[
  {
    "path": ".gitattributes",
    "content": "*.ipynb linguist-language=Python\n"
  },
  {
    "path": ".gitignore",
    "content": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nenv/\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\n*.egg-info/\n.installed.cfg\n*.egg\n\n# PyInstaller\n#  Usually these files are written by a python script from a template\n#  before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.coverage\n.cache\nnosetests.xml\ncoverage.xml\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\n\n# Sphinx documentation\ndocs/_build/\n\n# PyBuilder\ntarget/\n\n# IPython notebook\n.ipynb_checkpoints\n\n# Repo scratch directory\nscratch/\n\n# Misc\n.DS_Store"
  },
  {
    "path": "LICENSE",
    "content": "This repository contains a variety of content; some developed by Donne Martin,\nand some from third-parties.  The third-party content is distributed under the\nlicense provided by those parties.\n\nThe content developed by Donne Martin is distributed under the following license:\n\nI am providing code and resources in this repository to you under an open source\nlicense.  Because this is my personal repository, the license you receive to my\ncode and resources is from me and not my employer (Facebook).\n\nCopyright 2015 Donne Martin\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n   http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License."
  },
  {
    "path": "README.md",
    "content": "<br/>\n<p align=\"center\">\n  <img src=\"https://raw.githubusercontent.com/donnemartin/data-science-ipython-notebooks/master/images/README_1200x800.gif\">\n</p>\n\n<p align=\"center\">\n  <img src=\"https://raw.githubusercontent.com/donnemartin/data-science-ipython-notebooks/master/images/coversmall_alt.png\">\n  <br/>\n</p>\n\n# data-science-ipython-notebooks\n\n## Index\n\n* [deep-learning](#deep-learning)\n    * [tensorflow](#tensor-flow-tutorials)\n    * [theano](#theano-tutorials)\n    * [keras](#keras-tutorials)\n    * [caffe](#deep-learning-misc)\n* [scikit-learn](#scikit-learn)\n* [statistical-inference-scipy](#statistical-inference-scipy)\n* [pandas](#pandas)\n* [matplotlib](#matplotlib)\n* [numpy](#numpy)\n* [python-data](#python-data)\n* [kaggle-and-business-analyses](#kaggle-and-business-analyses)\n* [spark](#spark)\n* [mapreduce-python](#mapreduce-python)\n* [amazon web services](#aws)\n* [command lines](#commands)\n* [misc](#misc)\n* [notebook-installation](#notebook-installation)\n* [credits](#credits)\n* [contributing](#contributing)\n* [contact-info](#contact-info)\n* [license](#license)\n\n<br/>\n<p align=\"center\">\n  <img src=\"http://i.imgur.com/ZhKXrKZ.png\">\n</p>\n\n## deep-learning\n\nIPython Notebook(s) demonstrating deep learning functionality.\n\n<br/>\n<p align=\"center\">\n  <img src=\"https://avatars0.githubusercontent.com/u/15658638?v=3&s=100\">\n</p>\n\n### tensor-flow-tutorials\n\nAdditional TensorFlow tutorials:\n\n* [pkmital/tensorflow_tutorials](https://github.com/pkmital/tensorflow_tutorials)\n* [nlintz/TensorFlow-Tutorials](https://github.com/nlintz/TensorFlow-Tutorials)\n* [alrojo/tensorflow-tutorial](https://github.com/alrojo/tensorflow-tutorial)\n* [BinRoot/TensorFlow-Book](https://github.com/BinRoot/TensorFlow-Book)\n* [tuanavu/tensorflow-basic-tutorials](https://github.com/tuanavu/tensorflow-basic-tutorials)\n\n| Notebook | Description |\n|--------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| [tsf-basics](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-examples/notebooks/1_intro/basic_operations.ipynb) | Learn basic operations in TensorFlow, a library for various kinds of perceptual and language understanding tasks from Google. |\n| [tsf-linear](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-examples/notebooks/2_basic_classifiers/linear_regression.ipynb) | Implement linear regression in TensorFlow. |\n| [tsf-logistic](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-examples/notebooks/2_basic_classifiers/logistic_regression.ipynb) | Implement logistic regression in TensorFlow. |\n| [tsf-nn](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-examples/notebooks/2_basic_classifiers/nearest_neighbor.ipynb) | Implement nearest neighboars in TensorFlow. |\n| [tsf-alex](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-examples/notebooks/3_neural_networks/alexnet.ipynb) | Implement AlexNet in TensorFlow. |\n| [tsf-cnn](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-examples/notebooks/3_neural_networks/convolutional_network.ipynb) | Implement convolutional neural networks in TensorFlow. |\n| [tsf-mlp](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-examples/notebooks/3_neural_networks/multilayer_perceptron.ipynb) | Implement multilayer perceptrons in TensorFlow. |\n| [tsf-rnn](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-examples/notebooks/3_neural_networks/recurrent_network.ipynb) | Implement recurrent neural networks in TensorFlow. |\n| [tsf-gpu](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-examples/notebooks/4_multi_gpu/multigpu_basics.ipynb) | Learn about basic multi-GPU computation in TensorFlow. |\n| [tsf-gviz](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-examples/notebooks/5_ui/graph_visualization.ipynb) | Learn about graph visualization in TensorFlow. |\n| [tsf-lviz](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-examples/notebooks/5_ui/loss_visualization.ipynb) | Learn about loss visualization in TensorFlow. |\n\n### tensor-flow-exercises\n\n| Notebook | Description |\n|--------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| [tsf-not-mnist](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-exercises/1_notmnist.ipynb) | Learn simple data curation by creating a pickle with formatted datasets for training, development and testing in TensorFlow. |\n| [tsf-fully-connected](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-exercises/2_fullyconnected.ipynb) | Progressively train deeper and more accurate models using logistic regression and neural networks in TensorFlow. |\n| [tsf-regularization](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-exercises/3_regularization.ipynb) | Explore regularization techniques by training fully connected networks to classify notMNIST characters in TensorFlow. |\n| [tsf-convolutions](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-exercises/4_convolutions.ipynb) | Create convolutional neural networks in TensorFlow. |\n| [tsf-word2vec](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-exercises/5_word2vec.ipynb) | Train a skip-gram model over Text8 data in TensorFlow. |\n| [tsf-lstm](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-exercises/6_lstm.ipynb) | Train a LSTM character model over Text8 data in TensorFlow. |\n\n<br/>\n<p align=\"center\">\n  <img src=\"http://www.deeplearning.net/software/theano/_static/theano_logo_allblue_200x46.png\">\n</p>\n\n### theano-tutorials\n\n| Notebook | Description |\n|--------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| [theano-intro](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/theano-tutorial/intro_theano/intro_theano.ipynb) | Intro to Theano, which allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It can use GPUs and perform efficient symbolic differentiation. |\n| [theano-scan](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/theano-tutorial/scan_tutorial/scan_tutorial.ipynb) | Learn scans, a mechanism to perform loops in a Theano graph. |\n| [theano-logistic](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/theano-tutorial/intro_theano/logistic_regression.ipynb) | Implement logistic regression in Theano. |\n| [theano-rnn](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/theano-tutorial/rnn_tutorial/simple_rnn.ipynb) | Implement recurrent neural networks in Theano. |\n| [theano-mlp](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/theano-tutorial/theano_mlp/theano_mlp.ipynb) | Implement multilayer perceptrons in Theano. |\n\n<br/>\n<p align=\"center\">\n  <img src=\"http://i.imgur.com/L45Q8c2.jpg\">\n</p>\n\n### keras-tutorials\n\n| Notebook | Description |\n|--------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| keras | Keras is an open source neural network library written in Python. It is capable of running on top of either Tensorflow or Theano. |\n| [setup](https://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/keras-tutorial/0.%20Preamble.ipynb) | Learn about the tutorial goals and how to set up your Keras environment. |\n| [intro-deep-learning-ann](https://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/keras-tutorial/1.1%20Introduction%20-%20Deep%20Learning%20and%20ANN.ipynb) | Get an intro to deep learning with Keras and Artificial Neural Networks (ANN). |\n| [theano](https://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/keras-tutorial/1.2%20Introduction%20-%20Theano.ipynb) | Learn about Theano by working with weights matrices and gradients. |\n| [keras-otto](https://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/keras-tutorial/1.3%20Introduction%20-%20Keras.ipynb) | Learn about Keras by looking at the Kaggle Otto challenge. |\n| [ann-mnist](https://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/keras-tutorial/1.4%20%28Extra%29%20A%20Simple%20Implementation%20of%20ANN%20for%20MNIST.ipynb) | Review a simple implementation of ANN for MNIST using Keras. |\n| [conv-nets](https://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/keras-tutorial/2.1%20Supervised%20Learning%20-%20ConvNets.ipynb) | Learn about Convolutional Neural Networks (CNNs) with Keras. |\n| [conv-net-1](https://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/keras-tutorial/2.2.1%20Supervised%20Learning%20-%20ConvNet%20HandsOn%20Part%20I.ipynb) | Recognize handwritten digits from MNIST using Keras - Part 1. |\n| [conv-net-2](https://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/keras-tutorial/2.2.2%20Supervised%20Learning%20-%20ConvNet%20HandsOn%20Part%20II.ipynb) | Recognize handwritten digits from MNIST using Keras - Part 2. |\n| [keras-models](https://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/keras-tutorial/2.3%20Supervised%20Learning%20-%20Famous%20Models%20with%20Keras.ipynb) | Use pre-trained models such as VGG16, VGG19, ResNet50, and Inception v3 with Keras. |\n| [auto-encoders](https://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/keras-tutorial/3.1%20Unsupervised%20Learning%20-%20AutoEncoders%20and%20Embeddings.ipynb) | Learn about Autoencoders with Keras. |\n| [rnn-lstm](https://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/keras-tutorial/3.2%20RNN%20and%20LSTM.ipynb) | Learn about Recurrent Neural Networks (RNNs) with Keras. |\n| [lstm-sentence-gen](https://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/keras-tutorial/3.3%20%28Extra%29%20LSTM%20for%20Sentence%20Generation.ipynb) |  Learn about RNNs using Long Short Term Memory (LSTM) networks with Keras. |\n\n### deep-learning-misc\n\n| Notebook | Description |\n|--------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| [deep-dream](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/deep-dream/dream.ipynb) | Caffe-based computer vision program which uses a convolutional neural network to find and enhance patterns in images. |\n\n<br/>\n<p align=\"center\">\n  <img src=\"https://raw.githubusercontent.com/donnemartin/data-science-ipython-notebooks/master/images/scikitlearn.png\">\n</p>\n\n## scikit-learn\n\nIPython Notebook(s) demonstrating scikit-learn functionality.\n\n| Notebook | Description |\n|--------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| [intro](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scikit-learn/scikit-learn-intro.ipynb) | Intro notebook to scikit-learn.  Scikit-learn adds Python support for large, multi-dimensional arrays and matrices, along with a large library of high-level mathematical functions to operate on these arrays. |\n| [knn](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scikit-learn/scikit-learn-intro.ipynb#K-Nearest-Neighbors-Classifier) | Implement k-nearest neighbors in scikit-learn. |\n| [linear-reg](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scikit-learn/scikit-learn-linear-reg.ipynb) | Implement linear regression in scikit-learn. |\n| [svm](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scikit-learn/scikit-learn-svm.ipynb) | Implement support vector machine classifiers with and without kernels in scikit-learn. |\n| [random-forest](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scikit-learn/scikit-learn-random-forest.ipynb) | Implement random forest classifiers and regressors in scikit-learn. |\n| [k-means](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scikit-learn/scikit-learn-k-means.ipynb) | Implement k-means clustering in scikit-learn. |\n| [pca](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scikit-learn/scikit-learn-pca.ipynb) | Implement principal component analysis in scikit-learn. |\n| [gmm](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scikit-learn/scikit-learn-gmm.ipynb) | Implement Gaussian mixture models in scikit-learn. |\n| [validation](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scikit-learn/scikit-learn-validation.ipynb) | Implement validation and model selection in scikit-learn. |\n\n<br/>\n<p align=\"center\">\n  <img src=\"https://raw.githubusercontent.com/donnemartin/data-science-ipython-notebooks/master/images/scipy.png\">\n</p>\n\n## statistical-inference-scipy\n\nIPython Notebook(s) demonstrating statistical inference with SciPy functionality.\n\n| Notebook | Description |\n|--------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| scipy | SciPy is a collection of mathematical algorithms and convenience functions built on the Numpy extension of Python. It adds significant power to the interactive Python session by providing the user with high-level commands and classes for manipulating and visualizing data. |\n| [effect-size](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scipy/effect_size.ipynb) | Explore statistics that quantify effect size by analyzing the difference in height between men and women.  Uses data from the Behavioral Risk Factor Surveillance System (BRFSS) to estimate the mean and standard deviation of height for adult women and men in the United States. |\n| [sampling](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scipy/sampling.ipynb) | Explore random sampling by analyzing the average weight of men and women in the United States using BRFSS data. |\n| [hypothesis](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scipy/hypothesis.ipynb) | Explore hypothesis testing by analyzing the difference of first-born babies compared with others. |\n\n<br/>\n<p align=\"center\">\n  <img src=\"https://raw.githubusercontent.com/donnemartin/data-science-ipython-notebooks/master/images/pandas.png\">\n</p>\n\n## pandas\n\nIPython Notebook(s) demonstrating pandas functionality.\n\n| Notebook | Description |\n|--------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| [pandas](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/pandas/pandas.ipynb) | Software library written for data manipulation and analysis in Python. Offers data structures and operations for manipulating numerical tables and time series. |\n| [github-data-wrangling](https://github.com/donnemartin/viz/blob/master/githubstats/data_wrangling.ipynb) | Learn how to load, clean, merge, and feature engineer by analyzing GitHub data from the [`Viz`](https://github.com/donnemartin/viz) repo. |\n| [Introduction-to-Pandas](http://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/pandas/03.00-Introduction-to-Pandas.ipynb) | Introduction to Pandas. |\n| [Introducing-Pandas-Objects](http://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/pandas/03.01-Introducing-Pandas-Objects.ipynb) | Learn about Pandas objects. |\n| [Data Indexing and Selection](http://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/pandas/03.02-Data-Indexing-and-Selection.ipynb) | Learn about data indexing and selection in Pandas. |\n| [Operations-in-Pandas](http://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/pandas/03.03-Operations-in-Pandas.ipynb) | Learn about operating on data in Pandas. |\n| [Missing-Values](http://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/pandas/03.04-Missing-Values.ipynb) | Learn about handling missing data in Pandas. |\n| [Hierarchical-Indexing](http://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/pandas/03.05-Hierarchical-Indexing.ipynb) | Learn about hierarchical indexing in Pandas. |\n| [Concat-And-Append](http://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/pandas/03.06-Concat-And-Append.ipynb) | Learn about combining datasets: concat and append in Pandas. |\n| [Merge-and-Join](http://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/pandas/03.07-Merge-and-Join.ipynb) | Learn about combining datasets: merge and join in Pandas. |\n| [Aggregation-and-Grouping](http://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/pandas/03.08-Aggregation-and-Grouping.ipynb) | Learn about aggregation and grouping in Pandas. |\n| [Pivot-Tables](http://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/pandas/03.09-Pivot-Tables.ipynb) | Learn about pivot tables in Pandas. |\n| [Working-With-Strings](http://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/pandas/03.10-Working-With-Strings.ipynb) | Learn about vectorized string operations in Pandas. |\n| [Working-with-Time-Series](http://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/pandas/03.11-Working-with-Time-Series.ipynb) | Learn about working with time series in pandas. |\n| [Performance-Eval-and-Query](http://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/pandas/03.12-Performance-Eval-and-Query.ipynb) | Learn about high-performance Pandas: eval() and query() in Pandas. |\n\n<br/>\n<p align=\"center\">\n  <img src=\"https://raw.githubusercontent.com/donnemartin/data-science-ipython-notebooks/master/images/matplotlib.png\">\n</p>\n\n## matplotlib\n\nIPython Notebook(s) demonstrating matplotlib functionality.\n\n| Notebook | Description |\n|-----------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------|\n| [matplotlib](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/matplotlib/matplotlib.ipynb) | Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. |\n| [matplotlib-applied](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/matplotlib/matplotlib-applied.ipynb) | Apply matplotlib visualizations to Kaggle competitions for exploratory data analysis.  Learn how to create bar plots, histograms, subplot2grid, normalized plots, scatter plots, subplots, and kernel density estimation plots. |\n| [Introduction-To-Matplotlib](http://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/matplotlib/04.00-Introduction-To-Matplotlib.ipynb) | Introduction to Matplotlib. |\n| [Simple-Line-Plots](http://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/matplotlib/04.01-Simple-Line-Plots.ipynb) | Learn about simple line plots in Matplotlib. |\n| [Simple-Scatter-Plots](http://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/matplotlib/04.02-Simple-Scatter-Plots.ipynb) | Learn about simple scatter plots in Matplotlib. |\n| [Errorbars.ipynb](http://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/matplotlib/04.03-Errorbars.ipynb) | Learn about visualizing errors in Matplotlib. |\n| [Density-and-Contour-Plots](http://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/matplotlib/04.04-Density-and-Contour-Plots.ipynb) | Learn about density and contour plots in Matplotlib. |\n| [Histograms-and-Binnings](http://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/matplotlib/04.05-Histograms-and-Binnings.ipynb) | Learn about histograms, binnings, and density in Matplotlib. |\n| [Customizing-Legends](http://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/matplotlib/04.06-Customizing-Legends.ipynb) | Learn about customizing plot legends in Matplotlib. |\n| [Customizing-Colorbars](http://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/matplotlib/04.07-Customizing-Colorbars.ipynb) | Learn about customizing colorbars in Matplotlib. |\n| [Multiple-Subplots](http://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/matplotlib/04.08-Multiple-Subplots.ipynb) | Learn about multiple subplots in Matplotlib. |\n| [Text-and-Annotation](http://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/matplotlib/04.09-Text-and-Annotation.ipynb) | Learn about text and annotation in Matplotlib. |\n| [Customizing-Ticks](http://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/matplotlib/04.10-Customizing-Ticks.ipynb) | Learn about customizing ticks in Matplotlib. |\n| [Settings-and-Stylesheets](http://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/matplotlib/04.11-Settings-and-Stylesheets.ipynb) | Learn about customizing Matplotlib: configurations and stylesheets. |\n| [Three-Dimensional-Plotting](http://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/matplotlib/04.12-Three-Dimensional-Plotting.ipynb) | Learn about three-dimensional plotting in Matplotlib. |\n| [Geographic-Data-With-Basemap](http://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/matplotlib/04.13-Geographic-Data-With-Basemap.ipynb) | Learn about geographic data with basemap in Matplotlib. |\n| [Visualization-With-Seaborn](http://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/matplotlib/04.14-Visualization-With-Seaborn.ipynb) | Learn about visualization with Seaborn. |\n\n<br/>\n<p align=\"center\">\n  <img src=\"https://raw.githubusercontent.com/donnemartin/data-science-ipython-notebooks/master/images/numpy.png\">\n</p>\n\n## numpy\n\nIPython Notebook(s) demonstrating NumPy functionality.\n\n| Notebook | Description |\n|--------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| [numpy](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/numpy/numpy.ipynb) | Adds Python support for large, multi-dimensional arrays and matrices, along with a large library of high-level mathematical functions to operate on these arrays. |\n| [Introduction-to-NumPy](http://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/numpy/02.00-Introduction-to-NumPy.ipynb) | Introduction to NumPy. |\n| [Understanding-Data-Types](http://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/numpy/02.01-Understanding-Data-Types.ipynb) | Learn about data types in Python. |\n| [The-Basics-Of-NumPy-Arrays](http://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/numpy/02.02-The-Basics-Of-NumPy-Arrays.ipynb) | Learn about the basics of NumPy arrays. |\n| [Computation-on-arrays-ufuncs](http://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/numpy/02.03-Computation-on-arrays-ufuncs.ipynb) | Learn about computations on NumPy arrays: universal functions. |\n| [Computation-on-arrays-aggregates](http://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/numpy/02.04-Computation-on-arrays-aggregates.ipynb) | Learn about aggregations: min, max, and everything in between in NumPy. |\n| [Computation-on-arrays-broadcasting](http://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/numpy/02.05-Computation-on-arrays-broadcasting.ipynb) | Learn about computation on arrays: broadcasting in NumPy. |\n| [Boolean-Arrays-and-Masks](http://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/numpy/02.06-Boolean-Arrays-and-Masks.ipynb) | Learn about comparisons, masks, and boolean logic in NumPy. |\n| [Fancy-Indexing](http://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/numpy/02.07-Fancy-Indexing.ipynb) | Learn about fancy indexing in NumPy. |\n| [Sorting](http://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/numpy/02.08-Sorting.ipynb) | Learn about sorting arrays in NumPy. |\n| [Structured-Data-NumPy](http://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/numpy/02.09-Structured-Data-NumPy.ipynb) | Learn about structured data: NumPy's structured arrays. |\n\n<br/>\n<p align=\"center\">\n  <img src=\"https://raw.githubusercontent.com/donnemartin/data-science-ipython-notebooks/master/images/python.png\">\n</p>\n\n## python-data\n\nIPython Notebook(s) demonstrating Python functionality geared towards data analysis.\n\n| Notebook | Description |\n|-----------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------|\n| [data structures](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/python-data/structs.ipynb) | Learn Python basics with tuples, lists, dicts, sets. |\n| [data structure utilities](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/python-data/structs_utils.ipynb) | Learn Python operations such as slice, range, xrange, bisect, sort, sorted, reversed, enumerate, zip, list comprehensions. |\n| [functions](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/python-data/functions.ipynb) | Learn about more advanced Python features: Functions as objects, lambda functions, closures, *args, **kwargs currying, generators, generator expressions, itertools. |\n| [datetime](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/python-data/datetime.ipynb) | Learn how to work with Python dates and times: datetime, strftime, strptime, timedelta. |\n| [logging](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/python-data/logs.ipynb) | Learn about Python logging with RotatingFileHandler and TimedRotatingFileHandler. |\n| [pdb](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/python-data/pdb.ipynb) | Learn how to debug in Python with the interactive source code debugger. |\n| [unit tests](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/python-data/unit_tests.ipynb) | Learn how to test in Python with Nose unit tests. |\n\n<br/>\n<p align=\"center\">\n  <img src=\"https://raw.githubusercontent.com/donnemartin/data-science-ipython-notebooks/master/images/kaggle.png\">\n</p>\n\n## kaggle-and-business-analyses\n\nIPython Notebook(s) used in [kaggle](https://www.kaggle.com/) competitions and business analyses.\n\n| Notebook | Description |\n|-------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------|\n| [titanic](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/kaggle/titanic.ipynb) | Predict survival on the Titanic.  Learn data cleaning, exploratory data analysis, and machine learning. |\n| [churn-analysis](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/analyses/churn.ipynb) | Predict customer churn.  Exercise logistic regression, gradient boosting classifers, support vector machines, random forests, and k-nearest-neighbors.  Includes discussions of confusion matrices, ROC plots, feature importances, prediction probabilities, and calibration/descrimination.|\n\n<br/>\n<p align=\"center\">\n  <img src=\"https://raw.githubusercontent.com/donnemartin/data-science-ipython-notebooks/master/images/spark.png\">\n</p>\n\n## spark\n\nIPython Notebook(s) demonstrating spark and HDFS functionality.\n\n| Notebook | Description |\n|--------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------|\n| [spark](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/spark/spark.ipynb) | In-memory cluster computing framework, up to 100 times faster for certain applications and is well suited for machine learning algorithms. |\n| [hdfs](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/spark/hdfs.ipynb) | Reliably stores very large files across machines in a large cluster. |\n\n<br/>\n<p align=\"center\">\n  <img src=\"https://raw.githubusercontent.com/donnemartin/data-science-ipython-notebooks/master/images/mrjob.png\">\n</p>\n\n## mapreduce-python\n\nIPython Notebook(s) demonstrating Hadoop MapReduce with mrjob functionality.\n\n| Notebook | Description |\n|--------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------|\n| [mapreduce-python](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/mapreduce/mapreduce-python.ipynb) | Runs MapReduce jobs in Python, executing jobs locally or on Hadoop clusters. Demonstrates Hadoop Streaming in Python code with unit test and [mrjob](https://github.com/Yelp/mrjob) config file to analyze Amazon S3 bucket logs on Elastic MapReduce.  [Disco](https://github.com/discoproject/disco/) is another python-based alternative.|\n\n<br/>\n\n<p align=\"center\">\n  <img src=\"https://raw.githubusercontent.com/donnemartin/data-science-ipython-notebooks/master/images/aws.png\">\n</p>\n\n## aws\n\nIPython Notebook(s) demonstrating Amazon Web Services (AWS) and AWS tools functionality.\n\n\nAlso check out:\n\n* [SAWS](https://github.com/donnemartin/saws): A Supercharged AWS command line interface (CLI).\n* [Awesome AWS](https://github.com/donnemartin/awesome-aws): A curated list of libraries, open source repos, guides, blogs, and other resources.\n\n| Notebook | Description |\n|------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| [boto](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/aws/aws.ipynb#Boto) | Official AWS SDK for Python. |\n| [s3cmd](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/aws/aws.ipynb#s3cmd) | Interacts with S3 through the command line. |\n| [s3distcp](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/aws/aws.ipynb#s3distcp) | Combines smaller files and aggregates them together by taking in a pattern and target file.  S3DistCp can also be used to transfer large volumes of data from S3 to your Hadoop cluster. |\n| [s3-parallel-put](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/aws/aws.ipynb#s3-parallel-put) | Uploads multiple files to S3 in parallel. |\n| [redshift](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/aws/aws.ipynb#redshift) | Acts as a fast data warehouse built on top of technology from massive parallel processing (MPP). |\n| [kinesis](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/aws/aws.ipynb#kinesis) | Streams data in real time with the ability to process thousands of data streams per second. |\n| [lambda](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/aws/aws.ipynb#lambda) | Runs code in response to events, automatically managing compute resources. |\n\n<br/>\n<p align=\"center\">\n  <img src=\"https://raw.githubusercontent.com/donnemartin/data-science-ipython-notebooks/master/images/commands.png\">\n</p>\n\n## commands\n\nIPython Notebook(s) demonstrating various command lines for Linux, Git, etc.\n\n| Notebook | Description |\n|--------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| [linux](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/commands/linux.ipynb) | Unix-like and mostly POSIX-compliant computer operating system.  Disk usage, splitting files, grep, sed, curl, viewing running processes, terminal syntax highlighting, and Vim.|\n| [anaconda](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/commands/misc.ipynb#anaconda) | Distribution of the Python programming language for large-scale data processing, predictive analytics, and scientific computing, that aims to simplify package management and deployment. |\n| [ipython notebook](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/commands/misc.ipynb#ipython-notebook) | Web-based interactive computational environment where you can combine code execution, text, mathematics, plots and rich media into a single document. |\n| [git](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/commands/misc.ipynb#git) | Distributed revision control system with an emphasis on speed, data integrity, and support for distributed, non-linear workflows. |\n| [ruby](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/commands/misc.ipynb#ruby) | Used to interact with the AWS command line and for Jekyll, a blog framework that can be hosted on GitHub Pages. |\n| [jekyll](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/commands/misc.ipynb#jekyll) | Simple, blog-aware, static site generator for personal, project, or organization sites.  Renders Markdown or Textile and Liquid templates, and produces a complete, static website ready to be served by Apache HTTP Server, Nginx or another web server. |\n| [pelican](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/commands/misc.ipynb#pelican) | Python-based alternative to Jekyll. |\n| [django](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/commands/misc.ipynb#django) | High-level Python Web framework that encourages rapid development and clean, pragmatic design. It can be useful to share reports/analyses and for blogging. Lighter-weight alternatives include [Pyramid](https://github.com/Pylons/pyramid), [Flask](https://github.com/pallets/flask), [Tornado](https://github.com/tornadoweb/tornado), and [Bottle](https://github.com/bottlepy/bottle).\n\n## misc\n\nIPython Notebook(s) demonstrating miscellaneous functionality.\n\n| Notebook | Description |\n|--------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| [regex](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/misc/regex.ipynb) | Regular expression cheat sheet useful in data wrangling.|\n[algorithmia](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/misc/Algorithmia.ipynb) | Algorithmia is a marketplace for algorithms. This notebook showcases 4 different algorithms: Face Detection, Content Summarizer, Latent Dirichlet Allocation and Optical Character Recognition.|\n\n## notebook-installation\n\n### anaconda\n\nAnaconda is a free distribution of the Python programming language for large-scale data processing, predictive analytics, and scientific computing that aims to simplify package management and deployment.\n\nFollow instructions to install [Anaconda](https://docs.continuum.io/anaconda/install) or the more lightweight [miniconda](http://conda.pydata.org/miniconda.html).\n\n### dev-setup\n\nFor detailed instructions, scripts, and tools to set up your development environment for data analysis, check out the [dev-setup](https://github.com/donnemartin/dev-setup) repo.\n\n### running-notebooks\n\nTo view interactive content or to modify elements within the IPython notebooks, you must first clone or download the repository then run the notebook.  More information on IPython Notebooks can be found [here.](http://ipython.org/notebook.html)\n\n    $ git clone https://github.com/donnemartin/data-science-ipython-notebooks.git\n    $ cd data-science-ipython-notebooks\n    $ jupyter notebook\n\nNotebooks tested with Python 2.7.x.\n\n## credits\n\n* [Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython](http://www.amazon.com/Python-Data-Analysis-Wrangling-IPython/dp/1449319793) by Wes McKinney\n* [PyCon 2015 Scikit-learn Tutorial](https://github.com/jakevdp/sklearn_pycon2015) by Jake VanderPlas\n* [Python Data Science Handbook](https://github.com/jakevdp/PythonDataScienceHandbook) by Jake VanderPlas\n* [Parallel Machine Learning with scikit-learn and IPython](https://github.com/ogrisel/parallel_ml_tutorial) by Olivier Grisel\n* [Statistical Interference Using Computational Methods in Python](https://github.com/AllenDowney/CompStats) by Allen Downey\n* [TensorFlow Examples](https://github.com/aymericdamien/TensorFlow-Examples) by Aymeric Damien\n* [TensorFlow Tutorials](https://github.com/pkmital/tensorflow_tutorials) by Parag K Mital\n* [TensorFlow Tutorials](https://github.com/nlintz/TensorFlow-Tutorials) by Nathan Lintz\n* [TensorFlow Tutorials](https://github.com/alrojo/tensorflow-tutorial) by Alexander R Johansen\n* [TensorFlow Book](https://github.com/BinRoot/TensorFlow-Book) by Nishant Shukla\n* [Summer School 2015](https://github.com/mila-udem/summerschool2015) by mila-udem\n* [Keras tutorials](https://github.com/leriomaggio/deep-learning-keras-tensorflow) by Valerio Maggio\n* [Kaggle](https://www.kaggle.com/)\n* [Yhat Blog](http://blog.yhat.com/)\n\n## contributing\n\nContributions are welcome!  For bug reports or requests please [submit an issue](https://github.com/donnemartin/data-science-ipython-notebooks/issues).\n\n## contact-info\n\nFeel free to contact me to discuss any issues, questions, or comments.\n\n* Email: [donne.martin@gmail.com](mailto:donne.martin@gmail.com)\n* Twitter: [@donne_martin](https://twitter.com/donne_martin)\n* GitHub: [donnemartin](https://github.com/donnemartin)\n* LinkedIn: [donnemartin](https://www.linkedin.com/in/donnemartin)\n* Website: [donnemartin.com](http://donnemartin.com)\n\n## license\n\nThis repository contains a variety of content; some developed by Donne Martin, and some from third-parties.  The third-party content is distributed under the license provided by those parties.\n\nThe content developed by Donne Martin is distributed under the following license:\n\n*I am providing code and resources in this repository to you under an open source license.  Because this is my personal repository, the license you receive to my code and resources is from me and not my employer (Facebook).*\n\n    Copyright 2015 Donne Martin\n\n    Licensed under the Apache License, Version 2.0 (the \"License\");\n    you may not use this file except in compliance with the License.\n    You may obtain a copy of the License at\n\n       http://www.apache.org/licenses/LICENSE-2.0\n\n    Unless required by applicable law or agreed to in writing, software\n    distributed under the License is distributed on an \"AS IS\" BASIS,\n    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n    See the License for the specific language governing permissions and\n    limitations under the License.\n"
  },
  {
    "path": "__init__.py",
    "content": ""
  },
  {
    "path": "analyses/__init__.py",
    "content": ""
  },
  {
    "path": "analyses/churn.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"##Customer Churn##\\n\",\n    \"\\n\",\n    \"Credits: Forked from [growth-workshop](https://github.com/aprial/growth-workshop) by [aprial](https://github.com/aprial), as featured on the [yhat blog](http://blog.yhathq.com/posts/predicting-customer-churn-with-sklearn.html)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"\\\"Churn Rate\\\" is a business term describing the rate at which customers leave or cease paying for a product or service. It's a critical figure in many businesses, as it's often the case that acquiring new customers is a lot more costly than retaining existing ones (in some cases, 5 to 20 times more expensive).\\n\",\n    \"\\n\",\n    \"Understanding what keeps customers engaged, therefore, is incredibly valuable, as it is a logical foundation from which to develop retention strategies and roll out operational practices aimed to keep customers from walking out the door. Consequently, there's growing interest among companies to develop better churn-detection techniques, leading many to look to data mining and machine learning for new and creative approaches.\\n\",\n    \"\\n\",\n    \"Predicting churn is particularly important for businesses w/ subscription models such as cell phone, cable, or merchant credit card processing plans. But modeling churn has wide reaching applications in many domains. For example, casinos have used predictive models to predict ideal room conditions for keeping patrons at the blackjack table and when to reward unlucky gamblers with front row seats to Celine Dion. Similarly, airlines may offer first class upgrades to complaining customers. The list goes on.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Wait, don't go!\\n\",\n    \"\\n\",\n    \"So what are some of ops strategies that companies employ to prevent churn? Well, reducing churn, it turns out, often requires non-trivial resources. Specialized retention teams are common in many industries and exist expressly to call down lists of at-risk customers to plead for their continued business.\\n\",\n    \"\\n\",\n    \"![](http://blog.yhathq.com/static/img/netflix.png)\\n\",\n    \"\\n\",\n    \"Organizing and running such teams is tough. From an ops perspective, cross-geographic teams must be well organized and trained to respond to a huge spectrum of customer complaints. Customers must be accurately targeted based on churn-risk, and retention treatments must be well-conceived and correspond reasonably to match expected customer value to ensure the economics make sense. Spending $1,000 on someone who wasn't about to leave can get expensive pretty quickly.\\n\",\n    \"\\n\",\n    \"Within this frame of mind, efficiently dealing with turnover is an exercise of distinguishing who is likely to churn from who is not using the data at our disposal. The remainder of this post will explore a simple case study to show how Python and its scientific libraries can be used to predict churn and how you might deploy such a solution within operations to guide a retention team.\\n\",\n    \"\\n\",\n    \"##The Dataset##\\n\",\n    \"\\n\",\n    \"The data set we'll be using is a longstanding telecom customer data set.\\n\",\n    \"\\n\",\n    \"The data is straightforward. Each row represents a subscribing telephone customer. Each column contains customer attributes such as phone number, call minutes used during different times of day, charges incurred for services, lifetime account duration, and whether or not the customer is still a customer.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from __future__ import division\\n\",\n    \"import pandas as pd\\n\",\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import json\\n\",\n    \"\\n\",\n    \"from sklearn.cross_validation import KFold\\n\",\n    \"from sklearn.preprocessing import StandardScaler\\n\",\n    \"from sklearn.cross_validation import train_test_split\\n\",\n    \"from sklearn.svm import SVC\\n\",\n    \"from sklearn.ensemble import RandomForestClassifier as RF\\n\",\n    \"%matplotlib inline \\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Column names:\\n\",\n      \"['State', 'Account Length', 'Area Code', 'Phone', \\\"Int'l Plan\\\", 'VMail Plan', 'VMail Message', 'Day Mins', 'Day Calls', 'Day Charge', 'Eve Mins', 'Eve Calls', 'Eve Charge', 'Night Mins', 'Night Calls', 'Night Charge', 'Intl Mins', 'Intl Calls', 'Intl Charge', 'CustServ Calls', 'Churn?']\\n\",\n      \"\\n\",\n      \"Sample data:\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div style=\\\"max-height:1000px;max-width:1500px;overflow:auto;\\\">\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>State</th>\\n\",\n       \"      <th>Account Length</th>\\n\",\n       \"      <th>Area Code</th>\\n\",\n       \"      <th>Phone</th>\\n\",\n       \"      <th>Int'l Plan</th>\\n\",\n       \"      <th>VMail Plan</th>\\n\",\n       \"      <th>Night Charge</th>\\n\",\n       \"      <th>Intl Mins</th>\\n\",\n       \"      <th>Intl Calls</th>\\n\",\n       \"      <th>Intl Charge</th>\\n\",\n       \"      <th>CustServ Calls</th>\\n\",\n       \"      <th>Churn?</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td> KS</td>\\n\",\n       \"      <td> 128</td>\\n\",\n       \"      <td> 415</td>\\n\",\n       \"      <td> 382-4657</td>\\n\",\n       \"      <td>  no</td>\\n\",\n       \"      <td> yes</td>\\n\",\n       \"      <td> 11.01</td>\\n\",\n       \"      <td> 10.0</td>\\n\",\n       \"      <td> 3</td>\\n\",\n       \"      <td> 2.70</td>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"      <td> False.</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td> OH</td>\\n\",\n       \"      <td> 107</td>\\n\",\n       \"      <td> 415</td>\\n\",\n       \"      <td> 371-7191</td>\\n\",\n       \"      <td>  no</td>\\n\",\n       \"      <td> yes</td>\\n\",\n       \"      <td> 11.45</td>\\n\",\n       \"      <td> 13.7</td>\\n\",\n       \"      <td> 3</td>\\n\",\n       \"      <td> 3.70</td>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"      <td> False.</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td> NJ</td>\\n\",\n       \"      <td> 137</td>\\n\",\n       \"      <td> 415</td>\\n\",\n       \"      <td> 358-1921</td>\\n\",\n       \"      <td>  no</td>\\n\",\n       \"      <td>  no</td>\\n\",\n       \"      <td>  7.32</td>\\n\",\n       \"      <td> 12.2</td>\\n\",\n       \"      <td> 5</td>\\n\",\n       \"      <td> 3.29</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> False.</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td> OH</td>\\n\",\n       \"      <td>  84</td>\\n\",\n       \"      <td> 408</td>\\n\",\n       \"      <td> 375-9999</td>\\n\",\n       \"      <td> yes</td>\\n\",\n       \"      <td>  no</td>\\n\",\n       \"      <td>  8.86</td>\\n\",\n       \"      <td>  6.6</td>\\n\",\n       \"      <td> 7</td>\\n\",\n       \"      <td> 1.78</td>\\n\",\n       \"      <td> 2</td>\\n\",\n       \"      <td> False.</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td> OK</td>\\n\",\n       \"      <td>  75</td>\\n\",\n       \"      <td> 415</td>\\n\",\n       \"      <td> 330-6626</td>\\n\",\n       \"      <td> yes</td>\\n\",\n       \"      <td>  no</td>\\n\",\n       \"      <td>  8.41</td>\\n\",\n       \"      <td> 10.1</td>\\n\",\n       \"      <td> 3</td>\\n\",\n       \"      <td> 2.73</td>\\n\",\n       \"      <td> 3</td>\\n\",\n       \"      <td> False.</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5</th>\\n\",\n       \"      <td> AL</td>\\n\",\n       \"      <td> 118</td>\\n\",\n       \"      <td> 510</td>\\n\",\n       \"      <td> 391-8027</td>\\n\",\n       \"      <td> yes</td>\\n\",\n       \"      <td>  no</td>\\n\",\n       \"      <td>  9.18</td>\\n\",\n       \"      <td>  6.3</td>\\n\",\n       \"      <td> 6</td>\\n\",\n       \"      <td> 1.70</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> False.</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"  State  Account Length  Area Code     Phone Int'l Plan VMail Plan  \\\\\\n\",\n       \"0    KS             128        415  382-4657         no        yes   \\n\",\n       \"1    OH             107        415  371-7191         no        yes   \\n\",\n       \"2    NJ             137        415  358-1921         no         no   \\n\",\n       \"3    OH              84        408  375-9999        yes         no   \\n\",\n       \"4    OK              75        415  330-6626        yes         no   \\n\",\n       \"5    AL             118        510  391-8027        yes         no   \\n\",\n       \"\\n\",\n       \"   Night Charge  Intl Mins  Intl Calls  Intl Charge  CustServ Calls  Churn?  \\n\",\n       \"0         11.01       10.0           3         2.70               1  False.  \\n\",\n       \"1         11.45       13.7           3         3.70               1  False.  \\n\",\n       \"2          7.32       12.2           5         3.29               0  False.  \\n\",\n       \"3          8.86        6.6           7         1.78               2  False.  \\n\",\n       \"4          8.41       10.1           3         2.73               3  False.  \\n\",\n       \"5          9.18        6.3           6         1.70               0  False.  \"\n      ]\n     },\n     \"execution_count\": 2,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"churn_df = pd.read_csv('../data/churn.csv')\\n\",\n    \"col_names = churn_df.columns.tolist()\\n\",\n    \"\\n\",\n    \"print \\\"Column names:\\\"\\n\",\n    \"print col_names\\n\",\n    \"\\n\",\n    \"to_show = col_names[:6] + col_names[-6:]\\n\",\n    \"\\n\",\n    \"print \\\"\\\\nSample data:\\\"\\n\",\n    \"churn_df[to_show].head(6)\\n\",\n    \"      \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We'll be keeping the statistical model pretty simple for this example so the feature space is almost unchanged from what you see above. The following code simply drops irrelevant columns and converts strings to boolean values (since models don't handle \\\"yes\\\" and \\\"no\\\" very well). The rest of the numeric columns are left untouched.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Isolate target data\\n\",\n    \"churn_result = churn_df['Churn?']\\n\",\n    \"y = np.where(churn_result == 'True.',1,0)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# We don't need these columns\\n\",\n    \"to_drop = ['State','Area Code','Phone','Churn?']\\n\",\n    \"churn_feat_space = churn_df.drop(to_drop,axis=1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# 'yes'/'no' has to be converted to boolean values\\n\",\n    \"# NumPy converts these from boolean to 1. and 0. later\\n\",\n    \"yes_no_cols = [\\\"Int'l Plan\\\",\\\"VMail Plan\\\"]\\n\",\n    \"churn_feat_space[yes_no_cols] = churn_feat_space[yes_no_cols] == 'yes'\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Index([u'Account Length', u'Int'l Plan', u'VMail Plan', u'VMail Message', u'Day Mins', u'Day Calls', u'Day Charge', u'Eve Mins', u'Eve Calls', u'Eve Charge', u'Night Mins', u'Night Calls', u'Night Charge', u'Intl Mins', u'Intl Calls', u'Intl Charge', u'CustServ Calls'], dtype='object')\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Pull out features for future use\\n\",\n    \"features = churn_feat_space.columns\\n\",\n    \"print features\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Feature space holds 3333 observations and 17 features\\n\",\n      \"Unique target labels: [0 1]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"X = churn_feat_space.as_matrix().astype(np.float)\\n\",\n    \"\\n\",\n    \"# This is important\\n\",\n    \"scaler = StandardScaler()\\n\",\n    \"X = scaler.fit_transform(X)\\n\",\n    \"print \\\"Feature space holds %d observations and %d features\\\" % X.shape\\n\",\n    \"print \\\"Unique target labels:\\\", np.unique(y)\\n\",\n    \"      \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"One slight side note. Many predictors care about the relative size of different features even though those scales might be arbitrary. For instance: the number of points a basketball team scores per game will naturally be a couple orders of magnitude larger than their win percentage. But this doesn't mean that the latter is 100 times less signifigant. `StandardScaler` fixes this by normalizing each feature to a range of around 1.0 to -1.0 thereby preventing models from misbehaving. Well, at least for that reason.\\n\",\n    \"\\n\",\n    \"Great, I now have a feature space `X` and a set of target values `y`. On to the predictions!\\n\",\n    \"\\n\",\n    \"##How good is your model?##\\n\",\n    \"\\n\",\n    \"Express, test, cycle. A machine learning pipeline should be anything but static. There are always new features to design, new data to use, new classifiers to consider each with unique parameters to tune. And for every change it's critical to be able to ask, \\\"Is the new version better than the last?\\\" So how do I do that?\\n\",\n    \"\\n\",\n    \"As a good start, cross validation will be used throught this example. Cross validation attempts to avoid overfitting (training on and predicting the same datapoint) while still producing a prediction for each observation dataset. This is accomplished by systematically hiding different subsets of the data while training a set of models. After training, each model predicts on the subset that had been hidden to it, emulating multiple train-test splits. When done correctly, every observation will have a 'fair' corresponding prediction.\\n\",\n    \"\\n\",\n    \"Here's what that looks like using `scikit-learn` libraries.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.cross_validation import KFold\\n\",\n    \"\\n\",\n    \"def run_cv(X,y,clf_class,**kwargs):\\n\",\n    \"    # Construct a kfolds object\\n\",\n    \"    kf = KFold(len(y),n_folds=3,shuffle=True)\\n\",\n    \"    y_pred = y.copy()\\n\",\n    \"    \\n\",\n    \"    # Iterate through folds\\n\",\n    \"    for train_index, test_index in kf:\\n\",\n    \"        X_train, X_test = X[train_index], X[test_index]\\n\",\n    \"        y_train = y[train_index]\\n\",\n    \"        # Initialize a classifier with key word arguments\\n\",\n    \"        clf = clf_class(**kwargs)\\n\",\n    \"        clf.fit(X_train,y_train)\\n\",\n    \"        y_pred[test_index] = clf.predict(X_test)\\n\",\n    \"    return y_pred\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's compare three fairly unique algorithms support vector machines, random forest, and k-nearest-neighbors. Nothing fancy here, just passing each to cross validation and determining how often the classifier predicted the correct class.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Logistic Regression:\\n\",\n      \"0.862\\n\",\n      \"Gradient Boosting Classifier\\n\",\n      \"0.950\\n\",\n      \"Support vector machines:\\n\",\n      \"0.912\\n\",\n      \"Random forest:\\n\",\n      \"0.935\\n\",\n      \"K-nearest-neighbors:\\n\",\n      \"0.893\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from sklearn.svm import SVC\\n\",\n    \"from sklearn.ensemble import RandomForestClassifier as RF\\n\",\n    \"from sklearn.neighbors import KNeighborsClassifier as KNN\\n\",\n    \"from sklearn.linear_model import LogisticRegression as LR\\n\",\n    \"from sklearn.ensemble import GradientBoostingClassifier as GBC\\n\",\n    \"from sklearn.metrics import average_precision_score\\n\",\n    \"\\n\",\n    \"def accuracy(y_true,y_pred):\\n\",\n    \"    # NumPy interpretes True and False as 1. and 0.\\n\",\n    \"    return np.mean(y_true == y_pred)\\n\",\n    \"\\n\",\n    \"print \\\"Logistic Regression:\\\"\\n\",\n    \"print \\\"%.3f\\\" % accuracy(y, run_cv(X,y,LR))\\n\",\n    \"print \\\"Gradient Boosting Classifier\\\"\\n\",\n    \"print \\\"%.3f\\\" % accuracy(y, run_cv(X,y,GBC))\\n\",\n    \"print \\\"Support vector machines:\\\"\\n\",\n    \"print \\\"%.3f\\\" % accuracy(y, run_cv(X,y,SVC))\\n\",\n    \"print \\\"Random forest:\\\"\\n\",\n    \"print \\\"%.3f\\\" % accuracy(y, run_cv(X,y,RF))\\n\",\n    \"print \\\"K-nearest-neighbors:\\\"\\n\",\n    \"print \\\"%.3f\\\" % accuracy(y, run_cv(X,y,KNN))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"Random forest won, right?\\n\",\n    \"\\n\",\n    \"##Precision and recall##\\n\",\n    \"\\n\",\n    \"Measurements aren't golden formulas which always spit out high numbers for good models and low numbers for bad ones. Inherently they convey something sentiment about a model's performance, and it's the job of the human designer to determine each number's validity. The problem with accuracy is that outcomes aren't necessarily equal. If my classifier predicted a customer would churn and they didn't, that's not the best but it's forgivable. However, if my classifier predicted a customer would return, I didn't act, and then they churned... that's really bad.\\n\",\n    \"\\n\",\n    \"We'll be using another built in `scikit-learn` function to construction a confusion matrix. A confusion matrix is a way of visualizing predictions made by a classifier and is just a table showing the distribution of predictions for a specific class. The x-axis indicates the true class of each observation (if a customer churned or not) while the y-axis corresponds to the class predicted by the model (if my classifier said a customer would churned or not).\\n\",\n    \"\\n\",\n    \"## Confusion matrix and confusion tables: \\n\",\n    \"The columns represent the actual class and the rows represent the predicted class. Lets evaluate performance: \\n\",\n    \"\\n\",\n    \"|      | condition True | condition false|\\n\",\n    \"|------|----------------|---------------|\\n\",\n    \"|prediction true|True Positive|False positive|\\n\",\n    \"|Prediction False|False Negative|True Negative|\\n\",\n    \"\\n\",\n    \"Sensitivity, Recall or True Positive Rate quantify the models ability to predict our positive classes. \\n\",\n    \"\\n\",\n    \"$$TPR = \\\\frac{ TP}{TP + FN}$$ \\n\",\n    \"\\n\",\n    \"Specificity or True Negative Rate quantify the models ability to predict our Negative classes. \\n\",\n    \"\\n\",\n    \"$$TNR = \\\\frac{ TN}{FP + TN}$$ \\n\",\n    \"\\n\",\n    \"### Example:\\n\",\n    \"\\n\",\n    \"|      | Spam | Ham|\\n\",\n    \"|------|----------------|---------------|\\n\",\n    \"|prediction Spam|100|50|\\n\",\n    \"|Prediction Ham|75|900|\\n\",\n    \"\\n\",\n    \"$$TPR = \\\\frac{100}{100 + 75} = 57.14 \\\\%   Sensitive $$\\n\",\n    \"\\n\",\n    \"$$TNR = \\\\frac{ 900}{50 + 900} = 94.73 \\\\% Specific $$\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[2821   29]\\n\",\n      \" [ 244  239]]\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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dtsZgPIN0Mws9qqYjCpYpvNbAC5B2hmtVXFu8E4AJpZKdwDNLPaqmIw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\\nH4XA7bQpdsaKAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10c33edd0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[2814   36]\\n\",\n      \" [ 147  336]]\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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jxG1jU9bBy3Z2ZrWru6t3fD0B9G/3i+7m1EPNfL0NoZt6QXESskHQtc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\\nOOrbmSdSAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10c60b1d0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[2813   37]\\n\",\n      \" [ 317  166]]\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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Bd4u6R9IQVE4CXA7bXCdcm+DwG+mvNswLpk33sBe0k6pFWVHfTMrLzN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\\nz8yGioOemQ0VBz0zGyr/H2DgZHYpv+O0AAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10c7c6e10>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[2820   30]\\n\",\n      \" [ 124  359]]\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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T6MgRs2AETEjZLOI6VDWQ8cGwNpGI4Fvk2KxxeX3dkeZpnNzIam058l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\\nkDQzK/H/ASljNTZK+WWcAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10d212790>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[2769   81]\\n\",\n      \" [ 381  102]]\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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klzcLFvMxsK5c/erlPsOyKui4inR8ROEbETKantFRGLcbFvMxsK5TNI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\\n9MysVpz0zKxWnPTMrFac9MysVv4H2IlZ+21JrigAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10d38d7d0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.metrics import confusion_matrix\\n\",\n    \"from sklearn.metrics import precision_score\\n\",\n    \"from sklearn.metrics import recall_score\\n\",\n    \"\\n\",\n    \"def draw_confusion_matrices(confusion_matricies,class_names):\\n\",\n    \"    class_names = class_names.tolist()\\n\",\n    \"    for cm in confusion_matrices:\\n\",\n    \"        classifier, cm = cm[0], cm[1]\\n\",\n    \"        print(cm)\\n\",\n    \"        \\n\",\n    \"        fig = plt.figure()\\n\",\n    \"        ax = fig.add_subplot(111)\\n\",\n    \"        cax = ax.matshow(cm)\\n\",\n    \"        plt.title('Confusion matrix for %s' % classifier)\\n\",\n    \"        fig.colorbar(cax)\\n\",\n    \"        ax.set_xticklabels([''] + class_names)\\n\",\n    \"        ax.set_yticklabels([''] + class_names)\\n\",\n    \"        plt.xlabel('Predicted')\\n\",\n    \"        plt.ylabel('True')\\n\",\n    \"        plt.show()\\n\",\n    \"    \\n\",\n    \"y = np.array(y)\\n\",\n    \"class_names = np.unique(y)\\n\",\n    \"\\n\",\n    \"confusion_matrices = [\\n\",\n    \"    ( \\\"Support Vector Machines\\\", confusion_matrix(y,run_cv(X,y,SVC)) ),\\n\",\n    \"    ( \\\"Random Forest\\\", confusion_matrix(y,run_cv(X,y,RF)) ),\\n\",\n    \"    ( \\\"K-Nearest-Neighbors\\\", confusion_matrix(y,run_cv(X,y,KNN)) ),\\n\",\n    \"    ( \\\"Gradient Boosting Classifier\\\", confusion_matrix(y,run_cv(X,y,GBC)) ),\\n\",\n    \"    ( \\\"Logisitic Regression\\\", confusion_matrix(y,run_cv(X,y,LR)) )\\n\",\n    \"]\\n\",\n    \"\\n\",\n    \"# Pyplot code not included to reduce clutter\\n\",\n    \"# from churn_display import draw_confusion_matrices\\n\",\n    \"%matplotlib inline\\n\",\n    \"\\n\",\n    \"draw_confusion_matrices(confusion_matrices,class_names)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"An important question to ask might be, When an individual churns, how often does my classifier predict that correctly? This measurement is called \\\"recall\\\" and a quick look at these diagrams can demonstrate that random forest is clearly best for this criteria. Out of all the churn cases (outcome \\\"1\\\") random forest correctly retrieved 330 out of 482. This translates to a churn \\\"recall\\\" of about 68% (330/482≈2/3), far better than support vector machines (≈50%) or k-nearest-neighbors (≈35%).\\n\",\n    \"\\n\",\n    \"Another question of importance is \\\"precision\\\" or, When a classifier predicts an individual will churn, how often does that individual actually churn? The differences in sematic are small from the previous question, but it makes quite a different. Random forest again out preforms the other two at about 93% precision (330 out of 356) with support vector machines a little behind at about 87% (235 out of 269). K-nearest-neighbors lags at about 80%.\\n\",\n    \"\\n\",\n    \"While, just like accuracy, precision and recall still rank random forest above SVC and KNN, this won't always be true. When different measurements do return a different pecking order, understanding the values and tradeoffs of each rating should effect how you proceed.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## ROC Plots & AUC\\n\",\n    \"\\n\",\n    \"Another important metric to consider is ROC plots. We'll cover the majority of these concepts in lecture, but if you're itching for more, one of the best resources out there is this [academic paper](https://cours.etsmtl.ca/sys828/REFS/A1/Fawcett_PRL2006.pdf). \\n\",\n    \"\\n\",\n    \"Simply put, the area under the curve (AUC) of a receiver operating characteristic (ROC) curve is a way to reduce ROC performance to a single value representing expected performance.\\n\",\n    \"To explain with a little more detail, a ROC curve plots the true positives (sensitivity) vs. false positives (1 − specificity), for a binary classifier system as its discrimination threshold is varied. Since a random method describes a horizontal curve through the unit interval, it has an AUC of .5. Minimally, classifiers should perform better than this, and the extent to which they score higher than one another (meaning the area under the ROC curve is larger), they have better expected performance. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Support vector machines:\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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i7wyyLrR1Iscu7tLwVGDoFVRsOHhsOKw3xZc/Q8tvr1ZDyGvptlMtMr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\\n/wTICsX8iw7QAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10c334610>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Random forests:\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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IjqqQEap1grfJziH6v4K+IEy+OESuIUFglFBUJMjQKvkyB14qexxW8U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\\n/iAIggHj/wHSDCwebMwj3gAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10d933b50>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"K-nearest-neighbors:\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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NgeHfVddLn+T/ViAOKJYpgrrmFyoEFm8kX5aaVffJNYz3dieu9LgZUz\\n6rVmRkRbGBKvA693VC8/FKypx2Kx7Bfi80WxJmk0+fFH4NIZxDdMIKl+aJqrLjNrzdr4zQ/dQHVl\\nwW7H9HX1Zd7A3wRiXJvr+tblVg2v2BgYVrmxVzVVVf+B3IkBvj0AvooyCn45quWRaV3XxvrxWyyW\\nfUZ8PiE/bhDrkw6nruYQdqybTsWG4VK2KT1hy8bY+MIaThh/Z/2wnLr6oQV1MrDYFQsNrE1d4b9q\\n29VxUbjIio7zj0b90xtqZZJq4OfCwmhlCbt68MtQLYpwU/cZx5Y/zePxfBVpWRqxit9isbSJvPNp\\nBitSZlPrmk1MYDK9GkaRUt+vV3xN8uD1LtfA7+vrs184Piag/t2PQ7h5wi8bMrSwdHB1TvmYsg2B\\n4WXFKQkNJCxT1oyF7+LYTcnn0xWUT4gEeeysAs7uLGYdO7hrsVgAEJ8vieUp0yiNOYyowIFUbZpI\\n2boBlG9Iil+X44pZs1nrC3P41ahnSsYU4Rqwg7i4etH8tLyKTVnro7+Ojo2JIrphQJTUTtC6mln+\\nhqhpDZpw6HLv8nhYxi7lvhTImRwBT5qOIpIeO/uD7fFbLN0Q8fni2Jh4AAXxhyE6najK8aTKIHoH\\nMmOi/DGDvo8PDF/pqh65IVD1wrc/zixrKNojKdOcycfWjIurLhhVsXHH5O1bA8NLNSO5jr41sCFh\\nTwW/Dm3yGtDN8Xq9BwDPYPzy9zuSZjiwPX6LpZshPl80xTEj2NDrUAIyi9jABHr5h1G0KIu8VXGx\\nS7YQt3ZToGHbBq2qyY+6Pu1vBYfUjK5Kqo1OKk7eVLmpzyZd229NenpGjL9XdaJ/UFRd+YF+f83h\\ntUQf4idz2OL/5kXtrtyXAqsSVGsj2/JOgx+4ly7Syw/G9vgtlk6M+HxCrWsAq5JnU+s6mBidRFzt\\nCGo39WNIYqKrJkMGLIutHbHaVT5mQ6B8dF5t/RMFvx6yvGFR/G71IMw4cHzJ9IzaDZOLc0pm5tVH\\njyghI6WWYQLl7KngV6BaEYEmW9oRO7hrsXRSxOcTIJM1SQdRGnMoUTqVRP8okv39yaxL4etFEv3J\\nIk1Yu96vhRuortoS00C9/DrqqpLTAycnVcRXVOdm5tau7bc2bl3fdQnLN31XXrut0D+SupJDa6g+\\nvoroGX76J5jTNVXwy1AtiVjjLWHFKn6LJcKIz5fC1vhJFMQfCkwjvmEsSfWDkPx0cja5qB0QyKoc\\nXT5qrZSM39BQOmFrfc2Q8orEp+r+NuYNfWO3HnxcfJxmTUzLmzGsfNGROVWVh+UQM6qYjOQ6Rgok\\ns6cNfimwrTt50nQUjsfO2cCNXc6kY238Fkv4EZ8vgZKYsWxOnE2DzCA2MJ4k/xDS6zKJdcVQHUXU\\n4q+rErJfU1fheld19ea4eq2OApjDHL0w5sKo/PT8+PVZ6xO/mrYq5aXem+s2bl+zceBamFrP9uMr\\nqPxJOTGDamqHy4KCfiygH0apf8suBb/ZKvj9pxmPnW6D7fFbLHuJ+HzR1Msw1iYdQq3rYKL1AHr5\\nh5Na3wdK4llUACty6xMr0nZM5JjcKev9JZO31tUOq6hOjHXVjXy/4b0B93JvVGN90THRxKTG1Mto\\n3ThjbM2XJ6+i5ugNRI/bTu9EP2OAYcB69uzBr0e1IQKXoNvTJJJmp/TYCQVr6rFY9gLx+VzAQDYm\\nHkRpzCFE6WQSGkaRUt+XtPokimJha0K9qzBme5+vvi6v+eaePrXVuYk1Wr7TJDOTmQ1XpV2Vv6nP\\nJv/q/qsT1/Rfk7YlY8vW3LrNa3t/6485vJqCn5dQfkwxvRIDjAPGAFvZU8GvRrUuEtehJ+L1eo/D\\nuGl2Gb/8lrCK32JpgjOo2of8uMkUxs1GOJD4htEk+QeQUZdKUUBYVBhg9cbKmHWrqjJKtOx0uWrh\\njK3VtYP8NWlR4h8jyKiFUQurr/dfnwoQ5YoiJjVGA+mB8roBdWunTMQ3Zyk1J68mZvx2smICjAcm\\nADto3pOmKlLXw2Lwer3xQEZX7eUHYxW/pcciPl8qZdHjyU2YTUCmExsYS5J/MOl1GfhdwuYEoTCu\\njKKYrQNzXJtHLFpQ9d3GXx9dSUlKcD19pE/DLYNuWb+m/5roVQNWpW/I2hCbm5m7ojpQvar3F/CT\\nUnJ/s5WaiUVkRSkHABOBBnYPVdAYdGxHh18IS4/DKn5Lt0Z8vgRqXaPZnDCL2qiZRAcm0KthGKn1\\nvYkJxJATCyuLqlm9rtS1cXlFUu4KEiqK9L6Gh1/Pora/oOMUHeeP8tfn9Mop+WXZL0cIQmxSrNIb\\nf21GbQEprB04jfc8C6meu5S4MUUMEKPcJwKJNHWThKWobovgZbG0gdfrjfN4PN12QppV/JYuj/h8\\nMcBwNiccRHnMLFw6iUT/CFL8fUn0x5OXAHnxdRTFFlISu6HPZtfaI5bX5h2Sm1t3A8ff0kBDVNM6\\nr51w7Yp1Q9clrh6wuu/mzM3FZYlly4AVKYspu2wbmy9bSvTwHQxhl4LvA6ygqYKHXOtJ03UI8tg5\\nATi4K9vxW8MqfkuXwBlUHcz22MkUxh2CMIX4BjOZKaU+ie1xSm5CgA3FO1i3pkg2Li5N2rKY6MqN\\nqbUU93uGp/+dRtpIRccFJJBQllhWkJuRW/n7/N+P90f5Xa5Ml9T2qS3XFM0hheW9RvPRlYso/c03\\nxPevYAS7FPwQYC17KvgN1pOma9NdPHZCwSp+S6fBGVTtS0XUePISDqFBphEbGEOyfxCpdamUxyib\\nE4Q8KWVHQi47Elen5UQvOWVxVd7xVYWuAdQMOJMzri6hpFfTuk848IS1RROKeq3vtz69KKloLcIK\\nYEV0OWuuWUTxDZ+RkF7DWHYp+NHAZvZU8GusJ033oqtG0twf7AQuS4cjPl86dTKGLQmzqHNNJ1rH\\n08s/hDRXBnUuYXOii4K4Copj81n5fQFrPtmUtHWhxPpz+9ZTOriCiqy/8JdV05g2AzipKrZqa0FW\\nQel/+63zx+XH+eM1vr6+X700pDfkk8JqMvj+PwPf+/x3C9nue5GkxHomYJT7ScB4oIhdCv494B5g\\npfWk6TEcDkyjE+W+jRS2x2/ZL8Tn64VfRrEt7iDKo2cSpRNJbBhGan0fXBpNbiJsja+jKKaADVu2\\nUpa+OCln+Oc/21hUfApbE3vRMAoYdyVXnrqYxWlN6x8/fXxu6ezS5Py0/IaAK7AcY19fASz/7VcU\\n/OU/pMcGdnrQNC417N57b/SkKW1av6Vn4fV6pbv38oOxph7LPiM+XywwgqLYyRTHzkJ0MvGBEaTU\\n9yO+IYG8hAC5CQGK4rZTFLuJotjlLHyqKDH/ucwECob5qR5VSeUAP/7oK7ky/zROS/dH+fOKkooK\\nN2ZtrFkxcEX0f3f8d8S2mm2Z/t7+alLZSDpL6M/X9GKRZwGb//42faN1N+U+EYhlTwW/DNXCSF0r\\ni6UzYRW/pVXE54sChlAZNYFt8bNo4EBiA6NI9g8gyZ9EYZySmygUxpWwPXozuds3sOqzvKi8qC+v\\n9Z+w4kfk93HBOIxJZdyDPDj9FV5JDD5HbExsfcr0lJLSY0pT6qPrt7Cr974CWH7Rd2x89A0Gwh4K\\nPgNYzp6Bx7ZaTxpLUxxb/myPx+OLsCgRxyp+S+Ogan/MgOYYYAzKaMqiDyaxoQ+lMQE2J0ZREFdO\\ncdxWtseuJi/hB1Ykf5VaemZGNF/9NEDDhCqqBtdSmwBwFmf5L+Oy/PKE8q25mbllKweu1CVDliT9\\nUPfDiNJtpZmkU0Aaq8hiIWl8B6y4dAEb/vkWg9lTwQ8C1rDnjNaNdOP0fZb2I8hjZz1wZk8y6zSH\\nVfw9HPH5LgX+grF/rwZWUx69mX8NP5E1SXEUxd3BtuwNGdxS9Qr3ZGF67zuXd3hnwF/4S0xjfdGu\\naH90SnSNf6o/4Hf7o4CVBPXegRUXfs/Gx15vVsGPBHLYU8GvRbW+I66HpXvREz12QsF69fRQnF7+\\nHcAcYIa63asARJgEvAq8D1nPQOGlwJyBTKqsj6ovzEvL27S6/+q6xUMX65IhSwo3RW1KZS2xpLGe\\n3izyZ/i/87v8y4EVZy1j84sv7WaiOR24BfPQKGSXYn8L+F+MJ01Nx14JS3fF6/WOA57H+OX3eI+d\\nUAir4heRE4D7gCjgX6p6V5P9vYGngX6OLPeo6uPhlKkn4QzKPgyMBWar210IIMK5wP3w8e3gngV8\\n0XhMaa9SOf6q4wMao1sxE5h2edL0ZpvOow+7FPwZzucBQBW7FPwnwEMYT5ryjmmtpQdTAfwVeNr2\\n8kMjbKYeEYkCVgHHAlswiSLOUdUVQWXmAXGqeqPzEFgF9FVVf5O6rKlnLxGfLwV4BagGzla3u0qE\\nWIzv+kmwbi6MehvoK0jdqa5Tte7guoZ3f/TuncBfdB69YA83yQMwD+jm0vcVdXQbLRZL60TC1DMT\\nWKuqGx0BngdOw/QeG8kDJjvfU4CipkrfsveIzzcAeAfTk/+tut1+EQYALwHFwHTVkTtEuK9/TN9z\\n7uCPBxSPfyF/7tKP3h/8BUdg7KTp7O5F84bzmW89aSyWrk04Ff9AzFT4RnKBWU3KPAx8JCJbMXlC\\n54RRnh6B+HwTMEr/n8Bd6narCEcCz2HML39SJSC3SZx3qOes8TnHTh4Ue9u2IYuX+VzGo+bfGAWf\\nYz1pLJ0Jr9d7IHAJcIXH47G/zf0gnIo/lF7hTcAPquoWkZHAByIyRZuxCztmoUZ8quprHzG7D+Lz\\nHYHp1V+jbvfTIojA1bDtBsg6X5X3AfpfnXDUy89c+U7/wqlx1X3+5hlWsPThyEpusbRME4+dawhN\\nt/RIRMQNuNsqF07FvwUYHLQ+GNPrD2Y2cCeAqq4TkQ2YgcgFTStT1XnhEbN7ID7fHOBB4Bx1uz8U\\nIRl4BJ6YBhclgGbKbaQctDnp7y88c8u5MfUpVVnyzsSRBZ+ui7DoFkuLOL38xzHuv9Zjpw2cDrGv\\ncV1Ebm2unCuMMiwARovIMBGJBeZi7MTBrMQM/iIifTFKf30YZeqWiM93Ncar4ThH6Y+D+q9h7ji4\\ncCRoMpn84qwfMtf//dG/nptQ22tJfEND75ENL1qlb+m0eL3ew4H3MQ4Jp1ql336Ercevqn4R+Q3m\\nxkUBj6jqChG51Nk/H/gT8JiILMI8hK5X1eJwydTdcMIs/BU4DjhU3e4cEc6E/PkwqxByJgF+OYSl\\n91UNnz79tT8nRVP0eDlTLnGr274uWzo7XwJTPB5PXqQF6W7YmbtdFPH54jFzIHoDp3OUuxzzIJ0D\\n6dthx0FEU5Z+Ovrh+7NqKsr+JyOWHVfO0vP+EVHBLRZLh9GS7gynqccSJsTnywA+APzA8RzljnXW\\npwLTmbrjFgZSfOTJbP/h5ZOlouwPSUr08VbpWzorXq83se1SlvbC9vi7GOLzDQPexYQ/uIGj3DMx\\nnjxPcOIVf2TWg1e4Atz4zMvyzYzlvzxiCz8pC5BwlFtNqAaLpTMR5LFzBsasY9002xEbq6cbID7f\\ngRiFfxdHuR/AuLfNA37BPMkBPu9XTtXyB2KX5tbNm7GFaasCxJ3gVreNT2/pdDTx2DneKv2Ow/b4\\nuwji8x2PselfxlHud4F/wuuHEXX5h9yytRC45Ndf4/3ru6kX/MADVDPwa3Cd51Z3dWQlt1h2pxm/\\nfBtjJ0zYHn8XRny+CzFRLX/CUe58CHwJVzbAg0Np4BK+57OcbB7MLBt05Tf8s6aBXk8DN7rVbXtQ\\nls7IgZjxKOuXHyFsj78T44RUvhm4GDiRo9yjYMdjuGblE1g9EaDXAF4r2YpWMG3cIu7uDVG3uNU9\\nP6KCWyyWToH16uliiM8XDcwHTmd77KEc5T4XVnuJ7asEVk8kmrqT4rmpYiuT8zg7YRH39IaoC6zS\\nt1gsbWFNPZ0Q8fl6AS8A0dw/+nReG/goaRtSuHDqYh6vOxpY/1YdT5zk5+rF3PV+MTOPAI5zq3tR\\nZCW3WHbh2PKP8Xg870ZaFsvu2B5/J0N8vr6YWBvbOGP2rbzW38dx18GVI0aRVr1gFhxcXMeCE4k6\\n8wte+W8xMycCs63St3QmHI+db4HLvF5vVKTlseyO7fF3IsTnG4Px0X+Ko4/MIWPVu1xz9FaS8zKB\\n43QeAeClOlK/+oRX8iEqDTjcrW6b5crSKbAeO10Dq/g7CeLzHQz8m6qoeZw2fTrTfvlbfvSYEBd4\\nBPibzuN1f32JAAAgAElEQVR84O4SptyxiPsuwsQxucKtbpu4xtIp8Hq9YzCTCW0kzU6OVfydAPH5\\nTgP+xdKU67i74noOGzyUT4oSWM7LedXM7wf/Amat5reXbuX0+4G/AffYQGuWTkYRcDfwrO3ld26s\\nO2eEEZ/vcuBmnki7my0P30n+/BiWNMQADIBnNsGUaPjhC158tY4+84HL3ep+ObJSWyyWrsB+T+AS\\nkURVrWpfsXou4vO5gD+hnM4Daz4j/bZ7WFhQSXFDIlBzITz2GJwF3Ojjw2hwPQSc5lb3lxEV3GKx\\ndHna7PGLyGyMqSFZVQeLyFTAo6qXd4SAjgzdqscvPl8s8Cj1DeN5ztuXzI/78HjZ52yvPkpg01vw\\n7UkwtYG4OZ/y3jmYJPUnudVtE6dYIo7jsXMV8AuPx2PHmDox+9Pjvw84AXgdQFV/EJEj21m+HoP4\\nfKmovsqOwiy+umY8NVOXsWXmZLa/WdcXHloMU7KAPE48bBXXPwj0BQ5xq9smqLFElGY8dhoiK5Fl\\nXwnJ1KOqOSK7PTTsU34fEJ9vEIH6D8j9NpNvn8xg4VV36hfXmJyYz8kZmAfs7Z/w7vMB4t/ApKE8\\nzq3u2giKbbHY3LfdjFAUf46IHArg5M79LbAirFJ1Q+TD9yehDR+z/qVkXuxdzndvz9LikQsx1/Ru\\njDnnZB/ZpcBXwDPArdZzxxJpvF7vdOAdrF9+tyEUxf8r4H5gILAF+A/w63AK1d2Ql28/n9Spj7Dw\\nNT93911FyV/L4Z4VCEOBF4EC4CAf2RMxCelvdKv7sYgKbbHsYiEw0ePxbIu0IJb2IZTB3UNV9fO2\\ntoWTrjq4K7dJLMMveYp+Pz6LJ7ZW8eyb38N7hwH8GB56C34K/AX4q4/sc4F7gXPd6v4wknJbLJbu\\nQUu6MxTF/72qHtjWtnDSFRW//DF6JkPOe52+p/Xm+j7lLD1jE2ybCjRcCtkPwTgXnO0j+wvMgNkl\\nwI/d6l4WWcktPRmv15vi8XjKIi2HpX3Ya68eETkEmA30EZGrgcaDk7HB3VpEbpMkJOoORv7WQ9yP\\n4MIRq9h2QDr4p7qg8HXIOxkCwDTHnv8oMBE42K3u/MhKb+mpBHnsnOv1eid4PJ76SMtkCR+tKfBY\\njJKPcj6TnKUMY6KwNEFuk+NxxS3jgL/NoXQueI56gW0J08H/UQas2AB6sollcqKP7HpMQLYMwG2V\\nviVSBEXSnAYcaZV+9ycUU88wVd3YMeK0KEOnNvXIbdIbuJeYNDfjnxZ+GNaHe8dcqeUx8xGJKobb\\nesHFcfBzVD/yiW8oxkviA+Aat7qtP7Slw7GRNLs/+zOBq0pE7gEmAAnONlXVo9tTwK6I3CYCnAPc\\nS68J7zDyn0l82M/Fv0YcpjVRCxDpCzyTYd6apqGa7xPfdMxkuLvc6v5bJOW39HhGA5Owfvk9jlB6\\n/B9gskFdC1wKXAgUqur1YZdulwydrscvt8kQ4B/AEJJu8jLypHt4cFUeH74yV/XtrxE5AngWM+ll\\nHqp+n/hOAx4GLnGr+43ISd89ERHbW7X0WJr13tkPr57vVHWaiCxW1cnOtgWqOr3dJG6DzqT45TaJ\\nAi4HbgXuw7+whoMq7+KGR5ey+vGJwMqt8EJ/U+YiVN8F8InvSuB6TKC1BRFrQDemM/1OLJaOpEUF\\nvx+mnjrnM19ETga2Aun7J2bXRG6TAzA99gYaYg6jcsnNTNt0Dhf9dhk7Vk0G+CXE9jGhF2agutkn\\nviiMf/4xmBSJmyLXAktPxLHln+LxeF6JtCyWzkEoPf5TgE+BwcADQAowT1U7zFQR6Z6c3CZxwI3A\\nb4BbeGrxk5we8wX9vx/P7y/Jo75qSBRUPgdVZ8GTwI2o1vvE1wtj7ukF/NSt7h2RakNPINK/k85I\\nkxg7Z3o8nrrWj7B0Rdq9x6+qbzpfdwBup7KZ+ylnl0Fuk4OBR4B1wFT+UZ3EpetyGLMDrr75Fuqr\\n/rcP5H8MsePhl6i+DuATXz/gLWAxcJZb3fYPZ+kwrMeOpTVam8DlAk4HRgJLVfUdEZkO/AnIAqZ2\\njIiRw7Hnv4Xp6b+At+qnXLvqafrVrCHJf3DJtvXRD8LcK0BT4aeobgDwie8A4G2MWehPNtCapSPx\\ner0jgH9jI2laWqC1CVxezABlOnCziLwCPAE8BHRYuIYIMwUoZJ6+xOMVf+WPS59hYPV7DKqeqj85\\nfGwaLLwZPk+F2UFK/1ggG7jJre47rdK3RIBtwJ+BUy+99NLhIrJGRMpF5NTWDhKReSLyVCv7N4rI\\nMe0lpIh8LiJT2qu+7oqIxInIChHp3W6VqmqzC7AUcDnf4zGmnsyWyrdQxwnASmANcEMLZdzA9875\\nfC2U0b05b3stzONabkx6hDFln/LS59W8+tldt15wgShcplCoMCe4fDbZF2WTnZ9N9hGRkLenL5H6\\nnYQo20agCigH8oGngJQmZWYDH2Fmx+/ARGod36RMCiY50ianrrXA/7X03wQ+BK4IUcZbgada2b8B\\nOLqV/XcB253lf9s41ynAO5G+L+1wX/emzT8Bljn3dxlwWtC+eUC9c0/LnTLDgvZfB9zTSt26V9tb\\nqej71tZDuCBRzo9yGBAD/NDMjzjNuQCDnPXeeyN82G/q9ZmfcfoLJbz1SQUv/PuyGTBC4VmFRQpj\\nGstlky3ZZN+eTfbabLLHRvrH2FOXTq74dypNTFa1H4C7g/Yf4vzhr8A4A6QDtwPFwHCnTCwmtML7\\nwDhnWx+MLf/EFs67BjgmRBnn7avix8zxWQkMcJZlwKWt1PU2cM4+XsvoSN/PvW0zxjxeCRzvrJ/k\\nrPd21m8FnmzlXIOAQiCmhf26N9tbM/WME5EljQswNmh9cSvHNTITWKuqG1W1Hngek2wkmHOBV1Q1\\n15Fwewj1dggSV3YByX1mc1lWFL0azmTu6eMWweLnYDJwMKqrAXziiwOeBo7FpEhcFUm5LZ0fVS3A\\n5LU4IGjz3cATqvqAqlaqaomq3oJJyjPPKXM+xrvudFVd6fV6D5w/f/4/5s+ff48680WCEZF1wAjg\\nTREpE5EYERkgIm+ISJFjArqkJTlF5DwR2SQi20XkpjaadQGmR7pVVbcC92AmezZXbyxwFPBx0LaZ\\nIvKliJSIyFYReUBEYoL2B0TkchFZA6xytp0sIj84x3wuIpOCyv9eRNY67V4mIj9pQ/59IeQ2A6OA\\nClV9H0BV38Eo/pGNIrMrEOYeODqyBNNB2G9aU/zjMa9jjcuEoO+t2godBgKbg9ZznW3BjAYyRCRb\\nRBaIyHmhCh5ORHDRb9H99Jm2jVhe5qijqoArGyCxDF5EtRrAJ75MTLydWOBot7oLIym3pdMjACIy\\nCGMG/dpZT8T8oV9q5pgXgeOc78cC786fP9/v9Xpvw/T8X2fXXJvdUNWRmAHek1U1JagDlgP0xwRb\\n/JOIHLWHoCITMON5P8P0ZjMxvc6WmAAsClpfzO4PtmBGAwFHWTbiB650znMIZt7L5U2OOw2YAUwQ\\nkQMx3na/xAQ6nA+8EfSwWAscpqopwG3A0yLSrzlhRORc5+HR3FLs3K/9bfMiwO88rKKcB1GNcwyA\\nAqc4D+SlInJZM3WswIw77jctevXo/gdmC2VQMwYTEfAYIBH4UkS+UtU1TQuKyLygVZ+q+vZTvtYY\\ny8j3A2RNKwQW4PS4roP6S81cBnziG4kJtPYaJmNWIIzyWNoBkZB+k22i2nLPrLXTA685YSWSMAr7\\nDmdfBqYTltfMcflA46BeZlZWVg7G3LPXHjsiMhgzjnCiqtYBi0TkX5g3iewmxX8KvKmqnznH3oLx\\nbmuJJKA0aL3M2dYcaRiz1k5U9bug1U0i4gWOxGT/a+TPqrrDkccDzFfVb519TzpvJYcAn6jqy0F1\\nvygiN2KsEHvMP1LVZzHzbfaWkNusqpUicikm/E0s5mH9U3U6kZgH/HxMNr6DgVdEZIeqPh9UTTnm\\n2rWIiLhx3O5bI6Rk6/vIFsxraSODMb3+YDYD253GV4vIJ5gn2h6KX1XnhUnO5jicsW/VknR7Ig88\\nUAMcHQPVv4PPUC3xie8Q4FVgnlvd8ztQLst+sI8Ku91OjxnM+0hMHKc3genAN5hX+ACmF766yXH9\\nMbZdkpKS6oYNG3Yu4GHf/PIHAMWqWhm0LceRo7myO/+vqlolIkWt1F2BGXhuJNXZ1hwlmFDvOxGR\\nMZgZ7gdhOoHRmE5XMMEWhKHA+SJyRdC2GMz1QkTOB36HGWMEo5AzW5F/Xwi5zSIyDeMpebiqfue4\\nxr8hIieq6iJVDc5j/qWI3I95+AYr/mTMtWsRp0PsCzrvrc2VC2dClQXAaBEZ5tj05rLn0/Z14DDn\\n1ScRmAUsD6NMoRFbfgR916QTndSf1147E+BnsCkLnndm474FXGyVvmVfUNVPMG+OdznrlcCXwJxm\\nis/BeOZQUVHx6rffflt66aWXvrKPk7G2Ykyrwb3SIezZIWssu7Pj5vw/W1Ocy9h9bs8UjKdec6w1\\nVUr/oG3/wPz3R6lqKmbAuql+Cm5zDnCnqqYHLUmq+oKIDMUo2V8DGaqa7sjS7INfRH7muLs2t5S1\\nYurZmzYfA3zV+Gajqgswpr5jWyjfHOPZ3bS074Q4ep0I7LW3CnAiZiBmLXBj0Ej4pUFlrsVcwCXA\\nb/dmZDpcCyPfy+PumWvIzv4BODQa3syDMoXMbLLnZJP9fkfKY5cQ71sX8epx1ntjBvdmOeuHYnqL\\nV2B6dukYU1AxMNIpE4t5Q3gXGItRjJnATbTs1dP0vI0PnTiMo0I+u7yN5uF49WBs1eWOXLGYgct6\\nWvfqWY55Uxjo/Kc9rVyP1wny6sEowVswynmcozc+DdofAEYErR+EUf4znWN6AT/G9OwnANXAGIx3\\n4UWO7Be38z0Nuc3AjzBvblOc9QMxLqDHOuunOfdcnDZtAc4LOn6gU75dvHpCadypzk3YGCTwGx38\\np2lW+PCcSwdxzI2VPHXF+2Rn/0tVUThT4QNVJZvsV7PJvqgj22+Xzvc72QfZ9nCFxAyevhq0fijG\\n1t7ox/0mMKHJMSkYv/0cdvnx3wOkh3JeR4G8CRQ5x3qC9u3mUoix/W9yFM5NwPqWFL9T/i6n3iLa\\n9mk/iSA/fuBwzOBlOebhdBvGVt+4vyFY8TvbjmeXqWwrxn6e5Oy7w5GjEPirc13bVfG31WZM7z/4\\n4XYdJvRLufP5u6B9zzrXudy5Dr9pUle7+vGHFJYZOBrIVifBuogsVdWJrR7YjrQUaCg85+JsfjXp\\nQU6+fgGJg19Tt/ufiDwH+HxkP4exMw5zq7tVW5ul4+nI30m4CIqxcyEwzuPxVLd+RNdFRD4Dfq2q\\n7WO+6KaISBxm3sfh2oLLe0u//Za2hzK4W6+qO0R2O7b7erAkFB1F71XJJAwcCCxEJAFjsroK8/bz\\niVX6lnDQJJLmId1Z6QOo6mGRlqEroKq1GPt+uxGK4l8mIj8DokVkNPBb4Iv2FKJTMfTj4wikr0Bc\\nYzA+ticA36NagPjmYl4nLZZ2w0bStHQ0oSj+KzA/ylrgOcykkdvDKVSkECGd474cSMoBn1FcLHrG\\nGbXAmcDLPvGlA0dgJrRYLO3JQEyPzkbStHQIodj4p+nukys6nI6y3YrwYy4/4FmKB3/B8++fEAN/\\nrzNhJSb6yD4B+LFb3WeGWw7LvtEdbPwWy76wtzb+UPz47xWRlSJyu4h02IBuREjecgwZa+NZUzga\\n4HDj8rYcM7XcmnksFku3IJQMXG5nosUcYL6IpAAvqmr3M/cMzz6B6tRlrF03CeBm4yP9sk98fTCT\\ny86IqHyWLo1jy5+LteFbIkxIM3dVNU9V7wcuw8wc+0NYpYoAIsQz4JvRbPEvpLQ0Gsg70sQKeRVj\\n53/Xre7K1muxWJrH8dj5FtOBSoiwOJYeTps9fidK3xxM3IgijLnj6jDLFQlmMOr9er5KT4MSRsES\\nF6ShmuN489zfZg0WSxOsx46lMxJKj/9RTDag41X1SFV9SFW3hVmujqf3iuNJzYkidVg0SUmVvzBT\\nvV/2ia8/Jh7HexGW0NLF8Hq9QzC9/GkYj52nOlrpi8ihYlMvdmkkDKkX21T8qnqwqt6nqlva66Sd\\nkmEfn0RVnyXcfEtZzMsvX32tiWPyCnAW8IZb3TURltDS9SgA/ojJffuFiFQ5CjhfRJ5yxst2IiKz\\nReQjJzDYDjEJU8Y3KZMiIveJSZBS7iQb+T8RaSmA2h+Bv6lqsqruEZK4CW09lLSlMiJylJNXY4eI\\nbGijHkTkFKC0q8/aFZG7xCSq2S4i/9tG2Z+ISQrTmBzmtKB980SkvklwuGGwcwLXo8Dv20vuFhW/\\niLzkfC5pZgklA1eXQYQo+n13AK76N4Hpf3j6aY2Gzaiux3rzWPYRj8dT6/F4GiNpKiYhSjImiuMk\\n4ObGsiJyCGaOzL8xoYWHY8bTPheR4U6ZWEykzvGYN/BkTPz57ZjAXs0xhNAj3u6PK2wF8C9MTJlQ\\nuAyTd3ivEZFwhpMPGSe+/mmYTuJkTCKVS1somwU8A1ytJjnMdcCzQb14BZ5zHtDJahLnbAyq4jng\\nAgnKSrY/tNbjv9L5PJndM3E1Lt2JSYz4EPqnfgAM+91LL03DmHkGY6Ig/jey4lm6E9pOqRedugpV\\n9U6NcOpFVf1WVZ/BBIVrFbGpF9HOmnpRd6VFu1xN3tydC3umROvaDPzmxyQVKAc+EIXqsl61tadh\\nzDxzgNfc6m42tZ3FAsZjx+v1vu31elvKONVIu6ReVNWqUOTSjk29uDfY1IudNfViED8Cbmiy7aRm\\ntnVdhn58MmUDluKKmTYqNzcXiEd1NeI7G7gx0uJZ2ge5Tdon9eKtZiZkMx47rbn7tkvqRcxg8T4h\\n4U29uDfY1IthSr0YKi0qfhH5FeaJO1JElgTtSgY+b4+TdwZEEE5fPJllZS9Q8NDPj/D7azFmnpEY\\n+6gvshJa2otGhd0eNImkGUqMnf1OvYix5Q/YD7HDmXpxb7CpF8OUejFUWrPxP4ux5b/B7nb+g1S1\\nOwUqG87Qj+P4piCFl16aHvvJJ1OAlzFmnlfc6vZHWD5LJ8Pr9Q7DuPfeA5y6t4HVdB9TL2LGmo53\\nTEP7QjhTL+4NNvViZ029CKQ4n5mYV9HdlpaOC8dCGDMrMeqda7ghrRqRjYBmR0WtV1Wyyf4hm+wj\\nO7Kddum8v5Omy/z581P2UrbunnpRgHhM7oqNTv2xrVwPm3oxgqkXW+vxP+d8Lmxh6R4M+vJUNmat\\nQXWoKzFRZzc0POsT3zigD/BZpMWzdE48Hk/Z/hyvJpPSEzhjZar6OSaV4BmY3vZGTA/yMFVd55Sp\\nw/QQVwIfYOzLX2M6Y1+FeOpzMOaPrZhwJH9Q1Y8axXIWVHUZpsf8rFO2mN1NLU05EqgC3sa8KVTT\\n+qTH+cB5QevXYiLhlmF668+zew9/t/EZVV2IGdh90JFtDWasAlVdjkm3+CXmwTaRMPyXVXU+xmS3\\nBDNI+6aqehv3O4O05zhl/4Px3HpVRMoxVoU7VbXRY3Cu04YyzO/iz6oa7O56LvC4mgH6/abNsMyd\\nAQljuF2Ze0Ypy778hGX5Jw8ZNcq/ae3a6T6yfwKku9V9VTjOaQkP4fideL3eLI/H0/1mqncCxKZe\\nDAkJQ+rFNmfuipnyneR8P09E7nVsaF0eia3IYvCXyawrEIAZ8fHVG7hgMXA2dtJWj8br9cZ6vd7b\\ngB+8Xm9KmwdY9hpVPcwq/bZR1VpVHd+S0t8XQonV80+gSkxMjauB9cCT7SVARBn/77OQQC21envi\\nGWdUHwHvbOLCSZjoiaG+Olu6GUGRNA8CZuyvWcdi6WyE4sfvV9WAM+Hg76r6LxG5ONyCdQj9vz+d\\nkhHL017/14q6mJj4n82Z839LjK3tRbe6O78NzNKuNPHLvw540kbStHRHQlH85c7EiJ8Dh4tIFMZf\\ntuvTe/k0qjMeOvGbb366ZtCgurLyM77BxNOYG2nRLBEhAzO1/kCPx9O9gxJaejShKP65mBHli1U1\\nX0SGAH8Jr1jhRzLXJPOLhemUDnk8vrb2oaySkuWbuHCaszuiOYYtkcHj8eRjQhRYLN2aUMIy52F6\\nwWkicjJQo6pd38Y/9vWz8SfU6PyFa4tSU2fVRUe/hRnUfd6aeSwWS3cmlAxcczA9/MZIeg+KyHWq\\n2lxAqa5D75VnUjR6JSITlj7zTGJhcsaLGB/kkyMtmiW8OLb8C4BHPB5PINLyWCwdTShePTcDM1T1\\nfFU9HxMd75bwitUBZK45iC/y8vq5XNk5CxbIi2fFp2JmUrY05drSDfB6vVMxs19Po4WAWhZLdycU\\nxS/sChIFJu9uWCZTdRQy+6/x9FvUm03r6woCgayYoqKC+FrOAl6wZp7uSZBf/n+A/wNO6QlummJT\\nL3Z5JBKpFzHTrt8XkQtF5CLgHUyskK5L8pa5VGbVxtTXTgeoGzvBhwmGZSdtdUO8Xm8/TC//IIzH\\nzhMRyH27Ubp36sXrxGTnKxOR9SJybWsViU29uFvqxaAysY6S3xkeQzsy9WLQSa/DTOKajEkXN19V\\nr28vASJCypaz2DB4Qz0MjIqLC/x07bRNwDa3uldGWjRLWCgE/oDp5UfKTbO7p14EE3snDZNk5jci\\n0ppbtE29uHvqxUauA7ax5wO2XVMvthZ5bgwmgt4y56SD9iF63QmYgFJrgBtaKTcDk4HnjL2JMLfP\\nUfV+Ob2AUbPeAtQ1dWrNS5nZz2aTfWN7nsMuHb+09++knWVrGiXzbuDtoPVPgQebOe4dTEpGgEsw\\nQccSQzznOqABEzytDDP/ZgAm1HqR87+8JKj8PJzonM76ecAmzIPlpqZtaOPc92PeNJrbF+vINCBo\\n20xMULUSTFC4BwiKRImJznm5I/M6Z9vJmBg2JZgcIZOCyv8eE/65zNFhPwnDPf2iyfW7CPiyhbKz\\ngYIm27bhRGd11odjHtInAJubqWM1cMTe/PZb2t5aj/9R4C3gTIxf+99aKbsHzkSvB51GTADOafra\\nGlTuLoxJKexjB3LlqFj6rOjTd9PKCgCdcqBkFnEs1sxjCT89IvWiiAhwBC07StjUi3umXgTzsLvR\\n2d4c7ZZ6sTXFn6SqD6vqSlX9C+ZptDfMBNaqydPb+IPbw6aFiT/+MrsPIIePrdPPomREfX596YI/\\nzZ794czME9cJbHSre32HnN8SVrxe74derzej2Z0i2i7LvtGYerEMo3jXsW+pF5srE5oAu1Iv3qCq\\ndWrs642pF5uyM/WimnDQt2B63aEwz/l8rIX9zaZeVNVvVDWgqpswoZmPbHLcn1V1hxqb987Ui2p4\\nEqjFSUauqi+rar7z/UXMm0Kz5jBVfVZ3T+gSvGSoSXTeHHuVehETv/8FjGJ/BrhUndSLInI6Jlry\\n6y2cCzoi9SIQLyZdGJgfbYKzLpjXh7Zmtw5k9/jducCs4AIiMhDzMDga8yQP/4BbbNkcth2wFpb8\\n9N25c5ee9USfvtjefpcmKMYOmACCzaenC1No7xDpEakXReQ3OOFdtOXY8Tb1YlDqRczbyd2YJDat\\n0W6pF1tT/PmYZAYtre/xetiEUJT4fcDvVVWd18MW/5giMi9o1aeqvhDq35PU3FlTvxn8DnDa5xMn\\nbr5hJX0wdlRLF8Txy38cR0l5PJ4nIipQCKjqJyLSmHrxKDWJuBtTL37cpHjT1It3iEhiqOaeJuxM\\nvaiqjQqqtdSLO02zEkLqRSd44/UYO3Rr6Sh3pl5UExkATOrFhcBc53pchTEzB9Nc6sU/NSNHY+rF\\nozE2dxWR72kl9SLGgaU5FJjQQq+/MfVi4wNqr1Ivikhw6sWhwKdGDRILpIpIHmYMIMcpMx6TCa1F\\nRMQNuFsrA60oflVt8+A22EJQzk7ne9OLdxDwvNPY3sCJIlKvzbidqeq8/ZQHuU0SSE/MenhJbTHw\\neny166iEalIwr4GWLobX6+2PcS3+Paan35Vm4d4H/E5EZqnq15g2vC8iKzEPsmjgGsxb8gznmKcw\\n5oJXHMW4BpOu71Lge1Vt1c1aVTeLyBfAnx13y7HAxZhYXE15BfhKRA7FhKj+I62Yhh3leSfmQbax\\nDTnqROS/GAXVmOkvCWPKqBKRcZgIqa0lwHkY+LdTz7eYtwQ35sHZC6OwtwMup/c/sRV5nsGYXvaW\\nJ4GrReQdzEPlasygdnMsAq4XkSmqusgZozgc+DvmYRE8jnAoZny0MT1jo3WkzUxrTofY17guIre2\\nVDBcXgzRGDvmMMwT7AdgfCvlHyPMXj1cPHsOl8ysD8Cn34wdO/eAB7IrPyJ7QbiugV3Cv8yfP7/X\\nzvvbhbx6nG0PAa8GrR8KZGMUYCnGHDShyTEpmAloOU65tZheYHoo58WYYN/EePWsJShHLHAr8GTQ\\n+vns7tWzvmkbgsqux9jYy4OWh1q5HicB7wStH44ZvCzH5AW+DfgkaH8DQTl3nW3Hs8tUthVjsk1y\\n9t3htLEQY6nIpp1z7jrnucs5TxHwv032LWX3vMLXOTqx3Pn8XQt1uoGcJtuuA+5pRY5mf/stbQ9r\\n6kXHfnUfJuHxI6r650Y/VzX5KoPLPoYZTHq1mXpU28E+Kxcd+eqQzQMmf/jh832mP/PMz0/yDbjH\\n8zCfu9XdPfIL9HDa63di6RjEpl4MCYlE6sX9QVXfVdWxqjpKVf/sbJvfVOk72y9qTum3K1F1ky9Z\\nvbZ4DCSXnnfeqxO/91dhEiVbOjler3dgpGWwtC9qUy+GhEYi9aKIuMTk2v2Dsz5ERFqaJdi5cTX0\\nm75tWR8FIS2tdsyG6Dh296O1dDKCYux85/V6+0RaHoulOxBKj/8hjG9s4wBQhbOtSyFCWholCVW1\\n1f0BGDIkKqOYgVjF32kJiqR5EDDN4/F0zFwPi6WbE0rMi1mqeqDjDoWqFrdbvIiOZXY/zQssMw+u\\nuIQ+Q1yiVLnVbZVJJ8PmvrVYwksoir/OCasAgIj0oWu5zRmiq45Mb6iKWmm8AxhQ23u72Nj7nZVe\\nGH/XF+EAACAASURBVP9ym/vWYgkDoSj+BzARA7NE5E+Yqdw3t35IJ6TXtiN7V0ZVCv5q4uKqJuwY\\nUIwd2O2UeDyeEkzAK4vFEgbaVPyq+rSILMTMPAMz7XxFeMVqX0SIp1/J5H6VDcVe+PLZ996LO/M8\\nzcLa9y0WSw8klJy7QzApCd90NqmIDNFd04i7AmNJzSnoV6l1mMkeJ/cvkKaR8SwdjGPL9wBej8dT\\nF2l5LJaeQihePe9gkpC/hYkVsp6ul4EriZTcuv4V1Ppdrh0xdQyMqWcwZqagJQIEeeycgM19GzbE\\npl7s8kgkUi+q6kRVneQsozGhTVuNF9EJSSR5K/0q8G/OyqoetpEqgXVudddGWrCeRgu5b4sjLFbY\\nke6fevF3IrLOkb1ARB4TkeTmyjrlberFoNSLzkO33rmn5U6ZYRCh1ItNURNdblabBTsXiSTnSVYl\\nur5//9oxq6nDDux2OF6vN5MI576NIN099eLrwHQ1iU/GOef9n1bK29SLu6deVOA55wGdrCZxzsag\\nKto19WIoM3evCVquE5HnMJE3uxK96FUQlVtK4kvR0ZkjVtQFsPb9SFAM3EBkc99GHFUtwLzxBGdr\\nuhuTYvEBVa1U1RJVvQXzdj3PKXM+Jsrt6aq60qmrUFXv1GYic4rIOmAE8KbTg4wRkQHOm0SRYwK6\\npCU5nRn7m5ze7E1ttGm9qjbGindhXL6bTRrjPMCOIigEtYjMFJEvxWS92ioiDwQrOREJ/H97Zx4m\\nR1W27/uZSWayk4UkECCCJIABRNkEQRlENOx8ICIgAp/a8vEDvT5EQD8VEERURFzBBiSCrAqIaFAR\\nCJshKEsCCEIQEAiErGSykVne3x/v6VDT6S2T7unp7nNfV13T3XWq6j3VPW+dc+rU80g6RdLzwL/C\\nZwdLeiJs85CkHRPlzw69oUzr+vBC8feSE3DhtHnmMtQXAyfmKTsJWG5mfwYws+n4vdOtMyFT4MJr\\nLgu9hGA0s8GUoD53TmL5P9yKbVC5Ve6KxJBTYa707e1zfH7Xl3dpYjlgX5h4Yfu93HtQX9YhLpVf\\nNvR3UuHYXgT2C683xxse3wzvh+DWg/vk2O5EYF54fSNwdS+Om1TnvB+X/G3Bex5v4lLKkPDcxW0F\\n24G9Q9kfAB0U8NzFn+5/C0/61xcotz2eBJOf7Yz3Wppwbfp/Al9KrO/Ge0QjgVZcsng+Llkt/KL4\\nIsGnF592vkl4/Un8wc1NCsS9JM+ymDx+48BSYLfE+12AZXnKDsUbzAfjopWH4wqrg8P6c8L+FuHP\\nF52cYx+3A6etz28/3+cFu0zhwa0RZvblQuVqgCEMXtz6ajetAO9fNLGF2OJvODRjRlmGlaytrTdD\\nIhnrRcNvZt9O76wX/96LY3sA71gvHmBupzhbUsZ68d6s4mutF8O23wBOLbR/M7seH76YBPxG0v+a\\n2Q9zFM1pvZh4+7KkjPViUt/+O2a2NMSz1noxrLsm9Er2xOWcf5vY982SvopfWHJ5fVwPXF+obnlY\\nL+vFMAx0E34hXQN8woL1Iu6t/Av8YrYH7rmw1MxuTOym8taLkgaYWWeYFSALl48aZQjNSwbNhwFI\\n3Zt1jofcrkORMhBm7PwUOCqVSvXaI7bc9DJhl+3wNID1Yig7N9zoPBu/gZ9NtF5MWC+a2Wzr+WzU\\nTEk/wi++ycRfNuvFQmP8j4S/TwC3h/G+I8NyRDkO3ocMZUX7UIDBg0a2Lx43YFGbtdXyhaxfkjVj\\n5wq8tRrJwszux5+I/254vwLIWC9mk229+HG5DWJvWGu9mPiskPXiWgc9lWC9mMVAIJ895FrrxcRn\\nl+HDO5PMbCN8WDk7P+WyXkwaow8zs5v0jvXi/wNGm9kofPgkr/ViYjZN9rJM0ua5tuMd68UM62W9\\nCCStF0vhPfgN/w2mUOLPnKRB+LjTR/DxqYOBQ8px8D5j8MIRTQtdXmh064Q188dTyA800guylDQb\\nbcZOb7gU2F1SZobc2fisjdMkDZc0StIF+Ay680KZa/FW7y2StpVLpo+R9DW56VFBzOwVIGO92Crp\\nvbj14q9zFL8FODj0+Fsobr34ObmOF5KmhPrckieONfhFrC3xcS7rxUJcAZwcbgpL0lBJB4WLWrb1\\n4kkUsV60d2bTZC8jLLffLrxjvThBbo14Om6bmYvZwIcUnlvQO9aLs8P7w8J3Lrns/Rfx4UDC+pKs\\nF0ulUOIfK+l0fNrjUzmW2mHkS2OGrmlZvRd0btOyg82bwEvVDqmeSKfTo/Chi8y8/IadsVMq5qYa\\nv8JnOWFmD+FWgkfgre2X8Bbk3mb2QiizBm8hPgvchY8vz2L9EsIx+PDHPOBW/AbzPZmwwoKZPY23\\nmK8PZRfTc6glmw8CT0pqx6ekXkPuYZ4MvwCOT7w/A7/Jugxvrd9IzxZ+j0aEmT0KfB4fUlyM+w9/\\nJqz7J34zeibe69wBeLBALL3C3FDqDjxHzsHviaQz6yU9JemYUPYv+MytW8M5+i3eY/lrKH50qMMy\\n/HfxHTNLTnc9FphmZh3liD2v9aLc4T2f8zxmdl6+deVGG2ipp23vmL7dXift8MzVizqv3/Te4Tcd\\nzZW3/7Dtq+WMsdFJp9ODUqnU6mrGsKG/k0jfomi9WBKqgPVioVk9b/Rlcq8oQ+dvNK59wPJumldt\\nvJCJj+wepRrKTbWTfqT2MLO9qx1DLWD+5O57ihZcDyrqudtvGLpg+Pj2ppUr2HrNshGseWNT5lc7\\npFolnU5vVe0YIpHIhlEo8a/P3eb+zZBFwya0s/otdtBLW7KannNvIyWQmLEzK51O55vlEIlEaoC8\\nid/MSpqzWxMMXjxk83Zb3c62rS9sTTf+hFykRHLM2InPQEQiNUy/EDuqOIMXD3rh9eUj/8qKsc9u\\nYwNAscVfAtH7NhKpTxoj8bcubL184fJdmvgpm04+fA1xqKdUBgDjiN63kUhd0RiJv3nBAIAhDOH1\\nzSRgVZEtIkAqlVpJ8QdpIpFIjdEYs3qaFzUDDKKlq7uZt6wtyjVEIpHGpe4Tvz55VBNaKoCBGvg2\\n8cbuOoQZO19Op9O91YCJ9FMUrRdrHlXDerHmGbR0U5Z7PtOAppXE8f0epNPp9+NSv/viqoiRCqA6\\nt15MxNcSklQheYdovZhlvZgos875s/5gvVhzmCbSNar7g2zfPWzk+BeJLX6gx7z8P+O6JoekUqmc\\nj4NHykK9Wy9m+Apu7lLsIhKtF3taL2bId/761nqx5mnu2Jyhm3VexPe0yeln/ZrY4iedTo/AW/m7\\nAO9LpVJxmmYfYnVovRjKb4U79H2HAhcRRetFbF3rxYLnz8psvdgIiX/ChAVbdjSzUrN2Hv02MfGT\\nSqWW4bKvh6RSqShR3XcIQK7vPhVX1sxo3e8J/CbHNjcD+4fXHwXuNLN8Ovc9MLOtcd36g4O8cAeu\\nevkfvFfxCeBCSfuuE6hLK/8cT0QTcC3+Yk9s/wT4KlBMt2ky0B2SZYZO4EvhOHvi+vWnZG13GG61\\nOCXIGl+FK3SOxtU+f5+4WMzFlU1H4LLWv5a0Sa5gJB0bLh65lsXKr8c/hZ76+HPoeTFPMhvoDBer\\n5nAhWk1PJ8Bi5+8ZvLe4wfSLLlNFaX570x1fe1f3UF7sertlt+HEoR4AUqnUfcVL1RczVB7rxTaL\\n1os59v1fuNrv7ZLaioQSrRezrBdLPH+Vt14sF5Km4qYTzcCVZvbdrPXHAWfi/xjtwP+YWfn8cJs7\\nxm+/YBOG8PxK2G0jGqzFn06nFYdxnF4m7HJRt9aLkobiQ1VFzWAC0XoxYb2I905KOX99Yr24wcjN\\n2n+Kd2unAMdkz1IA/g182MzeC5yPn5zy0fz22K3fGtM0lJeXAqNooBZ/mLEzKypq9i/q0HpxMp6I\\nH5D7eNwCbCrpdUkTc5SP1os9rRcnUdr56xPrxXKwOzDXzF5KjC/2mMJkZjPNLNMKn0XxccT1Y8Dq\\nMStWvNnyFx5fzbx5k2gAk/WsGTs/heg41g+pJ+vFJ/H/253C8jlgfni9zv9btF5cx3rxKYqcP/Wh\\n9WI52Iye3bNXw2f5+CwwvZwBtJqNmd51x8CTeG0yjz66HfByOfff30jMy48zdvox9WS9aGZdZvZm\\nZsGHIzKfdeeJI1ovBuvFEs9f31gvlmXn0pHAVDP7fHj/aeADZnZajrL7Aj8D9jKzJVnrjHdaPQAz\\nzGxGKTFM+czuCwdf2znqMR5v4vzzV7D33ltZW9uC4lvWHul0ehjegjgPuLbREr6i9WJNoWi9WBJa\\nD+vFcGO4LbHqnPW1XiwHr5EYJwyv1+k2hS7nFfhFIufNCzM7tzcBbLRm0LClvNENNDFs2AC8+1eX\\npFKp5el0ertUKlWWVkEkUkmi9WJprI/1YmgQz8i8l3ROrnKVHur5BzBZ0pZhnPBosqZThZsXtwKf\\nNrO55Ty4zlNrS1fTwFWs9CteS8u8ehdoi0k/EokUo6ItfjPrlHQqfpOxGbjKzJ4J81kzY2TfxGfb\\nXCYJoMPM8j1+vr6MHbxq5JrVrGoBoLW1oH5ILZFOp7cBnm+04ZxIJLLhVHwef3iU/M6sz36ReP05\\n/C52JRg3eOXojp14tx4ZP3DR0uHDX6zQcfqMLFesD+JT4yKRSKRk6l2yYdyglSO7zuewFZPOPXcm\\n48ZlPxxTU+SYsROTfiQSWW/qXbJheOvbw2hhuV4fs+1IanQqZ1Yr/wwacMZOJBIpH/We+GntbG1u\\nYQlLhw3bmBpN/Pgc5uF4Kz+KqkUikQ2i7hN/S+fAplbam1a1tGxCjSb+MFPn9GrHEYlE6oN6H+On\\npXNAs5pWtnQ3NY3Cn0CMRCJVRNJYuctUa7Vj6e9Ieq+kh8q937pP/N2rVzXdOmg+PP30Qmtr66p2\\nPIUIGjtnp9PpUdWOJVJe5NaLbyvLIlHS43KTkVxiZpWMpy0cNyNE9lyQOk6WkaSvhHUr5aYsF4Zn\\ncpLldpc0Xa5fv0jSLEknFjj82cDV4cGkmkTSaEm3SVoevttjipT/hqRX5Jaa98r9DjLrZkhalRCG\\neyazLigVL5V0cDnjr/vEv3TVq01fWXl/M5dfPqjasRQiMWNnb1yvO1JfGK5EuzZByB2jBlPcprBS\\nvJYRIsNNUH4uKWkk8mNcD+d4XETtAFxl8uZMAbll5N24nv/WZjYGn4QwNdcBQyv/M+QWhiuK+ont\\nIi4vsxoYh5vVXJZM5knkJvcn46Jso3ENoaTtpOHSFRlhuOyndK8Dclo69pa6T/zW0eF1bG5eVuVQ\\ncpLH+3Z+lcOKVIZfE4TEAifgCo9rtVSCaubFoXX9hqTLJA0K60ZK+oOkN+XOUHcE1cbMtjMkfUvS\\ng6EV/+fsHkY+wvM2iwjSAJIm4wn8WDObZWbdQfzsSGCq3jEL+T4uHvZ9M1sc9vWYmX0qz6E+ACxN\\num9JOknSP0PMLyR7HqFn8qqkM+WSxVeFnkjGWnGhpJskjUps8xu5pPFSSfflS8i9Re4/cATwDTNb\\nGQT2bqen6FyS7YEHg0pxN57Is2MqpDF1H7CfyuS3C42Q+DvXZE5oWQwMykk6nR6MG3FEJc0+QJLl\\nWtan/AaG8DAwQtJ2cq+Ko1m35XsRrs++U/i7Gf50O/j/61W4hv5EYBWuTpnkGNz3dRzeczyjWFBy\\needDcSORx8PH+wGvBN34tQSJ4oeB/eUa/XvgSpOlsiPBMzfBfOCg0PM4CfihXLY4w3j86f6JeMv3\\ni8ChwIdx45UleAs8wx/xczcWeAxPtDmR9HPlt118Is9m2wCdWRIzs8lvu3g3sKekySF5n0DWQ624\\nVPaCcNHeJ7nCzF4DOoBt89Vjfan7xN/ZFYYR86jaVZNUKrUK/yFH79vG4Vq81b8/bjzyWmaFJOFD\\nK6eb2VIzW44bb38KwMwWm9ltZrY6rLsQtyfMYPjY+VwzW40PySSNQrKZIGkJsBK4DTg+IwGN2z2+\\nkWe718P6keS3jMxHLtvF6Wb2Ynh9P25E/6FEkW5cZbIj1OsLwNfNTc47cDXaT0hqCvuYZm5Yn1m3\\nk6Qejl+JY5+SZeaSXPKdu2G4fHSSdrJcxRLHeASX4P4Xfq6PpOcsvbOArXDXszRwh6R359h/WWwX\\noQGmc3Z0hRb/mjX5fsRVJZVKzap2DI3C+ko2V0Di2fDE/wD+j95jmAdvoQ4BHvVrAIT1TbDWBeuH\\nuG5/ZmhjmOSavOF98ne+ijwesIF5ZrZFuFl7EfA1SbeE4YiFBBvDHEzA71cUsozMx2LWtV08ADgH\\nd/Jqws9B0n51QfAiyLAlcJukpNZ/JzBe0pvAt3HP4LEhPsMvVD0uOBtAtuUieG8p5/7lemX74WYr\\nb+BDQvdI2t7MVoULQ4Zrwo3iA+nZmyurX3jdt/gnMoERU3btpLk527+zT0mn01EnPoKZ/QdPmgfg\\nqrRJFuLJekqi1TkyDIEAfBkfZtjd3J5wH/zCsEG/rZBUz8KTV2ac+h5gC0m7JcvKDds/ANxtbhQ+\\nE0+ypTIn1CGzv1bc7et7wDhzm8Tp9KxT9hDbf3AJ92TrfIiZvY4blhwK7BfO0VYUOEeSLld+28Un\\n89ThOWCApEmJzwrZLk4Fbgg9lG4z+xV+4S5Jajncx2lh3SGyXlP3iX9/9mXE2ReJn/2svF6+60GY\\nsfNoOp3ONwYYaSw+C3wkJM61hJb2FcClksaC/9NL+lgoMgy/MLwlaTTeSs6mVxeBMCzyA+DM8P45\\n4HLgOkkfkNQcZvzcAtxl77h2nQmcKOmMzI1kSTtJuiHPof4OjJSUMY1vCctCoDu0/j+WZ9sMlwMX\\nKkyBlT8XcGhYNwx4G1gcbsJeWKTeJ1t+28Ud82yzAr9of0vSEEl7A4fQc6ZOkjnAJyWNC/dTjsdH\\nW+ZK2kjSxyUNkjRA0nH4MNefEtvvg19oyya5XveJv4lWrKljpbW1re7rY2fN2LkUH9ONNDhm9m8L\\nptuZjxKvz8IVVx+W9BZusZhpIV+KT/9ciHvn3sm6reFsu8JCN6Sz1/0SGJdIoqcCV+I3oNvD8e7B\\nx6gzdZkJfCQsL0hahNsq/jHnAb13MQ34dHjfjt+svRkfBjoGnyFTKM4f4b4ef5G0DO91ZKTcr8Gf\\n0H8Nb4HPzLF9OTgF/y7exM/PyWb2DLjHSOgxZPzDL8Bb63Pw4bEvAUea2TJgIHB+2M8C3O7ysKwb\\nx8fhF7uyUVHrxXKhXlrq6dymo/56wfSb/3vamrkvf/rQyZWILR+hlT8N9ylNxZu3lae3v5NI3yJp\\nY/w+x/tq+SGuvkDuTniZme1VpFzO336+z+v75m7noGZ1D4Sm5X1qwJJOp1vxVsB3iUqakUgPzGfY\\nlTS+3eiEJ3cLJv3eUNeJf0j7mFYQXc1rp6j1CalU6u10Ov3eVCrVryUiIpFIY1LXY/wbtY8ddF/T\\n/Sz5/e+aFZ5+7Cti0o9EIv2Vuk78my0ZNeKSrktYef31J1F4PnOvSafT26fT6eZK7DsSiUQqQV0P\\n9UxaMGDM46zMvC3XwxvAOq5Y+wJPl3P/kUgkUinqusU/9i2N7aILoKOcswdyeN/GpB+JRGqGum7x\\nD223jQFoaipLaz+dTg8AvkH0vo1EIjVMXSd+W2MZTZByDfN0A81E79tIJFLD1PUDXB+fcvINb77+\\n3KeeWD3zClu1KlV8i0gt0+gPcEk6FzdDyacLH6lT1vcBrroe4x8/YIvOT7zn63DnnV+sdiyRxkVu\\nzbcyPMb/hqRrJWWrO5aD/t+Ki/QL6nqop3vB9s89tYfARZtKJszYORP4ZRzSiZQBAw42s3skjce1\\nm75OEESLRPqaum7xX2dTH73xqKZua2sruSWUmLGzBz6mH4mUDTObjxuNbA+QsBBcJulpSYdnyko6\\nMTgyfV9utfhvSVMT67eSWwsuk/QXXHOexPpDwz6XyA2+t0useykoas4JPZGrJI2XdKektyTdJals\\nxh+R/kVdJ35GdAyjo6mk5J3H+7ZfmrdEahIBBMXGqUDGgGcusHfQ3D8P+HXoFWTYHXgWGINr1l+V\\nWHc93kgZgys8nkAY7pG0TVj/RfyCMB13dsr08g33jd0Pt/Q7GFffPBu3bWwK20bqkLoe6mFExxA6\\nVVQ6IZ1OD8TlW18nztipW9Lp9Lnk1rA/L5VKnVti+ZxliyDgd3LP3mG47PAFAGa21q/WzG6W9FXc\\n6OT34eOXzewqAEnXAD+XNA4YBOyK6/p3AA9IuiNxzKOBP5jZ3WHbi3E54A8C94cyPzGzBWH9A8B8\\nM5sd3t+GXxQidUh9J/5l123HvYvQYR+abGbP5yuWSqU60un0Z4HZcV5+/RIS9rmVKl8AwzXW75H0\\nYeAOPGk/IukzwP/idoLgF4YxiW3X9jrNbGWwZByGt8qXZJm5vIzb+4HbI/4nsa1JegU3b88wP/F6\\nVdb71VRI5iRSfeo78S/57kQufa0V78b+sFDRVCr1RN8EFWlkzOx+ST8BvivpBNxxa19gZkjOj1Oa\\ni9brwChJQ8wso0vyLiDTw30NWOsgJb9ibEHC3D0HDTsVttGo6Bi/pKmSnpX0vKSz8pT5cVg/W9L7\\nyxvAmqHh1doHuKKgWqQfcCk+dr85PoFgIdAk6SRgh1J2YGYvA/8AzpM0MNj/HZwo8hvgIEkfkTQQ\\n9+tdjTt3RRqciiV+Sc24S/xUYApwjKT3ZJU5EJhkZpOBFHBZWYPo7s48ubsMenjf7pZ/o0iksgQj\\nkl8BX8EnEszEh3R2AB5MFqWwteKx+P2AxcA3wz4zx/gXbm/4E9zS7yDgEDPrLBRakWNH6oSKPbkr\\naU/gHDObGt6fDWBmFyXKXA7ca2Y3hffPAvuEKW/JffXOenH06L+zZMmuQ4cOPfSSSy7ZlaixU9c0\\n+pO7kcalP1kvbob7zWZ4FW+dFCuzOT1vMvWerq5hW2yxBWecccYluNlxnLETiUQankqO8Zfaos6+\\nGpWtJd681Vb/POHkkzsWLFhwOT4vPyb9SCTS8FSyxf8aPosgwxZ4i75Qmc3JM+sgCFBlmGFmM4oF\\n0HXppSde0tW10Yr99ss+biQSidQdktqAtqLlKjjGPwAfXtkPmAc8AhxjZs8kyhwInGpmB0raA7jU\\nzPbIsa84dhspSvydRBqVfjPGb2adkk7FJRCagavM7BlJXwjrf2Fm0yUdKGkusAI4qVLxRCKRSMSp\\naz3+SGMRfyeRRqXftPgjkWoQ9HAikUgBYuKP1A2xtR+JlEZ9yzKz9i53QxHr3BjEOjcGlahz3Sd+\\nSpjaVIe0VTuAKtBW7QCqQFu1A6gCbdUOoAq0lXuHjZD4I5FIJJIgJv5IJBJpMGpmOme1Y4hEIpFa\\nJOc0z1pI/JFIJBIpH3GoJxKJRBqMmPgjkUikwaibxF91m8cqUKzOko4LdZ0j6SFJ761GnOWklO85\\nlNtNUqekI/oyvnJT4u+6TdLjkp6SNKOPQyw7JfyuN5b0J0lPhDqfWIUwy4akX0qaL+nJAmXKm7vM\\nrOYXXARuLrAlMBB4AnhPVpkDgenh9QeAh6sddx/UeU9go/B6aiPUOVHuHuAPwJHVjrvC3/FI4Glg\\n8/B+42rH3Qd1Phf4Tqa+wCJgQLVj34A6fwh4P/BknvVlz1310uLfHZhrZi+ZWQdwI3BYVplDCZ6k\\nZjYLGClpfN+GWVaK1tnMZprZW+HtLNzvoJYp5XsGOA34Le41W8uUUt9jgVvM7FVY6+dby5RS59eB\\nEeH1CGCRFfYS7teY2QPAkgJFyp676iXx57Jw3KyEMrWcCEupc5LPAtMrGlHlKVpnSZvhieKy8FEt\\nT1sr5TueDIyWdK+kf0g6vs+iqwyl1PkKYHtJ84DZwJf6KLZqUfbcVS8ibVW3eawCJccuaV/gv4G9\\nKhdOn1BKnS8FzjYzkyTW/c5riVLqOxDYGTc8GgLMlPSwmT1f0cgqRyl1/hrwhJm1SdoauEvSTmbW\\nXuHYqklZc1e9JP6y2jzWCKXUmXBD9wpgqpkV6k7WAqXUeRfgRs/5bAwcIKnDzH7fNyGWlVLq+wqw\\n0MxWAask3Q/sBNRq4i+lzh8Evg1gZi9IehHYFvhHn0TY95Q9d9XLUM8/gMmStpTUAhwNZP+j/x74\\nDECweVxqZvP7NsyyUrTOkiYCtwKfNrO5VYix3BSts5m928y2MrOt8HH+/6nRpA+l/a5vB/aW1Cxp\\nCH7z7599HGc5KaXOzwIfBQhj3dsC/+7TKPuWsueuumjxWwPaPJZSZ+CbwCjgstAC7jCz3asV84ZS\\nYp3rhhJ/189K+hMwB+gGrjCzmk38JX7HFwJXS5qNN17PNLPFVQt6A5F0A7APsLGkV4Bz8CG8iuWu\\nKNkQiUQiDUa9DPVEIpFIpERi4o9EIpEGIyb+SCQSaTBi4o9EIpEGIyb+SCQSaTBi4o9EIpEGIyb+\\nSL9BUleQF84sEwuUXV6G402T9O9wrEfDwzHru48rJG0XXn8ta91DGxpj2E/mvMyRdKukYUXK7yTp\\ngHIcO1KfxHn8kX6DpHYzG17usgX2cTVwh5ndKml/4GIz22kD9rfBMRXbr6RpuHzvDwqUPxHYxcxO\\nK3cskfogtvgj/RZJQyX9NbTG50g6NEeZTSXdH1rET0raO3z+MUl/C9veLGlovsOEvw8Ak8K2p4d9\\nPSnpS4lY/hjMP56UdFT4fIakXSRdBAwOcVwb1i0Pf2+UdGAi5mmSjpDUJOn7kh4JBhupEk7LTGDr\\nsJ/dQx0fkxvtbBNkDr4FHB1iOSrE/ktJs0LZdc5jpMGotglBXOKSWYBO4PGw3II/sj88rNsYeD5R\\ntj38/TLwtfC6CRgWyt4HDA6fnwV8I8fxriYYtQBH4Ul1Z1z+YDAwFHgKeB9wJJBObDsi/L0X2DkZ\\nU44YDwemhdctwH+AViAF/F/4vBX4O7Bljjgz+2kO5+WU8H440BxefxT4bXh9AvDjxPYXAseF1yOB\\nfwFDqv19x6V6S11o9UTqhlVmttZWTtJA4DuSPoTr0EyQNM7M3kxs8wjwy1D2d2Y2W1IbMAX4J8BI\\nrwAAAkJJREFUW9AoagH+luN4Ar4v6evAm7hnwf7AreZql0i6FXdI+hNwcWjZ/8HMHlyPev0J+FFo\\njR8A3Gdmb0v6GLCjpE+EciPwXsdLWdsPlvQ4rsv+EnB5+HwkcI2kSbhMb+b/OVuO+mPAIZLOCO9b\\ncbXHf61HHSJ1REz8kf7McXjrfWcz65LL7w5KFjCzB8KF4WBgmqRLcDeju8zs2CL7N+AMM7s184Gk\\nj9IzacoPY8/LvU4PAi6QdLeZnV9KJcxstdwL9+PAJ4EbEqtPNbO7iuxilZm9X9JgXLzsMOA24Hzg\\nbjP7L0nvAmYU2McRVrsa/ZEyE8f4I/2ZEcCbIenvC7wru0CY+bPAzK4ErsS9Sx8G9pKbdGTG5yfn\\nOUa2wcUDwOGSBof7AocDD0jaFFhtZtcBF4fjZNMhKV9j6ibcDCfTewBP4qdktglj9EPybE/ohXwR\\n+La8KzMCmBdWJxUbl+HDQBn+HLYjHGfDzbojNU1M/JH+RPYUs+uAXSXNAY4HnslRdl/gCUmP4a3p\\nH5n7zp4I3BCke/+Ga7YXPaaZPQ5Mw4eQHsZljmcDOwKzwpDLN4ELcuwrDczJ3NzN2vdfgA/jPZGM\\nP+yVuHb+Y5KexO0ic1041u7HzJ7Azcg/CXwPHwp7DB//z5S7F5iSubmL9wwGhhvkTwHn5TkXkQYh\\nTueMRCKRBiO2+CORSKTBiIk/EolEGoyY+CORSKTBiIk/EolEGoyY+CORSKTBiIk/EolEGoyY+COR\\nSKTBiIk/EolEGoz/D22TQ/P8eg05AAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10d93b510>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Gradient Boosting Classifier:\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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BJwTSaT6RTHG2P8kbKjxsa+wFQ83loPTAcetIaG+dU2pBCS+gHb42nU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\\nSsb/AEsHkVSl0gvmAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10dcfa1d0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.metrics import roc_curve, auc\\n\",\n    \"from scipy import interp\\n\",\n    \"\\n\",\n    \"def plot_roc(X, y, clf_class, **kwargs):\\n\",\n    \"    kf = KFold(len(y), n_folds=5, shuffle=True)\\n\",\n    \"    y_prob = np.zeros((len(y),2))\\n\",\n    \"    mean_tpr = 0.0\\n\",\n    \"    mean_fpr = np.linspace(0, 1, 100)\\n\",\n    \"    all_tpr = []\\n\",\n    \"    for i, (train_index, test_index) in enumerate(kf):\\n\",\n    \"        X_train, X_test = X[train_index], X[test_index]\\n\",\n    \"        y_train = y[train_index]\\n\",\n    \"        clf = clf_class(**kwargs)\\n\",\n    \"        clf.fit(X_train,y_train)\\n\",\n    \"        # Predict probabilities, not classes\\n\",\n    \"        y_prob[test_index] = clf.predict_proba(X_test)\\n\",\n    \"        fpr, tpr, thresholds = roc_curve(y[test_index], y_prob[test_index, 1])\\n\",\n    \"        mean_tpr += interp(mean_fpr, fpr, tpr)\\n\",\n    \"        mean_tpr[0] = 0.0\\n\",\n    \"        roc_auc = auc(fpr, tpr)\\n\",\n    \"        plt.plot(fpr, tpr, lw=1, label='ROC fold %d (area = %0.2f)' % (i, roc_auc))\\n\",\n    \"    mean_tpr /= len(kf)\\n\",\n    \"    mean_tpr[-1] = 1.0\\n\",\n    \"    mean_auc = auc(mean_fpr, mean_tpr)\\n\",\n    \"    plt.plot(mean_fpr, mean_tpr, 'k--',label='Mean ROC (area = %0.2f)' % mean_auc, lw=2)\\n\",\n    \"    \\n\",\n    \"    plt.plot([0, 1], [0, 1], '--', color=(0.6, 0.6, 0.6), label='Random')\\n\",\n    \"    plt.xlim([-0.05, 1.05])\\n\",\n    \"    plt.ylim([-0.05, 1.05])\\n\",\n    \"    plt.xlabel('False Positive Rate')\\n\",\n    \"    plt.ylabel('True Positive Rate')\\n\",\n    \"    plt.title('Receiver operating characteristic')\\n\",\n    \"    plt.legend(loc=\\\"lower right\\\")\\n\",\n    \"    plt.show()\\n\",\n    \"      \\n\",\n    \"\\n\",\n    \"print \\\"Support vector machines:\\\"\\n\",\n    \"plot_roc(X,y,SVC,probability=True)\\n\",\n    \"\\n\",\n    \"print \\\"Random forests:\\\"\\n\",\n    \"plot_roc(X,y,RF,n_estimators=18)\\n\",\n    \"\\n\",\n    \"print \\\"K-nearest-neighbors:\\\"\\n\",\n    \"plot_roc(X,y,KNN)\\n\",\n    \"\\n\",\n    \"print \\\"Gradient Boosting Classifier:\\\"\\n\",\n    \"plot_roc(X,y,GBC)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Feature Importance\\n\",\n    \"\\n\",\n    \"Now that we understand the accuracy of each individual model for our particular dataset, let's dive a little deeper to get a better understanding of what features or behaviours are causing our customers to churn. In the next section, we will be using a `RandomForestClassifer` to build an ensemble of decision trees to predict whether a customer will churn or not churn. One of the first steps in building a decision tree to calculating the _information gain_ associated with splitting on a particular feature. (More on this later.)\\n\",\n    \"\\n\",\n    \"Let's look at the Top 10 features in our dataset that contribute to customer churn:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Feature ranking:\\n\",\n      \"1. Account Length (0.188157)\\n\",\n      \"2. Int'l Plan (0.102871)\\n\",\n      \"3. VMail Plan (0.096911)\\n\",\n      \"4. VMail Message (0.071246)\\n\",\n      \"5. Day Mins (0.062060)\\n\",\n      \"6. Day Calls (0.032456)\\n\",\n      \"7. Day Charge (0.030478)\\n\",\n      \"8. Eve Mins (0.026043)\\n\",\n      \"9. Eve Calls (0.025411)\\n\",\n      \"10. Eve Charge (0.021707)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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AR7RERhEuwREYVJsEdEFCbBHhFRmAR7RERhEuwREYVpG+ySlkvaIul+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\\n17PcAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10d5ceed0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"train_index,test_index = train_test_split(churn_df.index)\\n\",\n    \"\\n\",\n    \"forest = RF()\\n\",\n    \"forest_fit = forest.fit(X[train_index], y[train_index])\\n\",\n    \"forest_predictions = forest_fit.predict(X[test_index])\\n\",\n    \"\\n\",\n    \"importances = forest_fit.feature_importances_[:10]\\n\",\n    \"std = np.std([tree.feature_importances_ for tree in forest.estimators_],\\n\",\n    \"             axis=0)\\n\",\n    \"indices = np.argsort(importances)[::-1]\\n\",\n    \"\\n\",\n    \"# Print the feature ranking\\n\",\n    \"print(\\\"Feature ranking:\\\")\\n\",\n    \"\\n\",\n    \"for f in range(10):\\n\",\n    \"    print(\\\"%d. %s (%f)\\\" % (f + 1, features[f], importances[indices[f]]))\\n\",\n    \"\\n\",\n    \"# Plot the feature importances of the forest\\n\",\n    \"#import pylab as pl\\n\",\n    \"plt.figure()\\n\",\n    \"plt.title(\\\"Feature importances\\\")\\n\",\n    \"plt.bar(range(10), importances[indices], yerr=std[indices], color=\\\"r\\\", align=\\\"center\\\")\\n\",\n    \"plt.xticks(range(10), indices)\\n\",\n    \"plt.xlim([-1, 10])\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Thinking in Probabilities\\n\",\n    \"\\n\",\n    \"Decision making often favors probability over simple classifications. There's plainly more information in statements like \\\"there's a 20% chance of rain tomorrow\\\" and \\\"about 55% of test takers pass the California bar exam\\\" than just saying \\\"it shouldn't rain tomorrow\\\" or \\\"you'll probably pass.\\\" Probability predictions for churn also allow us to gauge a customers expected value, and their expected loss. Who do you want to reach out to first, the client with a 80% churn risk who pays 20,000 annually, or the client who's worth 100,000 a year with a 40% risk? How much should you spend on each client?\\n\",\n    \"\\n\",\n    \"While I'm moving a bit away from my expertise, being able to ask that question requires producing predictions a little differently. However, `scikit-learn` makes moving to probabilities easy; my three models have `predict_proba()` built right into their class objects. This is the same cross validation code with only a few lines changed.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def run_prob_cv(X, y, clf_class, roc=False, **kwargs):\\n\",\n    \"    kf = KFold(len(y), n_folds=5, shuffle=True)\\n\",\n    \"    y_prob = np.zeros((len(y),2))\\n\",\n    \"    for train_index, test_index in kf:\\n\",\n    \"        X_train, X_test = X[train_index], X[test_index]\\n\",\n    \"        y_train = y[train_index]\\n\",\n    \"        clf = clf_class(**kwargs)\\n\",\n    \"        clf.fit(X_train,y_train)\\n\",\n    \"        # Predict probabilities, not classes\\n\",\n    \"        y_prob[test_index] = clf.predict_proba(X_test)\\n\",\n    \"    return y_prob\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## How good is good?\\n\",\n    \"\\n\",\n    \"Determining how good a predictor which gives probabilities rather than classes is a bit more difficult. If I predict there's a 20% likelihood of rain tomorrow I don't get to live out all the possible outcomes of the universe. It either rains or it doesn't.\\n\",\n    \"\\n\",\n    \"What helps is that the predictors aren't making one prediction, they're making 3000+. So for every time I predict an event to occur 20% of the time I can see how often those events actually happen. Here's we'll use `pandas` to help me compare the predictions made by random forest against the actual outcomes.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.0    1796\\n\",\n       \"0.1     681\\n\",\n       \"0.2     258\\n\",\n       \"0.3     112\\n\",\n       \"0.8      94\\n\",\n       \"0.9      89\\n\",\n       \"0.7      79\\n\",\n       \"0.4      74\\n\",\n       \"0.5      53\\n\",\n       \"0.6      50\\n\",\n       \"1.0      47\\n\",\n       \"dtype: int64\"\n      ]\n     },\n     \"execution_count\": 14,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"import warnings\\n\",\n    \"warnings.filterwarnings('ignore')\\n\",\n    \"\\n\",\n    \"# Use 10 estimators so predictions are all multiples of 0.1\\n\",\n    \"pred_prob = run_prob_cv(X, y, RF, n_estimators=10)\\n\",\n    \"pred_churn = pred_prob[:,1]\\n\",\n    \"is_churn = y == 1\\n\",\n    \"\\n\",\n    \"# Number of times a predicted probability is assigned to an observation\\n\",\n    \"counts = pd.value_counts(pred_churn)\\n\",\n    \"counts[:]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div style=\\\"max-height:1000px;max-width:1500px;overflow:auto;\\\">\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>pred_prob</th>\\n\",\n       \"      <th>count</th>\\n\",\n       \"      <th>true_prob</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0 </th>\\n\",\n       \"      <td> 0.0</td>\\n\",\n       \"      <td> 1796</td>\\n\",\n       \"      <td> 0.027840</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1 </th>\\n\",\n       \"      <td> 0.1</td>\\n\",\n       \"      <td>  681</td>\\n\",\n       \"      <td> 0.020558</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2 </th>\\n\",\n       \"      <td> 0.2</td>\\n\",\n       \"      <td>  258</td>\\n\",\n       \"      <td> 0.081395</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3 </th>\\n\",\n       \"      <td> 0.3</td>\\n\",\n       \"      <td>  112</td>\\n\",\n       \"      <td> 0.089286</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4 </th>\\n\",\n       \"      <td> 0.4</td>\\n\",\n       \"      <td>   74</td>\\n\",\n       \"      <td> 0.270270</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5 </th>\\n\",\n       \"      <td> 0.5</td>\\n\",\n       \"      <td>   53</td>\\n\",\n       \"      <td> 0.622642</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>6 </th>\\n\",\n       \"      <td> 0.6</td>\\n\",\n       \"      <td>   50</td>\\n\",\n       \"      <td> 0.800000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>7 </th>\\n\",\n       \"      <td> 0.7</td>\\n\",\n       \"      <td>   79</td>\\n\",\n       \"      <td> 0.898734</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>8 </th>\\n\",\n       \"      <td> 0.8</td>\\n\",\n       \"      <td>   94</td>\\n\",\n       \"      <td> 0.957447</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>9 </th>\\n\",\n       \"      <td> 0.9</td>\\n\",\n       \"      <td>   89</td>\\n\",\n       \"      <td> 0.977528</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>10</th>\\n\",\n       \"      <td> 1.0</td>\\n\",\n       \"      <td>   47</td>\\n\",\n       \"      <td> 1.000000</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"    pred_prob  count  true_prob\\n\",\n       \"0         0.0   1796   0.027840\\n\",\n       \"1         0.1    681   0.020558\\n\",\n       \"2         0.2    258   0.081395\\n\",\n       \"3         0.3    112   0.089286\\n\",\n       \"4         0.4     74   0.270270\\n\",\n       \"5         0.5     53   0.622642\\n\",\n       \"6         0.6     50   0.800000\\n\",\n       \"7         0.7     79   0.898734\\n\",\n       \"8         0.8     94   0.957447\\n\",\n       \"9         0.9     89   0.977528\\n\",\n       \"10        1.0     47   1.000000\"\n      ]\n     },\n     \"execution_count\": 15,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from collections import defaultdict\\n\",\n    \"true_prob = defaultdict(float)\\n\",\n    \"\\n\",\n    \"# calculate true probabilities\\n\",\n    \"for prob in counts.index:\\n\",\n    \"    true_prob[prob] = np.mean(is_churn[pred_churn == prob])\\n\",\n    \"true_prob = pd.Series(true_prob)\\n\",\n    \"\\n\",\n    \"# pandas-fu\\n\",\n    \"counts = pd.concat([counts,true_prob], axis=1).reset_index()\\n\",\n    \"counts.columns = ['pred_prob', 'count', 'true_prob']\\n\",\n    \"counts\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We can see that random forests predicted that 75 individuals would have a 0.9 proability of churn and in actuality that group had a ~0.97 rate.\\n\",\n    \"\\n\",\n    \"## Calibration and Descrimination\\n\",\n    \"\\n\",\n    \"Using the `DataFrame` above we can draw a pretty simple graph to help visualize probability measurements. The x axis represents the churn probabilities which random forest assigned to a group of individuals. The y axis is the actual rate of churn within that group, and each point is scaled relative to the size of the group.\\n\",\n    \"\\n\",\n    \"![](http://blog.yhathq.com/static/img/ggplot.png)\\n\",\n    \"\\n\",\n    \"Calibration is a relatively simple measurement and can be summed up as so: Events predicted to happen 60% of the time should happen 60% of the time. For all individuals I predict to have a churn risk of between 30 and 40%, the true churn rate for that group should be about 35%. For the graph above think of it as, How close are my predictions to the red line?\\n\",\n    \"\\n\",\n    \"Discrimination measures _How far are my predictions away from the green line?_ Why is that important?\\n\",\n    \"\\n\",\n    \"Well, if we assign a churn probability of 15% to every individual we'll have near perfect calibration due to averages, but I'll be lacking any real insight. Discrimination gives a model a better score if it's able to isolate groups which are further from the base set.\\n\",\n    \"\\n\",\n    \"Equations are replicated from [Yang, Yates, and Smith (1991)](https://www.google.com/search?q=Measures+of+Discrimination+Skill+in+Probabilistic+Judgment&oq=Measures+of+Discrimination+Skill+in+Probabilistic+Judgment) and the code Yhat wrote can be found on GitHub [here](https://github.com/EricChiang/churn/blob/master/churn_measurements.py).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from churn_measurements import calibration, discrimination\\n\",\n    \"from sklearn.metrics import roc_curve, auc\\n\",\n    \"from scipy import interp\\n\",\n    \"from __future__ import division \\n\",\n    \"from operator import idiv\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"      \\n\",\n    \"def print_measurements(pred_prob):\\n\",\n    \"    churn_prob, is_churn = pred_prob[:,1], y == 1\\n\",\n    \"    print \\\"  %-20s %.4f\\\" % (\\\"Calibration Error\\\", calibration(churn_prob, is_churn))\\n\",\n    \"    print \\\"  %-20s %.4f\\\" % (\\\"Discrimination\\\", discrimination(churn_prob,is_churn))\\n\",\n    \"\\n\",\n    \"    print \\\"Note -- Lower calibration is better, higher discrimination is better\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Support vector machines:\\n\",\n      \"  Calibration Error    0.0016\\n\",\n      \"  Discrimination       0.0678\\n\",\n      \"Note -- Lower calibration is better, higher discrimination is better\\n\",\n      \"Random forests:\\n\",\n      \"  Calibration Error    0.0072\\n\",\n      \"  Discrimination       0.0845\\n\",\n      \"Note -- Lower calibration is better, higher discrimination is better\\n\",\n      \"K-nearest-neighbors:\\n\",\n      \"  Calibration Error    0.0023\\n\",\n      \"  Discrimination       0.0443\\n\",\n      \"Note -- Lower calibration is better, higher discrimination is better\\n\",\n      \"Gradient Boosting Classifier:\\n\",\n      \"  Calibration Error    0.0017\\n\",\n      \"  Discrimination       0.0859\\n\",\n      \"Note -- Lower calibration is better, higher discrimination is better\\n\",\n      \"Random Forest:\\n\",\n      \"  Calibration Error    0.0062\\n\",\n      \"  Discrimination       0.0782\\n\",\n      \"Note -- Lower calibration is better, higher discrimination is better\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print \\\"Support vector machines:\\\"\\n\",\n    \"print_measurements(run_prob_cv(X,y,SVC,probability=True))\\n\",\n    \"\\n\",\n    \"print \\\"Random forests:\\\"\\n\",\n    \"print_measurements(run_prob_cv(X,y,RF,n_estimators=18))\\n\",\n    \"\\n\",\n    \"print \\\"K-nearest-neighbors:\\\"\\n\",\n    \"print_measurements(run_prob_cv(X,y,KNN))\\n\",\n    \"\\n\",\n    \"print \\\"Gradient Boosting Classifier:\\\"\\n\",\n    \"print_measurements(run_prob_cv(X,y,GBC))\\n\",\n    \"\\n\",\n    \"print \\\"Random Forest:\\\"\\n\",\n    \"print_measurements(run_prob_cv(X,y,RF))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Unlike the classification comparisons earlier, random forest isn't as clearly the front-runner here. While it's good at differentiating between high and low probability churn events, it has trouble assigning an accurate probability estimate to those events. For example the group which random forest predicts to have a 30% churn rate actually had a true churn rate of 14%. Clearly there's more work to be done, but I leave that to you as a challenge.\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 2\",\n   \"language\": \"python\",\n   \"name\": \"python2\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 2\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython2\",\n   \"version\": \"2.7.10\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "analyses/churn_measurements.py",
    "content": "from __future__ import division\nimport numpy as np\n\n__author__ = \"Eric Chiang\"\n__email__  = \"eric[at]yhathq.com\"\n\n\"\"\"\n\nMeasurements inspired by Philip Tetlock's \"Expert Political Judgment\"\n\nEquations take from Yaniv, Yates, & Smith (1991):\n  \"Measures of Descrimination Skill in Probabilistic Judgement\"\n\n\"\"\"\n\n\ndef calibration(prob,outcome,n_bins=10):\n    \"\"\"Calibration measurement for a set of predictions.\n\n    When predicting events at a given probability, how far is frequency\n    of positive outcomes from that probability?\n    NOTE: Lower scores are better\n\n    prob: array_like, float\n        Probability estimates for a set of events\n\n    outcome: array_like, bool\n        If event predicted occurred\n\n    n_bins: int\n        Number of judgement categories to prefrom calculation over.\n        Prediction are binned based on probability, since \"descrete\" \n        probabilities aren't required. \n\n    \"\"\"\n    prob = np.array(prob)\n    outcome = np.array(outcome)\n\n    c = 0.0\n    # Construct bins\n    judgement_bins = np.arange(n_bins + 1) / n_bins\n    # Which bin is each prediction in?\n    bin_num = np.digitize(prob,judgement_bins)\n    for j_bin in np.unique(bin_num):\n        # Is event in bin\n        in_bin = bin_num == j_bin\n        # Predicted probability taken as average of preds in bin\n        predicted_prob = np.mean(prob[in_bin])\n        # How often did events in this bin actually happen?\n        true_bin_prob = np.mean(outcome[in_bin])\n        # Squared distance between predicted and true times num of obs\n        c += np.sum(in_bin) * ((predicted_prob - true_bin_prob) ** 2)\n    return c / len(prob)\n\ndef discrimination(prob,outcome,n_bins=10):\n    \"\"\"Discrimination measurement for a set of predictions.\n\n    For each judgement category, how far from the base probability\n    is the true frequency of that bin?\n    NOTE: High scores are better\n\n    prob: array_like, float\n        Probability estimates for a set of events\n\n    outcome: array_like, bool\n        If event predicted occurred\n\n    n_bins: int\n        Number of judgement categories to prefrom calculation over.\n        Prediction are binned based on probability, since \"descrete\" \n        probabilities aren't required. \n\n    \"\"\"\n    prob = np.array(prob)\n    outcome = np.array(outcome)\n\n    d = 0.0\n    # Base frequency of outcomes\n    base_prob = np.mean(outcome)\n    # Construct bins\n    judgement_bins = np.arange(n_bins + 1) / n_bins\n    # Which bin is each prediction in?\n    bin_num = np.digitize(prob,judgement_bins)\n    for j_bin in np.unique(bin_num):\n        in_bin = bin_num == j_bin\n        true_bin_prob = np.mean(outcome[in_bin])\n        # Squared distance between true and base times num of obs\n        d += np.sum(in_bin) * ((true_bin_prob - base_prob) ** 2)\n    return d / len(prob)\n"
  },
  {
    "path": "aws/__init__.py",
    "content": ""
  },
  {
    "path": "aws/aws.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This notebook was prepared by [Donne Martin](http://donnemartin.com). Source and license info is on [GitHub](https://github.com/donnemartin/data-science-ipython-notebooks).\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Amazon Web Services (AWS)\\n\",\n    \"\\n\",\n    \"* SSH to EC2\\n\",\n    \"* Boto\\n\",\n    \"* S3cmd\\n\",\n    \"* s3-parallel-put\\n\",\n    \"* S3DistCp\\n\",\n    \"* Redshift\\n\",\n    \"* Kinesis\\n\",\n    \"* Lambda\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<h2 id=\\\"ssh-to-ec2\\\">SSH to EC2</h2>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Connect to an Ubuntu EC2 instance through SSH with the given key:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!ssh -i key.pem ubuntu@ipaddress\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Connect to an Amazon Linux EC2 instance through SSH with the given key:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!ssh -i key.pem ec2-user@ipaddress\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Boto\\n\",\n    \"\\n\",\n    \"[Boto](https://github.com/boto/boto) is the official AWS SDK for Python.\\n\",\n    \"\\n\",\n    \"Install Boto:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!pip install Boto\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Configure boto by creating a ~/.boto file with the following:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"aws_access_key_id = YOURACCESSKEY\\n\",\n    \"aws_secret_access_key = YOURSECRETKEY\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Work with S3:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import boto\\n\",\n    \"s3 = boto.connect_s3()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Work with EC2:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import boto.ec2\\n\",\n    \"ec2 = boto.ec2.connect_to_region('us-east-1')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Create a bucket and put an object in that bucket:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import boto\\n\",\n    \"import time\\n\",\n    \"s3 = boto.connect_s3()\\n\",\n    \"\\n\",\n    \"# Create a new bucket. Buckets must have a globally unique name (not just\\n\",\n    \"# unique to your account).\\n\",\n    \"bucket = s3.create_bucket('boto-demo-%s' % int(time.time()))\\n\",\n    \"\\n\",\n    \"# Create a new key/value pair.\\n\",\n    \"key = bucket.new_key('mykey')\\n\",\n    \"key.set_contents_from_string(\\\"Hello World!\\\")\\n\",\n    \"\\n\",\n    \"# Sleep to ensure the data is eventually there.\\n\",\n    \"# This is often referred to as \\\"S3 eventual consistency\\\".\\n\",\n    \"time.sleep(2)\\n\",\n    \"\\n\",\n    \"# Retrieve the contents of ``mykey``.\\n\",\n    \"print key.get_contents_as_string()\\n\",\n    \"\\n\",\n    \"# Delete the key.\\n\",\n    \"key.delete()\\n\",\n    \"\\n\",\n    \"# Delete the bucket.\\n\",\n    \"bucket.delete()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Each service supports a different set of commands. Refer to the following for more details:\\n\",\n    \"* [AWS Docs](https://aws.amazon.com/documentation/)\\n\",\n    \"* [Boto Docs](http://boto.readthedocs.org/en/latest/index.html)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<h2 id=\\\"s3cmd\\\">S3cmd</h2>\\n\",\n    \"\\n\",\n    \"Before I discovered [S3cmd](http://s3tools.org/s3cmd), I had been using the [S3 console](http://aws.amazon.com/console/) to do basic operations and [boto](https://boto.readthedocs.org/en/latest/) to do more of the heavy lifting.  However, sometimes I just want to hack away at a command line to do my work.\\n\",\n    \"\\n\",\n    \"I've found S3cmd to be a great command line tool for interacting with S3 on AWS.  S3cmd is written in Python, is open source, and is free even for commercial use.  It offers more advanced features than those found in the [AWS CLI](http://aws.amazon.com/cli/).\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Install s3cmd:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!sudo apt-get install s3cmd\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Running the following command will prompt you to enter your AWS access and AWS secret keys. To follow security best practices, make sure you are using an IAM account as opposed to using the root account.\\n\",\n    \"\\n\",\n    \"I also suggest enabling GPG encryption which will encrypt your data at rest, and enabling HTTPS to encrypt your data in transit.  Note this might impact performance.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!s3cmd --configure\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Frequently used S3cmds:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# List all buckets\\n\",\n    \"!s3cmd ls\\n\",\n    \"\\n\",\n    \"# List the contents of the bucket\\n\",\n    \"!s3cmd ls s3://my-bucket-name\\n\",\n    \"\\n\",\n    \"# Upload a file into the bucket (private)\\n\",\n    \"!s3cmd put myfile.txt s3://my-bucket-name/myfile.txt\\n\",\n    \"\\n\",\n    \"# Upload a file into the bucket (public)\\n\",\n    \"!s3cmd put --acl-public --guess-mime-type myfile.txt s3://my-bucket-name/myfile.txt\\n\",\n    \"\\n\",\n    \"# Recursively upload a directory to s3\\n\",\n    \"!s3cmd put --recursive my-local-folder-path/ s3://my-bucket-name/mydir/\\n\",\n    \"\\n\",\n    \"# Download a file\\n\",\n    \"!s3cmd get s3://my-bucket-name/myfile.txt myfile.txt\\n\",\n    \"\\n\",\n    \"# Recursively download files that start with myfile\\n\",\n    \"!s3cmd --recursive get s3://my-bucket-name/myfile\\n\",\n    \"\\n\",\n    \"# Delete a file\\n\",\n    \"!s3cmd del s3://my-bucket-name/myfile.txt\\n\",\n    \"\\n\",\n    \"# Delete a bucket\\n\",\n    \"!s3cmd del --recursive s3://my-bucket-name/\\n\",\n    \"\\n\",\n    \"# Create a bucket\\n\",\n    \"!s3cmd mb s3://my-bucket-name\\n\",\n    \"\\n\",\n    \"# List bucket disk usage (human readable)\\n\",\n    \"!s3cmd du -H s3://my-bucket-name/\\n\",\n    \"\\n\",\n    \"# Sync local (source) to s3 bucket (destination)\\n\",\n    \"!s3cmd sync my-local-folder-path/ s3://my-bucket-name/\\n\",\n    \"\\n\",\n    \"# Sync s3 bucket (source) to local (destination)\\n\",\n    \"!s3cmd sync s3://my-bucket-name/ my-local-folder-path/\\n\",\n    \"\\n\",\n    \"# Do a dry-run (do not perform actual sync, but get information about what would happen)\\n\",\n    \"!s3cmd --dry-run sync s3://my-bucket-name/ my-local-folder-path/\\n\",\n    \"\\n\",\n    \"# Apply a standard shell wildcard include to sync s3 bucket (source) to local (destination)\\n\",\n    \"!s3cmd --include '2014-05-01*' sync s3://my-bucket-name/ my-local-folder-path/\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<h2 id=\\\"s3-parallel-put\\\">s3-parallel-put</h2>\\n\",\n    \"\\n\",\n    \"[s3-parallel-put](https://github.com/twpayne/s3-parallel-put.git) is a great tool for uploading multiple files to S3 in parallel.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Install package dependencies:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!sudo apt-get install boto\\n\",\n    \"!sudo apt-get install git\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Clone the s3-parallel-put repo:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!git clone https://github.com/twpayne/s3-parallel-put.git\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Setup AWS keys for s3-parallel-put:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!export AWS_ACCESS_KEY_ID=XXX\\n\",\n    \"!export AWS_SECRET_ACCESS_KEY=XXX\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Sample usage:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!s3-parallel-put --bucket=bucket --prefix=PREFIX SOURCE\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Dry run of putting files in the current directory on S3 with the given S3 prefix, do not check first if they exist:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!s3-parallel-put --bucket=bucket --host=s3.amazonaws.com --put=stupid --dry-run --prefix=prefix/ ./\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<h2 id=\\\"s3distcp\\\">S3DistCp</h2>\\n\",\n    \"\\n\",\n    \"[S3DistCp](http://docs.aws.amazon.com/ElasticMapReduce/latest/DeveloperGuide/UsingEMR_s3distcp.html) is an extension of DistCp that is optimized to work with Amazon S3.  S3DistCp is useful for combining smaller files and aggregate them together, taking in a pattern and target file to combine smaller input files to larger ones.  S3DistCp can also be used to transfer large volumes of data from S3 to your Hadoop cluster.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"To run S3DistCp with the EMR command line, ensure you are using the proper version of Ruby:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!rvm --default ruby-1.8.7-p374\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The EMR command line below executes the following:\\n\",\n    \"* Create a master node and slave nodes of type m1.small\\n\",\n    \"* Runs S3DistCp on the source bucket location and concatenates files that match the date regular expression, resulting in files that are roughly 1024 MB or 1 GB\\n\",\n    \"* Places the results in the destination bucket\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!./elastic-mapreduce --create --instance-group master --instance-count 1 \\\\\\n\",\n    \"--instance-type m1.small --instance-group core --instance-count 4 \\\\\\n\",\n    \"--instance-type m1.small --jar /home/hadoop/lib/emr-s3distcp-1.0.jar \\\\\\n\",\n    \"--args \\\"--src,s3://my-bucket-source/,--groupBy,.*([0-9]{4}-01).*,\\\\\\n\",\n    \"--dest,s3://my-bucket-dest/,--targetSize,1024\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"For further optimization, compression can be helpful to save on AWS storage and bandwidth costs, to speed up the S3 to/from EMR transfer, and to reduce disk I/O. Note that compressed files are not easy to split for Hadoop. For example, Hadoop uses a single mapper per GZIP file, as it does not know about file boundaries.\\n\",\n    \"\\n\",\n    \"What type of compression should you use?\\n\",\n    \"\\n\",\n    \"* Time sensitive job: Snappy or LZO\\n\",\n    \"* Large amounts of data: GZIP\\n\",\n    \"* General purpose: GZIP, as it’s supported by most platforms\\n\",\n    \"\\n\",\n    \"You can specify the compression codec (gzip, lzo, snappy, or none) to use for copied files with S3DistCp with –outputCodec. If no value is specified, files are copied with no compression change. The code below sets the compression to lzo:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"--outputCodec,lzo\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<h2 id=\\\"redshift\\\">Redshift</h2>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Copy values from the given S3 location containing CSV files to a Redshift cluster:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"copy table_name from 's3://source/part'\\n\",\n    \"credentials 'aws_access_key_id=XXX;aws_secret_access_key=XXX'\\n\",\n    \"csv;\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Copy values from the given location containing TSV files to a Redshift cluster:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"copy table_name from 's3://source/part'\\n\",\n    \"credentials 'aws_access_key_id=XXX;aws_secret_access_key=XXX'\\n\",\n    \"csv delimiter '\\\\t';\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"View Redshift errors:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"select * from stl_load_errors;\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Vacuum Redshift in full:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"VACUUM FULL;\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Analyze the compression of a table:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"analyze compression table_name;\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Cancel the query with the specified id:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"cancel 18764;\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The CANCEL command will not abort a transaction. To abort or roll back a transaction, you must use the ABORT or ROLLBACK command. To cancel a query associated with a transaction, first cancel the query then abort the transaction.\\n\",\n    \"\\n\",\n    \"If the query that you canceled is associated with a transaction, use the ABORT or ROLLBACK. command to cancel the transaction and discard any changes made to the data:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"abort;\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Reference table creation and setup:\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"![alt text](http://docs.aws.amazon.com/redshift/latest/dg/images/tutorial-optimize-tables-ssb-data-model.png)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"CREATE TABLE part (\\n\",\n    \"  p_partkey             integer         not null        sortkey distkey,\\n\",\n    \"  p_name                varchar(22)     not null,\\n\",\n    \"  p_mfgr                varchar(6)      not null,\\n\",\n    \"  p_category            varchar(7)      not null,\\n\",\n    \"  p_brand1              varchar(9)      not null,\\n\",\n    \"  p_color               varchar(11)     not null,\\n\",\n    \"  p_type                varchar(25)     not null,\\n\",\n    \"  p_size                integer         not null,\\n\",\n    \"  p_container           varchar(10)     not null\\n\",\n    \");\\n\",\n    \"\\n\",\n    \"CREATE TABLE supplier (\\n\",\n    \"  s_suppkey             integer        not null sortkey,\\n\",\n    \"  s_name                varchar(25)    not null,\\n\",\n    \"  s_address             varchar(25)    not null,\\n\",\n    \"  s_city                varchar(10)    not null,\\n\",\n    \"  s_nation              varchar(15)    not null,\\n\",\n    \"  s_region              varchar(12)    not null,\\n\",\n    \"  s_phone               varchar(15)    not null)\\n\",\n    \"diststyle all;\\n\",\n    \"\\n\",\n    \"CREATE TABLE customer (\\n\",\n    \"  c_custkey             integer        not null sortkey,\\n\",\n    \"  c_name                varchar(25)    not null,\\n\",\n    \"  c_address             varchar(25)    not null,\\n\",\n    \"  c_city                varchar(10)    not null,\\n\",\n    \"  c_nation              varchar(15)    not null,\\n\",\n    \"  c_region              varchar(12)    not null,\\n\",\n    \"  c_phone               varchar(15)    not null,\\n\",\n    \"  c_mktsegment          varchar(10)    not null)\\n\",\n    \"diststyle all;\\n\",\n    \"\\n\",\n    \"CREATE TABLE dwdate (\\n\",\n    \"  d_datekey            integer       not null sortkey,\\n\",\n    \"  d_date               varchar(19)   not null,\\n\",\n    \"  d_dayofweek          varchar(10)   not null,\\n\",\n    \"  d_month              varchar(10)   not null,\\n\",\n    \"  d_year               integer       not null,\\n\",\n    \"  d_yearmonthnum       integer       not null,\\n\",\n    \"  d_yearmonth          varchar(8)    not null,\\n\",\n    \"  d_daynuminweek       integer       not null,\\n\",\n    \"  d_daynuminmonth      integer       not null,\\n\",\n    \"  d_daynuminyear       integer       not null,\\n\",\n    \"  d_monthnuminyear     integer       not null,\\n\",\n    \"  d_weeknuminyear      integer       not null,\\n\",\n    \"  d_sellingseason      varchar(13)   not null,\\n\",\n    \"  d_lastdayinweekfl    varchar(1)    not null,\\n\",\n    \"  d_lastdayinmonthfl   varchar(1)    not null,\\n\",\n    \"  d_holidayfl          varchar(1)    not null,\\n\",\n    \"  d_weekdayfl          varchar(1)    not null)\\n\",\n    \"diststyle all;\\n\",\n    \"\\n\",\n    \"CREATE TABLE lineorder (\\n\",\n    \"  lo_orderkey               integer     not null,\\n\",\n    \"  lo_linenumber         integer         not null,\\n\",\n    \"  lo_custkey            integer         not null,\\n\",\n    \"  lo_partkey            integer         not null distkey,\\n\",\n    \"  lo_suppkey            integer         not null,\\n\",\n    \"  lo_orderdate          integer         not null sortkey,\\n\",\n    \"  lo_orderpriority      varchar(15)     not null,\\n\",\n    \"  lo_shippriority       varchar(1)      not null,\\n\",\n    \"  lo_quantity           integer         not null,\\n\",\n    \"  lo_extendedprice      integer         not null,\\n\",\n    \"  lo_ordertotalprice    integer         not null,\\n\",\n    \"  lo_discount           integer         not null,\\n\",\n    \"  lo_revenue            integer         not null,\\n\",\n    \"  lo_supplycost         integer         not null,\\n\",\n    \"  lo_tax                integer         not null,\\n\",\n    \"  lo_commitdate         integer         not null,\\n\",\n    \"  lo_shipmode           varchar(10)     not null\\n\",\n    \");\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"| Table name | Sort Key     | Distribution Style |\\n\",\n    \"|------------|--------------|--------------------|\\n\",\n    \"| LINEORDER  | lo_orderdate | lo_partkey         |\\n\",\n    \"| PART       | p_partkey    | p_partkey          |\\n\",\n    \"| CUSTOMER   | c_custkey    | ALL                |\\n\",\n    \"| SUPPLIER   | s_suppkey    | ALL                |\\n\",\n    \"| DWDATE     | d_datekey    | ALL                |\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"[Sort Keys](http://docs.aws.amazon.com/redshift/latest/dg/tutorial-tuning-tables-sort-keys.html)\\n\",\n    \"\\n\",\n    \"When you create a table, you can specify one or more columns as the sort key. Amazon Redshift stores your data on disk in sorted order according to the sort key. How your data is sorted has an important effect on disk I/O, columnar compression, and query performance.\\n\",\n    \"\\n\",\n    \"Choose sort keys for based on these best practices:\\n\",\n    \"\\n\",\n    \"If recent data is queried most frequently, specify the timestamp column as the leading column for the sort key.\\n\",\n    \"\\n\",\n    \"If you do frequent range filtering or equality filtering on one column, specify that column as the sort key.\\n\",\n    \"\\n\",\n    \"If you frequently join a (dimension) table, specify the join column as the sort key.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"[Distribution Styles](http://docs.aws.amazon.com/redshift/latest/dg/c_choosing_dist_sort.html)\\n\",\n    \"\\n\",\n    \"When you create a table, you designate one of three distribution styles: KEY, ALL, or EVEN.\\n\",\n    \"\\n\",\n    \"**KEY distribution**\\n\",\n    \"\\n\",\n    \"The rows are distributed according to the values in one column. The leader node will attempt to place matching values on the same node slice. If you distribute a pair of tables on the joining keys, the leader node collocates the rows on the slices according to the values in the joining columns so that matching values from the common columns are physically stored together.\\n\",\n    \"\\n\",\n    \"**ALL distribution**\\n\",\n    \"\\n\",\n    \"A copy of the entire table is distributed to every node. Where EVEN distribution or KEY distribution place only a portion of a table's rows on each node, ALL distribution ensures that every row is collocated for every join that the table participates in.\\n\",\n    \"\\n\",\n    \"**EVEN distribution**\\n\",\n    \"\\n\",\n    \"The rows are distributed across the slices in a round-robin fashion, regardless of the values in any particular column. EVEN distribution is appropriate when a table does not participate in joins or when there is not a clear choice between KEY distribution and ALL distribution. EVEN distribution is the default distribution style.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<h2 id=\\\"kinesis\\\">Kinesis</h2>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Create a stream:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!aws kinesis create-stream --stream-name Foo --shard-count 1 --profile adminuser\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"List all streams:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!aws kinesis list-streams --profile adminuser\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Get info about the stream:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!aws kinesis describe-stream --stream-name Foo --profile adminuser\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Put a record to the stream:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!aws kinesis put-record --stream-name Foo --data \\\"SGVsbG8sIHRoaXMgaXMgYSB0ZXN0IDEyMy4=\\\" --partition-key shardId-000000000000 --region us-east-1 --profile adminuser\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Get records from a given shard:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!SHARD_ITERATOR=$(aws kinesis get-shard-iterator --shard-id shardId-000000000000 --shard-iterator-type TRIM_HORIZON --stream-name Foo --query 'ShardIterator' --profile adminuser)\\n\",\n    \"aws kinesis get-records --shard-iterator $SHARD_ITERATOR\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Delete a stream:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!aws kinesis delete-stream --stream-name Foo --profile adminuser\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<h2 id=\\\"lambda\\\">Lambda</h2>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"List lambda functions:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!aws lambda list-functions \\\\\\n\",\n    \"    --region us-east-1 \\\\\\n\",\n    \"    --max-items 10\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Upload a lambda function:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!aws lambda upload-function \\\\\\n\",\n    \"    --region us-east-1 \\\\\\n\",\n    \"    --function-name foo \\\\\\n\",\n    \"    --function-zip file-path/foo.zip \\\\\\n\",\n    \"    --role IAM-role-ARN \\\\\\n\",\n    \"    --mode event \\\\\\n\",\n    \"    --handler foo.handler \\\\\\n\",\n    \"    --runtime nodejs \\\\\\n\",\n    \"    --debug\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Invoke a lambda function:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!aws lambda  invoke-async \\\\\\n\",\n    \"    --function-name foo \\\\\\n\",\n    \"    --region us-east-1 \\\\\\n\",\n    \"    --invoke-args foo.txt \\\\\\n\",\n    \"    --debug\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Update a function:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!aws lambda update-function-configuration \\\\\\n\",\n    \"   --function-name foo  \\\\\\n\",\n    \"   --region us-east-1 \\\\\\n\",\n    \"   --timeout timeout-in-seconds \\\\\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Return metadata for a specific function:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!aws lambda get-function-configuration \\\\\\n\",\n    \"    --function-name foo \\\\\\n\",\n    \"    --region us-east-1 \\\\\\n\",\n    \"    --debug\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Return metadata for a specific function along with a presigned URL that you can use to download the function's .zip file that you uploaded:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!aws lambda get-function \\\\\\n\",\n    \"    --function-name foo \\\\\\n\",\n    \"    --region us-east-1 \\\\\\n\",\n    \"    --debug\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Add an event source:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!aws lambda add-event-source \\\\\\n\",\n    \"    --region us-east-1 \\\\\\n\",\n    \"    --function-name ProcessKinesisRecords \\\\\\n\",\n    \"    --role invocation-role-arn  \\\\\\n\",\n    \"    --event-source kinesis-stream-arn \\\\\\n\",\n    \"    --batch-size 100\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Add permissions:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!aws lambda add-permission \\\\\\n\",\n    \"    --function-name CreateThumbnail \\\\\\n\",\n    \"    --region us-west-2 \\\\\\n\",\n    \"    --statement-id some-unique-id \\\\\\n\",\n    \"    --action \\\"lambda:InvokeFunction\\\" \\\\\\n\",\n    \"    --principal s3.amazonaws.com \\\\\\n\",\n    \"    --source-arn arn:aws:s3:::sourcebucket \\\\\\n\",\n    \"    --source-account bucket-owner-account-id\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Check policy permissions:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!aws lambda get-policy \\\\\\n\",\n    \"    --function-name function-name\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Delete a lambda function:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!aws lambda delete-function \\\\\\n\",\n    \"    --function-name foo \\\\\\n\",\n    \"    --region us-east-1 \\\\\\n\",\n    \"    --debug\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 2\",\n   \"language\": \"python\",\n   \"name\": \"python2\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 2\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython2\",\n   \"version\": \"2.7.10\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "commands/__init__.py",
    "content": ""
  },
  {
    "path": "commands/linux.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This notebook was prepared by [Donne Martin](http://donnemartin.com). Source and license info is on [GitHub](https://github.com/donnemartin/data-science-ipython-notebooks).\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Linux Commands\\n\",\n    \"\\n\",\n    \"* Disk Usage\\n\",\n    \"* Splitting Files\\n\",\n    \"* Grep, Sed\\n\",\n    \"* Compression\\n\",\n    \"* Curl\\n\",\n    \"* View Running Processes\\n\",\n    \"* Terminal Syntax Highlighting\\n\",\n    \"* Vim\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Disk Usage\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Display human-readable (-h) free disk space:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!df -h\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Display human-readable (-h) disk usage statistics:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!du -h ./\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Display human-readable (-h) disk usage statistics, showing only the total usage (-s):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!du -sh ../\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Display the human-readable (-h) disk usage statistics, showing also the grand total for all file types (-c):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!du -csh ./\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Splitting Files\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Count number of lines in a file with wc:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!wc -l < file.txt\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Count the number of lines in a file with grep:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!grep -c \\\".\\\" file.txt\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Split a file into multiple files based on line count:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!split -l 20 file.txt new\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Split a file into multiple files based on line count, use suffix of length 1:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!split -l 802 -a 1 file.csv dir/part-user-csv.tbl-\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Grep, Sed\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"List number of files matching “.txt\\\":\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!ls -1 | grep .txt | wc -l\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Check number of MapReduce records processed, outputting the results to the terminal:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!cat * | grep -c \\\"foo\\\" folder/part*\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Delete matching lines in place:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!sed -i '/Important Lines: /d’ original_file\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Compression\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Compress zip\\n\",\n    \"!zip -r archive_name.zip folder_to_compress\\n\",\n    \"\\n\",\n    \"# Compress zip without invisible Mac resources\\n\",\n    \"!zip -r -X archive_name.zip folder_to_compress\\n\",\n    \"\\n\",\n    \"# Extract zip\\n\",\n    \"!unzip archive_name.zip\\n\",\n    \"\\n\",\n    \"# Compress TAR.GZ\\n\",\n    \"!tar -zcvf archive_name.tar.gz folder_to_compress\\n\",\n    \"\\n\",\n    \"# Extract TAR.GZ\\n\",\n    \"!tar -zxvf archive_name.tar.gz\\n\",\n    \"\\n\",\n    \"# Compress TAR.BZ2\\n\",\n    \"!tar -jcvf archive_name.tar.bz2 folder_to_compress\\n\",\n    \"\\n\",\n    \"# Extract TAR.BZ2\\n\",\n    \"!tar -jxvf archive_name.tar.bz2\\n\",\n    \"\\n\",\n    \"# Extract GZ\\n\",\n    \"!gunzip archivename.gz\\n\",\n    \"\\n\",\n    \"# Uncompress all tar.gz in current directory to another directory\\n\",\n    \"!for i in *.tar.gz; do echo working on $i; tar xvzf $i -C directory/ ; done\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Curl\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Display the curl output:\\n\",\n    \"!curl donnemartin.com\\n\",\n    \"\\n\",\n    \"# Download the curl output to a file:\\n\",\n    \"!curl donnemartin.com > donnemartin.html\\n\",\n    \"\\n\",\n    \"# Download the curl output to a file -o\\n\",\n    \"!curl -o image.png http://i1.wp.com/donnemartin.com/wp-content/uploads/2015/02/splunk_cover.png\\n\",\n    \"\\n\",\n    \"# Download the curl output to a file, keeping the original file name -O\\n\",\n    \"!curl -O http://i1.wp.com/donnemartin.com/wp-content/uploads/2015/02/splunk_cover.png\\n\",\n    \"    \\n\",\n    \"# Download multiple files, attempting to reuse the same connection\\n\",\n    \"!curl -O url1 -O url2\\n\",\n    \"\\n\",\n    \"# Follow redirects -L\\n\",\n    \"!curl -L url\\n\",\n    \"\\n\",\n    \"# Resume a previous download -C -\\n\",\n    \"!curl -C - -O url\\n\",\n    \"\\n\",\n    \"# Authenticate -u\\n\",\n    \"!curl -u username:password url\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## View Running Processes\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Display sorted info about processes\\n\",\n    \"!top\\n\",\n    \"\\n\",\n    \"# Display all running processes\\n\",\n    \"!ps aux | less\\n\",\n    \"\\n\",\n    \"# Display all matching running processes with full formatting\\n\",\n    \"!ps -ef | grep python\\n\",\n    \"\\n\",\n    \"# See processes run by user dmartin\\n\",\n    \"!ps -u dmartin\\n\",\n    \"\\n\",\n    \"# Display running processes as a tree\\n\",\n    \"!pstree\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Terminal Syntax Highlighting\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Add the following to your ~/.bash_profile:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"export PS1='\\\\[\\\\033[01;32m\\\\]\\\\u@\\\\h\\\\[\\\\033[00m\\\\]:\\\\[\\\\033[01;34m\\\\]\\\\W\\\\[\\\\033[00m\\\\]\\\\$ '\\n\",\n    \"export CLICOLOR=1\\n\",\n    \"export LSCOLORS=ExFxBxDxCxegedabagacad\\n\",\n    \"alias ls='ls -GFh'\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Reload .bash_profile:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!source ~/.bash_profile\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Vim\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"Normal mode:  esc\\n\",\n    \"\\n\",\n    \"Basic movement:  h, j, k, l\\n\",\n    \"Word movement:  w, W, e, E, b, B\\n\",\n    \"\\n\",\n    \"Go to matching parenthesis:  %\\n\",\n    \"Go to start of the line:  0\\n\",\n    \"Go to end of the line:  $\\n\",\n    \"\\n\",\n    \"Find character:  f\\n\",\n    \"\\n\",\n    \"Insert mode:  i\\n\",\n    \"Append to line:  A\\n\",\n    \"\\n\",\n    \"Delete character:  x\\n\",\n    \"Delete command:  d\\n\",\n    \"Delete line:  dd\\n\",\n    \"\\n\",\n    \"Replace command:  r\\n\",\n    \"Change command:  c\\n\",\n    \"\\n\",\n    \"Undo:  u (U for all changes on a line)\\n\",\n    \"Redo:  CTRL-R\\n\",\n    \"\\n\",\n    \"Copy the current line:  yy\\n\",\n    \"Paste the current line:  p (P for paste above cursor)\\n\",\n    \"\\n\",\n    \"Quit without saving changes:  q!\\n\",\n    \"Write the current file and quit:  :wq\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Run the following command to enable the tutorial:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!vimtutor\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Run the following commands to enable syntax colors:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!cd ~\\n\",\n    \"!vim .vimrc\\n\",\n    \"# Add the following to ~/.vimrc\\n\",\n    \"syntax on\\n\",\n    \":wq\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 2\",\n   \"language\": \"python\",\n   \"name\": \"python2\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 2\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython2\",\n   \"version\": \"2.7.10\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "commands/misc.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This notebook was prepared by [Donne Martin](http://donnemartin.com). Source and license info is on [GitHub](https://github.com/donnemartin/data-science-ipython-notebooks).\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Misc Commands\\n\",\n    \"\\n\",\n    \"* Anaconda\\n\",\n    \"* IPython Notebook\\n\",\n    \"* Git\\n\",\n    \"* Ruby\\n\",\n    \"* Jekyll\\n\",\n    \"* Pelican\\n\",\n    \"* Django\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<h2 id=\\\"anaconda\\\">Anaconda</h2>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"[Anaconda](https://store.continuum.io/cshop/anaconda/) is a scientific python distribution containing Python, NumPy, SciPy, Pandas, IPython, Matplotlib, Numba, Blaze, Bokeh, and other great Python data analysis tools.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# See Anaconda installed packages\\n\",\n    \"!conda list\\n\",\n    \"\\n\",\n    \"# List environments\\n\",\n    \"!conda info -e\\n\",\n    \"\\n\",\n    \"# Create Python 3 environment\\n\",\n    \"!conda create -n py3k python=3 anaconda\\n\",\n    \"\\n\",\n    \"# Activate Python 3 environment\\n\",\n    \"!source activate py3k\\n\",\n    \"\\n\",\n    \"# Deactivate Python 3 environment\\n\",\n    \"!source deactivate\\n\",\n    \"\\n\",\n    \"# Update Anaconda\\n\",\n    \"!conda update conda\\n\",\n    \"\\n\",\n    \"# Update a package with Anaconda\\n\",\n    \"!conda update ipython\\n\",\n    \"\\n\",\n    \"# Update a package\\n\",\n    \"!conda update scipy\\n\",\n    \"\\n\",\n    \"# Update all packages\\n\",\n    \"!conda update all\\n\",\n    \"\\n\",\n    \"# Install specific version of a package\\n\",\n    \"!conda install scipy=0.12.0\\n\",\n    \"\\n\",\n    \"# Cleanup: Conda can accumulate a lot of disk space\\n\",\n    \"# because it doesn’t remove old unused packages\\n\",\n    \"!conda clean -p\\n\",\n    \"\\n\",\n    \"# Cleanup tarballs which are kept for caching purposes\\n\",\n    \"!conda clean -t\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<h2 id=\\\"ipython-notebook\\\">IPython Notebook</h2>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"[IPython Notebook](http://ipython.org/notebook.html) is a \\\"web-based interactive computational environment where you can combine code execution, text, mathematics, plots and rich media into a single document.\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Start IPython Notebook\\n\",\n    \"ipython notebook\\n\",\n    \"\\n\",\n    \"# Start IPython Notebook with built-in mode to work cleanly \\n\",\n    \"# with matplotlib figures\\n\",\n    \"ipython notebook --pylab inline\\n\",\n    \"\\n\",\n    \"# Start IPython Notebook with a profile\\n\",\n    \"ipython notebook --profile=dark-bg\\n\",\n    \"\\n\",\n    \"# Load the contents of a file\\n\",\n    \"%load dir/file.py\\n\",\n    \"\\n\",\n    \"# Time execution of a Python statement or expression\\n\",\n    \"%timeit\\n\",\n    \"%%time\\n\",\n    \"\\n\",\n    \"# Activate the interactive debugger\\n\",\n    \"%debug\\n\",\n    \"\\n\",\n    \"# Write the contents of the cell to a file\\n\",\n    \"%writefile\\n\",\n    \"\\n\",\n    \"# Run a cell via a shell command\\n\",\n    \"%%script\\n\",\n    \"\\n\",\n    \"# Run cells with bash in a subprocess\\n\",\n    \"# This is a shortcut for %%script bash\\n\",\n    \"%%bash\\n\",\n    \"\\n\",\n    \"# Run cells with python2 in a subprocess\\n\",\n    \"%%python2\\n\",\n    \"\\n\",\n    \"# Run cells with python3 in a subprocess\\n\",\n    \"%%python3\\n\",\n    \"\\n\",\n    \"# Convert a notebook to a basic HTML file \\n\",\n    \"!ipython nbconvert --to html --template basic file.ipynb \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"| Command   | Description                              |\\n\",\n    \"|-----------|------------------------------------------|\\n\",\n    \"| ?         | Intro and overview of IPython's features |\\n\",\n    \"| %quickref | Quick reference                          |\\n\",\n    \"| help      | Python help                              |\\n\",\n    \"| object?   | Object details, also use object??        |\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Apply css styling based on a css file:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from IPython.core.display import HTML\\n\",\n    \"\\n\",\n    \"def css_styling():\\n\",\n    \"    styles = open(\\\"styles/custom.css\\\", \\\"r\\\").read()\\n\",\n    \"    return HTML(styles)\\n\",\n    \"css_styling()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<h2 id=\\\"git\\\">Git</h2>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"[Git](http://git-scm.com/) is a distributed revision control system.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Configure git\\n\",\n    \"!git config --global user.name 'First Last'\\n\",\n    \"!git config --global user.email 'name@domain.com'\\n\",\n    \"!git init\\n\",\n    \"\\n\",\n    \"# View status and log\\n\",\n    \"!git status\\n\",\n    \"!git log\\n\",\n    \"\\n\",\n    \"# Add or remove from staging area\\n\",\n    \"!git add [target]\\n\",\n    \"!git reset [target file or commit]\\n\",\n    \"!git reset --hard origin/master\\n\",\n    \"\\n\",\n    \"# Automatically stage tracked files, \\n\",\n    \"# including deleting the previously tracked files\\n\",\n    \"# Does not add untracked files\\n\",\n    \"!git add -u\\n\",\n    \"\\n\",\n    \"# Delete files and stage them\\n\",\n    \"!git rm [target]\\n\",\n    \"\\n\",\n    \"# Commit\\n\",\n    \"!git commit -m “Add commit message here”\\n\",\n    \"\\n\",\n    \"# Add new origin\\n\",\n    \"!git remote add origin https://github.com/donnemartin/ipython-data-notebooks.git\\n\",\n    \"\\n\",\n    \"# Set to new origin\\n\",\n    \"!git remote set-url origin https://github.com/donnemartin/pydatasnippets.git\\n\",\n    \"    \\n\",\n    \"# Push to master, -u saves config so you can just do \\\"git push\\\" afterwards\\n\",\n    \"!git push -u origin master\\n\",\n    \"!git push\\n\",\n    \"\\n\",\n    \"# Diff files\\n\",\n    \"!git diff HEAD\\n\",\n    \"!git diff --staged\\n\",\n    \"!git diff --cached\\n\",\n    \"\\n\",\n    \"# Show log message of commit and diff\\n\",\n    \"!git show $COMMIT\\n\",\n    \"\\n\",\n    \"# Undo a file that has not been added\\n\",\n    \"!git checkout — [target]\\n\",\n    \"\\n\",\n    \"# Revert a commit\\n\",\n    \"!git revert\\n\",\n    \"\\n\",\n    \"# Undo a push and leave local repo intact\\n\",\n    \"!git push -f origin HEAD^:master\\n\",\n    \"\\n\",\n    \"# Undo commit but leave files and index\\n\",\n    \"!git reset --soft HEAD~1\\n\",\n    \"\\n\",\n    \"# Amend commit message of most recent change\\n\",\n    \"!git commit --amend\\n\",\n    \"!git push --force [branch]\\n\",\n    \"\\n\",\n    \"# Take the dirty state of your working directory\\n\",\n    \"# and save it on a stack of unfinished changes\\n\",\n    \"!git stash\\n\",\n    \"\\n\",\n    \"# Get list of stashes\\n\",\n    \"!git stash list\\n\",\n    \"\\n\",\n    \"# Apply the top stash, re-modifying the \\n\",\n    \"# uncommitted files when the stash was saved\\n\",\n    \"!git stash apply\\n\",\n    \"\\n\",\n    \"# Apply a stash at the specified index\\n\",\n    \"!git stash apply stash@{1}\\n\",\n    \"\\n\",\n    \"# Create a branch\\n\",\n    \"!git branch [branch]\\n\",\n    \"\\n\",\n    \"# Check branches\\n\",\n    \"!git branch\\n\",\n    \"\\n\",\n    \"# Switch branches\\n\",\n    \"!git checkout [branch]\\n\",\n    \"\\n\",\n    \"# Merge branch to master\\n\",\n    \"!git merge [branch]\\n\",\n    \"\\n\",\n    \"# Delete branch\\n\",\n    \"!git branch -d [branch]\\n\",\n    \"\\n\",\n    \"# Clone\\n\",\n    \"!git clone git@github.com:repo folder-name\\n\",\n    \"!git clone https://donnemartin@bitbucket.org/donnemartin/tutorial.git\\n\",\n    \"    \\n\",\n    \"# Update a local repository with changes from a remote repository\\n\",\n    \"# (pull down from master)\\n\",\n    \"!git pull origin master\\n\",\n    \"\\n\",\n    \"# Configuring a remote for a fork\\n\",\n    \"!git remote add upstream [target]\\n\",\n    \"\\n\",\n    \"# Set remote upstream\\n\",\n    \"git branch --set-upstream-to origin/branch\\n\",\n    \"\\n\",\n    \"# Check remotes\\n\",\n    \"!git remote -v\\n\",\n    \"\\n\",\n    \"# Syncing a fork\\n\",\n    \"!git fetch upstream\\n\",\n    \"!git checkout master\\n\",\n    \"!git merge upstream/master\\n\",\n    \"\\n\",\n    \"# Create a file containing a patch\\n\",\n    \"# git format-patch are like normal patch files, but they also carry information \\n\",\n    \"# about the git commit that created the patch: the author, the date, and the \\n\",\n    \"# commit log message are all there at the top of the patch.\\n\",\n    \"!git format-patch origin/master\\n\",\n    \"\\n\",\n    \"# Clean up .git folder:\\n\",\n    \"!git repack -a -d --depth=250 --window=250\\n\",\n    \"\\n\",\n    \"# GitHub tutorial:\\n\",\n    \"http://try.github.io/levels/1/challenges/9\\n\",\n    \"\\n\",\n    \"# BitBucket Setup\\n\",\n    \"!cd /path/to/my/repo\\n\",\n    \"!git init\\n\",\n    \"!git remote add origin https://donnemartin@bitbucket.org/donnemartin/repo.git\\n\",\n    \"!git push -u origin --all # pushes up the repo and its refs for the first time\\n\",\n    \"!git push -u origin --tags # pushes up any tags\\n\",\n    \"\\n\",\n    \"# Open Hatch missions\\n\",\n    \"!git clone https://openhatch.org/git-mission-data/git/dmartin git_missions\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<h2 id=\\\"ruby\\\">Ruby</h2>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"[Ruby](https://www.ruby-lang.org/en/) is used to interact with the AWS command line and for Jekyll, a blog framework that can be hosted on GitHub Pages.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Update Ruby\\n\",\n    \"!rvm get stable\\n\",\n    \"\\n\",\n    \"# Reload Ruby (or open a new terminal)\\n\",\n    \"!rvm reload\\n\",\n    \"\\n\",\n    \"# List all known RVM installable rubies\\n\",\n    \"!rvm list known\\n\",\n    \"\\n\",\n    \"# List all installed Ruby versions\\n\",\n    \"!rvm list\\n\",\n    \"\\n\",\n    \"# Install a specific Ruby version\\n\",\n    \"!rvm install 2.1.5\\n\",\n    \"\\n\",\n    \"# Set Ruby version\\n\",\n    \"!rvm --default ruby-1.8.7\\n\",\n    \"!rvm --default ruby-2.1.5\\n\",\n    \"\\n\",\n    \"# Check Ruby version\\n\",\n    \"!ruby -v\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<h2 id=\\\"jekyll\\\">Jekyll</h2>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"[Jekyll](http://jekyllrb.com/) is a blog framework that can be hosted on GitHub Pages.\\n\",\n    \"\\n\",\n    \"In addition to donnemartin.com, I’ve started to build up its mirror site donnemartin.github.io to try out Jekyll. So far I love that I can use my existing developer tools to generate content (SublimeText, Terminal, and GitHub).\\n\",\n    \"\\n\",\n    \"Here are other features I like about Jekyll:\\n\",\n    \"\\n\",\n    \"* Converts Markdown to produce fast, static pages\\n\",\n    \"* Simple to get started, no backend or manual updates\\n\",\n    \"* Hosted on GitHub Pages\\n\",\n    \"* Open source on GitHub\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Many Jekyll themes require a Ruby version of 2 and above.  However, the AWS CLI requires Ruby 1.8.7.  Run the proper version of Ruby for Jekyll:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!rvm --default ruby-2.1.5\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Build and run the localy Jekyll server:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# => The current folder will be generated into ./_site\\n\",\n    \"!bundle exec jekyll build\\n\",\n    \"\\n\",\n    \"# => A development server will run at http://localhost:4000/\\n\",\n    \"# Auto-regeneration: enabled. Use `--no-watch` to disable.\\n\",\n    \"!bundle exec jekyll serve\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<h2 id=\\\"pelican\\\">Pelican</h2>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"I've switched my personal website [donnemartin.com](http://donnemartin.com/) to run off Pelican, a python-based alternative to Jekyll.  Previous iterations ran off Wordpress and Jekyll.\\n\",\n    \"\\n\",\n    \"Setup [reference](http://nafiulis.me/making-a-static-blog-with-pelican.html).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Install\\n\",\n    \"!pip install pelican\\n\",\n    \"!pip install markdown\\n\",\n    \"!pip install ghp-import\\n\",\n    \"\\n\",\n    \"# Quick retup\\n\",\n    \"!pelican-quickstart\\n\",\n    \"\\n\",\n    \"# Run server\\n\",\n    \"!make devserver\\n\",\n    \"\\n\",\n    \"# Stop server\\n\",\n    \"!make stopserver\\n\",\n    \"\\n\",\n    \"# Run ghp-import on output folder\\n\",\n    \"# Review https://pypi.python.org/pypi/ghp-import\\n\",\n    \"# There's a \\\"Big Fat Warning\\\" section\\n\",\n    \"!ghp-import output\\n\",\n    \"\\n\",\n    \"# Update gh-pages (if using a project page)\\n\",\n    \"!git push origin gh-pages\\n\",\n    \"\\n\",\n    \"# Update gh-pages (if using a user or org page)\\n\",\n    \"!git merge gh-pages master\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Django\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"[Django](https://www.djangoproject.com) is a high-level Python Web framework that encourages rapid development and clean, pragmatic design.  It can be useful to share reports/analyses and for blogging. Lighter-weight alternatives include [Pyramid](https://github.com/Pylons/pyramid), [Flask](https://github.com/mitsuhiko/flask), [Tornado](https://github.com/tornadoweb/tornado), and [Bottle](https://github.com/bottlepy/bottle).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Check version of Django\\n\",\n    \"!python -c \\\"import django; print(django.get_version())\\\"\\n\",\n    \"\\n\",\n    \"# Create and setup a project\\n\",\n    \"!django-admin startproject mysite\\n\",\n    \"\\n\",\n    \"# Sync db\\n\",\n    \"!python manage.py syncdb\\n\",\n    \"\\n\",\n    \"# The migrate command looks at the INSTALLED_APPS setting and \\n\",\n    \"# creates any necessary database tables according to the database \\n\",\n    \"# settings in your mysite/settings.py file and the database \\n\",\n    \"# migrations shipped with the app\\n\",\n    \"!python manage.py migrate\\n\",\n    \"\\n\",\n    \"# Run the dev server\\n\",\n    \"!python manage.py runserver\\n\",\n    \"1python manage.py runserver 8080\\n\",\n    \"!python manage.py runserver 0.0.0.0:8000\\n\",\n    \"\\n\",\n    \"# Create app\\n\",\n    \"!python manage.py startapp [app_label]\\n\",\n    \"\\n\",\n    \"# Run tests\\n\",\n    \"python manage.py test [app_label]\\n\",\n    \"\\n\",\n    \"# Tell Django that you’ve made some changes to your models \\n\",\n    \"# and that you’d like the changes to be stored as a migration.\\n\",\n    \"!python manage.py makemigrations [app_label]\\n\",\n    \"\\n\",\n    \"# Take migration names and returns their SQL\\n\",\n    \"!python manage.py sqlmigrate [app_label] [migration_number]\\n\",\n    \"\\n\",\n    \"# Checks for any problems in your project without making \\n\",\n    \"# migrations or touching the database.\\n\",\n    \"!python manage.py check\\n\",\n    \"\\n\",\n    \"# Create a user who can login to the admin site\\n\",\n    \"!python manage.py createsuperuser\\n\",\n    \"\\n\",\n    \"# Locate Django source files\\n\",\n    \"!python -c \\\"\\n\",\n    \"import sys\\n\",\n    \"sys.path = sys.path[1:]\\n\",\n    \"import django\\n\",\n    \"print(django.__path__)\\\"\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.4.3\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "commands/styles/custom.css",
    "content": "<style>\n    @font-face {\n        font-family: \"Computer Modern\";\n        src: url('http://mirrors.ctan.org/fonts/cm-unicode/fonts/otf/cmunss.otf');\n    }\n    div.cell{\n        width:800px;\n        margin-left:16% !important;\n        margin-right:auto;\n    }\n    h1 {\n        font-family: Helvetica, serif;\n    }\n    h4{\n        margin-top:12px;\n        margin-bottom: 3px;\n       }\n    div.text_cell_render{\n        font-family: Computer Modern, \"Helvetica Neue\", Arial, Helvetica, Geneva, sans-serif;\n        line-height: 145%;\n        font-size: 130%;\n        width:800px;\n        margin-left:auto;\n        margin-right:auto;\n    }\n    .CodeMirror{\n            font-family: \"Source Code Pro\", source-code-pro,Consolas, monospace;\n    }\n    .prompt{\n        display: None;\n    }\n    .text_cell_render h5 {\n        font-weight: 300;\n        font-size: 22pt;\n        color: #4057A1;\n        font-style: italic;\n        margin-bottom: .5em;\n        margin-top: 0.5em;\n        display: block;\n    }\n    \n    .warning{\n        color: rgb( 240, 20, 20 )\n        }  \n</style>\n<script>\n    MathJax.Hub.Config({\n                        TeX: {\n                           extensions: [\"AMSmath.js\"]\n                           },\n                tex2jax: {\n                    inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n                    displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ]\n                },\n                displayAlign: 'center', // Change this to 'center' to center equations.\n                \"HTML-CSS\": {\n                    styles: {'.MathJax_Display': {\"margin\": 4}}\n                }\n        });\n</script>"
  },
  {
    "path": "data/churn.csv",
    "content": "State,Account Length,Area Code,Phone,Int'l Plan,VMail Plan,VMail Message,Day Mins,Day Calls,Day Charge,Eve Mins,Eve Calls,Eve Charge,Night Mins,Night Calls,Night Charge,Intl Mins,Intl Calls,Intl Charge,CustServ Calls,Churn?\r\nKS,128,415,382-4657,no,yes,25,265.100000,110,45.070000,197.400000,99,16.780000,244.700000,91,11.010000,10.000000,3,2.700000,1,False.\r\nOH,107,415,371-7191,no,yes,26,161.600000,123,27.470000,195.500000,103,16.620000,254.400000,103,11.450000,13.700000,3,3.700000,1,False.\r\nNJ,137,415,358-1921,no,no,0,243.400000,114,41.380000,121.200000,110,10.300000,162.600000,104,7.320000,12.200000,5,3.290000,0,False.\r\nOH,84,408,375-9999,yes,no,0,299.400000,71,50.900000,61.900000,88,5.260000,196.900000,89,8.860000,6.600000,7,1.780000,2,False.\r\nOK,75,415,330-6626,yes,no,0,166.700000,113,28.340000,148.300000,122,12.610000,186.900000,121,8.410000,10.100000,3,2.730000,3,False.\r\nAL,118,510,391-8027,yes,no,0,223.400000,98,37.980000,220.600000,101,18.750000,203.900000,118,9.180000,6.300000,6,1.700000,0,False.\r\nMA,121,510,355-9993,no,yes,24,218.200000,88,37.090000,348.500000,108,29.620000,212.600000,118,9.570000,7.500000,7,2.030000,3,False.\r\nMO,147,415,329-9001,yes,no,0,157.000000,79,26.690000,103.100000,94,8.760000,211.800000,96,9.530000,7.100000,6,1.920000,0,False.\r\nLA,117,408,335-4719,no,no,0,184.500000,97,31.370000,351.600000,80,29.890000,215.800000,90,9.710000,8.700000,4,2.350000,1,False.\r\nWV,141,415,330-8173,yes,yes,37,258.600000,84,43.960000,222.000000,111,18.870000,326.400000,97,14.690000,11.200000,5,3.020000,0,False.\r\nIN,65,415,329-6603,no,no,0,129.100000,137,21.950000,228.500000,83,19.420000,208.800000,111,9.400000,12.700000,6,3.430000,4,True.\r\nRI,74,415,344-9403,no,no,0,187.700000,127,31.910000,163.400000,148,13.890000,196.000000,94,8.820000,9.100000,5,2.460000,0,False.\r\nIA,168,408,363-1107,no,no,0,128.800000,96,21.900000,104.900000,71,8.920000,141.100000,128,6.350000,11.200000,2,3.020000,1,False.\r\nMT,95,510,394-8006,no,no,0,156.600000,88,26.620000,247.600000,75,21.050000,192.300000,115,8.650000,12.300000,5,3.320000,3,False.\r\nIA,62,415,366-9238,no,no,0,120.700000,70,20.520000,307.200000,76,26.110000,203.000000,99,9.140000,13.100000,6,3.540000,4,False.\r\nNY,161,415,351-7269,no,no,0,332.900000,67,56.590000,317.800000,97,27.010000,160.600000,128,7.230000,5.400000,9,1.460000,4,True.\r\nID,85,408,350-8884,no,yes,27,196.400000,139,33.390000,280.900000,90,23.880000,89.300000,75,4.020000,13.800000,4,3.730000,1,False.\r\nVT,93,510,386-2923,no,no,0,190.700000,114,32.420000,218.200000,111,18.550000,129.600000,121,5.830000,8.100000,3,2.190000,3,False.\r\nVA,76,510,356-2992,no,yes,33,189.700000,66,32.250000,212.800000,65,18.090000,165.700000,108,7.460000,10.000000,5,2.700000,1,False.\r\nTX,73,415,373-2782,no,no,0,224.400000,90,38.150000,159.500000,88,13.560000,192.800000,74,8.680000,13.000000,2,3.510000,1,False.\r\nFL,147,415,396-5800,no,no,0,155.100000,117,26.370000,239.700000,93,20.370000,208.800000,133,9.400000,10.600000,4,2.860000,0,False.\r\nCO,77,408,393-7984,no,no,0,62.400000,89,10.610000,169.900000,121,14.440000,209.600000,64,9.430000,5.700000,6,1.540000,5,True.\r\nAZ,130,415,358-1958,no,no,0,183.000000,112,31.110000,72.900000,99,6.200000,181.800000,78,8.180000,9.500000,19,2.570000,0,False.\r\nSC,111,415,350-2565,no,no,0,110.400000,103,18.770000,137.300000,102,11.670000,189.600000,105,8.530000,7.700000,6,2.080000,2,False.\r\nVA,132,510,343-4696,no,no,0,81.100000,86,13.790000,245.200000,72,20.840000,237.000000,115,10.670000,10.300000,2,2.780000,0,False.\r\nNE,174,415,331-3698,no,no,0,124.300000,76,21.130000,277.100000,112,23.550000,250.700000,115,11.280000,15.500000,5,4.190000,3,False.\r\nWY,57,408,357-3817,no,yes,39,213.000000,115,36.210000,191.100000,112,16.240000,182.700000,115,8.220000,9.500000,3,2.570000,0,False.\r\nMT,54,408,418-6412,no,no,0,134.300000,73,22.830000,155.500000,100,13.220000,102.100000,68,4.590000,14.700000,4,3.970000,3,False.\r\nMO,20,415,353-2630,no,no,0,190.000000,109,32.300000,258.200000,84,21.950000,181.500000,102,8.170000,6.300000,6,1.700000,0,False.\r\nHI,49,510,410-7789,no,no,0,119.300000,117,20.280000,215.100000,109,18.280000,178.700000,90,8.040000,11.100000,1,3.000000,1,False.\r\nIL,142,415,416-8428,no,no,0,84.800000,95,14.420000,136.700000,63,11.620000,250.500000,148,11.270000,14.200000,6,3.830000,2,False.\r\nNH,75,510,370-3359,no,no,0,226.100000,105,38.440000,201.500000,107,17.130000,246.200000,98,11.080000,10.300000,5,2.780000,1,False.\r\nLA,172,408,383-1121,no,no,0,212.000000,121,36.040000,31.200000,115,2.650000,293.300000,78,13.200000,12.600000,10,3.400000,3,False.\r\nAZ,12,408,360-1596,no,no,0,249.600000,118,42.430000,252.400000,119,21.450000,280.200000,90,12.610000,11.800000,3,3.190000,1,True.\r\nOK,57,408,395-2854,no,yes,25,176.800000,94,30.060000,195.000000,75,16.580000,213.500000,116,9.610000,8.300000,4,2.240000,0,False.\r\nGA,72,415,362-1407,no,yes,37,220.000000,80,37.400000,217.300000,102,18.470000,152.800000,71,6.880000,14.700000,6,3.970000,3,False.\r\nAK,36,408,341-9764,no,yes,30,146.300000,128,24.870000,162.500000,80,13.810000,129.300000,109,5.820000,14.500000,6,3.920000,0,False.\r\nMA,78,415,353-3305,no,no,0,130.800000,64,22.240000,223.700000,116,19.010000,227.800000,108,10.250000,10.000000,5,2.700000,1,False.\r\nAK,136,415,402-1381,yes,yes,33,203.900000,106,34.660000,187.600000,99,15.950000,101.700000,107,4.580000,10.500000,6,2.840000,3,False.\r\nNJ,149,408,332-9891,no,no,0,140.400000,94,23.870000,271.800000,92,23.100000,188.300000,108,8.470000,11.100000,9,3.000000,1,False.\r\nGA,98,408,372-9976,no,no,0,126.300000,102,21.470000,166.800000,85,14.180000,187.800000,135,8.450000,9.400000,2,2.540000,3,False.\r\nMD,135,408,383-6029,yes,yes,41,173.100000,85,29.430000,203.900000,107,17.330000,122.200000,78,5.500000,14.600000,15,3.940000,0,True.\r\nAR,34,510,353-7289,no,no,0,124.800000,82,21.220000,282.200000,98,23.990000,311.500000,78,14.020000,10.000000,4,2.700000,2,False.\r\nID,160,415,390-7274,no,no,0,85.800000,77,14.590000,165.300000,110,14.050000,178.500000,92,8.030000,9.200000,4,2.480000,3,False.\r\nWI,64,510,352-1237,no,no,0,154.000000,67,26.180000,225.800000,118,19.190000,265.300000,86,11.940000,3.500000,3,0.950000,1,False.\r\nOR,59,408,353-3061,no,yes,28,120.900000,97,20.550000,213.000000,92,18.110000,163.100000,116,7.340000,8.500000,5,2.300000,2,False.\r\nMI,65,415,363-5450,no,no,0,211.300000,120,35.920000,162.600000,122,13.820000,134.700000,118,6.060000,13.200000,5,3.560000,3,False.\r\nDE,142,408,364-1995,no,no,0,187.000000,133,31.790000,134.600000,74,11.440000,242.200000,127,10.900000,7.400000,5,2.000000,2,False.\r\nID,119,415,398-1294,no,no,0,159.100000,114,27.050000,231.300000,117,19.660000,143.200000,91,6.440000,8.800000,3,2.380000,5,True.\r\nWY,97,415,405-7146,no,yes,24,133.200000,135,22.640000,217.200000,58,18.460000,70.600000,79,3.180000,11.000000,3,2.970000,1,False.\r\nIA,52,408,413-4957,no,no,0,191.900000,108,32.620000,269.800000,96,22.930000,236.800000,87,10.660000,7.800000,5,2.110000,3,False.\r\nIN,60,408,420-5645,no,no,0,220.600000,57,37.500000,211.100000,115,17.940000,249.000000,129,11.210000,6.800000,3,1.840000,1,False.\r\nVA,10,408,349-4396,no,no,0,186.100000,112,31.640000,190.200000,66,16.170000,282.800000,57,12.730000,11.400000,6,3.080000,2,False.\r\nUT,96,415,404-3211,no,no,0,160.200000,117,27.230000,267.500000,67,22.740000,228.500000,68,10.280000,9.300000,5,2.510000,2,False.\r\nWY,87,415,353-3759,no,no,0,151.000000,83,25.670000,219.700000,116,18.670000,203.900000,127,9.180000,9.700000,3,2.620000,5,True.\r\nIN,81,408,363-5947,no,no,0,175.500000,67,29.840000,249.300000,85,21.190000,270.200000,98,12.160000,10.200000,3,2.750000,1,False.\r\nCO,141,415,340-5121,no,no,0,126.900000,98,21.570000,180.000000,62,15.300000,140.800000,128,6.340000,8.000000,2,2.160000,1,False.\r\nCO,121,408,370-7574,no,yes,30,198.400000,129,33.730000,75.300000,77,6.400000,181.200000,77,8.150000,5.800000,3,1.570000,3,True.\r\nWI,68,415,403-9733,no,no,0,148.800000,70,25.300000,246.500000,164,20.950000,129.800000,103,5.840000,12.100000,3,3.270000,3,False.\r\nOK,125,408,355-7251,no,no,0,229.300000,103,38.980000,177.400000,126,15.080000,189.300000,95,8.520000,12.000000,8,3.240000,1,False.\r\nID,174,408,359-5893,no,no,0,192.100000,97,32.660000,169.900000,94,14.440000,166.600000,54,7.500000,11.400000,4,3.080000,1,False.\r\nCA,116,415,405-3371,no,yes,34,268.600000,83,45.660000,178.200000,142,15.150000,166.300000,106,7.480000,11.600000,3,3.130000,2,False.\r\nMN,74,510,344-5117,no,yes,33,193.700000,91,32.930000,246.100000,96,20.920000,138.000000,92,6.210000,14.600000,3,3.940000,2,False.\r\nSD,149,408,332-8160,no,yes,28,180.700000,92,30.720000,187.800000,64,15.960000,265.500000,53,11.950000,12.600000,3,3.400000,3,False.\r\nNC,38,408,359-4081,no,no,0,131.200000,98,22.300000,162.900000,97,13.850000,159.000000,106,7.150000,8.200000,6,2.210000,2,False.\r\nWA,40,415,352-8305,no,yes,41,148.100000,74,25.180000,169.500000,88,14.410000,214.100000,102,9.630000,6.200000,5,1.670000,2,False.\r\nWY,43,415,329-9847,yes,no,0,251.500000,105,42.760000,212.800000,104,18.090000,157.800000,67,7.100000,9.300000,4,2.510000,0,False.\r\nMN,113,408,365-9011,yes,no,0,125.200000,93,21.280000,206.400000,119,17.540000,129.300000,139,5.820000,8.300000,8,2.240000,0,False.\r\nUT,126,408,338-9472,no,no,0,211.600000,70,35.970000,216.900000,80,18.440000,153.500000,60,6.910000,7.800000,1,2.110000,1,False.\r\nTX,150,510,374-8042,no,no,0,178.900000,101,30.410000,169.100000,110,14.370000,148.600000,100,6.690000,13.800000,3,3.730000,4,True.\r\nNJ,138,408,359-1231,no,no,0,241.800000,93,41.110000,170.500000,83,14.490000,295.300000,104,13.290000,11.800000,7,3.190000,3,False.\r\nMN,162,510,413-7170,no,yes,46,224.900000,97,38.230000,188.200000,84,16.000000,254.600000,61,11.460000,12.100000,2,3.270000,0,False.\r\nNM,147,510,415-2935,no,no,0,248.600000,83,42.260000,148.900000,85,12.660000,172.500000,109,7.760000,8.000000,4,2.160000,3,False.\r\nNV,90,415,399-4246,no,no,0,203.400000,146,34.580000,226.700000,117,19.270000,152.400000,105,6.860000,7.300000,4,1.970000,1,False.\r\nHI,85,415,362-5889,no,no,0,235.800000,109,40.090000,157.200000,94,13.360000,188.200000,99,8.470000,12.000000,3,3.240000,0,False.\r\nMN,50,415,350-8921,no,no,0,157.100000,90,26.710000,223.300000,72,18.980000,181.400000,111,8.160000,6.100000,2,1.650000,1,False.\r\nDC,82,415,374-5353,no,no,0,300.300000,109,51.050000,181.000000,100,15.390000,270.100000,73,12.150000,11.700000,4,3.160000,0,True.\r\nNY,144,408,360-1171,no,no,0,61.600000,117,10.470000,77.100000,85,6.550000,173.000000,99,7.790000,8.200000,7,2.210000,4,True.\r\nMN,46,415,355-8887,no,no,0,214.100000,72,36.400000,164.400000,104,13.970000,177.500000,113,7.990000,8.200000,3,2.210000,2,False.\r\nMD,70,408,333-1967,no,no,0,170.200000,98,28.930000,155.200000,102,13.190000,228.600000,76,10.290000,15.000000,2,4.050000,1,False.\r\nWV,144,415,354-4577,no,no,0,201.100000,99,34.190000,303.500000,74,25.800000,224.000000,119,10.080000,13.200000,2,3.560000,1,False.\r\nOR,116,415,331-7425,yes,no,0,215.400000,104,36.620000,204.800000,79,17.410000,278.500000,109,12.530000,12.600000,5,3.400000,3,False.\r\nCO,55,408,419-2637,no,yes,25,165.600000,123,28.150000,136.100000,95,11.570000,175.700000,90,7.910000,11.000000,2,2.970000,3,False.\r\nGA,70,415,411-1530,no,yes,24,249.500000,101,42.420000,259.700000,98,22.070000,222.700000,68,10.020000,9.800000,4,2.650000,1,False.\r\nTX,106,510,395-3026,no,no,0,210.600000,96,35.800000,249.200000,85,21.180000,191.400000,88,8.610000,12.400000,1,3.350000,2,True.\r\nVT,128,510,388-6441,no,yes,29,179.300000,104,30.480000,225.900000,86,19.200000,323.000000,78,14.540000,8.600000,7,2.320000,0,False.\r\nIN,94,408,402-1251,no,no,0,157.900000,105,26.840000,155.000000,101,13.180000,189.600000,84,8.530000,8.000000,5,2.160000,4,True.\r\nWV,111,510,412-9997,no,no,0,214.300000,118,36.430000,208.500000,76,17.720000,182.400000,98,8.210000,12.000000,2,3.240000,1,False.\r\nKY,74,415,346-7302,no,yes,35,154.100000,104,26.200000,123.400000,84,10.490000,202.100000,57,9.090000,10.900000,9,2.940000,2,False.\r\nNJ,128,415,358-9095,no,no,0,237.900000,125,40.440000,247.600000,93,21.050000,208.900000,68,9.400000,13.900000,4,3.750000,1,True.\r\nDC,82,510,400-9770,no,no,0,143.900000,61,24.460000,194.900000,105,16.570000,109.600000,94,4.930000,11.100000,2,3.000000,1,False.\r\nLA,155,415,334-1275,no,no,0,203.400000,100,34.580000,190.900000,104,16.230000,196.000000,119,8.820000,8.900000,4,2.400000,0,True.\r\nAR,80,415,340-4953,no,no,0,124.300000,100,21.130000,173.000000,107,14.710000,253.200000,62,11.390000,7.900000,9,2.130000,1,False.\r\nME,78,415,400-9510,no,no,0,252.900000,93,42.990000,178.400000,112,15.160000,263.900000,105,11.880000,9.500000,7,2.570000,3,False.\r\nAZ,90,415,387-6103,no,no,0,179.100000,71,30.450000,190.600000,81,16.200000,127.700000,91,5.750000,10.600000,7,2.860000,3,False.\r\nAK,104,408,366-4467,no,no,0,278.400000,106,47.330000,81.000000,113,6.890000,163.200000,137,7.340000,9.800000,5,2.650000,1,False.\r\nMT,73,415,370-3450,no,no,0,160.100000,110,27.220000,213.300000,72,18.130000,174.100000,72,7.830000,13.000000,4,3.510000,0,False.\r\nAZ,99,415,327-3954,no,no,0,198.200000,87,33.690000,207.300000,76,17.620000,190.900000,113,8.590000,8.700000,3,2.350000,4,False.\r\nMS,120,408,355-6291,no,no,0,212.100000,131,36.060000,209.400000,104,17.800000,167.200000,96,7.520000,5.300000,5,1.430000,1,True.\r\nID,77,415,362-9748,no,no,0,251.800000,72,42.810000,205.700000,126,17.480000,275.200000,109,12.380000,9.800000,7,2.650000,2,True.\r\nIA,98,510,379-6506,no,yes,21,161.200000,114,27.400000,252.200000,83,21.440000,160.200000,92,7.210000,4.400000,8,1.190000,4,False.\r\nMA,108,415,347-7741,no,no,0,178.300000,137,30.310000,189.000000,76,16.070000,129.100000,102,5.810000,14.600000,5,3.940000,0,False.\r\nVT,135,415,354-3783,no,no,0,151.700000,82,25.790000,119.000000,105,10.120000,180.000000,100,8.100000,10.500000,6,2.840000,0,False.\r\nKY,95,408,401-7594,no,no,0,135.000000,99,22.950000,183.600000,106,15.610000,245.300000,102,11.040000,12.500000,9,3.380000,1,False.\r\nIN,122,408,397-4976,no,no,0,170.500000,94,28.990000,173.700000,109,14.760000,248.600000,75,11.190000,11.300000,2,3.050000,1,False.\r\nAZ,95,408,334-2577,no,no,0,238.100000,65,40.480000,187.200000,98,15.910000,190.000000,115,8.550000,11.800000,4,3.190000,4,False.\r\nMI,36,510,400-3637,no,yes,29,281.400000,102,47.840000,202.200000,76,17.190000,187.200000,113,8.420000,9.000000,6,2.430000,2,False.\r\nNM,93,510,383-4361,no,yes,21,117.900000,131,20.040000,164.500000,115,13.980000,217.000000,86,9.760000,9.800000,3,2.650000,1,False.\r\nCO,141,415,371-4306,no,yes,32,148.600000,91,25.260000,131.100000,97,11.140000,219.400000,142,9.870000,10.100000,1,2.730000,1,False.\r\nUT,157,408,403-4298,no,no,0,229.800000,90,39.070000,147.900000,121,12.570000,241.400000,108,10.860000,9.600000,7,2.590000,3,False.\r\nMI,120,408,409-3786,no,no,0,165.000000,100,28.050000,317.200000,83,26.960000,119.200000,86,5.360000,8.300000,8,2.240000,1,False.\r\nMA,103,415,337-4697,no,no,0,185.000000,117,31.450000,223.300000,94,18.980000,222.800000,91,10.030000,12.600000,2,3.400000,2,False.\r\nAL,98,408,383-1509,no,no,0,161.000000,117,27.370000,190.900000,113,16.230000,227.700000,113,10.250000,12.100000,4,3.270000,4,False.\r\nDE,125,408,359-9794,no,no,0,126.700000,108,21.540000,206.000000,90,17.510000,247.800000,114,11.150000,13.300000,7,3.590000,1,False.\r\nAZ,63,415,407-7035,no,no,0,58.900000,125,10.010000,169.600000,59,14.420000,211.400000,88,9.510000,9.400000,3,2.540000,1,False.\r\nME,36,510,363-1069,yes,yes,42,196.800000,89,33.460000,254.900000,122,21.670000,138.300000,126,6.220000,20.000000,6,5.400000,0,True.\r\nNJ,64,510,391-4652,no,no,0,162.600000,83,27.640000,152.300000,109,12.950000,57.500000,122,2.590000,14.200000,3,3.830000,1,False.\r\nNV,74,415,355-6837,no,no,0,282.500000,114,48.030000,219.900000,48,18.690000,170.000000,115,7.650000,9.400000,4,2.540000,1,True.\r\nMO,112,510,409-1244,no,yes,36,113.700000,117,19.330000,157.500000,82,13.390000,177.600000,118,7.990000,10.000000,3,2.700000,2,False.\r\nID,97,408,328-3266,no,no,0,239.800000,125,40.770000,214.800000,111,18.260000,143.300000,81,6.450000,8.700000,5,2.350000,2,False.\r\nNE,46,408,352-7072,no,no,0,210.200000,92,35.730000,227.300000,77,19.320000,200.100000,116,9.000000,13.100000,7,3.540000,1,False.\r\nTX,41,408,370-7550,no,yes,22,213.800000,102,36.350000,141.800000,86,12.050000,142.200000,123,6.400000,7.200000,3,1.940000,0,False.\r\nMD,121,510,369-5526,no,no,0,190.700000,103,32.420000,183.500000,117,15.600000,220.800000,103,9.940000,9.800000,4,2.650000,3,False.\r\nMS,193,415,329-4391,no,no,0,170.900000,124,29.050000,132.300000,95,11.250000,112.900000,89,5.080000,11.600000,3,3.130000,1,False.\r\nNV,130,510,408-4195,no,no,0,154.200000,119,26.210000,110.200000,98,9.370000,227.400000,117,10.230000,9.200000,5,2.480000,2,False.\r\nAZ,85,408,354-4445,no,no,0,201.400000,52,34.240000,229.400000,104,19.500000,252.500000,106,11.360000,12.000000,3,3.240000,1,False.\r\nMS,162,415,335-4858,no,no,0,70.700000,108,12.020000,157.500000,87,13.390000,154.800000,82,6.970000,9.100000,3,2.460000,4,True.\r\nMS,61,510,414-8718,no,yes,27,187.500000,124,31.880000,146.600000,103,12.460000,225.700000,129,10.160000,6.400000,6,1.730000,4,True.\r\nTX,92,408,409-5939,no,no,0,91.700000,90,15.590000,193.700000,123,16.460000,175.000000,86,7.880000,9.200000,4,2.480000,2,False.\r\nNE,131,408,331-4902,no,yes,36,214.200000,115,36.410000,161.700000,117,13.740000,264.700000,102,11.910000,9.500000,4,2.570000,3,False.\r\nNE,90,415,353-6870,no,no,0,145.500000,92,24.740000,217.700000,114,18.500000,146.900000,123,6.610000,10.900000,2,2.940000,3,False.\r\nCA,75,408,355-2909,no,no,0,166.300000,125,28.270000,158.200000,86,13.450000,256.700000,80,11.550000,6.100000,5,1.650000,1,False.\r\nNJ,78,415,390-6101,no,no,0,231.000000,115,39.270000,230.400000,140,19.580000,261.400000,120,11.760000,9.500000,3,2.570000,1,False.\r\nTX,82,408,400-3446,no,no,0,200.300000,96,34.050000,201.200000,102,17.100000,206.100000,60,9.270000,7.100000,1,1.920000,4,False.\r\nAR,163,408,411-5859,no,no,0,197.000000,109,33.490000,202.600000,128,17.220000,206.400000,80,9.290000,9.100000,10,2.460000,1,False.\r\nAL,91,510,387-2919,yes,no,0,129.900000,112,22.080000,173.300000,83,14.730000,247.200000,130,11.120000,11.200000,3,3.020000,3,False.\r\nNY,75,415,374-8525,no,yes,21,175.800000,97,29.890000,217.500000,106,18.490000,237.500000,134,10.690000,5.300000,4,1.430000,5,False.\r\nFL,91,510,379-5592,no,no,0,203.100000,106,34.530000,210.100000,113,17.860000,195.600000,129,8.800000,12.000000,3,3.240000,3,False.\r\nAK,127,510,345-8237,no,yes,36,183.200000,117,31.140000,126.800000,76,10.780000,263.300000,71,11.850000,11.200000,8,3.020000,1,False.\r\nNV,113,415,422-6690,no,yes,23,205.000000,101,34.850000,152.000000,60,12.920000,158.600000,59,7.140000,10.200000,5,2.750000,2,False.\r\nDE,110,510,346-2359,no,no,0,148.500000,115,25.250000,276.400000,84,23.490000,193.600000,112,8.710000,12.400000,3,3.350000,1,False.\r\nMD,120,415,374-3534,no,yes,39,200.300000,68,34.050000,220.400000,97,18.730000,253.800000,116,11.420000,10.500000,4,2.840000,0,False.\r\nMI,157,415,381-4756,no,yes,28,192.600000,107,32.740000,195.500000,74,16.620000,109.700000,139,4.940000,6.800000,5,1.840000,3,False.\r\nVT,103,510,390-2805,no,no,0,246.500000,47,41.910000,195.500000,84,16.620000,200.500000,96,9.020000,11.700000,4,3.160000,1,False.\r\nVT,117,408,390-2390,yes,no,0,167.100000,86,28.410000,177.500000,87,15.090000,249.400000,132,11.220000,14.100000,7,3.810000,2,True.\r\nMI,140,415,419-9097,no,no,0,231.900000,101,39.420000,160.100000,94,13.610000,110.400000,98,4.970000,14.300000,6,3.860000,3,False.\r\nWA,127,408,386-7281,no,no,0,146.700000,91,24.940000,203.500000,78,17.300000,203.400000,110,9.150000,13.700000,3,3.700000,1,False.\r\nUT,83,408,380-3561,yes,no,0,271.500000,87,46.160000,216.300000,126,18.390000,121.100000,105,5.450000,11.700000,4,3.160000,1,False.\r\nLA,121,408,390-8760,no,no,0,181.500000,121,30.860000,218.400000,98,18.560000,161.600000,103,7.270000,8.500000,5,2.300000,1,False.\r\nRI,145,408,366-6730,no,yes,43,257.700000,97,43.810000,162.100000,95,13.780000,286.900000,86,12.910000,11.100000,4,3.000000,2,False.\r\nIA,113,408,395-5285,no,no,0,193.800000,99,32.950000,221.400000,125,18.820000,172.300000,67,7.750000,10.600000,6,2.860000,1,False.\r\nNE,117,415,354-3436,no,no,0,102.800000,119,17.480000,206.700000,91,17.570000,299.000000,105,13.460000,10.100000,7,2.730000,1,False.\r\nOH,65,408,336-7600,no,no,0,187.900000,116,31.940000,157.600000,117,13.400000,227.300000,86,10.230000,7.500000,6,2.030000,1,False.\r\nRI,56,415,383-6293,no,no,0,226.000000,112,38.420000,248.500000,118,21.120000,140.500000,142,6.320000,6.900000,11,1.860000,1,False.\r\nOK,96,415,362-4596,no,no,0,260.400000,115,44.270000,146.000000,46,12.410000,269.500000,87,12.130000,11.500000,4,3.110000,5,False.\r\nLA,151,408,401-3926,no,no,0,178.700000,116,30.380000,292.100000,138,24.830000,265.900000,101,11.970000,9.800000,4,2.650000,0,False.\r\nOH,83,415,370-9116,no,no,0,337.400000,120,57.360000,227.400000,116,19.330000,153.900000,114,6.930000,15.800000,7,4.270000,0,True.\r\nVA,139,510,328-6289,no,yes,23,157.600000,129,26.790000,247.000000,96,21.000000,259.200000,112,11.660000,13.700000,2,3.700000,0,False.\r\nMO,6,510,350-9994,no,no,0,183.600000,117,31.210000,256.700000,72,21.820000,178.600000,79,8.040000,10.200000,2,2.750000,1,False.\r\nFL,115,510,351-4616,no,yes,24,142.100000,124,24.160000,183.400000,129,15.590000,164.800000,114,7.420000,9.600000,4,2.590000,1,False.\r\nSC,87,415,360-5779,no,no,0,136.300000,97,23.170000,172.200000,108,14.640000,137.500000,101,6.190000,7.100000,5,1.920000,0,False.\r\nVA,141,415,417-4885,no,no,0,217.100000,110,36.910000,241.500000,111,20.530000,253.500000,103,11.410000,12.000000,6,3.240000,0,False.\r\nIA,141,510,406-4710,no,yes,36,187.500000,99,31.880000,241.400000,116,20.520000,229.500000,105,10.330000,10.500000,5,2.840000,3,False.\r\nMI,62,415,409-8743,no,no,0,98.900000,103,16.810000,135.400000,122,11.510000,236.600000,82,10.650000,12.200000,1,3.290000,1,False.\r\nOK,146,415,335-4584,no,no,0,206.300000,151,35.070000,148.600000,89,12.630000,167.200000,91,7.520000,6.100000,3,1.650000,1,False.\r\nDE,92,415,361-9845,no,yes,33,243.100000,92,41.330000,213.800000,92,18.170000,228.700000,104,10.290000,12.100000,2,3.270000,2,False.\r\nGA,185,510,366-5699,no,yes,31,189.800000,126,32.270000,163.300000,133,13.880000,264.800000,126,11.920000,7.500000,3,2.030000,1,False.\r\nDC,148,415,329-9364,no,no,0,202.000000,102,34.340000,243.200000,128,20.670000,261.300000,90,11.760000,10.900000,3,2.940000,1,False.\r\nAZ,94,408,390-7434,no,yes,38,170.100000,124,28.920000,193.300000,116,16.430000,105.900000,73,4.770000,12.800000,4,3.460000,1,False.\r\nAL,32,510,404-9680,no,no,0,230.900000,87,39.250000,187.400000,90,15.930000,154.000000,53,6.930000,6.300000,2,1.700000,0,False.\r\nCO,68,408,338-9398,no,no,0,237.100000,105,40.310000,223.500000,105,19.000000,97.400000,79,4.380000,13.200000,2,3.560000,1,False.\r\nNH,64,408,394-2445,no,yes,27,182.100000,91,30.960000,169.700000,98,14.420000,164.700000,86,7.410000,10.600000,5,2.860000,2,False.\r\nNM,25,415,381-2709,no,no,0,119.300000,87,20.280000,211.500000,101,17.980000,268.900000,86,12.100000,10.500000,4,2.840000,3,False.\r\nOR,65,415,397-5060,no,no,0,116.800000,87,19.860000,178.900000,93,15.210000,182.400000,150,8.210000,14.100000,2,3.810000,1,False.\r\nLA,179,408,415-2393,no,no,0,219.200000,92,37.260000,149.400000,125,12.700000,244.700000,104,11.010000,6.100000,5,1.650000,0,False.\r\nNE,94,415,377-1765,no,no,0,252.600000,104,42.940000,169.000000,125,14.370000,170.900000,106,7.690000,11.100000,7,3.000000,2,False.\r\nMN,62,415,409-2111,no,no,0,147.100000,91,25.010000,190.400000,107,16.180000,195.200000,115,8.780000,12.200000,3,3.290000,0,False.\r\nMI,127,415,401-3170,no,no,0,202.100000,103,34.360000,229.400000,86,19.500000,195.200000,113,8.780000,11.500000,3,3.110000,2,False.\r\nAR,116,408,405-5681,no,no,0,173.500000,93,29.500000,194.100000,76,16.500000,208.000000,112,9.360000,16.200000,10,4.370000,3,False.\r\nKS,70,408,411-4582,no,no,0,232.100000,122,39.460000,292.300000,112,24.850000,201.200000,112,9.050000,0.000000,0,0.000000,3,False.\r\nWV,94,510,355-5009,yes,yes,23,197.100000,125,33.510000,214.500000,136,18.230000,282.200000,103,12.700000,9.500000,5,2.570000,4,False.\r\nAK,126,415,372-3750,no,no,0,58.200000,94,9.890000,138.700000,118,11.790000,136.800000,91,6.160000,11.900000,1,3.210000,5,True.\r\nNY,67,408,405-2888,no,yes,36,115.600000,111,19.650000,237.700000,94,20.200000,169.900000,103,7.650000,9.900000,12,2.670000,2,False.\r\nNH,19,408,361-3337,no,no,0,186.100000,98,31.640000,254.300000,57,21.620000,214.000000,127,9.630000,14.600000,7,3.940000,2,False.\r\nVA,170,510,350-1639,yes,no,0,259.900000,68,44.180000,245.000000,122,20.830000,134.400000,121,6.050000,8.400000,3,2.270000,3,False.\r\nNM,73,415,333-3221,no,no,0,214.300000,145,36.430000,268.500000,135,22.820000,241.200000,92,10.850000,10.800000,13,2.920000,1,False.\r\nNY,106,408,422-1471,no,no,0,158.700000,74,26.980000,64.300000,139,5.470000,198.500000,103,8.930000,10.200000,4,2.750000,1,False.\r\nAZ,93,415,399-7865,no,no,0,271.600000,71,46.170000,229.400000,108,19.500000,77.300000,121,3.480000,10.900000,3,2.940000,2,False.\r\nWY,164,510,373-4819,no,no,0,160.600000,111,27.300000,163.200000,126,13.870000,187.100000,112,8.420000,9.000000,3,2.430000,1,False.\r\nWA,51,408,338-6981,no,no,0,232.400000,109,39.510000,187.400000,95,15.930000,231.200000,107,10.400000,9.100000,3,2.460000,1,False.\r\nCO,107,415,418-4365,no,no,0,133.800000,85,22.750000,180.500000,94,15.340000,112.200000,115,5.050000,8.900000,4,2.400000,0,False.\r\nTX,130,415,359-5461,no,no,0,176.900000,109,30.070000,90.700000,104,7.710000,238.000000,69,10.710000,9.500000,2,2.570000,1,False.\r\nKY,80,408,375-3586,no,no,0,209.900000,74,35.680000,195.100000,77,16.580000,208.200000,119,9.370000,8.800000,4,2.380000,2,False.\r\nMT,94,415,407-8376,no,no,0,137.500000,118,23.380000,203.200000,88,17.270000,150.000000,131,6.750000,13.400000,2,3.620000,0,False.\r\nOK,118,408,408-6496,no,yes,23,289.500000,52,49.220000,166.600000,111,14.160000,119.100000,88,5.360000,9.500000,4,2.570000,1,False.\r\nMD,117,415,385-7688,no,yes,23,198.100000,86,33.680000,177.000000,86,15.050000,180.500000,92,8.120000,6.800000,6,1.840000,1,False.\r\nTN,78,415,332-6934,no,no,0,149.700000,119,25.450000,182.200000,115,15.490000,261.500000,126,11.770000,9.700000,8,2.620000,0,False.\r\nTX,208,510,378-3625,no,no,0,326.500000,67,55.510000,176.300000,113,14.990000,181.700000,102,8.180000,10.700000,6,2.890000,2,True.\r\nME,131,510,353-7292,yes,yes,26,292.900000,101,49.790000,199.700000,97,16.970000,255.300000,127,11.490000,13.800000,7,3.730000,4,True.\r\nDC,63,408,399-6786,no,no,0,83.000000,64,14.110000,177.000000,106,15.050000,245.700000,89,11.060000,13.000000,3,3.510000,0,False.\r\nMN,53,415,358-3261,no,yes,24,145.700000,146,24.770000,220.500000,136,18.740000,249.900000,96,11.250000,13.100000,5,3.540000,3,False.\r\nDE,62,408,377-9932,no,no,0,182.300000,101,30.990000,328.200000,93,27.900000,245.000000,131,11.030000,11.200000,1,3.020000,2,False.\r\nMD,97,415,397-4030,no,no,0,218.000000,86,37.060000,184.000000,94,15.640000,240.500000,110,10.820000,6.400000,8,1.730000,3,False.\r\nMI,105,510,367-1062,no,no,0,140.600000,109,23.900000,178.600000,51,15.180000,217.000000,83,9.760000,6.800000,3,1.840000,2,False.\r\nWA,157,415,341-8467,no,no,0,152.700000,105,25.960000,257.500000,80,21.890000,198.100000,93,8.910000,9.400000,4,2.540000,1,False.\r\nMO,66,415,339-9453,no,yes,36,106.700000,76,18.140000,209.800000,77,17.830000,190.400000,117,8.570000,12.100000,2,3.270000,1,False.\r\nIN,122,415,344-3388,no,no,0,243.800000,98,41.450000,83.900000,72,7.130000,179.800000,84,8.090000,13.700000,8,3.700000,2,False.\r\nOR,38,415,375-8013,no,no,0,194.400000,94,33.050000,186.700000,95,15.870000,223.300000,90,10.050000,10.800000,5,2.920000,3,False.\r\nMD,106,510,408-4142,no,no,0,213.900000,95,36.360000,151.900000,70,12.910000,260.100000,124,11.700000,12.200000,5,3.290000,3,False.\r\nRI,99,510,386-3671,no,no,0,217.200000,112,36.920000,246.700000,89,20.970000,226.100000,89,10.170000,15.800000,7,4.270000,3,False.\r\nLA,99,415,411-2284,no,no,0,241.100000,72,40.990000,155.600000,98,13.230000,188.200000,109,8.470000,11.600000,10,3.130000,1,False.\r\nAZ,144,510,346-7795,yes,no,0,203.500000,100,34.600000,247.600000,103,21.050000,194.300000,94,8.740000,11.900000,11,3.210000,0,False.\r\nPA,82,415,333-5609,no,yes,24,155.200000,131,26.380000,244.500000,106,20.780000,122.400000,68,5.510000,10.700000,3,2.890000,1,False.\r\nAZ,86,408,405-1842,no,yes,31,167.600000,139,28.490000,113.000000,118,9.610000,246.900000,121,11.110000,12.200000,6,3.290000,1,False.\r\nFL,70,510,366-6345,yes,no,0,226.700000,98,38.540000,228.100000,115,19.390000,73.200000,93,3.290000,17.600000,4,4.750000,2,True.\r\nLA,93,415,337-9345,no,no,0,179.300000,93,30.480000,178.600000,98,15.180000,225.200000,131,10.130000,11.500000,6,3.110000,3,False.\r\nFL,93,415,328-6770,no,no,0,151.400000,89,25.740000,186.400000,76,15.840000,172.500000,120,7.760000,10.900000,3,2.940000,0,False.\r\nFL,120,415,380-7321,no,no,0,180.000000,80,30.600000,224.200000,82,19.060000,265.400000,91,11.940000,4.700000,7,1.270000,3,False.\r\nMD,136,415,375-1476,no,no,0,250.200000,121,42.530000,267.100000,118,22.700000,151.000000,114,6.800000,13.000000,2,3.510000,1,True.\r\nAL,106,415,356-1567,no,no,0,223.000000,121,37.910000,110.100000,98,9.360000,188.700000,107,8.490000,7.100000,12,1.920000,0,False.\r\nWA,81,415,422-6685,no,no,0,183.600000,116,31.210000,152.600000,98,12.970000,212.200000,99,9.550000,12.200000,6,3.290000,3,False.\r\nTN,127,408,336-1090,no,yes,22,166.000000,114,28.220000,174.500000,103,14.830000,244.900000,68,11.020000,10.200000,6,2.750000,1,False.\r\nMS,65,415,343-2095,no,no,0,136.100000,112,23.140000,272.900000,96,23.200000,220.200000,104,9.910000,4.400000,2,1.190000,1,False.\r\nME,35,408,345-3934,no,no,0,149.300000,113,25.380000,242.200000,122,20.590000,174.300000,104,7.840000,8.900000,6,2.400000,2,False.\r\nOK,88,408,338-8050,no,no,0,65.400000,97,11.120000,168.200000,76,14.300000,236.000000,113,10.620000,13.800000,1,3.730000,2,False.\r\nIN,65,415,388-9568,no,no,0,213.400000,111,36.280000,234.500000,94,19.930000,250.100000,123,11.250000,2.700000,4,0.730000,1,False.\r\nMO,123,415,402-6591,no,no,0,206.900000,85,35.170000,244.700000,78,20.800000,221.500000,136,9.970000,7.700000,2,2.080000,3,False.\r\nIA,126,408,403-6419,no,yes,27,186.200000,78,31.650000,189.600000,83,16.120000,76.500000,139,3.440000,9.600000,3,2.590000,2,False.\r\nVA,104,415,386-9790,no,yes,23,280.200000,136,47.630000,220.500000,92,18.740000,136.900000,102,6.160000,13.300000,3,3.590000,4,False.\r\nKY,45,415,378-5692,no,yes,22,196.600000,84,33.420000,313.200000,92,26.620000,163.300000,108,7.350000,11.900000,3,3.210000,0,False.\r\nMD,93,408,360-3324,yes,no,0,312.000000,109,53.040000,129.400000,100,11.000000,217.600000,74,9.790000,10.500000,2,2.840000,0,True.\r\nOH,63,415,410-3719,yes,yes,36,199.000000,110,33.830000,291.300000,111,24.760000,197.600000,92,8.890000,11.000000,6,2.970000,1,False.\r\nOK,100,415,352-4221,no,no,0,203.100000,96,34.530000,217.000000,126,18.450000,180.900000,122,8.140000,13.500000,2,3.650000,3,False.\r\nNV,53,415,327-6179,no,no,0,168.800000,97,28.700000,220.300000,87,18.730000,154.300000,113,6.940000,10.900000,2,2.940000,0,False.\r\nID,92,415,359-6196,yes,no,0,173.100000,140,29.430000,240.300000,105,20.430000,233.200000,117,10.490000,9.000000,5,2.430000,1,False.\r\nMN,139,510,374-9107,no,no,0,134.400000,106,22.850000,211.300000,98,17.960000,193.600000,125,8.710000,10.200000,2,2.750000,5,True.\r\nSD,110,408,357-4078,no,yes,40,202.600000,103,34.440000,118.800000,128,10.100000,234.900000,98,10.570000,9.000000,9,2.430000,2,False.\r\nIL,110,408,366-5780,no,no,0,74.500000,117,12.670000,200.800000,98,17.070000,192.200000,101,8.650000,9.800000,7,2.650000,3,False.\r\nWY,215,510,393-9619,no,no,0,83.600000,148,14.210000,120.900000,91,10.280000,226.600000,110,10.200000,10.700000,9,2.890000,0,False.\r\nAL,73,415,355-9295,no,no,0,192.200000,86,32.670000,168.600000,116,14.330000,139.800000,87,6.290000,9.400000,6,2.540000,1,False.\r\nNJ,138,510,400-5751,no,no,0,220.200000,89,37.430000,88.300000,125,7.510000,195.300000,79,8.790000,12.900000,5,3.480000,0,False.\r\nNV,137,415,338-1027,yes,no,0,135.100000,95,22.970000,134.100000,102,11.400000,223.100000,81,10.040000,12.300000,2,3.320000,2,True.\r\nIN,36,415,405-8867,no,no,0,253.400000,77,43.080000,182.400000,151,15.500000,275.800000,103,12.410000,8.400000,2,2.270000,1,False.\r\nWV,85,408,336-5616,no,no,0,225.000000,81,38.250000,176.900000,63,15.040000,194.300000,110,8.740000,7.100000,2,1.920000,3,False.\r\nVA,108,408,335-1697,no,no,0,198.500000,99,33.750000,267.800000,60,22.760000,354.900000,75,15.970000,9.400000,3,2.540000,0,True.\r\nSC,22,408,331-5138,no,no,0,110.300000,107,18.750000,166.500000,93,14.150000,202.300000,96,9.100000,9.500000,5,2.570000,0,False.\r\nRI,107,415,385-8240,no,yes,37,60.000000,102,10.200000,102.200000,80,8.690000,261.800000,106,11.780000,11.100000,3,3.000000,0,False.\r\nIN,51,510,348-1359,no,no,0,214.800000,94,36.520000,149.700000,58,12.720000,283.400000,66,12.750000,10.200000,5,2.750000,0,False.\r\nAZ,94,408,354-7339,no,no,0,181.800000,85,30.910000,202.400000,98,17.200000,245.900000,97,11.070000,9.200000,2,2.480000,4,False.\r\nNM,119,510,349-1687,no,yes,23,154.000000,114,26.180000,278.000000,137,23.630000,228.400000,112,10.280000,11.800000,4,3.190000,2,False.\r\nOR,33,415,380-2558,no,yes,29,157.400000,99,26.760000,117.900000,80,10.020000,279.200000,79,12.560000,13.900000,11,3.750000,4,True.\r\nNJ,106,415,365-2153,no,no,0,207.900000,91,35.340000,172.000000,109,14.620000,191.800000,143,8.630000,14.400000,7,3.890000,4,False.\r\nMS,82,408,345-6043,no,no,0,207.000000,90,35.190000,232.900000,83,19.800000,172.400000,108,7.760000,9.100000,8,2.460000,3,False.\r\nMI,86,510,349-2808,no,yes,41,119.000000,101,20.230000,230.000000,134,19.550000,236.900000,58,10.660000,9.500000,3,2.570000,0,False.\r\nTX,97,415,411-1715,yes,no,0,143.700000,117,24.430000,273.000000,82,23.210000,178.300000,81,8.020000,10.900000,3,2.940000,0,False.\r\nFL,106,408,385-2488,no,yes,32,165.900000,126,28.200000,216.500000,93,18.400000,173.100000,86,7.790000,14.100000,8,3.810000,4,False.\r\nDC,108,510,377-7177,no,no,0,138.600000,122,23.560000,172.300000,117,14.650000,231.600000,92,10.420000,9.800000,3,2.650000,1,False.\r\nTX,114,415,342-1099,no,no,0,84.700000,118,14.400000,249.900000,86,21.240000,193.400000,95,8.700000,14.500000,8,3.920000,1,False.\r\nKS,92,408,386-4170,yes,no,0,62.600000,111,10.640000,180.600000,126,15.350000,221.700000,80,9.980000,10.400000,2,2.810000,1,True.\r\nUT,59,510,413-1269,no,no,0,155.200000,79,26.380000,235.300000,123,20.000000,169.400000,80,7.620000,8.700000,4,2.350000,1,False.\r\nMN,24,510,396-4460,no,yes,25,164.900000,110,28.030000,209.300000,105,17.790000,231.200000,55,10.400000,6.700000,9,1.810000,1,False.\r\nIL,151,408,334-2730,no,no,0,134.500000,88,22.870000,143.100000,112,12.160000,223.900000,61,10.080000,15.400000,1,4.160000,1,False.\r\nNM,117,415,340-3182,no,no,0,143.300000,103,24.360000,211.300000,108,17.960000,185.200000,96,8.330000,11.500000,3,3.110000,1,False.\r\nSC,78,510,377-8608,no,no,0,168.300000,110,28.610000,221.200000,73,18.800000,241.000000,136,10.850000,12.500000,1,3.380000,1,False.\r\nNC,155,408,417-3676,no,no,0,262.400000,55,44.610000,194.600000,113,16.540000,146.500000,85,6.590000,8.300000,6,2.240000,2,False.\r\nWV,114,510,417-6774,no,yes,30,206.200000,79,35.050000,260.000000,91,22.100000,291.600000,83,13.120000,11.400000,6,3.080000,1,False.\r\nRI,114,510,411-9554,no,yes,28,225.800000,94,38.390000,193.000000,117,16.410000,232.400000,100,10.460000,8.400000,9,2.270000,4,False.\r\nNH,119,408,420-3192,no,no,0,138.300000,89,23.510000,170.500000,78,14.490000,263.900000,98,11.880000,13.500000,6,3.650000,3,False.\r\nMO,64,510,389-1475,no,yes,48,94.400000,104,16.050000,136.200000,101,11.580000,147.400000,89,6.630000,4.500000,4,1.220000,0,False.\r\nMA,118,408,343-7734,yes,no,0,160.000000,123,27.200000,175.400000,96,14.910000,184.800000,99,8.320000,9.900000,3,2.670000,2,False.\r\nPA,101,415,410-3390,no,no,0,206.600000,105,35.120000,224.900000,117,19.120000,249.900000,100,11.250000,14.600000,3,3.940000,0,False.\r\nOK,117,415,344-6495,no,no,0,134.700000,121,22.900000,180.000000,83,15.300000,200.900000,104,9.040000,7.700000,3,2.080000,1,False.\r\nAL,49,415,331-6229,no,yes,28,214.400000,78,36.450000,235.200000,100,19.990000,206.200000,107,9.280000,8.000000,13,2.160000,3,False.\r\nWY,139,415,337-7501,no,no,0,192.800000,104,32.780000,234.400000,96,19.920000,203.200000,101,9.140000,13.000000,3,3.510000,3,False.\r\nPA,92,408,339-9631,no,yes,28,151.100000,90,25.690000,194.800000,79,16.560000,239.200000,114,10.760000,10.000000,3,2.700000,1,False.\r\nWA,83,415,369-4384,no,no,0,221.400000,103,37.640000,231.800000,103,19.700000,122.500000,100,5.510000,9.800000,5,2.650000,3,False.\r\nHI,148,510,416-3915,yes,no,0,218.900000,88,37.210000,208.000000,85,17.680000,203.300000,99,9.150000,11.100000,4,3.000000,0,False.\r\nSD,144,408,339-3049,no,yes,48,189.800000,96,32.270000,123.400000,67,10.490000,214.200000,106,9.640000,6.500000,2,1.760000,2,True.\r\nAL,131,415,361-7998,no,yes,25,192.700000,85,32.760000,225.900000,105,19.200000,254.200000,59,11.440000,10.900000,6,2.940000,2,False.\r\nVT,146,510,355-4842,yes,no,0,204.400000,135,34.750000,219.100000,90,18.620000,222.700000,114,10.020000,10.500000,6,2.840000,3,False.\r\nMT,143,415,387-6440,no,no,0,172.300000,97,29.290000,174.000000,108,14.790000,188.200000,119,8.470000,13.000000,4,3.510000,2,False.\r\nMN,81,415,369-2625,no,no,0,198.400000,93,33.730000,210.900000,108,17.930000,193.300000,71,8.700000,10.400000,6,2.810000,2,False.\r\nAK,48,415,389-7073,no,yes,37,211.700000,115,35.990000,159.900000,84,13.590000,144.100000,80,6.480000,12.200000,1,3.290000,1,False.\r\nMI,86,415,370-8463,no,yes,28,221.600000,74,37.670000,288.400000,100,24.510000,240.300000,105,10.810000,9.000000,2,2.430000,1,False.\r\nDE,71,415,362-7318,no,no,0,197.900000,108,33.640000,181.500000,109,15.430000,281.400000,56,12.660000,6.700000,5,1.810000,3,False.\r\nSD,145,408,412-1194,no,yes,24,147.500000,90,25.080000,175.700000,108,14.930000,252.100000,102,11.340000,15.600000,3,4.210000,2,False.\r\nMI,137,510,355-9508,no,no,0,206.400000,122,35.090000,128.000000,102,10.880000,194.500000,84,8.750000,8.800000,5,2.380000,2,False.\r\nKS,137,408,352-8202,no,no,0,205.900000,88,35.000000,209.300000,86,17.790000,289.900000,84,13.050000,14.500000,4,3.920000,2,False.\r\nAL,167,510,335-5882,no,no,0,207.600000,88,35.290000,132.400000,63,11.250000,255.200000,98,11.480000,14.100000,5,3.810000,0,False.\r\nOK,89,510,352-6976,no,no,0,303.900000,95,51.660000,260.900000,114,22.180000,312.100000,89,14.040000,5.300000,3,1.430000,1,True.\r\nCT,199,415,393-6733,no,yes,34,230.600000,121,39.200000,219.400000,99,18.650000,299.300000,94,13.470000,8.000000,2,2.160000,0,False.\r\nNE,132,510,335-1838,no,no,0,99.500000,110,16.920000,129.100000,80,10.970000,125.100000,124,5.630000,9.700000,3,2.620000,0,False.\r\nWI,94,510,355-6930,no,no,0,177.100000,112,30.110000,194.000000,112,16.490000,146.700000,108,6.600000,5.900000,4,1.590000,1,False.\r\nCT,96,415,387-5860,no,yes,37,172.700000,93,29.360000,120.100000,116,10.210000,216.100000,86,9.720000,10.300000,5,2.780000,5,True.\r\nWI,96,510,343-2605,no,yes,18,172.700000,86,29.360000,133.400000,113,11.340000,259.500000,70,11.680000,9.800000,3,2.650000,1,False.\r\nIN,166,510,350-6759,no,no,0,204.200000,115,34.710000,179.900000,152,15.290000,216.800000,109,9.760000,9.500000,5,2.570000,1,False.\r\nDC,74,415,371-1514,no,no,0,85.700000,83,14.570000,247.700000,67,21.050000,142.400000,85,6.410000,10.100000,5,2.730000,2,False.\r\nAR,36,415,346-9317,no,no,0,157.600000,117,26.790000,184.300000,58,15.670000,240.400000,99,10.820000,11.900000,1,3.210000,0,False.\r\nME,113,415,398-4313,no,no,0,215.500000,129,36.640000,218.700000,117,18.590000,207.100000,91,9.320000,6.600000,9,1.780000,4,False.\r\nMN,94,415,412-4399,no,no,0,181.500000,98,30.860000,199.900000,88,16.990000,287.700000,114,12.950000,6.600000,5,1.780000,1,False.\r\nMD,67,415,330-1835,no,no,0,171.700000,80,29.190000,110.400000,81,9.380000,195.400000,111,8.790000,11.900000,4,3.210000,2,False.\r\nFL,127,415,416-1676,no,no,0,266.600000,106,45.320000,264.800000,168,22.510000,207.200000,119,9.320000,5.900000,2,1.590000,1,True.\r\nRI,121,408,329-7347,no,no,0,170.400000,108,28.970000,350.500000,68,29.790000,297.000000,87,13.370000,11.200000,3,3.020000,0,True.\r\nIA,158,415,360-6868,no,no,0,158.000000,106,26.860000,292.500000,114,24.860000,241.100000,89,10.850000,9.100000,4,2.460000,1,False.\r\nAZ,136,510,405-6641,no,no,0,92.000000,117,15.640000,253.600000,77,21.560000,214.100000,90,9.630000,10.300000,10,2.780000,1,False.\r\nMO,196,415,393-2373,no,no,0,234.000000,109,39.780000,249.500000,114,21.210000,173.100000,70,7.790000,9.100000,5,2.460000,2,False.\r\nVT,113,415,419-1714,no,no,0,272.100000,111,46.260000,268.500000,118,22.820000,213.800000,105,9.620000,8.500000,10,2.300000,1,True.\r\nIN,122,408,336-3819,no,no,0,296.400000,99,50.390000,214.800000,89,18.260000,133.900000,107,6.030000,11.400000,3,3.080000,4,True.\r\nRI,112,510,341-3464,no,no,0,194.400000,101,33.050000,190.300000,82,16.180000,183.400000,107,8.250000,11.400000,2,3.080000,0,False.\r\nSD,209,415,413-5310,no,no,0,227.200000,128,38.620000,258.400000,92,21.960000,183.500000,74,8.260000,8.900000,4,2.400000,3,False.\r\nMN,62,415,366-7912,no,no,0,248.700000,109,42.280000,220.000000,118,18.700000,265.700000,78,11.960000,13.200000,2,3.560000,1,True.\r\nTX,110,415,399-8845,no,yes,38,236.300000,102,40.170000,195.900000,112,16.650000,183.500000,82,8.260000,9.700000,6,2.620000,1,False.\r\nVA,16,510,368-2583,no,no,0,205.600000,69,34.950000,169.500000,93,14.410000,220.100000,64,9.900000,10.900000,3,2.940000,0,False.\r\nMA,73,408,360-6309,no,no,0,94.100000,136,16.000000,280.300000,122,23.830000,205.000000,77,9.230000,9.800000,4,2.650000,0,False.\r\nID,128,408,359-5890,no,no,0,125.200000,99,21.280000,205.400000,107,17.460000,254.400000,111,11.450000,18.900000,2,5.100000,0,False.\r\nMA,39,408,332-2462,no,no,0,60.400000,158,10.270000,306.200000,120,26.030000,123.900000,46,5.580000,12.400000,3,3.350000,1,False.\r\nGA,103,415,381-9196,no,yes,28,121.000000,105,20.570000,270.400000,100,22.980000,160.500000,76,7.220000,7.700000,4,2.080000,2,False.\r\nRI,119,415,329-3222,no,yes,29,117.800000,66,20.030000,256.800000,114,21.830000,147.600000,76,6.640000,7.600000,3,2.050000,3,False.\r\nID,173,510,363-5819,no,yes,21,232.400000,96,39.510000,211.900000,118,18.010000,273.000000,102,12.290000,5.000000,5,1.350000,3,False.\r\nSD,128,510,413-9269,yes,yes,32,223.500000,81,38.000000,188.800000,74,16.050000,154.900000,101,6.970000,9.400000,2,2.540000,2,True.\r\nMA,86,510,330-7483,no,no,0,176.300000,79,29.970000,259.200000,97,22.030000,287.400000,78,12.930000,6.200000,3,1.670000,0,False.\r\nWY,114,415,403-7775,no,yes,32,125.200000,79,21.280000,177.800000,105,15.110000,232.400000,89,10.460000,12.900000,3,3.480000,1,False.\r\nVA,104,408,360-2479,no,no,0,138.700000,107,23.580000,256.900000,113,21.840000,234.900000,74,10.570000,10.000000,3,2.700000,0,False.\r\nOR,148,415,394-3791,no,no,0,86.300000,134,14.670000,246.600000,92,20.960000,251.600000,91,11.320000,11.300000,4,3.050000,1,False.\r\nVA,129,408,384-2632,no,no,0,207.000000,91,35.190000,154.900000,121,13.170000,245.100000,112,11.030000,13.400000,5,3.620000,3,False.\r\nME,100,510,359-8466,no,yes,30,58.800000,104,10.000000,219.500000,107,18.660000,152.300000,118,6.850000,7.100000,3,1.920000,0,False.\r\nAL,121,408,331-8909,no,yes,35,68.700000,95,11.680000,209.200000,69,17.780000,197.400000,42,8.880000,11.400000,4,3.080000,1,False.\r\nGA,143,408,359-5160,no,yes,33,239.200000,109,40.660000,235.500000,112,20.020000,156.300000,95,7.030000,9.500000,4,2.570000,1,False.\r\nIA,76,510,330-9833,no,no,0,198.300000,130,33.710000,217.100000,86,18.450000,188.400000,96,8.480000,12.500000,3,3.380000,0,False.\r\nAZ,158,510,362-2314,no,no,0,205.200000,97,34.880000,240.600000,77,20.450000,79.700000,108,3.590000,14.400000,12,3.890000,0,False.\r\nFL,116,510,338-8478,no,no,0,192.100000,98,32.660000,312.900000,135,26.600000,130.200000,94,5.860000,7.900000,2,2.130000,3,False.\r\nMT,54,415,387-5453,no,no,0,272.600000,83,46.340000,248.700000,74,21.140000,197.400000,111,8.880000,9.500000,2,2.570000,1,True.\r\nAL,86,415,380-3437,no,no,0,128.300000,121,21.810000,197.100000,93,16.750000,138.400000,152,6.230000,12.200000,5,3.290000,7,True.\r\nDE,108,510,365-8779,no,no,0,169.600000,99,28.830000,264.100000,87,22.450000,206.300000,78,9.280000,9.300000,4,2.510000,0,False.\r\nMT,66,510,407-2750,no,no,0,201.300000,95,34.220000,152.800000,66,12.990000,233.200000,101,10.490000,7.500000,4,2.030000,1,False.\r\nKY,151,408,396-8265,no,yes,17,214.700000,97,36.500000,138.500000,90,11.770000,169.100000,44,7.610000,8.600000,4,2.320000,1,False.\r\nSC,99,510,397-4304,no,no,0,169.200000,70,28.760000,271.500000,77,23.080000,170.200000,104,7.660000,10.600000,2,2.860000,0,False.\r\nWA,55,415,333-2611,no,no,0,194.100000,121,33.000000,176.600000,110,15.010000,302.800000,136,13.630000,7.000000,7,1.890000,2,False.\r\nOR,77,510,409-8814,no,no,0,233.800000,104,39.750000,266.500000,94,22.650000,212.700000,104,9.570000,7.600000,3,2.050000,2,False.\r\nAK,78,408,336-5406,no,no,0,225.100000,67,38.270000,199.200000,127,16.930000,175.500000,102,7.900000,14.600000,2,3.940000,0,False.\r\nGA,89,415,343-6940,no,no,0,213.000000,63,36.210000,176.600000,71,15.010000,262.600000,126,11.820000,9.100000,1,2.460000,1,True.\r\nMN,101,415,361-9923,no,no,0,183.900000,115,31.260000,255.900000,101,21.750000,275.000000,145,12.380000,10.800000,11,2.920000,1,False.\r\nIL,44,415,350-6639,no,yes,34,221.800000,105,37.710000,161.700000,85,13.740000,227.700000,62,10.250000,14.000000,7,3.780000,0,False.\r\nIN,98,408,376-4300,no,yes,21,64.600000,98,10.980000,176.100000,86,14.970000,244.800000,84,11.020000,0.000000,0,0.000000,2,False.\r\nSC,64,510,349-6567,no,yes,37,154.600000,92,26.280000,83.400000,103,7.090000,165.900000,99,7.470000,13.300000,3,3.590000,1,False.\r\nVA,141,415,333-7749,no,no,0,260.200000,131,44.230000,179.200000,120,15.230000,135.000000,119,6.080000,7.200000,8,1.940000,3,False.\r\nWI,81,415,408-6089,no,yes,33,161.600000,117,27.470000,123.000000,90,10.460000,261.300000,101,11.760000,12.200000,5,3.290000,1,False.\r\nVT,162,510,375-2165,no,no,0,220.600000,117,37.500000,155.200000,121,13.190000,186.700000,89,8.400000,10.500000,11,2.840000,1,False.\r\nAZ,83,415,400-6999,no,yes,41,155.900000,122,26.500000,162.300000,107,13.800000,127.600000,105,5.740000,13.100000,5,3.540000,3,False.\r\nFL,100,510,420-7823,no,no,0,107.000000,63,18.190000,105.700000,67,8.980000,243.100000,74,10.940000,12.800000,3,3.460000,4,True.\r\nAK,59,510,366-5241,no,no,0,182.500000,104,31.030000,204.700000,95,17.400000,229.900000,100,10.350000,11.300000,8,3.050000,4,False.\r\nAR,179,415,413-3412,yes,yes,38,220.100000,78,37.420000,234.300000,71,19.920000,237.300000,85,10.680000,10.100000,4,2.730000,4,False.\r\nAR,79,408,406-2752,no,no,0,152.200000,112,25.870000,177.200000,132,15.060000,96.400000,87,4.340000,5.300000,3,1.430000,1,False.\r\nAK,117,415,337-8078,no,no,0,181.500000,95,30.860000,205.100000,88,17.430000,204.000000,82,9.180000,14.700000,9,3.970000,2,False.\r\nMS,64,408,402-1942,yes,no,0,236.200000,77,40.150000,218.600000,85,18.580000,194.100000,97,8.730000,13.200000,2,3.560000,2,True.\r\nME,31,415,371-7917,no,no,0,166.100000,105,28.240000,79.300000,93,6.740000,213.700000,98,9.620000,12.700000,2,3.430000,1,False.\r\nCA,124,408,343-6374,yes,no,0,244.600000,89,41.580000,188.800000,80,16.050000,206.000000,114,9.270000,11.300000,4,3.050000,1,False.\r\nNM,122,408,385-8730,no,yes,23,134.200000,85,22.810000,227.300000,132,19.320000,122.400000,96,5.510000,8.500000,2,2.300000,2,False.\r\nNE,37,408,393-7892,yes,yes,39,149.700000,122,25.450000,211.100000,75,17.940000,114.300000,90,5.140000,9.200000,4,2.480000,1,False.\r\nSC,90,408,407-6748,no,yes,29,150.100000,109,25.520000,264.700000,103,22.500000,178.400000,97,8.030000,5.800000,4,1.570000,1,False.\r\nCO,159,408,341-4463,yes,no,0,257.100000,53,43.710000,312.200000,127,26.540000,183.000000,82,8.240000,8.800000,6,2.380000,1,True.\r\nDE,148,415,351-2587,no,no,0,124.400000,83,21.150000,179.700000,81,15.270000,253.000000,99,11.390000,11.300000,6,3.050000,0,False.\r\nOH,39,415,421-9752,no,yes,36,141.700000,121,24.090000,232.300000,113,19.750000,222.100000,131,9.990000,12.000000,5,3.240000,1,False.\r\nMS,77,408,356-4001,no,no,0,230.000000,87,39.100000,103.200000,138,8.770000,309.600000,136,13.930000,11.300000,3,3.050000,2,False.\r\nOK,194,408,328-9869,no,no,0,162.300000,88,27.590000,213.700000,118,18.160000,192.100000,81,8.640000,10.900000,2,2.940000,0,False.\r\nCO,154,415,343-5709,no,no,0,350.800000,75,59.640000,216.500000,94,18.400000,253.900000,100,11.430000,10.100000,9,2.730000,1,True.\r\nNC,112,415,334-1872,no,no,0,193.300000,96,32.860000,264.100000,123,22.450000,128.600000,115,5.790000,9.100000,3,2.460000,4,False.\r\nMD,45,415,350-1040,no,no,0,78.200000,127,13.290000,253.400000,108,21.540000,255.000000,100,11.480000,18.000000,3,4.860000,1,False.\r\nKS,132,415,369-3214,no,no,0,83.400000,110,14.180000,232.200000,137,19.740000,146.700000,114,6.600000,7.600000,5,2.050000,1,False.\r\nMA,128,415,385-6778,no,no,0,195.600000,99,33.250000,267.800000,120,22.760000,164.900000,76,7.420000,16.000000,2,4.320000,0,False.\r\nNC,135,415,383-7689,no,no,0,201.800000,81,34.310000,225.000000,114,19.130000,204.400000,82,9.200000,10.300000,6,2.780000,1,False.\r\nNM,56,408,385-5722,no,no,0,197.000000,110,33.490000,222.800000,102,18.940000,225.300000,91,10.140000,10.600000,6,2.860000,2,False.\r\nCA,151,415,357-1909,yes,no,0,218.000000,57,37.060000,114.400000,88,9.720000,269.200000,95,12.110000,12.400000,1,3.350000,0,True.\r\nNY,32,415,364-3567,no,no,0,164.800000,98,28.020000,229.900000,96,19.540000,167.300000,108,7.530000,14.800000,2,4.000000,2,False.\r\nAZ,90,415,422-4241,no,no,0,179.200000,77,30.460000,210.700000,99,17.910000,276.900000,58,12.460000,9.200000,6,2.480000,2,False.\r\nSD,87,415,370-2957,no,yes,21,214.000000,113,36.380000,180.000000,114,15.300000,134.500000,82,6.050000,10.600000,5,2.860000,0,False.\r\nDC,138,415,329-6562,no,no,0,170.500000,87,28.990000,118.200000,116,10.050000,187.900000,111,8.460000,11.200000,7,3.020000,2,False.\r\nND,79,408,363-3515,no,no,0,205.700000,123,34.970000,214.500000,108,18.230000,226.100000,106,10.170000,6.700000,18,1.810000,1,False.\r\nMO,95,415,374-7787,yes,no,0,165.500000,84,28.140000,286.200000,112,24.330000,198.900000,89,8.950000,11.500000,2,3.110000,1,True.\r\nKS,127,415,345-2931,no,no,0,221.000000,100,37.570000,160.700000,113,13.660000,233.100000,96,10.490000,6.800000,4,1.840000,2,False.\r\nSD,137,510,373-5732,no,no,0,242.100000,118,41.160000,191.000000,93,16.240000,218.600000,50,9.840000,14.700000,2,3.970000,3,False.\r\nOK,97,415,348-7437,no,no,0,151.600000,107,25.770000,155.400000,96,13.210000,240.000000,112,10.800000,14.700000,4,3.970000,1,False.\r\nOR,149,415,332-9460,yes,no,0,176.200000,87,29.950000,145.000000,81,12.330000,249.500000,92,11.230000,5.700000,4,1.540000,0,False.\r\nIN,117,415,355-6531,yes,yes,22,196.000000,82,33.320000,322.700000,82,27.430000,225.600000,120,10.150000,3.700000,5,1.000000,1,False.\r\nOH,84,408,336-9390,no,no,0,159.500000,125,27.120000,247.100000,90,21.000000,187.900000,82,8.460000,7.200000,4,1.940000,2,False.\r\nKS,137,415,346-8581,no,no,0,230.200000,113,39.130000,220.400000,79,18.730000,204.700000,111,9.210000,10.700000,7,2.890000,4,False.\r\nCT,99,415,363-8824,no,no,0,146.700000,64,24.940000,274.000000,99,23.290000,321.300000,98,14.460000,8.900000,1,2.400000,3,False.\r\nNH,54,510,353-3351,no,no,0,210.500000,102,35.790000,204.500000,83,17.380000,127.800000,53,5.750000,8.500000,5,2.300000,1,False.\r\nWI,85,415,360-4320,no,no,0,102.000000,95,17.340000,270.200000,139,22.970000,148.200000,105,6.670000,10.700000,3,2.890000,1,False.\r\nMS,150,510,417-6252,no,no,0,126.000000,99,21.420000,238.500000,73,20.270000,285.100000,100,12.830000,10.200000,6,2.750000,3,False.\r\nWV,43,415,393-4949,no,no,0,168.400000,125,28.630000,243.800000,89,20.720000,214.700000,102,9.660000,11.100000,2,3.000000,1,False.\r\nMA,35,415,401-3156,no,no,0,105.600000,129,17.950000,258.200000,129,21.950000,213.100000,77,9.590000,8.700000,3,2.350000,0,False.\r\nMD,98,415,338-6283,no,no,0,206.500000,92,35.110000,176.200000,152,14.980000,232.800000,115,10.480000,12.400000,5,3.350000,5,False.\r\nPA,112,510,352-9017,no,no,0,217.100000,76,36.910000,205.200000,100,17.440000,185.700000,91,8.360000,9.400000,3,2.540000,2,False.\r\nWI,16,510,405-5305,no,no,0,229.600000,78,39.030000,205.700000,108,17.480000,166.200000,91,7.480000,10.800000,2,2.920000,0,True.\r\nTN,98,415,376-9249,no,yes,22,278.300000,89,47.310000,93.400000,143,7.940000,107.600000,42,4.840000,9.700000,5,2.620000,0,False.\r\nTX,84,408,339-7139,no,no,0,138.600000,102,23.560000,199.000000,93,16.920000,204.100000,137,9.180000,7.800000,4,2.110000,0,False.\r\nOR,94,415,328-6011,no,no,0,234.400000,103,39.850000,279.300000,109,23.740000,234.200000,121,10.540000,2.000000,2,0.540000,1,True.\r\nIL,84,510,378-1303,no,no,0,181.500000,129,30.860000,130.700000,112,11.110000,186.500000,118,8.390000,8.500000,4,2.300000,1,False.\r\nDC,66,415,402-5155,no,no,0,167.300000,91,28.440000,230.000000,68,19.550000,191.700000,118,8.630000,10.600000,5,2.860000,1,True.\r\nGA,98,415,333-5430,no,yes,31,121.000000,105,20.570000,218.900000,98,18.610000,226.700000,110,10.200000,12.000000,1,3.240000,1,False.\r\nID,74,415,365-9696,no,no,0,221.100000,124,37.590000,110.800000,94,9.420000,240.100000,112,10.800000,10.600000,3,2.860000,0,False.\r\nUT,96,408,410-4023,no,yes,26,145.800000,108,24.790000,192.200000,89,16.340000,165.100000,96,7.430000,9.900000,2,2.670000,1,False.\r\nKY,119,510,411-7649,no,no,0,222.800000,122,37.880000,163.200000,107,13.870000,160.600000,112,7.230000,11.200000,6,3.020000,1,False.\r\nOH,73,415,338-4065,no,no,0,183.400000,80,31.180000,242.000000,115,20.570000,201.400000,100,9.060000,7.500000,3,2.030000,4,False.\r\nWI,92,415,421-9401,yes,no,0,264.300000,91,44.930000,160.900000,115,13.680000,198.600000,73,8.940000,9.300000,5,2.510000,0,False.\r\nIL,21,415,343-9658,no,no,0,146.000000,78,24.820000,109.700000,79,9.320000,247.400000,108,11.130000,6.800000,7,1.840000,0,False.\r\nDE,122,510,332-5521,no,no,0,157.100000,134,26.710000,184.900000,122,15.720000,197.200000,59,8.870000,8.500000,5,2.300000,4,True.\r\nRI,133,510,349-4369,yes,no,0,127.300000,108,21.640000,251.300000,81,21.360000,135.000000,88,6.080000,10.300000,3,2.780000,1,False.\r\nTX,145,415,351-4288,no,no,0,187.900000,110,31.940000,197.000000,117,16.750000,167.000000,108,7.520000,4.800000,4,1.300000,2,False.\r\nOR,25,408,422-5874,no,no,0,178.800000,90,30.400000,141.200000,72,12.000000,203.000000,99,9.140000,8.400000,5,2.270000,2,False.\r\nNV,64,415,396-2324,no,no,0,97.200000,80,16.520000,186.200000,90,15.830000,189.000000,92,8.500000,10.400000,6,2.810000,2,False.\r\nNE,85,415,416-5662,no,no,0,259.800000,85,44.170000,242.300000,117,20.600000,168.800000,72,7.600000,5.400000,1,1.460000,0,False.\r\nMS,126,415,363-9663,no,no,0,256.500000,112,43.610000,199.500000,90,16.960000,188.300000,122,8.470000,7.000000,5,1.890000,3,False.\r\nOR,76,415,410-9477,no,no,0,169.500000,77,28.820000,124.000000,87,10.540000,219.400000,92,9.870000,10.000000,3,2.700000,0,False.\r\nDE,113,415,352-4418,no,no,0,239.700000,47,40.750000,282.900000,110,24.050000,238.400000,88,10.730000,8.700000,3,2.350000,2,True.\r\nDE,224,510,361-6563,yes,no,0,171.500000,99,29.160000,160.000000,103,13.600000,212.400000,102,9.560000,5.000000,2,1.350000,1,True.\r\nAZ,117,408,417-4404,no,no,0,239.900000,84,40.780000,174.800000,106,14.860000,209.500000,93,9.430000,9.800000,2,2.650000,0,False.\r\nSD,128,408,372-8048,no,yes,34,142.300000,73,24.190000,194.800000,79,16.560000,239.300000,81,10.770000,16.000000,6,4.320000,1,False.\r\nNV,115,415,356-3646,no,no,0,184.100000,98,31.300000,327.000000,73,27.800000,212.500000,106,9.560000,7.500000,6,2.030000,2,False.\r\nNM,141,415,351-9604,no,yes,28,206.900000,126,35.170000,264.400000,126,22.470000,171.800000,124,7.730000,9.300000,11,2.510000,2,False.\r\nMN,51,510,355-9581,no,no,0,259.900000,114,44.180000,176.200000,94,14.980000,77.200000,112,3.470000,15.300000,1,4.130000,1,False.\r\nNJ,100,415,396-5189,no,no,0,203.800000,122,34.650000,283.100000,76,24.060000,197.300000,83,8.880000,12.500000,3,3.380000,0,False.\r\nIN,96,415,356-9187,no,yes,45,248.800000,124,42.300000,140.300000,77,11.930000,263.600000,102,11.860000,10.300000,2,2.780000,3,False.\r\nDC,112,415,394-5537,no,yes,16,221.600000,110,37.670000,130.200000,123,11.070000,200.000000,108,9.000000,11.300000,3,3.050000,1,False.\r\nMA,129,510,408-2712,yes,no,0,192.900000,131,32.790000,185.500000,101,15.770000,205.200000,130,9.230000,10.900000,4,2.940000,1,False.\r\nME,163,415,404-4486,no,no,0,122.400000,129,20.810000,113.400000,108,9.640000,180.200000,97,8.110000,12.500000,7,3.380000,1,False.\r\nNH,67,415,355-1113,no,yes,40,104.900000,65,17.830000,216.300000,93,18.390000,217.400000,128,9.780000,9.600000,9,2.590000,1,False.\r\nAZ,140,408,411-4674,no,no,0,173.200000,91,29.440000,196.800000,106,16.730000,209.300000,128,9.420000,11.200000,5,3.020000,3,False.\r\nOR,49,510,376-4519,no,no,0,119.400000,69,20.300000,273.300000,92,23.230000,214.400000,153,9.650000,12.400000,7,3.350000,2,False.\r\nKS,46,510,365-5979,no,no,0,250.300000,100,42.550000,260.600000,90,22.150000,195.000000,104,8.780000,13.300000,2,3.590000,2,True.\r\nNE,148,415,382-2879,no,no,0,178.300000,98,30.310000,282.600000,110,24.020000,181.000000,98,8.150000,11.400000,4,3.080000,1,False.\r\nMI,112,510,420-1383,no,no,0,243.400000,77,41.380000,182.100000,97,15.480000,259.200000,94,11.660000,12.800000,2,3.460000,1,False.\r\nSC,78,415,411-7390,no,no,0,155.000000,106,26.350000,175.300000,101,14.900000,155.600000,125,7.000000,11.800000,5,3.190000,2,False.\r\nPA,61,408,383-8848,no,yes,31,288.700000,101,49.080000,203.800000,102,17.320000,203.200000,49,9.140000,8.600000,3,2.320000,0,False.\r\nMT,58,510,387-9301,no,yes,29,240.400000,80,40.870000,118.900000,91,10.110000,164.200000,108,7.390000,11.200000,3,3.020000,1,False.\r\nNM,155,415,399-3164,no,no,0,190.300000,123,32.350000,301.300000,96,25.610000,214.600000,134,9.660000,8.000000,3,2.160000,1,False.\r\nOH,100,510,385-8997,no,no,0,278.000000,76,47.260000,176.700000,74,15.020000,219.500000,126,9.880000,8.300000,4,2.240000,0,True.\r\nWY,113,510,352-6573,no,no,0,155.000000,93,26.350000,330.600000,106,28.100000,189.400000,123,8.520000,13.500000,3,3.650000,1,False.\r\nMI,81,415,408-3384,no,no,0,153.500000,99,26.100000,197.600000,102,16.800000,198.500000,86,8.930000,6.300000,2,1.700000,2,False.\r\nAR,135,510,419-6033,no,yes,27,273.400000,141,46.480000,154.000000,99,13.090000,245.800000,112,11.060000,12.300000,6,3.320000,1,False.\r\nFL,99,408,336-2090,no,no,0,155.300000,93,26.400000,265.700000,95,22.580000,145.700000,67,6.560000,12.400000,4,3.350000,0,False.\r\nAR,59,510,343-7242,no,yes,29,133.100000,114,22.630000,221.200000,82,18.800000,131.600000,103,5.920000,6.800000,3,1.840000,1,False.\r\nMO,135,510,376-1713,no,no,0,246.800000,129,41.960000,187.800000,121,15.960000,154.500000,109,6.950000,12.600000,5,3.400000,1,False.\r\nWI,85,408,381-5878,yes,no,0,165.400000,107,28.120000,196.000000,126,16.660000,349.200000,110,15.710000,9.600000,7,2.590000,2,False.\r\nTX,70,510,390-5470,no,no,0,59.500000,103,10.120000,257.200000,106,21.860000,208.300000,86,9.370000,11.100000,6,3.000000,0,False.\r\nTX,88,510,414-4803,no,no,0,138.300000,116,23.510000,236.000000,138,20.060000,179.100000,110,8.060000,9.600000,4,2.590000,3,False.\r\nNM,55,510,382-5478,no,no,0,286.700000,100,48.740000,134.400000,121,11.420000,192.900000,122,8.680000,6.900000,5,1.860000,2,False.\r\nGA,75,415,333-7637,no,no,0,117.300000,114,19.940000,201.100000,61,17.090000,107.900000,82,4.860000,12.200000,3,3.290000,1,False.\r\nID,79,510,341-1647,no,yes,21,264.300000,79,44.930000,202.800000,118,17.240000,173.400000,92,7.800000,6.300000,3,1.700000,4,False.\r\nAL,85,408,411-4232,no,no,0,127.900000,107,21.740000,271.200000,124,23.050000,202.200000,76,9.100000,12.500000,5,3.380000,0,False.\r\nKS,86,408,339-2616,no,yes,23,225.500000,107,38.340000,246.300000,105,20.940000,245.700000,81,11.060000,9.800000,2,2.650000,0,False.\r\nSD,91,510,327-3850,no,no,0,149.000000,115,25.330000,245.300000,105,20.850000,260.000000,94,11.700000,8.300000,3,2.240000,0,False.\r\nLA,149,415,328-7209,no,yes,20,198.900000,77,33.810000,274.000000,88,23.290000,190.700000,76,8.580000,14.300000,9,3.860000,1,False.\r\nOH,97,408,405-6189,no,no,0,256.400000,125,43.590000,273.900000,100,23.280000,222.700000,101,10.020000,11.100000,1,3.000000,1,True.\r\nMA,88,415,418-6737,no,no,0,264.800000,124,45.020000,245.400000,112,20.860000,160.500000,115,7.220000,14.800000,2,4.000000,1,True.\r\nAZ,60,415,366-2212,no,no,0,98.200000,88,16.690000,180.500000,69,15.340000,223.600000,69,10.060000,9.300000,2,2.510000,2,False.\r\nKY,54,408,356-1420,no,no,0,159.800000,99,27.170000,264.000000,64,22.440000,115.700000,70,5.210000,9.700000,7,2.620000,2,False.\r\nDC,11,415,343-1323,no,yes,28,190.600000,86,32.400000,220.100000,122,18.710000,180.300000,80,8.110000,6.000000,3,1.620000,3,False.\r\nWA,109,415,361-8239,no,no,0,184.000000,120,31.280000,120.400000,119,10.230000,153.700000,86,6.920000,11.000000,3,2.970000,0,False.\r\nUT,90,415,384-1621,no,no,0,261.800000,128,44.510000,220.600000,104,18.750000,136.600000,91,6.150000,9.600000,5,2.590000,1,False.\r\nRI,115,408,360-3525,no,no,0,147.900000,109,25.140000,228.400000,117,19.410000,299.700000,90,13.490000,9.600000,9,2.590000,3,False.\r\nOH,144,415,392-3813,no,yes,18,106.400000,109,18.090000,108.100000,113,9.190000,208.400000,111,9.380000,10.100000,5,2.730000,1,False.\r\nNV,91,408,337-6898,no,no,0,133.700000,75,22.730000,195.300000,87,16.600000,280.500000,89,12.620000,5.900000,2,1.590000,0,False.\r\nND,105,415,366-8036,no,yes,23,193.500000,85,32.900000,220.200000,90,18.720000,272.400000,111,12.260000,8.500000,5,2.300000,0,False.\r\nNV,71,415,352-8327,yes,no,0,178.200000,113,30.290000,167.800000,94,14.260000,182.100000,111,8.190000,13.600000,3,3.670000,3,True.\r\nFL,132,510,334-9505,no,yes,36,226.200000,103,38.450000,181.600000,125,15.440000,258.800000,102,11.650000,10.500000,5,2.840000,3,True.\r\nMD,112,415,336-5702,no,no,0,170.400000,103,28.970000,200.200000,71,17.020000,258.300000,100,11.620000,11.600000,4,3.130000,1,False.\r\nAZ,86,415,392-2381,no,yes,32,70.900000,163,12.050000,166.700000,121,14.170000,244.900000,105,11.020000,11.100000,5,3.000000,3,False.\r\nAL,41,510,369-6880,no,yes,34,194.400000,63,33.050000,254.900000,110,21.670000,160.200000,115,7.210000,17.200000,9,4.640000,2,False.\r\nNE,44,415,416-8697,no,no,0,240.300000,146,40.850000,164.600000,83,13.990000,240.700000,106,10.830000,10.600000,2,2.860000,1,False.\r\nNV,78,408,345-3451,no,no,0,75.000000,116,12.750000,248.700000,87,21.140000,176.000000,83,7.920000,9.500000,6,2.570000,3,False.\r\nIL,149,408,379-2514,no,no,0,69.100000,117,11.750000,136.300000,100,11.590000,181.700000,53,8.180000,6.300000,3,1.700000,1,False.\r\nWV,72,510,418-6651,no,yes,33,96.600000,59,16.420000,315.400000,98,26.810000,163.300000,117,7.350000,6.200000,4,1.670000,4,True.\r\nMI,139,415,421-3528,no,yes,20,214.600000,101,36.480000,235.100000,132,19.980000,162.800000,132,7.330000,14.800000,12,4.000000,0,False.\r\nAR,74,510,329-9046,no,no,0,148.500000,111,25.250000,146.500000,42,12.450000,289.200000,83,13.010000,9.900000,6,2.670000,3,False.\r\nUT,50,510,406-3890,no,no,0,258.100000,106,43.880000,161.400000,106,13.720000,225.100000,110,10.130000,11.700000,5,3.160000,1,False.\r\nGA,141,510,403-8904,no,yes,23,149.700000,112,25.450000,162.500000,118,13.810000,220.300000,115,9.910000,7.600000,2,2.050000,3,False.\r\nAZ,140,408,393-4086,no,no,0,149.800000,134,25.470000,164.400000,98,13.970000,294.700000,124,13.260000,8.100000,2,2.190000,1,False.\r\nID,99,408,400-1367,no,no,0,190.400000,102,32.370000,158.100000,107,13.440000,271.500000,92,12.220000,11.200000,4,3.020000,2,False.\r\nHI,166,408,377-9473,no,no,0,181.400000,108,30.840000,253.800000,54,21.570000,112.300000,94,5.050000,11.600000,6,3.130000,1,False.\r\nNV,124,408,396-3068,no,no,0,151.100000,123,25.690000,187.400000,104,15.930000,255.400000,93,11.490000,5.300000,3,1.430000,1,False.\r\nMD,74,415,331-9293,no,no,0,155.700000,116,26.470000,173.700000,63,14.760000,257.400000,97,11.580000,8.100000,4,2.190000,0,False.\r\nGA,117,510,347-1914,no,no,0,149.900000,95,25.480000,256.100000,110,21.770000,212.700000,92,9.570000,13.300000,13,3.590000,2,False.\r\nGA,85,510,395-1962,no,no,0,222.300000,132,37.790000,231.500000,101,19.680000,223.500000,75,10.060000,11.000000,2,2.970000,3,False.\r\nUT,36,415,401-5485,no,yes,16,149.400000,111,25.400000,131.800000,113,11.200000,132.700000,87,5.970000,6.700000,2,1.810000,0,False.\r\nMA,102,510,355-6560,yes,no,0,233.800000,103,39.750000,221.600000,131,18.840000,146.900000,106,6.610000,12.800000,3,3.460000,0,False.\r\nIN,76,415,363-3911,no,no,0,204.200000,100,34.710000,292.600000,139,24.870000,244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,367-7039,no,no,0,47.400000,125,8.060000,167.800000,90,14.260000,163.100000,107,7.340000,10.500000,8,2.840000,2,False.\r\nMI,30,510,391-6607,no,no,0,227.400000,88,38.660000,182.500000,100,15.510000,191.700000,134,8.630000,12.500000,3,3.380000,0,False.\r\nNJ,95,415,379-6652,no,yes,22,40.900000,126,6.950000,133.400000,90,11.340000,264.200000,91,11.890000,11.900000,7,3.210000,0,False.\r\nID,46,415,384-1833,no,no,0,124.800000,133,21.220000,157.300000,143,13.370000,199.300000,72,8.970000,8.600000,4,2.320000,2,False.\r\nDC,100,510,403-2455,yes,no,0,68.500000,110,11.650000,337.100000,115,28.650000,205.200000,99,9.230000,12.100000,9,3.270000,0,False.\r\nNY,47,415,391-1348,no,yes,37,163.500000,77,27.800000,203.100000,102,17.260000,232.000000,87,10.440000,7.800000,4,2.110000,2,False.\r\nAL,77,415,408-4174,no,no,0,163.000000,112,27.710000,219.100000,89,18.620000,233.400000,66,10.500000,6.700000,3,1.810000,2,False.\r\nOR,98,415,366-4334,no,yes,38,213.700000,61,36.330000,253.000000,104,21.510000,207.700000,73,9.350000,10.700000,5,2.890000,2,False.\r\nOK,125,415,406-5059,no,yes,36,201.300000,117,34.220000,42.200000,78,3.590000,125.700000,104,5.660000,5.400000,3,1.460000,1,False.\r\nLA,67,510,373-6784,no,no,0,310.400000,97,52.770000,66.500000,123,5.650000,246.500000,99,11.090000,9.200000,10,2.480000,4,False.\r\nNE,194,408,408-3532,no,no,0,48.400000,101,8.230000,281.100000,138,23.890000,218.500000,87,9.830000,18.200000,1,4.910000,1,False.\r\nTX,128,415,350-8680,no,yes,40,171.200000,88,29.100000,145.700000,109,12.380000,196.800000,93,8.860000,14.000000,6,3.780000,1,False.\r\nUT,190,415,398-9870,no,yes,22,166.500000,93,28.310000,183.000000,92,15.560000,121.000000,102,5.440000,8.500000,3,2.300000,0,False.\r\nOR,165,415,343-3356,no,no,0,216.600000,126,36.820000,190.800000,104,16.220000,224.700000,123,10.110000,12.400000,8,3.350000,0,False.\r\nNY,59,408,415-4609,no,no,0,107.800000,113,18.330000,216.600000,125,18.410000,217.500000,92,9.790000,9.900000,3,2.670000,2,False.\r\nAL,47,408,404-5387,no,yes,28,141.300000,94,24.020000,168.000000,108,14.280000,113.500000,84,5.110000,7.800000,2,2.110000,1,False.\r\nRI,150,415,415-8151,no,yes,29,209.900000,77,35.680000,158.000000,52,13.430000,141.900000,113,6.390000,6.600000,1,1.780000,0,False.\r\nMN,152,415,416-2778,yes,yes,20,237.500000,120,40.380000,253.400000,94,21.540000,265.200000,80,11.930000,14.200000,3,3.830000,9,True.\r\nNC,26,415,393-3300,no,no,0,234.500000,109,39.870000,216.500000,129,18.400000,191.600000,94,8.620000,3.500000,6,0.950000,3,False.\r\nMD,79,510,391-7661,no,yes,31,103.100000,90,17.530000,243.000000,135,20.660000,76.400000,92,3.440000,12.200000,8,3.290000,3,False.\r\nRI,95,510,339-4317,no,yes,27,129.500000,106,22.020000,248.900000,90,21.160000,268.000000,115,12.060000,11.900000,3,3.210000,1,False.\r\nWI,69,510,418-6455,yes,no,0,279.800000,90,47.570000,248.700000,91,21.140000,171.000000,118,7.690000,8.400000,10,2.270000,2,True.\r\nVT,95,510,378-3508,yes,yes,41,136.800000,91,23.260000,200.800000,61,17.070000,133.700000,67,6.020000,10.300000,9,2.780000,5,True.\r\nCT,31,415,390-9359,no,yes,31,100.100000,54,17.020000,246.300000,97,20.940000,255.000000,131,11.480000,5.900000,3,1.590000,0,False.\r\nOK,121,408,364-2495,no,yes,31,237.100000,63,40.310000,205.600000,117,17.480000,196.700000,85,8.850000,10.100000,5,2.730000,4,False.\r\nAK,111,415,364-7719,no,no,0,172.800000,58,29.380000,183.100000,108,15.560000,158.800000,104,7.150000,7.900000,3,2.130000,4,True.\r\nNY,157,415,421-1189,no,no,0,224.500000,111,38.170000,200.700000,99,17.060000,116.600000,118,5.250000,11.500000,2,3.110000,2,False.\r\nGA,44,510,419-8987,no,no,0,288.100000,112,48.980000,258.000000,92,21.930000,192.400000,90,8.660000,10.200000,4,2.750000,3,True.\r\nUT,61,510,402-9980,yes,no,0,78.200000,103,13.290000,195.900000,149,16.650000,108.000000,100,4.860000,10.100000,6,2.730000,2,False.\r\nNM,65,415,376-5908,no,no,0,148.700000,80,25.280000,259.000000,94,22.020000,149.500000,107,6.730000,12.700000,6,3.430000,2,False.\r\nNE,74,415,400-3150,no,yes,25,194.600000,84,33.080000,119.900000,103,10.190000,175.500000,75,7.900000,13.100000,2,3.540000,2,False.\r\nNJ,123,408,336-1749,no,no,0,159.500000,77,27.120000,303.800000,92,25.820000,226.900000,120,10.210000,12.000000,4,3.240000,0,False.\r\nTX,58,408,420-1259,no,yes,20,194.500000,110,33.070000,213.700000,89,18.160000,236.600000,92,10.650000,9.500000,2,2.570000,1,False.\r\nMT,74,408,339-7541,no,no,0,174.100000,96,29.600000,251.100000,94,21.340000,257.600000,123,11.590000,8.300000,5,2.240000,2,True.\r\nCO,125,415,378-9029,no,no,0,131.800000,97,22.410000,136.700000,100,11.620000,308.200000,119,13.870000,7.700000,6,2.080000,2,False.\r\nVT,80,415,342-7514,no,no,0,160.600000,103,27.300000,237.000000,109,20.150000,245.100000,88,11.030000,10.700000,1,2.890000,1,False.\r\nRI,53,408,422-4956,no,yes,18,146.800000,107,24.960000,310.000000,84,26.350000,178.700000,130,8.040000,7.200000,7,1.940000,0,False.\r\nWY,99,408,389-8606,no,yes,28,200.700000,88,34.120000,264.200000,116,22.460000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0000,237.400000,118,10.680000,7.500000,3,2.030000,0,False.\r\nMT,13,415,347-9421,no,yes,31,265.300000,94,45.100000,147.600000,95,12.550000,259.300000,117,11.670000,12.900000,1,3.480000,1,False.\r\nDE,106,415,419-3167,no,no,0,128.600000,83,21.860000,134.000000,114,11.390000,210.600000,113,9.480000,11.400000,2,3.080000,0,False.\r\nND,88,415,414-4162,no,no,0,161.500000,92,27.460000,173.500000,108,14.750000,206.200000,95,9.280000,7.900000,4,2.130000,2,False.\r\nVA,74,408,416-5341,no,no,0,165.300000,120,28.100000,198.500000,106,16.870000,208.500000,102,9.380000,9.800000,3,2.650000,1,False.\r\nIL,83,415,368-8600,no,no,0,195.000000,92,33.150000,210.500000,83,17.890000,180.600000,92,8.130000,11.000000,13,2.970000,0,False.\r\nOK,49,415,336-6085,no,no,0,213.800000,79,36.350000,265.100000,93,22.530000,239.800000,128,10.790000,15.600000,7,4.210000,0,False.\r\nCO,111,510,377-1479,no,yes,24,205.500000,114,34.940000,219.300000,99,18.640000,215.900000,95,9.720000,14.000000,4,3.780000,1,False.\r\nMT,50,415,360-2107,no,yes,22,252.900000,112,42.990000,177.900000,99,15.120000,158.400000,146,7.130000,8.500000,4,2.300000,3,False.\r\nWV,153,408,405-9384,no,yes,28,235.600000,74,40.050000,227.900000,37,19.370000,170.300000,103,7.660000,15.400000,9,4.160000,0,False.\r\nME,88,415,420-5179,no,no,0,192.000000,91,32.640000,127.600000,127,10.850000,155.600000,125,7.000000,7.500000,5,2.030000,1,False.\r\nWI,131,415,331-3174,no,yes,39,69.100000,122,11.750000,101.300000,136,8.610000,104.800000,94,4.720000,9.100000,4,2.460000,0,False.\r\nMO,79,408,411-5958,no,no,0,261.700000,97,44.490000,210.600000,48,17.900000,256.700000,83,11.550000,6.000000,3,1.620000,3,True.\r\nNY,140,415,333-8180,no,no,0,235.500000,81,40.040000,257.200000,130,21.860000,103.100000,111,4.640000,11.500000,4,3.110000,2,False.\r\nCT,105,408,357-2679,no,no,0,213.400000,100,36.280000,204.900000,52,17.420000,179.700000,93,8.090000,9.500000,6,2.570000,1,False.\r\nAR,54,415,396-2867,no,yes,39,206.900000,143,35.170000,127.800000,72,10.860000,199.200000,120,8.960000,9.200000,1,2.480000,3,False.\r\nWY,87,415,341-9443,no,yes,22,263.800000,65,44.850000,103.400000,115,8.790000,208.100000,109,9.360000,8.500000,3,2.300000,3,False.\r\nCA,96,510,341-4103,no,yes,31,183.400000,126,31.180000,195.500000,106,16.620000,180.100000,93,8.100000,10.500000,5,2.840000,1,False.\r\nCA,79,510,416-8701,no,no,0,157.600000,85,26.790000,194.100000,92,16.500000,231.500000,86,10.420000,9.400000,10,2.540000,5,True.\r\nMN,55,415,397-6109,no,no,0,175.600000,147,29.850000,161.800000,118,13.750000,289.500000,55,13.030000,9.300000,4,2.510000,0,False.\r\nAK,130,415,392-5587,no,no,0,242.500000,101,41.230000,102.800000,114,8.740000,142.400000,89,6.410000,9.300000,2,2.510000,2,False.\r\nVA,34,415,392-9342,no,no,0,151.000000,102,25.670000,131.400000,101,11.170000,186.600000,86,8.400000,9.900000,7,2.670000,0,False.\r\nCO,139,415,368-2845,no,no,0,138.100000,103,23.480000,164.500000,100,13.980000,134.900000,63,6.070000,8.300000,2,2.240000,1,False.\r\nMT,109,408,405-4920,no,no,0,264.700000,69,45.000000,305.000000,120,25.930000,197.400000,86,8.880000,9.500000,9,2.570000,1,True.\r\nSD,65,408,348-7484,no,yes,31,282.300000,70,47.990000,152.000000,89,12.920000,225.500000,93,10.150000,12.000000,4,3.240000,1,False.\r\nNE,63,415,338-5207,no,no,0,211.200000,80,35.900000,237.700000,93,20.200000,259.200000,58,11.660000,12.300000,2,3.320000,0,False.\r\nVT,152,415,418-7846,no,no,0,197.100000,126,33.510000,130.100000,76,11.060000,78.100000,100,3.510000,7.400000,4,2.000000,3,False.\r\nND,147,408,358-8729,no,no,0,205.300000,95,34.900000,166.700000,128,14.170000,240.600000,84,10.830000,7.800000,4,2.110000,1,False.\r\nGA,112,415,349-1943,no,yes,22,181.800000,110,30.910000,228.100000,123,19.390000,262.700000,141,11.820000,9.200000,4,2.480000,2,False.\r\nOR,120,415,368-8283,no,no,0,252.000000,120,42.840000,150.200000,106,12.770000,151.800000,96,6.830000,9.600000,1,2.590000,2,False.\r\nMT,27,510,345-1419,no,no,0,193.800000,102,32.950000,118.900000,104,10.110000,135.900000,124,6.120000,9.200000,3,2.480000,0,False.\r\nWY,171,415,358-8025,no,no,0,231.200000,135,39.300000,188.700000,74,16.040000,206.900000,124,9.310000,12.300000,1,3.320000,1,False.\r\nGA,101,415,383-8695,no,yes,33,200.100000,108,34.020000,188.900000,122,16.060000,205.100000,90,9.230000,15.500000,4,4.190000,0,False.\r\nWV,32,408,370-7565,no,yes,26,266.700000,109,45.340000,232.300000,107,19.750000,212.800000,98,9.580000,16.300000,4,4.400000,1,False.\r\nCT,3,415,401-6162,no,yes,36,118.100000,117,20.080000,221.500000,125,18.830000,103.900000,89,4.680000,11.900000,6,3.210000,2,False.\r\nIL,151,408,386-5303,no,no,0,175.300000,106,29.800000,144.300000,87,12.270000,160.200000,88,7.210000,11.800000,5,3.190000,0,False.\r\nCO,60,408,351-6552,no,no,0,125.100000,99,21.270000,248.800000,62,21.150000,211.300000,79,9.510000,11.200000,3,3.020000,3,False.\r\nDE,119,415,345-5338,no,no,0,176.800000,90,30.060000,224.700000,81,19.100000,204.600000,77,9.210000,7.500000,15,2.030000,1,False.\r\nLA,43,415,330-2849,no,no,0,241.900000,101,41.120000,129.400000,121,11.000000,264.800000,104,11.920000,5.900000,3,1.590000,1,False.\r\nMA,42,408,364-6801,no,no,0,241.200000,134,41.000000,116.500000,114,9.900000,152.200000,91,6.850000,10.600000,4,2.860000,0,False.\r\nIN,84,408,375-3003,no,no,0,217.100000,99,36.910000,236.000000,68,20.060000,118.300000,120,5.320000,9.400000,4,2.540000,1,False.\r\nNY,65,510,383-8878,no,no,0,195.400000,110,33.220000,181.200000,109,15.400000,178.500000,105,8.030000,8.900000,4,2.400000,0,False.\r\nTX,75,415,384-2372,yes,no,0,222.400000,78,37.810000,327.000000,111,27.800000,208.000000,104,9.360000,8.700000,9,2.350000,1,True.\r\nKS,116,510,377-7107,no,no,0,189.500000,90,32.220000,189.800000,118,16.130000,205.800000,83,9.260000,13.100000,2,3.540000,1,False.\r\nWV,107,415,361-1581,no,no,0,123.100000,100,20.930000,158.400000,82,13.460000,256.100000,82,11.520000,9.300000,5,2.510000,0,False.\r\nNE,189,415,417-7888,no,yes,38,256.700000,98,43.640000,150.500000,120,12.790000,123.000000,87,5.540000,11.400000,3,3.080000,3,False.\r\nND,123,408,383-8364,no,no,0,159.100000,94,27.050000,241.600000,119,20.540000,202.400000,120,9.110000,6.500000,1,1.760000,1,False.\r\nAK,110,408,396-2335,no,no,0,100.100000,90,17.020000,233.300000,93,19.830000,204.400000,57,9.200000,11.100000,8,3.000000,3,False.\r\nCO,63,415,408-4530,no,yes,32,30.900000,113,5.250000,187.000000,113,15.900000,230.800000,101,10.390000,8.600000,7,2.320000,1,False.\r\nME,176,415,408-6621,no,no,0,223.200000,76,37.940000,214.400000,131,18.220000,154.400000,80,6.950000,10.100000,2,2.730000,3,False.\r\nSC,108,510,393-7522,no,no,0,187.400000,101,31.860000,199.900000,126,16.990000,216.100000,107,9.720000,12.600000,8,3.400000,1,False.\r\nMN,13,510,338-7120,no,yes,21,315.600000,105,53.650000,208.900000,71,17.760000,260.100000,123,11.700000,12.100000,3,3.270000,3,False.\r\nCO,71,415,357-4265,no,no,0,277.500000,104,47.180000,131.800000,121,11.200000,126.900000,101,5.710000,8.200000,2,2.210000,1,False.\r\nKS,88,415,398-8801,no,no,0,189.800000,111,32.270000,197.300000,101,16.770000,234.500000,111,10.550000,14.900000,3,4.020000,2,False.\r\nMS,137,510,346-2347,no,no,0,147.200000,119,25.020000,192.800000,91,16.390000,172.700000,105,7.770000,10.200000,4,2.750000,1,False.\r\nNE,82,408,343-2741,no,no,0,185.800000,36,31.590000,276.500000,134,23.500000,192.100000,104,8.640000,5.700000,7,1.540000,4,False.\r\nNJ,92,510,420-8242,no,yes,29,155.400000,110,26.420000,188.500000,104,16.020000,254.900000,118,11.470000,8.000000,4,2.160000,3,False.\r\nWI,165,510,402-7746,no,no,0,154.200000,91,26.210000,268.600000,108,22.830000,188.800000,99,8.500000,10.900000,4,2.940000,6,False.\r\nMT,96,415,332-1494,no,no,0,97.600000,98,16.590000,105.500000,118,8.970000,220.200000,105,9.910000,11.600000,9,3.130000,1,False.\r\nAR,156,415,388-6223,no,no,0,178.800000,94,30.400000,178.400000,97,15.160000,169.200000,77,7.610000,7.500000,3,2.030000,1,False.\r\nWA,63,408,404-9539,no,no,0,149.300000,104,25.380000,273.600000,75,23.260000,206.600000,72,9.300000,9.100000,4,2.460000,0,False.\r\nNH,37,415,341-7332,no,no,0,206.000000,89,35.020000,186.000000,88,15.810000,307.100000,86,13.820000,8.400000,11,2.270000,0,False.\r\nIA,98,415,338-7886,no,no,0,216.800000,86,36.860000,190.800000,114,16.220000,187.500000,79,8.440000,11.000000,9,2.970000,0,False.\r\nWV,121,415,332-5596,no,no,0,103.300000,110,17.560000,129.100000,82,10.970000,167.100000,113,7.520000,10.700000,3,2.890000,0,False.\r\nRI,94,415,348-9945,no,no,0,139.400000,95,23.700000,159.100000,92,13.520000,128.200000,129,5.770000,7.700000,3,2.080000,0,False.\r\nKS,99,415,407-1896,no,no,0,191.200000,110,32.500000,163.900000,102,13.930000,243.600000,114,10.960000,14.100000,3,3.810000,1,False.\r\nMT,163,510,398-9408,no,yes,23,160.000000,104,27.200000,189.400000,64,16.100000,229.900000,118,10.350000,10.400000,7,2.810000,1,False.\r\nMO,161,510,369-8005,no,no,0,221.700000,95,37.690000,193.000000,82,16.410000,194.100000,113,8.730000,6.500000,4,1.760000,3,False.\r\nHI,99,415,346-2530,no,no,0,62.900000,81,10.690000,231.000000,64,19.640000,168.900000,121,7.600000,8.500000,5,2.300000,1,False.\r\nCO,108,415,400-5984,no,no,0,215.600000,78,36.650000,195.300000,119,16.600000,194.400000,65,8.750000,3.600000,5,0.970000,1,False.\r\nCT,84,510,351-1007,no,yes,42,165.300000,97,28.100000,223.500000,118,19.000000,260.800000,72,11.740000,7.600000,7,2.050000,3,False.\r\nID,83,415,345-5980,yes,yes,32,94.700000,111,16.100000,154.400000,98,13.120000,200.400000,109,9.020000,10.600000,6,2.860000,2,False.\r\nDC,139,510,368-8964,no,no,0,203.200000,81,34.540000,152.500000,99,12.960000,197.800000,76,8.900000,9.700000,3,2.620000,2,False.\r\nTN,69,510,358-1912,no,no,0,195.300000,70,33.200000,216.700000,108,18.420000,259.900000,119,11.700000,12.500000,4,3.380000,3,False.\r\nWY,129,510,379-3132,no,no,0,143.700000,114,24.430000,297.800000,98,25.310000,212.600000,86,9.570000,11.400000,8,3.080000,4,False.\r\nMO,106,415,340-9910,no,no,0,114.400000,104,19.450000,78.300000,101,6.660000,232.700000,78,10.470000,0.000000,0,0.000000,2,False.\r\nVA,158,415,396-2719,no,no,0,222.800000,101,37.880000,203.000000,128,17.260000,210.600000,106,9.480000,6.900000,2,1.860000,2,False.\r\nSD,168,415,369-6204,no,yes,22,175.900000,70,29.900000,211.700000,105,17.990000,174.500000,81,7.850000,7.300000,5,1.970000,2,False.\r\nWV,115,510,420-9971,yes,no,0,249.900000,95,42.480000,242.500000,104,20.610000,151.700000,121,6.830000,15.300000,6,4.130000,1,True.\r\nGA,57,408,410-3782,yes,yes,30,234.500000,130,39.870000,195.200000,116,16.590000,268.800000,94,12.100000,11.400000,4,3.080000,2,False.\r\nAZ,67,415,404-4481,no,no,0,210.700000,116,35.820000,219.200000,86,18.630000,179.700000,83,8.090000,7.200000,6,1.940000,1,False.\r\nAK,127,408,383-9255,no,no,0,182.300000,124,30.990000,169.900000,110,14.440000,184.000000,116,8.280000,9.300000,3,2.510000,1,False.\r\nAK,78,510,418-9385,no,no,0,190.300000,88,32.350000,194.500000,89,16.530000,256.500000,109,11.540000,11.700000,5,3.160000,2,False.\r\nCT,100,415,360-9676,no,yes,38,177.100000,88,30.110000,163.700000,108,13.910000,242.700000,72,10.920000,7.400000,2,2.000000,0,False.\r\nUT,103,510,327-3587,no,yes,36,87.200000,92,14.820000,169.300000,110,14.390000,166.700000,80,7.500000,10.900000,5,2.940000,6,True.\r\nKY,113,415,385-4715,no,no,0,215.600000,96,36.650000,193.400000,127,16.440000,105.400000,115,4.740000,13.500000,3,3.650000,1,False.\r\nMI,78,510,414-2695,no,no,0,137.400000,109,23.360000,237.600000,49,20.200000,206.700000,136,9.300000,14.000000,11,3.780000,3,False.\r\nOR,129,510,331-5999,no,yes,36,192.800000,103,32.780000,177.000000,83,15.050000,216.500000,118,9.740000,16.400000,5,4.430000,1,False.\r\nTN,57,510,337-7739,no,no,0,149.300000,100,25.380000,200.200000,110,17.020000,231.700000,101,10.430000,11.900000,3,3.210000,2,False.\r\nWV,82,408,388-6658,no,no,0,143.700000,116,24.430000,170.700000,99,14.510000,287.700000,95,12.950000,7.800000,5,2.110000,1,False.\r\nNJ,64,415,405-6943,no,no,0,224.800000,111,38.220000,190.000000,101,16.150000,221.400000,110,9.960000,9.200000,2,2.480000,1,False.\r\nMS,86,510,382-4084,no,yes,39,261.200000,122,44.400000,214.200000,101,18.210000,154.900000,101,6.970000,12.700000,5,3.430000,2,False.\r\nME,151,415,352-8249,no,yes,26,196.500000,98,33.410000,175.800000,111,14.940000,221.800000,124,9.980000,13.400000,5,3.620000,0,False.\r\nWY,94,510,353-8363,no,no,0,271.200000,105,46.100000,202.600000,105,17.220000,221.600000,51,9.970000,11.500000,3,3.110000,3,True.\r\nWY,90,415,416-2825,no,no,0,207.200000,121,35.220000,292.500000,104,24.860000,226.300000,103,10.180000,8.000000,1,2.160000,2,False.\r\nIN,48,510,342-6696,no,no,0,300.400000,94,51.070000,133.200000,103,11.320000,197.400000,94,8.880000,7.200000,5,1.940000,2,False.\r\nNM,85,408,338-9210,no,yes,37,229.600000,123,39.030000,132.300000,90,11.250000,211.900000,76,9.540000,9.500000,8,2.570000,2,False.\r\nNJ,93,415,328-1768,yes,yes,20,187.500000,110,31.880000,169.800000,94,14.430000,175.300000,127,7.890000,12.100000,4,3.270000,1,False.\r\nDC,169,415,406-5870,yes,no,0,57.100000,98,9.710000,199.700000,78,16.970000,274.700000,103,12.360000,6.500000,6,1.760000,3,False.\r\nUT,68,415,398-3834,no,no,0,162.100000,86,27.560000,155.000000,86,13.180000,189.700000,87,8.540000,11.000000,9,2.970000,5,True.\r\nKY,91,415,330-7754,yes,no,0,145.000000,89,24.650000,175.800000,102,14.940000,223.700000,151,10.070000,16.700000,3,4.510000,2,True.\r\nKS,68,510,414-9054,no,no,0,159.500000,123,27.120000,240.800000,93,20.470000,210.300000,76,9.460000,11.400000,3,3.080000,1,False.\r\nMI,101,510,350-2832,no,no,0,190.700000,72,32.420000,208.600000,103,17.730000,203.800000,111,9.170000,8.800000,8,2.380000,1,False.\r\nUT,67,510,414-9027,no,yes,20,230.600000,40,39.200000,189.100000,58,16.070000,162.200000,115,7.300000,9.400000,2,2.540000,1,False.\r\nNE,66,415,337-1225,no,no,0,34.000000,133,5.780000,278.600000,61,23.680000,129.600000,120,5.830000,11.500000,3,3.110000,0,False.\r\nFL,116,415,394-6577,no,yes,17,193.400000,112,32.880000,240.600000,131,20.450000,248.100000,98,11.160000,11.400000,3,3.080000,5,False.\r\nLA,158,408,359-6995,no,no,0,202.000000,126,34.340000,163.500000,86,13.900000,195.400000,84,8.790000,10.400000,6,2.810000,1,False.\r\nMT,78,415,377-7561,no,no,0,191.700000,122,32.590000,241.400000,88,20.520000,203.500000,86,9.160000,9.100000,5,2.460000,1,False.\r\nWA,119,415,380-6631,no,yes,26,161.300000,97,27.420000,250.300000,110,21.280000,142.400000,92,6.410000,6.600000,8,1.780000,1,False.\r\nMI,120,415,390-8876,no,no,0,150.600000,85,25.600000,119.000000,128,10.120000,232.900000,123,10.480000,6.400000,2,1.730000,1,False.\r\nKY,155,510,413-2201,no,no,0,184.600000,102,31.380000,196.000000,117,16.660000,226.500000,122,10.190000,7.800000,1,2.110000,1,False.\r\nLA,106,408,374-2073,no,no,0,220.700000,120,37.520000,270.200000,95,22.970000,121.600000,113,5.470000,8.700000,5,2.350000,1,False.\r\nNM,87,510,417-1272,yes,no,0,167.300000,119,28.440000,198.500000,119,16.870000,133.100000,88,5.990000,11.000000,6,2.970000,1,False.\r\nAL,146,415,358-1129,no,yes,32,154.000000,80,26.180000,185.500000,91,15.770000,148.200000,107,6.670000,8.200000,4,2.210000,3,False.\r\nMO,101,415,394-1211,no,yes,29,121.100000,116,20.590000,186.400000,100,15.840000,241.700000,75,10.880000,10.100000,6,2.730000,0,False.\r\nCO,22,510,327-1319,no,yes,23,182.100000,94,30.960000,164.600000,59,13.990000,128.800000,102,5.800000,12.700000,4,3.430000,3,False.\r\nTX,90,415,399-4413,no,no,0,109.600000,88,18.630000,137.600000,108,11.700000,159.700000,121,7.190000,11.000000,5,2.970000,2,False.\r\nNY,41,415,393-9985,no,no,0,209.900000,105,35.680000,121.900000,105,10.360000,253.700000,104,11.420000,9.600000,4,2.590000,1,False.\r\nOR,69,415,401-8377,no,no,0,167.500000,76,28.480000,242.100000,92,20.580000,101.200000,103,4.550000,11.400000,4,3.080000,2,False.\r\nWY,33,415,331-3202,no,no,0,213.900000,88,36.360000,239.800000,119,20.380000,148.700000,71,6.690000,9.800000,14,2.650000,2,False.\r\nUT,112,415,358-5953,no,no,0,115.800000,108,19.690000,243.300000,111,20.680000,184.600000,78,8.310000,13.100000,5,3.540000,1,False.\r\nLA,108,510,380-7624,no,yes,30,276.600000,99,47.020000,220.100000,113,18.710000,177.900000,95,8.010000,9.800000,6,2.650000,2,False.\r\nNV,136,415,416-5261,no,yes,21,179.400000,88,30.500000,181.100000,97,15.390000,320.700000,120,14.430000,9.500000,4,2.570000,2,False.\r\nNC,128,510,417-5067,no,no,0,187.300000,84,31.840000,270.800000,95,23.020000,206.400000,68,9.290000,10.100000,5,2.730000,1,False.\r\nNC,27,408,345-6515,no,no,0,201.200000,128,34.200000,227.200000,100,19.310000,145.800000,91,6.560000,8.400000,3,2.270000,2,False.\r\nWY,161,415,406-1349,yes,no,0,189.600000,78,32.230000,267.400000,117,22.730000,184.500000,137,8.300000,1.300000,6,0.350000,1,False.\r\nTN,33,415,360-9038,no,yes,35,186.800000,124,31.760000,261.000000,69,22.190000,317.800000,103,14.300000,15.000000,5,4.050000,0,False.\r\nFL,120,415,348-3444,no,yes,31,153.500000,83,26.100000,219.100000,96,18.620000,237.400000,76,10.680000,11.400000,4,3.080000,0,False.\r\nCA,113,415,370-2892,no,no,0,187.600000,97,31.890000,208.200000,118,17.700000,158.900000,101,7.150000,8.700000,6,2.350000,2,False.\r\nPA,122,415,383-4061,yes,no,0,230.900000,132,39.250000,243.200000,99,20.670000,182.400000,57,8.210000,11.000000,2,2.970000,0,True.\r\nWV,148,415,391-7937,no,yes,26,244.900000,150,41.630000,118.000000,138,10.030000,236.000000,91,10.620000,15.200000,4,4.100000,2,False.\r\nNJ,74,415,389-4083,no,no,0,230.900000,93,39.250000,223.000000,78,18.960000,157.800000,101,7.100000,9.700000,2,2.620000,3,False.\r\nMT,106,415,410-9633,no,no,0,187.100000,104,31.810000,250.200000,117,21.270000,144.900000,81,6.520000,11.000000,3,2.970000,1,False.\r\nMN,179,415,418-9502,no,no,0,170.700000,54,29.020000,191.100000,108,16.240000,214.600000,107,9.660000,13.300000,4,3.590000,1,False.\r\nWI,149,415,339-6637,yes,yes,28,126.900000,97,21.570000,166.900000,102,14.190000,145.200000,77,6.530000,8.800000,3,2.380000,5,True.\r\nID,77,510,356-3403,no,no,0,189.500000,112,32.220000,207.000000,95,17.600000,214.100000,91,9.630000,9.200000,7,2.480000,0,False.\r\nMA,127,408,371-9457,yes,no,0,176.900000,110,30.070000,167.900000,100,14.270000,182.200000,138,8.200000,7.700000,2,2.080000,1,True.\r\nOR,80,415,391-8087,no,no,0,161.100000,99,27.390000,198.800000,81,16.900000,228.400000,116,10.280000,10.600000,4,2.860000,1,False.\r\nMT,106,510,392-6420,no,no,0,169.400000,107,28.800000,197.200000,71,16.760000,202.200000,79,9.100000,10.700000,4,2.890000,1,False.\r\nAL,61,415,399-4094,no,yes,20,254.400000,133,43.250000,161.700000,96,13.740000,251.400000,91,11.310000,10.500000,4,2.840000,0,False.\r\nND,135,510,378-4013,yes,yes,24,127.700000,54,21.710000,215.000000,105,18.280000,234.300000,84,10.540000,5.800000,4,1.570000,2,False.\r\nLA,115,415,386-6306,no,yes,26,170.500000,107,28.990000,217.200000,77,18.460000,225.700000,71,10.160000,13.600000,5,3.670000,6,False.\r\nND,167,408,359-3618,yes,no,0,219.100000,100,37.250000,242.900000,90,20.650000,168.900000,101,7.600000,10.100000,4,2.730000,2,False.\r\nMS,107,510,340-8875,yes,no,0,273.500000,104,46.500000,183.800000,68,15.620000,153.800000,67,6.920000,11.000000,9,2.970000,2,False.\r\nWV,112,415,330-2693,yes,no,0,161.900000,138,27.520000,200.900000,114,17.080000,134.000000,134,6.030000,10.700000,4,2.890000,1,False.\r\nWI,35,510,403-7627,no,yes,27,241.700000,87,41.090000,142.000000,101,12.070000,288.900000,68,13.000000,9.400000,4,2.540000,1,False.\r\nKS,103,408,342-3678,yes,no,0,62.800000,124,10.680000,170.400000,66,14.480000,280.200000,78,12.610000,9.400000,4,2.540000,3,False.\r\nMO,107,415,344-9943,no,yes,22,281.100000,83,47.790000,143.700000,130,12.210000,239.400000,128,10.770000,11.200000,9,3.020000,1,False.\r\nPA,69,415,390-5686,no,no,0,228.200000,70,38.790000,263.700000,80,22.410000,142.600000,60,6.420000,10.700000,5,2.890000,3,False.\r\nSD,85,408,358-5826,no,no,0,209.800000,82,35.670000,194.500000,94,16.530000,200.400000,85,9.020000,11.300000,3,3.050000,0,False.\r\nNJ,24,408,393-7826,no,no,0,265.600000,86,45.150000,208.800000,102,17.750000,182.500000,105,8.210000,11.100000,6,3.000000,2,True.\r\nAL,90,415,335-9786,no,no,0,214.900000,97,36.530000,117.800000,117,10.010000,133.700000,78,6.020000,11.800000,2,3.190000,2,False.\r\nME,137,510,368-9860,no,no,0,110.500000,79,18.790000,223.200000,111,18.970000,169.500000,64,7.630000,10.500000,3,2.840000,3,False.\r\nAZ,92,415,416-9522,yes,yes,45,281.100000,88,47.790000,198.000000,103,16.830000,94.300000,76,4.240000,7.500000,3,2.030000,0,False.\r\nVT,38,415,416-7307,no,no,0,137.800000,86,23.430000,286.300000,76,24.340000,167.000000,77,7.520000,14.100000,3,3.810000,2,False.\r\nNV,69,510,397-6789,yes,yes,33,271.500000,98,46.160000,253.400000,102,21.540000,165.400000,85,7.440000,8.200000,2,2.210000,1,True.\r\nWI,45,408,335-9501,no,no,0,112.800000,108,19.180000,218.800000,120,18.600000,240.200000,106,10.810000,9.000000,3,2.430000,2,False.\r\nHI,73,408,388-1250,no,no,0,187.300000,118,31.840000,239.700000,90,20.370000,167.500000,108,7.540000,15.100000,2,4.080000,1,False.\r\nDE,92,415,386-1374,no,no,0,197.000000,84,33.490000,269.300000,105,22.890000,158.900000,105,7.150000,10.800000,4,2.920000,1,False.\r\nAZ,113,415,346-8112,no,yes,32,180.400000,89,30.670000,129.400000,124,11.000000,166.900000,124,7.510000,8.400000,2,2.270000,1,False.\r\nVA,68,408,364-9040,yes,no,0,148.500000,126,25.250000,219.400000,125,18.650000,198.500000,121,8.930000,14.500000,7,3.920000,1,True.\r\nGA,135,415,366-3944,no,yes,22,197.100000,113,33.510000,259.400000,95,22.050000,134.700000,135,6.060000,14.600000,5,3.940000,2,False.\r\nAZ,100,415,331-9861,no,yes,26,153.700000,115,26.130000,137.800000,146,11.710000,213.500000,104,9.610000,15.900000,5,4.290000,1,False.\r\nMN,96,415,330-2881,no,yes,27,261.300000,96,44.420000,220.900000,101,18.780000,179.400000,97,8.070000,11.300000,2,3.050000,1,False.\r\nME,108,510,402-9558,no,no,0,246.200000,102,41.850000,202.400000,134,17.200000,180.100000,95,8.100000,9.400000,5,2.540000,1,False.\r\nFL,84,510,341-3180,no,no,0,191.000000,88,32.470000,318.800000,119,27.100000,247.300000,79,11.130000,6.500000,4,1.760000,0,False.\r\nWA,134,408,371-8598,no,no,0,208.300000,86,35.410000,253.600000,89,21.560000,291.000000,86,13.100000,12.600000,3,3.400000,1,False.\r\nMT,72,415,398-8385,no,no,0,253.000000,73,43.010000,219.300000,78,18.640000,210.800000,89,9.490000,9.800000,4,2.650000,0,False.\r\nAL,83,408,333-4154,no,no,0,202.300000,87,34.390000,201.500000,111,17.130000,101.700000,82,4.580000,6.800000,4,1.840000,0,False.\r\nWV,137,408,330-3589,no,no,0,174.400000,120,29.650000,156.300000,98,13.290000,136.500000,121,6.140000,10.200000,5,2.750000,0,False.\r\nGA,56,415,417-1477,no,yes,30,127.100000,89,21.610000,172.100000,116,14.630000,194.600000,111,8.760000,12.100000,3,3.270000,1,False.\r\nOH,61,510,327-5525,yes,yes,16,143.500000,76,24.400000,242.600000,58,20.620000,147.700000,95,6.650000,11.300000,3,3.050000,0,False.\r\nDE,171,510,363-8244,no,yes,17,186.900000,94,31.770000,240.000000,138,20.400000,200.900000,64,9.040000,5.800000,3,1.570000,1,False.\r\nNE,123,510,419-9104,no,no,0,194.000000,118,32.980000,242.000000,114,20.570000,146.300000,108,6.580000,12.100000,4,3.270000,1,False.\r\nFL,58,510,363-1560,no,no,0,234.800000,89,39.920000,106.800000,131,9.080000,178.500000,122,8.030000,9.900000,6,2.670000,0,False.\r\nAK,156,510,341-4075,no,no,0,123.700000,96,21.030000,103.000000,80,8.760000,189.400000,82,8.520000,13.100000,4,3.540000,1,False.\r\nWI,166,510,366-9074,no,no,0,173.900000,103,29.560000,276.400000,83,23.490000,190.800000,113,8.590000,15.300000,5,4.130000,0,False.\r\nWA,75,510,367-1424,no,yes,41,130.900000,115,22.250000,203.400000,110,17.290000,171.700000,68,7.730000,12.400000,4,3.350000,1,False.\r\nKY,75,415,341-1191,no,no,0,314.600000,102,53.480000,169.800000,86,14.430000,285.100000,100,12.830000,5.700000,3,1.540000,2,True.\r\nOH,83,510,342-9480,no,no,0,227.900000,78,38.740000,207.500000,115,17.640000,211.700000,100,9.530000,12.100000,5,3.270000,1,False.\r\nUT,243,510,355-9360,no,no,0,95.500000,92,16.240000,163.700000,63,13.910000,264.200000,118,11.890000,6.600000,6,1.780000,2,False.\r\nNM,153,408,343-1538,no,no,0,185.300000,127,31.500000,208.000000,73,17.680000,206.100000,124,9.270000,15.100000,3,4.080000,1,False.\r\nMN,150,415,335-2331,no,no,0,146.300000,133,24.870000,202.700000,95,17.230000,234.700000,103,10.560000,13.100000,3,3.540000,1,False.\r\nWV,92,510,335-7257,no,yes,16,184.000000,99,31.280000,76.400000,134,6.490000,185.100000,96,8.330000,12.700000,3,3.430000,2,False.\r\nMN,80,415,332-2137,no,no,0,105.800000,110,17.990000,43.900000,88,3.730000,189.600000,87,8.530000,13.100000,5,3.540000,0,False.\r\nAL,134,415,352-2998,no,no,0,178.000000,110,30.260000,153.800000,64,13.070000,236.600000,105,10.650000,11.700000,4,3.160000,1,False.\r\nPA,77,510,346-6941,no,yes,24,149.400000,74,25.400000,123.900000,72,10.530000,174.300000,84,7.840000,10.100000,6,2.730000,1,False.\r\nDE,147,510,400-2203,no,no,0,209.400000,104,35.600000,132.500000,78,11.260000,149.400000,123,6.720000,11.300000,3,3.050000,2,False.\r\nMO,74,415,421-2955,no,no,0,172.100000,105,29.260000,211.700000,99,17.990000,182.200000,105,8.200000,11.600000,6,3.130000,1,False.\r\nIL,138,510,331-6629,yes,no,0,169.300000,82,28.780000,217.900000,147,18.520000,184.200000,77,8.290000,9.400000,9,2.540000,1,False.\r\nFL,143,415,343-6314,no,no,0,119.100000,117,20.250000,287.700000,136,24.450000,223.000000,100,10.040000,12.200000,4,3.290000,0,False.\r\nHI,64,415,414-6638,no,no,0,194.200000,147,33.010000,173.400000,87,14.740000,268.700000,114,12.090000,5.500000,2,1.490000,2,False.\r\nME,120,510,350-5883,no,no,0,198.800000,56,33.800000,230.100000,73,19.560000,119.800000,81,5.390000,9.900000,3,2.670000,2,False.\r\nCO,121,408,409-4447,yes,no,0,167.700000,94,28.510000,93.700000,121,7.960000,241.300000,115,10.860000,13.400000,1,3.620000,3,True.\r\nNH,88,415,376-4856,no,no,0,202.200000,86,34.370000,216.800000,93,18.430000,239.400000,99,10.770000,11.800000,2,3.190000,2,False.\r\nSC,87,408,335-1874,no,no,0,322.500000,106,54.830000,204.600000,93,17.390000,186.200000,128,8.380000,9.400000,4,2.540000,2,True.\r\nIN,100,510,397-6255,no,no,0,216.200000,107,36.750000,215.600000,84,18.330000,138.400000,127,6.230000,10.200000,3,2.750000,0,False.\r\nFL,104,415,381-5047,no,no,0,76.400000,116,12.990000,115.600000,74,9.830000,226.300000,94,10.180000,9.400000,3,2.540000,3,False.\r\nGA,27,510,403-6850,no,no,0,72.700000,75,12.360000,208.600000,117,17.730000,65.800000,71,2.960000,9.900000,3,2.670000,1,False.\r\nIL,81,415,375-3658,no,yes,31,210.400000,100,35.770000,225.500000,97,19.170000,168.700000,120,7.590000,9.700000,4,2.620000,0,False.\r\nNC,64,510,341-2603,yes,yes,33,127.200000,93,21.620000,162.900000,104,13.850000,247.400000,109,11.130000,8.100000,13,2.190000,0,False.\r\nVT,107,510,342-5062,no,yes,28,201.800000,79,34.310000,304.900000,128,25.920000,225.600000,133,10.150000,11.900000,8,3.210000,1,False.\r\nDC,88,415,354-1558,no,yes,17,219.500000,78,37.320000,222.100000,94,18.880000,188.300000,92,8.470000,16.100000,5,4.350000,1,False.\r\nVT,111,408,351-9537,no,no,0,99.300000,112,16.880000,270.500000,136,22.990000,225.300000,94,10.140000,9.000000,6,2.430000,3,False.\r\nNV,77,415,401-1252,no,no,0,239.200000,114,40.660000,150.000000,115,12.750000,160.800000,81,7.240000,10.300000,2,2.780000,5,False.\r\nOR,67,415,366-9538,yes,no,0,120.900000,58,20.550000,235.000000,88,19.980000,95.100000,130,4.280000,11.400000,11,3.080000,2,False.\r\nAL,102,408,364-7622,no,no,0,224.700000,81,38.200000,129.400000,112,11.000000,167.600000,109,7.540000,15.800000,6,4.270000,1,False.\r\nND,146,408,393-9918,no,yes,19,176.600000,88,30.020000,162.700000,66,13.830000,215.500000,98,9.700000,14.600000,6,3.940000,1,False.\r\nFL,144,415,376-4484,no,yes,51,283.900000,98,48.260000,192.000000,109,16.320000,196.300000,85,8.830000,10.000000,4,2.700000,1,False.\r\nNE,96,415,410-6791,no,no,0,180.600000,92,30.700000,190.900000,114,16.230000,295.600000,125,13.300000,10.300000,4,2.780000,1,True.\r\nND,70,415,343-2392,no,yes,31,125.900000,101,21.400000,196.400000,102,16.690000,252.700000,75,11.370000,10.300000,4,2.780000,1,False.\r\nME,149,408,408-4323,no,no,0,237.600000,79,40.390000,192.400000,107,16.350000,207.400000,111,9.330000,9.100000,9,2.460000,0,False.\r\nIL,129,415,395-1718,no,no,0,198.400000,91,33.730000,264.700000,106,22.500000,111.400000,101,5.010000,9.200000,2,2.480000,2,False.\r\nWA,166,408,354-9492,no,no,0,274.300000,110,46.630000,52.900000,109,4.500000,246.100000,119,11.070000,10.900000,5,2.940000,0,False.\r\nMA,136,408,367-8168,yes,no,0,199.600000,89,33.930000,211.400000,96,17.970000,72.400000,84,3.260000,11.000000,4,2.970000,3,True.\r\nKS,149,510,340-3500,no,no,0,217.700000,91,37.010000,273.500000,74,23.250000,226.900000,99,10.210000,9.600000,3,2.590000,3,False.\r\nRI,70,415,369-4962,no,no,0,134.700000,96,22.900000,235.900000,90,20.050000,260.200000,113,11.710000,7.600000,6,2.050000,3,False.\r\nMO,120,415,334-8967,no,yes,24,212.700000,73,36.160000,257.500000,103,21.890000,227.800000,119,10.250000,9.700000,13,2.620000,2,False.\r\nIA,66,510,402-2377,no,no,0,256.300000,135,43.570000,180.200000,106,15.320000,187.300000,135,8.430000,6.200000,7,1.670000,2,False.\r\nWY,104,408,366-3917,no,no,0,183.600000,133,31.210000,120.700000,98,10.260000,215.100000,112,9.680000,12.700000,2,3.430000,1,False.\r\nNV,160,415,333-3531,no,no,0,176.200000,90,29.950000,196.000000,115,16.660000,263.900000,95,11.880000,9.200000,4,2.480000,1,False.\r\nWI,129,415,333-8954,no,yes,37,205.000000,94,34.850000,165.400000,103,14.060000,185.000000,81,8.320000,11.700000,8,3.160000,1,False.\r\nAL,93,408,374-9203,no,no,0,267.900000,114,45.540000,223.000000,74,18.960000,262.700000,90,11.820000,11.300000,3,3.050000,3,True.\r\nHI,169,415,334-3289,no,no,0,179.200000,111,30.460000,175.200000,130,14.890000,228.600000,92,10.290000,9.900000,6,2.670000,2,False.\r\nMO,58,415,353-7822,no,no,0,149.400000,145,25.400000,196.500000,105,16.700000,209.500000,108,9.430000,14.900000,3,4.020000,1,False.\r\nCA,75,510,350-1422,no,yes,38,163.600000,132,27.810000,146.700000,113,12.470000,345.800000,115,15.560000,13.100000,3,3.540000,3,False.\r\nMO,45,408,385-8406,no,no,0,207.600000,71,35.290000,152.700000,94,12.980000,217.800000,125,9.800000,12.400000,13,3.350000,1,False.\r\nCT,155,510,380-7277,no,no,0,165.400000,108,28.120000,183.700000,103,15.610000,80.200000,108,3.610000,8.900000,4,2.400000,3,False.\r\nMD,52,415,352-1798,no,no,0,209.800000,114,35.670000,171.300000,82,14.560000,154.600000,119,6.960000,9.900000,9,2.670000,4,False.\r\nOH,119,415,385-7922,no,yes,27,220.100000,128,37.420000,268.200000,133,22.800000,146.500000,80,6.590000,11.100000,3,3.000000,0,False.\r\nNV,86,510,353-7730,no,no,0,141.300000,72,24.020000,154.300000,95,13.120000,210.600000,91,9.480000,8.200000,5,2.210000,1,False.\r\nMD,42,408,337-7163,no,no,0,196.500000,89,33.410000,241.300000,123,20.510000,143.200000,105,6.440000,4.000000,7,1.080000,0,False.\r\nNE,127,510,348-5567,yes,no,0,180.900000,114,30.750000,209.500000,118,17.810000,249.900000,105,11.250000,7.400000,4,2.000000,2,False.\r\nOH,123,408,420-9575,no,no,0,105.000000,150,17.850000,251.600000,90,21.390000,258.000000,93,11.610000,14.900000,5,4.020000,0,False.\r\nMA,98,510,366-3358,no,no,0,271.400000,119,46.140000,190.400000,102,16.180000,284.700000,118,12.810000,11.100000,6,3.000000,4,True.\r\nOK,149,510,359-9972,no,yes,43,206.700000,79,35.140000,174.600000,122,14.840000,241.500000,80,10.870000,10.900000,3,2.940000,1,False.\r\nMA,160,408,387-3332,no,no,0,166.800000,109,28.360000,236.000000,117,20.060000,307.600000,77,13.840000,9.300000,1,2.510000,1,False.\r\nWA,103,415,354-6960,no,no,0,204.900000,107,34.830000,135.200000,102,11.490000,208.200000,106,9.370000,10.400000,3,2.810000,5,False.\r\nHI,132,415,405-3335,no,yes,15,154.600000,128,26.280000,245.600000,106,20.880000,148.600000,90,6.690000,9.100000,4,2.460000,1,False.\r\nCO,137,415,379-4257,no,no,0,127.000000,107,21.590000,323.200000,75,27.470000,143.900000,127,6.480000,7.500000,2,2.030000,1,False.\r\nFL,129,415,355-4992,yes,no,0,267.400000,78,45.460000,204.200000,85,17.360000,111.700000,146,5.030000,5.900000,4,1.590000,1,False.\r\nWI,62,415,383-6373,no,no,0,281.000000,66,47.770000,160.600000,108,13.650000,77.900000,74,3.510000,0.000000,0,0.000000,1,False.\r\nID,122,510,382-7993,no,yes,33,270.800000,96,46.040000,220.400000,110,18.730000,169.900000,104,7.650000,11.800000,8,3.190000,4,False.\r\nWY,32,408,422-5865,no,no,0,171.200000,82,29.100000,185.600000,102,15.780000,203.300000,64,9.150000,10.200000,7,2.750000,1,False.\r\nGA,86,510,410-9961,no,no,0,124.100000,82,21.100000,202.600000,120,17.220000,289.600000,119,13.030000,6.700000,8,1.810000,3,False.\r\nFL,130,415,343-9946,no,no,0,162.800000,113,27.680000,290.300000,111,24.680000,114.900000,140,5.170000,7.200000,3,1.940000,1,False.\r\nWY,42,408,357-7060,no,no,0,146.300000,84,24.870000,255.900000,113,21.750000,45.000000,117,2.030000,8.000000,12,2.160000,1,False.\r\nDE,73,415,355-9541,no,no,0,254.800000,85,43.320000,143.400000,80,12.190000,153.900000,102,6.930000,15.000000,7,4.050000,2,False.\r\nME,66,408,378-4145,no,yes,26,254.900000,108,43.330000,243.200000,135,20.670000,190.800000,95,8.590000,5.400000,3,1.460000,2,False.\r\nDC,103,510,386-2317,no,yes,31,107.700000,124,18.310000,188.900000,104,16.060000,196.200000,98,8.830000,8.900000,3,2.400000,0,False.\r\nIA,128,408,335-8146,no,no,0,158.800000,75,27.000000,264.800000,91,22.510000,270.000000,77,12.150000,7.600000,7,2.050000,1,False.\r\nCO,104,415,377-2235,no,no,0,182.900000,113,31.090000,239.600000,85,20.370000,229.800000,104,10.340000,5.500000,4,1.490000,2,False.\r\nMN,103,415,386-9141,no,no,0,198.500000,112,33.750000,42.500000,90,3.610000,179.200000,124,8.060000,12.400000,5,3.350000,0,False.\r\nVT,124,415,416-5623,no,no,0,178.400000,72,30.330000,233.600000,134,19.860000,179.400000,91,8.070000,12.000000,2,3.240000,0,False.\r\nAZ,87,510,327-3053,no,no,0,110.900000,91,18.850000,158.500000,115,13.470000,207.500000,131,9.340000,6.200000,5,1.670000,1,False.\r\nLA,109,415,395-6195,no,yes,27,166.900000,85,28.370000,221.200000,92,18.800000,197.300000,97,8.880000,12.300000,4,3.320000,1,True.\r\nMO,167,415,397-8772,yes,no,0,244.800000,91,41.620000,60.800000,105,5.170000,176.700000,110,7.950000,10.700000,3,2.890000,2,False.\r\nME,97,510,346-7656,no,no,0,120.800000,96,20.540000,169.800000,101,14.430000,194.100000,63,8.730000,11.900000,3,3.210000,4,True.\r\nMD,106,415,343-2350,no,no,0,165.300000,118,28.100000,210.000000,101,17.850000,187.200000,93,8.420000,8.500000,3,2.300000,2,False.\r\nVT,125,415,372-4722,no,no,0,126.700000,113,21.540000,155.500000,131,13.220000,206.200000,112,9.280000,14.400000,7,3.890000,2,False.\r\nDC,108,408,399-8615,no,yes,35,215.900000,106,36.700000,200.600000,107,17.050000,195.400000,107,8.790000,15.500000,7,4.190000,0,False.\r\nWY,125,415,379-8248,no,no,0,140.100000,132,23.820000,209.600000,126,17.820000,264.100000,77,11.880000,8.000000,2,2.160000,1,False.\r\nVA,89,415,414-6219,no,yes,32,209.900000,113,35.680000,249.800000,104,21.230000,224.200000,92,10.090000,8.700000,7,2.350000,1,False.\r\nVA,72,510,387-1343,yes,yes,29,139.800000,114,23.770000,138.200000,91,11.750000,221.000000,88,9.950000,5.500000,6,1.490000,0,False.\r\nCT,23,510,370-5527,no,no,0,321.600000,107,54.670000,251.600000,115,21.390000,141.100000,158,6.350000,11.300000,3,3.050000,2,True.\r\nHI,149,510,393-8736,no,no,0,166.600000,61,28.320000,218.800000,107,18.600000,208.300000,131,9.370000,8.200000,6,2.210000,7,False.\r\nWI,73,415,402-7626,no,no,0,214.200000,90,36.410000,196.800000,78,16.730000,157.900000,112,7.110000,5.900000,8,1.590000,0,False.\r\nMD,61,415,370-2688,no,no,0,260.000000,123,44.200000,210.500000,127,17.890000,234.700000,70,10.560000,9.000000,3,2.430000,1,True.\r\nWV,161,415,418-9036,no,no,0,191.900000,113,32.620000,70.900000,87,6.030000,204.800000,107,9.220000,13.400000,4,3.620000,4,True.\r\nVT,73,408,417-2035,no,no,0,213.000000,95,36.210000,188.800000,104,16.050000,136.200000,89,6.130000,13.500000,3,3.650000,0,False.\r\nUT,118,415,355-3602,no,yes,24,118.100000,83,20.080000,109.600000,72,9.320000,245.500000,73,11.050000,16.900000,2,4.560000,1,False.\r\nCO,23,408,393-4027,no,no,0,190.200000,89,32.330000,166.400000,108,14.140000,219.800000,73,9.890000,15.000000,4,4.050000,6,False.\r\nNC,127,415,418-5141,no,yes,25,82.200000,95,13.970000,163.300000,109,13.880000,264.900000,104,11.920000,5.100000,6,1.380000,0,False.\r\nNJ,42,415,406-1247,no,yes,32,163.800000,80,27.850000,177.800000,123,15.110000,190.400000,106,8.570000,8.100000,5,2.190000,0,False.\r\nAR,118,415,402-3892,no,no,0,267.800000,145,45.530000,316.400000,121,26.890000,208.600000,91,9.390000,14.400000,11,3.890000,5,True.\r\nIA,45,510,332-2965,no,no,0,159.800000,91,27.170000,120.400000,86,10.230000,163.000000,93,7.340000,10.600000,3,2.860000,2,False.\r\nGA,50,408,377-1218,no,yes,24,214.300000,129,36.430000,289.800000,55,24.630000,312.500000,130,14.060000,10.600000,4,2.860000,1,False.\r\nMO,179,510,355-2464,no,no,0,287.300000,123,48.840000,288.000000,114,24.480000,266.000000,112,11.970000,10.500000,4,2.840000,0,True.\r\nMO,152,408,404-4611,no,no,0,101.200000,122,17.200000,141.600000,87,12.040000,198.500000,124,8.930000,7.500000,3,2.030000,2,False.\r\nWY,105,415,373-2339,no,no,0,102.800000,74,17.480000,281.700000,125,23.940000,228.100000,113,10.260000,13.200000,5,3.560000,2,False.\r\nHI,72,415,410-3503,no,no,0,109.100000,97,18.550000,115.700000,96,9.830000,295.800000,84,13.310000,8.300000,6,2.240000,0,False.\r\nPA,52,408,410-4739,no,no,0,215.900000,67,36.700000,217.000000,108,18.450000,342.800000,130,15.430000,5.200000,2,1.400000,1,False.\r\nTX,125,415,365-3562,no,no,0,203.400000,110,34.580000,128.700000,97,10.940000,190.500000,113,8.570000,11.000000,4,2.970000,1,False.\r\nVA,143,510,387-7641,no,no,0,110.100000,113,18.720000,169.000000,59,14.370000,166.700000,94,7.500000,9.200000,2,2.480000,1,False.\r\nRI,65,415,385-9744,no,no,0,111.000000,51,18.870000,219.800000,84,18.680000,202.000000,89,9.090000,4.400000,14,1.190000,1,False.\r\nWI,80,415,398-5006,no,no,0,239.900000,121,40.780000,142.300000,51,12.100000,364.300000,106,16.390000,9.300000,5,2.510000,1,False.\r\nMS,1,415,408-3977,no,no,0,144.800000,107,24.620000,112.500000,66,9.560000,218.700000,79,9.840000,13.800000,3,3.730000,1,False.\r\nKY,60,408,334-2729,no,no,0,135.400000,134,23.020000,205.900000,85,17.500000,204.000000,103,9.180000,7.900000,4,2.130000,1,False.\r\nNC,43,415,334-7685,no,no,0,84.200000,134,14.310000,80.800000,103,6.870000,196.100000,79,8.820000,10.800000,2,2.920000,1,False.\r\nNV,143,415,350-9228,no,no,0,209.100000,127,35.550000,106.100000,80,9.020000,179.600000,90,8.080000,14.000000,6,3.780000,0,False.\r\nUT,81,415,407-5774,no,yes,24,130.100000,117,22.120000,196.000000,61,16.660000,139.300000,123,6.270000,11.400000,5,3.080000,0,False.\r\nME,205,510,413-4039,no,yes,24,175.800000,139,29.890000,155.000000,98,13.180000,180.700000,64,8.130000,7.800000,5,2.110000,2,False.\r\nHI,24,415,343-2077,no,no,0,241.900000,104,41.120000,145.200000,112,12.340000,214.500000,105,9.650000,6.600000,5,1.780000,1,False.\r\nOH,74,415,336-5661,no,no,0,136.700000,106,23.240000,228.600000,105,19.430000,265.300000,114,11.940000,9.800000,4,2.650000,0,False.\r\nWV,77,510,355-4143,no,no,0,67.700000,68,11.510000,195.700000,86,16.630000,236.500000,137,10.640000,12.000000,2,3.240000,1,False.\r\nOK,74,415,366-5918,no,no,0,200.400000,87,34.070000,309.200000,105,26.280000,152.100000,118,6.840000,10.000000,2,2.700000,1,False.\r\nKY,74,510,368-7555,yes,no,0,125.800000,103,21.390000,207.700000,96,17.650000,207.400000,143,9.330000,14.100000,4,3.810000,1,True.\r\nAL,200,408,408-2119,no,no,0,128.200000,87,21.790000,133.200000,105,11.320000,177.600000,123,7.990000,11.200000,2,3.020000,1,False.\r\nMD,86,408,329-2789,no,no,0,226.300000,88,38.470000,223.000000,107,18.960000,255.600000,92,11.500000,13.000000,3,3.510000,4,False.\r\nNE,91,510,334-1508,no,yes,37,162.300000,107,27.590000,233.900000,115,19.880000,277.400000,94,12.480000,9.200000,4,2.480000,0,False.\r\nDC,76,415,337-1506,no,no,0,224.400000,121,38.150000,147.900000,97,12.570000,183.800000,74,8.270000,6.700000,2,1.810000,2,False.\r\nTX,130,415,396-8400,no,no,0,120.500000,127,20.490000,189.700000,52,16.120000,270.100000,107,12.150000,14.300000,2,3.860000,1,False.\r\nOH,56,408,349-2654,no,no,0,91.100000,90,15.490000,179.300000,115,15.240000,300.700000,89,13.530000,11.900000,8,3.210000,2,False.\r\nDE,117,415,417-2716,no,no,0,168.800000,137,28.700000,241.400000,107,20.520000,204.800000,106,9.220000,15.500000,4,4.190000,0,False.\r\nIA,63,510,402-1725,no,no,0,153.500000,81,26.100000,287.300000,115,24.420000,230.200000,85,10.360000,6.500000,5,1.760000,2,False.\r\nVT,126,415,403-3229,no,no,0,226.200000,88,38.450000,140.300000,114,11.930000,208.900000,110,9.400000,6.400000,2,1.730000,0,False.\r\nOR,132,510,353-6056,no,no,0,191.900000,107,32.620000,206.900000,127,17.590000,272.000000,88,12.240000,12.600000,2,3.400000,1,False.\r\nNV,81,415,399-9802,no,yes,28,167.900000,147,28.540000,190.700000,105,16.210000,193.000000,103,8.690000,9.200000,6,2.480000,4,True.\r\nNC,122,415,366-7069,no,no,0,180.000000,88,30.600000,145.000000,77,12.330000,233.700000,120,10.520000,11.500000,6,3.110000,2,False.\r\nNJ,46,408,332-5949,no,no,0,257.400000,67,43.760000,261.100000,91,22.190000,204.400000,107,9.200000,13.400000,5,3.620000,2,True.\r\nMN,150,415,393-6376,no,yes,28,174.400000,75,29.650000,169.900000,80,14.440000,201.600000,130,9.070000,11.000000,4,2.970000,1,False.\r\nID,99,408,354-7025,no,no,0,159.700000,83,27.150000,155.400000,121,13.210000,255.700000,114,11.510000,8.400000,3,2.270000,1,False.\r\nAL,87,408,351-6585,no,no,0,237.200000,124,40.320000,222.600000,87,18.920000,173.300000,81,7.800000,11.000000,3,2.970000,1,False.\r\nAK,108,415,330-5462,no,no,0,103.000000,129,17.510000,242.300000,103,20.600000,170.200000,89,7.660000,7.900000,3,2.130000,1,False.\r\nVT,101,415,407-2292,no,no,0,153.800000,89,26.150000,234.000000,89,19.890000,196.300000,77,8.830000,11.600000,2,3.130000,4,False.\r\nPA,53,415,340-3011,no,no,0,205.100000,86,34.870000,160.500000,95,13.640000,149.500000,142,6.730000,10.700000,2,2.890000,3,False.\r\nAK,132,415,345-9153,no,yes,39,175.700000,93,29.870000,187.200000,94,15.910000,225.500000,118,10.150000,8.600000,3,2.320000,2,False.\r\nCA,158,510,379-5503,no,no,0,155.900000,123,26.500000,224.200000,112,19.060000,221.000000,116,9.950000,8.600000,8,2.320000,2,False.\r\nMS,114,415,389-6790,no,yes,34,154.400000,109,26.250000,221.400000,142,18.820000,208.500000,103,9.380000,10.300000,5,2.780000,0,False.\r\nAR,77,415,408-3610,no,yes,23,209.700000,73,35.650000,183.600000,63,15.610000,205.500000,111,9.250000,7.100000,3,1.920000,2,False.\r\nNV,144,415,375-8238,yes,no,0,150.000000,69,25.500000,285.900000,73,24.300000,190.600000,121,8.580000,9.400000,15,2.540000,0,False.\r\nAL,91,510,378-5633,no,yes,23,232.400000,97,39.510000,186.000000,88,15.810000,190.500000,128,8.570000,12.300000,3,3.320000,3,False.\r\nGA,58,408,389-9120,no,no,0,165.400000,100,28.120000,115.700000,87,9.830000,193.800000,118,8.720000,12.800000,5,3.460000,2,False.\r\nAR,5,415,380-2758,no,no,0,199.200000,106,33.860000,187.300000,12,15.920000,214.000000,85,9.630000,13.300000,3,3.590000,3,False.\r\nMA,97,408,402-2728,no,no,0,217.600000,81,36.990000,320.500000,51,27.240000,150.700000,110,6.780000,4.200000,3,1.130000,0,False.\r\nNJ,107,415,354-9062,no,no,0,212.100000,95,36.060000,150.100000,88,12.760000,219.800000,111,9.890000,7.700000,2,2.080000,3,False.\r\nMO,142,415,417-2054,no,yes,30,154.000000,75,26.180000,165.800000,97,14.090000,270.000000,83,12.150000,10.800000,5,2.920000,1,False.\r\nNY,9,408,353-1941,no,yes,31,193.800000,130,32.950000,202.600000,98,17.220000,191.200000,102,8.600000,13.300000,2,3.590000,1,False.\r\nAZ,73,415,328-1522,no,no,0,175.400000,130,29.820000,248.100000,105,21.090000,122.400000,85,5.510000,12.200000,4,3.290000,0,False.\r\nNJ,48,510,408-4529,no,yes,22,152.000000,63,25.840000,258.800000,131,22.000000,263.200000,109,11.840000,15.700000,5,4.240000,2,True.\r\nWV,43,408,417-5320,no,no,0,230.200000,147,39.130000,186.700000,121,15.870000,128.400000,100,5.780000,9.200000,3,2.480000,0,False.\r\nNM,122,408,370-9755,no,yes,33,174.900000,103,29.730000,248.200000,105,21.100000,164.600000,116,7.410000,13.500000,3,3.650000,1,True.\r\nSC,93,415,372-4835,no,no,0,190.200000,68,32.330000,262.200000,64,22.290000,130.000000,92,5.850000,8.800000,4,2.380000,0,False.\r\nVT,85,415,334-6605,no,no,0,176.400000,122,29.990000,224.900000,123,19.120000,219.600000,50,9.880000,11.500000,1,3.110000,1,False.\r\nTN,59,415,399-5564,no,no,0,160.900000,95,27.350000,251.200000,65,21.350000,273.400000,97,12.300000,5.000000,5,1.350000,3,False.\r\nLA,87,415,392-2887,no,no,0,228.700000,90,38.880000,163.000000,99,13.860000,154.100000,90,6.930000,11.800000,3,3.190000,1,False.\r\nOH,137,408,328-2110,no,no,0,144.000000,90,24.480000,181.600000,100,15.440000,128.100000,93,5.760000,12.300000,2,3.320000,0,False.\r\nOR,21,510,383-5976,no,yes,31,135.900000,90,23.100000,271.000000,84,23.040000,179.100000,89,8.060000,9.500000,7,2.570000,6,False.\r\nDE,129,510,332-6181,no,no,0,334.300000,118,56.830000,192.100000,104,16.330000,191.000000,83,8.590000,10.400000,6,2.810000,0,True.\r\nKY,104,415,330-5255,no,no,0,130.500000,77,22.190000,131.200000,117,11.150000,264.700000,63,11.910000,13.000000,6,3.510000,0,False.\r\nGA,93,408,413-5190,no,yes,21,134.200000,105,22.810000,162.500000,128,13.810000,186.600000,90,8.400000,11.800000,2,3.190000,4,True.\r\nVT,63,415,394-7447,no,no,0,278.000000,102,47.260000,266.400000,114,22.640000,224.100000,118,10.080000,13.100000,4,3.540000,4,True.\r\nOR,161,415,353-7096,no,no,0,105.400000,70,17.920000,214.800000,122,18.260000,223.600000,126,10.060000,7.800000,5,2.110000,0,False.\r\nTX,50,510,395-6002,no,no,0,188.900000,94,32.110000,203.900000,104,17.330000,151.800000,124,6.830000,11.600000,8,3.130000,3,False.\r\nMO,103,408,372-9816,no,yes,24,111.800000,85,19.010000,239.600000,102,20.370000,268.300000,81,12.070000,6.900000,4,1.860000,1,False.\r\nND,84,415,400-7253,no,yes,33,159.100000,106,27.050000,149.800000,101,12.730000,213.400000,108,9.600000,13.000000,18,3.510000,1,False.\r\nMN,92,510,344-7470,no,no,0,212.400000,105,36.110000,224.600000,118,19.090000,221.300000,105,9.960000,9.000000,4,2.430000,1,False.\r\nNV,77,415,378-8572,no,no,0,142.300000,112,24.190000,306.300000,111,26.040000,196.500000,82,8.840000,9.900000,1,2.670000,1,False.\r\nNY,64,415,345-9140,yes,no,0,346.800000,55,58.960000,249.500000,79,21.210000,275.400000,102,12.390000,13.300000,9,3.590000,1,True.\r\nFL,159,415,340-5460,no,yes,15,113.900000,102,19.360000,145.300000,146,12.350000,195.200000,137,8.780000,11.800000,9,3.190000,1,False.\r\nKS,110,415,369-8024,yes,yes,27,267.900000,103,45.540000,263.300000,74,22.380000,178.100000,106,8.010000,8.300000,2,2.240000,1,True.\r\nNV,138,510,395-8595,no,no,0,171.400000,117,29.140000,115.200000,128,9.790000,224.500000,115,10.100000,17.000000,4,4.590000,3,False.\r\nNV,178,408,359-4587,no,no,0,275.400000,150,46.820000,187.500000,62,15.940000,147.100000,126,6.620000,13.600000,3,3.670000,1,False.\r\nSC,38,415,375-5439,no,yes,31,197.200000,118,33.520000,249.900000,70,21.240000,298.900000,104,13.450000,3.900000,2,1.050000,0,False.\r\nMI,50,415,361-3779,no,yes,35,192.600000,97,32.740000,135.200000,101,11.490000,216.200000,101,9.730000,7.900000,2,2.130000,2,False.\r\nMI,45,510,375-8934,no,yes,26,91.700000,104,15.590000,150.600000,119,12.800000,63.300000,103,2.850000,7.700000,5,2.080000,1,False.\r\nTN,70,510,395-4757,no,no,0,126.300000,99,21.470000,141.600000,106,12.040000,255.900000,96,11.520000,9.600000,2,2.590000,0,False.\r\nNY,147,510,421-7205,no,yes,33,251.500000,107,42.760000,234.100000,110,19.900000,213.400000,87,9.600000,10.400000,6,2.810000,3,False.\r\nNV,94,510,379-8805,no,no,0,190.600000,108,32.400000,152.300000,95,12.950000,144.700000,97,6.510000,7.500000,5,2.030000,1,False.\r\nIL,179,510,348-2150,no,no,0,116.100000,101,19.740000,201.800000,99,17.150000,181.900000,103,8.190000,11.600000,5,3.130000,0,False.\r\nMS,116,415,417-9128,no,no,0,217.300000,91,36.940000,216.100000,95,18.370000,148.100000,76,6.660000,11.300000,3,3.050000,2,False.\r\nND,59,510,351-4226,no,no,0,179.400000,80,30.500000,232.500000,99,19.760000,175.800000,105,7.910000,14.700000,3,3.970000,0,False.\r\nNC,165,415,330-6630,no,no,0,207.700000,109,35.310000,164.800000,94,14.010000,54.500000,91,2.450000,7.900000,3,2.130000,0,False.\r\nMI,133,408,387-9137,no,no,0,277.300000,138,47.140000,228.400000,117,19.410000,117.300000,103,5.280000,12.800000,4,3.460000,2,True.\r\nTN,140,415,372-3987,no,no,0,125.300000,84,21.300000,167.600000,121,14.250000,260.600000,94,11.730000,8.400000,4,2.270000,1,False.\r\nVT,93,408,408-5183,no,yes,32,138.100000,91,23.480000,167.300000,72,14.220000,238.900000,115,10.750000,6.800000,3,1.840000,2,False.\r\nOK,52,510,412-9357,no,yes,38,169.300000,88,28.780000,225.900000,97,19.200000,172.000000,86,7.740000,8.200000,3,2.210000,0,False.\r\nDE,64,415,402-3599,no,yes,27,201.300000,101,34.220000,143.800000,89,12.220000,150.200000,127,6.760000,12.300000,3,3.320000,1,False.\r\nND,12,510,379-5211,yes,no,0,216.700000,117,36.840000,116.500000,126,9.900000,220.000000,110,9.900000,9.800000,4,2.650000,2,False.\r\nOR,48,415,405-9217,no,no,0,190.400000,92,32.370000,317.500000,85,26.990000,133.400000,113,6.000000,8.300000,4,2.240000,2,False.\r\nNY,181,408,340-9200,no,no,0,143.300000,91,24.360000,195.500000,58,16.620000,223.300000,95,10.050000,6.000000,7,1.620000,1,False.\r\nMO,168,415,339-9026,no,yes,42,97.400000,57,16.560000,203.600000,98,17.310000,173.900000,124,7.830000,11.400000,2,3.080000,2,False.\r\nFL,155,415,343-4772,no,no,0,181.400000,111,30.840000,167.700000,92,14.250000,168.500000,122,7.580000,11.300000,3,3.050000,3,False.\r\nND,105,510,345-2108,no,no,0,246.400000,83,41.890000,256.200000,101,21.780000,169.000000,151,7.610000,3.800000,4,1.030000,0,False.\r\nNY,11,415,401-4650,no,no,0,143.400000,130,24.380000,289.400000,50,24.600000,194.000000,100,8.730000,9.700000,6,2.620000,2,False.\r\nND,182,415,400-3945,no,no,0,104.900000,111,17.830000,198.500000,120,16.870000,258.200000,91,11.620000,8.000000,5,2.160000,2,False.\r\nNY,104,415,338-3781,no,no,0,156.200000,93,26.550000,193.000000,54,16.410000,222.700000,94,10.020000,13.100000,5,3.540000,1,False.\r\nOH,102,415,342-6316,no,no,0,114.800000,125,19.520000,81.900000,126,6.960000,304.300000,101,13.690000,12.000000,4,3.240000,0,False.\r\nAL,122,415,336-5920,no,no,0,232.500000,96,39.530000,205.500000,120,17.470000,213.700000,91,9.620000,11.900000,2,3.210000,0,False.\r\nSC,41,415,332-1060,no,no,0,143.600000,117,24.410000,152.400000,108,12.950000,194.400000,110,8.750000,8.600000,3,2.320000,1,False.\r\nGA,132,415,418-3426,no,no,0,176.700000,132,30.040000,244.100000,80,20.750000,176.300000,120,7.930000,9.100000,4,2.460000,2,False.\r\nWY,76,415,408-6326,no,no,0,263.400000,148,44.780000,230.300000,69,19.580000,170.600000,101,7.680000,11.400000,5,3.080000,1,True.\r\nWV,13,415,334-6142,no,no,0,146.400000,74,24.890000,148.500000,92,12.620000,216.700000,96,9.750000,11.300000,3,3.050000,1,False.\r\nHI,115,415,336-6128,no,yes,33,145.000000,72,24.650000,194.500000,157,16.530000,242.300000,138,10.900000,14.200000,3,3.830000,2,False.\r\nWV,67,408,406-6708,no,no,0,167.800000,91,28.530000,167.700000,69,14.250000,110.300000,71,4.960000,8.400000,12,2.270000,1,False.\r\nLA,154,510,388-8670,no,no,0,166.900000,99,28.370000,154.900000,97,13.170000,189.400000,89,8.520000,7.200000,5,1.940000,0,False.\r\nAR,100,510,363-5853,no,no,0,142.500000,87,24.230000,195.700000,88,16.630000,122.100000,117,5.490000,7.800000,8,2.110000,2,False.\r\nAK,146,510,383-6544,yes,no,0,133.000000,65,22.610000,262.800000,93,22.340000,214.300000,128,9.640000,11.200000,3,3.020000,1,False.\r\nDC,148,415,378-2940,no,yes,11,252.900000,129,42.990000,284.300000,88,24.170000,262.800000,99,11.830000,12.300000,1,3.320000,1,False.\r\nAL,67,415,338-7683,no,yes,28,95.000000,94,16.150000,291.200000,73,24.750000,159.600000,114,7.180000,10.000000,2,2.700000,2,False.\r\nUT,161,510,329-2786,yes,no,0,194.200000,106,33.010000,249.400000,105,21.200000,254.900000,129,11.470000,12.900000,1,3.480000,1,True.\r\nKS,70,415,369-9465,no,no,0,222.800000,114,37.880000,215.900000,113,18.350000,223.500000,122,10.060000,0.000000,0,0.000000,1,False.\r\nMN,116,510,337-3769,no,no,0,201.800000,82,34.310000,231.500000,95,19.680000,226.100000,130,10.170000,16.500000,5,4.460000,0,False.\r\nVA,99,415,400-6257,no,yes,42,216.000000,125,36.720000,232.300000,104,19.750000,215.500000,100,9.700000,9.300000,4,2.510000,2,True.\r\nUT,87,415,411-6663,no,no,0,146.300000,108,24.870000,171.800000,102,14.600000,167.500000,66,7.540000,5.300000,9,1.430000,1,False.\r\nIN,87,510,384-3101,no,no,0,234.800000,85,39.920000,140.900000,91,11.980000,204.300000,93,9.190000,9.500000,5,2.570000,1,False.\r\nCT,70,408,339-5329,no,no,0,198.600000,111,33.760000,213.900000,115,18.180000,171.200000,105,7.700000,10.600000,6,2.860000,2,False.\r\nDE,131,408,388-9944,no,no,0,94.400000,80,16.050000,215.100000,101,18.280000,179.700000,108,8.090000,13.100000,9,3.540000,2,False.\r\nVT,119,510,403-1769,no,no,0,190.400000,74,32.370000,215.600000,113,18.330000,161.200000,111,7.250000,10.000000,1,2.700000,2,False.\r\nMO,119,408,390-1612,no,yes,32,142.600000,77,24.240000,208.200000,126,17.700000,171.000000,102,7.690000,12.000000,2,3.240000,3,False.\r\nRI,87,510,421-7214,yes,no,0,134.200000,80,22.810000,165.000000,71,14.030000,173.100000,102,7.790000,10.700000,5,2.890000,0,False.\r\nCA,112,415,338-6962,no,no,0,111.900000,92,19.020000,114.000000,143,9.690000,146.800000,79,6.610000,14.100000,3,3.810000,5,True.\r\nRI,75,415,333-9826,no,no,0,122.800000,89,20.880000,211.300000,104,17.960000,261.400000,91,11.760000,10.700000,2,2.890000,2,False.\r\nCT,150,510,411-8549,no,no,0,189.300000,77,32.180000,220.900000,105,18.780000,238.700000,117,10.740000,9.200000,5,2.480000,4,False.\r\nIN,161,510,381-9234,no,yes,38,240.400000,112,40.870000,201.800000,102,17.150000,206.100000,112,9.270000,16.100000,6,4.350000,0,False.\r\nFL,91,510,387-9855,yes,yes,24,93.500000,112,15.900000,183.400000,128,15.590000,240.700000,133,10.830000,9.900000,3,2.670000,0,False.\r\nKS,124,415,371-6990,no,no,0,158.600000,104,26.960000,211.200000,77,17.950000,179.300000,104,8.070000,10.200000,8,2.750000,3,False.\r\nNY,94,510,417-3046,yes,no,0,243.200000,109,41.340000,147.000000,88,12.500000,94.900000,99,4.270000,7.200000,4,1.940000,4,False.\r\nTX,217,408,385-7082,no,no,0,176.400000,115,29.990000,158.800000,128,13.500000,306.600000,107,13.800000,9.300000,3,2.510000,4,False.\r\nRI,158,510,365-5886,no,no,0,220.900000,129,37.550000,242.200000,108,20.590000,233.300000,75,10.500000,6.400000,5,1.730000,0,False.\r\nNV,102,415,373-5196,no,no,0,144.400000,87,24.550000,266.500000,128,22.650000,217.600000,59,9.790000,7.100000,7,1.920000,0,False.\r\nUT,85,510,327-6194,no,no,0,212.300000,107,36.090000,228.400000,103,19.410000,163.300000,116,7.350000,7.700000,3,2.080000,0,False.\r\nOK,79,510,381-4565,no,no,0,147.000000,72,24.990000,165.700000,102,14.080000,243.200000,107,10.940000,8.400000,9,2.270000,1,False.\r\nMN,139,510,357-9832,no,yes,25,96.200000,112,16.350000,178.900000,70,15.210000,182.100000,84,8.190000,12.900000,10,3.480000,2,False.\r\nNC,103,415,396-4845,no,no,0,263.400000,118,44.780000,179.100000,69,15.220000,214.700000,112,9.660000,10.300000,2,2.780000,0,False.\r\nOR,98,415,378-6772,yes,no,0,12.500000,67,2.130000,256.600000,90,21.810000,169.400000,88,7.620000,7.700000,9,2.080000,1,False.\r\nNH,78,408,353-4296,no,no,0,162.300000,116,27.590000,192.400000,86,16.350000,240.600000,100,10.830000,10.100000,3,2.730000,2,False.\r\nAK,50,408,362-8331,no,no,0,183.600000,107,31.210000,58.600000,118,4.980000,202.600000,99,9.120000,8.700000,3,2.350000,1,False.\r\nOR,161,415,362-4685,no,no,0,178.100000,109,30.280000,146.500000,86,12.450000,137.600000,78,6.190000,8.500000,2,2.300000,1,False.\r\nKS,67,408,383-1431,no,no,0,201.400000,101,34.240000,97.600000,122,8.300000,202.500000,119,9.110000,7.000000,3,1.890000,0,False.\r\nWV,86,415,332-2258,no,yes,38,123.000000,158,20.910000,133.900000,119,11.380000,138.200000,103,6.220000,13.300000,4,3.590000,1,False.\r\nGA,92,510,375-8304,no,no,0,208.000000,125,35.360000,198.900000,76,16.910000,76.400000,97,3.440000,8.600000,6,2.320000,0,False.\r\nNM,174,415,340-5580,no,no,0,239.200000,72,40.660000,188.500000,124,16.020000,105.600000,116,4.750000,8.800000,3,2.380000,1,False.\r\nOH,124,510,362-1490,no,no,0,193.000000,97,32.810000,89.800000,99,7.630000,172.800000,104,7.780000,15.300000,3,4.130000,1,False.\r\nND,132,415,336-4281,no,yes,31,174.500000,101,29.670000,245.600000,105,20.880000,172.800000,76,7.780000,10.300000,9,2.780000,1,False.\r\nRI,190,415,361-1315,no,yes,26,116.700000,71,19.840000,145.900000,88,12.400000,175.100000,103,7.880000,9.900000,3,2.670000,1,False.\r\nHI,101,510,342-5906,no,no,0,93.800000,127,15.950000,150.000000,104,12.750000,241.100000,116,10.850000,10.700000,2,2.890000,1,False.\r\nWY,185,415,405-7904,yes,yes,30,154.100000,114,26.200000,118.700000,106,10.090000,258.400000,105,11.630000,12.900000,3,3.480000,2,False.\r\nNY,68,415,349-4762,no,yes,29,239.500000,82,40.720000,203.800000,105,17.320000,167.800000,70,7.550000,9.900000,6,2.670000,0,False.\r\nKS,117,510,385-3263,no,yes,25,216.000000,140,36.720000,224.100000,69,19.050000,267.900000,112,12.060000,11.800000,4,3.190000,0,False.\r\nLA,118,408,405-9496,no,no,0,187.400000,97,31.860000,177.800000,89,15.110000,233.400000,97,10.500000,12.200000,6,3.290000,1,False.\r\nPA,124,415,396-6775,no,no,0,167.400000,119,28.460000,233.200000,143,19.820000,109.600000,115,4.930000,10.300000,5,2.780000,0,False.\r\nNV,22,510,393-6475,no,no,0,160.400000,108,27.270000,218.100000,88,18.540000,192.900000,115,8.680000,12.500000,4,3.380000,1,False.\r\nMN,75,415,379-7779,no,no,0,143.200000,92,24.340000,209.100000,142,17.770000,173.000000,96,7.790000,11.900000,9,3.210000,1,False.\r\nPA,134,408,408-8650,no,no,0,205.300000,122,34.900000,240.500000,155,20.440000,179.100000,107,8.060000,5.000000,9,1.350000,1,False.\r\nMO,164,408,400-3497,no,yes,25,219.100000,88,37.250000,151.500000,99,12.880000,50.100000,60,2.250000,14.300000,6,3.860000,1,False.\r\nNY,44,510,407-9244,no,no,0,143.200000,77,24.340000,169.800000,114,14.430000,215.800000,77,9.710000,7.600000,4,2.050000,1,False.\r\nME,177,415,406-8809,no,no,0,232.800000,106,39.580000,175.200000,97,14.890000,212.200000,77,9.550000,12.500000,7,3.380000,2,False.\r\nNH,110,408,358-1778,no,no,0,162.000000,81,27.540000,247.500000,89,21.040000,155.500000,99,7.000000,8.900000,8,2.400000,0,False.\r\nWY,53,415,337-4339,no,yes,27,25.900000,119,4.400000,206.500000,96,17.550000,228.100000,64,10.260000,6.500000,7,1.760000,1,False.\r\nNY,108,415,344-7197,no,no,0,154.200000,123,26.210000,112.300000,86,9.550000,246.400000,75,11.090000,15.400000,4,4.160000,4,True.\r\nME,80,408,333-7631,no,no,0,322.300000,113,54.790000,222.000000,95,18.870000,162.800000,123,7.330000,6.700000,8,1.810000,0,True.\r\nMN,158,408,372-6623,no,no,0,209.900000,112,35.680000,221.300000,82,18.810000,210.000000,93,9.450000,8.200000,3,2.210000,1,False.\r\nOH,114,415,363-2602,no,no,0,191.500000,88,32.560000,175.200000,78,14.890000,220.300000,118,9.910000,0.000000,0,0.000000,0,False.\r\nNC,64,408,387-7757,no,yes,19,291.100000,150,49.490000,226.700000,123,19.270000,219.100000,67,9.860000,7.500000,2,2.030000,1,False.\r\nSD,88,415,397-5381,no,no,0,215.600000,115,36.650000,216.200000,85,18.380000,171.300000,65,7.710000,11.800000,1,3.190000,3,False.\r\nUT,82,510,406-4604,yes,no,0,208.800000,101,35.500000,213.700000,87,18.160000,175.100000,86,7.880000,12.400000,6,3.350000,3,False.\r\nKY,111,415,376-9513,no,no,0,255.900000,97,43.500000,204.100000,129,17.350000,171.300000,84,7.710000,12.300000,5,3.320000,3,False.\r\nMT,60,408,360-1852,no,no,0,252.700000,97,42.960000,221.100000,121,18.790000,109.900000,100,4.950000,12.400000,4,3.350000,2,False.\r\nNJ,113,408,421-7270,no,no,0,132.100000,72,22.460000,247.500000,107,21.040000,246.200000,123,11.080000,6.900000,6,1.860000,3,False.\r\nFL,109,408,371-9482,no,no,0,217.000000,115,36.890000,207.000000,142,17.600000,268.000000,106,12.060000,8.200000,4,2.210000,1,False.\r\nIN,105,510,337-4101,no,yes,42,101.900000,79,17.320000,223.100000,97,18.960000,241.600000,77,10.870000,12.900000,2,3.480000,0,False.\r\nDE,85,510,420-2796,no,no,0,211.500000,100,35.960000,184.600000,88,15.690000,164.300000,131,7.390000,13.300000,4,3.590000,2,False.\r\nAL,131,510,366-4225,no,no,0,153.400000,86,26.080000,198.500000,81,16.870000,164.400000,83,7.400000,10.400000,3,2.810000,0,False.\r\nME,59,510,398-4567,no,no,0,166.300000,95,28.270000,239.300000,87,20.340000,123.200000,108,5.540000,10.000000,3,2.700000,2,False.\r\nSD,148,408,388-4571,no,no,0,185.200000,87,31.480000,170.400000,96,14.480000,165.100000,104,7.430000,9.500000,13,2.570000,1,False.\r\nVA,210,408,360-8666,no,no,0,104.600000,121,17.780000,149.500000,71,12.710000,255.100000,67,11.480000,6.500000,8,1.760000,2,False.\r\nAK,115,415,333-3704,no,no,0,245.200000,105,41.680000,159.000000,109,13.520000,229.900000,74,10.350000,7.200000,8,1.940000,0,False.\r\nID,106,510,383-2566,no,no,0,274.400000,120,46.650000,198.600000,82,16.880000,160.800000,62,7.240000,6.000000,3,1.620000,1,False.\r\nRI,93,415,406-5584,no,no,0,98.400000,78,16.730000,249.600000,129,21.220000,248.200000,114,11.170000,14.200000,4,3.830000,1,False.\r\nKY,57,415,344-4691,no,yes,29,279.900000,121,47.580000,223.100000,109,18.960000,251.700000,94,11.330000,13.000000,2,3.510000,1,False.\r\nND,98,510,347-9737,no,no,0,187.200000,127,31.820000,195.600000,88,16.630000,181.800000,129,8.180000,5.100000,4,1.380000,1,False.\r\nHI,157,415,333-7961,no,no,0,276.200000,95,46.950000,165.800000,119,14.090000,151.600000,79,6.820000,2.200000,4,0.590000,3,False.\r\nWI,116,415,364-2439,no,yes,35,200.400000,104,34.070000,272.800000,89,23.190000,214.500000,100,9.650000,8.300000,4,2.240000,1,False.\r\nWV,30,510,411-8043,no,no,0,162.300000,96,27.590000,244.000000,122,20.740000,180.100000,89,8.100000,9.100000,4,2.460000,2,False.\r\nNJ,111,510,332-5084,no,no,0,176.900000,128,30.070000,102.800000,56,8.740000,213.700000,84,9.620000,10.500000,2,2.840000,4,True.\r\nKS,52,415,413-4831,no,no,0,165.500000,78,28.140000,205.500000,89,17.470000,213.600000,124,9.610000,12.200000,6,3.290000,0,False.\r\nAL,72,415,343-4806,no,no,0,217.800000,93,37.030000,189.700000,113,16.120000,182.600000,91,8.220000,10.400000,5,2.810000,4,False.\r\nNJ,135,510,401-8735,no,yes,28,201.400000,100,34.240000,246.500000,117,20.950000,154.800000,131,6.970000,12.900000,4,3.480000,2,True.\r\nNC,86,510,391-8626,no,no,0,190.500000,115,32.390000,179.600000,130,15.270000,258.500000,89,11.630000,10.100000,5,2.730000,3,False.\r\nDE,98,415,327-5817,no,yes,29,179.900000,97,30.580000,189.200000,89,16.080000,164.300000,76,7.390000,12.800000,7,3.460000,3,False.\r\nWY,151,510,381-4712,no,no,0,235.900000,104,40.100000,80.600000,91,6.850000,212.800000,116,9.580000,5.800000,2,1.570000,3,False.\r\nID,118,415,335-3320,no,no,0,140.400000,112,23.870000,187.100000,60,15.900000,207.900000,155,9.360000,7.900000,1,2.130000,0,False.\r\nNY,117,415,415-8780,no,no,0,144.600000,115,24.580000,258.800000,66,22.000000,253.200000,113,11.390000,7.400000,9,2.000000,2,False.\r\nMO,55,510,362-1146,no,no,0,189.000000,100,32.130000,118.500000,99,10.070000,248.100000,87,11.160000,17.100000,6,4.620000,0,False.\r\nID,82,408,352-7413,no,no,0,101.000000,93,17.170000,155.600000,104,13.230000,304.400000,93,13.700000,13.300000,2,3.590000,0,False.\r\nIA,152,415,387-6716,no,no,0,206.300000,98,35.070000,292.800000,82,24.890000,43.700000,121,1.970000,10.600000,4,2.860000,1,False.\r\nTN,108,408,352-1127,no,yes,15,165.100000,85,28.070000,267.000000,93,22.700000,250.700000,114,11.280000,10.900000,4,2.940000,1,False.\r\nOH,98,408,368-1288,no,no,0,165.000000,129,28.050000,202.600000,113,17.220000,172.300000,94,7.750000,12.500000,7,3.380000,1,True.\r\nIL,130,415,403-5279,no,no,0,155.900000,95,26.500000,256.100000,97,21.770000,262.900000,103,11.830000,11.700000,3,3.160000,3,False.\r\nFL,136,408,397-9333,yes,no,0,199.200000,122,33.860000,214.700000,114,18.250000,150.900000,105,6.790000,11.800000,7,3.190000,1,False.\r\nMA,47,415,411-7353,no,no,0,155.300000,116,26.400000,188.200000,85,16.000000,247.000000,73,11.120000,12.300000,4,3.320000,3,False.\r\nOK,189,415,383-2537,no,no,0,208.300000,106,35.410000,236.700000,123,20.120000,179.100000,120,8.060000,11.300000,5,3.050000,3,False.\r\nKY,107,415,330-4419,no,no,0,157.100000,79,26.710000,162.600000,124,13.820000,150.000000,138,6.750000,12.100000,6,3.270000,1,False.\r\nMI,91,415,390-7930,no,no,0,154.400000,165,26.250000,168.300000,121,14.310000,239.900000,81,10.800000,11.700000,4,3.160000,5,True.\r\nNE,159,415,362-5111,no,no,0,189.100000,105,32.150000,246.100000,147,20.920000,242.000000,106,10.890000,10.400000,5,2.810000,1,True.\r\nVA,11,408,358-6796,no,yes,24,131.500000,98,22.360000,230.200000,111,19.570000,283.700000,87,12.770000,10.000000,3,2.700000,2,False.\r\nNY,167,415,409-7494,no,no,0,166.400000,85,28.290000,243.200000,135,20.670000,229.200000,95,10.310000,9.900000,5,2.670000,1,False.\r\nMT,111,408,400-1636,no,no,0,142.300000,75,24.190000,122.800000,106,10.440000,229.500000,94,10.330000,12.800000,9,3.460000,2,False.\r\nVT,99,408,389-2747,no,yes,19,87.700000,103,14.910000,223.000000,86,18.960000,182.300000,112,8.200000,7.300000,4,1.970000,2,False.\r\nKS,159,415,415-2176,no,yes,19,184.100000,78,31.300000,194.500000,71,16.530000,225.600000,101,10.150000,16.900000,3,4.560000,0,False.\r\nVA,114,415,377-8067,yes,yes,31,174.500000,104,29.670000,224.200000,92,19.060000,116.300000,91,5.230000,12.300000,10,3.320000,2,False.\r\nAL,71,415,362-7835,no,no,0,103.300000,103,17.560000,138.500000,79,11.770000,164.800000,98,7.420000,9.000000,2,2.430000,2,False.\r\nPA,122,415,361-5225,no,no,0,35.100000,62,5.970000,180.800000,89,15.370000,251.600000,58,11.320000,12.700000,2,3.430000,1,False.\r\nAL,100,408,333-3447,no,yes,25,246.600000,94,41.920000,141.400000,112,12.020000,189.800000,109,8.540000,13.000000,5,3.510000,1,False.\r\nWV,83,415,341-3044,no,yes,37,78.500000,109,13.350000,210.500000,101,17.890000,179.700000,102,8.090000,11.800000,4,3.190000,1,False.\r\nNV,64,408,350-7306,no,no,0,148.100000,73,25.180000,164.900000,101,14.020000,216.000000,125,9.720000,12.300000,2,3.320000,5,True.\r\nTN,105,408,353-8849,no,no,0,206.200000,84,35.050000,256.400000,138,21.790000,117.100000,91,5.270000,9.000000,3,2.430000,1,False.\r\nID,144,415,402-3476,no,yes,33,251.600000,87,42.770000,197.600000,118,16.800000,209.200000,97,9.410000,12.200000,3,3.290000,2,False.\r\nWY,106,415,338-6018,yes,yes,26,270.300000,111,45.950000,215.200000,90,18.290000,254.000000,133,11.430000,14.400000,7,3.890000,1,True.\r\nTX,19,510,409-3520,no,yes,34,156.600000,97,26.620000,224.200000,97,19.060000,260.900000,135,11.740000,11.300000,1,3.050000,1,False.\r\nMA,46,408,357-1085,no,no,0,139.400000,81,23.700000,223.700000,113,19.010000,173.100000,77,7.790000,13.600000,6,3.670000,1,False.\r\nIL,127,510,353-3285,no,no,0,220.200000,108,37.430000,188.400000,124,16.010000,172.700000,113,7.770000,11.700000,3,3.160000,2,False.\r\nLA,9,415,409-9885,no,yes,39,214.100000,108,36.400000,169.200000,115,14.380000,189.700000,117,8.540000,10.100000,3,2.730000,1,False.\r\nCO,157,415,388-7701,no,no,0,196.000000,74,33.320000,213.400000,96,18.140000,196.800000,81,8.860000,7.900000,6,2.130000,1,False.\r\nUT,105,415,385-8184,no,no,0,106.400000,71,18.090000,240.100000,83,20.410000,147.700000,114,6.650000,5.300000,4,1.430000,6,True.\r\nMI,105,415,345-2863,no,yes,29,179.400000,113,30.500000,275.400000,100,23.410000,246.100000,105,11.070000,10.000000,5,2.700000,0,False.\r\nNH,155,408,353-6300,no,no,0,216.700000,30,36.840000,144.300000,125,12.270000,135.300000,106,6.090000,10.800000,1,2.920000,2,False.\r\nID,31,415,389-5649,no,no,0,177.300000,129,30.140000,152.800000,105,12.990000,162.900000,92,7.330000,5.100000,2,1.380000,0,False.\r\nWA,161,415,378-8137,no,no,0,151.600000,117,25.770000,219.400000,87,18.650000,224.700000,68,10.110000,4.000000,5,1.080000,1,False.\r\nMN,95,408,340-4627,no,yes,32,262.200000,123,44.570000,165.200000,82,14.040000,194.300000,57,8.740000,10.600000,5,2.860000,0,False.\r\nNY,122,415,352-6833,no,no,0,173.600000,110,29.510000,91.700000,84,7.790000,211.700000,103,9.530000,9.700000,7,2.620000,3,False.\r\nMD,37,415,420-2000,yes,no,0,106.600000,76,18.120000,147.400000,89,12.530000,235.800000,113,10.610000,9.600000,8,2.590000,2,False.\r\nGA,132,415,368-5437,no,no,0,193.300000,106,32.860000,128.300000,94,10.910000,162.100000,119,7.290000,11.600000,4,3.130000,5,True.\r\nHI,119,408,418-8170,no,yes,24,217.200000,94,36.920000,138.700000,52,11.790000,139.300000,85,6.270000,11.300000,4,3.050000,0,False.\r\nAL,16,408,403-9417,no,no,0,209.500000,89,35.620000,172.800000,85,14.690000,94.100000,102,4.230000,8.800000,4,2.380000,1,False.\r\nCT,99,408,330-6165,no,no,0,95.400000,105,16.220000,207.200000,101,17.610000,136.000000,117,6.120000,5.600000,5,1.510000,1,False.\r\nCO,76,408,412-4185,no,yes,26,214.600000,110,36.480000,205.200000,87,17.440000,134.600000,140,6.060000,8.100000,2,2.190000,1,False.\r\nKS,167,415,409-4734,no,no,0,131.600000,120,22.370000,211.300000,96,17.960000,168.300000,97,7.570000,11.100000,4,3.000000,4,True.\r\nNJ,129,415,348-9828,no,no,0,168.400000,117,28.630000,217.100000,129,18.450000,81.600000,100,3.670000,11.800000,7,3.190000,1,False.\r\nFL,116,408,418-8850,no,no,0,146.400000,123,24.890000,176.600000,113,15.010000,212.600000,102,9.570000,7.800000,5,2.110000,1,False.\r\nCA,60,415,330-8351,yes,no,0,183.000000,110,31.110000,206.700000,93,17.570000,203.800000,119,9.170000,11.100000,6,3.000000,1,False.\r\nKS,128,415,347-7773,no,no,0,103.300000,122,17.560000,245.900000,123,20.900000,161.100000,95,7.250000,6.400000,7,1.730000,0,False.\r\nTX,47,408,392-6841,no,yes,28,112.200000,70,19.070000,154.800000,106,13.160000,166.700000,105,7.500000,10.600000,2,2.860000,0,False.\r\nMN,40,510,354-2189,yes,no,0,170.700000,55,29.020000,179.100000,108,15.220000,281.900000,89,12.690000,8.200000,9,2.210000,3,False.\r\nAK,173,510,349-9060,no,no,0,172.500000,78,29.330000,142.600000,91,12.120000,102.000000,63,4.590000,10.900000,2,2.940000,0,False.\r\nOR,157,510,334-1311,no,yes,30,194.300000,107,33.030000,243.200000,108,20.670000,322.200000,114,14.500000,7.100000,5,1.920000,1,False.\r\nMS,66,408,415-3120,no,yes,32,187.800000,117,31.930000,129.800000,90,11.030000,132.300000,113,5.950000,12.000000,3,3.240000,2,False.\r\nVT,50,415,387-5891,yes,yes,26,307.100000,94,52.210000,289.400000,78,24.600000,174.900000,109,7.870000,8.000000,3,2.160000,0,False.\r\nUT,72,415,368-8026,no,no,0,118.200000,106,20.090000,167.200000,136,14.210000,214.200000,106,9.640000,12.200000,3,3.290000,3,False.\r\nKS,130,510,332-9446,no,no,0,154.000000,95,26.180000,205.900000,106,17.500000,233.700000,75,10.520000,12.900000,1,3.480000,1,False.\r\nNV,143,408,393-5284,no,no,0,155.500000,101,26.440000,213.400000,89,18.140000,237.900000,61,10.710000,7.600000,11,2.050000,1,False.\r\nDE,89,510,376-1677,yes,no,0,125.600000,108,21.350000,213.000000,90,18.110000,181.700000,108,8.180000,5.400000,5,1.460000,1,False.\r\nIN,108,415,392-2268,no,no,0,199.300000,104,33.880000,224.200000,92,19.060000,140.100000,57,6.300000,15.200000,2,4.100000,0,False.\r\nTX,32,408,396-4311,no,no,0,157.900000,88,26.840000,180.800000,132,15.370000,132.500000,102,5.960000,12.800000,3,3.460000,1,False.\r\nMA,166,415,363-4005,no,no,0,203.400000,81,34.580000,167.700000,110,14.250000,132.000000,124,5.940000,9.200000,5,2.480000,2,False.\r\nVT,109,408,344-9966,no,no,0,222.200000,113,37.770000,218.500000,122,18.570000,266.000000,88,11.970000,10.900000,5,2.940000,1,False.\r\nCA,72,408,337-7377,no,yes,39,92.800000,98,15.780000,271.200000,115,23.050000,167.100000,83,7.520000,5.800000,7,1.570000,1,False.\r\nIA,134,415,373-7037,no,yes,32,216.800000,78,36.860000,102.200000,111,8.690000,174.000000,83,7.830000,8.600000,2,2.320000,0,False.\r\nNH,13,415,356-7580,no,no,0,193.200000,89,32.840000,194.400000,90,16.520000,186.500000,104,8.390000,9.700000,2,2.620000,4,False.\r\nGA,90,415,390-3401,no,no,0,113.200000,108,19.240000,189.300000,63,16.090000,271.800000,124,12.230000,14.100000,4,3.810000,3,False.\r\nWI,111,415,350-9313,no,yes,36,166.200000,54,28.250000,238.800000,109,20.300000,108.800000,92,4.900000,11.200000,2,3.020000,3,False.\r\nCO,101,408,420-9009,no,yes,23,262.200000,101,44.570000,157.000000,80,13.350000,129.100000,100,5.810000,7.300000,14,1.970000,1,False.\r\nSC,72,415,415-2641,no,no,0,207.800000,92,35.330000,195.700000,110,16.630000,184.800000,124,8.320000,13.100000,4,3.540000,0,False.\r\nMI,67,510,392-4929,no,yes,35,245.400000,89,41.720000,148.200000,102,12.600000,274.000000,136,12.330000,7.500000,6,2.030000,1,False.\r\nMD,172,408,346-5068,no,no,0,287.100000,108,48.810000,178.400000,125,15.160000,153.200000,98,6.890000,14.400000,2,3.890000,3,True.\r\nIN,154,510,389-2631,no,yes,32,192.300000,82,32.690000,165.300000,134,14.050000,205.000000,74,9.230000,9.000000,1,2.430000,2,False.\r\nME,69,510,368-3808,no,no,0,194.200000,122,33.010000,242.100000,81,20.580000,215.800000,80,9.710000,9.700000,3,2.620000,2,False.\r\nDC,123,415,406-8599,no,no,0,211.000000,92,35.870000,217.000000,102,18.450000,214.800000,104,9.670000,9.800000,7,2.650000,3,False.\r\nTN,130,415,386-7456,no,yes,12,141.900000,92,24.120000,228.900000,102,19.460000,195.100000,101,8.780000,8.700000,5,2.350000,0,False.\r\nFL,142,415,357-4936,no,yes,26,220.500000,94,37.490000,239.500000,126,20.360000,254.300000,109,11.440000,5.900000,9,1.590000,2,False.\r\nWA,29,415,397-7411,no,no,0,157.400000,122,26.760000,145.000000,75,12.330000,281.800000,92,12.680000,9.300000,2,2.510000,1,False.\r\nHI,87,408,353-6218,no,yes,28,143.500000,106,24.400000,223.500000,147,19.000000,175.400000,69,7.890000,11.200000,5,3.020000,0,False.\r\nNE,149,415,369-5942,no,no,0,156.000000,56,26.520000,56.000000,116,4.760000,163.300000,104,7.350000,8.900000,8,2.400000,0,False.\r\nTN,146,408,416-8543,yes,no,0,160.100000,63,27.220000,208.400000,112,17.710000,177.600000,98,7.990000,9.200000,3,2.480000,2,False.\r\nMD,88,415,358-4576,yes,no,0,235.100000,98,39.970000,251.800000,79,21.400000,285.900000,76,12.870000,7.200000,2,1.940000,4,True.\r\nNM,119,415,352-5118,yes,yes,15,160.000000,95,27.200000,209.500000,110,17.810000,82.300000,107,3.700000,8.700000,5,2.350000,5,True.\r\nVT,48,510,408-2621,no,no,0,188.400000,63,32.030000,165.900000,89,14.100000,205.700000,71,9.260000,13.200000,2,3.560000,1,False.\r\nOR,135,415,353-3994,no,no,0,194.800000,97,33.120000,235.300000,118,20.000000,174.400000,126,7.850000,11.000000,3,2.970000,1,False.\r\nIN,100,510,367-4277,no,no,0,247.800000,117,42.130000,130.000000,95,11.050000,134.300000,125,6.040000,6.900000,2,1.860000,1,False.\r\nMO,98,415,354-3237,no,no,0,221.200000,80,37.600000,213.600000,104,18.160000,291.800000,89,13.130000,11.900000,3,3.210000,4,False.\r\nFL,75,510,394-8504,no,yes,26,118.500000,86,20.150000,213.900000,118,18.180000,132.600000,99,5.970000,13.400000,3,3.620000,2,False.\r\nDC,180,415,370-4139,no,yes,33,231.800000,78,39.410000,232.900000,79,19.800000,206.900000,121,9.310000,7.600000,4,2.050000,1,False.\r\nVT,100,415,382-6135,no,yes,25,215.900000,90,36.700000,257.900000,92,21.920000,180.200000,157,8.110000,11.600000,4,3.130000,1,False.\r\nOH,119,415,359-5718,no,yes,35,217.100000,92,36.910000,220.800000,134,18.770000,249.500000,93,11.230000,8.000000,5,2.160000,2,False.\r\nMO,86,415,385-3111,no,no,0,83.500000,96,14.200000,221.100000,63,18.790000,349.700000,75,15.740000,12.600000,3,3.400000,0,False.\r\nLA,155,408,353-4880,no,yes,39,183.300000,106,31.160000,205.100000,101,17.430000,263.700000,90,11.870000,5.100000,7,1.380000,1,False.\r\nIL,78,415,343-7019,yes,no,0,236.800000,141,40.260000,265.300000,101,22.550000,152.400000,77,6.860000,9.500000,2,2.570000,1,True.\r\nNJ,153,415,419-6133,no,no,0,193.800000,90,32.950000,195.300000,121,16.600000,182.700000,108,8.220000,8.500000,3,2.300000,1,False.\r\nIA,92,510,350-7344,no,yes,25,134.000000,112,22.780000,206.000000,111,17.510000,180.600000,118,8.130000,9.700000,4,2.620000,0,False.\r\nWA,13,510,397-6064,no,yes,25,176.600000,65,30.020000,172.700000,96,14.680000,104.500000,128,4.700000,11.300000,5,3.050000,2,False.\r\nNE,154,415,363-6896,no,no,0,191.400000,93,32.540000,205.400000,119,17.460000,205.700000,121,9.260000,10.200000,3,2.750000,3,False.\r\nCT,144,510,416-9021,yes,yes,35,174.800000,127,29.720000,219.600000,93,18.670000,255.800000,90,11.510000,12.800000,3,3.460000,0,False.\r\nWV,48,408,408-3269,no,no,0,275.200000,67,46.780000,180.200000,108,15.320000,159.000000,110,7.150000,7.900000,2,2.130000,1,False.\r\nND,94,408,408-9463,no,no,0,174.000000,85,29.580000,241.100000,114,20.490000,207.800000,94,9.350000,7.900000,1,2.130000,1,False.\r\nUT,139,415,340-7062,no,no,0,165.000000,132,28.050000,249.700000,86,21.220000,170.300000,128,7.660000,12.600000,8,3.400000,1,False.\r\nMS,126,415,417-4309,no,no,0,228.700000,102,38.880000,168.700000,99,14.340000,223.500000,100,10.060000,11.800000,4,3.190000,1,False.\r\nMN,122,415,421-2659,no,no,0,107.900000,88,18.340000,235.800000,109,20.040000,228.600000,119,10.290000,9.500000,3,2.570000,2,False.\r\nNH,139,510,383-2017,no,no,0,221.300000,140,37.620000,157.800000,89,13.410000,192.500000,89,8.660000,11.300000,6,3.050000,1,False.\r\nLA,95,415,411-6294,no,no,0,141.100000,84,23.990000,211.400000,108,17.970000,103.700000,127,4.670000,5.900000,6,1.590000,3,False.\r\nME,80,408,332-9525,no,yes,31,166.400000,92,28.290000,238.300000,74,20.260000,150.700000,84,6.780000,10.700000,4,2.890000,4,False.\r\nKS,131,415,401-5012,no,yes,28,249.600000,87,42.430000,227.200000,138,19.310000,239.900000,92,10.800000,7.600000,3,2.050000,3,False.\r\nKS,36,510,368-8835,no,no,0,178.600000,83,30.360000,213.100000,103,18.110000,198.000000,119,8.910000,10.900000,5,2.940000,1,False.\r\nNV,180,510,351-1382,no,no,0,139.000000,96,23.630000,224.900000,64,19.120000,170.800000,118,7.690000,15.700000,5,4.240000,2,False.\r\nMT,25,415,359-7694,no,no,0,134.300000,98,22.830000,202.300000,109,17.200000,195.900000,100,8.820000,12.600000,5,3.400000,2,False.\r\nMT,113,415,419-5505,no,no,0,215.900000,93,36.700000,240.100000,85,20.410000,156.700000,123,7.050000,4.900000,5,1.320000,3,False.\r\nID,88,415,341-4570,no,yes,31,181.600000,91,30.870000,213.200000,120,18.120000,207.800000,104,9.350000,11.400000,4,3.080000,1,False.\r\nAK,120,415,366-6991,no,no,0,178.400000,97,30.330000,168.300000,113,14.310000,120.500000,93,5.420000,9.300000,9,2.510000,1,False.\r\nUT,74,415,377-7399,no,no,0,106.400000,84,18.090000,140.200000,104,11.920000,90.900000,81,4.090000,11.400000,3,3.080000,1,False.\r\nAR,109,510,377-9092,no,no,0,170.700000,101,29.020000,240.200000,82,20.420000,119.000000,112,5.360000,11.400000,4,3.080000,2,False.\r\nLA,162,510,373-6681,no,yes,33,184.500000,139,31.370000,183.200000,78,15.570000,127.400000,106,5.730000,12.300000,6,3.320000,0,False.\r\nKS,124,510,417-7736,no,yes,37,161.200000,109,27.400000,204.200000,79,17.360000,231.500000,87,10.420000,8.900000,4,2.400000,1,False.\r\nOR,177,408,393-2812,no,no,0,84.900000,77,14.430000,257.500000,109,21.890000,210.500000,66,9.470000,7.500000,5,2.030000,2,False.\r\nWI,91,510,398-3176,no,no,0,217.900000,71,37.040000,230.100000,116,19.560000,232.100000,110,10.440000,10.600000,2,2.860000,1,False.\r\nCO,105,408,334-8694,no,no,0,270.900000,98,46.050000,226.200000,110,19.230000,178.800000,60,8.050000,8.800000,5,2.380000,0,True.\r\nKS,24,510,369-5449,no,no,0,243.000000,91,41.310000,183.900000,77,15.630000,184.300000,109,8.290000,15.300000,6,4.130000,0,True.\r\nIL,48,510,380-5246,no,no,0,128.200000,71,21.790000,48.100000,78,4.090000,116.300000,80,5.230000,8.900000,3,2.400000,0,False.\r\nIA,86,408,390-3873,no,no,0,126.300000,115,21.470000,168.800000,112,14.350000,154.600000,95,6.960000,9.800000,7,2.650000,2,False.\r\nAZ,163,510,354-4568,no,no,0,178.700000,56,30.380000,215.700000,79,18.330000,152.700000,84,6.870000,10.600000,2,2.860000,4,False.\r\nNE,91,510,339-6968,no,no,0,159.000000,109,27.030000,255.100000,142,21.680000,82.400000,73,3.710000,10.100000,4,2.730000,0,False.\r\nID,56,510,379-5933,no,no,0,150.900000,79,25.650000,161.800000,87,13.750000,167.700000,115,7.550000,11.700000,5,3.160000,3,False.\r\nOH,147,415,365-5682,yes,yes,24,219.900000,118,37.380000,208.500000,116,17.720000,352.500000,111,15.860000,8.100000,4,2.190000,3,False.\r\nTX,64,415,382-8518,no,no,0,168.000000,116,28.560000,192.400000,94,16.350000,166.500000,98,7.490000,10.100000,3,2.730000,2,False.\r\nTN,108,510,356-8449,no,yes,34,162.100000,83,27.560000,171.800000,117,14.600000,259.800000,76,11.690000,9.600000,3,2.590000,4,True.\r\nOK,159,510,333-3460,no,no,0,198.800000,107,33.800000,195.500000,91,16.620000,213.300000,120,9.600000,16.500000,7,4.460000,5,False.\r\nND,136,510,375-8596,yes,no,0,256.800000,90,43.660000,230.100000,104,19.560000,143.600000,82,6.460000,9.100000,10,2.460000,3,False.\r\nMT,116,415,384-5907,no,yes,35,182.800000,122,31.080000,212.700000,119,18.080000,193.800000,103,8.720000,11.000000,2,2.970000,1,False.\r\nNC,45,408,373-2903,no,yes,38,196.800000,92,33.460000,254.200000,108,21.610000,261.800000,85,11.780000,7.700000,2,2.080000,1,False.\r\nMN,122,415,361-7702,no,no,0,140.100000,120,23.820000,231.400000,128,19.670000,188.100000,127,8.460000,11.200000,5,3.020000,2,False.\r\nMN,138,415,388-5850,no,no,0,194.300000,83,33.030000,189.900000,97,16.140000,232.200000,102,10.450000,9.000000,3,2.430000,5,False.\r\nMA,132,415,405-6298,no,no,0,117.600000,66,19.990000,214.000000,108,18.190000,239.500000,94,10.780000,8.800000,5,2.380000,1,False.\r\nPA,101,415,368-2074,yes,no,0,193.700000,108,32.930000,186.600000,98,15.860000,223.000000,100,10.040000,11.600000,8,3.130000,0,False.\r\nGA,58,510,328-5050,no,no,0,243.100000,105,41.330000,231.400000,108,19.670000,180.900000,120,8.140000,7.800000,4,2.110000,2,False.\r\nNV,81,415,395-5783,no,no,0,145.400000,132,24.720000,129.300000,91,10.990000,186.400000,109,8.390000,5.200000,4,1.400000,1,False.\r\nTX,87,415,420-7301,no,no,0,169.100000,105,28.750000,169.900000,102,14.440000,244.900000,106,11.020000,9.900000,10,2.670000,3,False.\r\nME,116,510,328-2478,no,no,0,229.300000,93,38.980000,184.500000,111,15.680000,168.200000,91,7.570000,8.800000,3,2.380000,2,False.\r\nRI,85,415,381-2460,yes,no,0,197.200000,97,33.520000,211.700000,115,17.990000,210.100000,133,9.450000,8.300000,4,2.240000,4,False.\r\nMN,62,510,390-9811,no,yes,33,186.400000,84,31.690000,201.000000,136,17.090000,286.700000,103,12.900000,11.100000,3,3.000000,2,True.\r\nNH,90,415,373-5670,no,no,0,76.100000,121,12.940000,290.300000,73,24.680000,236.900000,89,10.660000,10.800000,3,2.920000,0,False.\r\nTN,98,415,351-7016,no,no,0,162.800000,65,27.680000,185.000000,109,15.730000,219.500000,104,9.880000,6.000000,3,1.620000,2,False.\r\nUT,73,415,394-9934,no,no,0,182.300000,115,30.990000,199.200000,97,16.930000,120.200000,113,5.410000,18.000000,5,4.860000,1,False.\r\nRI,107,510,422-8268,yes,no,0,194.400000,83,33.050000,247.800000,84,21.060000,245.400000,93,11.040000,11.200000,7,3.020000,2,False.\r\nNH,55,408,373-7690,no,yes,20,189.300000,95,32.180000,118.600000,113,10.080000,250.200000,102,11.260000,12.500000,4,3.380000,2,False.\r\nAK,76,415,366-9781,no,yes,22,160.100000,107,27.220000,168.700000,136,14.340000,23.200000,102,1.040000,9.500000,4,2.570000,3,False.\r\nNC,30,510,404-5427,no,no,0,145.000000,76,24.650000,240.700000,112,20.460000,197.100000,134,8.870000,7.100000,4,1.920000,3,False.\r\nAZ,157,415,389-9783,no,no,0,220.700000,105,37.520000,119.300000,127,10.140000,165.100000,113,7.430000,11.500000,7,3.110000,4,False.\r\nMA,40,408,351-7005,no,yes,31,224.700000,69,38.200000,134.500000,81,11.430000,120.300000,104,5.410000,7.500000,5,2.030000,1,True.\r\nTN,72,408,348-2009,no,no,0,147.000000,79,24.990000,162.300000,103,13.800000,162.900000,80,7.330000,10.500000,4,2.840000,1,False.\r\nWY,95,415,340-4236,no,yes,39,260.800000,130,44.340000,213.400000,111,18.140000,195.600000,97,8.800000,10.100000,5,2.730000,1,False.\r\nIA,42,415,348-1528,no,no,0,155.400000,127,26.420000,164.100000,45,13.950000,157.700000,128,7.100000,9.000000,3,2.430000,0,False.\r\nIN,86,415,365-5039,no,no,0,166.200000,112,28.250000,255.300000,81,21.700000,228.100000,97,10.260000,5.400000,7,1.460000,1,False.\r\nNJ,131,415,411-1810,no,no,0,211.800000,115,36.010000,260.500000,102,22.140000,144.200000,96,6.490000,10.800000,7,2.920000,0,False.\r\nFL,55,510,364-7644,no,yes,45,130.500000,114,22.190000,208.400000,94,17.710000,141.600000,114,6.370000,11.000000,5,2.970000,4,True.\r\nMT,74,415,335-9066,no,no,0,162.700000,102,27.660000,292.000000,105,24.820000,183.300000,80,8.250000,8.700000,6,2.350000,0,False.\r\nND,81,408,362-7581,yes,yes,37,237.100000,76,40.310000,264.200000,125,22.460000,271.300000,120,12.210000,7.900000,3,2.130000,1,False.\r\nMI,81,408,346-1095,no,no,0,166.200000,102,28.250000,217.600000,112,18.500000,220.200000,68,9.910000,13.200000,2,3.560000,4,False.\r\nMT,28,415,357-9136,no,no,0,121.700000,48,20.690000,125.800000,112,10.690000,261.600000,122,11.770000,8.300000,2,2.240000,6,True.\r\nAL,111,510,390-7863,no,no,0,176.400000,62,29.990000,201.000000,124,17.090000,150.400000,138,6.770000,11.200000,2,3.020000,0,False.\r\nNV,3,510,344-2416,no,yes,27,67.400000,116,11.460000,244.000000,78,20.740000,281.100000,93,12.650000,11.400000,2,3.080000,2,False.\r\nMI,51,415,373-1448,no,no,0,229.700000,129,39.050000,336.000000,104,28.560000,192.800000,128,8.680000,9.600000,1,2.590000,1,True.\r\nFL,68,415,360-7076,no,yes,24,176.000000,118,29.920000,277.900000,116,23.620000,174.700000,71,7.860000,14.700000,7,3.970000,1,False.\r\nNY,163,408,413-2241,no,no,0,247.700000,77,42.110000,269.500000,108,22.910000,167.300000,82,7.530000,9.600000,7,2.590000,0,True.\r\nKS,87,510,343-3961,no,no,0,115.400000,90,19.620000,262.600000,68,22.320000,245.700000,69,11.060000,13.100000,5,3.540000,2,False.\r\nNC,58,510,375-4107,no,no,0,112.200000,95,19.070000,209.600000,108,17.820000,260.900000,78,11.740000,13.900000,1,3.750000,0,True.\r\nMN,109,408,414-7410,no,no,0,162.600000,138,27.640000,154.000000,109,13.090000,209.700000,118,9.440000,11.500000,4,3.110000,0,False.\r\nRI,111,415,351-2535,no,no,0,229.400000,107,39.000000,214.100000,99,18.200000,289.600000,95,13.030000,10.400000,6,2.810000,4,False.\r\nUT,144,415,416-9615,no,no,0,139.600000,96,23.730000,124.200000,93,10.560000,95.600000,75,4.300000,15.000000,4,4.050000,2,False.\r\nOR,135,415,377-1293,no,no,0,263.800000,66,44.850000,251.300000,116,21.360000,200.100000,112,9.000000,8.400000,2,2.270000,5,True.\r\nSC,109,415,388-6479,no,yes,46,217.500000,123,36.980000,233.700000,84,19.860000,163.900000,99,7.380000,9.000000,3,2.430000,4,False.\r\nIL,107,415,390-2755,no,yes,14,114.300000,132,19.430000,199.800000,91,16.980000,194.700000,74,8.760000,7.500000,8,2.030000,1,False.\r\nOH,149,408,340-5930,no,no,0,196.300000,108,33.370000,136.800000,96,11.630000,154.700000,87,6.960000,7.700000,3,2.080000,2,False.\r\nMA,56,510,401-3622,no,no,0,253.200000,95,43.040000,188.000000,116,15.980000,142.000000,133,6.390000,4.400000,4,1.190000,1,False.\r\nOR,129,408,382-1104,no,no,0,98.000000,99,16.660000,240.700000,62,20.460000,254.800000,123,11.470000,10.500000,4,2.840000,0,False.\r\nCA,92,408,355-9324,no,no,0,249.400000,118,42.400000,211.500000,95,17.980000,169.000000,116,7.610000,9.100000,3,2.460000,0,False.\r\nWV,67,415,393-4843,no,yes,30,129.600000,107,22.030000,233.000000,104,19.810000,297.000000,93,13.370000,14.500000,5,3.920000,1,False.\r\nVT,120,415,401-4052,no,no,0,221.300000,106,37.620000,267.600000,98,22.750000,111.500000,80,5.020000,9.300000,7,2.510000,0,False.\r\nID,166,415,385-1830,no,no,0,220.700000,106,37.520000,177.800000,118,15.110000,206.100000,102,9.270000,12.400000,9,3.350000,1,False.\r\nOR,66,408,348-7409,no,no,0,87.600000,76,14.890000,262.000000,111,22.270000,184.600000,125,8.310000,9.200000,5,2.480000,1,False.\r\nGA,76,408,361-4910,no,no,0,203.600000,61,34.610000,161.700000,127,13.740000,175.900000,97,7.920000,8.400000,3,2.270000,0,False.\r\nME,79,415,415-6578,no,no,0,213.600000,110,36.310000,234.900000,121,19.970000,229.600000,157,10.330000,8.800000,3,2.380000,2,False.\r\nHI,98,415,395-5015,no,yes,31,181.600000,112,30.870000,220.700000,100,18.760000,236.300000,121,10.630000,12.900000,4,3.480000,2,False.\r\nAZ,141,510,369-6012,no,yes,22,215.400000,123,36.620000,328.700000,98,27.940000,160.500000,89,7.220000,7.800000,6,2.110000,1,False.\r\nUT,49,415,394-4520,no,no,0,266.300000,90,45.270000,207.800000,117,17.660000,205.000000,98,9.230000,14.000000,2,3.780000,2,True.\r\nIA,46,510,380-5873,no,no,0,199.200000,111,33.860000,175.100000,83,14.880000,210.600000,84,9.480000,10.200000,2,2.750000,3,False.\r\nCT,137,415,372-5384,no,no,0,115.000000,130,19.550000,137.800000,83,11.710000,224.000000,61,10.080000,7.300000,4,1.970000,3,False.\r\nWA,171,408,419-1863,no,no,0,270.500000,69,45.990000,230.000000,112,19.550000,136.000000,111,6.120000,9.600000,5,2.590000,1,True.\r\nVA,10,415,352-5697,no,no,0,222.200000,127,37.770000,153.100000,125,13.010000,227.400000,80,10.230000,12.900000,4,3.480000,1,False.\r\nCO,88,510,373-4274,no,no,0,61.900000,78,10.520000,262.600000,114,22.320000,212.500000,110,9.560000,8.800000,2,2.380000,3,False.\r\nLA,89,415,382-4024,no,no,0,141.100000,92,23.990000,249.100000,126,21.170000,136.000000,73,6.120000,10.800000,2,2.920000,2,False.\r\nTX,82,415,395-9215,no,no,0,189.200000,81,32.160000,184.400000,117,15.670000,255.800000,83,11.510000,10.600000,5,2.860000,3,True.\r\nSD,139,510,331-9149,no,no,0,196.000000,135,33.320000,186.000000,146,15.810000,153.000000,92,6.890000,9.800000,1,2.650000,3,False.\r\nVA,87,415,347-3958,no,no,0,171.600000,119,29.170000,205.000000,107,17.430000,170.600000,114,7.680000,13.800000,4,3.730000,0,False.\r\nNY,137,415,389-2540,yes,no,0,174.000000,123,29.580000,161.300000,115,13.710000,260.700000,98,11.730000,11.400000,3,3.080000,2,False.\r\nWA,45,510,399-3083,no,no,0,78.600000,106,13.360000,187.300000,110,15.920000,184.200000,111,8.290000,7.400000,5,2.000000,1,True.\r\nWI,90,415,420-8308,no,no,0,200.900000,92,34.150000,164.300000,91,13.970000,249.000000,98,11.210000,8.900000,7,2.400000,1,False.\r\nTN,103,415,384-7724,no,no,0,141.300000,123,24.020000,253.600000,87,21.560000,215.800000,96,9.710000,6.400000,2,1.730000,1,False.\r\nCT,100,415,389-2114,no,no,0,235.800000,130,40.090000,176.000000,69,14.960000,63.600000,122,2.860000,7.300000,1,1.970000,2,False.\r\nWA,110,510,335-4414,no,no,0,185.100000,100,31.470000,165.100000,88,14.030000,111.600000,104,5.020000,6.300000,4,1.700000,3,False.\r\nNH,124,415,370-5361,no,no,0,254.300000,113,43.230000,78.900000,104,6.710000,153.200000,69,6.890000,11.800000,2,3.190000,2,False.\r\nMT,10,510,374-5965,no,no,0,183.000000,103,31.110000,214.800000,77,18.260000,206.400000,73,9.290000,8.700000,6,2.350000,2,False.\r\nNE,89,415,420-6414,no,yes,29,163.500000,80,27.800000,274.800000,136,23.360000,381.900000,147,17.190000,7.500000,5,2.030000,2,False.\r\nWA,121,408,392-2708,no,no,0,207.900000,98,35.340000,210.500000,96,17.890000,109.600000,114,4.930000,7.700000,2,2.080000,2,False.\r\nWI,101,415,352-7234,no,no,0,248.600000,102,42.260000,174.900000,93,14.870000,207.200000,86,9.320000,6.100000,3,1.650000,3,False.\r\nAR,103,415,337-9878,no,yes,31,185.400000,105,31.520000,197.600000,126,16.800000,147.100000,110,6.620000,14.500000,4,3.920000,2,False.\r\nWI,51,408,350-7288,no,no,0,197.800000,60,33.630000,221.000000,64,18.790000,168.600000,134,7.590000,8.900000,5,2.400000,1,False.\r\nDE,2,415,415-8448,yes,no,0,132.100000,42,22.460000,138.900000,88,11.810000,192.600000,119,8.670000,9.100000,1,2.460000,2,True.\r\nNH,111,510,392-6331,no,no,0,197.100000,117,33.510000,227.800000,128,19.360000,214.000000,101,9.630000,9.300000,11,2.510000,0,False.\r\nVA,118,415,392-3315,no,no,0,154.600000,112,26.280000,184.200000,105,15.660000,217.400000,102,9.780000,12.600000,5,3.400000,2,False.\r\nTN,17,510,382-5401,no,yes,31,153.100000,115,26.030000,185.900000,59,15.800000,224.300000,102,10.090000,10.000000,1,2.700000,6,True.\r\nVA,130,408,389-7012,no,no,0,211.200000,119,35.900000,231.100000,120,19.640000,220.900000,80,9.940000,6.300000,9,1.700000,2,False.\r\nID,193,415,411-4714,no,no,0,96.800000,92,16.460000,142.600000,103,12.120000,210.100000,115,9.450000,10.900000,5,2.940000,2,True.\r\nTN,114,510,343-3846,no,no,0,172.000000,145,29.240000,276.400000,101,23.490000,193.700000,100,8.720000,10.100000,9,2.730000,1,False.\r\nAZ,137,415,370-4395,no,no,0,141.100000,91,23.990000,147.200000,100,12.510000,254.700000,75,11.460000,8.000000,7,2.160000,2,False.\r\nMT,185,408,422-4394,no,yes,29,151.100000,121,25.690000,244.700000,88,20.800000,154.400000,91,6.950000,13.800000,2,3.730000,2,False.\r\nOK,101,408,405-1780,no,no,0,209.600000,107,35.630000,228.800000,96,19.450000,172.400000,85,7.760000,7.600000,2,2.050000,3,False.\r\nID,95,415,393-2220,no,yes,32,247.000000,109,41.990000,125.600000,91,10.680000,226.500000,90,10.190000,10.500000,4,2.840000,3,False.\r\nNV,7,408,355-8299,no,yes,30,221.400000,114,37.640000,165.800000,116,14.090000,247.000000,105,11.120000,10.800000,12,2.920000,1,False.\r\nKS,126,408,379-8681,no,no,0,321.300000,99,54.620000,167.900000,93,14.270000,193.600000,106,8.710000,8.000000,4,2.160000,1,True.\r\nWY,71,415,409-7034,no,no,0,243.700000,124,41.430000,60.000000,90,5.100000,189.000000,129,8.500000,11.300000,2,3.050000,0,False.\r\nMS,124,415,358-1922,no,no,0,251.500000,85,42.760000,214.200000,98,18.210000,186.100000,71,8.370000,11.100000,6,3.000000,3,False.\r\nWY,97,510,346-1629,yes,no,0,236.900000,107,40.270000,157.600000,105,13.400000,241.000000,120,10.850000,7.300000,2,1.970000,0,True.\r\nTX,28,415,347-1870,no,no,0,159.700000,79,27.150000,216.700000,131,18.420000,206.700000,116,9.300000,9.300000,3,2.510000,2,False.\r\nWA,90,415,374-9576,yes,no,0,148.200000,96,25.190000,220.400000,111,18.730000,134.200000,97,6.040000,9.200000,1,2.480000,4,True.\r\nHI,190,408,380-1096,no,no,0,150.900000,86,25.650000,268.600000,129,22.830000,179.900000,73,8.100000,14.700000,1,3.970000,1,False.\r\nIL,31,415,396-5790,no,yes,28,210.500000,101,35.790000,250.500000,86,21.290000,241.600000,125,10.870000,11.500000,2,3.110000,1,False.\r\nAK,52,415,356-5244,no,yes,24,170.900000,71,29.050000,201.400000,80,17.120000,159.000000,124,7.150000,4.100000,5,1.110000,2,False.\r\nMD,73,510,341-1412,no,no,0,254.700000,80,43.300000,90.200000,79,7.670000,153.400000,60,6.900000,10.600000,8,2.860000,0,False.\r\nMA,111,415,387-7371,no,no,0,284.400000,89,48.350000,157.000000,113,13.350000,242.800000,91,10.930000,8.400000,8,2.270000,0,True.\r\nSD,98,415,392-2555,no,no,0,0.000000,0,0.000000,159.600000,130,13.570000,167.100000,88,7.520000,6.800000,1,1.840000,4,True.\r\nPA,106,408,403-9167,yes,no,0,133.700000,45,22.730000,187.800000,107,15.960000,181.900000,89,8.190000,10.700000,2,2.890000,1,True.\r\nNC,111,408,338-6550,no,no,0,224.900000,117,38.230000,191.900000,127,16.310000,229.900000,97,10.350000,10.300000,3,2.780000,0,False.\r\nVT,59,408,357-5801,no,no,0,151.800000,98,25.810000,209.900000,92,17.840000,266.900000,86,12.010000,11.900000,5,3.210000,1,False.\r\nKY,71,510,403-1953,no,yes,22,141.400000,107,24.040000,163.000000,105,13.860000,220.000000,99,9.900000,5.600000,3,1.510000,2,False.\r\nWA,55,408,357-6039,no,no,0,285.700000,124,48.570000,230.900000,106,19.630000,230.700000,140,10.380000,14.800000,7,4.000000,0,True.\r\nLA,13,415,388-9653,no,no,0,58.400000,121,9.930000,262.200000,64,22.290000,159.000000,115,7.150000,11.900000,5,3.210000,1,False.\r\nWA,136,415,359-2915,no,yes,16,90.400000,105,15.370000,201.300000,109,17.110000,227.100000,115,10.220000,13.100000,4,3.540000,0,False.\r\nME,123,408,381-4562,no,no,0,114.400000,91,19.450000,216.600000,123,18.410000,250.600000,102,11.280000,11.000000,3,2.970000,0,False.\r\nWI,105,408,406-2213,no,no,0,147.700000,103,25.110000,222.700000,78,18.930000,163.500000,102,7.360000,12.800000,3,3.460000,2,False.\r\nTX,50,408,330-6436,no,yes,31,302.700000,93,51.460000,240.500000,119,20.440000,193.900000,103,8.730000,13.600000,14,3.670000,3,False.\r\nIA,118,415,332-4289,no,no,0,136.100000,120,23.140000,204.200000,103,17.360000,228.200000,90,10.270000,11.300000,4,3.050000,1,False.\r\nAZ,97,408,386-3596,no,no,0,169.700000,84,28.850000,165.900000,86,14.100000,191.900000,83,8.640000,12.800000,6,3.460000,3,False.\r\nND,51,510,337-3740,no,no,0,227.200000,89,38.620000,194.400000,106,16.520000,243.400000,126,10.950000,14.900000,2,4.020000,0,False.\r\nVT,73,415,414-1496,no,no,0,217.800000,91,37.030000,220.600000,97,18.750000,277.300000,89,12.480000,10.300000,6,2.780000,1,True.\r\nHI,35,415,349-7291,no,no,0,124.200000,102,21.110000,123.900000,115,10.530000,135.700000,100,6.110000,13.100000,8,3.540000,2,False.\r\nWY,64,415,385-1985,no,no,0,206.200000,76,35.050000,232.400000,76,19.750000,251.600000,96,11.320000,13.600000,2,3.670000,1,False.\r\nWV,63,510,329-7102,no,no,0,132.900000,122,22.590000,67.000000,62,5.700000,160.400000,121,7.220000,9.900000,2,2.670000,3,False.\r\nOK,117,415,394-2553,no,yes,31,104.900000,115,17.830000,237.600000,125,20.200000,263.400000,104,11.850000,7.700000,6,2.080000,3,False.\r\nCT,115,415,339-1330,no,no,0,245.000000,97,41.650000,250.700000,75,21.310000,270.200000,124,12.160000,13.700000,8,3.700000,1,True.\r\nUT,162,408,398-1959,no,no,0,184.500000,118,31.370000,224.000000,95,19.040000,180.500000,82,8.120000,11.600000,3,3.130000,1,False.\r\nNY,89,415,408-7015,no,no,0,89.500000,66,15.220000,179.300000,104,15.240000,225.100000,116,10.130000,12.300000,1,3.320000,3,False.\r\nVA,94,415,384-9254,yes,no,0,235.600000,131,40.050000,194.800000,107,16.560000,170.600000,93,7.680000,8.600000,4,2.320000,1,False.\r\nVT,129,408,355-9475,no,no,0,186.000000,127,31.620000,262.300000,96,22.300000,98.900000,63,4.450000,11.500000,6,3.110000,4,False.\r\nSD,86,415,334-1337,no,no,0,223.900000,75,38.060000,155.700000,109,13.230000,150.200000,143,6.760000,7.300000,9,1.970000,1,False.\r\nPA,96,510,359-1441,no,no,0,179.500000,125,30.520000,162.300000,139,13.800000,264.500000,133,11.900000,6.600000,2,1.780000,1,False.\r\nND,190,415,391-5442,no,no,0,169.400000,102,28.800000,253.500000,113,21.550000,197.100000,93,8.870000,8.900000,5,2.400000,1,False.\r\nCT,80,408,374-1551,no,no,0,118.100000,90,20.080000,144.300000,77,12.270000,225.100000,86,10.130000,8.200000,6,2.210000,1,False.\r\nSC,108,415,399-6233,no,no,0,112.000000,105,19.040000,193.700000,110,16.460000,208.900000,93,9.400000,4.100000,4,1.110000,4,True.\r\nMI,97,408,337-4749,no,yes,32,168.400000,129,28.630000,225.900000,97,19.200000,191.800000,95,8.630000,8.500000,7,2.300000,0,False.\r\nVT,84,415,403-5552,no,yes,42,214.300000,112,36.430000,188.200000,107,16.000000,333.500000,117,15.010000,11.300000,10,3.050000,0,False.\r\nOH,65,415,405-3097,no,no,0,245.700000,139,41.770000,241.900000,113,20.560000,285.300000,117,12.840000,4.200000,5,1.130000,4,True.\r\nVT,131,415,364-9240,no,yes,34,156.600000,134,26.620000,71.000000,95,6.040000,261.700000,120,11.780000,13.400000,10,3.620000,1,False.\r\nIL,58,415,404-9348,yes,yes,43,142.800000,96,24.280000,272.300000,100,23.150000,193.400000,105,8.700000,8.900000,4,2.400000,1,False.\r\nMO,36,415,336-1462,no,no,0,202.400000,115,34.410000,230.700000,115,19.610000,202.000000,127,9.090000,10.200000,2,2.750000,3,False.\r\nWI,54,415,364-8981,no,no,0,116.800000,119,19.860000,123.100000,123,10.460000,217.500000,101,9.790000,12.000000,2,3.240000,1,False.\r\nNJ,45,510,412-7606,no,no,0,155.700000,110,26.470000,260.300000,103,22.130000,192.200000,98,8.650000,11.000000,1,2.970000,1,False.\r\nGA,125,415,380-6342,no,yes,39,236.100000,107,40.140000,289.200000,110,24.580000,175.400000,107,7.890000,9.100000,4,2.460000,2,False.\r\nVT,72,415,418-3017,no,yes,21,138.100000,113,23.480000,260.100000,83,22.110000,135.400000,118,6.090000,8.200000,2,2.210000,2,False.\r\nCT,141,408,367-3648,no,no,0,51.900000,108,8.820000,162.000000,83,13.770000,223.500000,115,10.060000,10.100000,3,2.730000,3,False.\r\nAZ,113,510,341-5892,no,no,0,81.300000,116,13.820000,220.600000,124,18.750000,235.700000,113,10.610000,8.900000,3,2.400000,0,False.\r\nSD,20,415,334-4678,no,yes,35,171.500000,98,29.160000,153.100000,127,13.010000,165.600000,125,7.450000,7.100000,3,1.920000,0,False.\r\nCT,212,415,366-6751,no,no,0,126.000000,96,21.420000,144.300000,80,12.270000,302.800000,102,13.630000,7.600000,3,2.050000,1,False.\r\nIN,99,415,397-8512,yes,no,0,197.200000,127,33.520000,156.000000,92,13.260000,204.100000,99,9.180000,9.900000,6,2.670000,4,False.\r\nOH,94,510,367-9495,no,no,0,194.100000,62,33.000000,227.200000,54,19.310000,190.400000,115,8.570000,15.300000,4,4.130000,1,False.\r\nNY,40,510,379-2991,no,no,0,115.700000,105,19.670000,127.800000,113,10.860000,107.500000,91,4.840000,9.300000,6,2.510000,1,False.\r\nNE,86,510,356-4832,no,yes,29,157.200000,118,26.720000,196.300000,136,16.690000,226.700000,109,10.200000,8.400000,5,2.270000,3,False.\r\nOK,101,415,413-4040,no,no,0,269.700000,85,45.850000,207.600000,86,17.650000,214.200000,107,9.640000,4.500000,15,1.220000,3,True.\r\nNC,170,415,366-4444,no,no,0,246.400000,107,41.890000,228.100000,124,19.390000,166.400000,95,7.490000,9.100000,8,2.460000,0,False.\r\nHI,105,510,394-3806,no,no,0,227.400000,121,38.660000,268.500000,89,22.820000,143.300000,82,6.450000,13.000000,4,3.510000,1,False.\r\nUT,103,415,368-5647,no,no,0,189.800000,110,32.270000,115.500000,83,9.820000,191.300000,103,8.610000,12.200000,4,3.290000,0,False.\r\nMD,140,415,412-1076,yes,yes,27,188.900000,124,32.110000,160.900000,102,13.680000,197.700000,100,8.900000,11.500000,5,3.110000,4,False.\r\nVT,101,510,413-7655,no,no,0,0.000000,0,0.000000,192.100000,119,16.330000,168.800000,95,7.600000,7.200000,4,1.940000,1,False.\r\nTX,98,408,371-2316,no,yes,19,110.500000,87,18.790000,227.800000,97,19.360000,243.600000,84,10.960000,11.000000,4,2.970000,1,False.\r\nAZ,104,408,420-3346,no,no,0,167.600000,116,28.490000,219.200000,112,18.630000,215.900000,94,9.720000,11.700000,2,3.160000,4,False.\r\nVA,115,415,367-3971,no,no,0,132.000000,90,22.440000,197.500000,75,16.790000,175.800000,114,7.910000,0.000000,0,0.000000,3,False.\r\nWI,112,408,417-5813,no,no,0,167.800000,88,28.530000,247.900000,81,21.070000,155.100000,108,6.980000,11.900000,4,3.210000,0,False.\r\nNE,70,415,421-8535,no,no,0,213.400000,86,36.280000,204.700000,77,17.400000,256.600000,101,11.550000,5.700000,4,1.540000,1,False.\r\nKY,126,510,375-1721,no,no,0,175.400000,120,29.820000,98.300000,71,8.360000,201.900000,93,9.090000,10.600000,1,2.860000,0,False.\r\nNV,87,510,331-8484,no,yes,39,82.600000,113,14.040000,224.400000,63,19.070000,163.600000,88,7.360000,9.500000,1,2.570000,3,False.\r\nMT,125,510,366-5829,no,no,0,143.200000,80,24.340000,88.100000,94,7.490000,233.200000,135,10.490000,8.800000,7,2.380000,4,True.\r\nMO,86,415,367-7906,no,no,0,125.500000,139,21.340000,269.800000,93,22.930000,235.800000,110,10.610000,8.900000,8,2.400000,1,False.\r\nMS,73,415,412-2520,no,yes,31,82.300000,105,13.990000,256.100000,91,21.770000,229.600000,98,10.330000,11.800000,2,3.190000,6,True.\r\nNM,232,408,386-9177,no,no,0,165.600000,104,28.150000,195.900000,115,16.650000,118.300000,77,5.320000,11.800000,3,3.190000,1,False.\r\nNJ,1,415,420-6780,no,yes,30,183.100000,95,31.130000,232.600000,110,19.770000,248.300000,110,11.170000,8.400000,2,2.270000,0,False.\r\nNH,133,408,401-1454,no,no,0,162.100000,91,27.560000,212.100000,94,18.030000,260.400000,78,11.720000,12.200000,5,3.290000,1,False.\r\nNC,103,510,379-2508,no,no,0,166.600000,84,28.320000,192.400000,91,16.350000,167.900000,115,7.560000,7.700000,6,2.080000,1,False.\r\nMT,131,415,353-3492,no,yes,24,135.900000,60,23.100000,233.200000,78,19.820000,210.600000,121,9.480000,9.400000,4,2.540000,1,False.\r\nSD,95,408,406-4840,no,yes,20,165.700000,78,28.170000,215.600000,94,18.330000,243.300000,91,10.950000,9.800000,6,2.650000,0,False.\r\nVA,182,415,391-7982,no,no,0,176.100000,90,29.940000,174.900000,106,14.870000,234.700000,134,10.560000,9.700000,4,2.620000,1,False.\r\nLA,99,510,379-9821,no,no,0,142.300000,89,24.190000,204.500000,95,17.380000,203.100000,114,9.140000,9.100000,1,2.460000,0,False.\r\nNV,27,510,398-7414,no,no,0,177.600000,121,30.190000,296.800000,92,25.230000,192.900000,106,8.680000,7.600000,3,2.050000,3,False.\r\nAK,141,408,338-8566,no,no,0,83.200000,74,14.140000,190.600000,104,16.200000,150.500000,79,6.770000,10.700000,7,2.890000,1,False.\r\nOH,29,415,397-3058,yes,yes,37,235.000000,101,39.950000,183.300000,79,15.580000,139.800000,106,6.290000,5.700000,7,1.540000,2,False.\r\nNM,65,415,389-8096,no,no,0,105.700000,95,17.970000,141.800000,100,12.050000,180.500000,105,8.120000,6.600000,12,1.780000,2,False.\r\nMI,81,415,393-6840,yes,no,0,149.400000,68,25.400000,171.900000,98,14.610000,214.500000,97,9.650000,17.900000,3,4.830000,3,True.\r\nMN,37,415,360-7404,no,yes,20,264.700000,81,45.000000,216.500000,110,18.400000,210.700000,102,9.480000,10.400000,7,2.810000,0,False.\r\nWY,107,510,411-5740,no,yes,31,160.300000,45,27.250000,221.500000,70,18.830000,261.600000,109,11.770000,5.600000,1,1.510000,1,False.\r\nWY,127,415,412-3726,yes,yes,28,95.900000,117,16.300000,159.500000,131,13.560000,152.800000,132,6.880000,10.400000,3,2.810000,1,False.\r\nWA,78,408,372-7326,no,no,0,140.700000,77,23.920000,195.200000,114,16.590000,252.900000,107,11.380000,11.700000,5,3.160000,0,False.\r\nNM,55,510,338-9873,no,no,0,119.700000,148,20.350000,231.800000,96,19.700000,222.300000,113,10.000000,4.600000,2,1.240000,2,False.\r\nVA,86,415,383-4322,no,yes,30,99.900000,84,16.980000,263.500000,125,22.400000,254.700000,90,11.460000,9.800000,7,2.650000,2,False.\r\nRI,176,415,401-7654,no,no,0,250.900000,108,42.650000,171.400000,100,14.570000,148.600000,85,6.690000,9.600000,6,2.590000,2,False.\r\nAL,96,415,410-5455,yes,no,0,200.600000,117,34.100000,289.500000,120,24.610000,98.300000,95,4.420000,11.200000,5,3.020000,2,False.\r\nWV,11,510,419-4310,no,yes,38,209.800000,130,35.670000,196.600000,84,16.710000,233.000000,79,10.490000,7.000000,7,1.890000,1,False.\r\nWV,48,415,367-2056,no,yes,34,198.000000,70,33.660000,273.700000,121,23.260000,217.900000,71,9.810000,7.600000,4,2.050000,1,False.\r\nNJ,127,510,363-6695,no,no,0,239.800000,107,40.770000,128.900000,121,10.960000,249.900000,110,11.250000,11.300000,5,3.050000,1,False.\r\nTN,63,415,374-6217,no,no,0,164.500000,75,27.970000,147.900000,118,12.570000,252.700000,97,11.370000,11.200000,2,3.020000,0,False.\r\nMI,79,510,337-9569,no,no,0,220.900000,107,37.550000,192.200000,97,16.340000,161.000000,74,7.250000,12.200000,2,3.290000,1,False.\r\nUT,47,408,350-9720,no,yes,37,112.800000,150,19.180000,243.900000,97,20.730000,178.700000,112,8.040000,13.200000,6,3.560000,2,False.\r\nIL,89,415,380-4080,yes,yes,19,112.600000,114,19.140000,261.700000,132,22.240000,123.500000,116,5.560000,11.100000,2,3.000000,0,True.\r\nMI,83,510,333-7460,no,yes,26,226.400000,117,38.490000,234.700000,97,19.950000,133.600000,82,6.010000,10.800000,7,2.920000,1,False.\r\nWI,126,415,378-3722,yes,yes,34,244.900000,118,41.630000,219.600000,105,18.670000,210.800000,136,9.490000,9.700000,6,2.620000,4,False.\r\nND,60,510,353-9339,no,no,0,203.200000,99,34.540000,235.800000,131,20.040000,224.900000,112,10.120000,15.100000,6,4.080000,2,False.\r\nVT,122,415,386-6535,no,no,0,136.700000,115,23.240000,243.100000,137,20.660000,188.900000,110,8.500000,8.600000,4,2.320000,0,False.\r\nWA,136,408,403-5575,no,no,0,152.600000,97,25.940000,208.900000,85,17.760000,119.100000,99,5.360000,5.000000,10,1.350000,1,False.\r\nNC,172,408,331-5962,no,yes,47,274.900000,102,46.730000,186.600000,118,15.860000,245.000000,123,11.030000,8.800000,2,2.380000,1,False.\r\nME,102,510,390-9627,no,no,0,195.700000,116,33.270000,209.100000,87,17.770000,201.100000,73,9.050000,8.300000,3,2.240000,1,True.\r\nSD,113,415,406-4560,yes,no,0,204.300000,82,34.730000,188.800000,115,16.050000,139.400000,97,6.270000,9.200000,7,2.480000,1,False.\r\nWV,79,415,359-6931,no,no,0,222.300000,99,37.790000,146.200000,82,12.430000,275.600000,82,12.400000,8.900000,4,2.400000,3,False.\r\nID,55,510,331-7342,no,yes,8,222.500000,104,37.830000,171.500000,94,14.580000,377.500000,114,16.990000,9.700000,2,2.620000,1,False.\r\nLA,111,415,367-2227,no,yes,28,128.800000,104,21.900000,157.300000,52,13.370000,147.400000,76,6.630000,10.300000,2,2.780000,2,False.\r\nGA,160,415,335-8836,no,no,0,174.300000,105,29.630000,171.300000,107,14.560000,220.800000,131,9.940000,8.300000,2,2.240000,0,False.\r\nFL,110,415,398-6703,no,no,0,242.500000,110,41.230000,162.300000,140,13.800000,184.100000,86,8.280000,7.800000,3,2.110000,4,False.\r\nCO,192,415,370-8379,no,no,0,221.600000,101,37.670000,285.200000,50,24.240000,167.400000,83,7.530000,12.700000,6,3.430000,4,False.\r\nNV,93,408,335-3880,no,no,0,114.300000,100,19.430000,221.100000,103,18.790000,126.300000,88,5.680000,10.900000,9,2.940000,0,False.\r\nIN,101,415,375-8761,no,yes,33,219.700000,137,37.350000,186.800000,94,15.880000,184.500000,113,8.300000,9.500000,2,2.570000,2,False.\r\nVA,77,510,367-1398,no,no,0,144.900000,136,24.630000,151.300000,115,12.860000,252.400000,73,11.360000,12.300000,3,3.320000,2,False.\r\nUT,105,415,395-7857,no,yes,40,236.500000,111,40.210000,117.000000,110,9.950000,221.100000,115,9.950000,8.100000,3,2.190000,2,False.\r\nUT,133,408,398-8745,no,yes,44,174.000000,80,29.580000,209.400000,113,17.800000,224.100000,87,10.080000,14.100000,7,3.810000,2,True.\r\nMO,131,408,386-3717,no,no,0,109.500000,95,18.620000,332.100000,48,28.230000,258.600000,108,11.640000,6.600000,7,1.780000,1,False.\r\nCT,106,510,330-1175,no,yes,33,81.600000,120,13.870000,235.600000,85,20.030000,150.900000,113,6.790000,9.900000,4,2.670000,1,False.\r\nHI,118,415,418-6752,no,no,0,133.400000,113,22.680000,121.000000,92,10.290000,254.700000,129,11.460000,5.900000,4,1.590000,1,False.\r\nMD,125,408,349-6464,no,no,0,137.100000,94,23.310000,209.800000,83,17.830000,238.400000,114,10.730000,8.600000,4,2.320000,1,False.\r\nVA,95,415,366-7331,no,no,0,197.000000,88,33.490000,190.400000,68,16.180000,211.900000,104,9.540000,16.100000,8,4.350000,0,False.\r\nMT,80,415,361-8288,no,no,0,198.100000,160,33.680000,156.700000,87,13.320000,182.100000,76,8.190000,9.300000,3,2.510000,3,False.\r\nSC,145,408,377-6635,no,no,0,39.500000,78,6.720000,264.300000,106,22.470000,185.800000,90,8.360000,10.000000,6,2.700000,0,False.\r\nCO,37,408,408-1513,no,no,0,199.500000,107,33.920000,207.500000,110,17.640000,83.900000,123,3.780000,8.100000,4,2.190000,2,False.\r\nID,87,415,370-7546,no,no,0,156.800000,93,26.660000,215.800000,68,18.340000,223.300000,77,10.050000,7.600000,6,2.050000,1,False.\r\nAL,69,415,389-4278,no,no,0,183.400000,85,31.180000,237.600000,100,20.200000,228.000000,94,10.260000,9.000000,5,2.430000,3,False.\r\nCO,83,510,379-3012,no,no,0,132.400000,120,22.510000,121.600000,101,10.340000,197.700000,84,8.900000,8.600000,2,2.320000,1,False.\r\nUT,195,415,355-3620,no,no,0,63.200000,108,10.740000,220.200000,88,18.720000,184.000000,99,8.280000,5.100000,3,1.380000,0,False.\r\nDE,67,415,413-7743,yes,yes,35,181.100000,59,30.790000,215.900000,116,18.350000,216.300000,106,9.730000,16.900000,4,4.560000,0,True.\r\nOH,75,510,372-2296,no,yes,27,117.500000,102,19.980000,206.800000,127,17.580000,194.400000,114,8.750000,4.200000,7,1.130000,3,False.\r\nRI,123,415,333-9728,no,yes,27,218.700000,79,37.180000,163.400000,78,13.890000,173.800000,116,7.820000,15.000000,1,4.050000,0,False.\r\nFL,41,415,415-6110,no,yes,41,207.300000,95,35.240000,137.300000,120,11.670000,115.700000,74,5.210000,5.900000,3,1.590000,1,False.\r\nOH,75,415,340-9803,no,no,0,150.600000,99,25.600000,301.500000,83,25.630000,158.700000,104,7.140000,8.100000,5,2.190000,0,False.\r\nMD,76,415,400-7002,yes,no,0,273.300000,66,46.460000,263.600000,121,22.410000,165.200000,84,7.430000,12.000000,7,3.240000,1,True.\r\nIL,86,415,395-7435,yes,no,0,266.100000,120,45.240000,182.000000,92,15.470000,206.500000,103,9.290000,10.300000,4,2.780000,1,False.\r\nPA,140,408,336-7143,no,no,0,112.800000,89,19.180000,156.700000,65,13.320000,249.600000,85,11.230000,16.300000,6,4.400000,0,False.\r\nAZ,70,415,352-2175,no,no,0,104.700000,112,17.800000,82.200000,104,6.990000,169.400000,110,7.620000,15.800000,7,4.270000,3,False.\r\nNH,121,510,346-6352,no,yes,35,193.800000,62,32.950000,197.600000,97,16.800000,218.800000,95,9.850000,5.900000,4,1.590000,0,False.\r\nRI,112,415,405-7467,no,no,0,168.600000,102,28.660000,298.000000,117,25.330000,194.700000,110,8.760000,9.800000,5,2.650000,1,False.\r\nHI,118,415,379-8526,no,no,0,253.200000,122,43.040000,201.000000,78,17.090000,195.300000,108,8.790000,9.700000,7,2.620000,2,False.\r\nNJ,66,415,410-5713,no,yes,16,174.700000,92,29.700000,232.100000,105,19.730000,305.400000,98,13.740000,8.900000,2,2.400000,1,False.\r\nWI,78,408,408-5916,no,no,0,87.000000,102,14.790000,193.600000,64,16.460000,205.800000,120,9.260000,11.000000,5,2.970000,0,False.\r\nMD,129,415,370-5626,no,yes,34,204.500000,79,34.770000,132.800000,113,11.290000,190.100000,117,8.550000,14.800000,9,4.000000,2,False.\r\nOR,6,408,408-1331,no,no,0,226.500000,93,38.510000,152.100000,122,12.930000,164.400000,98,7.400000,9.400000,4,2.540000,3,False.\r\nNV,107,510,419-9688,yes,no,0,234.100000,91,39.800000,163.100000,105,13.860000,282.500000,100,12.710000,10.000000,3,2.700000,1,False.\r\nAR,107,415,343-5219,yes,no,0,133.300000,106,22.660000,182.900000,89,15.550000,241.100000,123,10.850000,12.900000,2,3.480000,3,True.\r\nMT,138,415,401-5586,no,no,0,133.900000,87,22.760000,166.400000,110,14.140000,193.500000,139,8.710000,15.400000,3,4.160000,1,False.\r\nCT,103,510,377-9178,no,no,0,160.200000,104,27.230000,138.900000,70,11.810000,312.500000,97,14.060000,9.700000,2,2.620000,0,False.\r\nUT,116,415,345-5639,no,yes,44,230.600000,94,39.200000,224.100000,103,19.050000,244.000000,76,10.980000,11.100000,2,3.000000,0,False.\r\nGA,189,408,336-3488,no,no,0,227.400000,84,38.660000,176.000000,81,14.960000,206.100000,120,9.270000,6.300000,4,1.700000,2,False.\r\nNV,161,415,414-6426,no,no,0,72.800000,120,12.380000,267.100000,120,22.700000,222.500000,91,10.010000,11.800000,2,3.190000,2,False.\r\nTN,1,415,335-5591,no,no,0,196.100000,107,33.340000,296.500000,82,25.200000,211.500000,91,9.520000,7.000000,2,1.890000,1,False.\r\nDE,89,408,421-9144,no,no,0,197.100000,110,33.510000,165.900000,115,14.100000,227.300000,106,10.230000,12.800000,3,3.460000,1,False.\r\nNY,64,408,422-7728,no,no,0,219.600000,126,37.330000,303.300000,100,25.780000,154.500000,65,6.950000,9.700000,5,2.620000,1,False.\r\nMT,126,415,344-3466,no,yes,30,153.400000,90,26.080000,151.400000,97,12.870000,153.800000,97,6.920000,12.800000,4,3.460000,4,True.\r\nIA,129,415,398-7978,no,no,0,216.000000,85,36.720000,186.900000,114,15.890000,210.700000,109,9.480000,4.900000,10,1.320000,2,False.\r\nVT,128,510,346-8368,no,yes,32,222.900000,136,37.890000,262.000000,80,22.270000,191.400000,101,8.610000,10.800000,4,2.920000,0,False.\r\nLA,81,415,392-2722,no,yes,36,115.900000,120,19.700000,236.600000,95,20.110000,255.000000,90,11.480000,11.700000,6,3.160000,3,False.\r\nMT,114,510,393-3274,no,no,0,189.800000,101,32.270000,147.700000,80,12.550000,172.700000,121,7.770000,10.600000,5,2.860000,1,False.\r\nNH,50,408,339-4636,no,no,0,154.700000,102,26.300000,298.000000,108,25.330000,210.200000,95,9.460000,11.100000,3,3.000000,0,False.\r\nWV,86,415,349-7138,no,no,0,136.400000,104,23.190000,202.500000,110,17.210000,230.700000,86,10.380000,11.500000,1,3.110000,3,False.\r\nID,96,408,363-3295,no,no,0,170.500000,86,28.990000,277.500000,88,23.590000,162.500000,117,7.310000,12.200000,6,3.290000,1,False.\r\nAZ,72,510,407-9830,no,no,0,272.400000,88,46.310000,107.900000,125,9.170000,185.500000,81,8.350000,12.700000,2,3.430000,0,False.\r\nSC,64,510,333-8822,no,yes,40,210.000000,116,35.700000,232.700000,89,19.780000,168.800000,94,7.600000,5.900000,4,1.590000,8,False.\r\nWV,57,415,419-6418,yes,yes,17,236.500000,94,40.210000,163.100000,94,13.860000,236.700000,117,10.650000,12.200000,3,3.290000,2,False.\r\nOH,65,510,351-8955,no,no,0,153.900000,117,26.160000,220.100000,122,18.710000,280.500000,147,12.620000,8.500000,3,2.300000,2,False.\r\nMD,163,408,338-1840,no,no,0,223.000000,120,37.910000,227.000000,98,19.300000,188.300000,125,8.470000,8.800000,5,2.380000,1,False.\r\nMD,136,415,336-6997,no,no,0,252.400000,74,42.910000,167.900000,81,14.270000,248.300000,110,11.170000,10.700000,3,2.890000,2,False.\r\nMN,116,408,408-6266,no,no,0,197.900000,84,33.640000,168.100000,113,14.290000,239.800000,145,10.790000,12.000000,6,3.240000,1,False.\r\nNE,93,408,332-4291,no,no,0,152.400000,74,25.910000,274.600000,88,23.340000,252.200000,120,11.350000,6.600000,5,1.780000,3,False.\r\nMN,142,510,355-7895,no,yes,40,237.400000,105,40.360000,175.900000,93,14.950000,210.300000,110,9.460000,9.200000,3,2.480000,3,False.\r\nNY,92,408,348-2916,no,no,0,265.600000,82,45.150000,180.700000,75,15.360000,211.100000,113,9.500000,8.600000,2,2.320000,1,False.\r\nHI,70,415,339-8132,no,no,0,197.300000,91,33.540000,305.800000,81,25.990000,171.000000,105,7.690000,6.700000,6,1.810000,1,False.\r\nMO,22,408,374-1684,no,yes,14,199.100000,100,33.850000,221.800000,103,18.850000,65.700000,91,2.960000,4.200000,1,1.130000,1,False.\r\nNV,37,415,362-7604,no,no,0,233.700000,114,39.730000,207.900000,109,17.670000,212.700000,101,9.570000,12.000000,2,3.240000,2,False.\r\nMA,51,415,389-3206,no,no,0,183.100000,99,31.130000,160.100000,107,13.610000,311.800000,121,14.030000,7.000000,3,1.890000,1,False.\r\nNH,174,408,336-2829,no,no,0,139.400000,96,23.700000,143.400000,108,12.190000,225.200000,107,10.130000,10.000000,5,2.700000,2,False.\r\nUT,68,415,403-8916,no,no,0,213.900000,112,36.360000,260.500000,100,22.140000,233.800000,97,10.520000,8.400000,3,2.270000,1,True.\r\nFL,130,415,384-1135,no,no,0,207.100000,70,35.210000,200.100000,115,17.010000,194.200000,100,8.740000,12.400000,2,3.350000,1,False.\r\nWA,104,415,390-2320,no,no,0,139.700000,78,23.750000,202.600000,119,17.220000,203.600000,102,9.160000,11.300000,5,3.050000,2,False.\r\nCT,134,408,398-8578,no,no,0,177.200000,91,30.120000,228.700000,105,19.440000,194.300000,113,8.740000,8.900000,3,2.400000,2,False.\r\nKY,108,415,393-9424,no,yes,35,169.800000,136,28.870000,173.700000,101,14.760000,214.600000,105,9.660000,9.500000,7,2.570000,2,False.\r\nNM,103,415,417-6330,no,no,0,173.500000,83,29.500000,244.300000,65,20.770000,221.600000,66,9.970000,9.700000,2,2.620000,3,False.\r\nND,62,510,340-6339,no,no,0,159.900000,100,27.180000,172.200000,99,14.640000,263.200000,109,11.840000,5.600000,4,1.510000,1,False.\r\nNV,162,415,380-6571,no,no,0,115.100000,89,19.570000,196.800000,111,16.730000,212.400000,98,9.560000,11.400000,3,3.080000,2,False.\r\nCA,93,510,368-6488,no,yes,19,136.800000,113,23.260000,179.500000,105,15.260000,71.100000,95,3.200000,12.500000,3,3.380000,2,False.\r\nID,42,415,363-2193,no,no,0,92.200000,108,15.670000,211.200000,120,17.950000,129.100000,73,5.810000,13.100000,6,3.540000,1,False.\r\nOK,155,415,328-1206,no,yes,23,243.900000,112,41.460000,133.000000,106,11.310000,213.700000,123,9.620000,13.400000,11,3.620000,2,False.\r\nIA,36,510,385-3540,no,no,0,117.100000,94,19.910000,235.400000,117,20.010000,221.300000,108,9.960000,9.000000,2,2.430000,0,False.\r\nOH,143,415,337-7167,no,no,0,223.300000,99,37.960000,167.100000,128,14.200000,203.000000,84,9.140000,4.500000,4,1.220000,0,True.\r\nNJ,197,510,372-8405,no,no,0,154.800000,111,26.320000,171.500000,102,14.580000,227.300000,86,10.230000,10.600000,2,2.860000,3,False.\r\nIA,81,510,377-1273,no,no,0,261.400000,141,44.440000,215.700000,102,18.330000,271.800000,96,12.230000,8.000000,6,2.160000,1,True.\r\nDE,138,510,380-7816,yes,no,0,46.500000,104,7.910000,186.000000,114,15.810000,167.500000,95,7.540000,9.600000,4,2.590000,4,True.\r\nCA,103,415,402-6744,no,yes,18,149.900000,84,25.480000,170.900000,84,14.530000,171.500000,112,7.720000,11.500000,7,3.110000,0,True.\r\nWY,127,510,400-2181,yes,no,0,242.200000,102,41.170000,226.100000,80,19.220000,252.000000,96,11.340000,13.900000,5,3.750000,2,True.\r\nOR,136,510,366-1613,no,no,0,259.400000,99,44.100000,172.700000,125,14.680000,293.700000,78,13.220000,10.700000,6,2.890000,1,True.\r\nME,99,415,347-8205,no,no,0,222.400000,102,37.810000,185.800000,89,15.790000,237.700000,81,10.700000,12.000000,8,3.240000,2,False.\r\nAR,95,415,328-2982,no,no,0,69.400000,79,11.800000,190.800000,109,16.220000,219.900000,102,9.900000,8.900000,5,2.400000,0,False.\r\nME,118,408,384-8723,yes,yes,21,156.500000,122,26.610000,209.200000,125,17.780000,158.700000,81,7.140000,11.100000,3,3.000000,4,True.\r\nWV,113,415,341-7686,no,no,0,61.200000,111,10.400000,92.300000,88,7.850000,197.400000,114,8.880000,13.700000,3,3.700000,5,True.\r\nPA,128,408,353-6038,yes,no,0,245.200000,112,41.680000,101.500000,101,8.630000,152.300000,116,6.850000,10.700000,5,2.890000,0,False.\r\nHI,117,408,416-8827,no,no,0,102.300000,100,17.390000,135.200000,104,11.490000,199.700000,93,8.990000,15.700000,10,4.240000,3,False.\r\nMT,48,415,418-8450,no,yes,36,230.900000,92,39.250000,167.600000,121,14.250000,270.000000,87,12.150000,7.600000,4,2.050000,3,False.\r\nDC,81,510,385-7861,yes,no,0,227.400000,105,38.660000,211.500000,120,17.980000,258.200000,113,11.620000,11.900000,3,3.210000,0,False.\r\nAR,57,510,393-3507,no,no,0,192.800000,68,32.780000,158.000000,86,13.430000,235.500000,105,10.600000,12.700000,6,3.430000,1,False.\r\nMS,140,408,372-5262,no,no,0,162.600000,98,27.640000,206.200000,109,17.530000,141.600000,66,6.370000,8.200000,2,2.210000,1,False.\r\nOH,107,510,411-3095,no,yes,38,219.400000,92,37.300000,180.500000,73,15.340000,104.100000,91,4.680000,11.000000,1,2.970000,2,False.\r\nMO,56,415,331-5919,no,no,0,137.200000,111,23.320000,165.900000,119,14.100000,182.300000,72,8.200000,14.300000,4,3.860000,1,False.\r\nTX,159,415,402-1556,no,no,0,87.700000,103,14.910000,278.200000,97,23.650000,170.600000,93,7.680000,10.500000,10,2.840000,1,False.\r\nMD,102,415,349-7362,no,no,0,271.100000,80,46.090000,172.000000,133,14.620000,169.200000,105,7.610000,10.300000,5,2.780000,1,False.\r\nCT,107,408,339-2734,no,no,0,103.400000,94,17.580000,189.300000,125,16.090000,227.200000,125,10.220000,14.400000,3,3.890000,1,False.\r\nSC,106,408,330-4914,no,no,0,52.200000,106,8.870000,220.100000,113,18.710000,112.300000,95,5.050000,11.400000,2,3.080000,2,False.\r\nMI,225,415,371-2500,no,no,0,165.400000,106,28.120000,273.700000,109,23.260000,210.000000,93,9.450000,8.700000,3,2.350000,0,True.\r\nSD,75,408,335-3681,no,no,0,147.500000,110,25.080000,191.700000,97,16.290000,135.000000,68,6.080000,16.400000,3,4.430000,2,False.\r\nCO,86,415,405-1132,no,no,0,217.800000,93,37.030000,214.700000,95,18.250000,228.700000,70,10.290000,11.300000,7,3.050000,0,False.\r\nID,169,415,399-9239,no,no,0,235.700000,79,40.070000,136.900000,85,11.640000,220.900000,97,9.940000,13.300000,10,3.590000,1,False.\r\nAZ,122,510,350-7227,no,yes,22,204.500000,92,34.770000,139.600000,121,11.870000,205.000000,103,9.230000,8.600000,5,2.320000,2,False.\r\nFL,106,408,384-6654,no,no,0,178.400000,143,30.330000,247.000000,123,21.000000,259.900000,105,11.700000,9.600000,2,2.590000,0,False.\r\nMN,52,415,376-4271,no,yes,32,130.100000,68,22.120000,247.200000,77,21.010000,289.400000,87,13.020000,13.500000,5,3.650000,2,False.\r\nDE,79,415,391-8124,no,yes,34,103.700000,100,17.630000,236.300000,78,20.090000,256.600000,102,11.550000,14.800000,4,4.000000,2,False.\r\nMI,135,415,393-2524,no,no,0,239.900000,91,40.780000,177.100000,104,15.050000,217.200000,118,9.770000,5.900000,3,1.590000,2,False.\r\nMS,70,408,384-4385,no,no,0,148.400000,110,25.230000,267.100000,90,22.700000,151.500000,101,6.820000,8.900000,4,2.400000,0,False.\r\nMA,80,408,377-8266,no,no,0,148.600000,106,25.260000,210.800000,65,17.920000,203.700000,86,9.170000,10.000000,2,2.700000,2,False.\r\nCA,37,415,345-1243,no,no,0,191.100000,69,32.490000,129.200000,113,10.980000,207.500000,117,9.340000,12.900000,1,3.480000,0,False.\r\nMN,161,415,394-8086,no,yes,39,218.500000,76,37.150000,112.700000,94,9.580000,205.100000,121,9.230000,7.300000,4,1.970000,1,False.\r\nVT,137,510,348-9145,no,no,0,97.500000,95,16.580000,195.800000,82,16.640000,288.800000,78,13.000000,0.000000,0,0.000000,1,False.\r\nCT,123,415,376-5201,no,no,0,128.700000,126,21.880000,117.600000,94,10.000000,198.400000,132,8.930000,10.800000,5,2.920000,0,False.\r\nWV,80,415,356-2093,no,yes,38,236.600000,69,40.220000,197.500000,68,16.790000,209.500000,102,9.430000,9.500000,10,2.570000,2,False.\r\nWV,94,415,353-2080,no,no,0,85.900000,113,14.600000,226.700000,91,19.270000,279.600000,110,12.580000,15.600000,16,4.210000,0,False.\r\nNE,105,415,397-7500,no,yes,27,141.200000,96,24.000000,167.700000,94,14.250000,274.400000,101,12.350000,11.400000,2,3.080000,1,False.\r\nNC,73,415,414-5786,no,yes,31,194.400000,104,33.050000,176.000000,84,14.960000,230.100000,110,10.350000,11.500000,3,3.110000,0,False.\r\nNE,112,415,388-4282,no,no,0,167.600000,100,28.490000,154.500000,90,13.130000,281.400000,107,12.660000,17.300000,3,4.670000,2,False.\r\nIL,179,408,415-5132,no,no,0,234.500000,134,39.870000,164.200000,94,13.960000,191.400000,72,8.610000,6.100000,4,1.650000,1,False.\r\nMA,57,510,352-4541,no,no,0,154.200000,78,26.210000,196.700000,85,16.720000,253.500000,97,11.410000,10.100000,9,2.730000,1,False.\r\nAZ,127,415,373-5928,no,yes,14,143.200000,99,24.340000,169.900000,91,14.440000,221.600000,77,9.970000,11.600000,1,3.130000,1,False.\r\nSD,122,415,406-7737,yes,yes,40,216.400000,80,36.790000,249.700000,90,21.220000,185.900000,99,8.370000,12.700000,4,3.430000,1,False.\r\nMT,33,510,332-7607,no,yes,35,161.900000,85,27.520000,151.200000,82,12.850000,191.000000,131,8.590000,8.500000,2,2.300000,1,False.\r\nVT,94,408,359-7788,no,no,0,118.700000,90,20.180000,205.100000,57,17.430000,172.200000,100,7.750000,10.400000,6,2.810000,3,False.\r\nUT,100,408,384-1549,no,no,0,179.100000,123,30.450000,196.600000,132,16.710000,186.700000,116,8.400000,10.200000,10,2.750000,1,False.\r\nHI,106,415,352-8508,no,no,0,147.900000,97,25.140000,209.300000,99,17.790000,162.100000,80,7.290000,8.800000,5,2.380000,2,False.\r\nDC,148,415,404-1002,no,yes,38,209.200000,110,35.560000,116.600000,73,9.910000,109.600000,105,4.930000,16.500000,4,4.460000,2,False.\r\nWI,120,415,414-2905,no,yes,29,244.300000,140,41.530000,322.300000,89,27.400000,166.800000,83,7.510000,10.600000,6,2.860000,0,False.\r\nUT,91,415,380-9849,no,yes,34,175.300000,96,29.800000,262.300000,122,22.300000,143.900000,76,6.480000,5.600000,11,1.510000,1,False.\r\nWA,86,510,387-6498,no,no,0,150.500000,92,25.590000,120.300000,95,10.230000,271.200000,96,12.200000,9.000000,2,2.430000,1,False.\r\nSD,78,415,360-6024,no,yes,25,197.400000,73,33.560000,295.700000,113,25.130000,211.700000,73,9.530000,13.200000,2,3.560000,0,False.\r\nMT,94,510,352-5815,no,no,0,163.500000,136,27.800000,143.700000,111,12.210000,253.400000,82,11.400000,12.600000,5,3.400000,1,False.\r\nNJ,85,415,366-2273,no,no,0,236.900000,93,40.270000,197.700000,113,16.800000,309.100000,78,13.910000,11.400000,7,3.080000,2,True.\r\nCT,89,415,414-9119,no,no,0,82.300000,77,13.990000,167.200000,80,14.210000,194.700000,70,8.760000,7.200000,4,1.940000,1,False.\r\nVA,128,415,409-8796,no,no,0,216.000000,111,36.720000,153.700000,115,13.060000,227.000000,74,10.220000,12.700000,4,3.430000,1,False.\r\nNC,115,415,337-2442,yes,no,0,180.000000,119,30.600000,198.800000,126,16.900000,217.100000,70,9.770000,12.400000,3,3.350000,1,False.\r\nAK,76,415,404-1931,no,no,0,143.700000,55,24.430000,173.100000,108,14.710000,239.100000,95,10.760000,5.800000,6,1.570000,1,False.\r\nMD,75,415,367-9765,no,yes,39,198.200000,107,33.690000,280.400000,132,23.830000,129.600000,73,5.830000,11.300000,7,3.050000,1,False.\r\nIL,90,415,378-7299,no,yes,29,185.600000,106,31.550000,219.700000,113,18.670000,152.100000,120,6.840000,11.100000,5,3.000000,2,False.\r\nCT,30,408,410-5192,no,no,0,137.600000,108,23.390000,162.000000,80,13.770000,187.700000,126,8.450000,5.800000,10,1.570000,3,False.\r\nKS,105,415,405-1108,yes,no,0,273.900000,119,46.560000,278.600000,103,23.680000,255.300000,90,11.490000,10.900000,7,2.940000,1,True.\r\nMA,102,415,392-1734,no,yes,31,125.300000,92,21.300000,141.200000,108,12.000000,168.200000,68,7.570000,6.300000,2,1.700000,3,False.\r\nNJ,83,415,395-6030,no,no,0,178.800000,102,30.400000,167.900000,84,14.270000,178.900000,65,8.050000,8.600000,4,2.320000,3,False.\r\nAR,63,510,330-5168,no,yes,49,214.900000,86,36.530000,198.200000,89,16.850000,170.800000,139,7.690000,8.200000,5,2.210000,0,False.\r\nMS,155,408,334-3142,no,no,0,163.000000,93,27.710000,203.900000,102,17.330000,159.000000,109,7.150000,15.100000,4,4.080000,2,False.\r\nND,82,415,362-9983,no,yes,29,163.800000,77,27.850000,134.900000,112,11.470000,79.300000,95,3.570000,8.800000,2,2.380000,2,False.\r\nIN,87,510,414-2606,no,no,0,189.500000,113,32.220000,204.900000,100,17.420000,221.700000,93,9.980000,13.400000,3,3.620000,1,False.\r\nMI,115,415,402-4501,no,yes,26,155.200000,110,26.380000,230.900000,133,19.630000,261.600000,100,11.770000,4.500000,4,1.220000,0,False.\r\nAR,99,510,387-2604,yes,no,0,242.300000,102,41.190000,350.900000,102,29.830000,163.100000,93,7.340000,11.300000,3,3.050000,0,True.\r\nVT,121,415,400-3343,yes,yes,44,254.100000,127,43.200000,180.200000,108,15.320000,196.200000,129,8.830000,8.700000,4,2.350000,3,False.\r\nWV,54,510,353-2450,no,yes,33,112.000000,90,19.040000,208.000000,112,17.680000,150.300000,83,6.760000,11.300000,4,3.050000,2,False.\r\nME,105,408,406-2032,no,no,0,115.500000,73,19.640000,267.300000,83,22.720000,114.200000,90,5.140000,13.300000,5,3.590000,3,False.\r\nIA,73,415,409-4462,no,no,0,137.100000,102,23.310000,210.800000,114,17.920000,191.400000,120,8.610000,11.100000,4,3.000000,1,False.\r\nCT,95,415,392-5941,no,no,0,198.400000,113,33.730000,235.900000,144,20.050000,325.600000,99,14.650000,10.100000,3,2.730000,0,False.\r\nNM,21,415,334-9182,no,yes,19,132.700000,94,22.560000,204.600000,101,17.390000,154.700000,78,6.960000,12.900000,7,3.480000,3,False.\r\nOR,163,408,346-3445,no,yes,25,219.600000,99,37.330000,210.400000,99,17.880000,242.700000,88,10.920000,13.800000,8,3.730000,2,False.\r\nVT,57,415,368-9507,no,no,0,169.600000,96,28.830000,234.700000,112,19.950000,285.400000,83,12.840000,11.200000,4,3.020000,0,False.\r\nRI,104,408,382-3966,yes,no,0,160.400000,73,27.270000,293.900000,103,24.980000,306.600000,90,13.800000,12.600000,5,3.400000,4,False.\r\nRI,83,415,334-5844,no,yes,20,95.000000,89,16.150000,167.900000,92,14.270000,200.600000,79,9.030000,11.200000,2,3.020000,0,False.\r\nNM,141,415,362-9411,no,no,0,160.100000,87,27.220000,256.700000,120,21.820000,270.000000,107,12.150000,7.000000,1,1.890000,2,False.\r\nAL,95,415,390-3565,no,no,0,194.600000,114,33.080000,232.800000,106,19.790000,173.400000,92,7.800000,3.800000,2,1.030000,3,False.\r\nMT,184,415,417-4810,no,no,0,236.400000,73,40.190000,287.300000,120,24.420000,192.000000,94,8.640000,13.800000,4,3.730000,1,True.\r\nCT,74,408,384-3389,no,no,0,157.100000,95,26.710000,213.100000,36,18.110000,280.400000,77,12.620000,7.600000,3,2.050000,2,False.\r\nTN,67,415,414-9717,no,no,0,179.800000,125,30.570000,173.200000,86,14.720000,272.800000,97,12.280000,10.900000,4,2.940000,0,False.\r\nID,104,415,357-1700,yes,no,0,148.200000,108,25.190000,161.800000,113,13.750000,259.300000,103,11.670000,11.000000,4,2.970000,0,False.\r\nTX,71,415,376-7207,no,yes,39,183.200000,103,31.140000,209.400000,111,17.800000,172.400000,109,7.760000,11.900000,6,3.210000,1,False.\r\nNH,149,415,368-7706,no,no,0,119.200000,88,20.260000,168.300000,110,14.310000,204.700000,119,9.210000,12.200000,6,3.290000,4,True.\r\nND,154,408,346-4216,no,yes,35,224.000000,102,38.080000,192.000000,99,16.320000,163.100000,100,7.340000,9.600000,2,2.590000,0,False.\r\nSC,138,510,370-9533,no,yes,21,19.500000,149,3.320000,140.900000,109,11.980000,179.700000,111,8.090000,7.900000,1,2.130000,0,False.\r\nKS,117,415,372-1493,no,no,0,184.800000,83,31.420000,248.600000,101,21.130000,133.100000,113,5.990000,9.600000,8,2.590000,1,False.\r\nME,130,408,387-6031,no,no,0,176.300000,140,29.970000,201.000000,104,17.090000,161.900000,123,7.290000,11.300000,5,3.050000,1,False.\r\nRI,73,415,366-6248,no,no,0,241.700000,115,41.090000,168.500000,133,14.320000,169.800000,122,7.640000,11.100000,2,3.000000,2,False.\r\nWI,100,510,369-3756,no,yes,38,224.700000,121,38.200000,294.000000,131,24.990000,290.000000,61,13.050000,9.800000,6,2.650000,0,False.\r\nNC,149,510,363-1719,no,no,0,207.300000,115,35.240000,198.400000,82,16.860000,114.100000,83,5.130000,8.600000,4,2.320000,1,False.\r\nOH,29,408,402-6666,no,no,0,196.800000,81,33.460000,168.000000,110,14.280000,132.600000,98,5.970000,12.700000,7,3.430000,2,False.\r\nWY,131,510,408-9779,no,no,0,110.900000,74,18.850000,115.600000,90,9.830000,190.500000,114,8.570000,15.800000,9,4.270000,1,False.\r\nNJ,153,510,407-2441,no,no,0,122.500000,145,20.830000,273.300000,103,23.230000,197.800000,71,8.900000,8.000000,3,2.160000,2,False.\r\nND,84,510,384-5027,no,no,0,226.900000,144,38.570000,201.600000,122,17.140000,130.200000,121,5.860000,13.200000,5,3.560000,2,False.\r\nWI,133,510,380-3161,no,no,0,187.000000,65,31.790000,141.400000,128,12.020000,238.200000,108,10.720000,10.000000,8,2.700000,2,False.\r\nKY,112,415,360-8135,no,no,0,170.500000,113,28.990000,193.200000,129,16.420000,188.000000,91,8.460000,11.200000,6,3.020000,0,False.\r\nNY,87,415,399-5426,no,no,0,204.800000,101,34.820000,161.000000,80,13.690000,285.700000,89,12.860000,9.500000,3,2.570000,0,False.\r\nMO,72,415,385-2564,no,no,0,165.900000,114,28.200000,235.900000,97,20.050000,210.100000,120,9.450000,12.000000,5,3.240000,2,False.\r\nAZ,66,510,337-8618,no,no,0,154.000000,133,26.180000,198.900000,121,16.910000,151.900000,100,6.840000,9.500000,3,2.570000,4,True.\r\nMN,65,510,354-8491,no,yes,29,158.100000,104,26.880000,322.200000,81,27.390000,210.000000,96,9.450000,8.900000,6,2.400000,1,False.\r\nCO,74,415,394-6278,no,no,0,225.200000,93,38.280000,215.100000,120,18.280000,241.800000,95,10.880000,9.100000,2,2.460000,2,False.\r\nMD,116,408,405-2276,no,no,0,159.400000,79,27.100000,179.500000,88,15.260000,167.800000,71,7.550000,9.700000,2,2.620000,6,True.\r\nAR,68,510,376-1000,no,no,0,172.700000,95,29.360000,139.100000,90,11.820000,174.300000,99,7.840000,11.700000,1,3.160000,2,False.\r\nTN,68,415,397-1659,no,no,0,222.800000,99,37.880000,175.800000,85,14.940000,202.000000,111,9.090000,11.000000,3,2.970000,3,False.\r\nDE,54,415,379-3953,yes,no,0,214.100000,77,36.400000,240.500000,94,20.440000,188.900000,75,8.500000,10.100000,3,2.730000,1,False.\r\nTN,99,408,418-6512,no,no,0,54.800000,92,9.320000,173.000000,103,14.710000,195.100000,125,8.780000,7.500000,3,2.030000,1,False.\r\nWI,107,408,392-5296,no,no,0,134.000000,104,22.780000,174.500000,94,14.830000,311.100000,79,14.000000,7.300000,3,1.970000,3,False.\r\nWV,124,510,355-3814,no,no,0,184.800000,74,31.420000,175.100000,84,14.880000,158.200000,95,7.120000,10.500000,6,2.840000,1,False.\r\nCT,95,415,375-2098,no,yes,36,283.100000,112,48.130000,286.200000,86,24.330000,261.700000,129,11.780000,11.300000,3,3.050000,3,False.\r\nMN,173,510,372-7990,no,no,0,291.800000,143,49.610000,214.300000,134,18.220000,151.200000,119,6.800000,9.900000,2,2.670000,0,True.\r\nMO,110,408,356-4558,no,no,0,222.700000,94,37.860000,105.800000,98,8.990000,214.800000,78,9.670000,13.500000,4,3.650000,1,False.\r\nVA,102,510,398-5788,no,no,0,174.500000,79,29.670000,236.800000,136,20.130000,270.400000,110,12.170000,8.500000,5,2.300000,0,False.\r\nNH,130,408,390-4003,no,no,0,68.400000,86,11.630000,193.300000,110,16.430000,171.500000,139,7.720000,10.400000,4,2.810000,0,False.\r\nOK,91,408,332-8103,no,yes,31,273.000000,78,46.410000,215.500000,98,18.320000,104.700000,114,4.710000,9.600000,2,2.590000,1,False.\r\nCT,64,415,406-9926,yes,no,0,225.300000,134,38.300000,108.200000,87,9.200000,139.600000,132,6.280000,17.300000,9,4.670000,1,True.\r\nTN,176,415,418-2402,no,yes,23,283.200000,130,48.140000,162.600000,74,13.820000,177.700000,104,8.000000,7.200000,6,1.940000,1,False.\r\nMD,93,510,384-3299,yes,no,0,131.400000,78,22.340000,219.700000,106,18.670000,155.700000,103,7.010000,11.100000,2,3.000000,1,True.\r\nWI,84,510,378-9090,no,yes,12,89.700000,87,15.250000,138.600000,73,11.780000,165.800000,114,7.460000,10.700000,2,2.890000,1,False.\r\nSD,138,510,350-6473,no,no,0,127.100000,102,21.610000,247.700000,106,21.050000,207.700000,75,9.350000,5.000000,3,1.350000,3,False.\r\nND,101,415,379-4583,no,yes,28,105.900000,132,18.000000,231.700000,107,19.690000,281.300000,120,12.660000,10.700000,5,2.890000,1,False.\r\nVA,136,408,411-5078,no,no,0,142.300000,79,24.190000,158.000000,113,13.430000,177.500000,75,7.990000,6.000000,11,1.620000,2,False.\r\nUT,111,510,347-4982,no,no,0,191.300000,80,32.520000,138.500000,94,11.770000,246.000000,107,11.070000,6.400000,3,1.730000,2,False.\r\nMA,132,408,341-9274,no,yes,36,201.900000,93,34.320000,156.300000,75,13.290000,131.300000,92,5.910000,13.700000,5,3.700000,0,False.\r\nSD,128,415,353-7461,no,no,0,247.300000,91,42.040000,182.700000,60,15.530000,143.200000,112,6.440000,14.700000,2,3.970000,3,False.\r\nAL,92,408,371-7366,no,yes,38,242.200000,96,41.170000,159.700000,144,13.570000,210.000000,108,9.450000,8.900000,1,2.400000,1,False.\r\nAL,197,415,395-7923,yes,no,0,127.300000,80,21.640000,222.300000,115,18.900000,173.900000,95,7.830000,13.700000,5,3.700000,5,True.\r\nWV,191,408,351-8398,no,no,0,162.000000,104,27.540000,241.200000,120,20.500000,210.400000,83,9.470000,10.900000,7,2.940000,1,False.\r\nSC,99,415,329-2204,no,yes,33,179.100000,93,30.450000,238.300000,102,20.260000,165.700000,96,7.460000,10.600000,1,2.860000,2,False.\r\nFL,106,415,407-7507,no,yes,31,197.400000,125,33.560000,123.400000,110,10.490000,115.600000,101,5.200000,12.300000,4,3.320000,3,False.\r\nKY,88,415,405-8075,no,no,0,148.200000,82,25.190000,308.700000,67,26.240000,235.400000,79,10.590000,6.400000,4,1.730000,2,False.\r\nUT,78,415,390-9698,no,no,0,193.100000,85,32.830000,172.100000,105,14.630000,129.600000,119,5.830000,10.200000,1,2.750000,0,False.\r\nNY,98,408,403-4917,no,no,0,171.700000,99,29.190000,174.800000,87,14.860000,189.600000,130,8.530000,7.800000,6,2.110000,1,False.\r\nMS,17,408,391-6709,no,yes,35,198.500000,123,33.750000,270.600000,74,23.000000,209.900000,130,9.450000,8.100000,10,2.190000,1,False.\r\nNH,56,415,389-5988,no,yes,24,121.700000,87,20.690000,184.000000,76,15.640000,266.600000,98,12.000000,12.700000,3,3.430000,1,False.\r\nVA,84,415,372-1534,no,no,0,130.200000,105,22.130000,278.000000,60,23.630000,305.400000,74,13.740000,14.000000,6,3.780000,2,False.\r\nVT,95,510,395-6369,no,no,0,203.400000,96,34.580000,168.600000,61,14.330000,173.000000,105,7.790000,13.700000,3,3.700000,2,False.\r\nWY,16,415,400-3197,no,no,0,174.700000,83,29.700000,280.800000,122,23.870000,171.700000,80,7.730000,10.500000,8,2.840000,5,False.\r\nNV,76,510,377-4169,yes,no,0,241.000000,120,40.970000,231.800000,96,19.700000,220.200000,67,9.910000,9.900000,1,2.670000,1,True.\r\nWV,93,415,384-5343,no,no,0,141.700000,95,24.090000,221.000000,100,18.790000,227.100000,71,10.220000,10.200000,3,2.750000,0,False.\r\nWA,83,408,338-4472,no,no,0,134.800000,96,22.920000,167.200000,78,14.210000,161.500000,123,7.270000,7.700000,5,2.080000,2,False.\r\nKS,123,415,332-2126,no,no,0,163.100000,119,27.730000,249.400000,51,21.200000,168.200000,77,7.570000,9.000000,10,2.430000,1,False.\r\nVT,64,408,349-2157,no,no,0,145.500000,116,24.740000,228.400000,110,19.410000,273.400000,91,12.300000,8.900000,8,2.400000,1,False.\r\nOK,82,510,393-4823,no,no,0,329.800000,73,56.070000,208.300000,120,17.710000,267.100000,102,12.020000,10.600000,6,2.860000,0,True.\r\nGA,107,510,385-2683,no,no,0,194.500000,97,33.070000,186.300000,131,15.840000,178.300000,106,8.020000,12.700000,1,3.430000,2,False.\r\nCO,110,510,345-8350,no,no,0,131.900000,93,22.420000,272.700000,106,23.180000,192.800000,105,8.680000,7.100000,4,1.920000,1,False.\r\nAK,96,408,334-4506,no,yes,29,150.000000,91,25.500000,159.400000,75,13.550000,228.100000,55,10.260000,8.500000,3,2.300000,1,False.\r\nTN,47,415,332-3544,no,yes,30,196.600000,93,33.420000,241.400000,140,20.520000,226.000000,118,10.170000,12.900000,4,3.480000,2,False.\r\nKY,115,510,380-5102,no,no,0,99.700000,107,16.950000,145.100000,96,12.330000,149.400000,99,6.720000,14.100000,4,3.810000,2,False.\r\nPA,69,415,395-6149,no,no,0,143.600000,88,24.410000,141.800000,86,12.050000,194.000000,83,8.730000,10.800000,5,2.920000,3,False.\r\nCT,163,408,398-8122,no,yes,40,231.900000,56,39.420000,211.800000,91,18.000000,268.500000,74,12.080000,12.300000,3,3.320000,2,False.\r\nCT,90,415,334-4438,no,no,0,37.800000,80,6.430000,155.300000,105,13.200000,175.000000,111,7.880000,14.200000,5,3.830000,3,False.\r\nMN,98,415,384-7459,no,no,0,72.800000,107,12.380000,186.400000,103,15.840000,175.300000,110,7.890000,10.500000,4,2.840000,3,False.\r\nWY,90,408,368-3931,no,yes,39,94.800000,89,16.120000,219.100000,91,18.620000,197.400000,65,8.880000,11.400000,5,3.080000,1,False.\r\nPA,174,415,353-1352,no,yes,15,221.800000,143,37.710000,210.600000,115,17.900000,221.800000,109,9.980000,12.400000,9,3.350000,1,False.\r\nOR,95,415,348-5725,no,no,0,269.000000,120,45.730000,233.700000,120,19.860000,179.300000,61,8.070000,7.300000,4,1.970000,2,True.\r\nPA,79,415,365-2008,no,no,0,268.300000,114,45.610000,185.500000,111,15.770000,264.600000,88,11.910000,6.300000,7,1.700000,1,True.\r\nOK,123,415,393-3635,no,yes,27,198.700000,127,33.780000,249.000000,105,21.170000,173.200000,124,7.790000,12.500000,5,3.380000,1,False.\r\nVT,99,415,380-8727,no,no,0,115.500000,75,19.640000,218.100000,111,18.540000,254.900000,98,11.470000,11.500000,7,3.110000,7,True.\r\nID,114,415,381-2376,no,no,0,202.100000,100,34.360000,195.700000,102,16.630000,291.800000,120,13.130000,13.300000,5,3.590000,2,False.\r\nPA,141,510,365-8114,no,no,0,215.600000,113,36.650000,200.600000,81,17.050000,153.800000,107,6.920000,12.400000,6,3.350000,1,False.\r\nNM,132,408,415-5008,no,no,0,169.900000,107,28.880000,209.400000,121,17.800000,206.100000,79,9.270000,11.500000,2,3.110000,1,False.\r\nFL,133,510,392-8318,no,no,0,201.700000,85,34.290000,169.400000,116,14.400000,286.300000,80,12.880000,6.000000,4,1.620000,0,False.\r\nTX,133,408,401-4007,no,no,0,221.100000,133,37.590000,160.200000,140,13.620000,161.800000,84,7.280000,8.400000,3,2.270000,4,False.\r\nVT,93,510,338-7709,no,yes,32,218.700000,117,37.180000,115.000000,61,9.780000,192.700000,85,8.670000,9.400000,5,2.540000,2,False.\r\nMA,34,415,374-1981,no,no,0,293.700000,89,49.930000,272.500000,71,23.160000,178.200000,76,8.020000,11.000000,10,2.970000,2,True.\r\nOR,140,415,333-5101,no,no,0,120.300000,108,20.450000,240.400000,84,20.430000,216.400000,74,9.740000,7.700000,3,2.080000,4,True.\r\nDE,96,415,345-3734,no,yes,26,175.800000,96,29.890000,206.600000,84,17.560000,178.000000,105,8.010000,11.100000,2,3.000000,2,False.\r\nFL,144,510,384-5004,no,no,0,278.500000,95,47.350000,240.700000,90,20.460000,120.000000,90,5.400000,11.600000,5,3.130000,1,True.\r\nID,24,408,341-9396,no,yes,29,236.300000,105,40.170000,190.800000,114,16.220000,129.000000,105,5.810000,7.200000,2,1.940000,3,False.\r\nMD,54,415,408-6302,no,no,0,273.800000,113,46.550000,119.600000,156,10.170000,267.600000,117,12.040000,11.700000,3,3.160000,1,False.\r\nWV,50,408,348-7193,no,no,0,131.100000,129,22.290000,160.500000,94,13.640000,206.900000,88,9.310000,5.600000,9,1.510000,5,True.\r\nID,92,415,417-4063,no,yes,23,167.400000,83,28.460000,258.600000,129,21.980000,116.400000,110,5.240000,11.200000,3,3.020000,4,False.\r\nNV,96,408,375-6911,no,no,0,197.700000,68,33.610000,250.500000,53,21.290000,181.200000,67,8.150000,10.500000,3,2.840000,3,False.\r\nOH,146,415,358-3604,no,no,0,169.500000,93,28.820000,230.900000,71,19.630000,269.800000,115,12.140000,9.000000,7,2.430000,2,False.\r\nID,138,415,339-7485,yes,yes,17,225.200000,116,38.280000,173.400000,88,14.740000,145.800000,99,6.560000,11.700000,4,3.160000,0,False.\r\nSC,102,408,368-3078,no,no,0,174.500000,73,29.670000,213.700000,114,18.160000,164.700000,116,7.410000,10.300000,5,2.780000,4,False.\r\nMO,76,510,418-7055,no,no,0,129.700000,84,22.050000,177.500000,80,15.090000,228.900000,87,10.300000,7.500000,3,2.030000,5,True.\r\nNE,99,415,386-9981,no,no,0,200.000000,66,34.000000,107.900000,104,9.170000,233.700000,82,10.520000,11.400000,2,3.080000,3,False.\r\nNC,83,510,366-2541,no,yes,36,95.900000,87,16.300000,261.600000,105,22.240000,228.600000,109,10.290000,13.300000,4,3.590000,0,False.\r\nME,36,510,335-3110,no,yes,25,152.800000,110,25.980000,242.800000,67,20.640000,147.400000,74,6.630000,9.100000,2,2.460000,1,False.\r\nMI,70,510,400-7809,no,no,0,129.900000,102,22.080000,208.700000,133,17.740000,231.400000,93,10.410000,14.300000,3,3.860000,1,False.\r\nAZ,109,415,404-3106,yes,no,0,268.400000,85,45.630000,150.600000,131,12.800000,297.900000,84,13.410000,9.700000,8,2.620000,2,True.\r\nAZ,100,415,333-2337,no,no,0,188.500000,152,32.050000,148.300000,115,12.610000,179.800000,88,8.090000,15.200000,5,4.100000,2,False.\r\nHI,104,408,353-6482,no,no,0,170.600000,97,29.000000,162.100000,111,13.780000,210.700000,131,9.480000,6.100000,1,1.650000,1,False.\r\nNJ,106,415,397-8162,no,no,0,191.400000,124,32.540000,200.700000,116,17.060000,230.100000,76,10.350000,8.200000,3,2.210000,1,False.\r\nMS,84,510,380-6722,no,no,0,75.300000,96,12.800000,179.900000,113,15.290000,193.800000,134,8.720000,12.300000,1,3.320000,1,False.\r\nMA,80,510,329-2918,no,no,0,149.800000,123,25.470000,276.300000,75,23.490000,241.400000,75,10.860000,10.900000,7,2.940000,2,False.\r\nSC,100,510,348-8022,no,no,0,115.900000,87,19.700000,111.300000,56,9.460000,170.200000,77,7.660000,7.100000,4,1.920000,1,False.\r\nMN,99,408,388-4459,no,no,0,128.800000,86,21.900000,203.900000,105,17.330000,282.600000,131,12.720000,14.100000,4,3.810000,2,False.\r\nWV,50,510,358-3114,no,no,0,131.700000,108,22.390000,216.500000,103,18.400000,196.100000,126,8.820000,11.000000,5,2.970000,1,False.\r\nMS,105,415,343-9654,no,no,0,101.400000,48,17.240000,159.100000,119,13.520000,259.200000,53,11.660000,12.200000,2,3.290000,1,False.\r\nVA,113,415,401-9909,no,yes,23,149.000000,104,25.330000,235.800000,67,20.040000,201.800000,76,9.080000,9.500000,5,2.570000,4,False.\r\nMS,111,415,404-9978,no,yes,36,96.800000,123,16.460000,170.600000,105,14.500000,166.000000,85,7.470000,13.400000,4,3.620000,2,False.\r\nNM,161,408,397-8011,no,no,0,107.500000,121,18.280000,256.400000,46,21.790000,247.200000,131,11.120000,12.600000,3,3.400000,2,False.\r\nTX,70,415,341-8719,no,no,0,232.800000,95,39.580000,303.400000,111,25.790000,255.600000,104,11.500000,12.900000,7,3.480000,0,True.\r\nHI,97,415,408-1242,no,yes,43,121.100000,105,20.590000,260.200000,115,22.120000,222.400000,100,10.010000,8.300000,5,2.240000,3,False.\r\nWA,130,510,406-7726,no,no,0,124.300000,70,21.130000,270.700000,99,23.010000,239.500000,83,10.780000,3.500000,6,0.950000,0,False.\r\nWI,92,415,351-2773,no,no,0,157.700000,101,26.810000,298.600000,100,25.380000,216.900000,99,9.760000,13.800000,3,3.730000,1,False.\r\nCO,119,408,368-6174,no,no,0,124.300000,68,21.130000,207.100000,88,17.600000,157.400000,93,7.080000,14.800000,1,4.000000,0,False.\r\nNV,115,415,334-5029,no,no,0,286.400000,125,48.690000,205.700000,74,17.480000,191.400000,141,8.610000,6.900000,6,1.860000,1,True.\r\nRI,134,415,413-1789,no,no,0,141.700000,95,24.090000,205.600000,101,17.480000,218.500000,60,9.830000,8.800000,6,2.380000,0,False.\r\nVA,127,408,414-1246,no,yes,25,173.000000,91,29.410000,245.800000,64,20.890000,300.000000,99,13.500000,4.800000,3,1.300000,0,False.\r\nNJ,80,415,330-4978,no,no,0,268.700000,120,45.680000,301.000000,147,25.590000,167.000000,140,7.520000,5.800000,1,1.570000,2,True.\r\nND,153,415,386-1631,no,yes,31,218.500000,130,37.150000,134.200000,103,11.410000,118.900000,105,5.350000,9.400000,6,2.540000,0,False.\r\nMN,85,415,363-1208,no,no,0,255.300000,114,43.400000,194.600000,83,16.540000,276.600000,78,12.450000,3.700000,5,1.000000,3,False.\r\nHI,79,415,334-5263,no,no,0,41.900000,124,7.120000,211.000000,95,17.940000,237.900000,55,10.710000,11.400000,5,3.080000,1,False.\r\nND,35,415,361-4137,no,no,0,260.800000,87,44.340000,258.100000,78,21.940000,131.300000,123,5.910000,5.800000,2,1.570000,1,False.\r\nWI,120,408,374-8187,no,yes,26,239.400000,94,40.700000,259.400000,88,22.050000,238.000000,132,10.710000,7.700000,3,2.080000,0,False.\r\nMN,68,510,370-1525,no,no,0,226.700000,94,38.540000,168.400000,129,14.310000,188.700000,117,8.490000,10.200000,1,2.750000,0,False.\r\nDC,60,408,355-3801,no,no,0,179.300000,147,30.480000,208.900000,89,17.760000,248.200000,98,11.170000,13.500000,6,3.650000,1,True.\r\nKS,120,510,392-5605,no,no,0,158.000000,110,26.860000,197.000000,103,16.750000,154.900000,132,6.970000,10.000000,5,2.700000,1,False.\r\nMT,71,510,363-1366,no,yes,23,175.700000,82,29.870000,258.900000,136,22.010000,268.400000,154,12.080000,14.100000,7,3.810000,1,False.\r\nWV,124,415,358-5274,no,no,0,157.400000,107,26.760000,167.800000,112,14.260000,188.800000,102,8.500000,8.800000,3,2.380000,3,False.\r\nME,23,510,376-9607,no,no,0,113.100000,74,19.230000,168.800000,95,14.350000,262.900000,126,11.830000,6.900000,2,1.860000,1,True.\r\nWY,225,415,374-1213,no,no,0,182.700000,142,31.060000,246.500000,63,20.950000,218.000000,103,9.810000,8.800000,2,2.380000,1,False.\r\nNY,181,415,421-8537,yes,no,0,161.300000,83,27.420000,124.400000,83,10.570000,262.000000,98,11.790000,14.100000,3,3.810000,0,True.\r\nVT,63,415,351-5576,no,no,0,142.500000,92,24.230000,208.300000,102,17.710000,228.900000,120,10.300000,7.500000,2,2.030000,2,False.\r\nNC,54,415,407-7258,yes,no,0,190.500000,108,32.390000,259.700000,108,22.070000,141.500000,111,6.370000,9.700000,2,2.620000,2,True.\r\nMO,80,408,405-4420,yes,yes,15,159.300000,110,27.080000,170.600000,120,14.500000,141.200000,82,6.350000,11.900000,5,3.210000,1,False.\r\nNC,118,408,340-2855,yes,yes,39,153.800000,106,26.150000,123.300000,111,10.480000,117.800000,103,5.300000,9.200000,6,2.480000,1,False.\r\nNJ,42,408,342-8002,yes,no,0,180.700000,127,30.720000,174.600000,94,14.840000,165.300000,114,7.440000,12.000000,6,3.240000,2,False.\r\nOH,134,408,355-6826,no,no,0,202.700000,105,34.460000,224.900000,90,19.120000,253.900000,108,11.430000,12.100000,7,3.270000,0,False.\r\nTX,66,415,402-3886,no,yes,35,190.800000,100,32.440000,261.300000,93,22.210000,209.500000,108,9.430000,8.900000,6,2.400000,0,False.\r\nWA,66,415,336-5900,no,no,0,205.100000,102,34.870000,232.700000,109,19.780000,259.900000,95,11.700000,9.200000,6,2.480000,2,False.\r\nTN,127,415,339-7684,no,yes,28,235.600000,124,40.050000,236.800000,113,20.130000,241.200000,127,10.850000,7.700000,2,2.080000,1,False.\r\nHI,146,510,390-2433,no,no,0,189.300000,77,32.180000,155.900000,128,13.250000,186.000000,83,8.370000,7.400000,3,2.000000,0,False.\r\nWY,93,408,360-7246,no,yes,42,166.900000,101,28.370000,273.200000,84,23.220000,171.000000,106,7.690000,11.500000,1,3.110000,1,False.\r\nCT,77,415,335-6508,no,no,0,245.200000,87,41.680000,254.100000,83,21.600000,239.400000,91,10.770000,7.500000,4,2.030000,0,True.\r\nNM,111,415,348-6720,no,no,0,132.600000,125,22.540000,221.100000,67,18.790000,127.900000,101,5.760000,12.700000,2,3.430000,4,True.\r\nNJ,125,415,406-6400,no,no,0,182.300000,64,30.990000,139.800000,121,11.880000,171.600000,96,7.720000,11.600000,7,3.130000,2,False.\r\nAL,115,510,390-7370,no,yes,14,192.300000,86,32.690000,88.700000,90,7.540000,229.400000,120,10.320000,10.500000,3,2.840000,2,False.\r\nMN,115,510,390-5055,yes,no,0,122.000000,110,20.740000,220.200000,100,18.720000,179.700000,124,8.090000,10.800000,2,2.920000,2,True.\r\nNC,114,408,405-7542,no,no,0,193.000000,101,32.810000,250.000000,81,21.250000,133.300000,79,6.000000,9.600000,2,2.590000,2,False.\r\nOH,106,415,364-4927,no,no,0,158.600000,112,26.960000,220.000000,114,18.700000,252.900000,106,11.380000,9.100000,3,2.460000,0,False.\r\nND,118,415,329-3458,no,yes,39,91.500000,125,15.560000,219.900000,113,18.690000,229.000000,99,10.310000,12.700000,8,3.430000,2,False.\r\nCO,59,510,331-3842,no,no,0,153.600000,92,26.110000,205.500000,88,17.470000,114.500000,89,5.150000,12.500000,10,3.380000,1,False.\r\nND,87,415,343-4147,yes,yes,40,221.600000,79,37.670000,157.100000,74,13.350000,222.400000,124,10.010000,11.500000,3,3.110000,1,False.\r\nNY,21,415,335-2274,no,no,0,244.700000,81,41.600000,168.000000,117,14.280000,281.500000,87,12.670000,6.600000,1,1.780000,1,False.\r\nWI,142,408,343-3227,no,yes,24,239.800000,103,40.770000,285.900000,65,24.300000,256.700000,106,11.550000,9.500000,4,2.570000,0,False.\r\nWY,62,415,336-6907,no,no,0,172.400000,132,29.310000,230.500000,100,19.590000,228.200000,109,10.270000,11.000000,5,2.970000,0,False.\r\nOR,149,415,331-1391,no,no,0,242.500000,83,41.230000,245.400000,97,20.860000,219.600000,80,9.880000,10.000000,3,2.700000,3,True.\r\nCO,54,510,360-1643,no,yes,39,117.600000,82,19.990000,159.200000,60,13.530000,236.400000,113,10.640000,11.300000,10,3.050000,2,False.\r\nLA,112,510,410-2518,no,no,0,174.500000,127,29.670000,259.300000,71,22.040000,170.500000,120,7.670000,11.300000,7,3.050000,1,False.\r\nAL,68,510,344-4970,no,no,0,157.300000,83,26.740000,220.900000,85,18.780000,218.900000,129,9.850000,12.000000,7,3.240000,1,False.\r\nTX,201,415,408-1486,no,yes,21,192.000000,97,32.640000,239.100000,81,20.320000,116.100000,125,5.220000,15.100000,3,4.080000,1,False.\r\nPA,88,510,396-1648,no,no,0,218.200000,76,37.090000,169.300000,60,14.390000,141.100000,99,6.350000,8.000000,1,2.160000,1,False.\r\nIL,85,415,391-2022,no,yes,29,144.600000,97,24.580000,140.000000,102,11.900000,165.400000,148,7.440000,10.900000,3,2.940000,1,False.\r\nDE,51,415,420-6465,yes,no,0,153.600000,108,26.110000,232.900000,85,19.800000,214.200000,92,9.640000,14.100000,4,3.810000,0,True.\r\nMO,45,510,398-2628,no,yes,29,135.800000,104,23.090000,222.500000,101,18.910000,235.600000,92,10.600000,7.900000,6,2.130000,2,False.\r\nAR,116,510,409-5519,no,no,0,160.700000,69,27.320000,146.800000,106,12.480000,287.800000,144,12.950000,8.200000,5,2.210000,0,False.\r\nOH,146,408,391-8554,no,yes,31,202.500000,91,34.430000,241.400000,108,20.520000,169.600000,77,7.630000,7.800000,2,2.110000,1,False.\r\nWI,63,510,395-1693,no,yes,34,152.200000,119,25.870000,227.100000,91,19.300000,195.700000,103,8.810000,12.300000,5,3.320000,1,False.\r\nGA,133,510,393-3194,no,no,0,227.400000,90,38.660000,73.200000,135,6.220000,114.300000,99,5.140000,4.700000,7,1.270000,0,False.\r\nKY,125,408,328-3402,no,no,0,191.600000,115,32.570000,205.600000,108,17.480000,210.200000,123,9.460000,9.200000,3,2.480000,2,False.\r\nOH,72,510,411-4781,no,no,0,138.900000,111,23.610000,211.600000,102,17.990000,179.500000,91,8.080000,10.800000,3,2.920000,1,False.\r\nTN,130,408,401-2581,no,no,0,127.000000,102,21.590000,206.900000,107,17.590000,231.700000,99,10.430000,6.100000,6,1.650000,0,False.\r\nSD,97,415,385-1214,no,no,0,168.600000,87,28.660000,259.200000,105,22.030000,279.800000,123,12.590000,7.300000,4,1.970000,1,False.\r\nNY,54,415,348-6853,no,no,0,286.600000,73,48.720000,223.200000,108,18.970000,203.700000,107,9.170000,11.500000,5,3.110000,1,True.\r\nGA,160,415,341-8412,no,yes,29,164.600000,121,27.980000,262.800000,108,22.340000,123.800000,131,5.570000,15.200000,4,4.100000,1,False.\r\nTX,79,415,330-8142,no,no,0,144.000000,90,24.480000,135.800000,91,11.540000,212.400000,129,9.560000,13.000000,4,3.510000,1,False.\r\nWV,92,415,361-1404,no,yes,47,141.600000,95,24.070000,207.900000,130,17.670000,203.600000,95,9.160000,10.200000,11,2.750000,0,False.\r\nMI,59,415,375-9671,no,no,0,204.300000,65,34.730000,247.300000,123,21.020000,214.700000,94,9.660000,12.000000,4,3.240000,1,False.\r\nND,132,510,372-1824,no,no,0,163.200000,80,27.740000,167.600000,90,14.250000,87.500000,90,3.940000,6.200000,10,1.670000,1,False.\r\nNE,21,510,408-3606,no,no,0,225.000000,110,38.250000,244.200000,111,20.760000,221.200000,93,9.950000,10.700000,4,2.890000,0,False.\r\nSD,93,415,333-3595,no,no,0,176.100000,103,29.940000,199.700000,130,16.970000,263.900000,96,11.880000,8.500000,6,2.300000,2,False.\r\nNJ,147,415,379-7009,no,yes,36,254.200000,78,43.210000,228.100000,105,19.390000,98.000000,125,4.410000,13.800000,7,3.730000,5,False.\r\nAK,101,510,411-4940,no,no,0,174.900000,105,29.730000,262.000000,75,22.270000,210.000000,93,9.450000,8.500000,5,2.300000,1,False.\r\nCT,125,415,409-7523,yes,no,0,187.300000,118,31.840000,160.700000,111,13.660000,263.800000,112,11.870000,9.600000,2,2.590000,0,True.\r\nCO,63,415,408-6725,no,no,0,211.800000,84,36.010000,230.900000,137,19.630000,217.100000,99,9.770000,10.700000,9,2.890000,3,False.\r\nMD,107,415,350-2384,no,no,0,241.900000,102,41.120000,126.900000,117,10.790000,185.600000,92,8.350000,11.700000,6,3.160000,0,False.\r\nND,110,408,348-1706,no,no,0,196.100000,103,33.340000,199.700000,123,16.970000,135.900000,71,6.120000,12.900000,1,3.480000,3,False.\r\nNH,83,415,415-6145,no,no,0,231.300000,100,39.320000,210.400000,84,17.880000,217.400000,106,9.780000,12.400000,2,3.350000,3,False.\r\nMN,117,408,373-3731,no,no,0,161.600000,104,27.470000,196.300000,119,16.690000,294.800000,111,13.270000,13.800000,2,3.730000,1,False.\r\nKY,124,415,341-3349,no,no,0,194.000000,103,32.980000,241.000000,116,20.490000,227.500000,153,10.240000,11.900000,5,3.210000,0,False.\r\nNH,115,510,399-8859,no,no,0,109.700000,148,18.650000,223.800000,87,19.020000,240.300000,96,10.810000,15.400000,8,4.160000,3,False.\r\nCO,156,408,377-4518,yes,no,0,277.000000,119,47.090000,238.300000,106,20.260000,94.400000,96,4.250000,8.300000,3,2.240000,1,False.\r\nKY,89,408,341-1594,no,no,0,192.100000,83,32.660000,163.600000,88,13.910000,169.700000,138,7.640000,6.100000,3,1.650000,0,False.\r\nKY,72,415,418-8770,no,no,0,198.400000,147,33.730000,216.900000,121,18.440000,112.800000,125,5.080000,13.100000,4,3.540000,0,False.\r\nIN,101,415,332-9118,no,yes,42,209.200000,82,35.560000,159.700000,74,13.570000,181.600000,100,8.170000,9.500000,3,2.570000,0,False.\r\nOR,53,415,386-1418,no,no,0,184.800000,98,31.420000,216.400000,125,18.390000,141.100000,116,6.350000,18.400000,3,4.970000,2,False.\r\nSD,116,408,393-3535,no,no,0,167.800000,119,28.530000,142.000000,123,12.070000,190.700000,128,8.580000,7.300000,4,1.970000,2,False.\r\nDE,78,408,328-9006,no,no,0,139.200000,140,23.660000,191.400000,113,16.270000,286.500000,125,12.890000,11.800000,3,3.190000,3,False.\r\nOR,117,415,402-2482,no,yes,17,221.300000,82,37.620000,167.600000,100,14.250000,262.700000,87,11.820000,4.400000,4,1.190000,0,False.\r\nNE,56,510,408-4865,no,no,0,121.600000,84,20.670000,165.300000,115,14.050000,243.900000,95,10.980000,8.900000,2,2.400000,1,False.\r\nOH,123,408,396-6247,no,yes,39,270.400000,99,45.970000,245.100000,110,20.830000,108.900000,113,4.900000,15.400000,7,4.160000,1,False.\r\nOH,127,408,396-9462,no,no,0,139.600000,94,23.730000,240.900000,112,20.480000,127.100000,88,5.720000,8.800000,4,2.380000,2,False.\r\nAR,116,415,396-9279,no,yes,23,253.000000,78,43.010000,138.900000,121,11.810000,277.800000,104,12.500000,11.800000,3,3.190000,2,False.\r\nKS,138,510,363-8715,no,yes,26,183.900000,83,31.260000,240.700000,93,20.460000,185.700000,125,8.360000,15.000000,3,4.050000,1,False.\r\nTX,120,415,356-1358,no,no,0,203.300000,108,34.560000,259.900000,66,22.090000,115.900000,103,5.220000,7.800000,2,2.110000,3,False.\r\nWI,102,408,360-7839,no,no,0,200.600000,106,34.100000,152.500000,127,12.960000,199.400000,128,8.970000,7.700000,2,2.080000,3,False.\r\nOR,95,415,364-8774,no,no,0,167.600000,96,28.490000,176.000000,89,14.960000,250.900000,113,11.290000,13.400000,6,3.620000,2,False.\r\nVA,102,408,348-5038,no,no,0,156.500000,67,26.610000,204.300000,103,17.370000,141.900000,72,6.390000,9.900000,2,2.670000,2,False.\r\nAR,89,415,365-4728,no,yes,25,215.100000,140,36.570000,197.400000,69,16.780000,162.100000,117,7.290000,10.600000,10,2.860000,1,False.\r\nCT,50,408,351-9037,no,no,0,301.700000,82,51.290000,167.100000,118,14.200000,72.200000,89,3.250000,10.500000,6,2.840000,1,False.\r\nOH,93,415,397-9184,no,yes,42,152.300000,90,25.890000,267.500000,102,22.740000,266.900000,130,12.010000,11.300000,5,3.050000,7,False.\r\nWY,68,510,398-4538,no,no,0,195.400000,116,33.220000,212.100000,101,18.030000,138.400000,134,6.230000,15.100000,11,4.080000,1,False.\r\nIL,70,408,382-6827,no,no,0,208.700000,97,35.480000,275.500000,83,23.420000,182.500000,122,8.210000,8.000000,3,2.160000,2,False.\r\nMO,138,415,408-1340,no,yes,29,190.100000,87,32.320000,223.200000,123,18.970000,256.200000,130,11.530000,14.200000,6,3.830000,0,False.\r\nDC,141,415,333-9511,no,yes,37,185.400000,87,31.520000,178.500000,128,15.170000,218.300000,107,9.820000,8.000000,3,2.160000,4,False.\r\nMA,112,415,358-7379,no,yes,17,183.200000,95,31.140000,252.800000,125,21.490000,156.700000,95,7.050000,9.700000,3,2.620000,0,False.\r\nNH,117,510,397-1766,yes,no,0,54.200000,100,9.210000,303.200000,84,25.770000,171.800000,84,7.730000,8.600000,2,2.320000,1,True.\r\nIA,1,408,331-2144,no,yes,26,208.000000,115,35.360000,185.000000,113,15.730000,177.700000,144,8.000000,8.100000,9,2.190000,1,False.\r\nAL,70,415,345-7014,no,no,0,230.300000,110,39.150000,77.900000,87,6.620000,247.100000,105,11.120000,13.200000,4,3.560000,1,False.\r\nOR,87,510,395-1898,no,yes,22,240.800000,102,40.940000,75.900000,106,6.450000,224.600000,115,10.110000,7.100000,3,1.920000,2,False.\r\nWV,52,510,373-8920,no,yes,21,195.700000,119,33.270000,106.200000,95,9.030000,157.400000,94,7.080000,5.300000,3,1.430000,2,False.\r\nWA,97,408,373-8908,no,no,0,276.100000,82,46.940000,201.100000,106,17.090000,231.300000,73,10.410000,8.900000,4,2.400000,0,True.\r\nNV,105,408,415-1203,no,no,0,166.100000,93,28.240000,175.900000,106,14.950000,243.500000,55,10.960000,16.200000,3,4.370000,2,False.\r\nSC,77,510,369-7017,no,yes,28,135.900000,117,23.100000,244.500000,102,20.780000,207.500000,74,9.340000,11.500000,3,3.110000,4,True.\r\nNC,80,415,420-8435,yes,no,0,189.100000,122,32.150000,223.200000,92,18.970000,269.000000,116,12.110000,13.900000,3,3.750000,2,True.\r\nNH,120,510,395-2579,no,yes,43,177.900000,117,30.240000,175.100000,70,14.880000,161.300000,117,7.260000,11.500000,4,3.110000,1,False.\r\nWY,54,408,405-7850,no,yes,39,143.900000,73,24.460000,210.300000,117,17.880000,129.200000,117,5.810000,12.500000,8,3.380000,2,False.\r\nFL,148,510,394-7710,yes,no,0,148.200000,138,25.190000,159.600000,123,13.570000,197.400000,62,8.880000,8.600000,3,2.320000,2,False.\r\nPA,119,408,342-4122,no,no,0,287.100000,115,48.810000,159.300000,99,13.540000,216.800000,86,9.760000,13.900000,1,3.750000,2,True.\r\nNC,162,408,340-1876,no,yes,26,179.700000,144,30.550000,218.100000,129,18.540000,212.300000,105,9.550000,9.300000,8,2.510000,1,True.\r\nMO,85,510,383-6095,no,no,0,165.800000,96,28.190000,190.000000,141,16.150000,144.000000,116,6.480000,10.900000,3,2.940000,5,True.\r\nKS,101,510,413-1061,no,yes,25,144.100000,144,24.500000,167.600000,105,14.250000,240.000000,107,10.800000,14.500000,3,3.920000,1,False.\r\nKY,172,415,343-5347,no,no,0,172.500000,85,29.330000,253.100000,71,21.510000,221.600000,113,9.970000,5.900000,6,1.590000,0,False.\r\nDE,80,415,376-4861,no,no,0,199.800000,138,33.970000,167.100000,91,14.200000,271.800000,94,12.230000,5.500000,4,1.490000,1,False.\r\nWI,67,510,417-2265,no,no,0,109.100000,134,18.550000,142.300000,76,12.100000,91.200000,86,4.100000,10.900000,5,2.940000,2,False.\r\nCO,86,408,419-7415,no,no,0,171.800000,106,29.210000,301.700000,44,25.640000,139.400000,108,6.270000,9.700000,5,2.620000,1,False.\r\nNM,107,415,407-2259,no,no,0,222.300000,101,37.790000,286.000000,111,24.310000,249.400000,117,11.220000,12.100000,4,3.270000,1,True.\r\nDE,133,510,333-2906,no,no,0,245.800000,102,41.790000,264.700000,90,22.500000,111.700000,103,5.030000,11.200000,7,3.020000,0,False.\r\nIL,116,510,360-7477,no,no,0,164.600000,110,27.980000,270.600000,103,23.000000,230.400000,109,10.370000,8.000000,3,2.160000,0,False.\r\nWA,63,408,342-5243,no,no,0,211.700000,107,35.990000,271.700000,77,23.090000,203.300000,108,9.150000,7.400000,7,2.000000,0,False.\r\nMA,119,408,417-3999,yes,yes,16,147.200000,103,25.020000,160.100000,96,13.610000,184.000000,120,8.280000,7.700000,2,2.080000,0,True.\r\nOH,133,408,379-1720,yes,no,0,254.700000,103,43.300000,252.200000,80,21.440000,178.100000,103,8.010000,8.000000,3,2.160000,0,True.\r\nTN,94,408,368-3117,yes,no,0,170.100000,113,28.920000,271.800000,94,23.100000,110.700000,78,4.980000,8.700000,4,2.350000,1,False.\r\nMA,69,510,352-5000,no,no,0,195.100000,91,33.170000,261.500000,57,22.230000,203.800000,90,9.170000,11.400000,5,3.080000,0,False.\r\nMI,146,408,405-7676,no,no,0,149.300000,83,25.380000,187.100000,130,15.900000,149.800000,100,6.740000,7.900000,4,2.130000,7,True.\r\nTX,119,510,361-2349,no,no,0,81.900000,75,13.920000,253.800000,114,21.570000,213.100000,125,9.590000,8.900000,1,2.400000,2,True.\r\nNH,142,408,383-2901,yes,yes,25,191.100000,109,32.490000,149.600000,120,12.720000,227.800000,60,10.250000,9.800000,3,2.650000,0,False.\r\nMD,123,408,369-7049,no,no,0,206.900000,115,35.170000,224.400000,86,19.070000,197.400000,60,8.880000,8.300000,2,2.240000,3,False.\r\nMS,101,408,387-5533,no,no,0,239.000000,156,40.630000,273.000000,106,23.210000,278.200000,93,12.520000,13.500000,8,3.650000,1,True.\r\nAZ,43,415,362-3660,no,no,0,179.300000,97,30.480000,252.700000,126,21.480000,227.500000,114,10.240000,8.000000,5,2.160000,0,False.\r\nIN,69,408,357-3577,no,no,0,185.300000,91,31.500000,219.100000,88,18.620000,243.600000,107,10.960000,5.500000,5,1.490000,0,False.\r\nNY,15,510,394-3312,yes,no,0,141.400000,80,24.040000,123.900000,76,10.530000,323.500000,88,14.560000,8.100000,3,2.190000,2,False.\r\nWI,107,510,395-8330,no,yes,25,248.600000,91,42.260000,119.300000,115,10.140000,194.300000,83,8.740000,12.000000,1,3.240000,1,False.\r\nWV,67,510,373-8895,no,no,0,152.500000,131,25.930000,252.400000,107,21.450000,185.400000,104,8.340000,4.900000,3,1.320000,2,False.\r\nNY,99,415,386-4581,no,no,0,145.600000,102,24.750000,230.900000,87,19.630000,181.500000,86,8.170000,11.400000,7,3.080000,1,False.\r\nOK,46,415,354-8191,no,no,0,164.200000,116,27.910000,196.200000,153,16.680000,236.100000,119,10.620000,8.100000,1,2.190000,1,False.\r\nNC,55,408,359-7562,yes,no,0,221.000000,115,37.570000,165.400000,97,14.060000,235.400000,117,10.590000,9.700000,4,2.620000,1,False.\r\nKY,39,415,359-4336,no,no,0,295.400000,126,50.220000,232.100000,117,19.730000,204.400000,123,9.200000,11.500000,2,3.110000,1,True.\r\nTX,92,510,336-9901,no,no,0,139.800000,98,23.770000,174.900000,143,14.870000,201.600000,135,9.070000,9.400000,7,2.540000,2,False.\r\nCT,56,415,406-3069,no,no,0,162.300000,99,27.590000,149.100000,78,12.670000,255.500000,115,11.500000,14.800000,1,4.000000,4,True.\r\nNE,76,415,334-6519,no,no,0,272.700000,97,46.360000,236.400000,95,20.090000,235.500000,105,10.600000,7.700000,2,2.080000,0,True.\r\nHI,132,408,361-8113,yes,yes,33,200.300000,75,34.050000,226.600000,67,19.260000,198.800000,91,8.950000,12.900000,3,3.480000,2,False.\r\nSC,140,510,347-9769,no,yes,28,157.100000,77,26.710000,172.400000,97,14.650000,184.500000,94,8.300000,11.100000,9,3.000000,1,False.\r\nAK,51,510,352-9130,yes,yes,12,135.800000,60,23.090000,200.600000,134,17.050000,192.400000,98,8.660000,12.300000,7,3.320000,2,False.\r\nSD,27,408,378-4557,no,no,0,236.700000,110,40.240000,231.900000,92,19.710000,164.700000,85,7.410000,12.700000,6,3.430000,1,False.\r\nID,224,510,360-8919,no,no,0,111.400000,133,18.940000,175.000000,66,14.880000,217.200000,106,9.770000,5.500000,6,1.490000,3,False.\r\nOK,105,510,405-4109,yes,yes,28,156.100000,89,26.540000,107.100000,114,9.100000,167.700000,95,7.550000,14.700000,3,3.970000,0,True.\r\nWA,117,408,381-2498,no,no,0,191.100000,93,32.490000,282.800000,56,24.040000,84.800000,118,3.820000,12.000000,4,3.240000,2,False.\r\nSD,91,415,357-5696,no,no,0,153.000000,123,26.010000,141.100000,127,11.990000,171.500000,76,7.720000,10.300000,15,2.780000,1,True.\r\nOH,135,415,412-2947,no,no,0,218.800000,123,37.200000,242.800000,64,20.640000,85.800000,80,3.860000,10.300000,3,2.780000,4,False.\r\nVA,146,415,363-3571,no,no,0,205.400000,101,34.920000,134.900000,77,11.470000,310.500000,83,13.970000,10.300000,2,2.780000,3,False.\r\nWI,147,415,405-5403,yes,no,0,225.200000,111,38.280000,184.900000,98,15.720000,143.200000,146,6.440000,9.900000,1,2.670000,0,True.\r\nIN,68,510,330-9354,no,no,0,249.900000,127,42.480000,254.500000,118,21.630000,273.200000,98,12.290000,8.900000,6,2.400000,2,True.\r\nNM,68,408,396-7091,no,no,0,131.600000,89,22.370000,137.000000,109,11.650000,256.300000,107,11.530000,10.200000,5,2.750000,3,False.\r\nHI,86,408,398-3004,no,yes,21,197.900000,99,33.640000,165.600000,100,14.080000,208.000000,120,9.360000,10.100000,9,2.730000,0,False.\r\nKY,131,415,400-4020,no,no,0,166.500000,129,28.310000,210.200000,107,17.870000,257.200000,93,11.570000,9.900000,5,2.670000,1,False.\r\nOH,86,415,356-3448,no,yes,29,225.400000,79,38.320000,187.100000,112,15.900000,281.100000,112,12.650000,12.900000,3,3.480000,1,False.\r\nVT,159,415,335-2019,no,no,0,275.800000,103,46.890000,189.500000,108,16.110000,223.900000,93,10.080000,7.400000,5,2.000000,2,True.\r\nAZ,134,415,332-6633,no,yes,40,142.900000,105,24.290000,88.600000,61,7.530000,290.000000,96,13.050000,10.800000,6,2.920000,1,False.\r\nVT,113,510,359-7648,no,no,0,207.200000,113,35.220000,256.000000,80,21.760000,211.000000,87,9.490000,9.900000,1,2.670000,1,False.\r\nMO,132,408,412-9190,no,no,0,206.200000,100,35.050000,211.200000,118,17.950000,196.200000,122,8.830000,10.200000,6,2.750000,1,False.\r\nAL,85,415,368-9007,no,no,0,210.300000,66,35.750000,195.800000,76,16.640000,221.600000,82,9.970000,11.200000,7,3.020000,1,False.\r\nNJ,93,510,384-5632,yes,yes,38,225.700000,117,38.370000,119.600000,122,10.170000,193.200000,125,8.690000,14.000000,7,3.780000,1,True.\r\nWA,174,408,352-6068,no,yes,33,167.800000,91,28.530000,205.300000,91,17.450000,130.000000,132,5.850000,14.500000,4,3.920000,4,True.\r\nNY,61,415,343-9645,no,no,0,197.700000,118,33.610000,152.200000,96,12.940000,221.000000,93,9.950000,7.000000,3,1.890000,2,False.\r\nDC,91,415,384-7873,no,yes,39,169.800000,105,28.870000,65.200000,116,5.540000,144.400000,92,6.500000,10.900000,4,2.940000,1,False.\r\nNE,88,408,396-2187,no,yes,28,190.600000,104,32.400000,237.300000,105,20.170000,211.600000,116,9.520000,9.800000,1,2.650000,2,False.\r\nMA,88,408,383-5109,no,yes,45,80.300000,140,13.650000,153.300000,101,13.030000,309.200000,123,13.910000,12.800000,3,3.460000,2,False.\r\nVT,195,415,377-7843,no,yes,36,231.700000,110,39.390000,225.100000,88,19.130000,201.700000,89,9.080000,12.100000,2,3.270000,0,False.\r\nNM,182,415,382-7999,no,no,0,69.100000,114,11.750000,230.300000,109,19.580000,256.700000,96,11.550000,6.500000,4,1.760000,0,False.\r\nCO,118,408,328-1222,no,no,0,188.800000,60,32.100000,217.400000,64,18.480000,220.100000,100,9.900000,8.200000,7,2.210000,4,False.\r\nNH,103,408,371-1727,no,no,0,150.600000,125,25.600000,169.100000,126,14.370000,221.200000,104,9.950000,10.400000,8,2.810000,8,True.\r\nIL,65,510,369-8871,no,no,0,192.000000,89,32.640000,139.500000,88,11.860000,187.400000,102,8.430000,5.500000,4,1.490000,2,False.\r\nUT,61,408,335-9726,no,yes,25,163.700000,78,27.830000,113.200000,112,9.620000,134.100000,118,6.030000,9.900000,3,2.670000,3,False.\r\nWV,172,415,357-3709,no,no,0,211.700000,100,35.990000,198.700000,101,16.890000,301.700000,136,13.580000,6.500000,9,1.760000,1,False.\r\nNJ,72,415,422-9964,no,no,0,175.500000,103,29.840000,132.300000,120,11.250000,242.900000,96,10.930000,11.800000,3,3.190000,1,False.\r\nNM,113,510,366-9211,no,no,0,150.100000,120,25.520000,200.100000,85,17.010000,266.700000,105,12.000000,11.000000,3,2.970000,2,False.\r\nND,177,408,384-9033,no,no,0,189.500000,99,32.220000,176.300000,117,14.990000,225.900000,112,10.170000,14.200000,2,3.830000,1,False.\r\nWA,100,408,382-4932,no,no,0,70.800000,94,12.040000,215.600000,102,18.330000,230.800000,125,10.390000,9.500000,1,2.570000,6,True.\r\nNM,67,415,404-7518,no,no,0,215.500000,102,36.640000,190.700000,95,16.210000,214.500000,106,9.650000,8.600000,6,2.320000,1,False.\r\nDE,136,415,353-1954,no,no,0,101.700000,105,17.290000,202.800000,99,17.240000,136.200000,119,6.130000,9.400000,6,2.540000,3,False.\r\nGA,71,415,391-7166,no,no,0,258.400000,132,43.930000,126.800000,119,10.780000,182.400000,87,8.210000,9.700000,8,2.620000,4,False.\r\nHI,134,408,370-9000,no,no,0,242.400000,126,41.210000,152.900000,115,13.000000,318.300000,115,14.320000,11.800000,6,3.190000,1,False.\r\nCT,124,415,332-3642,no,no,0,131.800000,82,22.410000,284.300000,119,24.170000,305.500000,101,13.750000,11.300000,2,3.050000,1,False.\r\nNJ,84,415,412-3898,no,no,0,190.200000,102,32.330000,197.700000,141,16.800000,247.500000,102,11.140000,9.800000,6,2.650000,2,False.\r\nME,39,408,366-5640,no,no,0,154.100000,104,26.200000,204.200000,112,17.360000,196.200000,92,8.830000,9.800000,4,2.650000,2,False.\r\nOK,110,510,356-2302,no,no,0,188.000000,127,31.960000,90.500000,118,7.690000,150.300000,64,6.760000,15.300000,3,4.130000,3,False.\r\nTN,102,510,345-9018,no,no,0,103.100000,70,17.530000,275.000000,129,23.380000,141.100000,92,6.350000,11.200000,5,3.020000,1,False.\r\nWY,70,415,365-6205,no,no,0,175.400000,130,29.820000,159.500000,130,13.560000,260.600000,96,11.730000,11.600000,4,3.130000,0,False.\r\nNY,142,415,337-1151,no,no,0,145.400000,93,24.720000,209.100000,98,17.770000,214.000000,96,9.630000,10.900000,1,2.940000,1,False.\r\nDE,81,510,374-4664,yes,no,0,250.600000,85,42.600000,187.900000,50,15.970000,120.300000,131,5.410000,7.800000,5,2.110000,1,False.\r\nRI,17,415,396-9656,no,no,0,161.500000,123,27.460000,214.200000,81,18.210000,315.000000,106,14.180000,8.600000,5,2.320000,1,False.\r\nPA,119,408,377-5043,no,no,0,260.100000,101,44.220000,256.500000,68,21.800000,229.100000,89,10.310000,10.000000,2,2.700000,1,True.\r\nHI,105,415,401-7359,no,no,0,281.300000,124,47.820000,301.500000,96,25.630000,202.800000,109,9.130000,8.700000,3,2.350000,0,True.\r\nMD,108,415,375-2184,yes,yes,42,130.100000,90,22.120000,167.000000,128,14.200000,244.700000,80,11.010000,13.600000,5,3.670000,3,True.\r\nVA,90,415,367-6005,no,no,0,102.000000,118,17.340000,113.300000,134,9.630000,188.600000,105,8.490000,11.400000,3,3.080000,2,False.\r\nIN,100,415,364-2166,no,yes,33,218.700000,104,37.180000,155.000000,144,13.180000,99.000000,117,4.460000,12.100000,4,3.270000,1,False.\r\nOR,155,408,414-4741,no,yes,30,128.500000,86,21.850000,188.400000,91,16.010000,254.400000,85,11.450000,6.800000,6,1.840000,1,False.\r\nAZ,113,510,403-9719,no,no,0,128.700000,100,21.880000,227.100000,67,19.300000,178.100000,135,8.010000,9.200000,4,2.480000,2,True.\r\nWI,123,415,371-8452,no,no,0,172.200000,92,29.270000,162.600000,76,13.820000,250.300000,101,11.260000,8.700000,4,2.350000,1,False.\r\nVA,145,408,392-6239,no,no,0,199.200000,124,33.860000,126.000000,86,10.710000,289.200000,135,13.010000,7.600000,3,2.050000,1,False.\r\nMO,42,415,410-5250,no,no,0,184.500000,98,31.370000,200.500000,93,17.040000,279.200000,91,12.560000,8.800000,3,2.380000,2,False.\r\nNV,125,510,336-1574,no,no,0,168.600000,99,28.660000,175.600000,107,14.930000,243.300000,92,10.950000,10.900000,7,2.940000,0,False.\r\nHI,131,415,406-8324,no,yes,30,174.000000,118,29.580000,205.300000,81,17.450000,218.200000,90,9.820000,6.700000,3,1.810000,1,False.\r\nWA,107,415,411-7110,no,no,0,230.400000,65,39.170000,257.400000,80,21.880000,107.300000,88,4.830000,8.500000,3,2.300000,1,False.\r\nIL,48,408,341-9907,no,no,0,198.200000,73,33.690000,202.800000,115,17.240000,146.400000,73,6.590000,5.100000,5,1.380000,1,False.\r\nIL,76,510,400-8952,no,no,0,186.100000,96,31.640000,211.600000,100,17.990000,230.600000,100,10.380000,8.000000,4,2.160000,0,False.\r\nLA,128,415,333-9266,no,no,0,148.500000,105,25.250000,243.000000,106,20.660000,255.200000,114,11.480000,6.800000,2,1.840000,1,False.\r\nWI,73,415,419-4894,no,no,0,157.100000,109,26.710000,268.800000,83,22.850000,181.500000,91,8.170000,10.000000,8,2.700000,0,False.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0000,138.600000,106,6.240000,10.200000,4,2.750000,0,False.\r\nAZ,59,408,385-9657,no,no,0,150.200000,70,25.530000,185.700000,98,15.780000,212.500000,128,9.560000,12.100000,2,3.270000,1,False.\r\nMT,124,415,420-5652,no,yes,30,144.500000,35,24.570000,262.300000,101,22.300000,226.500000,82,10.190000,12.000000,7,3.240000,2,False.\r\nDE,99,415,415-1141,no,no,0,140.700000,88,23.920000,210.900000,98,17.930000,229.900000,125,10.350000,12.400000,4,3.350000,2,False.\r\nVA,150,510,334-5634,no,no,0,169.200000,123,28.760000,216.800000,83,18.430000,179.400000,107,8.070000,12.600000,3,3.400000,1,False.\r\nMA,81,510,403-4200,no,no,0,220.800000,77,37.540000,148.500000,87,12.620000,183.900000,100,8.280000,7.600000,3,2.050000,2,False.\r\nIN,86,510,357-7893,no,no,0,216.300000,96,36.770000,266.300000,77,22.640000,214.000000,110,9.630000,4.500000,3,1.220000,0,False.\r\nMD,84,510,369-2899,no,no,0,169.500000,96,28.820000,157.600000,94,13.400000,98.200000,70,4.420000,10.600000,7,2.860000,0,False.\r\nNV,118,510,381-1026,no,yes,35,256.300000,119,43.570000,258.100000,91,21.940000,215.500000,130,9.700000,11.700000,1,3.160000,1,False.\r\nCO,89,415,388-8722,no,no,0,179.700000,128,30.550000,299.800000,92,25.480000,185.300000,120,8.340000,7.600000,3,2.050000,1,False.\r\nKS,93,415,418-3135,no,no,0,266.000000,120,45.220000,130.100000,84,11.060000,165.800000,63,7.460000,13.100000,6,3.540000,3,False.\r\nAR,85,415,380-3974,no,no,0,96.700000,97,16.440000,193.800000,95,16.470000,171.700000,88,7.730000,9.700000,3,2.620000,2,False.\r\nWY,160,408,338-7232,no,no,0,82.700000,116,14.060000,194.600000,95,16.540000,159.000000,54,7.150000,10.900000,9,2.940000,0,False.\r\nPA,28,415,334-5223,no,no,0,168.200000,87,28.590000,161.700000,92,13.740000,192.400000,112,8.660000,10.100000,3,2.730000,3,False.\r\nTX,73,408,340-8323,no,no,0,286.400000,109,48.690000,178.200000,67,15.150000,214.200000,152,9.640000,10.700000,14,2.890000,1,True.\r\nNY,156,408,337-6851,no,no,0,174.300000,95,29.630000,186.600000,128,15.860000,258.200000,105,11.620000,12.900000,5,3.480000,3,False.\r\nOR,33,415,344-5973,yes,no,0,190.600000,100,32.400000,161.700000,104,13.740000,189.900000,136,8.550000,13.000000,6,3.510000,1,False.\r\nCA,77,510,335-2261,no,no,0,175.500000,86,29.840000,205.100000,78,17.430000,245.200000,100,11.030000,17.800000,3,4.810000,4,False.\r\nNY,119,415,343-1458,no,no,0,133.400000,102,22.680000,204.600000,71,17.390000,196.900000,103,8.860000,11.100000,7,3.000000,1,False.\r\nAR,91,510,415-4875,no,yes,27,204.600000,96,34.780000,136.000000,93,11.560000,210.500000,82,9.470000,6.600000,2,1.780000,3,False.\r\nMI,102,510,381-2726,no,no,0,242.200000,88,41.170000,233.200000,89,19.820000,188.500000,121,8.480000,6.200000,6,1.670000,3,False.\r\nOK,86,415,395-3852,no,yes,33,253.100000,112,43.030000,210.100000,94,17.860000,95.000000,98,4.270000,11.900000,4,3.210000,3,False.\r\nTX,82,415,358-7914,no,no,0,130.000000,110,22.100000,185.300000,88,15.750000,178.700000,105,8.040000,8.300000,4,2.240000,0,False.\r\nNC,89,408,332-6958,no,no,0,105.900000,151,18.000000,189.600000,142,16.120000,170.900000,67,7.690000,12.700000,7,3.430000,0,False.\r\nID,86,415,355-1019,no,no,0,194.200000,98,33.010000,193.800000,95,16.470000,192.000000,123,8.640000,9.300000,7,2.510000,3,False.\r\nIL,134,408,382-9447,no,no,0,183.800000,111,31.250000,123.500000,92,10.500000,160.700000,105,7.230000,6.100000,2,1.650000,1,False.\r\nOR,92,415,386-8536,no,no,0,196.500000,82,33.410000,190.000000,89,16.150000,163.200000,99,7.340000,10.800000,2,2.920000,2,False.\r\nSD,87,510,363-3818,no,no,0,184.500000,81,31.370000,172.000000,103,14.620000,183.400000,96,8.250000,13.700000,3,3.700000,3,False.\r\nNE,64,408,360-6416,no,no,0,261.900000,113,44.520000,148.100000,99,12.590000,145.200000,74,6.530000,13.800000,4,3.730000,0,False.\r\nRI,80,510,332-8764,no,no,0,202.400000,118,34.410000,260.200000,67,22.120000,177.400000,112,7.980000,9.200000,5,2.480000,3,False.\r\nMD,165,415,398-4814,no,yes,39,167.400000,113,28.460000,172.700000,94,14.680000,192.600000,113,8.670000,9.500000,4,2.570000,3,False.\r\nID,153,415,410-5963,no,yes,22,167.700000,104,28.510000,246.800000,91,20.980000,203.900000,117,9.180000,7.500000,11,2.030000,1,False.\r\nME,41,415,399-6642,no,yes,30,191.700000,109,32.590000,193.000000,86,16.410000,149.400000,93,6.720000,11.100000,4,3.000000,1,False.\r\nSD,108,415,390-9986,no,no,0,240.200000,78,40.830000,230.300000,109,19.580000,217.000000,83,9.760000,5.200000,1,1.400000,2,False.\r\nNY,104,415,391-1793,no,yes,26,189.100000,112,32.150000,178.200000,97,15.150000,199.300000,104,8.970000,11.100000,4,3.000000,1,False.\r\nDE,115,408,352-5542,no,no,0,127.700000,67,21.710000,182.900000,90,15.550000,172.900000,92,7.780000,10.600000,7,2.860000,1,False.\r\nOK,87,415,386-8118,no,no,0,205.200000,106,34.880000,99.500000,122,8.460000,189.500000,75,8.530000,13.400000,3,3.620000,1,False.\r\nSC,159,415,394-9825,no,yes,23,153.600000,93,26.110000,216.900000,88,18.440000,161.300000,91,7.260000,12.600000,3,3.400000,2,False.\r\nIN,119,510,382-4952,no,no,0,154.500000,129,26.270000,193.600000,87,16.460000,180.900000,145,8.140000,13.400000,3,3.620000,2,False.\r\nNV,69,415,387-2698,no,no,0,153.700000,109,26.130000,194.000000,105,16.490000,256.100000,114,11.520000,14.100000,6,3.810000,1,False.\r\nIL,87,408,417-1360,yes,yes,36,171.200000,138,29.100000,185.800000,102,15.790000,227.600000,97,10.240000,10.800000,3,2.920000,1,False.\r\nSD,93,510,408-4836,no,no,0,328.100000,106,55.780000,151.700000,89,12.890000,303.500000,114,13.660000,8.700000,3,2.350000,1,True.\r\nOK,154,415,374-8329,yes,no,0,145.900000,69,24.800000,208.200000,141,17.700000,180.900000,106,8.140000,14.400000,10,3.890000,0,True.\r\nKS,57,415,363-8424,no,yes,37,201.200000,76,34.200000,280.100000,122,23.810000,154.200000,110,6.940000,11.800000,1,3.190000,1,False.\r\nIA,130,510,408-8910,no,no,0,139.100000,72,23.650000,246.000000,112,20.910000,207.200000,121,9.320000,11.400000,9,3.080000,5,False.\r\nNJ,151,415,399-3840,no,no,0,118.900000,128,20.210000,278.300000,65,23.660000,194.800000,61,8.770000,13.200000,10,3.560000,2,False.\r\nNJ,162,408,367-8692,no,no,0,217.600000,87,36.990000,279.000000,71,23.720000,250.700000,65,11.280000,10.400000,4,2.810000,2,True.\r\nMT,60,415,387-4504,no,no,0,145.000000,133,24.650000,209.100000,92,17.770000,328.500000,112,14.780000,14.600000,2,3.940000,1,False.\r\nIA,81,510,328-2647,no,no,0,203.500000,89,34.600000,289.600000,69,24.620000,212.900000,71,9.580000,8.700000,3,2.350000,3,False.\r\nWA,132,415,369-7903,no,no,0,240.100000,115,40.820000,180.400000,91,15.330000,133.400000,122,6.000000,8.000000,6,2.160000,3,False.\r\nNE,86,408,399-6852,no,no,0,83.800000,121,14.250000,240.200000,96,20.420000,158.600000,108,7.140000,6.700000,8,1.810000,1,False.\r\nTX,136,408,335-4888,no,no,0,269.800000,106,45.870000,228.800000,101,19.450000,257.500000,106,11.590000,10.100000,8,2.730000,1,True.\r\nMS,121,415,344-2260,no,yes,21,126.300000,84,21.470000,209.600000,102,17.820000,192.500000,129,8.660000,10.600000,2,2.860000,1,False.\r\nIA,105,510,349-4070,no,yes,15,88.100000,125,14.980000,175.900000,142,14.950000,269.900000,85,12.150000,9.700000,1,2.620000,2,False.\r\nWI,105,415,394-6505,no,yes,34,218.500000,61,37.150000,196.700000,74,16.720000,151.100000,103,6.800000,9.900000,4,2.670000,2,False.\r\nMT,51,415,419-3612,no,yes,26,236.800000,61,40.260000,263.400000,97,22.390000,181.100000,91,8.150000,11.200000,8,3.020000,1,False.\r\nGA,64,408,356-1952,no,no,0,124.100000,117,21.100000,192.800000,108,16.390000,162.900000,84,7.330000,6.400000,5,1.730000,3,False.\r\nMT,80,510,416-7866,yes,yes,30,184.200000,132,31.310000,167.500000,109,14.240000,212.800000,114,9.580000,10.000000,10,2.700000,0,False.\r\nND,56,415,398-1759,no,no,0,222.700000,133,37.860000,277.000000,89,23.550000,101.800000,94,4.580000,13.600000,4,3.670000,4,False.\r\nVT,120,415,338-9950,no,no,0,149.200000,98,25.360000,193.600000,88,16.460000,248.900000,119,11.200000,11.100000,5,3.000000,1,False.\r\nSD,103,510,412-7278,no,no,0,206.500000,125,35.110000,180.200000,113,15.320000,220.600000,95,9.930000,12.200000,4,3.290000,3,False.\r\nOH,164,510,347-1263,no,yes,27,159.700000,102,27.150000,168.800000,113,14.350000,244.100000,127,10.980000,9.600000,9,2.590000,3,False.\r\nOH,116,415,386-5684,no,yes,27,204.700000,118,34.800000,209.400000,91,17.800000,212.900000,67,9.580000,7.000000,2,1.890000,2,False.\r\nMT,121,408,334-4354,no,no,0,213.200000,79,36.240000,120.700000,116,10.260000,244.400000,102,11.000000,7.500000,4,2.030000,1,False.\r\nIL,55,415,398-5970,yes,no,0,269.600000,121,45.830000,171.700000,91,14.590000,219.000000,98,9.860000,8.200000,6,2.210000,1,False.\r\nNV,183,415,330-3429,no,no,0,116.700000,92,19.840000,213.800000,112,18.170000,214.300000,112,9.640000,9.700000,6,2.620000,2,False.\r\nUT,104,408,418-4637,no,no,0,263.400000,101,44.780000,235.500000,117,20.020000,102.000000,146,4.590000,13.000000,4,3.510000,0,False.\r\nNH,90,408,393-7322,no,no,0,140.200000,97,23.830000,213.900000,102,18.180000,120.000000,126,5.400000,7.100000,2,1.920000,1,False.\r\nLA,82,415,353-5557,no,no,0,197.700000,101,33.610000,127.600000,83,10.850000,142.100000,103,6.390000,13.500000,3,3.650000,1,False.\r\nVT,101,415,411-5334,no,no,0,136.200000,92,23.150000,220.900000,110,18.780000,196.900000,116,8.860000,13.300000,7,3.590000,3,False.\r\nNY,9,415,398-8588,no,yes,16,88.500000,87,15.050000,178.800000,108,15.200000,228.700000,96,10.290000,11.500000,3,3.110000,2,False.\r\nCT,97,415,374-7285,no,no,0,215.300000,58,36.600000,242.400000,91,20.600000,279.800000,105,12.590000,12.100000,9,3.270000,0,False.\r\nKS,94,408,379-7215,no,no,0,269.200000,104,45.760000,193.800000,144,16.470000,257.600000,61,11.590000,8.900000,2,2.400000,3,True.\r\nMI,127,510,357-7875,no,yes,25,203.800000,118,34.650000,267.100000,48,22.700000,225.100000,105,10.130000,7.300000,5,1.970000,0,False.\r\nSD,125,510,393-9677,no,yes,34,268.400000,112,45.630000,222.200000,108,18.890000,117.600000,102,5.290000,10.300000,3,2.780000,0,False.\r\nME,140,415,345-9598,no,no,0,159.100000,104,27.050000,269.800000,106,22.930000,220.400000,116,9.920000,10.300000,4,2.780000,1,False.\r\nMD,90,415,353-3203,no,no,0,114.400000,122,19.450000,127.700000,154,10.850000,253.100000,109,11.390000,10.100000,5,2.730000,2,False.\r\nVA,67,415,330-7486,no,no,0,138.900000,65,23.610000,208.900000,109,17.760000,232.400000,82,10.460000,9.200000,3,2.480000,2,False.\r\nNJ,113,415,397-6425,no,no,0,186.000000,55,31.620000,237.400000,105,20.180000,148.100000,83,6.660000,12.200000,6,3.290000,1,False.\r\nTN,121,510,338-1815,no,yes,26,170.400000,91,28.970000,254.500000,90,21.630000,219.600000,122,9.880000,15.100000,5,4.080000,0,False.\r\nDC,93,408,406-5023,no,no,0,164.500000,95,27.970000,230.900000,87,19.630000,149.900000,91,6.750000,9.900000,3,2.670000,4,False.\r\nDC,121,408,368-2458,no,no,0,168.600000,121,28.660000,168.600000,94,14.330000,95.300000,59,4.290000,12.300000,4,3.320000,1,False.\r\nOR,53,408,400-8375,no,no,0,261.200000,119,44.400000,250.800000,105,21.320000,176.000000,112,7.920000,9.800000,2,2.650000,0,True.\r\nRI,75,415,387-8201,no,no,0,190.500000,91,32.390000,178.400000,75,15.160000,162.400000,113,7.310000,13.100000,5,3.540000,1,False.\r\nAK,132,510,346-4360,no,no,0,181.100000,121,30.790000,314.400000,109,26.720000,246.700000,81,11.100000,4.200000,9,1.130000,2,False.\r\nWI,162,408,412-8811,no,no,0,177.100000,131,30.110000,114.700000,122,9.750000,153.600000,88,6.910000,6.500000,6,1.760000,3,False.\r\nIN,140,415,413-3990,no,no,0,160.500000,114,27.290000,240.500000,103,20.440000,233.500000,121,10.510000,11.300000,4,3.050000,1,False.\r\nMI,91,408,345-2448,no,no,0,134.700000,116,22.900000,295.300000,98,25.100000,195.500000,121,8.800000,6.600000,5,1.780000,2,False.\r\nID,73,510,394-4512,no,yes,28,198.200000,107,33.690000,139.100000,123,11.820000,199.100000,139,8.960000,8.800000,1,2.380000,2,False.\r\nNH,95,408,400-8538,yes,no,0,228.900000,134,38.910000,255.700000,71,21.730000,208.000000,120,9.360000,10.100000,2,2.730000,4,True.\r\nMN,145,408,412-8769,no,no,0,241.700000,137,41.090000,135.800000,100,11.540000,277.600000,123,12.490000,13.100000,3,3.540000,0,False.\r\nAZ,100,415,390-1552,no,no,0,131.100000,108,22.290000,176.200000,81,14.980000,89.700000,81,4.040000,4.300000,4,1.160000,1,False.\r\nMN,122,415,389-2477,no,no,0,234.100000,101,39.800000,200.200000,121,17.020000,237.400000,89,10.680000,13.100000,9,3.540000,2,False.\r\nMO,109,415,389-4695,no,no,0,200.100000,72,34.020000,300.900000,120,25.580000,236.000000,68,10.620000,11.900000,5,3.210000,0,False.\r\nAL,82,408,406-8037,no,no,0,154.000000,107,26.180000,94.400000,114,8.020000,287.600000,95,12.940000,10.100000,7,2.730000,1,False.\r\nPA,65,415,382-9138,no,yes,23,224.200000,106,38.110000,189.600000,100,16.120000,222.800000,75,10.030000,9.800000,4,2.650000,0,False.\r\nAK,52,510,414-7942,no,no,0,148.300000,83,25.210000,181.600000,79,15.440000,155.600000,104,7.000000,8.300000,6,2.240000,3,False.\r\nMS,136,415,348-7071,no,yes,24,174.600000,76,29.680000,176.600000,114,15.010000,214.400000,91,9.650000,8.800000,5,2.380000,2,False.\r\nIA,75,415,404-2942,no,no,0,138.500000,110,23.550000,153.200000,86,13.020000,215.600000,103,9.700000,11.100000,7,3.000000,1,False.\r\nWY,146,408,348-3581,no,no,0,109.000000,69,18.530000,265.800000,98,22.590000,228.300000,80,10.270000,12.600000,2,3.400000,1,False.\r\nNE,105,408,327-6764,no,no,0,162.300000,99,27.590000,212.500000,95,18.060000,214.700000,114,9.660000,11.100000,8,3.000000,4,False.\r\nND,48,415,405-2831,no,no,0,210.800000,84,35.840000,189.600000,98,16.120000,157.600000,99,7.090000,16.400000,3,4.430000,2,False.\r\nCT,45,415,416-4351,no,no,0,142.400000,107,24.210000,318.700000,78,27.090000,224.100000,108,10.080000,11.100000,7,3.000000,1,False.\r\nNC,106,415,419-3196,no,yes,37,223.500000,104,38.000000,235.100000,99,19.980000,140.100000,90,6.300000,10.600000,5,2.860000,2,False.\r\nCT,33,510,411-6211,no,no,0,182.500000,65,31.030000,232.100000,96,19.730000,149.200000,82,6.710000,7.500000,2,2.030000,2,False.\r\nND,68,408,391-8369,no,no,0,219.600000,97,37.330000,141.100000,144,11.990000,205.700000,101,9.260000,10.800000,4,2.920000,2,False.\r\nWA,106,408,416-4464,no,no,0,193.600000,66,32.910000,238.200000,82,20.250000,176.400000,107,7.940000,12.900000,3,3.480000,0,False.\r\nNV,141,415,347-1814,no,no,0,192.400000,111,32.710000,156.900000,87,13.340000,175.800000,82,7.910000,11.000000,6,2.970000,0,False.\r\nCO,98,408,386-7337,no,no,0,236.200000,122,40.150000,189.400000,110,16.100000,153.600000,104,6.910000,13.300000,4,3.590000,0,False.\r\nKS,94,510,375-8505,no,yes,28,233.200000,88,39.640000,113.300000,102,9.630000,118.000000,71,5.310000,16.100000,4,4.350000,0,False.\r\nCO,65,510,407-5056,no,no,0,158.800000,53,27.000000,188.500000,132,16.020000,189.300000,87,8.520000,9.800000,4,2.650000,2,False.\r\nMO,85,415,367-8924,no,no,0,126.100000,112,21.440000,274.700000,126,23.350000,184.400000,95,8.300000,9.800000,4,2.650000,1,False.\r\nMA,71,510,419-5171,no,no,0,290.400000,108,49.370000,253.900000,92,21.580000,263.300000,126,11.850000,10.100000,5,2.730000,3,True.\r\nNY,112,408,396-7687,no,yes,30,60.600000,113,10.300000,165.900000,96,14.100000,132.800000,99,5.980000,13.300000,7,3.590000,0,False.\r\nAK,110,415,394-4548,no,no,0,148.400000,95,25.230000,193.800000,98,16.470000,206.000000,106,9.270000,6.900000,6,1.860000,0,False.\r\nWI,111,415,382-6438,no,no,0,246.500000,108,41.910000,216.300000,89,18.390000,179.600000,99,8.080000,12.700000,3,3.430000,2,False.\r\nNH,74,408,413-2194,no,no,0,298.100000,112,50.680000,201.300000,100,17.110000,214.700000,88,9.660000,9.700000,4,2.620000,2,True.\r\nTN,105,510,366-2622,no,no,0,119.300000,82,20.280000,185.100000,111,15.730000,157.000000,74,7.070000,10.900000,4,2.940000,2,False.\r\nNY,40,408,416-7591,no,no,0,242.500000,82,41.230000,232.900000,97,19.800000,154.000000,86,6.930000,9.600000,7,2.590000,0,False.\r\nGA,128,408,355-2634,yes,yes,18,222.100000,89,37.760000,160.600000,109,13.650000,218.800000,102,9.850000,13.600000,2,3.670000,0,True.\r\nMN,123,408,422-5350,no,no,0,236.200000,135,40.150000,273.900000,88,23.280000,227.000000,77,10.220000,10.100000,6,2.730000,2,True.\r\nMI,122,510,329-2388,no,no,0,144.200000,87,24.510000,212.200000,74,18.040000,169.300000,87,7.620000,9.500000,4,2.570000,0,False.\r\nID,114,408,381-5273,no,yes,19,154.600000,100,26.280000,241.600000,109,20.540000,160.000000,112,7.200000,12.600000,1,3.400000,3,False.\r\nCT,102,415,421-6694,no,yes,25,137.400000,100,23.360000,176.700000,83,15.020000,188.200000,93,8.470000,10.200000,6,2.750000,2,False.\r\nNC,126,415,342-1702,no,no,0,103.700000,93,17.630000,127.000000,107,10.800000,329.300000,66,14.820000,14.400000,1,3.890000,0,False.\r\nLA,150,415,381-4029,no,no,0,136.600000,112,23.220000,209.400000,81,17.800000,161.100000,78,7.250000,12.200000,2,3.290000,4,True.\r\nNJ,60,408,335-2967,no,no,0,289.800000,101,49.270000,255.600000,115,21.730000,242.800000,76,10.930000,11.700000,4,3.160000,2,True.\r\nTX,123,408,416-6594,no,no,0,260.900000,85,44.350000,168.500000,103,14.320000,178.300000,91,8.020000,13.300000,5,3.590000,3,False.\r\nCA,138,510,388-6026,yes,no,0,196.200000,129,33.350000,176.500000,86,15.000000,232.400000,108,10.460000,15.200000,1,4.100000,0,True.\r\nMD,29,510,367-1024,no,no,0,195.600000,71,33.250000,126.400000,74,10.740000,148.600000,87,6.690000,14.200000,4,3.830000,1,False.\r\nWY,111,415,386-7118,no,no,0,222.200000,96,37.770000,162.500000,111,13.810000,184.900000,120,8.320000,11.900000,7,3.210000,4,False.\r\nTX,37,510,346-2020,yes,no,0,172.900000,119,29.390000,183.000000,86,15.560000,226.400000,100,10.190000,9.800000,1,2.650000,0,True.\r\nCA,111,408,329-9067,no,no,0,249.800000,109,42.470000,242.400000,106,20.600000,231.800000,78,10.430000,11.600000,4,3.130000,0,True.\r\nUT,81,510,329-6144,no,no,0,154.500000,84,26.270000,216.200000,91,18.380000,229.800000,82,10.340000,13.700000,3,3.700000,1,False.\r\nWA,46,510,332-1502,no,no,0,90.400000,108,15.370000,276.200000,77,23.480000,146.500000,111,6.590000,12.700000,2,3.430000,1,False.\r\nMS,69,510,342-8320,no,yes,27,268.800000,78,45.700000,246.600000,89,20.960000,271.900000,102,12.240000,16.400000,3,4.430000,0,False.\r\nOH,125,408,411-5748,no,no,0,106.100000,95,18.040000,157.600000,113,13.400000,192.500000,69,8.660000,8.100000,3,2.190000,1,False.\r\nKS,43,415,381-9367,no,no,0,27.000000,117,4.590000,160.900000,97,13.680000,279.500000,96,12.580000,10.700000,3,2.890000,3,False.\r\nRI,127,415,400-1280,no,yes,27,140.100000,59,23.820000,223.400000,111,18.990000,257.900000,73,11.610000,3.800000,10,1.030000,1,False.\r\nIN,94,510,360-5794,no,no,0,245.000000,112,41.650000,180.400000,91,15.330000,262.900000,105,11.830000,9.700000,6,2.620000,1,False.\r\nVT,46,408,373-3538,no,no,0,196.700000,85,33.440000,205.900000,74,17.500000,216.600000,112,9.750000,11.200000,5,3.020000,3,False.\r\nMT,73,408,394-9942,no,yes,26,131.200000,98,22.300000,106.500000,97,9.050000,221.700000,96,9.980000,10.200000,6,2.750000,2,False.\r\nCT,146,408,380-3329,no,yes,23,149.600000,96,25.430000,239.800000,124,20.380000,293.500000,135,13.210000,7.400000,4,2.000000,2,False.\r\nNM,93,415,334-7618,no,no,0,239.800000,70,40.770000,251.800000,99,21.400000,168.600000,112,7.590000,10.900000,10,2.940000,1,False.\r\nOH,52,408,327-9289,no,yes,31,142.100000,77,24.160000,193.000000,97,16.410000,253.400000,88,11.400000,11.000000,4,2.970000,1,False.\r\nGA,202,510,351-2589,no,no,0,115.400000,137,19.620000,178.700000,70,15.190000,185.700000,113,8.360000,6.000000,3,1.620000,3,False.\r\nMN,129,510,368-6892,no,yes,31,193.000000,99,32.810000,224.800000,87,19.110000,197.600000,91,8.890000,10.300000,8,2.780000,2,False.\r\nCT,94,415,337-9303,no,no,0,206.100000,49,35.040000,224.600000,115,19.090000,256.700000,74,11.550000,13.000000,1,3.510000,1,False.\r\nAL,100,415,377-5258,no,no,0,160.300000,138,27.250000,221.300000,92,18.810000,150.400000,120,6.770000,11.200000,2,3.020000,0,False.\r\nWV,43,415,348-5767,no,no,0,199.900000,108,33.980000,288.400000,80,24.510000,180.600000,103,8.130000,11.300000,7,3.050000,1,False.\r\nNH,130,415,373-3549,no,no,0,213.100000,105,36.230000,206.200000,108,17.530000,163.400000,93,7.350000,8.900000,3,2.400000,0,False.\r\nWY,124,415,422-8344,no,no,0,178.300000,102,30.310000,235.000000,120,19.980000,239.700000,119,10.790000,10.900000,1,2.940000,3,False.\r\nVA,92,510,411-2958,yes,no,0,252.300000,120,42.890000,207.000000,112,17.600000,284.600000,95,12.810000,12.000000,5,3.240000,3,True.\r\nVT,48,415,384-2908,no,no,0,197.700000,64,33.610000,136.700000,126,11.620000,244.400000,81,11.000000,13.200000,5,3.560000,4,False.\r\nOH,98,510,347-6393,no,yes,29,111.100000,105,18.890000,217.900000,101,18.520000,248.100000,108,11.160000,6.600000,3,1.780000,2,False.\r\nMT,100,415,385-7148,no,no,0,96.500000,86,16.410000,210.200000,133,17.870000,146.400000,106,6.590000,12.500000,3,3.380000,1,True.\r\nMA,79,415,419-2767,no,no,0,156.900000,109,26.670000,122.200000,87,10.390000,189.100000,103,8.510000,11.300000,5,3.050000,3,False.\r\nVA,164,415,375-1746,no,no,0,123.300000,78,20.960000,170.000000,85,14.450000,165.900000,78,7.470000,12.700000,2,3.430000,1,False.\r\nNM,105,415,362-7870,no,no,0,193.700000,108,32.930000,183.200000,124,15.570000,293.700000,72,13.220000,10.800000,5,2.920000,1,False.\r\nAR,89,408,410-3725,yes,no,0,206.900000,134,35.170000,167.700000,105,14.250000,155.700000,86,7.010000,10.900000,4,2.940000,0,False.\r\nNE,126,415,387-1535,no,no,0,249.800000,96,42.470000,261.900000,92,22.260000,166.800000,108,7.510000,12.700000,4,3.430000,3,True.\r\nWY,96,408,329-2045,no,no,0,144.000000,102,24.480000,224.700000,73,19.100000,227.700000,91,10.250000,10.000000,7,2.700000,1,False.\r\nIA,120,415,341-6743,no,yes,33,299.500000,83,50.920000,163.400000,84,13.890000,146.700000,88,6.600000,11.600000,5,3.130000,0,False.\r\nSC,212,415,336-8343,no,no,0,226.000000,127,38.420000,304.600000,83,25.890000,181.200000,132,8.150000,12.600000,4,3.400000,2,True.\r\nNC,72,415,368-5758,no,no,0,137.600000,106,23.390000,143.500000,94,12.200000,273.700000,110,12.320000,9.600000,6,2.590000,2,False.\r\nHI,155,415,346-8362,yes,yes,26,211.700000,121,35.990000,139.200000,123,11.830000,146.700000,89,6.600000,11.100000,3,3.000000,1,False.\r\nUT,89,415,345-9690,no,no,0,89.700000,80,15.250000,179.800000,81,15.280000,145.700000,120,6.560000,9.500000,4,2.570000,2,False.\r\nWY,126,408,339-9798,yes,no,0,197.600000,126,33.590000,246.500000,112,20.950000,285.300000,104,12.840000,12.500000,8,3.380000,2,False.\r\nAL,172,408,359-5731,no,no,0,270.000000,102,45.900000,256.600000,111,21.810000,168.500000,104,7.580000,12.000000,5,3.240000,2,True.\r\nVA,75,415,373-2091,no,no,0,224.700000,116,38.200000,192.000000,79,16.320000,212.200000,98,9.550000,11.300000,11,3.050000,3,False.\r\nWI,143,510,367-3439,no,no,0,194.300000,99,33.030000,123.600000,133,10.510000,229.500000,99,10.330000,10.200000,2,2.750000,2,False.\r\nFL,166,510,367-1681,yes,no,0,47.700000,89,8.110000,264.400000,95,22.470000,235.200000,97,10.580000,13.200000,3,3.560000,0,True.\r\nKS,132,415,420-9973,no,no,0,190.100000,105,32.320000,182.200000,116,15.490000,279.800000,105,12.590000,13.000000,2,3.510000,1,False.\r\nNV,94,408,351-4025,yes,no,0,89.500000,94,15.220000,339.900000,106,28.890000,172.900000,76,7.780000,7.900000,1,2.130000,1,True.\r\nNY,99,415,393-5897,no,no,0,182.600000,83,31.040000,154.500000,111,13.130000,196.000000,57,8.820000,12.100000,5,3.270000,0,False.\r\nVA,136,415,384-7216,no,yes,35,205.500000,86,34.940000,298.500000,119,25.370000,214.200000,104,9.640000,6.900000,4,1.860000,1,False.\r\nKS,119,415,384-4595,no,no,0,231.500000,82,39.360000,266.900000,97,22.690000,211.000000,118,9.490000,7.400000,10,2.000000,1,False.\r\nNC,115,510,329-9667,yes,no,0,251.300000,69,42.720000,252.500000,96,21.460000,118.300000,112,5.320000,9.900000,1,2.670000,3,True.\r\nMO,160,415,347-5063,no,no,0,171.200000,103,29.100000,243.500000,121,20.700000,178.200000,92,8.020000,13.000000,3,3.510000,2,False.\r\nND,166,510,345-8433,no,no,0,197.900000,89,33.640000,251.000000,113,21.340000,138.300000,85,6.220000,11.200000,2,3.020000,2,False.\r\nCA,120,510,339-7602,no,no,0,134.800000,94,22.920000,204.100000,106,17.350000,238.400000,109,10.730000,6.700000,8,1.810000,1,False.\r\nWV,173,415,332-1109,no,no,0,191.400000,114,32.540000,168.500000,138,14.320000,109.300000,99,4.920000,10.300000,3,2.780000,1,False.\r\nIL,156,415,343-3296,no,no,0,174.500000,65,29.670000,197.400000,116,16.780000,238.500000,86,10.730000,10.600000,2,2.860000,0,False.\r\nNY,70,415,366-2536,no,no,0,177.400000,125,30.160000,226.200000,104,19.230000,254.100000,72,11.430000,10.900000,4,2.940000,0,False.\r\nNV,41,510,355-2293,no,no,0,182.100000,89,30.960000,211.500000,104,17.980000,207.400000,124,9.330000,6.800000,1,1.840000,1,False.\r\nAL,132,408,350-9318,no,no,0,222.400000,85,37.810000,165.400000,76,14.060000,208.400000,97,9.380000,11.200000,4,3.020000,0,False.\r\nKS,47,510,418-5300,yes,no,0,47.800000,120,8.130000,178.900000,123,15.210000,152.600000,96,6.870000,13.300000,7,3.590000,0,True.\r\nND,160,510,395-2626,no,no,0,121.800000,97,20.710000,89.300000,97,7.590000,150.700000,92,6.780000,10.300000,5,2.780000,1,False.\r\nOK,180,415,402-7372,no,no,0,143.500000,121,24.400000,189.300000,111,16.090000,174.900000,82,7.870000,8.800000,5,2.380000,3,False.\r\nUT,93,415,337-9710,no,no,0,164.900000,68,28.030000,210.400000,86,17.880000,229.400000,104,10.320000,7.800000,4,2.110000,2,False.\r\nOH,109,415,363-4967,no,no,0,193.600000,58,32.910000,148.700000,115,12.640000,282.500000,105,12.710000,13.100000,1,3.540000,2,False.\r\nWY,80,510,400-5389,no,no,0,101.100000,121,17.190000,263.200000,110,22.370000,137.700000,74,6.200000,7.300000,5,1.970000,0,False.\r\nNM,54,415,416-9162,no,yes,24,92.300000,88,15.690000,193.100000,98,16.410000,99.300000,119,4.470000,11.600000,3,3.130000,2,False.\r\nAL,121,415,414-6541,no,no,0,168.900000,128,28.710000,123.900000,99,10.530000,266.300000,105,11.980000,2.900000,7,0.780000,2,False.\r\nDC,157,510,392-6647,no,yes,29,219.200000,102,37.260000,206.000000,109,17.510000,192.400000,117,8.660000,15.000000,5,4.050000,1,False.\r\nID,170,510,343-2465,no,yes,37,178.100000,130,30.280000,242.800000,103,20.640000,243.000000,93,10.930000,13.000000,4,3.510000,0,False.\r\nAR,138,510,338-9171,no,no,0,146.500000,101,24.910000,284.500000,142,24.180000,176.000000,98,7.920000,14.000000,6,3.780000,3,False.\r\nID,92,415,405-4606,no,yes,31,172.300000,116,29.290000,266.200000,91,22.630000,228.200000,90,10.270000,11.800000,5,3.190000,1,False.\r\nTX,126,415,386-9711,no,no,0,190.900000,143,32.450000,149.700000,72,12.720000,191.400000,87,8.610000,13.000000,3,3.510000,1,False.\r\nNM,41,415,327-8495,no,no,0,232.100000,74,39.460000,327.100000,88,27.800000,226.500000,119,10.190000,10.900000,2,2.940000,3,True.\r\nWA,167,415,416-5660,no,no,0,169.200000,124,28.760000,173.300000,108,14.730000,216.500000,64,9.740000,12.400000,4,3.350000,4,True.\r\nUT,91,510,370-3032,no,no,0,123.800000,107,21.050000,319.000000,125,27.120000,237.600000,78,10.690000,7.300000,4,1.970000,2,False.\r\nRI,127,510,331-8462,no,no,0,96.000000,117,16.320000,177.000000,68,15.050000,162.200000,127,7.300000,9.700000,4,2.620000,3,False.\r\nNC,88,408,414-4037,no,yes,27,93.400000,106,15.880000,252.000000,92,21.420000,189.000000,104,8.500000,10.900000,1,2.940000,1,False.\r\nRI,113,415,415-2865,no,no,0,90.600000,130,15.400000,170.600000,100,14.500000,137.400000,74,6.180000,5.400000,9,1.460000,1,False.\r\nNY,78,510,362-2353,no,no,0,152.900000,81,25.990000,256.600000,82,21.810000,173.600000,112,7.810000,5.300000,6,1.430000,2,False.\r\nTX,123,408,329-5114,no,no,0,257.900000,92,43.840000,211.600000,71,17.990000,189.300000,104,8.520000,9.400000,2,2.540000,2,False.\r\nDE,136,408,351-1389,yes,yes,29,85.200000,98,14.480000,230.400000,85,19.580000,243.600000,104,10.960000,9.000000,3,2.430000,2,False.\r\nMS,68,415,375-3668,no,yes,34,160.000000,72,27.200000,184.500000,119,15.680000,208.300000,101,9.370000,6.100000,10,1.650000,1,False.\r\nOH,132,415,375-5414,no,yes,10,182.900000,54,31.090000,292.400000,68,24.850000,142.300000,116,6.400000,11.500000,4,3.110000,0,False.\r\nLA,133,415,360-7079,no,no,0,216.200000,67,36.750000,222.200000,133,18.890000,192.000000,95,8.640000,3.100000,1,0.840000,2,False.\r\nFL,127,415,344-9302,no,no,0,261.700000,105,44.490000,181.800000,107,15.450000,100.900000,131,4.540000,3.300000,5,0.890000,0,False.\r\nWA,110,415,418-1775,no,no,0,241.200000,105,41.000000,174.300000,85,14.820000,245.300000,59,11.040000,8.500000,4,2.300000,2,False.\r\nWV,121,510,401-2468,no,no,0,177.200000,142,30.120000,123.500000,88,10.500000,213.200000,51,9.590000,8.400000,6,2.270000,1,False.\r\nNY,116,510,346-4984,no,no,0,89.500000,128,15.220000,180.800000,137,15.370000,193.100000,94,8.690000,14.000000,3,3.780000,2,False.\r\nNE,112,415,351-2928,yes,yes,16,200.300000,72,34.050000,197.800000,91,16.810000,151.100000,92,6.800000,10.400000,3,2.810000,1,False.\r\nPA,97,510,365-7774,yes,no,0,145.000000,103,24.650000,294.300000,93,25.020000,239.800000,120,10.790000,11.000000,2,2.970000,4,True.\r\nMS,43,510,358-3691,no,no,0,159.500000,99,27.120000,119.700000,149,10.170000,173.900000,126,7.830000,6.800000,3,1.840000,2,False.\r\nIN,110,415,364-9059,no,no,0,151.800000,106,25.810000,138.000000,126,11.730000,233.500000,112,10.510000,11.200000,8,3.020000,3,False.\r\nVA,67,408,356-7208,no,no,0,176.200000,120,29.950000,236.000000,138,20.060000,152.500000,104,6.860000,10.600000,4,2.860000,1,False.\r\nMN,166,408,333-5551,no,no,0,152.100000,95,25.860000,121.000000,105,10.290000,198.000000,126,8.910000,9.800000,5,2.650000,0,False.\r\nDE,129,408,362-6528,no,no,0,161.300000,122,27.420000,220.600000,95,18.750000,224.700000,104,10.110000,9.600000,3,2.590000,1,False.\r\nOR,103,510,394-2560,yes,no,0,171.700000,78,29.190000,144.500000,86,12.280000,157.900000,106,7.110000,6.800000,3,1.840000,3,False.\r\nUT,71,415,367-3220,no,no,0,278.900000,110,47.410000,190.200000,67,16.170000,255.200000,84,11.480000,11.700000,7,3.160000,0,True.\r\nCT,112,415,418-5708,no,yes,27,213.000000,121,36.210000,226.200000,101,19.230000,189.800000,99,8.540000,11.100000,3,3.000000,4,False.\r\nAL,8,415,421-2245,no,yes,36,242.900000,67,41.290000,170.900000,59,14.530000,177.300000,130,7.980000,4.800000,12,1.300000,1,False.\r\nTX,98,415,406-2242,no,no,0,217.200000,121,36.920000,303.400000,73,25.790000,197.100000,71,8.870000,12.400000,2,3.350000,0,True.\r\nCT,90,415,347-6994,no,no,0,175.900000,111,29.900000,285.200000,115,24.240000,150.800000,122,6.790000,13.000000,7,3.510000,1,False.\r\nMS,13,415,413-7468,no,no,0,303.200000,133,51.540000,170.500000,86,14.490000,227.600000,80,10.240000,11.500000,3,3.110000,0,True.\r\nAR,58,415,389-6082,no,no,0,238.900000,107,40.610000,187.200000,88,15.910000,181.100000,84,8.150000,11.800000,3,3.190000,2,False.\r\nCA,137,415,415-3689,no,yes,22,189.600000,42,32.230000,179.000000,137,15.220000,179.600000,126,8.080000,11.400000,5,3.080000,2,False.\r\nMI,116,408,379-2503,no,no,0,133.300000,94,22.660000,247.800000,126,21.060000,219.000000,78,9.860000,11.300000,5,3.050000,5,True.\r\nWV,94,415,396-1106,no,yes,28,92.700000,107,15.760000,127.800000,86,10.860000,225.600000,86,10.150000,9.900000,4,2.670000,3,False.\r\nDE,87,415,379-4372,no,no,0,177.200000,72,30.120000,248.900000,105,21.160000,200.800000,87,9.040000,8.600000,7,2.320000,3,False.\r\nFL,120,415,336-3738,no,no,0,184.500000,103,31.370000,209.000000,86,17.770000,169.700000,70,7.640000,10.200000,6,2.750000,2,False.\r\nAK,97,415,380-2600,no,yes,24,176.100000,109,29.940000,159.400000,81,13.550000,269.100000,94,12.110000,12.100000,9,3.270000,0,False.\r\nID,134,415,345-4473,no,no,0,204.700000,108,34.800000,143.100000,105,12.160000,165.800000,84,7.460000,11.000000,4,2.970000,6,False.\r\nOH,68,510,380-9990,no,no,0,143.600000,80,24.410000,134.300000,65,11.420000,215.600000,84,9.700000,15.500000,5,4.190000,2,False.\r\nNH,93,408,411-1045,no,no,0,179.300000,93,30.480000,188.800000,65,16.050000,253.200000,88,11.390000,12.100000,5,3.270000,1,False.\r\nMA,120,415,413-5306,no,no,0,137.300000,100,23.340000,212.200000,129,18.040000,152.700000,92,6.870000,10.500000,2,2.840000,1,False.\r\nSC,41,408,417-6906,no,no,0,237.800000,92,40.430000,223.500000,155,19.000000,217.400000,90,9.780000,10.200000,6,2.750000,2,False.\r\nOR,80,510,331-4807,no,no,0,203.700000,92,34.630000,216.400000,97,18.390000,154.200000,66,6.940000,7.600000,5,2.050000,2,False.\r\nOH,83,415,376-5375,no,yes,25,191.300000,95,32.520000,250.700000,136,21.310000,249.400000,86,11.220000,17.600000,5,4.750000,2,False.\r\nNC,109,510,361-9839,yes,no,0,209.100000,141,35.550000,205.000000,93,17.430000,119.400000,111,5.370000,7.800000,3,2.110000,2,False.\r\nKY,66,510,348-8679,no,yes,33,88.800000,104,15.100000,109.600000,94,9.320000,172.700000,107,7.770000,7.100000,9,1.920000,3,False.\r\nID,104,510,403-6565,no,no,0,97.200000,88,16.520000,155.600000,85,13.230000,261.600000,105,11.770000,12.400000,5,3.350000,0,False.\r\nWA,89,510,346-5287,no,no,0,137.900000,96,23.440000,192.600000,63,16.370000,255.700000,125,11.510000,11.000000,5,2.970000,1,False.\r\nWV,127,510,413-6769,no,no,0,224.300000,112,38.130000,185.700000,103,15.780000,159.400000,83,7.170000,10.000000,1,2.700000,2,False.\r\nRI,117,408,370-5042,no,yes,13,207.600000,65,35.290000,152.700000,77,12.980000,232.800000,95,10.480000,9.700000,3,2.620000,1,False.\r\nKS,128,510,397-9486,no,no,0,268.100000,95,45.580000,120.500000,126,10.240000,220.800000,121,9.940000,14.400000,6,3.890000,2,False.\r\nNV,88,415,364-3286,no,no,0,166.700000,61,28.340000,179.300000,88,15.240000,242.700000,131,10.920000,6.800000,7,1.840000,4,True.\r\nNE,61,408,420-8897,no,no,0,267.100000,104,45.410000,180.400000,131,15.330000,230.600000,106,10.380000,17.300000,4,4.670000,1,True.\r\nFL,22,415,378-9506,no,no,0,181.800000,108,30.910000,198.600000,148,16.880000,206.600000,96,9.300000,9.300000,3,2.510000,2,False.\r\nWY,78,415,399-6259,no,no,0,147.100000,80,25.010000,199.700000,100,16.970000,160.700000,106,7.230000,13.700000,7,3.700000,0,False.\r\nWA,56,415,335-5806,no,yes,29,37.700000,115,6.410000,144.100000,111,12.250000,226.600000,101,10.200000,4.900000,3,1.320000,1,False.\r\nCO,192,415,401-6392,no,no,0,185.000000,88,31.450000,224.900000,98,19.120000,212.400000,105,9.560000,11.400000,3,3.080000,2,False.\r\nWI,70,415,379-9859,no,no,0,156.400000,108,26.590000,171.000000,116,14.540000,196.100000,96,8.820000,8.600000,4,2.320000,2,False.\r\nKS,148,510,415-4051,no,no,0,239.300000,84,40.680000,195.700000,85,16.630000,232.600000,104,10.470000,10.900000,3,2.940000,1,False.\r\nRI,65,415,368-5612,no,yes,29,215.500000,129,36.640000,161.900000,77,13.760000,128.300000,91,5.770000,8.800000,5,2.380000,2,False.\r\nMT,119,510,374-5301,no,no,0,134.900000,70,22.930000,211.500000,74,17.980000,188.500000,105,8.480000,11.300000,6,3.050000,1,False.\r\nCO,80,415,406-5710,no,no,0,194.800000,116,33.120000,209.900000,93,17.840000,194.100000,100,8.730000,12.800000,3,3.460000,0,False.\r\nCT,152,408,354-7077,no,yes,20,239.100000,105,40.650000,209.100000,111,17.770000,268.200000,130,12.070000,13.300000,3,3.590000,5,False.\r\nFL,113,510,343-3340,no,no,0,92.600000,85,15.740000,177.600000,92,15.100000,159.800000,72,7.190000,14.400000,4,3.890000,3,False.\r\nVT,75,510,377-8267,no,no,0,209.400000,133,35.600000,211.500000,121,17.980000,291.200000,123,13.100000,7.200000,4,1.940000,1,False.\r\nOH,80,415,382-2453,no,no,0,197.600000,83,33.590000,164.500000,86,13.980000,94.000000,98,4.230000,6.400000,6,1.730000,1,False.\r\nNH,148,408,333-7449,no,no,0,17.600000,121,2.990000,161.700000,125,13.740000,203.100000,82,9.140000,10.600000,6,2.860000,1,False.\r\nRI,63,415,366-4287,yes,no,0,62.900000,112,10.690000,202.900000,111,17.250000,259.000000,58,11.660000,8.900000,8,2.400000,1,False.\r\nFL,97,415,415-2285,no,yes,28,202.300000,97,34.390000,69.200000,84,5.880000,257.600000,64,11.590000,6.700000,3,1.810000,1,False.\r\nMD,166,415,381-1328,no,no,0,136.100000,116,23.140000,181.400000,93,15.420000,131.400000,108,5.910000,11.300000,4,3.050000,0,False.\r\nWY,94,408,344-4022,no,no,0,207.000000,109,35.190000,167.400000,80,14.230000,238.200000,117,10.720000,2.600000,6,0.700000,1,False.\r\nFL,85,408,415-6601,no,yes,33,207.900000,95,35.340000,233.500000,88,19.850000,221.300000,92,9.960000,13.500000,3,3.650000,1,False.\r\nTN,80,415,351-7309,yes,no,0,276.500000,122,47.010000,195.600000,79,16.630000,210.300000,78,9.460000,7.200000,3,1.940000,1,True.\r\nNC,210,415,363-7802,no,yes,31,313.800000,87,53.350000,147.700000,103,12.550000,192.700000,97,8.670000,10.100000,7,2.730000,3,False.\r\nIN,88,415,408-4870,yes,yes,25,288.500000,114,49.050000,203.400000,74,17.290000,228.400000,117,10.280000,13.000000,5,3.510000,1,False.\r\nIA,100,408,378-9478,no,no,0,210.900000,85,35.850000,329.300000,69,27.990000,127.100000,78,5.720000,9.400000,5,2.540000,4,False.\r\nWV,154,408,401-4778,no,yes,35,64.900000,76,11.030000,184.100000,91,15.650000,151.600000,75,6.820000,14.600000,1,3.940000,1,False.\r\nUT,32,510,370-9563,no,yes,26,243.500000,137,41.400000,236.800000,108,20.130000,173.300000,149,7.800000,9.000000,9,2.430000,1,False.\r\nGA,18,408,394-6382,no,no,0,197.000000,97,33.490000,203.700000,107,17.310000,202.000000,105,9.090000,8.700000,3,2.350000,3,False.\r\nAK,126,415,333-5295,no,yes,31,278.000000,88,47.260000,253.200000,65,21.520000,223.200000,114,10.040000,8.700000,4,2.350000,0,False.\r\nUT,144,510,370-2451,no,yes,37,219.900000,102,37.380000,222.100000,77,18.880000,118.500000,111,5.330000,10.000000,4,2.700000,1,False.\r\nMS,29,510,401-6982,no,no,0,313.200000,103,53.240000,216.300000,151,18.390000,218.400000,106,9.830000,12.800000,4,3.460000,2,True.\r\nAR,86,408,329-8115,no,yes,16,145.700000,88,24.770000,191.000000,129,16.240000,215.500000,82,9.700000,11.300000,7,3.050000,0,False.\r\nAK,138,415,340-3409,no,yes,37,75.800000,102,12.890000,173.600000,147,14.760000,162.600000,96,7.320000,8.200000,13,2.210000,0,False.\r\nAK,146,415,397-5911,no,no,0,195.900000,86,33.300000,228.600000,82,19.430000,303.500000,94,13.660000,12.200000,4,3.290000,3,False.\r\nME,175,415,415-6127,no,no,0,132.000000,95,22.440000,231.200000,74,19.650000,313.400000,108,14.100000,8.700000,10,2.350000,1,False.\r\nWV,74,510,392-6073,no,no,0,124.000000,102,21.080000,262.100000,101,22.280000,268.200000,98,12.070000,11.700000,2,3.160000,2,False.\r\nCT,48,415,419-6564,no,no,0,171.900000,98,29.220000,159.000000,127,13.520000,139.500000,101,6.280000,7.600000,3,2.050000,2,False.\r\nGA,74,510,340-8245,no,yes,31,249.400000,70,42.400000,209.500000,59,17.810000,180.600000,75,8.130000,9.900000,2,2.670000,4,False.\r\nNV,105,415,376-4540,yes,no,0,228.400000,100,38.830000,145.100000,108,12.330000,245.300000,140,11.040000,7.700000,7,2.080000,0,False.\r\nVT,157,510,361-5936,no,no,0,168.600000,71,28.660000,205.100000,48,17.430000,175.800000,88,7.910000,5.900000,2,1.590000,3,False.\r\nDC,217,415,421-9846,no,no,0,123.700000,138,21.030000,248.500000,105,21.120000,269.600000,78,12.130000,13.300000,4,3.590000,0,False.\r\nTN,68,415,356-1582,no,no,0,178.700000,61,30.380000,252.300000,84,21.450000,255.700000,76,11.510000,8.400000,4,2.270000,1,False.\r\nOR,80,415,375-4900,no,no,0,113.200000,86,19.240000,185.500000,97,15.770000,237.300000,145,10.680000,9.500000,5,2.570000,1,False.\r\nMS,38,415,420-8953,no,yes,25,142.400000,106,24.210000,313.700000,109,26.660000,126.600000,117,5.700000,13.400000,6,3.620000,2,False.\r\nNC,107,415,376-4035,no,yes,38,204.200000,57,34.710000,205.900000,92,17.500000,286.500000,80,12.890000,8.300000,4,2.240000,0,False.\r\nCO,140,415,344-5206,no,no,0,149.700000,71,25.450000,212.500000,97,18.060000,245.900000,67,11.070000,12.600000,4,3.400000,3,False.\r\nAR,98,415,328-7833,no,no,0,227.100000,116,38.610000,120.500000,103,10.240000,117.000000,102,5.270000,4.700000,4,1.270000,5,True.\r\nPA,114,415,417-4266,no,no,0,155.300000,75,26.400000,169.900000,87,14.440000,207.000000,133,9.320000,12.600000,5,3.400000,2,False.\r\nMN,46,408,351-6574,no,no,0,156.400000,105,26.590000,185.500000,98,15.770000,226.700000,96,10.200000,11.800000,3,3.190000,1,False.\r\nNM,118,415,372-8925,no,yes,42,148.700000,105,25.280000,167.300000,105,14.220000,270.600000,105,12.180000,10.400000,7,2.810000,0,False.\r\nUT,37,510,340-5678,no,no,0,271.700000,112,46.190000,155.100000,96,13.180000,199.500000,97,8.980000,6.600000,4,1.780000,3,False.\r\nNE,34,415,361-6814,no,no,0,193.700000,74,32.930000,126.900000,84,10.790000,221.200000,166,9.950000,8.800000,4,2.380000,0,False.\r\nMS,98,415,336-7155,yes,yes,23,245.500000,54,41.740000,292.700000,83,24.880000,184.000000,90,8.280000,10.800000,7,2.920000,1,False.\r\nNV,113,510,342-8167,no,no,0,245.300000,108,41.700000,259.900000,140,22.090000,204.300000,115,9.190000,10.700000,2,2.890000,3,True.\r\nOR,69,408,375-9180,no,no,0,196.100000,87,33.340000,236.800000,66,20.130000,182.300000,75,8.200000,11.900000,1,3.210000,0,False.\r\nVA,121,415,357-7064,no,no,0,134.100000,112,22.800000,195.100000,104,16.580000,159.600000,139,7.180000,10.500000,2,2.840000,2,False.\r\nNJ,59,510,347-5354,yes,yes,31,225.000000,78,38.250000,191.300000,79,16.260000,226.700000,79,10.200000,9.100000,3,2.460000,1,False.\r\nWV,59,510,362-9391,no,no,0,189.700000,100,32.250000,115.900000,133,9.850000,220.600000,115,9.930000,7.400000,4,2.000000,0,False.\r\nOR,190,415,386-8984,no,no,0,142.900000,96,24.290000,177.900000,96,15.120000,113.300000,117,5.100000,6.600000,4,1.780000,0,False.\r\nKY,109,415,384-6372,no,no,0,175.600000,80,29.850000,238.000000,94,20.230000,198.400000,103,8.930000,10.200000,6,2.750000,1,False.\r\nMO,136,415,358-1329,no,no,0,92.400000,109,15.710000,219.000000,115,18.620000,212.600000,80,9.570000,12.900000,4,3.480000,2,False.\r\nTX,86,510,400-7987,no,no,0,92.800000,92,15.780000,159.600000,87,13.570000,148.700000,115,6.690000,8.800000,5,2.380000,1,False.\r\nMN,100,415,327-8732,no,yes,27,221.700000,100,37.690000,236.100000,70,20.070000,192.700000,91,8.670000,8.000000,4,2.160000,0,False.\r\nFL,106,510,389-6955,no,no,0,159.600000,94,27.130000,276.800000,118,23.530000,223.500000,65,10.060000,8.800000,3,2.380000,0,False.\r\nPA,104,415,396-1800,no,no,0,144.500000,107,24.570000,180.500000,85,15.340000,226.000000,94,10.170000,17.000000,6,4.590000,2,False.\r\nWV,129,415,349-4979,no,no,0,159.100000,100,27.050000,202.500000,90,17.210000,233.100000,96,10.490000,11.500000,6,3.110000,2,False.\r\nIN,205,510,361-5864,no,no,0,49.900000,123,8.480000,150.700000,81,12.810000,188.200000,67,8.470000,10.100000,4,2.730000,2,False.\r\nOK,93,415,418-4658,no,yes,32,116.900000,120,19.870000,232.400000,97,19.750000,127.700000,112,5.750000,11.000000,9,2.970000,0,False.\r\nNE,123,415,330-6208,no,no,0,150.000000,98,25.500000,89.800000,95,7.630000,326.000000,91,14.670000,11.100000,3,3.000000,3,False.\r\nDE,99,415,416-6628,no,no,0,254.400000,120,43.250000,159.300000,92,13.540000,264.400000,94,11.900000,6.000000,5,1.620000,1,False.\r\nMI,61,415,349-5617,no,yes,33,270.700000,53,46.020000,200.700000,116,17.060000,201.700000,102,9.080000,10.900000,3,2.940000,3,False.\r\nIL,71,415,397-8051,no,no,0,207.000000,112,35.190000,173.800000,96,14.770000,178.400000,61,8.030000,12.100000,3,3.270000,1,False.\r\nUT,4,510,413-6346,yes,no,0,145.300000,89,24.700000,303.800000,93,25.820000,206.100000,82,9.270000,8.900000,4,2.400000,0,False.\r\nIL,148,408,395-9270,no,yes,25,230.700000,102,39.220000,233.800000,109,19.870000,215.800000,90,9.710000,13.500000,2,3.650000,3,False.\r\nWA,141,415,401-7575,no,no,0,151.500000,104,25.760000,242.200000,114,20.590000,304.200000,109,13.690000,10.800000,2,2.920000,1,False.\r\nNM,56,408,332-5964,no,no,0,146.100000,57,24.840000,196.200000,97,16.680000,310.100000,110,13.950000,9.200000,3,2.480000,0,False.\r\nMD,160,415,348-3338,no,no,0,256.000000,111,43.520000,187.400000,61,15.930000,119.100000,81,5.360000,11.500000,4,3.110000,3,False.\r\nVA,43,408,387-5411,no,yes,35,200.200000,105,34.030000,244.400000,88,20.770000,207.200000,97,9.320000,11.600000,4,3.130000,3,False.\r\nMT,42,415,378-7872,no,no,0,150.700000,52,25.620000,246.700000,96,20.970000,103.800000,118,4.670000,7.000000,4,1.890000,2,False.\r\nCT,135,415,389-6037,yes,no,0,186.000000,107,31.620000,66.000000,94,5.610000,213.100000,105,9.590000,12.900000,4,3.480000,1,False.\r\nAL,106,415,349-3732,no,no,0,212.900000,110,36.190000,187.000000,69,15.900000,128.100000,71,5.760000,6.300000,3,1.700000,3,False.\r\nWV,106,510,347-9738,no,no,0,194.800000,133,33.120000,213.400000,73,18.140000,190.800000,92,8.590000,11.500000,7,3.110000,0,False.\r\nMO,83,415,380-6074,no,yes,30,272.500000,105,46.330000,253.000000,83,21.510000,180.800000,123,8.140000,8.700000,6,2.350000,3,False.\r\nND,110,415,338-4307,no,no,0,135.100000,109,22.970000,205.200000,99,17.440000,166.300000,119,7.480000,11.700000,4,3.160000,1,False.\r\nAR,153,408,339-3636,no,no,0,154.600000,56,26.280000,263.000000,84,22.360000,367.700000,89,16.550000,15.500000,2,4.190000,1,False.\r\nGA,109,510,328-9315,no,yes,35,230.500000,116,39.190000,265.800000,130,22.590000,269.700000,69,12.140000,10.600000,6,2.860000,5,False.\r\nFL,31,510,402-3634,no,no,0,165.400000,84,28.120000,203.700000,107,17.310000,201.700000,65,9.080000,8.200000,1,2.210000,1,False.\r\nLA,124,510,348-4316,no,no,0,143.300000,120,24.360000,230.700000,111,19.610000,214.300000,91,9.640000,7.800000,2,2.110000,4,True.\r\nUT,110,415,375-3826,no,no,0,271.100000,108,46.090000,237.000000,122,20.150000,239.900000,122,10.800000,9.800000,5,2.650000,2,True.\r\nAZ,124,415,360-1406,no,no,0,253.500000,104,43.100000,117.900000,123,10.020000,248.500000,104,11.180000,14.000000,2,3.780000,0,False.\r\nNY,82,415,356-5475,no,no,0,167.100000,77,28.410000,131.800000,79,11.200000,187.400000,98,8.430000,9.400000,1,2.540000,6,True.\r\nKY,122,415,363-9969,no,no,0,168.300000,96,28.610000,87.600000,91,7.450000,247.200000,87,11.120000,8.400000,6,2.270000,1,False.\r\nAL,137,415,350-4367,no,no,0,104.700000,115,17.800000,249.800000,144,21.230000,192.300000,99,8.650000,8.900000,2,2.400000,1,False.\r\nIN,69,510,348-1592,no,no,0,135.400000,101,23.020000,238.100000,124,20.240000,195.600000,102,8.800000,10.600000,2,2.860000,1,False.\r\nIN,46,415,368-9751,no,yes,34,191.400000,102,32.540000,361.800000,96,30.750000,147.500000,132,6.640000,7.200000,2,1.940000,1,False.\r\nFL,103,415,380-6413,no,no,0,158.700000,90,26.980000,198.400000,117,16.860000,181.100000,76,8.150000,10.500000,4,2.840000,1,False.\r\nNM,16,510,367-9259,no,no,0,144.800000,84,24.620000,164.900000,141,14.020000,231.500000,75,10.420000,8.200000,4,2.210000,2,False.\r\nAL,119,415,404-8765,no,no,0,98.800000,97,16.800000,146.900000,68,12.490000,190.700000,105,8.580000,10.000000,4,2.700000,3,False.\r\nMN,124,510,371-6284,yes,no,0,157.800000,71,26.830000,203.200000,114,17.270000,168.700000,82,7.590000,10.000000,2,2.700000,3,True.\r\nNY,122,415,403-9468,no,yes,37,163.000000,107,27.710000,312.800000,118,26.590000,200.000000,85,9.000000,11.600000,5,3.130000,1,False.\r\nMD,139,415,335-3133,no,no,0,181.600000,119,30.870000,335.700000,118,28.530000,149.800000,64,6.740000,8.300000,6,2.240000,4,False.\r\nKS,67,510,366-4426,no,no,0,129.000000,78,21.930000,188.000000,116,15.980000,235.000000,102,10.580000,11.200000,3,3.020000,2,False.\r\nWV,84,408,354-4752,no,no,0,86.000000,83,14.620000,260.700000,86,22.160000,98.600000,109,4.440000,8.900000,4,2.400000,1,False.\r\nID,101,510,406-4768,no,yes,17,193.900000,71,32.960000,189.800000,81,16.130000,196.300000,97,8.830000,12.600000,7,3.400000,0,False.\r\nLA,40,510,367-9257,no,no,0,109.400000,107,18.600000,244.700000,102,20.800000,276.900000,123,12.460000,7.100000,7,1.920000,0,False.\r\nMI,61,415,342-8348,no,no,0,188.900000,105,32.110000,153.600000,116,13.060000,213.300000,106,9.600000,10.200000,2,2.750000,2,False.\r\nTN,120,408,410-7611,yes,no,0,179.900000,72,30.580000,170.000000,98,14.450000,190.600000,89,8.580000,13.800000,2,3.730000,1,True.\r\nCA,95,415,341-3270,no,no,0,183.400000,98,31.180000,281.300000,95,23.910000,105.200000,113,4.730000,8.200000,8,2.210000,1,False.\r\nFL,98,408,416-7452,no,no,0,288.100000,101,48.980000,137.900000,93,11.720000,206.500000,88,9.290000,0.000000,0,0.000000,0,False.\r\nLA,114,415,356-8982,no,no,0,169.200000,96,28.760000,149.900000,83,12.740000,196.900000,119,8.860000,4.600000,4,1.240000,2,False.\r\nIL,68,415,340-6908,yes,yes,29,195.500000,113,33.240000,171.600000,96,14.590000,204.000000,85,9.180000,13.500000,9,3.650000,1,True.\r\nAL,149,415,348-6659,no,yes,20,264.400000,102,44.950000,219.600000,123,18.670000,200.400000,89,9.020000,11.300000,3,3.050000,2,False.\r\nCT,22,408,345-2401,no,no,0,207.700000,116,35.310000,210.600000,99,17.900000,238.200000,88,10.720000,9.600000,5,2.590000,0,False.\r\nPA,176,415,422-5264,no,no,0,169.500000,151,28.820000,112.900000,84,9.600000,56.600000,99,2.550000,8.700000,4,2.350000,0,False.\r\nTX,152,415,422-1799,no,no,0,141.500000,102,24.060000,263.000000,94,22.360000,207.100000,113,9.320000,3.400000,4,0.920000,2,False.\r\nVA,118,408,404-2877,no,no,0,154.800000,71,26.320000,244.000000,73,20.740000,159.600000,81,7.180000,12.800000,4,3.460000,0,False.\r\nMO,101,415,417-7913,yes,no,0,133.500000,51,22.700000,219.600000,96,18.670000,210.000000,74,9.450000,11.700000,4,3.160000,1,False.\r\nMT,102,408,399-2457,no,no,0,273.200000,85,46.440000,211.100000,82,17.940000,203.700000,129,9.170000,13.100000,7,3.540000,2,True.\r\nND,118,408,419-3427,no,no,0,224.600000,94,38.180000,225.900000,120,19.200000,269.000000,105,12.110000,12.500000,8,3.380000,2,False.\r\nFL,105,415,358-2490,no,no,0,273.800000,97,46.550000,289.700000,106,24.620000,269.100000,126,12.110000,5.800000,3,1.570000,2,True.\r\nWI,153,510,349-3112,no,no,0,159.500000,103,27.120000,275.500000,90,23.420000,176.700000,126,7.950000,10.100000,2,2.730000,1,True.\r\nKY,71,415,414-5422,no,no,0,104.000000,92,17.680000,197.000000,125,16.750000,110.100000,123,4.950000,14.600000,8,3.940000,0,False.\r\nMD,71,415,386-3766,no,yes,31,115.400000,90,19.620000,217.400000,78,18.480000,239.900000,102,10.800000,13.100000,4,3.540000,1,False.\r\nIN,68,415,386-9724,no,no,0,222.100000,107,37.760000,199.400000,102,16.950000,162.400000,107,7.310000,9.400000,3,2.540000,2,False.\r\nMA,66,415,416-7393,no,no,0,116.400000,98,19.790000,95.600000,74,8.130000,181.500000,94,8.170000,10.500000,3,2.840000,3,False.\r\nND,101,415,395-1380,no,no,0,217.700000,118,37.010000,231.700000,128,19.690000,185.300000,128,8.340000,0.000000,0,0.000000,3,False.\r\nVT,116,415,408-4911,no,no,0,129.400000,84,22.000000,157.300000,89,13.370000,215.500000,77,9.700000,13.300000,3,3.590000,0,False.\r\nCT,54,415,387-4064,no,yes,33,161.800000,73,27.510000,273.000000,58,23.210000,153.900000,76,6.930000,13.700000,4,3.700000,0,False.\r\nVA,112,408,380-5667,no,yes,29,198.800000,122,33.800000,238.600000,114,20.280000,289.500000,69,13.030000,11.500000,5,3.110000,1,False.\r\nMS,122,408,402-8930,no,yes,45,147.800000,85,25.130000,147.400000,93,12.530000,203.500000,110,9.160000,14.000000,5,3.780000,1,False.\r\nAK,74,415,336-6533,no,no,0,262.300000,114,44.590000,198.900000,96,16.910000,165.900000,90,7.470000,6.600000,5,1.780000,3,False.\r\nWY,90,415,359-9992,no,no,0,246.400000,83,41.890000,160.300000,88,13.630000,170.900000,99,7.690000,7.600000,7,2.050000,1,False.\r\nNY,112,415,391-1737,no,no,0,174.300000,123,29.630000,140.200000,124,11.920000,215.400000,89,9.690000,9.000000,6,2.430000,4,True.\r\nNC,85,510,404-2871,no,no,0,183.400000,111,31.180000,168.800000,98,14.350000,199.700000,97,8.990000,9.900000,4,2.670000,4,False.\r\nIL,100,415,420-6121,no,no,0,191.900000,95,32.620000,200.900000,101,17.080000,271.900000,74,12.240000,18.200000,3,4.910000,1,False.\r\nOH,114,415,369-4012,no,no,0,187.800000,109,31.930000,154.600000,97,13.140000,213.900000,102,9.630000,10.100000,3,2.730000,2,False.\r\nRI,83,510,357-2294,no,no,0,259.700000,106,44.150000,152.700000,116,12.980000,224.700000,92,10.110000,10.200000,2,2.750000,0,False.\r\nWY,157,415,348-9938,yes,no,0,180.400000,123,30.670000,194.000000,98,16.490000,227.300000,88,10.230000,8.400000,5,2.270000,0,False.\r\nOR,51,510,394-3023,no,no,0,51.800000,107,8.810000,230.200000,104,19.570000,227.500000,118,10.240000,10.400000,4,2.810000,2,False.\r\nNV,42,415,352-5466,no,no,0,303.900000,106,51.660000,232.200000,54,19.740000,147.100000,76,6.620000,5.800000,3,1.570000,1,True.\r\nND,101,415,364-5510,no,yes,36,123.700000,125,21.030000,172.600000,106,14.670000,280.500000,127,12.620000,8.800000,4,2.380000,1,True.\r\nOR,112,510,396-6462,no,no,0,206.200000,122,35.050000,164.500000,94,13.980000,140.300000,101,6.310000,12.600000,7,3.400000,3,False.\r\nND,56,510,384-5335,no,no,0,164.300000,92,27.930000,233.700000,107,19.860000,187.300000,104,8.430000,11.800000,1,3.190000,2,False.\r\nNJ,53,408,416-6886,no,no,0,228.600000,117,38.860000,132.800000,123,11.290000,227.200000,124,10.220000,10.100000,2,2.730000,9,True.\r\nWV,64,415,357-2748,no,yes,22,200.400000,80,34.070000,131.100000,84,11.140000,230.700000,67,10.380000,7.600000,5,2.050000,1,False.\r\nVA,123,408,386-7976,no,no,0,154.300000,107,26.230000,183.000000,111,15.560000,54.000000,134,2.430000,10.900000,8,2.940000,0,False.\r\nID,68,510,403-9199,no,yes,30,122.900000,93,20.890000,233.500000,91,19.850000,199.500000,144,8.980000,9.600000,2,2.590000,2,False.\r\nCT,40,510,361-1900,yes,no,0,220.800000,100,37.540000,265.700000,106,22.580000,212.800000,94,9.580000,6.400000,3,1.730000,0,False.\r\nNM,132,408,405-3848,no,no,0,214.600000,78,36.480000,251.700000,98,21.390000,240.800000,88,10.840000,13.900000,2,3.750000,0,False.\r\nCT,120,408,344-1136,yes,no,0,202.000000,123,34.340000,184.300000,78,15.670000,176.000000,89,7.920000,7.400000,2,2.000000,2,True.\r\nMI,108,408,378-6276,no,yes,32,209.500000,108,35.620000,109.600000,64,9.320000,189.700000,145,8.540000,9.100000,6,2.460000,6,True.\r\nSC,161,510,343-2592,no,no,0,297.900000,141,50.640000,238.100000,107,20.240000,240.500000,93,10.820000,8.900000,5,2.400000,1,True.\r\nSC,130,415,396-4410,no,no,0,212.800000,102,36.180000,189.800000,137,16.130000,170.100000,105,7.650000,10.600000,4,2.860000,0,True.\r\nNY,122,510,397-3943,no,no,0,145.600000,102,24.750000,284.700000,111,24.200000,228.200000,91,10.270000,12.200000,5,3.290000,0,False.\r\nMT,130,408,347-3821,no,yes,19,152.900000,87,25.990000,213.200000,99,18.120000,205.300000,114,9.240000,10.800000,6,2.920000,2,False.\r\nWY,90,510,400-8069,no,no,0,125.400000,158,21.320000,269.100000,83,22.870000,238.600000,103,10.740000,11.000000,7,2.970000,1,False.\r\nNE,139,415,346-5349,no,yes,25,138.300000,96,23.510000,80.600000,79,6.850000,163.700000,83,7.370000,8.300000,8,2.240000,0,False.\r\nIN,57,415,345-5089,no,no,0,189.300000,157,32.180000,174.900000,70,14.870000,221.900000,117,9.990000,11.200000,5,3.020000,3,False.\r\nCO,128,510,410-4613,no,no,0,199.300000,86,33.880000,194.800000,102,16.560000,298.200000,82,13.420000,14.300000,2,3.860000,4,False.\r\nWY,127,510,356-4706,yes,no,0,247.500000,99,42.080000,108.500000,118,9.220000,232.000000,72,10.440000,10.600000,3,2.860000,2,False.\r\nMD,107,415,347-3406,no,no,0,294.900000,71,50.130000,192.800000,78,16.390000,148.100000,87,6.660000,13.200000,5,3.560000,1,True.\r\nOK,177,408,333-9133,no,no,0,175.400000,99,29.820000,155.300000,83,13.200000,179.400000,86,8.070000,11.500000,3,3.110000,1,False.\r\nSD,121,415,392-2459,no,no,0,179.400000,70,30.500000,143.000000,93,12.160000,116.300000,113,5.230000,11.200000,5,3.020000,1,False.\r\nSC,99,510,401-3685,yes,yes,39,126.800000,94,21.560000,293.600000,115,24.960000,174.100000,91,7.830000,8.400000,4,2.270000,0,False.\r\nNY,126,415,352-7752,yes,no,0,239.700000,87,40.750000,281.700000,92,23.940000,183.500000,113,8.260000,11.400000,1,3.080000,1,True.\r\nNY,77,415,388-9285,no,yes,33,143.000000,101,24.310000,212.200000,102,18.040000,104.900000,120,4.720000,15.300000,4,4.130000,5,True.\r\nWV,21,415,332-5582,no,no,0,91.900000,109,15.620000,198.400000,111,16.860000,171.700000,125,7.730000,13.000000,7,3.510000,2,False.\r\nWA,56,408,376-2550,no,no,0,210.400000,80,35.770000,176.600000,96,15.010000,149.700000,56,6.740000,15.500000,4,4.190000,1,False.\r\nID,92,415,333-4594,no,yes,29,201.300000,130,34.220000,203.700000,115,17.310000,129.900000,113,5.850000,6.400000,6,1.730000,1,True.\r\nMN,81,510,375-2522,no,no,0,145.600000,59,24.750000,287.900000,131,24.470000,181.700000,121,8.180000,9.200000,4,2.480000,2,False.\r\nTX,139,510,388-2240,yes,yes,31,203.500000,82,34.600000,200.300000,72,17.030000,214.000000,112,9.630000,13.400000,6,3.620000,1,True.\r\nAZ,68,415,420-1782,no,no,0,232.400000,76,39.510000,153.300000,103,13.030000,214.600000,107,9.660000,10.500000,2,2.840000,1,False.\r\nDE,183,415,384-8890,no,yes,8,86.500000,119,14.710000,285.200000,97,24.240000,180.400000,133,8.120000,8.700000,2,2.350000,2,False.\r\nCO,90,408,371-4788,no,no,0,109.900000,102,18.680000,220.800000,114,18.770000,104.000000,133,4.680000,10.900000,6,2.940000,0,False.\r\nWI,165,408,360-5636,no,no,0,156.000000,88,26.520000,276.100000,81,23.470000,175.900000,94,7.920000,9.300000,5,2.510000,1,False.\r\nWI,89,415,373-4264,no,no,0,326.300000,112,55.470000,165.100000,110,14.030000,162.900000,97,7.330000,7.500000,1,2.030000,1,True.\r\nNJ,59,510,352-9836,no,no,0,195.000000,58,33.150000,198.500000,88,16.870000,304.300000,110,13.690000,14.800000,9,4.000000,0,False.\r\nIL,16,415,342-2013,yes,no,0,110.000000,91,18.700000,147.300000,75,12.520000,190.500000,73,8.570000,6.400000,7,1.730000,0,False.\r\nDC,114,415,354-5689,no,no,0,147.100000,119,25.010000,161.000000,111,13.690000,275.900000,106,12.420000,9.000000,3,2.430000,5,True.\r\nIA,113,510,335-8427,no,no,0,156.000000,141,26.520000,256.800000,72,21.830000,175.300000,123,7.890000,11.900000,5,3.210000,2,False.\r\nGA,120,408,409-6753,no,no,0,98.200000,99,16.690000,186.700000,85,15.870000,146.700000,96,6.600000,9.300000,4,2.510000,2,False.\r\nAK,115,415,349-1756,no,no,0,210.600000,120,35.800000,153.100000,84,13.010000,262.200000,79,11.800000,11.000000,5,2.970000,0,False.\r\nCA,37,510,328-8980,no,no,0,239.900000,120,40.780000,261.600000,88,22.240000,207.100000,88,9.320000,8.900000,4,2.400000,2,True.\r\nMN,100,415,399-2151,yes,no,0,159.900000,94,27.180000,179.900000,95,15.290000,154.400000,102,6.950000,11.600000,2,3.130000,2,True.\r\nCO,132,415,379-9524,no,no,0,197.800000,66,33.630000,133.900000,119,11.380000,177.300000,94,7.980000,10.900000,3,2.940000,4,True.\r\nKY,38,408,352-9947,no,yes,36,115.400000,98,19.620000,166.200000,83,14.130000,184.700000,79,8.310000,15.200000,6,4.100000,2,False.\r\nSC,1,408,336-1043,no,no,0,123.800000,113,21.050000,236.200000,77,20.080000,73.200000,81,3.290000,3.700000,2,1.000000,0,False.\r\nMT,97,415,416-7013,no,yes,15,117.600000,97,19.990000,196.300000,126,16.690000,157.400000,113,7.080000,6.400000,3,1.730000,1,False.\r\nKY,55,415,345-4551,no,yes,28,105.300000,82,17.900000,197.400000,109,16.780000,187.500000,91,8.440000,8.600000,6,2.320000,2,False.\r\nWV,75,415,405-9864,no,no,0,111.700000,121,18.990000,237.300000,119,20.170000,253.500000,110,11.410000,13.100000,6,3.540000,1,False.\r\nID,83,415,350-4297,no,no,0,159.300000,104,27.080000,202.300000,98,17.200000,229.000000,73,10.310000,9.500000,3,2.570000,2,False.\r\nMN,40,510,350-7114,no,no,0,81.700000,123,13.890000,210.200000,108,17.870000,212.000000,64,9.540000,11.300000,3,3.050000,6,True.\r\nMA,101,415,400-4244,no,yes,21,238.000000,88,40.460000,209.600000,84,17.820000,233.000000,95,10.490000,10.000000,5,2.700000,2,False.\r\nMD,120,415,417-2608,no,yes,40,128.100000,99,21.780000,247.700000,78,21.050000,199.700000,121,8.990000,15.600000,3,4.210000,0,False.\r\nSD,183,415,372-2990,no,yes,31,171.200000,104,29.100000,193.600000,74,16.460000,196.500000,85,8.840000,10.200000,4,2.750000,1,False.\r\nPA,75,510,353-9998,no,no,0,109.000000,88,18.530000,259.300000,120,22.040000,182.100000,119,8.190000,13.300000,3,3.590000,4,True.\r\nKY,80,415,330-3008,no,no,0,220.000000,114,37.400000,207.700000,76,17.650000,168.400000,137,7.580000,12.100000,3,3.270000,2,False.\r\nID,88,415,384-8629,no,no,0,55.600000,65,9.450000,242.700000,121,20.630000,176.300000,134,7.930000,11.300000,4,3.050000,0,False.\r\nNY,112,415,404-9504,no,yes,23,286.600000,79,48.720000,315.300000,102,26.800000,193.900000,101,8.730000,10.300000,6,2.780000,1,False.\r\nNM,63,510,405-8753,no,no,0,207.600000,96,35.290000,229.000000,112,19.470000,162.600000,131,7.320000,13.300000,2,3.590000,1,False.\r\nCA,105,415,409-9911,no,yes,31,109.600000,108,18.630000,249.300000,119,21.190000,321.200000,101,14.450000,8.300000,4,2.240000,4,True.\r\nID,92,415,394-4260,no,no,0,197.200000,113,33.520000,242.300000,116,20.600000,192.000000,76,8.640000,11.000000,5,2.970000,2,False.\r\nWY,177,415,380-9063,no,no,0,175.700000,120,29.870000,168.600000,90,14.330000,198.900000,110,8.950000,14.600000,4,3.940000,1,False.\r\nNM,118,510,395-4509,no,no,0,205.200000,115,34.880000,184.800000,137,15.710000,176.100000,115,7.920000,7.000000,6,1.890000,0,False.\r\nHI,111,408,401-6671,no,yes,13,193.100000,104,32.830000,111.600000,98,9.490000,227.400000,94,10.230000,12.100000,4,3.270000,1,False.\r\nID,82,510,405-7204,no,yes,34,232.600000,121,39.540000,153.200000,115,13.020000,286.700000,77,12.900000,4.700000,3,1.270000,3,False.\r\nNC,74,415,329-5377,no,no,0,102.700000,89,17.460000,149.300000,100,12.690000,188.100000,114,8.460000,11.000000,5,2.970000,0,False.\r\nTX,121,415,408-9572,no,yes,31,263.100000,70,44.730000,279.300000,118,23.740000,127.100000,143,5.720000,9.700000,4,2.620000,5,False.\r\nGA,131,408,380-9879,no,no,0,197.000000,79,33.490000,201.000000,114,17.090000,151.200000,111,6.800000,11.600000,5,3.130000,1,False.\r\nAL,125,408,384-9243,no,no,0,169.300000,90,28.780000,156.000000,138,13.260000,210.800000,106,9.490000,11.600000,6,3.130000,2,False.\r\nME,19,415,404-5597,no,no,0,201.500000,123,34.260000,129.200000,110,10.980000,220.600000,98,9.930000,12.900000,4,3.480000,1,False.\r\nVA,138,415,359-7521,no,no,0,251.000000,119,42.670000,91.200000,96,7.750000,142.200000,87,6.400000,13.800000,3,3.730000,3,False.\r\nID,119,415,327-4795,no,no,0,230.400000,117,39.170000,225.000000,101,19.130000,198.500000,111,8.930000,7.600000,6,2.050000,3,False.\r\nNY,137,510,338-7955,no,no,0,109.800000,120,18.670000,230.500000,86,19.590000,255.800000,103,11.510000,11.900000,6,3.210000,1,False.\r\nNC,182,415,379-6970,no,no,0,279.500000,118,47.520000,203.200000,113,17.270000,174.200000,101,7.840000,10.700000,4,2.890000,2,True.\r\nOH,135,415,351-7807,no,no,0,173.400000,107,29.480000,222.000000,84,18.870000,64.200000,94,2.890000,13.700000,6,3.700000,1,False.\r\nHI,134,415,342-9394,no,yes,38,214.400000,93,36.450000,211.700000,57,17.990000,165.000000,79,7.430000,10.000000,8,2.700000,1,False.\r\nDC,45,415,384-6264,no,no,0,96.100000,103,16.340000,246.800000,134,20.980000,229.700000,92,10.340000,9.700000,4,2.620000,1,False.\r\nTN,129,408,352-4534,no,no,0,101.400000,145,17.240000,249.100000,116,21.170000,157.600000,107,7.090000,7.100000,6,1.920000,1,False.\r\nVT,142,415,378-4617,no,no,0,232.500000,74,39.530000,181.800000,142,15.450000,203.100000,86,9.140000,10.400000,6,2.810000,4,False.\r\nMD,130,415,364-9567,no,yes,45,174.500000,120,29.670000,217.500000,95,18.490000,220.300000,67,9.910000,12.200000,2,3.290000,1,False.\r\nLA,163,408,371-5875,no,yes,23,224.000000,126,38.080000,233.500000,89,19.850000,293.900000,104,13.230000,8.800000,4,2.380000,1,False.\r\nHI,105,415,383-6489,no,no,0,211.100000,99,35.890000,176.700000,66,15.020000,221.500000,96,9.970000,14.700000,7,3.970000,4,False.\r\nFL,119,415,345-7117,no,no,0,109.200000,96,18.560000,153.100000,80,13.010000,240.000000,102,10.800000,9.800000,5,2.650000,2,False.\r\nWY,78,408,384-3902,no,no,0,220.000000,95,37.400000,179.900000,121,15.290000,188.200000,109,8.470000,11.500000,5,3.110000,0,False.\r\nNE,92,415,386-2759,no,no,0,181.400000,98,30.840000,164.500000,98,13.980000,171.000000,110,7.690000,10.900000,4,2.940000,2,False.\r\nWY,146,415,356-1270,no,yes,11,180.700000,82,30.720000,173.700000,90,14.760000,231.500000,89,10.420000,10.100000,4,2.730000,2,False.\r\nOR,125,408,379-1336,no,yes,32,96.500000,109,16.410000,145.800000,109,12.390000,174.400000,82,7.850000,9.400000,2,2.540000,1,False.\r\nIN,88,415,354-7201,no,no,0,183.500000,93,31.200000,170.500000,80,14.490000,193.800000,88,8.720000,8.300000,5,2.240000,3,False.\r\nMD,83,408,404-5057,no,yes,38,107.900000,90,18.340000,140.400000,94,11.930000,253.600000,79,11.410000,10.500000,2,2.840000,0,False.\r\nTN,3,510,407-8012,yes,no,0,161.000000,96,27.370000,244.900000,82,20.820000,180.800000,103,8.140000,7.700000,6,2.080000,1,False.\r\nWV,152,510,332-6139,yes,yes,41,146.800000,128,24.960000,285.600000,96,24.280000,213.600000,80,9.610000,4.300000,2,1.160000,1,True.\r\nWA,48,510,328-1373,no,no,0,149.200000,146,25.360000,161.900000,109,13.760000,197.900000,109,8.910000,8.300000,2,2.240000,3,False.\r\nMS,189,415,411-6501,no,no,0,227.800000,124,38.730000,169.500000,112,14.410000,201.100000,91,9.050000,5.600000,4,1.510000,3,False.\r\nOH,95,415,329-8056,no,yes,23,160.300000,87,27.250000,202.400000,101,17.200000,191.100000,122,8.600000,7.400000,3,2.000000,0,False.\r\nIN,129,415,415-4564,no,no,0,137.800000,120,23.430000,225.800000,110,19.190000,145.200000,95,6.530000,10.200000,6,2.750000,1,True.\r\nCO,66,408,329-6192,no,yes,40,141.700000,87,24.090000,268.300000,89,22.810000,241.300000,68,10.860000,8.500000,7,2.300000,0,False.\r\nTX,80,510,384-3904,no,yes,22,196.400000,115,33.390000,150.300000,109,12.780000,176.200000,75,7.930000,9.300000,1,2.510000,0,False.\r\nAK,1,408,373-1028,no,no,0,175.200000,74,29.780000,151.700000,79,12.890000,230.500000,109,10.370000,5.300000,3,1.430000,1,False.\r\nWV,84,408,369-1220,no,no,0,146.800000,133,24.960000,171.700000,73,14.590000,234.500000,69,10.550000,9.900000,3,2.670000,1,False.\r\nMA,96,415,359-9369,no,no,0,173.900000,111,29.560000,287.400000,105,24.430000,204.800000,91,9.220000,9.100000,7,2.460000,1,False.\r\nTN,123,415,415-3016,no,yes,34,305.200000,80,51.880000,156.500000,109,13.300000,280.000000,81,12.600000,13.200000,7,3.560000,1,False.\r\nID,116,510,414-7090,yes,yes,29,162.300000,91,27.590000,279.300000,79,23.740000,192.700000,131,8.670000,11.700000,2,3.160000,3,True.\r\nDE,105,415,350-2250,yes,no,0,150.000000,106,25.500000,293.800000,123,24.970000,250.700000,65,11.280000,10.300000,7,2.780000,3,False.\r\nVA,80,415,383-9355,no,no,0,197.500000,114,33.580000,206.900000,119,17.590000,163.600000,109,7.360000,11.300000,4,3.050000,1,False.\r\nMT,157,408,417-3257,no,no,0,240.200000,67,40.830000,153.000000,98,13.010000,249.000000,72,11.210000,10.200000,6,2.750000,2,False.\r\nID,67,510,336-8010,no,yes,30,186.200000,117,31.650000,286.700000,76,24.370000,164.300000,113,7.390000,12.900000,3,3.480000,2,False.\r\nIN,141,415,354-7718,no,yes,39,116.900000,127,19.870000,276.500000,88,23.500000,289.900000,125,13.050000,12.300000,2,3.320000,0,False.\r\nMD,79,415,358-4412,no,yes,17,236.700000,95,40.240000,263.500000,56,22.400000,259.600000,107,11.680000,12.000000,4,3.240000,1,False.\r\nMS,76,408,368-8972,no,no,0,173.200000,93,29.440000,131.200000,80,11.150000,170.900000,104,7.690000,5.400000,3,1.460000,0,False.\r\nWA,111,510,407-9841,no,no,0,152.200000,114,25.870000,137.200000,102,11.660000,185.900000,97,8.370000,9.800000,3,2.650000,0,False.\r\nOH,94,415,393-5208,no,no,0,181.300000,135,30.820000,182.400000,108,15.500000,180.600000,103,8.130000,6.700000,2,1.810000,0,False.\r\nRI,143,408,332-2889,no,no,0,167.800000,72,28.530000,211.000000,99,17.940000,153.500000,109,6.910000,10.500000,6,2.840000,4,True.\r\nAR,109,510,374-5530,no,no,0,175.400000,125,29.820000,250.700000,87,21.310000,289.300000,74,13.020000,9.800000,9,2.650000,1,False.\r\nAZ,138,415,332-3381,no,no,0,87.600000,112,14.890000,266.900000,107,22.690000,214.600000,104,9.660000,9.800000,10,2.650000,2,False.\r\nSC,73,415,344-9347,no,no,0,203.300000,45,34.560000,141.900000,87,12.060000,200.700000,71,9.030000,8.500000,6,2.300000,0,False.\r\nKY,21,415,412-1991,no,no,0,92.600000,95,15.740000,161.900000,70,13.760000,285.000000,78,12.830000,11.300000,3,3.050000,5,True.\r\nAL,148,415,393-4528,no,yes,21,262.900000,135,44.690000,149.500000,96,12.710000,140.500000,109,6.320000,8.100000,4,2.190000,1,False.\r\nNE,103,408,347-2378,no,no,0,160.800000,91,27.340000,155.800000,82,13.240000,254.300000,103,11.440000,8.500000,3,2.300000,1,False.\r\nMT,143,408,385-2699,no,yes,22,141.800000,116,24.110000,167.300000,99,14.220000,178.100000,130,8.010000,7.800000,3,2.110000,1,False.\r\nMN,79,408,383-4319,no,yes,32,50.600000,62,8.600000,201.400000,87,17.120000,146.800000,121,6.610000,4.200000,4,1.130000,2,False.\r\nNV,89,415,352-7915,no,no,0,134.900000,59,22.930000,156.000000,152,13.260000,197.500000,112,8.890000,10.200000,5,2.750000,1,False.\r\nCA,120,415,375-5547,no,no,0,252.100000,110,42.860000,226.100000,103,19.220000,155.600000,83,7.000000,13.800000,3,3.730000,1,False.\r\nUT,121,415,337-2348,no,yes,41,215.500000,95,36.640000,241.800000,92,20.550000,147.000000,108,6.610000,9.600000,3,2.590000,1,False.\r\nIL,101,415,342-8702,no,no,0,124.800000,66,21.220000,257.200000,85,21.860000,193.200000,115,8.690000,13.400000,4,3.620000,0,False.\r\nDC,115,408,393-5802,no,no,0,178.700000,114,30.380000,271.000000,96,23.040000,245.900000,94,11.070000,16.400000,5,4.430000,2,False.\r\nIN,168,415,384-2219,no,no,0,183.200000,131,31.140000,179.200000,73,15.230000,292.800000,100,13.180000,9.900000,5,2.670000,2,False.\r\nNM,90,415,347-6164,no,no,0,167.500000,96,28.480000,139.100000,104,11.820000,138.400000,87,6.230000,13.000000,1,3.510000,1,False.\r\nMS,70,510,376-9940,no,no,0,147.100000,105,25.010000,200.000000,135,17.000000,234.900000,65,10.570000,12.500000,9,3.380000,3,False.\r\nVT,138,415,354-4352,no,no,0,230.100000,107,39.120000,212.000000,120,18.020000,174.900000,119,7.870000,13.200000,4,3.560000,1,False.\r\nVT,43,408,331-8713,no,no,0,135.800000,125,23.090000,163.200000,88,13.870000,229.800000,106,10.340000,12.600000,3,3.400000,0,False.\r\nUT,117,510,341-3663,no,yes,20,205.700000,98,34.970000,136.100000,107,11.570000,159.400000,147,7.170000,8.700000,3,2.350000,2,False.\r\nKY,108,408,376-4665,no,no,0,73.800000,105,12.550000,143.400000,114,12.190000,170.200000,98,7.660000,10.900000,3,2.940000,2,False.\r\nVA,118,408,421-9034,no,no,0,189.300000,119,32.180000,233.500000,112,19.850000,270.900000,104,12.190000,10.000000,1,2.700000,2,False.\r\nOH,169,408,401-5169,no,no,0,147.200000,115,25.020000,161.900000,123,13.760000,142.100000,103,6.390000,7.200000,6,1.940000,3,False.\r\nAZ,62,408,370-8262,no,yes,42,137.300000,95,23.340000,184.200000,94,15.660000,231.400000,70,10.410000,10.200000,3,2.750000,0,False.\r\nNY,86,510,387-2041,no,no,0,70.700000,125,12.020000,211.000000,113,17.940000,174.600000,107,7.860000,0.000000,0,0.000000,2,False.\r\nVA,44,408,356-4146,no,no,0,204.600000,117,34.780000,205.200000,94,17.440000,164.600000,84,7.410000,10.700000,5,2.890000,0,False.\r\nMD,111,510,372-8883,no,no,0,123.100000,88,20.930000,213.900000,84,18.180000,184.900000,88,8.320000,12.000000,2,3.240000,5,True.\r\nMA,127,510,336-1880,no,yes,19,129.700000,115,22.050000,160.800000,101,13.670000,265.000000,63,11.930000,12.200000,3,3.290000,2,False.\r\nIL,151,415,347-5843,yes,no,0,198.700000,70,33.780000,209.500000,106,17.810000,281.900000,126,12.690000,12.400000,4,3.350000,0,False.\r\nLA,53,415,370-8023,no,no,0,145.100000,116,24.670000,233.700000,82,19.860000,208.700000,95,9.390000,7.900000,5,2.130000,2,False.\r\nMO,15,415,417-9814,no,no,0,135.200000,101,22.980000,152.500000,79,12.960000,224.800000,83,10.120000,8.400000,5,2.270000,2,False.\r\nDC,123,408,387-3422,no,yes,28,124.700000,105,21.200000,250.400000,78,21.280000,216.400000,128,9.740000,7.800000,8,2.110000,1,False.\r\nPA,137,415,365-1664,no,no,0,215.900000,76,36.700000,145.400000,118,12.360000,186.900000,129,8.410000,12.100000,4,3.270000,1,False.\r\nTN,106,415,367-2436,no,no,0,119.200000,142,20.260000,228.400000,139,19.410000,197.900000,61,8.910000,8.400000,9,2.270000,2,False.\r\nNJ,88,510,344-6258,no,no,0,144.300000,116,24.530000,156.400000,74,13.290000,214.700000,90,9.660000,7.800000,10,2.110000,3,False.\r\nVA,106,415,353-8928,no,no,0,235.200000,121,39.980000,220.600000,87,18.750000,236.300000,91,10.630000,11.800000,4,3.190000,1,False.\r\nTN,95,510,365-7784,no,no,0,174.000000,57,29.580000,281.100000,118,23.890000,197.200000,94,8.870000,9.700000,2,2.620000,0,False.\r\nNJ,57,510,330-2635,yes,no,0,115.000000,65,19.550000,122.300000,96,10.400000,245.000000,75,11.030000,6.400000,1,1.730000,0,True.\r\nWA,184,408,344-3131,no,no,0,151.700000,93,25.790000,178.500000,77,15.170000,229.100000,111,10.310000,13.100000,8,3.540000,2,False.\r\nAR,109,510,378-4294,no,no,0,153.100000,102,26.030000,234.100000,77,19.900000,329.200000,74,14.810000,9.900000,9,2.670000,3,False.\r\nWI,127,415,343-9365,no,no,0,218.600000,93,37.160000,149.900000,130,12.740000,204.600000,131,9.210000,9.200000,5,2.480000,2,False.\r\nWA,82,510,362-5579,no,no,0,265.200000,122,45.080000,178.700000,102,15.190000,174.700000,90,7.860000,10.700000,9,2.890000,2,False.\r\nRI,180,415,366-7616,no,no,0,143.300000,134,24.360000,180.500000,113,15.340000,184.200000,87,8.290000,10.100000,4,2.730000,1,False.\r\nME,174,415,397-2870,no,no,0,190.300000,98,32.350000,252.700000,70,21.480000,220.600000,97,9.930000,7.200000,9,1.940000,2,False.\r\nCO,92,415,408-3262,no,no,0,184.700000,60,31.400000,262.000000,73,22.270000,239.500000,120,10.780000,12.300000,6,3.320000,2,True.\r\nCO,81,408,372-9091,no,no,0,115.300000,99,19.600000,224.700000,117,19.100000,152.500000,98,6.860000,18.000000,2,4.860000,1,False.\r\nRI,125,408,410-3159,no,no,0,113.000000,108,19.210000,169.200000,107,14.380000,156.600000,61,7.050000,9.200000,5,2.480000,2,True.\r\nCT,119,408,344-5181,no,no,0,294.200000,100,50.010000,232.500000,53,19.760000,195.000000,64,8.780000,9.000000,1,2.430000,0,True.\r\nNC,122,415,396-8662,no,no,0,215.600000,86,36.650000,167.800000,59,14.260000,207.000000,67,9.320000,6.400000,8,1.730000,3,False.\r\nWY,34,408,339-6446,no,no,0,128.800000,80,21.900000,208.700000,93,17.740000,202.100000,103,9.090000,14.000000,7,3.780000,1,False.\r\nOR,138,415,384-7236,yes,yes,28,211.200000,117,35.900000,312.500000,98,26.560000,178.000000,118,8.010000,10.700000,2,2.890000,3,True.\r\nFL,90,415,353-5257,no,yes,24,71.200000,82,12.100000,181.600000,103,15.440000,186.900000,111,8.410000,12.900000,1,3.480000,1,False.\r\nKY,73,408,369-7295,no,no,0,94.900000,121,16.130000,253.200000,83,21.520000,175.100000,86,7.880000,14.200000,2,3.830000,2,False.\r\nSC,19,510,408-5322,no,no,0,259.400000,116,44.100000,269.700000,109,22.920000,175.300000,130,7.890000,9.500000,3,2.570000,1,True.\r\nWA,120,408,344-9620,no,yes,28,215.800000,123,36.690000,285.200000,76,24.240000,192.100000,78,8.640000,6.900000,3,1.860000,1,False.\r\nMT,160,415,329-8436,no,no,0,97.500000,113,16.580000,268.100000,69,22.790000,255.300000,62,11.490000,13.200000,4,3.560000,3,False.\r\nPA,141,510,414-6739,no,no,0,146.500000,121,24.910000,169.900000,125,14.440000,238.800000,112,10.750000,8.200000,5,2.210000,0,False.\r\nMA,90,408,406-1730,no,no,0,157.900000,72,26.840000,234.000000,93,19.890000,210.000000,86,9.450000,12.200000,5,3.290000,2,False.\r\nVT,72,415,336-9327,no,no,0,139.900000,117,23.780000,223.600000,96,19.010000,240.800000,93,10.840000,12.700000,4,3.430000,2,False.\r\nMN,117,408,378-7418,no,yes,21,153.200000,112,26.040000,263.300000,110,22.380000,135.000000,85,6.080000,11.900000,7,3.210000,1,False.\r\nIL,79,408,412-6019,yes,no,0,103.500000,134,17.600000,319.300000,111,27.140000,239.900000,124,10.800000,8.400000,4,2.270000,2,False.\r\nAR,87,408,390-4789,no,no,0,185.800000,119,31.590000,192.300000,83,16.350000,200.000000,96,9.000000,6.600000,4,1.780000,1,False.\r\nMD,102,415,386-9774,no,no,0,129.500000,56,22.020000,354.200000,118,30.110000,145.500000,93,6.550000,10.900000,3,2.940000,1,False.\r\nMT,49,408,353-8970,no,no,0,236.600000,91,40.220000,220.900000,146,18.780000,146.800000,114,6.610000,8.900000,2,2.400000,1,False.\r\nVT,67,408,410-5370,no,no,0,260.400000,107,44.270000,208.200000,104,17.700000,207.900000,115,9.360000,10.000000,2,2.700000,6,False.\r\nCO,107,415,404-4421,no,no,0,167.300000,100,28.440000,163.900000,79,13.930000,185.900000,100,8.370000,6.700000,5,1.810000,2,False.\r\nNC,190,408,409-3353,no,no,0,182.200000,101,30.970000,212.300000,95,18.050000,233.000000,123,10.490000,9.300000,4,2.510000,2,False.\r\nWA,118,510,422-2571,no,no,0,113.000000,80,19.210000,150.100000,87,12.760000,204.300000,115,9.190000,10.800000,4,2.920000,2,False.\r\nTN,120,415,412-3404,no,no,0,185.700000,133,31.570000,235.100000,149,19.980000,256.400000,78,11.540000,16.900000,6,4.560000,0,False.\r\nAR,94,408,333-2964,no,no,0,136.200000,114,23.150000,165.100000,118,14.030000,137.900000,71,6.210000,9.600000,5,2.590000,0,False.\r\nDC,115,510,406-6669,no,yes,29,222.600000,81,37.840000,190.300000,109,16.180000,201.200000,87,9.050000,11.500000,2,3.110000,1,False.\r\nMN,61,415,409-8802,no,no,0,197.300000,67,33.540000,264.500000,106,22.480000,210.500000,116,9.470000,9.000000,6,2.430000,1,False.\r\nIA,143,510,354-6183,no,yes,33,141.400000,130,24.040000,186.400000,114,15.840000,210.000000,111,9.450000,7.700000,6,2.080000,1,False.\r\nKS,110,510,354-2434,no,no,0,208.000000,69,35.360000,95.100000,94,8.080000,178.500000,129,8.030000,8.000000,11,2.160000,1,False.\r\nND,104,415,389-7620,no,no,0,118.500000,92,20.150000,177.800000,109,15.110000,255.700000,98,11.510000,12.100000,4,3.270000,1,False.\r\nMT,16,510,338-1724,no,no,0,153.200000,65,26.040000,229.700000,90,19.520000,148.200000,94,6.670000,10.700000,8,2.890000,1,False.\r\nIL,183,510,399-1750,no,no,0,108.300000,87,18.410000,183.600000,116,15.610000,176.600000,109,7.950000,13.500000,2,3.650000,0,False.\r\nDC,147,408,354-8914,no,no,0,168.600000,92,28.660000,187.700000,107,15.950000,216.500000,95,9.740000,14.400000,8,3.890000,2,False.\r\nKY,58,415,409-2983,no,no,0,247.200000,116,42.020000,303.700000,103,25.810000,105.400000,94,4.740000,9.300000,2,2.510000,2,True.\r\nMS,102,510,329-9689,yes,no,0,224.200000,81,38.110000,243.300000,90,20.680000,147.800000,66,6.650000,12.000000,8,3.240000,3,False.\r\nTN,123,415,337-8950,no,no,0,166.900000,98,28.370000,221.800000,77,18.850000,243.900000,114,10.980000,12.800000,4,3.460000,3,False.\r\nIA,64,415,374-1836,no,yes,43,118.400000,100,20.130000,144.100000,108,12.250000,158.100000,91,7.110000,8.500000,6,2.300000,1,False.\r\nAK,103,510,359-9454,no,no,0,190.900000,62,32.450000,226.600000,53,19.260000,230.100000,96,10.350000,7.800000,3,2.110000,2,False.\r\nMN,152,415,378-9542,no,no,0,317.800000,60,54.030000,152.900000,100,13.000000,123.400000,63,5.550000,10.400000,7,2.810000,1,True.\r\nWV,124,415,344-1970,no,no,0,312.000000,112,53.040000,180.000000,109,15.300000,168.600000,94,7.590000,12.800000,4,3.460000,1,True.\r\nOR,97,415,417-2774,no,no,0,146.000000,121,24.820000,203.000000,141,17.260000,151.800000,120,6.830000,13.300000,2,3.590000,1,False.\r\nMS,131,415,333-9002,no,no,0,131.600000,95,22.370000,179.300000,109,15.240000,251.200000,129,11.300000,15.500000,3,4.190000,1,True.\r\nME,57,415,369-8576,no,yes,33,193.400000,105,32.880000,231.600000,79,19.690000,226.200000,90,10.180000,11.100000,11,3.000000,2,False.\r\nMN,157,510,372-6920,no,no,0,185.100000,92,31.470000,213.000000,85,18.110000,196.100000,85,8.820000,8.500000,5,2.300000,2,False.\r\nGA,194,510,333-6575,no,no,0,193.300000,106,32.860000,169.000000,150,14.370000,225.200000,122,10.130000,11.800000,4,3.190000,0,False.\r\nDC,66,415,410-1190,no,no,0,146.400000,107,24.890000,196.500000,99,16.700000,230.100000,106,10.350000,7.800000,2,2.110000,1,False.\r\nGA,155,510,376-1641,no,no,0,71.200000,90,12.100000,304.400000,119,25.870000,183.300000,103,8.250000,8.600000,4,2.320000,0,False.\r\nNY,123,415,329-6731,no,no,0,123.200000,104,20.940000,190.000000,117,16.150000,170.300000,95,7.660000,12.900000,5,3.480000,4,True.\r\nOK,116,510,393-3976,no,no,0,205.000000,90,34.850000,140.900000,114,11.980000,272.600000,96,12.270000,7.500000,4,2.030000,2,False.\r\nOK,63,415,388-7355,no,no,0,128.700000,78,21.880000,240.800000,133,20.470000,237.700000,121,10.700000,12.800000,6,3.460000,1,False.\r\nGA,64,510,412-7791,no,no,0,216.900000,78,36.870000,211.000000,115,17.940000,179.800000,116,8.090000,11.400000,5,3.080000,3,False.\r\nNJ,96,510,368-6111,no,no,0,150.000000,122,25.500000,218.500000,116,18.570000,212.400000,89,9.560000,9.800000,1,2.650000,3,False.\r\nMN,53,415,401-9420,no,no,0,164.100000,106,27.900000,206.000000,56,17.510000,194.700000,124,8.760000,11.400000,2,3.080000,1,False.\r\nME,105,510,352-5750,no,no,0,212.000000,113,36.040000,226.600000,128,19.260000,193.600000,114,8.710000,8.900000,7,2.400000,3,False.\r\nMI,53,510,417-3702,no,yes,37,167.300000,99,28.440000,194.700000,99,16.550000,236.700000,112,10.650000,12.000000,9,3.240000,2,False.\r\nMT,101,415,353-5714,no,no,0,154.400000,130,26.250000,217.200000,101,18.460000,185.400000,52,8.340000,13.900000,4,3.750000,1,False.\r\nNE,129,510,409-9494,no,yes,30,177.300000,95,30.140000,211.800000,102,18.000000,240.200000,108,10.810000,9.300000,7,2.510000,3,False.\r\nND,122,408,395-1901,no,no,0,231.200000,141,39.300000,267.800000,136,22.760000,240.300000,100,10.810000,8.800000,5,2.380000,1,True.\r\nVA,163,415,378-8342,no,no,0,202.900000,100,34.490000,178.600000,46,15.180000,203.800000,116,9.170000,12.800000,3,3.460000,5,False.\r\nVT,93,408,417-6044,no,no,0,149.600000,120,25.430000,200.700000,85,17.060000,181.200000,107,8.150000,14.300000,9,3.860000,0,False.\r\nOH,115,510,348-1163,yes,no,0,345.300000,81,58.700000,203.400000,106,17.290000,217.500000,107,9.790000,11.800000,8,3.190000,1,True.\r\nAL,25,408,337-4600,no,no,0,264.900000,80,45.030000,281.200000,66,23.900000,166.100000,80,7.470000,8.400000,4,2.270000,1,True.\r\nDC,73,408,355-5922,no,no,0,122.000000,92,20.740000,138.300000,114,11.760000,224.200000,128,10.090000,5.800000,5,1.570000,1,False.\r\nND,120,415,369-5810,no,no,0,177.200000,88,30.120000,270.400000,99,22.980000,231.500000,90,10.420000,14.000000,2,3.780000,2,False.\r\nTN,196,415,340-8291,no,no,0,133.100000,80,22.630000,206.500000,120,17.550000,221.600000,96,9.970000,10.300000,8,2.780000,1,False.\r\nDE,97,510,354-7397,no,no,0,225.100000,90,38.270000,279.500000,127,23.760000,233.800000,103,10.520000,8.800000,4,2.380000,0,True.\r\nNY,148,408,407-7464,no,no,0,208.400000,120,35.430000,174.400000,99,14.820000,310.700000,105,13.980000,11.200000,4,3.020000,1,False.\r\nAL,85,408,386-6411,no,yes,30,173.100000,107,29.430000,247.200000,101,21.010000,158.700000,104,7.140000,11.500000,5,3.110000,1,False.\r\nOK,86,510,397-3746,yes,no,0,162.400000,131,27.610000,167.000000,102,14.200000,128.900000,118,5.800000,11.400000,2,3.080000,2,True.\r\nMS,78,415,410-5236,no,yes,13,281.200000,93,47.800000,178.200000,101,15.150000,244.200000,129,10.990000,6.400000,5,1.730000,2,False.\r\nMD,106,415,409-2412,no,no,0,208.300000,89,35.410000,169.400000,67,14.400000,102.000000,90,4.590000,15.900000,4,4.290000,3,False.\r\nNE,147,415,400-7280,no,yes,38,243.400000,126,41.380000,273.800000,109,23.270000,282.900000,91,12.730000,14.100000,8,3.810000,2,False.\r\nAR,145,415,332-5820,no,no,0,224.200000,89,38.110000,314.900000,121,26.770000,182.900000,121,8.230000,16.100000,3,4.350000,1,True.\r\nIL,91,415,373-4483,no,no,0,189.300000,100,32.180000,239.300000,107,20.340000,89.700000,89,4.040000,9.900000,3,2.670000,3,False.\r\nIN,81,408,347-6717,no,yes,46,168.300000,124,28.610000,270.900000,103,23.030000,222.500000,98,10.010000,6.700000,2,1.810000,4,False.\r\nUT,116,415,380-2929,no,yes,24,232.900000,90,39.590000,152.100000,94,12.930000,344.300000,82,15.490000,10.700000,6,2.890000,1,False.\r\nLA,69,415,420-7692,no,yes,37,155.000000,98,26.350000,142.400000,105,12.100000,143.700000,117,6.470000,5.900000,4,1.590000,1,False.\r\nID,135,510,380-6437,no,no,0,154.400000,130,26.250000,203.800000,90,17.320000,158.700000,59,7.140000,11.800000,3,3.190000,0,False.\r\nKY,73,510,377-8309,no,no,0,234.700000,102,39.900000,195.700000,110,16.630000,253.400000,71,11.400000,8.400000,8,2.270000,2,False.\r\nMI,48,415,407-2718,no,no,0,240.000000,88,40.800000,141.000000,117,11.990000,128.900000,137,5.800000,7.100000,9,1.920000,1,False.\r\nNH,125,415,357-1938,yes,no,0,298.400000,78,50.730000,270.500000,142,22.990000,107.300000,84,4.830000,12.200000,2,3.290000,0,True.\r\nWV,100,415,381-3735,no,no,0,166.000000,102,28.220000,236.100000,97,20.070000,134.300000,93,6.040000,10.900000,4,2.940000,1,False.\r\nOR,165,415,409-8453,no,yes,33,111.600000,140,18.970000,213.300000,111,18.130000,267.600000,115,12.040000,16.000000,3,4.320000,0,False.\r\nSD,64,415,395-6758,no,no,0,174.500000,98,29.670000,180.200000,103,15.320000,179.000000,89,8.060000,10.700000,2,2.890000,2,False.\r\nMD,116,510,399-5424,yes,yes,27,175.500000,137,29.840000,210.600000,60,17.900000,294.800000,121,13.270000,6.900000,5,1.860000,1,False.\r\nND,147,408,409-4671,yes,yes,35,157.500000,109,26.780000,189.600000,67,16.120000,227.000000,76,10.220000,11.100000,2,3.000000,3,True.\r\nTN,115,415,374-6525,no,no,0,206.200000,113,35.050000,176.400000,102,14.990000,297.100000,119,13.370000,11.000000,7,2.970000,1,False.\r\nMO,84,415,406-8665,no,yes,35,207.500000,138,35.280000,201.000000,116,17.090000,164.500000,107,7.400000,7.500000,16,2.030000,4,False.\r\nIL,86,510,342-7716,no,yes,16,144.800000,105,24.620000,206.200000,111,17.530000,255.400000,117,11.490000,11.600000,2,3.130000,4,False.\r\nUT,134,415,417-2221,no,no,0,258.800000,85,44.000000,129.500000,114,11.010000,193.600000,106,8.710000,10.900000,7,2.940000,2,False.\r\nMI,105,415,376-5213,no,no,0,226.900000,106,38.570000,182.200000,77,15.490000,203.900000,107,9.180000,11.600000,2,3.130000,0,True.\r\nAR,88,408,348-7448,no,no,0,152.900000,119,25.990000,171.200000,107,14.550000,257.000000,106,11.570000,12.000000,5,3.240000,2,False.\r\nTX,90,408,328-8179,no,yes,27,156.700000,51,26.640000,236.500000,118,20.100000,123.200000,111,5.540000,12.600000,6,3.400000,2,False.\r\nAK,86,408,389-4602,no,no,0,150.800000,85,25.640000,295.900000,88,25.150000,247.200000,104,11.120000,12.500000,1,3.380000,1,False.\r\nTN,37,415,413-2238,no,no,0,221.000000,126,37.570000,204.500000,110,17.380000,118.000000,98,5.310000,6.800000,3,1.840000,4,False.\r\nNH,141,415,402-3370,no,yes,32,322.400000,92,54.810000,283.200000,107,24.070000,209.500000,111,9.430000,6.700000,3,1.810000,1,True.\r\nNM,148,408,348-6008,no,no,0,153.600000,148,26.110000,262.100000,87,22.280000,225.500000,99,10.150000,9.800000,3,2.650000,2,False.\r\nMN,163,408,371-5655,no,yes,22,215.100000,91,36.570000,138.900000,102,11.810000,146.200000,109,6.580000,12.400000,2,3.350000,2,False.\r\nIA,89,415,374-5224,no,yes,35,174.400000,108,29.650000,196.700000,100,16.720000,127.400000,74,5.730000,11.800000,3,3.190000,1,False.\r\nRI,63,415,371-1187,no,no,0,180.500000,126,30.690000,230.000000,98,19.550000,232.500000,73,10.460000,10.600000,3,2.860000,0,False.\r\nAL,102,415,337-1100,no,no,0,123.100000,106,20.930000,182.000000,102,15.470000,244.600000,75,11.010000,12.600000,7,3.400000,2,False.\r\nNC,76,510,421-8141,no,no,0,165.700000,94,28.170000,257.400000,80,21.880000,170.800000,114,7.690000,10.000000,4,2.700000,1,False.\r\nSD,104,408,406-2678,no,no,0,200.200000,92,34.030000,118.700000,87,10.090000,236.600000,65,10.650000,6.000000,6,1.620000,2,False.\r\nMT,109,510,415-9649,no,no,0,154.800000,82,26.320000,287.700000,109,24.450000,208.400000,80,9.380000,5.900000,9,1.590000,3,False.\r\nHI,105,510,364-8128,no,no,0,125.400000,116,21.320000,261.500000,95,22.230000,241.600000,104,10.870000,11.400000,9,3.080000,2,False.\r\nMT,63,415,356-7817,no,yes,33,184.200000,111,31.310000,312.600000,89,26.570000,264.000000,55,11.880000,12.200000,4,3.290000,1,False.\r\nKY,105,415,404-6357,no,yes,24,274.700000,99,46.700000,193.500000,118,16.450000,299.600000,109,13.480000,10.800000,3,2.920000,3,False.\r\nDC,68,415,398-2138,yes,yes,39,142.000000,140,24.140000,241.600000,89,20.540000,302.000000,72,13.590000,11.300000,5,3.050000,1,False.\r\nCO,63,408,378-8029,yes,yes,21,151.500000,99,25.760000,147.800000,89,12.560000,210.400000,114,9.470000,10.000000,4,2.700000,1,False.\r\nMI,74,415,386-4215,no,no,0,124.800000,114,21.220000,133.000000,121,11.310000,160.300000,85,7.210000,10.600000,7,2.860000,3,False.\r\nAL,76,415,367-8156,no,no,0,179.200000,85,30.460000,222.900000,66,18.950000,188.200000,113,8.470000,12.400000,2,3.350000,0,False.\r\nKS,91,408,382-8079,yes,no,0,246.400000,110,41.890000,182.000000,98,15.470000,157.600000,106,7.090000,12.100000,2,3.270000,2,True.\r\nNC,101,415,354-2985,no,no,0,232.700000,114,39.560000,186.400000,123,15.840000,153.300000,122,6.900000,11.500000,6,3.110000,5,False.\r\nSC,116,408,373-6922,no,no,0,288.000000,120,48.960000,255.800000,90,21.740000,233.400000,99,10.500000,13.400000,4,3.620000,0,True.\r\nCO,131,415,397-7125,no,yes,23,170.800000,145,29.040000,236.700000,93,20.120000,294.500000,100,13.250000,12.700000,1,3.430000,1,False.\r\nMN,84,415,333-6296,no,no,0,216.100000,114,36.740000,197.500000,107,16.790000,217.800000,104,9.800000,9.800000,3,2.650000,1,False.\r\nWY,104,415,365-6022,no,no,0,138.700000,100,23.580000,215.400000,58,18.310000,164.300000,98,7.390000,4.900000,4,1.320000,2,False.\r\nFL,108,510,365-1688,no,no,0,210.700000,112,35.820000,238.700000,73,20.290000,253.600000,90,11.410000,9.200000,5,2.480000,3,False.\r\nNY,111,415,382-4872,no,no,0,181.800000,117,30.910000,158.100000,91,13.440000,266.200000,123,11.980000,9.700000,9,2.620000,0,False.\r\nOK,155,408,367-6136,no,yes,30,61.600000,103,10.470000,255.100000,110,21.680000,225.900000,96,10.170000,12.400000,5,3.350000,1,False.\r\nME,66,510,404-3592,no,no,0,207.700000,85,35.310000,196.700000,112,16.720000,261.700000,83,11.780000,6.800000,3,1.840000,1,False.\r\nNE,64,510,415-2949,no,no,0,219.200000,73,37.260000,167.000000,65,14.200000,161.400000,119,7.260000,10.000000,5,2.700000,1,False.\r\nOH,69,415,375-8880,no,no,0,227.000000,122,38.590000,258.700000,111,21.990000,169.700000,87,7.640000,8.900000,1,2.400000,2,False.\r\nCT,116,415,335-6832,no,no,0,245.900000,73,41.800000,240.100000,87,20.410000,158.700000,89,7.140000,8.900000,5,2.400000,3,False.\r\nDC,101,415,361-8367,no,no,0,257.300000,84,43.740000,184.800000,115,15.710000,108.900000,109,4.900000,13.500000,7,3.650000,0,False.\r\nOK,15,415,408-2002,no,no,0,121.100000,130,20.590000,216.000000,86,18.360000,235.100000,33,10.580000,16.100000,5,4.350000,2,False.\r\nNJ,88,415,347-8659,no,no,0,301.500000,136,51.260000,257.700000,72,21.900000,132.900000,118,5.980000,13.400000,2,3.620000,4,True.\r\nIA,197,415,376-2922,no,no,0,233.900000,96,39.760000,218.900000,111,18.610000,182.900000,109,8.230000,9.500000,3,2.570000,0,False.\r\nVA,50,415,382-2182,yes,no,0,99.600000,108,16.930000,308.700000,102,26.240000,161.200000,62,7.250000,13.700000,6,3.700000,2,True.\r\nVA,172,510,408-2089,no,no,0,169.800000,123,28.870000,183.100000,94,15.560000,395.000000,72,17.770000,12.700000,7,3.430000,2,False.\r\nNM,188,415,369-6890,yes,yes,26,198.800000,115,33.800000,166.600000,67,14.160000,198.500000,118,8.930000,14.400000,3,3.890000,1,True.\r\nFL,85,408,347-2951,yes,no,0,116.200000,86,19.750000,229.700000,127,19.520000,204.200000,109,9.190000,10.100000,3,2.730000,3,False.\r\nRI,103,510,420-6324,yes,no,0,255.900000,128,43.500000,140.900000,92,11.980000,308.900000,130,13.900000,12.100000,2,3.270000,1,True.\r\nNJ,136,408,402-7650,no,yes,27,187.700000,84,31.910000,221.000000,147,18.790000,145.700000,110,6.560000,10.000000,4,2.700000,3,False.\r\nNE,155,408,391-2702,no,yes,21,195.900000,91,33.300000,213.900000,84,18.180000,88.200000,111,3.970000,8.600000,4,2.320000,0,False.\r\nWV,145,415,383-3375,no,no,0,129.400000,97,22.000000,185.400000,101,15.760000,204.700000,106,9.210000,1.100000,2,0.300000,2,False.\r\nWY,116,510,392-2733,no,yes,12,221.000000,108,37.570000,151.000000,118,12.840000,179.000000,80,8.060000,9.000000,6,2.430000,2,False.\r\nSC,152,408,397-9933,no,no,0,140.500000,92,23.890000,186.800000,96,15.880000,227.000000,89,10.220000,9.500000,5,2.570000,2,False.\r\nMS,65,415,383-9306,yes,no,0,277.900000,123,47.240000,155.800000,112,13.240000,256.900000,71,11.560000,9.200000,10,2.480000,0,True.\r\nND,180,415,369-1929,no,no,0,224.900000,105,38.230000,250.000000,101,21.250000,216.100000,73,9.720000,6.700000,5,1.810000,3,True.\r\nIL,67,415,369-4377,no,no,0,109.100000,117,18.550000,217.400000,124,18.480000,188.400000,141,8.480000,12.800000,6,3.460000,0,False.\r\nOR,60,415,366-9430,no,no,0,207.800000,109,35.330000,123.500000,112,10.500000,291.600000,115,13.120000,5.700000,9,1.540000,0,False.\r\nUT,138,510,353-7407,no,no,0,205.900000,96,35.000000,257.100000,94,21.850000,209.000000,63,9.400000,12.100000,8,3.270000,0,False.\r\nIA,44,415,359-7426,no,no,0,308.600000,139,52.460000,150.800000,94,12.820000,198.700000,66,8.940000,7.300000,3,1.970000,4,True.\r\nME,25,510,332-7391,no,no,0,242.600000,69,41.240000,209.000000,117,17.770000,219.700000,82,9.890000,14.400000,6,3.890000,2,False.\r\nWY,145,408,405-6559,no,no,0,229.600000,82,39.030000,138.100000,103,11.740000,250.800000,109,11.290000,3.300000,3,0.890000,1,False.\r\nWI,122,510,338-8784,no,yes,28,166.000000,62,28.220000,233.900000,88,19.880000,170.100000,84,7.650000,7.700000,3,2.080000,2,False.\r\nSC,121,415,415-6347,no,no,0,144.800000,126,24.620000,200.600000,82,17.050000,208.800000,81,9.400000,13.300000,9,3.590000,0,True.\r\nDC,55,510,354-5058,yes,no,0,106.100000,77,18.040000,123.500000,100,10.500000,96.400000,92,4.340000,12.900000,3,3.480000,0,False.\r\nCT,77,415,342-5701,no,no,0,221.800000,84,37.710000,166.000000,125,14.110000,210.200000,72,9.460000,13.200000,4,3.560000,1,False.\r\nOR,12,415,378-4179,no,no,0,204.600000,98,34.780000,212.500000,90,18.060000,182.100000,95,8.190000,9.800000,7,2.650000,2,False.\r\nOR,64,510,407-6391,no,no,0,213.500000,93,36.300000,166.600000,114,14.160000,122.000000,78,5.490000,14.100000,3,3.810000,3,False.\r\nNV,92,415,404-3105,no,yes,44,152.000000,95,25.840000,274.900000,73,23.370000,162.400000,121,7.310000,10.000000,1,2.700000,2,False.\r\nMN,125,415,390-9735,yes,yes,29,260.800000,81,44.340000,163.700000,112,13.910000,271.700000,117,12.230000,17.000000,6,4.590000,1,True.\r\nOK,160,408,350-4820,no,no,0,166.400000,117,28.290000,317.000000,129,26.950000,160.400000,121,7.220000,10.000000,2,2.700000,1,False.\r\nKS,79,415,383-8807,no,no,0,177.900000,83,30.240000,167.300000,84,14.220000,223.700000,142,10.070000,15.200000,8,4.100000,0,False.\r\nRI,36,415,366-8382,no,no,0,235.100000,97,39.970000,196.800000,104,16.730000,259.700000,110,11.690000,7.000000,7,1.890000,3,False.\r\nDC,102,415,402-9704,no,no,0,186.800000,92,31.760000,173.700000,123,14.760000,250.900000,131,11.290000,9.700000,4,2.620000,2,False.\r\nIL,138,408,405-2209,yes,no,0,268.400000,81,45.630000,174.400000,115,14.820000,193.500000,96,8.710000,11.600000,4,3.130000,1,False.\r\nUT,164,510,397-3939,no,no,0,192.100000,95,32.660000,249.800000,94,21.230000,132.600000,100,5.970000,7.300000,3,1.970000,3,False.\r\nMN,125,415,343-2689,no,no,0,240.700000,82,40.920000,269.400000,85,22.900000,187.100000,74,8.420000,10.100000,3,2.730000,0,True.\r\nWI,72,408,383-9448,no,no,0,179.900000,113,30.580000,149.800000,112,12.730000,168.200000,79,7.570000,9.800000,7,2.650000,2,False.\r\nMI,74,415,359-6232,no,no,0,314.100000,86,53.400000,222.400000,99,18.900000,259.000000,121,11.660000,12.300000,5,3.320000,3,True.\r\nMI,134,415,369-9772,no,yes,41,162.000000,82,27.540000,324.700000,77,27.600000,160.100000,112,7.200000,11.900000,5,3.210000,0,False.\r\nMA,145,415,381-7003,no,no,0,175.800000,89,29.890000,274.300000,119,23.320000,226.600000,69,10.200000,12.400000,4,3.350000,1,False.\r\nAL,136,510,352-6732,no,no,0,109.400000,91,18.600000,207.500000,111,17.640000,135.000000,107,6.080000,11.600000,5,3.130000,0,False.\r\nSC,209,510,388-7540,no,no,0,255.100000,124,43.370000,230.600000,110,19.600000,218.000000,69,9.810000,8.500000,5,2.300000,3,True.\r\nWI,66,415,356-3333,yes,no,0,208.700000,84,35.480000,173.300000,88,14.730000,264.700000,107,11.910000,8.300000,3,2.240000,3,False.\r\nVT,152,510,333-9664,no,yes,20,214.600000,108,36.480000,96.600000,82,8.210000,170.700000,145,7.680000,7.900000,5,2.130000,1,False.\r\nCT,162,408,363-3763,no,no,0,49.200000,121,8.360000,143.900000,136,12.230000,203.000000,97,9.140000,12.100000,13,3.270000,1,False.\r\nOR,72,510,345-7900,no,no,0,141.300000,133,24.020000,134.900000,96,11.470000,227.500000,97,10.240000,11.200000,3,3.020000,2,False.\r\nHI,101,415,400-5511,no,no,0,253.200000,89,43.040000,237.900000,114,20.220000,154.300000,85,6.940000,9.700000,7,2.620000,4,False.\r\nWV,125,415,381-7597,no,no,0,206.000000,128,35.020000,198.100000,71,16.840000,135.900000,116,6.120000,13.200000,3,3.560000,0,False.\r\nRI,46,408,404-9775,no,no,0,40.400000,105,6.870000,172.400000,83,14.650000,145.100000,89,6.530000,9.000000,2,2.430000,2,False.\r\nMI,132,408,389-4608,no,no,0,291.200000,104,49.500000,234.200000,132,19.910000,191.700000,87,8.630000,8.900000,3,2.400000,1,True.\r\nME,193,415,403-1742,no,yes,31,71.200000,58,12.100000,124.700000,105,10.600000,155.500000,108,7.000000,11.700000,3,3.160000,0,False.\r\nWV,63,510,328-9797,no,no,0,261.800000,69,44.510000,245.000000,135,20.830000,202.100000,94,9.090000,14.700000,4,3.970000,0,True.\r\nNE,124,510,359-9223,no,no,0,191.300000,134,32.520000,261.500000,113,22.230000,182.300000,111,8.200000,10.000000,3,2.700000,1,False.\r\nDC,144,415,336-7696,no,no,0,133.300000,101,22.660000,255.500000,127,21.720000,228.600000,68,10.290000,11.600000,2,3.130000,0,False.\r\nNH,116,408,369-2214,no,yes,24,183.600000,138,31.210000,203.800000,90,17.320000,166.900000,89,7.510000,6.000000,3,1.620000,2,False.\r\nMD,189,415,411-1325,no,yes,30,155.200000,116,26.380000,195.500000,50,16.620000,170.100000,108,7.650000,15.400000,6,4.160000,1,False.\r\nNH,97,408,410-7553,no,yes,28,283.100000,93,48.130000,185.400000,98,15.760000,312.800000,78,14.080000,6.100000,8,1.650000,1,False.\r\nWV,137,510,376-4284,no,yes,50,186.500000,94,31.710000,178.000000,106,15.130000,215.600000,100,9.700000,12.100000,4,3.270000,2,False.\r\nIL,142,415,334-2800,no,yes,38,163.300000,104,27.760000,136.000000,114,11.560000,249.100000,127,11.210000,4.300000,6,1.160000,0,False.\r\nOK,84,510,369-1904,no,no,0,203.400000,125,34.580000,182.900000,88,15.550000,213.700000,121,9.620000,13.800000,2,3.730000,1,False.\r\nNH,119,415,359-3833,no,yes,19,178.100000,110,30.280000,212.800000,100,18.090000,226.300000,123,10.180000,10.000000,6,2.700000,1,False.\r\nMI,158,415,348-5569,no,no,0,195.900000,103,33.300000,89.100000,95,7.570000,302.200000,82,13.600000,10.300000,3,2.780000,1,False.\r\nND,50,415,342-1960,no,no,0,295.300000,127,50.200000,127.400000,100,10.830000,166.800000,105,7.510000,9.600000,6,2.590000,1,False.\r\nLA,98,408,352-9050,no,no,0,136.100000,82,23.140000,156.300000,118,13.290000,158.800000,83,7.150000,10.100000,5,2.730000,2,False.\r\nHI,101,415,390-5316,no,yes,24,114.100000,95,19.400000,161.500000,86,13.730000,176.300000,90,7.930000,13.000000,9,3.510000,2,False.\r\nNJ,182,415,418-8568,no,no,0,279.100000,124,47.450000,180.500000,108,15.340000,217.500000,104,9.790000,9.500000,11,2.570000,2,True.\r\nWV,51,408,401-4844,no,no,0,169.300000,111,28.780000,139.500000,69,11.860000,197.000000,87,8.870000,12.000000,3,3.240000,0,False.\r\nNC,117,510,376-5471,no,no,0,214.400000,94,36.450000,138.000000,149,11.730000,148.700000,102,6.690000,9.900000,1,2.670000,2,False.\r\nPA,92,415,409-2917,yes,no,0,255.800000,125,43.490000,142.700000,111,12.130000,181.200000,101,8.150000,11.700000,3,3.160000,0,False.\r\nMI,86,408,369-6308,no,no,0,148.200000,71,25.190000,285.100000,91,24.230000,166.400000,155,7.490000,6.200000,3,1.670000,2,False.\r\nWY,122,415,357-7385,no,no,0,119.300000,93,20.280000,223.900000,103,19.030000,211.900000,122,9.540000,8.700000,4,2.350000,2,False.\r\nNJ,156,408,405-7119,no,yes,27,192.300000,137,32.690000,199.900000,115,16.990000,244.200000,112,10.990000,14.800000,8,4.000000,1,False.\r\nNJ,127,510,405-3309,no,no,0,245.200000,91,41.680000,217.200000,92,18.460000,243.100000,128,10.940000,13.900000,6,3.750000,0,True.\r\nNC,130,408,384-4938,yes,no,0,216.200000,106,36.750000,363.700000,86,30.910000,126.700000,123,5.700000,16.900000,2,4.560000,5,True.\r\nNM,158,408,377-2725,no,no,0,172.400000,114,29.310000,256.600000,69,21.810000,235.300000,104,10.590000,0.000000,0,0.000000,2,False.\r\nMS,145,510,405-6398,yes,yes,30,175.300000,107,29.800000,153.300000,116,13.030000,233.600000,85,10.510000,11.100000,3,3.000000,1,False.\r\nTX,90,415,355-9366,yes,yes,26,169.000000,104,28.730000,188.800000,104,16.050000,213.300000,76,9.600000,13.300000,3,3.590000,0,True.\r\nOK,127,510,403-1128,no,yes,27,2.600000,113,0.440000,254.000000,102,21.590000,242.700000,156,10.920000,9.200000,5,2.480000,3,False.\r\nID,109,415,384-9682,no,no,0,184.100000,143,31.300000,211.700000,105,17.990000,243.000000,116,10.930000,9.900000,2,2.670000,1,False.\r\nAL,88,415,352-5393,no,no,0,181.900000,90,30.920000,151.500000,87,12.880000,143.000000,100,6.440000,7.500000,3,2.030000,1,False.\r\nWY,101,510,395-1229,no,yes,9,160.100000,116,27.220000,210.000000,121,17.850000,139.100000,65,6.260000,10.800000,9,2.920000,0,False.\r\nHI,171,510,361-9195,no,no,0,189.800000,122,32.270000,173.700000,85,14.760000,257.100000,84,11.570000,10.300000,1,2.780000,0,False.\r\nVA,21,415,351-6366,no,no,0,223.200000,142,37.940000,216.500000,114,18.400000,214.700000,111,9.660000,12.400000,2,3.350000,1,False.\r\nWV,145,408,346-4919,no,yes,31,216.000000,94,36.720000,225.100000,123,19.130000,234.700000,109,10.560000,10.700000,1,2.890000,2,False.\r\nDE,90,415,354-9068,no,no,0,198.500000,124,33.750000,266.600000,100,22.660000,243.300000,80,10.950000,8.000000,7,2.160000,2,False.\r\nCA,33,408,369-2743,no,no,0,159.500000,115,27.120000,195.400000,118,16.610000,102.400000,86,4.610000,7.100000,7,1.920000,1,False.\r\nPA,61,408,343-1347,no,yes,40,105.000000,78,17.850000,180.600000,100,15.350000,174.100000,115,7.830000,10.200000,2,2.750000,2,True.\r\nCO,107,415,336-5495,no,no,0,204.500000,108,34.770000,162.400000,110,13.800000,155.000000,102,6.980000,13.400000,1,3.620000,3,False.\r\nMD,147,408,376-4292,no,no,0,274.000000,92,46.580000,231.800000,82,19.700000,283.600000,83,12.760000,6.200000,1,1.670000,0,True.\r\nAL,117,510,391-8677,no,no,0,158.700000,84,26.980000,181.700000,91,15.440000,177.300000,67,7.980000,7.700000,10,2.080000,2,False.\r\nAL,95,415,350-7273,no,no,0,229.900000,116,39.080000,202.400000,110,17.200000,171.400000,105,7.710000,14.200000,6,3.830000,1,False.\r\nKS,186,510,400-6454,no,no,0,137.800000,97,23.430000,187.700000,118,15.950000,146.400000,85,6.590000,8.700000,6,2.350000,1,False.\r\nMI,128,415,422-3052,no,no,0,179.400000,94,30.500000,270.400000,92,22.980000,191.000000,88,8.590000,7.900000,4,2.130000,0,False.\r\nAK,55,408,365-6756,no,yes,39,139.300000,101,23.680000,178.300000,117,15.160000,246.500000,104,11.090000,8.100000,1,2.190000,3,False.\r\nOH,134,415,406-4158,no,no,0,7.800000,86,1.330000,171.400000,100,14.570000,186.500000,80,8.390000,12.900000,2,3.480000,2,False.\r\nIN,96,415,383-4641,no,yes,23,183.100000,88,31.130000,147.400000,89,12.530000,350.200000,108,15.760000,11.300000,7,3.050000,1,False.\r\nSC,107,415,368-5165,no,no,0,206.900000,79,35.170000,262.400000,117,22.300000,149.300000,69,6.720000,10.700000,3,2.890000,0,False.\r\nKS,123,415,378-2432,no,no,0,140.000000,106,23.800000,153.700000,101,13.060000,50.100000,87,2.250000,12.500000,1,3.380000,2,False.\r\nOK,35,415,362-4159,no,no,0,179.200000,59,30.460000,283.300000,101,24.080000,285.400000,83,12.840000,5.800000,7,1.570000,2,False.\r\nWI,74,408,363-7979,no,no,0,177.400000,136,30.160000,240.300000,104,20.430000,237.300000,133,10.680000,12.000000,3,3.240000,0,False.\r\nIN,130,408,334-9818,no,no,0,115.600000,129,19.650000,167.800000,104,14.260000,141.800000,124,6.380000,12.600000,9,3.400000,1,False.\r\nIL,137,408,352-5787,yes,no,0,237.300000,103,40.340000,176.700000,84,15.020000,263.400000,81,11.850000,14.200000,4,3.830000,0,True.\r\nTN,88,415,332-3617,no,no,0,181.500000,116,30.860000,187.000000,119,15.900000,220.300000,96,9.910000,10.500000,7,2.840000,1,False.\r\nDC,80,408,327-9957,no,no,0,51.500000,90,8.760000,164.000000,98,13.940000,169.400000,80,7.620000,9.500000,4,2.570000,3,False.\r\nNC,116,408,338-7527,no,yes,19,155.700000,104,26.470000,185.400000,118,15.760000,192.700000,116,8.670000,8.200000,2,2.210000,3,False.\r\nRI,123,510,348-8711,no,yes,23,245.000000,88,41.650000,265.000000,105,22.530000,239.700000,108,10.790000,14.900000,3,4.020000,2,False.\r\nMS,120,415,421-3226,no,no,0,131.700000,99,22.390000,163.100000,109,13.860000,201.100000,116,9.050000,10.700000,3,2.890000,1,False.\r\nVA,146,415,391-4358,yes,no,0,111.100000,126,18.890000,313.400000,95,26.640000,215.700000,82,9.710000,10.500000,6,2.840000,1,False.\r\nKY,106,510,379-2523,no,yes,9,88.500000,100,15.050000,324.800000,109,27.610000,79.900000,86,3.600000,8.200000,4,2.210000,3,False.\r\nNV,121,408,419-2369,no,yes,44,116.000000,85,19.720000,150.100000,120,12.760000,246.800000,98,11.110000,12.000000,2,3.240000,1,False.\r\nWI,137,510,382-1227,no,no,0,155.500000,81,26.440000,133.100000,94,11.310000,253.100000,77,11.390000,9.100000,2,2.460000,1,False.\r\nNH,84,408,409-5749,no,yes,30,106.500000,65,18.110000,225.700000,108,19.180000,188.600000,61,8.490000,5.700000,3,1.540000,2,False.\r\nNE,67,510,362-7951,no,yes,31,175.200000,68,29.780000,199.200000,73,16.930000,219.800000,99,9.890000,13.200000,6,3.560000,1,False.\r\nWI,161,408,415-3537,no,no,0,154.700000,84,26.300000,177.800000,125,15.110000,172.900000,90,7.780000,5.900000,2,1.590000,4,True.\r\nNJ,134,510,373-3959,no,yes,34,247.200000,105,42.020000,225.500000,133,19.170000,186.300000,76,8.380000,6.100000,5,1.650000,2,True.\r\nME,62,415,358-1346,yes,yes,32,218.400000,93,37.130000,236.700000,132,20.120000,192.200000,137,8.650000,13.200000,3,3.560000,0,True.\r\nWY,120,415,381-8422,no,yes,24,227.500000,81,38.680000,234.900000,71,19.970000,166.400000,128,7.490000,9.000000,13,2.430000,1,False.\r\nIN,130,408,360-9005,no,yes,30,185.000000,117,31.450000,249.500000,141,21.210000,157.800000,103,7.100000,7.400000,7,2.000000,0,False.\r\nWI,20,408,344-5967,no,no,0,186.800000,89,31.760000,253.400000,51,21.540000,273.100000,105,12.290000,12.300000,6,3.320000,2,False.\r\nVT,68,415,396-6390,no,no,0,158.800000,119,27.000000,211.800000,105,18.000000,198.100000,101,8.910000,10.300000,3,2.780000,1,False.\r\nCA,112,415,346-5036,no,no,0,208.700000,150,35.480000,212.800000,104,18.090000,178.100000,98,8.010000,8.500000,4,2.300000,0,False.\r\nIN,77,408,328-7252,no,no,0,185.900000,95,31.600000,212.000000,98,18.020000,282.300000,81,12.700000,11.300000,4,3.050000,3,False.\r\nSC,109,415,360-1745,no,no,0,222.500000,74,37.830000,169.700000,75,14.420000,264.300000,94,11.890000,9.000000,3,2.430000,2,False.\r\nIN,108,415,358-2046,no,no,0,201.100000,101,34.190000,170.700000,86,14.510000,237.400000,113,10.680000,11.600000,3,3.130000,3,False.\r\nIA,79,415,344-6935,no,yes,17,167.900000,114,28.540000,243.700000,93,20.710000,211.900000,114,9.540000,9.100000,2,2.460000,1,False.\r\nMD,119,408,401-9665,no,no,0,239.100000,88,40.650000,243.500000,79,20.700000,230.900000,92,10.390000,10.900000,3,2.940000,3,True.\r\nMN,38,510,399-5291,no,no,0,175.700000,109,29.870000,211.800000,97,18.000000,137.900000,109,6.210000,9.200000,3,2.480000,5,True.\r\nAR,109,415,409-6588,no,yes,29,111.200000,90,18.900000,263.500000,98,22.400000,224.700000,128,10.110000,9.000000,6,2.430000,6,True.\r\nMS,78,415,358-5721,no,no,0,87.700000,74,14.910000,214.800000,58,18.260000,201.300000,147,9.060000,10.800000,6,2.920000,1,False.\r\nMN,134,415,414-7446,no,no,0,244.100000,99,41.500000,246.900000,111,20.990000,200.000000,133,9.000000,7.200000,2,1.940000,0,False.\r\nWA,47,415,329-9517,no,yes,27,165.000000,89,28.050000,127.300000,118,10.820000,284.400000,95,12.800000,7.700000,4,2.080000,2,False.\r\nMS,59,408,415-9553,no,yes,27,127.400000,110,21.660000,103.300000,99,8.780000,164.200000,73,7.390000,9.100000,3,2.460000,0,False.\r\nID,151,415,413-3177,no,no,0,194.800000,106,33.120000,292.700000,103,24.880000,224.600000,82,10.110000,5.500000,3,1.490000,0,False.\r\nNC,129,415,347-5113,no,no,0,54.700000,131,9.300000,256.100000,105,21.770000,176.600000,135,7.950000,11.100000,4,3.000000,1,False.\r\nVA,107,510,330-2662,no,yes,27,283.400000,104,48.180000,224.100000,152,19.050000,241.300000,63,10.860000,14.400000,7,3.890000,2,False.\r\nMT,137,408,330-5824,yes,no,0,258.000000,112,43.860000,246.500000,117,20.950000,173.200000,100,7.790000,10.900000,3,2.940000,0,True.\r\nMI,76,510,411-1261,no,no,0,90.500000,142,15.390000,211.700000,75,17.990000,194.900000,76,8.770000,9.300000,2,2.510000,1,False.\r\nHI,24,415,329-8788,no,no,0,235.600000,132,40.050000,115.900000,129,9.850000,185.400000,136,8.340000,16.200000,2,4.370000,0,False.\r\nNC,169,408,333-7869,no,no,0,142.500000,82,24.230000,231.400000,110,19.670000,131.200000,67,5.900000,10.000000,4,2.700000,2,False.\r\nMN,30,408,399-4800,no,no,0,54.000000,68,9.180000,179.300000,96,15.240000,247.200000,101,11.120000,10.200000,8,2.750000,1,False.\r\nWV,70,415,402-2072,no,no,0,214.800000,87,36.520000,131.000000,114,11.140000,216.900000,104,9.760000,9.400000,3,2.540000,3,False.\r\nSD,52,510,403-6187,yes,no,0,251.400000,118,42.740000,196.600000,80,16.710000,192.000000,53,8.640000,11.000000,2,2.970000,0,True.\r\nHI,3,408,355-2872,no,no,0,139.000000,99,23.630000,250.700000,108,21.310000,286.200000,87,12.880000,6.100000,3,1.650000,4,False.\r\nMS,38,415,386-2970,no,no,0,117.300000,114,19.940000,208.700000,105,17.740000,203.400000,98,9.150000,14.400000,2,3.890000,2,False.\r\nNY,104,415,389-6081,no,no,0,264.000000,108,44.880000,132.200000,75,11.240000,177.700000,91,8.000000,10.600000,8,2.860000,3,False.\r\nLA,27,408,348-7556,no,no,0,82.600000,105,14.040000,204.000000,99,17.340000,224.200000,122,10.090000,9.100000,4,2.460000,1,False.\r\nKS,166,415,334-9163,yes,yes,28,175.800000,126,29.890000,253.600000,76,21.560000,128.500000,72,5.780000,11.400000,5,3.080000,1,False.\r\nMA,13,408,411-4293,no,no,0,220.400000,100,37.470000,211.200000,79,17.950000,259.300000,112,11.670000,13.600000,8,3.670000,2,False.\r\nAK,52,408,375-5562,no,no,0,217.000000,104,36.890000,152.300000,83,12.950000,134.300000,109,6.040000,11.800000,4,3.190000,2,False.\r\nSD,114,415,386-3823,no,yes,25,129.000000,77,21.930000,290.000000,110,24.650000,177.100000,110,7.970000,11.600000,5,3.130000,1,False.\r\nCO,156,408,364-6445,no,no,0,150.500000,106,25.590000,152.900000,112,13.000000,215.900000,86,9.720000,3.500000,3,0.950000,1,False.\r\nNH,90,415,383-2251,no,yes,42,193.300000,66,32.860000,263.300000,85,22.380000,214.400000,97,9.650000,11.100000,4,3.000000,0,False.\r\nWV,62,415,351-3169,no,no,0,189.500000,122,32.220000,103.800000,95,8.820000,180.600000,106,8.130000,10.800000,5,2.920000,2,False.\r\nAZ,82,415,413-6380,no,yes,33,137.800000,95,23.430000,235.500000,128,20.020000,268.100000,70,12.060000,11.000000,6,2.970000,2,False.\r\nME,52,510,380-9674,no,no,0,129.300000,80,21.980000,142.700000,101,12.130000,258.300000,89,11.620000,12.300000,4,3.320000,3,False.\r\nNH,146,510,345-2319,no,no,0,115.600000,77,19.650000,213.600000,100,18.160000,218.400000,72,9.830000,10.700000,6,2.890000,1,False.\r\nRI,120,415,377-5441,no,yes,23,221.900000,114,37.720000,254.700000,84,21.650000,250.500000,117,11.270000,7.200000,5,1.940000,2,False.\r\nID,130,415,358-3692,no,no,0,263.700000,113,44.830000,186.500000,103,15.850000,195.300000,99,8.790000,18.300000,6,4.940000,1,True.\r\nWI,90,408,400-5831,no,no,0,61.300000,91,10.420000,194.400000,94,16.520000,143.100000,80,6.440000,11.400000,9,3.080000,1,False.\r\nNM,147,408,357-5995,yes,no,0,183.800000,113,31.250000,164.700000,110,14.000000,111.000000,87,5.000000,10.100000,4,2.730000,1,False.\r\nWY,159,415,391-2159,no,no,0,167.400000,68,28.460000,143.800000,74,12.220000,140.100000,111,6.300000,10.300000,3,2.780000,0,True.\r\nIL,74,510,398-5954,no,yes,27,154.100000,122,26.200000,195.300000,150,16.600000,276.700000,86,12.450000,13.200000,2,3.560000,4,False.\r\nTX,130,408,385-6175,no,no,0,252.000000,101,42.840000,170.200000,105,14.470000,209.200000,64,9.410000,5.700000,5,1.540000,0,False.\r\nRI,155,408,334-2961,yes,no,0,163.100000,94,27.730000,291.700000,108,24.790000,96.400000,111,4.340000,11.200000,3,3.020000,0,False.\r\nMI,87,415,405-4303,no,no,0,198.300000,80,33.710000,187.000000,89,15.900000,133.500000,96,6.010000,16.600000,4,4.480000,2,False.\r\nOR,81,415,406-4100,no,no,0,324.700000,48,55.200000,236.400000,82,20.090000,187.600000,78,8.440000,13.100000,5,3.540000,0,True.\r\nVA,99,510,352-4401,no,no,0,128.300000,78,21.810000,215.300000,120,18.300000,143.700000,140,6.470000,14.300000,9,3.860000,2,False.\r\nSD,131,510,347-1473,no,no,0,187.900000,110,31.940000,200.500000,101,17.040000,202.600000,125,9.120000,10.200000,11,2.750000,2,False.\r\nAL,89,510,347-2016,no,no,0,129.200000,71,21.960000,214.100000,68,18.200000,214.900000,100,9.670000,10.300000,4,2.780000,5,True.\r\nMS,123,415,388-8948,yes,no,0,125.500000,106,21.340000,128.900000,96,10.960000,251.900000,129,11.340000,6.300000,6,1.700000,4,True.\r\nMS,130,510,402-5509,no,yes,26,257.200000,108,43.720000,224.300000,122,19.070000,204.000000,118,9.180000,12.600000,4,3.400000,1,False.\r\nHI,99,415,367-2598,no,no,0,124.600000,90,21.180000,146.400000,70,12.440000,169.400000,95,7.620000,10.500000,6,2.840000,1,False.\r\nWV,36,408,370-5001,no,no,0,175.100000,144,29.770000,216.900000,69,18.440000,243.700000,146,10.970000,9.900000,3,2.670000,1,False.\r\nWV,87,415,332-3693,no,no,0,124.300000,91,21.130000,173.400000,105,14.740000,256.300000,109,11.530000,7.500000,5,2.030000,3,False.\r\nWV,139,415,403-9766,no,no,0,271.600000,130,46.170000,156.000000,131,13.260000,136.300000,108,6.130000,11.600000,9,3.130000,2,False.\r\nIN,189,510,363-2407,no,no,0,219.900000,80,37.380000,143.300000,117,12.180000,130.600000,69,5.880000,11.700000,7,3.160000,1,False.\r\nNM,96,415,395-9214,no,yes,33,183.300000,115,31.160000,201.400000,87,17.120000,177.400000,84,7.980000,10.400000,15,2.810000,3,False.\r\nDE,112,408,351-8894,no,no,0,101.100000,119,17.190000,214.400000,67,18.220000,179.500000,112,8.080000,10.300000,5,2.780000,2,False.\r\nNC,75,408,406-5003,no,no,0,203.300000,70,34.560000,228.900000,97,19.460000,222.200000,118,10.000000,14.300000,3,3.860000,1,False.\r\nNM,178,415,398-1332,no,yes,35,175.400000,88,29.820000,190.000000,65,16.150000,138.700000,94,6.240000,10.500000,3,2.840000,2,False.\r\nSC,112,415,363-8033,no,no,0,266.000000,97,45.220000,214.600000,94,18.240000,306.200000,100,13.780000,14.200000,2,3.830000,2,True.\r\nNJ,108,415,333-1012,no,yes,41,171.600000,110,29.170000,136.100000,78,11.570000,183.400000,103,8.250000,10.800000,7,2.920000,0,False.\r\nAZ,100,510,391-6260,no,no,0,78.700000,98,13.380000,225.600000,102,19.180000,150.400000,106,6.770000,14.000000,8,3.780000,0,False.\r\nRI,121,510,336-1353,no,yes,20,211.900000,110,36.020000,215.100000,120,18.280000,238.500000,107,10.730000,9.400000,2,2.540000,0,False.\r\nSD,116,415,365-5629,no,no,0,63.700000,101,10.830000,195.800000,95,16.640000,210.100000,87,9.450000,10.000000,6,2.700000,1,False.\r\nNH,161,415,349-4397,no,no,0,173.400000,100,29.480000,213.700000,74,18.160000,141.500000,69,6.370000,11.500000,4,3.110000,1,False.\r\nAL,19,415,380-3910,yes,no,0,237.700000,98,40.410000,207.100000,121,17.600000,182.200000,95,8.200000,4.500000,4,1.220000,0,False.\r\nWV,104,415,354-7820,no,no,0,225.900000,123,38.400000,162.800000,106,13.840000,272.100000,85,12.240000,10.100000,4,2.730000,1,False.\r\nID,119,415,338-9952,yes,yes,32,173.000000,101,29.410000,209.400000,93,17.800000,231.100000,91,10.400000,12.200000,4,3.290000,0,False.\r\nMD,125,415,405-1821,no,no,0,224.900000,102,38.230000,143.800000,87,12.220000,198.900000,105,8.950000,8.000000,2,2.160000,0,False.\r\nIN,156,510,329-8669,no,no,0,237.700000,122,40.410000,181.500000,91,15.430000,185.700000,151,8.360000,7.700000,4,2.080000,1,False.\r\nWY,109,415,328-8808,no,no,0,137.000000,128,23.290000,217.000000,116,18.450000,182.100000,86,8.190000,10.000000,5,2.700000,2,False.\r\nNH,95,510,409-3018,no,no,0,142.500000,109,24.230000,176.100000,107,14.970000,189.600000,88,8.530000,8.200000,3,2.210000,2,False.\r\nMN,90,408,421-5994,no,no,0,142.400000,126,24.210000,126.200000,118,10.730000,274.200000,71,12.340000,4.600000,4,1.240000,1,False.\r\nMA,105,415,354-4448,no,yes,21,147.000000,112,24.990000,197.300000,43,16.770000,267.400000,93,12.030000,8.700000,3,2.350000,2,False.\r\nNH,101,510,352-5081,no,no,0,220.300000,124,37.450000,188.600000,101,16.030000,278.400000,98,12.530000,10.600000,4,2.860000,1,False.\r\nMN,95,415,401-7803,no,no,0,149.200000,96,25.360000,260.700000,116,22.160000,201.000000,120,9.050000,8.100000,2,2.190000,1,False.\r\nIA,123,415,420-6052,no,no,0,204.400000,88,34.750000,137.500000,111,11.690000,226.000000,100,10.170000,10.000000,4,2.700000,0,False.\r\nNY,160,408,352-6084,no,no,0,216.800000,77,36.860000,207.300000,117,17.620000,228.600000,117,10.290000,5.600000,2,1.510000,1,False.\r\nAL,141,510,388-8583,no,yes,28,308.000000,123,52.360000,247.800000,128,21.060000,152.900000,103,6.880000,7.400000,3,2.000000,1,False.\r\nWV,87,415,415-3158,no,no,0,58.000000,125,9.860000,67.500000,116,5.740000,185.900000,136,8.370000,11.500000,3,3.110000,0,False.\r\nNM,81,415,402-9304,no,no,0,173.200000,80,29.440000,236.200000,94,20.080000,240.200000,84,10.810000,11.800000,6,3.190000,2,False.\r\nCA,75,415,341-1916,no,yes,19,210.300000,90,35.750000,241.800000,87,20.550000,215.700000,102,9.710000,13.100000,3,3.540000,4,False.\r\nMA,126,408,381-2745,no,yes,24,58.900000,125,10.010000,305.500000,90,25.970000,158.900000,73,7.150000,12.100000,6,3.270000,0,False.\r\nME,28,415,402-5014,no,no,0,236.800000,102,40.260000,167.100000,87,14.200000,280.200000,115,12.610000,9.700000,3,2.620000,3,False.\r\nFL,153,415,399-8846,no,no,0,228.900000,102,38.910000,160.700000,136,13.660000,203.100000,109,9.140000,12.500000,2,3.380000,0,False.\r\nNH,97,408,328-9267,no,yes,32,90.000000,87,15.300000,276.300000,113,23.490000,185.200000,107,8.330000,8.600000,6,2.320000,2,True.\r\nIN,115,408,348-7224,no,no,0,146.700000,128,24.940000,106.200000,74,9.030000,197.700000,104,8.900000,11.100000,4,3.000000,2,False.\r\nWI,95,408,336-2190,no,no,0,237.300000,83,40.340000,154.000000,65,13.090000,237.000000,105,10.670000,11.200000,6,3.020000,1,False.\r\nMA,17,415,376-4705,yes,no,0,162.800000,118,27.680000,229.600000,91,19.520000,332.700000,94,14.970000,13.600000,3,3.670000,0,True.\r\nNH,105,415,381-4076,no,yes,20,186.900000,114,31.770000,256.300000,91,21.790000,334.700000,104,15.060000,8.900000,2,2.400000,1,False.\r\nTX,121,415,348-8464,no,no,0,86.100000,100,14.640000,259.800000,113,22.080000,148.000000,79,6.660000,9.100000,9,2.460000,2,False.\r\nNC,125,408,412-7020,no,no,0,212.300000,89,36.090000,215.400000,127,18.310000,186.800000,73,8.410000,11.300000,2,3.050000,2,False.\r\nCT,124,415,386-8432,no,no,0,151.000000,98,25.670000,120.600000,119,10.250000,152.800000,81,6.880000,9.200000,2,2.480000,2,False.\r\nNJ,35,408,337-1802,no,no,0,158.600000,67,26.960000,130.400000,96,11.080000,229.800000,80,10.340000,6.900000,5,1.860000,2,False.\r\nWY,134,510,366-1084,no,no,0,296.000000,93,50.320000,226.400000,117,19.240000,246.800000,98,11.110000,12.300000,10,3.320000,0,True.\r\nLA,123,510,352-4182,no,yes,32,212.300000,77,36.090000,251.500000,78,21.380000,208.700000,85,9.390000,6.600000,2,1.780000,3,False.\r\nNV,124,415,368-5628,no,no,0,234.400000,61,39.850000,179.300000,111,15.240000,285.500000,117,12.850000,10.400000,6,2.810000,3,False.\r\nWV,133,510,333-8996,no,no,0,176.800000,92,30.060000,187.500000,97,15.940000,196.800000,88,8.860000,6.500000,3,1.760000,2,False.\r\nAR,185,415,353-2557,no,yes,19,157.300000,123,26.740000,257.700000,94,21.900000,190.400000,107,8.570000,9.600000,6,2.590000,0,False.\r\nSC,1,415,356-8621,no,yes,26,146.600000,68,24.920000,172.800000,67,14.690000,173.800000,113,7.820000,10.000000,2,2.700000,1,False.\r\nKS,107,415,354-6942,no,no,0,260.500000,108,44.290000,102.400000,110,8.700000,129.700000,148,5.840000,9.800000,5,2.650000,1,False.\r\nNY,91,415,377-9829,no,yes,20,146.100000,98,24.840000,277.400000,104,23.580000,137.700000,100,6.200000,6.200000,3,1.670000,1,False.\r\nMI,178,408,348-4660,yes,no,0,124.500000,134,21.170000,141.200000,78,12.000000,268.200000,113,12.070000,11.400000,2,3.080000,2,True.\r\nMA,123,415,410-5199,no,no,0,209.400000,49,35.600000,237.400000,117,20.180000,239.200000,98,10.760000,9.800000,11,2.650000,1,False.\r\nUT,170,415,397-6542,no,no,0,285.700000,44,48.570000,167.500000,144,14.240000,260.000000,97,11.700000,8.700000,4,2.350000,1,True.\r\nHI,135,415,333-4492,no,no,0,190.900000,44,32.450000,161.400000,109,13.720000,231.900000,100,10.440000,8.400000,2,2.270000,1,False.\r\nPA,85,408,405-9573,no,no,0,144.400000,88,24.550000,264.600000,105,22.490000,185.400000,94,8.340000,9.900000,3,2.670000,1,False.\r\nOR,134,415,359-7255,no,yes,50,208.800000,130,35.500000,132.900000,104,11.300000,136.700000,107,6.150000,11.100000,4,3.000000,2,False.\r\nTN,148,415,419-5501,no,yes,36,77.600000,141,13.190000,207.000000,60,17.600000,255.700000,115,11.510000,10.900000,2,2.940000,1,False.\r\nCT,93,415,404-4809,no,no,0,271.100000,101,46.090000,237.400000,133,20.180000,145.400000,103,6.540000,8.400000,6,2.270000,1,True.\r\nAZ,138,415,344-6334,no,no,0,240.800000,104,40.940000,144.500000,92,12.280000,125.700000,98,5.660000,11.600000,1,3.130000,0,False.\r\nOR,159,510,400-1899,no,no,0,114.800000,98,19.520000,192.600000,101,16.370000,259.000000,108,11.660000,12.200000,5,3.290000,0,False.\r\nDE,103,415,346-5053,no,yes,34,138.800000,80,23.600000,142.000000,108,12.070000,183.800000,77,8.270000,11.800000,7,3.190000,1,False.\r\nMA,150,408,398-2148,no,yes,27,209.800000,112,35.670000,155.000000,80,13.180000,251.500000,111,11.320000,7.200000,6,1.940000,0,False.\r\nCT,37,408,347-7675,no,no,0,134.900000,98,22.930000,248.400000,130,21.110000,236.200000,113,10.630000,14.700000,2,3.970000,3,False.\r\nAR,33,415,411-8956,yes,no,0,164.000000,99,27.880000,153.100000,102,13.010000,123.800000,104,5.570000,6.400000,4,1.730000,0,False.\r\nSD,55,415,390-3761,no,no,0,245.500000,130,41.740000,192.700000,54,16.380000,141.700000,83,6.380000,9.100000,4,2.460000,1,False.\r\nCT,134,408,377-3876,no,yes,32,80.300000,94,13.650000,199.900000,124,16.990000,170.800000,117,7.690000,16.600000,3,4.480000,0,False.\r\nCT,107,408,345-2476,no,no,0,90.700000,90,15.420000,207.500000,109,17.640000,169.400000,96,7.620000,5.600000,5,1.510000,2,False.\r\nMA,80,408,337-7879,no,yes,36,190.300000,115,32.350000,256.600000,78,21.810000,214.900000,145,9.670000,3.800000,4,1.030000,1,False.\r\nMS,78,408,361-7283,no,no,0,108.600000,108,18.460000,209.900000,126,17.840000,222.600000,117,10.020000,7.900000,5,2.130000,1,True.\r\nMT,85,408,372-4868,no,yes,17,89.800000,88,15.270000,233.200000,75,19.820000,165.700000,116,7.460000,9.300000,7,2.510000,4,True.\r\nAK,61,415,346-8863,no,yes,15,252.400000,106,42.910000,187.800000,69,15.960000,259.600000,137,11.680000,10.000000,3,2.700000,2,False.\r\nDE,97,415,390-5267,no,yes,32,183.400000,94,31.180000,269.100000,120,22.870000,203.500000,38,9.160000,6.700000,4,1.810000,5,False.\r\nOH,136,408,392-1547,yes,no,0,183.400000,103,31.180000,141.900000,113,12.060000,200.400000,122,9.020000,10.400000,9,2.810000,2,False.\r\nMN,135,408,340-8177,no,no,0,155.200000,100,26.380000,135.900000,84,11.550000,184.600000,82,8.310000,3.800000,9,1.030000,3,False.\r\nCA,87,415,383-4802,no,yes,19,165.800000,122,28.190000,186.900000,89,15.890000,249.700000,78,11.240000,0.000000,0,0.000000,1,False.\r\nOH,165,510,378-8567,no,no,0,209.400000,67,35.600000,273.800000,89,23.270000,150.200000,88,6.760000,12.800000,1,3.460000,0,False.\r\nNV,148,415,406-7844,no,no,0,279.300000,104,47.480000,201.600000,87,17.140000,280.800000,99,12.640000,7.900000,2,2.130000,2,True.\r\nSC,99,415,402-9173,no,no,0,174.100000,102,29.600000,99.100000,118,8.420000,211.600000,126,9.520000,7.700000,2,2.080000,2,False.\r\nWV,123,415,352-3440,no,no,0,175.700000,78,29.870000,184.600000,96,15.690000,156.900000,92,7.060000,9.100000,2,2.460000,2,False.\r\nNM,127,415,363-1413,yes,no,0,256.500000,87,43.610000,222.100000,101,18.880000,156.700000,122,7.050000,13.000000,3,3.510000,1,False.\r\nWY,151,415,394-8861,no,no,0,170.200000,89,28.930000,187.500000,83,15.940000,119.500000,100,5.380000,4.300000,3,1.160000,0,False.\r\nCA,185,408,358-4036,no,no,0,139.600000,92,23.730000,250.200000,115,21.270000,158.100000,79,7.110000,10.800000,4,2.920000,1,False.\r\nKS,65,415,392-4680,no,yes,34,208.800000,119,35.500000,142.100000,106,12.080000,214.600000,87,9.660000,12.500000,4,3.380000,4,False.\r\nWY,58,510,354-2762,no,no,0,210.100000,126,35.720000,248.900000,108,21.160000,158.600000,88,7.140000,14.400000,2,3.890000,4,False.\r\nOK,104,415,371-5811,no,no,0,113.600000,87,19.310000,158.600000,98,13.480000,187.700000,87,8.450000,10.500000,6,2.840000,2,False.\r\nUT,44,415,387-2014,no,no,0,202.600000,89,34.440000,163.000000,96,13.860000,268.100000,151,12.060000,8.300000,3,2.240000,0,False.\r\nMA,58,408,411-6598,no,no,0,174.400000,112,29.650000,265.800000,122,22.590000,182.400000,87,8.210000,0.000000,0,0.000000,4,False.\r\nUT,108,415,355-9356,no,no,0,210.600000,117,35.800000,164.200000,103,13.960000,201.400000,68,9.060000,9.400000,5,2.540000,0,False.\r\nMN,132,510,406-4720,no,no,0,121.500000,88,20.660000,253.000000,124,21.510000,195.700000,120,8.810000,10.700000,4,2.890000,1,False.\r\nNE,80,415,356-7239,no,no,0,127.800000,67,21.730000,181.600000,112,15.440000,197.300000,63,8.880000,15.900000,2,4.290000,2,False.\r\nOH,162,415,328-8747,no,no,0,135.200000,98,22.980000,242.000000,107,20.570000,246.900000,96,11.110000,10.200000,2,2.750000,2,False.\r\nKY,110,510,388-9464,no,no,0,99.400000,62,16.900000,275.000000,86,23.380000,212.100000,94,9.540000,16.700000,3,4.510000,2,False.\r\nWA,96,415,406-2866,no,no,0,276.900000,105,47.070000,246.900000,94,20.990000,254.400000,107,11.450000,10.300000,3,2.780000,1,True.\r\nNY,168,408,333-5729,no,no,0,163.400000,134,27.780000,240.100000,87,20.410000,164.000000,147,7.380000,11.600000,2,3.130000,2,True.\r\nIN,72,510,391-1499,no,no,0,287.400000,116,48.860000,235.300000,126,20.000000,292.100000,114,13.140000,5.000000,3,1.350000,4,True.\r\nCO,125,408,386-8690,no,yes,23,120.500000,104,20.490000,227.800000,115,19.360000,158.500000,100,7.130000,10.200000,3,2.750000,1,False.\r\nDC,170,510,391-2231,no,no,0,184.100000,106,31.300000,204.900000,70,17.420000,224.300000,133,10.090000,9.800000,3,2.650000,2,False.\r\nAK,71,510,332-2275,no,no,0,185.000000,84,31.450000,232.500000,129,19.760000,191.100000,82,8.600000,14.900000,4,4.020000,3,False.\r\nSC,124,415,340-4028,no,no,0,160.900000,109,27.350000,144.200000,152,12.260000,120.400000,97,5.420000,12.900000,12,3.480000,1,False.\r\nKS,68,415,363-3486,no,no,0,207.600000,68,35.290000,251.600000,123,21.390000,191.600000,100,8.620000,10.900000,6,2.940000,2,False.\r\nUT,97,415,418-3181,no,no,0,209.200000,134,35.560000,0.000000,0,0.000000,175.400000,94,7.890000,11.800000,6,3.190000,1,False.\r\nIL,98,510,351-3316,yes,no,0,158.400000,71,26.930000,306.600000,66,26.060000,144.200000,93,6.490000,2.100000,4,0.570000,1,False.\r\nDC,24,408,369-3626,no,no,0,149.000000,73,25.330000,131.000000,81,11.140000,238.600000,69,10.740000,8.600000,3,2.320000,2,True.\r\nDC,136,510,353-2763,no,no,0,204.500000,63,34.770000,208.800000,95,17.750000,224.000000,119,10.080000,9.800000,2,2.650000,0,False.\r\nOK,44,408,358-7165,no,no,0,288.800000,86,49.100000,175.900000,87,14.950000,215.400000,106,9.690000,9.500000,2,2.570000,0,True.\r\nKY,96,415,353-3223,no,yes,40,108.600000,90,18.460000,206.400000,154,17.540000,126.300000,118,5.680000,13.400000,4,3.620000,0,False.\r\nNE,31,415,338-9044,no,no,0,97.500000,129,16.580000,260.400000,78,22.130000,88.700000,100,3.990000,7.000000,5,1.890000,1,False.\r\nAL,72,415,341-7296,no,no,0,166.500000,102,28.310000,261.000000,103,22.190000,262.700000,85,11.820000,13.300000,5,3.590000,0,False.\r\nHI,24,415,398-4431,no,no,0,156.200000,104,26.550000,90.000000,101,7.650000,205.100000,116,9.230000,7.300000,5,1.970000,1,False.\r\nSC,112,415,408-5601,no,yes,31,225.200000,89,38.280000,256.800000,117,21.830000,249.700000,87,11.240000,11.500000,1,3.110000,4,False.\r\nGA,117,415,328-5188,yes,no,0,287.400000,118,48.860000,259.600000,84,22.070000,153.200000,86,6.890000,10.000000,3,2.700000,1,True.\r\nKS,137,415,329-4474,no,yes,19,175.300000,96,29.800000,241.300000,146,20.510000,211.400000,109,9.510000,7.800000,2,2.110000,0,False.\r\nPA,136,408,357-4573,no,no,0,102.100000,75,17.360000,219.500000,97,18.660000,73.700000,92,3.320000,9.800000,5,2.650000,0,False.\r\nUT,95,415,341-7112,no,no,0,157.900000,103,26.840000,259.600000,90,22.070000,230.000000,117,10.350000,14.000000,2,3.780000,0,False.\r\nOR,82,415,400-3147,no,yes,19,146.500000,73,24.910000,246.400000,65,20.940000,199.000000,114,8.960000,4.100000,4,1.110000,1,False.\r\nND,145,415,378-1936,no,no,0,245.800000,116,41.790000,286.700000,91,24.370000,240.700000,115,10.830000,9.000000,13,2.430000,1,True.\r\nKY,56,415,347-1640,yes,no,0,177.700000,114,30.210000,215.600000,110,18.330000,236.700000,67,10.650000,10.500000,5,2.840000,1,False.\r\nMN,155,408,374-9531,yes,no,0,250.800000,146,42.640000,152.500000,105,12.960000,148.100000,104,6.660000,10.000000,5,2.700000,2,False.\r\nOH,133,408,380-4374,no,no,0,117.800000,100,20.030000,199.200000,105,16.930000,244.100000,119,10.980000,11.800000,4,3.190000,0,True.\r\nTX,53,415,380-9409,no,no,0,119.700000,113,20.350000,189.700000,84,16.120000,256.200000,108,11.530000,12.900000,7,3.480000,2,False.\r\nWY,123,415,387-1116,no,no,0,242.200000,87,41.170000,226.100000,101,19.220000,268.600000,121,12.090000,8.200000,3,2.210000,5,True.\r\nGA,136,415,400-7509,no,no,0,163.400000,83,27.780000,249.300000,119,21.190000,249.700000,90,11.240000,9.800000,4,2.650000,7,False.\r\nTX,57,415,403-6225,no,no,0,161.000000,113,27.370000,208.000000,134,17.680000,208.100000,81,9.360000,8.400000,4,2.270000,3,False.\r\nWV,62,408,382-8274,no,no,0,128.700000,111,21.880000,169.500000,104,14.410000,193.600000,97,8.710000,10.300000,5,2.780000,1,False.\r\nNM,112,415,354-5764,no,no,0,81.600000,94,13.870000,268.100000,112,22.790000,140.800000,75,6.340000,8.600000,18,2.320000,1,False.\r\nMI,55,415,387-6912,no,yes,20,207.700000,91,35.310000,199.700000,113,16.970000,216.500000,110,9.740000,7.300000,1,1.970000,1,False.\r\nNC,95,408,384-3413,no,no,0,128.600000,115,21.860000,216.200000,88,18.380000,255.300000,96,11.490000,6.300000,2,1.700000,6,True.\r\nNY,125,415,367-3950,no,no,0,233.300000,65,39.660000,209.800000,93,17.830000,210.600000,109,9.480000,9.100000,4,2.460000,1,False.\r\nTX,1,415,396-4254,no,no,0,182.100000,106,30.960000,134.900000,106,11.470000,152.300000,75,6.850000,10.000000,3,2.700000,5,True.\r\nKY,98,415,333-3010,no,yes,36,168.000000,81,28.560000,163.200000,125,13.870000,172.700000,120,7.770000,8.000000,2,2.160000,6,True.\r\nSD,105,415,393-1891,no,no,0,251.600000,88,42.770000,175.100000,103,14.880000,184.400000,112,8.300000,5.400000,5,1.460000,1,False.\r\nID,113,415,336-9053,no,yes,30,183.800000,102,31.250000,183.400000,123,15.590000,235.000000,52,10.580000,11.600000,7,3.130000,0,False.\r\nOR,99,408,353-3372,no,no,0,256.400000,44,43.590000,214.500000,105,18.230000,233.700000,75,10.520000,7.900000,1,2.130000,3,True.\r\nWI,103,415,386-8943,no,no,0,180.200000,134,30.630000,97.700000,85,8.300000,181.700000,134,8.180000,8.400000,3,2.270000,1,False.\r\nWV,177,408,376-9716,no,no,0,227.800000,81,38.730000,161.800000,97,13.750000,217.000000,106,9.760000,8.000000,5,2.160000,1,False.\r\nSC,149,415,370-8676,no,yes,20,147.800000,132,25.130000,276.800000,94,23.530000,149.900000,110,6.750000,10.200000,6,2.750000,0,False.\r\nCT,160,415,360-2329,no,no,0,234.900000,136,39.930000,270.800000,134,23.020000,219.300000,101,9.870000,13.900000,2,3.750000,1,True.\r\nNV,116,408,360-1320,no,no,0,110.900000,54,18.850000,213.400000,82,18.140000,186.200000,116,8.380000,7.900000,2,2.130000,2,False.\r\nND,90,415,329-8638,no,yes,22,124.500000,94,21.170000,231.700000,90,19.690000,222.200000,108,10.000000,6.400000,12,1.730000,1,False.\r\nMI,148,415,385-1118,yes,no,0,233.500000,81,39.700000,187.700000,71,15.950000,122.300000,97,5.500000,9.600000,2,2.590000,0,True.\r\nMT,147,415,408-8269,no,yes,35,197.300000,134,33.540000,141.100000,99,11.990000,212.100000,90,9.540000,10.100000,4,2.730000,2,True.\r\nNE,95,510,391-2334,no,no,0,58.200000,96,9.890000,202.100000,126,17.180000,210.500000,97,9.470000,10.400000,5,2.810000,0,False.\r\nUT,201,510,373-8900,no,no,0,212.700000,72,36.160000,225.200000,90,19.140000,195.100000,99,8.780000,7.000000,6,1.890000,1,False.\r\nWV,80,415,382-3512,no,no,0,151.500000,89,25.760000,131.700000,78,11.190000,235.300000,131,10.590000,11.800000,4,3.190000,0,False.\r\nAR,122,415,420-4089,yes,no,0,146.300000,117,24.870000,218.700000,93,18.590000,236.000000,97,10.620000,11.500000,5,3.110000,1,False.\r\nMT,132,408,406-8465,no,no,0,195.100000,100,33.170000,148.800000,95,12.650000,224.500000,117,10.100000,6.700000,2,1.810000,0,False.\r\nUT,83,510,386-6114,no,no,0,208.900000,71,35.510000,214.800000,92,18.260000,247.900000,108,11.160000,13.000000,5,3.510000,1,False.\r\nHI,99,408,413-9328,no,no,0,135.700000,107,23.070000,208.400000,103,17.710000,209.000000,95,9.400000,8.800000,3,2.380000,7,True.\r\nKS,84,415,335-7144,no,no,0,225.900000,86,38.400000,275.600000,105,23.430000,201.400000,108,9.060000,14.300000,3,3.860000,3,True.\r\nNY,46,415,414-5177,no,no,0,122.200000,67,20.770000,167.200000,62,14.210000,194.800000,98,8.770000,9.700000,6,2.620000,1,False.\r\nOH,87,510,350-5993,no,no,0,153.300000,106,26.060000,224.500000,117,19.080000,273.400000,152,12.300000,8.900000,5,2.400000,2,False.\r\nHI,150,415,370-1465,no,no,0,214.000000,117,36.380000,192.400000,89,16.350000,242.600000,99,10.920000,7.900000,4,2.130000,1,False.\r\nKS,73,408,385-2370,no,no,0,194.800000,112,33.120000,167.200000,85,14.210000,100.300000,61,4.510000,10.800000,6,2.920000,1,False.\r\nIN,7,415,358-9146,no,no,0,206.700000,87,35.140000,281.100000,83,23.890000,158.500000,77,7.130000,11.000000,5,2.970000,3,False.\r\nOR,89,415,357-8515,no,yes,12,188.000000,105,31.960000,151.300000,107,12.860000,201.900000,132,9.090000,10.500000,3,2.840000,2,False.\r\nNY,131,408,406-8995,yes,no,0,122.300000,83,20.790000,118.800000,94,10.100000,147.900000,95,6.660000,13.700000,3,3.700000,3,True.\r\nVA,105,415,344-3145,no,no,0,259.300000,96,44.080000,175.200000,97,14.890000,222.400000,36,10.010000,12.000000,5,3.240000,3,False.\r\nMI,108,408,341-9890,yes,no,0,115.100000,114,19.570000,211.300000,70,17.960000,136.100000,85,6.120000,13.800000,3,3.730000,2,True.\r\nID,47,415,365-4387,no,yes,28,172.900000,109,29.390000,137.600000,94,11.700000,203.800000,109,9.170000,8.300000,6,2.240000,1,False.\r\nMO,101,415,375-3341,yes,no,0,156.400000,116,26.590000,130.400000,114,11.080000,207.300000,109,9.330000,7.300000,5,1.970000,1,False.\r\nAL,182,415,418-3096,no,yes,24,128.100000,104,21.780000,143.400000,127,12.190000,191.000000,98,8.590000,11.600000,3,3.130000,1,False.\r\nOR,161,408,378-3879,no,no,0,196.600000,73,33.420000,170.200000,79,14.470000,194.300000,79,8.740000,12.500000,3,3.380000,1,False.\r\nVT,128,408,344-1362,no,no,0,227.900000,130,38.740000,302.600000,71,25.720000,191.500000,82,8.620000,5.500000,7,1.490000,1,True.\r\nAZ,69,415,419-3937,no,yes,31,194.900000,63,33.130000,191.600000,90,16.290000,153.000000,129,6.890000,13.200000,2,3.560000,2,False.\r\nVA,113,408,348-4961,no,yes,34,44.900000,63,7.630000,134.200000,82,11.410000,168.400000,118,7.580000,13.300000,3,3.590000,1,False.\r\nPA,87,408,329-1410,no,yes,30,262.800000,114,44.680000,215.800000,130,18.340000,154.800000,88,6.970000,7.800000,2,2.110000,0,False.\r\nCO,71,415,332-9896,no,no,0,211.200000,70,35.900000,252.700000,122,21.480000,225.800000,104,10.160000,12.300000,3,3.320000,0,False.\r\nKY,76,415,407-8575,no,no,0,204.000000,69,34.680000,225.100000,110,19.130000,240.300000,85,10.810000,9.600000,5,2.590000,1,False.\r\nNJ,87,510,387-2799,no,no,0,223.200000,109,37.940000,127.500000,86,10.840000,289.300000,83,13.020000,14.500000,4,3.920000,3,False.\r\nIL,117,408,373-9108,no,no,0,119.000000,82,20.230000,187.500000,108,15.940000,189.300000,97,8.520000,11.500000,3,3.110000,1,False.\r\nWA,177,415,345-3947,no,no,0,266.100000,91,45.240000,225.200000,79,19.140000,224.700000,58,10.110000,8.900000,8,2.400000,3,True.\r\nWV,95,415,356-7511,no,no,0,134.400000,104,22.850000,152.400000,95,12.950000,236.500000,80,10.640000,9.400000,3,2.540000,1,False.\r\nRI,76,415,343-4516,no,no,0,171.100000,78,29.090000,257.200000,83,21.860000,91.600000,92,4.120000,16.200000,3,4.370000,1,False.\r\nOH,66,415,408-6305,no,no,0,170.500000,103,28.990000,254.300000,77,21.620000,197.300000,138,8.880000,10.500000,2,2.840000,2,False.\r\nMO,110,415,338-7305,no,no,0,178.500000,124,30.350000,146.900000,141,12.490000,217.100000,102,9.770000,9.900000,7,2.670000,1,False.\r\nMD,204,510,401-3077,no,no,0,205.200000,145,34.880000,154.800000,95,13.160000,191.400000,77,8.610000,14.100000,5,3.810000,3,False.\r\nOH,32,415,401-6977,no,yes,31,232.800000,97,39.580000,183.500000,111,15.600000,206.800000,111,9.310000,13.000000,2,3.510000,0,False.\r\nVA,133,408,385-1464,no,yes,39,239.900000,107,40.780000,253.800000,77,21.570000,128.700000,85,5.790000,6.700000,3,1.810000,5,False.\r\nFL,185,408,417-5034,no,no,0,55.600000,97,9.450000,288.700000,83,24.540000,111.200000,110,5.000000,12.100000,3,3.270000,2,False.\r\nCO,103,415,420-7066,no,yes,37,153.500000,78,26.100000,241.900000,108,20.560000,244.700000,110,11.010000,10.600000,3,2.860000,1,False.\r\nNY,91,510,394-8256,no,no,0,109.800000,100,18.670000,189.600000,104,16.120000,206.700000,85,9.300000,11.100000,3,3.000000,1,False.\r\nWV,131,415,362-5044,no,no,0,196.100000,89,33.340000,185.500000,87,15.770000,250.000000,132,11.250000,5.200000,6,1.400000,2,False.\r\nLA,153,510,350-2075,no,no,0,166.800000,127,28.360000,143.500000,121,12.200000,210.700000,130,9.480000,11.800000,4,3.190000,0,False.\r\nMA,132,415,343-5372,no,yes,25,113.200000,96,19.240000,269.900000,107,22.940000,229.100000,87,10.310000,7.100000,7,1.920000,2,False.\r\nUT,148,510,377-9520,no,no,0,203.000000,92,34.510000,150.900000,125,12.830000,245.500000,131,11.050000,14.600000,9,3.940000,1,False.\r\nAL,141,408,391-6773,no,no,0,242.800000,90,41.280000,234.100000,80,19.900000,211.500000,104,9.520000,6.000000,3,1.620000,5,False.\r\nME,105,415,403-4442,no,no,0,156.500000,102,26.610000,140.200000,134,11.920000,227.400000,111,10.230000,12.200000,2,3.290000,2,False.\r\nTX,169,408,379-5885,no,no,0,266.700000,105,45.340000,158.200000,88,13.450000,287.700000,111,12.950000,13.800000,3,3.730000,3,True.\r\nND,127,415,399-1021,no,yes,23,182.000000,80,30.940000,216.100000,85,18.370000,156.900000,82,7.060000,9.800000,4,2.650000,1,False.\r\nCO,57,415,342-4004,no,no,0,85.900000,92,14.600000,193.900000,127,16.480000,231.500000,93,10.420000,10.100000,2,2.730000,0,False.\r\nLA,123,415,382-7659,no,yes,33,146.600000,87,24.920000,114.800000,59,9.760000,220.400000,99,9.920000,2.900000,7,0.780000,0,False.\r\nMT,103,510,342-1004,no,yes,35,110.500000,101,18.790000,208.300000,81,17.710000,87.400000,77,3.930000,13.900000,2,3.750000,4,True.\r\nOR,101,415,398-5851,no,no,0,118.600000,89,20.160000,199.600000,97,16.970000,53.300000,61,2.400000,11.500000,5,3.110000,1,False.\r\nNH,123,415,396-4869,no,yes,22,197.600000,105,33.590000,80.000000,86,6.800000,120.800000,82,5.440000,15.600000,12,4.210000,2,False.\r\nNE,78,510,422-8333,no,yes,32,210.300000,116,35.750000,192.200000,83,16.340000,246.100000,92,11.070000,10.800000,4,2.920000,6,False.\r\nWV,101,415,367-9127,no,yes,28,220.300000,96,37.450000,285.800000,72,24.290000,203.000000,111,9.140000,9.400000,6,2.540000,4,False.\r\nNV,129,415,420-3028,no,no,0,150.000000,98,25.500000,232.400000,101,19.750000,261.200000,123,11.750000,12.500000,6,3.380000,1,False.\r\nMA,67,415,357-6348,no,yes,34,161.700000,114,27.490000,207.600000,115,17.650000,205.700000,114,9.260000,9.200000,4,2.480000,0,False.\r\nMI,37,415,386-1131,no,no,0,191.400000,116,32.540000,167.400000,99,14.230000,216.500000,112,9.740000,14.000000,5,3.780000,3,False.\r\nKY,64,415,349-8391,yes,no,0,146.700000,83,24.940000,148.300000,91,12.610000,238.600000,69,10.740000,12.500000,3,3.380000,3,False.\r\nWV,173,510,421-1484,no,no,0,109.400000,103,18.600000,101.300000,111,8.610000,167.300000,106,7.530000,7.800000,7,2.110000,1,False.\r\nKY,135,510,414-2663,no,no,0,144.100000,115,24.500000,249.800000,68,21.230000,211.400000,82,9.510000,13.600000,3,3.670000,1,False.\r\nNJ,75,415,327-6989,no,yes,42,248.900000,93,42.310000,170.800000,108,14.520000,104.500000,91,4.700000,11.200000,8,3.020000,1,False.\r\nME,88,415,405-5513,no,no,0,85.700000,112,14.570000,221.600000,70,18.840000,190.600000,75,8.580000,11.600000,3,3.130000,4,True.\r\nTX,112,415,345-9168,no,no,0,214.800000,112,36.520000,209.700000,104,17.820000,164.400000,97,7.400000,9.400000,5,2.540000,3,False.\r\nMN,113,408,417-5146,no,no,0,158.900000,137,27.010000,242.800000,109,20.640000,247.800000,97,11.150000,6.500000,4,1.760000,0,False.\r\nVA,121,510,339-2792,no,yes,28,110.000000,94,18.700000,141.500000,76,12.030000,237.300000,87,10.680000,6.400000,3,1.730000,2,False.\r\nDC,70,415,345-8397,no,no,0,152.800000,145,25.980000,183.600000,102,15.610000,151.800000,75,6.830000,10.500000,2,2.840000,1,False.\r\nMD,90,415,344-6404,no,no,0,145.600000,103,24.750000,197.100000,137,16.750000,294.500000,83,13.250000,10.500000,4,2.840000,0,False.\r\nRI,39,408,417-9455,no,no,0,93.300000,83,15.860000,199.600000,114,16.970000,206.200000,104,9.280000,6.500000,4,1.760000,0,False.\r\nMA,142,408,343-1009,no,no,0,216.800000,134,36.860000,187.800000,106,15.960000,138.100000,108,6.210000,8.300000,2,2.240000,0,False.\r\nMD,176,408,365-3493,no,no,0,201.900000,101,34.320000,154.700000,78,13.150000,164.400000,79,7.400000,9.000000,2,2.430000,1,False.\r\nNM,105,408,376-7043,no,no,0,146.400000,81,24.890000,225.100000,80,19.130000,230.100000,117,10.350000,8.500000,2,2.300000,1,False.\r\nMN,57,415,348-5728,no,no,0,272.700000,74,46.360000,224.900000,85,19.120000,178.200000,104,8.020000,10.500000,3,2.840000,2,True.\r\nMI,110,510,357-5784,no,no,0,18.900000,92,3.210000,258.400000,81,21.960000,109.600000,74,4.930000,14.800000,4,4.000000,1,False.\r\nAZ,88,415,417-9844,no,no,0,172.800000,81,29.380000,193.400000,90,16.440000,89.600000,107,4.030000,12.800000,5,3.460000,2,False.\r\nAL,95,408,333-7225,no,no,0,190.200000,119,32.330000,157.100000,70,13.350000,181.500000,120,8.170000,14.000000,6,3.780000,0,False.\r\nMI,147,415,382-4943,no,no,0,130.600000,83,22.200000,208.100000,144,17.690000,204.600000,72,9.210000,15.600000,3,4.210000,3,False.\r\nSC,101,415,345-4589,no,no,0,158.400000,92,26.930000,188.000000,117,15.980000,219.700000,125,9.890000,13.500000,4,3.650000,4,True.\r\nMS,115,415,404-6337,no,no,0,166.500000,111,28.310000,236.200000,98,20.080000,205.600000,92,9.250000,15.600000,2,4.210000,1,False.\r\nMS,103,415,412-1470,no,no,0,129.300000,103,21.980000,202.800000,89,17.240000,233.000000,126,10.490000,12.900000,2,3.480000,2,False.\r\nCA,82,415,394-9220,no,no,0,199.300000,112,33.880000,193.400000,120,16.440000,254.400000,117,11.450000,7.000000,10,1.890000,0,False.\r\nMD,141,415,364-5362,no,no,0,185.100000,126,31.470000,233.000000,98,19.810000,152.200000,106,6.850000,9.100000,7,2.460000,0,False.\r\nND,149,408,372-9852,no,no,0,175.400000,80,29.820000,197.400000,127,16.780000,188.200000,102,8.470000,9.700000,2,2.620000,2,False.\r\nIL,131,510,394-9984,no,no,0,263.400000,123,44.780000,151.900000,74,12.910000,218.500000,101,9.830000,10.700000,2,2.890000,2,False.\r\nSD,119,510,402-1668,no,no,0,94.200000,108,16.010000,264.100000,100,22.450000,203.700000,79,9.170000,7.300000,3,1.970000,1,False.\r\nAL,112,510,339-5659,no,no,0,189.400000,83,32.200000,219.000000,89,18.620000,168.000000,116,7.560000,7.100000,8,1.920000,3,False.\r\nNV,116,510,341-7279,no,yes,35,118.000000,103,20.060000,167.200000,106,14.210000,205.700000,102,9.260000,11.800000,2,3.190000,2,False.\r\nLA,94,415,371-3236,no,no,0,212.100000,98,36.060000,189.400000,89,16.100000,352.200000,95,15.850000,8.400000,5,2.270000,3,False.\r\nVA,90,408,343-5679,no,no,0,222.000000,93,37.740000,187.000000,103,15.900000,282.300000,124,12.700000,12.400000,6,3.350000,2,False.\r\nDE,114,415,347-4626,no,yes,31,222.800000,98,37.880000,180.500000,105,15.340000,151.300000,101,6.810000,13.000000,4,3.510000,0,False.\r\nCT,63,408,344-8498,no,yes,25,190.000000,137,32.300000,116.600000,76,9.910000,141.500000,110,6.370000,12.200000,2,3.290000,1,False.\r\nCO,130,408,349-3005,no,no,0,271.800000,129,46.210000,237.200000,128,20.160000,210.100000,91,9.450000,8.700000,2,2.350000,4,True.\r\nMA,122,408,371-3498,no,yes,29,195.400000,83,33.220000,268.200000,93,22.800000,168.000000,95,7.560000,8.400000,6,2.270000,3,False.\r\nOR,166,510,367-4853,no,no,0,199.600000,93,33.930000,214.300000,99,18.220000,196.800000,110,8.860000,7.200000,5,1.940000,3,False.\r\nWA,62,415,422-3454,no,no,0,100.000000,98,17.000000,173.500000,95,14.750000,218.000000,122,9.810000,10.100000,4,2.730000,0,False.\r\nSC,78,415,403-8915,no,yes,21,160.600000,85,27.300000,223.100000,79,18.960000,124.000000,92,5.580000,9.500000,1,2.570000,2,False.\r\nIN,148,415,371-2418,no,yes,26,158.700000,91,26.980000,160.500000,127,13.640000,218.300000,88,9.820000,9.900000,3,2.670000,1,False.\r\nMD,154,510,411-2977,no,no,0,154.500000,122,26.270000,214.200000,71,18.210000,178.000000,105,8.010000,12.000000,2,3.240000,3,True.\r\nNV,110,408,389-8163,no,yes,34,192.300000,114,32.690000,129.300000,114,10.990000,136.300000,102,6.130000,6.300000,12,1.700000,1,False.\r\nTX,75,415,417-4456,no,no,0,305.100000,106,51.870000,188.000000,115,15.980000,235.400000,116,10.590000,8.500000,5,2.300000,0,True.\r\nND,84,408,351-1894,no,yes,38,193.000000,106,32.810000,153.600000,106,13.060000,260.400000,87,11.720000,7.400000,5,2.000000,2,False.\r\nWV,113,510,386-6408,no,no,0,72.500000,88,12.330000,204.000000,112,17.340000,117.900000,118,5.310000,6.600000,3,1.780000,1,False.\r\nCO,181,510,370-9592,no,yes,40,105.200000,61,17.880000,341.300000,79,29.010000,165.700000,97,7.460000,6.300000,3,1.700000,2,False.\r\nTX,51,415,397-9251,no,no,0,180.500000,88,30.690000,134.700000,102,11.450000,170.700000,97,7.680000,10.000000,3,2.700000,2,False.\r\nFL,102,408,395-6913,no,yes,29,214.700000,86,36.500000,314.300000,109,26.720000,280.200000,110,12.610000,14.300000,2,3.860000,0,False.\r\nAL,107,408,332-3804,no,no,0,86.800000,95,14.760000,108.100000,85,9.190000,204.300000,87,9.190000,13.200000,3,3.560000,1,False.\r\nWV,88,510,421-1326,no,no,0,131.500000,99,22.360000,174.800000,128,14.860000,184.200000,83,8.290000,7.900000,2,2.130000,5,True.\r\nMI,82,415,415-8200,no,no,0,135.400000,102,23.020000,237.100000,122,20.150000,118.300000,91,5.320000,17.500000,4,4.730000,0,False.\r\nNY,204,415,371-9414,no,no,0,174.300000,85,29.630000,254.100000,95,21.600000,176.400000,96,7.940000,5.900000,3,1.590000,6,False.\r\nMS,130,510,347-3895,no,no,0,203.900000,63,34.660000,191.800000,93,16.300000,132.500000,125,5.960000,12.100000,4,3.270000,3,False.\r\nMO,174,510,342-5854,no,no,0,235.500000,108,40.040000,142.300000,143,12.100000,316.700000,131,14.250000,12.500000,5,3.380000,0,False.\r\nAR,129,415,379-7192,no,no,0,157.000000,113,26.690000,256.900000,97,21.840000,185.500000,126,8.350000,12.100000,2,3.270000,2,False.\r\nMS,190,415,394-5753,yes,no,0,111.900000,55,19.020000,223.000000,124,18.960000,243.200000,81,10.940000,10.000000,7,2.700000,3,False.\r\nNY,54,510,390-6932,yes,no,0,236.300000,91,40.170000,152.800000,130,12.990000,160.300000,98,7.210000,11.200000,8,3.020000,3,False.\r\nSD,78,408,394-3171,no,no,0,163.600000,88,27.810000,283.400000,93,24.090000,262.100000,108,11.790000,8.600000,9,2.320000,0,False.\r\nAK,100,415,394-5202,no,yes,29,213.600000,127,36.310000,175.900000,82,14.950000,207.200000,100,9.320000,8.900000,3,2.400000,1,False.\r\nWV,70,510,348-3777,no,yes,30,143.400000,72,24.380000,170.000000,92,14.450000,127.900000,68,5.760000,9.400000,4,2.540000,3,False.\r\nSC,111,510,407-3949,no,no,0,78.300000,119,13.310000,198.200000,94,16.850000,248.500000,94,11.180000,12.100000,4,3.270000,1,False.\r\nVA,117,408,363-4779,no,no,0,97.100000,98,16.510000,228.000000,131,19.380000,240.000000,111,10.800000,10.600000,3,2.860000,1,False.\r\nMS,68,415,340-2239,no,no,0,94.100000,93,16.000000,147.600000,80,12.550000,213.500000,85,9.610000,10.100000,2,2.730000,0,False.\r\nSD,27,510,359-3423,no,no,0,226.300000,95,38.470000,274.300000,109,23.320000,242.700000,119,10.920000,8.200000,3,2.210000,2,True.\r\nMN,91,415,382-9297,no,no,0,133.800000,61,22.750000,158.800000,96,13.500000,189.600000,92,8.530000,10.500000,2,2.840000,1,False.\r\nAL,181,415,330-9294,no,yes,27,190.300000,93,32.350000,249.000000,127,21.170000,215.700000,82,9.710000,10.600000,4,2.860000,1,False.\r\nCO,118,415,362-8763,no,yes,36,294.900000,106,50.130000,165.700000,115,14.080000,189.200000,63,8.510000,9.800000,5,2.650000,3,False.\r\nME,112,415,403-4816,no,no,0,185.400000,114,31.520000,191.400000,119,16.270000,144.000000,78,6.480000,10.000000,11,2.700000,2,False.\r\nGA,93,415,371-2155,no,no,0,179.500000,121,30.520000,191.900000,131,16.310000,165.500000,125,7.450000,12.000000,4,3.240000,0,False.\r\nAZ,102,408,334-1339,no,no,0,158.000000,94,26.860000,207.900000,100,17.670000,190.400000,120,8.570000,10.100000,10,2.730000,0,False.\r\nMA,93,415,341-7412,no,no,0,173.000000,131,29.410000,190.400000,108,16.180000,290.000000,66,13.050000,10.400000,2,2.810000,0,False.\r\nAZ,107,415,375-4770,no,yes,32,134.200000,101,22.810000,211.900000,145,18.010000,167.600000,138,7.540000,8.200000,5,2.210000,1,False.\r\nIN,100,415,406-7643,no,yes,32,125.200000,123,21.280000,230.900000,101,19.630000,192.000000,106,8.640000,12.600000,9,3.400000,3,False.\r\nDE,115,415,415-8164,no,no,0,195.900000,111,33.300000,227.000000,108,19.300000,313.200000,113,14.090000,13.200000,1,3.560000,2,False.\r\nWI,63,510,354-3545,no,yes,13,214.200000,61,36.410000,181.200000,88,15.400000,174.000000,68,7.830000,10.300000,2,2.780000,0,False.\r\nME,57,415,377-3139,no,no,0,221.100000,101,37.590000,236.700000,65,20.120000,252.300000,137,11.350000,9.500000,1,2.570000,0,False.\r\nDC,119,408,418-7478,no,yes,26,132.000000,100,22.440000,173.300000,121,14.730000,203.500000,108,9.160000,11.600000,5,3.130000,1,False.\r\nGA,73,408,385-6952,no,no,0,157.600000,92,26.790000,198.300000,87,16.860000,364.900000,106,16.420000,9.100000,4,2.460000,1,False.\r\nHI,98,408,381-8593,no,yes,30,110.300000,71,18.750000,182.400000,108,15.500000,183.800000,88,8.270000,11.000000,8,2.970000,2,False.\r\nVA,139,415,365-9371,yes,no,0,161.500000,121,27.460000,192.900000,137,16.400000,168.300000,96,7.570000,11.200000,13,3.020000,0,False.\r\nNY,31,408,401-7335,no,yes,28,171.800000,116,29.210000,240.700000,125,20.460000,245.500000,80,11.050000,10.600000,7,2.860000,1,False.\r\nPA,129,510,364-5126,no,yes,32,211.000000,99,35.870000,155.100000,89,13.180000,234.800000,96,10.570000,11.400000,5,3.080000,1,False.\r\nAR,115,415,385-7157,no,no,0,139.300000,89,23.680000,192.300000,95,16.350000,151.000000,75,6.800000,9.300000,3,2.510000,7,True.\r\nHI,108,415,385-4766,no,no,0,291.600000,99,49.570000,221.100000,93,18.790000,229.200000,110,10.310000,14.000000,9,3.780000,2,True.\r\nDE,139,408,390-1760,no,no,0,139.000000,110,23.630000,132.900000,93,11.300000,272.000000,120,12.240000,12.100000,1,3.270000,0,False.\r\nWV,102,408,365-8831,no,no,0,234.800000,125,39.920000,199.200000,99,16.930000,163.200000,88,7.340000,10.000000,1,2.700000,4,False.\r\nOK,149,408,353-4002,no,no,0,187.600000,83,31.890000,201.400000,81,17.120000,264.200000,79,11.890000,8.800000,1,2.380000,1,False.\r\nOR,113,415,367-5923,no,no,0,159.800000,143,27.170000,210.100000,93,17.860000,175.100000,86,7.880000,13.100000,7,3.540000,2,False.\r\nND,131,408,393-9548,no,yes,33,177.100000,100,30.110000,194.000000,85,16.490000,253.400000,124,11.400000,5.200000,5,1.400000,1,False.\r\nMO,83,408,362-2356,no,no,0,117.900000,101,20.040000,160.400000,92,13.630000,235.300000,150,10.590000,11.400000,10,3.080000,0,False.\r\nAR,96,415,365-2341,no,yes,21,247.600000,95,42.090000,256.300000,150,21.790000,158.600000,72,7.140000,10.800000,6,2.920000,2,False.\r\nGA,98,408,388-8797,no,no,0,169.900000,77,28.880000,138.300000,155,11.760000,142.600000,105,6.420000,8.500000,7,2.300000,1,False.\r\nTN,3,415,400-4713,no,no,0,185.000000,120,31.450000,203.700000,129,17.310000,170.500000,89,7.670000,14.100000,3,3.810000,3,False.\r\nMA,77,408,420-3042,no,yes,17,204.900000,84,34.830000,201.000000,102,17.090000,219.700000,97,9.890000,11.300000,5,3.050000,0,False.\r\nND,75,408,396-4171,no,yes,24,225.500000,119,38.340000,182.000000,108,15.470000,270.900000,106,12.190000,9.400000,2,2.540000,3,False.\r\nIA,40,510,389-8417,no,no,0,169.700000,115,28.850000,141.400000,123,12.020000,253.000000,115,11.390000,10.500000,3,2.840000,4,True.\r\nNJ,108,415,339-4068,no,no,0,239.300000,102,40.680000,223.400000,127,18.990000,251.400000,104,11.310000,10.600000,6,2.860000,0,False.\r\nMT,100,415,341-4873,no,no,0,113.300000,96,19.260000,197.900000,89,16.820000,284.500000,93,12.800000,11.700000,2,3.160000,4,True.\r\nAL,16,415,336-2322,no,no,0,161.900000,100,27.520000,230.100000,138,19.560000,148.800000,78,6.700000,10.200000,11,2.750000,3,False.\r\nNY,115,510,402-1607,no,yes,16,133.300000,110,22.660000,185.700000,111,15.780000,161.500000,113,7.270000,5.600000,4,1.510000,2,False.\r\nPA,108,510,379-3037,no,yes,25,170.700000,88,29.020000,109.900000,113,9.340000,165.700000,99,7.460000,8.700000,1,2.350000,3,False.\r\nVT,107,510,382-1399,no,no,0,189.700000,76,32.250000,156.100000,65,13.270000,244.000000,91,10.980000,8.300000,3,2.240000,5,False.\r\nNC,161,415,394-5489,no,no,0,322.300000,100,54.790000,230.400000,135,19.580000,241.500000,104,10.870000,7.800000,5,2.110000,2,True.\r\nCT,147,415,387-6065,no,no,0,124.400000,74,21.150000,320.900000,78,27.280000,157.200000,126,7.070000,10.400000,4,2.810000,2,False.\r\nMO,107,415,327-6087,no,no,0,146.900000,94,24.970000,114.300000,111,9.720000,114.500000,97,5.150000,11.400000,5,3.080000,3,False.\r\nWV,120,510,341-8667,no,no,0,192.600000,123,32.740000,206.400000,105,17.540000,283.200000,93,12.740000,10.800000,3,2.920000,1,False.\r\nNJ,107,408,338-9612,no,yes,36,96.300000,83,16.370000,179.600000,91,15.270000,166.300000,121,7.480000,10.300000,2,2.780000,1,False.\r\nAK,58,510,364-1134,no,no,0,131.900000,96,22.420000,167.600000,107,14.250000,205.900000,106,9.270000,14.700000,5,3.970000,3,False.\r\nLA,91,408,413-4811,no,no,0,147.200000,121,25.020000,175.200000,87,14.890000,136.300000,80,6.130000,13.300000,3,3.590000,2,False.\r\nAL,13,415,354-4333,no,no,0,143.100000,139,24.330000,239.600000,88,20.370000,221.700000,123,9.980000,7.100000,5,1.920000,2,False.\r\nIN,104,408,382-2026,no,no,0,280.400000,127,47.670000,179.400000,79,15.250000,150.600000,77,6.780000,15.200000,6,4.100000,5,False.\r\nMA,93,415,368-3287,no,yes,31,237.200000,85,40.320000,213.100000,100,18.110000,192.700000,87,8.670000,10.700000,4,2.890000,1,False.\r\nDE,95,510,367-8298,no,no,0,184.200000,95,31.310000,181.600000,101,15.440000,143.400000,113,6.450000,12.800000,4,3.460000,2,False.\r\nSC,104,415,391-1783,no,no,0,109.100000,141,18.550000,187.100000,140,15.900000,216.600000,100,9.750000,10.000000,4,2.700000,3,False.\r\nNH,35,408,393-8762,no,no,0,138.100000,115,23.480000,158.200000,82,13.450000,215.700000,118,9.710000,10.300000,2,2.780000,5,True.\r\nLA,62,415,385-1423,no,no,0,186.800000,94,31.760000,207.600000,92,17.650000,195.000000,98,8.780000,8.800000,4,2.380000,3,False.\r\nMS,143,510,406-7670,no,no,0,155.400000,112,26.420000,290.900000,92,24.730000,228.400000,91,10.280000,13.900000,5,3.750000,1,False.\r\nNM,62,415,339-5423,no,no,0,245.300000,91,41.700000,122.900000,130,10.450000,228.400000,102,10.280000,8.500000,4,2.300000,4,False.\r\nWA,60,415,366-8939,yes,no,0,205.900000,97,35.000000,277.400000,117,23.580000,202.000000,139,9.090000,11.000000,2,2.970000,0,True.\r\nSC,41,510,353-2391,no,no,0,207.200000,138,35.220000,214.100000,83,18.200000,193.000000,105,8.690000,11.900000,4,3.210000,1,False.\r\nMT,34,415,372-4203,no,yes,14,151.500000,100,25.760000,248.700000,126,21.140000,199.800000,120,8.990000,10.700000,5,2.890000,2,False.\r\nME,56,408,385-5688,no,no,0,221.900000,112,37.720000,278.200000,122,23.650000,288.100000,85,12.960000,7.100000,5,1.920000,0,True.\r\nNM,183,415,397-7453,no,no,0,190.000000,100,32.300000,246.600000,78,20.960000,304.200000,107,13.690000,9.500000,4,2.570000,1,False.\r\nAZ,94,415,366-9015,no,no,0,220.800000,111,37.540000,156.200000,67,13.280000,187.900000,89,8.460000,10.500000,4,2.840000,2,False.\r\nCT,73,415,356-1654,no,yes,47,173.700000,117,29.530000,204.000000,114,17.340000,174.600000,94,7.860000,6.300000,3,1.700000,2,False.\r\nIL,123,408,337-3932,no,no,0,114.800000,94,19.520000,150.000000,104,12.750000,268.600000,119,12.090000,9.600000,4,2.590000,2,False.\r\nIN,64,408,350-1126,no,no,0,113.800000,97,19.350000,192.300000,97,16.350000,214.900000,89,9.670000,10.400000,1,2.810000,3,False.\r\nAR,127,415,416-3649,yes,no,0,143.200000,60,24.340000,179.500000,159,15.260000,171.800000,122,7.730000,6.200000,4,1.670000,4,True.\r\nRI,33,415,349-1726,no,no,0,184.400000,111,31.350000,203.800000,110,17.320000,237.400000,100,10.680000,9.300000,5,2.510000,3,False.\r\nND,27,415,405-1589,no,no,0,227.400000,67,38.660000,248.000000,115,21.080000,61.400000,109,2.760000,7.800000,6,2.110000,1,False.\r\nNH,123,408,366-7560,no,no,0,224.000000,99,38.080000,210.700000,80,17.910000,231.900000,75,10.440000,2.100000,5,0.570000,0,False.\r\nNV,148,510,333-9643,no,no,0,216.200000,95,36.750000,185.700000,105,15.780000,300.000000,143,13.500000,10.000000,5,2.700000,2,False.\r\nUT,81,415,355-6422,no,no,0,129.900000,121,22.080000,230.100000,105,19.560000,140.500000,123,6.320000,13.300000,3,3.590000,0,False.\r\nNC,122,510,329-5400,no,yes,30,230.100000,108,39.120000,287.600000,76,24.450000,177.100000,85,7.970000,6.900000,3,1.860000,2,False.\r\nMI,52,415,383-6356,no,no,0,204.400000,97,34.750000,273.200000,128,23.220000,179.600000,118,8.080000,11.000000,5,2.970000,1,False.\r\nWI,91,408,377-7276,no,yes,44,216.600000,101,36.820000,173.100000,98,14.710000,242.100000,95,10.890000,9.100000,3,2.460000,1,False.\r\nAR,54,415,337-1586,no,no,0,247.500000,85,42.080000,225.400000,93,19.160000,244.300000,132,10.990000,10.200000,2,2.750000,2,True.\r\nNH,152,510,336-9273,no,no,0,228.100000,93,38.780000,136.400000,106,11.590000,197.300000,107,8.880000,9.000000,2,2.430000,1,False.\r\nTX,201,415,415-5476,no,no,0,225.900000,110,38.400000,299.100000,86,25.420000,251.300000,81,11.310000,11.200000,4,3.020000,1,True.\r\nID,78,415,332-2650,no,no,0,103.500000,115,17.600000,117.900000,102,10.020000,201.000000,94,9.050000,12.000000,3,3.240000,4,True.\r\nCT,67,415,418-8257,no,no,0,115.500000,70,19.640000,252.200000,143,21.440000,208.900000,91,9.400000,7.500000,6,2.030000,0,False.\r\nNH,100,408,407-3121,no,no,0,218.800000,125,37.200000,148.300000,102,12.610000,277.800000,97,12.500000,9.700000,6,2.620000,1,False.\r\nWY,41,510,381-2413,no,no,0,223.800000,67,38.050000,244.800000,74,20.810000,223.800000,156,10.070000,12.300000,5,3.320000,3,False.\r\nOR,133,415,378-1144,no,no,0,143.800000,71,24.450000,184.000000,131,15.640000,275.500000,132,12.400000,12.900000,4,3.480000,3,False.\r\nSC,36,408,359-5091,no,yes,43,29.900000,123,5.080000,129.100000,117,10.970000,325.900000,105,14.670000,8.600000,6,2.320000,2,False.\r\nMD,51,510,378-6986,no,yes,28,276.700000,121,47.040000,203.700000,99,17.310000,246.200000,88,11.080000,8.300000,3,2.240000,0,False.\r\nNY,122,415,386-6580,no,no,0,141.400000,128,24.040000,146.400000,70,12.440000,123.000000,75,5.540000,8.100000,4,2.190000,0,False.\r\nNM,84,408,419-9713,no,yes,41,153.900000,102,26.160000,140.700000,117,11.960000,217.700000,101,9.800000,12.800000,5,3.460000,1,False.\r\nLA,91,415,382-6153,no,no,0,190.500000,128,32.390000,205.500000,103,17.470000,130.700000,63,5.880000,13.800000,5,3.730000,0,False.\r\nUT,110,408,332-1690,no,no,0,192.600000,102,32.740000,178.900000,118,15.210000,214.600000,74,9.660000,9.400000,4,2.540000,1,False.\r\nAL,91,408,348-9383,yes,no,0,151.800000,115,25.810000,103.600000,116,8.810000,156.300000,86,7.030000,12.200000,4,3.290000,1,False.\r\nDE,121,408,420-3857,no,no,0,215.600000,74,36.650000,192.900000,98,16.400000,144.000000,103,6.480000,10.100000,4,2.730000,5,False.\r\nWV,109,415,405-2653,no,no,0,180.000000,100,30.600000,229.000000,103,19.470000,139.400000,105,6.270000,7.800000,8,2.110000,3,False.\r\nKY,95,510,417-9278,no,no,0,157.300000,116,26.740000,197.500000,77,16.790000,128.200000,111,5.770000,8.400000,4,2.270000,2,False.\r\nNC,72,415,352-5663,no,no,0,196.500000,88,33.410000,158.600000,129,13.480000,269.300000,118,12.120000,6.800000,3,1.840000,0,False.\r\nWV,73,415,370-8786,no,no,0,240.300000,130,40.850000,162.500000,83,13.810000,231.900000,136,10.440000,11.900000,3,3.210000,0,False.\r\nAZ,108,415,415-6333,no,no,0,193.300000,126,32.860000,154.700000,85,13.150000,174.800000,98,7.870000,9.400000,6,2.540000,3,False.\r\nWV,58,408,391-6558,no,yes,39,211.900000,40,36.020000,274.400000,76,23.320000,210.500000,139,9.470000,5.400000,4,1.460000,1,False.\r\nND,148,415,396-4234,yes,no,0,218.700000,111,37.180000,155.600000,133,13.230000,277.400000,62,12.480000,8.200000,5,2.210000,1,False.\r\nWA,76,510,345-6961,yes,no,0,246.800000,110,41.960000,206.300000,63,17.540000,208.400000,123,9.380000,13.200000,5,3.560000,0,True.\r\nID,103,415,346-5992,no,no,0,174.700000,151,29.700000,148.000000,56,12.580000,168.200000,109,7.570000,15.800000,3,4.270000,6,True.\r\nCT,87,415,402-3908,no,no,0,240.000000,83,40.800000,134.100000,106,11.400000,189.100000,84,8.510000,9.300000,2,2.510000,0,True.\r\nOK,35,510,350-2340,no,yes,37,181.200000,76,30.800000,177.600000,98,15.100000,228.000000,136,10.260000,5.000000,3,1.350000,2,False.\r\nIA,88,415,410-2015,no,no,0,113.700000,67,19.330000,165.100000,127,14.030000,141.500000,142,6.370000,10.800000,3,2.920000,1,False.\r\nNE,67,415,380-3311,no,yes,41,174.700000,86,29.700000,160.600000,93,13.650000,155.300000,108,6.990000,13.400000,1,3.620000,0,False.\r\nID,77,510,399-7029,no,yes,29,211.100000,89,35.890000,223.500000,97,19.000000,148.400000,106,6.680000,9.700000,9,2.620000,2,False.\r\nOR,124,510,337-3868,no,no,0,169.300000,108,28.780000,178.600000,91,15.180000,242.300000,82,10.900000,12.200000,3,3.290000,1,False.\r\nSD,30,415,354-8088,no,no,0,247.400000,107,42.060000,175.900000,76,14.950000,287.400000,90,12.930000,11.300000,2,3.050000,0,False.\r\nDE,53,415,416-9723,no,yes,32,131.200000,63,22.300000,227.400000,125,19.330000,178.900000,105,8.050000,12.800000,2,3.460000,2,False.\r\nWA,152,510,337-4403,no,no,0,161.400000,84,27.440000,163.600000,88,13.910000,153.200000,121,6.890000,11.800000,5,3.190000,1,False.\r\nCT,100,510,416-1536,yes,no,0,107.200000,98,18.220000,86.800000,122,7.380000,156.200000,117,7.030000,9.700000,4,2.620000,1,False.\r\nMN,59,408,386-3796,no,yes,32,211.900000,120,36.020000,202.900000,136,17.250000,213.500000,95,9.610000,8.800000,5,2.380000,1,False.\r\nWA,143,510,340-4989,no,no,0,160.400000,120,27.270000,285.900000,104,24.300000,182.500000,85,8.210000,6.900000,4,1.860000,3,False.\r\nPA,142,510,340-6221,no,yes,40,230.700000,101,39.220000,256.800000,88,21.830000,263.900000,92,11.880000,6.400000,3,1.730000,1,False.\r\nID,105,408,363-3469,no,no,0,232.600000,96,39.540000,253.400000,117,21.540000,154.000000,101,6.930000,10.500000,9,2.840000,1,False.\r\nMS,111,408,345-3787,no,no,0,294.700000,90,50.100000,294.600000,72,25.040000,260.100000,121,11.700000,10.800000,3,2.920000,1,True.\r\nWA,143,510,362-3107,no,no,0,133.400000,107,22.680000,223.900000,117,19.030000,180.400000,85,8.120000,10.200000,13,2.750000,1,False.\r\nDC,93,408,345-1994,no,yes,22,306.200000,123,52.050000,189.700000,83,16.120000,240.300000,107,10.810000,11.700000,2,3.160000,0,False.\r\nKY,79,415,377-5417,no,no,0,236.800000,135,40.260000,186.400000,87,15.840000,126.900000,112,5.710000,10.400000,5,2.810000,2,False.\r\nOH,68,415,369-8574,yes,yes,24,125.700000,92,21.370000,275.900000,98,23.450000,214.500000,108,9.650000,14.200000,6,3.830000,3,True.\r\nTN,93,510,344-6847,yes,no,0,168.400000,114,28.630000,276.000000,127,23.460000,196.200000,48,8.830000,11.400000,3,3.080000,1,False.\r\nID,103,415,391-7528,no,no,0,70.900000,134,12.050000,134.500000,112,11.430000,168.800000,164,7.600000,12.000000,6,3.240000,2,False.\r\nWV,144,510,393-6053,no,yes,38,105.000000,86,17.850000,121.800000,123,10.350000,221.500000,122,9.970000,3.700000,4,1.000000,0,False.\r\nWI,93,415,392-6286,no,no,0,152.100000,141,25.860000,215.500000,107,18.320000,262.400000,111,11.810000,12.000000,7,3.240000,1,False.\r\nOK,149,510,365-9079,yes,no,0,180.900000,79,30.750000,194.900000,83,16.570000,197.800000,109,8.900000,8.800000,9,2.380000,3,False.\r\nWV,23,510,399-3089,no,yes,31,156.600000,84,26.620000,161.500000,96,13.730000,294.600000,107,13.260000,9.400000,6,2.540000,1,False.\r\nSD,221,510,365-2192,no,yes,24,180.500000,85,30.690000,224.100000,92,19.050000,205.700000,103,9.260000,2.400000,3,0.650000,0,False.\r\nKS,164,510,394-3051,no,yes,30,238.800000,100,40.600000,230.000000,121,19.550000,206.300000,66,9.280000,13.200000,8,3.560000,1,False.\r\nNC,104,415,357-2429,no,yes,18,182.100000,66,30.960000,213.600000,65,18.160000,193.000000,108,8.690000,13.400000,9,3.620000,2,False.\r\nNY,150,415,421-6268,no,yes,35,139.600000,72,23.730000,332.800000,170,28.290000,213.800000,105,9.620000,8.800000,2,2.380000,2,False.\r\nWI,184,408,401-5915,no,yes,12,200.300000,76,34.050000,253.600000,105,21.560000,149.300000,93,6.720000,10.200000,5,2.750000,0,False.\r\nSC,88,408,348-6057,no,no,0,153.500000,94,26.100000,251.700000,118,21.390000,182.200000,99,8.200000,8.500000,6,2.300000,1,False.\r\nUT,61,415,349-3843,yes,yes,29,128.200000,119,21.790000,171.700000,83,14.590000,250.900000,114,11.290000,11.700000,6,3.160000,3,False.\r\nNC,110,408,396-5561,no,no,0,159.500000,145,27.120000,202.300000,101,17.200000,256.000000,96,11.520000,16.700000,2,4.510000,2,False.\r\nIN,115,415,370-9622,no,no,0,226.400000,101,38.490000,276.800000,60,23.530000,213.400000,82,9.600000,12.300000,4,3.320000,3,True.\r\nAR,33,408,371-9602,no,no,0,251.900000,81,42.820000,194.600000,96,16.540000,211.200000,87,9.500000,8.400000,3,2.270000,2,False.\r\nME,100,510,351-2815,no,no,0,264.500000,117,44.970000,194.000000,111,16.490000,262.700000,111,11.820000,7.500000,4,2.030000,2,True.\r\nNY,209,415,369-8703,no,no,0,153.700000,105,26.130000,188.600000,87,16.030000,200.800000,95,9.040000,10.700000,2,2.890000,0,False.\r\nOR,27,510,355-2840,no,no,0,232.100000,81,39.460000,210.800000,101,17.920000,165.400000,87,7.440000,15.000000,6,4.050000,5,False.\r\nIL,117,415,372-1115,no,no,0,201.900000,86,34.320000,212.300000,96,18.050000,176.900000,98,7.960000,7.800000,10,2.110000,1,False.\r\nMA,87,408,337-2986,no,no,0,186.900000,79,31.770000,182.600000,105,15.520000,143.100000,90,6.440000,4.200000,14,1.130000,1,False.\r\nCT,129,510,404-3238,no,yes,27,196.600000,89,33.420000,180.600000,95,15.350000,245.000000,83,11.030000,6.600000,5,1.780000,1,False.\r\nWI,142,510,397-4968,no,no,0,232.100000,102,39.460000,168.200000,110,14.300000,197.300000,120,8.880000,9.900000,3,2.670000,1,False.\r\nOK,112,415,327-1058,no,no,0,166.000000,79,28.220000,74.600000,100,6.340000,247.900000,74,11.160000,6.300000,7,1.700000,0,False.\r\nDE,75,510,419-9509,no,yes,28,200.600000,96,34.100000,164.100000,111,13.950000,169.600000,153,7.630000,2.500000,5,0.680000,1,False.\r\nAZ,97,408,349-7282,no,yes,25,141.000000,101,23.970000,212.000000,85,18.020000,175.200000,138,7.880000,4.900000,2,1.320000,3,False.\r\nAK,121,408,382-5743,no,yes,34,245.000000,95,41.650000,216.900000,66,18.440000,112.400000,125,5.060000,7.500000,8,2.030000,0,False.\r\nMI,142,415,358-2694,yes,no,0,140.800000,140,23.940000,228.600000,119,19.430000,152.900000,88,6.880000,10.900000,7,2.940000,1,False.\r\nWA,121,510,378-1884,no,no,0,255.100000,93,43.370000,266.900000,97,22.690000,197.700000,118,8.900000,8.800000,3,2.380000,3,True.\r\nSD,87,415,330-1627,no,yes,33,125.000000,99,21.250000,235.300000,81,20.000000,215.300000,95,9.690000,10.200000,7,2.750000,2,False.\r\nSD,34,408,392-5716,no,no,0,180.600000,65,30.700000,280.400000,99,23.830000,292.400000,105,13.160000,5.000000,3,1.350000,1,False.\r\nAK,177,415,384-6132,yes,no,0,248.700000,118,42.280000,172.300000,73,14.650000,191.900000,87,8.640000,11.300000,2,3.050000,1,True.\r\nMA,58,415,359-2740,no,yes,30,178.100000,111,30.280000,236.700000,109,20.120000,264.000000,118,11.880000,8.400000,2,2.270000,0,False.\r\nAR,113,415,338-6714,yes,no,0,122.200000,112,20.770000,131.700000,94,11.190000,169.500000,106,7.630000,10.300000,9,2.780000,5,True.\r\nKS,101,415,347-9968,no,no,0,231.300000,87,39.320000,224.700000,88,19.100000,214.600000,69,9.660000,7.200000,7,1.940000,1,False.\r\nOR,89,415,343-3399,no,no,0,111.200000,101,18.900000,122.100000,94,10.380000,180.800000,85,8.140000,12.600000,2,3.400000,3,False.\r\nNC,77,408,334-6129,yes,yes,44,103.200000,117,17.540000,236.300000,86,20.090000,203.500000,101,9.160000,11.900000,2,3.210000,0,True.\r\nOK,146,510,377-4975,no,no,0,138.400000,104,23.530000,158.900000,122,13.510000,47.400000,73,2.130000,3.900000,9,1.050000,4,True.\r\nNJ,93,415,405-3533,no,no,0,146.300000,85,24.870000,216.600000,95,18.410000,233.000000,82,10.490000,11.500000,3,3.110000,0,False.\r\nOH,160,415,337-9326,no,no,0,206.300000,66,35.070000,241.100000,109,20.490000,227.800000,102,10.250000,11.700000,6,3.160000,0,False.\r\nNM,55,415,338-6556,no,no,0,132.000000,103,22.440000,279.600000,114,23.770000,180.000000,74,8.100000,13.500000,4,3.650000,0,False.\r\nOH,88,408,354-3040,no,no,0,274.600000,105,46.680000,161.100000,121,13.690000,194.400000,123,8.750000,9.200000,4,2.480000,2,False.\r\nMI,63,510,396-1278,no,no,0,185.300000,87,31.500000,225.300000,87,19.150000,194.300000,93,8.740000,11.700000,3,3.160000,0,False.\r\nKS,127,415,354-6810,no,yes,24,154.800000,69,26.320000,177.200000,105,15.060000,207.600000,102,9.340000,9.000000,4,2.430000,1,False.\r\nIL,57,415,403-6237,no,yes,30,179.200000,105,30.460000,283.200000,83,24.070000,228.100000,77,10.260000,14.700000,5,3.970000,1,False.\r\nRI,138,510,411-6823,yes,no,0,286.200000,61,48.650000,187.200000,60,15.910000,146.200000,114,6.580000,11.000000,4,2.970000,2,True.\r\nAR,115,408,338-1400,no,no,0,268.000000,115,45.560000,153.600000,106,13.060000,232.300000,65,10.450000,17.000000,1,4.590000,3,False.\r\nNY,171,415,412-6245,no,no,0,137.500000,110,23.380000,198.100000,109,16.840000,292.700000,131,13.170000,13.300000,5,3.590000,2,False.\r\nWY,148,408,377-3417,no,no,0,243.000000,115,41.310000,191.800000,91,16.300000,117.800000,93,5.300000,13.400000,5,3.620000,2,False.\r\nNC,127,510,343-2597,no,no,0,134.900000,79,22.930000,221.500000,114,18.830000,113.800000,118,5.120000,15.000000,2,4.050000,1,False.\r\nOR,61,415,388-8282,no,no,0,234.200000,76,39.810000,216.700000,108,18.420000,130.600000,122,5.880000,13.900000,2,3.750000,1,False.\r\nVT,131,415,416-8394,no,no,0,175.100000,73,29.770000,171.900000,116,14.610000,131.100000,94,5.900000,7.300000,6,1.970000,1,False.\r\nSD,88,408,343-6643,no,no,0,142.200000,107,24.170000,262.400000,84,22.300000,139.200000,99,6.260000,10.10000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  {
    "path": "data/titanic/genderclassmodel.csv",
    "content": "PassengerId,Survived\r\n892,0\r\n893,1\r\n894,0\r\n895,0\r\n896,1\r\n897,0\r\n898,1\r\n899,0\r\n900,1\r\n901,0\r\n902,0\r\n903,0\r\n904,1\r\n905,0\r\n906,1\r\n907,1\r\n908,0\r\n909,0\r\n910,1\r\n911,1\r\n912,0\r\n913,0\r\n914,1\r\n915,0\r\n916,1\r\n917,0\r\n918,1\r\n919,0\r\n920,0\r\n921,0\r\n922,0\r\n923,0\r\n924,0\r\n925,0\r\n926,0\r\n927,0\r\n928,1\r\n929,1\r\n930,0\r\n931,0\r\n932,0\r\n933,0\r\n934,0\r\n935,1\r\n936,1\r\n937,0\r\n938,0\r\n939,0\r\n940,1\r\n941,1\r\n942,0\r\n943,0\r\n944,1\r\n945,1\r\n946,0\r\n947,0\r\n948,0\r\n949,0\r\n950,0\r\n951,1\r\n952,0\r\n953,0\r\n954,0\r\n955,1\r\n956,0\r\n957,1\r\n958,1\r\n959,0\r\n960,0\r\n961,1\r\n962,1\r\n963,0\r\n964,1\r\n965,0\r\n966,1\r\n967,0\r\n968,0\r\n969,1\r\n970,0\r\n971,1\r\n972,0\r\n973,0\r\n974,0\r\n975,0\r\n976,0\r\n977,0\r\n978,1\r\n979,1\r\n980,1\r\n981,0\r\n982,1\r\n983,0\r\n984,1\r\n985,0\r\n986,0\r\n987,0\r\n988,1\r\n989,0\r\n990,1\r\n991,0\r\n992,1\r\n993,0\r\n994,0\r\n995,0\r\n996,1\r\n997,0\r\n998,0\r\n999,0\r\n1000,0\r\n1001,0\r\n1002,0\r\n1003,1\r\n1004,1\r\n1005,1\r\n1006,1\r\n1007,0\r\n1008,0\r\n1009,1\r\n1010,0\r\n1011,1\r\n1012,1\r\n1013,0\r\n1014,1\r\n1015,0\r\n1016,0\r\n1017,1\r\n1018,0\r\n1019,0\r\n1020,0\r\n1021,0\r\n1022,0\r\n1023,0\r\n1024,0\r\n1025,0\r\n1026,0\r\n1027,0\r\n1028,0\r\n1029,0\r\n1030,1\r\n1031,0\r\n1032,0\r\n1033,1\r\n1034,0\r\n1035,0\r\n1036,0\r\n1037,0\r\n1038,0\r\n1039,0\r\n1040,0\r\n1041,0\r\n1042,1\r\n1043,0\r\n1044,0\r\n1045,1\r\n1046,0\r\n1047,0\r\n1048,1\r\n1049,1\r\n1050,0\r\n1051,1\r\n1052,1\r\n1053,0\r\n1054,1\r\n1055,0\r\n1056,0\r\n1057,0\r\n1058,0\r\n1059,0\r\n1060,1\r\n1061,1\r\n1062,0\r\n1063,0\r\n1064,0\r\n1065,0\r\n1066,0\r\n1067,1\r\n1068,1\r\n1069,0\r\n1070,1\r\n1071,1\r\n1072,0\r\n1073,0\r\n1074,1\r\n1075,0\r\n1076,1\r\n1077,0\r\n1078,1\r\n1079,0\r\n1080,0\r\n1081,0\r\n1082,0\r\n1083,0\r\n1084,0\r\n1085,0\r\n1086,0\r\n1087,0\r\n1088,0\r\n1089,1\r\n1090,0\r\n1091,1\r\n1092,1\r\n1093,0\r\n1094,0\r\n1095,1\r\n1096,0\r\n1097,0\r\n1098,1\r\n1099,0\r\n1100,1\r\n1101,0\r\n1102,0\r\n1103,0\r\n1104,0\r\n1105,1\r\n1106,1\r\n1107,0\r\n1108,1\r\n1109,0\r\n1110,1\r\n1111,0\r\n1112,1\r\n1113,0\r\n1114,1\r\n1115,0\r\n1116,1\r\n1117,1\r\n1118,0\r\n1119,1\r\n1120,0\r\n1121,0\r\n1122,0\r\n1123,1\r\n1124,0\r\n1125,0\r\n1126,0\r\n1127,0\r\n1128,0\r\n1129,0\r\n1130,1\r\n1131,1\r\n1132,1\r\n1133,1\r\n1134,0\r\n1135,0\r\n1136,0\r\n1137,0\r\n1138,1\r\n1139,0\r\n1140,1\r\n1141,1\r\n1142,1\r\n1143,0\r\n1144,0\r\n1145,0\r\n1146,0\r\n1147,0\r\n1148,0\r\n1149,0\r\n1150,1\r\n1151,0\r\n1152,0\r\n1153,0\r\n1154,1\r\n1155,1\r\n1156,0\r\n1157,0\r\n1158,0\r\n1159,0\r\n1160,1\r\n1161,0\r\n1162,0\r\n1163,0\r\n1164,1\r\n1165,1\r\n1166,0\r\n1167,1\r\n1168,0\r\n1169,0\r\n1170,0\r\n1171,0\r\n1172,1\r\n1173,0\r\n1174,1\r\n1175,1\r\n1176,0\r\n1177,0\r\n1178,0\r\n1179,0\r\n1180,0\r\n1181,0\r\n1182,0\r\n1183,1\r\n1184,0\r\n1185,0\r\n1186,0\r\n1187,0\r\n1188,1\r\n1189,0\r\n1190,0\r\n1191,0\r\n1192,0\r\n1193,0\r\n1194,0\r\n1195,0\r\n1196,1\r\n1197,1\r\n1198,0\r\n1199,0\r\n1200,0\r\n1201,1\r\n1202,0\r\n1203,0\r\n1204,0\r\n1205,1\r\n1206,1\r\n1207,1\r\n1208,0\r\n1209,0\r\n1210,0\r\n1211,0\r\n1212,0\r\n1213,0\r\n1214,0\r\n1215,0\r\n1216,1\r\n1217,0\r\n1218,1\r\n1219,0\r\n1220,0\r\n1221,0\r\n1222,1\r\n1223,0\r\n1224,0\r\n1225,1\r\n1226,0\r\n1227,0\r\n1228,0\r\n1229,0\r\n1230,0\r\n1231,0\r\n1232,0\r\n1233,0\r\n1234,0\r\n1235,1\r\n1236,0\r\n1237,1\r\n1238,0\r\n1239,1\r\n1240,0\r\n1241,1\r\n1242,1\r\n1243,0\r\n1244,0\r\n1245,0\r\n1246,0\r\n1247,0\r\n1248,1\r\n1249,0\r\n1250,0\r\n1251,1\r\n1252,0\r\n1253,1\r\n1254,1\r\n1255,0\r\n1256,1\r\n1257,0\r\n1258,0\r\n1259,0\r\n1260,1\r\n1261,0\r\n1262,0\r\n1263,1\r\n1264,0\r\n1265,0\r\n1266,1\r\n1267,1\r\n1268,1\r\n1269,0\r\n1270,0\r\n1271,0\r\n1272,0\r\n1273,0\r\n1274,1\r\n1275,1\r\n1276,0\r\n1277,1\r\n1278,0\r\n1279,0\r\n1280,0\r\n1281,0\r\n1282,0\r\n1283,1\r\n1284,0\r\n1285,0\r\n1286,0\r\n1287,1\r\n1288,0\r\n1289,1\r\n1290,0\r\n1291,0\r\n1292,1\r\n1293,0\r\n1294,1\r\n1295,0\r\n1296,0\r\n1297,0\r\n1298,0\r\n1299,0\r\n1300,1\r\n1301,1\r\n1302,1\r\n1303,1\r\n1304,1\r\n1305,0\r\n1306,1\r\n1307,0\r\n1308,0\r\n1309,0\r\n"
  },
  {
    "path": "data/titanic/genderclassmodel.py",
    "content": "\"\"\" Now that the user can read in a file this creates a model which uses the price, class and gender\nAuthor : AstroDave\nDate : 18th September 2012\nRevised : 28 March 2014\n\n\"\"\"\n\n\nimport csv as csv\nimport numpy as np\n\ncsv_file_object = csv.reader(open('train.csv', 'rb'))       # Load in the csv file\nheader = csv_file_object.next()                             # Skip the fist line as it is a header\ndata=[]                                                     # Create a variable to hold the data\n\nfor row in csv_file_object:                 # Skip through each row in the csv file\n    data.append(row)                        # adding each row to the data variable\ndata = np.array(data)                       # Then convert from a list to an array\n\n# In order to analyse the price column I need to bin up that data\n# here are my binning parameters, the problem we face is some of the fares are very large\n# So we can either have a lot of bins with nothing in them or we can just lose some\n# information by just considering that anythng over 39 is simply in the last bin.\n# So we add a ceiling\nfare_ceiling = 40\n# then modify the data in the Fare column to = 39, if it is greater or equal to the ceiling\ndata[ data[0::,9].astype(np.float) >= fare_ceiling, 9 ] = fare_ceiling - 1.0\n\nfare_bracket_size = 10\nnumber_of_price_brackets = fare_ceiling / fare_bracket_size\nnumber_of_classes = 3                             # I know there were 1st, 2nd and 3rd classes on board.\nnumber_of_classes = len(np.unique(data[0::,2]))   # But it's better practice to calculate this from the Pclass directly:\n                                                  # just take the length of an array of UNIQUE values in column index 2\n\n\n# This reference matrix will show the proportion of survivors as a sorted table of\n# gender, class and ticket fare.\n# First initialize it with all zeros\nsurvival_table = np.zeros([2,number_of_classes,number_of_price_brackets],float)\n\n# I can now find the stats of all the women and men on board\nfor i in xrange(number_of_classes):\n    for j in xrange(number_of_price_brackets):\n\n        women_only_stats = data[ (data[0::,4] == \"female\") \\\n                                 & (data[0::,2].astype(np.float) == i+1) \\\n                                 & (data[0:,9].astype(np.float) >= j*fare_bracket_size) \\\n                                 & (data[0:,9].astype(np.float) < (j+1)*fare_bracket_size), 1]\n\n        men_only_stats = data[ (data[0::,4] != \"female\") \\\n                                 & (data[0::,2].astype(np.float) == i+1) \\\n                                 & (data[0:,9].astype(np.float) >= j*fare_bracket_size) \\\n                                 & (data[0:,9].astype(np.float) < (j+1)*fare_bracket_size), 1]\n\n                                 #if i == 0 and j == 3:\n\n        survival_table[0,i,j] = np.mean(women_only_stats.astype(np.float))  # Female stats\n        survival_table[1,i,j] = np.mean(men_only_stats.astype(np.float))    # Male stats\n\n# Since in python if it tries to find the mean of an array with nothing in it\n# (such that the denominator is 0), then it returns nan, we can convert these to 0\n# by just saying where does the array not equal the array, and set these to 0.\nsurvival_table[ survival_table != survival_table ] = 0.\n\n# Now I have my proportion of survivors, simply round them such that if <0.5\n# I predict they dont surivive, and if >= 0.5 they do\nsurvival_table[ survival_table < 0.5 ] = 0\nsurvival_table[ survival_table >= 0.5 ] = 1\n\n# Now I have my indicator I can read in the test file and write out\n# if a women then survived(1) if a man then did not survived (0)\n# First read in test\ntest_file = open('test.csv', 'rb')\ntest_file_object = csv.reader(test_file)\nheader = test_file_object.next()\n\n# Also open the a new file so I can write to it. \npredictions_file = open(\"genderclassmodel.csv\", \"wb\")\npredictions_file_object = csv.writer(predictions_file)\npredictions_file_object.writerow([\"PassengerId\", \"Survived\"])\n\n# First thing to do is bin up the price file\nfor row in test_file_object:\n    for j in xrange(number_of_price_brackets):\n        # If there is no fare then place the price of the ticket according to class\n        try:\n            row[8] = float(row[8])    # No fare recorded will come up as a string so\n                                      # try to make it a float\n        except:                       # If fails then just bin the fare according to the class\n            bin_fare = 3 - float(row[1])\n            break                     # Break from the loop and move to the next row\n        if row[8] > fare_ceiling:     # Otherwise now test to see if it is higher\n                                      # than the fare ceiling we set earlier\n            bin_fare = number_of_price_brackets - 1\n            break                     # And then break to the next row\n\n        if row[8] >= j*fare_bracket_size\\\n            and row[8] < (j+1)*fare_bracket_size:     # If passed these tests then loop through\n                                                      # each bin until you find the right one\n                                                      # append it to the bin_fare\n                                                      # and move to the next loop\n            bin_fare = j\n            break\n        # Now I have the binned fare, passenger class, and whether female or male, we can\n        # just cross ref their details with our survival table\n    if row[3] == 'female':\n        predictions_file_object.writerow([row[0], \"%d\" % int(survival_table[ 0, float(row[1]) - 1, bin_fare ])])\n    else:\n        predictions_file_object.writerow([row[0], \"%d\" % int(survival_table[ 1, float(row[1]) - 1, bin_fare])])\n\n# Close out the files\ntest_file.close()\npredictions_file.close()"
  },
  {
    "path": "data/titanic/gendermodel.csv",
    "content": "PassengerId,Survived\r\n892,0\r\n893,1\r\n894,0\r\n895,0\r\n896,1\r\n897,0\r\n898,1\r\n899,0\r\n900,1\r\n901,0\r\n902,0\r\n903,0\r\n904,1\r\n905,0\r\n906,1\r\n907,1\r\n908,0\r\n909,0\r\n910,1\r\n911,1\r\n912,0\r\n913,0\r\n914,1\r\n915,0\r\n916,1\r\n917,0\r\n918,1\r\n919,0\r\n920,0\r\n921,0\r\n922,0\r\n923,0\r\n924,1\r\n925,1\r\n926,0\r\n927,0\r\n928,1\r\n929,1\r\n930,0\r\n931,0\r\n932,0\r\n933,0\r\n934,0\r\n935,1\r\n936,1\r\n937,0\r\n938,0\r\n939,0\r\n940,1\r\n941,1\r\n942,0\r\n943,0\r\n944,1\r\n945,1\r\n946,0\r\n947,0\r\n948,0\r\n949,0\r\n950,0\r\n951,1\r\n952,0\r\n953,0\r\n954,0\r\n955,1\r\n956,0\r\n957,1\r\n958,1\r\n959,0\r\n960,0\r\n961,1\r\n962,1\r\n963,0\r\n964,1\r\n965,0\r\n966,1\r\n967,0\r\n968,0\r\n969,1\r\n970,0\r\n971,1\r\n972,0\r\n973,0\r\n974,0\r\n975,0\r\n976,0\r\n977,0\r\n978,1\r\n979,1\r\n980,1\r\n981,0\r\n982,1\r\n983,0\r\n984,1\r\n985,0\r\n986,0\r\n987,0\r\n988,1\r\n989,0\r\n990,1\r\n991,0\r\n992,1\r\n993,0\r\n994,0\r\n995,0\r\n996,1\r\n997,0\r\n998,0\r\n999,0\r\n1000,0\r\n1001,0\r\n1002,0\r\n1003,1\r\n1004,1\r\n1005,1\r\n1006,1\r\n1007,0\r\n1008,0\r\n1009,1\r\n1010,0\r\n1011,1\r\n1012,1\r\n1013,0\r\n1014,1\r\n1015,0\r\n1016,0\r\n1017,1\r\n1018,0\r\n1019,1\r\n1020,0\r\n1021,0\r\n1022,0\r\n1023,0\r\n1024,1\r\n1025,0\r\n1026,0\r\n1027,0\r\n1028,0\r\n1029,0\r\n1030,1\r\n1031,0\r\n1032,1\r\n1033,1\r\n1034,0\r\n1035,0\r\n1036,0\r\n1037,0\r\n1038,0\r\n1039,0\r\n1040,0\r\n1041,0\r\n1042,1\r\n1043,0\r\n1044,0\r\n1045,1\r\n1046,0\r\n1047,0\r\n1048,1\r\n1049,1\r\n1050,0\r\n1051,1\r\n1052,1\r\n1053,0\r\n1054,1\r\n1055,0\r\n1056,0\r\n1057,1\r\n1058,0\r\n1059,0\r\n1060,1\r\n1061,1\r\n1062,0\r\n1063,0\r\n1064,0\r\n1065,0\r\n1066,0\r\n1067,1\r\n1068,1\r\n1069,0\r\n1070,1\r\n1071,1\r\n1072,0\r\n1073,0\r\n1074,1\r\n1075,0\r\n1076,1\r\n1077,0\r\n1078,1\r\n1079,0\r\n1080,1\r\n1081,0\r\n1082,0\r\n1083,0\r\n1084,0\r\n1085,0\r\n1086,0\r\n1087,0\r\n1088,0\r\n1089,1\r\n1090,0\r\n1091,1\r\n1092,1\r\n1093,0\r\n1094,0\r\n1095,1\r\n1096,0\r\n1097,0\r\n1098,1\r\n1099,0\r\n1100,1\r\n1101,0\r\n1102,0\r\n1103,0\r\n1104,0\r\n1105,1\r\n1106,1\r\n1107,0\r\n1108,1\r\n1109,0\r\n1110,1\r\n1111,0\r\n1112,1\r\n1113,0\r\n1114,1\r\n1115,0\r\n1116,1\r\n1117,1\r\n1118,0\r\n1119,1\r\n1120,0\r\n1121,0\r\n1122,0\r\n1123,1\r\n1124,0\r\n1125,0\r\n1126,0\r\n1127,0\r\n1128,0\r\n1129,0\r\n1130,1\r\n1131,1\r\n1132,1\r\n1133,1\r\n1134,0\r\n1135,0\r\n1136,0\r\n1137,0\r\n1138,1\r\n1139,0\r\n1140,1\r\n1141,1\r\n1142,1\r\n1143,0\r\n1144,0\r\n1145,0\r\n1146,0\r\n1147,0\r\n1148,0\r\n1149,0\r\n1150,1\r\n1151,0\r\n1152,0\r\n1153,0\r\n1154,1\r\n1155,1\r\n1156,0\r\n1157,0\r\n1158,0\r\n1159,0\r\n1160,1\r\n1161,0\r\n1162,0\r\n1163,0\r\n1164,1\r\n1165,1\r\n1166,0\r\n1167,1\r\n1168,0\r\n1169,0\r\n1170,0\r\n1171,0\r\n1172,1\r\n1173,0\r\n1174,1\r\n1175,1\r\n1176,1\r\n1177,0\r\n1178,0\r\n1179,0\r\n1180,0\r\n1181,0\r\n1182,0\r\n1183,1\r\n1184,0\r\n1185,0\r\n1186,0\r\n1187,0\r\n1188,1\r\n1189,0\r\n1190,0\r\n1191,0\r\n1192,0\r\n1193,0\r\n1194,0\r\n1195,0\r\n1196,1\r\n1197,1\r\n1198,0\r\n1199,0\r\n1200,0\r\n1201,1\r\n1202,0\r\n1203,0\r\n1204,0\r\n1205,1\r\n1206,1\r\n1207,1\r\n1208,0\r\n1209,0\r\n1210,0\r\n1211,0\r\n1212,0\r\n1213,0\r\n1214,0\r\n1215,0\r\n1216,1\r\n1217,0\r\n1218,1\r\n1219,0\r\n1220,0\r\n1221,0\r\n1222,1\r\n1223,0\r\n1224,0\r\n1225,1\r\n1226,0\r\n1227,0\r\n1228,0\r\n1229,0\r\n1230,0\r\n1231,0\r\n1232,0\r\n1233,0\r\n1234,0\r\n1235,1\r\n1236,0\r\n1237,1\r\n1238,0\r\n1239,1\r\n1240,0\r\n1241,1\r\n1242,1\r\n1243,0\r\n1244,0\r\n1245,0\r\n1246,1\r\n1247,0\r\n1248,1\r\n1249,0\r\n1250,0\r\n1251,1\r\n1252,0\r\n1253,1\r\n1254,1\r\n1255,0\r\n1256,1\r\n1257,1\r\n1258,0\r\n1259,1\r\n1260,1\r\n1261,0\r\n1262,0\r\n1263,1\r\n1264,0\r\n1265,0\r\n1266,1\r\n1267,1\r\n1268,1\r\n1269,0\r\n1270,0\r\n1271,0\r\n1272,0\r\n1273,0\r\n1274,1\r\n1275,1\r\n1276,0\r\n1277,1\r\n1278,0\r\n1279,0\r\n1280,0\r\n1281,0\r\n1282,0\r\n1283,1\r\n1284,0\r\n1285,0\r\n1286,0\r\n1287,1\r\n1288,0\r\n1289,1\r\n1290,0\r\n1291,0\r\n1292,1\r\n1293,0\r\n1294,1\r\n1295,0\r\n1296,0\r\n1297,0\r\n1298,0\r\n1299,0\r\n1300,1\r\n1301,1\r\n1302,1\r\n1303,1\r\n1304,1\r\n1305,0\r\n1306,1\r\n1307,0\r\n1308,0\r\n1309,0\r\n"
  },
  {
    "path": "data/titanic/gendermodel.py",
    "content": "\"\"\" This simple code is desinged to teach a basic user to read in the files in python, simply find what proportion of males and females survived and make a predictive model based on this\nAuthor : AstroDave\nDate : 18 September 2012\nRevised: 28 March 2014\n\n\"\"\"\n\n\nimport csv as csv\nimport numpy as np\n\ncsv_file_object = csv.reader(open('train.csv', 'rb')) \t# Load in the csv file\nheader = csv_file_object.next() \t\t\t\t\t\t# Skip the fist line as it is a header\ndata=[] \t\t\t\t\t\t\t\t\t\t\t\t# Create a variable to hold the data\n\nfor row in csv_file_object: \t\t\t\t\t\t\t# Skip through each row in the csv file,\n    data.append(row[0:]) \t\t\t\t\t\t\t\t# adding each row to the data variable\ndata = np.array(data) \t\t\t\t\t\t\t\t\t# Then convert from a list to an array.\n\n# Now I have an array of 12 columns and 891 rows\n# I can access any element I want, so the entire first column would\n# be data[0::,0].astype(np.float) -- This means all of the rows (from start to end), in column 0\n# I have to add the .astype() command, because\n# when appending the rows, python thought it was a string - so needed to convert\n\n# Set some variables\nnumber_passengers = np.size(data[0::,1].astype(np.float))\nnumber_survived = np.sum(data[0::,1].astype(np.float))\nproportion_survivors = number_survived / number_passengers \n\n# I can now find the stats of all the women on board,\n# by making an array that lists True/False whether each row is female\nwomen_only_stats = data[0::,4] == \"female\" \t# This finds where all the women are\nmen_only_stats = data[0::,4] != \"female\" \t# This finds where all the men are (note != means 'not equal')\n\n# I can now filter the whole data, to find statistics for just women, by just placing\n# women_only_stats as a \"mask\" on my full data -- Use it in place of the '0::' part of the array index. \n# You can test it by placing it there, and requesting column index [4], and the output should all read 'female'\n# e.g. try typing this:   data[women_only_stats,4]\nwomen_onboard = data[women_only_stats,1].astype(np.float)\nmen_onboard = data[men_only_stats,1].astype(np.float)\n\n# and derive some statistics about them\nproportion_women_survived = np.sum(women_onboard) / np.size(women_onboard)\nproportion_men_survived = np.sum(men_onboard) / np.size(men_onboard)\n\nprint 'Proportion of women who survived is %s' % proportion_women_survived\nprint 'Proportion of men who survived is %s' % proportion_men_survived\n\n# Now that I have my indicator that women were much more likely to survive,\n# I am done with the training set.\n# Now I will read in the test file and write out my simplistic prediction:\n# if female, then model that she survived (1) \n# if male, then model that he did not survive (0)\n\n# First, read in test.csv\ntest_file = open('test.csv', 'rb')\ntest_file_object = csv.reader(test_file)\nheader = test_file_object.next()\n\n# Also open the a new file so I can write to it. Call it something descriptive\n# Finally, loop through each row in the train file, and look in column index [3] (which is 'Sex')\n# Write out the PassengerId, and my prediction.\n\npredictions_file = open(\"gendermodel.csv\", \"wb\")\npredictions_file_object = csv.writer(predictions_file)\npredictions_file_object.writerow([\"PassengerId\", \"Survived\"])\t# write the column headers\nfor row in test_file_object:\t\t\t\t\t\t\t\t\t# For each row in test file,\n    if row[3] == 'female':\t\t\t\t\t\t\t\t\t\t# is it a female, if yes then\n        predictions_file_object.writerow([row[0], \"1\"])\t\t\t# write the PassengerId, and predict 1\n    else:\t\t\t\t\t\t\t\t\t\t\t\t\t\t# or else if male,\n        predictions_file_object.writerow([row[0], \"0\"])\t\t\t# write the PassengerId, and predict 0.\ntest_file.close()\t\t\t\t\t\t\t\t\t\t\t\t# Close out the files.\npredictions_file.close()\n\n"
  },
  {
    "path": "data/titanic/myfirstforest.py",
    "content": "\"\"\" Writing my first randomforest code.\nAuthor : AstroDave\nDate : 23rd September 2012\nRevised: 15 April 2014\nplease see packages.python.org/milk/randomforests.html for more\n\n\"\"\" \nimport pandas as pd\nimport numpy as np\nimport csv as csv\nfrom sklearn.ensemble import RandomForestClassifier\n\n# Data cleanup\n# TRAIN DATA\ntrain_df = pd.read_csv('train.csv', header=0)        # Load the train file into a dataframe\n\n# I need to convert all strings to integer classifiers.\n# I need to fill in the missing values of the data and make it complete.\n\n# female = 0, Male = 1\ntrain_df['Gender'] = train_df['Sex'].map( {'female': 0, 'male': 1} ).astype(int)\n\n# Embarked from 'C', 'Q', 'S'\n# Note this is not ideal: in translating categories to numbers, Port \"2\" is not 2 times greater than Port \"1\", etc.\n\n# All missing Embarked -> just make them embark from most common place\nif len(train_df.Embarked[ train_df.Embarked.isnull() ]) > 0:\n    train_df.Embarked[ train_df.Embarked.isnull() ] = train_df.Embarked.dropna().mode().values\n\nPorts = list(enumerate(np.unique(train_df['Embarked'])))    # determine all values of Embarked,\nPorts_dict = { name : i for i, name in Ports }              # set up a dictionary in the form  Ports : index\ntrain_df.Embarked = train_df.Embarked.map( lambda x: Ports_dict[x]).astype(int)     # Convert all Embark strings to int\n\n# All the ages with no data -> make the median of all Ages\nmedian_age = train_df['Age'].dropna().median()\nif len(train_df.Age[ train_df.Age.isnull() ]) > 0:\n    train_df.loc[ (train_df.Age.isnull()), 'Age'] = median_age\n\n# Remove the Name column, Cabin, Ticket, and Sex (since I copied and filled it to Gender)\ntrain_df = train_df.drop(['Name', 'Sex', 'Ticket', 'Cabin', 'PassengerId'], axis=1) \n\n\n# TEST DATA\ntest_df = pd.read_csv('test.csv', header=0)        # Load the test file into a dataframe\n\n# I need to do the same with the test data now, so that the columns are the same as the training data\n# I need to convert all strings to integer classifiers:\n# female = 0, Male = 1\ntest_df['Gender'] = test_df['Sex'].map( {'female': 0, 'male': 1} ).astype(int)\n\n# Embarked from 'C', 'Q', 'S'\n# All missing Embarked -> just make them embark from most common place\nif len(test_df.Embarked[ test_df.Embarked.isnull() ]) > 0:\n    test_df.Embarked[ test_df.Embarked.isnull() ] = test_df.Embarked.dropna().mode().values\n# Again convert all Embarked strings to int\ntest_df.Embarked = test_df.Embarked.map( lambda x: Ports_dict[x]).astype(int)\n\n\n# All the ages with no data -> make the median of all Ages\nmedian_age = test_df['Age'].dropna().median()\nif len(test_df.Age[ test_df.Age.isnull() ]) > 0:\n    test_df.loc[ (test_df.Age.isnull()), 'Age'] = median_age\n\n# All the missing Fares -> assume median of their respective class\nif len(test_df.Fare[ test_df.Fare.isnull() ]) > 0:\n    median_fare = np.zeros(3)\n    for f in range(0,3):                                              # loop 0 to 2\n        median_fare[f] = test_df[ test_df.Pclass == f+1 ]['Fare'].dropna().median()\n    for f in range(0,3):                                              # loop 0 to 2\n        test_df.loc[ (test_df.Fare.isnull()) & (test_df.Pclass == f+1 ), 'Fare'] = median_fare[f]\n\n# Collect the test data's PassengerIds before dropping it\nids = test_df['PassengerId'].values\n# Remove the Name column, Cabin, Ticket, and Sex (since I copied and filled it to Gender)\ntest_df = test_df.drop(['Name', 'Sex', 'Ticket', 'Cabin', 'PassengerId'], axis=1) \n\n\n# The data is now ready to go. So lets fit to the train, then predict to the test!\n# Convert back to a numpy array\ntrain_data = train_df.values\ntest_data = test_df.values\n\n\nprint 'Training...'\nforest = RandomForestClassifier(n_estimators=100)\nforest = forest.fit( train_data[0::,1::], train_data[0::,0] )\n\nprint 'Predicting...'\noutput = forest.predict(test_data).astype(int)\n\n\npredictions_file = open(\"myfirstforest.csv\", \"wb\")\nopen_file_object = csv.writer(predictions_file)\nopen_file_object.writerow([\"PassengerId\",\"Survived\"])\nopen_file_object.writerows(zip(ids, output))\npredictions_file.close()\nprint 'Done.'\n"
  },
  {
    "path": "data/titanic/results-rf.csv",
    "content": "PassengerId,Survived\n892,0.0\n893,0.0\n894,0.0\n895,1.0\n896,1.0\n897,0.0\n898,0.0\n899,0.0\n900,1.0\n901,0.0\n902,0.0\n903,0.0\n904,1.0\n905,0.0\n906,1.0\n907,1.0\n908,0.0\n909,1.0\n910,1.0\n911,1.0\n912,0.0\n913,1.0\n914,1.0\n915,1.0\n916,1.0\n917,0.0\n918,1.0\n919,1.0\n920,1.0\n921,0.0\n922,0.0\n923,0.0\n924,1.0\n925,0.0\n926,1.0\n927,1.0\n928,0.0\n929,0.0\n930,0.0\n931,1.0\n932,0.0\n933,1.0\n934,0.0\n935,1.0\n936,1.0\n937,0.0\n938,1.0\n939,0.0\n940,1.0\n941,1.0\n942,0.0\n943,0.0\n944,1.0\n945,1.0\n946,0.0\n947,0.0\n948,0.0\n949,0.0\n950,0.0\n951,1.0\n952,0.0\n953,0.0\n954,0.0\n955,1.0\n956,1.0\n957,1.0\n958,1.0\n959,0.0\n960,0.0\n961,1.0\n962,1.0\n963,0.0\n964,0.0\n965,0.0\n966,1.0\n967,0.0\n968,0.0\n969,1.0\n970,0.0\n971,1.0\n972,1.0\n973,0.0\n974,0.0\n975,0.0\n976,0.0\n977,0.0\n978,1.0\n979,0.0\n980,0.0\n981,1.0\n982,1.0\n983,0.0\n984,1.0\n985,0.0\n986,0.0\n987,0.0\n988,1.0\n989,0.0\n990,0.0\n991,0.0\n992,1.0\n993,0.0\n994,0.0\n995,0.0\n996,1.0\n997,0.0\n998,0.0\n999,0.0\n1000,0.0\n1001,0.0\n1002,0.0\n1003,0.0\n1004,1.0\n1005,0.0\n1006,1.0\n1007,0.0\n1008,0.0\n1009,1.0\n1010,0.0\n1011,1.0\n1012,1.0\n1013,0.0\n1014,1.0\n1015,0.0\n1016,0.0\n1017,1.0\n1018,0.0\n1019,0.0\n1020,0.0\n1021,0.0\n1022,1.0\n1023,0.0\n1024,0.0\n1025,0.0\n1026,0.0\n1027,0.0\n1028,0.0\n1029,0.0\n1030,0.0\n1031,0.0\n1032,0.0\n1033,1.0\n1034,0.0\n1035,0.0\n1036,1.0\n1037,0.0\n1038,0.0\n1039,0.0\n1040,1.0\n1041,0.0\n1042,1.0\n1043,0.0\n1044,0.0\n1045,1.0\n1046,0.0\n1047,0.0\n1048,1.0\n1049,0.0\n1050,1.0\n1051,1.0\n1052,0.0\n1053,1.0\n1054,1.0\n1055,0.0\n1056,0.0\n1057,1.0\n1058,0.0\n1059,0.0\n1060,1.0\n1061,0.0\n1062,0.0\n1063,0.0\n1064,0.0\n1065,0.0\n1066,0.0\n1067,1.0\n1068,1.0\n1069,0.0\n1070,1.0\n1071,1.0\n1072,0.0\n1073,0.0\n1074,1.0\n1075,0.0\n1076,1.0\n1077,0.0\n1078,1.0\n1079,0.0\n1080,0.0\n1081,0.0\n1082,0.0\n1083,0.0\n1084,1.0\n1085,0.0\n1086,1.0\n1087,0.0\n1088,1.0\n1089,0.0\n1090,0.0\n1091,0.0\n1092,0.0\n1093,1.0\n1094,0.0\n1095,1.0\n1096,0.0\n1097,0.0\n1098,0.0\n1099,0.0\n1100,1.0\n1101,0.0\n1102,0.0\n1103,0.0\n1104,0.0\n1105,1.0\n1106,0.0\n1107,0.0\n1108,0.0\n1109,0.0\n1110,1.0\n1111,0.0\n1112,1.0\n1113,0.0\n1114,1.0\n1115,1.0\n1116,0.0\n1117,1.0\n1118,0.0\n1119,0.0\n1120,0.0\n1121,0.0\n1122,0.0\n1123,1.0\n1124,0.0\n1125,0.0\n1126,1.0\n1127,0.0\n1128,0.0\n1129,1.0\n1130,1.0\n1131,1.0\n1132,0.0\n1133,1.0\n1134,0.0\n1135,0.0\n1136,0.0\n1137,0.0\n1138,1.0\n1139,0.0\n1140,1.0\n1141,0.0\n1142,1.0\n1143,0.0\n1144,0.0\n1145,0.0\n1146,0.0\n1147,0.0\n1148,0.0\n1149,0.0\n1150,1.0\n1151,0.0\n1152,0.0\n1153,0.0\n1154,1.0\n1155,1.0\n1156,0.0\n1157,0.0\n1158,0.0\n1159,0.0\n1160,0.0\n1161,0.0\n1162,0.0\n1163,0.0\n1164,1.0\n1165,0.0\n1166,0.0\n1167,1.0\n1168,0.0\n1169,0.0\n1170,0.0\n1171,0.0\n1172,0.0\n1173,1.0\n1174,0.0\n1175,0.0\n1176,1.0\n1177,0.0\n1178,0.0\n1179,0.0\n1180,0.0\n1181,0.0\n1182,0.0\n1183,0.0\n1184,0.0\n1185,0.0\n1186,0.0\n1187,0.0\n1188,1.0\n1189,0.0\n1190,0.0\n1191,0.0\n1192,0.0\n1193,0.0\n1194,0.0\n1195,0.0\n1196,0.0\n1197,1.0\n1198,1.0\n1199,1.0\n1200,0.0\n1201,0.0\n1202,0.0\n1203,1.0\n1204,0.0\n1205,0.0\n1206,1.0\n1207,1.0\n1208,0.0\n1209,0.0\n1210,0.0\n1211,0.0\n1212,0.0\n1213,0.0\n1214,0.0\n1215,1.0\n1216,1.0\n1217,0.0\n1218,1.0\n1219,0.0\n1220,0.0\n1221,0.0\n1222,1.0\n1223,1.0\n1224,0.0\n1225,1.0\n1226,0.0\n1227,0.0\n1228,1.0\n1229,0.0\n1230,0.0\n1231,0.0\n1232,0.0\n1233,0.0\n1234,0.0\n1235,1.0\n1236,0.0\n1237,0.0\n1238,0.0\n1239,1.0\n1240,0.0\n1241,1.0\n1242,1.0\n1243,0.0\n1244,0.0\n1245,0.0\n1246,1.0\n1247,0.0\n1248,1.0\n1249,0.0\n1250,0.0\n1251,1.0\n1252,0.0\n1253,1.0\n1254,1.0\n1255,1.0\n1256,1.0\n1257,0.0\n1258,0.0\n1259,0.0\n1260,1.0\n1261,1.0\n1262,0.0\n1263,1.0\n1264,0.0\n1265,0.0\n1266,1.0\n1267,1.0\n1268,0.0\n1269,0.0\n1270,0.0\n1271,0.0\n1272,0.0\n1273,0.0\n1274,1.0\n1275,1.0\n1276,0.0\n1277,1.0\n1278,0.0\n1279,0.0\n1280,0.0\n1281,0.0\n1282,0.0\n1283,1.0\n1284,0.0\n1285,0.0\n1286,0.0\n1287,1.0\n1288,0.0\n1289,1.0\n1290,0.0\n1291,0.0\n1292,1.0\n1293,0.0\n1294,1.0\n1295,0.0\n1296,0.0\n1297,0.0\n1298,0.0\n1299,0.0\n1300,0.0\n1301,1.0\n1302,0.0\n1303,1.0\n1304,0.0\n1305,0.0\n1306,1.0\n1307,0.0\n1308,0.0\n1309,0.0\n"
  },
  {
    "path": "data/titanic/test.csv",
    "content": "PassengerId,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked\r\n892,3,\"Kelly, Mr. James\",male,34.5,0,0,330911,7.8292,,Q\r\n893,3,\"Wilkes, Mrs. James (Ellen Needs)\",female,47,1,0,363272,7,,S\r\n894,2,\"Myles, Mr. Thomas Francis\",male,62,0,0,240276,9.6875,,Q\r\n895,3,\"Wirz, Mr. Albert\",male,27,0,0,315154,8.6625,,S\r\n896,3,\"Hirvonen, Mrs. Alexander (Helga E Lindqvist)\",female,22,1,1,3101298,12.2875,,S\r\n897,3,\"Svensson, Mr. Johan Cervin\",male,14,0,0,7538,9.225,,S\r\n898,3,\"Connolly, Miss. Kate\",female,30,0,0,330972,7.6292,,Q\r\n899,2,\"Caldwell, Mr. Albert Francis\",male,26,1,1,248738,29,,S\r\n900,3,\"Abrahim, Mrs. Joseph (Sophie Halaut Easu)\",female,18,0,0,2657,7.2292,,C\r\n901,3,\"Davies, Mr. John Samuel\",male,21,2,0,A/4 48871,24.15,,S\r\n902,3,\"Ilieff, Mr. Ylio\",male,,0,0,349220,7.8958,,S\r\n903,1,\"Jones, Mr. Charles Cresson\",male,46,0,0,694,26,,S\r\n904,1,\"Snyder, Mrs. John Pillsbury (Nelle Stevenson)\",female,23,1,0,21228,82.2667,B45,S\r\n905,2,\"Howard, Mr. Benjamin\",male,63,1,0,24065,26,,S\r\n906,1,\"Chaffee, Mrs. Herbert Fuller (Carrie Constance Toogood)\",female,47,1,0,W.E.P. 5734,61.175,E31,S\r\n907,2,\"del Carlo, Mrs. Sebastiano (Argenia Genovesi)\",female,24,1,0,SC/PARIS 2167,27.7208,,C\r\n908,2,\"Keane, Mr. Daniel\",male,35,0,0,233734,12.35,,Q\r\n909,3,\"Assaf, Mr. Gerios\",male,21,0,0,2692,7.225,,C\r\n910,3,\"Ilmakangas, Miss. Ida Livija\",female,27,1,0,STON/O2. 3101270,7.925,,S\r\n911,3,\"Assaf Khalil, Mrs. Mariana (Miriam\"\")\"\"\",female,45,0,0,2696,7.225,,C\r\n912,1,\"Rothschild, Mr. Martin\",male,55,1,0,PC 17603,59.4,,C\r\n913,3,\"Olsen, Master. Artur Karl\",male,9,0,1,C 17368,3.1708,,S\r\n914,1,\"Flegenheim, Mrs. Alfred (Antoinette)\",female,,0,0,PC 17598,31.6833,,S\r\n915,1,\"Williams, Mr. Richard Norris II\",male,21,0,1,PC 17597,61.3792,,C\r\n916,1,\"Ryerson, Mrs. Arthur Larned (Emily Maria Borie)\",female,48,1,3,PC 17608,262.375,B57 B59 B63 B66,C\r\n917,3,\"Robins, Mr. Alexander A\",male,50,1,0,A/5. 3337,14.5,,S\r\n918,1,\"Ostby, Miss. Helene Ragnhild\",female,22,0,1,113509,61.9792,B36,C\r\n919,3,\"Daher, Mr. Shedid\",male,22.5,0,0,2698,7.225,,C\r\n920,1,\"Brady, Mr. John Bertram\",male,41,0,0,113054,30.5,A21,S\r\n921,3,\"Samaan, Mr. Elias\",male,,2,0,2662,21.6792,,C\r\n922,2,\"Louch, Mr. Charles Alexander\",male,50,1,0,SC/AH 3085,26,,S\r\n923,2,\"Jefferys, Mr. Clifford Thomas\",male,24,2,0,C.A. 31029,31.5,,S\r\n924,3,\"Dean, Mrs. Bertram (Eva Georgetta Light)\",female,33,1,2,C.A. 2315,20.575,,S\r\n925,3,\"Johnston, Mrs. Andrew G (Elizabeth Lily\"\" Watson)\"\"\",female,,1,2,W./C. 6607,23.45,,S\r\n926,1,\"Mock, Mr. Philipp Edmund\",male,30,1,0,13236,57.75,C78,C\r\n927,3,\"Katavelas, Mr. Vassilios (Catavelas Vassilios\"\")\"\"\",male,18.5,0,0,2682,7.2292,,C\r\n928,3,\"Roth, Miss. Sarah A\",female,,0,0,342712,8.05,,S\r\n929,3,\"Cacic, Miss. Manda\",female,21,0,0,315087,8.6625,,S\r\n930,3,\"Sap, Mr. Julius\",male,25,0,0,345768,9.5,,S\r\n931,3,\"Hee, Mr. Ling\",male,,0,0,1601,56.4958,,S\r\n932,3,\"Karun, Mr. Franz\",male,39,0,1,349256,13.4167,,C\r\n933,1,\"Franklin, Mr. Thomas Parham\",male,,0,0,113778,26.55,D34,S\r\n934,3,\"Goldsmith, Mr. Nathan\",male,41,0,0,SOTON/O.Q. 3101263,7.85,,S\r\n935,2,\"Corbett, Mrs. Walter H (Irene Colvin)\",female,30,0,0,237249,13,,S\r\n936,1,\"Kimball, Mrs. Edwin Nelson Jr (Gertrude Parsons)\",female,45,1,0,11753,52.5542,D19,S\r\n937,3,\"Peltomaki, Mr. Nikolai Johannes\",male,25,0,0,STON/O 2. 3101291,7.925,,S\r\n938,1,\"Chevre, Mr. Paul Romaine\",male,45,0,0,PC 17594,29.7,A9,C\r\n939,3,\"Shaughnessy, Mr. Patrick\",male,,0,0,370374,7.75,,Q\r\n940,1,\"Bucknell, Mrs. William Robert (Emma Eliza Ward)\",female,60,0,0,11813,76.2917,D15,C\r\n941,3,\"Coutts, Mrs. William (Winnie Minnie\"\" Treanor)\"\"\",female,36,0,2,C.A. 37671,15.9,,S\r\n942,1,\"Smith, Mr. Lucien Philip\",male,24,1,0,13695,60,C31,S\r\n943,2,\"Pulbaum, Mr. Franz\",male,27,0,0,SC/PARIS 2168,15.0333,,C\r\n944,2,\"Hocking, Miss. Ellen Nellie\"\"\"\"\",female,20,2,1,29105,23,,S\r\n945,1,\"Fortune, Miss. Ethel Flora\",female,28,3,2,19950,263,C23 C25 C27,S\r\n946,2,\"Mangiavacchi, Mr. Serafino Emilio\",male,,0,0,SC/A.3 2861,15.5792,,C\r\n947,3,\"Rice, Master. Albert\",male,10,4,1,382652,29.125,,Q\r\n948,3,\"Cor, Mr. Bartol\",male,35,0,0,349230,7.8958,,S\r\n949,3,\"Abelseth, Mr. Olaus Jorgensen\",male,25,0,0,348122,7.65,F G63,S\r\n950,3,\"Davison, Mr. Thomas Henry\",male,,1,0,386525,16.1,,S\r\n951,1,\"Chaudanson, Miss. Victorine\",female,36,0,0,PC 17608,262.375,B61,C\r\n952,3,\"Dika, Mr. Mirko\",male,17,0,0,349232,7.8958,,S\r\n953,2,\"McCrae, Mr. Arthur Gordon\",male,32,0,0,237216,13.5,,S\r\n954,3,\"Bjorklund, Mr. Ernst Herbert\",male,18,0,0,347090,7.75,,S\r\n955,3,\"Bradley, Miss. Bridget Delia\",female,22,0,0,334914,7.725,,Q\r\n956,1,\"Ryerson, Master. John Borie\",male,13,2,2,PC 17608,262.375,B57 B59 B63 B66,C\r\n957,2,\"Corey, Mrs. Percy C (Mary Phyllis Elizabeth Miller)\",female,,0,0,F.C.C. 13534,21,,S\r\n958,3,\"Burns, Miss. Mary Delia\",female,18,0,0,330963,7.8792,,Q\r\n959,1,\"Moore, Mr. Clarence Bloomfield\",male,47,0,0,113796,42.4,,S\r\n960,1,\"Tucker, Mr. Gilbert Milligan Jr\",male,31,0,0,2543,28.5375,C53,C\r\n961,1,\"Fortune, Mrs. Mark (Mary McDougald)\",female,60,1,4,19950,263,C23 C25 C27,S\r\n962,3,\"Mulvihill, Miss. Bertha E\",female,24,0,0,382653,7.75,,Q\r\n963,3,\"Minkoff, Mr. Lazar\",male,21,0,0,349211,7.8958,,S\r\n964,3,\"Nieminen, Miss. Manta Josefina\",female,29,0,0,3101297,7.925,,S\r\n965,1,\"Ovies y Rodriguez, Mr. Servando\",male,28.5,0,0,PC 17562,27.7208,D43,C\r\n966,1,\"Geiger, Miss. Amalie\",female,35,0,0,113503,211.5,C130,C\r\n967,1,\"Keeping, Mr. Edwin\",male,32.5,0,0,113503,211.5,C132,C\r\n968,3,\"Miles, Mr. Frank\",male,,0,0,359306,8.05,,S\r\n969,1,\"Cornell, Mrs. Robert Clifford (Malvina Helen Lamson)\",female,55,2,0,11770,25.7,C101,S\r\n970,2,\"Aldworth, Mr. Charles Augustus\",male,30,0,0,248744,13,,S\r\n971,3,\"Doyle, Miss. Elizabeth\",female,24,0,0,368702,7.75,,Q\r\n972,3,\"Boulos, Master. Akar\",male,6,1,1,2678,15.2458,,C\r\n973,1,\"Straus, Mr. Isidor\",male,67,1,0,PC 17483,221.7792,C55 C57,S\r\n974,1,\"Case, Mr. Howard Brown\",male,49,0,0,19924,26,,S\r\n975,3,\"Demetri, Mr. Marinko\",male,,0,0,349238,7.8958,,S\r\n976,2,\"Lamb, Mr. John Joseph\",male,,0,0,240261,10.7083,,Q\r\n977,3,\"Khalil, Mr. Betros\",male,,1,0,2660,14.4542,,C\r\n978,3,\"Barry, Miss. Julia\",female,27,0,0,330844,7.8792,,Q\r\n979,3,\"Badman, Miss. Emily Louisa\",female,18,0,0,A/4 31416,8.05,,S\r\n980,3,\"O'Donoghue, Ms. Bridget\",female,,0,0,364856,7.75,,Q\r\n981,2,\"Wells, Master. Ralph Lester\",male,2,1,1,29103,23,,S\r\n982,3,\"Dyker, Mrs. Adolf Fredrik (Anna Elisabeth Judith Andersson)\",female,22,1,0,347072,13.9,,S\r\n983,3,\"Pedersen, Mr. Olaf\",male,,0,0,345498,7.775,,S\r\n984,1,\"Davidson, Mrs. Thornton (Orian Hays)\",female,27,1,2,F.C. 12750,52,B71,S\r\n985,3,\"Guest, Mr. Robert\",male,,0,0,376563,8.05,,S\r\n986,1,\"Birnbaum, Mr. Jakob\",male,25,0,0,13905,26,,C\r\n987,3,\"Tenglin, Mr. Gunnar Isidor\",male,25,0,0,350033,7.7958,,S\r\n988,1,\"Cavendish, Mrs. Tyrell William (Julia Florence Siegel)\",female,76,1,0,19877,78.85,C46,S\r\n989,3,\"Makinen, Mr. Kalle Edvard\",male,29,0,0,STON/O 2. 3101268,7.925,,S\r\n990,3,\"Braf, Miss. 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Catherine Helen \"\"Carrie\"\"\",female,,1,2,W./C. 6607,23.45,,S\r\n890,1,1,\"Behr, Mr. Karl Howell\",male,26,0,0,111369,30,C148,C\r\n891,0,3,\"Dooley, Mr. Patrick\",male,32,0,0,370376,7.75,,Q\r\n"
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  {
    "path": "deep-learning/deep-dream/dream.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"RMhGdYHuOZM8\"\n   },\n   \"source\": [\n    \"# Deep Dreams (with Caffe)\\n\",\n    \"\\n\",\n    \"Credits: Forked from [DeepDream](https://github.com/google/deepdream) by Google\\n\",\n    \"\\n\",\n    \"This notebook demonstrates how to use the [Caffe](http://caffe.berkeleyvision.org/) neural network framework to produce \\\"dream\\\" visuals shown in the [Google Research blog post](http://googleresearch.blogspot.ch/2015/06/inceptionism-going-deeper-into-neural.html).\\n\",\n    \"\\n\",\n    \"It'll be interesting to see what imagery people are able to generate using the described technique. If you post images to Google+, Facebook, or Twitter, be sure to tag them with **#deepdream** so other researchers can check them out too.\\n\",\n    \"\\n\",\n    \"##Dependencies\\n\",\n    \"This notebook is designed to have as few dependencies as possible:\\n\",\n    \"* Standard Python scientific stack: [NumPy](http://www.numpy.org/), [SciPy](http://www.scipy.org/), [PIL](http://www.pythonware.com/products/pil/), [IPython](http://ipython.org/). Those libraries can also be installed as a part of one of the scientific packages for Python, such as [Anaconda](http://continuum.io/downloads) or [Canopy](https://store.enthought.com/).\\n\",\n    \"* [Caffe](http://caffe.berkeleyvision.org/) deep learning framework ([installation instructions](http://caffe.berkeleyvision.org/installation.html)).\\n\",\n    \"* Google [protobuf](https://developers.google.com/protocol-buffers/) library that is used for Caffe model manipulation.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"cellView\": \"both\",\n    \"colab_type\": \"code\",\n    \"collapsed\": false,\n    \"id\": \"Pqz5k4syOZNA\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# imports and basic notebook setup\\n\",\n    \"from cStringIO import StringIO\\n\",\n    \"import numpy as np\\n\",\n    \"import scipy.ndimage as nd\\n\",\n    \"import PIL.Image\\n\",\n    \"from IPython.display import clear_output, Image, display\\n\",\n    \"from google.protobuf import text_format\\n\",\n    \"\\n\",\n    \"import caffe\\n\",\n    \"\\n\",\n    \"# If your GPU supports CUDA and Caffe was built with CUDA support,\\n\",\n    \"# uncomment the following to run Caffe operations on the GPU.\\n\",\n    \"# caffe.set_mode_gpu()\\n\",\n    \"# caffe.set_device(0) # select GPU device if multiple devices exist\\n\",\n    \"\\n\",\n    \"def showarray(a, fmt='jpeg'):\\n\",\n    \"    a = np.uint8(np.clip(a, 0, 255))\\n\",\n    \"    f = StringIO()\\n\",\n    \"    PIL.Image.fromarray(a).save(f, fmt)\\n\",\n    \"    display(Image(data=f.getvalue()))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"AeF9mG-COZNE\"\n   },\n   \"source\": [\n    \"## Loading DNN model\\n\",\n    \"In this notebook we are going to use a [GoogLeNet](https://github.com/BVLC/caffe/tree/master/models/bvlc_googlenet) model trained on [ImageNet](http://www.image-net.org/) dataset.\\n\",\n    \"Feel free to experiment with other models from Caffe [Model Zoo](https://github.com/BVLC/caffe/wiki/Model-Zoo). One particularly interesting [model](http://places.csail.mit.edu/downloadCNN.html) was trained in [MIT Places](http://places.csail.mit.edu/) dataset. It produced many visuals from the [original blog post](http://googleresearch.blogspot.ch/2015/06/inceptionism-going-deeper-into-neural.html).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"cellView\": \"both\",\n    \"colab_type\": \"code\",\n    \"collapsed\": false,\n    \"id\": \"i9hkSm1IOZNR\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model_path = '../caffe/models/bvlc_googlenet/' # substitute your path here\\n\",\n    \"net_fn   = model_path + 'deploy.prototxt'\\n\",\n    \"param_fn = model_path + 'bvlc_googlenet.caffemodel'\\n\",\n    \"\\n\",\n    \"# Patching model to be able to compute gradients.\\n\",\n    \"# Note that you can also manually add \\\"force_backward: true\\\" line to \\\"deploy.prototxt\\\".\\n\",\n    \"model = caffe.io.caffe_pb2.NetParameter()\\n\",\n    \"text_format.Merge(open(net_fn).read(), model)\\n\",\n    \"model.force_backward = True\\n\",\n    \"open('tmp.prototxt', 'w').write(str(model))\\n\",\n    \"\\n\",\n    \"net = caffe.Classifier('tmp.prototxt', param_fn,\\n\",\n    \"                       mean = np.float32([104.0, 116.0, 122.0]), # ImageNet mean, training set dependent\\n\",\n    \"                       channel_swap = (2,1,0)) # the reference model has channels in BGR order instead of RGB\\n\",\n    \"\\n\",\n    \"# a couple of utility functions for converting to and from Caffe's input image layout\\n\",\n    \"def preprocess(net, img):\\n\",\n    \"    return np.float32(np.rollaxis(img, 2)[::-1]) - net.transformer.mean['data']\\n\",\n    \"def deprocess(net, img):\\n\",\n    \"    return np.dstack((img + net.transformer.mean['data'])[::-1])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"UeV_fJ4QOZNb\"\n   },\n   \"source\": [\n    \"##  Producing dreams\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"9udrp3efOZNd\"\n   },\n   \"source\": [\n    \"Making the \\\"dream\\\" images is very simple. Essentially it is just a gradient ascent process that tries to maximize the L2 norm of activations of a particular DNN layer. Here are a few simple tricks that we found useful for getting good images:\\n\",\n    \"* offset image by a random jitter\\n\",\n    \"* normalize the magnitude of gradient ascent steps\\n\",\n    \"* apply ascent across multiple scales (octaves)\\n\",\n    \"\\n\",\n    \"First we implement a basic gradient ascent step function, applying the first two tricks:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {\n    \"cellView\": \"both\",\n    \"colab_type\": \"code\",\n    \"collapsed\": false,\n    \"id\": \"pN43nMsHOZNg\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def objective_L2(dst):\\n\",\n    \"    dst.diff[:] = dst.data \\n\",\n    \"\\n\",\n    \"def make_step(net, step_size=1.5, end='inception_4c/output', \\n\",\n    \"              jitter=32, clip=True, objective=objective_L2):\\n\",\n    \"    '''Basic gradient ascent step.'''\\n\",\n    \"\\n\",\n    \"    src = net.blobs['data'] # input image is stored in Net's 'data' blob\\n\",\n    \"    dst = net.blobs[end]\\n\",\n    \"\\n\",\n    \"    ox, oy = np.random.randint(-jitter, jitter+1, 2)\\n\",\n    \"    src.data[0] = np.roll(np.roll(src.data[0], ox, -1), oy, -2) # apply jitter shift\\n\",\n    \"            \\n\",\n    \"    net.forward(end=end)\\n\",\n    \"    objective(dst)  # specify the optimization objective\\n\",\n    \"    net.backward(start=end)\\n\",\n    \"    g = src.diff[0]\\n\",\n    \"    # apply normalized ascent step to the input image\\n\",\n    \"    src.data[:] += step_size/np.abs(g).mean() * g\\n\",\n    \"\\n\",\n    \"    src.data[0] = np.roll(np.roll(src.data[0], -ox, -1), -oy, -2) # unshift image\\n\",\n    \"            \\n\",\n    \"    if clip:\\n\",\n    \"        bias = net.transformer.mean['data']\\n\",\n    \"        src.data[:] = np.clip(src.data, -bias, 255-bias)    \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"nphEdlBgOZNk\"\n   },\n   \"source\": [\n    \"Next we implement an ascent through different scales. We call these scales \\\"octaves\\\".\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"cellView\": \"both\",\n    \"colab_type\": \"code\",\n    \"collapsed\": false,\n    \"id\": \"ZpFIn8l0OZNq\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def deepdream(net, base_img, iter_n=10, octave_n=4, octave_scale=1.4, \\n\",\n    \"              end='inception_4c/output', clip=True, **step_params):\\n\",\n    \"    # prepare base images for all octaves\\n\",\n    \"    octaves = [preprocess(net, base_img)]\\n\",\n    \"    for i in xrange(octave_n-1):\\n\",\n    \"        octaves.append(nd.zoom(octaves[-1], (1, 1.0/octave_scale,1.0/octave_scale), order=1))\\n\",\n    \"    \\n\",\n    \"    src = net.blobs['data']\\n\",\n    \"    detail = np.zeros_like(octaves[-1]) # allocate image for network-produced details\\n\",\n    \"    for octave, octave_base in enumerate(octaves[::-1]):\\n\",\n    \"        h, w = octave_base.shape[-2:]\\n\",\n    \"        if octave > 0:\\n\",\n    \"            # upscale details from the previous octave\\n\",\n    \"            h1, w1 = detail.shape[-2:]\\n\",\n    \"            detail = nd.zoom(detail, (1, 1.0*h/h1,1.0*w/w1), order=1)\\n\",\n    \"\\n\",\n    \"        src.reshape(1,3,h,w) # resize the network's input image size\\n\",\n    \"        src.data[0] = octave_base+detail\\n\",\n    \"        for i in xrange(iter_n):\\n\",\n    \"            make_step(net, end=end, clip=clip, **step_params)\\n\",\n    \"            \\n\",\n    \"            # visualization\\n\",\n    \"            vis = deprocess(net, src.data[0])\\n\",\n    \"            if not clip: # adjust image contrast if clipping is disabled\\n\",\n    \"                vis = vis*(255.0/np.percentile(vis, 99.98))\\n\",\n    \"            showarray(vis)\\n\",\n    \"            print octave, i, end, vis.shape\\n\",\n    \"            clear_output(wait=True)\\n\",\n    \"            \\n\",\n    \"        # extract details produced on the current octave\\n\",\n    \"        detail = src.data[0]-octave_base\\n\",\n    \"    # returning the resulting image\\n\",\n    \"    return deprocess(net, src.data[0])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"QrcdU-lmOZNx\"\n   },\n   \"source\": [\n    \"Now we are ready to let the neural network reveal its dreams! Let's take a [cloud image](https://commons.wikimedia.org/wiki/File:Appearance_of_sky_for_weather_forecast,_Dhaka,_Bangladesh.JPG) as a starting point:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"cellView\": \"both\",\n    \"colab_type\": \"code\",\n    \"collapsed\": false,\n    \"executionInfo\": null,\n    \"id\": \"40p5AqqwOZN5\",\n    \"outputId\": \"f62cde37-79e8-420a-e448-3b9b48ee1730\",\n    \"pinned\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/jpeg\": 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6nBY4IPOK5iO+dXDFjnvjirSXwYN8/4VfsmjP2qZea7YsUDYUHvUD3q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\\nlx2NGKU8+tAhv1FKRwOlBPrR0x2oA//Z\\n\",\n      \"text/plain\": [\n       \"<IPython.core.display.Image at 0x104adf450>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"img = np.float32(PIL.Image.open('sky1024px.jpg'))\\n\",\n    \"showarray(img)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"Z9_215_GOZOL\"\n   },\n   \"source\": [\n    \"Running the next code cell starts the detail generation process. You may see how new patterns start to form, iteration by iteration, octave by octave.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"cellView\": \"both\",\n    \"colab_type\": \"code\",\n    \"collapsed\": false,\n    \"executionInfo\": null,\n    \"id\": \"HlnVnDTlOZOL\",\n    \"outputId\": \"425dfc83-b474-4a69-8386-30d86361bbf6\",\n    \"pinned\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/jpeg\": 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cEPVSeXaMcdM1fnjlEsgdshWwAOc1iXEmZH28AGuuNmjlej1Ekm3f8A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\\nBUABOc9hRjGM0pVgMECk2sD1NCZCb6i9qRskdqUGnMuF3DjNNxsUpXP/2Q==\\n\",\n      \"text/plain\": [\n       \"<IPython.core.display.Image at 0x114a8d850>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"3 9 inception_4c/output (575, 1024, 3)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"_=deepdream(net, img)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"Rp9kOCQTOZOQ\"\n   },\n   \"source\": [\n    \"The complexity of the details generated depends on which layer's activations we try to maximize. Higher layers produce complex features, while lower ones enhance edges and textures, giving the image an impressionist feeling:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"cellView\": \"both\",\n    \"colab_type\": \"code\",\n    \"collapsed\": false,\n    \"executionInfo\": null,\n    \"id\": \"eHOX0t93OZOR\",\n    \"outputId\": \"0de0381c-4681-4619-912f-9b6a2cdec0c6\",\n    \"pinned\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/jpeg\": 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xNDx/eQ5FTIQ4BDZHfPUUAxsjlEVF++/A9vekWNYogme5JPvTIn82R5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\\n\",\n      \"text/plain\": [\n       \"<IPython.core.display.Image at 0x132236290>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"3 9 inception_3b/5x5_reduce (575, 1024, 3)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"_=deepdream(net, img, end='inception_3b/5x5_reduce')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"rkzHz9E8OZOb\"\n   },\n   \"source\": [\n    \"We encourage readers to experiment with layer selection to see how it affects the results. Execute the next code cell to see the list of different layers. You can modify the `make_step` function to make it follow some different objective, say to select a subset of activations to maximize, or to maximize multiple layers at once. There is a huge design space to explore!\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"cellView\": \"both\",\n    \"colab_type\": \"code\",\n    \"collapsed\": false,\n    \"id\": \"OIepVN6POZOc\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"net.blobs.keys()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"vs2uUpMCOZOe\"\n   },\n   \"source\": [\n    \"What if we feed the `deepdream` function its own output, after applying a little zoom to it? It turns out that this leads to an endless stream of impressions of the things that the network saw during training. Some patterns fire more often than others, suggestive of basins of attraction.\\n\",\n    \"\\n\",\n    \"We will start the process from the same sky image as above, but after some iteration the original image becomes irrelevant; even random noise can be used as the starting point.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"cellView\": \"both\",\n    \"colab_type\": \"code\",\n    \"collapsed\": false,\n    \"id\": \"IB48CnUfOZOe\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!mkdir frames\\n\",\n    \"frame = img\\n\",\n    \"frame_i = 0\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"cellView\": \"both\",\n    \"colab_type\": \"code\",\n    \"collapsed\": false,\n    \"id\": \"fj0E-fKDOZOi\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"h, w = frame.shape[:2]\\n\",\n    \"s = 0.05 # scale coefficient\\n\",\n    \"for i in xrange(100):\\n\",\n    \"    frame = deepdream(net, frame)\\n\",\n    \"    PIL.Image.fromarray(np.uint8(frame)).save(\\\"frames/%04d.jpg\\\"%frame_i)\\n\",\n    \"    frame = nd.affine_transform(frame, [1-s,1-s,1], [h*s/2,w*s/2,0], order=1)\\n\",\n    \"    frame_i += 1\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"XzZGGME_OZOk\"\n   },\n   \"source\": [\n    \"Be careful running the code above, it can bring you into very strange realms!\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"cellView\": \"both\",\n    \"colab_type\": \"code\",\n    \"collapsed\": false,\n    \"executionInfo\": null,\n    \"id\": \"ZCZcz2p1OZOt\",\n    \"outputId\": \"d3773436-2b5d-4e79-be9d-0f12ab839fff\",\n    \"pinned\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/jpeg\": 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hMA0sf8ZYcEehzViHWLh0ktbezaNlXKvt+X6Vm3d5rn2YXEnlraq2X8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\\n\",\n      \"text/plain\": [\n       \"<IPython.core.display.Image at 0x132235e90>\"\n      ]\n     },\n     \"execution_count\": 189,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"Image(filename='frames/0029.jpg')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Controlling dreams\\n\",\n    \"\\n\",\n    \"The image detail generation method described above tends to produce some patterns more often the others. One easy way to improve the generated image diversity is to tweak the optimization objective. Here we show just one of many ways to do that. Let's use one more input image. We'd call it a \\\"*guide*\\\".\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/jpeg\": 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nGDTpVeaTTKjNyepYFrDHbwvHOzTOxMnHyoM8D3NWEt0mYA/KB/dHJq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\\nF3c7f8ax08N39vfLdfaVlbeC+flOPauiKUn7z2Ksn1P/2Q==\\n\",\n      \"text/plain\": [\n       \"<IPython.core.display.Image at 0x114adbfd0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"guide = np.float32(PIL.Image.open('flowers.jpg'))\\n\",\n    \"showarray(guide)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Note that the neural network we use was trained on images downscaled to 224x224 size. So high resolution images might have to be downscaled, so that the network could pick up their features. The image we use here is already small enough.\\n\",\n    \"\\n\",\n    \"Now we pick some target layer and extract guide image features.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 51,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"end = 'inception_3b/output'\\n\",\n    \"h, w = guide.shape[:2]\\n\",\n    \"src, dst = net.blobs['data'], net.blobs[end]\\n\",\n    \"src.reshape(1,3,h,w)\\n\",\n    \"src.data[0] = preprocess(net, guide)\\n\",\n    \"net.forward(end=end)\\n\",\n    \"guide_features = dst.data[0].copy()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Instead of maximizing the L2-norm of current image activations, we try to maximize the dot-products between activations of current image, and their best matching correspondences from the guide image.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 53,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/jpeg\": 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FFJ3E23oNLsH2MBzypHemtCHuElwMqKTeW3Bh88ZB+oqx04FUwT7FW7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\\nwmfmap1lVQoHYVry2fma2T1P/9k=\\n\",\n      \"text/plain\": [\n       \"<IPython.core.display.Image at 0x10fde1950>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"3 9 inception_3b/output (575, 1024, 3)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"def objective_guide(dst):\\n\",\n    \"    x = dst.data[0].copy()\\n\",\n    \"    y = guide_features\\n\",\n    \"    ch = x.shape[0]\\n\",\n    \"    x = x.reshape(ch,-1)\\n\",\n    \"    y = y.reshape(ch,-1)\\n\",\n    \"    A = x.T.dot(y) # compute the matrix of dot-products with guide features\\n\",\n    \"    dst.diff[0].reshape(ch,-1)[:] = y[:,A.argmax(1)] # select ones that match best\\n\",\n    \"\\n\",\n    \"_=deepdream(net, img, end=end, objective=objective_guide)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This way we can affect the style of generated images without using a different training set.\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"colabVersion\": \"0.3.1\",\n  \"default_view\": {},\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.4.3\"\n  },\n  \"views\": {}\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "deep-learning/keras-tutorial/0. Preamble.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Credits: Forked from [deep-learning-keras-tensorflow](https://github.com/leriomaggio/deep-learning-keras-tensorflow) by Valerio Maggio\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"-\"\n    }\n   },\n   \"source\": [\n    \"<div>\\n\",\n    \"    <h1 style=\\\"text-align: center;\\\">Deep Learning with Keras</h1>\\n\",\n    \"    <img style=\\\"text-align: left\\\" src=\\\"imgs/keras-logo-small.jpg\\\" width=\\\"10%\\\" />\\n\",\n    \"<div>\\n\",\n    \"\\n\",\n    \"<div>\\n\",\n    \"    <h2 style=\\\"text-align: center;\\\">Tutorial @ EuroScipy 2016</h2>\\n\",\n    \"    <img style=\\\"text-align: left\\\" src=\\\"imgs/euroscipy_2016_logo.png\\\" width=\\\"40%\\\" />\\n\",\n    \"</div>    \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"##### Yam Peleg,  Valerio Maggio\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"-\"\n    }\n   },\n   \"source\": [\n    \"# Goal of this Tutorial\\n\",\n    \"\\n\",\n    \"- **Introduce** main features of Keras\\n\",\n    \"- **Learn** how simple and Pythonic is doing Deep Learning with Keras\\n\",\n    \"- **Understand** how easy is to do basic and *advanced* DL models in Keras;\\n\",\n    \"    - **Examples and Hand-on Excerises** along the way.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Source\\n\",\n    \"\\n\",\n    \"https://github.com/leriomaggio/deep-learning-keras-euroscipy2016/\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"# (Tentative) Schedule \\n\",\n    \"\\n\",\n    \"## Attention: Spoilers Warning!\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"- **Setup** (`10 mins`)\\n\",\n    \"\\n\",\n    \"- **Part I**: **Introduction** (`~65 mins`)\\n\",\n    \"\\n\",\n    \"    - Intro to ANN (`~20 mins`)\\n\",\n    \"        - naive pure-Python implementation\\n\",\n    \"        - fast forward, sgd, backprop\\n\",\n    \"        \\n\",\n    \"    - Intro to Theano (`15 mins`)\\n\",\n    \"        - Model + SGD with Theano\\n\",\n    \"        \\n\",\n    \"    - Introduction to Keras (`30 mins`)\\n\",\n    \"        - Overview and main features\\n\",\n    \"            - Theano backend\\n\",\n    \"            - Tensorflow backend\\n\",\n    \"        - Multi-Layer Perceptron and Fully Connected\\n\",\n    \"            - Examples with `keras.models.Sequential` and `Dense`\\n\",\n    \"            - HandsOn: MLP with keras\\n\",\n    \"            \\n\",\n    \"- **Coffe Break** (`30 mins`)\\n\",\n    \"\\n\",\n    \"- **Part II**: **Supervised Learning and Convolutional Neural Nets** (`~45 mins`)\\n\",\n    \"    \\n\",\n    \"    - Intro: Focus on Image Classification (`5 mins`)\\n\",\n    \"\\n\",\n    \"    - Intro to CNN (`25 mins`)\\n\",\n    \"        - meaning of convolutional filters\\n\",\n    \"            - examples from ImageNet    \\n\",\n    \"        - Meaning of dimensions of Conv filters (through an exmple of ConvNet) \\n\",\n    \"        - Visualising ConvNets\\n\",\n    \"        - HandsOn: ConvNet with keras \\n\",\n    \"\\n\",\n    \"    - Advanced CNN (`10 mins`)\\n\",\n    \"        - Dropout\\n\",\n    \"        - MaxPooling\\n\",\n    \"        - Batch Normalisation\\n\",\n    \"        \\n\",\n    \"    - Famous Models in Keras (likely moved somewhere else) (`10 mins`)\\n\",\n    \"        (ref: https://github.com/fchollet/deep-learning-models)\\n\",\n    \"            - VGG16\\n\",\n    \"            - VGG19\\n\",\n    \"            - ResNet50\\n\",\n    \"            - Inception v3\\n\",\n    \"        - HandsOn: Fine tuning a network on new dataset \\n\",\n    \"        \\n\",\n    \"- **Part III**: **Unsupervised Learning** (`10 mins`)\\n\",\n    \"\\n\",\n    \"    - AutoEncoders (`5 mins`)\\n\",\n    \"    - word2vec & doc2vec (gensim) & `keras.datasets` (`5 mins`)\\n\",\n    \"        - `Embedding`\\n\",\n    \"        - word2vec and CNN\\n\",\n    \"    - Exercises\\n\",\n    \"\\n\",\n    \"- **Part IV**: **Advanced Materials** (`20 mins`)\\n\",\n    \"    - RNN and LSTM (`10 mins`)\\n\",\n    \"        -  RNN, LSTM, GRU  \\n\",\n    \"    - Example of RNN and LSTM with Text (`~10 mins`) -- *Tentative*\\n\",\n    \"    - HandsOn: IMDB\\n\",\n    \"\\n\",\n    \"- **Wrap up and Conclusions** (`5 mins`)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Requirements\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This tutorial requires the following packages:\\n\",\n    \"\\n\",\n    \"- Python version 3.4+ \\n\",\n    \"    - likely Python 2.7 would be fine, but *who knows*? :P\\n\",\n    \"- `numpy` version 1.10 or later: http://www.numpy.org/\\n\",\n    \"- `scipy` version 0.16 or later: http://www.scipy.org/\\n\",\n    \"- `matplotlib` version 1.4 or later: http://matplotlib.org/\\n\",\n    \"- `pandas` version 0.16 or later: http://pandas.pydata.org\\n\",\n    \"- `scikit-learn` version 0.15 or later: http://scikit-learn.org\\n\",\n    \"- `keras` version 1.0 or later: http://keras.io\\n\",\n    \"- `theano` version 0.8 or later: http://deeplearning.net/software/theano/\\n\",\n    \"- `ipython`/`jupyter` version 4.0 or later, with notebook support\\n\",\n    \"\\n\",\n    \"(Optional but recommended):\\n\",\n    \"\\n\",\n    \"- `pyyaml`\\n\",\n    \"- `hdf5` and `h5py` (required if you use model saving/loading functions in keras)\\n\",\n    \"- **NVIDIA cuDNN** if you have NVIDIA GPUs on your machines.\\n\",\n    \"    [https://developer.nvidia.com/rdp/cudnn-download]()\\n\",\n    \"\\n\",\n    \"The easiest way to get (most) these is to use an all-in-one installer such as [Anaconda](http://www.continuum.io/downloads) from Continuum. These are available for multiple architectures.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Python Version\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"I'm currently running this tutorial with **Python 3** on **Anaconda**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Python 3.5.2\\r\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!python --version\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# How to set up your environment\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The quickest and simplest way to setup the environment is to use [conda](https://store.continuum.io) environment manager. \\n\",\n    \"\\n\",\n    \"We provide in the materials a `deep-learning.yml` that is complete and **ready to use** to set up your virtual environment with conda.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"name: deep-learning\\r\\n\",\n      \"channels:\\r\\n\",\n      \"- conda-forge\\r\\n\",\n      \"- defaults\\r\\n\",\n      \"dependencies:\\r\\n\",\n      \"- accelerate=2.3.0=np111py35_3\\r\\n\",\n      \"- accelerate_cudalib=2.0=0\\r\\n\",\n      \"- bokeh=0.12.1=py35_0\\r\\n\",\n      \"- cffi=1.6.0=py35_0\\r\\n\",\n      \"- backports.shutil_get_terminal_size=1.0.0=py35_0\\r\\n\",\n      \"- blas=1.1=openblas\\r\\n\",\n      \"- ca-certificates=2016.8.2=3\\r\\n\",\n      \"- cairo=1.12.18=8\\r\\n\",\n      \"- certifi=2016.8.2=py35_0\\r\\n\",\n      \"- cycler=0.10.0=py35_0\\r\\n\",\n      \"- cython=0.24.1=py35_0\\r\\n\",\n      \"- decorator=4.0.10=py35_0\\r\\n\",\n      \"- entrypoints=0.2.2=py35_0\\r\\n\",\n      \"- fontconfig=2.11.1=3\\r\\n\",\n      \"- freetype=2.6.3=1\\r\\n\",\n      \"- gettext=0.19.7=1\\r\\n\",\n      \"- glib=2.48.0=4\\r\\n\",\n      \"- h5py=2.6.0=np111py35_6\\r\\n\",\n      \"- harfbuzz=1.0.6=0\\r\\n\",\n      \"- hdf5=1.8.17=2\\r\\n\",\n      \"- icu=56.1=4\\r\\n\",\n      \"- ipykernel=4.3.1=py35_1\\r\\n\",\n      \"- ipython=5.1.0=py35_0\\r\\n\",\n      \"- ipywidgets=5.2.2=py35_0\\r\\n\",\n      \"- jinja2=2.8=py35_1\\r\\n\",\n      \"- jpeg=9b=0\\r\\n\",\n      \"- jsonschema=2.5.1=py35_0\\r\\n\",\n      \"- jupyter_client=4.3.0=py35_0\\r\\n\",\n      \"- jupyter_console=5.0.0=py35_0\\r\\n\",\n      \"- jupyter_core=4.1.1=py35_1\\r\\n\",\n      \"- libffi=3.2.1=2\\r\\n\",\n      \"- libiconv=1.14=3\\r\\n\",\n      \"- libpng=1.6.24=0\\r\\n\",\n      \"- libsodium=1.0.10=0\\r\\n\",\n      \"- libtiff=4.0.6=6\\r\\n\",\n      \"- libxml2=2.9.4=0\\r\\n\",\n      \"- markupsafe=0.23=py35_0\\r\\n\",\n      \"- matplotlib=1.5.2=np111py35_6\\r\\n\",\n      \"- mistune=0.7.3=py35_0\\r\\n\",\n      \"- nbconvert=4.2.0=py35_0\\r\\n\",\n      \"- nbformat=4.0.1=py35_0\\r\\n\",\n      \"- ncurses=5.9=8\\r\\n\",\n      \"- nose=1.3.7=py35_1\\r\\n\",\n      \"- notebook=4.2.2=py35_0\\r\\n\",\n      \"- numpy=1.11.1=py35_blas_openblas_201\\r\\n\",\n      \"- openblas=0.2.18=4\\r\\n\",\n      \"- openssl=1.0.2h=2\\r\\n\",\n      \"- pandas=0.18.1=np111py35_1\\r\\n\",\n      \"- pango=1.40.1=0\\r\\n\",\n      \"- path.py=8.2.1=py35_0\\r\\n\",\n      \"- pcre=8.38=1\\r\\n\",\n      \"- pexpect=4.2.0=py35_1\\r\\n\",\n      \"- pickleshare=0.7.3=py35_0\\r\\n\",\n      \"- pip=8.1.2=py35_0\\r\\n\",\n      \"- pixman=0.32.6=0\\r\\n\",\n      \"- prompt_toolkit=1.0.6=py35_0\\r\\n\",\n      \"- protobuf=3.0.0b3=py35_1\\r\\n\",\n      \"- ptyprocess=0.5.1=py35_0\\r\\n\",\n      \"- pygments=2.1.3=py35_1\\r\\n\",\n      \"- pyparsing=2.1.7=py35_0\\r\\n\",\n      \"- python=3.5.2=2\\r\\n\",\n      \"- python-dateutil=2.5.3=py35_0\\r\\n\",\n      \"- pytz=2016.6.1=py35_0\\r\\n\",\n      \"- pyyaml=3.11=py35_0\\r\\n\",\n      \"- pyzmq=15.4.0=py35_0\\r\\n\",\n      \"- qt=4.8.7=0\\r\\n\",\n      \"- qtconsole=4.2.1=py35_0\\r\\n\",\n      \"- readline=6.2=0\\r\\n\",\n      \"- requests=2.11.0=py35_0\\r\\n\",\n      \"- scikit-learn=0.17.1=np111py35_blas_openblas_201\\r\\n\",\n      \"- scipy=0.18.0=np111py35_blas_openblas_201\\r\\n\",\n      \"- setuptools=25.1.6=py35_0\\r\\n\",\n      \"- simplegeneric=0.8.1=py35_0\\r\\n\",\n      \"- sip=4.18=py35_0\\r\\n\",\n      \"- six=1.10.0=py35_0\\r\\n\",\n      \"- sqlite=3.13.0=1\\r\\n\",\n      \"- terminado=0.6=py35_0\\r\\n\",\n      \"- tk=8.5.19=0\\r\\n\",\n      \"- tornado=4.4.1=py35_1\\r\\n\",\n      \"- traitlets=4.2.2=py35_0\\r\\n\",\n      \"- wcwidth=0.1.7=py35_0\\r\\n\",\n      \"- wheel=0.29.0=py35_0\\r\\n\",\n      \"- widgetsnbextension=1.2.6=py35_3\\r\\n\",\n      \"- xz=5.2.2=0\\r\\n\",\n      \"- yaml=0.1.6=0\\r\\n\",\n      \"- zeromq=4.1.5=0\\r\\n\",\n      \"- zlib=1.2.8=3\\r\\n\",\n      \"- cudatoolkit=7.5=0\\r\\n\",\n      \"- ipython_genutils=0.1.0=py35_0\\r\\n\",\n      \"- jupyter=1.0.0=py35_3\\r\\n\",\n      \"- libgfortran=3.0.0=1\\r\\n\",\n      \"- llvmlite=0.11.0=py35_0\\r\\n\",\n      \"- mkl=11.3.3=0\\r\\n\",\n      \"- mkl-service=1.1.2=py35_2\\r\\n\",\n      \"- numba=0.26.0=np111py35_0\\r\\n\",\n      \"- pycparser=2.14=py35_1\\r\\n\",\n      \"- pyqt=4.11.4=py35_4\\r\\n\",\n      \"- snakeviz=0.4.1=py35_0\\r\\n\",\n      \"- pip:\\r\\n\",\n      \"  - backports.shutil-get-terminal-size==1.0.0\\r\\n\",\n      \"  - certifi==2016.8.2\\r\\n\",\n      \"  - cycler==0.10.0\\r\\n\",\n      \"  - cython==0.24.1\\r\\n\",\n      \"  - decorator==4.0.10\\r\\n\",\n      \"  - h5py==2.6.0\\r\\n\",\n      \"  - ipykernel==4.3.1\\r\\n\",\n      \"  - ipython==5.1.0\\r\\n\",\n      \"  - ipython-genutils==0.1.0\\r\\n\",\n      \"  - ipywidgets==5.2.2\\r\\n\",\n      \"  - jinja2==2.8\\r\\n\",\n      \"  - jsonschema==2.5.1\\r\\n\",\n      \"  - jupyter-client==4.3.0\\r\\n\",\n      \"  - jupyter-console==5.0.0\\r\\n\",\n      \"  - jupyter-core==4.1.1\\r\\n\",\n      \"  - keras==1.0.7\\r\\n\",\n      \"  - mako==1.0.4\\r\\n\",\n      \"  - markupsafe==0.23\\r\\n\",\n      \"  - matplotlib==1.5.2\\r\\n\",\n      \"  - mistune==0.7.3\\r\\n\",\n      \"  - nbconvert==4.2.0\\r\\n\",\n      \"  - nbformat==4.0.1\\r\\n\",\n      \"  - nose==1.3.7\\r\\n\",\n      \"  - notebook==4.2.2\\r\\n\",\n      \"  - numpy==1.11.1\\r\\n\",\n      \"  - pandas==0.18.1\\r\\n\",\n      \"  - path.py==8.2.1\\r\\n\",\n      \"  - pexpect==4.2.0\\r\\n\",\n      \"  - pickleshare==0.7.3\\r\\n\",\n      \"  - pip==8.1.2\\r\\n\",\n      \"  - prompt-toolkit==1.0.6\\r\\n\",\n      \"  - protobuf==3.0.0b2\\r\\n\",\n      \"  - ptyprocess==0.5.1\\r\\n\",\n      \"  - pygments==2.1.3\\r\\n\",\n      \"  - pygpu==0.2.1\\r\\n\",\n      \"  - pyparsing==2.1.7\\r\\n\",\n      \"  - python-dateutil==2.5.3\\r\\n\",\n      \"  - pytz==2016.6.1\\r\\n\",\n      \"  - pyyaml==3.11\\r\\n\",\n      \"  - pyzmq==15.4.0\\r\\n\",\n      \"  - qtconsole==4.2.1\\r\\n\",\n      \"  - requests==2.11.0\\r\\n\",\n      \"  - scikit-learn==0.17.1\\r\\n\",\n      \"  - scipy==0.18.0\\r\\n\",\n      \"  - setuptools==25.1.4\\r\\n\",\n      \"  - simplegeneric==0.8.1\\r\\n\",\n      \"  - six==1.10.0\\r\\n\",\n      \"  - tensorflow==0.10.0rc0\\r\\n\",\n      \"  - terminado==0.6\\r\\n\",\n      \"  - theano==0.8.2\\r\\n\",\n      \"  - tornado==4.4.1\\r\\n\",\n      \"  - traitlets==4.2.2\\r\\n\",\n      \"  - wcwidth==0.1.7\\r\\n\",\n      \"  - wheel==0.29.0\\r\\n\",\n      \"  - widgetsnbextension==1.2.6\\r\\n\",\n      \"prefix: /home/valerio/anaconda3/envs/deep-learning\\r\\n\",\n      \"\\r\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!cat deep-learning.yml\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Recreate the Conda Environment\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### A. Create the Environment\\n\",\n    \"\\n\",\n    \"```\\n\",\n    \"conda env create -f deep-learning.yml  # this file is for Linux channels.\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"If you're using a **Mac OSX**, we also provided in the repo the conda file \\n\",\n    \"that is compatible with `osx-channels`:\\n\",\n    \"\\n\",\n    \"```\\n\",\n    \"conda env create -f deep-learning-osx.yml  # this file is for OSX channels.\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"#### B. Activate the new `deep-learning` Environment\\n\",\n    \"\\n\",\n    \"```\\n\",\n    \"source activate deep-learning\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Optionals\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 1. Enabling Conda-Forge\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"It is strongly suggested to enable [**conda forge**](https://conda-forge.github.io/) in your Anaconda installation.\\n\",\n    \"\\n\",\n    \"**Conda-Forge** is a github organisation containing repositories of conda recipies.\\n\",\n    \"\\n\",\n    \"To add `conda-forge` as an additional anaconda channel it is just required to type:\\n\",\n    \"\\n\",\n    \"```shell\\n\",\n    \"conda config --add channels conda-forge\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 2. Configure Theano\\n\",\n    \"\\n\",\n    \"1) Create the `theanorc` file:\\n\",\n    \"\\n\",\n    \"```shell\\n\",\n    \"touch $HOME/.theanorc\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"2) Copy the following content into the file:\\n\",\n    \"\\n\",\n    \"```\\n\",\n    \"[global]\\n\",\n    \"floatX = float32\\n\",\n    \"device = gpu  # switch to cpu if no GPU is available on your machine\\n\",\n    \"\\n\",\n    \"[nvcc]\\n\",\n    \"fastmath = True\\n\",\n    \"\\n\",\n    \"[lib]\\n\",\n    \"cnmem=.90\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**More on [theano documentation](http://theano.readthedocs.io/en/latest/library/config.html)**\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 3. Installing Tensorflow as backend \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"```shell\\n\",\n    \"# Ubuntu/Linux 64-bit, GPU enabled, Python 3.5\\n\",\n    \"# Requires CUDA toolkit 7.5 and CuDNN v4. For other versions, see \\\"Install from sources\\\" below.\\n\",\n    \"export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.10.0rc0-cp35-cp35m-linux_x86_64.whl\\n\",\n    \"\\n\",\n    \"pip install --ignore-installed --upgrade $TF_BINARY_URL\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**More on [tensorflow documentation](https://www.tensorflow.org/versions/r0.10/get_started/os_setup.html)**\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Test if everything is up&running\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 1. Check import\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import scipy as sp\\n\",\n    \"import pandas as pd\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import sklearn\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Using Theano backend.\\n\",\n      \"Using gpu device 0: GeForce GTX 760 (CNMeM is enabled with initial size: 90.0% of memory, cuDNN 4007)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import keras\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 2. Check installeded Versions\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"numpy: 1.11.1\\n\",\n      \"scipy: 0.18.0\\n\",\n      \"matplotlib: 1.5.2\\n\",\n      \"iPython: 5.1.0\\n\",\n      \"scikit-learn: 0.17.1\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import numpy\\n\",\n    \"print('numpy:', numpy.__version__)\\n\",\n    \"\\n\",\n    \"import scipy\\n\",\n    \"print('scipy:', scipy.__version__)\\n\",\n    \"\\n\",\n    \"import matplotlib\\n\",\n    \"print('matplotlib:', matplotlib.__version__)\\n\",\n    \"\\n\",\n    \"import IPython\\n\",\n    \"print('iPython:', IPython.__version__)\\n\",\n    \"\\n\",\n    \"import sklearn\\n\",\n    \"print('scikit-learn:', sklearn.__version__)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"keras:  1.0.7\\n\",\n      \"Theano:  0.8.2\\n\",\n      \"Tensorflow:  0.10.0rc0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import keras\\n\",\n    \"print('keras: ', keras.__version__)\\n\",\n    \"\\n\",\n    \"import theano\\n\",\n    \"print('Theano: ', theano.__version__)\\n\",\n    \"\\n\",\n    \"# optional\\n\",\n    \"import tensorflow as tf\\n\",\n    \"print('Tensorflow: ', tf.__version__)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<br>\\n\",\n    \"<h1 style=\\\"text-align: center;\\\">If everything worked till down here, you're ready to start!</h1>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Consulting Material\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"You have two options to go through the material presented in this tutorial:\\n\",\n    \"\\n\",\n    \"* Read (and execute) the material as **iPython/Jupyter** notebooks\\n\",\n    \"* (just) read the material as (HTML) slides\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"In the first case, all you need to do is just execute `ipython notebook` (or `jupyter notebook`) depending on the version of `iPython` you have installed on your machine\\n\",\n    \"\\n\",\n    \"(`jupyter` command works in case you have `iPython 4.0.x` installed)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"In the second case, you may simply convert the provided notebooks in `HTML` slides and see them into your browser\\n\",\n    \"thanks to `nbconvert`.\\n\",\n    \"\\n\",\n    \"Thus, move to the folder where notebooks are stored and execute the following command:\\n\",\n    \"\\n\",\n    \"    jupyter nbconvert --to slides ./*.ipynb --post serve\\n\",\n    \"    \\n\",\n    \"   \\n\",\n    \"(Please substitute `jupyter` with `ipython` in the previous command if you have `iPython 3.x` installed on your machine)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## In case...\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"..you wanna do **both** (interactive and executable slides), I highly suggest to install the terrific `RISE` ipython notebook extension: [https://github.com/damianavila/RISE](https://github.com/damianavila/RISE)\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.4.3\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "deep-learning/keras-tutorial/1.1 Introduction - Deep Learning and ANN.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Credits: Forked from [deep-learning-keras-tensorflow](https://github.com/leriomaggio/deep-learning-keras-tensorflow) by Valerio Maggio\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"nbpresent\": {\n     \"id\": \"24ca50a0-b9ad-425d-a28f-6bd3ca3ac378\"\n    },\n    \"slideshow\": {\n     \"slide_type\": \"slide\"\n    }\n   },\n   \"source\": [\n    \"# Introduction to Deep Learning\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"nbpresent\": {\n     \"id\": \"3896ecb2-47fa-4dbc-a2ff-9c76e9dfe93e\"\n    },\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"Deep learning allows computational models that are composed of multiple processing **layers** to learn representations of data with multiple levels of abstraction.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"nbpresent\": {\n     \"id\": \"8c3060aa-fee9-438c-bc60-4685c0eb4750\"\n    },\n    \"slideshow\": {\n     \"slide_type\": \"fragment\"\n    }\n   },\n   \"source\": [\n    \"These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"nbpresent\": {\n     \"id\": \"6287766f-972f-4b4d-bee7-5418f0af74de\"\n    },\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"**Deep learning** is one of the leading tools in data analysis these days and one of the most common frameworks for deep learning is **Keras**. \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"nbpresent\": {\n     \"id\": \"2f1c6299-954a-461a-b5c1-a86a1d12ad15\"\n    },\n    \"slideshow\": {\n     \"slide_type\": \"fragment\"\n    }\n   },\n   \"source\": [\n    \"The Tutorial will provide an introduction to deep learning using `keras` with practical code examples.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"nbpresent\": {\n     \"id\": \"5e13607b-3ec5-4a95-a2d8-f898f20748da\"\n    },\n    \"slideshow\": {\n     \"slide_type\": \"slide\"\n    }\n   },\n   \"source\": [\n    \"# Artificial Neural Networks (ANN)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"nbpresent\": {\n     \"id\": \"4fa2e86a-be32-4e78-96d9-f511a07e3908\"\n    },\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"In machine learning and cognitive science, an artificial neural network (ANN) is a network inspired by biological neural networks which are used to estimate or approximate functions that can depend on a large number of inputs that are generally unknown\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"nbpresent\": {\n     \"id\": \"df0121bc-10f1-4ace-840e-6fc89c6fdc7f\"\n    },\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"An ANN is built from nodes (neurons) stacked in layers between the feature vector and the target vector. \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"nbpresent\": {\n     \"id\": \"c25d7194-10bd-4196-9d4c-592bf6e188f9\"\n    },\n    \"slideshow\": {\n     \"slide_type\": \"fragment\"\n    }\n   },\n   \"source\": [\n    \"A node in a neural network is built from Weights and Activation function\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"nbpresent\": {\n     \"id\": \"15260f90-13d1-4fcc-afc6-379c507cb950\"\n    },\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"An early version of ANN built from one node was called the **Perceptron**\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"nbpresent\": {\n     \"id\": \"92d4603e-7e39-4156-818c-785df6189fe8\"\n    },\n    \"slideshow\": {\n     \"slide_type\": \"-\"\n    }\n   },\n   \"source\": [\n    \"<img src =\\\"imgs/Perceptron.png\\\" width=\\\"85%\\\">\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true,\n    \"nbpresent\": {\n     \"id\": \"356d5ec7-3392-4daa-9671-4cc7111c5c91\"\n    },\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"The Perceptron is an algorithm for supervised learning of binary classifiers. functions that can decide whether an input (represented by a vector of numbers) belongs to one class or another.\\n\",\n    \"\\n\",\n    \"Much like logistic regression, the weights in a neural net are being multiplied by the input vertor summed up and feeded into the activation function's input.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"A Perceptron Network can be designed to have *multiple layers*, leading to the **Multi-Layer Perceptron** (aka `MLP`)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<img src =\\\"imgs/MLP.png\\\" width=\\\"85%\\\">\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true,\n    \"nbpresent\": {\n     \"id\": \"5678486b-caf4-440b-be62-2f1286982c71\"\n    },\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"The weights of each neuron are learned by **gradient descent**, where each neuron's error is derived with respect to it's weight.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"Optimization is done for each layer with respect to the previous layer in a technique known as **BackPropagation**.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<img src =\\\"imgs/backprop.png\\\" width=\\\"80%\\\">\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"slide\"\n    }\n   },\n   \"source\": [\n    \"# Building Neural Nets from scratch \\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"### Idea:\\n\",\n    \"\\n\",\n    \"We will build the neural networks from first principles. \\n\",\n    \"We will create a very simple model and understand how it works. We will also be implementing backpropagation algorithm. \\n\",\n    \"\\n\",\n    \"**Please note that this code is not optimized and not to be used in production**. \\n\",\n    \"\\n\",\n    \"This is for instructive purpose - for us to understand how ANN works. \\n\",\n    \"\\n\",\n    \"Libraries like `theano` have highly optimized code.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"skip\"\n    }\n   },\n   \"source\": [\n    \"(*The following code is inspired from [these](https://github.com/dennybritz/nn-from-scratch) terrific notebooks*)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true,\n    \"slideshow\": {\n     \"slide_type\": \"skip\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Import the required packages\\n\",\n    \"import numpy as np\\n\",\n    \"import pandas as pd\\n\",\n    \"import matplotlib\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import scipy\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": true,\n    \"slideshow\": {\n     \"slide_type\": \"skip\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Display plots inline \\n\",\n    \"%matplotlib inline\\n\",\n    \"# Define plot's default figure size\\n\",\n    \"matplotlib.rcParams['figure.figsize'] = (10.0, 8.0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": true,\n    \"slideshow\": {\n     \"slide_type\": \"skip\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import random\\n\",\n    \"random.seed(123)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": true,\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"#read the datasets\\n\",\n    \"train = pd.read_csv(\\\"data/intro_to_ann.csv\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": true,\n    \"slideshow\": {\n     \"slide_type\": \"fragment\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"X, y = np.array(train.ix[:,0:2]), np.array(train.ix[:,2])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"slideshow\": {\n     \"slide_type\": \"fragment\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(500, 2)\"\n      ]\n     },\n     \"execution_count\": 6,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"X.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"slideshow\": {\n     \"slide_type\": \"fragment\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(500,)\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"y.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.collections.PathCollection at 0x110b4b0f0>\"\n      ]\n     },\n     \"execution_count\": 8,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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+Xc9evX4ebmhgGDBsOpTRvcvnUL69etxebNm9GtWzeFjy8Wi3H9+nXk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\\nDX9u7LtHVykAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10eada2b0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"#Let's plot the dataset and see how it is\\n\",\n    \"plt.scatter(X[:,0], X[:,1], s=40, c=y, cmap=plt.cm.BuGn)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true,\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"## Start Building our ANN building blocks\\n\",\n    \"\\n\",\n    \"Note: This process will eventually result in our own Neural Networks class\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"### A look at the details\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<img src=\\\"imgs/mlp_details.png\\\" width=\\\"85%\\\" />\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"### Function to generate a random number, given two numbers\\n\",\n    \"\\n\",\n    \"**Where will it be used?**: When we initialize the neural networks, the weights have to be randomly assigned.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": true,\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# calculate a random number where:  a <= rand < b\\n\",\n    \"def rand(a, b):\\n\",\n    \"    return (b-a)*random.random() + a\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": true,\n    \"slideshow\": {\n     \"slide_type\": \"fragment\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Make a matrix \\n\",\n    \"def makeMatrix(I, J, fill=0.0):\\n\",\n    \"    return np.zeros([I,J])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"### Define our activation function. Let's use sigmoid function\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# our sigmoid function\\n\",\n    \"def sigmoid(x):\\n\",\n    \"    #return math.tanh(x)\\n\",\n    \"    return 1/(1+np.exp(-x))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"### Derivative of our activation function. \\n\",\n    \"\\n\",\n    \"Note: We need this when we run the backpropagation algorithm\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# derivative of our sigmoid function, in terms of the output (i.e. y)\\n\",\n    \"def dsigmoid(y):\\n\",\n    \"    return y - y**2\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"### Our neural networks class\\n\",\n    \"\\n\",\n    \"When we first create a neural networks architecture, we need to know the number of inputs, number of hidden layers and number of outputs.\\n\",\n    \"\\n\",\n    \"The weights have to be randomly initialized.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"```python\\n\",\n    \"class ANN:\\n\",\n    \"    def __init__(self, ni, nh, no):\\n\",\n    \"        # number of input, hidden, and output nodes\\n\",\n    \"        self.ni = ni + 1 # +1 for bias node\\n\",\n    \"        self.nh = nh\\n\",\n    \"        self.no = no\\n\",\n    \"\\n\",\n    \"        # activations for nodes\\n\",\n    \"        self.ai = [1.0]*self.ni\\n\",\n    \"        self.ah = [1.0]*self.nh\\n\",\n    \"        self.ao = [1.0]*self.no\\n\",\n    \"        \\n\",\n    \"        # create weights\\n\",\n    \"        self.wi = makeMatrix(self.ni, self.nh)\\n\",\n    \"        self.wo = makeMatrix(self.nh, self.no)\\n\",\n    \"        \\n\",\n    \"        # set them to random vaules\\n\",\n    \"        self.wi = rand(-0.2, 0.2, size=self.wi.shape)\\n\",\n    \"        self.wo = rand(-2.0, 2.0, size=self.wo.shape)\\n\",\n    \"\\n\",\n    \"        # last change in weights for momentum   \\n\",\n    \"        self.ci = makeMatrix(self.ni, self.nh)\\n\",\n    \"        self.co = makeMatrix(self.nh, self.no)\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"### Activation Function\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"```python\\n\",\n    \"def activate(self, inputs):\\n\",\n    \"        \\n\",\n    \"    if len(inputs) != self.ni-1:\\n\",\n    \"        print(inputs)\\n\",\n    \"        raise ValueError('wrong number of inputs')\\n\",\n    \"\\n\",\n    \"    # input activations\\n\",\n    \"    for i in range(self.ni-1):\\n\",\n    \"        self.ai[i] = inputs[i]\\n\",\n    \"\\n\",\n    \"    # hidden activations\\n\",\n    \"    for j in range(self.nh):\\n\",\n    \"        sum_h = 0.0\\n\",\n    \"        for i in range(self.ni):\\n\",\n    \"            sum_h += self.ai[i] * self.wi[i][j]\\n\",\n    \"        self.ah[j] = sigmoid(sum_h)\\n\",\n    \"\\n\",\n    \"    # output activations\\n\",\n    \"    for k in range(self.no):\\n\",\n    \"        sum_o = 0.0\\n\",\n    \"        for j in range(self.nh):\\n\",\n    \"            sum_o += self.ah[j] * self.wo[j][k]\\n\",\n    \"        self.ao[k] = sigmoid(sum_o)\\n\",\n    \"\\n\",\n    \"    return self.ao[:]\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"### BackPropagation\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"```python\\n\",\n    \"def backPropagate(self, targets, N, M):\\n\",\n    \"        \\n\",\n    \"    if len(targets) != self.no:\\n\",\n    \"        print(targets)\\n\",\n    \"        raise ValueError('wrong number of target values')\\n\",\n    \"\\n\",\n    \"    # calculate error terms for output\\n\",\n    \"    output_deltas = np.zeros(self.no)\\n\",\n    \"    for k in range(self.no):\\n\",\n    \"        error = targets[k]-self.ao[k]\\n\",\n    \"        output_deltas[k] = dsigmoid(self.ao[k]) * error\\n\",\n    \"\\n\",\n    \"    # calculate error terms for hidden\\n\",\n    \"    hidden_deltas = np.zeros(self.nh)\\n\",\n    \"    for j in range(self.nh):\\n\",\n    \"        error = 0.0\\n\",\n    \"        for k in range(self.no):\\n\",\n    \"            error += output_deltas[k]*self.wo[j][k]\\n\",\n    \"        hidden_deltas[j] = dsigmoid(self.ah[j]) * error\\n\",\n    \"\\n\",\n    \"    # update output weights\\n\",\n    \"    for j in range(self.nh):\\n\",\n    \"        for k in range(self.no):\\n\",\n    \"            change = output_deltas[k] * self.ah[j]\\n\",\n    \"            self.wo[j][k] += N*change + \\n\",\n    \"                             M*self.co[j][k]\\n\",\n    \"            self.co[j][k] = change\\n\",\n    \"\\n\",\n    \"    # update input weights\\n\",\n    \"    for i in range(self.ni):\\n\",\n    \"        for j in range(self.nh):\\n\",\n    \"            change = hidden_deltas[j]*self.ai[i]\\n\",\n    \"            self.wi[i][j] += N*change + \\n\",\n    \"                             M*self.ci[i][j]\\n\",\n    \"            self.ci[i][j] = change\\n\",\n    \"\\n\",\n    \"    # calculate error\\n\",\n    \"    error = 0.0\\n\",\n    \"    for k in range(len(targets)):\\n\",\n    \"        error += 0.5*(targets[k]-self.ao[k])**2\\n\",\n    \"    return error\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Putting all together\\n\",\n    \"\\n\",\n    \"class ANN:\\n\",\n    \"    def __init__(self, ni, nh, no):\\n\",\n    \"        # number of input, hidden, and output nodes\\n\",\n    \"        self.ni = ni + 1 # +1 for bias node\\n\",\n    \"        self.nh = nh\\n\",\n    \"        self.no = no\\n\",\n    \"\\n\",\n    \"        # activations for nodes\\n\",\n    \"        self.ai = [1.0]*self.ni\\n\",\n    \"        self.ah = [1.0]*self.nh\\n\",\n    \"        self.ao = [1.0]*self.no\\n\",\n    \"        \\n\",\n    \"        # create weights\\n\",\n    \"        self.wi = makeMatrix(self.ni, self.nh)\\n\",\n    \"        self.wo = makeMatrix(self.nh, self.no)\\n\",\n    \"        \\n\",\n    \"        # set them to random vaules\\n\",\n    \"        for i in range(self.ni):\\n\",\n    \"            for j in range(self.nh):\\n\",\n    \"                self.wi[i][j] = rand(-0.2, 0.2)\\n\",\n    \"        for j in range(self.nh):\\n\",\n    \"            for k in range(self.no):\\n\",\n    \"                self.wo[j][k] = rand(-2.0, 2.0)\\n\",\n    \"\\n\",\n    \"        # last change in weights for momentum   \\n\",\n    \"        self.ci = makeMatrix(self.ni, self.nh)\\n\",\n    \"        self.co = makeMatrix(self.nh, self.no)\\n\",\n    \"        \\n\",\n    \"\\n\",\n    \"    def backPropagate(self, targets, N, M):\\n\",\n    \"        \\n\",\n    \"        if len(targets) != self.no:\\n\",\n    \"            print(targets)\\n\",\n    \"            raise ValueError('wrong number of target values')\\n\",\n    \"\\n\",\n    \"        # calculate error terms for output\\n\",\n    \"        output_deltas = np.zeros(self.no)\\n\",\n    \"        for k in range(self.no):\\n\",\n    \"            error = targets[k]-self.ao[k]\\n\",\n    \"            output_deltas[k] = dsigmoid(self.ao[k]) * error\\n\",\n    \"\\n\",\n    \"        # calculate error terms for hidden\\n\",\n    \"        hidden_deltas = np.zeros(self.nh)\\n\",\n    \"        for j in range(self.nh):\\n\",\n    \"            error = 0.0\\n\",\n    \"            for k in range(self.no):\\n\",\n    \"                error += output_deltas[k]*self.wo[j][k]\\n\",\n    \"            hidden_deltas[j] = dsigmoid(self.ah[j]) * error\\n\",\n    \"\\n\",\n    \"        # update output weights\\n\",\n    \"        for j in range(self.nh):\\n\",\n    \"            for k in range(self.no):\\n\",\n    \"                change = output_deltas[k] * self.ah[j]\\n\",\n    \"                self.wo[j][k] += N*change + M*self.co[j][k]\\n\",\n    \"                self.co[j][k] = change\\n\",\n    \"\\n\",\n    \"        # update input weights\\n\",\n    \"        for i in range(self.ni):\\n\",\n    \"            for j in range(self.nh):\\n\",\n    \"                change = hidden_deltas[j]*self.ai[i]\\n\",\n    \"                self.wi[i][j] += N*change + M*self.ci[i][j]\\n\",\n    \"                self.ci[i][j] = change\\n\",\n    \"\\n\",\n    \"        # calculate error\\n\",\n    \"        error = 0.0\\n\",\n    \"        for k in range(len(targets)):\\n\",\n    \"            error += 0.5*(targets[k]-self.ao[k])**2\\n\",\n    \"        return error\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"    def test(self, patterns):\\n\",\n    \"        self.predict = np.empty([len(patterns), self.no])\\n\",\n    \"        for i, p in enumerate(patterns):\\n\",\n    \"            self.predict[i] = self.activate(p)\\n\",\n    \"            #self.predict[i] = self.activate(p[0])\\n\",\n    \"            \\n\",\n    \"    def activate(self, inputs):\\n\",\n    \"        \\n\",\n    \"        if len(inputs) != self.ni-1:\\n\",\n    \"            print(inputs)\\n\",\n    \"            raise ValueError('wrong number of inputs')\\n\",\n    \"\\n\",\n    \"        # input activations\\n\",\n    \"        for i in range(self.ni-1):\\n\",\n    \"            self.ai[i] = inputs[i]\\n\",\n    \"\\n\",\n    \"        # hidden activations\\n\",\n    \"        for j in range(self.nh):\\n\",\n    \"            sum_h = 0.0\\n\",\n    \"            for i in range(self.ni):\\n\",\n    \"                sum_h += self.ai[i] * self.wi[i][j]\\n\",\n    \"            self.ah[j] = sigmoid(sum_h)\\n\",\n    \"\\n\",\n    \"        # output activations\\n\",\n    \"        for k in range(self.no):\\n\",\n    \"            sum_o = 0.0\\n\",\n    \"            for j in range(self.nh):\\n\",\n    \"                sum_o += self.ah[j] * self.wo[j][k]\\n\",\n    \"            self.ao[k] = sigmoid(sum_o)\\n\",\n    \"\\n\",\n    \"        return self.ao[:]\\n\",\n    \"    \\n\",\n    \"\\n\",\n    \"    def train(self, patterns, iterations=1000, N=0.5, M=0.1):\\n\",\n    \"        # N: learning rate\\n\",\n    \"        # M: momentum factor\\n\",\n    \"        patterns = list(patterns)\\n\",\n    \"        for i in range(iterations):\\n\",\n    \"            error = 0.0\\n\",\n    \"            for p in patterns:\\n\",\n    \"                inputs = p[0]\\n\",\n    \"                targets = p[1]\\n\",\n    \"                self.activate(inputs)\\n\",\n    \"                error += self.backPropagate([targets], N, M)\\n\",\n    \"            if i % 5 == 0:\\n\",\n    \"                print('error in interation %d : %-.5f' % (i,error))\\n\",\n    \"            print('Final training error: %-.5f' % error)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"### Running the model on our dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"error in interation 0 : 53.62995\\n\",\n      \"Final training error: 53.62995\\n\",\n      \"Final training error: 47.35136\\n\",\n      \"1 loop, best of 1: 97.6 ms per loop\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# create a network with two inputs, one hidden, and one output nodes\\n\",\n    \"ann = ANN(2, 1, 1)\\n\",\n    \"\\n\",\n    \"%timeit -n 1 -r 1 ann.train(zip(X,y), iterations=2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"### Predicting on training dataset and measuring in-sample accuracy\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"1 loop, best of 1: 22.6 ms per loop\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%timeit -n 1 -r 1 ann.test(X)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"slideshow\": {\n     \"slide_type\": \"fragment\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>actual</th>\\n\",\n       \"      <th>prediction</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>0.491100</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>0.495469</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.097362</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.400006</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>0.489664</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   actual  prediction\\n\",\n       \"0     1.0    0.491100\\n\",\n       \"1     1.0    0.495469\\n\",\n       \"2     0.0    0.097362\\n\",\n       \"3     0.0    0.400006\\n\",\n       \"4     1.0    0.489664\"\n      ]\n     },\n     \"execution_count\": 16,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"prediction = pd.DataFrame(data=np.array([y, np.ravel(ann.predict)]).T, \\n\",\n    \"                          columns=[\\\"actual\\\", \\\"prediction\\\"])\\n\",\n    \"prediction.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"slideshow\": {\n     \"slide_type\": \"fragment\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.076553078113180129\"\n      ]\n     },\n     \"execution_count\": 17,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.min(prediction.prediction)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"### Let's visualize and observe the results\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {\n    \"collapsed\": true,\n    \"slideshow\": {\n     \"slide_type\": \"skip\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Helper function to plot a decision boundary.\\n\",\n    \"# This generates the contour plot to show the decision boundary visually\\n\",\n    \"def plot_decision_boundary(nn_model):\\n\",\n    \"    # Set min and max values and give it some padding\\n\",\n    \"    x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\\n\",\n    \"    y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\\n\",\n    \"    h = 0.01\\n\",\n    \"    # Generate a grid of points with distance h between them\\n\",\n    \"    xx, yy = np.meshgrid(np.arange(x_min, x_max, h), \\n\",\n    \"                         np.arange(y_min, y_max, h))\\n\",\n    \"    # Predict the function value for the whole gid\\n\",\n    \"    nn_model.test(np.c_[xx.ravel(), yy.ravel()])\\n\",\n    \"    Z = nn_model.predict\\n\",\n    \"    Z[Z>=0.5] = 1\\n\",\n    \"    Z[Z<0.5] = 0\\n\",\n    \"    Z = Z.reshape(xx.shape)\\n\",\n    \"    # Plot the contour and training examples\\n\",\n    \"    plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral)\\n\",\n    \"    plt.scatter(X[:, 0], X[:, 1], s=40,  c=y, cmap=plt.cm.BuGn)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.text.Text at 0x110bdb940>\"\n      ]\n     },\n     \"execution_count\": 19,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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nXXIS7ceE/7PqsSbMwiBS05c8pJp03TNM6cOkGxQtkTbFzxbfLmzkyd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\\npwAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10eb09208>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plot_decision_boundary(ann)\\n\",\n    \"plt.title(\\\"Our initial model\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"**Exercise**: \\n\",\n    \"\\n\",\n    \"Create Neural networks with 10 hidden nodes on the above code. \\n\",\n    \"\\n\",\n    \"What's the impact on accuracy?\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Put your code here \\n\",\n    \"#(or load the solution if you wanna cheat :-)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"error in interation 0 : 34.91394\\n\",\n      \"Final training error: 34.91394\\n\",\n      \"Final training error: 25.36183\\n\",\n      \"1 loop, best of 1: 288 ms per loop\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.text.Text at 0x11151f630>\"\n      ]\n     },\n     \"execution_count\": 21,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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M8i87N4LvkImTYKXx8fTkVEcK9XPUZ4Bld304QQolxdw9dXeR1KKbTW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     \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10eb09908>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load solutions/sol_111.py\\n\",\n    \"ann = ANN(2, 10, 1)\\n\",\n    \"%timeit -n 1 -r 1 ann.train(zip(X,y), iterations=2)\\n\",\n    \"plot_decision_boundary(ann)\\n\",\n    \"plt.title(\\\"Our next model with 10 hidden units\\\")\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"**Exercise:**\\n\",\n    \"\\n\",\n    \"Train the neural networks by increasing the epochs. \\n\",\n    \"\\n\",\n    \"What's the impact on accuracy?\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"#Put your code here\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"scrolled\": false,\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"error in interation 0 : 31.63185\\n\",\n      \"Final training error: 31.63185\\n\",\n      \"Final training error: 25.12319\\n\",\n      \"Final training error: 24.92547\\n\",\n      \"Final training error: 24.89692\\n\",\n      \"Final training error: 24.88124\\n\",\n      \"error in interation 5 : 24.86083\\n\",\n      \"Final training error: 24.86083\\n\",\n      \"Final training error: 24.83512\\n\",\n      \"Final training error: 24.80603\\n\",\n      \"Final training error: 24.77544\\n\",\n      \"Final training error: 24.74477\\n\",\n      \"error in interation 10 : 24.71498\\n\",\n      \"Final training error: 24.71498\\n\",\n      \"Final training error: 24.68669\\n\",\n      \"Final training error: 24.66021\\n\",\n      \"Final training error: 24.63569\\n\",\n      \"Final training error: 24.61313\\n\",\n      \"error in interation 15 : 24.59246\\n\",\n      \"Final training error: 24.59246\\n\",\n      \"Final training error: 24.57359\\n\",\n      \"Final training error: 24.55639\\n\",\n      \"Final training error: 24.54072\\n\",\n      \"Final training error: 24.52646\\n\",\n      \"error in interation 20 : 24.51350\\n\",\n      \"Final training error: 24.51350\\n\",\n      \"Final training error: 24.50171\\n\",\n      \"Final training error: 24.49100\\n\",\n      \"Final training error: 24.48127\\n\",\n      \"Final training error: 24.47242\\n\",\n      \"error in interation 25 : 24.46436\\n\",\n      \"Final training error: 24.46436\\n\",\n      \"Final training error: 24.45702\\n\",\n      \"Final training error: 24.45030\\n\",\n      \"Final training error: 24.44414\\n\",\n      \"Final training error: 24.43846\\n\",\n      \"error in interation 30 : 24.43319\\n\",\n      \"Final training error: 24.43319\\n\",\n      \"Final training error: 24.42828\\n\",\n      \"Final training error: 24.42366\\n\",\n      \"Final training error: 24.41929\\n\",\n      \"Final training error: 24.41510\\n\",\n      \"error in interation 35 : 24.41107\\n\",\n      \"Final training error: 24.41107\\n\",\n      \"Final training error: 24.40715\\n\",\n      \"Final training error: 24.40331\\n\",\n      \"Final training error: 24.39952\\n\",\n      \"Final training error: 24.39576\\n\",\n      \"error in interation 40 : 24.39200\\n\",\n      \"Final training error: 24.39200\\n\",\n      \"Final training error: 24.38821\\n\",\n      \"Final training error: 24.38438\\n\",\n      \"Final training error: 24.38048\\n\",\n      \"Final training error: 24.37649\\n\",\n      \"error in interation 45 : 24.37237\\n\",\n      \"Final training error: 24.37237\\n\",\n      \"Final training error: 24.36806\\n\",\n      \"Final training error: 24.36353\\n\",\n      \"Final training error: 24.35868\\n\",\n      \"Final training error: 24.35340\\n\",\n      \"error in interation 50 : 24.34754\\n\",\n      \"Final training error: 24.34754\\n\",\n      \"Final training error: 24.34086\\n\",\n      \"Final training error: 24.33302\\n\",\n      \"Final training error: 24.32348\\n\",\n      \"Final training error: 24.31138\\n\",\n      \"error in interation 55 : 24.29529\\n\",\n      \"Final training error: 24.29529\\n\",\n      \"Final training error: 24.27275\\n\",\n      \"Final training error: 24.23928\\n\",\n      \"Final training error: 24.18646\\n\",\n      \"Final training error: 24.09789\\n\",\n      \"error in interation 60 : 23.94185\\n\",\n      \"Final training error: 23.94185\\n\",\n      \"Final training error: 23.66093\\n\",\n      \"Final training error: 23.16905\\n\",\n      \"Final training error: 22.38000\\n\",\n      \"Final training error: 21.27360\\n\",\n      \"error in interation 65 : 19.93871\\n\",\n      \"Final training error: 19.93871\\n\",\n      \"Final training error: 18.52756\\n\",\n      \"Final training error: 17.16893\\n\",\n      \"Final training error: 15.93090\\n\",\n      \"Final training error: 14.83582\\n\",\n      \"error in interation 70 : 13.88300\\n\",\n      \"Final training error: 13.88300\\n\",\n      \"Final training error: 13.06081\\n\",\n      \"Final training error: 12.35255\\n\",\n      \"Final training error: 11.74050\\n\",\n      \"Final training error: 11.20877\\n\",\n      \"error in interation 75 : 10.74440\\n\",\n      \"Final training error: 10.74440\\n\",\n      \"Final training error: 10.33728\\n\",\n      \"Final training error: 9.97939\\n\",\n      \"Final training error: 9.66423\\n\",\n      \"Final training error: 9.38631\\n\",\n      \"error in interation 80 : 9.14093\\n\",\n      \"Final training error: 9.14093\\n\",\n      \"Final training error: 8.92402\\n\",\n      \"Final training error: 8.73205\\n\",\n      \"Final training error: 8.56193\\n\",\n      \"Final training error: 8.41096\\n\",\n      \"error in interation 85 : 8.27675\\n\",\n      \"Final training error: 8.27675\\n\",\n      \"Final training error: 8.15722\\n\",\n      \"Final training error: 8.05052\\n\",\n      \"Final training error: 7.95506\\n\",\n      \"Final training error: 7.86944\\n\",\n      \"error in interation 90 : 7.79246\\n\",\n      \"Final training error: 7.79246\\n\",\n      \"Final training error: 7.72306\\n\",\n      \"Final training error: 7.66035\\n\",\n      \"Final training error: 7.60354\\n\",\n      \"Final training error: 7.55195\\n\",\n      \"error in interation 95 : 7.50499\\n\",\n      \"Final training error: 7.50499\\n\",\n      \"Final training error: 7.46215\\n\",\n      \"Final training error: 7.42298\\n\",\n      \"Final training error: 7.38707\\n\",\n      \"Final training error: 7.35410\\n\",\n      \"1 loop, best of 1: 14.5 s per loop\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.text.Text at 0x1115951d0>\"\n      ]\n     },\n     \"execution_count\": 23,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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cIut1whhBBClE8GFbUda9RgGYBpWuu2QHfgYaVU66IbKKUigGZa6xbA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     \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x11156e160>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load solutions/sol_112.py\\n\",\n    \"ann = ANN(2, 10, 1)\\n\",\n    \"%timeit -n 1 -r 1 ann.train(zip(X,y), iterations=100)\\n\",\n    \"plot_decision_boundary(ann)\\n\",\n    \"plt.title(\\\"Our model with 10 hidden units and 100 iterations\\\")\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"# Addendum\\n\",\n    \"\\n\",\n    \"There is an additional notebook in the repo, i.e. [A simple implementation of ANN for MNIST](1.4 (Extra) A Simple Implementation of ANN for MNIST.ipynb) for a *naive* implementation of **SGD** and **MLP** applied on **MNIST** dataset.\\n\",\n    \"\\n\",\n    \"This accompanies the online text http://neuralnetworksanddeeplearning.com/ . The book is highly recommended. \"\n   ]\n  }\n ],\n \"metadata\": {\n  \"anaconda-cloud\": {},\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.4.3\"\n  },\n  \"nbpresent\": {\n   \"slides\": {\n    \"5445cab1-b2b9-4492-a3be-c4063bd67610\": {\n     \"id\": \"5445cab1-b2b9-4492-a3be-c4063bd67610\",\n     \"prev\": \"ef2af7e1-6294-42cf-a434-b1e17cf679a7\",\n     \"regions\": {\n      \"4baa3e75-a346-47d1-a015-c9040545adbd\": {\n       \"attrs\": {\n        \"height\": 0.4,\n        \"width\": 0.8,\n        \"x\": 0.1,\n        \"y\": 0.5\n       },\n       \"content\": {\n        \"cell\": 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    "path": "deep-learning/keras-tutorial/1.2 Introduction - Theano.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Credits: Forked from [deep-learning-keras-tensorflow](https://github.com/leriomaggio/deep-learning-keras-tensorflow) by Valerio Maggio\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"slide\"\n    }\n   },\n   \"source\": [\n    \"Theano \\n\",\n    \"===\\n\",\n    \"A language in a language\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Dealing with weights matrices and gradients can be tricky and sometimes not trivial.\\n\",\n    \"Theano is a great framework for handling vectors, matrices and high dimensional tensor algebra. \\n\",\n    \"Most of this tutorial will refer to Theano however TensorFlow is another great framework capable of providing an incredible abstraction for complex algebra.\\n\",\n    \"More on TensorFlow in the next chapters.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"slideshow\": {\n     \"slide_type\": \"fragment\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import theano\\n\",\n    \"import theano.tensor as T\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"slide\"\n    }\n   },\n   \"source\": [\n    \"Symbolic variables\\n\",\n    \"==========\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Theano has it's own variables and functions, defined the following\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"slideshow\": {\n     \"slide_type\": \"fragment\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x = T.scalar()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true,\n    \"slideshow\": {\n     \"slide_type\": \"fragment\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"slide\"\n    }\n   },\n   \"source\": [\n    \"Variables can be used in expressions\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"slideshow\": {\n     \"slide_type\": \"-\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"y = 3*(x**2) + 1\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"fragment\"\n    }\n   },\n   \"source\": [\n    \"y is an expression now \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"fragment\"\n    }\n   },\n   \"source\": [\n    \"Result is symbolic as well\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"slideshow\": {\n     \"slide_type\": \"-\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Shape.0\"\n      ]\n     },\n     \"execution_count\": 9,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"type(y)\\n\",\n    \"y.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"slide\"\n    }\n   },\n   \"source\": [\n    \"#####printing\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"fragment\"\n    }\n   },\n   \"source\": [\n    \"As we are about to see, normal printing isn't the best when it comes to theano\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"slideshow\": {\n     \"slide_type\": \"fragment\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Elemwise{add,no_inplace}.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(y)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"slideshow\": {\n     \"slide_type\": \"fragment\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'((TensorConstant{3} * (<TensorType(float32, scalar)> ** TensorConstant{2})) + TensorConstant{1})'\"\n      ]\n     },\n     \"execution_count\": 11,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"theano.pprint(y)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"slideshow\": {\n     \"slide_type\": \"fragment\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Elemwise{add,no_inplace} [@A] ''   \\n\",\n      \" |Elemwise{mul,no_inplace} [@B] ''   \\n\",\n      \" | |TensorConstant{3} [@C]\\n\",\n      \" | |Elemwise{pow,no_inplace} [@D] ''   \\n\",\n      \" |   |<TensorType(float32, scalar)> [@E]\\n\",\n      \" |   |TensorConstant{2} [@F]\\n\",\n      \" |TensorConstant{1} [@G]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"theano.printing.debugprint(y)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"slide\"\n    }\n   },\n   \"source\": [\n    \"Evaluating expressions\\n\",\n    \"============\\n\",\n    \"\\n\",\n    \"Supply a `dict` mapping variables to values\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array(13.0, dtype=float32)\"\n      ]\n     },\n     \"execution_count\": 26,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"y.eval({x: 2})\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"slide\"\n    }\n   },\n   \"source\": [\n    \"Or compile a function\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"f = theano.function([x], y)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"slideshow\": {\n     \"slide_type\": \"fragment\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array(13.0, dtype=float32)\"\n      ]\n     },\n     \"execution_count\": 28,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"f(2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"slide\"\n    }\n   },\n   \"source\": [\n    \"Other tensor types\\n\",\n    \"==========\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"metadata\": {\n    \"collapsed\": true,\n    \"slideshow\": {\n     \"slide_type\": \"-\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"X = T.vector()\\n\",\n    \"X = T.matrix()\\n\",\n    \"X = T.tensor3()\\n\",\n    \"X = T.tensor4()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"slide\"\n    }\n   },\n   \"source\": [\n    \"Automatic differention\\n\",\n    \"============\\n\",\n    \"- Gradients are free!\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x = T.scalar()\\n\",\n    \"y = T.log(x)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"slideshow\": {\n     \"slide_type\": \"fragment\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Elemwise{true_div}.0\\n\",\n      \"0.5\\n\",\n      \"Elemwise{mul,no_inplace}.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"gradient = T.grad(y, x)\\n\",\n    \"print gradient\\n\",\n    \"print gradient.eval({x: 2})\\n\",\n    \"print (2 * gradient)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"slide\"\n    }\n   },\n   \"source\": [\n    \"# Shared Variables\\n\",\n    \"\\n\",\n    \"- Symbolic + Storage\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 39,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"x = theano.shared(np.zeros((2, 3), dtype=theano.config.floatX))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 40,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<CudaNdarrayType(float32, matrix)>\"\n      ]\n     },\n     \"execution_count\": 40,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"x\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"slide\"\n    }\n   },\n   \"source\": [\n    \"We can get and set the variable's value\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 41,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"slideshow\": {\n     \"slide_type\": \"-\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(2, 3)\\n\",\n      \"[[ 0.  0.  0.]\\n\",\n      \" [ 0.  0.  0.]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"values = x.get_value()\\n\",\n    \"print(values.shape)\\n\",\n    \"print(values)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 42,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x.set_value(values)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"slide\"\n    }\n   },\n   \"source\": [\n    \"Shared variables can be used in expressions as well\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 43,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"slideshow\": {\n     \"slide_type\": \"-\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Elemwise{pow,no_inplace}.0\"\n      ]\n     },\n     \"execution_count\": 43,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"(x + 2) ** 2\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"fragment\"\n    }\n   },\n   \"source\": [\n    \"Their value is used as input when evaluating\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 44,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[ 4.,  4.,  4.],\\n\",\n       \"       [ 4.,  4.,  4.]], dtype=float32)\"\n      ]\n     },\n     \"execution_count\": 44,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"((x + 2) ** 2).eval()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 45,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[ 4.,  4.,  4.],\\n\",\n       \"       [ 4.,  4.,  4.]], dtype=float32)\"\n      ]\n     },\n     \"execution_count\": 45,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"theano.function([], (x + 2) ** 2)()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"slide\"\n    }\n   },\n   \"source\": [\n    \"# Updates\\n\",\n    \"\\n\",\n    \"- Store results of function evalution\\n\",\n    \"- `dict` mapping shared variables to new values\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 46,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"slideshow\": {\n     \"slide_type\": \"slide\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"count = theano.shared(0)\\n\",\n    \"new_count = count + 1\\n\",\n    \"updates = {count: new_count}\\n\",\n    \"\\n\",\n    \"f = theano.function([], count, updates=updates)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 47,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"slideshow\": {\n     \"slide_type\": \"fragment\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array(0)\"\n      ]\n     },\n     \"execution_count\": 47,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"f()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 48,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"slideshow\": {\n     \"slide_type\": \"fragment\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array(1)\"\n      ]\n     },\n     \"execution_count\": 48,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"f()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 49,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"slideshow\": {\n     \"slide_type\": \"fragment\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array(2)\"\n      ]\n     },\n     \"execution_count\": 49,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"f()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Warming up! Logistic Regression\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%matplotlib inline\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Using Theano backend.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import pandas as pd\\n\",\n    \"import theano\\n\",\n    \"import theano.tensor as T\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from sklearn.preprocessing import StandardScaler\\n\",\n    \"from sklearn.preprocessing import LabelEncoder \\n\",\n    \"from keras.utils import np_utils\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"For this section we will use the Kaggle otto challenge.\\n\",\n    \"If you want to follow, Get the data from Kaggle: https://www.kaggle.com/c/otto-group-product-classification-challenge/data\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### About the data\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The Otto Group is one of the world’s biggest e-commerce companies, A consistent analysis of the performance of products is crucial. However, due to diverse global infrastructure, many identical products get classified differently.\\n\",\n    \"For this competition, we have provided a dataset with 93 features for more than 200,000 products. The objective is to build a predictive model which is able to distinguish between our main product categories. \\n\",\n    \"Each row corresponds to a single product. There are a total of 93 numerical features, which represent counts of different events. All features have been obfuscated and will not be defined any further.\\n\",\n    \"\\n\",\n    \"https://www.kaggle.com/c/otto-group-product-classification-challenge/data\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 37,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def load_data(path, train=True):\\n\",\n    \"    \\\"\\\"\\\"Load data from a CSV File\\n\",\n    \"    \\n\",\n    \"    Parameters\\n\",\n    \"    ----------\\n\",\n    \"    path: str\\n\",\n    \"        The path to the CSV file\\n\",\n    \"        \\n\",\n    \"    train: bool (default True)\\n\",\n    \"        Decide whether or not data are *training data*.\\n\",\n    \"        If True, some random shuffling is applied.\\n\",\n    \"        \\n\",\n    \"    Return\\n\",\n    \"    ------\\n\",\n    \"    X: numpy.ndarray \\n\",\n    \"        The data as a multi dimensional array of floats\\n\",\n    \"    ids: numpy.ndarray\\n\",\n    \"        A vector of ids for each sample\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    df = pd.read_csv(path)\\n\",\n    \"    X = df.values.copy()\\n\",\n    \"    if train:\\n\",\n    \"        np.random.shuffle(X)  # https://youtu.be/uyUXoap67N8\\n\",\n    \"        X, labels = X[:, 1:-1].astype(np.float32), X[:, -1]\\n\",\n    \"        return X, labels\\n\",\n    \"    else:\\n\",\n    \"        X, ids = X[:, 1:].astype(np.float32), X[:, 0].astype(str)\\n\",\n    \"        return X, ids\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 38,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def preprocess_data(X, scaler=None):\\n\",\n    \"    \\\"\\\"\\\"Preprocess input data by standardise features \\n\",\n    \"    by removing the mean and scaling to unit variance\\\"\\\"\\\"\\n\",\n    \"    if not scaler:\\n\",\n    \"        scaler = StandardScaler()\\n\",\n    \"        scaler.fit(X)\\n\",\n    \"    X = scaler.transform(X)\\n\",\n    \"    return X, scaler\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def preprocess_labels(labels, encoder=None, categorical=True):\\n\",\n    \"    \\\"\\\"\\\"Encode labels with values among 0 and `n-classes-1`\\\"\\\"\\\"\\n\",\n    \"    if not encoder:\\n\",\n    \"        encoder = LabelEncoder()\\n\",\n    \"        encoder.fit(labels)\\n\",\n    \"    y = encoder.transform(labels).astype(np.int32)\\n\",\n    \"    if categorical:\\n\",\n    \"        y = np_utils.to_categorical(y)\\n\",\n    \"    return y, encoder\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 41,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Loading data...\\n\",\n      \"[[  0.   0.   0.   0.   0.   0.   0.   0.   0.   3.   0.   0.   0.   3.\\n\",\n      \"    2.   1.   0.   0.   0.   0.   0.   0.   0.   5.   3.   1.   1.   0.\\n\",\n      \"    0.   0.   0.   0.   1.   0.   0.   1.   0.   1.   0.   1.   0.   0.\\n\",\n      \"    0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\\n\",\n      \"    0.   0.   0.   0.   0.   0.   0.   3.   0.   0.   0.   0.   1.   1.\\n\",\n      \"    0.   1.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\\n\",\n      \"    0.  11.   1.  20.   0.   0.   0.   0.   0.]]\\n\",\n      \"(9L, 'classes')\\n\",\n      \"(93L, 'dims')\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(\\\"Loading data...\\\")\\n\",\n    \"X, labels = load_data('train.csv', train=True)\\n\",\n    \"X, scaler = preprocess_data(X)\\n\",\n    \"Y, encoder = preprocess_labels(labels)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"X_test, ids = load_data('test.csv', train=False)\\n\",\n    \"X_test, ids = X_test[:1000], ids[:1000]\\n\",\n    \"\\n\",\n    \"#Plotting the data\\n\",\n    \"print(X_test[:1])\\n\",\n    \"\\n\",\n    \"X_test, _ = preprocess_data(X_test, scaler)\\n\",\n    \"\\n\",\n    \"nb_classes = Y.shape[1]\\n\",\n    \"print(nb_classes, 'classes')\\n\",\n    \"\\n\",\n    \"dims = X.shape[1]\\n\",\n    \"print(dims, 'dims')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now lets create and train a logistic regression model.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Hands On - Logistic Regression\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 46,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch 1\\n\",\n      \"target values for Data:\\n\",\n      \"[ 0.  0.  1. ...,  0.  0.  0.]\\n\",\n      \"prediction on training set:\\n\",\n      \"[0 0 0 ..., 0 0 0]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"#Based on example from DeepLearning.net\\n\",\n    \"rng = np.random\\n\",\n    \"N = 400\\n\",\n    \"feats = 93\\n\",\n    \"training_steps = 1\\n\",\n    \"\\n\",\n    \"# Declare Theano symbolic variables\\n\",\n    \"x = T.matrix(\\\"x\\\")\\n\",\n    \"y = T.vector(\\\"y\\\")\\n\",\n    \"w = theano.shared(rng.randn(feats), name=\\\"w\\\")\\n\",\n    \"b = theano.shared(0., name=\\\"b\\\")\\n\",\n    \"\\n\",\n    \"# Construct Theano expression graph\\n\",\n    \"p_1 = 1 / (1 + T.exp(-T.dot(x, w) - b))   # Probability that target = 1\\n\",\n    \"prediction = p_1 > 0.5                    # The prediction thresholded\\n\",\n    \"xent = -y * T.log(p_1) - (1-y) * T.log(1-p_1) # Cross-entropy loss function\\n\",\n    \"cost = xent.mean() + 0.01 * (w ** 2).sum()# The cost to minimize\\n\",\n    \"gw, gb = T.grad(cost, [w, b])             # Compute the gradient of the cost\\n\",\n    \"                                          # (we shall return to this in a\\n\",\n    \"                                          # following section of this tutorial)\\n\",\n    \"\\n\",\n    \"# Compile\\n\",\n    \"train = theano.function(\\n\",\n    \"          inputs=[x,y],\\n\",\n    \"          outputs=[prediction, xent],\\n\",\n    \"          updates=((w, w - 0.1 * gw), (b, b - 0.1 * gb)),\\n\",\n    \"          allow_input_downcast=True)\\n\",\n    \"predict = theano.function(inputs=[x], outputs=prediction, allow_input_downcast=True)\\n\",\n    \"\\n\",\n    \"#Transform for class1\\n\",\n    \"y_class1 = []\\n\",\n    \"for i in Y:\\n\",\n    \"    y_class1.append(i[0])\\n\",\n    \"y_class1 = np.array(y_class1)\\n\",\n    \"\\n\",\n    \"# Train\\n\",\n    \"for i in range(training_steps):\\n\",\n    \"    print('Epoch %s' % (i+1,))\\n\",\n    \"    pred, err = train(X, y_class1)\\n\",\n    \"\\n\",\n    \"print(\\\"target values for Data:\\\")\\n\",\n    \"print(y_class1)\\n\",\n    \"print(\\\"prediction on training set:\\\")\\n\",\n    \"print(predict(X))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.4.3\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "deep-learning/keras-tutorial/1.3 Introduction - Keras.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Credits: Forked from [deep-learning-keras-tensorflow](https://github.com/leriomaggio/deep-learning-keras-tensorflow) by Valerio Maggio\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%matplotlib inline\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Using Theano backend.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import pandas as pd\\n\",\n    \"import theano\\n\",\n    \"import theano.tensor as T\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import keras \\n\",\n    \"from sklearn.preprocessing import StandardScaler\\n\",\n    \"from sklearn.preprocessing import LabelEncoder \\n\",\n    \"from keras.utils import np_utils\\n\",\n    \"from sklearn.cross_validation import train_test_split\\n\",\n    \"from keras.callbacks import EarlyStopping, ModelCheckpoint\\n\",\n    \"from keras.models import Sequential\\n\",\n    \"from keras.layers import Dense, Activation\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"For this section we will use the Kaggle otto challenge.\\n\",\n    \"If you want to follow, Get the data from Kaggle: https://www.kaggle.com/c/otto-group-product-classification-challenge/data\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### About the data\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The Otto Group is one of the world’s biggest e-commerce companies, A consistent analysis of the performance of products is crucial. However, due to diverse global infrastructure, many identical products get classified differently.\\n\",\n    \"For this competition, we have provided a dataset with 93 features for more than 200,000 products. The objective is to build a predictive model which is able to distinguish between our main product categories. \\n\",\n    \"Each row corresponds to a single product. There are a total of 93 numerical features, which represent counts of different events. All features have been obfuscated and will not be defined any further.\\n\",\n    \"\\n\",\n    \"https://www.kaggle.com/c/otto-group-product-classification-challenge/data\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def load_data(path, train=True):\\n\",\n    \"    \\\"\\\"\\\"Load data from a CSV File\\n\",\n    \"    \\n\",\n    \"    Parameters\\n\",\n    \"    ----------\\n\",\n    \"    path: str\\n\",\n    \"        The path to the CSV file\\n\",\n    \"        \\n\",\n    \"    train: bool (default True)\\n\",\n    \"        Decide whether or not data are *training data*.\\n\",\n    \"        If True, some random shuffling is applied.\\n\",\n    \"        \\n\",\n    \"    Return\\n\",\n    \"    ------\\n\",\n    \"    X: numpy.ndarray \\n\",\n    \"        The data as a multi dimensional array of floats\\n\",\n    \"    ids: numpy.ndarray\\n\",\n    \"        A vector of ids for each sample\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    df = pd.read_csv(path)\\n\",\n    \"    X = df.values.copy()\\n\",\n    \"    if train:\\n\",\n    \"        np.random.shuffle(X)  # https://youtu.be/uyUXoap67N8\\n\",\n    \"        X, labels = X[:, 1:-1].astype(np.float32), X[:, -1]\\n\",\n    \"        return X, labels\\n\",\n    \"    else:\\n\",\n    \"        X, ids = X[:, 1:].astype(np.float32), X[:, 0].astype(str)\\n\",\n    \"        return X, ids\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def preprocess_data(X, scaler=None):\\n\",\n    \"    \\\"\\\"\\\"Preprocess input data by standardise features \\n\",\n    \"    by removing the mean and scaling to unit variance\\\"\\\"\\\"\\n\",\n    \"    if not scaler:\\n\",\n    \"        scaler = StandardScaler()\\n\",\n    \"        scaler.fit(X)\\n\",\n    \"    X = scaler.transform(X)\\n\",\n    \"    return X, scaler\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def preprocess_labels(labels, encoder=None, categorical=True):\\n\",\n    \"    \\\"\\\"\\\"Encode labels with values among 0 and `n-classes-1`\\\"\\\"\\\"\\n\",\n    \"    if not encoder:\\n\",\n    \"        encoder = LabelEncoder()\\n\",\n    \"        encoder.fit(labels)\\n\",\n    \"    y = encoder.transform(labels).astype(np.int32)\\n\",\n    \"    if categorical:\\n\",\n    \"        y = np_utils.to_categorical(y)\\n\",\n    \"    return y, encoder\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Loading data...\\n\",\n      \"[[  0.   0.   0.   0.   0.   0.   0.   0.   0.   3.   0.   0.   0.   3.\\n\",\n      \"    2.   1.   0.   0.   0.   0.   0.   0.   0.   5.   3.   1.   1.   0.\\n\",\n      \"    0.   0.   0.   0.   1.   0.   0.   1.   0.   1.   0.   1.   0.   0.\\n\",\n      \"    0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\\n\",\n      \"    0.   0.   0.   0.   0.   0.   0.   3.   0.   0.   0.   0.   1.   1.\\n\",\n      \"    0.   1.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\\n\",\n      \"    0.  11.   1.  20.   0.   0.   0.   0.   0.]]\\n\",\n      \"(9L, 'classes')\\n\",\n      \"(93L, 'dims')\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(\\\"Loading data...\\\")\\n\",\n    \"X, labels = load_data('train.csv', train=True)\\n\",\n    \"X, scaler = preprocess_data(X)\\n\",\n    \"Y, encoder = preprocess_labels(labels)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"X_test, ids = load_data('test.csv', train=False)\\n\",\n    \"X_test, ids = X_test[:1000], ids[:1000]\\n\",\n    \"\\n\",\n    \"#Plotting the data\\n\",\n    \"print(X_test[:1])\\n\",\n    \"\\n\",\n    \"X_test, _ = preprocess_data(X_test, scaler)\\n\",\n    \"\\n\",\n    \"nb_classes = Y.shape[1]\\n\",\n    \"print(nb_classes, 'classes')\\n\",\n    \"\\n\",\n    \"dims = X.shape[1]\\n\",\n    \"print(dims, 'dims')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now lets create and train a logistic regression model.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Keras\\n\",\n    \"\\n\",\n    \"## Deep Learning library for Theano and TensorFlow\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Keras is a minimalist, highly modular neural networks library, written in Python and capable of running on top of either TensorFlow or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.\\n\",\n    \"ref: https://keras.io/\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Why this name, Keras?\\n\",\n    \"\\n\",\n    \"Keras (κέρας) means _horn_ in Greek. It is a reference to a literary image from ancient Greek and Latin literature, first found in the _Odyssey_, where dream spirits (_Oneiroi_, singular _Oneiros_) are divided between those who deceive men with false visions, who arrive to Earth through a gate of ivory, and those who announce a future that will come to pass, who arrive through a gate of horn. It's a play on the words κέρας (horn) / κραίνω (fulfill), and ἐλέφας (ivory) / ἐλεφαίρομαι (deceive).\\n\",\n    \"\\n\",\n    \"Keras was initially developed as part of the research effort of project ONEIROS (Open-ended Neuro-Electronic Intelligent Robot Operating System).\\n\",\n    \"\\n\",\n    \">_\\\"Oneiroi are beyond our unravelling --who can be sure what tale they tell? Not all that men look for comes to pass. Two gates there are that give passage to fleeting Oneiroi; one is made of horn, one of ivory. The Oneiroi that pass through sawn ivory are deceitful, bearing a message that will not be fulfilled; those that come out through polished horn have truth behind them, to be accomplished for men who see them.\\\"_ Homer, Odyssey 19. 562 ff (Shewring translation).\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Hands On - Keras Logistic Regression\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(93L, 'dims')\\n\",\n      \"Building model...\\n\",\n      \"(9L, 'classes')\\n\",\n      \"Epoch 1/10\\n\",\n      \"61878/61878 [==============================] - 1s - loss: 1.0574     \\n\",\n      \"Epoch 2/10\\n\",\n      \"61878/61878 [==============================] - 1s - loss: 0.7730     \\n\",\n      \"Epoch 3/10\\n\",\n      \"61878/61878 [==============================] - 1s - loss: 0.7297     \\n\",\n      \"Epoch 4/10\\n\",\n      \"61878/61878 [==============================] - 1s - loss: 0.7080     \\n\",\n      \"Epoch 5/10\\n\",\n      \"61878/61878 [==============================] - 1s - loss: 0.6948     \\n\",\n      \"Epoch 6/10\\n\",\n      \"61878/61878 [==============================] - 1s - loss: 0.6854     \\n\",\n      \"Epoch 7/10\\n\",\n      \"61878/61878 [==============================] - 1s - loss: 0.6787     \\n\",\n      \"Epoch 8/10\\n\",\n      \"61878/61878 [==============================] - 1s - loss: 0.6734     \\n\",\n      \"Epoch 9/10\\n\",\n      \"61878/61878 [==============================] - 1s - loss: 0.6691     \\n\",\n      \"Epoch 10/10\\n\",\n      \"61878/61878 [==============================] - 1s - loss: 0.6657     \\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<keras.callbacks.History at 0x23d330f0>\"\n      ]\n     },\n     \"execution_count\": 6,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"dims = X.shape[1]\\n\",\n    \"print(dims, 'dims')\\n\",\n    \"print(\\\"Building model...\\\")\\n\",\n    \"\\n\",\n    \"nb_classes = Y.shape[1]\\n\",\n    \"print(nb_classes, 'classes')\\n\",\n    \"\\n\",\n    \"model = Sequential()\\n\",\n    \"model.add(Dense(nb_classes, input_shape=(dims,)))\\n\",\n    \"model.add(Activation('softmax'))\\n\",\n    \"model.compile(optimizer='sgd', loss='categorical_crossentropy')\\n\",\n    \"model.fit(X, Y)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Simplicity is pretty impressive right? :)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now lets understand:\\n\",\n    \"<pre>The core data structure of Keras is a <b>model</b>, a way to organize layers. The main type of model is the <b>Sequential</b> model, a linear stack of layers.</pre>\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"What we did here is stacking a Fully Connected (<b>Dense</b>) layer of trainable weights from the input to the output and an <b>Activation</b> layer on top of the weights layer.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"##### Dense\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"```python\\n\",\n    \"from keras.layers.core import Dense\\n\",\n    \"\\n\",\n    \"Dense(output_dim, init='glorot_uniform', activation='linear', \\n\",\n    \"      weights=None, W_regularizer=None, b_regularizer=None,\\n\",\n    \"      activity_regularizer=None, W_constraint=None, \\n\",\n    \"      b_constraint=None, bias=True, input_dim=None)\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"##### Activation\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"```python\\n\",\n    \"from keras.layers.core import Activation\\n\",\n    \"\\n\",\n    \"Activation(activation)\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"##### Optimizer\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"If you need to, you can further configure your optimizer. A core principle of Keras is to make things reasonably simple, while allowing the user to be fully in control when they need to (the ultimate control being the easy extensibility of the source code).\\n\",\n    \"Here we used <b>SGD</b> (stochastic gradient descent) as an optimization algorithm for our trainable weights.  \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\\"Data Sciencing\\\" this example a little bit more\\n\",\n    \"=====\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"What we did here is nice, however in the real world it is not useable because of overfitting.\\n\",\n    \"Lets try and solve it with cross validation.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"##### Overfitting\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"In overfitting, a statistical model describes random error or noise instead of the underlying relationship. Overfitting occurs when a model is excessively complex, such as having too many parameters relative to the number of observations. \\n\",\n    \"\\n\",\n    \"A model that has been overfit has poor predictive performance, as it overreacts to minor fluctuations in the training data.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"source\": [\n    \"\\n\",\n    \"<img src =\\\"imgs/overfitting.png\\\">\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<pre>To avoid overfitting, we will first split out data to training set and test set and test out model on the test set.\\n\",\n    \"Next: we will use two of keras's callbacks <b>EarlyStopping</b> and <b>ModelCheckpoint</b></pre>\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Train on 19835 samples, validate on 3501 samples\\n\",\n      \"Epoch 1/20\\n\",\n      \"19835/19835 [==============================] - 0s - loss: 0.6391 - val_loss: 0.6680\\n\",\n      \"Epoch 2/20\\n\",\n      \"19835/19835 [==============================] - 0s - loss: 0.6386 - val_loss: 0.6689\\n\",\n      \"Epoch 3/20\\n\",\n      \"19835/19835 [==============================] - 0s - loss: 0.6384 - val_loss: 0.6695\\n\",\n      \"Epoch 4/20\\n\",\n      \"19835/19835 [==============================] - 0s - loss: 0.6381 - val_loss: 0.6702\\n\",\n      \"Epoch 5/20\\n\",\n      \"19835/19835 [==============================] - 0s - loss: 0.6378 - val_loss: 0.6709\\n\",\n      \"Epoch 6/20\\n\",\n      \"19328/19835 [============================>.] - ETA: 0s - loss: 0.6380Epoch 00005: early stopping\\n\",\n      \"19835/19835 [==============================] - 0s - loss: 0.6375 - val_loss: 0.6716\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<keras.callbacks.History at 0x1d7245f8>\"\n      ]\n     },\n     \"execution_count\": 13,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"X, X_test, Y, Y_test = train_test_split(X, Y, test_size=0.15, random_state=42)\\n\",\n    \"\\n\",\n    \"fBestModel = 'best_model.h5' \\n\",\n    \"early_stop = EarlyStopping(monitor='val_loss', patience=4, verbose=1) \\n\",\n    \"best_model = ModelCheckpoint(fBestModel, verbose=0, save_best_only=True)\\n\",\n    \"model.fit(X, Y, validation_data = (X_test, Y_test), nb_epoch=20, \\n\",\n    \"          batch_size=128, verbose=True, validation_split=0.15, \\n\",\n    \"          callbacks=[best_model, early_stop]) \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Multi-Layer Perceptron and Fully Connected\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"So, how hard can it be to build a Multi-Layer percepton with keras?\\n\",\n    \"It is baiscly the same, just add more layers!\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = Sequential()\\n\",\n    \"model.add(Dense(100, input_shape=(dims,)))\\n\",\n    \"model.add(Dense(nb_classes))\\n\",\n    \"model.add(Activation('softmax'))\\n\",\n    \"model.compile(optimizer='sgd', loss='categorical_crossentropy')\\n\",\n    \"model.fit(X, Y)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Your Turn!\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Hands On - Keras Fully Connected\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Take couple of minutes and Try and optimize the number of layers and the number of parameters in the layers to get the best results. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = Sequential()\\n\",\n    \"model.add(Dense(100, input_shape=(dims,)))\\n\",\n    \"\\n\",\n    \"# ...\\n\",\n    \"# ...\\n\",\n    \"# Play with it! add as much layers as you want! try and get better results.\\n\",\n    \"\\n\",\n    \"model.add(Dense(nb_classes))\\n\",\n    \"model.add(Activation('softmax'))\\n\",\n    \"model.compile(optimizer='sgd', loss='categorical_crossentropy')\\n\",\n    \"model.fit(X, Y)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Building a question answering system, an image classification model, a Neural Turing Machine, a word2vec embedder or any other model is just as fast. The ideas behind deep learning are simple, so why should their implementation be painful?\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Theoretical Motivations for depth\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \">Much has been studied about the depth of neural nets. Is has been proven mathematically[1] and empirically that convolutional neural network benifit from depth! \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"[1] - On the Expressive Power of Deep Learning: A Tensor Analysis - Cohen, et al 2015\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Theoretical Motivations for depth\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"One much quoted theorem about neural network states that:\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \">Universal approximation theorem states[1] that a feed-forward network with a single hidden layer containing a finite number of neurons (i.e., a multilayer perceptron), can approximate continuous functions on compact subsets of $\\\\mathbb{R}^n$, under mild assumptions on the activation function. The theorem thus states that simple neural networks can represent a wide variety of interesting functions when given appropriate parameters; however, it does not touch upon the algorithmic learnability of those parameters.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"[1] - Approximation Capabilities of Multilayer Feedforward Networks - Kurt Hornik 1991\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.4.3\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "deep-learning/keras-tutorial/1.4 (Extra) A Simple Implementation of ANN for MNIST.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Credits: Forked from [deep-learning-keras-tensorflow](https://github.com/leriomaggio/deep-learning-keras-tensorflow) by Valerio Maggio\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"# A simple implementation of ANN for MNIST\\n\",\n    \"\\n\",\n    \"This code was taken from: https://github.com/mnielsen/neural-networks-and-deep-learning\\n\",\n    \"\\n\",\n    \"This accompanies the online text http://neuralnetworksanddeeplearning.com/ . The book is highly recommended. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Using Theano backend.\\n\",\n      \"Using gpu device 0: GeForce GTX 760 (CNMeM is enabled with initial size: 90.0% of memory, cuDNN 4007)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Import libraries\\n\",\n    \"import random\\n\",\n    \"import numpy as np\\n\",\n    \"import keras\\n\",\n    \"from keras.datasets import mnist\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Set the full path to mnist.pkl.gz\\n\",\n    \"# Point this to the data folder inside the repository\\n\",\n    \"path_to_dataset = \\\"euroscipy2016_dl-tutorial/data/mnist.pkl.gz\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!mkdir -p $HOME/.keras/datasets/euroscipy2016_dl-tutorial/data/\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Downloading data from https://s3.amazonaws.com/img-datasets/mnist.pkl.gz\\n\",\n      \"15286272/15296311 [============================>.] - ETA: 0s\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Load the datasets\\n\",\n    \"(X_train, y_train), (X_test, y_test) = mnist.load_data(path_to_dataset)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(60000, 28, 28) (60000,)\\n\",\n      \"(10000, 28, 28) (10000,)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(X_train.shape, y_train.shape)\\n\",\n    \"print(X_test.shape, y_test.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"\\\"\\\"\\\"\\n\",\n    \"network.py\\n\",\n    \"~~~~~~~~~~\\n\",\n    \"A module to implement the stochastic gradient descent learning\\n\",\n    \"algorithm for a feedforward neural network.  Gradients are calculated\\n\",\n    \"using backpropagation.  Note that I have focused on making the code\\n\",\n    \"simple, easily readable, and easily modifiable.  It is not optimized,\\n\",\n    \"and omits many desirable features.\\n\",\n    \"\\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"#### Libraries\\n\",\n    \"# Standard library\\n\",\n    \"import random\\n\",\n    \"\\n\",\n    \"# Third-party libraries\\n\",\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"class Network(object):\\n\",\n    \"\\n\",\n    \"    def __init__(self, sizes):\\n\",\n    \"        \\\"\\\"\\\"The list ``sizes`` contains the number of neurons in the\\n\",\n    \"        respective layers of the network.  For example, if the list\\n\",\n    \"        was [2, 3, 1] then it would be a three-layer network, with the\\n\",\n    \"        first layer containing 2 neurons, the second layer 3 neurons,\\n\",\n    \"        and the third layer 1 neuron.  The biases and weights for the\\n\",\n    \"        network are initialized randomly, using a Gaussian\\n\",\n    \"        distribution with mean 0, and variance 1.  Note that the first\\n\",\n    \"        layer is assumed to be an input layer, and by convention we\\n\",\n    \"        won't set any biases for those neurons, since biases are only\\n\",\n    \"        ever used in computing the outputs from later layers.\\\"\\\"\\\"\\n\",\n    \"        self.num_layers = len(sizes)\\n\",\n    \"        self.sizes = sizes\\n\",\n    \"        self.biases = [np.random.randn(y, 1) for y in sizes[1:]]\\n\",\n    \"        self.weights = [np.random.randn(y, x)\\n\",\n    \"                        for x, y in zip(sizes[:-1], sizes[1:])]\\n\",\n    \"\\n\",\n    \"    def feedforward(self, a):\\n\",\n    \"        \\\"\\\"\\\"Return the output of the network if ``a`` is input.\\\"\\\"\\\"\\n\",\n    \"        for b, w in zip(self.biases, self.weights):\\n\",\n    \"            a = sigmoid(np.dot(w, a)+b)\\n\",\n    \"        return a\\n\",\n    \"\\n\",\n    \"    def SGD(self, training_data, epochs, mini_batch_size, eta,\\n\",\n    \"            test_data=None):\\n\",\n    \"        \\\"\\\"\\\"Train the neural network using mini-batch stochastic\\n\",\n    \"        gradient descent.  The ``training_data`` is a list of tuples\\n\",\n    \"        ``(x, y)`` representing the training inputs and the desired\\n\",\n    \"        outputs.  The other non-optional parameters are\\n\",\n    \"        self-explanatory.  If ``test_data`` is provided then the\\n\",\n    \"        network will be evaluated against the test data after each\\n\",\n    \"        epoch, and partial progress printed out.  This is useful for\\n\",\n    \"        tracking progress, but slows things down substantially.\\\"\\\"\\\"\\n\",\n    \"        training_data = list(training_data)\\n\",\n    \"        test_data = list(test_data)\\n\",\n    \"        if test_data: n_test = len(test_data)\\n\",\n    \"        n = len(training_data)\\n\",\n    \"        for j in range(epochs):\\n\",\n    \"            random.shuffle(training_data)\\n\",\n    \"            mini_batches = [\\n\",\n    \"                training_data[k:k+mini_batch_size]\\n\",\n    \"                for k in range(0, n, mini_batch_size)]\\n\",\n    \"            for mini_batch in mini_batches:\\n\",\n    \"                self.update_mini_batch(mini_batch, eta)\\n\",\n    \"            if test_data:\\n\",\n    \"                print( \\\"Epoch {0}: {1} / {2}\\\".format(\\n\",\n    \"                    j, self.evaluate(test_data), n_test))\\n\",\n    \"            else:\\n\",\n    \"                print( \\\"Epoch {0} complete\\\".format(j))\\n\",\n    \"\\n\",\n    \"    def update_mini_batch(self, mini_batch, eta):\\n\",\n    \"        \\\"\\\"\\\"Update the network's weights and biases by applying\\n\",\n    \"        gradient descent using backpropagation to a single mini batch.\\n\",\n    \"        The ``mini_batch`` is a list of tuples ``(x, y)``, and ``eta``\\n\",\n    \"        is the learning rate.\\\"\\\"\\\"\\n\",\n    \"        nabla_b = [np.zeros(b.shape) for b in self.biases]\\n\",\n    \"        nabla_w = [np.zeros(w.shape) for w in self.weights]\\n\",\n    \"        for x, y in mini_batch:\\n\",\n    \"            delta_nabla_b, delta_nabla_w = self.backprop(x, y)\\n\",\n    \"            nabla_b = [nb+dnb for nb, dnb in zip(nabla_b, delta_nabla_b)]\\n\",\n    \"            nabla_w = [nw+dnw for nw, dnw in zip(nabla_w, delta_nabla_w)]\\n\",\n    \"        self.weights = [w-(eta/len(mini_batch))*nw\\n\",\n    \"                        for w, nw in zip(self.weights, nabla_w)]\\n\",\n    \"        self.biases = [b-(eta/len(mini_batch))*nb\\n\",\n    \"                       for b, nb in zip(self.biases, nabla_b)]\\n\",\n    \"\\n\",\n    \"    def backprop(self, x, y):\\n\",\n    \"        \\\"\\\"\\\"Return a tuple ``(nabla_b, nabla_w)`` representing the\\n\",\n    \"        gradient for the cost function C_x.  ``nabla_b`` and\\n\",\n    \"        ``nabla_w`` are layer-by-layer lists of numpy arrays, similar\\n\",\n    \"        to ``self.biases`` and ``self.weights``.\\\"\\\"\\\"\\n\",\n    \"        nabla_b = [np.zeros(b.shape) for b in self.biases]\\n\",\n    \"        nabla_w = [np.zeros(w.shape) for w in self.weights]\\n\",\n    \"        # feedforward\\n\",\n    \"        activation = x\\n\",\n    \"        activations = [x] # list to store all the activations, layer by layer\\n\",\n    \"        zs = [] # list to store all the z vectors, layer by layer\\n\",\n    \"        for b, w in zip(self.biases, self.weights):\\n\",\n    \"            z = np.dot(w, activation)+b\\n\",\n    \"            zs.append(z)\\n\",\n    \"            activation = sigmoid(z)\\n\",\n    \"            activations.append(activation)\\n\",\n    \"        # backward pass\\n\",\n    \"        delta = self.cost_derivative(activations[-1], y) * \\\\\\n\",\n    \"            sigmoid_prime(zs[-1])\\n\",\n    \"        nabla_b[-1] = delta\\n\",\n    \"        nabla_w[-1] = np.dot(delta, activations[-2].transpose())\\n\",\n    \"        # Note that the variable l in the loop below is used a little\\n\",\n    \"        # differently to the notation in Chapter 2 of the book.  Here,\\n\",\n    \"        # l = 1 means the last layer of neurons, l = 2 is the\\n\",\n    \"        # second-last layer, and so on.  It's a renumbering of the\\n\",\n    \"        # scheme in the book, used here to take advantage of the fact\\n\",\n    \"        # that Python can use negative indices in lists.\\n\",\n    \"        for l in range(2, self.num_layers):\\n\",\n    \"            z = zs[-l]\\n\",\n    \"            sp = sigmoid_prime(z)\\n\",\n    \"            delta = np.dot(self.weights[-l+1].transpose(), delta) * sp\\n\",\n    \"            nabla_b[-l] = delta\\n\",\n    \"            nabla_w[-l] = np.dot(delta, activations[-l-1].transpose())\\n\",\n    \"        return (nabla_b, nabla_w)\\n\",\n    \"\\n\",\n    \"    def evaluate(self, test_data):\\n\",\n    \"        \\\"\\\"\\\"Return the number of test inputs for which the neural\\n\",\n    \"        network outputs the correct result. Note that the neural\\n\",\n    \"        network's output is assumed to be the index of whichever\\n\",\n    \"        neuron in the final layer has the highest activation.\\\"\\\"\\\"\\n\",\n    \"        test_results = [(np.argmax(self.feedforward(x)), y)\\n\",\n    \"                        for (x, y) in test_data]\\n\",\n    \"        return sum(int(x == y) for (x, y) in test_results)\\n\",\n    \"\\n\",\n    \"    def cost_derivative(self, output_activations, y):\\n\",\n    \"        \\\"\\\"\\\"Return the vector of partial derivatives \\\\partial C_x /\\n\",\n    \"        \\\\partial a for the output activations.\\\"\\\"\\\"\\n\",\n    \"        return (output_activations-y)\\n\",\n    \"\\n\",\n    \"#### Miscellaneous functions\\n\",\n    \"def sigmoid(z):\\n\",\n    \"    \\\"\\\"\\\"The sigmoid function.\\\"\\\"\\\"\\n\",\n    \"    return 1.0/(1.0+np.exp(-z))\\n\",\n    \"\\n\",\n    \"def sigmoid_prime(z):\\n\",\n    \"    \\\"\\\"\\\"Derivative of the sigmoid function.\\\"\\\"\\\"\\n\",\n    \"    return sigmoid(z)*(1-sigmoid(z))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def vectorized_result(j):\\n\",\n    \"    \\\"\\\"\\\"Return a 10-dimensional unit vector with a 1.0 in the jth\\n\",\n    \"    position and zeroes elsewhere.  This is used to convert a digit\\n\",\n    \"    (0...9) into a corresponding desired output from the neural\\n\",\n    \"    network.\\\"\\\"\\\"\\n\",\n    \"    e = np.zeros((10, 1))\\n\",\n    \"    e[j] = 1.0\\n\",\n    \"    return e\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"net = Network([784, 30, 10])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"training_inputs = [np.reshape(x, (784, 1)) for x in X_train.copy()]\\n\",\n    \"training_results = [vectorized_result(y) for y in y_train.copy()]\\n\",\n    \"training_data = zip(training_inputs, training_results)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"test_inputs = [np.reshape(x, (784, 1)) for x in X_test.copy()]\\n\",\n    \"test_data = zip(test_inputs, y_test.copy())\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch 0: 1348 / 10000\\n\",\n      \"Epoch 1: 1939 / 10000\\n\",\n      \"Epoch 2: 2046 / 10000\\n\",\n      \"Epoch 3: 1422 / 10000\\n\",\n      \"Epoch 4: 1365 / 10000\\n\",\n      \"Epoch 5: 1351 / 10000\\n\",\n      \"Epoch 6: 1879 / 10000\\n\",\n      \"Epoch 7: 1806 / 10000\\n\",\n      \"Epoch 8: 1754 / 10000\\n\",\n      \"Epoch 9: 1974 / 10000\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"net.SGD(training_data, 10, 10, 3.0, test_data=test_data)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch 0: 3526 / 10000\\n\",\n      \"Epoch 1: 3062 / 10000\\n\",\n      \"Epoch 2: 2946 / 10000\\n\",\n      \"Epoch 3: 2462 / 10000\\n\",\n      \"Epoch 4: 3617 / 10000\\n\",\n      \"Epoch 5: 3773 / 10000\\n\",\n      \"Epoch 6: 3568 / 10000\\n\",\n      \"Epoch 7: 4459 / 10000\\n\",\n      \"Epoch 8: 3009 / 10000\\n\",\n      \"Epoch 9: 2660 / 10000\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"net = Network([784, 10, 10])\\n\",\n    \"\\n\",\n    \"training_inputs = [np.reshape(x, (784, 1)) for x in X_train.copy()]\\n\",\n    \"training_results = [vectorized_result(y) for y in y_train.copy()]\\n\",\n    \"training_data = zip(training_inputs, training_results)\\n\",\n    \"\\n\",\n    \"test_inputs = [np.reshape(x, (784, 1)) for x in X_test.copy()]\\n\",\n    \"test_data = zip(test_inputs, y_test.copy())\\n\",\n    \"\\n\",\n    \"net.SGD(training_data, 10, 10, 1.0, test_data=test_data)\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.4.3\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "deep-learning/keras-tutorial/2.1 Supervised Learning - ConvNets.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Credits: Forked from [deep-learning-keras-tensorflow](https://github.com/leriomaggio/deep-learning-keras-tensorflow) by Valerio Maggio\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"slide\"\n    }\n   },\n   \"source\": [\n    \"# Convolutional Neural Network\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"skip\"\n    }\n   },\n   \"source\": [\n    \"### References:\\n\",\n    \"\\n\",\n    \"Some of the images and the content I used came from this great couple of blog posts \\\\[1\\\\] [https://adeshpande3.github.io/adeshpande3.github.io/]() and \\\\[2\\\\] the  terrific book, [\\\"Neural Networks and Deep Learning\\\"](http://neuralnetworksanddeeplearning.com/) by Michael Nielsen. (**Strongly recommend**)  \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"A convolutional neural network (CNN, or ConvNet) is a type of **feed-forward** artificial neural network in which the connectivity pattern between its neurons is inspired by the organization of the animal visual cortex.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"fragment\"\n    }\n   },\n   \"source\": [\n    \"The networks consist of multiple layers of small neuron collections which process portions of the input image, called **receptive fields**. \\n\",\n    \"\\n\",\n    \"The outputs of these collections are then tiled so that their input regions overlap, to obtain a _better representation_ of the original image; this is repeated for every such layer.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"## How does it look like?\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true,\n    \"slideshow\": {\n     \"slide_type\": \"-\"\n    }\n   },\n   \"source\": [\n    \"<img src=\\\"imgs/convnets_cover.png\\\" width=\\\"70%\\\" />\\n\",\n    \"\\n\",\n    \"> source: https://flickrcode.files.wordpress.com/2014/10/conv-net2.png\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"slide\"\n    }\n   },\n   \"source\": [\n    \"# The Problem Space \\n\",\n    \"\\n\",\n    \"## Image Classification\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"Image classification is the task of taking an input image and outputting a class (a cat, dog, etc) or a probability of classes that best describes the image. \\n\",\n    \"\\n\",\n    \"For humans, this task of recognition is one of the first skills we learn from the moment we are born and is one that comes naturally and effortlessly as adults.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"These skills of being able to quickly recognize patterns, *generalize* from prior knowledge, and adapt to different image environments are ones that we do not share with machines.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"## Inputs and Outputs\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<img src=\\\"imgs/cnn1.png\\\" width=\\\"70%\\\" />\\n\",\n    \"\\n\",\n    \"source: [http://www.pawbuzz.com/wp-content/uploads/sites/551/2014/11/corgi-puppies-21.jpg]()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"When a computer sees an image (takes an image as input), it will see an array of pixel values. \\n\",\n    \"\\n\",\n    \"Depending on the resolution and size of the image, it will see a 32 x 32 x 3 array of numbers (The 3 refers to RGB values).\\n\",\n    \"\\n\",\n    \"let's say we have a color image in JPG form and its size is 480 x 480. The representative array will be 480 x 480 x 3. Each of these numbers is given a value from 0 to 255 which describes the pixel intensity at that point.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"## Goal\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"What we want the computer to do is to be able to differentiate between all the images it’s given and figure out the unique features that make a dog a dog or that make a cat a cat. \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"fragment\"\n    }\n   },\n   \"source\": [\n    \"When we look at a picture of a dog, we can classify it as such if the picture has identifiable features such as paws or 4 legs. \\n\",\n    \"\\n\",\n    \"In a similar way, the computer should be able to perform image classification by looking for *low level* features such as edges and curves, and then building up to more abstract concepts through a series of **convolutional layers**.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"## Structure of a CNN\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"> A more detailed overview of what CNNs do would be that you take the image, pass it through a series of convolutional, nonlinear, pooling (downsampling), and fully connected layers, and get an output. As we said earlier, the output can be a single class or a probability of classes that best describes the image. \\n\",\n    \"\\n\",\n    \"source: [1]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"slide\"\n    }\n   },\n   \"source\": [\n    \"# Convolutional Layer\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"The first layer in a CNN is always a **Convolutional Layer**.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"<img src =\\\"imgs/conv.png\\\" width=\\\"50%\\\">\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"### Convolutional filters\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"A Convolutional Filter much like a **kernel** in image recognition is a small matrix useful for blurring, sharpening, embossing, edge detection, and more. \\n\",\n    \"\\n\",\n    \"This is accomplished by means of convolution between a kernel and an image.\\n\",\n    \"\\n\",\n    \"The main difference _here_ is that the conv matrices are **learned**.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"<img src=\\\"imgs/keDyv.png\\\" width=\\\"90%\\\">\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"As the filter is sliding, or **convolving**, around the input image, it is multiplying the values in the filter with the original pixel values of the image (aka computing **element wise multiplications**).\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<img src=\\\"imgs/cnn2.png\\\" width=\\\"80%\\\">\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"iNow, we repeat this process for every location on the input volume. (Next step would be moving the filter to the right by 1 unit, then right again by 1, and so on).\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"fragment\"\n    }\n   },\n   \"source\": [\n    \"After sliding the filter over all the locations, we are left with an array of numbers usually called an **activation map** or **feature map**.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"## High Level Perspective\\n\",\n    \"\\n\",\n    \"Let’s talk about briefly what this convolution is actually doing from a high level. \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"Each of these filters can be thought of as **feature identifiers** (e.g. *straight edges, simple colors, curves*)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"fragment\"\n    }\n   },\n   \"source\": [\n    \"<img src=\\\"imgs/cnn3.png\\\" width=\\\"70%\\\" />\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"### Visualisation of the Receptive Field\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<img src=\\\"imgs/cnn4.png\\\" width=\\\"80%\\\" />\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"<img src=\\\"imgs/cnn5.png\\\" width=\\\"80%\\\" />\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"<img src=\\\"imgs/cnn6.png\\\" width=\\\"80%\\\" />\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"fragment\"\n    }\n   },\n   \"source\": [\n    \"The value is much lower! This is because there wasn’t anything in the image section that responded to the curve detector filter. Remember, the output of this conv layer is an activation map. \\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"slide\"\n    }\n   },\n   \"source\": [\n    \"# Going Deeper Through the Network\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"Now in a traditional **convolutional neural network** architecture, there are other layers that are interspersed between these conv layers.\\n\",\n    \"\\n\",\n    \"<img src=\\\"https://adeshpande3.github.io/assets/Table.png\\\"/>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"## ReLU (Rectified Linear Units) Layer\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"fragment\"\n    }\n   },\n   \"source\": [\n    \" After each conv layer, it is convention to apply a *nonlinear layer* (or **activation layer**) immediately afterward.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"The purpose of this layer is to introduce nonlinearity to a system that basically has just been computing linear operations during the conv layers (just element wise multiplications and summations)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"In the past, nonlinear functions like tanh and sigmoid were used, but researchers found out that **ReLU layers** work far better because the network is able to train a lot faster (because of the computational efficiency) without making a significant difference to the accuracy.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"fragment\"\n    }\n   },\n   \"source\": [\n    \"It also helps to alleviate the **vanishing gradient problem**, which is the issue where the lower layers of the network train very slowly because the gradient decreases exponentially through the layers\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"(**very briefly**)\\n\",\n    \"\\n\",\n    \"Vanishing gradient problem depends on the choice of the activation function. \\n\",\n    \"\\n\",\n    \"Many common activation functions (e.g `sigmoid` or `tanh`) *squash* their input into a very small output range in a very non-linear fashion. \\n\",\n    \"\\n\",\n    \"For example, sigmoid maps the real number line onto a \\\"small\\\" range of [0, 1].\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"As a result, there are large regions of the input space which are mapped to an extremely small range. \\n\",\n    \"\\n\",\n    \"In these regions of the input space, even a large change in the input will produce a small change in the output - hence the **gradient is small**.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"### ReLu\\n\",\n    \"\\n\",\n    \"The **ReLu** function is defined as $f(x) = \\\\max(0, x),$ [2]\\n\",\n    \"\\n\",\n    \"A smooth approximation to the rectifier is the *analytic function*: $f(x) = \\\\ln(1 + e^x)$\\n\",\n    \"\\n\",\n    \"which is called the **softplus** function.\\n\",\n    \"\\n\",\n    \"The derivative of softplus is $f'(x) = e^x / (e^x + 1) = 1 / (1 + e^{-x})$, i.e. the **logistic function**.\\n\",\n    \"\\n\",\n    \"[2] [http://www.cs.toronto.edu/~fritz/absps/reluICML.pdf]() by G. E. Hinton \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"## Pooling Layers\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"fragment\"\n    }\n   },\n   \"source\": [\n    \" After some ReLU layers, it is customary to apply a **pooling layer** (aka *downsampling layer*).\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"fragment\"\n    }\n   },\n   \"source\": [\n    \"In this category, there are also several layer options, with **maxpooling** being the most popular. \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"Example of a MaxPooling filter\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<img src=\\\"imgs/MaxPool.png\\\" width=\\\"80%\\\" />\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"fragment\"\n    }\n   },\n   \"source\": [\n    \"Other options for pooling layers are average pooling and L2-norm pooling. \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"The intuition behind this Pooling layer is that once we know that a specific feature is in the original input volume (there will be a high activation value), its exact location is not as important as its relative location to the other features. \\n\",\n    \"\\n\",\n    \"Therefore this layer drastically reduces the spatial dimension (the length and the width but not the depth) of the input volume.\\n\",\n    \"\\n\",\n    \"This serves two main purposes: reduce the amount of parameters; controlling overfitting. \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"An intuitive explanation for the usefulness of pooling could be explained by an example: \\n\",\n    \"\\n\",\n    \"Lets assume that we have a filter that is used for detecting faces. The exact pixel location of the face is less relevant then the fact that there is a face \\\"somewhere at the top\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"## Dropout Layer\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"The **dropout layers** have the very specific function to *drop out* a random set of activations in that layers by setting them to zero in the forward pass. Simple as that. \\n\",\n    \"\\n\",\n    \"It allows to avoid *overfitting* but has to be used **only** at training time and **not** at test time. \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"## Fully Connected Layer\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"fragment\"\n    }\n   },\n   \"source\": [\n    \"The last layer, however, is an important one, namely the **Fully Connected Layer**.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"Basically, a FC layer looks at what high level features most strongly correlate to a particular class and has particular weights so that when you compute the products between the weights and the previous layer, you get the correct probabilities for the different classes.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<img src=\\\"imgs/ConvNet LeNet.png\\\" width=\\\"90%\\\" />\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"slide\"\n    }\n   },\n   \"source\": [\n    \"# CNN in Keras\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"**Keras** supports:\\n\",\n    \"\\n\",\n    \"- 1D Convolutional Layers;\\n\",\n    \"- 2D Convolutional Layers;\\n\",\n    \"- 3D Convolutional Layers;\\n\",\n    \"\\n\",\n    \"The corresponding `keras` package is `keras.layers.convolutional`\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"#### Convolution1D\\n\",\n    \"\\n\",\n    \"```python\\n\",\n    \"from keras.layers.convolutional import Convolution1D\\n\",\n    \"Convolution1D(nb_filter, filter_length, init='uniform',\\n\",\n    \"              activation='linear', weights=None,\\n\",\n    \"              border_mode='valid', subsample_length=1,\\n\",\n    \"              W_regularizer=None, b_regularizer=None,\\n\",\n    \"              activity_regularizer=None, W_constraint=None,\\n\",\n    \"              b_constraint=None, bias=True, input_dim=None,\\n\",\n    \"              input_length=None)\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"fragment\"\n    }\n   },\n   \"source\": [\n    \">Convolution operator for filtering neighborhoods of **one-dimensional inputs**. When using this layer as the first layer in a model, either provide the keyword argument `input_dim` (int, e.g. 128 for sequences of 128-dimensional vectors), or `input_shape` (tuple of integers, e.g. (10, 128) for sequences of 10 vectors of 128-dimensional vectors).\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"#### Example\\n\",\n    \"\\n\",\n    \"```python\\n\",\n    \"\\n\",\n    \"# apply a convolution 1d of length 3 to a sequence with 10 timesteps,\\n\",\n    \"# with 64 output filters\\n\",\n    \"model = Sequential()\\n\",\n    \"model.add(Convolution1D(64, 3, border_mode='same', input_shape=(10, 32)))\\n\",\n    \"# now model.output_shape == (None, 10, 64)\\n\",\n    \"\\n\",\n    \"# add a new conv1d on top\\n\",\n    \"model.add(Convolution1D(32, 3, border_mode='same'))\\n\",\n    \"# now model.output_shape == (None, 10, 32)\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"#### Convolution2D\\n\",\n    \"\\n\",\n    \"```python\\n\",\n    \"from keras.layers.convolutional import Convolution2D\\n\",\n    \"Convolution2D(nb_filter, nb_row, nb_col, \\n\",\n    \"              init='glorot_uniform',\\n\",\n    \"              activation='linear', weights=None,\\n\",\n    \"              border_mode='valid', subsample=(1, 1),\\n\",\n    \"              dim_ordering='default', W_regularizer=None,\\n\",\n    \"              b_regularizer=None, activity_regularizer=None,\\n\",\n    \"              W_constraint=None, b_constraint=None, \\n\",\n    \"              bias=True)\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"#### Example\\n\",\n    \"\\n\",\n    \"```python\\n\",\n    \"\\n\",\n    \"# apply a 3x3 convolution with 64 output filters on a 256x256 image:\\n\",\n    \"model = Sequential()\\n\",\n    \"model.add(Convolution2D(64, 3, 3, border_mode='same', \\n\",\n    \"          input_shape=(3, 256, 256)))\\n\",\n    \"# now model.output_shape == (None, 64, 256, 256)\\n\",\n    \"\\n\",\n    \"# add a 3x3 convolution on top, with 32 output filters:\\n\",\n    \"model.add(Convolution2D(32, 3, 3, border_mode='same'))\\n\",\n    \"# now model.output_shape == (None, 32, 256, 256)\\n\",\n    \"\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"## Dimensions of Conv filters in Keras\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"fragment\"\n    }\n   },\n   \"source\": [\n    \"The complex structure of ConvNets *may* lead to a representation that is challenging to understand.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"fragment\"\n    }\n   },\n   \"source\": [\n    \"Of course, the dimensions vary according to the dimension of the Convolutional filters (e.g. 1D, 2D)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"### Convolution1D\\n\",\n    \"\\n\",\n    \"**Input Shape**:\\n\",\n    \"\\n\",\n    \"**3D** tensor with shape: (`samples`, `steps`, `input_dim`).\\n\",\n    \"\\n\",\n    \"**Output Shape**:\\n\",\n    \"\\n\",\n    \"**3D** tensor with shape: (`samples`, `new_steps`, `nb_filter`).\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"### Convolution2D\\n\",\n    \"\\n\",\n    \"**Input Shape**:\\n\",\n    \"\\n\",\n    \"**4D** tensor with shape: \\n\",\n    \"\\n\",\n    \"- (`samples`, `channels`, `rows`, `cols`) if `dim_ordering='th'`\\n\",\n    \"- (`samples`, `rows`, `cols`, `channels`) if `dim_ordering='tf'`\\n\",\n    \"\\n\",\n    \"**Output Shape**:\\n\",\n    \"\\n\",\n    \"**4D** tensor with shape:\\n\",\n    \"\\n\",\n    \"- (`samples`, `nb_filter`, `new_rows`, `new_cols`) \\n\",\n    \"if `dim_ordering='th'`\\n\",\n    \"- (`samples`, `new_rows`, `new_cols`, `nb_filter`) if `dim_ordering='tf'`\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"celltoolbar\": \"Slideshow\",\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.4.3\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "deep-learning/keras-tutorial/2.2.1 Supervised Learning - ConvNet HandsOn Part I.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Credits: Forked from [deep-learning-keras-tensorflow](https://github.com/leriomaggio/deep-learning-keras-tensorflow) by Valerio Maggio\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"slide\"\n    }\n   },\n   \"source\": [\n    \"# ConvNet HandsOn with Keras\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"## Problem Definition\\n\",\n    \"\\n\",\n    \"*Recognize handwritten digits*\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"## Data\\n\",\n    \"\\n\",\n    \"The MNIST database ([link](http://yann.lecun.com/exdb/mnist)) has a database of handwritten digits. \\n\",\n    \"\\n\",\n    \"The training set has $60,000$ samples. \\n\",\n    \"The test set has $10,000$ samples.\\n\",\n    \"\\n\",\n    \"The digits are size-normalized and centered in a fixed-size image. \\n\",\n    \"\\n\",\n    \"The data page has description on how the data was collected. It also has reports the benchmark of various algorithms on the test dataset. \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"### Load the data\\n\",\n    \"\\n\",\n    \"The data is available in the repo's `data` folder. Let's load that using the `keras` library. \\n\",\n    \"\\n\",\n    \"For now, let's load the data and see how it looks.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Using Theano backend.\\n\",\n      \"Using gpu device 0: GeForce GTX 760 (CNMeM is enabled with initial size: 90.0% of memory, cuDNN 4007)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import keras\\n\",\n    \"from keras.datasets import mnist\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": true,\n    \"slideshow\": {\n     \"slide_type\": \"skip\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!mkdir -p $HOME/.keras/datasets/euroscipy_2016_dl-keras/data/\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Set the full path to mnist.pkl.gz\\n\",\n    \"path_to_dataset = \\\"euroscipy_2016_dl-keras/data/mnist.pkl.gz\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"code_folding\": [],\n    \"collapsed\": false,\n    \"slideshow\": {\n     \"slide_type\": \"fragment\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Downloading data from https://s3.amazonaws.com/img-datasets/mnist.pkl.gz\\n\",\n      \"15024128/15296311 [============================>.] - ETA: 0s\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Load the datasets\\n\",\n    \"(X_train, y_train), (X_test, y_test) = mnist.load_data(path_to_dataset)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"slide\"\n    }\n   },\n   \"source\": [\n    \"# Basic data analysis on the dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# What is the type of X_train?\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# What is the type of y_train?\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Find number of observations in training data\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Find number of observations in test data\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[[0, 0, 0, ..., 0, 0, 0],\\n\",\n       \"        [0, 0, 0, ..., 0, 0, 0],\\n\",\n       \"        [0, 0, 0, ..., 0, 0, 0],\\n\",\n       \"        ..., \\n\",\n       \"        [0, 0, 0, ..., 0, 0, 0],\\n\",\n       \"        [0, 0, 0, ..., 0, 0, 0],\\n\",\n       \"        [0, 0, 0, ..., 0, 0, 0]],\\n\",\n       \"\\n\",\n       \"       [[0, 0, 0, ..., 0, 0, 0],\\n\",\n       \"        [0, 0, 0, ..., 0, 0, 0],\\n\",\n       \"        [0, 0, 0, ..., 0, 0, 0],\\n\",\n       \"        ..., \\n\",\n       \"        [0, 0, 0, ..., 0, 0, 0],\\n\",\n       \"        [0, 0, 0, ..., 0, 0, 0],\\n\",\n       \"        [0, 0, 0, ..., 0, 0, 0]]], dtype=uint8)\"\n      ]\n     },\n     \"execution_count\": 23,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# Display first 2 records of X_train\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([5, 0, 4, 1, 9, 2, 1, 3, 1, 4], dtype=uint8)\"\n      ]\n     },\n     \"execution_count\": 24,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# Display the first 10 records of y_train\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=uint8),\\n\",\n       \" array([5923, 6742, 5958, 6131, 5842, 5421, 5918, 6265, 5851, 5949]))\"\n      ]\n     },\n     \"execution_count\": 26,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# Find the number of observations for each digit in the y_train dataset \\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=uint8),\\n\",\n       \" array([ 980, 1135, 1032, 1010,  982,  892,  958, 1028,  974, 1009]))\"\n      ]\n     },\n     \"execution_count\": 27,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# Find the number of observations for each digit in the y_test dataset \\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(60000, 28, 28)\"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# What is the dimension of X_train?. What does that mean?\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"slide\"\n    }\n   },\n   \"source\": [\n    \"### Display Images\\n\",\n    \"\\n\",\n    \"Let's now display some of the images and see how they look\\n\",\n    \"\\n\",\n    \"We will be using `matplotlib` library for displaying the image\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"collapsed\": true,\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from matplotlib import pyplot\\n\",\n    \"import matplotlib as mpl\\n\",\n    \"%matplotlib inline\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {\n    \"collapsed\": true,\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Displaying the first training data\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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WN3/8mgbeOy+/8ys7slfdvdb6xxXR7cA9qgUPHNbJqkDZK2SvLsa7Gk\\nG1W9v98vqVvS7e7eW+P6FB9og8Jn/II7p/hAG/CyXABHofhAQBQfCIjiAwFRfCAgig8ERPGBgCg+\\nEBDFBwKi+EBAFB8IiOIDAVF8ICCKDwRE8YGAKD4QUO7bazeCGe/CDZRJ09+BB0D5cFMfCIjiAwFR\\nfCAgig8ERPGBgP4fzOndNhthqDAAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x121299e80>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig = pyplot.figure()\\n\",\n    \"ax = fig.add_subplot(1,1,1)\\n\",\n    \"imgplot = ax.imshow(X_train[1], cmap=mpl.cm.Greys)\\n\",\n    \"imgplot.set_interpolation('nearest')\\n\",\n    \"ax.xaxis.set_ticks_position('top')\\n\",\n    \"ax.yaxis.set_ticks_position('left')\\n\",\n    \"pyplot.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true,\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Let's now display the 11th record\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 52,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAP4AAAD7CAYAAABKWyniAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\neQAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x124541dd8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"celltoolbar\": \"Slideshow\",\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.4.3\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "deep-learning/keras-tutorial/2.2.2 Supervised Learning - ConvNet HandsOn Part II.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Credits: Forked from [deep-learning-keras-tensorflow](https://github.com/leriomaggio/deep-learning-keras-tensorflow) by Valerio Maggio\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"slide\"\n    }\n   },\n   \"source\": [\n    \"# Convolution Nets for MNIST\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"Deep Learning models can take quite a bit of time to run, particularly if GPU isn't used. \\n\",\n    \"\\n\",\n    \"In the interest of time, you could sample a subset of observations (e.g. $1000$) that are a particular number of your choice (e.g. $6$) and $1000$ observations that aren't that particular number (i.e. $\\\\neq 6$). \\n\",\n    \"\\n\",\n    \"We will build a model using that and see how it performs on the test dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Using Theano backend.\\n\",\n      \"Using gpu device 0: GeForce GTX 760 (CNMeM is enabled with initial size: 90.0% of memory, cuDNN 4007)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"#Import the required libraries\\n\",\n    \"import numpy as np\\n\",\n    \"np.random.seed(1338)\\n\",\n    \"\\n\",\n    \"from keras.datasets import mnist\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": true,\n    \"slideshow\": {\n     \"slide_type\": \"fragment\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from keras.models import Sequential\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": true,\n    \"slideshow\": {\n     \"slide_type\": \"fragment\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from keras.layers.core import Dense, Dropout, Activation, Flatten\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"slideshow\": {\n     \"slide_type\": \"fragment\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from keras.layers.convolutional import Convolution2D\\n\",\n    \"from keras.layers.pooling import MaxPooling2D\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": true,\n    \"slideshow\": {\n     \"slide_type\": \"fragment\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from keras.utils import np_utils\\n\",\n    \"from keras.optimizers import SGD\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"## Loading Data\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {\n    \"collapsed\": true,\n    \"slideshow\": {\n     \"slide_type\": \"-\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"path_to_dataset = \\\"euroscipy_2016_dl-keras/data/mnist.pkl.gz\\\"\\n\",\n    \"\\n\",\n    \"#Load the training and testing data\\n\",\n    \"(X_train, y_train), (X_test, y_test) = mnist.load_data(path_to_dataset)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {\n    \"collapsed\": true,\n    \"slideshow\": {\n     \"slide_type\": \"skip\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"X_test_orig = X_test\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"## Data Preparation\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {\n    \"collapsed\": true,\n    \"slideshow\": {\n     \"slide_type\": \"-\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"img_rows, img_cols = 28, 28\\n\",\n    \"\\n\",\n    \"X_train = X_train.reshape(X_train.shape[0], 1, img_rows, img_cols)\\n\",\n    \"X_test = X_test.reshape(X_test.shape[0], 1, img_rows, img_cols)\\n\",\n    \"\\n\",\n    \"X_train = X_train.astype('float32')\\n\",\n    \"X_test = X_test.astype('float32')\\n\",\n    \"\\n\",\n    \"X_train /= 255\\n\",\n    \"X_test /= 255\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Seed for reproducibilty\\n\",\n    \"np.random.seed(1338)\\n\",\n    \"\\n\",\n    \"# Test data\\n\",\n    \"X_test = X_test.copy()\\n\",\n    \"Y = y_test.copy()\\n\",\n    \"\\n\",\n    \"# Converting the output to binary classification(Six=1,Not Six=0)\\n\",\n    \"Y_test = Y == 6\\n\",\n    \"Y_test = Y_test.astype(int)\\n\",\n    \"\\n\",\n    \"# Selecting the 5918 examples where the output is 6\\n\",\n    \"X_six = X_train[y_train == 6].copy()\\n\",\n    \"Y_six = y_train[y_train == 6].copy()\\n\",\n    \"\\n\",\n    \"# Selecting the examples where the output is not 6\\n\",\n    \"X_not_six = X_train[y_train != 6].copy()\\n\",\n    \"Y_not_six = y_train[y_train != 6].copy()\\n\",\n    \"\\n\",\n    \"# Selecting 6000 random examples from the data that \\n\",\n    \"# only contains the data where the output is not 6\\n\",\n    \"random_rows = np.random.randint(0,X_six.shape[0],6000)\\n\",\n    \"X_not_six = X_not_six[random_rows]\\n\",\n    \"Y_not_six = Y_not_six[random_rows]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {\n    \"collapsed\": true,\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Appending the data with output as 6 and data with output as <> 6\\n\",\n    \"X_train = np.append(X_six,X_not_six)\\n\",\n    \"\\n\",\n    \"# Reshaping the appended data to appropraite form\\n\",\n    \"X_train = X_train.reshape(X_six.shape[0] + X_not_six.shape[0], \\n\",\n    \"                          1, img_rows, img_cols)\\n\",\n    \"\\n\",\n    \"# Appending the labels and converting the labels to \\n\",\n    \"# binary classification(Six=1,Not Six=0)\\n\",\n    \"Y_labels = np.append(Y_six,Y_not_six)\\n\",\n    \"Y_train = Y_labels == 6 \\n\",\n    \"Y_train = Y_train.astype(int)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(11918, 1, 28, 28) (11918,) (10000, 1, 28, 28) (10000, 2)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(X_train.shape, Y_labels.shape, X_test.shape, Y_test.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"metadata\": {\n    \"collapsed\": true,\n    \"slideshow\": {\n     \"slide_type\": \"fragment\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Converting the classes to its binary categorical form\\n\",\n    \"nb_classes = 2\\n\",\n    \"Y_train = np_utils.to_categorical(Y_train, nb_classes)\\n\",\n    \"Y_test = np_utils.to_categorical(Y_test, nb_classes)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"slide\"\n    }\n   },\n   \"source\": [\n    \"# A simple CNN\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {\n    \"collapsed\": true,\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"#Initializing the values for the convolution neural network\\n\",\n    \"nb_epoch = 2\\n\",\n    \"batch_size = 128\\n\",\n    \"# number of convolutional filters to use\\n\",\n    \"nb_filters = 32\\n\",\n    \"# size of pooling area for max pooling\\n\",\n    \"nb_pool = 2\\n\",\n    \"# convolution kernel size\\n\",\n    \"nb_conv = 3\\n\",\n    \"\\n\",\n    \"sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"### Step 1: Model Definition\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = Sequential()\\n\",\n    \"\\n\",\n    \"model.add(Convolution2D(nb_filters, nb_conv, nb_conv,\\n\",\n    \"                        border_mode='valid',\\n\",\n    \"                        input_shape=(1, img_rows, img_cols)))\\n\",\n    \"model.add(Activation('relu'))\\n\",\n    \"\\n\",\n    \"model.add(Flatten())\\n\",\n    \"model.add(Dense(nb_classes))\\n\",\n    \"model.add(Activation('softmax'))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"### Step 2: Compile\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.compile(loss='categorical_crossentropy',\\n\",\n    \"              optimizer='sgd',\\n\",\n    \"              metrics=['accuracy'])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"### Step 3: Fit\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Train on 11918 samples, validate on 10000 samples\\n\",\n      \"Epoch 1/2\\n\",\n      \"11918/11918 [==============================] - 0s - loss: 0.2890 - acc: 0.9326 - val_loss: 0.1251 - val_acc: 0.9722\\n\",\n      \"Epoch 2/2\\n\",\n      \"11918/11918 [==============================] - 0s - loss: 0.1341 - acc: 0.9612 - val_loss: 0.1298 - val_acc: 0.9599\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<keras.callbacks.History at 0x7f6ccb68f630>\"\n      ]\n     },\n     \"execution_count\": 32,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"model.fit(X_train, Y_train, batch_size=batch_size, \\n\",\n    \"          nb_epoch=nb_epoch,verbose=1,\\n\",\n    \"          validation_data=(X_test, Y_test))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"### Step 4: Evaluate\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Test score: 0.129807630396\\n\",\n      \"Test accuracy: 0.9599\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Evaluating the model on the test data    \\n\",\n    \"score, accuracy = model.evaluate(X_test, Y_test, verbose=0)\\n\",\n    \"print('Test score:', score)\\n\",\n    \"print('Test accuracy:', accuracy)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"### Let's plot our model Predictions!\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"metadata\": {\n    \"collapsed\": true,\n    \"slideshow\": {\n     \"slide_type\": \"skip\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"%matplotlib inline\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 57,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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kNhWTiNhB6h8UGrGukLx1tTe2z9uvSMvKazJZu5jTe8p6WUFvnGbBjx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\\nWbumY8f6AAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f6cab246898>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"slice = 15\\n\",\n    \"predicted = model.predict(X_test[:slice]).argmax(-1)\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(16,8))\\n\",\n    \"for i in range(slice):\\n\",\n    \"    plt.subplot(1, slice, i+1)\\n\",\n    \"    plt.imshow(X_test_orig[i], interpolation='nearest')\\n\",\n    \"    plt.text(0, 0, predicted[i], color='black', \\n\",\n    \"             bbox=dict(facecolor='white', alpha=1))\\n\",\n    \"    plt.axis('off')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"slide\"\n    }\n   },\n   \"source\": [\n    \"# Adding more Dense Layers\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 44,\n   \"metadata\": {\n    \"collapsed\": true,\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = Sequential()\\n\",\n    \"model.add(Convolution2D(nb_filters, nb_conv, nb_conv,\\n\",\n    \"                        border_mode='valid',\\n\",\n    \"                        input_shape=(1, img_rows, img_cols)))\\n\",\n    \"model.add(Activation('relu'))\\n\",\n    \"\\n\",\n    \"model.add(Flatten())\\n\",\n    \"model.add(Dense(128))\\n\",\n    \"model.add(Activation('relu'))\\n\",\n    \"\\n\",\n    \"model.add(Dense(nb_classes))\\n\",\n    \"model.add(Activation('softmax'))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 45,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Train on 11918 samples, validate on 10000 samples\\n\",\n      \"Epoch 1/2\\n\",\n      \"11918/11918 [==============================] - 0s - loss: 0.3044 - acc: 0.9379 - val_loss: 0.1469 - val_acc: 0.9625\\n\",\n      \"Epoch 2/2\\n\",\n      \"11918/11918 [==============================] - 0s - loss: 0.1189 - acc: 0.9640 - val_loss: 0.1058 - val_acc: 0.9655\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<keras.callbacks.History at 0x7f6cf59f7358>\"\n      ]\n     },\n     \"execution_count\": 45,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"model.compile(loss='categorical_crossentropy',\\n\",\n    \"              optimizer='sgd',\\n\",\n    \"              metrics=['accuracy'])\\n\",\n    \"\\n\",\n    \"model.fit(X_train, Y_train, batch_size=batch_size, \\n\",\n    \"          nb_epoch=nb_epoch,verbose=1,\\n\",\n    \"          validation_data=(X_test, Y_test))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 46,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Test score: 0.105762729073\\n\",\n      \"Test accuracy: 0.9655\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"#Evaluating the model on the test data    \\n\",\n    \"score, accuracy = model.evaluate(X_test, Y_test, verbose=0)\\n\",\n    \"print('Test score:', score)\\n\",\n    \"print('Test accuracy:', accuracy)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"slide\"\n    }\n   },\n   \"source\": [\n    \"# Adding Dropout\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 47,\n   \"metadata\": {\n    \"collapsed\": true,\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = Sequential()\\n\",\n    \"\\n\",\n    \"model.add(Convolution2D(nb_filters, nb_conv, nb_conv,\\n\",\n    \"                        border_mode='valid',\\n\",\n    \"                        input_shape=(1, img_rows, img_cols)))\\n\",\n    \"model.add(Activation('relu'))\\n\",\n    \"\\n\",\n    \"model.add(Flatten())\\n\",\n    \"model.add(Dense(128))\\n\",\n    \"model.add(Activation('relu'))\\n\",\n    \"model.add(Dropout(0.5))\\n\",\n    \"model.add(Dense(nb_classes))\\n\",\n    \"model.add(Activation('softmax'))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 48,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Train on 11918 samples, validate on 10000 samples\\n\",\n      \"Epoch 1/2\\n\",\n      \"11918/11918 [==============================] - 0s - loss: 0.3128 - acc: 0.9097 - val_loss: 0.1438 - val_acc: 0.9624\\n\",\n      \"Epoch 2/2\\n\",\n      \"11918/11918 [==============================] - 0s - loss: 0.1362 - acc: 0.9580 - val_loss: 0.1145 - val_acc: 0.9628\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<keras.callbacks.History at 0x7f6ccb180208>\"\n      ]\n     },\n     \"execution_count\": 48,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"model.compile(loss='categorical_crossentropy',\\n\",\n    \"              optimizer='sgd',\\n\",\n    \"              metrics=['accuracy'])\\n\",\n    \"\\n\",\n    \"model.fit(X_train, Y_train, batch_size=batch_size, \\n\",\n    \"          nb_epoch=nb_epoch,verbose=1,\\n\",\n    \"          validation_data=(X_test, Y_test))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 49,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Test score: 0.11448907243\\n\",\n      \"Test accuracy: 0.9628\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"#Evaluating the model on the test data    \\n\",\n    \"score, accuracy = model.evaluate(X_test, Y_test, verbose=0)\\n\",\n    \"print('Test score:', score)\\n\",\n    \"print('Test accuracy:', accuracy)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"slide\"\n    }\n   },\n   \"source\": [\n    \"# Adding more Convolution Layers\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 50,\n   \"metadata\": {\n    \"collapsed\": true,\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = Sequential()\\n\",\n    \"model.add(Convolution2D(nb_filters, nb_conv, nb_conv,\\n\",\n    \"                        border_mode='valid',\\n\",\n    \"                        input_shape=(1, img_rows, img_cols)))\\n\",\n    \"model.add(Activation('relu'))\\n\",\n    \"model.add(Convolution2D(nb_filters, nb_conv, nb_conv))\\n\",\n    \"model.add(Activation('relu'))\\n\",\n    \"model.add(MaxPooling2D(pool_size=(nb_pool, nb_pool)))\\n\",\n    \"model.add(Dropout(0.25))\\n\",\n    \"    \\n\",\n    \"model.add(Flatten())\\n\",\n    \"model.add(Dense(128))\\n\",\n    \"model.add(Activation('relu'))\\n\",\n    \"model.add(Dropout(0.5))\\n\",\n    \"model.add(Dense(nb_classes))\\n\",\n    \"model.add(Activation('softmax'))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 51,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Train on 11918 samples, validate on 10000 samples\\n\",\n      \"Epoch 1/2\\n\",\n      \"11918/11918 [==============================] - 1s - loss: 0.4707 - acc: 0.8288 - val_loss: 0.2307 - val_acc: 0.9399\\n\",\n      \"Epoch 2/2\\n\",\n      \"11918/11918 [==============================] - 1s - loss: 0.1882 - acc: 0.9383 - val_loss: 0.1195 - val_acc: 0.9621\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<keras.callbacks.History at 0x7f6cc97b8748>\"\n      ]\n     },\n     \"execution_count\": 51,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"model.compile(loss='categorical_crossentropy',\\n\",\n    \"              optimizer='sgd',\\n\",\n    \"              metrics=['accuracy'])\\n\",\n    \"\\n\",\n    \"model.fit(X_train, Y_train, batch_size=batch_size, \\n\",\n    \"          nb_epoch=nb_epoch,verbose=1,\\n\",\n    \"          validation_data=(X_test, Y_test))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 53,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Test score: 0.11954063682\\n\",\n      \"Test accuracy: 0.9621\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"#Evaluating the model on the test data    \\n\",\n    \"score, accuracy = model.evaluate(X_test, Y_test, verbose=0)\\n\",\n    \"print('Test score:', score)\\n\",\n    \"print('Test accuracy:', accuracy)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Exercise\\n\",\n    \"\\n\",\n    \"The above code has been written as a function. \\n\",\n    \"\\n\",\n    \"Change some of the **hyperparameters** and see what happens. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 54,\n   \"metadata\": {\n    \"collapsed\": true,\n    \"slideshow\": {\n     \"slide_type\": \"skip\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Function for constructing the convolution neural network\\n\",\n    \"# Feel free to add parameters, if you want\\n\",\n    \"\\n\",\n    \"def build_model():\\n\",\n    \"    \\\"\\\"\\\"\\\"\\\"\\\"\\n\",\n    \"    model = Sequential()\\n\",\n    \"    model.add(Convolution2D(nb_filters, nb_conv, nb_conv,\\n\",\n    \"                        border_mode='valid',\\n\",\n    \"                        input_shape=(1, img_rows, img_cols)))\\n\",\n    \"    model.add(Activation('relu'))\\n\",\n    \"    model.add(Convolution2D(nb_filters, nb_conv, nb_conv))\\n\",\n    \"    model.add(Activation('relu'))\\n\",\n    \"    model.add(MaxPooling2D(pool_size=(nb_pool, nb_pool)))\\n\",\n    \"    model.add(Dropout(0.25))\\n\",\n    \"    \\n\",\n    \"    model.add(Flatten())\\n\",\n    \"    model.add(Dense(128))\\n\",\n    \"    model.add(Activation('relu'))\\n\",\n    \"    model.add(Dropout(0.5))\\n\",\n    \"    model.add(Dense(nb_classes))\\n\",\n    \"    model.add(Activation('softmax'))\\n\",\n    \"    \\n\",\n    \"    model.compile(loss='categorical_crossentropy',\\n\",\n    \"              optimizer='sgd',\\n\",\n    \"              metrics=['accuracy'])\\n\",\n    \"\\n\",\n    \"    model.fit(X_train, Y_train, batch_size=batch_size, \\n\",\n    \"              nb_epoch=nb_epoch,verbose=1,\\n\",\n    \"              validation_data=(X_test, Y_test))\\n\",\n    \"          \\n\",\n    \"\\n\",\n    \"    #Evaluating the model on the test data    \\n\",\n    \"    score, accuracy = model.evaluate(X_test, Y_test, verbose=0)\\n\",\n    \"    print('Test score:', score)\\n\",\n    \"    print('Test accuracy:', accuracy)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 55,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Train on 11918 samples, validate on 10000 samples\\n\",\n      \"Epoch 1/2\\n\",\n      \"11918/11918 [==============================] - 1s - loss: 0.5634 - acc: 0.7860 - val_loss: 0.3574 - val_acc: 0.9363\\n\",\n      \"Epoch 2/2\\n\",\n      \"11918/11918 [==============================] - 1s - loss: 0.2372 - acc: 0.9292 - val_loss: 0.2253 - val_acc: 0.9190\\n\",\n      \"Test score: 0.225333989978\\n\",\n      \"Test accuracy: 0.919\\n\",\n      \"1 loop, best of 1: 5.45 s per loop\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"#Timing how long it takes to build the model and test it.\\n\",\n    \"%timeit -n1 -r1 build_model()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"slide\"\n    }\n   },\n   \"source\": [\n    \"# Batch Normalisation\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Normalize the activations of the previous layer at each batch, i.e. applies a transformation that maintains the mean activation close to 0 and the activation standard deviation close to 1.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"## How to BatchNorm in Keras\\n\",\n    \"\\n\",\n    \"```python\\n\",\n    \"from keras.layers.normalization import BatchNormalization\\n\",\n    \"\\n\",\n    \"BatchNormalization(epsilon=1e-06, mode=0, \\n\",\n    \"                   axis=-1, momentum=0.99, \\n\",\n    \"                   weights=None, beta_init='zero', \\n\",\n    \"                   gamma_init='one')\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 58,\n   \"metadata\": {\n    \"collapsed\": true,\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Try to add a new BatchNormalization layer to the Model \\n\",\n    \"# (after the Dropout layer)\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"celltoolbar\": \"Slideshow\",\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.4.3\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "deep-learning/keras-tutorial/2.3 Supervised Learning - Famous Models with Keras.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Credits: Forked from [deep-learning-keras-tensorflow](https://github.com/leriomaggio/deep-learning-keras-tensorflow) by Valerio Maggio\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"slide\"\n    }\n   },\n   \"source\": [\n    \"# Practical Deep Learning\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"Constructing and training your own ConvNet from scratch can be Hard and a long task.\\n\",\n    \"\\n\",\n    \"A common trick used in Deep Learning is to use a **pre-trained** model and finetune it to the specific data it will be used for. \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"slide\"\n    }\n   },\n   \"source\": [\n    \"## Famous Models with Keras\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"This notebook contains code and reference for the following Keras models (gathered from [https://github.com/fchollet/deep-learning-models]())\\n\",\n    \"\\n\",\n    \"- VGG16\\n\",\n    \"- VGG19\\n\",\n    \"- ResNet50\\n\",\n    \"- Inception v3\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"skip\"\n    }\n   },\n   \"source\": [\n    \"## References\\n\",\n    \"\\n\",\n    \"- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://arxiv.org/abs/1409.1556) - please cite this paper if you use the VGG models in your work.\\n\",\n    \"- [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) - please cite this paper if you use the ResNet model in your work.\\n\",\n    \"- [Rethinking the Inception Architecture for Computer Vision](http://arxiv.org/abs/1512.00567) - please cite this paper if you use the Inception v3 model in your work.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"All architectures are compatible with both TensorFlow and Theano, and upon instantiation the models will be built according to the image dimension ordering set in your Keras configuration file at `~/.keras/keras.json`. \\n\",\n    \"\\n\",\n    \"For instance, if you have set `image_dim_ordering=tf`, then any model loaded from this repository will get built according to the TensorFlow dimension ordering convention, \\\"Width-Height-Depth\\\".\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"### Keras Configuration File\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"slideshow\": {\n     \"slide_type\": \"-\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"{\\r\\n\",\n      \"    \\\"image_dim_ordering\\\": \\\"th\\\",\\r\\n\",\n      \"    \\\"floatx\\\": \\\"float32\\\",\\r\\n\",\n      \"    \\\"epsilon\\\": 1e-07,\\r\\n\",\n      \"    \\\"backend\\\": \\\"theano\\\"\\r\\n\",\n      \"}\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!cat ~/.keras/keras.json\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"{\\r\\n\",\n      \"    \\\"image_dim_ordering\\\": \\\"th\\\",\\r\\n\",\n      \"    \\\"floatx\\\": \\\"float32\\\",\\r\\n\",\n      \"    \\\"epsilon\\\": 1e-07,\\r\\n\",\n      \"    \\\"backend\\\": \\\"tensorflow\\\"\\r\\n\",\n      \"}\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!sed -i 's/theano/tensorflow/g' ~/.keras/keras.json\\n\",\n    \"!cat ~/.keras/keras.json\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Using TensorFlow backend.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import keras\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"slideshow\": {\n     \"slide_type\": \"fragment\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Using gpu device 0: GeForce GTX 760 (CNMeM is enabled with initial size: 90.0% of memory, cuDNN 4007)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import theano\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"{\\r\\n\",\n      \"    \\\"image_dim_ordering\\\": \\\"th\\\",\\r\\n\",\n      \"    \\\"backend\\\": \\\"theano\\\",\\r\\n\",\n      \"    \\\"floatx\\\": \\\"float32\\\",\\r\\n\",\n      \"    \\\"epsilon\\\": 1e-07\\r\\n\",\n      \"}\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!sed -i 's/tensorflow/theano/g' ~/.keras/keras.json\\n\",\n    \"!cat ~/.keras/keras.json\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Using Theano backend.\\n\",\n      \"Using gpu device 0: GeForce GTX 760 (CNMeM is enabled with initial size: 90.0% of memory, cuDNN 4007)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import keras\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"slideshow\": {\n     \"slide_type\": \"skip\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Using Theano backend.\\n\",\n      \"Using gpu device 0: GeForce GTX 760 (CNMeM is enabled with initial size: 90.0% of memory, cuDNN 4007)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# %load deep_learning_models/imagenet_utils.py\\n\",\n    \"import numpy as np\\n\",\n    \"import json\\n\",\n    \"\\n\",\n    \"from keras.utils.data_utils import get_file\\n\",\n    \"from keras import backend as K\\n\",\n    \"\\n\",\n    \"CLASS_INDEX = None\\n\",\n    \"CLASS_INDEX_PATH = 'https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json'\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def preprocess_input(x, dim_ordering='default'):\\n\",\n    \"    if dim_ordering == 'default':\\n\",\n    \"        dim_ordering = K.image_dim_ordering()\\n\",\n    \"    assert dim_ordering in {'tf', 'th'}\\n\",\n    \"\\n\",\n    \"    if dim_ordering == 'th':\\n\",\n    \"        x[:, 0, :, :] -= 103.939\\n\",\n    \"        x[:, 1, :, :] -= 116.779\\n\",\n    \"        x[:, 2, :, :] -= 123.68\\n\",\n    \"        # 'RGB'->'BGR'\\n\",\n    \"        x = x[:, ::-1, :, :]\\n\",\n    \"    else:\\n\",\n    \"        x[:, :, :, 0] -= 103.939\\n\",\n    \"        x[:, :, :, 1] -= 116.779\\n\",\n    \"        x[:, :, :, 2] -= 123.68\\n\",\n    \"        # 'RGB'->'BGR'\\n\",\n    \"        x = x[:, :, :, ::-1]\\n\",\n    \"    return x\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def decode_predictions(preds):\\n\",\n    \"    global CLASS_INDEX\\n\",\n    \"    assert len(preds.shape) == 2 and preds.shape[1] == 1000\\n\",\n    \"    if CLASS_INDEX is None:\\n\",\n    \"        fpath = get_file('imagenet_class_index.json',\\n\",\n    \"                         CLASS_INDEX_PATH,\\n\",\n    \"                         cache_subdir='models')\\n\",\n    \"        CLASS_INDEX = json.load(open(fpath))\\n\",\n    \"    indices = np.argmax(preds, axis=-1)\\n\",\n    \"    results = []\\n\",\n    \"    for i in indices:\\n\",\n    \"        results.append(CLASS_INDEX[str(i)])\\n\",\n    \"    return results\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": true,\n    \"slideshow\": {\n     \"slide_type\": \"skip\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"IMAGENET_FOLDER = 'imgs/imagenet'  #in the repo\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"slide\"\n    }\n   },\n   \"source\": [\n    \"# VGG16\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# %load deep_learning_models/vgg16.py\\n\",\n    \"'''VGG16 model for Keras.\\n\",\n    \"\\n\",\n    \"# Reference:\\n\",\n    \"\\n\",\n    \"- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://arxiv.org/abs/1409.1556)\\n\",\n    \"\\n\",\n    \"'''\\n\",\n    \"from __future__ import print_function\\n\",\n    \"\\n\",\n    \"import numpy as np\\n\",\n    \"import warnings\\n\",\n    \"\\n\",\n    \"from keras.models import Model\\n\",\n    \"from keras.layers import Flatten, Dense, Input\\n\",\n    \"from keras.layers import Convolution2D, MaxPooling2D\\n\",\n    \"from keras.preprocessing import image\\n\",\n    \"from keras.utils.layer_utils import convert_all_kernels_in_model\\n\",\n    \"from keras.utils.data_utils import get_file\\n\",\n    \"from keras import backend as K\\n\",\n    \"\\n\",\n    \"TH_WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_th_dim_ordering_th_kernels.h5'\\n\",\n    \"TF_WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels.h5'\\n\",\n    \"TH_WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_th_dim_ordering_th_kernels_notop.h5'\\n\",\n    \"TF_WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5'\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def VGG16(include_top=True, weights='imagenet',\\n\",\n    \"          input_tensor=None):\\n\",\n    \"    '''Instantiate the VGG16 architecture,\\n\",\n    \"    optionally loading weights pre-trained\\n\",\n    \"    on ImageNet. Note that when using TensorFlow,\\n\",\n    \"    for best performance you should set\\n\",\n    \"    `image_dim_ordering=\\\"tf\\\"` in your Keras config\\n\",\n    \"    at ~/.keras/keras.json.\\n\",\n    \"\\n\",\n    \"    The model and the weights are compatible with both\\n\",\n    \"    TensorFlow and Theano. The dimension ordering\\n\",\n    \"    convention used by the model is the one\\n\",\n    \"    specified in your Keras config file.\\n\",\n    \"\\n\",\n    \"    # Arguments\\n\",\n    \"        include_top: whether to include the 3 fully-connected\\n\",\n    \"            layers at the top of the network.\\n\",\n    \"        weights: one of `None` (random initialization)\\n\",\n    \"            or \\\"imagenet\\\" (pre-training on ImageNet).\\n\",\n    \"        input_tensor: optional Keras tensor (i.e. output of `layers.Input()`)\\n\",\n    \"            to use as image input for the model.\\n\",\n    \"\\n\",\n    \"    # Returns\\n\",\n    \"        A Keras model instance.\\n\",\n    \"    '''\\n\",\n    \"    if weights not in {'imagenet', None}:\\n\",\n    \"        raise ValueError('The `weights` argument should be either '\\n\",\n    \"                         '`None` (random initialization) or `imagenet` '\\n\",\n    \"                         '(pre-training on ImageNet).')\\n\",\n    \"    # Determine proper input shape\\n\",\n    \"    if K.image_dim_ordering() == 'th':\\n\",\n    \"        if include_top:\\n\",\n    \"            input_shape = (3, 224, 224)\\n\",\n    \"        else:\\n\",\n    \"            input_shape = (3, None, None)\\n\",\n    \"    else:\\n\",\n    \"        if include_top:\\n\",\n    \"            input_shape = (224, 224, 3)\\n\",\n    \"        else:\\n\",\n    \"            input_shape = (None, None, 3)\\n\",\n    \"\\n\",\n    \"    if input_tensor is None:\\n\",\n    \"        img_input = Input(shape=input_shape)\\n\",\n    \"    else:\\n\",\n    \"        if not K.is_keras_tensor(input_tensor):\\n\",\n    \"            img_input = Input(tensor=input_tensor)\\n\",\n    \"        else:\\n\",\n    \"            img_input = input_tensor\\n\",\n    \"    # Block 1\\n\",\n    \"    x = Convolution2D(64, 3, 3, activation='relu', border_mode='same', name='block1_conv1')(img_input)\\n\",\n    \"    x = Convolution2D(64, 3, 3, activation='relu', border_mode='same', name='block1_conv2')(x)\\n\",\n    \"    x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)\\n\",\n    \"\\n\",\n    \"    # Block 2\\n\",\n    \"    x = Convolution2D(128, 3, 3, activation='relu', border_mode='same', name='block2_conv1')(x)\\n\",\n    \"    x = Convolution2D(128, 3, 3, activation='relu', border_mode='same', name='block2_conv2')(x)\\n\",\n    \"    x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)\\n\",\n    \"\\n\",\n    \"    # Block 3\\n\",\n    \"    x = Convolution2D(256, 3, 3, activation='relu', border_mode='same', name='block3_conv1')(x)\\n\",\n    \"    x = Convolution2D(256, 3, 3, activation='relu', border_mode='same', name='block3_conv2')(x)\\n\",\n    \"    x = Convolution2D(256, 3, 3, activation='relu', border_mode='same', name='block3_conv3')(x)\\n\",\n    \"    x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x)\\n\",\n    \"\\n\",\n    \"    # Block 4\\n\",\n    \"    x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block4_conv1')(x)\\n\",\n    \"    x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block4_conv2')(x)\\n\",\n    \"    x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block4_conv3')(x)\\n\",\n    \"    x = MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)\\n\",\n    \"\\n\",\n    \"    # Block 5\\n\",\n    \"    x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block5_conv1')(x)\\n\",\n    \"    x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block5_conv2')(x)\\n\",\n    \"    x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block5_conv3')(x)\\n\",\n    \"    x = MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x)\\n\",\n    \"\\n\",\n    \"    if include_top:\\n\",\n    \"        # Classification block\\n\",\n    \"        x = Flatten(name='flatten')(x)\\n\",\n    \"        x = Dense(4096, activation='relu', name='fc1')(x)\\n\",\n    \"        x = Dense(4096, activation='relu', name='fc2')(x)\\n\",\n    \"        x = Dense(1000, activation='softmax', name='predictions')(x)\\n\",\n    \"\\n\",\n    \"    # Create model\\n\",\n    \"    model = Model(img_input, x)\\n\",\n    \"\\n\",\n    \"    # load weights\\n\",\n    \"    if weights == 'imagenet':\\n\",\n    \"        print('K.image_dim_ordering:', K.image_dim_ordering())\\n\",\n    \"        if K.image_dim_ordering() == 'th':\\n\",\n    \"            if include_top:\\n\",\n    \"                weights_path = get_file('vgg16_weights_th_dim_ordering_th_kernels.h5',\\n\",\n    \"                                        TH_WEIGHTS_PATH,\\n\",\n    \"                                        cache_subdir='models')\\n\",\n    \"            else:\\n\",\n    \"                weights_path = get_file('vgg16_weights_th_dim_ordering_th_kernels_notop.h5',\\n\",\n    \"                                        TH_WEIGHTS_PATH_NO_TOP,\\n\",\n    \"                                        cache_subdir='models')\\n\",\n    \"            model.load_weights(weights_path)\\n\",\n    \"            if K.backend() == 'tensorflow':\\n\",\n    \"                warnings.warn('You are using the TensorFlow backend, yet you '\\n\",\n    \"                              'are using the Theano '\\n\",\n    \"                              'image dimension ordering convention '\\n\",\n    \"                              '(`image_dim_ordering=\\\"th\\\"`). '\\n\",\n    \"                              'For best performance, set '\\n\",\n    \"                              '`image_dim_ordering=\\\"tf\\\"` in '\\n\",\n    \"                              'your Keras config '\\n\",\n    \"                              'at ~/.keras/keras.json.')\\n\",\n    \"                convert_all_kernels_in_model(model)\\n\",\n    \"        else:\\n\",\n    \"            if include_top:\\n\",\n    \"                weights_path = get_file('vgg16_weights_tf_dim_ordering_tf_kernels.h5',\\n\",\n    \"                                        TF_WEIGHTS_PATH,\\n\",\n    \"                                        cache_subdir='models')\\n\",\n    \"            else:\\n\",\n    \"                weights_path = get_file('vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5',\\n\",\n    \"                                        TF_WEIGHTS_PATH_NO_TOP,\\n\",\n    \"                                        cache_subdir='models')\\n\",\n    \"            model.load_weights(weights_path)\\n\",\n    \"            if K.backend() == 'theano':\\n\",\n    \"                convert_all_kernels_in_model(model)\\n\",\n    \"    return model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"K.image_dim_ordering: th\\n\",\n      \"Input image shape: (1, 3, 224, 224)\\n\",\n      \"Predicted: [['n07745940', 'strawberry']]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import os\\n\",\n    \"\\n\",\n    \"model = VGG16(include_top=True, weights='imagenet')\\n\",\n    \"\\n\",\n    \"img_path = os.path.join(IMAGENET_FOLDER, 'strawberry_1157.jpeg')\\n\",\n    \"img = image.load_img(img_path, target_size=(224, 224))\\n\",\n    \"x = image.img_to_array(img)\\n\",\n    \"x = np.expand_dims(x, axis=0)\\n\",\n    \"x = preprocess_input(x)\\n\",\n    \"print('Input image shape:', x.shape)\\n\",\n    \"\\n\",\n    \"preds = model.predict(x)\\n\",\n    \"print('Predicted:', decode_predictions(preds))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"slide\"\n    }\n   },\n   \"source\": [\n    \"# Fine Tuning of a Pre-Trained Model\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"```python\\n\",\n    \"def VGG16_FT(weights_path = None, \\n\",\n    \"             img_width = 224, img_height = 224, \\n\",\n    \"             f_type = None, n_labels = None ):\\n\",\n    \"    \\n\",\n    \"    \\\"\\\"\\\"Fine Tuning of a VGG16 based Net\\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"    # VGG16 Up to the layer before the last!\\n\",\n    \"    model = Sequential()\\n\",\n    \"    model.add(ZeroPadding2D((1, 1), \\n\",\n    \"                            input_shape=(3, \\n\",\n    \"                            img_width, img_height)))\\n\",\n    \"\\n\",\n    \"    model.add(Convolution2D(64, 3, 3, activation='relu', \\n\",\n    \"                            name='conv1_1'))\\n\",\n    \"    model.add(ZeroPadding2D((1, 1)))\\n\",\n    \"    model.add(Convolution2D(64, 3, 3, activation='relu', \\n\",\n    \"                            name='conv1_2'))\\n\",\n    \"    model.add(MaxPooling2D((2, 2), strides=(2, 2)))\\n\",\n    \"\\n\",\n    \"    model.add(ZeroPadding2D((1, 1)))\\n\",\n    \"    model.add(Convolution2D(128, 3, 3, activation='relu', \\n\",\n    \"                            name='conv2_1'))\\n\",\n    \"    model.add(ZeroPadding2D((1, 1)))\\n\",\n    \"    model.add(Convolution2D(128, 3, 3, activation='relu', \\n\",\n    \"                            name='conv2_2'))\\n\",\n    \"    model.add(MaxPooling2D((2, 2), strides=(2, 2)))\\n\",\n    \"\\n\",\n    \"    model.add(ZeroPadding2D((1, 1)))\\n\",\n    \"    model.add(Convolution2D(256, 3, 3, activation='relu', \\n\",\n    \"                            name='conv3_1'))\\n\",\n    \"    model.add(ZeroPadding2D((1, 1)))\\n\",\n    \"    model.add(Convolution2D(256, 3, 3, activation='relu', \\n\",\n    \"                            name='conv3_2'))\\n\",\n    \"    model.add(ZeroPadding2D((1, 1)))\\n\",\n    \"    model.add(Convolution2D(256, 3, 3, activation='relu', \\n\",\n    \"                            name='conv3_3'))\\n\",\n    \"    model.add(MaxPooling2D((2, 2), strides=(2, 2)))\\n\",\n    \"\\n\",\n    \"    model.add(ZeroPadding2D((1, 1)))\\n\",\n    \"    model.add(Convolution2D(512, 3, 3, activation='relu', \\n\",\n    \"                            name='conv4_1'))\\n\",\n    \"    model.add(ZeroPadding2D((1, 1)))\\n\",\n    \"    model.add(Convolution2D(512, 3, 3, activation='relu', \\n\",\n    \"                            name='conv4_2'))\\n\",\n    \"    model.add(ZeroPadding2D((1, 1)))\\n\",\n    \"    model.add(Convolution2D(512, 3, 3, activation='relu', \\n\",\n    \"                            name='conv4_3'))\\n\",\n    \"    model.add(MaxPooling2D((2, 2), strides=(2, 2)))\\n\",\n    \"\\n\",\n    \"    model.add(ZeroPadding2D((1, 1)))\\n\",\n    \"    model.add(Convolution2D(512, 3, 3, activation='relu', \\n\",\n    \"                            name='conv5_1'))\\n\",\n    \"    model.add(ZeroPadding2D((1, 1)))\\n\",\n    \"    model.add(Convolution2D(512, 3, 3, activation='relu', \\n\",\n    \"                            name='conv5_2'))\\n\",\n    \"    model.add(ZeroPadding2D((1, 1)))\\n\",\n    \"    model.add(Convolution2D(512, 3, 3, activation='relu', \\n\",\n    \"                            name='conv5_3'))\\n\",\n    \"    model.add(MaxPooling2D((2, 2), strides=(2, 2)))\\n\",\n    \"    model.add(Flatten())\\n\",\n    \"\\n\",\n    \"    # Plugging new Layers\\n\",\n    \"    model.add(Dense(768, activation='sigmoid'))\\n\",\n    \"    model.add(Dropout(0.0))\\n\",\n    \"    model.add(Dense(768, activation='sigmoid'))\\n\",\n    \"    model.add(Dropout(0.0))\\n\",\n    \"    \\n\",\n    \"    last_layer = Dense(n_labels, activation='sigmoid')\\n\",\n    \"    loss = 'categorical_crossentropy'\\n\",\n    \"    optimizer = optimizers.Adam(lr=1e-4, epsilon=1e-08)\\n\",\n    \"    batch_size = 128\\n\",\n    \"    \\n\",\n    \"    assert os.path.exists(weights_path), 'Model weights not found (see \\\"weights_path\\\" variable in script).'\\n\",\n    \"    #model.load_weights(weights_path)\\n\",\n    \"    f = h5py.File(weights_path)\\n\",\n    \"    for k in range(len(f.attrs['layer_names'])):\\n\",\n    \"       g = f[f.attrs['layer_names'][k]]\\n\",\n    \"       weights = [g[g.attrs['weight_names'][p]] \\n\",\n    \"                   for p in range(len(g.attrs['weight_names']))]\\n\",\n    \"       if k >= len(model.layers):\\n\",\n    \"           break\\n\",\n    \"       else:\\n\",\n    \"           model.layers[k].set_weights(weights)\\n\",\n    \"    f.close()\\n\",\n    \"    print('Model loaded.')\\n\",\n    \"\\n\",\n    \"    model.add(last_layer)\\n\",\n    \"\\n\",\n    \"    # set the first 25 layers (up to the last conv block)\\n\",\n    \"    # to non-trainable (weights will not be updated)\\n\",\n    \"    for layer in model.layers[:25]:\\n\",\n    \"        layer.trainable = False\\n\",\n    \"\\n\",\n    \"    # compile the model with a SGD/momentum optimizer\\n\",\n    \"    # and a very slow learning rate.\\n\",\n    \"    model.compile(loss=loss,\\n\",\n    \"                  optimizer=optimizer,\\n\",\n    \"                  metrics=['accuracy'])\\n\",\n    \"    return model, batch_size\\n\",\n    \"\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"slide\"\n    }\n   },\n   \"source\": [\n    \"# Hands On:\\n\",\n    \"\\n\",\n    \"### Try to do the same with other models \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%load deep_learning_models/vgg19.py\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%load deep_learning_models/resnet50.py\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"celltoolbar\": \"Slideshow\",\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.4.3\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "deep-learning/keras-tutorial/3.1 Unsupervised Learning - AutoEncoders and Embeddings.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Credits: Forked from [deep-learning-keras-tensorflow](https://github.com/leriomaggio/deep-learning-keras-tensorflow) by Valerio Maggio\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Unsupervised learning\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### AutoEncoders  \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"An autoencoder, is an artificial neural network used for learning efficient codings. \\n\",\n    \"\\n\",\n    \"The aim of an autoencoder is to learn a representation (encoding) for a set of data, typically for the purpose of dimensionality reduction. \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"source\": [\n    \"<img src =\\\"imgs/autoencoder.png\\\" width=\\\"25%\\\">\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Unsupervised learning is a type of machine learning algorithm used to draw inferences from datasets consisting of input data without labeled responses. The most common unsupervised learning method is cluster analysis, which is used for exploratory data analysis to find hidden patterns or grouping in data.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Train on 60000 samples, validate on 10000 samples\\n\",\n      \"Epoch 1/50\\n\",\n      \"60000/60000 [==============================] - 20s - loss: 0.3832 - val_loss: 0.2730\\n\",\n      \"Epoch 2/50\\n\",\n      \"60000/60000 [==============================] - 19s - loss: 0.2660 - val_loss: 0.2557\\n\",\n      \"Epoch 3/50\\n\",\n      \"60000/60000 [==============================] - 18s - loss: 0.2455 - val_loss: 0.2331\\n\",\n      \"Epoch 4/50\\n\",\n      \"60000/60000 [==============================] - 19s - loss: 0.2254 - val_loss: 0.2152\\n\",\n      \"Epoch 5/50\\n\",\n      \"60000/60000 [==============================] - 19s - loss: 0.2099 - val_loss: 0.2018\\n\",\n      \"Epoch 6/50\\n\",\n      \"60000/60000 [==============================] - 19s - loss: 0.1982 - val_loss: 0.1917\\n\",\n      \"Epoch 7/50\\n\",\n      \"60000/60000 [==============================] - 19s - loss: 0.1893 - val_loss: 0.1840\\n\",\n      \"Epoch 8/50\\n\",\n      \"60000/60000 [==============================] - 19s - loss: 0.1824 - val_loss: 0.1778\\n\",\n      \"Epoch 9/50\\n\",\n      \"60000/60000 [==============================] - 19s - loss: 0.1766 - val_loss: 0.1725\\n\",\n      \"Epoch 10/50\\n\",\n      \"60000/60000 [==============================] - 19s - loss: 0.1715 - val_loss: 0.1676\\n\",\n      \"Epoch 11/50\\n\",\n      \"60000/60000 [==============================] - 18s - loss: 0.1669 - val_loss: 0.1632\\n\",\n      \"Epoch 12/50\\n\",\n      \"60000/60000 [==============================] - 19s - loss: 0.1626 - val_loss: 0.1593\\n\",\n      \"Epoch 13/50\\n\",\n      \"60000/60000 [==============================] - 19s - loss: 0.1587 - val_loss: 0.1554\\n\",\n      \"Epoch 14/50\\n\",\n      \"60000/60000 [==============================] - 19s - loss: 0.1551 - val_loss: 0.1520\\n\",\n      \"Epoch 15/50\\n\",\n      \"60000/60000 [==============================] - 19s - loss: 0.1519 - val_loss: 0.1489\\n\",\n      \"Epoch 16/50\\n\",\n      \"60000/60000 [==============================] - 19s - loss: 0.1489 - val_loss: 0.1461\\n\",\n      \"Epoch 17/50\\n\",\n      \"60000/60000 [==============================] - 19s - loss: 0.1461 - val_loss: 0.1435\\n\",\n      \"Epoch 18/50\\n\",\n      \"60000/60000 [==============================] - 19s - loss: 0.1435 - val_loss: 0.1408\\n\",\n      \"Epoch 19/50\\n\",\n      \"60000/60000 [==============================] - 19s - loss: 0.1410 - val_loss: 0.1385\\n\",\n      \"Epoch 20/50\\n\",\n      \"60000/60000 [==============================] - 19s - loss: 0.1387 - val_loss: 0.1362\\n\",\n      \"Epoch 21/50\\n\",\n      \"60000/60000 [==============================] - 19s - loss: 0.1365 - val_loss: 0.1340\\n\",\n      \"Epoch 22/50\\n\",\n      \"60000/60000 [==============================] - 19s - loss: 0.1344 - val_loss: 0.1320\\n\",\n      \"Epoch 23/50\\n\",\n      \"60000/60000 [==============================] - 19s - loss: 0.1324 - val_loss: 0.1301\\n\",\n      \"Epoch 24/50\\n\",\n      \"60000/60000 [==============================] - 19s - loss: 0.1305 - val_loss: 0.1282\\n\",\n      \"Epoch 25/50\\n\",\n      \"60000/60000 [==============================] - 18s - loss: 0.1287 - val_loss: 0.1265\\n\",\n      \"Epoch 26/50\\n\",\n      \"60000/60000 [==============================] - 18s - loss: 0.1270 - val_loss: 0.1248\\n\",\n      \"Epoch 27/50\\n\",\n      \"60000/60000 [==============================] - 18s - loss: 0.1254 - val_loss: 0.1232\\n\",\n      \"Epoch 28/50\\n\",\n      \"60000/60000 [==============================] - 19s - loss: 0.1238 - val_loss: 0.1217\\n\",\n      \"Epoch 29/50\\n\",\n      \"60000/60000 [==============================] - 19s - loss: 0.1224 - val_loss: 0.1203\\n\",\n      \"Epoch 30/50\\n\",\n      \"60000/60000 [==============================] - 19s - loss: 0.1210 - val_loss: 0.1189\\n\",\n      \"Epoch 31/50\\n\",\n      \"60000/60000 [==============================] - 19s - loss: 0.1197 - val_loss: 0.1176\\n\",\n      \"Epoch 32/50\\n\",\n      \"60000/60000 [==============================] - 19s - loss: 0.1184 - val_loss: 0.1163\\n\",\n      \"Epoch 33/50\\n\",\n      \"60000/60000 [==============================] - 19s - loss: 0.1173 - val_loss: 0.1152\\n\",\n      \"Epoch 34/50\\n\",\n      \"60000/60000 [==============================] - 19s - loss: 0.1161 - val_loss: 0.1141\\n\",\n      \"Epoch 35/50\\n\",\n      \"60000/60000 [==============================] - 19s - loss: 0.1151 - val_loss: 0.1131\\n\",\n      \"Epoch 36/50\\n\",\n      \"60000/60000 [==============================] - 19s - loss: 0.1141 - val_loss: 0.1122\\n\",\n      \"Epoch 37/50\\n\",\n      \"60000/60000 [==============================] - 19s - loss: 0.1132 - val_loss: 0.1112\\n\",\n      \"Epoch 38/50\\n\",\n      \"60000/60000 [==============================] - 19s - loss: 0.1123 - val_loss: 0.1104\\n\",\n      \"Epoch 39/50\\n\",\n      \"60000/60000 [==============================] - 19s - loss: 0.1115 - val_loss: 0.1095\\n\",\n      \"Epoch 40/50\\n\",\n      \"60000/60000 [==============================] - 19s - loss: 0.1107 - val_loss: 0.1088\\n\",\n      \"Epoch 41/50\\n\",\n      \"60000/60000 [==============================] - 19s - loss: 0.1100 - val_loss: 0.1081\\n\",\n      \"Epoch 42/50\\n\",\n      \"60000/60000 [==============================] - 19s - loss: 0.1093 - val_loss: 0.1074\\n\",\n      \"Epoch 43/50\\n\",\n      \"60000/60000 [==============================] - 19s - loss: 0.1087 - val_loss: 0.1068\\n\",\n      \"Epoch 44/50\\n\",\n      \"60000/60000 [==============================] - 19s - loss: 0.1081 - val_loss: 0.1062\\n\",\n      \"Epoch 45/50\\n\",\n      \"60000/60000 [==============================] - 19s - loss: 0.1075 - val_loss: 0.1057\\n\",\n      \"Epoch 46/50\\n\",\n      \"60000/60000 [==============================] - 19s - loss: 0.1070 - val_loss: 0.1052\\n\",\n      \"Epoch 47/50\\n\",\n      \"60000/60000 [==============================] - 20s - loss: 0.1065 - val_loss: 0.1047\\n\",\n      \"Epoch 48/50\\n\",\n      \"60000/60000 [==============================] - 17s - loss: 0.1061 - val_loss: 0.1043\\n\",\n      \"Epoch 49/50\\n\",\n      \"60000/60000 [==============================] - 29s - loss: 0.1056 - val_loss: 0.1039\\n\",\n      \"Epoch 50/50\\n\",\n      \"60000/60000 [==============================] - 21s - loss: 0.1052 - val_loss: 0.1034\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<keras.callbacks.History at 0x285017b8>\"\n      ]\n     },\n     \"execution_count\": 4,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# based on: https://blog.keras.io/building-autoencoders-in-keras.html\\n\",\n    \"\\n\",\n    \"encoding_dim = 32  \\n\",\n    \"input_img = Input(shape=(784,))\\n\",\n    \"encoded = Dense(encoding_dim, activation='relu')(input_img)\\n\",\n    \"decoded = Dense(784, activation='sigmoid')(encoded)\\n\",\n    \"autoencoder = Model(input=input_img, output=decoded)\\n\",\n    \"encoder = Model(input=input_img, output=encoded)\\n\",\n    \"\\n\",\n    \"encoded_input = Input(shape=(encoding_dim,))\\n\",\n    \"decoder_layer = autoencoder.layers[-1]\\n\",\n    \"decoder = Model(input=encoded_input, output=decoder_layer(encoded_input))\\n\",\n    \"\\n\",\n    \"autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')\\n\",\n    \"\\n\",\n    \"(x_train, _), (x_test, _) = mnist.load_data()\\n\",\n    \"\\n\",\n    \"x_train = x_train.astype('float32') / 255.\\n\",\n    \"x_test = x_test.astype('float32') / 255.\\n\",\n    \"x_train = x_train.reshape((len(x_train), np.prod(x_train.shape[1:])))\\n\",\n    \"x_test = x_test.reshape((len(x_test), np.prod(x_test.shape[1:])))\\n\",\n    \"\\n\",\n    \"#note: x_train, x_train :) \\n\",\n    \"autoencoder.fit(x_train, x_train,\\n\",\n    \"                nb_epoch=50,\\n\",\n    \"                batch_size=256,\\n\",\n    \"                shuffle=True,\\n\",\n    \"                validation_data=(x_test, x_test))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Testing the Autoencoder \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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f19xONxIWrC4bB4Ss1mM5yfn8Pn8xlFzR2gzxgkan777Tf87W9/QywW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\\nCBEiRIgQIUKECBEiRIgwI/j/3p++8Tt5LoQAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x27028cc0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"encoded_imgs = encoder.predict(x_test)\\n\",\n    \"decoded_imgs = decoder.predict(encoded_imgs)\\n\",\n    \"\\n\",\n    \"n = 10 \\n\",\n    \"plt.figure(figsize=(20, 4))\\n\",\n    \"for i in range(n):\\n\",\n    \"    # original\\n\",\n    \"    ax = plt.subplot(2, n, i + 1)\\n\",\n    \"    plt.imshow(x_test[i].reshape(28, 28))\\n\",\n    \"    plt.gray()\\n\",\n    \"    ax.get_xaxis().set_visible(False)\\n\",\n    \"    ax.get_yaxis().set_visible(False)\\n\",\n    \"\\n\",\n    \"    # reconstruction\\n\",\n    \"    ax = plt.subplot(2, n, i + 1 + n)\\n\",\n    \"    plt.imshow(decoded_imgs[i].reshape(28, 28))\\n\",\n    \"    plt.gray()\\n\",\n    \"    ax.get_xaxis().set_visible(False)\\n\",\n    \"    ax.get_yaxis().set_visible(False)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Sample generation with Autoencoder \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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3LCOgJKqqqt4XK5bCSky9r5PD+ZYwK2fQBS25MEBhBRdhqM1RVc2AkH\\nA9YPryUgCL1mfdF7+uT19PghdMjme72r6pXdNmmTDt2BSG8sjJn+UxXggJPv9zI8tkcGRvtolhvr\\nI74kgy6+k5f7a7LEfjKJOarezs/PB4Dl8fFxYK92zdN0+pLBdBDgzKuDOVfpWM6c0DAJxZpaXizn\\nHndmFQ3Ukwgau6Wvw/ZzPTw8NGCKXWO+e37AY+XfR0cvW3qTiEoZ4N8Gta4MM+FhW29gaHzhoJJ1\\nJLPvZ7Pm/N59sQxVvSStPBb/3ePiM/infa2lsZNxpberMDcZyDHXSZbhF1k/k8WMFwLdBEwSHzQS\\nIGwdTqLGzTq7a7wOCKq+Bf8812uHvTA5j311pXXayiTfbLP30UwoQMYkLjbG6FWgJBZB9vic44ys\\ndLIc25+h/1TNJFHDevN38Kz9gX0pfpFAL9fe/WdeXJXIWmVsRHO1hrGfdT7tzlitJ0c8xwSEbZaJ\\nOPpt25r9ND4H3/SufJblpPcc398JJPtkr+NkMmmVIjm32BnsiPthn4t/cEWQsYztTOq7CYWxW8bL\\n2S/3j74wHip587v+TFUNdtB4jnJOUl4tD0ls2gbsapBI3tLMeO1fsUWM17qciTgwNwlsrw2xy+Pj\\nY9spYtzI/L3VvhuFsB/LoNzCgqHC4Dmgs9IaeCQrneSJg0EWOUmgXYGwFQ+FNmOW5APCYCYsQYf7\\nYIPH4ngfW7LBu4wS/YCRT2AxdktWNwMyQARzx7xAuHAB/vJMmul0WldXV4Osvx2g55F1MUGEwWO9\\nAP2An3RM3ivoMjTWx58j42HjaQNc9c1oXFxc1Gw2q8vLy0FwiMJRJg6gQGYcVHjcY7e7u7sBkLAs\\nu5IiHbfXHQZ4NpsNzoLyWUOuXMDwkMWAJHt6ehoYLQyqSUc7MJ/TRKbLQbxJCAOVzeZblRNVJnZu\\nyV5vt9t2nhDOy2NC15KMJDikqszk1z5AKVlwkyx2RgThzKO3+ySAd0bRAYO3viQZgiNxRYtBSZLU\\n6BO6jU54HquGtjfH5mezZs5UuM3n8yZ3yBx9pMILe8G6V1XL1mGD0dd9BIee26wE8XYUkxIJ0gis\\nsPmZ3PC9bWdIBvA75tPBiUl3B/w09NN+K4EQa7Vardp20O12OzhnLrco+7uuNEifD+GKv7W+0X9X\\n4uwjqKh6sZvOxrKG9oXL5bLm83mr+Dw9PR1UauKv8F/WKwd92BqD/558mnChL4+Pj/Xly5dmfwny\\n+D8kvM9aMJbKsmufP5LJI8vedDptW4jwE9giQKh10TbTPhH/NHbzXPK83pYU5t/ZeCd8HBTiHyFD\\nTd6Z+M4kFGPks8YGzoajb71sr22YA5ueDtj3YScglmyjWc/T09O2nZTLcmB77kDXgUQPX4zRTG4x\\nt+lT0o/1+ua1Ydw+M8/+3Qlh+lBVg2qblGnjKON8vmscVFVNRubzeTuLLM/ocnCZskxlrElx7DAy\\nnvEKc2XC3rbOFQRjtkzkYO9cvct6VA3PuPQ62B4bF6K/Pk9zF46oei2rzIPl2oE4+m2yB7zkOaWf\\n9hk9gjCJT8exEH2eLxOju7BUxlP7aI63XMSArKMbZ2dnr0gdYxVkzkQx/2bdHfNlfFr1Gje6MML9\\ndUUoNpotR76m02nzmXd3dwPbzb3sU0wmuRiDsVDF7HhxF66iSuf29rb1uaq+Gy9+N5okC01zFYWB\\nIpPtbK0NjoHpZrMZHExa9VKp4MDEezqZYC90gh0cnBXPTLqrQPiMn2WwbQVAUe08zaAhMABMDlK6\\nu7sb9L9qyCo7k2kDsQ8wA8nihtGBqLFRYZ6n0+lgHK6mos84PH6fYK9qeCgwgg+bSWaBv6G0AAsc\\ni9cmmchknrm+fv3atnSR5XCmxETNfD5vBy3ambEF5eHhYbCPmrNoDKYBXrDjY7a7u7sB6UiWknF7\\nDZgvdMPbnapeqp8Iir1VKYP/4+Pjuri4GMjSdrtta4NeMqcOADebTbMJzt7ZJnivth0dZMJ8Pm+H\\n2AK6d5Wh26kZ1FXVq+CQuUROmCMIKvo9diOISqKTfuIATX4R6LiMlM+iTz5ElmaA62egz8iRq+Ay\\n8GKuMsjL8moTSD3n7TGjw8iygQfAABvvecLxeisjF/3GfrhseOxmcsXZNeyYiRo74e1228rk2fuM\\nDDp7VFXd+zNGvmPfh+yjg87asr3F8+XPYD+yIoh5zTNpql4yjwY6XsfVatV8K7bRlaYmRBw0AaS9\\n19z6PGYjaDUwpe/MK34QO8i2TewqvpW5s080yWif4EAh9abqNVFjwJnYBDvrtSaJkc938Okgk3ux\\n5nwPu+2Ahj4i69hl4y3LATYMYn3slkRNVQ0qMJ1Yc9KAgBlc4PHN5/O6urqqq6urury8HOi7yQAT\\nCk5akgSxL8SOZVLHWx6SqES2TFIwrx47AbADwMSXbAF20q6q2pZ9B1bph5nX9KljNpMK+Le3KkiM\\n+VMenBUHR1J96PFlApefJjaRYRNo+GCvDfcxDkpsSCLJJAvzytx67Z2sZizM0+fPn+vTp09dP2c9\\nduDvud4HRs3qDubSGM2ESB6kzzxXDQ9kt190dWAmTCzziTFyPTLOs+9LOeNnYsskavgO9zL561gD\\nHIXOuarP9sRraSLBfd+XTXXsDA6gf9hD1sb+zDjFZKMJm9y+ZnLHRI3Hhu1M3GJ953kmVahYtN01\\nSWNfneSQcVzyGYyBKjtsBjY7E2NV1fpze3tbVTU4VuKt9qsqalLJeLCNm1kynKCdmAPc9XrdToqG\\nKXYQyQRwOKy3LzkYNQmSextxqAYcmQ0h2KTPdhRmSJl0O10cXx4Et1qt6ueff66ff/55UNqYRA3K\\nwMJVVeuDM5RjNQMFGx87dxsEG8jn5+dWkcJcLpfLVsKbIJS18h5AbzthjGTmAMsYAtYHeeI5rAHO\\nAIUxaMoMlN9UQP8Zu8En46KCx+APmeKUcPqNsbUxgHzaF1GDbjCvVS9BNwaRbUUZrDpYA4ADKLns\\nVFhDjBjn9/hg5gRQ0+nLeT8QW9gD3hjjffTeq235A2ibcCMwwNBzfpBlxiWNkGw87/7+vsm+A8DZ\\n7OXNVLe3t21cBBdjN9YA/cqqGpeo27ZglxxYuxpuuVy2M30SCOJw0GfkArKNNwQhxyafsadsZ5tM\\nJoOyfdbQWT2TYOn8kNsM7PwsnLfXCz+Bvrmqy4A+s4sGY2M1SE5s3GQyaXuNnWHl7zQICMYwn7+c\\nE+Gtf05KcP/edl6eZVkx0M83HJiANlEKIYE+mTCvqrq/v2+6a1CS5xilLTZgS0DkagxX5/FcZAqi\\nax+A1LpoAgsdsa/ytk3sIUSJZSGreE2UeK2Qh8wOGpAyhxzoy1vQALlHR0ftc9vt9tWbNHrEkSs7\\neJ4BNLgpt7rNZsM36mCPkUkwBL7HoHuf1cI9ogbZZj2rXqoP881x1gHm4+jo23lpHz58qA8fPjQd\\nTLKFrC3zYqIGO+wEAT+TGHWgX1WD7/UICgeanmtkCj/hajoTS67oWK/XrSrDtqtHJO6bqMnn9SoL\\nwOn+Gy3XhrFQleJmfO/5R0b8ljXbM+bP6+0tdswNJCGYZT7/9gaY6+vrwRtGGTs+zJjlLcL3t7/9\\nba3X65ZINa73mJgH9IB53Be2cbxo8j7JDogrdLHqBdOTLIPEcXM8h93mnvhGx1vMn+chyb1eksJ9\\n7pGF3L9H1FQNK2Pz/3w27Qf9ciKtl/DaB6ZxQ/+QE9utqmo2DvIzKxNtD012GDsY3zAvJtg85vSX\\nTpzzXBMrs9lsEF94vpLosl55vqlMpN/+LvjEJCzjYR197EbVsKLGNrZqhIoaB6PJIDtzScdw3L3S\\ncAN9EzrO3hrEsGgYFp7tZ5qcsfNiEjF2lPnjFBkDDp1n0A8Ex+DNmcle6ZWBCYLs0tLMPGMYUITn\\n528HC6URGaNxzoeJDm8hgFRCqKks4HPT6fCNTFwG6g66HVjhLCaTl60MDjidKUTZqqqRepQvZ/bK\\nxrHqhfjyGiZI4T5WIu6RwMTrtFgs6urqqlV4MDcEWgYQZKjGbgDh5+dvr0p2/701Bj30egMWIHFy\\nDZ35zm18GMHcpuS3cVnXmWMM0Wq1qi9fvrwqz88Sxp5RZh0NFAnyeiW/NJ7vIMaZtawESECIPmZF\\n4RjNlUj0lWzsLsBt52DbgU1z9ptyVEBJgjcTGwQg3Dezz9g/A0avOXqfRDtj4R7IZhIvnvO088iE\\nnRoVjJCwSaZjFxyU7APUID9J+AJImFMIEoMDkhYE/ABvA3pXEbL+tmUZpGXSwkkF+x6ToraLnsf0\\n2946YzLCgbzBsNc1faR9PEDImUSqIHKbqe38mI03zLkSCZtCn5Eh1hGf9vDwUJPJpG2NcVUizbYE\\nv2D76jmxvfPnvIXHSSmAsNfO9s465P54Lq1r3NPyROYQYsGELAHwdrsdbCvJzK/t1j7W0JWlrJfX\\n0H3BppO4wL7hP7lOTk7q8vKyJQOMG71W2GJ/n8OmkSsHibuIF69P1fC1xbv8Ys+O+u/cj3u4vybj\\n0HG2JNqW5DORvX0QNSkbHnPV8M2xTkJV1aBq1wQwmOfy8rIuLy8H85X4z3bXweRms2n6jQzzGXwl\\n3/O60h9eW8yWeuTLCT0T1Iwz15e5JxEGhoXo97yYjGfusDfERfvwi07ks6bGDNZP1oAx+U2Xlvu0\\nVUkaEF84seBqFubOsWQSSMxXxof0OfXXMkpf/buez8rfm+AxngOLZxKavjB2Y/uxG3LpilGej064\\nWjFtL3bQ842/J+53PJ+Xx+nmxEBidq+BbWDaRRNqSZDbtjLHPqDYdrdXueU4A9vAcQqWnR5Z+Fb7\\nLlHj8k0Pko4ZXG0237LoGC2fD4EAAmQz+2Sj1FOudGwGnyZr0qEQAJmVdvaIxTWIMOi2wTPTXTXc\\nj8/3eR5EjZXJmU7AOqCexWPb1Njt/Py8KRZOzKW/GEpvL0nCyu+Dd5BPhcfd3d0gG2ngz5x7W87j\\n4+MrBUhme71etwMdrXAJ4llDB2qWA75nEslZCiufDQ/rSlWPxwfzj1Hi8/f39620bcy2WCzaWpEB\\nzD2+MPQmK9frdQumvOXQWUXWh/lxlUQGg8yJiYasjuB3VdXY79TRqmFlXjq3DF5MptI3wHYGKiaR\\nkA9INdY5sx88G3n5rxrTX9uyzNckjWUV+XXAbbbfATLgj2wxY8Que24dUKFnkG/MA4ClR9TwfYI0\\nb7fws2xHHUxZlhinHaZtQtXwTRLIwMnJSQvmrY9JBO1rDZ2NxE8xDkq9XVFpe8LaUfWFnOITJpOX\\ncnED8978OaD2T5Osnmf/rWoIfi0bXmf6al9uf2q5SMCKLNuvI++AQRN9+CDsj9c/g+8xGsFbJnt4\\nLn1nLiFnPGfL5XKQgU/CisALssWZeetYBmXOCJq4glBhXb1mnvckBWyjrWtVNdAVxvr169dXhJzn\\nB39ydHQ0IET90wH1PgLDqpegwgSN++m+eP4y+WQZt58k6ejAwkEvRI+xrbczeW0894l5ewGp14hx\\nWJdtVzKYcxCIXXSfsGFUjJ2fnw/e4koFaq6lbfOYLQPbtPtpx9Al7OXR0VGr0HflO5WmVPSif35G\\nJj58bTabVsEPRmWLg+9hH+Zs+Xa7bRXA2AfsH2vG9x0Ypr1zMsWZeRI/SdQ7buP3jqu8jXWs5oDU\\n62esiH2pqjYOk5lgc2QiZQHdsT1MAsVJd8stCQKvjYmaTFIkkeCGv3efEreYRLBOu78mTTlOAfl1\\n5bnnh/Xkc2M3KjXZYl5Vg0QNsb5xhHUVO+jmnTIZG2fchk30Glp2/B3HBUl8M7+5dsY42XeeiYwZ\\nj6Zf9/iJvyBqvEPD8s99ab+G9P5VRE1v+w4DBUhQhuTJwUDiFCAvyD70nBaOz5NkVs6DNVHjRcYI\\nETxYEV1N4PvxrMHkyFi7/NhnC9iB0x8E1a9VBHBDOhDk0zdXteyDIeX11awTQe4ukGym9vz8vJV8\\nJ1EDsMHAsMUIw0JQf3l5OdjvjsL1iDiMFn0E4NrgWZlzHVORDNpMdJyenjag64C56vUboVwlAJnG\\nli2THLe3t3V/f78Xso254zlkTgCZBvgmBNfrdcvmkCWkbNFnAMH+ou+usupVIHnOM7izs3t4eKib\\nm5u6ubl5FViYNK16+1W3NoYGIUk+WOcNvjCggBpaAlhvG9qHLvrMhwQadvgQZgDn1WrVgghvSTAB\\nzd+xf3ZncAeBAAAgAElEQVRgjNElqDzfh7B5LXcRNV5fysBdbWiylu8jL6mnXluCLuuw++OtXmzt\\n4WItWVvLydgNkO2EgefPNtU+YLPZ1PX1ddPF6fTlbJ3VajXItPI75tXNQNw/c16zf7Z/JvwyYLFN\\nAHAQaGTAb91z20XUIItsFyBAwX+YqLFe7iM4vLq6GmzpZCuUQXoGQKwF+IY30xHYz2azwbqQ/Ejf\\nCmmH/vo5Tp6QPQTngEWwCbYFvjIo8PomsU4ShzkwvnN1QfppiF7rGoGg19x/G7uhi8YFJverXr/x\\n0IkeCBreMsh62uZlxrbqxb7kmRkZVDrotK54npL8sNzTf1ovYPA9TNIkZnLgSz/wF6vVt/MVP336\\nVJvN5lU1aZLDYzd8n/+fiTjsme0dsQZbi9CRHlGTW4KdsEjdceIGXzmZTBppk+tmzJE/TWw7Ecn3\\nuL5+/Vr39/cNa5pQWK/XDQ/bH/gszyRpnExHBvYV3DOfHnMSGIwDPfQceM5yXm2/ejres29VNfC9\\njiWz9YhsJ4szlsCfOsj35zwmMGrPFlcNKwL9k3NUSIAbF+6bqCHm9cG3l5eXTYa9FRfy0hVBXFUv\\nWI6KGuJJ7mEiP8kuJ5cytvMaWI54pv9mssVEtbcju5mPSK7BhDE2AWzKOME2xGj0yUc4pI1+cz2+\\nt2CZFeDmZq1domtHwCAcSOD0k+hg0AZ7uwbirEk6qQzS0xjkQptlTjbOhhZwRECSjFwGmGR9vV+b\\n+dtFKNgQj90yIErnl+wmnwfMU8KZ2SefU+DAsZdBS+YZg+YMg88+wJh/zxDZeGWGuTfHvnogJ/tM\\nsMI2QAIgzgtAP3jm4+PjXogaB1z0jfHTJ+THbHOWVObaGRCZ7CGwcGDni7VJ8G9gUlXt7JfPnz83\\nEtOVTIzFa2U54e9pA3q6b5CcJOB0+nL2UgZgJgHQTUiSsZtJLo/J9itJKuaA7CcHmrK2i8Wi6WGS\\nKg4QIMecvaEvVS9bd5KoSQDTewZzbWI/Sb20sSbhE5gzV34uoAA5ze85W+E5HLv1gEKCiwxcsafY\\nE78xIeXcfgLf6nG66sEEv6vgDHIS2LGF04AxiVh01IEo5cwOZHINesCZtbQvZi622+Hrg+2feuBt\\nzOb+9saTAA27arDnZIXPGWIMDpLySt+UFVEpF7Zt/hwBgefV2zt7ASRjSDuEXPNsB/Veux7ZY73n\\nHmDFHuE4Rstxg7/4W/oBj5c1pKKEbTKcvdDDn/g+Lm8d9ra3qhromO1w4sWeDcmAJHFOj6ihpbzk\\nXHj9ISE2m037N1iC76V87msd6TtrVTXEqA7YWAtXI/icJAgWksPgDc99+hf0JpuJBOQrSTb8s8+r\\nsE3rrUtvbWyDGL9xmV960CPJ0w4b29s37cMvJjmFrqR80hhz+kYTw+lTeoRk+ofEVSZL7F8sB72k\\nFM/FprEWiS/zOdl6ftH2KKswSd7ZzqBz/5UA//dtGSNZ1i2LxECsueMKnx+5Xq8H8aHJC+I8r5fn\\n0wSW49jEHNzPcmB84/7vIrjdWOdcC8fGu3QYOQaPurLbdoz2BxM1VEZg/BgMv8/XU1rIyU4sl8uW\\nBUbYnSG1oplN7QUynsxdTozfGdg7OPTBXxiuHojKoNRsGP83aLFxTSFh8XheZnbc/31knX755Zc2\\nn+fn5zWdTtt2mXT6BLQ4cbIRl5eXA0XMM39gTAkinXnxWRisIXPWO/fA2VZIEK+9z9fJLIJ/Whn5\\naTbVhj+dMXLlwHa7/bZli7GjyFmNtI+M06dPn1r/l8tlTSaTRh7d3t4OSiWn0+ngsGCCidQvy9t2\\nu21vK7u9vW2H1Jn0SvDqAMfgj6vq2yHIfjOIdcxluJnp7YEQ9BLwk0GPwSffxea4b84ee92QF5zQ\\nPraw4XB7QDptEXpKALhcLttlooRqNYCZgU/Vy1vNHLxVVdM/ZNkVSL3gtUfaECj679h49CDBBc9y\\ngFlVzSnydz8/Axv6ju2xLjK/BlJjNr8xgGowg2iDj/Q9VCba9zF25s72zT7NwBGwlFtkklywbU/i\\naNeFLeydTeOWIMWN7wNGExDxXapPZ7PZqy2c+wgm3G5vb1vfrAc01hC55Iwvgnu/nQ07hk/BTuEL\\nN5vhuVn2EZm4qarWF+yVy73xA8/Pz91qGgNiLm/rSL1wwJtkGnKeVToOyLDlJpJ6ZNQ+Anz03T7Z\\nQaLH52z6ZDKpd+/eDapMTXjb/hkPEOgBxlnjJLOq6tXzemS0/57Elrc89IIMXyaCbI+TIM2f/ixJ\\nNu6JnWPsrGEv6P5Dm6um7bezEgtCxL6eGAMZZeuobRb3sN9I7M4zLb+pe4knPY+uak1SdNe6c3/I\\nMWyN3yrqqgMTkr2Az74dW+oA11WeYzePter12YH0qbd1u3fuWa8ykHGmT+yRJjzTfcs+JsawDnm9\\nLBs94smy0Ftv+9Z8pmNd+zyScNPptxey0Kf01WMnhl2xzyHcJDjBy8Y3y+Wyrq+vW1zCZbvFvBM7\\nk1S2zOcap516y+4Y81TVK5lIsofxvWUbvVYZI9svmoytGq41a8gzwTW5pf2t9quIGgNrJoSMu4Ef\\nwITsAkTNxcVFY7qtYCisFwaAywCSXXNLcGlAYAfkPmX5brJ16fjol4XAn1+v16/KGnuXy5udCTU7\\nm4I2Zvvll18awQLYNAllpnA2m7U92mSZCA6dxTdrboGsejnE1xlIfo8cmIXN8w+YKwy4y4SRHzsw\\nXw5eTORVDbftZWkmComc8z3Lz3a7HZBUBi8+BHMfa/j58+fBNgRA1Xq9rtvb20GwxuF1l5eX9f79\\n+y77CzNuHYCoYXtXBldu/l0voOdZGGVnJfi+SzcdWDDnrJftgJ9JgOA5B0A7S4EjsDxBHrHXFlnF\\nkezrrCGqkAwaqoavWrW8MhbW1FtKHTSh03ZUBJGutjF4cHbXeuHPJcFim9YLCgwQWTOTfJaPnh2G\\nXHe1jhvrjs0hgQBJ0ws0x2739/eDNz9BkK3X387UMjA+OjoanO/lKkQa68bYXCHjAAHAgN3LLTLe\\nWpNJgAz0er7Ta5WlzFkqbF3s+WkT8dgaBxYJwrBrgJkvX74MSLx9BIe3t7eviIich6pvusk5E8vl\\nss7Pz18FxQSVyKztjckXz8OuLWvWs6qXt1NlsOmkV1a5OUjMz+S68ZkMAvwdbIn12ImNnq5msLuP\\nJBTVYcinCXzbQpMRjOf6+rqurq4G29dIRiVRY13BHiMj9q9pK9Mfet6Nid1n9BpQn3rc01kTSOlf\\n3AeeZ1vrCpDEglXDrZbow9gNH239wW7QF/s6VwhzTpT12EQ6sYV/nz5rFxmWCdXEodYHMPDFxcWb\\nRFmup6tlmAfIpqyIMaHGZXthshTdtC9GB/eBUbEhPJ9n0ugn85VV+XluKa0XxPPvDMiZ33wma586\\nmfrbI2pSZ9IeOMbhO36GYyz3n3Ha7ia+IRFHf7If+yJq7P+xqZvNph2PYKLm4uKifvjhh7q+vh5U\\n7hoPWKe9BZGzDY0787I87CKCjN/z+46zHSd6HXK9e/fgefY3+XKQlHcw02w2a2QbMQbz9D2/+F2i\\nhky3SwI9yRZYOg/g4uLtFpkdxOBYwSaTycChpzNy20XS2OAT6EA8wArmd0wseNF7xgbnwQQz7vV6\\n3ZyCA04zxMybD4v0tiuDizHbp0+fWkUMZ8tkKanBGW85IttEFngXAKh6OazV5E9e3iqDM2IbAFlV\\nLkiS3n2YM8CxK2l83k4ae5yAyypZXwerbq6oAbATIBpoO5O+DyDz5cuXury8bK+2Pzk5qdvb21b9\\nYsVHt5bLZf3444+D+al6YZ+tb+xRh6xhr3QSPLsCOztLg9Jkoq231jmvvc/q8He4t+WuargtjMzW\\n8fHLG4nW63W7F3aMsacdQ6b2tYXNwZadtcEfjQCWNYd843yTXBvm24CO+YRgtI1jPhzkZebZLQOQ\\nXSSNAcwux2vQBGntihrWsUdgMX++B5VuCcAsJ2M1dKPqJZPPfmsOSefZVIxcXV3V9fX1q3VnrD0b\\n560w1i0Hc85Msb2Ea5cdyoCc5sDdcmMQnaSav+d7WUYgn9gW5+Z1nEy+vfr95uamEa2W8bHb3d1d\\nq+JhHT3/Bl9kFz98+FBXV1eDz1UN7RykBmPIdcbeGjg6mDKGyEDCJA3z3NPHTBb5d/SX9betSHtt\\ncqOq/2Yi9yPXy0B6H9iGCtL5fN7OlrHdzwCfqhkCfIg3kzR51ozX1+Rhksz8LuctSbDUS2Nj4wkC\\n916QmH2y3bYspBxUDas40HPmxbpt4ohklIPwMRt4hP4zt+hKVlrgEyHZCJh87p2DL6rPkAPrFs/3\\nGvQSs3m5r6y3q1uRBxpz5znmeXkg//Hx8SsbYfni9z6LjvXmczyPvjqZsQ+MmrjBcuS5Atv7vETW\\njNgImUMurI+9oB0SoPdM+rYrsdTDJkmwWVasG/hj9y3tt3H3LgLCsR/3cIWjP5O+Yux2d3fXtgxi\\nU7MChj5QJfz+/fv64Ycf2hmdJHrdbC/zvCjWKuc9+QGvV+qik13pC6teYnfjUycjemvOv71Ojnnt\\nK5LEAwPgs798+fJKd6lQeqv9qjNqkkCwIFrYYZe8v5AOmZn2QB0Y5ZUl2ny+10cbVwMYvoOzyYnM\\nQx9toHtA1n3msnL6otl44BQpmeYsFpwLf/v3f//37y3Nf6nxPMbsIJl5ns/ng4yvq52Y3wzKM9jK\\nLKHXJEFOD0Q46MyMhitlbm5uGkkBUYFx8JoiH8irFTTLwHuXv4cT3W63zTh5jFbYXYHuH9IAFw7s\\n7bCQIa83DLjn3ow2ToaL1wkbsKTj6+liMtK7sg8+r8FZLf5tm8J9XIXl+6OjrmJCTngrSjLWBi2z\\n2bctZEdHLwe38tnlcvld4/n7tmTfM0A30EQXXeFigLCL8WdOkQ2Tmn6W5X9XEJCAFJnhew46EzSk\\nrKTTNGnEGpq0yEoDmnWTiwQBRKUPGR67GWD74PTJZDLI6EI2QQre398PgjcD0qyASX9kf8hBfPf3\\n94O3EULUZPBPS1tL/wgunFyBnOfNN852pf9mPROkVA3tL2O2nPlvk8mkVeNeX183sr3nh8dqlj90\\nBJmGdGFrBcQL5G4mfYwHGFsGe6nvKduWMQIAdNr2n++4si0Jmx4m4XnWvV6Cigviw/pm+4wtwP8t\\nFovBId8OrvehjyQTv379OqiuQwbBpuArvwyBAN+l6zkPDqZYU9s1r2vV8FBckw/8f1fiyQkT5Agb\\nbgxumeNv3BtfmYmPJIySlMDXcV/bU9t/y8/Y57dBbjhRlyS77Y/lnL4zBzQThyYt0i/1fFEPv6b+\\nGgcfHR21LdMQza4KtR/zv/OcT1/ehm4cVFWD75lgNTaYTl8qNenj2dlZVQ3fXDhWI0nRI2tZD/tF\\n4ozENkmyeI2zWf5zbpMss12z/phss4z4e/lv25e8Ny11NG1yNuYF+XezPzeRuQ/yO+NFnj2dTtsh\\nwfR/uVw2vPXly5dmu/zSjLRfyCTJfcaVuxGS1Mor7ae3jLuBe/EF1mf0MzFJylISgo433Cfroe13\\n1bf1vbi4qA8fPtTR0VHT7+/Z0u8SNTQDPoMG773PPdIIvct2HexbYXoHE9vgWkn8O0+OCRYrkg14\\ngiIMa54v0iOJstEnjGTV8MBE+mvjgyKen58PHIEPYNoHmKGaiDFnxsDrxjYkB5Q4dI8tiZokacyI\\n2zEiFwYRzvbwOSuT2UfODrm9va2bm5sm7FSCWI7oJ31NAsGOwJ/LDBTgnO9S7pdz6KBk7OY9+Ak4\\n/HfWkOoJsv/WlSynNfhKPfBcsjZpQB3wV71k7DJQpKGvBIB5JkDeJ4mgBLk+84mD9jxGB/yQARA1\\nl5eXzQZA8PitM2M3g8cEa1TozefzluH0ujN3SdIZiFa9bHtKsroX+Cc49hrZcZq8NLClD+6LWwIf\\nZ3VdZWG9NJngdTM4T/tBEAagwM7sA8h4frzX2PNufZxMvmXVHh4eWsaQsUEq+7BA5iODaWTZb12w\\n7CK/mbRgbXw2l8G7q7aoMPBWLWeccg6SJEyyxuDGgRFjTD9iooZKwcQDYzX7Z/TdgZLPTPDLAUzi\\nEvgy56kLXsMMBN8iwXukD0SXn50+1MFhYpiULdv5BMk0kz7IcpIa4BrILGdVjS0A+WM2+yjbEmMy\\nnu3t3JylRwWN5wyfY2K5Z9eQHWNPkwf5WWMir30mCtALiEHPtYmDlDUwLpUy6K2fZ7xsXGedhKjh\\nGYzJ2GHsxkHl9o3GnLYlSdYwP/48a+HPWv7TJ+ac5FoZi3JZ/46OjhpJ48osjn1IHUtdRw4g3Xnj\\nDjg3iRr3N4maxHtVLxXlVS+VumM3qnX8fNsrsJ630rqSwcSD8ULim8QZ1hHrPuNOctXkDvfpkTQ9\\nott9y/H1bK19oTGU5TqJrN69EmsRN+8j1uAIi+32W4La65nHX1CRStzqJGOSmyZr8Gd5FpMJN+PT\\nXmyW8aZ/+rlpO0wWOWZPosa4K9fG85/EKFcmTGazWTsj1on1XRwD7VdX1CQLzSTjnJ2VcIYc41NV\\nA3BSNVSq3tYV7yGzYKdQO6hLJpXPo/zJ1pF957XbXvgeUWNyxmRVL2jy3PFvAgob1s1mM3jv+j7e\\n+mTFc0WNgRalw1kVxVq5KsFGlLXOrJCBh40SwtlTPM+5lZyAyIfdkmlIssZrSB972VwAXI+ttSFy\\nQIkT9OHC9NNlignUxmgu12VNLHeUdftAxM1m04JD79/3K2ndstwa/UiZ9Lz2sh4EPjaS1ofn5+cB\\nAcHc5fxhG3rO3OuMYYQUpn9JTvAZb888Oztr8mTCEb0YuznYMlgkGGULIKWnOX98x/OfQMQ2M8Gl\\ngzPseJJ+7ltm05ETbDW6YqLS97ANNQmHLbq/vx/Yh6oXIm6X48N2+OwNyvY9bvo4drMcExxh55Fr\\n77eveu3/ANHevuR5sH+wDWLOTNQk0ch3cu6x2w64M4iFqHFJumUws5QO8NK+es2tS54vy5Yz+YAj\\nDjbch1+kL9iFqhpUJyHbnM/GuiR4tSwwN/wtA8EkRpCHnv7Zvue8M7e5ZdFbYBzMeZ3ch/TXaW88\\nTpM09NEEJXLDAejeNk/FwdjNuDGJrKpqcwJWRcY558tnuti/9Oa/Z9uso71g0P006WhdT6KG34M/\\neoFDEtcmGagu8vwbN1sWXEnmJNrp6Wlbb5/z8mtK9X+fdnZ2NrAfkGWec9sgj93zU/ViX/wZ9BRi\\nwzbVNpy56FU6YiesKybkfYHFqBTy1nSvfSYSSUZC+tzc3NSXL1/a9zK2qaqBD7dcOPkCdiQGMZkx\\nVsMuJjb0WKnq6W0vpP8ZWCcZknpoeU6ipkfSJMnZ+yz/t/z0+tCLTdPu0mxLe89zXOmxmTxiTdHF\\nfcWLrlKpqsGZfH4LJDaDM2s5loEzPe1jMg4H1xNz8DwT0vg4E8/830UdiW2tbz6+AluJrWeek6RJ\\nfac6xkQ3z7HPdB/sW9AJfCRJi14FULbvEjWAX2dgE1T1Duo1m0pZvO/B5CRoWK1eDmVNosbCbSCL\\n4GYWxaxotlwIAlcb7gwyHEQS+LKoJi0ygKp6HVT3WDi2YOzDgLKOCJYNDU7GWy2cSXSmyXNX9UKO\\nIaCZ3aI6w3OS82XFs8Fl3txviIO8TN5YUawgPu9ms3k5EJA1cH+sXAlwaXZ8s9msKWaCi7EawHIy\\nmbSgDqdscqZ35ghzmWtjOa+qATjJrJ/lFfDWq6Qj+JxOpwPixN91Jh+iN52SQY0NtY0/65H6twtU\\n83lXeqU8Whf2kTkEVNmuokOWu7SlBj62TQlc/HfW3dkYAw5XRBJY5bMBDQYYPVLTpdSebxO1zhxm\\nZtKBkrO81j9knr9bL/EFbCd9fHwcZC3GXsOqb/bPB8QhX5ZzPktjXAAS20n+nkSNfSPZ1jyXxoRk\\nrjk65GD18vKyrq+v23V1ddWufFNc9sXBqW0EY68aVmKwvkki9gD3dDpt1VG2/TmPY62j1wO746Db\\nQNHNAYLnoaoGMmoQabLNv8+1T31OW5B2lMvYDEKJ57F2Bp+9pEoGwDk262oSePajPu8Eoi3JizGa\\n8YltSfbH/sr4jbW0/BmD9Oxrj/RCZrwGrszJec8g1bY2g7+s/Mi/mczh+06GWEcdBDKWDCBpEBEE\\nUz6Ufh/raL/IfJos4k1rHPzsZKIz8YyXoIq5NQnAnLpyxhjWgZ59tfUF4sGXK2LPzs7q8fHxFVHT\\n87GM39VoEDXgWsuMZcTrm0kc/zShtY84I+XWZDx/T+KX5s865nPgzGesA5koMKnhZ6a96v2d/+d4\\nMpGMjaD1YiPbkGy7yHk/Exnlc/jw7Xb4QpN9JRPt27IPJHd61WK2c7ZPxiWZhOr93uudc838ZRLC\\nc+jYzW9j6/U3yXn7gB5+th6lz+H+llnWKGOyh4eH9lbcN9fjewt2dnbWtgcB0AAHvHLbwWEyqb1s\\njgU5FzYDyV1AICcyFdkOz84aw+3PA6Bccph70KuGmUEWk98n6ZAK1DMmKawWmrHbycnJK9BmYsZV\\nNL78GsM0ZHYMJgGyKur5+XkgJ8yZq1ZyLy/PsTKkk+SCZKO6xp83yIYIo78cAJ1rmGfBpDG1zNoJ\\nuq/7yDh9+PChjZeDS5lXqjAs3xCpi8WigUMfNmpn4SDMF3LOnDCXPJOMUYJT9CgztKyDS2C5zLpj\\nawxMM/OC48jXOlpu/fkEt2R/CJadsdoX2VZV7ZDjJCpMmHgdq14fvG5b0rtsV6qGwbqzzsgHttyA\\nhu+ZqLEtzi2vVHD1AgzmOUnXzEKbqMGm+Lkek0G318rBq53i2GtY9XpPdTaDPHQHmbYuch/Pu+Xf\\nPjEvZ3yTiENu0DcqCZbLZTv874cffmivKeYA+Zw721sC4twWwPOSpOrJFJ81CIesMOG2WCyab6Za\\nZcwGsdwjuLzNNIOfJNI8fsaQRIzl3q9eThuXwXoGW9Y72z5nqCHJAfvMr5/dy0aSbDCes1xlcOk+\\nMmbeojeZfHvbHAQYZ26M3XiBAWPFztkn9XCZfbvXIPEp69nDm0miIDc81822OUl2+1haj8S0H+sF\\nNh4TiSmCLi6vm+2FSTkunuU57BE6YzTbfts11nOxWDQbxRY7J0uTMAUDMeaev/TvXNloHJu217oC\\nFsqEB3MPXsvg0Ikn28f1ej14mYMrx7FBxspp7002YjPoE7L1/PzcziIcu+EXbet72M3k4q6L7ztR\\nwPeTDOmRIr2YMfFdjxhy/+0/8YWpZyY8UxcdQ/hzJpt8JTmRybyq/qHIY7fEzr059byZBNxsNu1t\\ndf4++oA/4v8mMBPrZ2zNc5hH98m21IngzWYzeLNwVb1KVuOjvEaWqcQ1PM823/PCd42/bcuIdxz/\\nvNW+i2B5hzqvsn1+fm7VMxw45ODWTioDPi8ag8SA9QJwlxFX1Suni0FyNsFg30GOjWKSRwRqLjn0\\naxETRMHiU06KoHoezMD2yBkvZALFt4D/79vIMhMgbDab5lwIuPNU/cw6eS2YYwcaFvpdJ4Sb5EpA\\nmADCxsrP6VXtUFVjooZqE5MLduSLxeIVUZOVAW6M3evkTGbVC0DfB1Hzww8/1JcvXxrgXa1W7S1Q\\ny+VyIE+AE8574PXdDw8PTQ4S9CUh5go65hGZ4I0Zl5eXdXFxMZALqhkgPG30M7iwE0RWHFR4LtFn\\nOwuTaq4ochWFbY3/jx6kke5lrMZsgLjt9iV7n+SoiegeqEviNwlFdAYSKAlI7uPybOxZBhAmOpgP\\nZ1ysxwb3gO75fD7IrhgMO2uaJBPP61VT2UbyvSSO3K99raEBhu2YZRXSASLcPiaJSc+zfYJtXr7Z\\nKSvLDDZdSUCg8+7du7q+vq4ff/yxfvOb39RvfvOb+uGHH9obcC4vLwfy//z83PQZ/WYd7Nv9zKzM\\nSvmxfTcgdVXC6elpkxcC/rEbpCl2huDL4L5qWBHL71l34xRnXfm77aorQplHE+uQQ/hCEyPMq99i\\n6dfb5jZJtsFh5yDQCdJSD7kgr9CpBMBJ2BgoM6aqYeVxVbUte2M3qjGRI/AFa8W8YQt6WXrwkNfA\\nNtZY1hlm/90kkTFqz7dkEigDn/Q7yJGDHlegIGuWU9bTRA3rm3LlPpmE9bwZ2+4rOLTvh+CfTF62\\ndV9eXtaHDx8G284TW6dPZFyZLGDeTTJyDIKDR3CTt5ZygSu9tYKf2Grspu1bJpRYs9Xq25s3fUHc\\nGLM7MLQsscapqxA1yCqJzbEb569ZDpMMSaIm8VYG6oyPe/XW2Xg272NSOe9r/5S+qerFfhhH9PCW\\n5972Adnqfc921XLoe2Yyz7HmLkJ4jJbkXw97p81yPL1eD99+metuPYdb8Br6+banKeu2n55zfsez\\nvfNnu922N6XRL9vP3rhN0njO6YNlqEck+d6ORUcjarz1xSQCgKFH0HAZZLrTVTUQZmeaMhh3I5De\\n5dz8OZ5lksYOygDTQBjDSsUCY3bAgFFOkJrK/5aS0nrBVn5mjJbBE8LFwcYZ5L6VfUphZN6cdXCJ\\nPvPH3O0CfglQTNJkFtlMrA8Zvr+/b6ApK2oAkTzHVSW5bgnibFwZM87e4NxOZOx2fn7e3oLCHHN2\\nwnK5HJBkjBvj5O85sNgVLNkAsVY2Khz2yZYJj/vp6WkA7tzoj7dKcn8ARtUL0dADRxA0lF9CrPWy\\nphksJqAneLHdSmc/djNhxIVu+ADhXY457R338k8DeJM8SSRTnk2GMisL0i6lDcjAxN91gGofktUY\\nJoYYg33NZrMZkFces8lJ9ksDSk3sj908V1kZlNlO/Ba6Y8I8ScIcm8GfCegs22VNTIQTEKBv5+fn\\n7RXh79+/rx9//LH++I//uP7kT/6kfvzxx3bA6sXFxeD52BP0xSQ9NrDqBSzRD8tpji99iLfkGFxn\\n5cjYLZMB9vUQyR5Hgnu+x5hZe8u+A3wTJElA2zelrBvD+DwhzoNxttA6end3NwiAIWogazhvxPab\\n+7RAYgkAACAASURBVDCO9H+5hgbirhjIqix0ZewGEYEOGl/Qjwxwq4YZcNbdZLV1z0E7Mt/zm9yD\\nZ9keJmHueTRO9d+YU/CmyRcTvMiY7WYSNUkw9EjlqhpgK14qYVu6r+Cw5zeYK4gacAfkv/FMkln2\\nrfw0uY3s8HfwBgeYmxiHNHl4eBhgdhPheSgq33VlHn2DaEqfR0Waq2o4i8yylkmXrLLwTx8gbNyz\\nj5eWQAaxHozRfUuf8Fa8Y1voWOotUi6Jmnx2j8DhvrvsG7Yk++t/2xfSF8eBSQ4bL/C8/Du6sFqt\\nBvivl0wYu/VIr4zx04Ygw04SJrFhPUeHnXjLmD4rmhKf8h33wzGCsRd6xwtWjEnxg5Y198c20DFi\\nEjo9fG+7bL9kkvet9l0E++XLl7YNyIH8ZDIZlP65tK7q5VViqWgJzqtqENyzwDamFmgOwcog0ovJ\\nfjSz21w21qvVqru3FOFPJ+7A1sCZvnMopu9J8z1tVAhQEWIb7zHbzz//3AIEnK+BuYWGdbSy0cw6\\nO9OaRI23Ntj5moyzEUJGHHxQIfPly5fBK7h7r6N1Zh6Z4X7MLX3uZePdvySnMiBKWXaGhgqlfZBt\\nP/30U8usIM+Xl5ftYi4ZAywt64Bsemwpb4BJxmKH5m0yFxcXbZsEr7LO7Yp2UlxURWBYeeau+aKP\\nHpv1aDqdNlaerW3oMeudRLEDRGTBTsdl1/tonz9/bvfmcDNXtng7ou1SVrolKeX5Yg0N1vienY4z\\newb/GWga5AN67YCdBcEekzWws3JlyGbzcjZC6q3tpMmQzOji7CFlGVvPJozZqP5CpzIg9Do5ENhs\\nNi04tl56/kzQYWPwE6wLFT38LUErDfIPnXXVDP9eLpd1cXHRCDt8rINUB/om4Z3pyyxcku8ZRPcI\\nWoNwiALL1Njt48eP7Xkmt8ENPlSZdUSOPQ/Wqap6pUfGNZbLJPaNCXhWEq29hIqJUp7prccOHAkE\\n8aGZEDMQdtYzExLYDG8/SfvKuObzeavuHLtRtWlCm2oj/0S2mV+2fiTZZNvXs4PWDZMkSdRQmWRf\\nZT/oQI55xMZZh70lwIQdz7bumLyhn97ex8/0AXyu6gW7G+cYzxP4jN1cYWhSy5Uq+PXEDCaH+b+x\\ne8YK/j7jc7LPRA2JWwhOY5skSnqkJZglA92snsVvg3OSVDQpB8bLANI64Gp4+uxq6H0E+HkOlbGA\\ncQ1nn9GSpPZapk9jPI4tHSinToF9knjwZZvn+yd+yMTyrtYL4nvEXA/39rBAEibuwz7WEb/ul8rY\\nhmJTOXLB485CC/sDn8vngoEcv/2Iv28bBzYwWcNniVfANK48xdbZBiMnjmMz7rdepdyZwPbaWEbs\\nN+xLfg1G/S7yubm5GbDAZCxZTAwNAb4dm5WHDvlvCCtEh1+RnffAiHlRUmBxjiZpXPbNWRoue8rz\\nLUzUMLkJRpP5x5hjGM2Wp1Lb+GR2rhc4j9V+/vnnJkhk5ADovNnD5X0AOwCsDQJrkGA1z6Xh4rMG\\nrA7+MiPCvHJA8JcvX+rm5ubVAcLOTPaIGgcu/N5EXzKeOECU0GuSWUWDGAewEDX7yDj99NNP7VkE\\n8QRcl5eXA3msemHqYYpN1CQJScNouSKFy29J4plsfaKkPXXFum+yC2ec67CLsMG4Pzw8DNYHfX54\\neKj5fD4gajKA7ZGtDtIYZ1Z7jN0+f/7cDD62KokawAz6CBC1vTJQqxqCnaygydL/Hphh/riH1y33\\nETsY8XobDKcOmtR2ltc6y09f2Ar3l+c6aPYa8p19rF9VDfb3M6d25L6Qc6oJHCA7aHMlDjJKhtKk\\no0ka1nyX/pycnLTDN03UJElzfn4+ADQEgAbDyIHPc7O8pD73AGYPbFYNM1EpM/jKfRA1nz596iZ5\\nuEhksAXcRJUvdM6Zw6zGsN9jnZLoN15g3SFFkgRIksY6lYdM4xdIKCVR4/UwCWGCJ0l3EgaJcazP\\ngGmIGuZxzMah4Q4IeyQN2zurXqpNHOTaL1TVAMP05iTxDJVVJtJs05hbkyXGrq6U8Pcy6ck6k73e\\nRTIlPrHPt630301wJMZhDPitsdvDw8OApIXMYD3xecYrtjX8rUd+Gi+YIDap6cpsV25D1EDW4C+5\\nlzGS+2bCixjJdgJS2Lpj+9ojapLYBwOk73H8kSTGYrFoWHXsxnYXk1aM0wfU9w6p75ERXuMMyDNY\\nZp5zTrAJaZ8sQ55jP6OXrDPplvf5NRethy97xEtvXnqx5ZgN2+gklLGniRr7ZWyQyV3H+WBrH3OQ\\nY0iSBtvm5AGXP+d5gai5uLioy8vLAVFDgQnftd9Hz1mrvKeTo15PY2vrN3NpH5L+5Ne0X1VR4/Ih\\nb4UCRBFAzOfztsXCYMTAwwQAHcVA7iJqzFp5UZLRxuFh1Okvhh6h4rPb7bBKyCSNjQTGMTNKTLqD\\nDwuOM2F5T+YlA60ewzhG+/nnn+v8/LyBTgA6Py1cziQlc2+jmNuOepU0CCLrPp1OX1XUcL8UZk69\\n//z5c93c3LzaWmWgaaImFRvgVfVy+OcuouZ7B6z585YB1gyiZh8Zp59++mlQxmeyZLlcvjKSzug4\\n28d6GAAyZxlM2ThloMf/z8/P2/o58Mqgwyw49/ZauCUYS6LGoBXds2MxcDLL7gbQxXD//6qo+fTp\\nU6tiI3hxMOEAw8Cz6uWMHwM8r10CegPJtEU9osZO0+fKAFhxsH69pwk539cgEX/BtrjMPNiuOFBP\\ngpTEQBLKu16XzHyM3bCLDpIzm2mixjrpCkTmz36VMXgOptOX18aTIbe/8BzZB1EVgs13NY2ranil\\nJgkO7DT9q6qBr3NFjf3YLqLGwDnBD3213XZAu2+ihm1EjN96aJuHPNnXuaIGkngyGWZovUUjwbmD\\na3Q1fZb1u1dR5wtfmEkTfueqAW8rzaoc+zcnLxxYscZ8z7rO2LAREDUe11gN3Qf3OSBMu2r7vlqt\\nunY0K2V8foF9fpJhEDUkMME2NBNdrgyBNECvE2dZZpAP5hs7ZN2BGCTY6lUCWWdN+mbw6+AIO7Qv\\nooYg38Qvvo4zFI2xmR/mlou4gTGljcGe+Wwm6zLbAk2aWGe4Z1Yc9+IG1oI+mJS336VvxFD2x1VD\\nvMb6GJfjcxJjZcw0m82aHuxrDY3DTNSwTRP9ZK0cdOfFXFofGI9/Wj962AY8btlxIor/m6jhfm4O\\nxHukoftr/41OYw9tS/P+SUj535bhlOcxG76MuJndKMhaEjWeB9sax3nb7ba9lIgt34zZrUfSZMzt\\nKmrPB9+jj7w0wbtqiENpnH/H9jLsqrGN5SnJ9KphRY3lruql+tqXyXRjtl3tu8gHJ23SI8G+DXmW\\n9NCYbBucdHzer+ZqDAu9t3YAWDDkZA7TIOSrRmnT6bTu7u4GgcOuQB8HbONnR7LrSjLGIIc+4KgJ\\ndvdxGrvH5t85oDNZ4vFVvTZwvWyNyR07FILNlAGcmO9vAHxzczPYp2vA6yqRlMccJ881ebFLTnvO\\nwbJqOfXZBBjczOaM2bxFrMews6b034CzangyOfPHd7zWNPQeFh1CgWwSBq2qGsnqcmEO4EsDBfjJ\\nIC7Hkgaae9s5GQggDzDk6UB6TtWgECOcNm3sRt8MKlgDO3+AsfuZ9sTzZBCRREdvng0w3TJoQR8p\\n/c5MMnPEHFa92Ju0q+5vgpUEZPQ59Y/yamxy1YtsJ7GVczVWY208rw6ivYXUsuszLuhzfqfnLwA6\\nrI9lB0LNwRb9MvniOYJM4IBXAIzXFlvsqka/Lvbu7u4VUYOtyGoF5oG1RafxfQ4u38INY7escEGG\\nIVDx4fZ3uQ5UdyVYfCvwSPzjTLllI21C77uuSMVXUo3K2rF+/O7+/n5w2D/yBxlaNdxLnwSRx2pi\\nycSiSYhe9nislsGqK6TTnhr4098kB/l8D1gbtxgH5HlNNCcAbAttA6uq3bdnj/0598WJsqww5l5c\\nVcOss32E58e2MuXWybB9BIcmExJbQx5vNpuG/5EzEx15HxOJuRUtcUUvAYi9hvSez+dtO4VfE+7k\\nHH3hfu4Ha+9+2sdXDRO4lmnrvMfO5+wTTASZyDP+2kecYRyVW9Ys2/b5jDkJCOM1J916ZLHl2vJj\\nW+D7mojl38yLdYLxYAe8bpa77FfGedbhXUQEPsDzkvfY5YfGbhkTWR+pPkEnc3z2ieiAsTlVLR5L\\n1bCaNDFiEh2eF+u7d9VAznv70+np6Su8RCxC35MES13MMyQzBrXNZVwQQMiMsZpJnV3tu0SNM1pv\\nETV+BV2vpKcnZD2w0gui7VDsgBxAukQ5L++lTuHzHjz3NYXEoIP/m1mkDz7cL6sqcqx23oDUx8fH\\nur29/d6y/JcbTgaA7gDLQJTfTSaTBtxyLhLY+O8mQ2iQCzY4BAmcacI8PD+/nEhPMOA3pBgYZXCa\\ngW+CW8sXwGqXwvfImSxhdxbU+7sdjO2jeU2sN/5bkqA5P2TerNuUVDvw97ZBjDNGx1sfcvsbr5bk\\n3CYuKgowVhh/VyWkLiaZ679VfSM+zs7OBjp5dnb2Sn69/gYIOA/0FvIpg8axGgEgMpOgHZlDtnoA\\nxs7MeowDcdVa1curhk3OJHBKB2jZh3jjbWMOFvzsXYTpLntvQOO5TkKBe7AmEHZcPTtuB7uPZieN\\nP3IllOfV1YCAbLJUDoJdKeE5M6Ftm8e/PZ8mSLxNzn2C8Lq7u2t64jMEXJFxc3NTHz9+rI8fP9bn\\nz58HpE1m5wFh6/V6cCYSNseANAkjn4lmmWf8+wgsEhhXDbfpENC6OsWgEztpcG8ivWoI1Ph7+tCU\\ncbAWsuBAK0kD5g8iFbIGkubTp0/1yy+/tLW7vb0dVAVlYoF/G5jiA1JOGbtJjgSt9p15Hs4YrVea\\nn76EgJyx2rYm9sl1Yt7t4+x3k6hJoI9eONikT/bdtF6g6rXx2nvNnSzB5+PT6Qe40+uNP+Rvu55v\\nUnMfwSH99Xp6ywL42+QH48o4wUkJy4WrRHMunSQ0UUOAZaLGZ1cRe/hsL2IiSDTb5QyCq15vsUc+\\nseH4FJMLWc3JZV0w7qdvxmZjN/ywg3onzZh7Jx17xFVPvmxrjM0yQE4Z4qd9CjLPXHpbaPbHyQc3\\n+zbHVcY1Sdh43RMfuT/4Qf5W9WKX+btjtbFbEkjoALEuMkd/7EMsA9bZyeTl7W2z2azpmf0pc2of\\nl3Gln5UkDXIHOcNRAiZtGI/tBX302icJgy76vFsnRr1WXlMn47LS3LjxrfZdBItA94gakxcOvhJ0\\n0+me8GYAzWI4aLDxcuah6luQxj40V9R4W0gvq8LE59tnPME9xs8KAnNngw0xhJFKFtxCZ+Eks7Uv\\noobzLnrAxAwzBtQlXq5CMdhOI+QMRRqRXgDozA9BK+Dj9va2VdT0iBruZ2UxEdELEqte7zt3P9Px\\n812UGHDr+aPv9MfZmX009yv1id/35NdOH2IQY+zsU34HI3dxcTEAwNvtS3lgZhxxeF++fGkVa1x+\\nLrrIfTPj73EaFFunttttM75VLzp5cXHRXr9u4JTBFvLiN6mwxvuqqGGrYbLrSXjiHE2yOANXNTyw\\n0yAFWeQ+AJVehjflKeXHFTW8EcPkpW1pZpnesqk9shd5rHoBYNZfZIBgPw+K99ww3n2QbX6GiRpk\\niH46CACY0E/v/3YWEqduO8s8Z/YUktT653sZzCd59PT0VLe3t82vmax0Zvn29rY+ffpUnz59attQ\\nqc4wUTOfzwf+G5+Nbbc/d0sgZhJx30SNG/01UYNtw/8Q6DnQN6lmu+bxWb94Vg/Qgwtsf5MQ8Vo7\\n2Nlut41QxYd+/vy5Pn36VB8/fhwQbBmIGqCayABEgqt6GfyqYbVU1YstYu3c57HbLv3x1hH64bWx\\n7TN4N97MBMPd3V0D9IzVRI2DQZOx/Mxn2J7ZFvoerAvjQE9Wq1Uj5aiQMkEPIZPbbax/6XMyCctz\\nmYNe0DlWS8LNtt1BH+dBumLEfsW+MokLr3Um7myjmUvsLsFfJoEdZ1CVyL0eHh6aLEJQ9gK6XAdk\\nk75zX2SNecjttYzbASvxDXpNBSVVdmM377zw2rmqh/nP5JPXbBf5YJIl8asJGgfVrL3lGrK1aviG\\nUc5JouEb7dNMYBhLmajpJaGSoLP9yfX3Z7CzPLPqdSXovprHAMnCW149D46TkQFeqmK/jay6UpVd\\nO8gNJI6JK3CoybKM2TKpkFtgic/T74KFei2JGj/Dvsbr7osY1XKdyUTm9a32XaLGDqNXtVA1PDRu\\nV0th9M+8DIJQRAflTBYHJF5dXdVyuWws2vn5eV1eXtbV1VVdXV014GNDzuXskMHhrsb3etkmgxnu\\nybwAEDJwycB7X0G+9xjaEPYCfvrJfNuApID3gq6egKYRzX6Y5AEQGXyk8auqQfBhMOj70ewYct79\\n02vDPTLjkvKa6+k+jtl2yWium/XHQCsDIJoD+M1mM3CYnHHBWwK4pwN4SEbmH5KNiiiyQZYlPsu6\\nIB89oJ3jYL4ta5mB8qGoXJa3BAcOUrELbwGGP6RBDPf00AGPgyeI6Z6cYdP8ecuGHURm3xzImKwl\\nCGCrhN+6RrDKvDs4yD73CPC0ORnkJahJffT+ZECoS1F7oGjs5oCsl9VJEjNJ4R5BkwDXIIj90846\\nsW5Vw8N4szrNc2J7BmlU9c1+ogMnJyeDc2ju7u4GlTRkY+/v75u9QJ4Yn/URQNsj93s2swdQkc2x\\nWxKX6avoZwbSlm/Wgb+nvUp/wViZkyRfU8byGSa18t+Qa1ldg+76/A2+Z+LCmUMf/GkyNBMiqav8\\nZC5S38duWVWQASByz7+NS2iJaZBr1sDbYrxtF9mwD2btbHPpX9q7JHQhk5Is4cqkRW57chbaZJOx\\nmQNOE309H9ILxhJbjdVS9xKbGyvahhDwuSXZnz6g54csA8yHA6ne+XzEGFdXV7VYLNpaYDchy702\\nSQK62faZuKI6n7FAyFu2Mk5LjN3DsvtaQ/u0tJP8tI1PX5p2hbnxHPXmqkfaeusT37fuWa+TqDG5\\naplgPK5CzxjF8S3P5fu2FZZDPwvbkYSOfc2+iFPrguUWjJNkhO09vhEc1OMMql7k0cercDwJJA3j\\nTjKI+cxKcScWkrDBh63X65aM4szE9FNesyRpiPMtWz6Sw9wGMsRne7G2sdOu9l2ihr2XsLg+RLGq\\nXi1Aj3h5q9nYMiAroSfv+Ph4cBDi9fV1vXv3rt69e9cy6BAmEDeUFDkLkeSJJ7Jn9FAWH0rsxe/t\\nic5McYJ5hIAMAVl8guOxG2Ojn4vFohkZn9SembIsDc0yZmcQDFSZL5yM9wpeXV21dTs9PW1z5L3B\\nPq8ARdkVbK5Wq4FDu729bcGMHeRkMmnA02uNHGI4cIQeu2XFjsIZz6qXE8T3ERxCAvaIDIyogyM3\\ns9cG6T2S1GDJmaPM5jpI8FlCDgy+fv3aAh7Wzod2eg8zsmAQjc1hOwZrQR+9F9XVcSYdPGbW0aSh\\ny5oBwziQfWTxcQ6AQOwc1UjYBzs1G3dk1A6MzzjjRoCcZEAGAXz3+fl5sOXw8+fP7fIBuNvtsEIn\\nHbB1NAkDB649ctgBicvULWsJfAx4+ZvJwH1st0gSKomu7O9sNhu8uYTL5EwGx0nU5LYok0AJ4JB3\\nBz+sG0RM2j70BIDFZd3OQD8BjX2e9ShBaDbLo4nilJ2xGz7dNgQCBQK6qgZVQQ7m+J2DL3QBOaYZ\\nxLLu2BjrpQlnk9s9+2ui1MG+QbTXx2QM9mc2mw2qBa6vr+vq6qq90c9Z+V7A63E5ODRJkMTwmM3+\\nlzkw3kpiIUm2HE/O3Xa7HRAhaa+qhtssfdCmK2kcyGVVb2Zi3RJj8xl/P/WrR1RbjtxYL9ukJHv9\\n7H35RdbRsYT1zRWI9CtxdupkVTVCgnnvEcKZBObMiMlk0rZWGwtBzlxfX7c4ZLFYDCoReRELc5v4\\nxXbCgZr9tUm8JAAYE37SGNnjTd/7azL4v2/D5yQZj8yBdaw3zMEuG+91dRCcmNvbMnP7FVUPzCcv\\nKsnEA2fU0CdXo+EXMulPSzLadjGJ4x6BncTOLt/Ks4xvx274Ftp8Pm/k/8PDw+CzGRMZR/N3yB1j\\nFGJd46OstKFZNjxXrA3/dvGEd9iAV+07uUcvAeLEGv6SKtvlctm2byXZn0k5+gRvkIT6r90S/F2i\\nhlc3YxQxCp5AjByLk1mDNKJ8zr8zeDA76kU5PT2tDx8+1Pv37+v9+/eDtxblm1N8qDCLCLB0AIAg\\nodRZRmjHYFDnTFMyt15oCzACZQBjgIwB3xdR43JIAlOIGmdycx1NxmQgkuCQ9WQceeDaYrFoju3D\\nhw81n8+7J+2bUMvAD6eEjDw/Pw/Oh4DswwAYvJlZ9VqbYa8aljObqDFJ5D6wtiYwx2689ttz4WAR\\no+CzWWjIYwLO1E8DM4wLeua3e2Um9/7+vm2P8MHP2+22GeOeITVpxhgIZtGNh4eHwXr0iBoTcCY+\\nnAlNUthEosln5H6fRE3VcI8z/U17WPX6TKJeJgbARnPQl5mmzF5xL84j+fnnn+uXX35p5dFfvnwZ\\nlG6jg9YHAxEHLZmBpyVZsysYySCmF4R4Dj1P6PM+gEySpO5nkpjMGfPiuUpyxvLMPLC2zjDzO4ht\\nnu/gJYk6iJqnp6cGiEyoHB0dtW0FCWqTqLG9d1LFQJr5x9exXtmSoEg5clZ47JaHDhoo8jYn5Jz1\\nMFGa2MaBiIMJg3bLKCQNb64z3qD1sptJ1NhH9yrcrF9OfEwm36rRjKVM1JAccGDZa9zHW0y4CN7S\\nBozVTHYjcwbwgGbWxfiO5gDIgNvEZ5L9vgdygj4T1BvM245lUJBra9+cBFjvHimLibcZGzbC68ZP\\nbIXP9XDz/O6LqLGu256YqHHi2EGRA+Fe0It/tc1KP+kzvRzwgxuprAHDvn//vv08OzsbVNR4y5HJ\\nwefn51dEjfvi83SMQU3KOTnTSyIib9hf2+j5/Ns5O5DPYzYqP5NUoLE29p2OkXpEcGIF2zRss+W2\\nV1FzfHzc5pFK4Zubm8GZXVTVYLczhiABuouoSbnznJtsYb12ETXcy/fmPn6OK1LGbq5eZQ6o0ISk\\n6BG5xjgmK6gmMeEIBneylPiTIzBsn2zHsHusCWtGTGCShrgJosZxhOfS+ub4HVuAXed13/bj9MvE\\nMWOHGD0/P29zm4nEUYgahIzghYnz4mCUslyJfxtcuyV7CSGDYbQwn5+f148//lh//Md/XH/0R3/0\\nKkgzUeOtDDgnG00mtmpYmuTg3QF5ZkqynMpVDl54f9/zNZ1OG+ClogaFSAc5RvMhS2YY1+uXPb+z\\n2awx7QATGwMLsoUsHQlz6m0zBsNU1Lx//76m02l9/Phx8BYKEzUO8Hvj4QA3KycGkWwCQU5VDci1\\nLCt0dsLgNwOSLOvkPhmQjd0Ym4k/E01mbxkTP30QojM7bg6+MS4maiaTSSMUM4t7f39fHz9+rN/9\\n7ncDUqRnSMlQoUc+SwOQP5/PG0nD22WyQsFGOStqTNRAMDnwy22LOV+Ma19EjUlb+ou8OTBMoNIj\\nwDO42Gw2A+LVhKJtS2aDvn79Wl++fKmffvqpfvvb3zYgc3NzU1Uvmb7FYjFwYs5OO8B21ZcJxey3\\nbbLJA28FyEwVPzNTxf33nXFypYcddq/ijHnyliYuVxzmWwpYF+7rLDfVVz7clGc5sAQkITcQNcg1\\n80TlG88wGAWg8dNBq0n9nqyCBwC1CcgdRCBfGTym/IzZjBN6FTW29QDInizT36rXoDpl1HJj8s0+\\nraqa/UGvCfJ8VdVAzhNsum+94BSATJWAt4xz7kZP3zw2xmyMZPkheMsM5FjN/hefj/1JosZrY9KM\\nZjLC2CYBvgMvy7WJmuVyOdCNXZnbJGtcEUUwgoyA2XwvB/AZ8FkuGVuPqLE89M5YZA73TdQ46POc\\ngXOMbx4fH9t8mczaFUDZjiRxmRU1tsn2oVT1X11d1fv37+vDhw/tOj8/H2xFQz58gDwYqle5j637\\nHlHjqhUTBpn4wYd6nHyOeRq7mWhkDVkDJ2SMNauGgbi/l3Jgwj9JGpKvSdSwflXVsCrY5vPnz3V7\\nezs4PL/qxb9DKCAfHl/isV7fLcvYH38ndTeb7bbn1PhmH7po3IXueDstfiErKLNog7l0RQ2EqT+L\\nHwKf3t/fv/IVyAc2iOQSMYPvYyx1fn4+IEa8+6NqWJ2UZLn7b6LG51FZxi2jVS8kFedmPj8/t+11\\nJmm+h1G/S9RkdccuJhBj0QNsGbwDOAhKciHM1AEc5/N5XV5e1o8//lgfPnyod+/eDYIRkyYoLMLt\\nzKr3IvZKuXNxWPz5fP7qzTc5F1WvDwnrgRs7ZC+UyybHbtkHA0iMB86lavgaSFdO9ECBf1dVrxyD\\nWUW2qJG1oJmNpr+ZIcChTafTwboTmD48PLRsMNfR0dFAoXy4GcCOca/X6wZOrKzZFwem3MNG1g5o\\nzHZ5eTkAzegJQb6dfS+T46DSa2+ZcJbAh40SsPmsA+sShywSPDj4yC0fDtaSgPAcGjjydwAK8sVz\\nql5k1qw+cpNZF+aOcVlWq4Zvfhm7JcHg+WDcALwMJExA7sqweH5ms1lz5uib9dM67Nf3Ug4MEWA7\\njdM10WRixiDUgQ+y4WxMTwYMYDJ7ZtDkTKeDsl42ah/N43Yg5T5kv1kD1tpjsO9iPA42PJ8O1jJo\\n88V3rd9+DemuQNKgw1UavlfKHkSG7aNbyp9JGGTRGWDbsH1VmpK0cPUFgJr+5nwwBgevjKVXEcDv\\nM4DebDYtYWKsg257zRyAGlv1iCLubd1h/kx0u0LYB6R6TuzHkyhznx38OZDJPuwji98j8/yTOWTO\\n/FnLcWZTjdOclEAmjW2cJMnzEGzbeD7/xjb7jAMCmR4eot+Mx7Z4Op02W22ZtB3yT6+dA1vbcXBT\\nj/gbu3Gmi7PsPdvKGjjphE8w6W8MZp/C2uVbnkx4ZLWAfY6rt/McDDd0yLjLdgR5St3gDTn2Xj6p\\nJQAAIABJREFUzQ4s+bzJ7161vPXSMmdMP3ZzEtTYwFg1Y8Sq12Riypc/n8SxP0Oz3qNTxHzeyguG\\nNSm2i9Tu2RkTwf5O9jvbrjGkfqKXFDF4zZxwHLuZsHWFYMo0a2lcZlnjXknsuOqKz5mghoRNvTEe\\nmE6nrcKNI1HwXfhQCiGMQR1n+0IOql7iFxdnGGuyHhB5XnfHii6IYGcA80Wsyly8uR7fWzCXsfpm\\nBowEDgb+CShYMDpIti8Dlu32pSphMpm0qozz8/O6urqqH374oZUbZmmbAzEHq5lZT4Ul6PSp0g4C\\n6ZOrMQBxNqIedwZjGZCYKTdRA3E0dusRNYzTRA2GyixhBgBWsGSWbaw8dhM1ViacfwKTJES4Bxdr\\nYaIGJTJR4/MWUCqDYRSZsXpcjNVVUUku2XDaEO0jOLy8vBw8G2O+3W4H+sTfbETTIaRTsZ7mWpCF\\nMKg02YkuGewYzLClYFd1hasB6JsZclh2DL71jLWhnw6YWKusoDk9Pa3pdNp08PHxcQBs6P/Z2dno\\na1hVXf2wA2P8Ptvje8A4A3iDDK8zGVouZ+OpnsEuYhMNXiaT12//YJ0MKliHBEteD4KKlL0kajIo\\ncsbXl32OZX4fpKnHzfNtV/h7bx3s2F3R4TGh1zQHb5mV71XxGNRk0E9ZcYJI5MfZWgKiJOktd9bH\\nBKo5D1XDoB19SyxhPADg+d4rLH+ftlgsBiSD/T8AqlfVBcmbYI172ZeYZPF9sDPoA3NVNTzvoIep\\n0mc6CM8AyEEmfT8+Pm5bxrHP3t6Bf87Lz2W8+F2eDRjNfrgPYzbjxV24C9kyeVH1eltpVrtk9XAS\\nNSZQchsjRA0yzboiY65OMVGTgarXMINAB0GeA8ZqO2g76gqS3CZiogJbb6KG/ozdbm9v21Y7ZCu3\\n7IJTN5vhNm501TgtK5AyGCI4IybAz2W1ddXrQ3Jtt03MWcd9j7dIGsZm/SR26eFuE8LcN/tjH596\\nYIJr7OYKaeTRc+bkdPqfJGPewjw9kqZ3P1ce3t3dDc7gc6Leeu5qI+uLrx5Zw5qANyxz3C/JJF/8\\nznGyt3Nhc62Dx8fHezlvyInK5XLZ/ARnm9qemTxxRayTq0kMO/kDQYPtA5+AfzMRZaKGCpd37941\\nosaYgrecmXA3OZvFGyZqZrNZq/znaJXcOQNhmiRexhOr1aq9MdVETZKwO9fjewtGYMQNERIbnul0\\nOsjy9oJ4ByNVNQAV6VQtCCcnJ20hOJvm3bt3dX19PTDiLg9lErmSCNn1JgRvc8lshB1aHjyaQIqW\\nrGkCgqyoscCM3dKo2LAALB8fH5vSea2ymmYXWUPrZbcgV6iosUInMWAA5H8bTNop4ahwEEnUuGIq\\n14NA3WuX/0bxOMsk9aE3n/sAMldXV68CI+aQTDR/t4POCqBcpwyOMXLI6N3d3YBBBiTZ6ZmoQU94\\nZb3JzZQN7gdI8t9wkBhN1trgBV0lQEjCw/rLNimqZG5ublo1kA8yq/pmqNnbOnZLR28nB9FonWMu\\nWL+ernj9ekSNgR+AvGoIVvNwvbeImh5wtC3OihpnivlsBpYeW+/+2IAsSTdZxxzhQ6go2kcDfPhc\\nA8bD2vSCXYPorAZNYJZ63iPOfflvJmqYF9bCc+U5M1GTSQWT6v6ewahtuu1PEgrO2FVVy2ZR+YWO\\nmBDYB1Hj7aSMF5k7OjpqIJ7XhXpNbW8t14k/3Fg/AmAHBWnXvkfW2A4gb17XnO+qF5L07OysnUXD\\nWSroUia7+L7nyHYVogaZ6+mbybmxW2Y0/UyCe8uxA6OcT+tOBtXWA+Z7Mpk0v9GrjgMXY/vAy9hj\\nZN4vUUDmE4P2EkXgqEy6VA23Etm3/leImiS69k3UTKffsuQ9osakCwGXK+HBKLaxSZB5vR0TgAHB\\nqh6/599bo0hEeb2tp98jalhf9AmdQ1d79h25w0YyltxC6+SibTl6QKwxdjNJhOwZezHPufUefWCe\\nPO/GCNnS5vn3yC0XMR8XBB0VFw6YXRFkv70LwxpnoxtJWPT6nmSUsVQWH2QyEx/am5c/tIFNOI/p\\n4uJikETy2B0j5XZJ2zHs1Wq1quPj41bVknYQ2ewRNfhM5shEDRU/2IrNZvOKqDEh3numyVOfpQlR\\n42o1x32WCSeGneyBiEqiJndkdNfjewuWytwDnyyYG07dAN8BlEEHv2cQBvyXl5ftDU/sm+bQO4N0\\nKwkKiuMzOeOTq/MtQ2YGCcotFFx5GG0vQ5Mgjd/1qlMsiPs6wJT5cB8Zm9+o42wO69YDjDm+ZIWZ\\np17WAYeE3CTb7IyBjWS+nYP7JwNvtpS14//Zd5TIwWACPsaUzjHJOeSO+R67cU/PUy/jaSbb8mmw\\nYMfNPQ3u0QUMkcv/IWq8bdBgIzNOrjTIbJLl30bd91ssFgNn5HtsNpumh4wjAxvWz/Jkcir3uOd3\\n99F6gYGD2txuwXx47XbdlzkyIORyxRzZSezi3d3dwCaakGHOaDmftsPIDf3orUP6Ef7eI7wzsMCB\\nZiY516tnh8dqvXF5bnKectwmeJxgcEbYfjLBocdn3e3Ju59tuUM2nGRJoiyJmrx6epL+3gA4A0UT\\nCPahJn9ybGO3lJHFYtFwAD4zgy/PqVtPpv1Zz30mPvyct3yu7cKu5/B/g0afj8Shwe/evauzs7PB\\n9xJ4eow8H9lJ3fLa+Tv7XENvdwXX9J7n4CKxkOfctjOJTwcgiU+SWEhSChtuPwjmSxLIAZtJAX7H\\nZ53sc0BrvGJdc6bXJE0mPW2DPE+eo7Gb9cAYkDXLz/YwdNVL9YttCK1HyuXYTEba5xpn9vTDcp6f\\n99qk30h70vMXJgD4nnHnLvkjMAajOc7Yx9ltvbk0YdzzY73vpR3b5WNy/rgX6+eqDW/XT5zjtev5\\nKJN3jg8yduG7Oa6qGozLfc/fZyySY+th27Gb58E2wj7bfj6TK45DvI62072YILcl5XmaEOI+C4yL\\n8+W4r20iOuBqMhM1kNC25SZpssBgFy7J9WXdsfWWlyQS32rfJWp++9vfDg65g1Fj0XJxbWBtoBgE\\n/3ZW1QZ3Op0OAvLlctlKrzLo75FFDiRZcBTU256cLcZYOzC08KGwPpzK72nvsYwmRnogOQWY+duX\\nE8zDVNl+dHR0VJeXl68OQEuwSf96zsYZN5SRQ2h5heH5+XlbszTKaSC5n9nKJAAc5Gw2m3aOyfPz\\nc1MwtlYYeLqMuecseoFdAgHfwzKCQaCMduz2008/vSIP2TvpA3F7YNgA20YrtxqkvjrDimNzBRrA\\nmLUFHPhcmqpqOub+rFarAaAw2YrzIuOeZJiJmnQU1jXLGIbZhhybRZ/QW7Iv+6hu4zk++4XA8Ojo\\naKAL/D1lz+DCJI9BKs9J4I69TftIRiGBkmUhKxfzwn4YFAIkE/hbvgy4e4A4M8D0wSDIY+Yg6n1t\\nJeUcAfpE/3rAqufUE4z1Aj/bxgyyMkFgENRbs14wYHn0mieRa2Iht4E40EmCIG1pgtAkB/CxfJa+\\nkQG+v78ffR3v7u4G/99sNq3ijrk2RvAc95I06/V6YI+8vtgZgzQnjlJvesSY18AZ1iRrPJ+srQPw\\ni4uLlgSDCN9FThtQeu2x/eg8n80gEr8DJhu73dzcvMKkrJvHjt2nvz4DLIN3kyBJSCbxwSH29l+2\\nW7bRzEXv4Oeq12+P8mH5bAuyrbPt/h5R48pzY9kkZ3YRPMwbfnTs1qvIdJIiE2n8bpesgQ34G6Sx\\n8bd1HExDVZ/JNn/ONpBK+Pv7+9psNgMioFcdRV/ASsiOx2p/fnx8PNjywXN7xIXnhHufnp7Ww8ND\\n84mJucdud3d3bd6YZ8uQq2rpL/bFfUp/6X8noWF8gI4xR97uYsybJF/VC8nuWCPPjUMurdM9v2rC\\nhj4lCQsWc5LEfrlnl3InBjI4dvMbeP2WyKyMZ76Y+15sxxirhgRrnstqWwh+Q6/Q5fl8PtiOtFwu\\nWzK4J0u2t0n+eHxVL+fxmqjhGbzpyXyFbbjjG57JWnvM9rMkSUmUvtV+FVGDU8dYu2w5GSGDhASr\\nFkIWPRd2Pp83coZFyLcycPE8JscO1lubIGVc5m/hMAiDPLJAOrOxa39qVokgHGlwegFQAr19BPn3\\n9/eDZ63X67q6uqrj4+O2thhPP39XxiwNXFY1cR4NRI3fNJX3STbaDtmkhA1n7hWEsNlsNo1phSgz\\nw40y5xx7nXCKfN7jxaG6KgSiAhKDrSNjt59++ukVWQhRc3V19Woffa6Zsykut7ahzQZYWCwWDfgC\\nRFg71orAeT6fN/Y5WXMbz13l1i47NRmX4+D3mY3wmH0BxmgGUnzHToLzWsZuyJ8DmixjxY6m80nn\\nYCCLbjg4yIzWbDYbZBXsKAGXCeCSkE1AnWDeAAId9LraH3A/9MqBXgadmSl0tQbri2w8Pj4OSpzH\\nbhB+CeCSgOgRNR5XZs0MOj0228Feab5JHsuPwWwPUKbNM1mTpIDn11naJHroZwYECa5NcjCXOVf7\\nJmp698Q3EGwYZDn47hE1jNmZvx6RDID79OlTrdfrASDtVd3uImt6gYLJPp97YcxClpCtqb3nJdhm\\nLVKvkTn7a689mIygdux2c3MzwAckZJz8SyDvNU1Qn0SZM6HMby9xlFsjfNmW94It/DU2Ef/usyH8\\nSmX6zPZj29UkanrVND4PyzYsZdX/5xno49gtcUDVMLAzXkgSn8YaJmnKPVnnqmHVkokatnqTQJhM\\nXirEcxscMs1nvZXCb3tKAh+Z9PlOxkf2jVTCTSaTRkDRL/tq67+3G9GvHiYau93f37c+J0GMPcU+\\n0l+vyVvkjMeY6+9Kj4wFffRFrzrUa9QjaqwnTgRmTLfLFpukYex50QfPidcr7b7HtQ+ixjEC5IZ9\\nSWK/JLA8ZlrKXxI1rvoCg7Md0qQ45Ak7a3xuTtp3YjT01OPxVVWtQht7CFED/4EtIQ6yL3RSwP4S\\necxrvV43TMPr4d9q3yVqfvrpp3p+/nZY1/n5+QAALBaLAUtpB2HQ1TMKdmgs4nb7ba8lJbnv3r0b\\nCEQG6ulkzdLd3d21wzGz1MmC4cy0F8l9NFHgQx+zyigz8wa3jN+CnW3XXI3RMPB2BOv1t4NFLy4u\\nBgROGhf3z/PtANBjI1B31s57Bz1Hma0ymGcNHKT1skDuz2azaXLpoNN9dyYyx5ZBR+/3DlYA4sjj\\n8/Nz3d3d1efPn0dfw19++aUBONaOAH25XLbqCMubx2djb8fFHPVk0tkZggl0CMfigDGDeYPmzEQl\\n+WZnztr6XCAbOetWz2laHri8jcHAKGUSJ4ENGbslmKyqlkG1XiWQccCTGVlni01aWrec/f769Ws7\\n3MyHqr1VUeO13ZWBRT98vwxqM5hw8JEAJ8mLvBIYMCd2gvsI8Ferb68c5nnO5u+yLTmXWXGRBI5J\\nYo+fZ/l7Bq3MnYmFHlHzFrBysJpkjUEi/hN7w3q+ZduTpGHMfC5BNEHUPgg37mkyEqLGc05lSgLr\\nXUSNCW7rSmaXSSpZx5MowCfzewPGnNOUL3zu0dFRXVxcNHALyU+VRpIHztybQDK4ZZzIKs9CD0zU\\n7HO7BW8LMumdldLMPevjqqDe3Fuu7UsdzHkLpitqdl1VwySeg0YHryZqOCATsgaSApljrq1r2M23\\niBofyN6z50mo2h7ts6LGZJd9CXLD3NtP9MhwiAySaCYQbf9M1FRVS7aZKId0zQNQ8Z2uIMlgkH5k\\nrGMZAsdl0tuEN834xz4lfYHlkwoh63L6pLHa/f39IDYzZqAKNYkaxuWWfe35DMumK1TQsaw68fpZ\\nvx3H9CrOrCNp610ZlHjJMupx7bIPyGbOAfKwyw+Da8dsWXGCLFfVKyzSw3aJPy1zSdR4+5Eraki2\\ncbA9unx2dlaXl5d1dXU1eMtZ1XBXDetQVYPKcRM1xrvYCfTZ256Wy+UrH7Grogb/0iMd/f18Ictb\\n7btEzY8//tjABgfzEIjj+Bz4puI468PEWTkRdq58q5KdLPe7u7sbMJRV9eowXm9z6ilsCh7lU2Z3\\n3a/cnvGWM06WODNpuwAy87qPtlwuBwDQ1Q4u76XZ6TEmG0YLID+dJfAaIiN8Die/3X6rEOB1wFYk\\niACDPwdjNoI2siZxeoEQz02G00A918tZbJ5jQG4GlTVMkmuMRpWKjdIuwisDogwGPR84raenp4EB\\nrqoBcQGBwuGTNsCWG3QN4+PsLH1jTV0tx17TBGy5fhhJN9sV+mNQY0Nq52liw8YXApN5GLN5ziD3\\nvIYZIKCn/m6OxTaVaiPGZhLZ30M/cRp2Ys4SAGQIWHNLGyWcPCuDvcwwZjBvewJg8boaOHk8SbAZ\\nwHBfsiRjt+Vy2YAcQQzzm6QhQbvfqAPoS9IGwA6ASLucAQbJA9/LVREO2HqkGvLHuqBrDhoAUvjT\\n9KEG5T17YFnu2SJ/zskW8MZms2mHk4/dLi4uBnYUWYTsQ5Y8niQAqOJwZtENPXRCx8RqVTXg6tfG\\nWo+Ojo4aue3sXTbrAP4J3OLXbyOHDsAhxJIIt22yr80sKvJWNSTbIJcghcZuPgyZuZxOpw3UI5+s\\nJbrlwCEDfTfrVpLVSSKbCEdXwJ23t7eDN+tBdmELnJAkOOCwZy7joPV6PajqMbHK2ic5k1WJiZVM\\nGGy321aVTmUiQeE+1pG1wdYwThP9iWHSLyYRk37Resv37feRc0hofKIDZPsf7BoEGnHI/2Pu3Zva\\nyJLt7ZS4WCBudvc5cc73/3K/me62DUiAMUjvHx1P6alFCnxmShPvjqjABqlqX/KycmXuXSR6WHNe\\ntoBsMl5kwMF6bomzbyQBboKqqka7DVyhk9UKqcNTN2SZcWAf7SdYJ8dMHk+S+7mWXaIjfYkDY2TK\\n65tktg/uzbf72j5mH5DNqhrhmH2kTRLrfIfLY2DOsKvd2YH5rKka1Xuvr68DcemkOed/OjZmvuxP\\njWWd4KZyzf7OxA0Vez6L1Id4EzOY5ESX028Zwzg53dkK7C/xCLxAt44mphwnJV6znFoPq/62GxzU\\n/F77JaIG4SILC5lAgGPQbGdGprcbpBfQbJn3+3I/hGE+nw9ETZZeUkWDQzHb1WWKDMAwvAgm/cpM\\nRAb++4iaqvGhehlo+u9eNIL8rrLh320XFxejsWPMMaiAP/pmo0mzYeGnDQtBlTNMVW8rCDCeAMPc\\njsZPAxj6giLa0Dnwpw8u5d0XFDD/dvipWMiegZgdpJ0u83KIt5NU7d5Q4nntyKYMht6TVxy6gR8y\\naBDDGCFqTk5ORnplecFoscYJgh3YObO72WyGzG8SNV4PSD/bD/5mEJvVAMyNZQVH7OAT3Sf7PHXD\\n8TIHBhBJvlWN7Ue3zuiedcTkiuXWNinJFh8OneRREg7O5BKUmPQyqZrbqvAP9hH0kTHbnzgwMlle\\n9VaXvZVgPp8P/mTqdnl5ObINHUg0IHaGM8kmg86q3bksBmsOHvk8a0F5r0kW1s/y4+e46gEAylrY\\nPzu71SU+7DOc0UwfkjazAzt81hVtPMPk4NTraL9oogbQlcSqQR7Jnc6uel6oSjTo4x7cj2fyNjoD\\n8ufn50FnTMDZJqTtSzLPW2lMejImbL313nbJ69cRNd66kaTBfD4fgsmpWxJ4zPnp6Wmdn58PNsgk\\nbgaO+2SVZptlksPEZxI1JD+wf+v1enir3nq9Hp6V1S6uRr6+vh5VQh0dHY0CGlekJhFlm+kKcPrd\\nVUQ62AFrc/H2FOR+6sb6PD09DXLe4TwHvFU7n45e+O9JJNoum6jOABg8TKBqm2f9Zi4gIV0JsFqt\\nhjcpQszhl/xc9ANdzmor+2wnYGyT/AYq/J2JmsT49gFTtiRqjG+sY8i+4zz/PgmPJD46uXCzHfIW\\nlSS+6K/1KPWE+2XRgTFU1TgB1/W5I2nSVyZR44IEJ9E6XDBl442nm81mqDq17bMuQogzrsRkyDC6\\nnTFfFlY4CZtv0UuyhrlNvJ9FGa4OSn9tW0LymEqaPP8mSUPWBt/pNydzP54DYVm1O3MSP/zROv4S\\nUeNqFQxJ1e7ANmeRHOjyew8ygaAZamccbYRN1CCkVprZbDYAHNhrO10b4q6k2IGMP2PB/BWSxn3m\\n+XbijBslszAzn7PZ7EN27V9pl5eXg7CypxbjaIeQgDSDDhtXjAn/Zi7JfpvEMCniyie/dQtA5QwA\\na58kj42d5QqA5kA/jblZajuIqvEbZli7zOT/ClFzCCeY1Vaek45hf4+k8Zy5ooJXYyKDBGrMF4CS\\n1+ulTBPkmCl3n2xM5/N5XVxcjCppLi8vh+e4v9ZF2wBkxIE/emwWH5vlcTsj6gzabDYbHMEhdBFD\\nbgLE2TOvKeuMDlpWGY8dRwZH1gPuxfdcUZNbQrOipmp3XhFVJDzPBzTnPm4cJoEnQNW+w7qL3uwj\\nF7qKGs8Zth7baxJlyoaceg28vjT6b6LGOpigk++b/EzgiuxyT+wqf2NeTdjlcwjKkozjGQ7+DazW\\n6/UokPP96btBRwLzJJHd+BzE3/39/RBwEsBO3S4uLgYA56QApEnql8dT9faMlg7IIe+8lcLEiPUS\\nPbm/vx+dWYNf4UK20TXLvvtHpc9isRi20PhQWhN74ACCyX0Xc2TfW1Uj+58EOf4EPzp1Wy6Xo2di\\nYyBqsrLF2NA/M2hKctTYJpN3JpGRYdYT3XHg/vj4OPquz19xuT1bx7lms1k9PDwMcpREjdfQhJLP\\nJ0JuugtZAidSTbNarQayverwFTXMo7FkYmzrW9XYT+bfkO8cr4Ms42Bj0aOjo1H8YP8GSXNzczO8\\nPZWA02TN4+PjqH/oCdjK899ti0MGkcMM7PAtTky4ojWJGu4zdaPiy/beOMLBu5O94HDWijWmr/zf\\nviwTsTTmxskbr6+fyfeppCH+dNxnf+g+5Bokoe0x7Ou7155xMgZkhPv+pytqTFRS1Qn+8zw7ZiBW\\nM/a2TbFuuMrF+vL8/DwiZPaRNIvFYvQMx/l+VhZuQC75HFrjUhM1TkrmmnqtPL6Ml5Ejtk9W7fyl\\nqxrfax9qKgdZMcCq8T5bnDfGwVkW/o/yJhtpgbXCYSi5jw2ciRQrxNPT02gLjRUks9X83oADkExG\\nGHCEQ7ai+bueEyspjttKjQDnArvyIBV2qoZybTabYTwEpvf394Og25AzBxkk0HcbHcCjAw6E1/Kw\\n3W5HLKozeHZSAAIH3MyVFcUOk3EBfHCclOl1hjAbY8pyYROPDibT+WQmYMrGq8hNqhEoQ07SjyRr\\nDAiZExOKqR8ZCJt0dKBncsaBAoGOD+NFF9Fh1oRyQ2TKe7ezxJzvOFB0RpJn5PhdAfL09FSvr6/D\\nthjkNQPaQ+kidoFmso9Aoguw+az7i4OpGm+xxDHwZjrm0sRkt6ZJsvM8n8XlzOvT09NIB7KM3nuA\\nuZ/XJcF39zdXtFn20p6/B96nbgbPtvFZxTKbzd4ckpm+rwOaCfasw1mhg98w+DMB4/u5IsnAOAGm\\nv+egOy/PQZKAHTGcAb5lxz/T5yT5MVXrZB4/Q2Y/bYvXxcEGLe1HEhuM2cAyq31ze46faX+z7zus\\nLa/ednk62zRYW1e9ATRZG35mkJJra3005rKPSJJ5qrZYLEZ4sWpc6UeQ5nXyuvkn/Ux5Q98M8E20\\n5Nry01iHqh7ub4LGQchyuRyd03h1dTVU1CBz9M1ymniW+3eVAklWOLAyxk5d5P6HsKnOXJOMA1vy\\nfMte9s22dJ+sdkSNx44eOQtvHZ7NZqO30vqtW58+fRoFnL5IZqT/dszjfnQBfzc2LuNU9C9JeGIb\\n9ONQa+ixIKfY1Kq3r7K2TOHLcr39fxOjDnRdJZq4kM87tgMDVtUo+M9Y1DYs7XrKXfpyr193deNP\\nPG6b4jU7pE0lkWa/bPvgMWWfsafGaY51IYCz0sXVX64c84tTODzY26yq6o0Oe6v2arV6U5yRF7Yn\\n9Yh7o5NcrtrJ13tbx9JOWZaYQ5Iu77UPiRqACcAfpWHPLYNx0AMA8PdS4JgcZ4b4Hj8NHpg8D9IT\\nx8LkAV6pEGajDXA7UJmKZyVx4G9Gjc+enJzUcrkchJ0MqQGAmXuz/YcEpDZAP378qNVq1To4zxff\\npYqJdUwhJ6BGbigzY2+u14q1tQJYmQjkDd69t9ACb6Gnv1n2bHCSa+lxm2ijXDxlgPkCULjCw5mX\\nqRsn6SNTEBaPj491d3c3ki3LuUEh5BXnBOHETU55zTsdcgBa9XdgwpracDL3HViHMLQtcCABEMJ5\\n2n7MZrvtWozVjtpZL2SSUmrkEpKZ/nvNmTM+O3VbLpcjG+QgEEAHGLEdoq+ADOtN1U7HIb5yn20X\\nIJNB4Fwl64DX6/z8vN3ylBWKSdSgB/gOk8HMc8qYf6LLBEg8O6vIGPfr6+tARjrgmLpxVhtAEZuK\\n/gMkMjgyiEFmWVuTbkk4Ve3knPlgbMg5l22iEwFkMW2/aPvsqu2y/96RZLYjSax1hEiupe0mFTSW\\nSYD+lK1L+mw2u+1X4Bp01GvoIJ/mebAO8z3G4SplV7HlnCags8wY/GLTk3Cn2sS2jmAK3+UDDf12\\nyC6oSGLC8mO5w657nfH9UzeIJ547m/29lfX+/n6EtQhWc60zqHSw4UDYGXdjRut2BjYm0eyvCPj9\\nNlN83sXFRf3222/15cuX+vLly1B1en5+PvSbfj09PY2Ifwd+ueUJot1k7dHRrpo0tyv7PgS0Xuup\\nG5WaSaDY7qAH9IPPGMe7OZBGZn1xnwzycnuJq4yYL56NrTg+Ph6Imbu7u2GLG0GpCQbm1z7NFWFe\\nY4K9xGBJzCVZyD2Re99ru92+Oedvimb/Y2ID3Xef0094zWxXc82MFcEkTkQZLyGn6ZfS5ruiLXFq\\nh2/AQehSJmK6mIKxZDMBhY1I/2q/bbtq/Ddlc582m80b2fI8uLKTGI3+4pOQa+xM9+alrBJCThaL\\nxbAVFDvYERvWR1cEcli/18ExAnYRO5kcRmIh8IG3b2GDeDtut3unS174vu+1D4kaA2GYibVcAAAg\\nAElEQVQ7RIgav77MYMTZNsqz+Z0Na9VuHxd/w1EQxHRAD9DPZSacID8V3Yaalqyaqyg6YOr5sLLw\\nk/sD4FnMDigjuBzg57FO3VLJMZyUl9tQpeH3d+3U7DTTURJ08rlcq+4wSs81zzHwYY79HZr7gtIZ\\ncHeBqr+bBhQDsVwuhyA6S/35TpbeuSJoyoYe5bM5S8GZhQwu0N/FYjGUGjKfrF8XYHVgnTXGQDPv\\nyHPVuIzZpIKBPD/T+ULUkEXkVevIDPbFBAX9xomkQ5vNZgOR6y0NZrP5nlnzQwBSH3BJcI6MkZlL\\nHeRyRghdSB30WkPUdFkbSA1InU6v+DfbwHiW9cG6nSQfY2Mbhsl41qXLcPj3ZJpxgpTgOzuNDGT2\\nBpmZuj0+Pg4+DxBsOWf8BpNJtuV2SvuPJK7sU15eXgZbgPyYpPMcMl8AI9+P57HODjRtj1N/kzS1\\nTexAeOfDq2q0VgY22AMOSDXgmbp12WsnXby+SdJU9cA75yCxgjN+3uqbJKabZdygmbkDONpfQlhY\\n32wHsDcmaThral8Q6CqQJCYsG5YFbOmhEhiuEEJOn5+f6+7ubjjHJQ9Bp/F54yInEzsbhC2zD0ns\\n21VoJKkKkU62mH9fXl4OJM2XL1/q/Px8eHbK33q9HlVu2246OZOHsWfQCh7EvyJz3AO5TJJ2yuYk\\nSWd3+JvJGNvbrqrCQW6ufQa7DiJN4Jqomc/nLVHDyy84h8hn03CuFzEF48NPe079zLQzOZas9vN4\\nOqIGPSF4PgRGZf5pxjY/fvwYVSykX/C6JO6yvjlB4m2IHVHD/XLO7GuTgEZvM/n68+fPUWWO/Xlu\\nye5iivx9yrTxXdqlJOds7w7hFztMRmPuiLPyGA3jEOyhdRr/Z5LGBLHnhGTXcrkczuvKhJQxbVch\\nSvLBZC39qNqdE4Ntx2d5+3mSdt5aRfIBGcsYxYmoDl/9ij39pYoanJaZezIxgFGcYJIlXmQLlYXO\\njKoNTFaZOON/cnIy2ibjoN5K5EVP4Jv/d9aE/W8dI5aAmn7zrFR6BAOCyoYEgbCydeDv321pOLbb\\n8YGJZGsMBE3OoECsjdeVz2R2GIHOzJIJGwuoWfSqGubPhjODb+bbzhwHWFXDeTwZOCaoyd9jhAiq\\nvbcwyUIMrPt6CEAKwPYcUxny/Pw87Oek/wmc7ah8D+6NQ88KFc9JEkGvr6+D44J0ZC1Zdxspz7vX\\nKveiQjKwN5/yceuSg3Svh9fadoa5wkC70sDzk9nFqRvbvDJrQt+Y98w0VdVI57BzJqwYQ1bUONCk\\nYQNMenVBqEkC+uk1tV30NsEk36p2BF4CJK9TBkG2y/gWKrV8nwQ4BthTN840AWgb0Fs2DSCrduRE\\nEtv8zf9PX5Fz2gXFJgeOj/8+3NIA0mvvec6WhEyCIvuEBLxe/+7fJjw60MK6m1A6VGWUKzppDnAc\\npHpuMpBIn+T58NwzFh8Im9uWGL+fxxwSmHTAF1Ic+WP9EyDSD7YGUQXAixgyqGFcrCH6iL1y3+mv\\nbS/reijSFP/vgI8xPz09DckWVwR2hHSSxJZbEzXY01x35jXJRwf94OX5fD6QM5eXl8M5bZxNw7an\\nz58/jypirC8k2Kzf7rerDkzS5E98dmI7kzgOhEw8TtkSs6CXSdZ4/eiziUT+zrrsI2pcAcF38G3W\\nReZmNpu9Ob+NrRy8ibYjaagcd1Kqalzd5j7YpnssKZPe+oPsdbba+gyGJpCculk2bAuZK/rtuM+y\\naxvCz8TqXUVN4laSRolBsJOeY+bZMu31dcIHHMOYGHNWou8jabK5H5Y3y3viWPsTZPYQLWPkfJ7H\\nXVUj/2z8wd+SuE6iJv2ft3dfXFzU9fX1KMn5EVFjsob7MS731XbZvhWdTZLG5CzPSV3kM0nG2C55\\nPj/SxQ+JmryBH5BArWoHRACy+Z2qPuPE//lcJ+jcH6fnoD8XOQNv3y8BcBp1lBsA4EneB159HxyI\\niSn6ZBbY+5NhuVn4qZsPVGL+mIvMsBAEJjPojJgFmsvGykFLVpr4fl0mLoNF1joBJOSQS3ltzE0e\\nseY2AGlc92WBvbYZeLBWzA1Kn5mFKdrd3d1oHTx+Z+kcGLsM0Ic4O8PnceZ+ds+pq65ms9kbo8vp\\n8JYrbzmD5MKgXVxc1O+//17//d//Xf/zP/9Tv/322/CqunQOyAKkgY2rddLPJuijEWRwKKkNc8qd\\nKxGmbrZX9BGbmFUW1o8uYOSnbQsZCB8eavt6dnY2jK177SH/t+3jOVmmCsiExEkAafk0sOZ7fD5B\\ngf9v8pigifVzm83GBxfSrwyqp2i2kxB/SYYCCLxlM8nFqjH5Zll3UN2RMvajDgKss9vttk5PT9+U\\n8jP/fNeEPDYfW22gaJ9oW2vQbJ0zGHafPRaPtaoGcgH7ckib6rUhiKGZXIO4SQLXhKJ1rKp/A1ji\\nGO6BnvhwaK+79RJ94G+uUk0gCFmxXq9HQcnT01Pd3t7W7e3tcNipQa2DQQNz5sUVx5ls4aeTUJa3\\nqRuVRJ5P44H05R1uNF6xLuHvsKV+c1XiSvTdW+BNkjEH3BtiBsImDxC+vLysy8vLUf87m8Ca0F9I\\nKxM0PiA1A2QnpbI6x/KaydSpmzPf1kPLEi8nwBdZvph7j9VVvxkXbDabES5Ch5xsxrbbTplsfXh4\\nqNvb28HOcngwb6b1zoIuEYHcJGbhcyaOUq7dH+akakeWWE+xA/n2xakbmJJx5Th9oXNd5YLnnDFx\\n+S12vEXRuyAcUDvuoIoDQtuylTGKifR9MWYSok6QdHFy4pCOaPD9wfc8K8/hssxM3TLBWlUjWcx+\\n0wcwGPPtRMDd3d1QgZLJA8vrfP73S0Zubm7qt99+q8+fP9fl5eUQ25j87sif7u1Y9D2xJTJLjESC\\nw/bW3+eniddMxjAOKiC99d3bSF2R/lF12y+fUWPlSoEzCLERszNJQsAg3UqI8qYDTKbRAuEA2UYu\\nCRn304bQwNXKZ+VxY3J5vhUxhdcGtmq3FxLniDM12DoE080rBrm/58HMM4yg59aGj2ayJEmMo6Oj\\nN4x07tXeV62UTgh5eH5+HjlfhN2g0mQMfXKQl0QN33Ug8RFRY+PpigeTWFbaKRuHPnst2AqBYZjN\\ndieau+LMr8XDWdshMn7LpJ3P0dHR6MC12Ww23HM+nw8An+a5TrIHsHt9fT0iam5ubt4laugrjvDk\\n5GSQDWdreW46Qoga7udS+AQQkM0+DHmqloG75wl7mMR3BiLZZ+YFAMMFUQOYmM/nI2dGAOhAkLXP\\nCjHmJMlVbJnn3odupp6wNny2A/0el0uakVEHqqzny8tuG6AJpEMAUmTQ2ZAkplg/dO3oaPeWBz6X\\nJEX61w7EJXFCX7h/J8sEcNhZ1oq5Nqk5m82GREsSiQBDntMRNSZ9OmIpiam0lZmI4bOHWEdnzWzj\\nE3Mwb37VvOfZVcW2WwawHoODXc8hftOEWFWNzhqxb8wAI5+DzrKliWc/Pj7W9+/f69u3b8ObNriH\\n/Xj2sWqn40k2G185U+pqku5sgX+3kSAw1kz9sNxBwhP0s1b+aQLD58dQHt+RNNzbcsM8WefQfdvp\\nJGogaThAmGeYbDWpZN3cbv9OmmHTTdbg07kf44R8cv+ZD2xcRw5M2fLcL57vPpDQtH1w8JQJPJ97\\nY7kGI/qcCcuskzRZye3AHtmjctGvGsZXJsGStszrl/LLvGfVLN/PeCn9ZlUNhBEy7yBz6pZy4YRB\\nEjfMLd9LYtvft8+0Tp6fn785XyaJV2MX1tiY6ufPv99S6s+bqMmEVcZN9vn2x+m79xHaxoL2mVW7\\nOCt9J3ODzEzdiN+NO7O6jn8neeO++s1nq9XqDVFje4vvPDk5GQ5U/6//+q/BHpJwxO91hQBZqWqS\\n08kY4yxkAKLGyWj7N8ZsQp81sG1M3OnkKz6FJL+J4ffaL1XUdCw3LZXPBtXMF84gg2JXQwBOukA3\\nwU+SJQ7EO+a6M5QYQDs8GwSe143TlS8WUi8kTqRqB+zN/uJEqahxudbU7fHx8Q1DWjU+34MLgTT7\\nzLiSbKOZKOOyLHifp+9nQ0rFhWXBJA1z9fr6WsvlcphXG0kHBsx3kjjMfQYQlkmTNXZ+gABfVbvM\\nmeV76nZ3d/eGwT49PR2RN5AhbA18fn5+8yo8Ag2DcMZsmYQJ5m8GllU1nGlQ9fdeeRNk1iMA/+vr\\n61DGeHFxUZ8/fx6Imv/93/8diIUu4On0E8O4Xq+HIMqfsRNBtiArkB2vr9cYYHMIoiZLKmn0D5k0\\nQAVcuK+WXWSdLWN59oGz8Q7sOqKGNff5GRC9WfEHMeiAnqDTb1BwkORsVl4GQw4kGCM657fY8V3O\\nCTDpa2JuymYwBvHmjJ8raqrG57IhvxkU7SNtkqjJgAMd8+f9fcgxAH0CJBMnXi9IJvvL9KOMB5sB\\nQMn7d36Zln6D0nR8RgbaU7ZMtPDT82JgSGAHQW2bgbzl+jEPBF4JdDMIqXp7KKcJ1H0JEB9Yiv95\\nfd29FbBqV4mxXq/r69evA1Hjqg/7QvTImVv8hYGr5ZK+OyCy/526PT09jTBqBlKWPeypbXvaf383\\nq2lM1PBdN4hF5sXzZEKPhAX3fo+ocdCZ5GH6RdZns9m0JA1EjWULe8r3LL8OXDuSccr28PAwIvzd\\nD2SeoMr4EZm1XeJNh0nUmAgyUeOgCZvHWC0jHj8BPhhrNpuN9NFrlrbT9/P6YZ9N6jtx67F4bkyS\\nJlFJHGLsh15P3VIukC/PIZftIHbR4/SYvHZJ1LgCnOfwbGPjbruNz/biZwb83KtLPlieTNTk2vwK\\nSeNkQfpX1jL95iHsaVUNMmL7bh+RMa+JE8u1z2yiyqw7t9L+g8ODb25u6vfff6/lcjnYXuMtx477\\ntlM5qWjyz/OGbCCTljs3j92+m3smuQjZY7vkhCz2An/0XvuQqMnMkUFAMrssDg4uhWqfYXBA5UVP\\nsJcTB0iFAHLwbMdrFrBjOAlyALz0ycY1GdIMBp2p7MC2mTxaghs/a+rG65ttDDBSPN+GzkaC4Mdz\\nnJnqNDj7FMYAmGe52Rn63lXjQCKzScgB+1B9iKI/Q8Ywx4LRJ7BdLBajoIM1dH+SOMhs9tQNMGDF\\n7+YvgT9zhRNiHgGRmaXBcJl44fcGnX4eJAPniLB+3nqy3W6Hig8OB6OkMYMxHKcBf5ag8jkDkPw7\\nz3UWDOfrrHA6wgx6p2wY5SS3mXOAdRKPfMdEDTLsaicqlky6OJA2UUDriHAARucYDQS5v9cW55r2\\n1mRwOlF8RwLbDD6TvOiAjp3uIRprZbnxHNKHTAx0n6sal9bbzhgseetolnF38o7uuCLKwDWJhAxW\\n0/9x7yRf/JmO9M7qPMs9JIj9BQSWM4mHImpMfhqjoJMkbFy15M/Z/2NfrJeMzZl3Z/CRfWwRz8gA\\n0cEA6w94tuy4pU47IFmv13V7e1t3d3cD0c3lrbPguA6jeO0g1JIgT6LqUGRbBnX2OSZrUwdZa/5m\\nm+bLRIcravJemaxxIAdRw//BK64qT7nK6kvm26+4xV6kDufzXfVlu8Lz0hYnMZHzNHXzeTs0ghpI\\nfy5jzPRHSQpbH3M+8ieXz/ayfe9I444cZz34HPiIMzec4PNn7AMycO/WOMnJfb7Tcm8SduqWulg1\\nPtzY2CJtgW1YxknGrIlpuldrG0dyZbKS6lvbMn8mdx/wfNYQ0ja3PZkk9/hzrIypSxT44rv2CV7r\\nQ6yjCbecT7YX5lp2xJPn0wl7rwtxE/ppUpw5RoY7+7QPq3d2jDE4lkl8y/iS0N4XC6RO+vf5M7ER\\nsvLRGv4SUUPbbrcjp+MyfWcMfT7G8fHxwMbbEPnKkirvX7OBssG04/fgM0vOGGDyCE6YdCtXTpyd\\nthcyM0sd8KbPSeTwb7PMnSOZuvEqdRtQl7cfHR0NQT3jJhuRVQl2VjYugAaz0QQKNH/PBBZXZ6SQ\\nL1/JxGI8XcKLIpq4MNC08/TBfhcXF0NlB6SQ195GFgPDc22kpm684QGwleNJg+GDhZlbgg+ToZYJ\\nwCFlgMiE5YKgxfeA6OPV0/RvuVyO1peAns8ul8tRFZeN5+vr63BguYPPrMJK3TJZk/fkpPbNZjP0\\nY7PZDDqX4OEQgcXDw8NoLlkXAileCQ9RyHwnQWbQ2Tk3V4ZlJZzBmufKOgZwsQ1LWUjggg5RqurM\\nVQb/znQ5a5S23PKT/fBFFggdODTZxtjtB/FfTmYw5/sIVv++qkbyjIxnxsiZ27Q1tp0+n8pl3Psy\\n4p6vrr/2fZantD1J0jggZZ4MjAygIU84dLsLrqdqrFVmLZlzKg3ou7FJ6o0JFPty9NoVjt4agV20\\nTleNtxIb67i6ASzhNep8wWw2G2WPHx8fh7MDqLblomISbMP8mDw2kQBJbhtjEipl/xBrmPoE8fX6\\n+jr4MOams38OgmxXTWz76shX5CbBOHJkubB+mJxIMia3APF7thT4zJHUDweXDm6xo/aTyCd4iXHZ\\ntoKdjfGnbPg9226w3XK5HI2DahbGbF8HbrU8MN/+2V0d0dMRb52vSvxqucBXuGp4NpsNeuM5Z128\\nPp1ue21tH61zGV+QUDwkUcPcg1uqdoE/pGnGIvgEzx0tfUrqpfFHVtfbrvmMRubZtt6+0gkOywz4\\n1cko5MFkjYmp9BdeP/uQqrdVtMwbJEXKFfMzdXNcht3i9/uS7/ZPxuhOMOWWJX4yh9YRYysT2Cnf\\nJviSqLSMYedSLtLmutjD/qG7b9oI97Mj3pBTV87/Crb5JaImM3vn5+cjoqZqDODoRGYJ9hE1Jmcc\\nhL83+TgLg0AbWzfeo+59d4zHp4a7SuT19XUYn9lDZzwZn8efTDB/wyiYQGC+OkM6dWNrCs+hEcCi\\nIJvNZuR46I+Js32kGww0gNuHcaUsMFcQQV0g5vlO5fI8s0YEii8vLwP4BCza2DqI8vfYknN5eTmS\\nB9bMZ03AhDNHi8WittvtYJQOERyu1+s6Ozsb5tHAIhU+SSRnDFhTZ2w81+hJ6qSBiueF5wGoPCc/\\nf/5841gNqDg/xQaRvtq4J5AxQ2/bYOI3yRoO/+OV9HyOxn1sdw7hBB8eHkbZTeQmA72zs7MR2EoA\\n40DYFWE+m6DLJJr0SoeSJA2VeOlcWXNntUx2Xl9f13K5HLbHmcj3WuEguZ/Bpw97Zj2qdkRGOn/L\\nI2tnGZ+yPT4+DomLtIcAD4Nk282OXOVvmenJvdcASa9HgjevL+DDb7vYB/hSHui/x2a/lX/n97YT\\nAGkTNQ4UGRfnOrB9DaLmEPrn5iDf/tzrgD1jbS1vzD9gn3lj7N52MJ/PB2IjK2qwBa+vr0MwbWzD\\nfat2FXnYjaxCcEui5uHhYTTXHCzvikX6T9/QySQV8BXIV1br4bcT0B5iDZkPfDL2naA+8VZiN/wb\\nfjUPpM1KDN/H43Myi2dzIWOJ+YyL6Ddywv25+D2HdLIltbNxnS6ig8b1ll+CeGewvWWdMRwimcgb\\nXey7jSu8LZutCnw2SeHOJvnMtCRn/DNxlUlp4xf/DT+QCUZkw4QCbyGz7HLfqmpfn52kKxiB+ej0\\nyp/LC3s2deOYAvpjUhfZTqImic4uhmINkzBlXiFPbDPBqPjKh4eHYZ43m82wZdKBuhPMEA7G0l5D\\nKu/zLEfHn8aZmQSBqHECzvJDY746YuRQ/tHy65gRm58FC7nWPoz7I7LG8SHr6Ipw4w/LRfoTE2Cp\\nvxm/d3Gm16aT29T/TPCkHc+LZ+E/TCh+1D4kapJUSfKEz2DEEaTMBjkY2JfFtTNIANsZ0zws05OV\\ngBcjmvtbyWgnw0sfMIZ2aJnZNpmQwu5x2ihkoP9/WbR/pTkbYkDPfDP3BBnMu8dh5c1ACKV15jC3\\nsCGcHiNrgUK5DM3zZmNn2cjslMlDB+JWDAMYGPI8MNDBlwmpVD7Wj20eyNghyDbmBIdF5sj6YZ00\\n8eCxJwNupwChQXBoPcpn2ejO57sKHkgaMsYGUCZVAE/oK3qKfNhpWv+pPgGkunQ5SWU7B58K7/HY\\nedM6hzlVM1HLWqR8pZ1Nu0Bw53JqlwDnVhOvt4OZDpiaEKEKiWDMsg0JmIdhUpW2XC4H+2sgYvtr\\nm1O1y4bnAYFeF2/ZsvO177EPOIQudtsqbSvsu/jbeyR3zouBozN9rEWCP8uJbbKrSZ+fd6+LRIbe\\nAzMJLvmOfV+ClbTFGdh0/o1xA+j8PcaTAH7KdcRfmBD1eD1u9585zsoE7C2X15gxYmNNdOOj+H/3\\nBjbug0wk8ZABpf0B1Tz39/dvDk81UbNYLN4EBV7Dqt3rVg22s4GfmE9ke+qW2NQ4YZ/MsdbGcvw+\\nMWdWpGRFhoMm29Tj4+NRoJK4MfGz9R6ZTELVb1FZrVaDXmfSwePIIN1ElucnfZDn8j/RFovFiPy0\\n7NnX8TIDcGraVgdaVWOfYj/n9U0bZRLBZExudUnsZR31vzOh7HVHDkwI2HbQ3Le0tRnIdkGtx218\\nP2WD2LDcdq0jnug3381xpK3zHOTbSiGvXJFqTMhamTj3IdDoFH0xKeY3TnVbFxPr5NVhO9sFmvFn\\n4grbjkNg1Pdsf44FPEkfO+ydFSxJYFT1L6LwfHbPNXboEnXMVeIs99G2t7s3V3d2p8lvFwHkc8xx\\ndBX/nf90+5CoyawWEzmbzUb793IiPXnONOHMEIDsMIEoE5z7R+0wCQwuLi5GDD9sab7pxlmsBE5k\\nslFKG2dnR8mSOAgw0WACxEDMxplFIVg8Ozurnz9/1nq9Hko6p25sMfEc2rlRysp8pENL5pKxVO0I\\nBM9HAlBnpWyUkqhJ0o/7bLfbgZjIvakE+xhe74PMQIM1THmy403DaUONI2LcBMyMg1LkQ6wh20mo\\n/Dk/Px85daptIMtom81mtA3NDLd1k88yFtbHpJTnw2s9m82GNXEwQunoe0CIIAIChvm1gbdzhkwy\\nmKTPGD3bHvfH47DDs2Fnbcn0T90gln3Ar5+PI0hnk868CyLQDeY6wYIdpQM+tkLc3t7Wt2/f6uvX\\nr/Xnn3/WH3/8UX/88Uet1+shsMMmcOjb1dVV3dzc1JcvX+r6+npU2s36IJOPj4/DoXIup98XINlu\\nci9nAz037C12Nod1n7olKMwz2XIMGeg7yGdckEusUVaamKixDzOw9VxBGrv6zH9zEGe7W7U7vwy7\\nYVLaY3GwYGLBBwszvgTd9gWdnTGYYk2nbgn8qmrkC5A/gDvr8fDwMJIv2ycnbYxvHJAnEWbSjyAU\\nAvbTp08j0Mt9kHHWMg+CJ6BElpwp7s7zSuLONtpyC5mQAHufvCOD6PzUzf4E3GbSmjfD0PckOJPQ\\nQSaM+RJ7+vJY7auOj4+HuSYJ5cDFz83MruXeuvH4+Fjfvn2r29vbkU22P7QdNW7zfJmoQtaQKesG\\nekp1LDJ0CHyTJCm60JH9xvdp31kzJ7ZOT0+HLX00E+kmfTKgRqasj0nWMd/+vHWfseAHU2ZtP3i7\\nzadPn0ZYhrH58/Q3CRHbnBxP/jxkS1l0IjUJiyRosu/+vC/HMRnc+15UCVNVfXd3N7z57vb2tlar\\n1aBT6CfrQBUNFcsZM3Ska85/VnI4TuJZ7i+/p1lvM848xLp1RG+SmvZFVbuzJTPeSF/XJX+sE/b9\\nEEFO6GLzXF3oFxgl0WS9Zu5M4mXFTxc/2g9jLxmXq17pK7jK9ht8QCXrer2u9Xr9oT39JaImAbTZ\\nSjtzkzMpkF4Q7pPsWgKGLgABgPB63+vr67q5uRkmgCCOxcptOPnGEsCOJxdBpZ8+E4Tx5OGHSdRk\\nFsVKyGJ7LiCK/lNETRoZAIeJtS6optkRVL0l6TKjlaRXAr9URLOzZoxN1PhtNSZqkL+ULSuugZad\\nbQJU5sHBjIN5QDWVHvf39wdbQ6oUIGrM6AOmTNTY2flwNBsmyz7ADUOYjtJr5kxx1ThwtYzbRiTw\\n3xe4ZFDHunOZFOyYdQdQuX3EgSAOj2biBqLmEOsIwZa6yBqYaLHhz/nryoAt08xxR9YkGYCzu7u7\\nq2/fvtVff/01EDX//Oc/h20zEKYQSryl5Obmpj5//jzaNujAh+AOooatQzTbwgQGBpbp5D0np6en\\nQ4UV8ktF0NStI2roY9VbEs3NeoQM4w+8TqwNjtyJhw70dMAbcIou+LPMbQao6TfxYwBty6LljsDY\\nRE2CTwc/rvpI38HF+pHpnLrZ5rA2mdCA0KQvbOdLG2qixuvrNe3WySCVezgR4a0NzI+zc1X15juL\\nxWJEcpqkMYB2sMAzkqyxjqHL4L2q8UHL6UtZf+ZstVpNvoZ+NviArQngHvuN9C9J2lsekFOTNZZ1\\nqjtor6+vb2wY2M64kN/xfFeM4YPZTmaf9vj4OLzqlsReykGuQZJtjMtEBXKQGWb0gQrZqhq9RWzK\\nZvzNWEzUMDfYtI6owa45u45MnJ2djRKsTsaC8VnDfURNbit2XMR8J0nj+2DXLR+WX2wmVXR5vlg+\\ny0SN17dLzqTPOARRY5l2vGdfaD3js8yNm7HLPpuZMaVtl++DDDmwT6Lm/v5+2KZtjAVRA+Z2xbKf\\nmVjU896RNP67ySrbn5xT22PjqqlbkofWE/vtHBvJVicGUv72tS42eI+o8aHQ2ML0tYyjI2C9LTm3\\nvUH62H6enZ0N3/nIjzBnyDVrim0g4QZR85E9/SWixqDTAQSG3JObZb0dE0qHWdw0HmlAs+yRLMn1\\n9XV9+fKlvnz5UkdHRyOWjRO9s6ImX6tpYgCQtd1u3winHchmsxkOMsN4puH0fXF2Zh83m80wptPT\\n0+GAq/8/EDUIW1bd4MhsfAw+0zEwp17PzMiy3hhHQK/vaeD7XkVNEgSdgWBcPjchg+UENgacSRTY\\nECBbLy8vBwkOXVEDu5/7splLGxMbo66iBrDAHOPQuvlj7A7ECdoJ3JkDM+1pB5AjsqwOIiBUMlvl\\nueY+JvR4nreMeBsXc5EBiO/lgAhiYepGxiwBPZeJlgzg3iNqPqqoQV6ZJ8Zo8J3z/OkAACAASURB\\nVEJFzZ9//jkiajIAYc2zoma5XA7rjS3l2T4IkwOVWV+P0yQNgfJ7wW1H1KCnyPLUzQQNc54OuRtD\\nEiMEb8wZPwmuqXRar9ejA3e7zxv0cXnLH8kLmgOZqvF2FgdC+CvmOMFIJlK8vxx9ykDBtha/62yW\\ns11PT0+1Wq0O4hcNuJg7B2XYF5+l8xFRwzwnYHcVha+ciw7vVO3O0zGIBcgSTLN1l4NZbVPTJu5L\\nkKUPzMSKySLkBx/f2TXk5+Hhob5//z75GppwgETBLl1eXo4wSQbhtvnWy6wWyooaMChVGgb/1n37\\nK+QCmbYuuppts9mMtvsmhnWFnXXFa5d4hpbBLc8zPnJwhZ+pGp/1eAiiJn27+4cMsVWROUGebVvy\\nsFN8FZXrDv5MIvh5OSdZ4ZaBOmvlZt9rW+BEKD/dF4jZ+Xxeq9VqFBD7mTQTNSnDHUlD3w5B1LB2\\nNCdGTd4nJk9COEmP1Fl+ZnCcuM7rAPlprJNEDW9WNa7KihpvffEzjbszoDc+3ZdE9rwQ4O8bq4nf\\nQxA1lmnbM+9E6HyRt2nvq9z8iHT7iKixT3OS0QRbkqyea2MNbDHJIFcwe2cJfj35CmNQx4lORCW/\\nwbhM0vzbFTUIucGHA/iqnZNBcDrjkEGHFYnFspPJwCNfPUuAbraMCcjskdkyb3HKBXVWzUYj58KZ\\n++7+Fk4Le9Vua5GdtjMGHJw2dcNx4egAdrnNwmuWbK+FkHlyKbbnMoNKB9RujN0ZIx+YaaLLQIn7\\nI3MYrM1mM4B6Dk7M8zWyD4ylC/78bwcUlgPmi3I2+n2IhoKTVXLWEIfejcuBBIFI1S5Isw7ZkaBT\\nmaUwoYcRQ67T4bgvNpJ59obJHfqMDiXASEdvY+osIzJsY8q4Db4ZE/1xNdwh1rAjNTvSMDN2XUsH\\nAGHI97AtgHmTdQCVr1+/1l9//TVsdfrrr7/q+/fvQ5YJ+c/MogNy23/u7/MUHh4eRnqIPCB7GTwl\\n+JrP56PxJShNQLGvomWK5oBrNpsN5excrljokhHIddoYExWAnQR9jDnJ0w58J/nhZrsMCErfhqxW\\n7ex+gjfmO8mrTLJ4TdLeOjGSfsK6MnXDpqPzZK5JXmQAaLLLPsC2F9vmhMM+e2gfm42/E1xw3d3d\\njV7NbKBvvGHZYv2Y6/SBGSAnWWP/b3+Pb+bfxjQOqpE9j3+qZnyKzlFhapnJ+WAsmQzKzyWBZzLM\\nRA3PZ93QW2fyOSOIgJDLQQT2jTm3j3TCJatmmYsMyLuWgV8Sht0cVY0D76kbWzqxQ2lfkghxX0z4\\n0+e0Z/bp9g1dMGo9n8/n7TmGfsMiyRHbfJNLtuHb7W6rhMlu7mO5cD+NX+wr92FV44Ln5+d2jqZu\\nJpXAjeADJxN5vnXPcQBz4wDd6+gY0/4h1x/8//DwMGznJgH19evXgaTBnpJkMUHbJUOraq+PckyT\\nZ4AZWzs29vzhf9yQW2xwJummbmAz8NrJyd/ncNJHSAwn1pkzfpeYp8MnGR8ay5j0oiWx4pgbTNXh\\nKubQZJnjkIzhIcGNvbELGSNtt7tzQ+ElXD3mJIFtrc87+gjbfEjUeNLoJE6rKynqSBoTNVl9YoPD\\n/Wygk6SBqPFr0ZIxRTk7sgZgzTMRvJOTkzfEhJUlDX/eM0vF7Wxw8AB6z4vJntlsVovFoq6urj5a\\nlv9zy7JxADEB/kcOeh/IMfuZgN7z688lu+hyM58BgKPqqqtw2qw7z395eRkFhpA1fgPVPqXwuud4\\nkTP6nwEpz2WLyCECfDJtBMDsYQZI5PphLBPEUz0F0NnH9Juo8Xput9sROMI4O1uVwQlzybPyzSNs\\nhUnyheem3NlYJutNQOVgwmvJfGR2wAEy4zjEOhLEGYS4YisvZ2dtj5kLZMPVRF5r5gMbYKAPQfPn\\nn38OP/k3RM3j4+PgqCkhN1mDLma24+npqe7u7uru7m4gfHCAyBcy4bVPosY2ABDQOd+s8PA1dUMX\\nCeCzisD7mQ2aAWEmIS2fDuzwYZlRpO0DQr4MEDu7ZyBkQGFyBx3Gb3dEWLc9Of9tfctAEltE9ivJ\\n7g7kTdEgaih5xzaenJwMb0F0JryrSnKSgrU0WE375eDcLYk8bNF8Ph8RNWTh8GeJrRKsZjC+j6hB\\nBh2M+G8G38YFEDX2G+4XOl+1O6tvymY/hT5ip2wvOpzjueb/jMt/Z5yupAGPeu1MUrHG3vJ5f38/\\nbF0yGW9caSKzqt5gWvvrTCol/vaYcr1TPjK46YIn9J9Ez5SNKvjEzgRItEy2mEg0CZCZ/vS7WV1u\\nO4at5N5OEEPS8XpmLkjJxJpJtiMX6JpjHeaVNbP/d+P73Trmhe93BYifMWVDZj1/vJyDwDV9N31x\\nXIW+GZ+a+EiixklTk2NUo97f3w/45h//+Ef98ccf9eeff9bXr1/r9vZ2RBCZALQPQ1YylsGnI7fZ\\n3/TBH5EW+0gO2/iXl5c3icYpG+PB3uCXmRvrG3PC74nDWVfbj27s9o0muxwjZoWtKwuRCfptvbAd\\ns1xk/JZEDevGuEl4Ou7IdaHS0tV2jAn5RgfBQOfn50Pf32u/RNRUjfdG0wEcfk7uvkVJ5p6FtHDa\\nMBn8JUnjLS/0z8SJ9696EVwGz3NzT+tHbFwSQK4McDmqHbz3mRtY2JFA1ByikSlE0BAmXrWeAWln\\nJExeIfw4HYykCbckxJIpNbmWyvf4+DgqK82ggGd0zhjjTEmZFdqkYtfS8aXCIwNVu3NM6D/guWPE\\np2gm9mC5IWnYcpRAk3VyptUG9+TkZMhkdftJ7Uj5iRzZSXgNTHB4PmHgqZzy+jw8PLwJBj2Wjqih\\nvw5OLA8OTLMs1tsA0X8CMb/y9BDr6OqLqr/1AoCWW72YE4OWDNzthFwyz7hsk20nf/z4MdreBHAh\\n00RAwbZMAtg8iwRHjY8w2QpJA1HjrTQE4w5arWtdtpG5sxyYpGHMrvI4RGkw60B/5vPd9j+eacfu\\nNUD/0E2DHwJN5sogPwFO+lr/riNcDJ6RuxyTfTk/7Q9zq5MBbFdRA6GX20zdT9uHk5OTUQCaY5q6\\nHR0djUqoGd9yuRzNn7Oi2DfbSH5H37OKLf1Jkh4dSWObh63kIG50mOdlBZ6rX9MG4wOY2/R5WVnh\\n3+/LDlbtKgX5yXhcUXMIosbBEQEMWxV+/vw5qlD0OjB+vm9Mk5+h78aiBOzWPRJ1xifIFwmku7u7\\nWq1WI2IXnwXJzv222+0IW2YixPcwkb8vGKwaJ9CwP/vImqoxGfmfJmpINPz8+XPoG7KLvcgkDP4k\\nAzEIPO4LhrLvYGyJeTP+MFnDz6Ojo2FLGPLu5JqxtMeQNtX2zv0y9jI26chY21qIPifXkfWpm+Mx\\n/A023ue4mezldxkLmazKSumsMmWtsZ3z+bx+/PgxbHG6vb0dYZ1//vOf9ddff9W3b9/q7u7uDRnJ\\nmuC78F/MH3bDuCXtkCuXLdfpw61r6KTJH/8fO2Yfewh8w3iwO8gW+A+/Sb+9Xd9ncnm93yOnkhQ2\\nvs2YO48yASOBT5hH2zH7VeMy/p8kjXEXeKWriqJB1FBAwlrZH0L8mGA24f5e+9BrumLFTs3K4uDB\\nLVkn7pdETQquf3ZssY0xbLHBFkE6AbsPYjR7NZuNS9RTQBwIVNXImaaw+D4Gx87se6xWdj6HgB0C\\nzCQQ7AD+PoUyYEwW3/Nk2bAB83rmZ82Q5iFUVbtgImWA+6AEvoezVt53yD2zT/wugWc+O8FAAhnL\\n/6GIGsYM+LWhyvnxOC13ZsJNbuzTb7IyHdHGcwyGunlF7721Lbd20PeugqqbB4ys1y37xuezP/6O\\nbVwHWqduKT8el/vdEVS5lm4ma3h7FuPwurr6L7c73d7e1t3d3RAQpl3Ks6G8V5kglj3DvFkBfXSV\\nox1gjjtlwKDT5E7VGOgiD11V0iGa18NBEmuBXhFcWF9MetvmGOSlb/J3DdQ7osbzan1OYJTJlS7Q\\n6/QyM42uft1XUWPfb6KA9p6v7/RlinZycjL4BQKLBNm5195khW2Qx2ESx3aGAJR5tb2CZDWJx5q4\\nosZZ2u12t43CNot/e84zOw8W2mw2bz7jfng9jHEYc/rBJPdNvB5CF8FLzFX2x37bfUzMwt+7ADb9\\nhckRnmXdxwZ7uxM2EcLGyaecV+uik1fYY8bE9x3I0bfEtanbbu/57H3X1M24xTbKWNGNtc6Kxby8\\nxkk4ety2ccavx8fHI2LGF5VVi8Wi5vP5SIfxiU5g8RwH7B0OSD+fBJp9QWIEmnWOceX2p6kbhJLx\\nVJcY6GINB+O2PUl+uCIYwtokCu3p6am+ffs2XK4W9pvTTER4rpx08HZS1jV92EfxX8rbPgxoX55k\\neecjD6GLXjP0xngE4tT9Y3729bEjvz1uSI1MvL9HpLynR/m7Lga2Xeywj21IZy/sLzMh033O13a7\\nI9o/qor6kBHgpHRn5DabzZvSpjQqyTraWbKgVTW6J4DXoIFydwMif9dsGwHB3d3dcFDU169fhz3B\\nPj/EApNOm8UxANtutyOipgNOjI/veq8xwusAOIUVBTgEQ4og8EwMEUEcfScDSOsEPQkABNngxKWq\\nfm7VGAjb2BqAkiVPoMFFwMkWKVdmmKQzMcB62pFm4MTf3A8rmoMXSmRZSzI0BnBTN5wvCg7Yx3Ay\\n1/n8NGqWSZ+nkORnBhkdyJvNZqN1zGyHZciMOOAYBtqBOdkMAj+vhwkrV6ek48jAwkbS7HtXxfP0\\n9DSQEFO3s7OzUR+RGdtVHJ91y4DUc+91Z/1cJZUEnLdq/vXXX8P17du3oZKDANMlmi75Z52QG9bj\\n5eWl1uv18EYFtj5BmpqocSBv4IPdJ/BHlr3mkD4Eqsinszyeo6mb9Qed9HhYKwhQ64PtTGZl7Gdt\\nW121kYF8R3bx/6qdTie46khNz5kDtiRo3queMZFnoLvvufsAbAdwpm5nZ2ejPmAXttvtm4DAvigz\\ncaw3jXVzBpt7E1xD7tmHJMnGlVUVJkQcKKZ9z0Dt9PT0DSENUYPf8uuHDT6RK/w2Mmuywjrs5yae\\nmnoNPX9UCmM/TbDh27w9BbvFXBjLIoMdGdVhE96IdHt7O9jU79+/1/fv34cDS6kuRGdS/jPhYBlM\\nXOVxdfeBxGYdOkK3S7Ch85YnqpPQ4anb5eXlG1KFqnnGTX8z4PP60/ckp5AFZJgErivA0FFjJbZB\\nXl5e1vX19fDmzeVyOfhB+tCR5KwdzbiEz3TbReinbb8D3gwsM5bK+UL/wY2H8ItUWnFvJwMfHx/f\\nTYBhb3O7In7RRI0rID3Xnr+Hh4ehSpiE1J9//jlUC1M5bfvnLWj59kLmPGNhr1WX+Dfpx/p7DtKH\\nI//EGY6Xra/7SOUpmskXdNC6j1ynvWStWXsqZD1PXeGC7Rxry/zbZnt+ra8kc/GBOT9JHnk+jXEc\\nJ3A/E6Q0sDEVltgAr5vXOQ8gT8zOnO1rHyKfxWIxYtQQEpcbWQATABiweFL2sVbJbJphywECGCjv\\nI1sBUcNrZh0g+BkICMrnAJP7V+1KwpOooVIjK0j4rs+FgT1LAfV36MehiBqvDUIGYw1J4wAgg0Bf\\nNjJ2NhA1BCh8xs3MudfX5aEAro6kMeBiTslSUUHlLU8+nNiZBa+ZQe3R0W6LDmy7S/IhQgC99Jn9\\n5YckamxU7DxM1FTVG8W3bNvhI5d2/q4a2xdUus1msyGrkee9JGmTTmY2mw1lgHnOhbfXeA336Qpj\\nsawTLCMrzj4ynsweIk/My9SNoCozlK+vr8MZWgbKDjQc9NEya8Xn2ULjeffa/vjxY3RGzd3d3eAs\\nkRPkLM9l8Julqmp0bwIVAJGr23C4jAsZSCKCElsHYQ6O6T9renS028LiuTlU5jCzKVkBZqLGMo8N\\nsqxV7chVgCO/S6KmanwAOJ8zMLRPtUw4U2nAxb0TyBismTxNooaf3pbc7e03KMsrfaJxwCGz+IvF\\n4g1BAujaR9QYe0AauqqFtWH+PC5vuSQgtE9zJZVtppMZXmvuj67ap2X1h/2VbQi+1lvYfAZZyoyx\\njYlD3/Po6Gh0hhW+9BBrCPFtP75YLIb5cHBgohpCGuCfOsR6MgfMlcm1JG54Q9nd3d1oGynbL0xa\\nJ95kPlM3Ei/RsJve9uS/5RafJAS7/ic+cLCMjwcjTt0gahyI8TyvX5Lk/J3xWK8St6JbbAfymZKO\\nTwjUZrNZffr0aUTUeMuTiViTZcicyQWaCXnbZq8heuTPeU25R9oKPpexFfYBHT8UUeOzEjPJ9/j4\\nOLJHjnvAz9g/J+s9Vidq2C5vP+l/r1aroVr4jz/+GCprfPYeMQcynxgU+8U6I0OpQ44PM9lSNfZn\\nGcibuLA+8j1XyGVV5yGbMSiyZx/gn1U7mwNONd42Zrd80uxX5vP5qDLXiWMwo+NQdMJYqiPJTCQl\\nRjTOgazh/vYtvo8rzPk8+C15DJM/GVNPQtTk2SqpFAZ3KJUVEGNlcGkSJx0FC4zhYqFQVC+sn+st\\nL66o+fbt2xB4pPKgNJ2zQvEASc7mEhDA/HXAh4DCwoPTcUbUjbk6FFGTpVcEtA4ALdysVwqW59Dz\\nZwNKtjsVEoNMsGXDloHovuDD80t2gnUHBHkrFT83m91ZIAbQZlsdQP38+XPIAry+7s714fv8nv4B\\nZA4V4NNXdCmdh40Pn2GMyUKjZ3w2M6YmakxsdsB8Pp+PSlBZY2cBndG3E3YFjZ0jhprMPPKATllX\\nbPCwV+iaZcVybaLOxCX2BuN7KDCTgAUdgnzA8dBHdMNBazoPg560ry5Z9blKBBNfv36t1Wo1khkH\\nKSZpfGCaMxjoP4HK9+/fRwRqEjXInjOYdvCsP3bXNthnQRwf/33IJ6/89hiQ2albEhj5s6uowcZ5\\n24SBPUSNSRl/L7M0Xvf0XwYjgCBnKi1zgJ/M9hhoZKaxI1V9JVEDQHqPpLGvsE02WJ26cSYcz2V9\\nWDvbMQNqbA8AkmAo7T7z7OTDy8vLoD9es9wS6rV0MsNzhg46EPfacWWFQZL6uYUtK2pSljxm+xOT\\nl+zZn8/nwxkZh9BFnuH+2o93uMGkF/OGjKYcpn+0rnGZRIaohqShooZtoGy3SJzl5IJtd24DcCOI\\nzYqu+Xw+Ot+lIy7SNyRRY//O/Vw9NXW7uLgYVcD6GQ5eGR9rDAa0fe2qGhzg4mtdUcN9HaQRNJ6f\\nnw+vfM+DpDNu8GWixvY6iVhvkbV9N/mTOOXp6Wm4lwNhmgmjql2FQ+r0lA385WcYr1H5wBitQ8wT\\nY06ihot1M8azzLD2d3d3w5k0//jHP4bq3tvb2yF5b6IG3JV+jjmrqtH9LXOdjbYftW9NH47spZya\\nFHDSzvJ2CMKU9fGzWYvEG7S0XSYz6CcY0USNbZELPx4fHweM4aRIh4OcNLd+5/wYT9iee5ysq5OI\\nGev4mSSnTKxadvkOlTeWm1zz99qHyIf3kxvQZYYrA3gm3IOez+dD4JcOvgMQBj0sohfSTGTV3weR\\nEaznGQs2il60BPMOzler1RsywluwfE4N96NPWcLHwrB4PI+xdYz51M2HlHlrA4bJBE6XTbNwG/R0\\nDrEjvRxEep9pR9SYBPE9HJwbEL++vo7Ov+CeXNkP5t+OygQFsur9shgalI97eM9kBptTN4JWOyUU\\n3oYDIiDBidloG1n3u8tkJyhi7A7iqmooJybocBDDZaLr6OhoMGDn5+dvzrjg/iZpOP/Eb/PyelhG\\nZrPZCKSyLt6aYbIBBwsJcAgwWrU7U2Gz2Qz99Bx3lRmd3HotTfxmcMJ8ZPWabSoVLNlP+nRxcVHn\\n5+dDphq7jUxh61gfnw3Gsw2GcV6LxWIoJb+4uKjLy8tBFlgXX7PZbAgAkUfW2NWUvIENpzl1Mwlo\\nYLUPCNv+IY/4QgfdtlcGESZS0An7z6xgA7B3QYq/l6ScKyv4mW+6MSHVbYdylsngbt9lAtE+23Nh\\nEmLK5nXqCCNAdlW96ZdxUdXukHD7se6+PM+gzVl+64y3CPN/+oUOZTCHD8d+UFXx9PQ0/P7p6WmQ\\nJYJb1tuHZzrpYB9MPwxGDbjtNyFvsQtTNwe/kBZgGgC8ZShxDOsIUWpyzoke20wnihykudIafMh2\\n7O6AcGSaOcztAtg31qEjAi1jvleXDLC9MnlvP+lneCvC3d1dPT4+jkjgKRtvXuvIY1fkmVQFu1g3\\nuTID7+B3NpuNzrmAqOMgcdvlxWIxbHnirK2q3TYHP8/y4ud3LXEt826dMgGXwbKJIPtGPksfndyw\\n7B2ied4cUGfg77fgOj5gPphXvt9VeGFfrI+OOTkTygewe+y27ZkgTPuXxKbjFv/k99w/E5xJYLkl\\nGVA1PpfS8mUfYSw/VbOd8xEI2Kbc1sy4rL+OKzpskbLSyTWJWCcC3FhD4mzmDFns7EJiK8ckXgPk\\nj7FfXV3V9fV1XV9f19XVVV1cXLxJ6CaWR18dL6d9+hXC7cNo8v7+fjR4OuGzIxw42gD4bwwCUJ8G\\nmO++R9SQkc9A/vX1dTgXgRJTv77SYM/9yMZkUqKbINHPs+Hz4qBUBg3MQRob5pW5QxgPcaK+D9uk\\nf+w99N65rjzSP71W+4gas877rgS9JvSs9BloODthB8xamExLEshAOS/WgXt7T2TVLtOajicBNUYn\\nDcoUjTJ9Mi30q2p86vhyuRzKze08HCA4cHS/bWQcqNsg2pjZ0OE00KF9BzgbkJyeng5lxc7IG5ix\\n9s/Pz8NB4QBhwLJlhjXbR9Tkm2g6h39IMOPSbMCjgypXLFjnHOQxl9a3lHX+hq1ymbd1uKpGpaOs\\nr52qCRTPmYPH7XbbEjVdeTlBFPIKWXN5eTns/TdJxXjIKkLUmHhyReVqtRqVoR6i2Sa9vLwMWS8T\\nmsyLx2C5spM2EDSYxNmbHDFhxzo6MWD7aN9Fy0DLoMUgDPLXZxRlUJ+HB3PZBrov+RP5BYR2/jbn\\nbaq2j6jh/8yL57qq3vgz4wnW3+DT4+ferBWXK2r8Zqfc+oIO+FBg1jazfZyjBrgmE+2qrpeX3dso\\nWNM8z4KxOyBx8OX5gezGph6aqGE+HXC70og+QYwae1n+bE9sMzPwRucZt79ju4cP9HmGDhgtN0ms\\n0FdwB364C8rSP3R+nPsRdO0jHS3DVTWMCVztA42nbpBAyBjyxfitm6l/Tv55S2ISAca5xkbYbWNv\\nk6FdAiED9HzJiKvi3ByQmqSxTUa2WAcTFl1Aa39t7JxB/kfk0b/bwFm2eSmL2BYnGd5LKDAfmTR2\\nANyR/BzivY+ocdVYVoV6+2cXh5okSbIG2c2krmMpmmU6P0fDTrxnj6ZuHicxEToDqX12dlYXFxcj\\nebM96UgxY4e0W6yn5xF7hS1nHTKWs03PRIgrJ9NfO66x37Zf5Lq+vq6bm5u6vr4ecCqJS56NXDl+\\nspwbr/5fSNNfqqhx5xHeT58+1XK5HBayA1/JjuNMn5+fBzD3XkYgFSQdkAXJbxn5qKKGagM3k0gu\\nKXSwn47d4Jg+GTSz0BZeG17GZsPEHE/dqKhB2VEugsQkaTIzbHIsA0Ove5IxySK6dDuVErAE2EqC\\nxU4HmeEeDgi7ihqMmWXMPy2bzBOyVbU7gyPJqySEzP5O3TgvqmqXzbEhJIC9uLio4+PjwTnP57sD\\ng5MUdQajA3kdYWPj6AwgjovMMBUvrsrx3EASQtSwrYYzXCwz3NevqKVsGaMPKDcQMUFgoibPYbCs\\nZDn01C23VhiMmqSBEEkbQbP8A8JdvcLfnal3BRJyhBNO8tJBOHvycUz0i2cxdwZHrH9XUeOD2Njz\\nb6LGZFA6Zp9BhNzxXJc20w6R/Z3NdtVbBFMcTIt8O+DviArrXtXbs1tSJ531s/3Cr6Cb6Hzes7OB\\n3JvL25wArJBpEDUd6O6qarAv2FbPg+fCdv3x8fFNQOb5mLoZ9Oda+e802137OQNF2ztkmL+ZqLH/\\nINhAZ/ymH1d3Pj8/D1VOBI2ZJYRs8bYu7GOXiMHvcnWfQ5bs113x6LX2urKmfhPn1M26yFqgI1TU\\nYOccFHfBMsD+o4oa38Ofz4oa/m17aLI8ddFZa0gl+94kgP3v7l5eI+w12NbYOAMf+0+IGipqnPSZ\\nsiVRQ9+pjnI/k6AxXmPNqGCy/tmu4YOdQIBgdoCWVb8mSRzQ+pBqEwMZiHvNPA6vnX1lJq+xKf4u\\na+zz2qyr/8mKGvrqxApjQb6JcSCZ0qYah/Bdz6GD8R8/fgx4yTEHVWBJkJoEok/dQfjYP5PQSQqa\\noDE5ZvuQa5f+xj/dJ9tRbGlW1PwniBowI5WZxGdnZ2d1dXVVs9lssHUZAzKm1Kkkanylbu87zBg5\\nsY/ld9ZRE6YdtvJa2HdvNpvBD56entbNzU3d3NzU58+fh2QiiUuPw6Q78X5H8k1aUUOz4qTzoCMd\\nK9oBUwA2xjiNvo0gATsLu48IMmmTGXEW0gvSGVobQO7RObN0dJ4PjL+DKgufnVwGyA5gp242bvvW\\nlb7yM9ne/Hw6nWSNbdj2zaUNu8GigwYuZxMwXDZimR3yve3IfJ6E+2qlzrJZFNDybwORQOsQjtDB\\nAwbAmVAHCwbtBumZjU8H6eAcR9tl6BwYch8bVwMDnu356TK2boBKl4+b/HElk21CZrNpdhAdCez7\\nQdYdqrotyWPLlecjyWHrWOqfbWJmehKk5fPYKoejchDO5coXKp5M1jhQ8dYx2xDLKk7++vq6Pn/+\\nPBB1yEmOH/0zeZMOuXN4hwju6VsCMz/LPjA/az9ZNd7fTZ9tV0xwOni27TKYtH4msco9ea5tbW5d\\ngjjlPAauLqjvtsqkbUmM0M2RyaS0YwD+Q7SOfHY1xnskWpeRQzY7H2ps4oRP2s2spnFSDDtuffUa\\nuNLRPhObwJphM5OQt3/DpoB77AuROY83169qXPE5dbM9z+fafib+6PCQcYnJHxNprlICK7BeuXUt\\nEzgOWKhUg5zmTUIEnFwESFQ9ekwO4jMYSh/n8SVGYh6N39K/8BnL1pSN14QvrQAAIABJREFUtTFJ\\nZeIxE2/2bb5slwgw8Svn5+ejpAf2BplnLl296Ovk5GTQ1ZSRTA6mjUOfrFMk/xJHJjHlv6NrJpn8\\nXV9ec9tTY8gpW/rgrG5DbvhcN2epuyZSE/sQG7pKis+bzKjaJYiM55lrdI/tLJB5/rx1vlvzXKcM\\n2FOvksADT+2Lr/KzhyBLs6Vf9FEZYI+q8VYp2zvuUfX2TYBOemS8ZGIjfVMSPMyPYwEnHxJbZYKB\\nlngFP4veX15e1tXVVV1dXY0OmXa1n++fZyhlEQGfhYh6r/3S67mTpOkIDwdNHngHOjEQKKodRNWu\\neoE3eDAROakJgnIBzcIxYXweo2AF2mw2Q2Dp7QlJLKC0GAUbSIS2qt6AVgumy5VZUJNYh2oJKD1v\\nyf5jODoDhKJled++kq99Rth9ShKEIMFnI9hA+N657ztBPn3ifufn56PDgVkTZMLMO81G1HORTO8h\\nSJqqGsCBM4auDKn6W/by4EZnWCFjrMPWP4OfDDC8vnnAWgZiBoIAH2cteHMQ8s5bRGy4XTLOdsYE\\nwBhUbFASySYb2E5jueS7Zrm5zyFImqoa7aXuQBZOh99ns/7awFtPHUDY1uR3CRjsNHyAHp8jqwiA\\nob+eP4IUB3PIi4mvxWJRFxcX9eXLl/rtt9/q999/r6urq6ECwHJjXeM+BmNpQ5Az24ZD2FODgs7h\\ne/6R0bR99iMJ1vlcB74t32R+0LkMtrvgLTP0/M4ANckZr33e10QNvtpBl7N+lk9fttfMDWtLxvMQ\\nwDSxCb6NOUhS2IH+fD4fyA7k08kE4xAHWJ4fByn7gr3EOibWkkjLt7ElsZu+mgDWMuKGHGMfsEtg\\nFj6TfsTy9+nTp6FE/vz8fPI1RAeSJPOcmkxBd1OnunkyXnt6eqr1ej3YQ7b+Q+JgA101w3pB+qEv\\n8/l8IKnJ0HproSszqKQlyWc5dP99ZhTgH0xrLGe5p3/dPLDmnF9m/HqI1uFMB4f4Rftp/5+kDoTu\\nbPZ3BRWVuK5SqKpRbODtgFklaOLTMu9EYYf9HNyDlZlvYhyq95P8RXd8/4yBeH4XuHv+uBdjPWQy\\n0baccTGH4L3NZndOpu2rSaeUCWyl9RM7al9kDOr5caIW7Gx77+3X3mFAXNYlgdNmJ6mRCYwsSkgd\\nxCYZV3g90QW+59h76masD3Zz5fPx8d87RVar1TDXrCMtiUDjQVe/pz22/f758+eoIisrvy1r6JKT\\nEUkIWt+59pFgGT84eZVET3IknjuKPjgzMfEDn3uv/dLruat2CtMRNRZYOt21DC6S+aIZHCCcvm/n\\nLKzQBu9J1GAMElzwXJTXZJLvn6wbhqBz8l1Wg592iHwOQToE022Ql0QN85VgkPlI4oP+ucrF994X\\nxHdzafIuM5OAzyRWTNQY7BpE27E5aDeo9VsM6KMrnuwYbTwNgk0wWf4OEVSYSML5+VBcPlNVI9CB\\nDplMoc/OXAECAYmeYz6XWX3W2OucpA0ynoGFDz2GGDK48aud2c6YlW7YHq8Xsg0g9pk0lkueNZuN\\nD4XmfmRUpm44FOuAA2aTEek4mPMMLGzokzwxUWH7ZlIVGfHaWF+dYQQQeb2dTUYG02ERsC2Xy7q6\\nuhpImt9//31EACVgcx+xVThxZ2+SkORzhwCkyAgt/SKO2YdvZgDo7wJWOtCd5IpBhNfCoHUfWeOt\\nrQZG+ypqIMlN4vgZvh82Icm0rDBljRP4utnHY9sOkcVPP1FVQ5CGHBF4Z6k54881qhoTUZ5ngv4k\\nY4yhTETbb1oerK+2qc52GjjyPVrKkOXDfTIRjr+YzWZtdUfKXf4fYmPqhg8hwYI9ZAzeMuBK2Y4A\\ntV2t6omaqt2WWgcpPhclt9Q4Mw8BcnV1VZ8/f67ffvutlsvloG/z+Xz06uEkO5Fb1pG1y8oP5gF7\\nbBzXEfbWXz5vm5qJsKlbFzAhk5YdJ4ZN1KS9hVjkTVx+xmw2GwIuB2Ld2+tsd1gPkmaJfYyhM1bg\\nueDM5+fnEdZPHIA8m5jjc9y/i8nsR7KaI33y1C3nBLlH/026dPqSY/Sce3wO5G1j/BnPu+OLnBsT\\nNZzvaExPEjGxrUm49L2WK0jTbp289sbktkE01swkVW5ln6p5jiA2qKq+vLwcraHtk8njXA+TP8fH\\nx6Nt88T6TnRgd01421clMZf+NeNZ5omEPf4y5zgPSraPpP/Ig0mXjK2JoYlZePEQLXHde+1D5JMO\\nphM4C2v+3QJoQ+SBeiL5nic8WWorSWacMqBOomY2mw2LiKLTn+y/++178136wTgtTDaOjMc/cbAG\\n24zvEC3nJIE/n0Gh0hkn6DMB4uAvA7qPjIgDLGfxyfA6cDSYSJIms9ZWGrPbfrWiMw0ODOz8LKtJ\\nNNEw6J6PQ2bxGZezZ4wD4GgCJpl4O3LG6meYrDApYgNpwiNJkzTczJHPPeAeAEkHdZQXc94U+4z9\\nSsWsqkgCirHx6tbc6pZraMDHfZJAnqrhXNMmut92emnMu6AiHbvtMmtsfXDwlGeRXFxcDK8SNBlt\\nR+rS78xysubMHWOkzP/q6qpubm7qy5cvQ0bZfqEDvZ3PyUCWsX369Gnk4A9B1OA3PK+WK+bF2808\\nRq+VM9W2f52M2A77c5aRJOIcyNk+J1Fje/Le67YTLBkEsz4mHUzUGMB0diLl2D7rUIFF1dhfeI4A\\nV0koMWd8PolUbJR/T+BlcJqBnYkPz0EStq5QzIoa+wPrjeWOOcXe4CP5ntfFOmYbwjgcDDkYNKg9\\nOjoa+aApm4PvDkfat7hS1uSvMVvaGgclVHl7HHmmTZ6FgW7Zl5ycnAyHU1JRg64Zr2Yw6PE46M5D\\n6PF1Tnoa4+QWSutwJnR8f+bxEKSpg/GqnYwi609PT2/mJdeXalhvH8PfZrBoOQUbenuvD0V3Is+B\\n2kdVcIzH//Z3qXbyGjCW+Xw+8qtdrJHy6pZ2I33HR8Hhv7qGmeCzzbJfQE+M3xlXYpkuPmIO02cy\\ndsesmVBIv+jD8v1dV6x0xJz/z/NNeqOPXjePy+v0nr1mXoipXl9fh7coH4Ko4Xn0yZXwZ2dnQ0IO\\nYtQynrFicgdeq46E8+8tG2mLSCrYFrAjhrPc7K959vHx8YiY81x/+vRplJyynGT8b9m1DHbYHFlK\\nHWQ8H9nTXzpM2IDPpVyZMTGbZnbQnbfSMTm5CA7oOLcA4YTAqaqRwU5FB3x4Ytw/HFIXqGfwY2LA\\nQmFDlAuCEfH5HHYMCaQIWHH0UzezlhguGyc7l8ywZZmYwSRB8HK5fEMEZeDkQI4LJ8llgOfDSwGR\\nJmvs2KwwzgryfxtNlwZ3xrHq7VtK8nMJKmDe/VaiqVsG7gAJl1YiZ7nH2s6S8RhwWr7n8/lQ/eCz\\nE2yInK1Avrg6MgWQa+MNGEkCsGp3iCFv0cisiwkC1gjAbWfJ/ZNoS6LR8+HqmkOUleJEvE5JXqUx\\nN9BIm+IA7vj47y2bAP5cb+sNmQUcE7aA7G5uncK2kSXwZV1zM0hZLpejk/Ovrq5G9p3P2VlX7Q4M\\nNCFEf5ADSqm99nz3EPbUxIPnM/2Zy72ZvwTKCfZcIVBVo/s6cDShZ9uaJDKVF0ncuB/pew16U34M\\nQm0TE8RmpaN1PStITO75Oay138A3ZcvtpNjVJB0Ys/9vosX6ut1uRxWMnnPkMbduEHBgq1i7xAzW\\nWQeW/NuJCEq/M6HkLKn7z1on4LbdNHazrKV9yaQFFSmH0MVv376N5p/APoOk+Xw+2q7y+jreKmjQ\\nbFzmQMFBvgljJw34HNviqt5uz4KowdbmeW18/+zsbCAb0s5vNps3VXDIqNcniWpkzJdtbp6XlDiZ\\n/kzdIGKMuZ1kwOZktSeVG+ic9YiKC+bd9sg2Gn26uLioi4uL4TsZlDN2vzDB85Xb0qhEwDdZTlNn\\n8PHEOmTi86yjjphkniBDsTMmRJJ8OMQagumQZVd3JSlurGCMti8O8+W/82//Lkko7J19nDEVxNxi\\nsRjFedkPZC7tvmMM2x4H+W5OkCah5892xHaO+RDkN3GAsYGTs5kgs2/n9xATrLcxEHbFMst3TWig\\nC8i/1wSsjF6kP2IuHRdyIDj6fnZ2Nko2edt3Vg+jh/5J8oVnIf/Yj+12OzyXsZjMZVwfkaa/RNTw\\nFhacR5cZN+gzCE1HzkK7GQDhZH2h0EmQJLtqQ+Bn2uAa/GA83ScD7bzM3u8L4vm9g1gbro70MPhm\\nn+3UzRkYjImFNcFLR9JAgOCItttdSSNzyRoCfjBeODVOBmfM7g9lplwoVZfF5bsAYSsOzeSfs0z0\\nzTKSxi+rCZKoyTUmwOek+UO8hpQxMyYfeGaD4iyUg3rWz/JtII+BJMPgknEMa2ay0oiaQEFOAAUG\\nUASXNmo2gnlIoHU+gzzrkR1GZvkT5Nhxe44BXff39/Xw8DD5Oj4/Pw/z7ADZxh05M9GY4CJl10Ek\\nsm9Qy7hTx5xdwCYsFovRq0ZNlCATbHVCpgy+eKZJpsvLy+HkfIKU8/PzwRZ6nRwkdiDTskCfqAKz\\nfvP3qRu+pCMFkUOTZA7KWR/fC/+TwA6bSsNuumLD/zcIcMBYtfOzvkyoW8b4vVsGFX6zkwMZE+f2\\nl7ahWYWX5ARts9kMAf6hdNF+MUGpA2ODvi74TYzDWhkzMUfersHFXLoKLskadNZkTRI2rhj1Fg3u\\naVLRZESS7/67cUpVvfm/P599JmiEeJ+6ffv2bXT+mRMyEKWWW8udCSWCBuaHcaY+mcT0vKF7qXfY\\ngqrdFozT09O6uroaDi/NCmQTPfad9m0QggQX+HsTNfSRfhgPm4RwwoZMOW94MsFkfz51o0LAZHXV\\neJseuMf6RUIUosZrg64x7txi4+AMHWJrh7GN7RtbdkzUOBFpIiiTzB6TyRqT6PZznXxZHn1hX4yl\\n/JpwB66MYerGWhAXINsm3vCZtIzRPE/7iIqOuKF1/m0+n7+JKffhKnBE2nienUQNz0iixr7U+Cbv\\nxbx0ZE73syONpm5OejEubJBlib44kWEMkGuRRA1yyzykbHO/JDiYd2xbxwk4Fs+3ennLsH1FvvmL\\ntT06OhrhYbYtEnMa34HhmS8KTk5PT0f+HmzakenZPiRqcKx0OoXbTi6VIxWIybMx5XcOqjkNn20L\\n3MuZQ5eez+fzUUWNjaCdEv/GCey7Moi3kTHwdHDI5y1wNtZVu6CLANsCDRvnfdBTtpeXlwEM+HWD\\nXJnZ6wwZ47DzAtx4fCZWchtUEm026BA1PAtl8v0wqJ5/BB6lNqg18ZffSwOXQUICL8thgloTNbwm\\neOqWWSCDM4/X8861z9hnhQrsL+tsgg0jlVkkgECSPsi1S3xtPNN+GPz4wN0kWDPYcUPOfF/GbYcI\\n2EX2DPoAdKvVqr5//z75OlKeaZvHuln+ttvtSIZNjmewX7UjKEzWGFhYfrh4Jfbl5eWw5QmCdL1e\\nD/fBPkEmsxWNsxRM2NP32Ww2cnw+PPPq6mpEoKbNtG7mtoVO95EXZMBEzaEqaqr6A9Ttz/AfrjLJ\\nYKsD4SY2rLv5GeuT58cywc8EA0kAvEfUOAC3fWF93A98SY7LgUm3XYB+GoBDyvmciSnbjx8/Bt0y\\nwGbeMvvrzOB7wYIb9+WMBtaKKkN0KYlI2zy2XLy8vLzx3UnUgJ2qdoF3ZiJtI7fb7UAasZZ81uOy\\nbmagYLxluWIen5+fh7PGpm7fv3+v5XJZR0dHw1l2Jmss49gRrtRdJzaSdLOudwRXEgBOkOBXnHnn\\nvC5INRO9Jmufnp5GRDBrNpvNRtvfTDLZNhjDGNs6yKFh5x8fH2u1Wo2SAM54H8KmMk6CeMbvBIQr\\n1UwMp61hXMy7iZqsfCJpybbfy8vLoWIG/bGN9SuSTdR0FYTYE2Mik/weH/4dfc3q807OWEtn9qt2\\nfg+yjbU2UXOoihrWkDjORA3rQL8tl/tIKb5ne7TP5uY9jZkyOLe/s+2irx/ZQtuE2Ww2CvjTlxp/\\n5v0Yr2MS/8xYJftyKKLG5LXPakpyF7vGZ3ihBD6LOUJmsR/MOWtv39eRNJnQMd6y3nte0Jujo93r\\nxJfL5Yi0sx/1Vm92IiAT6/V6sInYkuQfTNRU7WJiEgZgGNsKHzWxr31I1NgI8ZOKBgfay+VytBc2\\n2UYMRCesdogZyPM5K3oXbDuA9fcNHt8DvNzDf2cMNpydkUx23M2AxWQTP121QHb6PdD3r7anp6fR\\neM0CZsbT6wWY6dbODjQDYQuinZflAGe2j+l2NY1JFIPkql2miH46EM2A3etWtf/smcx8JIlj2WKe\\nOEPFBM6Ubb1eD/NoErCrmkhD7jF0OodB9v1//vw5WhuAyenpaT08PNR6vR7JRedcuA9zB9BJkiYZ\\nazPNybBbF1P/OnLRZ3NkNaDJEfqK0cSeTN0AyRCzzAOEWoLjXDOTLtyPnwTS/j1j43fOkli/TH5Y\\nrrH9zhradqD/9JH/Hx3150wlYZtZ/KpdJYyfn5VbZAVx7swnhCmZVAiFKZv9gAmVDmB3FSWsSQey\\nPC8AnCSFrRu5Tt3r0Ts/Z93J4Nq200GkA1T0pLP1GUB1OpwVQA4yGJ/Jg7ThU7T1ej0iMA0+rScQ\\nbO6v5y39ka/chsOcIK/YVSclFovFaOuC5Z4thFRk+KB9g1fLSga9iYE6EtSfcWLGcrAPq5ggMol6\\niDW0bfrx48cw113yziTGdrsdBWm2jZlwMvZwUGWQnliB73FPE6LOvPv+xreJhXMs/nwSEthY2xfu\\nmevAWkGg+vXwxmoc5o9tP0RzkJ24zXPJfNqWERQZw6QvQ05cEYrugXP8xi22XTMPrlJxPJBrk8F1\\nkt62GcQCPMv2083jMZGb+MWxBf7AY4JcOgROTbwAcVNVo/ViHviZukO8Z3xqe5t2pNMPkzXWuTzL\\nyXbf90q5ycRC+rT06/sIls5/d/Jqcq2qRj4ztw5N3TwW1pBn57g8Ns8fY3P1iM+ATAzoWNTriAzz\\nNlvjk6wkTnuZ829SNAna5XI52GMnqxxbgAWSS/D6V9XIB2R/qcYxyfRvEzVMIoabB1KuBBifz+cj\\nYOFyQzKmDMwDMcvPYsEqU7LuycrAxeCIoNWAysQIBtwK2BEt6SRToFJBbTzSUDtghCFkoV2t4Cod\\nlwVO1XiGn+9sPc1OHuGxgCejaaVgXi2YAB2yiZ4DAu3OeBooJUmzD7Awbw7C+Q59s3NIg+nG/1Go\\nVES+a0dI0Hio4PDu7m503g+kFKXA+4DZZrMZBWAeCzoM22z9Neil0o0sCdseIKgMOrh/GqEuk+TL\\nz3YG2GPJdUpj6P6ajDAQS4CMvXDgP5/vXoE+dQOEQMyiM5DM78mb7WgXNGUg0sk4uuItHoAR67+r\\nqJBxvxmrc4gGoVU1KjWlKsGl82l7HXigU7bZlhH3ZTabDdsr7u/vBzDKOk7dEqBXjQ+IdHBrYO2g\\nzEAs5y2Bm/UpAwGemWef5D0sW7ab/MwgKQmbzjd0xJ7/7wx2B8ITTLH2jMNvETuEX+T1ogniIE49\\nFuwdc2m7k4GEyWD7NK+tx/n8/DwQLicnJ3V/fz86nNaydH5+PiJqvL3Aa4KMWTcZJ+NIkNuRM74y\\n0DIWcnOVFf3Hxk3dbC+o8APXGFflHGTVFPqZlbj4CuZzXzV3BojWs6oayYWDROu9sZQDXttb20zG\\nbhvv9ci1sf9gThzgm+w2kQg5QTL2EPjGNseVbdgckzGZYOH7rprmO7Z/+J6Hh4chcPc6sH4EhiZq\\nwK3+neOZLoD175ChjBdeXl5GuMO6aHudQX0SgujabDYb1o97s+3QW5izr1M09IOtnGANJ2msi/z0\\n5dgiZTrxAnOc90VWMg7pSFgKD/y8JGkYE3gj1x5bYn9rYtjjRTYtl/6d4yM3YzAwMomAqRvyZlLC\\nSeGUdeaOf/N35s82mngE/+c3uXrMzJ8PcU8OwEUAXZyehIqxGOOhQpWqTGMw9wVZJpZFTjrZNUHo\\nooFMqLhv77VfImqcqbMTY8IRFoAyLJoNqxWgalwybiOURA0T2jGuVlieU1VvyugQEsZjZbBDTaBj\\nJjWNgBfFAC2DKxsEBAuHh/FcrVZVtSuRZhxTtoeHhzo5ORkZaQJY9kAnGHcJNmtFS6LGZIAdLvJC\\nYOnyY5Maua8z93jaMNtJsQ7eZmADTADuktNkM63UNr4OngyMvL6UCa/X61GVxyHW8Pb2dpTxeX39\\n+9DYi4uL1jBlcI+hMAmQ8uxD8qieYX0cqJO5enx8HBF43JNmgtREaQc67bxYAy6TjJklMbDryL48\\n86rbagTIcYBPFcjUDVAFEfHy8jKw+8h2zmWSNBn48lnk37bPjtOfMVGDntAAeB1Z420tqQ+2FbPZ\\nbLQf2DaQ/fHomG2pn//w8DCSZcsnhAh9YLva169f6+fP3Ws7D03U2C6ZlMw5wi505FYHPh3Q87kM\\nvPBtXqMELPgyy1YG3Pavab8zs4TdcxDsvvqZJq46P9sRNQSIVEVBxB3Cpq5Wq4GszoRCbrNgjgiG\\nMqA0QLPdsR/s1pa54jBYAhuvqb9zdnY2bNFgu2IGtan7NNsW5tkJK/tFy08SfSmDlikDdH4emqgx\\n5pzNZsPW4C4D7KCa8XYBnZNHmUy0Pvi+xlHGDraLrqrhuYzD+oTumEzr1tHBisnWffYikzr2uQ5I\\nTXzf3d2NMN4hiBr8Um4bMe6y77cdsV9L+QefsHZUsTqQ8podHx8PFS65DRu8l0QNz+ff9NlyRrMt\\nZP5dAenPZtLRY3PcY0xRVSNS4enpqe7v7+v29nbw8UmsTtVc2UUf8b+2fyZfwMxJ4ndEftXbt0Jx\\nT1oGzNY5y5crJ2wnHCO6Wi/PIEK2Mj7gu06aWRcti1mdkUSNxwnB+PDwMDzbfmXKZhxGHxeLxRvi\\n2/PfETW5G4fvo5M/fvyo1Wo1SiyZgCRmfXp6ekOMYceT6MrEg/vH7/YRNeBT+zvGQp/8LBekZDya\\nsnZ8fDxs7zTWNcm1r31I1JhIsdNFsJMd5iyKBH9V+5lTdziZTIBt3i+BLcCJhWQCLXQWogRf7zk1\\ngLKfnaREFzRBQgDmCDQw9j77g3s6MzZlS+bP267YiuI5NJD0eA3GfuXq1ubo6Gi0HSbZ7S7TZKXj\\nfiZTOoIIh+vMdWdkMJ5WZAcpNsbJHKdBt1xP3exkCVzMsCdATOdgQEPDYCGnVNF4+xlBg7P2s9ls\\nyNSsVqtRxtfEqJ0Pv7cdSb3hOxg7Bx/cnzF0lTJee5NMJmn4HEbYjpH+m2yYukGkuIrFlRCWbTsK\\nAzQHVAasnkO+Y8fp35k8MxiBkPf5Gdgp7EVXBpv2GXuWwQ7P9fgBG6y1K2osxwQv9MXNb+N4fX0d\\nycPULYOq7vJnvJ4pyzQHJKyT1zqDaT5HcEVg3/kwB3r7/Au21DqU9ti+2n2wHHTg6CMg0oFkA7v3\\n+v3vNEjnPG/L85HJJvrYzQ92JwlKJ2s6P7nZbEZ6/+nTp0H3AKk83/vqfeih59E+z2NBFp24wsd3\\npK/750CW/fteGxPfxk4dqTlls49wxZ2TM4wfG9IFUF1wlzikI2pSL5MU6rCJfXTKvreNZDUN96RZ\\nv+bz3dsYIcb8+UymMAb7hDzgGsIUwpxK2kPYVNsY48wuucMYvHbGaFThpZ654oPkJfOU+Njn5ZlQ\\ntQ9MMsH9cfDaETkpUwSruTasYcYzlic/z9jCdg296EjGqZqJtQzQ0UXmwDpgXLfPh2az/en+tk8+\\n8m9VYyyfFbu+jBOrepI6EyX2m4nnEgPQbBP4vwsm/MxD+EXWodO/bs6NT712EOh5/hl/cxW7sRI+\\nmbiUPlTtKu9y3lLOTajl2Oyn/cYv+uexey2MCdLe0+/0HbavJtQtf/vkmPahtb25uXljEFIoMxij\\nw3aenjADgs5h+t5poO1QExhyb/6PIOzbS9pdqdAsKgKSRJF/ZpAyn+/OXwHo2RFxJgWL7oWduvEa\\nMgfgOHay27CcGE5XNtg5WnANKNLBu4Q2Gc4kDCwLlikrqOWD/lseDJaZZ8bM3LsZcHgsgB6U2fdF\\nhu1UycSydh8FJf9q8wnlAHT6AsuOU+RNXD7o2bLpOUBnkUNA+Onp6cjwucJis9kM62vnBlkF+Kga\\nVxdYXghObFvcRxs9Mt2bzd9vUfG5JyZi7BBhzJ0ZhYSybTIzzj32OaQp2mKxGOwl8jmbzYZ19OGS\\nJslxEvx+HxlQNd4r7s8ZtJvMtq1D3r31wjptfe50OolTP8PEtn3Dy8vuzR0ZaDnIYc4gYQ2oCGiR\\n/QTtU7asRLJd9773dPTpxwxsmCPbOT/P/iaTGgZCDmYsw/53Byy9Rq5IcwUa4+p8b47NLatXO1tv\\novTk5GQ4987l0VM3HzLvUmwT4ElyQ7h0YNHzm7igI9D9WRPd2K71ej0EyU4i0G/jKBOw3Hdf0qAL\\nFAz8HUA50EpSHN2sGtt5qmdIJrBd5BAHmF5cXLRBGM02abvdjraTGTv4O12gyFx4jpIkRxeRnQw0\\n3T/skklzLnyricMO92ayynKUfeRvHvPJye5NkO6HD9NFF+kHfZy6QQK5OgrCM986yRYskgEfBcdc\\nJhCpGMpAir/5rU7GotYJB9KJF9LGdUF9/h0749/zWSfi7BO7RPF2u6tMxCdeX1+PEu2HwKk3Nzej\\nMRrXEA/hT6yv/BuZ9lqCs/e1Tuctz14vEppV4/MZjXMgJl1Rta+yjWc5WM+EhpPd7rN9SdoSnmF5\\nq/qbzDw7OxvmhD4dotmuZLKms0WpG1ndkjqELGazLyZG9TiNgSxnXjtvmUeP4CWcwM0tlvbz2KL0\\nBbYf4GJ8tn0ilVqOix3/vBcLZ/slosYTmM4sGUImxAEwlQBmi204LRg2Ut0kGfykkUuD7AAyjaqf\\nl8GKhZHgPP+27z5ZamfAiwPhswiI9/0dqiVRQ+UOgmRiomrH8rtPXm9/piNqEuwmsE8Awf0TGOW8\\nGEwS4GYGzOuEkanaEVEGMnzOjprnAAK8FYsxPzw8DIbDRM0hnaDSKisPAAAgAElEQVSJGoyNHWEa\\nSROjBOWMy47Fzn8+nw/3RofQn4eHhyHTm4DXe9kBCfsynQ6AfICudS63v5kcpaydy8GDbYeJ0txa\\nZyDDOKpqRGAeah0halgvxvXysttK5i1qJisht/i3QbNlPgkx6xEyDEFr+2k5cBaRdSNgzuCuI2ky\\nQ5pytt1uR2cemAR1Js5g4fX1deRYkf31ej3cC6KGcR+KqGFtABTIaW4LyCDMwMxy4CAqExkOPlib\\nnCdna7w2+4JF/pZJlKxIy/M0OpKGlj41Zcsko2Xa4Ie5NIniA2mnbNjtqvG2D28vxQ8lBuiwQIdn\\n8jsJdk128BzGj70yDrLPs++ExEwf62DAa+NAxYFuJibQ/dfX15HdtXxst9uhAu/h4WF4JrrPdhG2\\neU/ZlsvlSMYNevm/+4lfqxq/qbKqRnbfc26fUtW/6pr7+nyQlAk339eBpLdbEMwwDjBMBv3IBPd9\\nfX0dVTHze8uEqw2xG6w3RA3E4HK5HGG8Q9hUnwnFdsmzs7NRQshVDyTfbO/5u32M4w3GCu7FPxCI\\ne+5JSiXhlrre6Xza118hcvz7/ExVjeQY7MszbUv4HoRfVQ0YyNWX3uo8Vbu5uRkRavhoXoTB2+m8\\nda1rxuJd6+KCjqhBZ03SmEShj8avJmrYGurEke1wEmy29Y5hXIFGv8EQv5LAIAlDfJPVSlM36w1y\\n50pA65b7zLqZXPFlO5fbB92wAfzbFdz0z1gqiZrc4mdeIqvsu6o6Y2/sMA2ZdvLS8YrlEDL4+/fv\\ndXt7+yZpUPX2bcNd+5Co+fz580go9oE8OysTHK7MyEnIlve2UqRSEhQzmR60we2vVNT4vs42WCiS\\nwLED57tVu3NAuEza2FDMZrMhGGMsdhhTN/a/e88c42DfnImRVA5+R+P/dmwdQWPWtCPF+HcaKOY1\\nSQeTXBz26sDQlRl+HjLpPhmwGABgdFk3V20QTBNk8Tl+5pinbJxFkGcZdUSN5yszvzYKNhy5hzeJ\\np9VqNcg0BgwHsl6vB+cLWHXmAmLA5Ynug0Flng9ggsX7SjuiBp3n/q6osTFFrtgjy5qzjYDvH4I8\\n5fBnZwkgajjkkAAqSSMT0s7ebrc7Rj+rTdKGuvzZtpt+JGhIXQcg73M41kXbOwcJ8/n48Hk74QRG\\n3Bsg720kzNn379+H56ccHLqihv6xZnmuloMCbAafrxq/bQzdcuWKg2nm3EETOmj/y1pW9Vs+PyJq\\nsqLGW3zeI34sP362f390dPTGx5kQ8BZFdJG1nrrhgx1k+0ofjywbyHbN8t7pRW6bAuBhr3NvuxMM\\nzCvrxfx1GMfA2sCb77A2Btcm6bbb7VAx9vLyMnoduG3wZrOp79+/1+vr60DUuJp1Ntsd9j1144w2\\n7EbiSHTy7OxstM7MH/6GoJ25tJw7qLM8o98OREyg7gsoTcDzWdurDHCMQXPtjEt9X+u0MZZlkaSF\\nSVNX1NhvOiP88PAw+Tq6gheC5Pz8fETUML/MCUQN8jufz0c4O5PDrCVr40ph41h+T6Du9UtSxI2/\\nZRCfBHomJT8ic1KOSfaw1sbATlA8PT0N2GixWNTPnz/r/v5+kLup283NzZsqSNaGBB8yl8Fp6obH\\n78/YJvM7/0ziIMkakyjIvbd5+ydkfVc4kOtDH5JAz752/tBYyvaGe9BvsBM6YFs2dWMsTlibhEL/\\nPM+ZLDZ2dYzrbcadzDN238/xqOeEuUyixgmHqrfHPCRR41jR2BvS1r9nHNgIbOlyuRz1+/X1tVar\\nVf3111/1//7f/xv5T89zZ0vcfun13BbMzWYzGDUCNx+MZqBhwU5hz0yAg6jMxJOlByyYYUwjxyI7\\nU85nLegoL4bB96FfCE0Kb2Yo0lky6QbQzpqiANzDgbXLmadsrhRhbpNgOz4+rvPz8zcMr8fieTK4\\ncOUSBtFbJd6ba5NgfJf5MfjMLBDzZ/bdwQyO2opuhe8AHcDd22MwKDgeSoIhZhx0GxhM3dARG5jt\\ndjtkfi4uLmo+nw/nIjAPGZTZIQI0f/78OYzLQUhm/FmvLgjnGTQDpAxC+D2gI4lECBkuO7P8nK+c\\nd+yJddJOkFJSG1bG+CtM97/SfOArOoXDhYQk+HEmHt1xIGBg78M07Tg9tiRZ/HeTnuk4ktDpgn+a\\nbR7ZSDtsiAdnjw1sGAvjcmaFc5Gc7WId/Vzr4kdO8F9p6JgJDbZm4RftzwygPZcGB9jYqt2hjPgp\\nAxJ+7893SZH31g37xVrngdsmClwdlQmM92TBem8bjx+HbDJx7rfJbTa713OzxodoJuWprIBUYA66\\nigvbCF8OkjabzUA8gWc8J8ZPvFzAW4YzWeI+V+1sMYGY32KDnNjup35nIJHEEg1Azv3xDX4r18vL\\nS52entb19fVINox5SC5M2bAxPAefWFX19PRUNzc3g31DhvAvEPPGJegSBLir+tKfModp87jSP5nA\\nTjvsRF1iZtvpxLyWj8RsJq/8Gfs2Z4Z5E+HR0e4Q9s1mMyRZ8B+HWMeOXHp+fq77+/uqGm/fdqW7\\nMZftDH03NmQejbPRi+fnvw8Qvru7G5E2qetdEoKfmcxNP58kgteSsWelhXFzBvJc9qG2PYkbEsdN\\n3ZwwtH5U7bZjMa8muTuSxfYu58+/57uZEM572u6iN8hG2tt8frb0fV7rjC09zm793Uz2ZpLatjqT\\nfFM34qOqnS6SFDOGxBd4XNa9lH8Ti0762NYR6yf2SD8LseX42oRmJkScgDa/kPGuiw+SHPLzTLiB\\nVZgzJ4IdW4N77+/vB99i3LavfYh8MrOME+QBl5eXdXV11Z5g70UClGE4bdiYSEAAje/xM7MXbn4e\\nwbSdiwUOYeoMqIkaC1fVzoAmWZPVHO5HZlABDM7qYyxwAodQPM8Dzaf7e++jtw/ROuBAcMk8W5hx\\nFrwSMIGs59oAnuyd5QiFIMOa97EiZXVNAiCPwbLDuiZZaGDugBpnj+PLyqFDBIcmaiAvnP2dzWbD\\n73NPaee0DCgMEN1/HIYzhA4ckwRKHcrLawsY5DKA5aAvGGjfI4NIr3c6RQcttif0FbtFViKDlkOs\\nY2Yj02AvFou6vLwcAVDLImuVGUYTISbCc9uSnRj6YbvE5Zbr+F6zzTQgsu12sI5c0RcH6PzbxO9q\\ntar1ej34IfTWAZLX8VBEjQ+jy20iZ2dnw3walKQPQ5e8rdIgzfOdP2n4NOYziRR/jt/b9naHctuf\\nowdJkleNXyO+D9hmEAXAIjPvQBi/mERk1WGIGgM7yOKqGsj5i4uL4UwsdAl920dqMjYTjlTRsf7Y\\nH1cPsYVvtVqN8IsD8Qw4mDMOGDZBWFVvfCM22MFNgs6OGGRc2A7mAkxXtdtGtFgsRudbmag5xFuf\\nXOZuogayBrAMAV5Vo+20DiwSiLv60POSONTEqKuJ8wD7rpoK22E86CAnk0ysBc/LIN7BAnNjAocx\\nMFYqSpA5sM3Z2dkoCZeE6tSN+1tGIWp+/PgxYIGzs7MBtySZjZ1ivMwfPx38ed5ms9lwuDC4Dlte\\nVW/WzWtnu2aiqCNmLFtJklqvk/Sh2Z9m4tGkDXqXCc9DB/jd1kaP1VjEa+a1cH+7+cu+JxngufT3\\nE/MaF+cRGXmfHA8/O/IMOUubsS/Y972YH+ubdT3XEf81dSNGr9oReuDOp6enoSrEWNGkKZjW8+gx\\nvL6+jqpIjXeQ28T4vtA920y+bx2nf4lp6B9brTMW8jp0iZL0mfj6LM7w1v7Ly8tB9tfr9Zt4/L32\\nS0SNDchmsxleR7xarQbgf3V19WZ/VtVOAGF4XYqPEAAWIWrS6Dgj4EDbYKIjEAAKVtAuc+IAJ9s+\\nEsfjdKnwfD7fW3Jvp0CQhPEi2DhUtsJbeugLGbyHh4c6Pz+v6+vrkUEDgGZGh8ulX0nGoNAEVAnS\\nPRdWwMxQJJnjszuq/r/2zm25bSTZosnLuEWJot3z//9oixJ1p3ge+ixwYbNoa2aIme6OygiEZYkE\\nUFl52bmzUBgHLxze+sNZvFdOEnQmeHyPnNvL9Rw4SfYmas6B90uIu/c89gXA4tXdX79+HTHK1ilj\\nMsli30EvZspd/CZRYwBvXdpPMln5nvwYE+MxGXN1dVU3Nze1Xq9Hc5ZsuQNzdqB8PRMbnIdz2VZ8\\n71PI09PTCWjwCsDb29tRAeTOn1erefmrV7exIg2dUBDzumN38V0MQ4aS1Lhm1fgNE15Vwfz7Z4MR\\nzsG9E2edZA0+TGhn4UIeyQ3jiJ2cx51fd1cvKdgsgGU+nw/xlA0q0SEEPD+7UYEdVv1hf26CZD4y\\nqEviJIkuE3YZ1wxcnXv9BgR3nwyOMjcR++z3tgf70TmiZr/fj/bf2u12w99Muk1F1DjekJeJ9exZ\\nRXEIIQ65ZH+w3q0Td7EPh8OoU++VMBTM3sSSuJvi+A0Q5dFYF//GTV7xnIVsqzh00ekmFWLydDab\\n1WazqdVqVZvNpu7v74e4Rs7hPi8tgG2PC9KB63/58qWur68HHRD/slBKgsR+ZJ911zWvTRx+f38f\\n+VXmrdzjJ7vKiVFNZGADzrXoGewBiV11+rhz1bHA9Z4c5L/FYjGsNN3v94Ofmgy5tBgvEyPwlcPh\\nUL///nstl8uhQDTuY+zkliQi+b8LP+vAzQ2+Rw4iv+SRhE0S2xYX8UnE2d+IJY6VxjO2CeMZE6Pg\\nNPCP8Z2J2Sny4v39/UnjOkkMEzXgzMxNzidVY4IqCeQkTY03uW7GtZxvEyN5bUvmuBYJl01MX8e6\\nOEfUnKs3GH8SOFPMo/EH43FDcDabjXI2+nXOo+ZEB2kH/B5bB5dTJ3iVfOYkNxqTgHR94ZrJ5ztH\\n1GS9mCtaPRf208wXScSiox8/ftRut6vtdjsQel7gck5+GW3tGNwMBbq7PgCTLLSZDAA0AL3FQGeR\\njEKqTg08i4B0Fk+cDYJxIP45WTOfPw9/x8bmvxuceQwGSBiFjfBckPhPxPozuWD2kACK0ZgtdPHk\\nbqeXzdroktxxwLY+MgFZx+mgWfR7zjjXbDYbBUuTcEm0eZ44L86cnQo/N217zQ5G1XHl1VTCPFaN\\nn4V00jbBWlUj4oak53M5AJslz31EfD0nCwtzBxh18iMIE4jdybfOALcUwgYbdGZbwMlEEuPmb1k4\\nYTfMYRIHrWL5EkL8Q1e+D+aQPQIyLnKv3lzRvyMe+zGOXH1TdYxJ/puL+ozfWRQ4zhuYtECJCx+D\\nMfumi1F/x3EHAh5y0qs9WwDoHGi+hLRiEveepDbXT9IwcxHxxGDe43P8TH2nTvnX8+S5dGFw7tGM\\nnE+u0wKg/I3fZ5z2Zzy3CYA8ngSLUwigr0VoJxhOsG+7bxVQLh7sQ5Cy5JWXl5dhfw7+5qLLWMf3\\n4k6h9xJygZfzYNtI7OJVTdnN5DyeP2PBqho2K82CDAC+XC6Hx2kuKS0smI0kVislaDfWPFcYuqjg\\nes6z+JEJSH+P89FEahGerbk5FysRxwDynklY471WLMc+vaKGuIT9OLeQez9TWPw7kveJntOnzuF0\\nYoRtmEcOU5/WQcajFibKJmXiBA7bAfPuuU1cDIlgm8h6gu9nkevVzvgauCAxqq83ZUzl3pzb0o7x\\nTRPHmfM9Lzn/zinGT3l4nvPcKWlXSQadk5yftCUE/+ezOT7Em3Ub1zPPHMYAU+VGjyttyL6QZEo2\\nMGzLWQNnLQyx6GaicxmrW/04MffmfEwM4DysxGMltLdJ8PWJOYmZM5YbGziWJAlrW2jZSiuWteSX\\nRA3Oxo1yI/P5/OQVzwY9Bj9MFB1cFAnjagXkzZN8PMgEU1lM8V0z61ncJxBrgV6+l92lDA4OJiQY\\nvpuTa8bORTDjdBF9SfGzhCZF3Dl/fX2t7XZb+/1+xD5m8eVzuLjMOTCYbBVxaaBJuPj7dKY8pxmw\\nk3zJpMR3W3OYAcb3lJ9nnr1yi65PFl+XFAI0NsScLRaLWq/XAyu93W7r4+NjIEEgPOi4Aia5Z/sd\\nZJUf5cgk8f7+PnTfKL6ZA/s6dgyxwuMD1rWLDM+pH3vi1aDMKedrPa7B/ODbBuUfH8c30DH+Fthy\\nkG51s/9TObd6y7b2/Pxc379/r9fX4+aq8/l89Ahf60iAYD/KvX6IZ8yDC/fsmOb3DC5d2FeNY63j\\nAzHHpE+SVBnPsUGO3KzcyR4y0deZiqhhBRJjwfdms9lJXjwcxqu3IHszqVfV4AfYKzrKxP8zOUek\\nVY03C863ObXI7yTSyP2c077DfXl1AbHSRRHfA0MAsvb7/eCT5Cbnxc8Amn9ViIlVx0cvGEOu/slC\\nmhiDfgzwDMTI7+ghu7fEcMBirmBMsgvA6tWIAFpjrqoauo7oDh8j9i8WiyGv5upEr0bLOTQhagLq\\n7u5uWNrN4SKfR48uKQbczuf4Ic3Bh4eHms1mwwbIjkOM8ZykLyZpkE0LFxfEMK5BPLJ/GR8msdD6\\nHUcS6y2Mm3jJ/seqKK8CapGK+TjpFL7IaiuPwUSD/TR153yyWPyx59bt7e1ohb7H1iK3Wpg+bZzY\\n7Tnz/bpwPhezIeqxI+4Z20gf9veTjDxHYPlf43SPZ4oCPwtgH84XxEvXVUmmmBTw733fiTnsC/43\\nm0+t7x8Oh9HqSDfHnEs9/9ZxqwDHz+zbrVjSIgDJDX4DoX3aOerSwjnJFbZPasmXl5fabrf1+vo6\\nYHF0M5sd9wOjlnBM5G/YM9iOGGSi1fmI3/kNS/yd87kG4bi9va3b29vhrWN+JBXbg6ixL7nGsw0k\\n1jbetF1AdPtYLBZ1fX09wlK/8sVfEjVJiGBMgAWCDs8Du7MNQGXSXCzYkVoAkcH7X/+MEhl8VZ04\\npIGGx+NxOQDyWTuYz/UroobA0yJqqo7MHcubXRQ6YSTpcQnJJfKMD0egCGTJMhsfOQi0ApLH4Pki\\nCVJ4JMBoFRtO/iQTlizzenE/3mJJdjkL1tYKLgc+kwcAAu7T18qEbqIGPTPmS4tXMrUIj+Xyj7f9\\nAJb9Vo6qI1nHufzICzp0dzA7GV5e6x3xPQcu/G0DnKNqnFRd7Ps+WkSNu1ZJ0JiocdfJqwUIkozf\\nz+U76TlBTkXUuItddbR3YuPLy0v9+PGjXl9fBz2wD4sTeib3jJ+c88uXL0McNlhNUJPdc99z+lAu\\n987zMi6KAQMe3x+kpx8J8edsdz5y7CYO/xtEjZM514CowY5YbUDhyN9M1jCPJnSSQM0i2eCulTdT\\nx/49/pnL9lu5zWCSe7AdOP+5ACAPtjppPg9EjQtV4wsXalM0MK6vr0dz2rLtqvEm5K08ZqDJfTIf\\niQMcU21H9mkDfotj6mq1GkAnj6m1MBD5DLxke0sA7OZM4qEkKTzn2DqAlp/96JPv5ZKS5IQJDB45\\ngqhxTncsbMWkxKWJg60TPufvYjvGdC7yjKGII7Zz5j4Lv7SNxNG2Fc5/TmeeK3fruS4rF/1739sl\\nJR+Lwz7RORjWj3eil4xxV1dXQ8w11uG8GTPzZ8+1c0zavm3Bds55HKOzKMP/+awxgWOo7Ro516RJ\\n/WV9wj1OSdRwn7bjVvFKzjHu4sjVDJkPkRyLdef8n0RNy2b4mbwFacPvTN7Yd5NcSR9lPK5V8Vea\\nOX6E1GTA6+vrgFNtsz97zO4SYqIG33PTs6oG/O+9WVjl4rnwPTsP+P7f399Hb3dzXWAMbxxqPMM9\\nmUxzXbDZbGq9XtfNzc2Apd3AdU60vULU7Ha7EVZq5WfjIds6eZA5nM/nA+74bBz91IoaHCuLLcD/\\nfr8fbsIBw6zafD4flImxc6M4TGvwWfwnUeOlt+5aOijawR1gs8g0027Q6/NlQvS5EpC2AiEGyWZC\\nDvDc3xTP/+azqiaimEeIGoCOE2cmN4/bCcesInOOjSSgt51YDEAganJD2fwOnze4zMKFuSQIEzyd\\nRAkCFAsJ2DJxA8555pakPYW4k8Q939zcDDqiKCa4exkly/9cXBpY21+sA8+bH3MDwHk+HTj5HT87\\ngKHPvJ59HYKJuXdCxDZbjz/Zhw6H0/1ssI/39/fa7XZ1d3dX2+325FWzXrlxafGY3Qng/xA1xDcD\\nHm9wlkCyRdZwPcZuEoDkmbpB8hxJMH2WqCHZmRBNUgj74s1j/hx252LCqxeSsEq/n4qoQfcs2U0g\\n46KLpob32DJwzAKTHJm5i8NFcEqr8PQ1W+CnFTvz+i5wAW+ZU9JGMl/bTimkDMghjh2PfI5Ly9XV\\n1cnqOZpJzBWFNjnLfsa/+ehB5ir7ZKsgASRm8dkqLgC6xMecR77HfOS1TIbi946l2A336vlNgph7\\nQgdeKewOIqt+pmhiMFaOqjFBD/54eHgYcBpkfys+nMN4rY63i9IsDJNk5D6SHLBujbGRjOueE7BW\\n+rzv2+Pwzxy5v4nziuOu9TOFL6ZtZBwyUYOvGiuaIMPectVzi+hK8Zzg+94zpEXWcj7yKzoyNs5V\\nasREz1uSrY77+CznSgyQY0p78ernVhP7EtLKCVnDmbigSG8RII4h5+ap6vQx5CRgjCX8PT6TOZK4\\nCdY0YeYDP814kJ9lvPhZNjN4QcLDw8NoxQg6oF5MjEUNMEW9yHx4bOQYciZ1BhjZBKRrf+eSbCyD\\nz6lHeJOn8aXxfXIHnIN44PjrGsEkDatoGY+fbLFNGLs+PT2NmmiMJ7G2v+t5f3p6Gr0NlJo789Y5\\n+dSKGiaOwDE6wf87JhsH+eDGDR5QjsGZr+VExqQ7eeTAbBB2Risu/81i3wE+E3wKv7M+kin3WJzc\\nAdcY8q/IhksKem45kIEdRsXYMrlxj14u72TE2BGAoMEKksDGjybN5/OhY3hzczNsNovk20laRAPJ\\nzIRMAmAOinNAtkGnE30+dsL5sBf0MUWBz/P9jMOkCKts0CtzWVVDUcXvKUTW63Wt1+sR2WS7yNUk\\nLZ/m//gO4JHgS6Lm81n8OVZ43iBoONzVS99B3y0ARTHLzyQWFz3WTdrJFEnQvpjdn1wxRtyYz+fD\\n0swkgp30rQP7XAJ9g6hWXGc+fG8GMui8BUoMjEzsW8f4DOPNe/Y9eV+aBKQGufZpvp9L9i8p2Ief\\np+ZnAKY3Ns7EnKQvwKAFTLOo5zv4HeDGIL9FrrFEOd/q5MKCGJj5DD1mns/l94hjpzfnOwe8uRcD\\ndGwUe7m0OKZYb4yX/Jevk0Uciww6s8hwTEGyyEuShvlxh76qhrlzwZyEA3Zl4oRCMouic2O3PRiv\\ntX52IXiucMLep5KWbo21ZrPjEvvHx8cTHeOf6APbZfVzC996XloxFLsF+3rPBd9ngv4sCE36Wq8U\\nKrlqIG3V53UsyUebHVtbhE+O/ZIC/uA6SXrZJ52TGHOr4bPf70fNmtb8IdlMtA9g3xSsLhw9F94r\\nCiGOuiFiHOrVG6wmSEyEtPJ15l/bCuc0qVd1uuL8UuJ4x5w4vpmgAYcZLxuPG+N6DmwXfAbMwGeI\\nw8xVjtX6Rx8mtJyv0u+tQ9ehSfhknem54ufEM+jI54d0aMWZzxT5/44Yj9q2sUXnm8R11qV9paqG\\nFewvLy+jJiExknkz7vQ2B3AH4A03/Xzf2Xh6e3sbYv7b29vw2DB7w5mo4buOt8635FKv1PO/53yz\\n6lirMK/4/q+aiZ8ias4V976RVqGUgWg+nw9Ow0C9hBTDtYHnM3oOzHZkFJpEAP+eI2rsWMnenWO7\\nWkRPqxBlUnJzQBci5+7r0uIA5+451/M9G4DjHGbgra+q4yspPfYEQO5YZmHKEjXvl8Dv/cYE5HA4\\nnLCs6AzH5sBBTSZ53jlIIARKkxetjqITdxb0SVxdSiBq0sbxJfsGvkPScYH99evX+vr1a63X67q6\\nuqq7u7v68ePH6FEmfDGTIjpOQAgQRRcuJs2co18XAE7ezKlJGu8nkN1q9G1SyYWBH2/KgggbIEmQ\\nbBj/FIRp1emGe4yltTIFYEpScuGNrvmsi2ADoxZo529Z4FnwzywKDf4cv9G57xN7ciGUq++yIMgO\\nFQQbb1NKcgvdJUnjHDOFZPzK/ZZM9qb9WhK4tWIlOZaCykSMH+tzjM8NYXPVjws85wDbBTptrSJI\\nHf/s8KNB+YZAF2Mmana7XVXV6J4vLY4nJvyrjmQH+7lkzvB9E7uIfbyxirxo0O3YlTEJwaYZu22F\\nnMz/bfc+p/XOowYZhylE0mexxSzks7hPki6JmrStKYka9JYYzPGUzi2xyMVC61GM19fXYfVzC6M5\\ndqF7i/da8ApEz7+LG85lXzOZW3X6GHfrsM7TZ8GlfiuW5yZxMd+bkqSpOiVqIJYpVE1+469+VM9F\\nj+cwsShj9L/EMT/+lYVx4h+OjLHZdMRXkxjP2iULd2MCzuGaJAtCz2PWLsYz9o9LC7nJ8SgJJOMU\\nbLFlv+jF2JXzZ52ZmJ45MJFiSaKmhUeNYVu273nI3NAiTJPscV7l/8ZWfA77N5GYep1C0GFiCeNV\\nxk2stE3Zh29ubkY5lp/9KBH5zqvlyHdshUBdg/7PNVKzJuc7buoyjnw0Ep+zv7vWNeHId3lT4+Pj\\n44DJcvW6/fvt7W2ob1nd+TP51KNPCaRQBEpLMOqJNjHAhJMwq45AyUbnBIQSSJYGmSgxg65/bv2b\\nn8OxOK+LfDOqre9kkYOhMhY6MgA3n6N1L1MSNQYKLvAwVgMzyAqKEJwluzkEmywQq46779tuDAop\\nHNgQ0UvS/BgbydgG77+bRJjN/tgjgpU4dOHZ1BM5VxCb8fXeMx5DrvKyI2cxeknJV1gT1CFjDBRy\\nqSufxU83m03d3NzUt2/f6u3tbdgUzEESRtx79mRCdNBGT+6AONCSbBwzeGyRFQHYgx974pEtF1Xo\\nOufD9/b+/l739/fD400uVA2EmDvAms8/1Yoa/Aa7OpfYTUJh216yb+AHiKk6xh8nrIyznqMEfXyP\\nIsN6xr9tK0jmCz7r+wIIEx/8XXcDuV/8crfb1Ww2G+bQY+fz7oZkgXNpMVHDUnsIG3dd8EP7KfNn\\nUMtnWnEHsAHRYXKbz/FZNxwcJ304VhjQEMsdU5nj7NSa0LbeEOMAAA54SURBVAe0+Hn7bMD4lckG\\ngTxihD2QK+mA0eCZyhezwHMR7eItwWXmKewAHTJnSdT4Ogb7jtXEJvBI1THemTy1f+FvjhvYC3O6\\nXC6Ht+h5T51zRA1zm5sc54oa8qDt2aSXbWhqcfGdBAv+hd16/oitXvXA/gRZ8HusWXxx7aojycge\\ney0C5JwNOB7yGcfl3E/IxLpjIPdlUpCV05Df6A27sx8Y001J1rgbDcajEQd28OqLqvFjvS0ymjyT\\n5KHrmKoa/OH5+Xlks8Y7SYBBfnhVk+fX91Q1Jmp8r16lnY0PMBENbq/SRE9IixAhvnG4WJzCFx1P\\n0Z2v5yZA1lQZT91QZo5M1OT4q8ZNYHKjCRnryr9PMrpFQLfsPn3nZ2SN7YbvJD5pYStshntKX5wC\\n3xAHjDOsL4+X2GICxOTpb7/9NrzshLlLkhx/d7zjZ15EQsO2qka25Dxjffh31LdsnO5cngQweZrv\\nYkv2U+MCavztdlv39/dDo/nm5uakbk0ClXzzq7ch/hL5ZGLAwTC41kobdy0M1NxhZ0K9HBFl+pzp\\nCHkk42rDZwLNNLuDlOfIn5msJHl+Fuww2NZhEsoTmKTBFERNC6i35tm/xyBT39YrjOl6vR4FJwpu\\nb4LqbrC7kKyaWK1WI/3ksjjr3MnODul7h3gwwIENraoRGeNVT/k7ExS+jsGZbXaqOUwbTWDI30iS\\nDiwQIh8fH8Mqmtvb25rP58NrOU1YoT8TddnNYdy5tNQFwsfHxxBob25uBqBsdp7x+C0mfsNXJjdA\\ndtVx1QKB1sBpv9+fMN28Rp77rDq+YQf7yoR0afH8cV3igEEI85rj5/f25QR2Bpb2W87hQtmf82Fg\\nyjUtGd8hJ10QGPRYvCLHRAzPKCeAJjdwHxDPLrItCVovLX7kkvt00yEfuYK0T4CSYM6P+3gcjjXZ\\n6VosFqMOoFf2ZHHJzya5W4UjRANgyEDVxW8W897HqmrcEXVcgEx2Iem8aPvjmlPMY3ZyrQvr42ed\\nWccMfJEcSCfRGML6d0x1DAK8XV9fD+QPfkTchQCHoPFb/BIcurHg8XBPzLUL1ufn58EeDVBN1Pix\\nKttk6qOqRn59SSF+O5amzu1n+KKbVwB29IRPGZ/ZTtxESp0aI5lUNnbMeJ/zb3//FfZ1bHe8c/73\\nPUKoGt8gSTyk3bcIuEtJ5hnbb2tlAz6R/urvVh3fVOcVhJkbTaJSk3AeY9HFYrxtA4Vornwj5rXs\\nwkUw8wGO4Xrct0nfw2G8qtJ5NkkGNypSx9bllGL9EgcS+3jOfH/4Fdgf/ZnsRM7ZZPqVfccxm9hn\\nHWZTivGkfflI3OLGmesL43Y+x7WMr6vGL55gPM5RGcsvJdkM89ygi6yJXBdwPD8/12+//VaPj4+1\\nXC6H8SeGyTqYz2ADzs3kvlbN7PnI85kbMJeRBCljtF9ls8J5ENKbld/Up9gr5yPfZw2X5HNLPkXU\\nuFuUDBRJy5uZQtQYnDFhPMbChK5Wq1FnBgN1we7HpVqgk+AJsMBhcjIyOZs1Q4EGNoyd+2Wi7VQE\\nAHSVgReywIW0ASnXn6q4RwB0Bud2LpNxjMVjMtFkoma5XNbt7e1JIb9arYZXorH5VK4OqKrRpoic\\nw+DCAMmEH3PnIJaEH04D2WRAS/Geq50MDtCNE6Bt0IVWi/i7tGBrBGuDEfTgDqxBIX5YVfXjx48h\\ngPAK6NfX14E1NtEKaeKVJlVH8OO3O3HQhSQAAUooOPxZ9Ej3jJU3XnnnQt2BlRjjN4wQq9ABhX9V\\njfYYIj5wXRfWBslTCBuJMaeODZl4UohjPMqQ3fXlcjl6k03VeIO1BDP2c5MAjo2Oy7Y327vJM/yG\\nYqe1vxBJnJ8dM10cAE6xNxMe2Cd5xbEjj0sLb8Wj8KMTiz6Il63Ooo8Ef/jF1dXVSSxiXrzyE7Bv\\nEOKOsu2MYsRFSxJF3Edu7OzHN6xXF+sJagzGEjQ7z9rvTFSw8oN5582SlxRWpyXYRyetRpQLKeKS\\nSUfyEasBXTxBwDB3jLuqRvpbLBbDqtDr6+t6fHw8WTm5Xq9rtVoN+enp6ekkx3tOub8E+Z6Tw+Ew\\nerU2xSi6SYDqAhPwjM0TQ2yfUxQVxh72g8SMzCV2y1xxuDkDzmUebAPMIUfGGccBE+luNLlYAZe4\\nY54EDJKFh5uJYB38MWONC0qKxhbeYj8MiuUkq/DpS4tXtnJNE0vGXyYoTBZnQ9I42/nKONxYEvxg\\nezU2XiwWo6cHiMM0JC3Wq/GE74FcQaFHfcJ8ur5wUZ85jdjgeoNrcn03S6YiaUxGGVPwN+Nmk/KM\\nD92abKImQf95Pa/Esb37GiZq+J514frEPye2dc3U8i/XOEk0umFlPz5HvmU94f/7/FPhm8QtXLNq\\n3OCwHs8RWLvdbqijEdfD6IjzOI84bvN5cqjjWtq0dWZ+gdW61IKOAy3Sk4Mmk7cwcS0C5iNeVY3J\\nmcz32JM3ZD4nnyJqKLRYvonSHKhQ2sPDwyiwvr6+jpaVPj8/nywTZ4AM3sU310aJVj4F3nK5HAo1\\nlAY4NLCkUOP8vE3Ggc+OlV0Tr4wxIEXp6Xgel4sxEgLioDJVcchqlZajA+CdgDMYZRfIxs3KDIOE\\n1WpVm82mvn37NrxeOUE688tqGuzj6elpYKNN1KBHliyjd2zDydtz6ns2SLm/vx8O5tu25vOx18nH\\nx8foUbAEBa3C51LiZIOzE0BN1OSjKmZt0cl+v6/dbje8NcqB7/n5edjXx5uqJbvvgs96QE8EK8cP\\njwE/YI8NEyfMbwZB7t2FAnPDzuyszAF8e/UNBch8Pq/NZlObzWa0oiYJiSmEhGWQ7ISc/1aNN3h2\\n4e4YaAI6AUv+nXk0gDApv1wuT5b/toCuSVIXe+jQK9QM+A1qsFEnXYtjD0tNOZ+TYIugmao4XK1W\\nIyCS5K5zFEQDh8fK+Pi/C0AXb+TaVu7z+exT/M0AEX21VlIxZ4fDYfQI03w+H701gcaLNwl2cWiA\\nnXOeNg8QdC4ib6d9TgFIveQ4SUuDRYpGi+8bu6TD7v21/KY84hMrZeznfvQWfHJ7ezuQPY+Pj8Mq\\nR4is9Xpd2+22np+f6/7+frTKJjuv5D7bJ3PuVQLb7bbu7u7q7u5uIGD8mmtjH+zA4BoduhhyM26K\\nOUyS1vHP5CZxh/lyrCX3PD8/D2T6YvHHpvu2T8hwiLSMY8YBLiLSlv2GLWNKrmH7s3hOTSyScx8e\\nHkav880iKzGSCxNjCz5bdfqGxiyYLyHEKxMr+FbGOObW5E3mNjeB+NfzbdyCrrM4I075UVJ0DamC\\nv0Ouo+u0R+vVG9C/vLyMSCDE9uIVcfxrAsFEjWMr9+pie0qMalKXOTz3/7wH12LEfueDbLy74Qbm\\na/liEjX4G/P0swaPYyX3hOQcc9/4MPHO17IOXC969b/tnPGm3pApsE1VDW8/bdkvWKRqXPd6Hjkg\\nRYjBYBtI/bQN/L/VDKcGsS3YP5IMTWyTNSjbqlB/upH/+Pg4PFlg/sA8g+M6zVM3nvb7/Qh3k1Ps\\nl9Q3JlFb8mmiBiUTzEjOsFskLH5vUgTWGDCH0zBhKJjCEBbKIM9BnMOdeAAoActFetWRrX1/fx9t\\nUJbspwF3VY0Y9iwUDVLMuBrIZOJxwiPYuisyleRz7k58AINWkV9VI2dwkvOmr1dXVyNwv1qtar1e\\n12azqdvb29G1bPzz+Xx43AVbSL1xPYP9BBCeQ7qLAF7vGM787Ha7Yf8SACkrEdyh43wAoPf392E5\\nu5c0e8XCVISbC3p8xrblIFZVo1UwBCkS4cvLS93f39fV1VX9/vvv9c9//nNYJYD+6Lih1wQ81r0f\\nt5jP50NwZo7wIwf8vGcTD+jQ920bMkGz2+2GfaywSQpF2zFEzffv34eYtl6vh5hm35hSII9NbhC4\\n7V/oLsGMu+4UT6wMgHSzzxqUuvv0j3/8Yyi27E/MlWNZ1XifJs+dARbnMbEEWeM4SVFhMuBXvuP4\\nSkfEdvWz49KCjrOji84hc/0IAfkNwX9dwGL79hX83uAlu/GQjVXjx1zdpTeJxLmsa9s/Or6/vx+A\\nBRgAm+FRNXKdwYsBF3kuu48uHH1txwGTP95s9FLi1bqOSwZ4JmrySNKJmEVO+/btWz08PFTVcZ84\\nHgUl3jIX7MlTVUNehBBgY2UX8vyd1TaPj4+D/jwO9Ap+gbywP9tPHx4earvd1vfv36uqRitKbON+\\n3K2qhnhrW3LTYyqiBnzmGOImhZsY3B8Y1vfK3Ly9vY2aMWAnF2Ng3s1mMyKvjFkhlvNxiiymTUgy\\nv8yLsQ1iHGSbpcgA27S+x+9cvCdxZZvMRtCURI1JfwS7SX0xH27u0txL8g0/yUaD86LjqHMsGMrx\\nnL87V0K+OpZ4zk3QV9VgW7x5pup0f0vyhd846nN4LrAr8lGuEuA+zsX+S0mLhGk1BFp5Bz9h1SB/\\n8zyBzb1C10QNPkQMQDJGo+dWPLf+sUHGRq5yHWLdgreIL1kHo4usF52nOXf6n+3XpMlU+Mar8jK3\\n+z6SWMU/jAk/Pj7qy5cvtdlsaj6fj4gaz7XP5VqGcWK/LPSAV+Az1pNtzSS0G1eMgXxqAod8yv0b\\nr5pY4TvWF9cxb5J1E/EF8udnMs1rTc7IlETEvyp/pnvp0uXPIJ/xian9pvtlly5/L+k+/a/J1ERx\\nly5dunTp0uWvIbOfgYLZbNYRw/9QDofDRRBun8f/nfQ5/HtIn8e/vvQ5/HtIn8e/vvQ5/HtIn8e/\\nvvQ5/HtIn8e/vpybw58SNV26dOnSpUuXLl26dOnSpUuXLl3+e/JfffSpS5cuXbp06dKlS5cuXbp0\\n6dKly3npRE2XLl26dOnSpUuXLl26dOnSpcufRDpR06VLly5dunTp0qVLly5dunTp8ieRTtR06dKl\\nS5cuXbp06dKlS5cuXbr8SaQTNV26dOnSpUuXLl26dOnSpUuXLn8S+T97gzP6gWJNGAAAAABJRU5E\\nrkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1bb23198>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"encoded_imgs = np.random.rand(10,32)\\n\",\n    \"decoded_imgs = decoder.predict(encoded_imgs)\\n\",\n    \"\\n\",\n    \"n = 10 \\n\",\n    \"plt.figure(figsize=(20, 4))\\n\",\n    \"for i in range(n):\\n\",\n    \"    # generation\\n\",\n    \"    ax = plt.subplot(2, n, i + 1 + n)\\n\",\n    \"    plt.imshow(decoded_imgs[i].reshape(28, 28))\\n\",\n    \"    plt.gray()\\n\",\n    \"    ax.get_xaxis().set_visible(False)\\n\",\n    \"    ax.get_yaxis().set_visible(False)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Pretraining encoders \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"One of the powerful tools of auto-encoders is using the encoder to generate meaningful representation from the feature vectors.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Use the encoder to pretrain a classifier \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Natural Language Processing using Artificial Neural Networks\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"> “In God we trust. All others must bring data.” – W. Edwards Deming, statistician\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Word Embeddings\\n\",\n    \"\\n\",\n    \"### What?\\n\",\n    \"Convert words to vectors in a high dimensional space. Each dimension denotes an aspect like gender, type of object / word.\\n\",\n    \"\\n\",\n    \"\\\"Word embeddings\\\" are a family of natural language processing techniques aiming at mapping semantic meaning into a geometric space. This is done by associating a numeric vector to every word in a dictionary, such that the distance (e.g. L2 distance or more commonly cosine distance) between any two vectors would capture part of the semantic relationship between the two associated words. The geometric space formed by these vectors is called an embedding space.\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Why?\\n\",\n    \"By converting words to vectors we build relations between words. More similar the words in a dimension, more closer their scores are.\\n\",\n    \"\\n\",\n    \"### Example\\n\",\n    \"_W(green) = (1.2, 0.98, 0.05, ...)_\\n\",\n    \"\\n\",\n    \"_W(red) = (1.1, 0.2, 0.5, ...)_\\n\",\n    \"\\n\",\n    \"Here the vector values of _green_ and _red_ are very similar in one dimension because they both are colours. The value for second dimension is very different because red might be depicting something negative in the training data while green is used for positiveness.\\n\",\n    \"\\n\",\n    \"By vectorizing we are indirectly building different kind of relations between words.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Example of `word2vec` using gensim\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Using gpu device 0: GeForce GTX 760 (CNMeM is enabled with initial size: 90.0% of memory, cuDNN 4007)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from gensim.models import word2vec\\n\",\n    \"from gensim.models.word2vec import Word2Vec\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Reading blog post from data directory\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"import pickle\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"DATA_DIRECTORY = os.path.join(os.path.abspath(os.path.curdir), 'data')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"male_posts = []\\n\",\n    \"female_post = []\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"with open(os.path.join(DATA_DIRECTORY,\\\"male_blog_list.txt\\\"),\\\"rb\\\") as male_file:\\n\",\n    \"    male_posts= pickle.load(male_file)\\n\",\n    \"    \\n\",\n    \"with open(os.path.join(DATA_DIRECTORY,\\\"female_blog_list.txt\\\"),\\\"rb\\\") as female_file:\\n\",\n    \"    female_posts = pickle.load(female_file)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"2252\\n\",\n      \"2611\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(len(female_posts))\\n\",\n    \"print(len(male_posts))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"filtered_male_posts = list(filter(lambda p: len(p) > 0, male_posts))\\n\",\n    \"filtered_female_posts = list(filter(lambda p: len(p) > 0, female_posts))\\n\",\n    \"posts = filtered_female_posts + filtered_male_posts\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"2247 2595 4842\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(len(filtered_female_posts), len(filtered_male_posts), len(posts))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Word2Vec\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"w2v = Word2Vec(size=200, min_count=1)\\n\",\n    \"w2v.build_vocab(map(lambda x: x.split(), posts[:100]), )\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"{'see.': <gensim.models.word2vec.Vocab at 0x7f61aa4f1908>,\\n\",\n       \" 'never.': <gensim.models.word2vec.Vocab at 0x7f61aa4f1dd8>,\\n\",\n       \" 'driving': <gensim.models.word2vec.Vocab at 0x7f61aa4f1e48>,\\n\",\n       \" 'buddy': <gensim.models.word2vec.Vocab at 0x7f61aa4f0240>,\\n\",\n       \" 'DEFENSE': <gensim.models.word2vec.Vocab at 0x7f61aa4f0438>,\\n\",\n       \" 'interval': <gensim.models.word2vec.Vocab at 0x7f61aa4f04e0>,\\n\",\n       \" 'Right': <gensim.models.word2vec.Vocab at 0x7f61aa4f06a0>,\\n\",\n       \" 'minds,': <gensim.models.word2vec.Vocab at 0x7f61aa4f06d8>,\\n\",\n       \" 'earth.': <gensim.models.word2vec.Vocab at 0x7f61aa4f0710>,\\n\",\n       \" 'pleasure': <gensim.models.word2vec.Vocab at 0x7f61aa4f08d0>,\\n\",\n       \" 'school,': <gensim.models.word2vec.Vocab at 0x7f61aa4f0cc0>,\\n\",\n       \" 'someone': <gensim.models.word2vec.Vocab at 0x7f61aa4f0ef0>,\\n\",\n       \" 'dangit...': <gensim.models.word2vec.Vocab at 0x7f61aa4f23c8>,\\n\",\n       \" 'one!': <gensim.models.word2vec.Vocab at 0x7f61aa4f2c88>,\\n\",\n       \" 'hard.': <gensim.models.word2vec.Vocab at 0x7f61aa4e25c0>,\\n\",\n       \" 'programs,': <gensim.models.word2vec.Vocab at 0x7f61aa4e27b8>,\\n\",\n       \" 'SEEEENNNIIIOOORS!!!': <gensim.models.word2vec.Vocab at 0x7f61aa4e27f0>,\\n\",\n       \" 'two)': <gensim.models.word2vec.Vocab at 0x7f61aa4e2828>,\\n\",\n       \" \\\"o'\\\": <gensim.models.word2vec.Vocab at 0x7f61aa4e28d0>,\\n\",\n       \" '--': <gensim.models.word2vec.Vocab at 0x7f61aa4e2a58>,\\n\",\n       \" 'this-actually': <gensim.models.word2vec.Vocab at 0x7f61aa4e2b70>,\\n\",\n       \" 'swimming.': <gensim.models.word2vec.Vocab at 0x7f61aa4e2c50>,\\n\",\n       \" 'people.': <gensim.models.word2vec.Vocab at 0x7f61aa4e2cc0>,\\n\",\n       \" 'turn': <gensim.models.word2vec.Vocab at 0x7f61aa4e2e48>,\\n\",\n       \" 'happened': <gensim.models.word2vec.Vocab at 0x7f61aa4e2fd0>,\\n\",\n       \" 'clothing:': <gensim.models.word2vec.Vocab at 0x7f61aa4e22e8>,\\n\",\n       \" 'it!': <gensim.models.word2vec.Vocab at 0x7f61aa4e2048>,\\n\",\n       \" 'church': <gensim.models.word2vec.Vocab at 0x7f61aa4e21d0>,\\n\",\n       \" 'boring.': <gensim.models.word2vec.Vocab at 0x7f61aa4e2240>,\\n\",\n       \" 'freaky': <gensim.models.word2vec.Vocab at 0x7f61aa4ea278>,\\n\",\n       \" 'Democrats,': <gensim.models.word2vec.Vocab at 0x7f61aa4ea320>,\\n\",\n       \" '*kick': <gensim.models.word2vec.Vocab at 0x7f61aa4ea358>,\\n\",\n       \" '\\\"It': <gensim.models.word2vec.Vocab at 0x7f61aa4ea550>,\\n\",\n       \" 'wet': <gensim.models.word2vec.Vocab at 0x7f61aa4ea6d8>,\\n\",\n       \" 'snooze': <gensim.models.word2vec.Vocab at 0x7f61aa4ea7b8>,\\n\",\n       \" 'points': <gensim.models.word2vec.Vocab at 0x7f61aa4ea978>,\\n\",\n       \" 'Sen.': <gensim.models.word2vec.Vocab at 0x7f61aa4ea9b0>,\\n\",\n       \" 'although': <gensim.models.word2vec.Vocab at 0x7f61aa4eaac8>,\\n\",\n       \" 'Charlotte': <gensim.models.word2vec.Vocab at 0x7f61aa4eab00>,\\n\",\n       \" 'lil...but': <gensim.models.word2vec.Vocab at 0x7f61aa4eab38>,\\n\",\n       \" 'oneo': <gensim.models.word2vec.Vocab at 0x7f61aa4eac50>,\\n\",\n       \" 'course;': <gensim.models.word2vec.Vocab at 0x7f61aa4eada0>,\\n\",\n       \" 'Bring': <gensim.models.word2vec.Vocab at 0x7f61aa4eadd8>,\\n\",\n       \" '(compared': <gensim.models.word2vec.Vocab at 0x7f61aa4eae48>,\\n\",\n       \" 'ugh.': <gensim.models.word2vec.Vocab at 0x7f61aa4eaef0>,\\n\",\n       \" 'sit': <gensim.models.word2vec.Vocab at 0x7f61aa553a20>,\\n\",\n       \" 'dipped?': <gensim.models.word2vec.Vocab at 0x7f61aa4eafd0>,\\n\",\n       \" 'based': <gensim.models.word2vec.Vocab at 0x7f61aa4ec978>,\\n\",\n       \" 'A.I.': <gensim.models.word2vec.Vocab at 0x7f61aa4ec080>,\\n\",\n       \" 'breathing.': <gensim.models.word2vec.Vocab at 0x7f61aa4ec128>,\\n\",\n       \" 'multi-millionaire': <gensim.models.word2vec.Vocab at 0x7f61aa4ec208>,\\n\",\n       \" 'groups': <gensim.models.word2vec.Vocab at 0x7f61aa4ec278>,\\n\",\n       \" 'on': <gensim.models.word2vec.Vocab at 0x7f61aa4ec2b0>,\\n\",\n       \" 'animals),': <gensim.models.word2vec.Vocab at 0x7f61aa4d8630>,\\n\",\n       \" 'Manners?': <gensim.models.word2vec.Vocab at 0x7f61aa4ec320>,\\n\",\n       \" 'you?]:': <gensim.models.word2vec.Vocab at 0x7f61aa445f60>,\\n\",\n       \" 'redistribute': <gensim.models.word2vec.Vocab at 0x7f61aa4dbba8>,\\n\",\n       \" 'omg.': <gensim.models.word2vec.Vocab at 0x7f61aa4ec470>,\\n\",\n       \" 'dance?:': <gensim.models.word2vec.Vocab at 0x7f61aa4ec4a8>,\\n\",\n       \" 'Canada)': <gensim.models.word2vec.Vocab at 0x7f61aa553b00>,\\n\",\n       \" 'came': <gensim.models.word2vec.Vocab at 0x7f61aa4ec550>,\\n\",\n       \" 'poof': <gensim.models.word2vec.Vocab at 0x7f61aa4ec588>,\\n\",\n       \" 'brownies.': <gensim.models.word2vec.Vocab at 0x7f61aa4ec630>,\\n\",\n       \" 'Not': <gensim.models.word2vec.Vocab at 0x7f61aa4ec710>,\\n\",\n       \" 'spaces': <gensim.models.word2vec.Vocab at 0x7f61aa4ec780>,\\n\",\n       \" 'destroy': <gensim.models.word2vec.Vocab at 0x7f61aa4ec860>,\\n\",\n       \" 'maybe.': <gensim.models.word2vec.Vocab at 0x7f61aa4ec898>,\\n\",\n       \" 'Industrial': <gensim.models.word2vec.Vocab at 0x7f61aa4ec9e8>,\\n\",\n       \" 'boring': <gensim.models.word2vec.Vocab at 0x7f61aa4ecb00>,\\n\",\n       \" 'is:': <gensim.models.word2vec.Vocab at 0x7f61aa4ecd30>,\\n\",\n       \" 'question.': <gensim.models.word2vec.Vocab at 0x7f61aa4ecd68>,\\n\",\n       \" 'long-lasting': <gensim.models.word2vec.Vocab at 0x7f61aa4ecda0>,\\n\",\n       \" 'sun': <gensim.models.word2vec.Vocab at 0x7f61aa5dc1d0>,\\n\",\n       \" 'CrAp*': <gensim.models.word2vec.Vocab at 0x7f61aa4ed080>,\\n\",\n       \" 'irresistable': <gensim.models.word2vec.Vocab at 0x7f61aa4ed0f0>,\\n\",\n       \" 'dont...i': <gensim.models.word2vec.Vocab at 0x7f61aa4ed128>,\\n\",\n       \" 'loss.': <gensim.models.word2vec.Vocab at 0x7f61aa4ed160>,\\n\",\n       \" 'easy': <gensim.models.word2vec.Vocab at 0x7f61aa4ed2b0>,\\n\",\n       \" 'wanna': <gensim.models.word2vec.Vocab at 0x7f61aa4635c0>,\\n\",\n       \" 'Gaviota': <gensim.models.word2vec.Vocab at 0x7f61aa4ed4a8>,\\n\",\n       \" 'nose': <gensim.models.word2vec.Vocab at 0x7f61aa4ed518>,\\n\",\n       \" 'slept': <gensim.models.word2vec.Vocab at 0x7f61aa4ed5c0>,\\n\",\n       \" 'hahahahah': <gensim.models.word2vec.Vocab at 0x7f61aa4ed5f8>,\\n\",\n       \" 'halloween': <gensim.models.word2vec.Vocab at 0x7f61aa4ed630>,\\n\",\n       \" 'shes': <gensim.models.word2vec.Vocab at 0x7f61aa553c50>,\\n\",\n       \" 'realize': <gensim.models.word2vec.Vocab at 0x7f61aa4ed860>,\\n\",\n       \" 'twice': <gensim.models.word2vec.Vocab at 0x7f61aa4ed908>,\\n\",\n       \" 'lift': <gensim.models.word2vec.Vocab at 0x7f61aa4eda90>,\\n\",\n       \" 'china,': <gensim.models.word2vec.Vocab at 0x7f61aa4edc88>,\\n\",\n       \" 'Standard.)': <gensim.models.word2vec.Vocab at 0x7f61aa4edcc0>,\\n\",\n       \" 'worried': <gensim.models.word2vec.Vocab at 0x7f61aa4edda0>,\\n\",\n       \" 'Opposite': <gensim.models.word2vec.Vocab at 0x7f61aa4eddd8>,\\n\",\n       \" 'chin.': <gensim.models.word2vec.Vocab at 0x7f61aa4edef0>,\\n\",\n       \" 'Garden': <gensim.models.word2vec.Vocab at 0x7f61aa4ebcc0>,\\n\",\n       \" 'guy': <gensim.models.word2vec.Vocab at 0x7f61aa4ebd68>,\\n\",\n       \" 'remmeber': <gensim.models.word2vec.Vocab at 0x7f61aa4ebef0>,\\n\",\n       \" 'fence,': <gensim.models.word2vec.Vocab at 0x7f61aa4eb128>,\\n\",\n       \" 'apologizing': <gensim.models.word2vec.Vocab at 0x7f61aa4eb160>,\\n\",\n       \" 'next.': <gensim.models.word2vec.Vocab at 0x7f61aa4eb2b0>,\\n\",\n       \" 'MATTERS': <gensim.models.word2vec.Vocab at 0x7f61aa4eb2e8>,\\n\",\n       \" 'rugs': <gensim.models.word2vec.Vocab at 0x7f61aa4eb320>,\\n\",\n       \" 'her...': <gensim.models.word2vec.Vocab at 0x7f61aa4eb438>,\\n\",\n       \" 'energy,': <gensim.models.word2vec.Vocab at 0x7f61aa4eb4a8>,\\n\",\n       \" 'recorded,': <gensim.models.word2vec.Vocab at 0x7f61aa4eb588>,\\n\",\n       \" 'pepsi.': <gensim.models.word2vec.Vocab at 0x7f61aa4eb710>,\\n\",\n       \" 'r': <gensim.models.word2vec.Vocab at 0x7f61aa4eb860>,\\n\",\n       \" '13': <gensim.models.word2vec.Vocab at 0x7f61aa4eb898>,\\n\",\n       \" 'at:': <gensim.models.word2vec.Vocab at 0x7f61aa5dc390>,\\n\",\n       \" 'cheaper': <gensim.models.word2vec.Vocab at 0x7f61aa4ee9b0>,\\n\",\n       \" 'children!': <gensim.models.word2vec.Vocab at 0x7f61aa5b6c88>,\\n\",\n       \" 'tree': <gensim.models.word2vec.Vocab at 0x7f61aa4eecc0>,\\n\",\n       \" 'met': <gensim.models.word2vec.Vocab at 0x7f61aa4eecf8>,\\n\",\n       \" 'one,': <gensim.models.word2vec.Vocab at 0x7f61aa4eeda0>,\\n\",\n       \" 'rejected?': <gensim.models.word2vec.Vocab at 0x7f61aa4eee48>,\\n\",\n       \" 'Marianne’s': <gensim.models.word2vec.Vocab at 0x7f61aa4eee80>,\\n\",\n       \" 'Icenhower': <gensim.models.word2vec.Vocab at 0x7f61aa4ee978>,\\n\",\n       \" 'day!': <gensim.models.word2vec.Vocab at 0x7f61aa4ee1d0>,\\n\",\n       \" 'leaving': <gensim.models.word2vec.Vocab at 0x7f61aa4ee240>,\\n\",\n       \" '2110': <gensim.models.word2vec.Vocab at 0x7f61aa4ee2b0>,\\n\",\n       \" 'kiss:': <gensim.models.word2vec.Vocab at 0x7f61aa4ee748>,\\n\",\n       \" 'nearest': <gensim.models.word2vec.Vocab at 0x7f61aa4ee780>,\\n\",\n       \" 'aimlessly': <gensim.models.word2vec.Vocab at 0x7f61aa4ee7b8>,\\n\",\n       \" 'sprint': <gensim.models.word2vec.Vocab at 0x7f61aa4ee898>,\\n\",\n       \" 'kids!)': <gensim.models.word2vec.Vocab at 0x7f61aa536048>,\\n\",\n       \" 'canteen': <gensim.models.word2vec.Vocab at 0x7f61aa5360f0>,\\n\",\n       \" 'weekend!': <gensim.models.word2vec.Vocab at 0x7f61aa536160>,\\n\",\n       \" 'him': <gensim.models.word2vec.Vocab at 0x7f61aa536198>,\\n\",\n       \" 'scariest': <gensim.models.word2vec.Vocab at 0x7f61aa5361d0>,\\n\",\n       \" 'this?': <gensim.models.word2vec.Vocab at 0x7f61aa536208>,\\n\",\n       \" '\\\"choosing': <gensim.models.word2vec.Vocab at 0x7f61aa536240>,\\n\",\n       \" 'Talk': <gensim.models.word2vec.Vocab at 0x7f61aa5362b0>,\\n\",\n       \" 'weeks': <gensim.models.word2vec.Vocab at 0x7f61aa5362e8>,\\n\",\n       \" \\\"You'll\\\": <gensim.models.word2vec.Vocab at 0x7f61aa536390>,\\n\",\n       \" 'goodnight': <gensim.models.word2vec.Vocab at 0x7f61aa5363c8>,\\n\",\n       \" 'skiing.': <gensim.models.word2vec.Vocab at 0x7f61aa536438>,\\n\",\n       \" 'KeEp': <gensim.models.word2vec.Vocab at 0x7f61aa5535f8>,\\n\",\n       \" 'week': <gensim.models.word2vec.Vocab at 0x7f61aa536550>,\\n\",\n       \" 'norwegian': <gensim.models.word2vec.Vocab at 0x7f61aa5366a0>,\\n\",\n       \" 'HAND:': <gensim.models.word2vec.Vocab at 0x7f61aa553780>,\\n\",\n       \" 'fact,': <gensim.models.word2vec.Vocab at 0x7f61aa5367f0>,\\n\",\n       \" 'thanksgiving': <gensim.models.word2vec.Vocab at 0x7f61aa536828>,\\n\",\n       \" 'me..argh...': <gensim.models.word2vec.Vocab at 0x7f61aa536860>,\\n\",\n       \" 'she': <gensim.models.word2vec.Vocab at 0x7f61aa536898>,\\n\",\n       \" 'Tree': <gensim.models.word2vec.Vocab at 0x7f61aa536908>,\\n\",\n       \" 'combat.': <gensim.models.word2vec.Vocab at 0x7f61aa536940>,\\n\",\n       \" 'mitosis': <gensim.models.word2vec.Vocab at 0x7f61aa536978>,\\n\",\n       \" 'offered': <gensim.models.word2vec.Vocab at 0x7f61aa5369e8>,\\n\",\n       \" 'no..': <gensim.models.word2vec.Vocab at 0x7f61aa536a20>,\\n\",\n       \" '(there': <gensim.models.word2vec.Vocab at 0x7f61aa536a58>,\\n\",\n       \" 'aspirations': <gensim.models.word2vec.Vocab at 0x7f61aa536a90>,\\n\",\n       \" 'page': <gensim.models.word2vec.Vocab at 0x7f61aa536ac8>,\\n\",\n       \" 'Least': <gensim.models.word2vec.Vocab at 0x7f61aa536b38>,\\n\",\n       \" 'each': <gensim.models.word2vec.Vocab at 0x7f61aa536b70>,\\n\",\n       \" 'ride...': <gensim.models.word2vec.Vocab at 0x7f61aa536ba8>,\\n\",\n       \" 'doesn’t': <gensim.models.word2vec.Vocab at 0x7f61aa536c18>,\\n\",\n       \" 'FUCK': <gensim.models.word2vec.Vocab at 0x7f61aa536c50>,\\n\",\n       \" 'gona': <gensim.models.word2vec.Vocab at 0x7f61aa536dd8>,\\n\",\n       \" 'window': <gensim.models.word2vec.Vocab at 0x7f61aa536e10>,\\n\",\n       \" 'end': <gensim.models.word2vec.Vocab at 0x7f61aa536e48>,\\n\",\n       \" 'expected': <gensim.models.word2vec.Vocab at 0x7f61aa536eb8>,\\n\",\n       \" 'well.': <gensim.models.word2vec.Vocab at 0x7f61aa536ef0>,\\n\",\n       \" 'called': <gensim.models.word2vec.Vocab at 0x7f61aa460748>,\\n\",\n       \" \\\"needn't\\\": <gensim.models.word2vec.Vocab at 0x7f61aa536f28>,\\n\",\n       \" 'doesnt': <gensim.models.word2vec.Vocab at 0x7f61aa536f60>,\\n\",\n       \" 'venturing': <gensim.models.word2vec.Vocab at 0x7f61aa440a90>,\\n\",\n       \" 'alex': <gensim.models.word2vec.Vocab at 0x7f61aa536fd0>,\\n\",\n       \" 'here:': <gensim.models.word2vec.Vocab at 0x7f61aa53b048>,\\n\",\n       \" 'ewWw': <gensim.models.word2vec.Vocab at 0x7f61aa53b0b8>,\\n\",\n       \" 'pole?': <gensim.models.word2vec.Vocab at 0x7f61aa53b0f0>,\\n\",\n       \" 'melody,': <gensim.models.word2vec.Vocab at 0x7f61aa5b6eb8>,\\n\",\n       \" 'motivated': <gensim.models.word2vec.Vocab at 0x7f61aa53b128>,\\n\",\n       \" 'Well,': <gensim.models.word2vec.Vocab at 0x7f61aa53b160>,\\n\",\n       \" 'says:': <gensim.models.word2vec.Vocab at 0x7f61aa53b198>,\\n\",\n       \" 'worm': <gensim.models.word2vec.Vocab at 0x7f61aa53b1d0>,\\n\",\n       \" '[some': <gensim.models.word2vec.Vocab at 0x7f61aa553f98>,\\n\",\n       \" 'name': <gensim.models.word2vec.Vocab at 0x7f61aa53b320>,\\n\",\n       \" 'Leave\\\"': <gensim.models.word2vec.Vocab at 0x7f61aa53b358>,\\n\",\n       \" '4th': <gensim.models.word2vec.Vocab at 0x7f61aa404ba8>,\\n\",\n       \" \\\"It's...\\\": <gensim.models.word2vec.Vocab at 0x7f61aa53b390>,\\n\",\n       \" 'problem??': <gensim.models.word2vec.Vocab at 0x7f61aa553fd0>,\\n\",\n       \" 'remember': <gensim.models.word2vec.Vocab at 0x7f61aa53b470>,\\n\",\n       \" 'o': <gensim.models.word2vec.Vocab at 0x7f61aa4e32b0>,\\n\",\n       \" 'letters.': <gensim.models.word2vec.Vocab at 0x7f61aa53b4a8>,\\n\",\n       \" 'jean': <gensim.models.word2vec.Vocab at 0x7f61aa53b4e0>,\\n\",\n       \" 'thing.': <gensim.models.word2vec.Vocab at 0x7f61aa53b518>,\\n\",\n       \" 'friend?]:': <gensim.models.word2vec.Vocab at 0x7f61aa53b588>,\\n\",\n       \" 'am!': <gensim.models.word2vec.Vocab at 0x7f61aa53b5c0>,\\n\",\n       \" 'side...': <gensim.models.word2vec.Vocab at 0x7f61aa53b6a0>,\\n\",\n       \" 'Yet': <gensim.models.word2vec.Vocab at 0x7f61aa53b6d8>,\\n\",\n       \" 'easier': <gensim.models.word2vec.Vocab at 0x7f61aa53b828>,\\n\",\n       \" 'babies': <gensim.models.word2vec.Vocab at 0x7f61aa53b860>,\\n\",\n       \" 'You?': <gensim.models.word2vec.Vocab at 0x7f61aa53b898>,\\n\",\n       \" 'wedding:': <gensim.models.word2vec.Vocab at 0x7f61aa53b8d0>,\\n\",\n       \" '2.)': <gensim.models.word2vec.Vocab at 0x7f61aa53b908>,\\n\",\n       \" 'first...then': <gensim.models.word2vec.Vocab at 0x7f61aa53b940>,\\n\",\n       \" 'LA:': <gensim.models.word2vec.Vocab at 0x7f61aa53b978>,\\n\",\n       \" 'but,)': <gensim.models.word2vec.Vocab at 0x7f61aa53b9b0>,\\n\",\n       \" 'not,': <gensim.models.word2vec.Vocab at 0x7f61aa53ba20>,\\n\",\n       \" 'possession': <gensim.models.word2vec.Vocab at 0x7f61aa53ba58>,\\n\",\n       \" 'its': <gensim.models.word2vec.Vocab at 0x7f61aa53ba90>,\\n\",\n       \" 'stop': <gensim.models.word2vec.Vocab at 0x7f61aa53bac8>,\\n\",\n       \" 'Thanks': <gensim.models.word2vec.Vocab at 0x7f61aa53bb00>,\\n\",\n       \" 'durin': <gensim.models.word2vec.Vocab at 0x7f61aa53bb38>,\\n\",\n       \" 'rings': <gensim.models.word2vec.Vocab at 0x7f61aa53bb70>,\\n\",\n       \" 'Specifics': <gensim.models.word2vec.Vocab at 0x7f61aa53bba8>,\\n\",\n       \" 'http://www.kingsofchaos.com/recruit.php?uniqid=jm8bja2z': <gensim.models.word2vec.Vocab at 0x7f61aa53bbe0>,\\n\",\n       \" 'lace': <gensim.models.word2vec.Vocab at 0x7f61aa53bc18>,\\n\",\n       \" 'pretended': <gensim.models.word2vec.Vocab at 0x7f61aa53bc50>,\\n\",\n       \" 'clothes': <gensim.models.word2vec.Vocab at 0x7f61aa53bd30>,\\n\",\n       \" 'wong': <gensim.models.word2vec.Vocab at 0x7f61aa53bd68>,\\n\",\n       \" '38': <gensim.models.word2vec.Vocab at 0x7f61aa5ce390>,\\n\",\n       \" 'country.': <gensim.models.word2vec.Vocab at 0x7f61aa53bda0>,\\n\",\n       \" 'criticism': <gensim.models.word2vec.Vocab at 0x7f61aa53bdd8>,\\n\",\n       \" 'NATIONAL': <gensim.models.word2vec.Vocab at 0x7f61aa53be48>,\\n\",\n       \" \\\"that's\\\": <gensim.models.word2vec.Vocab at 0x7f61aa53beb8>,\\n\",\n       \" 'conclusively': <gensim.models.word2vec.Vocab at 0x7f61aa53bef0>,\\n\",\n       \" 'cartoons,': <gensim.models.word2vec.Vocab at 0x7f61aa53bf28>,\\n\",\n       \" 'chest/lungs': <gensim.models.word2vec.Vocab at 0x7f61aa53bf60>,\\n\",\n       \" 'whilst': <gensim.models.word2vec.Vocab at 0x7f61aa5dc7b8>,\\n\",\n       \" \\\"I'm,\\\": <gensim.models.word2vec.Vocab at 0x7f61aa3feb38>,\\n\",\n       \" 'Tata.': <gensim.models.word2vec.Vocab at 0x7f61aa53bfd0>,\\n\",\n       \" 'mix': <gensim.models.word2vec.Vocab at 0x7f61aa533160>,\\n\",\n       \" 'popularity': <gensim.models.word2vec.Vocab at 0x7f61aa533390>,\\n\",\n       \" 'park)': <gensim.models.word2vec.Vocab at 0x7f61aa5333c8>,\\n\",\n       \" '(trampled': <gensim.models.word2vec.Vocab at 0x7f61aa5336a0>,\\n\",\n       \" 'reminded': <gensim.models.word2vec.Vocab at 0x7f61aa5339b0>,\\n\",\n       \" 'says.': <gensim.models.word2vec.Vocab at 0x7f61aa533a58>,\\n\",\n       \" 'repetition,': <gensim.models.word2vec.Vocab at 0x7f61aa533ac8>,\\n\",\n       \" 'Size?': <gensim.models.word2vec.Vocab at 0x7f61aa533c18>,\\n\",\n       \" \\\"hm...i'm\\\": <gensim.models.word2vec.Vocab at 0x7f61aa533e10>,\\n\",\n       \" 'interesting,': <gensim.models.word2vec.Vocab at 0x7f61aa560160>,\\n\",\n       \" 'exams': <gensim.models.word2vec.Vocab at 0x7f61aa533f28>,\\n\",\n       \" 'crusts.': <gensim.models.word2vec.Vocab at 0x7f61aa533f60>,\\n\",\n       \" 'filling': <gensim.models.word2vec.Vocab at 0x7f61aa533fd0>,\\n\",\n       \" 'gets': <gensim.models.word2vec.Vocab at 0x7f61aa4e51d0>,\\n\",\n       \" 'his': <gensim.models.word2vec.Vocab at 0x7f61aa4e5208>,\\n\",\n       \" 'Friday,': <gensim.models.word2vec.Vocab at 0x7f61aa4e5240>,\\n\",\n       \" 'f': <gensim.models.word2vec.Vocab at 0x7f61aa4e5278>,\\n\",\n       \" 'too!': <gensim.models.word2vec.Vocab at 0x7f61aa4e52e8>,\\n\",\n       \" 'Made': <gensim.models.word2vec.Vocab at 0x7f61aa4e5400>,\\n\",\n       \" 'accidentally': <gensim.models.word2vec.Vocab at 0x7f61aa4e5438>,\\n\",\n       \" '\\\"New': <gensim.models.word2vec.Vocab at 0x7f61aa4e5470>,\\n\",\n       \" 'COURSE.': <gensim.models.word2vec.Vocab at 0x7f61aa4e54a8>,\\n\",\n       \" '[please': <gensim.models.word2vec.Vocab at 0x7f61aa572240>,\\n\",\n       \" 'this...': <gensim.models.word2vec.Vocab at 0x7f61aa4e5630>,\\n\",\n       \" 'soon': <gensim.models.word2vec.Vocab at 0x7f61aa4e5710>,\\n\",\n       \" 'worry': <gensim.models.word2vec.Vocab at 0x7f61aa4e57b8>,\\n\",\n       \" 'Job]:': <gensim.models.word2vec.Vocab at 0x7f61aa4e58d0>,\\n\",\n       \" 'deal': <gensim.models.word2vec.Vocab at 0x7f61aa4e59b0>,\\n\",\n       \" 'pounding': <gensim.models.word2vec.Vocab at 0x7f61aa4e59e8>,\\n\",\n       \" '[Are': <gensim.models.word2vec.Vocab at 0x7f61aa4e5a90>,\\n\",\n       \" 'begin': <gensim.models.word2vec.Vocab at 0x7f61aa4e5b00>,\\n\",\n       \" 'isolated': <gensim.models.word2vec.Vocab at 0x7f61aa4e5c18>,\\n\",\n       \" 'anyways': <gensim.models.word2vec.Vocab at 0x7f61aa4e5c50>,\\n\",\n       \" 'garbage': <gensim.models.word2vec.Vocab at 0x7f61aa4e5c88>,\\n\",\n       \" 'awww': <gensim.models.word2vec.Vocab at 0x7f61aa4e5cf8>,\\n\",\n       \" 'intelligence': <gensim.models.word2vec.Vocab at 0x7f61aa4e5d68>,\\n\",\n       \" 'being': <gensim.models.word2vec.Vocab at 0x7f61aa4e5e48>,\\n\",\n       \" 'married?]:': <gensim.models.word2vec.Vocab at 0x7f61aa4e5eb8>,\\n\",\n       \" 'omg': <gensim.models.word2vec.Vocab at 0x7f61aa440dd8>,\\n\",\n       \" '...': <gensim.models.word2vec.Vocab at 0x7f61aa4e5f28>,\\n\",\n       \" 'highlight': <gensim.models.word2vec.Vocab at 0x7f61aa4e5fd0>,\\n\",\n       \" 'to': <gensim.models.word2vec.Vocab at 0x7f61aa4e8978>,\\n\",\n       \" 'AHH': <gensim.models.word2vec.Vocab at 0x7f61aa4e8b38>,\\n\",\n       \" 'OVER!!!!!!!!!': <gensim.models.word2vec.Vocab at 0x7f61aa4e8b70>,\\n\",\n       \" 'Cried': <gensim.models.word2vec.Vocab at 0x7f61aa4e8c18>,\\n\",\n       \" 'SAYING?!?!?': <gensim.models.word2vec.Vocab at 0x7f61aa4e8c50>,\\n\",\n       \" 'olivia.': <gensim.models.word2vec.Vocab at 0x7f61aa4e8da0>,\\n\",\n       \" \\\"she'll\\\": <gensim.models.word2vec.Vocab at 0x7f61aa4e8f60>,\\n\",\n       \" 'community,': <gensim.models.word2vec.Vocab at 0x7f61aa4e8f98>,\\n\",\n       \" 'cold.': <gensim.models.word2vec.Vocab at 0x7f61aa5dc978>,\\n\",\n       \" 'not': <gensim.models.word2vec.Vocab at 0x7f61aa4e8898>,\\n\",\n       \" 'transcripts': <gensim.models.word2vec.Vocab at 0x7f61aa4e8160>,\\n\",\n       \" 'promises...i': <gensim.models.word2vec.Vocab at 0x7f61aa5c7ba8>,\\n\",\n       \" 'totem': <gensim.models.word2vec.Vocab at 0x7f61aa4e82e8>,\\n\",\n       \" 'naked,': <gensim.models.word2vec.Vocab at 0x7f61aa554320>,\\n\",\n       \" 'hate': <gensim.models.word2vec.Vocab at 0x7f61aa4e8358>,\\n\",\n       \" 'gas': <gensim.models.word2vec.Vocab at 0x7f61aa4e85c0>,\\n\",\n       \" 'beat': <gensim.models.word2vec.Vocab at 0x7f61aa4e85f8>,\\n\",\n       \" 'Jungle': <gensim.models.word2vec.Vocab at 0x7f61aa4e8748>,\\n\",\n       \" 'band': <gensim.models.word2vec.Vocab at 0x7f61aa5697b8>,\\n\",\n       \" 'ought': <gensim.models.word2vec.Vocab at 0x7f61aa4e8828>,\\n\",\n       \" 'ishouldnt': <gensim.models.word2vec.Vocab at 0x7f61aa4e7128>,\\n\",\n       \" 'funni': <gensim.models.word2vec.Vocab at 0x7f61aa4e7208>,\\n\",\n       \" 'camera': <gensim.models.word2vec.Vocab at 0x7f61aa4e7278>,\\n\",\n       \" \\\"Mom's\\\": <gensim.models.word2vec.Vocab at 0x7f61aa4e7400>,\\n\",\n       \" 'invitations': <gensim.models.word2vec.Vocab at 0x7f61aa4e7438>,\\n\",\n       \" 'sheets,': <gensim.models.word2vec.Vocab at 0x7f61aa4e7470>,\\n\",\n       \" 'sony': <gensim.models.word2vec.Vocab at 0x7f61aa4e74a8>,\\n\",\n       \" 'Could': <gensim.models.word2vec.Vocab at 0x7f61aa4e7588>,\\n\",\n       \" '\\\"goodness\\\"': <gensim.models.word2vec.Vocab at 0x7f61aa4e75c0>,\\n\",\n       \" 'commentators': <gensim.models.word2vec.Vocab at 0x7f61aa4e7668>,\\n\",\n       \" 'learned': <gensim.models.word2vec.Vocab at 0x7f61aa4e7710>,\\n\",\n       \" 'quit': <gensim.models.word2vec.Vocab at 0x7f61aa4e7748>,\\n\",\n       \" \\\"mother's\\\": <gensim.models.word2vec.Vocab at 0x7f61aa5dc9e8>,\\n\",\n       \" 'Hussein,': <gensim.models.word2vec.Vocab at 0x7f61aa5b9320>,\\n\",\n       \" 'Funny,': <gensim.models.word2vec.Vocab at 0x7f61aa4e7860>,\\n\",\n       \" 'Actually': <gensim.models.word2vec.Vocab at 0x7f61aa4e7898>,\\n\",\n       \" 'upsetting.': <gensim.models.word2vec.Vocab at 0x7f61aa4e7a90>,\\n\",\n       \" 'ring!)': <gensim.models.word2vec.Vocab at 0x7f61aa4e7b00>,\\n\",\n       \" 'material': <gensim.models.word2vec.Vocab at 0x7f61aa4e7b38>,\\n\",\n       \" '…': <gensim.models.word2vec.Vocab at 0x7f61aa4e7be0>,\\n\",\n       \" 'kind': <gensim.models.word2vec.Vocab at 0x7f61aa4e7c18>,\\n\",\n       \" 'Moon\\\"': <gensim.models.word2vec.Vocab at 0x7f61aa4e7cc0>,\\n\",\n       \" 'james,': <gensim.models.word2vec.Vocab at 0x7f61aa4e7d30>,\\n\",\n       \" 'regardless': <gensim.models.word2vec.Vocab at 0x7f61aa4e7d68>,\\n\",\n       \" 'WATCHED': <gensim.models.word2vec.Vocab at 0x7f61aa4e7da0>,\\n\",\n       \" 'possibly': <gensim.models.word2vec.Vocab at 0x7f61aa5bbe10>,\\n\",\n       \" 'Make': <gensim.models.word2vec.Vocab at 0x7f61aa4e7ef0>,\\n\",\n       \" 'airplanes,': <gensim.models.word2vec.Vocab at 0x7f61aa463f60>,\\n\",\n       \" 'Exaggerated,': <gensim.models.word2vec.Vocab at 0x7f61aa4e7f98>,\\n\",\n       \" 'head,': <gensim.models.word2vec.Vocab at 0x7f61aa4e7fd0>,\\n\",\n       \" 'graceful': <gensim.models.word2vec.Vocab at 0x7f61aa4d9128>,\\n\",\n       \" 'but': <gensim.models.word2vec.Vocab at 0x7f61aa4d9550>,\\n\",\n       \" 'low': <gensim.models.word2vec.Vocab at 0x7f61aa4d95f8>,\\n\",\n       \" 'it!!!': <gensim.models.word2vec.Vocab at 0x7f61aa4d9710>,\\n\",\n       \" 'usual)': <gensim.models.word2vec.Vocab at 0x7f61aa4d97f0>,\\n\",\n       \" 'doing?:': <gensim.models.word2vec.Vocab at 0x7f61aa4d9908>,\\n\",\n       \" \\\"wat's\\\": <gensim.models.word2vec.Vocab at 0x7f61aa4d99b0>,\\n\",\n       \" 'disadvantages': <gensim.models.word2vec.Vocab at 0x7f61aa5dcb00>,\\n\",\n       \" 'breaks': <gensim.models.word2vec.Vocab at 0x7f61aa4d9cf8>,\\n\",\n       \" 'partner,': <gensim.models.word2vec.Vocab at 0x7f61aa4d8048>,\\n\",\n       \" 'totally': <gensim.models.word2vec.Vocab at 0x7f61aa4d80f0>,\\n\",\n       \" 'break?!': <gensim.models.word2vec.Vocab at 0x7f61aa4d81d0>,\\n\",\n       \" 'remember,': <gensim.models.word2vec.Vocab at 0x7f61aa3fedd8>,\\n\",\n       \" 'nose.': <gensim.models.word2vec.Vocab at 0x7f61aa4d82b0>,\\n\",\n       \" '...gets': <gensim.models.word2vec.Vocab at 0x7f61aa4d82e8>,\\n\",\n       \" 'circles': <gensim.models.word2vec.Vocab at 0x7f61aa4d8320>,\\n\",\n       \" 'list?': <gensim.models.word2vec.Vocab at 0x7f61aa4d84a8>,\\n\",\n       \" 'babble.': <gensim.models.word2vec.Vocab at 0x7f61aa4ec2e8>,\\n\",\n       \" 'Those': <gensim.models.word2vec.Vocab at 0x7f61aa5c19b0>,\\n\",\n       \" 'hers,': <gensim.models.word2vec.Vocab at 0x7f61aa554518>,\\n\",\n       \" 'Kucinich).': <gensim.models.word2vec.Vocab at 0x7f61aa4d8a90>,\\n\",\n       \" 'toxic,': <gensim.models.word2vec.Vocab at 0x7f61aa4d8ac8>,\\n\",\n       \" 'mates.': <gensim.models.word2vec.Vocab at 0x7f61aa4d8be0>,\\n\",\n       \" 'rock!': <gensim.models.word2vec.Vocab at 0x7f61aa4d8d68>,\\n\",\n       \" 'birthday': <gensim.models.word2vec.Vocab at 0x7f61aa4d8e48>,\\n\",\n       \" 'okay-': <gensim.models.word2vec.Vocab at 0x7f61aa4d8ef0>,\\n\",\n       \" 'Twenty-six': <gensim.models.word2vec.Vocab at 0x7f61aa4d8f60>,\\n\",\n       \" 'Molly': <gensim.models.word2vec.Vocab at 0x7f61aa4d8f98>,\\n\",\n       \" 'everyone.i': <gensim.models.word2vec.Vocab at 0x7f61aa4d8fd0>,\\n\",\n       \" 'brought': <gensim.models.word2vec.Vocab at 0x7f61aa4db320>,\\n\",\n       \" 'rusty.': <gensim.models.word2vec.Vocab at 0x7f61aa4db358>,\\n\",\n       \" \\\"Let's\\\": <gensim.models.word2vec.Vocab at 0x7f61aa4db390>,\\n\",\n       \" 'soon?': <gensim.models.word2vec.Vocab at 0x7f61aa4db400>,\\n\",\n       \" '19.': <gensim.models.word2vec.Vocab at 0x7f61aa4db4e0>,\\n\",\n       \" 'shuffle': <gensim.models.word2vec.Vocab at 0x7f61aa4db8d0>,\\n\",\n       \" \\\"you're\\\": <gensim.models.word2vec.Vocab at 0x7f61aa4dbac8>,\\n\",\n       \" 'somehow?': <gensim.models.word2vec.Vocab at 0x7f61aa4ec400>,\\n\",\n       \" 'naked?]:': <gensim.models.word2vec.Vocab at 0x7f61aa5d4c18>,\\n\",\n       \" '...i': <gensim.models.word2vec.Vocab at 0x7f61aa4dbd68>,\\n\",\n       \" 'friend': <gensim.models.word2vec.Vocab at 0x7f61aa4da048>,\\n\",\n       \" 'away;': <gensim.models.word2vec.Vocab at 0x7f61aa4da320>,\\n\",\n       \" 'tending': <gensim.models.word2vec.Vocab at 0x7f61aa4da358>,\\n\",\n       \" 'creates': <gensim.models.word2vec.Vocab at 0x7f61aa4da5f8>,\\n\",\n       \" 'certitude,': <gensim.models.word2vec.Vocab at 0x7f61aa4da668>,\\n\",\n       \" 'job...some': <gensim.models.word2vec.Vocab at 0x7f61aa4da6a0>,\\n\",\n       \" 'room.': <gensim.models.word2vec.Vocab at 0x7f61aa4da748>,\\n\",\n       \" '...will': <gensim.models.word2vec.Vocab at 0x7f61aa4da7b8>,\\n\",\n       \" 'mincing': <gensim.models.word2vec.Vocab at 0x7f61aa4da8d0>,\\n\",\n       \" 'dog/cat/bird/fish,': <gensim.models.word2vec.Vocab at 0x7f61aa4da908>,\\n\",\n       \" 'way,': <gensim.models.word2vec.Vocab at 0x7f61aa5bf0b8>,\\n\",\n       \" 'nvm...': <gensim.models.word2vec.Vocab at 0x7f61aa4daba8>,\\n\",\n       \" 'illness,': <gensim.models.word2vec.Vocab at 0x7f61aa4dac18>,\\n\",\n       \" 'good.': <gensim.models.word2vec.Vocab at 0x7f61aa4dacc0>,\\n\",\n       \" 'bother??': <gensim.models.word2vec.Vocab at 0x7f61aa4dada0>,\\n\",\n       \" 'curse': <gensim.models.word2vec.Vocab at 0x7f61aa4dadd8>,\\n\",\n       \" \\\"daughter's\\\": <gensim.models.word2vec.Vocab at 0x7f61aa4daf60>,\\n\",\n       \" '(albeit,': <gensim.models.word2vec.Vocab at 0x7f61aa4dc3c8>,\\n\",\n       \" 'okay.': <gensim.models.word2vec.Vocab at 0x7f61aa4ede48>,\\n\",\n       \" 'boxers': <gensim.models.word2vec.Vocab at 0x7f61aa4dc588>,\\n\",\n       \" 'Calculus,': <gensim.models.word2vec.Vocab at 0x7f61aa4dc6a0>,\\n\",\n       \" 'MEAN': <gensim.models.word2vec.Vocab at 0x7f61aa4dc7b8>,\\n\",\n       \" 'rosie.': <gensim.models.word2vec.Vocab at 0x7f61aa4dc9e8>,\\n\",\n       \" 'hard': <gensim.models.word2vec.Vocab at 0x7f61aa4dca90>,\\n\",\n       \" 'life...think': <gensim.models.word2vec.Vocab at 0x7f61aa4dcba8>,\\n\",\n       \" 'takes': <gensim.models.word2vec.Vocab at 0x7f61aa4dce48>,\\n\",\n       \" 'pretty.': <gensim.models.word2vec.Vocab at 0x7f61aa5c1c88>,\\n\",\n       \" 'award': <gensim.models.word2vec.Vocab at 0x7f61aa4dceb8>,\\n\",\n       \" 'their': <gensim.models.word2vec.Vocab at 0x7f61aa4dcf28>,\\n\",\n       \" 'plainly.': <gensim.models.word2vec.Vocab at 0x7f61aa4dcfd0>,\\n\",\n       \" 'noone': <gensim.models.word2vec.Vocab at 0x7f61aa4ca1d0>,\\n\",\n       \" 'say...no': <gensim.models.word2vec.Vocab at 0x7f61aa438978>,\\n\",\n       \" 'thats': <gensim.models.word2vec.Vocab at 0x7f61aa4ca2e8>,\\n\",\n       \" 'learning': <gensim.models.word2vec.Vocab at 0x7f61aa5dcd30>,\\n\",\n       \" 'sleep': <gensim.models.word2vec.Vocab at 0x7f61aa4ca4a8>,\\n\",\n       \" 'against': <gensim.models.word2vec.Vocab at 0x7f61aa4ca5c0>,\\n\",\n       \" 'rubbish': <gensim.models.word2vec.Vocab at 0x7f61aa565358>,\\n\",\n       \" 'years,': <gensim.models.word2vec.Vocab at 0x7f61aa4ca9b0>,\\n\",\n       \" 'theatre)': <gensim.models.word2vec.Vocab at 0x7f61aa4caa20>,\\n\",\n       \" '[Kissed': <gensim.models.word2vec.Vocab at 0x7f61aa4caa90>,\\n\",\n       \" 'love?': <gensim.models.word2vec.Vocab at 0x7f61aa4caba8>,\\n\",\n       \" 'Forgetting': <gensim.models.word2vec.Vocab at 0x7f61aa4cada0>,\\n\",\n       \" 'Whoever': <gensim.models.word2vec.Vocab at 0x7f61aa4cb358>,\\n\",\n       \" 'bacon': <gensim.models.word2vec.Vocab at 0x7f61aa4cb438>,\\n\",\n       \" 'wishing': <gensim.models.word2vec.Vocab at 0x7f61aa5ce940>,\\n\",\n       \" 'fantastic.': <gensim.models.word2vec.Vocab at 0x7f61aa4cb898>,\\n\",\n       \" 'rosalie...': <gensim.models.word2vec.Vocab at 0x7f61aa4cbc18>,\\n\",\n       \" 'souned': <gensim.models.word2vec.Vocab at 0x7f61aa5dce48>,\\n\",\n       \" 'bulbous': <gensim.models.word2vec.Vocab at 0x7f61aa4cbeb8>,\\n\",\n       \" 'in-depth': <gensim.models.word2vec.Vocab at 0x7f61aa4cbef0>,\\n\",\n       \" 'proof': <gensim.models.word2vec.Vocab at 0x7f61aa4cd240>,\\n\",\n       \" 'however,': <gensim.models.word2vec.Vocab at 0x7f61aa4cd278>,\\n\",\n       \" 'at': <gensim.models.word2vec.Vocab at 0x7f61aa4cd390>,\\n\",\n       \" \\\"you'll\\\": <gensim.models.word2vec.Vocab at 0x7f61aa4cd438>,\\n\",\n       \" 'Will': <gensim.models.word2vec.Vocab at 0x7f61aa4cd780>,\\n\",\n       \" 'Chotky': <gensim.models.word2vec.Vocab at 0x7f61aa4cda90>,\\n\",\n       \" 'o0o!': <gensim.models.word2vec.Vocab at 0x7f61aa4cf2b0>,\\n\",\n       \" 'overnight,': <gensim.models.word2vec.Vocab at 0x7f61aa442208>,\\n\",\n       \" '6.': <gensim.models.word2vec.Vocab at 0x7f61aa4cf400>,\\n\",\n       \" 'expensive': <gensim.models.word2vec.Vocab at 0x7f61aa4cf518>,\\n\",\n       \" 'employers': <gensim.models.word2vec.Vocab at 0x7f61aa4cf550>,\\n\",\n       \" 'especially': <gensim.models.word2vec.Vocab at 0x7f61aa4cf828>,\\n\",\n       \" 'lives,': <gensim.models.word2vec.Vocab at 0x7f61aa4cf860>,\\n\",\n       \" 'dumb': <gensim.models.word2vec.Vocab at 0x7f61aa4cf898>,\\n\",\n       \" 'EVERYONE!!!': <gensim.models.word2vec.Vocab at 0x7f61aa4cfc88>,\\n\",\n       \" 'mind,': <gensim.models.word2vec.Vocab at 0x7f61aa4cfd68>,\\n\",\n       \" 'terms': <gensim.models.word2vec.Vocab at 0x7f61aa4cffd0>,\\n\",\n       \" 'deception': <gensim.models.word2vec.Vocab at 0x7f61aa4ce390>,\\n\",\n       \" 'glad.': <gensim.models.word2vec.Vocab at 0x7f61aa4ce5c0>,\\n\",\n       \" '20:': <gensim.models.word2vec.Vocab at 0x7f61aa453e48>,\\n\",\n       \" 'disappeared!!!!!!!!': <gensim.models.word2vec.Vocab at 0x7f61aa4ce978>,\\n\",\n       \" 'candy:': <gensim.models.word2vec.Vocab at 0x7f61aa4ceba8>,\\n\",\n       \" 'PRODUCTIVE!!': <gensim.models.word2vec.Vocab at 0x7f61aa5d7048>,\\n\",\n       \" 'Goals': <gensim.models.word2vec.Vocab at 0x7f61aa4cec18>,\\n\",\n       \" 'like,': <gensim.models.word2vec.Vocab at 0x7f61aa4cecf8>,\\n\",\n       \" 'Carter': <gensim.models.word2vec.Vocab at 0x7f61aa4cedd8>,\\n\",\n       \" 'So': <gensim.models.word2vec.Vocab at 0x7f61aa4cef60>,\\n\",\n       \" '5:': <gensim.models.word2vec.Vocab at 0x7f61aa4d2048>,\\n\",\n       \" 'stalled.': <gensim.models.word2vec.Vocab at 0x7f61aa4d2208>,\\n\",\n       \" 'fewer': <gensim.models.word2vec.Vocab at 0x7f61aa4d26d8>,\\n\",\n       \" 'lies': <gensim.models.word2vec.Vocab at 0x7f61aa4d27b8>,\\n\",\n       \" 'faces': <gensim.models.word2vec.Vocab at 0x7f61aa5b9828>,\\n\",\n       \" 'im': <gensim.models.word2vec.Vocab at 0x7f61aa4d2898>,\\n\",\n       \" 'kina': <gensim.models.word2vec.Vocab at 0x7f61aa4d2ba8>,\\n\",\n       \" 'Each': <gensim.models.word2vec.Vocab at 0x7f61aa4d2e10>,\\n\",\n       \" 'know...even': <gensim.models.word2vec.Vocab at 0x7f61aa4d2e48>,\\n\",\n       \" 'thrown': <gensim.models.word2vec.Vocab at 0x7f61aa4d2eb8>,\\n\",\n       \" \\\"can't\\\": <gensim.models.word2vec.Vocab at 0x7f61aa4d3128>,\\n\",\n       \" 'close-minded.': <gensim.models.word2vec.Vocab at 0x7f61aa4d31d0>,\\n\",\n       \" 'aint': <gensim.models.word2vec.Vocab at 0x7f61aa4d3240>,\\n\",\n       \" 'the': <gensim.models.word2vec.Vocab at 0x7f61aa4d36d8>,\\n\",\n       \" 'Ikea': <gensim.models.word2vec.Vocab at 0x7f61aa4d37b8>,\\n\",\n       \" 'trying': <gensim.models.word2vec.Vocab at 0x7f61aa4d38d0>,\\n\",\n       \" 'Coulter': <gensim.models.word2vec.Vocab at 0x7f61aa4d3940>,\\n\",\n       \" 'cleaner,': <gensim.models.word2vec.Vocab at 0x7f61aa4d3b00>,\\n\",\n       \" 'Mix]\\\"': <gensim.models.word2vec.Vocab at 0x7f61aa4d3ba8>,\\n\",\n       \" 'surface,': <gensim.models.word2vec.Vocab at 0x7f61aa4d3c50>,\\n\",\n       \" 'mean,': <gensim.models.word2vec.Vocab at 0x7f61aa4d50b8>,\\n\",\n       \" 'Graham),': <gensim.models.word2vec.Vocab at 0x7f61aa4d5160>,\\n\",\n       \" 'Congress,': <gensim.models.word2vec.Vocab at 0x7f61aa4d5198>,\\n\",\n       \" 'animals': <gensim.models.word2vec.Vocab at 0x7f61aa4d5208>,\\n\",\n       \" 'small': <gensim.models.word2vec.Vocab at 0x7f61aa4d5278>,\\n\",\n       \" 'steps.': <gensim.models.word2vec.Vocab at 0x7f61aa4d5390>,\\n\",\n       \" '[relationship]': <gensim.models.word2vec.Vocab at 0x7f61aa4d5438>,\\n\",\n       \" '[Wanted': <gensim.models.word2vec.Vocab at 0x7f61aa4d55c0>,\\n\",\n       \" 'finals...too': <gensim.models.word2vec.Vocab at 0x7f61aa4d55f8>,\\n\",\n       \" 'definitely.': <gensim.models.word2vec.Vocab at 0x7f61aa554eb8>,\\n\",\n       \" 'I:': <gensim.models.word2vec.Vocab at 0x7f61aa4d56a0>,\\n\",\n       \" 'what...even': <gensim.models.word2vec.Vocab at 0x7f61aa4eef98>,\\n\",\n       \" '......': <gensim.models.word2vec.Vocab at 0x7f61aa4d5978>,\\n\",\n       \" 'lies).': <gensim.models.word2vec.Vocab at 0x7f61aa4d59e8>,\\n\",\n       \" 'longer': <gensim.models.word2vec.Vocab at 0x7f61aa4d5a90>,\\n\",\n       \" 'animals.': <gensim.models.word2vec.Vocab at 0x7f61aa5b9908>,\\n\",\n       \" 'mindless': <gensim.models.word2vec.Vocab at 0x7f61aa4d5b38>,\\n\",\n       \" 'disappear….': <gensim.models.word2vec.Vocab at 0x7f61aa4d5cc0>,\\n\",\n       \" 'places': <gensim.models.word2vec.Vocab at 0x7f61aa4d6c88>,\\n\",\n       \" 'sheets.': <gensim.models.word2vec.Vocab at 0x7f61aa4d6cf8>,\\n\",\n       \" 'here.': <gensim.models.word2vec.Vocab at 0x7f61aa4d6d68>,\\n\",\n       \" 'both,': <gensim.models.word2vec.Vocab at 0x7f61aa4d6e10>,\\n\",\n       \" 'xela': <gensim.models.word2vec.Vocab at 0x7f61aa4d6e80>,\\n\",\n       \" 'creeping': <gensim.models.word2vec.Vocab at 0x7f61aa4d6be0>,\\n\",\n       \" 'dressy': <gensim.models.word2vec.Vocab at 0x7f61aa4d6048>,\\n\",\n       \" 'melting': <gensim.models.word2vec.Vocab at 0x7f61aa4d6198>,\\n\",\n       \" '30': <gensim.models.word2vec.Vocab at 0x7f61aa4d6240>,\\n\",\n       \" 'Questions': <gensim.models.word2vec.Vocab at 0x7f61aa5b99e8>,\\n\",\n       \" 'indicates': <gensim.models.word2vec.Vocab at 0x7f61aa4d6390>,\\n\",\n       \" 'guess': <gensim.models.word2vec.Vocab at 0x7f61aa4d63c8>,\\n\",\n       \" '37': <gensim.models.word2vec.Vocab at 0x7f61aa4d6400>,\\n\",\n       \" 'strong,': <gensim.models.word2vec.Vocab at 0x7f61aa4d6588>,\\n\",\n       \" \\\"I'd\\\": <gensim.models.word2vec.Vocab at 0x7f61aa4d6668>,\\n\",\n       \" 'Band': <gensim.models.word2vec.Vocab at 0x7f61aa4d6940>,\\n\",\n       \" 'portly.': <gensim.models.word2vec.Vocab at 0x7f61aa4d6a20>,\\n\",\n       \" 'dere': <gensim.models.word2vec.Vocab at 0x7f61aa4d6ac8>,\\n\",\n       \" 'weeee': <gensim.models.word2vec.Vocab at 0x7f61aa5b9ef0>,\\n\",\n       \" 'reason': <gensim.models.word2vec.Vocab at 0x7f61aa4d6b00>,\\n\",\n       \" 'az': <gensim.models.word2vec.Vocab at 0x7f61aa4d7208>,\\n\",\n       \" 'pond..': <gensim.models.word2vec.Vocab at 0x7f61aa5e1ef0>,\\n\",\n       \" 'anyway).': <gensim.models.word2vec.Vocab at 0x7f61aa4d77b8>,\\n\",\n       \" 'adventurous': <gensim.models.word2vec.Vocab at 0x7f61aa4d7828>,\\n\",\n       \" 'supply': <gensim.models.word2vec.Vocab at 0x7f61aa4d70b8>,\\n\",\n       \" 'Bored': <gensim.models.word2vec.Vocab at 0x7f61aa4d7240>,\\n\",\n       \" 'black': <gensim.models.word2vec.Vocab at 0x7f61aa4d7278>,\\n\",\n       \" 'cambridge?': <gensim.models.word2vec.Vocab at 0x7f61aa4d7358>,\\n\",\n       \" 'noise': <gensim.models.word2vec.Vocab at 0x7f61aa4d7438>,\\n\",\n       \" 'Winnipeg.': <gensim.models.word2vec.Vocab at 0x7f61aa4d7470>,\\n\",\n       \" 'There': <gensim.models.word2vec.Vocab at 0x7f61aa4d74e0>,\\n\",\n       \" 'chat': <gensim.models.word2vec.Vocab at 0x7f61aa4d7588>,\\n\",\n       \" 'HERE': <gensim.models.word2vec.Vocab at 0x7f61aa4d76a0>,\\n\",\n       \" 'choose': <gensim.models.word2vec.Vocab at 0x7f61aa4d78d0>,\\n\",\n       \" 'morality,': <gensim.models.word2vec.Vocab at 0x7f61aa4d7a90>,\\n\",\n       \" 'favors': <gensim.models.word2vec.Vocab at 0x7f61aa4d7b38>,\\n\",\n       \" '[If': <gensim.models.word2vec.Vocab at 0x7f61aa4d7c50>,\\n\",\n       \" 'nvm,': <gensim.models.word2vec.Vocab at 0x7f61aa4d7cc0>,\\n\",\n       \" 'tragedy': <gensim.models.word2vec.Vocab at 0x7f61aa4d7d30>,\\n\",\n       \" 'japanese': <gensim.models.word2vec.Vocab at 0x7f61aa4d7da0>,\\n\",\n       \" 'invite': <gensim.models.word2vec.Vocab at 0x7f61aa54b780>,\\n\",\n       \" 'way.': <gensim.models.word2vec.Vocab at 0x7f61aa4d7e10>,\\n\",\n       \" 'HAPPY': <gensim.models.word2vec.Vocab at 0x7f61aa4d7f98>,\\n\",\n       \" 'fierce': <gensim.models.word2vec.Vocab at 0x7f61aa5e78d0>,\\n\",\n       \" 'fools': <gensim.models.word2vec.Vocab at 0x7f61aa4d13c8>,\\n\",\n       \" 'goes': <gensim.models.word2vec.Vocab at 0x7f61aa4d1400>,\\n\",\n       \" 'wafers': <gensim.models.word2vec.Vocab at 0x7f61aa4d1470>,\\n\",\n       \" ':-D': <gensim.models.word2vec.Vocab at 0x7f61aa5e7c88>,\\n\",\n       \" 'feathers': <gensim.models.word2vec.Vocab at 0x7f61aa5e7e10>,\\n\",\n       \" 'still...': <gensim.models.word2vec.Vocab at 0x7f61aa4425f8>,\\n\",\n       \" 'selene': <gensim.models.word2vec.Vocab at 0x7f61aa4d15c0>,\\n\",\n       \" 'dinner\\\"': <gensim.models.word2vec.Vocab at 0x7f61aa4d16a0>,\\n\",\n       \" 'EVERY': <gensim.models.word2vec.Vocab at 0x7f61aa4d16d8>,\\n\",\n       \" '(2)': <gensim.models.word2vec.Vocab at 0x7f61aa4d1710>,\\n\",\n       \" 'hormones': <gensim.models.word2vec.Vocab at 0x7f61aa4d1860>,\\n\",\n       \" 'singing': <gensim.models.word2vec.Vocab at 0x7f61aa4d1898>,\\n\",\n       \" 'carry': <gensim.models.word2vec.Vocab at 0x7f61aa4d1a58>,\\n\",\n       \" 'bestfriend': <gensim.models.word2vec.Vocab at 0x7f61aa4d1ac8>,\\n\",\n       \" 'AmeriCorps': <gensim.models.word2vec.Vocab at 0x7f61aa4d1b00>,\\n\",\n       \" 'tuesday': <gensim.models.word2vec.Vocab at 0x7f61aa4d1c50>,\\n\",\n       \" 'plants.': <gensim.models.word2vec.Vocab at 0x7f61aa4d1fd0>,\\n\",\n       \" 'Presidential': <gensim.models.word2vec.Vocab at 0x7f61aa4d1048>,\\n\",\n       \" 'dunno...i': <gensim.models.word2vec.Vocab at 0x7f61aa4d1278>,\\n\",\n       \" '[few': <gensim.models.word2vec.Vocab at 0x7f61aa4dd048>,\\n\",\n       \" 'exercise.': <gensim.models.word2vec.Vocab at 0x7f61aa4dd080>,\\n\",\n       \" 'WITH': <gensim.models.word2vec.Vocab at 0x7f61aa4dd198>,\\n\",\n       \" 'Figueroa': <gensim.models.word2vec.Vocab at 0x7f61aa4dd1d0>,\\n\",\n       \" 'softens': <gensim.models.word2vec.Vocab at 0x7f61aa4dd320>,\\n\",\n       \" 'true.': <gensim.models.word2vec.Vocab at 0x7f61aa466eb8>,\\n\",\n       \" 'ballpark': <gensim.models.word2vec.Vocab at 0x7f61aa4dd588>,\\n\",\n       \" 'sleep,': <gensim.models.word2vec.Vocab at 0x7f61aa4dd5f8>,\\n\",\n       \" 'names.': <gensim.models.word2vec.Vocab at 0x7f61aa4dd6d8>,\\n\",\n       \" 'you’re': <gensim.models.word2vec.Vocab at 0x7f61aa4dd710>,\\n\",\n       \" 'price': <gensim.models.word2vec.Vocab at 0x7f61aa4dd7f0>,\\n\",\n       \" 'pig': <gensim.models.word2vec.Vocab at 0x7f61aa4dd940>,\\n\",\n       \" 'time:': <gensim.models.word2vec.Vocab at 0x7f61aa4dda20>,\\n\",\n       \" 'Colella': <gensim.models.word2vec.Vocab at 0x7f61aa4dda90>,\\n\",\n       \" 'gift': <gensim.models.word2vec.Vocab at 0x7f61aa4ddb70>,\\n\",\n       \" 'american': <gensim.models.word2vec.Vocab at 0x7f61aa4ddba8>,\\n\",\n       \" 'poopie': <gensim.models.word2vec.Vocab at 0x7f61aa4ddcf8>,\\n\",\n       \" 'floor': <gensim.models.word2vec.Vocab at 0x7f61aa4ddd68>,\\n\",\n       \" 'talked': <gensim.models.word2vec.Vocab at 0x7f61aa4dddd8>,\\n\",\n       \" 'age': <gensim.models.word2vec.Vocab at 0x7f61aa4dde10>,\\n\",\n       \" 'sad.': <gensim.models.word2vec.Vocab at 0x7f61aa4dde48>,\\n\",\n       \" 'usually': <gensim.models.word2vec.Vocab at 0x7f61aa4dde80>,\\n\",\n       \" \\\"i'd\\\": <gensim.models.word2vec.Vocab at 0x7f61aa4040f0>,\\n\",\n       \" 'New]:': <gensim.models.word2vec.Vocab at 0x7f61aa4ddeb8>,\\n\",\n       \" 'out,': <gensim.models.word2vec.Vocab at 0x7f61aa4ddef0>,\\n\",\n       \" 'Secondly,': <gensim.models.word2vec.Vocab at 0x7f61aa4ddf28>,\\n\",\n       \" 'kicked': <gensim.models.word2vec.Vocab at 0x7f61aa4e0048>,\\n\",\n       \" 'stuff': <gensim.models.word2vec.Vocab at 0x7f61aa4e0080>,\\n\",\n       \" 'essences': <gensim.models.word2vec.Vocab at 0x7f61aa4e0128>,\\n\",\n       \" 'live': <gensim.models.word2vec.Vocab at 0x7f61aa4e0198>,\\n\",\n       \" 'aditi.': <gensim.models.word2vec.Vocab at 0x7f61aa4e01d0>,\\n\",\n       \" 'prepare,': <gensim.models.word2vec.Vocab at 0x7f61aa4e0320>,\\n\",\n       \" 'Ave': <gensim.models.word2vec.Vocab at 0x7f61aa4e0358>,\\n\",\n       \" 'Given': <gensim.models.word2vec.Vocab at 0x7f61aa4e0438>,\\n\",\n       \" 'C\\\"': <gensim.models.word2vec.Vocab at 0x7f61aa4e04e0>,\\n\",\n       \" 'touching': <gensim.models.word2vec.Vocab at 0x7f61aa4e0588>,\\n\",\n       \" 'Jeep),': <gensim.models.word2vec.Vocab at 0x7f61aa5df438>,\\n\",\n       \" 'Los': <gensim.models.word2vec.Vocab at 0x7f61aa4e0668>,\\n\",\n       \" 'wide.': <gensim.models.word2vec.Vocab at 0x7f61aa4e06d8>,\\n\",\n       \" 'though.': <gensim.models.word2vec.Vocab at 0x7f61aa4e0748>,\\n\",\n       \" 'sometime,': <gensim.models.word2vec.Vocab at 0x7f61aa554dd8>,\\n\",\n       \" 'had.': <gensim.models.word2vec.Vocab at 0x7f61aa4e07f0>,\\n\",\n       \" 'dreams': <gensim.models.word2vec.Vocab at 0x7f61aa4e0908>,\\n\",\n       \" 'jobs': <gensim.models.word2vec.Vocab at 0x7f61aa4e0940>,\\n\",\n       \" 'bike': <gensim.models.word2vec.Vocab at 0x7f61aa4e09b0>,\\n\",\n       \" 'waterfall': <gensim.models.word2vec.Vocab at 0x7f61aa4e09e8>,\\n\",\n       \" 'uhh....': <gensim.models.word2vec.Vocab at 0x7f61aa4e0a58>,\\n\",\n       \" 'strenuous': <gensim.models.word2vec.Vocab at 0x7f61aa4e0a90>,\\n\",\n       \" 'overly-perky': <gensim.models.word2vec.Vocab at 0x7f61aa554e48>,\\n\",\n       \" '....that': <gensim.models.word2vec.Vocab at 0x7f61aa4e0b70>,\\n\",\n       \" 'fraud': <gensim.models.word2vec.Vocab at 0x7f61aa4e0be0>,\\n\",\n       \" 'ahaha': <gensim.models.word2vec.Vocab at 0x7f61aa4e0c88>,\\n\",\n       \" 'New': <gensim.models.word2vec.Vocab at 0x7f61aa4e0cc0>,\\n\",\n       \" 'shopping': <gensim.models.word2vec.Vocab at 0x7f61aa4e0d30>,\\n\",\n       \" 'extra': <gensim.models.word2vec.Vocab at 0x7f61aa4e0d68>,\\n\",\n       \" 'use.': <gensim.models.word2vec.Vocab at 0x7f61aa4e0e10>,\\n\",\n       \" 'running--while': <gensim.models.word2vec.Vocab at 0x7f61aa4e0e80>,\\n\",\n       \" \\\"won't\\\": <gensim.models.word2vec.Vocab at 0x7f61aa4e0ef0>,\\n\",\n       \" 'no:': <gensim.models.word2vec.Vocab at 0x7f61aa4e0f28>,\\n\",\n       \" 'verb,': <gensim.models.word2vec.Vocab at 0x7f61aa4e0f60>,\\n\",\n       \" 'punch': <gensim.models.word2vec.Vocab at 0x7f61aa4e0f98>,\\n\",\n       \" 'tamar.': <gensim.models.word2vec.Vocab at 0x7f61aa4e0fd0>,\\n\",\n       \" 'summer': <gensim.models.word2vec.Vocab at 0x7f61aa4e3080>,\\n\",\n       \" 'got': <gensim.models.word2vec.Vocab at 0x7f61aa4e30f0>,\\n\",\n       \" 'breath,': <gensim.models.word2vec.Vocab at 0x7f61aa4e3240>,\\n\",\n       \" 'answer': <gensim.models.word2vec.Vocab at 0x7f61aa4e3278>,\\n\",\n       \" 'selves': <gensim.models.word2vec.Vocab at 0x7f61aa5d4eb8>,\\n\",\n       \" 'everthing': <gensim.models.word2vec.Vocab at 0x7f61aa4dd908>,\\n\",\n       \" 'nap,': <gensim.models.word2vec.Vocab at 0x7f61aa4e3470>,\\n\",\n       \" 'CBC': <gensim.models.word2vec.Vocab at 0x7f61aa4e34a8>,\\n\",\n       \" 'argument': <gensim.models.word2vec.Vocab at 0x7f61aa4e34e0>,\\n\",\n       \" 'if': <gensim.models.word2vec.Vocab at 0x7f61aa4e3550>,\\n\",\n       \" 'sorts': <gensim.models.word2vec.Vocab at 0x7f61aa5edd30>,\\n\",\n       \" 'fields,': <gensim.models.word2vec.Vocab at 0x7f61aa4e35c0>,\\n\",\n       \" 'canning': <gensim.models.word2vec.Vocab at 0x7f61aa4e36a0>,\\n\",\n       \" 'worry..': <gensim.models.word2vec.Vocab at 0x7f61aa4e36d8>,\\n\",\n       \" 'curtains!': <gensim.models.word2vec.Vocab at 0x7f61aa4e3828>,\\n\",\n       \" 'why…': <gensim.models.word2vec.Vocab at 0x7f61aa4e38d0>,\\n\",\n       \" 'fainting': <gensim.models.word2vec.Vocab at 0x7f61aa4e3940>,\\n\",\n       \" 'ONLY': <gensim.models.word2vec.Vocab at 0x7f61aa4e3978>,\\n\",\n       \" 'no-one': <gensim.models.word2vec.Vocab at 0x7f61aa4e3a58>,\\n\",\n       \" 'floating': <gensim.models.word2vec.Vocab at 0x7f61aa4e3a90>,\\n\",\n       \" 'messy,': <gensim.models.word2vec.Vocab at 0x7f61aa4e3b38>,\\n\",\n       \" 'third': <gensim.models.word2vec.Vocab at 0x7f61aa4e3ba8>,\\n\",\n       \" 'stood,': <gensim.models.word2vec.Vocab at 0x7f61aa4e3c18>,\\n\",\n       \" 'fishing?': <gensim.models.word2vec.Vocab at 0x7f61aa4e3cc0>,\\n\",\n       \" 'shall': <gensim.models.word2vec.Vocab at 0x7f61aa4e3d30>,\\n\",\n       \" 'everything': <gensim.models.word2vec.Vocab at 0x7f61aa4e3d68>,\\n\",\n       \" 'dog': <gensim.models.word2vec.Vocab at 0x7f61aa4e3da0>,\\n\",\n       \" 'semester!': <gensim.models.word2vec.Vocab at 0x7f61aa53ca20>,\\n\",\n       \" 'hurts': <gensim.models.word2vec.Vocab at 0x7f61aa4e3dd8>,\\n\",\n       \" 'blab': <gensim.models.word2vec.Vocab at 0x7f61aa4e3e10>,\\n\",\n       \" 'Cyan425:': <gensim.models.word2vec.Vocab at 0x7f61aa4e3e80>,\\n\",\n       \" 'kid': <gensim.models.word2vec.Vocab at 0x7f61aa4e3eb8>,\\n\",\n       \" 'Rumsfeld': <gensim.models.word2vec.Vocab at 0x7f61aa4e3ef0>,\\n\",\n       \" 'be:': <gensim.models.word2vec.Vocab at 0x7f61aa4e3f60>,\\n\",\n       \" 'character': <gensim.models.word2vec.Vocab at 0x7f61aa4e3f98>,\\n\",\n       \" 'too;': <gensim.models.word2vec.Vocab at 0x7f61aa4e3fd0>,\\n\",\n       \" 'cheese.': <gensim.models.word2vec.Vocab at 0x7f61aa4e4048>,\\n\",\n       \" 'showin': <gensim.models.word2vec.Vocab at 0x7f61aa4e4080>,\\n\",\n       \" 'DiFranco.': <gensim.models.word2vec.Vocab at 0x7f61aa4e40b8>,\\n\",\n       \" 'weeks.': <gensim.models.word2vec.Vocab at 0x7f61aa4e40f0>,\\n\",\n       \" 'authorized': <gensim.models.word2vec.Vocab at 0x7f61aa4e4128>,\\n\",\n       \" 'Or': <gensim.models.word2vec.Vocab at 0x7f61aa4e4208>,\\n\",\n       \" 'easier.': <gensim.models.word2vec.Vocab at 0x7f61aa4e4278>,\\n\",\n       \" 'deserve': <gensim.models.word2vec.Vocab at 0x7f61aa4e42b0>,\\n\",\n       \" 'reads': <gensim.models.word2vec.Vocab at 0x7f61aa4e42e8>,\\n\",\n       \" 'beautiful': <gensim.models.word2vec.Vocab at 0x7f61aa4e4390>,\\n\",\n       \" 'avril': <gensim.models.word2vec.Vocab at 0x7f61aa4e43c8>,\\n\",\n       \" 'days.': <gensim.models.word2vec.Vocab at 0x7f61aa5b9e80>,\\n\",\n       \" '\\\"can': <gensim.models.word2vec.Vocab at 0x7f61aa4e4400>,\\n\",\n       \" 'player:': <gensim.models.word2vec.Vocab at 0x7f61aa4e4470>,\\n\",\n       \" 'american??': <gensim.models.word2vec.Vocab at 0x7f61aa4e44a8>,\\n\",\n       \" 'Michelle': <gensim.models.word2vec.Vocab at 0x7f61aa4e44e0>,\\n\",\n       \" 'confusing,': <gensim.models.word2vec.Vocab at 0x7f61aa4e4550>,\\n\",\n       \" 'YoUr': <gensim.models.word2vec.Vocab at 0x7f61aa4e4588>,\\n\",\n       \" 'away...': <gensim.models.word2vec.Vocab at 0x7f61aa4e5358>,\\n\",\n       \" 'handed': <gensim.models.word2vec.Vocab at 0x7f61aa4e45f8>,\\n\",\n       \" 'casual': <gensim.models.word2vec.Vocab at 0x7f61aa4e46a0>,\\n\",\n       \" 'colorful': <gensim.models.word2vec.Vocab at 0x7f61aa4e4828>,\\n\",\n       \" 'lives.': <gensim.models.word2vec.Vocab at 0x7f61aa4e4898>,\\n\",\n       \" 'selfishness...busying': <gensim.models.word2vec.Vocab at 0x7f61aa4e4978>,\\n\",\n       \" 'shakes': <gensim.models.word2vec.Vocab at 0x7f61aa4e4a20>,\\n\",\n       \" 'workouts.': <gensim.models.word2vec.Vocab at 0x7f61aa4e4b70>,\\n\",\n       \" 'upon': <gensim.models.word2vec.Vocab at 0x7f61aa4e4cc0>,\\n\",\n       \" 'BACK': <gensim.models.word2vec.Vocab at 0x7f61aa4e4d68>,\\n\",\n       \" 'Radio': <gensim.models.word2vec.Vocab at 0x7f61aa4e4e48>,\\n\",\n       \" '\\\"Truly,': <gensim.models.word2vec.Vocab at 0x7f61aa4e4e80>,\\n\",\n       \" 'lord': <gensim.models.word2vec.Vocab at 0x7f61aa4e4ef0>,\\n\",\n       \" 'Opening': <gensim.models.word2vec.Vocab at 0x7f61aa4e4f28>,\\n\",\n       \" 'counts?': <gensim.models.word2vec.Vocab at 0x7f61aa4e4f60>,\\n\",\n       \" 'sorry?': <gensim.models.word2vec.Vocab at 0x7f61aa5df780>,\\n\",\n       \" 'His': <gensim.models.word2vec.Vocab at 0x7f61aa4e1710>,\\n\",\n       \" 'article': <gensim.models.word2vec.Vocab at 0x7f61aa4e1860>,\\n\",\n       \" '(Dear': <gensim.models.word2vec.Vocab at 0x7f61aa4e1898>,\\n\",\n       \" 'FAITH': <gensim.models.word2vec.Vocab at 0x7f61aa4e1b70>,\\n\",\n       \" 'Girl**': <gensim.models.word2vec.Vocab at 0x7f61aa4e1c18>,\\n\",\n       \" 'school': <gensim.models.word2vec.Vocab at 0x7f61aa5df7b8>,\\n\",\n       \" 'hheeh.': <gensim.models.word2vec.Vocab at 0x7f61aa4e1dd8>,\\n\",\n       \" 'done,': <gensim.models.word2vec.Vocab at 0x7f61aa4e1e48>,\\n\",\n       \" 'foot': <gensim.models.word2vec.Vocab at 0x7f61aa4e1eb8>,\\n\",\n       \" 'change...ppl': <gensim.models.word2vec.Vocab at 0x7f61aa4e1f28>,\\n\",\n       \" 'lungs': <gensim.models.word2vec.Vocab at 0x7f61aa4e1fd0>,\\n\",\n       \" \\\"didn't\\\": <gensim.models.word2vec.Vocab at 0x7f61aa4e1630>,\\n\",\n       \" ']': <gensim.models.word2vec.Vocab at 0x7f61aa4e1048>,\\n\",\n       \" 'summer.': <gensim.models.word2vec.Vocab at 0x7f61aa4e1080>,\\n\",\n       \" 'side,': <gensim.models.word2vec.Vocab at 0x7f61aa4e10b8>,\\n\",\n       \" 'this': <gensim.models.word2vec.Vocab at 0x7f61aa4e1128>,\\n\",\n       \" 'step': <gensim.models.word2vec.Vocab at 0x7f61aa4e1160>,\\n\",\n       \" 'sloth': <gensim.models.word2vec.Vocab at 0x7f61aa4e11d0>,\\n\",\n       \" 'essences,': <gensim.models.word2vec.Vocab at 0x7f61aa4e1438>,\\n\",\n       \" 'spice': <gensim.models.word2vec.Vocab at 0x7f61aa4e14e0>,\\n\",\n       \" 'Interesting:': <gensim.models.word2vec.Vocab at 0x7f61aa4e1518>,\\n\",\n       \" 'survive': <gensim.models.word2vec.Vocab at 0x7f61aa4e1588>,\\n\",\n       \" 'intelligence\\\"': <gensim.models.word2vec.Vocab at 0x7f61aa4e15c0>,\\n\",\n       \" 'cliff': <gensim.models.word2vec.Vocab at 0x7f61aa53c048>,\\n\",\n       \" 'dragging': <gensim.models.word2vec.Vocab at 0x7f61aa53c080>,\\n\",\n       \" 'Worst': <gensim.models.word2vec.Vocab at 0x7f61aa5c1ba8>,\\n\",\n       \" '\\\"L\\\"': <gensim.models.word2vec.Vocab at 0x7f61aa53c160>,\\n\",\n       \" 'columnists': <gensim.models.word2vec.Vocab at 0x7f61aa53c198>,\\n\",\n       \" 'shopping.': <gensim.models.word2vec.Vocab at 0x7f61aa53c1d0>,\\n\",\n       \" 'have...satisfied': <gensim.models.word2vec.Vocab at 0x7f61aa53c208>,\\n\",\n       \" 'lie.': <gensim.models.word2vec.Vocab at 0x7f61aa53c278>,\\n\",\n       \" 'flying': <gensim.models.word2vec.Vocab at 0x7f61aa53c320>,\\n\",\n       \" 'perhaps': <gensim.models.word2vec.Vocab at 0x7f61aa53c358>,\\n\",\n       \" 'myself..': <gensim.models.word2vec.Vocab at 0x7f61aa53c390>,\\n\",\n       \" 'thing.)': <gensim.models.word2vec.Vocab at 0x7f61aa53c3c8>,\\n\",\n       \" 'shattered': <gensim.models.word2vec.Vocab at 0x7f61aa53c400>,\\n\",\n       \" 'ACL': <gensim.models.word2vec.Vocab at 0x7f61aa53c438>,\\n\",\n       \" 'dressed,': <gensim.models.word2vec.Vocab at 0x7f61aa53c4a8>,\\n\",\n       \" 'someone...and': <gensim.models.word2vec.Vocab at 0x7f61aa53c588>,\\n\",\n       \" 'Random': <gensim.models.word2vec.Vocab at 0x7f61aa558a20>,\\n\",\n       \" 'painful': <gensim.models.word2vec.Vocab at 0x7f61aa53c5f8>,\\n\",\n       \" 'Florida?]:': <gensim.models.word2vec.Vocab at 0x7f61aa53c630>,\\n\",\n       \" 'Gulf': <gensim.models.word2vec.Vocab at 0x7f61aa53c668>,\\n\",\n       \" 'stupid': <gensim.models.word2vec.Vocab at 0x7f61aa53c6a0>,\\n\",\n       \" 'kneecap': <gensim.models.word2vec.Vocab at 0x7f61aa53c6d8>,\\n\",\n       \" '26th': <gensim.models.word2vec.Vocab at 0x7f61aa53c710>,\\n\",\n       \" 'recently': <gensim.models.word2vec.Vocab at 0x7f61aa53c748>,\\n\",\n       \" 'Eye': <gensim.models.word2vec.Vocab at 0x7f61aa53c780>,\\n\",\n       \" 'Insecure:': <gensim.models.word2vec.Vocab at 0x7f61aa53c7b8>,\\n\",\n       \" 'Organized:': <gensim.models.word2vec.Vocab at 0x7f61aa53c7f0>,\\n\",\n       \" 'school...*sigh*': <gensim.models.word2vec.Vocab at 0x7f61aa53c828>,\\n\",\n       \" 'shoulders': <gensim.models.word2vec.Vocab at 0x7f61aa53c860>,\\n\",\n       \" 'MoO': <gensim.models.word2vec.Vocab at 0x7f61aa53c898>,\\n\",\n       \" 'following': <gensim.models.word2vec.Vocab at 0x7f61aa53c8d0>,\\n\",\n       \" 'on,': <gensim.models.word2vec.Vocab at 0x7f61aa53c908>,\\n\",\n       \" 'pollution,': <gensim.models.word2vec.Vocab at 0x7f61aa53c940>,\\n\",\n       \" 'rosalie': <gensim.models.word2vec.Vocab at 0x7f61aa53c978>,\\n\",\n       \" 'law': <gensim.models.word2vec.Vocab at 0x7f61aa53c9b0>,\\n\",\n       \" 'norway,': <gensim.models.word2vec.Vocab at 0x7f61aa53c9e8>,\\n\",\n       \" 'have]': <gensim.models.word2vec.Vocab at 0x7f61aa5b6438>,\\n\",\n       \" '...cheers': <gensim.models.word2vec.Vocab at 0x7f61aa53ca58>,\\n\",\n       \" 'DrAmA': <gensim.models.word2vec.Vocab at 0x7f61aa53ca90>,\\n\",\n       \" 'searching': <gensim.models.word2vec.Vocab at 0x7f61aa5b4240>,\\n\",\n       \" 'people!': <gensim.models.word2vec.Vocab at 0x7f61aa53cb00>,\\n\",\n       \" 'fun!': <gensim.models.word2vec.Vocab at 0x7f61aa53cb38>,\\n\",\n       \" 'Yellowcard': <gensim.models.word2vec.Vocab at 0x7f61aa53cb70>,\\n\",\n       \" 'terminally': <gensim.models.word2vec.Vocab at 0x7f61aa53cba8>,\\n\",\n       \" 'right.': <gensim.models.word2vec.Vocab at 0x7f61aa53cbe0>,\\n\",\n       \" 'feet': <gensim.models.word2vec.Vocab at 0x7f61aa53cc18>,\\n\",\n       \" 'person.': <gensim.models.word2vec.Vocab at 0x7f61aa53cc50>,\\n\",\n       \" \\\"they're\\\": <gensim.models.word2vec.Vocab at 0x7f61aa53cc88>,\\n\",\n       \" 'Opposition': <gensim.models.word2vec.Vocab at 0x7f61aa53ccc0>,\\n\",\n       \" \\\"veterans'\\\": <gensim.models.word2vec.Vocab at 0x7f61aa53ccf8>,\\n\",\n       \" 'Quiz': <gensim.models.word2vec.Vocab at 0x7f61aa53cd30>,\\n\",\n       \" 'lying,': <gensim.models.word2vec.Vocab at 0x7f61aa53cd68>,\\n\",\n       \" '7.': <gensim.models.word2vec.Vocab at 0x7f61aa53cda0>,\\n\",\n       \" 'mention': <gensim.models.word2vec.Vocab at 0x7f61aa53cdd8>,\\n\",\n       \" 'weirdest': <gensim.models.word2vec.Vocab at 0x7f61aa53ce10>,\\n\",\n       \" '\\\"Stay': <gensim.models.word2vec.Vocab at 0x7f61aa5d7b00>,\\n\",\n       \" 'rear': <gensim.models.word2vec.Vocab at 0x7f61aa53ce48>,\\n\",\n       \" 'clairol': <gensim.models.word2vec.Vocab at 0x7f61aa53ce80>,\\n\",\n       \" 'nvm': <gensim.models.word2vec.Vocab at 0x7f61aa53ceb8>,\\n\",\n       \" 'minute': <gensim.models.word2vec.Vocab at 0x7f61aa558358>,\\n\",\n       \" 'getting': <gensim.models.word2vec.Vocab at 0x7f61aa53cf60>,\\n\",\n       \" 'prefer': <gensim.models.word2vec.Vocab at 0x7f61aa53cf98>,\\n\",\n       \" 'open': <gensim.models.word2vec.Vocab at 0x7f61aa53cfd0>,\\n\",\n       \" 'feeble': <gensim.models.word2vec.Vocab at 0x7f61aa54b048>,\\n\",\n       \" 'October': <gensim.models.word2vec.Vocab at 0x7f61aa5bb160>,\\n\",\n       \" 'LIKE': <gensim.models.word2vec.Vocab at 0x7f61aa54b0b8>,\\n\",\n       \" 'do': <gensim.models.word2vec.Vocab at 0x7f61aa54b0f0>,\\n\",\n       \" 'amount': <gensim.models.word2vec.Vocab at 0x7f61aa54b128>,\\n\",\n       \" 'gerbils': <gensim.models.word2vec.Vocab at 0x7f61aa54b160>,\\n\",\n       \" 'nasty': <gensim.models.word2vec.Vocab at 0x7f61aa558400>,\\n\",\n       \" 'Responsible:': <gensim.models.word2vec.Vocab at 0x7f61aa54b1d0>,\\n\",\n       \" 'America.': <gensim.models.word2vec.Vocab at 0x7f61aa54b208>,\\n\",\n       \" '\\\"I\\\\'d': <gensim.models.word2vec.Vocab at 0x7f61aa54b240>,\\n\",\n       \" 'game': <gensim.models.word2vec.Vocab at 0x7f61aa54b278>,\\n\",\n       \" 'behind\\\"': <gensim.models.word2vec.Vocab at 0x7f61aa54b2b0>,\\n\",\n       \" 'Free': <gensim.models.word2vec.Vocab at 0x7f61aa54b2e8>,\\n\",\n       \" '6:30.': <gensim.models.word2vec.Vocab at 0x7f61aa54b320>,\\n\",\n       \" 'doom,': <gensim.models.word2vec.Vocab at 0x7f61aa54b358>,\\n\",\n       \" 'family,': <gensim.models.word2vec.Vocab at 0x7f61aa54b390>,\\n\",\n       \" 'odd': <gensim.models.word2vec.Vocab at 0x7f61aa54b3c8>,\\n\",\n       \" 'bio': <gensim.models.word2vec.Vocab at 0x7f61aa54b400>,\\n\",\n       \" 'going...': <gensim.models.word2vec.Vocab at 0x7f61aa54b438>,\\n\",\n       \" 'post-its,': <gensim.models.word2vec.Vocab at 0x7f61aa54b470>,\\n\",\n       \" 'teachers': <gensim.models.word2vec.Vocab at 0x7f61aa54b4a8>,\\n\",\n       \" 'Time': <gensim.models.word2vec.Vocab at 0x7f61aa54b4e0>,\\n\",\n       \" '11:10': <gensim.models.word2vec.Vocab at 0x7f61aa54b518>,\\n\",\n       \" 'orchestra...': <gensim.models.word2vec.Vocab at 0x7f61aa569b38>,\\n\",\n       \" 'jacket': <gensim.models.word2vec.Vocab at 0x7f61aa54b588>,\\n\",\n       \" 'Talkative:': <gensim.models.word2vec.Vocab at 0x7f61aa54b5c0>,\\n\",\n       \" 'left-middle': <gensim.models.word2vec.Vocab at 0x7f61aa54b5f8>,\\n\",\n       \" 'radical': <gensim.models.word2vec.Vocab at 0x7f61aa54b630>,\\n\",\n       \" 'forever.': <gensim.models.word2vec.Vocab at 0x7f61aa54b668>,\\n\",\n       \" 'Guess': <gensim.models.word2vec.Vocab at 0x7f61aa560b38>,\\n\",\n       \" 'them,': <gensim.models.word2vec.Vocab at 0x7f61aa54b6d8>,\\n\",\n       \" 'normal,': <gensim.models.word2vec.Vocab at 0x7f61aa5dfc18>,\\n\",\n       \" \\\"lavigne's\\\": <gensim.models.word2vec.Vocab at 0x7f61aa54b748>,\\n\",\n       \" 'places.': <gensim.models.word2vec.Vocab at 0x7f61aa54b7b8>,\\n\",\n       \" 'laugh': <gensim.models.word2vec.Vocab at 0x7f61aa54b7f0>,\\n\",\n       \" 'vik': <gensim.models.word2vec.Vocab at 0x7f61aa54b860>,\\n\",\n       \" 'yet...or': <gensim.models.word2vec.Vocab at 0x7f61aa5cec50>,\\n\",\n       \" 'night..': <gensim.models.word2vec.Vocab at 0x7f61aa54b898>,\\n\",\n       \" 'states': <gensim.models.word2vec.Vocab at 0x7f61aa54b8d0>,\\n\",\n       \" 'done)': <gensim.models.word2vec.Vocab at 0x7f61aa54b908>,\\n\",\n       \" 'excuses': <gensim.models.word2vec.Vocab at 0x7f61aa5dfcc0>,\\n\",\n       \" 'treason.': <gensim.models.word2vec.Vocab at 0x7f61aa54b978>,\\n\",\n       \" 'Gold': <gensim.models.word2vec.Vocab at 0x7f61aa54b9b0>,\\n\",\n       \" 'words?': <gensim.models.word2vec.Vocab at 0x7f61aa54b9e8>,\\n\",\n       \" 'fall': <gensim.models.word2vec.Vocab at 0x7f61aa54ba20>,\\n\",\n       \" 'online': <gensim.models.word2vec.Vocab at 0x7f61aa54ba58>,\\n\",\n       \" 'lips,': <gensim.models.word2vec.Vocab at 0x7f61aa54ba90>,\\n\",\n       \" 'PLEAAAASSSSSSEEEEEEE': <gensim.models.word2vec.Vocab at 0x7f61aa54bac8>,\\n\",\n       \" 'God': <gensim.models.word2vec.Vocab at 0x7f61aa54bb00>,\\n\",\n       \" 'b/c': <gensim.models.word2vec.Vocab at 0x7f61aa54bb38>,\\n\",\n       \" 'worst': <gensim.models.word2vec.Vocab at 0x7f61aa54bb70>,\\n\",\n       \" 'cancelling': <gensim.models.word2vec.Vocab at 0x7f61aa54bba8>,\\n\",\n       \" 'by': <gensim.models.word2vec.Vocab at 0x7f61aa54bbe0>,\\n\",\n       \" 'BS': <gensim.models.word2vec.Vocab at 0x7f61aa54bc18>,\\n\",\n       \" 'bugs': <gensim.models.word2vec.Vocab at 0x7f61aa54bc50>,\\n\",\n       \" 'succumb': <gensim.models.word2vec.Vocab at 0x7f61aa54bc88>,\\n\",\n       \" 'baby...': <gensim.models.word2vec.Vocab at 0x7f61aa54bcc0>,\\n\",\n       \" 'seems': <gensim.models.word2vec.Vocab at 0x7f61aa54bcf8>,\\n\",\n       \" 'color(s):': <gensim.models.word2vec.Vocab at 0x7f61aa54bd30>,\\n\",\n       \" 'Washington-based': <gensim.models.word2vec.Vocab at 0x7f61aa54bd68>,\\n\",\n       \" 'support': <gensim.models.word2vec.Vocab at 0x7f61aa54bda0>,\\n\",\n       \" 'never)': <gensim.models.word2vec.Vocab at 0x7f61aa54bdd8>,\\n\",\n       \" 'afternoon': <gensim.models.word2vec.Vocab at 0x7f61aa54be10>,\\n\",\n       \" 'sprints.': <gensim.models.word2vec.Vocab at 0x7f61aa54be48>,\\n\",\n       \" 'tank': <gensim.models.word2vec.Vocab at 0x7f61aa54be80>,\\n\",\n       \" 'center': <gensim.models.word2vec.Vocab at 0x7f61aa445080>,\\n\",\n       \" 'repetition': <gensim.models.word2vec.Vocab at 0x7f61aa54bef0>,\\n\",\n       \" 'loneliness': <gensim.models.word2vec.Vocab at 0x7f61aa5e5a90>,\\n\",\n       \" '\\\"Fast': <gensim.models.word2vec.Vocab at 0x7f61aa54bf60>,\\n\",\n       \" 'UNDERWORLD': <gensim.models.word2vec.Vocab at 0x7f61aa438208>,\\n\",\n       \" '(hmm,': <gensim.models.word2vec.Vocab at 0x7f61aa54bfd0>,\\n\",\n       \" 'shoes.': <gensim.models.word2vec.Vocab at 0x7f61aa54f048>,\\n\",\n       \" '(chocolate': <gensim.models.word2vec.Vocab at 0x7f61aa54f080>,\\n\",\n       \" 'THE': <gensim.models.word2vec.Vocab at 0x7f61aa54f0b8>,\\n\",\n       \" 'bakin': <gensim.models.word2vec.Vocab at 0x7f61aa54f0f0>,\\n\",\n       \" 'those': <gensim.models.word2vec.Vocab at 0x7f61aa54f128>,\\n\",\n       \" 'post...my': <gensim.models.word2vec.Vocab at 0x7f61aa5dfe48>,\\n\",\n       \" 'about.': <gensim.models.word2vec.Vocab at 0x7f61aa54f198>,\\n\",\n       \" 'helped': <gensim.models.word2vec.Vocab at 0x7f61aa54f1d0>,\\n\",\n       \" 'hit': <gensim.models.word2vec.Vocab at 0x7f61aa54f240>,\\n\",\n       \" 'unlike': <gensim.models.word2vec.Vocab at 0x7f61aa54f278>,\\n\",\n       \" 'comments,': <gensim.models.word2vec.Vocab at 0x7f61aa54f2b0>,\\n\",\n       \" 'yellow.': <gensim.models.word2vec.Vocab at 0x7f61aa54f2e8>,\\n\",\n       \" 'youll': <gensim.models.word2vec.Vocab at 0x7f61aa54f320>,\\n\",\n       \" 'Finally': <gensim.models.word2vec.Vocab at 0x7f61aa54f358>,\\n\",\n       \" 'David': <gensim.models.word2vec.Vocab at 0x7f61aa54f390>,\\n\",\n       \" 'cover': <gensim.models.word2vec.Vocab at 0x7f61aa54f3c8>,\\n\",\n       \" 'Colin': <gensim.models.word2vec.Vocab at 0x7f61aa54f400>,\\n\",\n       \" 'complain': <gensim.models.word2vec.Vocab at 0x7f61aa54f438>,\\n\",\n       \" 'sometime': <gensim.models.word2vec.Vocab at 0x7f61aa54f470>,\\n\",\n       \" 'shore,': <gensim.models.word2vec.Vocab at 0x7f61aa54f4a8>,\\n\",\n       \" 'be?]:': <gensim.models.word2vec.Vocab at 0x7f61aa4420b8>,\\n\",\n       \" 'lee': <gensim.models.word2vec.Vocab at 0x7f61aa54f4e0>,\\n\",\n       \" 'Lonely': <gensim.models.word2vec.Vocab at 0x7f61aa54f518>,\\n\",\n       \" 'starred': <gensim.models.word2vec.Vocab at 0x7f61aa54f550>,\\n\",\n       \" 'sumtin': <gensim.models.word2vec.Vocab at 0x7f61aa54f588>,\\n\",\n       \" 'tints?': <gensim.models.word2vec.Vocab at 0x7f61aa54f5c0>,\\n\",\n       \" 'homework': <gensim.models.word2vec.Vocab at 0x7f61aa54f5f8>,\\n\",\n       \" 'towers': <gensim.models.word2vec.Vocab at 0x7f61aa54f630>,\\n\",\n       \" 'saddest': <gensim.models.word2vec.Vocab at 0x7f61aa46bcf8>,\\n\",\n       \" 'Garden,': <gensim.models.word2vec.Vocab at 0x7f61aa54f668>,\\n\",\n       \" 'green,': <gensim.models.word2vec.Vocab at 0x7f61aa54f6a0>,\\n\",\n       \" 'you:': <gensim.models.word2vec.Vocab at 0x7f61aa54f6d8>,\\n\",\n       \" 'sex?': <gensim.models.word2vec.Vocab at 0x7f61aa54f710>,\\n\",\n       \" 'black,': <gensim.models.word2vec.Vocab at 0x7f61aa54f748>,\\n\",\n       \" 'feasible,': <gensim.models.word2vec.Vocab at 0x7f61aa54f780>,\\n\",\n       \" 'YOU...': <gensim.models.word2vec.Vocab at 0x7f61aa54f7b8>,\\n\",\n       \" 'trouble?': <gensim.models.word2vec.Vocab at 0x7f61aa54f7f0>,\\n\",\n       \" 'me...appreciative': <gensim.models.word2vec.Vocab at 0x7f61aa4609e8>,\\n\",\n       \" 'learner': <gensim.models.word2vec.Vocab at 0x7f61aa54f860>,\\n\",\n       \" 'hours': <gensim.models.word2vec.Vocab at 0x7f61aa54f898>,\\n\",\n       \" 'feast': <gensim.models.word2vec.Vocab at 0x7f61aa54f8d0>,\\n\",\n       \" 'again!': <gensim.models.word2vec.Vocab at 0x7f61aa54f908>,\\n\",\n       \" 'tip': <gensim.models.word2vec.Vocab at 0x7f61aa54f940>,\\n\",\n       \" 'You...': <gensim.models.word2vec.Vocab at 0x7f61aa54f978>,\\n\",\n       \" 'KNOW': <gensim.models.word2vec.Vocab at 0x7f61aa54f9b0>,\\n\",\n       \" 'purple': <gensim.models.word2vec.Vocab at 0x7f61aa54f9e8>,\\n\",\n       \" 'Dreams': <gensim.models.word2vec.Vocab at 0x7f61aa54fa20>,\\n\",\n       \" 'here': <gensim.models.word2vec.Vocab at 0x7f61aa54fa58>,\\n\",\n       \" 'accused': <gensim.models.word2vec.Vocab at 0x7f61aa54fa90>,\\n\",\n       \" 'since': <gensim.models.word2vec.Vocab at 0x7f61aa54fac8>,\\n\",\n       \" 'HATE': <gensim.models.word2vec.Vocab at 0x7f61aa54fb00>,\\n\",\n       \" 'walk': <gensim.models.word2vec.Vocab at 0x7f61aa54fb38>,\\n\",\n       \" 'outta': <gensim.models.word2vec.Vocab at 0x7f61aa54fb70>,\\n\",\n       \" 'yet,': <gensim.models.word2vec.Vocab at 0x7f61aa54fba8>,\\n\",\n       \" \\\"other...we're\\\": <gensim.models.word2vec.Vocab at 0x7f61aa54fbe0>,\\n\",\n       \" 'look': <gensim.models.word2vec.Vocab at 0x7f61aa54fc18>,\\n\",\n       \" ':-/': <gensim.models.word2vec.Vocab at 0x7f61aa54fc50>,\\n\",\n       \" 'yet': <gensim.models.word2vec.Vocab at 0x7f61aa54fc88>,\\n\",\n       \" 'background': <gensim.models.word2vec.Vocab at 0x7f61aa54fcc0>,\\n\",\n       \" 'is.': <gensim.models.word2vec.Vocab at 0x7f61aa54fcf8>,\\n\",\n       \" 'now...': <gensim.models.word2vec.Vocab at 0x7f61aa54fd68>,\\n\",\n       \" 'grow': <gensim.models.word2vec.Vocab at 0x7f61aa54fda0>,\\n\",\n       \" 'dough': <gensim.models.word2vec.Vocab at 0x7f61aa54fdd8>,\\n\",\n       \" 'government,': <gensim.models.word2vec.Vocab at 0x7f61aa54fe10>,\\n\",\n       \" 'okie...that': <gensim.models.word2vec.Vocab at 0x7f61aa54fe48>,\\n\",\n       \" 'plan': <gensim.models.word2vec.Vocab at 0x7f61aa54fe80>,\\n\",\n       \" 'ummm...': <gensim.models.word2vec.Vocab at 0x7f61aa54feb8>,\\n\",\n       \" 'king....': <gensim.models.word2vec.Vocab at 0x7f61aa54fef0>,\\n\",\n       \" 'Marianne': <gensim.models.word2vec.Vocab at 0x7f61aa54ff60>,\\n\",\n       \" 'until': <gensim.models.word2vec.Vocab at 0x7f61aa54ff98>,\\n\",\n       \" 'mashed': <gensim.models.word2vec.Vocab at 0x7f61aa5e1208>,\\n\",\n       \" 'rain': <gensim.models.word2vec.Vocab at 0x7f61aa553048>,\\n\",\n       \" 'freshman': <gensim.models.word2vec.Vocab at 0x7f61aa553080>,\\n\",\n       \" 'calls': <gensim.models.word2vec.Vocab at 0x7f61aa5530b8>,\\n\",\n       \" \\\"us...we're\\\": <gensim.models.word2vec.Vocab at 0x7f61aa5530f0>,\\n\",\n       \" 'Soviet': <gensim.models.word2vec.Vocab at 0x7f61aa553128>,\\n\",\n       \" 'gears,': <gensim.models.word2vec.Vocab at 0x7f61aa553160>,\\n\",\n       \" 'knife': <gensim.models.word2vec.Vocab at 0x7f61aa553198>,\\n\",\n       \" 'Floods,': <gensim.models.word2vec.Vocab at 0x7f61aa5531d0>,\\n\",\n       \" '(and': <gensim.models.word2vec.Vocab at 0x7f61aa553208>,\\n\",\n       \" 'America': <gensim.models.word2vec.Vocab at 0x7f61aa553240>,\\n\",\n       \" 'shi,': <gensim.models.word2vec.Vocab at 0x7f61aa553278>,\\n\",\n       \" 'considering': <gensim.models.word2vec.Vocab at 0x7f61aa5532b0>,\\n\",\n       \" 'committed': <gensim.models.word2vec.Vocab at 0x7f61aa5532e8>,\\n\",\n       \" 'situation,': <gensim.models.word2vec.Vocab at 0x7f61aa553320>,\\n\",\n       \" 'stole': <gensim.models.word2vec.Vocab at 0x7f61aa553358>,\\n\",\n       \" 'brushing': <gensim.models.word2vec.Vocab at 0x7f61aa553390>,\\n\",\n       \" 'happily': <gensim.models.word2vec.Vocab at 0x7f61aa5533c8>,\\n\",\n       \" 'hand': <gensim.models.word2vec.Vocab at 0x7f61aa553400>,\\n\",\n       \" 'problem': <gensim.models.word2vec.Vocab at 0x7f61aa553438>,\\n\",\n       \" 'us': <gensim.models.word2vec.Vocab at 0x7f61aa553470>,\\n\",\n       \" 'color': <gensim.models.word2vec.Vocab at 0x7f61aa5534a8>,\\n\",\n       \" 'barely': <gensim.models.word2vec.Vocab at 0x7f61aa558a90>,\\n\",\n       \" '2:': <gensim.models.word2vec.Vocab at 0x7f61aa553518>,\\n\",\n       \" 'repetition.': <gensim.models.word2vec.Vocab at 0x7f61aa553550>,\\n\",\n       \" 'ready': <gensim.models.word2vec.Vocab at 0x7f61aa553588>,\\n\",\n       \" 'everynight,': <gensim.models.word2vec.Vocab at 0x7f61aa5535c0>,\\n\",\n       \" 'brownies': <gensim.models.word2vec.Vocab at 0x7f61aa5364a8>,\\n\",\n       \" 'freaked': <gensim.models.word2vec.Vocab at 0x7f61aa553630>,\\n\",\n       \" 'medium.': <gensim.models.word2vec.Vocab at 0x7f61aa447048>,\\n\",\n       \" 'IS': <gensim.models.word2vec.Vocab at 0x7f61aa5536a0>,\\n\",\n       \" 'helps': <gensim.models.word2vec.Vocab at 0x7f61aa5536d8>,\\n\",\n       \" 'sophie?': <gensim.models.word2vec.Vocab at 0x7f61aa553710>,\\n\",\n       \" '\\\"Trust': <gensim.models.word2vec.Vocab at 0x7f61aa553748>,\\n\",\n       \" 'Now,': <gensim.models.word2vec.Vocab at 0x7f61aa536748>,\\n\",\n       \" 'tact': <gensim.models.word2vec.Vocab at 0x7f61aa5537b8>,\\n\",\n       \" 'needs': <gensim.models.word2vec.Vocab at 0x7f61aa5537f0>,\\n\",\n       \" 'uniter,': <gensim.models.word2vec.Vocab at 0x7f61aa553828>,\\n\",\n       \" 'He': <gensim.models.word2vec.Vocab at 0x7f61aa553860>,\\n\",\n       \" 'family)': <gensim.models.word2vec.Vocab at 0x7f61aa553898>,\\n\",\n       \" 'again...or': <gensim.models.word2vec.Vocab at 0x7f61aa5538d0>,\\n\",\n       \" 'hearts': <gensim.models.word2vec.Vocab at 0x7f61aa553908>,\\n\",\n       \" 'react': <gensim.models.word2vec.Vocab at 0x7f61aa5e1400>,\\n\",\n       \" 'Flogging': <gensim.models.word2vec.Vocab at 0x7f61aa553978>,\\n\",\n       \" 'running,': <gensim.models.word2vec.Vocab at 0x7f61aa5539e8>,\\n\",\n       \" 'razors': <gensim.models.word2vec.Vocab at 0x7f61aa4eaf28>,\\n\",\n       \" 'rarely': <gensim.models.word2vec.Vocab at 0x7f61aa445630>,\\n\",\n       \" 'daunted:': <gensim.models.word2vec.Vocab at 0x7f61aa553a90>,\\n\",\n       \" 'very': <gensim.models.word2vec.Vocab at 0x7f61aa553ac8>,\\n\",\n       \" 'around': <gensim.models.word2vec.Vocab at 0x7f61aa4ec4e0>,\\n\",\n       \" 'except': <gensim.models.word2vec.Vocab at 0x7f61aa553b38>,\\n\",\n       \" 'war,\\\"': <gensim.models.word2vec.Vocab at 0x7f61aa553b70>,\\n\",\n       \" 'become': <gensim.models.word2vec.Vocab at 0x7f61aa553ba8>,\\n\",\n       \" 'know,,,': <gensim.models.word2vec.Vocab at 0x7f61aa553be0>,\\n\",\n       \" 'asleep': <gensim.models.word2vec.Vocab at 0x7f61aa553c18>,\\n\",\n       \" 'sad...that': <gensim.models.word2vec.Vocab at 0x7f61aa4ed668>,\\n\",\n       \" 'of,': <gensim.models.word2vec.Vocab at 0x7f61aa553c88>,\\n\",\n       \" 'week,': <gensim.models.word2vec.Vocab at 0x7f61aa553cc0>,\\n\",\n       \" 'SATs...fuun...sux...but': <gensim.models.word2vec.Vocab at 0x7f61aa553cf8>,\\n\",\n       \" '...[should': <gensim.models.word2vec.Vocab at 0x7f61aa553d30>,\\n\",\n       \" 'dropped': <gensim.models.word2vec.Vocab at 0x7f61aa4ee0b8>,\\n\",\n       \" 'sure,': <gensim.models.word2vec.Vocab at 0x7f61aa553da0>,\\n\",\n       \" 'cool.': <gensim.models.word2vec.Vocab at 0x7f61aa553dd8>,\\n\",\n       \" 'jetlag': <gensim.models.word2vec.Vocab at 0x7f61aa553e10>,\\n\",\n       \" 'fit.': <gensim.models.word2vec.Vocab at 0x7f61aa54b828>,\\n\",\n       \" 'Arrogant:': <gensim.models.word2vec.Vocab at 0x7f61aa553e80>,\\n\",\n       \" 'now?]:': <gensim.models.word2vec.Vocab at 0x7f61aa553eb8>,\\n\",\n       \" 'objectives': <gensim.models.word2vec.Vocab at 0x7f61aa553ef0>,\\n\",\n       \" 'me...they': <gensim.models.word2vec.Vocab at 0x7f61aa553f28>,\\n\",\n       \" 'call': <gensim.models.word2vec.Vocab at 0x7f61aa553f60>,\\n\",\n       \" 'Today': <gensim.models.word2vec.Vocab at 0x7f61aa53b240>,\\n\",\n       \" 'checking': <gensim.models.word2vec.Vocab at 0x7f61aa53b400>,\\n\",\n       \" 'tried': <gensim.models.word2vec.Vocab at 0x7f61aa554048>,\\n\",\n       \" 'old,': <gensim.models.word2vec.Vocab at 0x7f61aa554080>,\\n\",\n       \" 'glasses': <gensim.models.word2vec.Vocab at 0x7f61aa5540b8>,\\n\",\n       \" 'bill': <gensim.models.word2vec.Vocab at 0x7f61aa5540f0>,\\n\",\n       \" 'fourth,': <gensim.models.word2vec.Vocab at 0x7f61aa554128>,\\n\",\n       \" 'better': <gensim.models.word2vec.Vocab at 0x7f61aa554160>,\\n\",\n       \" 'ground': <gensim.models.word2vec.Vocab at 0x7f61aa554198>,\\n\",\n       \" 'More': <gensim.models.word2vec.Vocab at 0x7f61aa5541d0>,\\n\",\n       \" 'gameroom': <gensim.models.word2vec.Vocab at 0x7f61aa4e5588>,\\n\",\n       \" 'above': <gensim.models.word2vec.Vocab at 0x7f61aa554240>,\\n\",\n       \" 'eventful.': <gensim.models.word2vec.Vocab at 0x7f61aa554278>,\\n\",\n       \" 'happen': <gensim.models.word2vec.Vocab at 0x7f61aa5542b0>,\\n\",\n       \" 'Lazy': <gensim.models.word2vec.Vocab at 0x7f61aa5542e8>,\\n\",\n       \" 'license': <gensim.models.word2vec.Vocab at 0x7f61aa4e8320>,\\n\",\n       \" 'bleating': <gensim.models.word2vec.Vocab at 0x7f61aa554358>,\\n\",\n       \" 'start.': <gensim.models.word2vec.Vocab at 0x7f61aa554390>,\\n\",\n       \" 'will': <gensim.models.word2vec.Vocab at 0x7f61aa5543c8>,\\n\",\n       \" '?': <gensim.models.word2vec.Vocab at 0x7f61aa554400>,\\n\",\n       \" 'napping': <gensim.models.word2vec.Vocab at 0x7f61aa554438>,\\n\",\n       \" 'Better?': <gensim.models.word2vec.Vocab at 0x7f61aa554470>,\\n\",\n       \" 'linoleum': <gensim.models.word2vec.Vocab at 0x7f61aa5544a8>,\\n\",\n       \" 'SOMETHING!': <gensim.models.word2vec.Vocab at 0x7f61aa5544e0>,\\n\",\n       \" 'sophie': <gensim.models.word2vec.Vocab at 0x7f61aa4d8828>,\\n\",\n       \" 'reacts,': <gensim.models.word2vec.Vocab at 0x7f61aa554550>,\\n\",\n       \" 'Car\\\"': <gensim.models.word2vec.Vocab at 0x7f61aa554588>,\\n\",\n       \" 'extinct.': <gensim.models.word2vec.Vocab at 0x7f61aa5e1550>,\\n\",\n       \" 'knowin': <gensim.models.word2vec.Vocab at 0x7f61aa5545f8>,\\n\",\n       \" 'looks': <gensim.models.word2vec.Vocab at 0x7f61aa554630>,\\n\",\n       \" 'alex!': <gensim.models.word2vec.Vocab at 0x7f61aa554668>,\\n\",\n       \" 'analyze': <gensim.models.word2vec.Vocab at 0x7f61aa5546a0>,\\n\",\n       \" 'internet': <gensim.models.word2vec.Vocab at 0x7f61aa5546d8>,\\n\",\n       \" 'am,': <gensim.models.word2vec.Vocab at 0x7f61aa554710>,\\n\",\n       \" \\\"I'll\\\": <gensim.models.word2vec.Vocab at 0x7f61aa554748>,\\n\",\n       \" 'go:': <gensim.models.word2vec.Vocab at 0x7f61aa554780>,\\n\",\n       \" 'hardest': <gensim.models.word2vec.Vocab at 0x7f61aa5547b8>,\\n\",\n       \" 'bed:': <gensim.models.word2vec.Vocab at 0x7f61aa5547f0>,\\n\",\n       \" 'tower!!': <gensim.models.word2vec.Vocab at 0x7f61aa554828>,\\n\",\n       \" '(analyze': <gensim.models.word2vec.Vocab at 0x7f61aa554860>,\\n\",\n       \" 'Rice': <gensim.models.word2vec.Vocab at 0x7f61aa554898>,\\n\",\n       \" 'bravest': <gensim.models.word2vec.Vocab at 0x7f61aa5548d0>,\\n\",\n       \" ...}\"\n      ]\n     },\n     \"execution_count\": 10,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"w2v.vocab\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.082851942583535218\"\n      ]\n     },\n     \"execution_count\": 11,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"w2v.similarity('I', 'My')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"I've tried starting blog after blog and it just never feels right.  Then I read today that it feels strange to most people, but the more you do it the better it gets (hmm, sounds suspiciously like something else!) so I decided to give it another try.    My husband bought me a notepad at  urlLink McNally  (the best bookstore in Western Canada) with that title and a picture of a 50s housewife grinning desperately.  Each page has something funny like \\\"New curtains!  Hurrah!\\\".  For some reason it struck me as absolutely hilarious and has stuck in my head ever since.  What were those women thinking?\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.037229111896779618\"\n      ]\n     },\n     \"execution_count\": 12,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"print(posts[5])\\n\",\n    \"w2v.similarity('ring', 'husband')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.11547398696865138\"\n      ]\n     },\n     \"execution_count\": 15,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"w2v.similarity('ring', 'housewife')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"-0.14627530812290576\"\n      ]\n     },\n     \"execution_count\": 16,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"w2v.similarity('women', 'housewife')  # Diversity friendly\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Doc2Vec\\n\",\n    \"\\n\",\n    \"The same technique of word2vec is extrapolated to documents. Here, we do everything done in word2vec + we vectorize the documents too\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# 0 for male, 1 for female\\n\",\n    \"y_posts = np.concatenate((np.zeros(len(filtered_male_posts)),\\n\",\n    \"                          np.ones(len(filtered_female_posts))))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"4842\"\n      ]\n     },\n     \"execution_count\": 19,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"len(y_posts)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Convolutional Neural Networks for Sentence Classification\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Train convolutional network for sentiment analysis. Based on\\n\",\n    \"\\\"Convolutional Neural Networks for Sentence Classification\\\" by Yoon Kim\\n\",\n    \"http://arxiv.org/pdf/1408.5882v2.pdf\\n\",\n    \"\\n\",\n    \"For 'CNN-non-static' gets to 82.1% after 61 epochs with following settings:\\n\",\n    \"embedding_dim = 20          \\n\",\n    \"filter_sizes = (3, 4)\\n\",\n    \"num_filters = 3\\n\",\n    \"dropout_prob = (0.7, 0.8)\\n\",\n    \"hidden_dims = 100\\n\",\n    \"\\n\",\n    \"For 'CNN-rand' gets to 78-79% after 7-8 epochs with following settings:\\n\",\n    \"embedding_dim = 20          \\n\",\n    \"filter_sizes = (3, 4)\\n\",\n    \"num_filters = 150\\n\",\n    \"dropout_prob = (0.25, 0.5)\\n\",\n    \"hidden_dims = 150\\n\",\n    \"\\n\",\n    \"For 'CNN-static' gets to 75.4% after 7 epochs with following settings:\\n\",\n    \"embedding_dim = 100          \\n\",\n    \"filter_sizes = (3, 4)\\n\",\n    \"num_filters = 150\\n\",\n    \"dropout_prob = (0.25, 0.5)\\n\",\n    \"hidden_dims = 150\\n\",\n    \"\\n\",\n    \"* it turns out that such a small data set as \\\"Movie reviews with one\\n\",\n    \"sentence per review\\\"  (Pang and Lee, 2005) requires much smaller network\\n\",\n    \"than the one introduced in the original article:\\n\",\n    \"- embedding dimension is only 20 (instead of 300; 'CNN-static' still requires ~100)\\n\",\n    \"- 2 filter sizes (instead of 3)\\n\",\n    \"- higher dropout probabilities and\\n\",\n    \"- 3 filters per filter size is enough for 'CNN-non-static' (instead of 100)\\n\",\n    \"- embedding initialization does not require prebuilt Google Word2Vec data.\\n\",\n    \"Training Word2Vec on the same \\\"Movie reviews\\\" data set is enough to \\n\",\n    \"achieve performance reported in the article (81.6%)\\n\",\n    \"\\n\",\n    \"** Another distinct difference is slidind MaxPooling window of length=2\\n\",\n    \"instead of MaxPooling over whole feature map as in the article\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Using gpu device 0: GeForce GTX 760 (CNMeM is enabled with initial size: 90.0% of memory, cuDNN 4007)\\n\",\n      \"Using Theano backend.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import data_helpers\\n\",\n    \"from w2v import train_word2vec\\n\",\n    \"\\n\",\n    \"from keras.models import Sequential, Model\\n\",\n    \"from keras.layers import (Activation, Dense, Dropout, Embedding, \\n\",\n    \"                          Flatten, Input, Merge, \\n\",\n    \"                          Convolution1D, MaxPooling1D)\\n\",\n    \"\\n\",\n    \"np.random.seed(2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Parameters\\n\",\n    \"\\n\",\n    \"Model Variations. See Kim Yoon's Convolutional Neural Networks for \\n\",\n    \"Sentence Classification, Section 3 for detail.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Model variation is CNN-rand\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"model_variation = 'CNN-rand'  #  CNN-rand | CNN-non-static | CNN-static\\n\",\n    \"print('Model variation is %s' % model_variation)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Model Hyperparameters\\n\",\n    \"sequence_length = 56\\n\",\n    \"embedding_dim = 20          \\n\",\n    \"filter_sizes = (3, 4)\\n\",\n    \"num_filters = 150\\n\",\n    \"dropout_prob = (0.25, 0.5)\\n\",\n    \"hidden_dims = 150\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Training parameters\\n\",\n    \"batch_size = 32\\n\",\n    \"num_epochs = 100\\n\",\n    \"val_split = 0.1\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Word2Vec parameters, see train_word2vec\\n\",\n    \"min_word_count = 1  # Minimum word count                        \\n\",\n    \"context = 10        # Context window size    \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Data Preparation \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Loading data...\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Load data\\n\",\n    \"print(\\\"Loading data...\\\")\\n\",\n    \"x, y, vocabulary, vocabulary_inv = data_helpers.load_data()\\n\",\n    \"\\n\",\n    \"if model_variation=='CNN-non-static' or model_variation=='CNN-static':\\n\",\n    \"    embedding_weights = train_word2vec(x, vocabulary_inv, \\n\",\n    \"                                       embedding_dim, min_word_count, \\n\",\n    \"                                       context)\\n\",\n    \"    if model_variation=='CNN-static':\\n\",\n    \"        x = embedding_weights[0][x]\\n\",\n    \"elif model_variation=='CNN-rand':\\n\",\n    \"    embedding_weights = None\\n\",\n    \"else:\\n\",\n    \"    raise ValueError('Unknown model variation')    \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Shuffle data\\n\",\n    \"shuffle_indices = np.random.permutation(np.arange(len(y)))\\n\",\n    \"x_shuffled = x[shuffle_indices]\\n\",\n    \"y_shuffled = y[shuffle_indices].argmax(axis=1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Vocabulary Size: 18765\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(\\\"Vocabulary Size: {:d}\\\".format(len(vocabulary)))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Building CNN Model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"graph_in = Input(shape=(sequence_length, embedding_dim))\\n\",\n    \"convs = []\\n\",\n    \"for fsz in filter_sizes:\\n\",\n    \"    conv = Convolution1D(nb_filter=num_filters,\\n\",\n    \"                         filter_length=fsz,\\n\",\n    \"                         border_mode='valid',\\n\",\n    \"                         activation='relu',\\n\",\n    \"                         subsample_length=1)(graph_in)\\n\",\n    \"    pool = MaxPooling1D(pool_length=2)(conv)\\n\",\n    \"    flatten = Flatten()(pool)\\n\",\n    \"    convs.append(flatten)\\n\",\n    \"    \\n\",\n    \"if len(filter_sizes)>1:\\n\",\n    \"    out = Merge(mode='concat')(convs)\\n\",\n    \"else:\\n\",\n    \"    out = convs[0]\\n\",\n    \"\\n\",\n    \"graph = Model(input=graph_in, output=out)\\n\",\n    \"\\n\",\n    \"# main sequential model\\n\",\n    \"model = Sequential()\\n\",\n    \"if not model_variation=='CNN-static':\\n\",\n    \"    model.add(Embedding(len(vocabulary), embedding_dim, input_length=sequence_length,\\n\",\n    \"                        weights=embedding_weights))\\n\",\n    \"model.add(Dropout(dropout_prob[0], input_shape=(sequence_length, embedding_dim)))\\n\",\n    \"model.add(graph)\\n\",\n    \"model.add(Dense(hidden_dims))\\n\",\n    \"model.add(Dropout(dropout_prob[1]))\\n\",\n    \"model.add(Activation('relu'))\\n\",\n    \"model.add(Dense(1))\\n\",\n    \"model.add(Activation('sigmoid'))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Train on 9595 samples, validate on 1067 samples\\n\",\n      \"Epoch 1/100\\n\",\n      \"1s - loss: 0.6516 - acc: 0.6005 - val_loss: 0.5692 - val_acc: 0.7151\\n\",\n      \"Epoch 2/100\\n\",\n      \"1s - loss: 0.4556 - acc: 0.7896 - val_loss: 0.5154 - val_acc: 0.7573\\n\",\n      \"Epoch 3/100\\n\",\n      \"1s - loss: 0.3556 - acc: 0.8532 - val_loss: 0.5050 - val_acc: 0.7816\\n\",\n      \"Epoch 4/100\\n\",\n      \"1s - loss: 0.2978 - acc: 0.8779 - val_loss: 0.5335 - val_acc: 0.7901\\n\",\n      \"Epoch 5/100\\n\",\n      \"1s - loss: 0.2599 - acc: 0.8972 - val_loss: 0.5592 - val_acc: 0.7769\\n\",\n      \"Epoch 6/100\\n\",\n      \"1s - loss: 0.2248 - acc: 0.9112 - val_loss: 0.5559 - val_acc: 0.7685\\n\",\n      \"Epoch 7/100\\n\",\n      \"1s - loss: 0.1994 - acc: 0.9219 - val_loss: 0.5760 - val_acc: 0.7704\\n\",\n      \"Epoch 8/100\\n\",\n      \"1s - loss: 0.1801 - acc: 0.9326 - val_loss: 0.6014 - val_acc: 0.7788\\n\",\n      \"Epoch 9/100\\n\",\n      \"1s - loss: 0.1472 - acc: 0.9449 - val_loss: 0.6637 - val_acc: 0.7751\\n\",\n      \"Epoch 10/100\\n\",\n      \"1s - loss: 0.1269 - acc: 0.9537 - val_loss: 0.7281 - val_acc: 0.7563\\n\",\n      \"Epoch 11/100\\n\",\n      \"1s - loss: 0.1123 - acc: 0.9592 - val_loss: 0.7452 - val_acc: 0.7788\\n\",\n      \"Epoch 12/100\\n\",\n      \"1s - loss: 0.0897 - acc: 0.9658 - val_loss: 0.8504 - val_acc: 0.7591\\n\",\n      \"Epoch 13/100\\n\",\n      \"1s - loss: 0.0811 - acc: 0.9723 - val_loss: 0.8935 - val_acc: 0.7573\\n\",\n      \"Epoch 14/100\\n\",\n      \"1s - loss: 0.0651 - acc: 0.9764 - val_loss: 0.8738 - val_acc: 0.7685\\n\",\n      \"Epoch 15/100\\n\",\n      \"1s - loss: 0.0540 - acc: 0.9809 - val_loss: 0.9407 - val_acc: 0.7648\\n\",\n      \"Epoch 16/100\\n\",\n      \"1s - loss: 0.0408 - acc: 0.9857 - val_loss: 1.1880 - val_acc: 0.7638\\n\",\n      \"Epoch 17/100\\n\",\n      \"1s - loss: 0.0341 - acc: 0.9886 - val_loss: 1.2878 - val_acc: 0.7638\\n\",\n      \"Epoch 18/100\\n\",\n      \"1s - loss: 0.0306 - acc: 0.9901 - val_loss: 1.4448 - val_acc: 0.7573\\n\",\n      \"Epoch 19/100\\n\",\n      \"1s - loss: 0.0276 - acc: 0.9917 - val_loss: 1.5300 - val_acc: 0.7591\\n\",\n      \"Epoch 20/100\\n\",\n      \"1s - loss: 0.0249 - acc: 0.9917 - val_loss: 1.4825 - val_acc: 0.7666\\n\",\n      \"Epoch 21/100\\n\",\n      \"1s - loss: 0.0220 - acc: 0.9937 - val_loss: 1.4357 - val_acc: 0.7601\\n\",\n      \"Epoch 22/100\\n\",\n      \"1s - loss: 0.0188 - acc: 0.9945 - val_loss: 1.4081 - val_acc: 0.7657\\n\",\n      \"Epoch 23/100\\n\",\n      \"1s - loss: 0.0182 - acc: 0.9954 - val_loss: 1.7145 - val_acc: 0.7610\\n\",\n      \"Epoch 24/100\\n\",\n      \"1s - loss: 0.0129 - acc: 0.9964 - val_loss: 1.7047 - val_acc: 0.7704\\n\",\n      \"Epoch 25/100\\n\",\n      \"1s - loss: 0.0064 - acc: 0.9981 - val_loss: 1.9119 - val_acc: 0.7629\\n\",\n      \"Epoch 26/100\\n\",\n      \"1s - loss: 0.0108 - acc: 0.9969 - val_loss: 1.8306 - val_acc: 0.7704\\n\",\n      \"Epoch 27/100\\n\",\n      \"1s - loss: 0.0105 - acc: 0.9973 - val_loss: 1.9624 - val_acc: 0.7619\\n\",\n      \"Epoch 28/100\\n\",\n      \"1s - loss: 0.0112 - acc: 0.9973 - val_loss: 1.8552 - val_acc: 0.7694\\n\",\n      \"Epoch 29/100\\n\",\n      \"1s - loss: 0.0110 - acc: 0.9968 - val_loss: 1.8585 - val_acc: 0.7657\\n\",\n      \"Epoch 30/100\\n\",\n      \"1s - loss: 0.0071 - acc: 0.9983 - val_loss: 2.0571 - val_acc: 0.7694\\n\",\n      \"Epoch 31/100\\n\",\n      \"1s - loss: 0.0089 - acc: 0.9975 - val_loss: 2.0361 - val_acc: 0.7629\\n\",\n      \"Epoch 32/100\\n\",\n      \"1s - loss: 0.0074 - acc: 0.9978 - val_loss: 2.0010 - val_acc: 0.7648\\n\",\n      \"Epoch 33/100\\n\",\n      \"1s - loss: 0.0074 - acc: 0.9981 - val_loss: 2.0995 - val_acc: 0.7498\\n\",\n      \"Epoch 34/100\\n\",\n      \"1s - loss: 0.0125 - acc: 0.9971 - val_loss: 2.2003 - val_acc: 0.7610\\n\",\n      \"Epoch 35/100\\n\",\n      \"1s - loss: 0.0074 - acc: 0.9981 - val_loss: 2.1526 - val_acc: 0.7582\\n\",\n      \"Epoch 36/100\\n\",\n      \"1s - loss: 0.0068 - acc: 0.9984 - val_loss: 2.1754 - val_acc: 0.7648\\n\",\n      \"Epoch 37/100\\n\",\n      \"1s - loss: 0.0065 - acc: 0.9979 - val_loss: 2.0810 - val_acc: 0.7498\\n\",\n      \"Epoch 38/100\\n\",\n      \"1s - loss: 0.0078 - acc: 0.9980 - val_loss: 2.3443 - val_acc: 0.7460\\n\",\n      \"Epoch 39/100\\n\",\n      \"1s - loss: 0.0038 - acc: 0.9991 - val_loss: 2.1696 - val_acc: 0.7629\\n\",\n      \"Epoch 40/100\\n\",\n      \"1s - loss: 0.0062 - acc: 0.9985 - val_loss: 2.2752 - val_acc: 0.7545\\n\",\n      \"Epoch 41/100\\n\",\n      \"1s - loss: 0.0044 - acc: 0.9985 - val_loss: 2.3457 - val_acc: 0.7535\\n\",\n      \"Epoch 42/100\\n\",\n      \"1s - loss: 0.0066 - acc: 0.9985 - val_loss: 2.1172 - val_acc: 0.7629\\n\",\n      \"Epoch 43/100\\n\",\n      \"1s - loss: 0.0052 - acc: 0.9987 - val_loss: 2.3550 - val_acc: 0.7619\\n\",\n      \"Epoch 44/100\\n\",\n      \"1s - loss: 0.0024 - acc: 0.9993 - val_loss: 2.3832 - val_acc: 0.7610\\n\",\n      \"Epoch 45/100\\n\",\n      \"1s - loss: 0.0042 - acc: 0.9989 - val_loss: 2.4242 - val_acc: 0.7648\\n\",\n      \"Epoch 46/100\\n\",\n      \"1s - loss: 0.0048 - acc: 0.9990 - val_loss: 2.4529 - val_acc: 0.7563\\n\",\n      \"Epoch 47/100\\n\",\n      \"1s - loss: 0.0036 - acc: 0.9994 - val_loss: 2.8412 - val_acc: 0.7282\\n\",\n      \"Epoch 48/100\\n\",\n      \"1s - loss: 0.0037 - acc: 0.9991 - val_loss: 2.4515 - val_acc: 0.7619\\n\",\n      \"Epoch 49/100\\n\",\n      \"1s - loss: 0.0031 - acc: 0.9991 - val_loss: 2.4849 - val_acc: 0.7676\\n\",\n      \"Epoch 50/100\\n\",\n      \"1s - loss: 0.0078 - acc: 0.9990 - val_loss: 2.5083 - val_acc: 0.7563\\n\",\n      \"Epoch 51/100\\n\",\n      \"1s - loss: 0.0105 - acc: 0.9981 - val_loss: 2.3538 - val_acc: 0.7601\\n\",\n      \"Epoch 52/100\\n\",\n      \"1s - loss: 0.0076 - acc: 0.9986 - val_loss: 2.4405 - val_acc: 0.7685\\n\",\n      \"Epoch 53/100\\n\",\n      \"1s - loss: 0.0043 - acc: 0.9991 - val_loss: 2.5753 - val_acc: 0.7591\\n\",\n      \"Epoch 54/100\\n\",\n      \"1s - loss: 0.0044 - acc: 0.9989 - val_loss: 2.5550 - val_acc: 0.7582\\n\",\n      \"Epoch 55/100\\n\",\n      \"1s - loss: 0.0034 - acc: 0.9994 - val_loss: 2.6361 - val_acc: 0.7591\\n\",\n      \"Epoch 56/100\\n\",\n      \"1s - loss: 0.0041 - acc: 0.9994 - val_loss: 2.6753 - val_acc: 0.7563\\n\",\n      \"Epoch 57/100\\n\",\n      \"1s - loss: 0.0042 - acc: 0.9990 - val_loss: 2.6464 - val_acc: 0.7601\\n\",\n      \"Epoch 58/100\\n\",\n      \"1s - loss: 0.0037 - acc: 0.9992 - val_loss: 2.6616 - val_acc: 0.7582\\n\",\n      \"Epoch 59/100\\n\",\n      \"1s - loss: 0.0060 - acc: 0.9990 - val_loss: 2.6052 - val_acc: 0.7619\\n\",\n      \"Epoch 60/100\\n\",\n      \"1s - loss: 0.0051 - acc: 0.9990 - val_loss: 2.7033 - val_acc: 0.7498\\n\",\n      \"Epoch 61/100\\n\",\n      \"1s - loss: 0.0034 - acc: 0.9994 - val_loss: 2.7142 - val_acc: 0.7526\\n\",\n      \"Epoch 62/100\\n\",\n      \"1s - loss: 0.0047 - acc: 0.9994 - val_loss: 2.7656 - val_acc: 0.7591\\n\",\n      \"Epoch 63/100\\n\",\n      \"1s - loss: 0.0083 - acc: 0.9990 - val_loss: 2.7971 - val_acc: 0.7526\\n\",\n      \"Epoch 64/100\\n\",\n      \"1s - loss: 0.0046 - acc: 0.9992 - val_loss: 2.6585 - val_acc: 0.7545\\n\",\n      \"Epoch 65/100\\n\",\n      \"1s - loss: 0.0062 - acc: 0.9989 - val_loss: 2.6194 - val_acc: 0.7535\\n\",\n      \"Epoch 66/100\\n\",\n      \"1s - loss: 0.0062 - acc: 0.9993 - val_loss: 2.6255 - val_acc: 0.7694\\n\",\n      \"Epoch 67/100\\n\",\n      \"1s - loss: 0.0036 - acc: 0.9990 - val_loss: 2.6384 - val_acc: 0.7582\\n\",\n      \"Epoch 68/100\\n\",\n      \"1s - loss: 0.0066 - acc: 0.9991 - val_loss: 2.6743 - val_acc: 0.7648\\n\",\n      \"Epoch 69/100\\n\",\n      \"1s - loss: 0.0030 - acc: 0.9995 - val_loss: 2.8236 - val_acc: 0.7535\\n\",\n      \"Epoch 70/100\\n\",\n      \"1s - loss: 0.0048 - acc: 0.9993 - val_loss: 2.7829 - val_acc: 0.7610\\n\",\n      \"Epoch 71/100\\n\",\n      \"1s - loss: 0.0062 - acc: 0.9990 - val_loss: 2.6402 - val_acc: 0.7573\\n\",\n      \"Epoch 72/100\\n\",\n      \"1s - loss: 0.0037 - acc: 0.9992 - val_loss: 2.9089 - val_acc: 0.7526\\n\",\n      \"Epoch 73/100\\n\",\n      \"1s - loss: 0.0069 - acc: 0.9985 - val_loss: 2.7071 - val_acc: 0.7535\\n\",\n      \"Epoch 74/100\\n\",\n      \"1s - loss: 0.0033 - acc: 0.9995 - val_loss: 2.6727 - val_acc: 0.7601\\n\",\n      \"Epoch 75/100\\n\",\n      \"1s - loss: 0.0069 - acc: 0.9990 - val_loss: 2.6967 - val_acc: 0.7601\\n\",\n      \"Epoch 76/100\\n\",\n      \"1s - loss: 0.0089 - acc: 0.9989 - val_loss: 2.7479 - val_acc: 0.7666\\n\",\n      \"Epoch 77/100\\n\",\n      \"1s - loss: 0.0046 - acc: 0.9994 - val_loss: 2.7192 - val_acc: 0.7629\\n\",\n      \"Epoch 78/100\\n\",\n      \"1s - loss: 0.0069 - acc: 0.9989 - val_loss: 2.7173 - val_acc: 0.7629\\n\",\n      \"Epoch 79/100\\n\",\n      \"1s - loss: 8.6550e-04 - acc: 0.9998 - val_loss: 2.7283 - val_acc: 0.7601\\n\",\n      \"Epoch 80/100\\n\",\n      \"1s - loss: 0.0011 - acc: 0.9995 - val_loss: 2.8405 - val_acc: 0.7629\\n\",\n      \"Epoch 81/100\\n\",\n      \"1s - loss: 0.0040 - acc: 0.9994 - val_loss: 2.8725 - val_acc: 0.7619\\n\",\n      \"Epoch 82/100\\n\",\n      \"1s - loss: 0.0055 - acc: 0.9992 - val_loss: 2.8490 - val_acc: 0.7601\\n\",\n      \"Epoch 83/100\\n\",\n      \"1s - loss: 0.0059 - acc: 0.9989 - val_loss: 2.7838 - val_acc: 0.7545\\n\",\n      \"Epoch 84/100\\n\",\n      \"1s - loss: 0.0054 - acc: 0.9994 - val_loss: 2.8706 - val_acc: 0.7526\\n\",\n      \"Epoch 85/100\\n\",\n      \"1s - loss: 0.0060 - acc: 0.9992 - val_loss: 2.9374 - val_acc: 0.7516\\n\",\n      \"Epoch 86/100\\n\",\n      \"1s - loss: 0.0087 - acc: 0.9982 - val_loss: 2.7966 - val_acc: 0.7573\\n\",\n      \"Epoch 87/100\\n\",\n      \"1s - loss: 0.0084 - acc: 0.9991 - val_loss: 2.8620 - val_acc: 0.7619\\n\",\n      \"Epoch 88/100\\n\",\n      \"1s - loss: 0.0053 - acc: 0.9990 - val_loss: 2.8450 - val_acc: 0.7601\\n\",\n      \"Epoch 89/100\\n\",\n      \"1s - loss: 0.0054 - acc: 0.9990 - val_loss: 2.8303 - val_acc: 0.7629\\n\",\n      \"Epoch 90/100\\n\",\n      \"1s - loss: 0.0073 - acc: 0.9991 - val_loss: 2.8474 - val_acc: 0.7657\\n\",\n      \"Epoch 91/100\\n\",\n      \"1s - loss: 0.0037 - acc: 0.9994 - val_loss: 3.0151 - val_acc: 0.7432\\n\",\n      \"Epoch 92/100\\n\",\n      \"1s - loss: 0.0017 - acc: 0.9999 - val_loss: 2.9555 - val_acc: 0.7582\\n\",\n      \"Epoch 93/100\\n\",\n      \"1s - loss: 0.0080 - acc: 0.9991 - val_loss: 2.9178 - val_acc: 0.7554\\n\",\n      \"Epoch 94/100\\n\",\n      \"1s - loss: 0.0078 - acc: 0.9991 - val_loss: 2.8724 - val_acc: 0.7582\\n\",\n      \"Epoch 95/100\\n\",\n      \"1s - loss: 0.0012 - acc: 0.9997 - val_loss: 2.9582 - val_acc: 0.7545\\n\",\n      \"Epoch 96/100\\n\",\n      \"1s - loss: 0.0058 - acc: 0.9989 - val_loss: 2.8944 - val_acc: 0.7479\\n\",\n      \"Epoch 97/100\\n\",\n      \"1s - loss: 0.0094 - acc: 0.9985 - val_loss: 2.7146 - val_acc: 0.7516\\n\",\n      \"Epoch 98/100\\n\",\n      \"1s - loss: 0.0044 - acc: 0.9993 - val_loss: 2.9052 - val_acc: 0.7498\\n\",\n      \"Epoch 99/100\\n\",\n      \"1s - loss: 0.0030 - acc: 0.9995 - val_loss: 3.1474 - val_acc: 0.7470\\n\",\n      \"Epoch 100/100\\n\",\n      \"1s - loss: 0.0051 - acc: 0.9990 - val_loss: 3.1746 - val_acc: 0.7451\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<keras.callbacks.History at 0x7f78362ae400>\"\n      ]\n     },\n     \"execution_count\": 12,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"model.compile(loss='binary_crossentropy', optimizer='rmsprop', \\n\",\n    \"              metrics=['accuracy'])\\n\",\n    \"\\n\",\n    \"# Training model\\n\",\n    \"# ==================================================\\n\",\n    \"model.fit(x_shuffled, y_shuffled, batch_size=batch_size,\\n\",\n    \"          nb_epoch=num_epochs, validation_split=val_split, verbose=2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"# Another Example\\n\",\n    \"\\n\",\n    \"Using Keras + [**GloVe**](http://nlp.stanford.edu/projects/glove/) - **Global Vectors for Word Representation**\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Using pre-trained word embeddings in a Keras model\\n\",\n    \"\\n\",\n    \"**Reference:** [https://blog.keras.io/using-pre-trained-word-embeddings-in-a-keras-model.html]()\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.4.3\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "deep-learning/keras-tutorial/3.2 RNN and LSTM.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Credits: Forked from [deep-learning-keras-tensorflow](https://github.com/leriomaggio/deep-learning-keras-tensorflow) by Valerio Maggio\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Recurrent Neural networks\\n\",\n    \"=====\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### RNN  \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"source\": [\n    \"<img src =\\\"imgs/rnn.png\\\" width=\\\"20%\\\">\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"A recurrent neural network (RNN) is a class of artificial neural network where connections between units form a directed cycle. This creates an internal state of the network which allows it to exhibit dynamic temporal behavior.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"keras.layers.recurrent.SimpleRNN(output_dim, \\n\",\n    \"                                 init='glorot_uniform', inner_init='orthogonal', activation='tanh', \\n\",\n    \"                                 W_regularizer=None, U_regularizer=None, b_regularizer=None, \\n\",\n    \"                                 dropout_W=0.0, dropout_U=0.0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Backprop Through time  \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Contrary to feed-forward neural networks, the RNN is characterized by the ability of encoding longer past information, thus very suitable for sequential models. The BPTT extends the ordinary BP algorithm to suit the recurrent neural\\n\",\n    \"architecture.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": false,\n    \"scrolled\": true\n   },\n   \"source\": [\n    \"<img src =\\\"imgs/rnn2.png\\\" width=\\\"45%\\\">\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%matplotlib inline\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Using Theano backend.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import pandas as pd\\n\",\n    \"import theano\\n\",\n    \"import theano.tensor as T\\n\",\n    \"import keras \\n\",\n    \"from keras.models import Sequential\\n\",\n    \"from keras.layers import Dense, Activation\\n\",\n    \"from keras.preprocessing import image\\n\",\n    \"from __future__ import print_function\\n\",\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"from keras.datasets import imdb\\n\",\n    \"from keras.datasets import mnist\\n\",\n    \"from keras.models import Sequential\\n\",\n    \"from keras.layers import Dense, Dropout, Activation, Flatten\\n\",\n    \"from keras.layers import Convolution2D, MaxPooling2D\\n\",\n    \"from keras.utils import np_utils\\n\",\n    \"from keras.preprocessing import sequence\\n\",\n    \"from keras.layers.embeddings import Embedding\\n\",\n    \"from keras.layers.recurrent import LSTM, GRU, SimpleRNN\\n\",\n    \"from sklearn.preprocessing import LabelEncoder\\n\",\n    \"from sklearn.preprocessing import StandardScaler\\n\",\n    \"from sklearn.cross_validation import train_test_split\\n\",\n    \"from keras.layers.core import Activation, TimeDistributedDense, RepeatVector\\n\",\n    \"from keras.callbacks import EarlyStopping, ModelCheckpoint\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### IMDB sentiment classification task\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets. \\n\",\n    \"\\n\",\n    \"IMDB provided a set of 25,000 highly polar movie reviews for training, and 25,000 for testing. \\n\",\n    \"\\n\",\n    \"There is additional unlabeled data for use as well. Raw text and already processed bag of words formats are provided. \\n\",\n    \"\\n\",\n    \"http://ai.stanford.edu/~amaas/data/sentiment/\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Data Preparation - IMDB\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Loading data...\\n\",\n      \"20000 train sequences\\n\",\n      \"5000 test sequences\\n\",\n      \"Example:\\n\",\n      \"[ [1, 20, 28, 716, 48, 495, 79, 27, 493, 8, 5067, 7, 50, 5, 4682, 13075, 10, 5, 852, 157, 11, 5, 1716, 3351, 10, 5, 500, 7308, 6, 33, 256, 41, 13610, 7, 17, 23, 48, 1537, 3504, 26, 269, 929, 18, 2, 7, 2, 4284, 8, 105, 5, 2, 182, 314, 38, 98, 103, 7, 36, 2184, 246, 360, 7, 19, 396, 17, 26, 269, 929, 18, 1769, 493, 6, 116, 7, 105, 5, 575, 182, 27, 5, 1002, 1085, 130, 62, 17, 24, 89, 17, 13, 381, 1421, 8, 5167, 7, 5, 2723, 38, 325, 7, 17, 23, 93, 9, 156, 252, 19, 235, 20, 28, 5, 104, 76, 7, 17, 169, 35, 14764, 17, 23, 1460, 7, 36, 2184, 934, 56, 2134, 6, 17, 891, 214, 11, 5, 1552, 6, 92, 6, 33, 256, 82, 7]]\\n\",\n      \"Pad sequences (samples x time)\\n\",\n      \"X_train shape: (20000L, 100L)\\n\",\n      \"X_test shape: (5000L, 100L)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"max_features = 20000\\n\",\n    \"maxlen = 100  # cut texts after this number of words (among top max_features most common words)\\n\",\n    \"batch_size = 32\\n\",\n    \"\\n\",\n    \"print(\\\"Loading data...\\\")\\n\",\n    \"(X_train, y_train), (X_test, y_test) = imdb.load_data(nb_words=max_features, test_split=0.2)\\n\",\n    \"print(len(X_train), 'train sequences')\\n\",\n    \"print(len(X_test), 'test sequences')\\n\",\n    \"\\n\",\n    \"print('Example:')\\n\",\n    \"print(X_train[:1])\\n\",\n    \"\\n\",\n    \"print(\\\"Pad sequences (samples x time)\\\")\\n\",\n    \"X_train = sequence.pad_sequences(X_train, maxlen=maxlen)\\n\",\n    \"X_test = sequence.pad_sequences(X_test, maxlen=maxlen)\\n\",\n    \"print('X_train shape:', X_train.shape)\\n\",\n    \"print('X_test shape:', X_test.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Model building \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Build model...\\n\",\n      \"Train...\\n\",\n      \"Train on 20000 samples, validate on 5000 samples\\n\",\n      \"Epoch 1/1\\n\",\n      \"20000/20000 [==============================] - 174s - loss: 0.7213 - val_loss: 0.6179\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<keras.callbacks.History at 0x20519860>\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"print('Build model...')\\n\",\n    \"model = Sequential()\\n\",\n    \"model.add(Embedding(max_features, 128, input_length=maxlen))\\n\",\n    \"model.add(SimpleRNN(128))  \\n\",\n    \"model.add(Dropout(0.5))\\n\",\n    \"model.add(Dense(1))\\n\",\n    \"model.add(Activation('sigmoid'))\\n\",\n    \"\\n\",\n    \"# try using different optimizers and different optimizer configs\\n\",\n    \"model.compile(loss='binary_crossentropy', optimizer='adam', class_mode=\\\"binary\\\")\\n\",\n    \"\\n\",\n    \"print(\\\"Train...\\\")\\n\",\n    \"model.fit(X_train, y_train, batch_size=batch_size, nb_epoch=1, validation_data=(X_test, y_test), show_accuracy=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### LSTM  \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"A LSTM network is an artificial neural network that contains LSTM blocks instead of, or in addition to, regular network units. A LSTM block may be described as a \\\"smart\\\" network unit that can remember a value for an arbitrary length of time. \\n\",\n    \"\\n\",\n    \"Unlike traditional RNNs, an Long short-term memory network is well-suited to learn from experience to classify, process and predict time series when there are very long time lags of unknown size between important events.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": false,\n    \"scrolled\": true\n   },\n   \"source\": [\n    \"<img src =\\\"imgs/gru.png\\\" width=\\\"60%\\\">\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"keras.layers.recurrent.LSTM(output_dim, init='glorot_uniform', inner_init='orthogonal', \\n\",\n    \"                            forget_bias_init='one', activation='tanh', \\n\",\n    \"                            inner_activation='hard_sigmoid', \\n\",\n    \"                            W_regularizer=None, U_regularizer=None, b_regularizer=None, \\n\",\n    \"                            dropout_W=0.0, dropout_U=0.0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### GRU  \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Gated recurrent units are a gating mechanism in recurrent neural networks. \\n\",\n    \"\\n\",\n    \"Much similar to the LSTMs, they have fewer parameters than LSTM, as they lack an output gate.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"keras.layers.recurrent.GRU(output_dim, init='glorot_uniform', inner_init='orthogonal', \\n\",\n    \"                           activation='tanh', inner_activation='hard_sigmoid', \\n\",\n    \"                           W_regularizer=None, U_regularizer=None, b_regularizer=None, \\n\",\n    \"                           dropout_W=0.0, dropout_U=0.0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Your Turn! - Hands on Rnn\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"print('Build model...')\\n\",\n    \"model = Sequential()\\n\",\n    \"model.add(Embedding(max_features, 128, input_length=maxlen))\\n\",\n    \"\\n\",\n    \"# Play with those! try and get better results!\\n\",\n    \"#model.add(SimpleRNN(128))  \\n\",\n    \"#model.add(GRU(128))  \\n\",\n    \"#model.add(LSTM(128))  \\n\",\n    \"\\n\",\n    \"model.add(Dropout(0.5))\\n\",\n    \"model.add(Dense(1))\\n\",\n    \"model.add(Activation('sigmoid'))\\n\",\n    \"\\n\",\n    \"# try using different optimizers and different optimizer configs\\n\",\n    \"model.compile(loss='binary_crossentropy', optimizer='adam', class_mode=\\\"binary\\\")\\n\",\n    \"\\n\",\n    \"print(\\\"Train...\\\")\\n\",\n    \"model.fit(X_train, y_train, batch_size=batch_size, \\n\",\n    \"          nb_epoch=4, validation_data=(X_test, y_test), show_accuracy=True)\\n\",\n    \"score, acc = model.evaluate(X_test, y_test, batch_size=batch_size, show_accuracy=True)\\n\",\n    \"print('Test score:', score)\\n\",\n    \"print('Test accuracy:', acc)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Sentence Generation using RNN(LSTM)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Downloading data from https://s3.amazonaws.com/text-datasets/nietzsche.txt\\n\",\n      \"598016/600901 [============================>.] - ETA: 0s('corpus length:', 600901)\\n\",\n      \"('total chars:', 59)\\n\",\n      \"('nb sequences:', 200287)\\n\",\n      \"Vectorization...\\n\",\n      \"Build model...\\n\",\n      \"()\\n\",\n      \"--------------------------------------------------\\n\",\n      \"('Iteration', 1)\\n\",\n      \"Epoch 1/1\\n\",\n      \"200287/200287 [==============================] - 1367s - loss: 1.9977  \\n\",\n      \"()\\n\",\n      \"('----- diversity:', 0.2)\\n\",\n      \"----- Generating with seed: \\\"nd the frenzied\\n\",\n      \"speeches of the prophets\\\"\\n\",\n      \"nd the frenzied\\n\",\n      \"speeches of the prophets and the present and and the preases and the soul to the sense of the morals and the some the consequence of the most and one only the some of the proment and interent of the some devertal to the self-consertion of the some deverent of the some distiness and the sense of the some of the morality of the most proves and the some of the some in the seem of the self-conception of the sees of the sense()\\n\",\n      \"()\\n\",\n      \"('----- diversity:', 0.5)\\n\",\n      \"----- Generating with seed: \\\"nd the frenzied\\n\",\n      \"speeches of the prophets\\\"\\n\",\n      \"nd the frenzied\\n\",\n      \"speeches of the prophets of the preat weak to the master of man who onow in interervain of even which who with it is the isitaial conception of the some live the contented the one who exilfacied in the sees to raters, and the passe expecience the inte that the persented in the pass, in the experious of the soulity of the waith the morally distanding of the some of the most interman only and as a period of the sense and o()\\n\",\n      \"()\\n\",\n      \"('----- diversity:', 1.0)\\n\",\n      \"----- Generating with seed: \\\"nd the frenzied\\n\",\n      \"speeches of the prophets\\\"\\n\",\n      \"nd the frenzied\\n\",\n      \"speeches of the prophets of\\n\",\n      \"ar self now no ecerspoped ivent so not,\\n\",\n      \"that itsed undiswerbatarlials. what it is altrenively evok\\n\",\n      \"now be scotnew\\n\",\n      \"prigardiness intagualds, and coumond-grow to\\n\",\n      \"the respence you as penires never wand be\\n\",\n      \"natuented ost ablinice to love worts an who itnopeancew be than mrank againribl\\n\",\n      \"some something lines in the estlenbtupenies of korils divenowry apmains, curte, were,\\n\",\n      \"ind \\\"feulness.  a will, natur()\\n\",\n      \"()\\n\",\n      \"('----- diversity:', 1.2)\\n\",\n      \"----- Generating with seed: \\\"nd the frenzied\\n\",\n      \"speeches of the prophets\\\"\\n\",\n      \"nd the frenzied\\n\",\n      \"speeches of the prophets, ind someaterting will stroour hast-fards and lofe beausold, in souby in ruarest, we withquus. \\\"the capinistin and it a mode what it be\\n\",\n      \"my oc, to th[se condectay\\n\",\n      \"of ymo fre\\n\",\n      \"dunt and so asexthersess renieved concecunaulies tound\\\"), from glubiakeitiouals kenty am feelitafouer deceanw or sumpind, and by afolod peall--phasoos of sole\\n\",\n      \"iy copprajakias\\n\",\n      \"in\\n\",\n      \"in adcyont-mean to prives apf-rigionall thust wi()\\n\",\n      \"()\\n\",\n      \"--------------------------------------------------\\n\",\n      \"('Iteration', 2)\\n\",\n      \"Epoch 1/1\\n\",\n      \" 40576/200287 [=====>........................] - ETA: 1064s - loss: 1.6878\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from keras.models import Sequential\\n\",\n    \"from keras.layers import Dense, Activation, Dropout\\n\",\n    \"from keras.layers import LSTM\\n\",\n    \"from keras.optimizers import RMSprop\\n\",\n    \"from keras.utils.data_utils import get_file\\n\",\n    \"import numpy as np\\n\",\n    \"import random\\n\",\n    \"import sys\\n\",\n    \"\\n\",\n    \"path = get_file('nietzsche.txt', origin=\\\"https://s3.amazonaws.com/text-datasets/nietzsche.txt\\\")\\n\",\n    \"text = open(path).read().lower()\\n\",\n    \"print('corpus length:', len(text))\\n\",\n    \"\\n\",\n    \"chars = sorted(list(set(text)))\\n\",\n    \"print('total chars:', len(chars))\\n\",\n    \"char_indices = dict((c, i) for i, c in enumerate(chars))\\n\",\n    \"indices_char = dict((i, c) for i, c in enumerate(chars))\\n\",\n    \"\\n\",\n    \"# cut the text in semi-redundant sequences of maxlen characters\\n\",\n    \"maxlen = 40\\n\",\n    \"step = 3\\n\",\n    \"sentences = []\\n\",\n    \"next_chars = []\\n\",\n    \"for i in range(0, len(text) - maxlen, step):\\n\",\n    \"    sentences.append(text[i: i + maxlen])\\n\",\n    \"    next_chars.append(text[i + maxlen])\\n\",\n    \"print('nb sequences:', len(sentences))\\n\",\n    \"\\n\",\n    \"print('Vectorization...')\\n\",\n    \"X = np.zeros((len(sentences), maxlen, len(chars)), dtype=np.bool)\\n\",\n    \"y = np.zeros((len(sentences), len(chars)), dtype=np.bool)\\n\",\n    \"for i, sentence in enumerate(sentences):\\n\",\n    \"    for t, char in enumerate(sentence):\\n\",\n    \"        X[i, t, char_indices[char]] = 1\\n\",\n    \"    y[i, char_indices[next_chars[i]]] = 1\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# build the model: a single LSTM\\n\",\n    \"print('Build model...')\\n\",\n    \"model = Sequential()\\n\",\n    \"model.add(LSTM(128, input_shape=(maxlen, len(chars))))\\n\",\n    \"model.add(Dense(len(chars)))\\n\",\n    \"model.add(Activation('softmax'))\\n\",\n    \"\\n\",\n    \"optimizer = RMSprop(lr=0.01)\\n\",\n    \"model.compile(loss='categorical_crossentropy', optimizer=optimizer)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def sample(preds, temperature=1.0):\\n\",\n    \"    # helper function to sample an index from a probability array\\n\",\n    \"    preds = np.asarray(preds).astype('float64')\\n\",\n    \"    preds = np.log(preds) / temperature\\n\",\n    \"    exp_preds = np.exp(preds)\\n\",\n    \"    preds = exp_preds / np.sum(exp_preds)\\n\",\n    \"    probas = np.random.multinomial(1, preds, 1)\\n\",\n    \"    return np.argmax(probas)\\n\",\n    \"\\n\",\n    \"# train the model, output generated text after each iteration\\n\",\n    \"for iteration in range(1, 60):\\n\",\n    \"    print()\\n\",\n    \"    print('-' * 50)\\n\",\n    \"    print('Iteration', iteration)\\n\",\n    \"    model.fit(X, y, batch_size=128, nb_epoch=1)\\n\",\n    \"\\n\",\n    \"    start_index = random.randint(0, len(text) - maxlen - 1)\\n\",\n    \"\\n\",\n    \"    for diversity in [0.2, 0.5, 1.0, 1.2]:\\n\",\n    \"        print()\\n\",\n    \"        print('----- diversity:', diversity)\\n\",\n    \"\\n\",\n    \"        generated = ''\\n\",\n    \"        sentence = text[start_index: start_index + maxlen]\\n\",\n    \"        generated += sentence\\n\",\n    \"        print('----- Generating with seed: \\\"' + sentence + '\\\"')\\n\",\n    \"        sys.stdout.write(generated)\\n\",\n    \"\\n\",\n    \"        for i in range(400):\\n\",\n    \"            x = np.zeros((1, maxlen, len(chars)))\\n\",\n    \"            for t, char in enumerate(sentence):\\n\",\n    \"                x[0, t, char_indices[char]] = 1.\\n\",\n    \"\\n\",\n    \"            preds = model.predict(x, verbose=0)[0]\\n\",\n    \"            next_index = sample(preds, diversity)\\n\",\n    \"            next_char = indices_char[next_index]\\n\",\n    \"\\n\",\n    \"            generated += next_char\\n\",\n    \"            sentence = sentence[1:] + next_char\\n\",\n    \"\\n\",\n    \"            sys.stdout.write(next_char)\\n\",\n    \"            sys.stdout.flush()\\n\",\n    \"        print()\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.4.3\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "deep-learning/keras-tutorial/3.3 (Extra) LSTM for Sentence Generation.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Credits: Forked from [deep-learning-keras-tensorflow](https://github.com/leriomaggio/deep-learning-keras-tensorflow) by Valerio Maggio\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"source\": [\n    \"\\n\",\n    \"# RNN using LSTM \\n\",\n    \"       \\n\",\n    \"\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<img src=\\\"imgs/RNN-rolled.png\\\"/ width=\\\"80px\\\" height=\\\"80px\\\">\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<img src=\\\"imgs/RNN-unrolled.png\\\"/ width=\\\"400px\\\" height=\\\"400px\\\">\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<img src=\\\"imgs/LSTM3-chain.png\\\"/ width=\\\"60%\\\">\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"_source: http://colah.github.io/posts/2015-08-Understanding-LSTMs_\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from keras.optimizers import SGD\\n\",\n    \"from keras.preprocessing.text import one_hot, text_to_word_sequence, base_filter\\n\",\n    \"from keras.utils import np_utils\\n\",\n    \"from keras.models import Sequential\\n\",\n    \"from keras.layers.core import Dense, Dropout, Activation\\n\",\n    \"from keras.layers.embeddings import Embedding\\n\",\n    \"from keras.layers.recurrent import LSTM, GRU\\n\",\n    \"from keras.preprocessing import sequence\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Reading blog post from data directory\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"import pickle\\n\",\n    \"import numpy as np\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/valerio/deep-learning-keras-euroscipy2016/data\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"DATA_DIRECTORY = os.path.join(os.path.abspath(os.path.curdir), 'data')\\n\",\n    \"print(DATA_DIRECTORY)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"male_posts = []\\n\",\n    \"female_post = []\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"with open(os.path.join(DATA_DIRECTORY,\\\"male_blog_list.txt\\\"),\\\"rb\\\") as male_file:\\n\",\n    \"    male_posts= pickle.load(male_file)\\n\",\n    \"    \\n\",\n    \"with open(os.path.join(DATA_DIRECTORY,\\\"female_blog_list.txt\\\"),\\\"rb\\\") as female_file:\\n\",\n    \"    female_posts = pickle.load(female_file)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 85,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"filtered_male_posts = list(filter(lambda p: len(p) > 0, male_posts))\\n\",\n    \"filtered_female_posts = list(filter(lambda p: len(p) > 0, female_posts))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 86,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# text processing - one hot builds index of the words\\n\",\n    \"male_one_hot = []\\n\",\n    \"female_one_hot = []\\n\",\n    \"n = 30000\\n\",\n    \"for post in filtered_male_posts:\\n\",\n    \"    try:\\n\",\n    \"        male_one_hot.append(one_hot(post, n, split=\\\" \\\", filters=base_filter(), lower=True))\\n\",\n    \"    except:\\n\",\n    \"        continue\\n\",\n    \"\\n\",\n    \"for post in filtered_female_posts:\\n\",\n    \"    try:\\n\",\n    \"        female_one_hot.append(one_hot(post,n,split=\\\" \\\",filters=base_filter(),lower=True))\\n\",\n    \"    except:\\n\",\n    \"        continue\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 87,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# 0 for male, 1 for female\\n\",\n    \"concatenate_array_rnn = np.concatenate((np.zeros(len(male_one_hot)),\\n\",\n    \"                                        np.ones(len(female_one_hot))))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 88,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.cross_validation import train_test_split\\n\",\n    \"\\n\",\n    \"X_train_rnn, X_test_rnn, y_train_rnn, y_test_rnn = train_test_split(np.concatenate((female_one_hot,male_one_hot)),\\n\",\n    \"                                                                    concatenate_array_rnn, \\n\",\n    \"                                                                    test_size=0.2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 89,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"X_train_rnn shape: (3873, 100) (3873,)\\n\",\n      \"X_test_rnn shape: (969, 100) (969,)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"maxlen = 100\\n\",\n    \"X_train_rnn = sequence.pad_sequences(X_train_rnn, maxlen=maxlen)\\n\",\n    \"X_test_rnn = sequence.pad_sequences(X_test_rnn, maxlen=maxlen)\\n\",\n    \"print('X_train_rnn shape:', X_train_rnn.shape, y_train_rnn.shape)\\n\",\n    \"print('X_test_rnn shape:', X_test_rnn.shape, y_test_rnn.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 90,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"max_features = 30000\\n\",\n    \"dimension = 128\\n\",\n    \"output_dimension = 128\\n\",\n    \"model = Sequential()\\n\",\n    \"model.add(Embedding(max_features, dimension))\\n\",\n    \"model.add(LSTM(output_dimension))\\n\",\n    \"model.add(Dropout(0.5))\\n\",\n    \"model.add(Dense(1))\\n\",\n    \"model.add(Activation('sigmoid'))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 91,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.compile(loss='mean_squared_error', optimizer='sgd', metrics=['accuracy'])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 92,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Train on 3873 samples, validate on 969 samples\\n\",\n      \"Epoch 1/4\\n\",\n      \"3873/3873 [==============================] - 3s - loss: 0.2487 - acc: 0.5378 - val_loss: 0.2506 - val_acc: 0.5191\\n\",\n      \"Epoch 2/4\\n\",\n      \"3873/3873 [==============================] - 3s - loss: 0.2486 - acc: 0.5401 - val_loss: 0.2508 - val_acc: 0.5191\\n\",\n      \"Epoch 3/4\\n\",\n      \"3873/3873 [==============================] - 3s - loss: 0.2484 - acc: 0.5417 - val_loss: 0.2496 - val_acc: 0.5191\\n\",\n      \"Epoch 4/4\\n\",\n      \"3873/3873 [==============================] - 3s - loss: 0.2484 - acc: 0.5399 - val_loss: 0.2502 - val_acc: 0.5191\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<keras.callbacks.History at 0x7fa1e96ac4e0>\"\n      ]\n     },\n     \"execution_count\": 92,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"model.fit(X_train_rnn, y_train_rnn, batch_size=32,\\n\",\n    \"          nb_epoch=4, validation_data=(X_test_rnn, y_test_rnn))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 93,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"969/969 [==============================] - 0s     \\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"score, acc = model.evaluate(X_test_rnn, y_test_rnn, batch_size=32)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 94,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"0.250189056399 0.519091847357\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(score, acc)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Using TFIDF Vectorizer as an input instead of one hot encoder\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 95,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.feature_extraction.text import TfidfVectorizer\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 96,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"vectorizer = TfidfVectorizer(decode_error='ignore', norm='l2', min_df=5)\\n\",\n    \"tfidf_male = vectorizer.fit_transform(filtered_male_posts)\\n\",\n    \"tfidf_female = vectorizer.fit_transform(filtered_female_posts)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 97,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"flattened_array_tfidf_male = tfidf_male.toarray()\\n\",\n    \"flattened_array_tfidf_female = tfidf_male.toarray()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 98,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"y_rnn = np.concatenate((np.zeros(len(flattened_array_tfidf_male)),\\n\",\n    \"                                        np.ones(len(flattened_array_tfidf_female))))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 99,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"X_train_rnn, X_test_rnn, y_train_rnn, y_test_rnn = train_test_split(np.concatenate((flattened_array_tfidf_male, \\n\",\n    \"                                                                                    flattened_array_tfidf_female)),\\n\",\n    \"                                                                    y_rnn,test_size=0.2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 100,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"X_train_rnn shape: (4152, 100) (4152,)\\n\",\n      \"X_test_rnn shape: (1038, 100) (1038,)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"maxlen = 100\\n\",\n    \"X_train_rnn = sequence.pad_sequences(X_train_rnn, maxlen=maxlen)\\n\",\n    \"X_test_rnn = sequence.pad_sequences(X_test_rnn, maxlen=maxlen)\\n\",\n    \"print('X_train_rnn shape:', X_train_rnn.shape, y_train_rnn.shape)\\n\",\n    \"print('X_test_rnn shape:', X_test_rnn.shape, y_test_rnn.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 101,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"max_features = 30000\\n\",\n    \"model = Sequential()\\n\",\n    \"model.add(Embedding(max_features, dimension))\\n\",\n    \"model.add(LSTM(output_dimension))\\n\",\n    \"model.add(Dropout(0.5))\\n\",\n    \"model.add(Dense(1))\\n\",\n    \"model.add(Activation('sigmoid'))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 102,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.compile(loss='mean_squared_error',optimizer='sgd', metrics=['accuracy'])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 103,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Train on 4152 samples, validate on 1038 samples\\n\",\n      \"Epoch 1/4\\n\",\n      \"4152/4152 [==============================] - 3s - loss: 0.2502 - acc: 0.4988 - val_loss: 0.2503 - val_acc: 0.4865\\n\",\n      \"Epoch 2/4\\n\",\n      \"4152/4152 [==============================] - 3s - loss: 0.2507 - acc: 0.4843 - val_loss: 0.2500 - val_acc: 0.4865\\n\",\n      \"Epoch 3/4\\n\",\n      \"4152/4152 [==============================] - 3s - loss: 0.2504 - acc: 0.4952 - val_loss: 0.2501 - val_acc: 0.4865\\n\",\n      \"Epoch 4/4\\n\",\n      \"4152/4152 [==============================] - 3s - loss: 0.2506 - acc: 0.4913 - val_loss: 0.2500 - val_acc: 0.5135\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<keras.callbacks.History at 0x7fa1f466f278>\"\n      ]\n     },\n     \"execution_count\": 103,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"model.fit(X_train_rnn, y_train_rnn, \\n\",\n    \"          batch_size=32, nb_epoch=4,\\n\",\n    \"          validation_data=(X_test_rnn, y_test_rnn))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 104,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"1038/1038 [==============================] - 0s     \\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"score,acc = model.evaluate(X_test_rnn, y_test_rnn, \\n\",\n    \"                           batch_size=32)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 105,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"0.249981284572 0.513487476145\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(score, acc)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Sentence Generation using LSTM\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 106,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# reading all the male text data into one string\\n\",\n    \"male_post = ' '.join(filtered_male_posts)\\n\",\n    \"\\n\",\n    \"#building character set for the male posts\\n\",\n    \"character_set_male = set(male_post)\\n\",\n    \"#building two indices - character index and index of character\\n\",\n    \"char_indices = dict((c, i) for i, c in enumerate(character_set_male))\\n\",\n    \"indices_char = dict((i, c) for i, c in enumerate(character_set_male))\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# cut the text in semi-redundant sequences of maxlen characters\\n\",\n    \"maxlen = 20\\n\",\n    \"step = 1\\n\",\n    \"sentences = []\\n\",\n    \"next_chars = []\\n\",\n    \"for i in range(0, len(male_post) - maxlen, step):\\n\",\n    \"    sentences.append(male_post[i : i + maxlen])\\n\",\n    \"    next_chars.append(male_post[i + maxlen])\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 107,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(2552476, 20, 152) (2552476, 152)\\n\",\n      \"(2552476, 20, 152) (2552476, 152)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"#Vectorisation of input\\n\",\n    \"x_male = np.zeros((len(male_post), maxlen, len(character_set_male)), dtype=np.bool)\\n\",\n    \"y_male = np.zeros((len(male_post), len(character_set_male)), dtype=np.bool)\\n\",\n    \"\\n\",\n    \"print(x_male.shape, y_male.shape)\\n\",\n    \"\\n\",\n    \"for i, sentence in enumerate(sentences):\\n\",\n    \"    for t, char in enumerate(sentence):\\n\",\n    \"        x_male[i, t, char_indices[char]] = 1\\n\",\n    \"    y_male[i, char_indices[next_chars[i]]] = 1\\n\",\n    \"\\n\",\n    \"print(x_male.shape, y_male.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 109,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Build model...\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"\\n\",\n    \"# build the model: a single LSTM\\n\",\n    \"print('Build model...')\\n\",\n    \"model = Sequential()\\n\",\n    \"model.add(LSTM(128, input_shape=(maxlen, len(character_set_male))))\\n\",\n    \"model.add(Dense(len(character_set_male)))\\n\",\n    \"model.add(Activation('softmax'))\\n\",\n    \"\\n\",\n    \"optimizer = RMSprop(lr=0.01)\\n\",\n    \"model.compile(loss='categorical_crossentropy', optimizer=optimizer)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 74,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"auto_text_generating_male_model.compile(loss='mean_squared_error',optimizer='sgd')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 110,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import random, sys\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 111,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# helper function to sample an index from a probability array\\n\",\n    \"def sample(a, diversity=0.75):\\n\",\n    \"    if random.random() > diversity:\\n\",\n    \"        return np.argmax(a)\\n\",\n    \"    while 1:\\n\",\n    \"        i = random.randint(0, len(a)-1)\\n\",\n    \"        if a[i] > random.random():\\n\",\n    \"            return i\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 113,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \"--------------------------------------------------\\n\",\n      \"Iteration 1\\n\",\n      \"Epoch 1/1\\n\",\n      \"2552476/2552476 [==============================] - 226s - loss: 1.8022   \\n\",\n      \"\\n\",\n      \"----- diversity: 0.2\\n\",\n      \"----- Generating with seed: \\\"p from the lack of  \\\"\\n\",\n      \"sense of the search \\n\",\n      \"\\n\",\n      \"\\n\",\n      \"----- diversity: 0.4\\n\",\n      \"----- Generating with seed: \\\"p from the lack of  \\\"\\n\",\n      \"through that possibl\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"----- diversity: 0.6\\n\",\n      \"----- Generating with seed: \\\"p from the lack of  \\\"\\n\",\n      \". This is a \\\"   by p\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"----- diversity: 0.8\\n\",\n      \"----- Generating with seed: \\\"p from the lack of  \\\"\\n\",\n      \"d he latermal ta we \\n\",\n      \"\\n\",\n      \"\\n\",\n      \"--------------------------------------------------\\n\",\n      \"Iteration 2\\n\",\n      \"Epoch 1/1\\n\",\n      \"2552476/2552476 [==============================] - 228s - loss: 1.7312   \\n\",\n      \"\\n\",\n      \"----- diversity: 0.2\\n\",\n      \"----- Generating with seed: \\\"s Last Dance\\\" with t\\\"\\n\",\n      \" screening on the st\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"----- diversity: 0.4\\n\",\n      \"----- Generating with seed: \\\"s Last Dance\\\" with t\\\"\\n\",\n      \"r song think of the \\n\",\n      \"\\n\",\n      \"\\n\",\n      \"----- diversity: 0.6\\n\",\n      \"----- Generating with seed: \\\"s Last Dance\\\" with t\\\"\\n\",\n      \". I'm akin computer \\n\",\n      \"\\n\",\n      \"\\n\",\n      \"----- diversity: 0.8\\n\",\n      \"----- Generating with seed: \\\"s Last Dance\\\" with t\\\"\\n\",\n      \"played that comment \\n\",\n      \"\\n\",\n      \"\\n\",\n      \"--------------------------------------------------\\n\",\n      \"Iteration 3\\n\",\n      \"Epoch 1/1\\n\",\n      \"2552476/2552476 [==============================] - 229s - loss: 1.8693   \\n\",\n      \"\\n\",\n      \"----- diversity: 0.2\\n\",\n      \"----- Generating with seed: \\\", as maybe someone w\\\"\\n\",\n      \"the ssone the so the\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"----- diversity: 0.4\\n\",\n      \"----- Generating with seed: \\\", as maybe someone w\\\"\\n\",\n      \"the sasd nouts and t\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"----- diversity: 0.6\\n\",\n      \"----- Generating with seed: \\\", as maybe someone w\\\"\\n\",\n      \"p hin I had at f¿ to\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"----- diversity: 0.8\\n\",\n      \"----- Generating with seed: \\\", as maybe someone w\\\"\\n\",\n      \"oge rely bluy leanda\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"--------------------------------------------------\\n\",\n      \"Iteration 4\\n\",\n      \"Epoch 1/1\\n\",\n      \"2552476/2552476 [==============================] - 228s - loss: 1.9135   \\n\",\n      \"\\n\",\n      \"----- diversity: 0.2\\n\",\n      \"----- Generating with seed: \\\"o the package :(. Ah\\\"\\n\",\n      \" suadedbe teacher th\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"----- diversity: 0.4\\n\",\n      \"----- Generating with seed: \\\"o the package :(. Ah\\\"\\n\",\n      \"e a searingly the id\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"----- diversity: 0.6\\n\",\n      \"----- Generating with seed: \\\"o the package :(. Ah\\\"\\n\",\n      \"propost the bure so \\n\",\n      \"\\n\",\n      \"\\n\",\n      \"----- diversity: 0.8\\n\",\n      \"----- Generating with seed: \\\"o the package :(. Ah\\\"\\n\",\n      \"ing.Lever fan. By in\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"--------------------------------------------------\\n\",\n      \"Iteration 5\\n\",\n      \"Epoch 1/1\\n\",\n      \"2552476/2552476 [==============================] - 229s - loss: 4.5892   \\n\",\n      \"\\n\",\n      \"----- diversity: 0.2\\n\",\n      \"----- Generating with seed: \\\"ot as long as my fri\\\"\\n\",\n      \"atde getu  th> QQ.“]\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"----- diversity: 0.4\\n\",\n      \"----- Generating with seed: \\\"ot as long as my fri\\\"\\n\",\n      \"tQ t[we QaaefYhere Q\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"----- diversity: 0.6\\n\",\n      \"----- Generating with seed: \\\"ot as long as my fri\\\"\\n\",\n      \"ew[”*ing”e[ t[w that\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"----- diversity: 0.8\\n\",\n      \"----- Generating with seed: \\\"ot as long as my fri\\\"\\n\",\n      \" me]sQoonQ“]e” ti nw\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"--------------------------------------------------\\n\",\n      \"Iteration 6\\n\",\n      \"Epoch 1/1\\n\",\n      \"2552476/2552476 [==============================] - 229s - loss: 6.7174   \\n\",\n      \"\\n\",\n      \"----- diversity: 0.2\\n\",\n      \"----- Generating with seed: \\\"use I'm pretty damn \\\"\\n\",\n      \"me g 'o a  a   a   a\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"----- diversity: 0.4\\n\",\n      \"----- Generating with seed: \\\"use I'm pretty damn \\\"\\n\",\n      \" a o theT  a   o  a \\n\",\n      \"\\n\",\n      \"\\n\",\n      \"----- diversity: 0.6\\n\",\n      \"----- Generating with seed: \\\"use I'm pretty damn \\\"\\n\",\n      \"  n . thot auupe to \\n\",\n      \"\\n\",\n      \"\\n\",\n      \"----- diversity: 0.8\\n\",\n      \"----- Generating with seed: \\\"use I'm pretty damn \\\"\\n\",\n      \" tomalek ho tt Ion i\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"--------------------------------------------------\\n\",\n      \"Iteration 7\\n\",\n      \"Epoch 1/1\\n\",\n      \"2552476/2552476 [==============================] - 227s - loss: 6.9138   \\n\",\n      \"\\n\",\n      \"----- diversity: 0.2\\n\",\n      \"----- Generating with seed: \\\"ats all got along be\\\"\\n\",\n      \"  thrtg t ia thv i c\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"----- diversity: 0.4\\n\",\n      \"----- Generating with seed: \\\"ats all got along be\\\"\\n\",\n      \"th wtot..  t to gt? \\n\",\n      \"\\n\",\n      \"\\n\",\n      \"----- diversity: 0.6\\n\",\n      \"----- Generating with seed: \\\"ats all got along be\\\"\\n\",\n      \" ed dthwnn,is a ment\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"----- diversity: 0.8\\n\",\n      \"----- Generating with seed: \\\"ats all got along be\\\"\\n\",\n      \"    t incow . wmiyit\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"--------------------------------------------------\\n\",\n      \"Iteration 8\\n\",\n      \"Epoch 1/1\\n\",\n      \"2552476/2552476 [==============================] - 228s - loss: 11.0629   \\n\",\n      \"\\n\",\n      \"----- diversity: 0.2\\n\",\n      \"----- Generating with seed: \\\"oot of my sleeping b\\\"\\n\",\n      \"m g te>t e  s t anab\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"----- diversity: 0.4\\n\",\n      \"----- Generating with seed: \\\"oot of my sleeping b\\\"\\n\",\n      \" dttoe s s“snge es s\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"----- diversity: 0.6\\n\",\n      \"----- Generating with seed: \\\"oot of my sleeping b\\\"\\n\",\n      \"tut  hou wen a  onap\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"----- diversity: 0.8\\n\",\n      \"----- Generating with seed: \\\"oot of my sleeping b\\\"\\n\",\n      \"evtyr tt e io on tok\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"--------------------------------------------------\\n\",\n      \"Iteration 9\\n\",\n      \"Epoch 1/1\\n\",\n      \"2552476/2552476 [==============================] - 228s - loss: 8.7874   \\n\",\n      \"\\n\",\n      \"----- diversity: 0.2\\n\",\n      \"----- Generating with seed: \\\" I’ve always looked \\\"\\n\",\n      \"ea  e ton ann n ffee\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"----- diversity: 0.4\\n\",\n      \"----- Generating with seed: \\\" I’ve always looked \\\"\\n\",\n      \"o tire n a anV sia a\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"----- diversity: 0.6\\n\",\n      \"----- Generating with seed: \\\" I’ve always looked \\\"\\n\",\n      \"r i jooe  Vag  o en \\n\",\n      \"\\n\",\n      \"\\n\",\n      \"----- diversity: 0.8\\n\",\n      \"----- Generating with seed: \\\" I’ve always looked \\\"\\n\",\n      \"  ao at ge ena oro o\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# train the model, output generated text after each iteration\\n\",\n    \"for iteration in range(1,10):\\n\",\n    \"    print()\\n\",\n    \"    print('-' * 50)\\n\",\n    \"    print('Iteration', iteration)\\n\",\n    \"    model.fit(x_male, y_male, batch_size=128, nb_epoch=1)\\n\",\n    \"\\n\",\n    \"    start_index = random.randint(0, len(male_post) - maxlen - 1)\\n\",\n    \"\\n\",\n    \"    for diversity in [0.2, 0.4, 0.6, 0.8]:\\n\",\n    \"        print()\\n\",\n    \"        print('----- diversity:', diversity)\\n\",\n    \"\\n\",\n    \"        generated = ''\\n\",\n    \"        sentence = male_post[start_index : start_index + maxlen]\\n\",\n    \"        generated += sentence\\n\",\n    \"        print('----- Generating with seed: \\\"' + sentence + '\\\"')\\n\",\n    \"\\n\",\n    \"        for iteration in range(400):\\n\",\n    \"            try:\\n\",\n    \"                x = np.zeros((1, maxlen, len(character_set_male)))\\n\",\n    \"                for t, char in enumerate(sentence):\\n\",\n    \"                    x[0, t, char_indices[char]] = 1.\\n\",\n    \"\\n\",\n    \"                preds = model.predict(x, verbose=0)[0]\\n\",\n    \"                next_index = sample(preds, diversity)\\n\",\n    \"                next_char = indices_char[next_index]\\n\",\n    \"\\n\",\n    \"                generated += next_char\\n\",\n    \"                sentence = sentence[1:] + next_char\\n\",\n    \"            except:\\n\",\n    \"                continue\\n\",\n    \"                \\n\",\n    \"        print(sentence)\\n\",\n    \"        print()\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"celltoolbar\": \"Slideshow\",\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.4.3\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "deep-learning/keras-tutorial/4. Conclusions.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Credits: Forked from [deep-learning-keras-tensorflow](https://github.com/leriomaggio/deep-learning-keras-tensorflow) by Valerio Maggio\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"slide\"\n    }\n   },\n   \"source\": [\n    \"# Conclusions\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"* Keras is a powerful and battery-included framework for Deep Learning in Python\\n\",\n    \"\\n\",\n    \"* Keras is **simple** to use..\\n\",\n    \"\\n\",\n    \"* ...but it is **not** for simple things!\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"<img src=\\\"imgs/keras_rank_1.jpg\\\" width=\\\"65%\\\" />\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"subslide\"\n    }\n   },\n   \"source\": [\n    \"<img src=\\\"imgs/keras_rank_2.jpg\\\" width=\\\"65%\\\" />\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"slide\"\n    }\n   },\n   \"source\": [\n    \"## Some References for ..\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"fragment\"\n    }\n   },\n   \"source\": [\n    \"#### Cutting Edge\\n\",\n    \"\\n\",\n    \"* Fractal Net Implementation with Keras: https://github.com/snf/keras-fractalnet -\\n\",\n    \"* Please check out: [https://github.com/fchollet/keras-resources]()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"fragment\"\n    }\n   },\n   \"source\": [\n    \"#### Hyper-Cool\\n\",\n    \"\\n\",\n    \"* Hyperas: https://github.com/maxpumperla/hyperas\\n\",\n    \"    - A web dashboard for Keras Models\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"celltoolbar\": \"Slideshow\",\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.4.3\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "deep-learning/keras-tutorial/LICENSE",
    "content": "The MIT License (MIT)\n\nCopyright (c) 2017 MPBA\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n"
  },
  {
    "path": "deep-learning/keras-tutorial/data/intro_to_ann.csv",
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    "content": "simplistic , silly and tedious . \nit's so laddish and juvenile , only teenage boys could possibly find it funny . \nexploitative and largely devoid of the depth or sophistication that would make watching such a graphic treatment of the crimes bearable . \n[garbus] discards the potential for pathological study , exhuming instead , the skewed melodrama of the circumstantial situation . \na visually flashy but narratively opaque and emotionally vapid exercise in style and mystification . \nthe story is also as unoriginal as they come , already having been recycled more times than i'd care to count . \nabout the only thing to give the movie points for is bravado -- to take an entirely stale concept and push it through the audience's meat grinder one more time . \nnot so much farcical as sour . \nunfortunately the story and the actors are served with a hack script . \nall the more disquieting for its relatively gore-free allusions to the serial murders , but it falls down in its attempts to humanize its subject . \na sentimental mess that never rings true . \nwhile the performances are often engaging , this loose collection of largely improvised numbers would probably have worked better as a one-hour tv documentary . \ninteresting , but not compelling . \non a cutting room floor somewhere lies . . . footage that might have made no such thing a trenchant , ironic cultural satire instead of a frustrating misfire . \nwhile the ensemble player who gained notice in guy ritchie's lock , stock and two smoking barrels and snatch has the bod , he's unlikely to become a household name on the basis of his first starring vehicle . \nthere is a difference between movies with the courage to go over the top and movies that don't care about being stupid\nnothing here seems as funny as it did in analyze this , not even joe viterelli as de niro's right-hand goombah . \nsuch master screenwriting comes courtesy of john pogue , the yale grad who previously gave us \" the skulls \" and last year's \" rollerball . \" enough said , except : film overboard ! \nhere , common sense flies out the window , along with the hail of bullets , none of which ever seem to hit sascha . \nthis 100-minute movie only has about 25 minutes of decent material . \nthe execution is so pedestrian that the most positive comment we can make is that rob schneider actually turns in a pretty convincing performance as a prissy teenage girl . \non its own , it's not very interesting . as a remake , it's a pale imitation . \nit shows that some studios firmly believe that people have lost the ability to think and will forgive any shoddy product as long as there's a little girl-on-girl action . \na farce of a parody of a comedy of a premise , it isn't a comparison to reality so much as it is a commentary about our knowledge of films . \nas exciting as all this exoticism might sound to the typical pax viewer , the rest of us will be lulled into a coma . \nthe party scenes deliver some tawdry kicks . the rest of the film . . . is dudsville . \nour culture is headed down the toilet with the ferocity of a frozen burrito after an all-night tequila bender  and i know this because i've seen 'jackass : the movie . '\nthe criticism never rises above easy , cynical potshots at morally bankrupt characters . . . \nthe movie's something-borrowed construction feels less the product of loving , well integrated homage and more like a mere excuse for the wan , thinly sketched story . killing time , that's all that's going on here . \nstupid , infantile , redundant , sloppy , over-the-top , and amateurish . yep , it's \" waking up in reno . \" go back to sleep . \nsomewhere in the middle , the film compels , as demme experiments he harvests a few movie moment gems , but the field of roughage dominates . \nthe action clichs just pile up . \npayami tries to raise some serious issues about iran's electoral process , but the result is a film that's about as subtle as a party political broadcast . \nthe only surprise is that heavyweights joel silver and robert zemeckis agreed to produce this ; i assume the director has pictures of them cavorting in ladies' underwear . \nanother useless recycling of a brutal mid-'70s american sports movie . \ni didn't laugh . i didn't smile . i survived . \nplease , someone , stop eric schaeffer before he makes another film . \nmost of the problems with the film don't derive from the screenplay , but rather the mediocre performances by most of the actors involved\n . . . if you're just in the mood for a fun -- but bad -- movie , you might want to catch freaks as a matinee . \ncurling may be a unique sport but men with brooms is distinctly ordinary . \nthough the opera itself takes place mostly indoors , jacquot seems unsure of how to evoke any sort of naturalism on the set . \nthere's no getting around the fact that this is revenge of the nerds revisited -- again . \nthe effort is sincere and the results are honest , but the film is so bleak that it's hardly watchable . \nanalyze that regurgitates and waters down many of the previous film's successes , with a few new swings thrown in . \nwith flashbulb editing as cover for the absence of narrative continuity , undisputed is nearly incoherent , an excuse to get to the closing bout . . . by which time it's impossible to care who wins . \nstinks from start to finish , like a wet burlap sack of gloom . \nto the civilized mind , a movie like ballistic : ecks vs . sever is more of an ordeal than an amusement . \nequlibrium could pass for a thirteen-year-old's book report on the totalitarian themes of 1984 and farenheit 451 . \nthe lack of naturalness makes everything seem self-consciously poetic and forced . . . it's a pity that [nelson's] achievement doesn't match his ambition . \neverything is off . \nwhen seagal appeared in an orange prison jumpsuit , i wanted to stand up in the theater and shout , 'hey , kool-aid ! '\nan easy watch , except for the annoying demeanour of its lead character . \nimagine the cleanflicks version of 'love story , ' with ali macgraw's profanities replaced by romance-novel platitudes . \npc stability notwithstanding , the film suffers from a simplistic narrative and a pat , fairy-tale conclusion . \nforget the misleading title , what's with the unexplained baboon cameo ? \nan odd , haphazard , and inconsequential romantic comedy . \nthough her fans will assuredly have their funny bones tickled , others will find their humor-seeking dollars best spent elsewhere . \npascale bailly's rom-com provides amlie's audrey tautou with another fabuleux destin -- i . e . , a banal spiritual quest . \na static and sugary little half-hour , after-school special about interfaith understanding , stretched out to 90 minutes . \nwatching the chemistry between freeman and judd , however , almost makes this movie worth seeing . almost . \n . . . a pretentious and ultimately empty examination of a sick and evil woman . \nthe country bears has no scenes that will upset or frighten young viewers . unfortunately , there is almost nothing in this flat effort that will amuse or entertain them , either . \nthe cumulative effect of watching this 65-minute trifle is rather like being trapped while some weird relative trots out the video he took of the family vacation to stonehenge . before long , you're desperate for the evening to end . \nthe characters are never more than sketches . . . which leaves any true emotional connection or identification frustratingly out of reach . \nmattei's underdeveloped effort here is nothing but a convenient conveyor belt of brooding personalities that parade about as if they were coming back from stock character camp -- a drowsy drama infatuated by its own pretentious self-examination . \nonly in its final surprising shots does rabbit-proof fence find the authority it's looking for . \nisn't as sharp as the original . . . despite some visual virtues , 'blade ii' just doesn't cut it . \n . . . plays like a badly edited , 91-minute trailer ( and ) the director can't seem to get a coherent rhythm going . in fact , it doesn't even seem like she tried . \nmaybe leblanc thought , \" hey , the movie about the baseball-playing monkey was worse . \" \nwhat you expect is just what you get . . . assuming the bar of expectations hasn't been raised above sixth-grade height . \nbarry sonnenfeld owes frank the pug big time\nthe biggest problem with roger avary's uproar against the mpaa is that , even in all its director's cut glory , he's made a film that's barely shocking , barely interesting and most of all , barely anything . \nso riddled with unanswered questions that it requires gargantuan leaps of faith just to watch it plod along . \ni approached the usher and said that if she had to sit through it again , she should ask for a raise . \nearnest but heavy-handed . \nif sinise's character had a brain his ordeal would be over in five minutes but instead the plot goes out of its way to introduce obstacles for him to stumble over . \ntoo slow for a younger crowd , too shallow for an older one . \nthere's a reason the studio didn't offer an advance screening . \" the adventures of pluto nash \" is a big time stinker . \na punch line without a premise , a joke built entirely from musty memories of half-dimensional characters . \ntakes one character we don't like and another we don't believe , and puts them into a battle of wills that is impossible to care about and isn't very funny . \nthe things this movie tries to get the audience to buy just won't fly with most intelligent viewers . \neven if the enticing prospect of a lot of nubile young actors in a film about campus depravity didn't fade amid the deliberate , tiresome ugliness , it would be rendered tedious by avary's failure to construct a story with even a trace of dramatic interest . \nsitting through the last reel ( spoiler alert ! ) is significantly less charming than listening to a four-year-old with a taste for exaggeration recount his halloween trip to the haunted house . \nconfuses its message with an ultimate desire to please , and contorting itself into an idea of expectation is the last thing any of these three actresses , nor their characters , deserve . \ndeadly dull , pointless meditation on losers in a gone-to-seed hotel . \nwith this new rollerball , sense and sensibility have been overrun by what can only be characterized as robotic sentiment . \none can only assume that the jury who bestowed star hoffman's brother gordy with the waldo salt screenwriting award at 2002's sundance festival were honoring an attempt to do something different over actually pulling it off\na movie more to be prescribed than recommended -- as visually bland as a dentist's waiting room , complete with soothing muzak and a cushion of predictable narrative rhythms . \nsex ironically has little to do with the story , which becomes something about how lame it is to try and evade your responsibilities and that you should never , ever , leave a large dog alone with a toddler . but never mind all that ; the boobs are fantasti\nthe script covers huge , heavy topics in a bland , surfacey way that doesn't offer any insight into why , for instance , good things happen to bad people . \na portrait of alienation so perfect , it will certainly succeed in alienating most viewers . \nthe code talkers deserved better than a hollow tribute . \nskip the film and buy the philip glass soundtrack cd . \nfeels like a cold old man going through the motions . \ndignified ceo's meet at a rustic retreat and pee against a tree . can you bear the laughter ? \ndull and mechanical , kinda like a very goofy museum exhibit\nthere's no point of view , no contemporary interpretation of joan's prefeminist plight , so we're left thinking the only reason to make the movie is because present standards allow for plenty of nudity . \nbeware the quirky brit-com . they can and will turn on a dime from oddly humorous to tediously sentimental . \nhas its moments -- and almost as many subplots . \nthe gags , and the script , are a mixed bag . \ncompletely awful iranian drama . . . as much fun as a grouchy ayatollah in a cold mosque . \nnarratively , trouble every day is a plodding mess . \nthere's no point in extracting the bare bones of byatt's plot for purposes of bland hollywood romance . \ndirectors john musker and ron clements , the team behind the little mermaid , have produced sparkling retina candy , but they aren't able to muster a lot of emotional resonance in the cold vacuum of space . \nadam sandler's heart may be in the right place , but he needs to pull his head out of his butt\nthere's no doubting that this is a highly ambitious and personal project for egoyan , but it's also one that , next to his best work , feels clumsy and convoluted . \ndespite engaging offbeat touches , knockaround guys rarely seems interested in kicking around a raison d'etre that's as fresh-faced as its young-guns cast . \nit's all pretty tame . the most offensive thing about the movie is that hollywood expects people to pay to see it . \nthe movie is a mess from start to finish . \nthe trouble with making this queen a thoroughly modern maiden is that it also makes her appear foolish and shallow rather than , as was more likely , a victim of mental illness . \ni'm not saying that ice age doesn't have some fairly pretty pictures , but there's not enough substance in the story to actually give them life . \nin the telling of a story largely untold , bui chooses to produce something that is ultimately suspiciously familiar . \nthe plot is nothing but boilerplate clichs from start to finish , and the script assumes that not only would subtlety be lost on the target audience , but that it's also too stupid to realize that they've already seen this exact same movie a hundred times\nterminally brain dead production . \nsome episodes work , some don't . \nbeautifully filmed and well acted . . . but admittedly problematic in its narrative specifics . \nj . lo will earn her share of the holiday box office pie , although this movie makes one thing perfectly clear : she's a pretty woman , but she's no working girl . \nrymer doesn't trust laughs -- and doesn't conjure proper respect for followers of the whole dead-undead genre , who deserve more from a vampire pic than a few shrieky special effects . \nnot only are the film's sopranos gags incredibly dated and unfunny , they also demonstrate how desperate the makers of this 'we're -doing-it-for -the-cash' sequel were . \nwow . i have not been this disappointed by a movie in a long time . \noff the hook is overlong and not well-acted , but credit writer-producer-director adam watstein with finishing it at all . \nit's a drag how nettelbeck sees working women -- or at least this working woman -- for whom she shows little understanding . \nwatching harris ham it up while physically and emotionally disintegrating over the course of the movie has a certain poignancy in light of his recent death , but boyd's film offers little else of consequence . \nit's also curious to note that this film , like the similarly ill-timed antitrust , is easily as bad at a fraction the budget . \n'ejemplo de una cinta en que no importa el talento de su reparto o lo interesante que pudo haber resultado su premisa , pues el resultado es francamente aburrido y , por momentos , deplorable . '\nwill probably be one of those movies barely registering a blip on the radar screen of 2002 . \nthe problem is not that it's all derivative , because plenty of funny movies recycle old tropes . the problem is that van wilder does little that is actually funny with the material . \nthere's nothing interesting in unfaithful whatsoever . \nnone of this is half as moving as the filmmakers seem to think . \na processed comedy chop suey . \nas spent screen series go , star trek : nemesis is even more suggestive of a 65th class reunion mixer where only eight surviving members show up -- and there's nothing to drink . \nfails as a dystopian movie , as a retooling of fahrenheit 451 , and even as a rip-off of the matrix . \nfull of the kind of obnoxious chitchat that only self-aware neurotics engage in . \nan erotic thriller that's neither too erotic nor very thrilling , either . \nthe movie , like bartleby , is something of a stiff -- an extra-dry office comedy that seems twice as long as its 83 minutes . \nwith its parade of almost perpetually wasted characters . . . margarita feels like a hazy high that takes too long to shake . \nif you value your time and money , find an escape clause and avoid seeing this trite , predictable rehash . \nthe director and her capable cast appear to be caught in a heady whirl of new age-inspired good intentions , but the spell they cast isn't the least bit mesmerizing . \neverything is pegged into the groove of a new york dating comedy with 'issues' to simplify . \na dramatic comedy as pleasantly dishonest and pat as any hollywood fluff . \nthe cameo-packed , m : i-2-spoofing title sequence is the funniest 5 minutes to date in this spy comedy franchise . . . then mike myers shows up and ruins everything . \nit comes off as so silly that you wouldn't be surprised if ba , murdock and rest of the a-team were seen giving chase in a black and red van . \nthe 50-something lovebirds are too immature and unappealing to care about . \nso genial is the conceit , this is one of those rare pictures that you root for throughout , dearly hoping that the rich promise of the script will be realized on the screen . it never is , not fully . \neven in the summertime , the most restless young audience deserves the dignity of an action hero motivated by something more than franchise possibilities . \nwhat with all the blanket statements and dime-store ruminations on vanity , the worries of the rich and sudden wisdom , the film becomes a sermon for most of its running time . \none lousy movie . \nas gamely as the movie tries to make sense of its title character , there remains a huge gap between the film's creepy , clean-cut dahmer ( jeremy renner ) and fiendish acts that no amount of earnest textbook psychologizing can bridge . \nplodding , peevish and gimmicky . \nthe four feathers is definitely horse feathers , but if you go in knowing that , you might have fun in this cinematic sandbox . \noozes condescension from every pore . \nsolaris \" is a shapeless inconsequential move relying on the viewer to do most of the work . \nthe direction , by george hickenlooper , has no snap to it , no wiseacre crackle or hard-bitten cynicism . \n . . . hypnotically dull . \nthough this saga would be terrific to read about , it is dicey screen material that only a genius should touch . \nit has plenty of laughs . it just doesn't have much else . . . especially in a moral sense . \nan awful lot like one of [spears'] music videos in content -- except that it goes on for at least 90 more minutes and , worse , that you have to pay if you want to see it . \nconfusion is one of my least favourite emotions , especially when i have to put up with 146 minutes of it . \n[h]ad i suffered and bled on the hard ground of ia drang , i'd want something a bit more complex than we were soldiers to be remembered by . \noccasionally loud and offensive , but more often , it simply lulls you into a gentle waking coma . \nit may play well as a double feature with mainstream foreign mush like my big fat greek wedding\nby the time you reach the finale , you're likely wondering why you've been watching all this strutting and posturing . \njournalistically dubious , inept and often lethally dull . \nputting the primitive murderer inside a high-tech space station unleashes a pandora's box of special effects that run the gamut from cheesy to cheesier to cheesiest . \n at its best , it's black hawk down with more heart . at its worst , it's rambo- meets-john ford . \nexactly what you'd expect from a guy named kaos . \ncalculated swill . \nthis movie . . . doesn't deserve the energy it takes to describe how bad it is . \nwith or without ballast tanks , k-19 sinks to a harrison ford low . \ndirector oliver parker labors so hard to whip life into the importance of being earnest that he probably pulled a muscle or two . \nyou might be shocked to discover that seinfeld's real life is boring . \nit's not nearly as fresh or enjoyable as its predecessor , but there are enough high points to keep this from being a complete waste of time . \nwalsh can't quite negotiate the many inconsistencies in janice's behavior or compensate for them by sheer force of charm . \nthis 10th film in the series looks and feels tired . \nit leers , offering next to little insight into its intriguing subject . \ni found myself growing more and more frustrated and detached as vincent became more and more abhorrent . \none of the oddest and most inexplicable sequels in movie history . \nthere's nothing to gain from watching they . it isn't scary . it hates its characters . it finds no way to entertain or inspire its viewers . \nfear permeates the whole of stortelling , todd solondz' oftentimes funny , yet ultimately cowardly autocritique . \nthe skirmishes for power waged among victims and predators settle into an undistinguished rhythm of artificial suspense . \nthoroughly awful . \n . . . ice age treads predictably along familiar territory , making it a passable family film that won't win many fans over the age of 12 . \nthough the film is well-intentioned , one could rent the original and get the same love story and parable . \njust too silly and sophomoric to ensnare its target audience . \nthe video work is so grainy and rough , so dependent on being 'naturalistic' rather than carefully lit and set up , that it's exhausting to watch . \ntruly terrible . \na cleverly crafted but ultimately hollow mockumentary . \nit gets bogged down by hit-and-miss topical humour before getting to the truly good stuff . \nan achingly enthralling premise , the film is hindered by uneven dialogue and plot lapses . \nit's tommy's job to clean the peep booths surrounding her , and after viewing this one , you'll feel like mopping up , too . \nrifkin no doubt fancies himself something of a hubert selby jr . , but there isn't an ounce of honest poetry in his entire script ; it's simply crude and unrelentingly exploitative . \nfluffy and disposible . \nsuch a bad movie that its luckiest viewers will be seated next to one of those ignorant pinheads who talk throughout the show . \nif you go into the theater expecting a scary , action-packed chiller , you might soon be looking for a sign . an exit sign , that is . \nholds limited appeal to those who like explosions , sadism and seeing people beat each other to a pulp . \nthe dialogue is very choppy and monosyllabic despite the fact that it is being dubbed . \na feature-length , r-rated , road-trip version of mama's family . \nwhat you end up getting is the vertical limit of surfing movies - memorable stunts with lots of downtime in between . \nstealing harvard doesn't care about cleverness , wit or any other kind of intelligent humor . \nbigelow handles the nuclear crisis sequences evenly but milks drama when she should be building suspense , and drags out too many scenes toward the end that should move quickly . \nthere's undeniable enjoyment to be had from films crammed with movie references , but the fun wears thin -- then out -- when there's nothing else happening . \nimagine kevin smith , the blasphemous bad boy of suburban jersey , if he were stripped of most of his budget and all of his sense of humor . the result might look like vulgar . \nsuffers from a lack of clarity and audacity that a subject as monstrous and pathetic as dahmer demands . \nwhat soured me on the santa clause 2 was that santa bumps up against 21st century reality so hard , it's icky . \nit's an 88-minute highlight reel that's 86 minutes too long . \nthe film favors the scientific over the spectacular ( visually speaking ) . \nsuch an incomprehensible mess that it feels less like bad cinema than like being stuck in a dark pit having a nightmare about bad cinema . \nwith the exception of mccoist , the players don't have a clue on the park . the acting isn't much better . \nthe whole affair is as predictable as can be . \na not-so-divine secrets of the ya-ya sisterhood with a hefty helping of re-fried green tomatoes . \nthis cloying , voices-from-the-other-side story is hell . \na suffocating rape-payback horror show that hinges on the subgenre's most enabling victim . . . and an ebullient affection for industrial-model meat freezers . \nstar trek was kind of terrific once , but now it is a copy of a copy of a copy . \n[n]o matter how much good will the actors generate , showtime eventually folds under its own thinness . \nevery potential twist is telegraphed well in advance , every performance respectably muted ; the movie itself seems to have been made under the influence of rohypnol . \nputs on airs of a hal hartley wannabe film -- without the vital comic ingredient of the hilarious writer-director himself . \nver wiel's desperate attempt at wit is lost , leaving the character of critical jim two-dimensional and pointless . \ndespite a performance of sustained intelligence from stanford and another of subtle humour from bebe neuwirth , as an older woman who seduces oscar , the film founders on its lack of empathy for the social milieu - rich new york intelligentsia - and its off\nalthough disney follows its standard formula in this animated adventure , it feels more forced than usual . \ngaghan . . . has thrown every suspenseful clich in the book at this nonsensical story . \na sham construct based on theory , sleight-of-hand , and ill-wrought hypothesis . \n[p]artnering murphy with robert de niro for the tv-cops comedy showtime would seem to be surefire casting . the catch is that they're stuck with a script that prevents them from firing on all cylinders . \n'you'll laugh for not quite and hour and a half , but come out feeling strangely unsatisfied . you'll feel like you ate a reeses without the peanut butter . . . '\ngooding offers a desperately ingratiating performance . \nparker should be commended for taking a fresh approach to familiar material , but his determination to remain true to the original text leads him to adopt a somewhat mannered tone . . . that ultimately dulls the human tragedy at the story's core . \nthe director has injected self-consciousness into the proceedings at every turn . the results are far more alienating than involving . \nbogdanich is unashamedly pro-serbian and makes little attempt to give voice to the other side . \na lack of thesis makes maryam , in the end , play out with the intellectual and emotional impact of an after-school special . \nthe worst film of the year . \nby-the-numbers yarn . \nwithout shakespeare's eloquent language , the update is dreary and sluggish . \nif h . g . wells had a time machine and could take a look at his kin's reworked version , what would he say ? 'it looks good , sonny , but you missed the point . '\nduring the tuxedo's 90 minutes of screen time , there isn't one true 'chan moment' . \nbisset delivers a game performance , but she is unable to save the movie . \nwatching austin powers in goldmember is like binging on cotton candy . it's sweet and fluffy at the time , but it may leave you feeling a little sticky and unsatisfied . \nthe most anti-human big studio picture since 3000 miles to graceland . \nthe film can depress you about life itself . \ni'm sure the filmmakers found this a remarkable and novel concept , but anybody who has ever seen an independent film can report that it is instead a cheap clich . \nthe acting is fine but the script is about as interesting as a recording of conversations at the wal-mart checkout line . \nits weighty themes are too grave for youngsters , but the story is too steeped in fairy tales and other childish things to appeal much to teenagers . \nthe plot plummets into a comedy graveyard before janice comes racing to the rescue in the final reel . \nsometimes there are very , very good reasons for certain movies to be sealed in a jar and left on a remote shelf indefinitely . \nat 90 minutes this movie is short , but it feels much longer . \nhere's my advice , kev . start reading your scripts before signing that dotted line . \nan alternately raucous and sappy ethnic sitcom . . . you'd be wise to send your regrets . \nan ugly-duckling tale so hideously and clumsily told it feels accidental . \nunfortunately , it's also not very good . especially compared with the television series that inspired the movie . \nit wraps up a classic mother/daughter struggle in recycled paper with a shiny new bow and while the audience can tell it's not all new , at least it looks pretty . \nglazed with a tawdry b-movie scum . \nthis is the kind of movie during which you want to bang your head on the seat in front of you , at its cluelessness , at its idiocy , at its utterly misplaced earnestness . \nit winds up moving in many directions as it searches ( vainly , i think ) for something fresh to say . \nall in all , road to perdition is more in love with strangeness than excellence . \na big fat pain . \na mimetic approximation of better films like contempt and 8 1/2 . \nunintelligible , poorly acted , brain-slappingly bad , harvard man is ludicrous enough that it could become a cult classic . \nwatching the powerpuff girls movie , my mind kept returning to one anecdote for comparison : the cartoon in japan that gave people seizures . \naan opportunity wasted . \nan inelegant combination of two unrelated shorts that falls far short of the director's previous work in terms of both thematic content and narrative strength . \nto build a feel-good fantasy around a vain dictator-madman is off-putting , to say the least , not to mention inappropriate and wildly undeserved . \nwith the cheesiest monsters this side of a horror spoof , which they isn't , it is more likely to induce sleep than fright . \nmild , meandering teen flick . \nthough its atmosphere is intriguing . . . the drama is finally too predictable to leave much of an impression . \nthough this rude and crude film does deliver a few gut-busting laughs , its digs at modern society are all things we've seen before . \nalthough it tries to be much more , it's really just another major league . \nastonishing isn't the word -- neither is incompetent , incoherent or just plain crap . indeed , none of these words really gets at the very special type of badness that is deuces wild . \none thing is for sure : this movie does not tell you a whole lot about lily chou-chou . \nwith a tone as variable as the cinematography , schaeffer's film never settles into the light-footed enchantment the material needs , and the characters' quirks and foibles never jell into charm . \nto better understand why this didn't connect with me would require another viewing , and i won't be sitting through this one again . . . that in itself is commentary enough . \ncuba gooding jr . valiantly mugs his way through snow dogs , but even his boisterous energy fails to spark this leaden comedy . \ndiane lane's sophisticated performance can't rescue adrian lyne's unfaithful from its sleazy moralizing . \nnot at all clear what it's trying to say and even if it were  i doubt it would be all that interesting . \nstorytelling feels slight . \n[swimfan] falls victim to sloppy plotting , an insultingly unbelievable final act and a villainess who is too crazy to be interesting . \nthis remake of lina wertmuller's 1975 eroti-comedy might just be the biggest husband-and-wife disaster since john and bo derek made the ridiculous bolero . \nsilly , loud and goofy . \nwhy spend $9 on the same stuff you can get for a buck or so in that greasy little vidgame pit in the theater lobby ? \nthe french director has turned out nearly 21/2 hours of unfocused , excruciatingly tedious cinema that , half an hour in , starts making water torture seem appealing . \nthe basic premise is intriguing but quickly becomes distasteful and downright creepy . \nthe pool drowned me in boredom . \nit's like an all-star salute to disney's cheesy commercialism . \nit's hard to imagine any recent film , independent or otherwise , that makes as much of a mess as this one . \nsome of the computer animation is handsome , and various amusing sidekicks add much-needed levity to the otherwise bleak tale , but overall the film never rises above mediocrity . \nthere's an excellent 90-minute film here ; unfortunately , it runs for 170 . \nas saccharine movies go , this is likely to cause massive cardiac arrest if taken in large doses . \ndie another day is only intermittently entertaining but it's hard not to be a sucker for its charms , or perhaps it's just impossible not to feel nostalgia for movies you grew up with . \nas is often the case with ambitious , eager first-time filmmakers , mr . murray , a prolific director of music videos , stuffs his debut with more plot than it can comfortably hold . \nthe mystery of enigma is how a rich historical subject , combined with so much first-rate talent . . . could have yielded such a flat , plodding picture . \nit throws quirky characters , odd situations , and off-kilter dialogue at us , all as if to say , \" look at this ! this is an interesting movie ! \" but the film itself is ultimately quite unengaging . \nthe inherent limitations of using a video game as the source material movie are once again made all too clear in this schlocky horror/action hybrid . \nbad company . bad movie . just plain bad . \nit's not only dull because we've seen [eddie] murphy do the genial-rogue shtick to death , but because the plot is equally hackneyed . \navary's film never quite emerges from the shadow of ellis' book . \na poorly scripted , preachy fable that forgets about unfolding a coherent , believable story in its zeal to spread propaganda . \nwhile it is interesting to witness the conflict from the palestinian side , longley's film lacks balance . . . and fails to put the struggle into meaningful historical context . \nwoo has as much right to make a huge action sequence as any director , but how long will filmmakers copy the \" saving private ryan \" battle scenes before realizing steven spielberg got it right the first time ? \nit's sincere to a fault , but , unfortunately , not very compelling or much fun . \n . . . jones , despite a definitely distinctive screen presence , just isn't able to muster for a movie that , its title notwithstanding , should have been a lot nastier if it wanted to fully capitalize on its lead's specific gifts . \nthis follow-up seems so similar to the 1953 disney classic that it makes one long for a geriatric peter . \nwhy , you may ask , why should you buy the movie milk when the tv cow is free ? there's no good answer to that one . \nthis slow-moving swedish film offers not even a hint of joy , preferring to focus on the humiliation of martin as he defecates in bed and urinates on the plants at his own birthday party . \na muddled limp biscuit of a movie , a vampire soap opera that doesn't make much sense even on its own terms . \nthere's the plot , and a maddeningly insistent and repetitive piano score that made me want to scream . \nthis is a movie so insecure about its capacity to excite that it churns up not one but two flagrantly fake thunderstorms to underscore the action . \nthis is amusing for about three minutes . \nklein , charming in comedies like american pie and dead-on in election , delivers one of the saddest action hero performances ever witnessed . \nit's rare to see a movie that takes such a speedy swan dive from \" promising \" to \" interesting \" to \" familiar \" before landing squarely on \" stupid \" . \nthis is the sort of low-grade dreck that usually goes straight to video --with a lousy script , inept direction , pathetic acting , poorly dubbed dialogue and murky cinematography , complete with visible boom mikes . \nthe direction occasionally rises to the level of marginal competence , but for most of the film it is hard to tell who is chasing who or why . \nthere are few things more frustrating to a film buff than seeing an otherwise good movie marred beyond redemption by a disastrous ending . \nit won't harm anyone , but neither can i think of a very good reason to rush right out and see it . after all , it'll probably be in video stores by christmas , and it might just be better suited to a night in the living room than a night at the movies . \nlooks more like a travel-agency video targeted at people who like to ride bikes topless and roll in the mud than a worthwhile glimpse of independent-community guiding lights . \ngiven too much time to consider the looseness of the piece , the picture begins to resemble the shapeless , grasping actors' workshop that it is . \nthey kept much of the plot but jettisoned the stuff that would make this a moving experience for people who haven't read the book . \njust because a walk to remember is shrewd enough to activate girlish tear ducts doesn't mean it's good enough for our girls . \n[carvey's] characters are both overplayed and exaggerated , but then again , subtlety has never been his trademark . \nit's mildly interesting to ponder the peculiar american style of justice that plays out here , but it's so muddled and derivative that few will bother thinking it all through . \nthis dreadfully earnest inversion of the concubine love triangle eschews the previous film's historical panorama and roiling pathos for bug-eyed mugging and gay-niche condescension . \nbrown's saga , like many before his , makes for snappy prose but a stumblebum of a movie . \nthe boys' sparring , like the succession of blows dumped on guei , wears down the story's more cerebral , and likable , plot elements . \nthe script by vincent r . nebrida . . . tries to cram too many ingredients into one small pot . \nthe story is so light and sugary that were it a macy's thanksgiving day parade balloon , extra heavy-duty ropes would be needed to keep it from floating away . \noedekerk mugs mercilessly , and the genuinely funny jokes are few and far between . \nsince dahmer resorts to standard slasher flick thrills when it should be most in the mind of the killer , it misses a major opportunity to be truly revelatory about his psyche . \nonly those most addicted to film violence in all its forms will find anything here to appreciate . \ncold and scattered , minority report commands interest almost solely as an exercise in gorgeous visuals . that's not vintage spielberg and that , finally , is minimally satisfying . \nevery now and again , a movie comes along to remind us of how very bad a motion picture can truly be . frank mcklusky c . i . is that movie ! \nit's not difficult to spot the culprit early-on in this predictable thriller . \n[a] soulless , stupid sequel . . . \n . . . a mostly boring affair with a confusing sudden finale that's likely to irk viewers . \nthe trappings of i spy are so familiar you might as well be watching a rerun . \nwhat starts off as a potentially incredibly twisting mystery becomes simply a monster chase film . \nin the wake of saving private ryan , black hawk down and we were soldiers , you are likely to be as heartily sick of mayhem as cage's war-weary marine . \nit is messy , uncouth , incomprehensible , vicious and absurd . \nreally does feel like a short stretched out to feature length . \nhampered -- no , paralyzed -- by a self-indulgent script . . . that aims for poetry and ends up sounding like satire . \ncheap , vulgar dialogue and a plot that crawls along at a snail's pace . \nand if you appreciate the one-sided theme to lawrence's over-indulgent tirade , then knock yourself out and enjoy the big screen postcard that is a self-glorified martin lawrence lovefest . if you are willing to do this , then you so crazy ! \ndirected without the expected flair or imagination by hong kong master john woo , windtalkers airs just about every cliche in the war movie compendium across its indulgent two-hour-and-fifteen-minute length . \nit's a very tasteful rock and roll movie . you could put it on a coffee table anywhere . \nthe movie is loaded with good intentions , but in his zeal to squeeze the action and our emotions into the all-too-familiar dramatic arc of the holocaust escape story , minac drains his movie of all individuality . \nan infuriating film . just when you think you are making sense of it , something happens that tells you there is no sense . \nthe entire movie is so formulaic and forgettable that it's hardly over before it begins to fade from memory . \nthe setting turns out to be more interesting than any of the character dramas , which never reach satisfying conclusions . \nas an entertainment destination for the general public , kung pow sets a new benchmark for lameness . \nthis misty-eyed southern nostalgia piece , in treading the line between sappy and sanguine , winds up mired in tear-drenched quicksand . \nas pure over-the-top trash , any john waters movie has it beat by a country mile . \nwendigo wants to be a monster movie for the art-house crowd , but it falls into the trap of pretention almost every time . \nbigelow offers some flashy twists and turns that occasionally fortify this turgid fable . but for the most part , the weight of water comes off as a two-way time-switching myopic mystery that stalls in its lackluster gear of emotional blandness . \nthis film biggest problem ? no laughs . \nless-than-compelling documentary of a yiddish theater clan . \nthat the chuck norris \" grenade gag \" occurs about 7 times during windtalkers is a good indication of how serious-minded the film is . \nviewers are asked so often to suspend belief that were it not for holm's performance , the film would be a total washout . \nit's not exactly worth the bucks to expend the full price for a date , but when it comes out on video , it's well worth a rental . \ni can't begin to tell you how tedious , how resolutely unamusing , how thoroughly unrewarding all of this is , and what a reckless squandering of four fine acting talents . . . \neverybody loves a david and goliath story , and this one is told almost entirely from david's point of view . \nthrow smoochy from the train ! \neventually , they will have a showdown , but , by then , your senses are as mushy as peas and you don't care who fires the winning shot . \nirwin and his director never come up with an adequate reason why we should pay money for what we can get on television for free . \na light , engaging comedy that fumbles away almost all of its accumulated enjoyment with a crucial third act miscalculation . \ncedar somewhat defuses this provocative theme by submerging it in a hoary love triangle . \nto paraphrase a line from another dickens' novel , nicholas nickleby is too much like a fragment of an underdone potato . \nthe actresses may have worked up a back story for the women they portray so convincingly , but viewers don't get enough of that background for the characters to be involving as individuals rather than types . \nthe result , however well-intentioned , is ironically just the sort of disposable , kitchen-sink homage that illustrates why the whole is so often less than the sum of its parts in today's hollywood . \nan extremely unpleasant film . \na movie just for friday fans , critics be damned . if you already like this sort of thing , this is that sort of thing all over again . \na sincere but dramatically conflicted gay coming-of-age tale . \nwait for it to hit cable . \nultimately feels like just one more in the long line of films this year about the business of making movies . \na high-minded snoozer . \nnothing but one relentlessly depressing situation after another for its entire running time , something that you could easily be dealing with right now in your lives . \n77 minutes of pokemon may not last 4ever , it just seems like it does . my only wish is that celebi could take me back to a time before i saw this movie and i could just skip it . \nthe one not-so-small problem with expecting is that the entire exercise has no real point . \na movie you observe , rather than one you enter into . \n[at least] moore is a real charmer . \njohn carlen's script is full of unhappy , two-dimensional characters who are anything but compelling . \nlabute can't avoid a fatal mistake in the modern era : he's changed the male academic from a lower-class brit to an american , a choice that upsets the novel's exquisite balance and shreds the fabric of the film . \nthe notion of deleting emotion from people , even in an advanced prozac nation , is so insanely dysfunctional that the rampantly designed equilibrium becomes a concept doofus . \nstale first act , scrooge story , blatant product placement , some very good comedic songs , strong finish , dumb fart jokes . \nunsurprisingly , the way this all works out makes the women look more like stereotypical caretakers and moral teachers , instead of serious athletes . \na film that loses sight of its own story . \nadam sandler's eight crazy nights grows on you -- like a rash . \nthe big-screen scooby makes the silly original cartoon seem smart and well-crafted in comparison . \nfew of the increasingly far-fetched events that first-time writer-director neil burger follows up with are terribly convincing , which is a pity , considering barry's terrific performance . \ngets better after foster leaves that little room . \nthe movie is as padded as allen's jelly belly . \nresident evil isn't a product of its cinematic predecessors so much as an mtv , sugar hysteria , and playstation cocktail . \na rather average action film that benefits from several funny moments supplied by epps . \n . . . unspeakably , unbearably dull , featuring reams of flatly delivered dialogue and a heroine who comes across as both shallow and dim-witted . \nresembles a soft porn brian de palma pastiche . \nbluto blutarsky , we miss you . \ninnocuous enough to make even jean-claude van damme look good . \nit's a glorified sitcom , and a long , unfunny one at that . \nwoody allen can write and deliver a one liner as well as anybody . but i had a lot of problems with this movie . \nexecrable . \ndevoid of any of the qualities that made the first film so special . \nall movie long , city by the sea swings from one approach to the other , but in the end , it stays in formula -- which is a waste of de niro , mcdormand and the other good actors in the cast . \nplotless collection of moronic stunts is by far the worst movie of the year . \nhowever sincere it may be , the rising place never quite justifies its own existence . \nparker updates the setting in an attempt to make the film relevant today , without fully understanding what it was that made the story relevant in the first place . \nit's sort of a 21st century morality play with a latino hip hop beat . but the second half of the movie really goes downhill . \npaxton's uneven directorial debut fails to unlock the full potential of what is in many ways a fresh and dramatically substantial spin on the genre . \nthe script becomes lifeless and falls apart like a cheap lawn chair . \nthe script falls back on too many tried-and-true shenanigans that hardly distinguish it from the next teen comedy . \nthe film starts promisingly , but the ending is all too predictable and far too cliched to really work . \nlet's issue a moratorium , effective immediately , on treacly films about inspirational prep-school professors and the children they so heartwarmingly motivate . \nit's the element of condescension , as the filmmakers look down on their working-class subjects from their lofty perch , that finally makes sex with strangers , which opens today in the new york metropolitan area , so distasteful . \nalternately frustrating and rewarding . \nit's impossible to even categorize this as a smutty guilty pleasure . \ndespite suffering a sense-of-humour failure , the man who wrote rocky does not deserve to go down with a ship as leaky as this . \nswinging , the film makes it seem , is not a hobby that attracts the young and fit . or intelligent . \nthe most memorable moment was when green threw medical equipment at a window ; not because it was particularly funny , but because i had a serious urge to grab the old lady at the end of my aisle's walker and toss it at the screen in frustration . \nsee clockstoppers if you have nothing better to do with 94 minutes . but be warned , you too may feel time has decided to stand still . or that the battery on your watch has died . \nsuffers from over-familiarity since hit-hungry british filmmakers have strip-mined the monty formula mercilessly since 1997 . \nthere are enough throwaway references to faith and rainbows to plant smile-button faces on that segment of the populace that made a walk to remember a niche hit . \nyes , i have given this movie a rating of zero . but fans of the show should not consider this a diss . consider it 'perfection . '\nthe cumulative effect of the movie is repulsive and depressing . \nchildren and adults enamored of all things pokemon won't be disappointed . \ni don't even care that there's no plot in this antonio banderas-lucy liu faceoff . it's still terrible ! \nchildren of the century , though well dressed and well made , ultimately falls prey to the contradiction that afflicts so many movies about writers . \nit's not so much a movie as a joint promotion for the national basketball association and teenaged rap and adolescent poster-boy lil' bow wow . \nperalta's mythmaking could have used some informed , adult hindsight . \namazingly dopey . \na close-to-solid espionage thriller with the misfortune of being released a few decades too late . \ncloaks a familiar anti-feminist equation ( career - kids = misery ) in tiresome romantic-comedy duds . \nthe good girl is a film in which the talent is undeniable but the results are underwhelming . \njust a collection of this and that -- whatever fills time -- with no unified whole . \nthe animation and game phenomenon that peaked about three years ago is actually dying a slow death , if the poor quality of pokemon 4 ever is any indication . \nonly about as sexy and dangerous as an actress in a role that reminds at every turn of elizabeth berkley's flopping dolphin-gasm . \nkids who are into this thornberry stuff will probably be in wedgie heaven . anyone else who may , for whatever reason , be thinking about going to see this movie is hereby given fair warning . \nmr . soderbergh's direction and visual style struck me as unusually and unimpressively fussy and pretentious . \ndo you say \" hi \" to your lover when you wake up in the morning ? \nit makes me feel weird / thinking about all the bad things in the world / like puppies with broken legs / and butterflies that die / and movies starring pop queens\ndirector tom dey demonstrated a knack for mixing action and idiosyncratic humor in his charming 2000 debut shanghai noon , but showtime's uninspired send-up of tv cop show cliches mostly leaves him shooting blanks . \nbanal and predictable . \n'yes , that's right : it's forrest gump , angel of death . '\na wildly erratic drama with sequences that make you wince in embarrassment and others , thanks to the actors , that are quite touching . \nwhile easier to sit through than most of jaglom's self-conscious and gratingly irritating films , it's still tainted by cliches , painful improbability and murky points . \nabout as enjoyable , i would imagine , as searching for a quarter in a giant pile of elephant feces . . . positively dreadful . \na generic international version of a typical american horror film . \n . . . while certainly clever in spots , this too-long , spoofy update of shakespeare's macbeth doesn't sustain a high enough level of invention . \ncrikey indeed . \nlucas has in fact come closer than anyone could desire to the cheap , graceless , hackneyed sci-fi serials of the '30s and '40s . \nthere's a lot of good material here , but there's also a lot of redundancy and unsuccessful crudeness accompanying it . \nabsurdities and clichs accumulate like lint in a fat man's navel . \nif you think it's a riot to see rob schneider in a young woman's clothes , then you'll enjoy the hot chick . \nthe sheer dumbness of the plot ( other than its one good idea ) and the movie's inescapable air of sleaziness get you down . \nstrangely comes off as a kingdom more mild than wild . \nthe next big thing's not-so-big ( and not-so-hot ) directorial debut . \nyet another iteration of what's become one of the movies' creepiest conventions , in which the developmentally disabled are portrayed with almost supernatural powers to humble , teach and ultimately redeem their mentally \" superior \" friends , family . . . \nbond-inspired ? certainly . likely to have decades of life as a classic movie franchise ? let's hope not . \nthis flat run at a hip-hop tootsie is so poorly paced you could fit all of pootie tang in between its punchlines . \ndavis has energy , but she doesn't bother to make her heroine's book sound convincing , the gender-war ideas original , or the comic scenes fly . \nsurprisingly , considering that baird is a former film editor , the movie is rather choppy . \nnone of this is very original , and it isn't particularly funny . \n . . . a bland murder-on-campus yawner . \na humorless journey into a philosophical void . \nthese two are generating about as much chemistry as an iraqi factory poised to receive a un inspector . \nresponsvel direto pelo fracasso 'artstico' de doce lar , o roteirista c . jay cox no consegue sequer aproveitar os pouqussimos momentos em que escapa da mediocridade . \njust as the lousy tarantino imitations have subsided , here comes the first lousy guy ritchie imitation . \na passable romantic comedy , in need of another couple of passes through the word processor . \nthe movie attempts to mine laughs from a genre -- the gangster/crime comedy -- that wore out its welcome with audiences several years ago , and its cutesy reliance on movie-specific cliches isn't exactly endearing . \nshows holmes has the screen presence to become a major-league leading lady , ( but ) the movie itself is an underachiever , a psychological mystery that takes its sweet time building to a climax that's scarcely a surprise by the time it arrives . \nin the book-on-tape market , the film of \" the kid stays in the picture \" would be an abridged edition\nultimately . . . the movie is too heady for children , and too preachy for adults . \nit's just a little too self-satisfied . \nclever but not especially compelling . \nmckay seems embarrassed by his own invention and tries to rush through the intermediary passages , apparently hoping that the audience will not notice the glaring triteness of the plot device he has put in service . \na piece of mildly entertaining , inoffensive fluff that drifts aimlessly for 90 minutes before lodging in the cracks of that ever-growing category : unembarrassing but unmemorable . \nlabute was more fun when his characters were torturing each other psychologically and talking about their genitals in public . \nthe movie weighs no more than a glass of flat champagne . \nbrisk hack job . \nthe problem with antwone fisher is that it has a screenplay written by antwone fisher based on the book by antwone fisher . \nalarms for duvall's throbbing sincerity and his elderly propensity for patting people while he talks . \nthe maudlin way its story unfolds suggests a director fighting against the urge to sensationalize his material . \nwhat little grace [rifkin's] tale of precarious skid-row dignity achieves is pushed into the margins by predictable plotting and tiresome histrionics . \ntries to work in the same vein as the brilliance of animal house but instead comes closer to the failure of the third revenge of the nerds sequel . \nunfortunately , kapur modernizes a . e . w . mason's story to suit the sensibilities of a young american , a decision that plucks \" the four feathers \" bare . \n . . . what a banal bore the preachy circuit turns out to be\nfalsehoods pile up , undermining the movie's reality and stifling its creator's comic voice . \na mechanical action-comedy whose seeming purpose is to market the charismatic jackie chan to even younger audiences . \none of the most incoherent features in recent memory . \nlow rent from frame one . \neight legged freaks ? no big hairy deal . \nthe issues are presented in such a lousy way , complete with some of the year's ( unintentionally ) funniest moments , that it's impossible to care . \nlaggard drama wending its way to an uninspired philosophical epiphany . \nthe respective charms of sandra bullock and hugh grant have worn threadbare . \n'this movie sucks . '\nnone of this so-called satire has any sting to it , as if woody is afraid of biting the hand that has finally , to some extent , warmed up to him . \n . . . bibbidy-bobbidi-bland . \na few nonbelievers may rethink their attitudes when they see the joy the characters take in this creed , but skeptics aren't likely to enter the theater . \nbad in such a bizarre way that it's almost worth seeing , if only to witness the crazy confluence of purpose and taste . \nthere's more repetition than creativity throughout the movie . \nhugh grant's act is so consuming that sometimes it's difficult to tell who the other actors in the movie are . \nalthough the sequel has all the outward elements of the original , the first film's lovely flakiness is gone , replaced by the forced funniness found in the dullest kiddie flicks . \ni've had more interesting -- and , dare i say , thematically complex -- bowel movements than this long-on-the-shelf , point-and-shoot exercise in gimmicky crime drama . \nwhat we get . . . is caddyshack crossed with the loyal order of raccoons . \nthe jokes are flat , and the action looks fake . \nwhen a movie asks you to feel sorry for mick jagger's sex life , it already has one strike against it . \nthere are now two signs that m . night shyamalan's debut feature sucked up all he has to give to the mystic genres of cinema : unbreakable and signs . \n . . . hokey art house pretension . \n . . . a weak and ineffective ghost story without a conclusion or pay off . \ngussied up with so many distracting special effects and visual party tricks that it's not clear whether we're supposed to shriek or laugh . \nplodding , poorly written , murky and weakly acted , the picture feels as if everyone making it lost their movie mojo . \nthis is a fudged opportunity of gigantic proportions -- a lunar mission with no signs of life . \na baffling subplot involving smuggling drugs inside danish cows falls flat , and if you're going to alter the bard's ending , you'd better have a good alternative . \nsoderbergh seems capable only of delivering artfully lighted , earnest inquiries that lack the kind of genuine depth that would make them redeemable . \nthe only thing that distinguishes a randall wallace film from any other is the fact that there is nothing distinguishing in a randall wallace film . \nsilly stuff , all mixed up together like a term paper from a kid who can't quite distinguish one sci-fi work from another . \nthere is so much plodding sensitivity . \nthe town has kind of an authentic feel , but each one of these people stand out and everybody else is in the background and it just seems manufactured to me and artificial . \n . . . too sappy for its own good . \ncircuit queens won't learn a thing , they'll be too busy cursing the film's strategically placed white sheets . \nas an actress , madonna is one helluva singer . as the mediterranean sparkles , 'swept away' sinks . \nevery so often a film comes along that is so insanely stupid , so awful in so many ways that watching it leaves you giddy . half past dead is just such an achievement . \nexpanded to 65 minutes for theatrical release , it still feels somewhat unfinished . \nit all looks and plays like a $40 million version of a game you're more likely to enjoy on a computer . \n[javier bardem is] one of the few reasons to watch the film , which director gerardo vera has drenched in swoony music and fever-pitched melodrama . \nfeels shrill , simple and soapy . \nadults , other than the parents . . . will be hard pressed to succumb to the call of the wild . \nbrady achieves the remarkable feat of squandering a topnotch foursome of actors . . . by shoving them into every clichd white-trash situation imaginable . \nin the name of an allegedly inspiring and easily marketable flick , the emperor's club turns a blind eye to the very history it pretends to teach . \nno amount of blood and disintegrating vampire cadavers can obscure this movie's lack of ideas . \na direct-to-void release , heading nowhere . \ntypical anim , with cheapo animation ( like saturday morning tv in the '60s ) , a complex sword-and-sorcery plot and characters who all have big round eyes and japanese names . \nbelow is well below expectations . \nat no point during k-19 : the widowmaker did this viewer feel enveloped in a story that , though meant to be universal in its themes of loyalty , courage and dedication to a common goal , never seems to leave the lot . \n . . . standard guns versus martial arts cliche with little new added . \nempire can't make up its mind whether it wants to be a gangster flick or an art film . it doesn't work as either . \ngiven the fact that virtually no one is bound to show up at theatres for it , the project should have been made for the tube . \npossession is in the end an honorable , interesting failure . it falls far short of poetry , but it's not bad prose . \njonathan parker's bartleby should have been the be-all-end-all of the modern-office anomie films . \nthere may have been a good film in \" trouble every day , \" but it is not what is on the screen . \nunfortunately , carvey's rubber-face routine is no match for the insipid script he has crafted with harris goldberg . \nviewed as a comedy , a romance , a fairy tale , or a drama , there's nothing remotely triumphant about this motion picture . \nthere's something unintentionally comic in the film's drumbeat about authenticity , given the stale plot and pornographic way the film revels in swank apartments , clothes and parties . \nthe master of disguise is funny--not \" ha ha \" funny , \" dead circus performer \" funny . and for all the wrong reasons besides . \na zippy 96 minutes of mediocre special effects , hoary dialogue , fluxing accents , and -- worst of all -- silly-looking morlocks . \na 75-minute sample of puerile rubbish that is listless , witless , and devoid of anything resembling humor . \nyou leave feeling like you've endured a long workout without your pulse ever racing . \nthe waterlogged script plumbs uncharted depths of stupidity , incoherence and sub-sophomoric sexual banter . \nwith mcconaughey in an entirely irony-free zone and bale reduced mainly to batting his sensitive eyelids , there's not enough intelligence , wit or innovation on the screen to attract and sustain an older crowd . \nit's the type of stunt the academy loves : a powerful political message stuffed into an otherwise mediocre film . \nin theory , a middle-aged romance pairing clayburgh and tambor sounds promising , but in practice it's something else altogether -- clownish and offensive and nothing at all like real life . \nso mind-numbingly awful that you hope britney won't do it one more time , as far as movies are concerned . \nthe images are usually abbreviated in favor of mushy obviousness and telegraphed pathos , particularly where whitaker's misfit artist is concerned . \nif welles was unhappy at the prospect of the human race splitting in two , he probably wouldn't be too crazy with his great-grandson's movie splitting up in pretty much the same way . \nsets animation back 30 years , musicals back 40 years and judaism back at least 50 . \nweirdly , broomfield has compelling new material but he doesn't unveil it until the end , after endless scenes of him wheedling reluctant witnesses and pointing his camera through the smeared windshield of his rental car . \nmight best be enjoyed as a daytime soaper . \neventually arrives at its heart , as simple self-reflection meditation . \na showcase for both the scenic splendor of the mountains and for legendary actor michel serrault , the film is less successful on other levels . \nboy oh boy , it's a howler . \na few pieces of the film buzz and whir ; very little of it actually clicks . the thing just never gets off the ground . \n . . . contains very few laughs and even less surprises . \nthe actors must indeed be good to recite some of this laughable dialogue with a straight face . \nmost of the storylines feel like time fillers between surf shots . the movie isn't horrible , but you can see mediocre cresting on the next wave . \nhowever stale the material , lawrence's delivery remains perfect ; his great gift is that he can actually trick you into thinking some of this worn-out , pandering palaver is actually funny . \nthere's nothing remotely topical or sexy here . \nthe tuxedo wasn't just bad ; it was , as my friend david cross would call it , 'hungry-man portions of bad' . \nblue crush is so prolonged and boring it isn't even close to being the barn-burningly bad movie it promised it would be . \nthe sequel plays out like a flimsy excuse to give blade fans another look at wesley snipes' iconic hero doing battle with dozens of bad guys -- at once . \nwhile van wilder may not be the worst national lampoon film , it's far from being this generation's animal house . \nso devoid of pleasure or sensuality that it cannot even be dubbed hedonistic . \nreeks of rot and hack work from start to finish . \nan exhausting family drama about a porcelain empire and just as hard a flick as its subject matter . \nwoody allen has really found his groove these days . the problem is that it is one that allows him to churn out one mediocre movie after another . \nthe bland outweighs the nifty , and cletis tout never becomes the clever crime comedy it thinks it is . \nit's such a mechanical endeavor [that] it never bothers to question why somebody might devote time to see it . \nthe art direction is often exquisite , and the anthropomorphic animal characters are beautifully realized through clever makeup design , leaving one to hope that the eventual dvd release will offer subtitles and the original italian-language soundtrack . \n if the predictability of bland comfort food appeals to you , then the film is a pleasant enough dish . \nultimately , in the history of the academy , people may be wondering what all that jazz was about \" chicago \" in 2002 . zellweger's whiny pouty-lipped poof faced and spindly attempt at playing an ingenue makes her nomination as best actress even more of a an a\na seriously bad film with seriously warped logic by writer-director kurt wimmer at the screenplay level . \na pleasant and engaging enough sit , but in trying to have the best of both worlds it ends up falling short as a whole . \nits plot and animation offer daytime tv serviceability , but little more . \na tired , unimaginative and derivative variation of that already-shallow genre . \nhuman nature , in short , isn't nearly as funny as it thinks it is ; neither is it as smart . \na film with a great premise but only a great premise . \ninstead of building to a laugh riot we are left with a handful of disparate funny moments of no real consequence . \nkirshner and monroe seem to be in a contest to see who can out-bad-act the other . ( kirshner wins , but it's close . ) \na lame romantic comedy about an unsympathetic character and someone who would not likely be so stupid as to get involved with her . \nwhat started out as a taut contest of wills between bacon and theron , deteriorates into a protracted and borderline silly chase sequence . \n[sam's] self-flagellation is more depressing than entertaining . \nan ugly , pointless , stupid movie . \nsimply put , there should have been a more compelling excuse to pair susan sarandon and goldie hawn . \nthe master of disguise represents adam sandler's latest attempt to dumb down the universe . \nthis is an ungainly movie , ill-fitting , with its elbows sticking out where the knees should be . \ntoo silly to take seriously . \nobvious\nthe inevitable double- and triple-crosses arise , but the only drama is in waiting to hear how john malkovich's reedy consigliere will pronounce his next line . \neverything's serious , poetic , earnest and -- sadly -- dull . \ni like it . no , i hate it . no , i love it . . . hell , i dunno . \nthis mess of a movie is nothing short of a travesty of a transvestite comedy . \nit's clotted with heavy-handed symbolism , dime-store psychology and endless scenic shots that make 105 minutes seem twice as long . \na fifty car pileup of cliches . \nit's a stale , overused cocktail using the same olives since 1962 as garnish . not only is entry number twenty the worst of the brosnan bunch , it's one of the worst of the entire franchise . \nwhat ultimately makes windtalkers a disappointment is the superficial way it deals with its story . \nas an actor's showcase , hart's war has much to recommend it , even if the top-billed willis is not the most impressive player . as a story of dramatic enlightenment , the screenplay by billy ray and terry george leaves something to be desired . \na non-britney person might survive a screening with little harm done , except maybe for the last 15 minutes , which are as maudlin as any after-school special you can imagine . \nit's not hateful . it's simply stupid , irrelevant and deeply , truly , bottomlessly cynical . \npossibly not since grumpy old men have i heard a film so solidly connect with one demographic while striking out with another . \nthe drama was so uninspiring that even a story immersed in love , lust , and sin couldn't keep my attention . \na rather tired exercise in nostalgia . \nthe misery of these people becomes just another voyeuristic spectacle , to be consumed and forgotten . \nsome body often looks like an episode of the tv show blind date , only less technically proficient and without the pop-up comments . \nbad company has one of the most moronic screenplays of the year , full of holes that will be obvious even to those who aren't looking for them . \npredecessors the mummy and the mummy returns stand as intellectual masterpieces next to the scorpion king . \na markedly inactive film , city is conversational bordering on confessional . \nwhile kids will probably eat the whole thing up , most adults will be way ahead of the plot . \nits one-sidedness . . . flirts with propaganda . \ndespite an impressive roster of stars and direction from kathryn bigelow , the weight of water is oppressively heavy . \nwe've liked klein's other work but rollerball left us cold . \nthey were afraid to show this movie to reviewers before its opening , afraid of the bad reviews they thought they'd earn . they were right . \nit would be churlish to begrudge anyone for receiving whatever consolation that can be found in dragonfly , yet it is impossible to find the film anything but appalling , shamelessly manipulative and contrived , and totally lacking in conviction . \noffers no new insight on the matter , nor do its characters exactly spring to life . \nnohe has made a decent 'intro' documentary , but he feels like a spectator and not a participant . \napparently designed as a reverie about memory and regret , but the only thing you'll regret is remembering the experience of sitting through it . \na 94-minute travesty of unparalleled proportions , writer-director parker seems to go out of his way to turn the legendary wit's classic mistaken identity farce into brutally labored and unfunny hokum . \nguy gets girl , guy loses girl , audience falls asleep . \ntoo ordinary to restore [harmon] to prominence , despite some creepy scenes that evoke childish night terrors , and a praiseworthy attempt to generate suspense rather than gross out the audience . \nstirs potentially enticing ingredients into an uneasy blend of ghost and close encounters of the third kind . \nthe weird thing about the santa clause 2 , purportedly a children's movie , is that there is nothing in it to engage children emotionally . \nit's pretentious in a way that verges on the amateurish . \ncontains the humor , characterization , poignancy , and intelligence of a bad sitcom . \nit doesn't matter that the film is less than 90 minutes . it still feels like a prison stretch . \npartly a schmaltzy , by-the-numbers romantic comedy , partly a shallow rumination on the emptiness of success -- and entirely soulless . \nong chooses to present ah na's life as a slight , weightless fairy tale , whose most unpleasant details seem to melt away in the face of the character's blank-faced optimism . \nthe overall feel is not unlike watching a glorified episode of \" 7th heaven . \" \njust a kiss is a just a waste . \n . . . this isn't even a movie we can enjoy as mild escapism ; it is one in which fear and frustration are provoked to intolerable levels . \nfrankly , it's kind of insulting , both to men and women . and it's not that funny -- which is just generally insulting . \nas if drop dead gorgeous wasn't enough , this equally derisive clunker is fixated on the spectacle of small-town competition . \na wretched movie that reduces the second world war to one man's quest to find an old flame . \nthis is a remake by the numbers , linking a halfwit plot to a series of standup routines in which wilson and murphy show how funny they could have been in a more ambitious movie . \nit's better than mid-range steven seagal , but not as sharp as jet li on rollerblades . \nno dia em que aceitou dirigir esta continuao , harold ramis deve ter sado da cama com o p esquerdo . e aqueles que decidiram assistir a este filme tambm . \nthere's a reason why halftime is only fifteen minutes long . \nthe talk-heavy film plays like one of robert altman's lesser works . \nas happily glib and vicious as its characters . \none of those films that started with a great premise and then just fell apart . \n . . . a pretentious mess . . . \nno better or worse than 'truth or consequences , n . m . ' or any other interchangeable actioner with imbecilic mafia toolbags botching a routine assignment in a western backwater . \na sour attempt at making a farrelly brothers-style , down-and-dirty laugher for the female set . \n[macdowell] ventures beyond her abilities several times here and reveals how bad an actress she is . \ni can imagine this movie as a b&w british comedy , circa 1960 , with peter sellers , kenneth williams , et al . , but at this time , with this cast , this movie is hopeless . \ntalky , artificial and opaque . . . an interesting technical exercise , but a tedious picture . \nkurys never shows why , of all the period's volatile romantic lives , sand and musset are worth particular attention . \nalmost nothing else -- raunchy and graphic as it may be in presentation -- is one-sided , outwardly sexist or mean-spirited . and in a sense , that's a liability . \nit's easy to love robin tunney -- she's pretty and she can act -- but it gets harder and harder to understand her choices . \nif you've got a house full of tots -- don't worry , this will be on video long before they grow up and you can wait till then . \nthe new film of anton chekhov's the cherry orchard puts the 'ick' in 'classic . '\nhas an uppity musical beat that you can dance to , but its energy can't compare to the wit , humor and snappy dialogue of the original . if i want music , i'll buy the soundtrack . if i want a real movie , i'll buy the criterion dvd . \nan unremarkable , modern action/comedy buddy movie whose only nod to nostalgia is in the title . \nhas all the right elements but completely fails to gel together . \nwriter-director stephen gaghan has made the near-fatal mistake of being what the english call 'too clever by half . '\nseagal ran out of movies years ago , and this is just the proof . \nthe movie is so contrived , nonsensical and formulaic that , come to think of it , the day-old shelf would be a more appropriate location to store it . \nan awkwardly garish showcase that diverges from anything remotely probing or penetrating . \nthe name says it all . jackass is a vulgar and cheap-looking version of candid camera staged for the marquis de sade set . \nchildren , christian or otherwise , deserve to hear the full story of jonah's despair -- in all its agonizing , catch-22 glory -- even if they spend years trying to comprehend it . \npleasant but not more than recycled jock piffle . \n . . . the kind of movie you see because the theater has air conditioning . \nwith zoe clarke-williams's lackluster thriller \" new best friend \" , who needs enemies ? just another generic drama that has nothing going for it other than its exploitive array of obligatory cheap thrills . \nhip-hop prison thriller of stupefying absurdity . \nan uneven mix of dark satire and childhood awakening . \naside from showing us in explicit detail how difficult it is to win over the two-drink-minimum crowd , there's little to be learned from watching 'comedian'\na perfectly acceptable , perfectly bland , competently acted but by no means scary horror movie . \nthe film would have been more enjoyable had the balance shifted in favor of water-bound action over the land-based 'drama , ' but the emphasis on the latter leaves blue crush waterlogged . \nthe problem is the needlessly poor quality of its archival prints and film footage . the images lack contrast , are murky and are frequently too dark to be decipherable . \nlike a soft drink that's been sitting open too long : it's too much syrup and not enough fizz . \nas the movie dragged on , i thought i heard a mysterious voice , and felt myself powerfully drawn toward the light -- the light of the exit sign . i have returned from the beyond to warn you : this movie is 90 minutes long , and life is too short . \nthere are some fairly unsettling scenes , but they never succeed in really rattling the viewer . \nthe emperor's club is one of those films that possesses all the good intentions in the world , but . . . \nin the end , the weight of water comes to resemble the kind of soft-core twaddle you'd expect to see on showtime's 'red shoe diaries . '\na straight-ahead thriller that never rises above superficiality . \nglizty but formulaic and silly . . . cagney's 'top of the world' has been replaced by the bottom of the barrel . \nthe re- enactments , however fascinating they may be as history , are too crude to serve the work especially well . \neisenstein lacks considerable brio for a film about one of cinema's directorial giants . \nis anyone else out there getting tired of the whole slo-mo , double-pistoled , ballistic-pyrotechnic hong kong action aesthetic ? \nonce again , director chris columbus takes a hat-in-hand approach to rowling that stifles creativity and allows the film to drag on for nearly three hours . \nserving sara is little more than a mall movie designed to kill time . \ntoo smart to ignore but a little too smugly superior to like , this could be a movie that ends up slapping its target audience in the face by shooting itself in the foot . \na well-made but emotionally scattered film whose hero gives his heart only to the dog . \nthe most repugnant adaptation of a classic text since roland joff and demi moore's the scarlet letter . \nthe isolated moments of creative insanity finally are lost in the thin soup of canned humor . \nas a movie , it never seems fresh and vital . it never plays as dramatic even when dramatic things happen to people . it labours as storytelling . \nthe adventures of pluto nash is a whole lot of nada . \na really good premise is frittered away in middle-of-the-road blandness . \nlawrence should stick to his day job . he's a better actor than a standup comedian . \ndespite the fact that this film wasn't as bad as i thought it was going to be , it's still not a good movie\na well made indieflick in need of some trims and a more chemistry between its stars . \ni never thought i'd say this , but i'd much rather watch teens poking their genitals into fruit pies ! \na film neither bitter nor sweet , neither romantic nor comedic , neither warm nor fuzzy . \ntiresomely derivative and hammily acted . \nwe never truly come to care about the main characters and whether or not they'll wind up together , and michele's spiritual quest is neither amusing nor dramatic enough to sustain interest . \nthe plot grows thin soon , and you find yourself praying for a quick resolution . \ntoo bad maggio couldn't come up with a better script . \nmuch of the cast is stiff or just plain bad . \nrice is too pedestrian a filmmaker to bring any edge or personality to the rising place that would set it apart from other deep south stories . \nat best , cletis tout might inspire a trip to the video store -- in search of a better movie experience . \nwitless , pointless , tasteless and idiotic . \nnot really a thriller so much as a movie for teens to laugh , groan and hiss at . \nas plain and pedestrian as catsup--\nan improvement on the feeble examples of big-screen poke-mania that have preceded it . \ni know we're not supposed to take it seriously , but i can't shake the thought that undercover brother missed an opportunity to strongly present some profound social commentary . \nyour stomach for heaven depends largely on your appetite for canned corn . \na picture as erratic as its central character . \nwhatever satire lucky break was aiming for , it certainly got lost in the \" soon-to-be-forgettable \" section of the quirky rip-off prison romp pile . it's petty thievery like this that puts flimsy flicks like this behind bars\nthe package in which this fascinating -- and timely -- content comes wrapped is disappointingly generic . \nguys say mean things and shoot a lot of bullets . some of the characters die and others don't , and the film pretends that those living have learned some sort of lesson , and , really , nobody in the viewing audience cares . \nwildly incompetent but brilliantly named half past dead -- or for seagal pessimists : totally past his prime . \njust another combination of bad animation and mindless violence . . . lacking the slightest bit of wit or charm . \nall the movie's narrative gymnastics can't disguise the fact that it's inauthentic at its core and that its story just isn't worth telling . \nmuch like its easily dismissive take on the upscale lifestyle , there isn't much there here . \nthe film ultimately offers nothing more than people in an urban jungle needing other people to survive . . . \nfor all its shoot-outs , fistfights , and car chases , this movie is a phlegmatic bore , so tedious it makes the silly spy vs . spy film the sum of all fears , starring ben affleck , seem downright hitchcockian . \nthis mild-mannered farce , directed by one of its writers , john c . walsh , is corny in a way that bespeaks an expiration date passed a long time ago . \n[a] rather thinly-conceived movie . \na bit too eager to please . \nyou'd be hard put to find a movie character more unattractive or odorous [than leon] . \nkapur's contradictory feelings about his material result in a movie that works against itself . \n \" the road paved with good intentions leads to the video store \" \nanimated drivel meant to enhance the self-image of drooling idiots . \nso-so entertainment . \none scene after another in this supposedly funny movie falls to the floor with a sickening thud . \n'the chteau is never quite able to overcome the cultural moat surrounding its ludicrous and contrived plot . '\nmeyjes focuses too much on max when he should be filling the screen with this tortured , dull artist and monster-in-the- making . \njacobi , the most fluent of actors , is given relatively dry material from nijinsky's writings to perform , and the visuals , even erotically frank ones , become dullingly repetitive . \ncrudup's screen presence is the one thing that holds interest in the midst of a mushy , existential exploration of why men leave their families . \nthere is one surefire way to get a nomination for a best-foreign-film oscar : make a movie about whimsical folk who learn a nonchallenging , life-affirming lesson while walking around a foreign city with stunning architecture . \ndespite terrific special effects and funnier gags , harry potter and the chamber of secrets finds a way to make j . k . rowling's marvelous series into a deadly bore . \nan incredibly narrow in-joke targeted to the tiniest segment of an already obscure demographic . \nthe only thing i laughed at were the people who paid to see it . \nall of the elements are in place for a great film noir , but director george hickenlooper's approach to the material is too upbeat . \nthe hackneyed story about an affluent damsel in distress who decides to fight her bully of a husband is simply too overdone . \nthe phone rings and a voice tells you you've got seven days left to live . then you get another phone call warning you that if the video isn't back at blockbuster before midnight , you're going to face frightening late fees . o . k . , not really . \npossibly the most irresponsible picture ever released by a major film studio . \nthe film's overall mood and focus is interesting but constantly unfulfilling . \n . . . a cheap , ludicrous attempt at serious horror . \nthose of you who are not an eighth grade girl will most likely doze off during this one . \npunitively affirmational parable . \nbefuddled in its characterizations as it begins to seem as long as the two year affair which is its subject\nfrom beginning to end , this overheated melodrama plays like a student film . \nthe movie would seem less of a trifle if ms . sugarman followed through on her defiance of the saccharine . \nit's just not very smart . \nlike the excruciating end of days , collateral damage presents schwarzenegger as a tragic figure , but sympathy really belongs with any viewer forced to watch him try out so many complicated facial expressions . \nimagine ( if possible ) a pasolini film without passion or politics , or an almodovar movie without beauty or humor , and you have some idea of the glum , numb experience of watching o fantasma . \nin trying to be daring and original , it comes off as only occasionally satirical and never fresh . \n90 punitive minutes of eardrum-dicing gunplay , screeching-metal smashups , and flaccid odd-couple sniping . \nsadly , though many of the actors throw off a spark or two when they first appear , they can't generate enough heat in this cold vacuum of a comedy to start a reaction . \nnever capitalizes on this concept and opts for the breezy and amateurish feel of an after school special on the subject of tolerance . \nafter a while , hoffman's quirks and mannerisms , particularly his penchant for tearing up on cue -- things that seem so real in small doses -- become annoying and artificial . \nthis wretchedly unfunny wannabe comedy is inane and awful - no doubt , it's the worst movie i've seen this summer . \nit's drab . it's uninteresting . it squanders chan's uniqueness ; it could even be said to squander jennifer love hewitt ! \nthe movie keeps coming back to the achingly unfunny phonce and his several silly subplots . \nthis tale has been told and retold ; the races and rackets change , but the song remains the same . \na surprisingly flat retread , hobbled by half-baked setups and sluggish pacing . \nforget the psychology 101 study of romantic obsession and just watch the procession of costumes in castles and this won't seem like such a bore . \na film that should be relegated to a dark video store corner is somehow making its way instead to theaters . it's hard to imagine acting that could be any flatter . \nnew ways of describing badness need to be invented to describe exactly how bad it is . \nlots of effort and intelligence are on display but in execution it is all awkward , static , and lifeless rumblings . \nwhen cowering and begging at the feet a scruffy giannini , madonna gives her best performance since abel ferrara had her beaten to a pulp in his dangerous game . \ni suspect that there are more interesting ways of dealing with the subject . \ndecent but dull . \nthe film itself is about something very interesting and odd that would probably work better as a real documentary without the insinuation of mediocre acting or a fairly trite narrative . \nan unintentional parody of every teen movie made in the last five years . \nonly for young children , if them . their parents would do well to cram earplugs in their ears and put pillowcases over their heads for 87 minutes . \nfor all its violence , the movie is remarkably dull with only caine making much of an impression . \nno matter how firmly director john stainton has his tongue in his cheek , the fact remains that a wacky concept does not a movie make . \na sub-formulaic slap in the face to seasonal cheer . \nthe action is reasonably well-done . . . yet story , character and comedy bits are too ragged to ever fit smoothly together . \nseveral uninteresting , unlikeable people do bad things to and with each other in \" unfaithful . \" why anyone who is not a character in this movie should care is beyond me . \nthin period piece . \nhill looks to be going through the motions , beginning with the pale script . \nhoward conjures the past via surrealist flourishes so overwrought you'd swear he just stepped out of a buuel retrospective . \nthe best thing that can be said of the picture is that it does have a few cute moments . \nit's not a bad premise , just a bad movie . \nan already thin story boils down to surviving invaders seeking an existent anti-virus . if only there were one for this kind of movie . \nby the time the surprise ending is revealed , interest cannot be revived . \nthe heedless impetuousness of youth is on full , irritating display in [this] meandering and pointless french coming-of-age import from writer-director anne-sophie birot . \na peculiar misfire that even tunney can't save . \nwatching queen of the damned is like reading a research paper , with special effects tossed in . \ni can't remember the last time i saw worse stunt editing or cheaper action movie production values than in extreme ops . \ntoo much of nemesis has a tired , talky feel . \ni felt trapped and with no obvious escape for the entire 100 minutes . \nwhen mr . hundert tells us in his narration that 'this is a story without surprises , ' we nod in agreement . \nleaves us wondering less about its ideas and more about its characterization of hitler and the contrived nature of its provocative conclusion . \nit is that rare combination of bad writing , bad direction and bad acting -- the trifecta of badness . \neach scene wreaks of routine ; the film never manages to generate a single threat of suspense . \na soulless jumble of ineptly assembled cliches and pabulum that plays like a 95-minute commercial for nba properties . \nborstal boy represents the worst kind of filmmaking , the kind that pretends to be passionate and truthful but is really frustratingly timid and soggy . \nthe film's lack of personality permeates all its aspects  from the tv movie-esque , affected child acting to the dullest irish pub scenes ever filmed . \nworks on the whodunit level as its larger themes get lost in the murk of its own making\ncrush could be the worst film a man has made about women since valley of the dolls . \n4ever has the same sledgehammer appeal as pokemon videos , but it breathes more on the big screen and induces headaches more slowly . \nfeels like the work of someone who may indeed have finally aged past his prime . . . and , perhaps more than he realizes , just wants to be liked by the people who can still give him work . \ntrailer park magnolia : too long , too cutesy , too sure of its own importance , and possessed of that peculiar tension of being too dense & about nothing at all . \n[a] stuporously solemn film . \nviewers of barney's crushingly self-indulgent spectacle will see nothing in it to match the ordeal of sitting through it . \n . . . its stupidities wind up sticking in one's mind a lot more than the cool bits . \nsayles . . . once again strands his superb performers in the same old story . \nthe piano teacher , like its title character , is repellantly out of control . \ni have to admit i walked out of runteldat . i did go back and check out the last 10 minutes , but these were more repulsive than the first 30 or 40 minutes . \nthe filmmakers lack the nerve . . . to fully exploit the script's potential for sick humor . \nthe film wasn't preachy , but it was feminism by the book . \n . . . the same tired old gags , modernized for the extreme sports generation . there's already been too many of these films . . . \nseveral of steven soderbergh's earlier films were hailed as the works of an artist . sadly , full frontal plays like the work of a dilettante . \nclockstoppers is one of those crazy , mixed-up films that doesn't know what it wants to be when it grows up . \nalthough bright , well-acted and thought-provoking , tuck everlasting suffers from a laconic pace and a lack of traditional action . \n'the war of the roses , ' trailer-trash style . entertaining but like shooting fish in a barrel . \nsupposedly , pokemon can't be killed , but pokemon 4ever practically assures that the pocket monster movie franchise is nearly ready to keel over . \nwhite hasn't developed characters so much as caricatures , one-dimensional buffoons that get him a few laughs but nothing else . \nwhen you resurrect a dead man , hard copy should come a-knocking , no ? \ncattaneo should have followed the runaway success of his first film , the full monty , with something different . \nthe film feels formulaic , its plot and pacing typical hollywood war-movie stuff , while the performances elicit more of a sense of deja vu than awe . \nthis overproduced piece of dreck is shockingly bad and absolutely unnecessary . hmmmmight i suggest that the wayward wooden one end it all by stuffing himself into an electric pencil sharpener ? \nthe makers of mothman prophecies succeed in producing that most frightening of all movies -- a mediocre horror film too bad to be good and too good to be bad . \nmr . deeds is not really a film as much as it is a loose collection of not-so-funny gags , scattered moments of lazy humor . \nhow this one escaped the lifetime network i'll never know . \ncouldn't someone take rob schneider and have him switch bodies with a funny person ? \none of these days hollywood will come up with an original idea for a teen movie , but until then there's always these rehashes to feed to the younger generations . \nfor all its brilliant touches , dragon loses its fire midway , nearly flickering out by its perfunctory conclusion . \ni have to admit that i am baffled by jason x . \na mean-spirited film made by someone who surely read the catcher in the rye but clearly suffers from dyslexia\ninstead of a witty expose on the banality and hypocrisy of too much kid-vid , we get an ugly , mean-spirited lashing out by an adult who's apparently been forced by his kids to watch too many barney videos . \nthis is a film living far too much in its own head . \nthe umpteenth summer skinny dip in jerry bruckheimer's putrid pond of retread action twaddle . \nnational lampoon's van wilder may aim to be the next animal house , but it more closely resembles this year's version of tomcats . \nthe film thrusts the inchoate but already eldritch christian right propaganda machine into national media circles . \ndogtown is hollow , self-indulgent , and - worst of all - boring . \na movie so bad that it quickly enters the pantheon of wreckage that includes battlefield earth and showgirls . \nmore of a career curio than a major work . \nit's just too bad the screenwriters eventually shoot themselves in the feet with cop flick cliches like an oily arms dealer , squad car pile-ups and the requisite screaming captain . \ncox is far more concerned with aggrandizing madness , not the man , and the results might drive you crazy . \nto be influenced chiefly by humanity's greatest shame , reality shows -- reality shows for god's sake ! -- is a crime that should be punishable by chainsaw . \ntaken as a whole , the tuxedo doesn't add up to a whole lot . \nas we have come to learn -- as many times as we have fingers to count on -- jason is a killer who doesn't know the meaning of the word 'quit . ' the filmmakers might want to look it up . \na frustrating 'tweener' -- too slick , contrived and exploitative for the art houses and too cynical , small and decadent for the malls . \nwhat's surprising about this traditional thriller , moderately successful but not completely satisfying , is exactly how genteel and unsurprising the execution turns out to be . \ndrowning's too good for this sucker . \nan instantly forgettable snow-and-stuntwork extravaganza that likely will be upstaged by an avalanche of more appealing holiday-season product . \nfrankly , it's pretty stupid . i had more fun with ben stiller's zoolander , which i thought was rather clever . but there's plenty to offend everyone . . . \nlove liza is a festival film that would have been better off staying on the festival circuit . \nthere are things to like about murder by numbers -- but , in the end , the disparate elements don't gel . \n . . . tackling a low-budget movie in which inexperienced children play the two main characters might not be the best way to cut your teeth in the film industry . \nquite frankly , i can't see why any actor of talent would ever work in a mcculloch production again if they looked at how this movie turned out . \nmy precious new star wars movie is a lumbering , wheezy drag . . . \nthe innocence of holiday cheer ain't what it used to be . \ntoo campy to work as straight drama and too violent and sordid to function as comedy , vulgar is , truly and thankfully , a one-of-a-kind work . \nhorrid little propaganda film with fascinating connections not only to the serbs themselves but also to a network of american right-wing extremists . \nshould have gone straight to video . it looks like an action movie , but it's so poorly made , on all levels , that it doesn't even qualify as a spoof of such . \nit is supremely unfunny and unentertaining to watch middle-age and older men drink to excess , piss on trees , b . s . one another and put on a show in drag . \nconsider the film a celluloid litmus test for the intellectual and emotional pedigree of your date and a giant step backward for a director i admire . \na boring , pretentious muddle that uses a sensational , real-life 19th-century crime as a metaphor for -- well , i'm not exactly sure what -- and has all the dramatic weight of a raindrop . \nsheridan had a wonderful account to work from , but , curiously , he waters it down , turning grit and vulnerability into light reading . \nheavy with flabby rolls of typical toback machinations . \nit is very difficult to care about the character , and that is the central flaw of the film . \nsnow dogs finds its humour in a black man getting humiliated by a pack of dogs who are smarter than him\nwhole stretches of the film may be incomprehensible to moviegoers not already clad in basic black . \nreggio and glass so rhapsodize cynicism , with repetition and languorous slo-mo sequences , that glass's dirgelike score becomes a fang-baring lullaby . \nes triste tener que decirles que lo nico grato de la cinta es el cuerpo desnudo de la heather . . . \nends up offering nothing more than the latest schwarzenegger or stallone flick would . \nthe film makes strong arguments regarding the social status of america's indigenous people , but really only exists to try to eke out an emotional tug of the heart , one which it fails to get . \ncharlize chases kevin with a gun . courtney chases stuart with a cell phone . the sound of gunfire and cell phones ringing . \nif the tuxedo actually were a suit , it would fit chan like a $99 bargain-basement special . \nparents beware ; this is downright movie penance . \n . . . a complete shambles of a movie so sloppy , so uneven , so damn unpleasant that i can't believe any viewer , young or old , would have a good time here . \nhas nothing good to speak about other than the fact that it is relatively short , tries its best to hide the fact that seagal's overweight and out of shape . \na pathetically inane and unimaginative cross between xxx and vertical limit . \nimpeccably filmed , sexually charged , but ultimately lacking in substance , not to mention dragged down by a leaden closing act . \nfeels at times like a giant commercial for universal studios , where much of the action takes place . \nwhile the mystery unravels , the characters respond by hitting on each other . \nbritney spears' phoniness is nothing compared to the movie's contrived , lame screenplay and listless direction . \nevery sequel you skip will be two hours gained . consider this review life-affirming . \nif the movie were all comedy , it might work better . but it has an ambition to say something about its subjects , but not a willingness . \nthe movie , while beautiful , feels labored , with a hint of the writing exercise about it . \ntwenty-three movies into a mostly magnificent directorial career , clint eastwood's efficiently minimalist style finally has failed him . big time . \nthis heist flick about young brooklyn hoods is off the shelf after two years to capitalize on the popularity of vin diesel , seth green and barry pepper . it should have stayed there . \nthe film has a childlike quality about it . but the feelings evoked in the film are lukewarm and quick to pass . \nthe most opaque , self-indulgent and just plain goofy an excuse for a movie as you can imagine . \nit's not a film to be taken literally on any level , but its focus always appears questionable . \nbig fat liar is little more than home alone raised to a new , self-deprecating level . \nthe movie is gorgeously made , but it is also somewhat shallow and art-conscious . \nthe only time 8 crazy nights comes close to hitting a comedic or satirical target is during the offbeat musical numbers . \nloses its sense of humor in a vat of failed jokes , twitchy acting , and general boorishness . \nthere's a delightfully quirky movie to be made from curling , but brooms isn't it . \nthe story suffers a severe case of oversimplification , superficiality and silliness . \nchamber of secrets will find millions of eager fans . but if the essence of magic is its make-believe promise of life that soars above the material realm , this is the opposite of a truly magical movie . \ntoo clever by about nine-tenths . \nhas all the hallmarks of a movie designed strictly for children's home video , a market so insatiable it absorbs all manner of lame entertainment , as long as 3-year-olds find it diverting . \nwell-meant but unoriginal . \nbears about as much resemblance to the experiences of most battered women as spider-man does to the experiences of most teenagers . \ntoward the end sum of all fears morphs into a mundane '70s disaster flick . \ndirector carl franklin , so crisp and economical in one false move , bogs down in genre cliches here . \nmendes still doesn't quite know how to fill a frame . like the hanks character , he's a slow study : the action is stilted and the tabloid energy embalmed . \nthis thing is just garbage . \nas crimes go , writer-director michael kalesniko's how to kill your neighbor's dog is slight but unendurable . \nthere must be an audience that enjoys the friday series , but i wouldn't be interested in knowing any of them personally . \na bold ( and lovely ) experiment that will almost certainly bore most audiences into their own brightly colored dreams . \nan uplifting , largely bogus story . \nan empty exercise , a florid but ultimately vapid crime melodrama with lots of surface flash but little emotional resonance . \nif you are curious to see the darker side of what's going on with young tv actors ( dawson leery did what ? ! ? ) , or see some interesting storytelling devices , you might want to check it out , but there's nothing very attractive about this movie . \nmy own minority report is that it stinks . \ntrying to make head or tail of the story in the hip-hop indie snipes is enough to give you brain strain -- and the pay-off is negligible . \nthe script is high on squaddie banter , low on shocks . \n . . . if you , like me , think an action film disguised as a war tribute is disgusting to begin with , then you're in for a painful ride . \nwhile solondz tries and tries hard , storytelling fails to provide much more insight than the inside column of a torn book jacket . \nwith very little to add beyond the dark visions already relayed by superb recent predecessors like swimming with sharks and the player , this latest skewering . . . may put off insiders and outsiders alike . \n[davis] wants to cause his audience an epiphany , yet he refuses to give us real situations and characters . \nwithout a fresh infusion of creativity , 4ever is neither a promise nor a threat so much as wishful thinking . \n . . . unlike [scorsese's mean streets] , ash wednesday is essentially devoid of interesting characters or even a halfway intriguing plot . \nbeing unique doesn't necessarily equate to being good , no matter how admirably the filmmakers have gone for broke . \na few hours after you've seen it , you forget you've been to the movies . \nodd and weird . \nwaydowntown may not be an important movie , or even a good one , but it provides a nice change of mindless pace in collision with the hot oscar season currently underway . \nyes , i suppose it's lovely that cal works out his issues with his dad and comes to terms with his picture-perfect life -- but world traveler gave me no reason to care , so i didn't . \nshadyac , who belongs with the damned for perpetrating patch adams , trots out every ghost trick from the sixth sense to the mothman prophecies . \nthe photographer's show-don't-tell stance is admirable , but it can make him a problematic documentary subject . \nit is not the first time that director sara sugarman stoops to having characters drop their pants for laughs and not the last time she fails to provoke them . \ni'd be hard pressed to think of a film more cloyingly sappy than evelyn this year . \nnothing more than an amiable but unfocused bagatelle that plays like a loosely-connected string of acting-workshop exercises . \nmeanders between its powerful moments . \nwhat remains is a variant of the nincompoop benigni persona , here a more annoying , though less angry version of the irresponsible sandlerian manchild , undercut by the voice of the star of road trip . \na backhanded ode to female camaraderie penned by a man who has little clue about either the nature of women or of friendship . \npure of intention and passably diverting , his secret life is light , innocuous and unremarkable . \n . . . delivers few moments of inspiration amid the bland animation and simplistic story . \ntake away the controversy , and it's not much more watchable than a mexican soap opera . \nit's got the brawn , but not the brains . \nmindless and boring martial arts and gunplay with too little excitement and zero compelling storyline . \na lot of talent is wasted in this crass , low-wattage endeavor . \nto show these characters in the act and give them no feelings of remorse -- and to cut repeatedly to the flashback of the original rape -- is overkill to the highest degree . \n[t]oo many of these gross out scenes . . . \nabout one in three gags in white's intermittently wise script hits its mark ; the rest are padding unashamedly appropriated from the teen-exploitation playbook . \nlittle is done to support the premise other than fling gags at it to see which ones shtick . \nreno does what he can in a thankless situation , the film ricochets from humor to violence and back again , and ryoko hirosue makes us wonder if she is always like that . \nif jews were catholics , this would be catechism\none of those films that seems tailor made to air on pay cable to offer some modest amusements when one has nothing else to watch . \nthe big ending surprise almost saves the movie . it's too bad that the rest isn't more compelling . \ncharming , if overly complicated . . . \nschneider's mugging is relentless and his constant need to suddenly transpose himself into another character undermines the story's continuity and progression . \nall very stylish and beautifully photographed , but far more trouble than it's worth , with fantasy mixing with reality and actors playing more than one role just to add to the confusion . \nit's probably not easy to make such a worthless film . . . \nhope keeps arising that the movie will live up to the apparent skills of its makers and the talents of its actors , but it doesn't . \nhas no reason to exist , other than to employ hollywood kids and people who owe favors to their famous parents . \nfor a guy who has waited three years with breathless anticipation for a new hal hartley movie to pore over , no such thing is a big letdown . \nconstantly slips from the grasp of its maker . \nsmothered by its own solemnity . \n'christian bale's quinn [is] a leather clad grunge-pirate with a hairdo like gandalf in a wind-tunnel and a simply astounding cor-blimey-luv-a-duck cockney accent . '\nmight be one of those vanity projects in which a renowned filmmaker attempts to show off his talent by surrounding himself with untalented people . \nafter you laugh once ( maybe twice ) , you will have completely forgotten the movie by the time you get back to your car in the parking lot . \nnot one moment in the enterprise didn't make me want to lie down in a dark room with something cool to my brow . \nin the era of the sopranos , it feels painfully redundant and inauthentic . \nthe overall vibe is druggy and self-indulgent , like a spring-break orgy for pretentious arts majors . \nbreen's script is sketchy with actorish notations on the margin of acting . \nthere's no question that epps scores once or twice , but it's telling that his funniest moment comes when he falls about ten feet onto his head . \nif only merchant paid more attention the story . \nat the one-hour mark , herzog simply runs out of ideas and the pace turns positively leaden as the movie sputters to its inevitable tragic conclusion . \n . . . too contrived to be as naturally charming as it needs to be . \na simpler , leaner treatment would have been preferable ; after all , being about nothing is sometimes funnier than being about something . \nthe characters are based on stock clichs , and the attempt to complicate the story only defies credibility . \neverything about it from the bland songs to the colorful but flat drawings is completely serviceable and quickly forgettable . \nnot the great american comedy , but if you liked the previous movies in the series , you'll have a good time with this one too . \na domestic melodrama with weak dialogue and biopic cliches . \nmr . goyer's loose , unaccountable direction is technically sophisticated in the worst way . \nthe movie is so thoughtlessly assembled . \nbenigni presents himself as the boy puppet pinocchio , complete with receding hairline , weathered countenance and american breckin meyer's ridiculously inappropriate valley boy voice . \nplays like some corny television production from a bygone era\nthe end result is like cold porridge with only the odd enjoyably chewy lump . \nfor all the charm of kevin kline and a story that puts old-fashioned values under the microscope , there's something creepy about this movie . \ni was feeling this movie until it veered off too far into the exxon zone , and left me behind at the station looking for a return ticket to realism . \nproducer john penotti surveyed high school students . . . and came back with the astonishing revelation that \" they wanted to see something that didn't talk down to them . \" ignoring that , he made swimfan anyway\nnaipaul fans may be disappointed . those who are not acquainted with the author's work , on the other hand , may fall fast asleep . \nhoffman waits too long to turn his movie in an unexpected direction , and even then his tone retains a genteel , prep-school quality that feels dusty and leatherbound . \nif you're a crocodile hunter fan , you'll enjoy at least the \" real \" portions of the film . if you're looking for a story , don't bother . \nfull frontal had no effect and elicited no sympathies for any of the characters . by that measure , it is a failure . \na baffling mixed platter of gritty realism and magic realism with a hard-to-swallow premise . \nan affable but undernourished romantic comedy that fails to match the freshness of the actress-producer and writer's previous collaboration , miss congeniality . \nsometimes this modest little number clicks , and sometimes it doesn't . \nlike a pack of dynamite sticks , built for controversy . the film is explosive , but a few of those sticks are wet . \nhas its charming quirks and its dull spots . \nan admitted egomaniac , evans is no hollywood villain , and yet this grating showcase almost makes you wish he'd gone the way of don simpson . \nthe audience when i saw this one was chuckling at all the wrong times , and that's a bad sign when they're supposed to be having a collective heart attack . \neveryone's to blame here . \nyou get the impression that writer and director burr steers knows the territory . . . but his sense of humor has yet to lose the smug self-satisfaction usually associated with the better private schools . \nless a study in madness or love than a study in schoolgirl obsession . \nrice never clearly defines his characters or gives us a reason to care about them . \nit's a bizarre curiosity memorable mainly for the way it fritters away its potentially interesting subject matter via a banal script , unimpressive acting and indifferent direction . \na slight and obvious effort , even for one whose target demographic is likely still in the single digits , age-wise . \nsex with strangers will shock many with its unblinking frankness . but what is missing from it all is a moral . what is the filmmakers' point ? why did they deem it necessary to document all this emotional misery ? \nyou see robin williams and psycho killer , and you think , hmmmmm . you see the movie and you think , zzzzzzzzz . \ndownright transparent is the script's endless assault of embarrassingly ham-fisted sex jokes that reek of a script rewrite designed to garner the film a \" cooler \" pg-13 rating . \nthe movie slides downhill as soon as macho action conventions assert themselves . \nformulaic to the 51st power , more like . \ndraggin' about dragons\nhoward and his co-stars all give committed performances , but they're often undone by howard's self-conscious attempts to find a 'literary' filmmaking style to match his subject . \na respectable but uninspired thriller that's intelligent and considered in its details , but ultimately weak in its impact . \njones helps breathe some life into the insubstantial plot , but even he is overwhelmed by predictability . \nthe movie just has too much on its plate to really stay afloat for its just under ninety minute running time . \ncomes off more like a misdemeanor , a flat , unconvincing drama that never catches fire . \ncinematic poo . \noffers absolutely nothing i hadn't already seen . \n \" analyze that \" is one of those crass , contrived sequels that not only fails on its own , but makes you second-guess your affection for the original . \nyou might say tykwer has done all that heaven allows , if you wanted to make as anti-kieslowski a pun as possible . suffice to say its total promise is left slightly unfulfilled . \ncomplex , sinuously plotted and , somehow , off-puttingly cold . \nfirst-time writer-director dylan kidd also has a good ear for dialogue , and the characters sound like real people . \n . . . an airless , prepackaged julia roberts wannabe that stinks so badly of hard-sell image-mongering you'll wonder if lopez's publicist should share screenwriting credit . \ngoldmember has none of the visual wit of the previous pictures , and it looks as though jay roach directed the film from the back of a taxicab . \ncould as easily have been called 'under siege 3 : in alcatraz' . . . a cinematic corpse that never springs to life . \nin comparison to his earlier films it seems a disappointingly thin slice of lower-class london life ; despite the title . . . amounts to surprisingly little . \nlame sweet home leaves no southern stereotype unturned . \nslow , dry , poorly cast , but beautifully shot . \nthe jokes are sophomoric , stereotypes are sprinkled everywhere and the acting ranges from bad to bodacious . \nwill give many ministers and bible-study groups hours of material to discuss . but mainstream audiences will find little of interest in this film , which is often preachy and poorly acted . \nin its chicken heart , crush goes to absurd lengths to duck the very issues it raises . \nthis long and relentlessly saccharine film is a clear case of preaching to the converted . \nthe film is flat . \nthe movie is a lumbering load of hokum but . . . it's at least watchable . \nit's a boom-box of a movie that might have been titled 'the loud and the ludicrous' . . . the pandering to a moviegoing audience dominated by young males is all too calculated . \nan unbelievably stupid film , though occasionally fun enough to make you forget its absurdity . \nthe first fatal attraction was vile enough . do we really need the tiger beat version ? \nthis bond film goes off the beaten path , not necessarily for the better . \nthe problem is that the movie has no idea of it is serious or not . \nwhen the fire burns out , we've only come face-to-face with a couple dragons and that's where the film ultimately fails . \nit would work much better as a one-hour tv documentary . \nthe elements were all there but lack of a pyschological center knocks it flat . \nanemic chronicle of money grubbing new yorkers and their serial loveless hook ups . \nsimply doesn't have sufficient heft to justify its two-hour running time . \nan unsuccessful attempt at a movie of ideas . \nqueen of the damned as you might have guessed , makes sorry use of aaliyah in her one and only starring role -- she does little here but point at things that explode into flame . \nthis toothless dog , already on cable , loses all bite on the big screen . \nit made me feel unclean , and i'm the guy who liked there's something about mary and both american pie movies . oh , and booty call . \nnot only is it hokey , manipulative and as bland as wonder bread dipped in milk , but it also does the absolute last thing we need hollywood doing to us : it preaches . \nit's so crammed with scenes and vistas and pretty moments that it's left a few crucial things out , like character development and coherence . \nserving sara should be served an eviction notice at every theater stuck with it . \ndirector roger michell does so many of the little things right that it's difficult not to cuss him out severely for bungling the big stuff . \na loud , low-budget and tired formula film that arrives cloaked in the euphemism 'urban drama . '\nthe movie has a script ( by paul pender ) made of wood , and it's relentlessly folksy , a procession of stagy set pieces stacked with binary oppositions . \na pathetic exploitation film that tries to seem sincere , and just seems worse for the effort . \nat some point , all this visual trickery stops being clever and devolves into flashy , vaguely silly overkill . \ndespite some charm and heart , this quirky soccer import is forgettable\nmeyjes's movie , like max rothman's future , does not work . \nwhat's needed so badly but what is virtually absent here is either a saving dark humor or the feel of poetic tragedy . \nschneidermeister . . . makin' a fool of himself . . . losin' his fan base . . . \nan ambitious , serious film that manages to do virtually everything wrong ; sitting through it is something akin to an act of cinematic penance . \nnot once does it come close to being exciting . \na frankenstein mishmash that careens from dark satire to cartoonish slapstick , bartleby performs neither one very well . \nit plays like a big-budget , after-school special with a generous cast , who at times lift the material from its well-meaning clunkiness . \nthere's something not entirely convincing about the quiet american . and that holds true for both the movie and the title character played by brendan fraser . \none of those strained caper movies that's hardly any fun to watch and begins to vaporize from your memory minutes after it ends . \nneeded a little less bling-bling and a lot more romance . \nthe ending doesn't work . . . but most of the movie works so well i'm almost recommending it , anyway -- maybe not to everybody , but certainly to people with a curiosity about how a movie can go very right , and then step wrong . \nit's hard to believe these jokers are supposed to have pulled off four similar kidnappings before . \ni'm not exactly sure what this movie thinks it is about . \ncal is an unpleasantly shallow and immature character with whom to spend 110 claustrophobic minutes . \nso brisk is wang's pacing that none of the excellent cast are given air to breathe . \nthe bottom line , at least in my opinion , is imposter makes a better short story than it does a film . \nsome elements of it really blow the big one , but other parts are decent . \nit is just too bad the film's story does not live up to its style . \nunless you're a fanatic , the best advice is : 'scooby' don't . \na cautionary tale about the folly of superficiality that is itself endlessly superficial . \nfor single digits kidlets stuart little 2 is still a no brainer . if you're looking to rekindle the magic of the first film , you'll need a stronger stomach than us . \nshreve's graceful dual narrative gets clunky on the screen , and we keep getting torn away from the compelling historical tale to a less-compelling soap opera . \ncontains a few big laughs but many more that graze the funny bone or miss it altogether , in part because the consciously dumbed-down approach wears thin . \nnothing more than a widget cranked out on an assembly line to see if stupid americans will get a kick out of goofy brits with cute accents performing ages-old slapstick and unfunny tricks . \nthis is a film tailor-made for those who when they were in high school would choose the cliff-notes over reading a full-length classic . \nthe movie is undone by a filmmaking methodology that's just experimental enough to alienate the mainstream audience while ringing cliched to hardened indie-heads . \na jumbled fantasy comedy that did not figure out a coherent game plan at scripting , shooting or post-production stages . \na sad and rote exercise in milking a played-out idea -- a straight guy has to dress up in drag -- that shockingly manages to be even worse than its title would imply . \npersonal velocity ought to be exploring these women's inner lives , but it never moves beyond their surfaces . \nwe hate [madonna] within the film's first five minutes , and she lacks the skill or presence to regain any ground . \nsounding like arnold schwarzenegger , with a physique to match , [ahola] has a wooden delivery and encounters a substantial arc of change that doesn't produce any real transformation . \ntwo big things are missing -- anything approaching a visceral kick , and anything approaching even a vague reason to sit through it all . \na fascinating but choppy documentary . \nscarcely worth a mention apart from reporting on the number of tumbleweeds blowing through the empty theatres graced with its company . \nthe doofus-on- the-loose banter of welcome to collinwood has a cocky , after-hours loopiness to it . and as with most late-night bull sessions , eventually the content isn't nearly as captivating as the rowdy participants think it is . \ntoo stagey , talky -- and long -- for its own good . \napparently reassembled from the cutting-room floor of any given daytime soap . \nthe sinister inspiration that fuelled devito's early work is confused in death to smoochy into something both ugly and mindless . \ndespite auteuil's performance , it's a rather listless amble down the middle of the road , where the thematic ironies are too obvious and the sexual politics too smug . \ndirector boris von sychowski instead opts for a routine slasher film that was probably more fun to make than it is to sit through . \n . . . little more than a well-acted television melodrama shot for the big screen . \nnever comes together as a coherent whole . \nan unintentionally surreal kid's picture . . . in which actors in bad bear suits enact a sort of inter-species parody of a vh1 behind the music episode . \nfirst , for a movie that tries to be smart , it's kinda dumb . and second , what's with all the shooting ? \ndon't even bother to rent this on video . \nthere is something in full frontal , i guess , about artifice and acting and how it distorts reality for people who make movies and watch them , but like most movie riddles , it works only if you have an interest in the characters you see . \nthis is the kind of movie that gets a quick release before real contenders arrive in september . \nnot counting a few gross-out comedies i've been trying to forget , this is the first film in a long time that made me want to bolt the theater in the first 10 minutes . \nplays like one long , meandering sketch inspired by the works of john waters and todd solondz , rather than a fully developed story . \nthe film doesn't have enough innovation or pizazz to attract teenagers , and it lacks the novel charm that made spy kids a surprising winner with both adults and younger audiences . \na mawkish self-parody that plays like some weird masterpiece theater sketch with neither a point of view nor a compelling reason for being . \nan average b-movie with no aspirations to be anything more . \nbartlett's hero remains a reactive cipher , when opening the man's head and heart is the only imaginable reason for the film to be made . \ngibney and jarecki just want to string the bastard up . \nthe plot is plastered with one hollywood cliche after another , most of which involve precocious kids getting the better of obnoxious adults . \nless worrying about covering all the drama in frida's life and more time spent exploring her process of turning pain into art would have made this a superior movie . \na film that suffers because of its many excesses . \ntoo bland and fustily tasteful to be truly prurient . \nin any case , i would recommend big bad love only to winger fans who have missed her since 1995's forget paris . but even then , i'd recommend waiting for dvd and just skipping straight to her scenes . \ndepicts the sorriest and most sordid of human behavior on the screen , then laughs at how clever it's being . \nnijinsky says , 'i know how to suffer' and if you see this film you'll know too . \n'cq may one day be fondly remembered as roman coppola's brief pretentious period before going on to other films that actually tell a story worth caring about\nthe only thing scary about feardotcom is that the filmmakers and studio are brazen enough to attempt to pass this stinker off as a scary movie . \n . . . stale and uninspired . \na dreary movie . \n . . . the story simply putters along looking for astute observations and coming up blank . \ninstead of contriving a climactic hero's death for the beloved-major- character-who-shall- remain-nameless , why not invite some genuine spontaneity into the film by having the evil aliens' laser guns actually hit something for once ? \nit just didn't mean much to me and played too skewed to ever get a hold on ( or be entertained by ) . \nthis action-thriller/dark comedy is one of the most repellent things to pop up in a cinematic year already littered with celluloid garbage . \nmib ii is a movie that makes it possible for the viewer to doze off for a few minutes or make several runs to the concession stand and/or restroom and not feel as if he or she has missed anything . that's because relatively nothing happens . \nno amount of arty theorizing -- the special effects are 'german-expressionist , ' according to the press notes -- can render it anything but laughable . \nblue crush follows the formula , but throws in too many conflicts to keep the story compelling . \nboyd's screenplay ( co-written with guardian hack nick davies ) has a florid turn of phrase that owes more to guy ritchie than the bard of avon . \nthere's not a spark of new inspiration in it , just more of the same , done with noticeably less energy and imagination . \njackson shamefully strolls through this mess with a smug grin , inexplicably wearing a kilt and carrying a bag of golf clubs over one shoulder . \na moving picture that does not move . \nruh-roh ! romething's really wrong with this ricture ! \nits salient points are simultaneously buried , drowned and smothered in the excesses of writer-director roger avary . \ni'm not sure these words have ever been together in the same sentence : this erotic cannibal movie is boring . \n'god help us , but capra and cooper are rolling over in their graves . '\n . . . an hour-and-a-half of inoffensive , unmemorable filler . \nis it a comedy ? a drama ? a romance ? a cartoon ? \nze movie starts out so funny , then she is nothing . \ndid the film inform and educate me ? yes . did it move me to care about what happened in 1915 armenia ? no . and that is where ararat went astray . \nit's a bad sign in a thriller when you instantly know whodunit . \nhas a customarily jovial air but a deficit of flim-flam inventiveness . \neight legged freaks won't join the pantheon of great monster/science fiction flicks that we have come to love . . . \nit gets the details of its time frame right but it completely misses its emotions . \nwho , exactly , is fighting whom here ? ah , yes , that would be me : fighting off the urge to doze . \na kilted jackson is an unsettling sight , and indicative of his , if you will , out-of-kilter character , who rambles aimlessly through ill-conceived action pieces . \ncontrived , awkward and filled with unintended laughs , the film shows signs that someone other than the director got into the editing room and tried to improve things by making the movie go faster . \nstarts out with tremendous promise , introducing an intriguing and alluring premise , only to fall prey to a boatload of screenwriting cliches that sink it faster than a leaky freighter . \nthe film lapses too often into sugary sentiment and withholds delivery on the pell-mell pyrotechnics its punchy style promises . \nthe only question . . . is to determine how well the schmaltz is manufactured -- to assess the quality of the manipulative engineering . average , at best , i'm afraid . \nthis movie is so bad , that it's almost worth seeing because it's so bad . \na crisply made movie that is no more than mildly amusing . \nthis movie feel more like a non-stop cry for attention , than an attempt at any kind of satisfying entertainment . \noverall , it's a pretty mediocre family film . \nlove may have been in the air onscreen , but i certainly wasn't feeling any of it . \nin addition to the overcooked , ham-fisted direction , which has all the actors reaching for the back row , the dialogue sounds like horrible poetry . \nthe very definition of what critics have come to term an \" ambitious failure . \" \nit's as if de palma spent an hour setting a fancy table and then served up kraft macaroni and cheese . \nthe movie ends with outtakes in which most of the characters forget their lines and just utter 'uhhh , ' which is better than most of the writing in the movie . \nworthy of the gong . \nwhile certainly more naturalistic than its australian counterpart , amari's film falls short in building the drama of lilia's journey . \ni found the movie as divided against itself as the dysfunctional family it portrays . \nthe soul-searching deliberateness of the film , although leavened nicely with dry absurdist wit , eventually becomes too heavy for the plot . \nthe movie doesn't add anything fresh to the myth . \nas inept as big-screen remakes of the avengers and the wild wild west . \ncomes across as a relic from a bygone era , and its convolutions . . . feel silly rather than plausible . \nmoves in such odd plot directions and descends into such message-mongering moralism that its good qualities are obscured . \nit's a very sincere work , but it would be better as a diary or documentary . \nonce one experiences mr . haneke's own sadistic tendencies toward his audience , one is left with a sour taste in one's mouth , and little else . \noops , she's really done it this time . that chirpy songbird britney spears has popped up with more mindless drivel . \nit's a loathsome movie , it really is and it makes absolutely no sense . \na chiller resolutely without chills . \nfor those of us who respond more strongly to storytelling than computer-generated effects , the new star wars installment hasn't escaped the rut dug by the last one . \nthe director mostly plays it straight , turning leys' fable into a listless climb down the social ladder . \n \" bad \" is the operative word for \" bad company , \" and i don't mean that in a good way . \nthough frida is easier to swallow than julie taymor's preposterous titus , the eye candy here lacks considerable brio . \ndrumline is -- the mere suggestion , albeit a visually compelling one , of a fully realized story . \nthe whole movie is simply a lazy exercise in bad filmmaking that asks you to not only suspend your disbelief but your intelligence as well . \nthe film affords us intriguing glimpses of the insights gleaned from a lifetime of spiritual inquiry , but ram dass : fierce grace doesn't organize it with any particular insight . \npompous and garbled . \nbilly crystal and robert de niro sleepwalk through vulgarities in a sequel you can refuse . \nit's loud and boring ; watching it is like being trapped at a bad rock concert . \nmerely ( and literally ) tosses around sex toys and offers half-hearted paeans to empowerment that are repeatedly undercut by the brutality of the jokes , most at women's expense . \nif you want a movie time trip , the 1960 version is a far smoother ride . \ntraffics in the kind of prechewed racial clichs that have already been through the corporate stand-up-comedy mill . \nthe story is -- forgive me -- a little thin , and the filmmaking clumsy and rushed . \n . . . grows decidedly flimsier with its many out-sized , out of character and logically porous action set pieces . \ni wish windtalkers had had more faith in the dramatic potential of this true story . this would have been better than the fiction it has concocted , and there still could have been room for the war scenes . \naggressive self-glorification and a manipulative whitewash . stay for the credits and see a devastating comic impersonation by dustin hoffman that is revelatory . \nnone of the characters or plot-lines are fleshed-out enough to build any interest . \nas social expos , skins has its heart in the right place , but that's not much to hang a soap opera on . \nthe whole film has this sneaky feel to it  as if the director is trying to dupe the viewer into taking it all as very important simply because the movie is ugly to look at and not a hollywood product . \nthere's a bit of thematic meat on the bones of queen of the damned , as its origins in an anne rice novel dictate , but generally , it's a movie that emphasizes style over character and substance . \nthe only way to tolerate this insipid , brutally clueless film might be with a large dose of painkillers . \nthis one is certainly well-meaning , but it's also simple-minded and contrived . \ncoppola has made a film of intoxicating atmosphere and little else . \nbad and baffling from the get-go . \na series of immaculately composed shots of patch adams quietly freaking out does not make for much of a movie . \nat a time when we've learned the hard way just how complex international terrorism is , collateral damage paints an absurdly simplistic picture . \nthe impulses that produced this project . . . are commendable , but the results are uneven . \na well-acted movie that simply doesn't gel . \nlike a can of 2-day old coke . you can taste it , but there's no fizz . \nthere's no excuse for following up a delightful , well-crafted family film with a computer-generated cold fish . \nboth the crime story and the love story are unusual . but they don't fit well together and neither is well told . \nit's both sitcomishly predictable and cloying in its attempts to be poignant . \nother than a mildly engaging central romance , hospital is sickly entertainment at best and mind-destroying cinematic pollution at worst . \njaglom offers the none-too-original premise that everyone involved with moviemaking is a con artist and a liar . \noutside of burger's desire to make some kind of film , it's really unclear why this project was undertaken\nwas i scared ? only at the prospect of beck's next project . let's see , a haunted house , a haunted ship , what's next . . . ghost blimp ? \na fragile framework upon which to hang broad , mildly fleshed-out characters that seem to have been conjured up only 10 minutes prior to filming . \nby the time the plot grinds itself out in increasingly incoherent fashion , you might be wishing for a watch that makes time go faster rather than the other way around . \nsince lee is a sentimentalist , the film is more worshipful than your random e ! true hollywood story . \nwes craven's presence is felt ; not the craven of 'a nightmare on elm street' or 'the hills have eyes , ' but the sad schlock merchant of 'deadly friend . '\nsunshine state surveys the landscape and assesses the issues with a clear passion for sociology . but the cinematography is cloudy , the picture making becalmed . \nit's one long bore . \nit gets old quickly . watch barbershop again if you're in need of a cube fix--this isn't worth sitting through . \nit's leaden and predictable , and laughs are lacking . \n . . . a cinematic disaster so inadvertently sidesplitting it's worth the price of admission for the ridicule factor alone . \nskip this dreck , rent animal house and go back to the source . \nthe movie is a desperate miscalculation . it gives poor dana carvey nothing to do that is really funny , and then expects us to laugh because he acts so goofy all the time . \nwe just don't really care too much about this love story . in that setting , their struggle is simply too ludicrous and borderline insulting . \nthe transporter bombards the viewer with so many explosions and side snap kicks that it ends up being surprisingly dull . \nuzumaki's interesting social parallel and defiant aesthetic seems a prostituted muse . . . \n'men in black ii creates a new threat for the mib , but recycles the same premise . \nlarge budget notwithstanding , the movie is such a blip on the year's radar screen that it's tempting just to go with it for the ride . but this time , the old mib label stands for milder isn't better . \nfeels familiar and tired . \na retread of material already thoroughly plumbed by martin scorsese . \ninstead of making his own style , director marcus adams just copies from various sources  good sources , bad mixture\ncriminal conspiracies and true romances move so easily across racial and cultural lines in the film that it makes my big fat greek wedding look like an apartheid drama . \na bore that tends to hammer home every one of its points . \na story which fails to rise above its disgusting source material . \nit's fitting that a movie as artificial and soulless as the country bears owes its genesis to an animatronic display at disneyland . \nstarts as an intense political and psychological thriller but is sabotaged by ticking time bombs and other hollywood-action cliches . \ncompletely creatively stillborn and executed in a manner that i'm not sure could be a single iota worse . . . a soulless hunk of exploitative garbage . \nan uneven look into a grim future that doesn't come close to the level of intelligence and visual splendour that can be seen in other films based on philip k . dick stories . \nhorrible\nthe disjointed mess flows as naturally as jolie's hideous yellow 'do . \nbolstered by an astonishing voice cast ( excepting love hewitt ) , an interesting racial tension , and a storyline that i haven't encountered since at least pete's dragon . \nan authentically vague , but ultimately purposeless , study in total pandemonium . \nmakes a joke out of car chases for an hour and then gives us half an hour of car chases . \nas the sulking , moody male hustler in the title role , [franco] has all of dean's mannerisms and self-indulgence , but none of his sweetness and vulnerability . \nthe only thing to fear about \" fear dot com \" is hitting your head on the theater seat in front of you when you doze off thirty minutes into the film . \nit's too interested in jerking off in all its byzantine incarnations to bother pleasuring its audience . \n'synthetic' is the best description of this well-meaning , beautifully produced film that sacrifices its promise for a high-powered star pedigree . \nit concentrates far too much on the awkward interplay and utter lack of chemistry between chan and hewitt . \nimpostor can't think of a thing to do with these characters except have them run through dark tunnels , fight off various anonymous attackers , and evade elaborate surveillance technologies . \njudd's characters ought to pick up the durable best seller smart women , foolish choices for advice . \nthe script has less spice than a rat burger and the rock's fighting skills are more in line with steven seagal . \nthis ill-conceived and expensive project winds up looking like a bunch of talented thesps slumming it . \n . . . there's a choppy , surface-effect feeling to the whole enterprise . \ndoesn't get the job done , running off the limited chemistry created by ralph fiennes and jennifer lopez . \na particularly joyless , and exceedingly dull , period coming-of-age tale . \nit's impossible to indulge the fanciful daydreams of janice beard ( eileen walsh ) when her real-life persona is so charmless and vacant . \nit wasn't the subject matter that ultimately defeated the filmit was the unfulfilling , incongruous , \" wait a second , did i miss something ? \" ending . \nthis is a movie where the most notable observation is how long you've been sitting still . \npoor editing , bad bluescreen , and ultra-cheesy dialogue highlight the radical action . \nit's super- violent , super-serious and super-stupid . \nso earnest and well-meaning , and so stocked with talent , that you almost forget the sheer , ponderous awfulness of its script . \njust a string of stale gags , with no good inside dope , and no particular bite . \nit's splash without the jokes . \nthe chteau would have been benefited from a sharper , cleaner script before it went in front of the camera . not to mention a sharper , cleaner camera lens . \nshallow , noisy and pretentious . \nmorrissette has performed a difficult task indeed - he's taken one of the world's most fascinating stories and made it dull , lifeless , and irritating . \ngranddad of le nouvelle vague , jean-luc godard continues to baffle the faithful with his games of hide-and-seek . \nthis loud and thoroughly obnoxious comedy about a pair of squabbling working-class spouses is a deeply unpleasant experience . \nit's better than the phantom menace . but unless you're an absolute raving star wars junkie , it isn't much fun . \nphilosophically , intellectually and logistically a mess . \na standard police-oriented drama that , were it not for de niro's participation , would have likely wound up a tnt original . \ncoupling disgracefully written dialogue with flailing bodily movements that substitute for acting , circuit is the awkwardly paced soap opera-ish story . \nhollywood ending just isn't very funny . \nthough clearly well-intentioned , this cross-cultural soap opera is painfully formulaic and stilted . \na technical triumph and an extraordinary bore . \ni can easily imagine benigni's pinocchio becoming a christmas perennial . coal isn't as easy to come by as it used to be and this would be a worthy substitute for naughty children's stockings . \ntwo hours of junk . \nends up being mostly about ravishing costumes , eye-filling , wide-screen production design and joan's wacky decision to stand by her man , no matter how many times he demonstrates that he's a disloyal satyr . \nself-congratulatory , misguided , and ill-informed , if nonetheless compulsively watchable . \na clumsily manufactured exploitation flick , a style-free exercise in manipulation and mayhem . \nthe whole affair , true story or not , feels incredibly hokey . . . [it] comes off like a hallmark commercial . \nwhat's missing is what we call the 'wow' factor . \nthe nicest thing that can be said about stealing harvard ( which might have been called freddy gets molested by a dog ) is that it's not as obnoxious as tom green's freddie got fingered . \n . . . tara reid plays a college journalist , but she looks like the six-time winner of the miss hawaiian tropic pageant , so i don't know what she's doing in here . . . \nthe young stars are too cute ; the story and ensuing complications are too manipulative ; the message is too blatant ; the resolutions are too convenient . \nnormally , rohmer's talky films fascinate me , but when he moves his setting to the past , and relies on a historical text , he loses the richness of characterization that makes his films so memorable . \ni highly recommend irwin , but not in the way this film showcases him . \nit's been 13 months and 295 preview screenings since i last walked out on a movie , but resident evil really earned my indignant , preemptive departure . \nthis picture is mostly a lump of run-of-the-mill profanity sprinkled with a few remarks so geared toward engendering audience sympathy that you might think he was running for office -- or trying to win over a probation officer . \nmalone does have a gift for generating nightmarish images that will be hard to burn out of your brain . but the movie's narrative hook is way too muddled to be an effectively chilling guilty pleasure . \nthough it goes further than both , anyone who has seen the hunger or cat people will find little new here , but a tasty performance from vincent gallo lifts this tale of cannibal lust above the ordinary . \na blair witch- style adventure that plays like a bad soap opera , with passable performances from everyone in the cast . \ndiaz wears out her welcome in her most charmless performance\nit is too bad that this likable movie isn't more accomplished . the actors try hard but come off too amateurish and awkward . \nthe plot has a number of holes , and at times it's simply baffling . \nan ill-conceived jumble that's not scary , not smart and not engaging . \nbrainless , but enjoyably over-the-top , the retro gang melodrama , deuces wild represents fifties teen-gang machismo in a way that borders on rough-trade homo-eroticism . \ngets bogged down by an overly sillified plot and stop-and-start pacing . \nutter mush . . . conceited pap . \n \" what john does is heroic , but we don't condone it , \" one of the film's stars recently said , a tortuous comment that perfectly illustrates the picture's moral schizophrenia . \nlike coming into a long-running , well-written television series where you've missed the first half-dozen episodes and probably won't see the next six . \nthis is no \" waterboy ! \" \nits generic villains lack any intrigue ( other than their funny accents ) and the action scenes are poorly delivered . \ndripping with cliche and bypassing no opportunity to trivialize the material . \nhard-core slasher aficionados will find things to like . . . but overall the halloween series has lost its edge . \nstiff and schmaltzy and clumsily directed . \nthe story the movie tells is of brian de palma's addiction to the junk-calorie suspense tropes that have all but ruined his career . \ntoo many improbabilities and rose-colored situations temper what could've been an impacting film . \ngeneric slasher-movie nonsense , but it's not without style . \nwith tiny little jokes and nary an original idea , this sappy ethnic sleeper proves that not only blockbusters pollute the summer movie pool . \nit sticks rigidly to the paradigm , rarely permitting its characters more than two obvious dimensions and repeatedly placing them in contrived , well-worn situations . \never see one of those comedies that just seem like a bad idea from frame one ? \nonce ice-t sticks his mug in the window of the couple's bmw and begins haranguing the wife in bad stage dialogue , all credibility flies out the window . \nthe best drug addition movies are usually depressing but rewarding . quitting , however , manages just to be depressing , as the lead actor phones in his autobiographical performance . \nit would be great to see this turd squashed under a truck , preferably a semi . \nin the end , all you can do is admire the ensemble players and wonder what the point of it is . \nfor most movies , 84 minutes is short , but this one feels like a life sentence . \nwhile glover , the irrepressible eccentric of river's edge , dead man and back to the future , is perfect casting for the role , he represents bartleby's main overall flaw . \nsomething has been lost in the translation . . . another routine hollywood frightfest in which the slack execution italicizes the absurdity of the premise . \nshatner is probably the funniest person in the film , which gives you an idea just how bad it was . \ntries so hard to be quirky and funny that the strain is all too evident . \nas immaculate as stuart little 2 is , it could be a lot better if it were , well , more adventurous . \nsimply a re-hash of the other seven films . \nwith jump cuts , fast editing and lots of pyrotechnics , yu clearly hopes to camouflage how bad his movie is . he fails . \ni found myself more appreciative of what the director was trying to do than of what he had actually done . \na very depressing movie of many missed opportunities . \ngoes on and on to the point of nausea . \nby turns numbingly dull-witted and disquietingly creepy . \nboy , has this franchise ever run out of gas . \none problem with the movie , directed by joel schumacher , is that it jams too many prefabricated story elements into the running time . \nthe comedy is nonexistent . \n . . . a bland , pretentious mess . \nit's a pedestrian , flat drama that screams out 'amateur' in almost every frame . \na bland animated sequel that hardly seems worth the effort . \nit's not just the vampires that are damned in queen of the damned -- the viewers will feel they suffer the same fate . \nif religious films aren't your bailiwick , stay away . otherwise , this could be a passable date film . \nthis is the case of a pregnant premise being wasted by a script that takes few chances and manages to insult the intelligence of everyone in the audience . \nthe pace and the visuals are so hyped up that a curious sense of menace informs everything . \nstuffy , full of itself , morally ambiguous and nothing to shout about . \nit is most of the things costner movies are known for ; it's sanctimonious , self-righteous and so eager to earn our love that you want to slap it . \ndon't let the subtitles fool you ; the movie only proves that hollywood no longer has a monopoly on mindless action . \nchai's structure and pacing are disconcertingly slack . \nto be oblivious to the existence of this film would be very sweet indeed . \nthe director , with his fake backdrops and stately pacing , never settles on a consistent tone . \none just waits grimly for the next shock without developing much attachment to the characters . \ninstead of panoramic sweep , kapur gives us episodic choppiness , undermining the story's emotional thrust . \nthe director seems to take an unseemly pleasure in [the characters'] misery and at the same time to congratulate himself for having the guts to confront it . \nthis dubious product of a college-spawned ( colgate u . ) comedy ensemble known as broken lizard plays like a mix of cheech and chong and chips . \nthe movie doesn't think much of its characters , its protagonist , or of us . \nsuper troopers is an odd amalgam of comedy genres , existing somewhere between the often literal riffs of early zucker brothers/abrahams films , and the decidedly foul stylings of their post-modern contemporaries , the farrelly brothers . \n . . . will always be remembered for the 9-11 terrorist attacks . after seeing the film , i can tell you that there's no other reason why anyone should bother remembering it . \nmade me feel uneasy , even queasy , because [solondz's] cool compassion is on the border of bemused contempt . \nas a kind of colorful , dramatized pbs program , frida gets the job done . but , for that , why not watch a documentary ? \nwith minimal imagination , you could restage the whole thing in your bathtub . \nnights feels more like a quickie tv special than a feature film . . . it's not even a tv special you'd bother watching past the second commercial break . \nalthough . . . visually striking and slickly staged , it's also cold , grey , antiseptic and emotionally desiccated . \nyou can see where big bad love is trying to go , but it never quite gets there . \nfriday after next is the kind of film that could only be made by african-americans because of its broad racial insensitivity towards african-americans . \nit's not as awful as some of the recent hollywood trip tripe . . . but it's far from a groundbreaking endeavor . \nthe only thing \" swept away \" is the one hour and thirty-three minutes spent watching this waste of time . \none-sided documentary offers simplistic explanations to a very complex situation . . . . stylistically , the movie is a disaster . \nthe corpse count ultimately overrides what little we learn along the way about vicarious redemption . \nas violent , profane and exploitative as the most offensive action flick you've ever seen . \negoyan's work often elegantly considers various levels of reality and uses shifting points of view , but here he has constructed a film so labyrinthine that it defeats his larger purpose . \nlife or something like it has its share of high points , but it misses too many opportunities . \nthis is a truly , truly bad movie . \ndespite bearing the paramount imprint , it's a bargain-basement european pickup . what's hard to understand is why anybody picked it up . wiser souls would have tactfully pretended not to see it and left it lying there\nbelievability wasn't one of the film's virtues . \nsewer rats could watch this movie and be so skeeved out that they'd need a shower . \nthe santa clause 2's plot may sound like it was co-written by mattel executives and lobbyists for the tinsel industry . \nhis best film remains his shortest , the hole , which makes many of the points that this film does but feels less repetitive . \njust another disjointed , fairly predictable psychological thriller . \n . . . stumbles over every cheap trick in the book trying to make the outrage come even easier . \neven the hastily and amateurishly drawn animation cannot engage . \nkung pow is oedekerk's realization of his childhood dream to be in a martial-arts flick , and proves that sometimes the dreams of youth should remain just that . \nbusy urban comedy is clearly not zhang's forte , his directorial touch is neither light nor magical enough to bring off this kind of whimsy . \nwell-meaning but inert . \nnot completely loveable -- but what underdog movie since the bad news bears has been ? -- but certainly hard to hate . \na movie that can't get sufficient distance from leroy's delusions to escape their maudlin influence . \nit's young guns meets goodfellas in this easily skippable hayseeds-vs . -greaseballs mob action-comedy . \nlouiso lets the movie dawdle in classic disaffected-indie-film mode , and brother hoffman's script stumbles over a late-inning twist that just doesn't make sense . \nthe movie straddles the fence between escapism and social commentary , and on both sides it falls short . \nthe film is old-fashioned , occasionally charming and as subtle as boldface . \ncan't get enough of libidinous young city dwellers ? try this obscenely bad dark comedy , so crass that it makes edward burns' sidewalks of new york look like oscar wilde . \nin the new guy , even the bull gets recycled . \n . . . overly melodramatic . . . \nlargely , this is a movie that also does it by the numbers . \non top of a foundering performance , [madonna's] denied her own athleticism by lighting that emphasizes every line and sag . \nthe result is an 'action film' mired in stasis . \nthis is the first full scale wwii flick from hong kong's john woo . he's not good with people . \npatchy combination of soap opera , low-tech magic realism and , at times , ploddingly sociological commentary . \n[stevens is] so stoked to make an important film about human infidelity and happenstance that he tosses a kitchen sink onto a story already overladen with plot conceits . \nso boring that even its target audience talked all the way through it . \none of the more glaring signs of this movie's servitude to its superstar is the way it skirts around any scenes that might have required genuine acting from ms . spears . \nhollywood ending is the most disappointing woody allen movie ever . he has a great cast and a great idea . but the execution is a flop with the exception of about six gags that really work . \n . . . generically , forgettably pleasant from start to finish . \nit's just hard to believe that a life like this can sound so dull . \nwhen not wallowing in its characters' frustrations , the movie is busy contriving false , sitcom-worthy solutions to their problems . \nan overstylized , pured mlange of sex , psychology , drugs and philosophy . sometimes entertaining , sometimes indulgent -- but never less than pure wankery . \n'lovely and amazing , ' unhappily , is neither . . . excessively strained and contrived . \nringu is a disaster of a story , full of holes and completely lacking in chills . ignore the reputation , and ignore the film . \nthis one is a few bits funnier than malle's dud , if only because the cast is so engagingly messing around like slob city reductions of damon runyon crooks . \n'it's painful to watch witherspoon's talents wasting away inside unnecessary films like legally blonde and sweet home abomination , i mean , alabama . '\na plodding teen remake that's so mechanical you can smell the grease on the plot twists . \ntrying to figure out the rules of the country bear universe -- when are bears bears and when are they like humans , only hairier -- would tax einstein's brain . \neven in terms of the low-grade cheese standards on which it operates , it never quite makes the grade as tawdry trash . \namidst the action , the script carries arnold ( and the viewers ) into the forbidden zone of sympathizing with terrorist motivations by presenting the \" other side of the story . \" \nrife with nutty cliches and far too much dialogue . \nit's a 100-year old mystery that is constantly being interrupted by elizabeth hurley in a bathing suit . \n . . . one big laugh , three or four mild giggles , and a whole lot of not much else . \ntoo intensely focused on the travails of being hal hartley to function as pastiche , no such thing is hartley's least accessible screed yet . \nkenneth branagh's energetic sweet-and-sour performance as a curmudgeonly british playwright grounds this overstuffed , erratic dramedy in which he and his improbably forbearing wife contend with craziness and child-rearing in los angeles . \ndirector uwe boll and writer robert dean klein fail to generate any interest in an unsympathetic hero caught up in an intricate plot that while cleverly worked out , cannot overcome blah characters . \nms . phoenix is completely lacking in charm and charisma , and is unable to project either esther's initial anomie or her eventual awakening . \nthe movie fails to portray its literarily talented and notorious subject as anything much more than a dirty old man . \nextremely bad . \na clichd and shallow cautionary tale about the hard-partying lives of gay men . \nthe fetid underbelly of fame has never looked uglier . \na little weak -- and it isn't that funny . \nwhile it is welcome to see a chinese film depict a homosexual relationship in a mature and frank fashion , lan yu never catches dramatic fire . \nthe script boasts some tart tv-insider humor , but the film has not a trace of humanity or empathy . \ndespite the pyrotechnics , narc is strictly by the book . \nin both the writing and cutting , it does not achieve the kind of dramatic unity that transports you . you end up simply admiring this bit or that , this performance or that . \ncacoyannis is perhaps too effective in creating an atmosphere of dust-caked stagnation and labored gentility . \nworth seeing once , but its charm quickly fades . \nthe original wasn't a good movie but this remake makes it look like a masterpiece ! \none suspects that craven endorses they simply because this movie makes his own look much better by comparison . \ngere gives a good performance in a film that doesn't merit it . \nyour appreciation of it will depend on what experiences you bring to it and what associations you choose to make . \nincludes too much obvious padding . \nthere's no palpable chemistry between lopez and male lead ralph fiennes , plus the script by working girl scribe kevin wade is workmanlike in the extreme . \ni'm not sure which half of dragonfly is worse : the part where nothing's happening , or the part where something's happening , but it's stupid . \ndon't expect any subtlety from this latest entry in the increasingly threadbare gross-out comedy cycle . \nthe only camouflage carvey should now be considering is a paper bag to wear over his head when he goes out into public , to avoid being recognized as the man who bilked unsuspecting moviegoers . \nshot like a postcard and overacted with all the boozy self-indulgence that brings out the worst in otherwise talented actors . . . \nspain's greatest star wattage doesn't overcome the tumult of maudlin tragedy . \n . . . a movie that , quite simply , shouldn't have been made . \nconforms itself with creating a game of 'who's who' . . . where the characters' moves are often more predictable than their consequences . \nlooks and feels like a low-budget hybrid of scarface or carlito's way . \nthe script is a tired one , with few moments of joy rising above the stale material . \nsuffers from all the excesses of the genre . \nthe verdict : two bodies and hardly a laugh between them . \nthe latest adam sandler assault and possibly the worst film of the year . \ndownbeat , period-perfect biopic hammers home a heavy-handed moralistic message . \nwhile the film is competent , it's also uninspired , lacking the real talent and wit to elevate it beyond its formula to the level of classic romantic comedy to which it aspires . \nthey ought to be a whole lot scarier than they are in this tepid genre offering . \nit's harmless , diverting fluff . but it's hard to imagine a more generic effort in the genre . \nit's just plain lurid when it isn't downright silly . \ncomedy troupe broken lizard's first movie is very funny but too concerned with giving us a plot . \npap invested in undergraduate doubling subtexts and ridiculous stabs at existentialism reminding of the discovery of the wizard of god in the fifth trek flick . \na horror movie with seriously dumb characters , which somewhat dilutes the pleasure of watching them stalked by creepy-crawly bug things that live only in the darkness . \nit's a film with an idea buried somewhere inside its fabric , but never clearly seen or felt . \n'all in all , reign of fire will be a good ( successful ) rental . '\noccasionally funny , sometimes inspiring , often boring . \na movie in which two not very absorbing characters are engaged in a romance you can't wait to see end . \nthe predominantly amateur cast is painful to watch , so stilted and unconvincing are the performances . \nwho are 'they' ? well , they're 'they' . they're the unnamed , easily substitutable forces that serve as whatever terror the heroes of horror movies try to avoid . they exist for hushed lines like \" they're back ! \" , \" they're out there ! \" and \" they're coming ! \" \nelegantly crafted but emotionally cold , a puzzle whose intricate construction one can admire but is difficult to connect with on any deeper level . \nwere dylan thomas alive to witness first-time director ethan hawke's strained chelsea walls , he might have been tempted to change his landmark poem to , 'do not go gentle into that good theatre . '\nthe story has its redundancies , and the young actors , not very experienced , are sometimes inexpressive . \ni'm sure the filmmaker would disagree , but , honestly , i don't see the point . it's a visual rorschach test and i must have failed . \nthe film is really closer to porn than a serious critique of what's wrong with this increasingly pervasive aspect of gay culture . \nmurder by numbers just doesn't add up . \nclare peploe's airless movie adaptation could use a little american pie-like irreverence . \nvideo games are more involving than this mess . \nclayburgh and tambor are charming performers ; neither of them deserves eric schaeffer . \na pale xerox of other , better crime movies . \n . . . a hokey piece of nonsense that tries too hard to be emotional . \nilliterate , often inert sci-fi action thriller . \na perfect example of rancid , well-intentioned , but shamelessly manipulative movie making . \nthe adventure doesn't contain half the excitement of balto , or quarter the fun of toy story 2 . \nessentially a collection of bits -- and they're all naughty . \na mess . the screenplay does too much meandering , norton has to recite bland police procedural details , fiennes wanders around in an attempt to seem weird and distanced , hopkins looks like a drag queen . \nthe screenplay by james eric , james horton and director peter o'fallon . . . is so pat it makes your teeth hurt . \nbefore it takes a sudden turn and devolves into a bizarre sort of romantic comedy , steven shainberg's adaptation of mary gaitskill's harrowing short story . . . is a brilliantly played , deeply unsettling experience . \nsolaris is rigid and evasive in ways that soderbergh's best films , \" erin brockovich , \" \" out of sight \" and \" ocean's eleven , \" never were . \nseems like something american and european gay movies were doing 20 years ago . \nin the process of trimming the movie to an expeditious 84 minutes , director roger kumble seems to have dumped a whole lot of plot in favor of . . . outrageous gags . \nyou can see the would-be surprises coming a mile away , and the execution of these twists is delivered with a hammer . thumbs down . \nthe characters are paper thin and the plot is so cliched and contrived that it makes your least favorite james bond movie seem as cleverly plotted as the usual suspects . \n . . . del toro maintains a dark mood that makes the film seem like something to endure instead of enjoy . \nthe movie eventually snaps under the strain of its plot contrivances and its need to reassure . \nthe real question this movie poses is not 'who ? ' but 'why ? '\nnow here's a sadistic bike flick that would have made vittorio de sica proud . \na movie that's about as overbearing and over-the-top as the family it depicts . \na movie in which laughter and self-exploitation merge into jolly soft-porn 'empowerment . '\noccasionally interesting but essentially unpersuasive , a footnote to a still evolving story . \nif we're to slap protagonist genevieve leplouff because she's french , do we have that same option to slap her creators because they're clueless and inept ? \nmoretti plays giovanni , a psychiatrist who predictably finds it difficult to sustain interest in his profession after the family tragedy . too predictably , in fact . \nalternative medicine obviously has its merits . . . but ayurveda does the field no favors . \nthis thing works on no level whatsoever for me . \nit follows the basic plot trajectory of nearly every schwarzenegger film : someone crosses arnie . arnie blows things up . \nice age posits a heretofore unfathomable question : is it possible for computer-generated characters to go through the motions ? \nan incoherent jumble of a film that's rarely as entertaining as it could have been . \n . . . they missed the boat . \nmore dutiful than enchanting . . . terribly episodic and lacking the spark of imagination that might have made it an exhilarating treat . \nlaconic and very stilted in its dialogue , this indie flick never found its audience , probably because it's extremely hard to relate to any of the characters . \nthe comedy death to smoochy is a rancorous curiosity : a movie without an apparent audience . \nbarney's ideas about creation and identity don't really seem all that profound , at least by way of what can be gleaned from this three-hour endurance test built around an hour's worth of actual material . \naffleck merely creates an outline for a role he still needs to grow into , a role that ford effortlessly filled with authority . \ncinematic pyrotechnics aside , the only thing avary seems to care about are mean giggles and pulchritude . it makes sense that he went back to school to check out the girls -- his film is a frat boy's idea of a good time . \nthe narrative is so consistently unimaginative that probably the only way to have saved the film is with the aid of those wisecracking mystery science theater 3000 guys . \nnothing more or less than an outright bodice-ripper -- it should have ditched the artsy pretensions and revelled in the entertaining shallows . \na living testament to the power of the eccentric and the strange . the fact that it isn't very good is almost beside the point . \nfeels less like a cousin to blade runner than like a bottom-feeder sequel in the escape from new york series . \nwhat might have been acceptable on the printed page of iles' book does not translate well to the screen . \nif oscar had a category called best bad film you thought was going to be really awful but wasn't , guys would probably be duking it out with the queen of the damned for the honor . \na poky and pseudo-serious exercise in sham actor workshops and an affected malaise . \nmediocre fable from burkina faso . \nfessenden has nurtured his metaphors at the expense of his narrative , but he does display an original talent . \nsince the movie is based on a nicholas sparks best seller , you know death is lurking around the corner , just waiting to spoil things . \nbottom-rung new jack city wannabe . \nfincher takes no apparent joy in making movies , and he gives none to the audience . \nit's mildly amusing , but i certainly can't recommend it . \nnicholas nickleby celebrates the human spirit with such unrelenting dickensian decency that it turned me ( horrors ! ) into scrooge . \nfear dot com is more frustrating than a modem that disconnects every 10 seconds . \nfull of flatulence jokes and mild sexual references , kung pow ! is the kind of movie that's critic-proof , simply because it aims so low . \nmay cause you to bite your tongue to keep from laughing at the ridiculous dialog or the oh-so convenient plot twists . \nthere are just too many characters saying too many clever things and getting into too many pointless situations . where's the movie here ? \na dark , dull thriller with a parting shot that misfires . \nlacking substance and soul , crossroads comes up shorter than britney's cutoffs . \ncassavetes thinks he's making dog day afternoon with a cause , but all he's done is to reduce everything he touches to a shrill , didactic cartoon . \nburies an interesting storyline about morality and the choices we make underneath such a mountain of clichs and borrowed images that it might more accurately be titled mr . chips off the old block . \nalthough sensitive to a fault , it's often overwritten , with a surfeit of weighty revelations , flowery dialogue , and nostalgia for the past and roads not taken . \nit's so badly made on every level that i'm actually having a hard time believing people were paid to make it . \nwithout non-stop techno or the existential overtones of a kieslowski morality tale , maelstrm is just another winter sleepers . \nnicks , seemingly uncertain what's going to make people laugh , runs the gamut from stale parody to raunchy sex gags to formula romantic comedy . \n'a' for creativity but comes across more as a sketch for a full-length comedy . \nif there's one thing this world needs less of , it's movies about college that are written and directed by people who couldn't pass an entrance exam . \nthe script kicks in , and mr . hartley's distended pace and foot-dragging rhythms follow . \n[e]ventually , every idea in this film is flushed down the latrine of heroism . \ni am sorry that i was unable to get the full brunt of the comedy . \nno telegraphing is too obvious or simplistic for this movie . \nlooks and feels like a project better suited for the small screen . \nin its best moments , resembles a bad high school production of grease , without benefit of song . \nindifferently implausible popcorn programmer of a movie . \nit's inoffensive , cheerful , built to inspire the young people , set to an unending soundtrack of beach party pop numbers and aside from its remarkable camerawork and awesome scenery , it's about as exciting as a sunburn . \nhis comedy premises are often hackneyed or just plain crude , calculated to provoke shocked laughter , without following up on a deeper level . \nchristina ricci comedy about sympathy , hypocrisy and love is a misfire . \nat times , the suspense is palpable , but by the end there's a sense that the crux of the mystery hinges on a technicality that strains credulity and leaves the viewer haunted by the waste of potential . \nthey should have called it gutterball . \nthekids will probably stay amused at the kaleidoscope of big , colorful characters . mom and dad can catch some quality naptime along the way . \nit's too self-important and plodding to be funny , and too clipped and abbreviated to be an epic . \nthe best that can be said about the work here of scottish director ritchie . . . is that he obviously doesn't have his heart in it . \nless dizzying than just dizzy , the jaunt is practically over before it begins . \nslick piece of cross-promotion . \ntaylor appears to have blown his entire budget on soundtrack rights and had nothing left over for jokes . \nit believes it's revealing some great human truths , when , in reality , it's churning ground that has long passed the point of being fertile . \nby turns pretentious , fascinating , ludicrous , provocative and vainglorious . \nit all drags on so interminably it's like watching a miserable relationship unfold in real time . \nvilleneuve spends too much time wallowing in bibi's generic angst ( there are a lot of shots of her gazing out windows ) . \n[t]here's only so much anyone can do with a florid , overplotted , anne rice rock 'n' roll vampire novel before the built-in silliness of the whole affair defeats them . \nit's another video movie photographed like a film , with the bad lighting that's often written off as indie film naturalism . \nthe techno tux is good for a few laughs , as are chan and hewitt , but when such a good design turns out to be a cheap knockoff , we can't recommend anything but a rental for the tuxedo . \ni got a headache watching this meaningless downer . \napart from dazzling cinematography , we've seen just about everything in blue crush in one form or the other . \ntoo much of the humor falls flat . \ndetox is ultimately a pointless endeavor . \nvan wilder doesn't bring anything new to the proverbial table , but it does possess a coherence absent in recent crass-a-thons like tomcats , freddy got fingered , and slackers . \nthe piquant story needs more dramatic meat on its bones . \nvery special effects , brilliantly bold colors and heightened reality can't hide the giant achilles' heel in \" stuart little 2 \" : there's just no story , folks . \nthe plot combines the blues brothers and almost famous ( but with bears , and a g rating ) , with an excruciating dollop of disney sentimentality mixed in for good measure . \nno way i can believe this load of junk . \n \" roger michell ( \" notting hill \" ) directs a morality thriller . \" \nit's dumb , but more importantly , it's just not scary . \nthere is no pleasure in watching a child suffer . just embarrassment and a vague sense of shame . \nthe movie's accumulated force still feels like an ugly knot tightening in your stomach . but is that knot from dramatic tension or a symptom of artistic malnutrition ? \neven with a green mohawk and a sheet of fire-red flame tattoos covering his shoulder , however , kilmer seems to be posing , rather than acting . and that leaves a hole in the center of the salton sea . \nthere's just no currency in deriding james bond for being a clichd , doddering , misogynistic boy's club . \nwhen the film ended , i felt tired and drained and wanted to lie on my own deathbed for a while . \nfull of witless jokes , dealing in broad stereotypes and outrageously unbelievable scenarios , and saddled with a general air of misogyny\nthe film's hackneyed message is not helped by the thin characterizations , nonexistent plot and pretentious visual style . \nthe iditarod lasts for days - this just felt like it did . \n it feels like an after-school special gussied up with some fancy special effects , and watching its rote plot points connect is about as exciting as gazing at an egg timer for 93 minutes . \nthis movie is maddening . it conveys a simple message in a visual style that is willfully overwrought . \nshould have been someone else-\nthe film is based on truth and yet there is something about it that feels incomplete , as if the real story starts just around the corner . \nwhy make a documentary about these marginal historical figures ? wouldn't one about their famous dad , author of death in venice , etc . , be more valuable ? \nthe lower your expectations , the more you'll enjoy it . \nrarely has leukemia looked so shimmering and benign . \n is an arthritic attempt at directing by callie khouri . i had to look away - this was god awful . \neven in this less-than-magic kingdom , reese rules . \nvelocity represents everything wrong with ''independent film'' as a commodified , sold-out concept on the american filmmaking scene . \njust one bad idea after another . \nbecause of an unnecessary and clumsy last scene , 'swimfan' left me with a very bad feeling . \nthough moonlight mile is replete with acclaimed actors and actresses and tackles a subject that's potentially moving , the movie is too predictable and too self-conscious to reach a level of high drama . \na movie that hovers somewhere between an acute character study and a trite power struggle . \ni can't recommend it . but it's surprisingly harmless . \ncorpus collosum -- while undeniably interesting -- wore out its welcome well before the end credits rolled about 45 minutes in . \nthe last 20 minutes are somewhat redeeming , but most of the movie is the same teenage american road-trip drek we've seen before - only this time you have to read the fart jokes\nit's hard to like a film about a guy who is utterly unlikeable , and shiner , starring michael caine as an aging british boxing promoter desperate for a taste of fame and fortune , is certainly that . \na by-the-numbers effort that won't do much to enhance the franchise . \ninvolves two mysteries -- one it gives away and the other featuring such badly drawn characters that its outcome hardly matters . \noverall the film feels like a low-budget tv pilot that could not find a buyer to play it on the tube . \nit's of the quality of a lesser harrison ford movie - six days , seven nights , maybe , or that dreadful sabrina remake . \nit appears that something has been lost in the translation to the screen . \ndespite all evidence to the contrary , this clunker has somehow managed to pose as an actual feature movie , the kind that charges full admission and gets hyped on tv and purports to amuse small children and ostensible adults . \nshrewd but pointless . \nan unclassifiably awful study in self- and audience-abuse . \nsluggish , tonally uneven . \nthis movie is something of an impostor itself , stretching and padding its material in a blur of dead ends and distracting camera work . \nhey arnold ! the movie could have been made 40 years ago , and parents' appreciation of it may depend on whether they consider that a good thing . \nthis one is definitely one to skip , even for horror movie fanatics . \nexcessive , profane , packed with cartoonish violence and comic-strip characters . \nonce the 50 year old benigni appears as the title character , we find ourselves longing for the block of wood to come back . \na working class \" us vs . them \" opera that leaves no heartstring untugged and no liberal cause unplundered . \nif the movie succeeds in instilling a wary sense of 'there but for the grace of god , ' it is far too self-conscious to draw you deeply into its world . \nthere are simply too many ideas floating around -- part farce , part sliding doors , part pop video -- and yet failing to exploit them . \nit takes a strange kind of laziness to waste the talents of robert forster , anne meara , eugene levy , and reginald veljohnson all in the same movie . \nwe haven't seen such hilarity since say it isn't so ! \nexpect the same-old , lame-old slasher nonsense , just with different scenery . \nthe cold turkey would've been a far better title . \nthe idea of 49-year-old roberto benigni playing the wooden boy pinocchio is scary enough . the reality of the new live-action pinocchio he directed , cowrote and starred in borders on the grotesque . \nthe ga-zillionth airhead movie about a wife in distress who resorts to desperate measures . \nzaidan's script has barely enough plot to string the stunts together and not quite enough characterization to keep the faces straight . \ntry as i may , i can't think of a single good reason to see this movie , even though everyone in my group extemporaneously shouted , 'thank you ! ' when leguizamo finally plugged an irritating character late in the movie . \nwhile it's nice to watch a movie that hasn't been focus-grouped into tedium , yu's cinematic alchemy produces nearly as much lead as gold . \nit treats women like idiots . \nthough catch me if you can isn't badly made , the fun slowly leaks out of the movie . \njust an average comedic dateflick but not a waste of time . \na valueless kiddie paean to pro basketball underwritten by the nba . \nimpostor has a handful of thrilling moments and a couple of good performances , but the movie doesn't quite fly . for starters , the story is just too slim . \nso much facile technique , such cute ideas , so little movie . \nthe experience of going to a film festival is a rewarding one ; the experiencing of sampling one through this movie is not . \nthe film takes the materials of human tragedy and dresses them in lovely costumes , southern california locations and star power . \nit has its moments of swaggering camaraderie , but more often just feels generic , derivative and done to death . \nalmost gags on its own gore . \nhow do you spell clich ? \nit's sweet , harmless , dumb , occasionally funny and about as compelling as a fishing show . \nthe moviegoing equivalent of going to a dinner party and being forced to watch the host and hostess's home video of their baby's birth . \nwhile [hill] has learned new tricks , the tricks alone are not enough to salvage this lifeless boxing film . \nin the real world , an actor this uncharismatically beautiful would have a rsum loaded with credits like \" girl in bar #3 . \" \ntoo much of it feels unfocused and underdeveloped . \nunder 15 ? a giggle a minute . over age 15 ? big fat waste of time . \nhey arnold ! the movie is what happens when you blow up small potatoes to 10 times their natural size , and it ain't pretty . \nsometimes seems less like storytelling than something the otherwise compelling director needed to get off his chest . \nthis is not the undisputed worst boxing movie ever , but it's certainly not a champion - the big loser is the audience . \nyou really have to wonder how on earth anyone , anywhere could have thought they'd make audiences guffaw with a script as utterly diabolical as this . \nin the end , we are left with something like two ships passing in the night rather than any insights into gay love , chinese society or the price one pays for being dishonest . \nchokes on its own depiction of upper-crust decorum . \nwell-nigh unendurable . . . though the picture strains to become cinematic poetry , it remains depressingly prosaic and dull . \ni thought my own watch had stopped keeping time as i slogged my way through clockstoppers . \nwhile much of the cast has charm -- especially allodi and nolden -- the performers are sunk by the film's primitive approach to the mechanics of comedy . \nthis directorial debut from music video show-off higuchinsky is all flash . \nyes , ballistic is silly . unfortunately , it's not silly fun unless you enjoy really bad movies . \nthe twist that ends the movie is the one with the most emotional resonance , but twists are getting irritating , and this is the kind of material where the filmmakers should be very careful about raising eyebrows . \nthe longer the movie goes , the worse it gets , but it's actually pretty good in the first few minutes . \nwhile it's genuinely cool to hear characters talk about early rap records ( sugar hill gang , etc . ) , the constant referencing of hip-hop arcana can alienate even the savviest audiences . \nnot only unfunny , but downright repellent . \ncare deftly captures the wonder and menace of growing up , but he never really embraces the joy of fuhrman's destructive escapism or the grace-in-rebellion found by his characters . \nforced , familiar and thoroughly condescending . \ndoes little more than play an innocuous game of fill-in- the-blanks with a tragic past . \nk-19 exploits our substantial collective fear of nuclear holocaust to generate cheap hollywood tension . \nhas a long and clunky ending . . . which forces the audience to fidget through ten pseudo-serious minutes while waiting for the ending credits and the deleted scenes montage to break the audience's awkward silence\na ragbag of promising ideas and failed narrative , of good acting and plain old bad filmmaking . \nwhaley's determination to immerse you in sheer , unrelenting wretchedness is exhausting . \nuncommonly stylish but equally silly . . . the picture fails to generate much suspense , nor does it ask searching enough questions to justify its pretensions . \nthe entire movie is about a boring , sad man being boring and sad . \nthe plot convolutions ultimately add up to nothing more than jerking the audience's chain . \nconfirms the nagging suspicion that ethan hawke would be even worse behind the camera than he is in front of it . \nmade with no discernible craft and monstrously sanctimonious in dealing with childhood loss . \nit's a trifle of a movie , with a few laughs surrounding an unremarkable soft center . \nholden caulfield did it better . \na synthesis of cliches and absurdities that seems positively decadent in its cinematic flash and emptiness . \noh come on . like you couldn't smell this turkey rotting from miles away . \nif it's seldom boring , well , it's also rarely coherent . \nsimplistic fluff-ball of whimsy . \nnot exactly the bees knees\nit does nothing new with the old story , except to show fisticuffs in this sort of stop-go slow motion that makes the gang rumbles look like they're being streamed over a 28k modem . \nthe kind of spectacularly misconceived enterprise that only a sophisticated cinephile could have perpetrated . \nmakes for some truly odd , at times confusing , kids entertainment . . . but at least this time there's some centered storytelling to go along with all the weird stuff . \nthe film contains no good jokes , no good scenes , barely a moment when carvey's saturday night live-honed mimicry rises above the level of embarrassment . \njacquot's rendering of puccini's tale of devotion and double-cross is more than just a filmed opera . in his first stab at the form , jacquot takes a slightly anarchic approach that works only sporadically . \nchabrol has taken promising material for a black comedy and turned it instead into a somber chamber drama . \nit's as if you're watching a movie that was made in 1978 but not released then because it was so weak , and it has been unearthed and released now , when it has become even weaker . \nthis is nothing but familiar territory . \nin execution , this clever idea is far less funny than the original , killers from space . \none of the more irritating cartoons you will see this , or any , year . \na broad , melodramatic estrogen opera that's pretty toxic in its own right . \ntoo slow , too long and too little happens . \nthe film's few ideas are stretched to the point of evaporation ; the whole central section is one big chase that seems to have no goal and no urgency . it's just filler . \nsacrifices the value of its wealth of archival foot-age with its less-than-objective stance . \nutterly lacking in charm , wit and invention , roberto benigni's pinocchio is an astonishingly bad film . \na hamfisted romantic comedy that makes our girl the hapless facilitator of an extended cheap shot across the mason-dixon line . \nscores no points for originality , wit , or intelligence . it's a cookie-cutter movie , a cut-and-paste job . \nthey takes a long time to get to its gasp-inducing ending . \nbarely gets off the ground . \neven on those rare occasions when the narrator stops yammering , miller's hand often feels unsure . \npumpkin means to be an outrageous dark satire on fraternity life , but its ambitions far exceed the abilities of writer adam larson broder and his co-director , tony r . abrams , in their feature debut . \nat its best , queen is campy fun like the vincent price horror classics of the '60s . at its worst , it implodes in a series of very bad special effects . \nfrom the opening scenes , it's clear that all about the benjamins is a totally formulaic movie . \nit takes a certain kind of horror movie to qualify as 'worse than expected , ' but ghost ship somehow manages to do exactly that . \non the bright side , it contains jesse ventura's best work since the xfl . \ndespite impeccable acting . . . and a script that takes some rather unexpected ( even , at times , preposterous ) turns , love is just too , too precious in the end . \na tv style murder mystery with a few big screen moments ( including one that seems to be made for a different film altogether ) . \nby getting myself wrapped up in the visuals and eccentricities of many of the characters , i found myself confused when it came time to get to the heart of the movie . \ntoo often , the viewer isn't reacting to humor so much as they are wincing back in repugnance . \nwhat's next ? the porky's revenge : ultimate edition ? \ndilbert without the right-on satiric humor . \nmanages to show life in all of its banality when the intention is quite the opposite . \ndo not see this film . \nminority report is exactly what the title indicates , a report . \ndelivers the same old same old , tarted up with latin flava and turned out by hollywood playas . \nif you believe any of this , i can make you a real deal on leftover enron stock that will double in value a week from friday . \nto call the other side of heaven \" appalling \" would be to underestimate just how dangerous entertainments like it can be . \nin exactly 89 minutes , most of which passed as slowly as if i'd been sitting naked on an igloo , formula 51 sank from quirky to jerky to utter turkey . \nif only the story about a multi-million dollar con bothered to include the con . \ni'd have to say the star and director are the big problems here . \nwithout the dark spookiness of crystal lake camp , the horror concept completely loses its creepy menace . \nit's like every bad idea that's ever gone into an after-school special compiled in one place , minus those daytime programs' slickness and sophistication ( and who knew they even had any ? ) . \nwhile the resident evil games may have set new standards for thrills , suspense , and gore for video games , the movie really only succeeds in the third of these . \nfor close to two hours the audience is forced to endure three terminally depressed , mostly inarticulate , hyper dysfunctional families for the price of one . \nto my taste , the film's comic characters come perilously close to being amoses and andys for a new generation . \nwhat the director can&#8217 ; t do is make either of val kilmer&#8217 ; s two personas interesting or worth caring about . \nin an effort , i suspect , not to offend by appearing either too serious or too lighthearted , it offends by just being wishy-washy . \nit's difficult to imagine the process that produced such a script , but here's guessing that spray cheese and underarm noises played a crucial role . \nharland williams is so funny in drag he should consider permanent sex-reassignment . \n . . . nothing scary here except for some awful acting and lame special effects . \nit's not that kung pow isn't funny some of the time -- it just isn't any funnier than bad martial arts movies are all by themselves , without all oedekerk's impish augmentation . \na very long movie , dull in stretches , with entirely too much focus on meal preparation and igloo construction . \nnot an objectionable or dull film ; it merely lacks everything except good intentions . \na science-fiction pastiche so lacking in originality that if you stripped away its inspirations there would be precious little left . \nonce [kim] begins to overplay the shock tactics and bait-and-tackle metaphors , you may decide it's too high a price to pay for a shimmering picture postcard . \nthe words , 'frankly , my dear , i don't give a damn , ' have never been more appropriate . \nwhat's next : \" my mother the car ? \" \nall the amped-up tony hawk-style stunts and thrashing rap-metal can't disguise the fact that , really , we've been here , done that . \na sequel that's much too big for its britches . \nso unremittingly awful that labeling it a dog probably constitutes cruelty to canines . \nwhat was once original has been co-opted so frequently that it now seems pedestrian . \na perplexing example of promise unfulfilled , despite many charming moments . \nfor all the writhing and wailing , tears , rage and opium overdoses , there's no sense of actual passion being washed away in love's dissolution . \na coarse and stupid gross-out . \na nightmare date with a half-formed wit done a great disservice by a lack of critical distance and a sad trust in liberal arts college bumper sticker platitudes . \ndoesn't offer much besides glib soullessness , raunchy language and a series of brutal set pieces . . . that raise the bar on stylized screen violence . \nthere's something with potential here , but the movie decides , like lavinia , to go the conservative route . \nit's one pussy-ass world when even killer-thrillers revolve around group therapy sessions . \nthe stripped-down approach does give the film a certain timeless quality , but the measured pace and lack of dramatic inflection can also seem tedious . \nbut the power of these [subjects] is obscured by the majority of the film that shows a stationary camera on a subject that could be mistaken for giving a public oration , rather than contributing to a film's narrative . \nrarely has so much money delivered so little entertainment . \ntries to add some spice to its quirky sentiments but the taste is all too familiar . \npaid in full is so stale , in fact , that its most vibrant scene is one that uses clips from brian de palma's scarface . that's a cheat . \nharrison's flowers puts its heart in the right place , but its brains are in no particular place at all . \nthis re-do is so dumb and so exploitative in its violence that , ironically , it becomes everything that the rather clumsy original was railing against . \na string of rehashed sight gags based in insipid vulgarity . \nthe movie is dawn of the dead crossed with john carpenter's ghosts of mars , with zombies not as ghoulish as the first and trains not as big as the second . \nbasically a static series of semi-improvised ( and semi-coherent ) raps between the stars . \ntoo restrained to be a freak show , too mercenary and obvious to be cerebral , too dull and pretentious to be engaging . . . the isle defies an easy categorization . \nan unpredictable blend of gal-pal smart talk , romantic comedy and dark tragedy that bites off considerably more than writer/director john mckay can swallow . \nit's one of those baseball pictures where the hero is stoic , the wife is patient , the kids are as cute as all get-out and the odds against success are long enough to intimidate , but short enough to make a dream seem possible . \n \" the time machine \" is a movie that has no interest in itself . it doesn't believe in itself , it has no sense of humorit's just plain bored . \n . . . a hollow joke told by a cinematic gymnast having too much fun embellishing the misanthropic tale to actually engage it . \na morose little soap opera about three vapid , insensitive people who take turns hurting each other . it's a feature-length adaptation of one of those \" can this marriage be saved ? \" columns from ladies home journal . . . \nthe film's essentially over by the meet-cute . \ni'm sure if you're a hartley fan , you might enjoy yourself . . . me , i didn't care for it . \nit's about following your dreams , no matter what your parents think . socrates motions for hemlock . \nthe script isn't very good ; not even someone as gifted as hoffman ( the actor ) can make it work . \nwalter hill's pulpy , stylized boxing melodrama undisputed nearly overcomes its questionable in-the-ring match-up with solid fight choreography and gritty prison authenticity . \nit has all the excitement of eating oatmeal . \nit's hard to know whether or not to recommend this film because for every thing it does right there's at least one and occasionally two things it gets ever so wrong . \nalthough there are several truly jolting scares , there's also an abundance of hackneyed dialogue and more silly satanic business than you can shake a severed limb at . \ni'll bet the video game is a lot more fun than the film . \nstar trek : nemesis meekly goes where nearly every star trek movie has gone before . wince-inducing dialogue , thrift-shop costumes , prosthetic makeup by silly putty and kmart blue-light-special effects all conspire to test trekkie loyalty . \nlike all abstract art , the film does not make this statement in an easily accessible way , and -- unless prewarned -- it would be very possible for a reasonably intelligent person to sit through its tidal wave of imagery and not get this vision at all . \ni don't mind having my heartstrings pulled , but don't treat me like a fool . \n . . . although this idea is \" new \" the results are tired . \ni'm guessing the director is a magician . after all , he took three minutes of dialogue , 30 seconds of plot and turned them into a 90-minute movie that feels five hours long . \nan unencouraging threefold expansion on the former mtv series , accompanying the stunt-hungry dimwits in a random series of collected gags , pranks , pratfalls , dares , injuries , etc . \nthe script is a dim-witted pairing of teen-speak and animal gibberish . \nits well of thorn and vinegar ( and simple humanity ) has long been plundered by similar works featuring the insight and punch this picture so conspicuously lacks . \nfor all its impressive craftsmanship , and despite an overbearing series of third-act crescendos , lily chou-chou never really builds up a head of emotional steam . \ndon't be fooled by the impressive cast list - eye see you is pure junk . \nnot since freddy got fingered has a major release been so painful to sit through . \nthe documentary does little , apart from raising the topic , to further stoke the conversation . \nplays like a volatile and overlong w magazine fashion spread . \na better title , for all concerned , might be swept under the rug . \nthis movie seems to have been written using mad-libs . there can be no other explanation . hilariously inept and ridiculous . \nvera's technical prowess ends up selling his film short ; he smoothes over hard truths even as he uncovers them . \n[a] shapeless blob of desperate entertainment . \nfeels too formulaic and too familiar to produce the transgressive thrills of early underground work . \ngeneric thriller junk . teens only . \ngiven how heavy-handed and portent-heavy it is , this could be the worst thing soderbergh has ever done . \na by-the-numbers patient/doctor pic that covers all the usual ground\na dumb movie with dumb characters doing dumb things and you have to be really dumb not to see where this is going . \nstealing harvard aspires to comedic grand larceny but stands convicted of nothing more than petty theft of your time . \npretension , in its own way , is a form of bravery . for this reason and this reason only -- the power of its own steadfast , hoity-toity convictions -- chelsea walls deserves a medal . \nwith the exception of some fleetingly amusing improvisations by cedric the entertainer as perry's boss , there isn't a redeeming moment here . \nit's a grab bag of genres that don't add up to a whole lot of sense . \nmovie fans , get ready to take off . . . the other direction . \n[director] o'fallon manages to put some lovely pictures up on the big screen , but his skill at telling a story -- he also contributed to the screenplay -- falls short . \nthe intent is almost exactly the same [as the full monty] . all that's missing is the spontaneity , originality and delight . \nno one but a convict guilty of some truly heinous crime should have to sit through the master of disguise . \neven the finest chef can't make a hotdog into anything more than a hotdog , and robert de niro can't make this movie anything more than a trashy cop buddy comedy . \nthere's too much falseness to the second half , and what began as an intriguing look at youth fizzles into a dull , ridiculous attempt at heart-tugging . \nit's not without its pleasures , but i'll stick with the tune . \nmiller is playing so free with emotions , and the fact that children are hostages to fortune , that he makes the audience hostage to his swaggering affectation of seriousness . \ndespite the evocative aesthetics evincing the hollow state of modern love life , the film never percolates beyond a monotonous whine . \nmore maudlin than sharp . \nthis is an egotistical endeavor from the daughter of horror director dario argento ( a producer here ) , but her raw performance and utter fearlessness make it strangely magnetic . \nit's slow -- very , very slow . it's not the ultimate depression-era gangster movie . that's pure pr hype . \ncharacters still need to function according to some set of believable and comprehensible impulses , no matter how many drugs they do or how much artistic license avary employs . \ncomes . . . uncomfortably close to coasting in the treads of the bicycle thief . \nvisually rather stunning , but ultimately a handsome-looking bore , the true creativity would have been to hide treasure planet entirely and completely reimagine it . \nstealing harvard is evidence that the farrelly bros . -- peter and bobby -- and their brand of screen comedy are wheezing to an end , along with green's half-hearted movie career . \nthere seems to be no clear path as to where the story's going , or how long it's going to take to get there . \nif you're a wwf fan , or you related to the people who watched the robots getting butchered in a . i . , you'll probably like rollerball . \ni don't think i laughed out loud once . and when you're talking about a slapstick comedy , that's a pretty big problem . \nit's so mediocre , despite the dynamic duo on the marquee , that we just can't get no satisfaction . \nslapstick buffoonery can tickle many a preschooler's fancy , but when it costs a family of four about $40 to see a film in theaters , why spend money on a dog like this when you can rent a pedigree instead ? \n . . . turns so unforgivably trite in its last 10 minutes that anyone without a fortified sweet tooth will likely go into sugar shock . \nthe notion that bombing buildings is the funniest thing in the world goes entirely unexamined in this startlingly unfunny comedy . \nmy reaction in a word : disappointment . his last movie was poetically romantic and full of indelible images , but his latest has nothing going for it . \nit kinda works and qualifies as cool at times , but is just too lame to work or be cool at others . \nsustains its dreamlike glide through a succession of cheesy coincidences and voluptuous cheap effects , not the least of which is rebecca romijn-stamos . \nintriguing documentary which is emotionally diluted by focusing on the story's least interesting subject . \na non-mystery mystery . \nfeels haphazard , as if the writers mistakenly thought they could achieve an air of frantic spontaneity by simply tossing in lots of characters doing silly stuff and stirring the pot . \nwhat an embarrassment . \nfor each chuckle there are at least 10 complete misses , many coming from the amazingly lifelike tara reid , whose acting skills are comparable to a cardboard cutout . \nin its own way , joshua is as blasphemous and nonsensical as a luis buuel film without the latter's attendant intelligence , poetry , passion , and genius . \ni've always dreamed of attending cannes , but after seeing this film , it's not that big a deal . \nthe vintage is pure '87 , with a halfhearted twist on its cautionary message : fatal attraction = don't have an affair with a nutjob ; unfaithful = don't if you're married to one . \na workshop mentality prevails . \nit cannot be enjoyed , even on the level that one enjoys a bad slasher flick , primarily because it is dull . yes , dull . \npumpkin wants to have it both ways . \ndirector uwe boll and the actors provide scant reason to care in this crude '70s throwback . \n[w]hile long on amiable monkeys and worthy environmentalism , jane goodall's wild chimpanzees is short on the thrills the oversize medium demands . \nouter-space buffs might love this film , but others will find its pleasures intermittent . \nthis piece of channel 5 grade trash is , quite frankly , an insult to the intelligence of the true genre enthusiast . \nan occasionally funny , but overall limp , fish-out-of-water story . \na bloated gasbag thesis grotesquely impressed by its own gargantuan aura of self-importance . . . \nit's mighty tedious for the viewer who has to contend with unpleasant characters , hit-and-miss performances and awkwardly staged scenes . \nas a rumor of angels reveals itself to be a sudsy tub of supernatural hokum , not even ms . redgrave's noblest efforts can redeem it from hopeless sentimentality . \nnew best friend's playboy-mansion presentation of college life is laugh-out-loud ludicrous . \nan appalling 'ace ventura' rip-off that somehow manages to bring together kevin pollak , former wrestler chyna and dolly parton . if any of them list this 'credit' on their resumes in the future , that'll be much funnier than anything in the film . . . \nthe humor is forced and heavy-handed , and occasionally simply unpleasant . \nscorsese at his best makes gangster films that are equally lovely but also relentlessly brutal and brutally intelligent ; perdition , meanwhile , reads more like driving miss daisy than goodfellas . \nwhile the script starts promisingly , it loses steam towards the middle and never really develops beyond attacking obvious target . \nas the latest bid in the tv-to-movie franchise game , i spy makes its big-screen entry with little of the nervy originality of its groundbreaking small-screen progenitor . \nthis isn't even madonna's swept away . this is her blue lagoon . \nthe director knows how to apply textural gloss , but his portrait of sex-as-war is strictly sitcom . \n . . . the film suffers from a lack of humor ( something needed to balance out the violence ) . . . \nburns never really harnesses to full effect the energetic cast . \nan overemphatic , would-be wacky , ultimately tedious sex farce . \nhas all the depth of a wading pool . \nthis is the sort of burly action flick where one coincidence pummels another , narrative necessity is a drunken roundhouse , and whatever passes for logic is a factor of the last plot device left standing . \nthe so-inept- it's-surreal dubbing ( featuring the voices of glenn close , regis philbin and breckin meyer ) brings back memories of cheesy old godzilla flicks . \n . . . the movie is just a plain old monster . \nif this disposable tissue has one wild card , it's john turturro , who's simply fab as a spanish butler with a foot fetish . \none long string of cliches . \na noble failure . \nfancy a real downer ? [leigh] lays it on so thick this time that it feels like a suicide race . \nprofessionally speaking , it's tempting to jump ship in january to avoid ridiculous schlock like this shoddy suspense thriller . \nnelson's brutally unsentimental approach . . . sucks the humanity from the film , leaving behind an horrific but weirdly unemotional spectacle . \nweaves a spell over you , with its disturbingly close-up look at damaged psyches and its subtle undercurrents of danger . but its awkward structure keeps breaking the spell . \nat once half-baked and overheated . \nthere's a solid woman- finding-herself story somewhere in here , but you'd have to dig pretty deep to uncover it . \ni still can't relate to stuart : he's a mouse , for cryin' out loud , and all he does is milk it with despondent eyes and whine that nobody treats him human enough . \n[serry] wants to blend politics and drama , an admirable ambition . it's too bad that the helping hand he uses to stir his ingredients is also a heavy one . \nby the miserable standards to which the slasher genre has sunk , . . . actually pretty good . of course , by more objective measurements it's still quite bad . \nthe only entertainment you'll derive from this choppy and sloppy affair will be from unintentional giggles  several of them . \nsam mendes has become valedictorian at the school for soft landings and easy ways out . \nlstima por schwarzenegger , pero es hora de que deje la estafeta a las nuevas generaciones . \nexactly what it claims to be -- a simple diversion for the kids . \nits story may be a thousand years old , but why did it have to seem like it took another thousand to tell it to us ? \nwoefully pretentious . \nthe problem with this film is that it lacks focus . i sympathize with the plight of these families , but the movie doesn't do a very good job conveying the issue at hand . \na momentary escape from the summer heat and the sedentary doldrums that set in at this time of year . \n . . . think of it as american pie on valium . \ndull , lifeless , and amateurishly assembled . \npuportedly \" based on true events , \" a convolution of language that suggests it's impossible to claim that it is \" based on a true story \" with a straight face . \n . . . a plotline that's as lumpy as two-day old porridge . . . the filmmakers' paws , sad to say , were all over this \" un-bear-able \" project ! \n . . . irritating soul-searching garbage . \nit's a bad thing when a movie has about as much substance as its end credits blooper reel . \nwith its dogged hollywood naturalism and the inexorable passage of its characters toward sainthood , windtalkers is nothing but a sticky-sweet soap . \nsome of it is clever , but it is never melodic/\na relative letdown . \nbetter to just call it abc kiarostami . for aids and africa are nothing more than part of the scenery . \nno aspirations to social import inform the movie version . this is a shameless sham , calculated to cash in on the popularity of its stars . \nmanages to be somewhat well-acted , not badly art-directed and utterly unengaging no matter how hard it tries to be thrilling , touching or , yikes , uproarious . \nwarmed-over hash . \nthis rather superficial arthouse middle-brow film knows how to please a crowd , and that's about all it does well . \nit's clear the filmmakers weren't sure where they wanted their story to go , and even more clear that they lack the skills to get us to this undetermined destination . \nas vulgar as it is banal . \na puzzling experience . \nyou wonder why enough wasn't just a music video rather than a full-length movie . \nthe film's tone and pacing are off almost from the get-go . \nthe talented and clever robert rodriguez perhaps put a little too much heart into his first film and didn't reserve enough for his second . \nmore whiny downer than corruscating commentary . \ntambor and clayburgh make an appealing couple  he's understated and sardonic , she's appealingly manic and energetic . both deserve better . \nsuffocated by its fussy script and uptight characters , this musty adaptation is all the more annoying since it's been packaged and sold back to us by hollywood . \ncoughs and sputters on its own postmodern conceit . \na wildly inconsistent emotional experience . \nsit through this one , and you won't need a magic watch to stop time ; your dvd player will do it for you . \na sometimes tedious film . \nteen movies have really hit the skids . \nthere are plot holes big enough for shamu the killer whale to swim through . \n . . . plays like somebody spliced random moments of a chris rock routine into what is otherwise a cliche-riddled but self-serious spy thriller . \nnasty , ugly , pointless and depressing , even if you hate clowns . \nwhat is 100% missing here is a script of even the most elemental literacy , an inkling of genuine wit , and anything resembling acting . \ndoes paint some memorable images . . . , but makhmalbaf keeps her distance from the characters\nit uses the pain and violence of war as background material for color . \njust not campy enough\nthe movie , directed by mick jackson , leaves no cliche unturned , from the predictable plot to the characters straight out of central casting . \nit's everything you don't go to the movies for . \nlike watching a dress rehearsal the week before the show goes up : everything's in place but something's just a little off-kilter . \nthe affectionate loopiness that once seemed congenital to demme's perspective has a tough time emerging from between the badly dated cutesy-pie mystery scenario and the newfangled hollywood post-production effects . \nfor all its technical virtuosity , the film is so mired in juvenile and near-xenophobic pedagogy that it's enough to make one pine for the day when godard can no longer handle the rigors of filmmaking . \namerican chai encourages rueful laughter at stereotypes only an indian-american would recognize . and the lesson , in the end , is nothing new . \nit made me want to wrench my eyes out of my head and toss them at the screen . \ndue to some script weaknesses and the casting of the director's brother , the film trails off into inconsequentiality . \n . . . plot holes so large and obvious a marching band might as well be stomping through them in clown clothes , playing a college football fight song on untuned instruments . \nso devoid of any kind of intelligible story that it makes films like xxx and collateral damage seem like thoughtful treatises\ncombining quick-cut editing and a blaring heavy metal much of the time , beck seems to be under the illusion that he's shooting the latest system of a down video . \ndragonfly has no atmosphere , no tension -- nothing but costner , flailing away . it's a buggy drag . \nworks hard to establish rounded characters , but then has nothing fresh or particularly interesting to say about them . \nthe action switches between past and present , but the material link is too tenuous to anchor the emotional connections that purport to span a 125-year divide . \nnonsensical , dull \" cyber-horror \" flick is a grim , hollow exercise in flat scares and bad acting . \ninstead of hiding pinocchio from critics , miramax should have hidden it from everyone . \nmanages to be both repulsively sadistic and mundane . \na great ensemble cast can't lift this heartfelt enterprise out of the familiar . \nthere ought to be a directing license , so that ed burns can have his revoked . \nthe structure the film takes may find matt damon and ben affleck once again looking for residuals as this officially completes a good will hunting trilogy that was never planned . \nwhereas last year's exemplary sexy beast seemed to revitalize the british gangster movie , this equally brutal outing merely sustains it . \n . . . a boring parade of talking heads and technical gibberish that will do little to advance the linux cause . \ngreen might want to hang onto that ski mask , as robbery may be the only way to pay for his next project . \ni can take infantile humor . . . but this is the sort of infantile that makes you wonder about changing the director and writer's diapers . \nthere isn't nearly enough fun here , despite the presence of some appealing ingredients . \nthe tale of tok ( andy lau ) , a sleek sociopath on the trail of o ( takashi sorimachi ) , the most legendary of asian hitmen , is too scattershot to take hold . \ndirected in a paint-by-numbers manner . \na cheerful enough but imminently forgettable rip-off of [besson's] earlier work . \na lackluster , unessential sequel to the classic disney adaptation of j . m . barrie's peter pan . \nsamira makhmalbaf's new film blackboards is much like the ethos of a stream of consciousness , although , it's unfortunate for the viewer that the thoughts and reflections coming through are torpid and banal\n . . . routine , harmless diversion and little else . \nlate marriage's stiffness is unlikely to demonstrate the emotional clout to sweep u . s . viewers off their feet . \nthis time mr . burns is trying something in the martin scorsese street-realist mode , but his self-regarding sentimentality trips him up again . \nwhile there's something intrinsically funny about sir anthony hopkins saying 'get in the car , bitch , ' this jerry bruckheimer production has little else to offer\nit's hampered by a lifetime-channel kind of plot and a lead actress who is out of her depth . \nlet's hope -- shall we ? -- that the 'true story' by which all the queen's men is allegedly \" inspired \" was a lot funnier and more deftly enacted than what's been cobbled together onscreen . \nis there a group of more self-absorbed women than the mother and daughters featured in this film ? i don't think so . nothing wrong with performances here , but the whiney characters bugged me . \nthere is very little dread or apprehension , and though i like the creepy ideas , they are not executed with anything more than perfunctory skill . \nif you've ever entertained the notion of doing what the title of this film implies , what sex with strangers actually shows may put you off the idea forever . \nin the end , the movie collapses on its shaky foundation despite the best efforts of director joe carnahan . \nadults will wish the movie were less simplistic , obvious , clumsily plotted and shallowly characterized . but what are adults doing in the theater at all ? \nsticky sweet sentimentality , clumsy plotting and a rosily myopic view of life in the wwii-era mississippi delta undermine this adaptation . \nit's another stale , kill-by-numbers flick , complete with blade-thin characters and terrible , pun-laden dialogue . \nevery time you look , sweet home alabama is taking another bummer of a wrong turn . \npartway through watching this saccharine , easter-egg-colored concoction , you realize that it is made up of three episodes of a rejected tv show . \nthe overall effect is less like a children's movie than a recruitment film for future hollywood sellouts . \nportentous and pretentious , the weight of water is appropriately titled , given the heavy-handedness of it drama . \na fitfully amusing romp that , if nothing else , will appeal to fans of malcolm in the middle and its pubescent star , frankie muniz . \njason x is positively anti-darwinian : nine sequels and 400 years later , the teens are none the wiser and jason still kills on auto-pilot . \nto say this was done better in wilder's some like it hot is like saying the sun rises in the east . \nat the very least , if you don't know anything about derrida when you walk into the theater , you won't know much more when you leave . \nthe actors are appealing , but elysian fields is idiotic and absurdly sentimental . \nas 'chick flicks' go , this one is pretty miserable , resorting to string-pulling rather than legitimate character development and intelligent plotting . \nthe only excitement comes when the credits finally roll and you get to leave the theater . \nthere's no emotional pulse to solaris . with an emotional sterility to match its outer space setting , soderbergh's spectacular swing for the fence yields only a spectacular whiff . \nit can't decide if it wants to be a mystery/thriller , a romance or a comedy . \ndenis o'neill's script avoids the prime sports cliche , a last-second goal to win the championship , but it neglects few others . \nthe character of zigzag is not sufficiently developed to support a film constructed around him . \none of those pictures whose promising , if rather precious , premise is undercut by amateurish execution . \nserving sara doesn't serve up a whole lot of laughs . \nthe most hopelessly monotonous film of the year , noteworthy only for the gimmick of being filmed as a single unbroken 87-minute take . \nwith virtually no interesting elements for an audience to focus on , chelsea walls is a triple-espresso endurance challenge . \nstale , futile scenario . \ndeadeningly dull , mired in convoluted melodrama , nonsensical jargon and stiff-upper-lip laboriousness . \na misogynistic piece of filth that attempts to pass itself off as hip , young adult entertainment . \nlike the chelsea's denizens . . . burdette's collage-form scenario tends to over-romanticize the spiritual desolation of the struggling artiste . \nit's basically an overlong episode of tales from the crypt . \nthe film makes a fatal mistake : it asks us to care about a young man whose only apparent virtue is that he is not quite as unpleasant as some of the people in his life . \nanother in-your-face wallow in the lower depths made by people who have never sung those blues . \nshaky close-ups of turkey-on-rolls , stubbly chins , liver spots , red noses and the filmmakers new bobbed do draw easy chuckles but lead nowhere . \ni can't quite recommend it -- it's too patched together -- but i almost can ; it's the kind of movie that makes you want to like it . \ncomplete lack of originality , cleverness or even visible effort\nthough perry and hurley make inspiring efforts to breathe life into the disjointed , haphazard script by jay scherick and david ronn , neither the actors nor director reginald hudlin can make it more than fitfully entertaining . \nsomething akin to a japanese alice through the looking glass , except that it seems to take itself far more seriously . \nit takes talent to make a lifeless movie about the most heinous man who ever lived . \non the whole , the movie lacks wit , feeling and believability to compensate for its incessant coarseness and banality . \nthe story and the friendship proceeds in such a way that you're watching a soap opera rather than a chronicle of the ups and downs that accompany lifelong friendships . \noffers very little genuine romance and even fewer laughs . . . a sad sitcom of a movie , largely devoid of charm . \nmakes for a pretty unpleasant viewing experience . \nthe movie fails to live up to the sum of its parts . \nalthough huppert's intensity and focus has a raw exhilaration about it , the piano teacher is anything but fun . \nit showcases carvey's talent for voices , but not nearly enough and not without taxing every drop of one's patience to get to the good stuff . \nbad . very bad . stultifyingly , dumbfoundingly , mind-numbingly bad . \nmay reawaken discussion of the kennedy assassination but this fictional film looks made for cable rather than for the big screen . \nif looking for a thrilling sci-fi cinematic ride , don't settle for this imposter . \nnot really bad so much as distasteful : we need kidnapping suspense dramas right now like we need doomsday thrillers . \nthe result is a gaudy bag of stale candy , something from a halloween that died . \ndavis . . . is so enamored of her own creation that she can't see how insufferable the character is . \nthe man from elysian fields is a cold , bliss-less work that groans along thinking itself some important comment on how life throws us some beguiling curves . \nthe messages of compassion and mercy are clearly , squarely and specifically expounded via computer animated old testament tale of jonah and the whale . determined to be fun , and bouncy , with energetic musicals , the humor didn't quite engage this adult . \nhistorical dramas fused with love triangle is a well worn conceit . but this films lacks the passion required to sell the material . \nlong time dead ? not nearly long enough . \nnothing more substantial than a fitfully clever doodle . \na solid film . . . but more conscientious than it is truly stirring . \nthere's not enough here to justify the almost two hours . \nthe x potion gives the quickly named blossom , bubbles and buttercup supernatural powers that include extraordinary strength and laser-beam eyes , which unfortunately don't enable them to discern flimsy screenplays . \nperceptive in its vision of nascent industrialized world politics as a new art form , but far too clunky , didactic and saddled with scenes that seem simply an ill fit for this movie . \nverbinski implements every hack-artist trick to give us the ooky-spookies . \nmcconaughey's fun to watch , the dragons are okay , not much fire in the script . \nan unwise amalgam of broadcast news and vibes . \nskins has a right to yawp , and we have a right to our grains of salt . \nwho needs love like this ? \nhit and miss as far as the comedy goes and a big ole' miss in the way of story . \nreturning aggressively to his formula of dimwitted comedy and even dimmer characters , sandler , who also executive produces , has made a film that makes previous vehicles look smart and sassy . \nexists then as an occasionally insightful acting exercise . \ntrite , banal , cliched , mostly inoffensive . \nmattei is tiresomely grave and long-winded , as if circularity itself indicated profundity . \nit's not original , and , robbed of the element of surprise , it doesn't have any huge laughs in its story of irresponsible cops who love to play pranks . \nwhenever its story isn't bogged down by idiocy involving the cia and a lost u . s . satellite , hunter -- starring irwin and his american wife/colleague , terri -- is a movie children should enjoy . \nit offers little beyond the momentary joys of pretty and weightless intellectual entertainment . \na sequence of ridiculous shoot-'em-up scenes . \nnothing in waking up in reno ever inspired me to think of its inhabitants as anything more than markers in a screenplay . \ni'm just too bored to care . \nirwin is a man with enough charisma and audacity to carry a dozen films , but this particular result is ultimately held back from being something greater . \nnot a stereotype is omitted nor a clich left unsaid . \nas befits its title , this pg-13-rated piffle is ultimately as threatening as the snuggle fabric softener bear . \nattempts by this ensemble film to impart a message are so heavy-handed that they instead pummel the audience . \nit all feels like a monty python sketch gone horribly wrong . \nnervous breakdowns are not entertaining . \nscorsese doesn't give us a character worth giving a damn about . \na beautifully made piece of unwatchable drivel . \nlike being trapped at a perpetual frat party . . . how can something so gross be so boring ? \nthis is so bad . \neven film silliness needs a little gravity , beyond good hair and humping . \ni felt sad for lise not so much because of what happens as because she was captured by this movie when she obviously belongs in something lighter and sunnier , by rohmer , for example . \nprurient playthings aside , there's little to love about this english trifle . \nthis is a train wreck of an action film -- a stupefying attempt by the filmmakers to force-feed james bond into the mindless xxx mold and throw 40 years of cinematic history down the toilet in favor of bright flashes and loud bangs . \nthe film flat lines when it should peak and is more missed opportunity and trifle than dark , decadent truffle . \nit's played in the most straight-faced fashion , with little humor to lighten things up . the heavy-handed film is almost laughable as a consequence . \nvan wilder brings a whole new meaning to the phrase 'comedy gag . ' at least one scene is so disgusting that viewers may be hard pressed to retain their lunch . \na disappointment for those who love alternate versions of the bard , particularly ones that involve deep fryers and hamburgers . \nthe film tries too hard to be funny and tries too hard to be hip . the end result is a film that's neither . \nevery nanosecond of the the new guy reminds you that you could be doing something else far more pleasurable . something like scrubbing the toilet . or emptying rat traps . or doing last year's taxes with your ex-wife . \nscooby dooby doo / and shaggy too / you both look and sound great . / but daphne , you're too buff / fred thinks he's tough / and velma - wow , you've lost weight ! \nis the time really ripe for a warmed-over james bond adventure , with a village idiot as the 007 clone ? \nthere's enough melodrama in this magnolia primavera to make pta proud yet director muccino's characters are less worthy of puccini than they are of daytime television . \nhowever it may please those who love movies that blare with pop songs , young science fiction fans will stomp away in disgust . \nthe humor isn't as sharp , the effects not as innovative , nor the story as imaginative as in the original . but it could have been worse . \nsome of their jokes work , but most fail miserably and in the end , pumpkin is far more offensive than it is funny . \neven horror fans will most likely not find what they're seeking with trouble every day ; the movie lacks both thrills and humor . \ncomes off like a rejected abc afterschool special , freshened up by the dunce of a screenwriting 101 class . . . . designed to provide a mix of smiles and tears , \" crossroads \" instead provokes a handful of unintentional howlers and numerous yawns . \nit seems to me the film is about the art of ripping people off without ever letting them consciously know you have done so\nit's just disappointingly superficial -- a movie that has all the elements necessary to be a fascinating , involving character study , but never does more than scratch the surface . \nthe title not only describes its main characters , but the lazy people behind the camera as well . \nsometimes it feels as if it might have been made in the '70s or '80s , and starred chevy chase and goldie hawn . \nschaeffer has to find some hook on which to hang his persistently useless movies , and it might as well be the resuscitation of the middle-aged character . \ndemands too much of most viewers . \nthe story drifts so inexorably into cliches about tortured ( and torturing ) artists and consuming but impossible love that you can't help but become more disappointed as each overwrought new sequence plods on . \nit should be mentioned that the set design and interiors of the haunted vessel are more than effectively creepy and moodily lit . so i just did . \nshamelessly sappy and , worse , runs away from its own provocative theme . \naggravating and tedious . \nthe ring just left me cold and wet like i was out in the seattle drizzle without rainwear . \nthe film seems a dead weight . the lack of pace kills it , although , in a movie about cancer , this might be apt . \nfor anyone who grew up on disney's 1950 treasure island , or remembers the 1934 victor fleming classic , this one feels like an impostor . \na clutchy , indulgent and pretentious travelogue and diatribe against . . . well , just stuff . watching scarlet diva , one is poised for titillation , raw insight or both . instead , we just get messy anger , a movie as personal therapy . \nmeandering , sub-aquatic mess : it's so bad it's good , but only if you slide in on a freebie . \nthe ending is a cop-out . what happens to john q ? i don't have an i am sam clue . \nhas the feel of an unedited personal journal . \nremember when bond had more glamour than clamor ? no more . \ncry havoc and let slip the dogs of cheese , indeed . \nthis charmless nonsense ensues amid clanging film references that make jay and silent bob's excellent adventure seem understated . \nit doesn't quite deserve the gong , but there are more fascinating acts than \" confessions of a dangerous mind . \" \nthe subject of swinging still seems ripe for a documentary -- just not this one . \n . . . hudlin is stuck trying to light a fire with soggy leaves . \nunlike his directorial efforts , la femme nikita and the professional , the transporter lacks besson's perspective as a storyteller . \nthe overall effect is so completely inane that one would have to be mighty bored to even think of staying with this for more than , say , ten . . . make that three minutes . \nlacks depth . \nmost of the supporting characters in eastwood films are weak , as are most of the subplots . this one's weaker than most . \nthere's no real reason to see it , and no real reason not to . \naudiences will find no mention of political prisoners or persecutions that might paint the castro regime in less than saintly tones . \nunder-rehearsed and lifeless\nthe film takes too long getting to the good stuff , then takes too long figuring out what to do next . \nyou can practically smell the patchouli oil . \nto say analyze that is de niro's best film since meet the parents sums up the sad state of his recent career . \nthe actors don't inhabit their roles -- they're trapped by them , forced to change behavior in bizarre unjustified fashion and spout dialog that consists mostly of platitudes . \nan often-deadly boring , strange reading of a classic whose witty dialogue is treated with a baffling casual approach\nthis film was made to get laughs from the slowest person in the audience -- just pure slapstick with lots of inane , inoffensive screaming and exaggerated facial expressions . \nconsists of a plot and jokes done too often by people far more talented than ali g\nanother week , another gross-out college comedy--ugh . \nlaughably , irredeemably awful . \nmoderately involving despite bargain-basement photography and hackneyed romance . \nthere is no insight into the anguish of heidi's life -- only a depiction of pain , today's version of greek tragedy , the talk-show guest decrying her fate . \nthe editing is chaotic , the photography grainy and badly focused , the writing unintentionally hilarious , the direction unfocused , the performances as wooden . \nwhen [de palma's] bad , he's really bad , and femme fatale ranks with the worst he has done . \ntadpole is emblematic of the witless ageism afflicting films : young is cool , and too young is too cool . \ni doubt anyone will remember the picture by the time christmas really rolls around , but maybe it'll be on video by then . \nuncertain in tone . . . a garbled exercise in sexual politics , a junior varsity short cuts by way of very bad things . \nall's well that ends well , and rest assured , the consciousness-raising lessons are cloaked in gross-out gags . \nthe only thing worse than your substandard , run-of-the-mill hollywood picture is an angst-ridden attempt to be profound . \nif you think that jennifer lopez has shown poor judgment in planning to marry ben affleck , wait till you see maid in manhattan . \nstars matthew perry and elizabeth hurley illicit more than a chuckle , and more jokes land than crash , but ultimately serving sara doesn't distinguish itself from the herd . \nit's best to avoid imprisonment with the dull , nerdy folks that inhabit cherish . \nculkin exudes none of the charm or charisma that might keep a more general audience even vaguely interested in his bratty character . \nin the end , ted bundy's only justification is the director's common but unexplored fascination with the frustrated maniac ; there's no larger point , and little social context . \n . . . ( like ) channel surfing between the discovery channel and a late-night made-for-cable action movie . \na movie that , rather than skip along the seine , more or less slogs its way through soggy paris , tongue uncomfortably in cheek . \nshot perhaps 'artistically' with handheld cameras and apparently no movie lights by joaquin baca-asay , the low-budget production swings annoyingly between vertigo and opacity . \nimagine a really bad community theater production of west side story without the songs . \nsoul is what's lacking in every character in this movie and , subsequently , the movie itself . \na one-trick pony whose few t&a bits still can't save itself from being unoriginal , unfunny and unrecommendable . \nthe worst kind of independent ; the one where actors play dress down hicks and ponderously mope around trying to strike lightning as captured by their 1970s predecessors\nit may be a prize winner , but teacher is a bomb . \nthe production values are up there . the use of cgi and digital ink-and-paint make the thing look really slick . the voices are fine as well . the problem , it is with most of these things , is the script . \nit's got its heart in the right place , but it also wilts after awhile . \nproves that a movie about goodness is not the same thing as a good movie . \nwell , it does go on forever . \nthis overproduced and generally disappointing effort isn't likely to rouse the rush hour crowd . \ntopkapi this is not . \nif shayamalan wanted to tell a story about a man who loses his faith , why didn't he just do it , instead of using bad sci-fi as window dressing ? \nethan hawke has always fancied himself the bastard child of the beatnik generation and it's all over his chelsea walls . \nequal parts bodice-ripper and plodding costume drama . \ni'm not suggesting that you actually see it , unless you're the kind of person who has seen every wim wenders film of the '70s . \nwhile the film misfires at every level , the biggest downside is the paucity of laughter in what's supposed to be a comedy . \nif you liked the 1982 film then , you'll still like it now . \na 93-minute condensation of a 26-episode tv series , with all of the pitfalls of such you'd expect . \nguillen rarely gets beneath the surface of things . she lists ingredients , but never mixes and stirs . \naudiences can be expected to suspend their disbelief only so far -- and that does not include the 5 o'clock shadow on the tall wooden kid as he skips off to school . \nto imagine the life of harry potter as a martial arts adventure told by a lobotomized woody allen is to have some idea of the fate that lies in store for moviegoers lured to the mediocrity that is kung pow : enter the fist . \nit delivers some chills and sustained unease , but flounders in its quest for deeper meaning . \ncredibility levels are low and character development a non-starter . \ni would have preferred a transfer down the hall to mr . holland's class for the music , or to robin williams's lecture so i could listen to a teacher with humor , passion , and verve . \nfor the most part , the ingredients are there . but an unwillingness to explore beyond the surfaces of her characters prevents nettelbeck's film from coming together . \nan earnest , heartrending look at the divide between religious fundamentalists and their gay relatives . it's also heavy-handed and devotes too much time to bigoted views . \na mawkish , implausible platonic romance that makes chaplin's city lights seem dispassionate by comparison . \nyes , one enjoys seeing joan grow from awkward young woman to strong , determined monarch , but her love for the philandering philip only diminishes her stature . \nit's a film that hinges on its casting , and glover really doesn't fit the part . \nthis is a throwaway , junk-food movie whose rap soundtrack was better tended to than the film itself . \n . . . with the candy-like taste of it fading faster than 25-cent bubble gum , i realized this is a throwaway movie that won't stand the test of time . it's a trifle . \nliterally nothing in the pool is new , but if you grew up on the stalker flicks of the 1980's this one should appease you for 90 minutes . \narguably the year's silliest and most incoherent movie . \nanyway , for one reason or another , crush turns into a dire drama partway through . after that , it just gets stupid and maudlin . too bad , but thanks to some lovely comedic moments and several fine performances , it's not a total loss . \nchao was chen kaige's assistant for years in china . he has not learnt that storytelling is what the movies are about . \na mixed bag of a comedy that can't really be described as out of this world . \nunwieldy contraption . \nthe film is a travesty of the genre and even as spoof takes itself too seriously . \nmarries the amateurishness of the blair witch project with the illogic of series 7 : the contenders to create a completely crass and forgettable movie . \nthe piano teacher is the sort of movie that discourages american audiences from ever wanting to see another foreign film . \nif it's another regurgitated action movie you're after , there's no better film than half past dead . \nso what is the point ? lovingly choreographed bloodshed taking place in a pristine movie neverland , basically . \nthis is junk food cinema at its greasiest . \nwhen it's all wet , blue crush is highly enjoyable . when it's on dry land , though , this surfer-girl melodrama starts gasping like a beached grouper . \nmost new movies have a bright sheen . some , like ballistic , arrive stillborn . . . looking like the beaten , well-worn video box cover of seven years into the future . \nthe story is naturally poignant , but first-time screenwriter paul pender overloads it with sugary bits of business . \nyou see robert de niro singing - and dancing to - west side story show tunes . choose your reaction : a . ) that sure is funny ! b . ) that sure is pathetic ! \na sermonizing and lifeless paean to teenage dullards . \nthis dramatically shaky contest of wills only reiterates the old hollywood saw : evil is interesting and good is boring . \nbefore long , the film starts playing like general hospital crossed with a saturday night live spoof of dog day afternoon . \nthe charms of willful eccentricity , at least as evidenced by this latest cinematic essay , are beginning to wear a bit thin . \ninstead of accurately accounting a terrible true story , the film's more determined to become the next texas chainsaw massacre . but what about the countless other people who'd merely like to watch a solid tale about a universally interesting soul ? \na silly , self-indulgent film about a silly , self-indulgent filmmaker . \nscarlet diva has a voyeuristic tug , but all in all it's a lot less sensational than it wants to be . \nthe character is too forced and overwritten to be funny or believable much of the time , and clayburgh doesn't always improve the over-the-top mix . \nflashy , pretentious and as impenetrable as morvern's thick , working-class scottish accent . \na battle between bug-eye theatre and dead-eye matinee . \nthe movie is virtually without context -- journalistic or historical . what's worse is that pelosi knows it . \n . . . instead go rent \" shakes the clown \" , a much funnier film with a similar theme and an equally great robin williams performance . \nlame , haphazard teen comedy . \nit's the kind of movie that ends up festooning u . s . art house screens for no reason other than the fact that it's in french ( well , mostly ) with english subtitles and is magically 'significant' because of that . \nthis miserable excuse of a movie runs on empty , believing flatbush machismo will get it through . \nexpect to be reminded of other , better films , especially seven , which director william malone slavishly copies . \nnair stuffs the film with dancing , henna , ornamentation , and group song , but her narrative clichs and telegraphed episodes smell of old soap opera . \nit's getting harder and harder to ignore the fact that hollywood isn't laughing with us , folks . it's laughing at us . \nmight have been better off as a documentary , with less of mr . eyre's uninspired dramatics and more of his sense of observation and outrage . \nevery good actor needs to do his or her own hamlet . for benigni it wasn't shakespeare whom he wanted to define his career with but pinocchio . it might as well have been problem child iv . \narnold's jump from little screen to big will leave frowns on more than a few faces . \nboth awful and appealing . \nthe lack of opposing viewpoints soon grows tiresome -- the film feels more like a series of toasts at a testimonial dinner than a documentary . \nnothing plot-wise is worth e-mailing home about . \nwe are left with a superficial snapshot that , however engaging , is insufficiently enlightening and inviting . \nhelmer devito . . . attempts to do too many things in this story about ethics , payola , vice , murder , kids' tv and revenge . \nit wouldn't be my preferred way of spending 100 minutes or $7 . 00 . \ni hated every minute of it . \n[t]hose same extremes prevent us from taking its message seriously , and the stepford wives mentality doesn't work in a modern context . \nobvious politics and rudimentary animation reduce the chances that the appeal of hey arnold ! the movie will reach far beyond its core demographic . \nthere's no mistaking the fact that this hybrid misses the impact of the disney classic , and even that of the excellent 1934 mgm version . \na simple , sometimes maddeningly slow film that has just enough charm and good acting to make it interesting , but is ultimately pulled under by the pacing and lack of creativity within . \nroger michell , who did an appealing job directing persuasion and notting hill in england , gets too artsy in his american debut . \nthere is an almost poignant dimension to the way that every major stunt seagal's character . . . performs is shot from behind , as if it could fool us into thinking that we're not watching a double . \nanthony hopkins ? big deal ! we've already seen the prequel to the silence of the lambs and hannibal -- and it was better the first time . \nostensibly celebrates middle-aged girl power , even as it presents friendship between women as pathetic , dysfunctional and destructive . \nif this is an example of the type of project that robert redford's lab is willing to lend its imprimatur to , then perhaps it's time to rethink independent films . \npumpkin sits in a patch somewhere between mirthless todd solondzian satire and callow student film . \nnot so much funny as aggressively sitcom-cute , it's full of throwaway one-liners , not-quite jokes , and a determined tv amiability that allen personifies . \nit's not that waiting for happiness is a bad film , because it isn't . it's just incredibly dull . \nthe sad thing about knockaround guys is its lame aspiration for grasping the coolness vibes when in fact the film isn't as flippant or slick as it thinks it is . \na cumbersome and cliche-ridden movie greased with every emotional device known to man . \ndirector ferzan ozpetek creates an interesting dynamic with the members of this group , who live in the same apartment building . but he loses his focus when he concentrates on any single person . \negoyan's movie is too complicated to sustain involvement , and , if you'll excuse a little critical heresy , too intellectually ambitious . \nit wants to be thought of as a subversive little indie film , but it has all the qualities of a modern situation comedy . \ndespite apparent motives to the contrary , it ends up being , like [seinfeld's] revered tv show , about pretty much nothing . \nshadyac shoots his film like an m . night shyamalan movie , and he frequently maintains the same snail's pace ; he just forgot to add any genuine tension . \nplays less like a coming-of-age romance than an infomercial . \nthere are a few laughs and clever sight gags scattered about , but not enough to make this anything more than another big-budget bust . \nthe story's so preposterous that i didn't believe it for a second , despite the best efforts of everyone involved . \ni've heard that the fans of the first men in black have come away hating the second one . i wonder why . they felt like the same movie to me . \ndespite her relentless vim and winsome facial symmetry , witherspoon is just too dialed-up to be america's sweetheart . \nan ultra-low-budget indie debut that smacks more of good intentions than talent . \nbirot is a competent enough filmmaker , but her story has nothing fresh or very exciting about it . \nde niro and mcdormand give solid performances , but their screen time is sabotaged by the story's inability to create interest . \neven those of a single digit age will be able to recognize that this story is too goofy . . . even for disney . \nthat is essentially what's missing from blackboards -- the sense of something bigger , some ultimate point . \na compendium of solondz's own worst instincts in under 90 minutes . \nif the title is a jeopardy question , then the answer might be \" how does steven seagal come across these days ? \" or maybe \" how will you feel after an 88-minute rip-off of the rock with action confined to slo-mo gun firing and random glass-shattering ? \nit is a comedy that's not very funny and an action movie that is not very thrilling ( and an uneasy alliance , at that ) . \nthe story is familiar from its many predecessors ; like them , it eventually culminates in the not-exactly -stunning insight that crime doesn't pay . \nyou'll have more fun setting fire to yourself in the parking lot . you'll be more entertained getting hit by a bus . \ndissing a bond movie is quite like calling a dog stupid , but when it has the temerity to run over two hours , you feel like winding up with a kick . \nritchie's treatment of the class reversal is majorly ham-fisted , from the repetitive manifestos that keep getting thrown in people's faces to the fact amber is such a joke . \nflat , but with a revelatory performance by michelle williams . \nlittle more than a frothy vanity project . \nthe film goes from being an unusual sci-fi character study to a chase flick that detracts from its ending . \nverbinski substitutes atmosphere for action , tedium for thrills . \nfor all its surface frenzy , high crimes should be charged with loitering -- so much on view , so little to offer . \nthe sum of all fears is almost impossible to follow -- and there's something cringe-inducing about seeing an american football stadium nuked as pop entertainment . \nalex nohe's documentary plays like a travelogue for what mostly resembles a real-life , big-budget nc-17 version of tank girl . \nthe title trapped turns out to be a pretty fair description of how you feel while you're watching this ultra-manipulative thriller . \nthe appeal of the vulgar , sexist , racist humour went over my head or -- considering just how low brow it is -- perhaps it snuck under my feet . \nthe story really has no place to go since simone is not realshe can't provide any conflict . \nihops don't pile on this much syrup . \nfor the most part , i spy was an amusing lark that will probably rank as one of murphy's better performances in one of his lesser-praised movies . \nfocuses on joan's raging hormones and sledgehammers the audience with spanish inquisitions about her \" madness \" so much that i became mad that i wasted 123 minutes and $9 . 50 on this 21st century torture device . \nthis series should have died long ago , but they keep bringing it back another day as punishment for paying money to see the last james bond movie . \na bit of an unwieldy mess . \nwith a story as bizarre and mysterious as this , you don't want to be worrying about whether the ineffectual broomfield is going to have the courage to knock on that door . \nthe filmmakers juggle and juxtapose three story lines but fail to come up with one cogent point , unless it's that life stinks , especially for sensitive married women who really love other women . \nthe movie feels like it's going to be great , and it carries on feeling that way for a long time , but takeoff just never happens . \na gimmick in search of a movie : how to get carvey into as many silly costumes and deliver as many silly voices as possible , plot mechanics be damned . \n . . . the last time i saw a theater full of people constantly checking their watches was during my sats . \n . . . fifty minutes of tedious adolescent melodramatics followed by thirty-five minutes of inflated nonsense . \n . . . lacks the punch and verve needed to make this genre soar . \nit's often faintly amusing , but the problems of the characters never become important to us , and the story never takes hold . \nit's tough , astringent , darkly funny and . . . well , it's also generic , untidy , condescending and mild of impact rather than stunning . \nlargely a for-fans artifact . \nthere's no denying the elaborateness of the artist's conceptions , nor his ability to depict them with outrageous elan , but really the whole series is so much pretentious nonsense , lavishly praised by those who equate obscurity with profundity . \ncharacters wander into predictably treacherous situations even though they should know better . \nthere's plenty of style in guillermo del toro's sequel to the 1998 hit but why do we need 117 minutes to tell a tale that simply can't sustain more than 90 minutes . \n[i]f you've been to more than one indie flick in your life , chances are you've already seen this kind of thing . \nfirst-time director joo pedro rodrigues' unwillingness to define his hero's background or motivations becomes more and more frustrating as the film goes on . \nno reason for anyone to invest their hard-earned bucks into a movie which obviously didn't invest much into itself either . \na strong first quarter , slightly less so second quarter , and average second half . \na boring , wincingly cute and nauseatingly politically correct cartoon guaranteed to drive anyone much over age 4 screaming from the theater . \nthe vampire thriller blade ii starts off as a wild hoot and then sucks the blood out of its fun  toward the end , you can feel your veins cringing from the workout . \nwhen the first few villians are introduced as \" spider \" and \" snake \" you know you're in for a real winner , creativity at its peak . \nan afterschool special without the courage of its convictions . \nthe final result makes for adequate entertainment , i suppose , but anyone who has seen chicago on stage will leave the theater feeling they've watched nothing but a pale imitation of the real deal . \n[director] byler may yet have a great movie in him , but charlotte sometimes is only half of one . \nso few movies explore religion that it's disappointing to see one reduce it to an idea that fits in a sampler . \nit's also clear from the start that the transporter is running purely on adrenaline , and once the initial high wears off , the film's shortcomings start to shine through . \nwatching it is rather like viewing a long soap opera in which only the first episode was any good . \nit's fun , but a psychological mess , with austin powers bumping his head on the way out of the closet . \nthere are touching moments in etoiles , but for the most part this is a dull , dour documentary on what ought to be a joyful or at least fascinating subject . \noverwrought , melodramatic bodice-ripper . \ncould the whole plan here have been to produce something that makes fatal attraction look like a classic by comparison ? that's the only sane rationale i can think of for swimfan's existence . \ni didn't laugh at the ongoing efforts of cube , and his skinny buddy mike epps , to make like laurel and hardy 'n the hood . \nthe only way this supernatural snore-fest could give anyone a case of the frights is if they were put to sleep by the movie and had a nightmare . \ni wonder what the reaction of israelis will be to this supposedly evenhanded presentation . \nthe film would work much better as a video installation in a museum , where viewers would be free to leave . immediately . \nhuman nature initially succeeds by allowing itself to go crazy , but ultimately fails by spinning out of control . \n[it's] a prison soccer movie starring charismatic tough guy vinnie jones , but it had too much spitting for me to enjoy . \nnot even the hanson brothers can save it\nthe thriller side of this movie is falling flat , as the stalker doesn't do much stalking , and no cop or lawyer grasps the concept of actually investigating the case . \n . . . [a] strained comedy that jettisons all opportunities for rock to make his mark by serving up the usual chaotic nonsense . \na sour , nasty offering . \nfeels like one of those contrived , only-in -hollywood productions where name actors deliver big performances created for the sole purpose of generating oscar talk . \nobstacles are too easily overcome and there isn't much in the way of character development in the script . \nit tells more than it shows . \nearnest falls short of its ideal predecessor largely due to parker's ill-advised meddling with the timeless source material . \nthe film might have been more satisfying if it had , in fact , been fleshed out a little more instead of going for easy smiles . \npretentious editing ruins a potentially terrific flick . \nnot every animated film from disney will become a classic , but forgive me if i've come to expect more from this studio than some 79-minute after-school \" cartoon \" . \ndo not , under any circumstances , consider taking a child younger than middle school age to this wallow in crude humor . \nnothing debases a concept comedy quite like the grinding of bad ideas , and showtime is crammed full of them . \nmuch-anticipated and ultimately lackluster movie . \nthis is really just another genre picture . \neach story on its own could have been expanded and worked into a compelling single feature , but in its current incarnation , storytelling never quite gets over its rather lopsided conception . \nwilliam shatner , as a pompous professor , is the sole bright spot . . . \na trite psychological thriller designed to keep the audience guessing and guessing -- which is not to be confused with suspecting -- until it comes time to wrap things up and send the viewers home . \nneither funny nor suspenseful nor particularly well-drawn . \n'carente de imaginacin , mal dirigida , peor actuada y sin un pice de romance , es una verdadera prdida de tiempo y dinero'\nacting , particularly by tambor , almost makes \" never again \" worthwhile , but [writer/director] schaeffer should follow his titular advice\nboth stars manage to be funny , but , like the recent i spy , the star chemistry begs the question of whether random gags add up to a movie . \n \" collateral damage \" goes by the numbers and reps decent action entertainment  until the silly showdown ending that forces the viewer to totally suspend disbelief\n'enigma' is a good name for a movie this delibrately obtuse and unapproachable . a waste of good performances . \na dreary , incoherent , self-indulgent mess of a movie in which a bunch of pompous windbags drone on inanely for two hours . . . a cacophony of pretentious , meaningless prattle . \ni kept thinking over and over again , 'i should be enjoying this . ' but i wasn't . \nas conceived by mr . schaeffer , christopher and grace are little more than collections of quirky traits lifted from a screenwriter's outline and thrown at actors charged with the impossible task of making them jell . \nlike so many other allegedly scary movies , it gets so tangled up in the twist that it chokes the energy right out of the very audience it seeks to frighten . \nthe essential problem in orange county is that , having created an unusually vivid set of characters worthy of its strong cast , the film flounders when it comes to giving them something to do . \njust like hearst's enormous yacht , it's slow and unwieldy and takes a long time to reach its destination . \nthere is not a character in the movie with a shred of plausibility , not an event that is believable , not a confrontation that is not staged , not a moment that is not false . \nwhere last time jokes flowed out of cho's life story , which provided an engrossing dramatic through line , here the comedian hides behind obviously constructed routines . \nwhy come up with something even quasi-original , when you can pillage from shirley jackson , richard matheson . . . and puke up something like rose red ? \na broadly played , lowbrow comedy in which the cast delivers mildly amusing performances and no farm animals were injured by any of the gags . \n . . . too gory to be a comedy and too silly to be an effective horror film . \nlacks heart , depth and , most of all , purpose . \nthough a bit of a patchwork in script and production , a glossy , rich green , environment almost makes the picture work . \nperfectly enjoyable , instantly forgettable , nothing to write home about . \nwasabi is slight fare indeed , with the entire project having the feel of something tossed off quickly ( like one of hubert's punches ) , but it should go down smoothly enough with popcorn . \nsomehow both wildly implausible and strangely conventional . \nthis ill-fitting tuxedo is strictly off-the-rack . \nis \" ballistic \" worth the price of admission ? absolutely not . it sucked . would i see it again ? please see previous answer . \nthe premise is in extremely bad taste , and the film's supposed insights are so poorly thought-out and substance-free that even a high school senior taking his or her first psychology class could dismiss them . \nsets up a nice concept for its fiftysomething leading ladies , but fails loudly in execution . \nkidman is really the only thing that's worth watching in birthday girl , a film by the stage-trained jez butterworth ( mojo ) that serves as yet another example of the sad decline of british comedies in the post-full monty world . \nimagine the james woods character from videodrome making a home movie of audrey rose and showing it to the kid from the sixth sense and you've imagined the ring . \nthis time kaufman's imagination has failed him . \nan intermittently pleasing but mostly routine effort . \nit becomes gimmicky instead of compelling . \" interview \" loses its overall sense of mystery and becomes a tv episode rather than a documentary that you actually buy into . \n'unfaithful' cheats on itself and retreats to comfortable territory . too bad . \nfrenetic but not really funny . \ntaken individually or collectively , the stories never add up to as much as they promise . \nif you're not a prepubescent girl , you'll be laughing at britney spears' movie-starring debut whenever it doesn't have you impatiently squinting at your watch . \na didactic and dull documentary glorifying software anarchy . \nan awful snooze . \nsluggishly directed by episodic tv veteran joe zwick , it's a sitcom without the snap-crackle . \nyou could nap for an hour and not miss a thing . \ndirector clare kilner's debut is never as daft as it should have been . \nnew best friend shouldn't have gone straight to video ; it should have gone straight to a mystery science theater 3000 video . \nwallace seems less like he's been burning to tell a war story than he's been itching to somehow tack one together\nthe thrill is ( long ) gone . \njust plain silly . \nbegan life as a computer game , then morphed into a movie -- a bad one , of course . \npart comedy , part drama , the movie winds up accomplishing neither in full , and leaves us feeling touched and amused by several moments and ideas , but nevertheless dissatisfied with the movie as a whole . \ngodawful boring slug of a movie . \ndull , if not devoid of wit , this shaggy dog longs to frisk through the back alleys of history , but scarcely manages more than a modest , snoozy charm . \nscene-by-scene , things happen , but you'd be hard-pressed to say what or why . \n . . . an unimaginative , nasty , glibly cynical piece of work . \nit is , by conventional standards , a fairly terrible movie . . . but it is also weirdly fascinating , a ready-made eurotrash cult object . it is also , at times , curiously moving . \nthe tug-of-war at the core of beijing bicycle becomes weighed down with agonizing contrivances , overheated pathos and long , wistful gazes . \nvile and tacky are the two best adjectives to describe ghost ship . \nsome decent actors inflict big damage upon their reputations . \nbeing author wells' great-grandson , you'd think filmmaker simon wells would have more reverence for the material . but this costly dud is a far cry from either the book or the beloved film . \noffensive in the way it exploits the hot-button issue of domestic abuse for cheap thrills and disgusting in the manner it repeatedly puts a small child in jeopardy , treating her as little more than a prop to be cruelly tormented . \nmark me down as a non-believer in werewolf films that are not serious and rely on stupidity as a substitute for humor . \nthoughtless , random , superficial humour and a lot of very bad scouse accents\naspires for the piquant but only really achieves a sort of ridiculous sourness . \naccuracy and realism are terrific , but if your film becomes boring , and your dialogue isn't smart , then you need to use more poetic license . \nfor all its highfalutin title and corkscrew narrative , the movie turns out to be not much more than a shaggy human tale . \na soggy , cliche-bound epic-horror yarn that ends up being even dumber than its title . \none groan-inducing familiarity begets another . \nalthough based on a real-life person , john , in the movie , is a rather dull person to be stuck with for two hours . \nfeeble comedy . \ngreat story , bad idea for a movie . \nwith spy kids 2 : the island of lost dreams writer/director/producer robert rodriguez has cobbled together a film that feels like a sugar high gone awry . \nthere is no entry portal in the rules of attraction , and i spent most of the movie feeling depressed by the shallow , selfish , greedy characters . \nit's hard to tell with all the crashing and banging where the salesmanship ends and the movie begins . \n \" my god , i'm behaving like an idiot ! \" yes , you are , ben kingsley . \na dreadful live-action movie . \ndivertingly ridiculous , headbangingly noisy . \nan earnest racial-issues picture that might have gotten respectful critical praise in a different era -- say , the '60s . \na hideous , confusing spectacle , one that may well put the nail in the coffin of any future rice adaptations . \nall i can say is fuhgeddaboutit . \nbetween bedroom scenes , viewers may find themselves wishing they could roll over and take a nap . \nif you collected all the moments of coherent dialogue , they still wouldn't add up to the time required to boil a four- minute egg . \ndespite all the talking , by the time the bloody climax arrives we still don't feel enough of an attachment to these guys to care one way or another . \nevery bit as bogus as most disney live action family movies are -- no real plot , no real conflict , no real point . \na sensual performance from abbass buoys the flimsy story , but her inner journey is largely unexplored and we're left wondering about this exotic-looking woman whose emotional depths are only hinted at . \nnever engaging , utterly predictable and completely void of anything remotely interesting or suspenseful . \nspousal abuse is a major problem in contemporary society , but the film reduces this domestic tragedy to florid melodrama . \noedekerk wrote patch adams , for which he should not be forgiven . why he was given free reign over this project -- he wrote , directed , starred and produced -- is beyond me . \nthe creaking , rusty ship makes a fine backdrop , but the ghosts' haunting is routine . \nwhatever eyre's failings as a dramatist , he deserves credit for bringing audiences into this hard and bitter place . \nscotland , pa . blurs the line between black comedy and black hole . \nit tries too hard , and overreaches the logic of its own world . \nthe whole damn thing is ripe for the jerry springer crowd . it's all pretty cynical and condescending , too . \ni cry for i spy -- or i would if this latest and laziest imaginable of all vintage-tv spinoffs were capable of engendering an emotional response of any kind . \na boring , formulaic mix of serial killers and stalk'n'slash . \nwhat you would end up with if you took orwell , bradbury , kafka , george lucas and the wachowski brothers and threw them into a blender . but that's just the problem with it - the director hasn't added enough of his own ingredients . \nwith recent tensions rekindled by the kathleen soliah trial and the upcoming trial of sla members emily and william harris , not to mention sept . 11 , its difficult these days to appreciate fire's bright side . \nflaunts its quirky excesses like a new year's eve drunk sporting a paper party hat . \nwrithing under dialogue like 'you're from two different worlds' and 'tonight the maid is a lie and this , this is who you are , ' this schlock-filled fairy tale hits new depths of unoriginality and predictability . \n . . . too slow , too boring , and occasionally annoying . \nis there enough material to merit a documentary on the making of wilco's last album ? \nfaultlessly professional but finally slight . \nschmaltzy and unfunny , adam sandler's cartoon about hanukkah is numbingly bad , little nicky bad , 10 worst list bad . \nit's really yet another anemic and formulaic lethal weapon-derived buddy-cop movie , trying to pass off its lack of imagination as hip knowingness . \nscotland , pa . is a strangely drab romp . some studio pizazz might have helped . \nwhen perry fists a bull at the moore farm , it's only a matter of time before he gets the upper hand in matters of the heart . \nthere's more scatological action in 8 crazy nights than a proctologist is apt to encounter in an entire career . \ntoo loud , too long and too frantic by half , die another day suggests that the bond franchise has run into a creative wall that 007 cannot fly over , tunnel under or barrel through . \nthe cartoon is about as true to the spirit of the festival of lights as mr . deeds was to that of frank capra . \n . . . the sum of the parts equals largely a confused mediocrity . \nthe tone shifts abruptly from tense to celebratory to soppy . \nif we don't demand a standard of quality for the art that we choose , we deserve the trash that we get . \na modest and messy metaphysical thriller offering more questions than answers . \njulia is played with exasperating blandness by laura regan . \nmorrissette's script and direction show a fair amount of intelligence and wit -- but it doesn't signify a whole lot either . \nthere's suspension of disbelief and then there's bad screenwriting . . . this film packs a wallop of the latter . \nall ms . jovovich , as the sanctified heroine , has to do is look radiant , grimly purposeful and mildly alarmed while forcing open doors , wielding wrenches and fleeing monsters . \nmocking kung fu pictures when they were a staple of exploitation theater programming was witty . mocking them now is an exercise in pointlessness . \nthis is a particularly toxic little bonbon , palatable to only a chosen and very jaundiced few . \nthis isn't just the cliffsnotes version of nicholas nickleby , it's the cliffsnotes with pages missing . \nconsidering the harsh locations and demanding stunts , this must have been a difficult shoot , but the movie proves rough going for the audience as well . \nbetter at putting you to sleep than a sound machine . \nso clichd that , at one point , they literally upset an apple cart . \nit's a decent glimpse into a time period , and an outcast , that is no longer accessible , but it doesn't necessarily shed more light on its subject than the popular predecessor . \nit might be the first sci-fi comedy that could benefit from a three's company-style laugh track . \nwhen the plot kicks in , the film loses credibility . \ncredibility sinks into a mire of sentiment . \nthe movie takes itself too seriously and , as a result , it makes for only intermittent fun . \n[davis] has a bright , chipper style that keeps things moving , while never quite managing to connect her wish-fulfilling characters to the human race . \ndoesn't amount to much of anything . \nbullock's complete lack of focus and ability quickly derails the film\nso stupid , so ill-conceived , so badly drawn , it created whole new levels of ugly . \nthe truth is that the truth about charlie gets increasingly tiresome . \nenduring love but exhausting cinema . \nsome of seagal's action pictures are guilty pleasures , but this one is so formulaic that it seems to be on auto-pilot . \na ponderous meditation on love that feels significantly longer than its relatively scant 97 minutes . \na long-winded and stagy session of romantic contrivances that never really gels like the shrewd feminist fairy tale it could have been . \na little too pat for its own good . \nthere are films that try the patience of even the most cinema-besotted critic -- and this was one of them . \nwatching trouble every day , at least if you don't know what's coming , is like biting into what looks like a juicy , delicious plum on a hot summer day and coming away with your mouth full of rotten pulp and living worms . \nwell-intentioned though it may be , its soap-opera morality tales have the antiseptic , preprogrammed feel of an after-school special . \nwhat would jesus do if he was a film director ? he'd create a movie better than this . \nwhen a set of pre-shooting guidelines a director came up with for his actors turns out to be cleverer , better written and of considerable more interest than the finished film , that's a bad sign . a very bad sign . \nit's a deeply serious movie that cares passionately about its subject , but too often becomes ponderous in its teaching of history , or lost in the intricate connections and multiple timelines of its story . \ncan't kick about the assembled talent and the russos show genuine promise as comic filmmakers . still , this thing feels flimsy and ephemeral . \ndiane lane shines in unfaithful . almost everything else is wan . \ni'm afraid you won't get through this frankly fantastical by-the-numbers b-flick with just a suspension of disbelief . rather , you'll have to wrestle disbelief to the ground and then apply the chloroform-soaked handkerchief . \nso relentlessly wholesome it made me want to swipe something . \nthe title helpfully offers the most succinct review of it you'll read anywhere . \nthe makers of divine secrets of the ya-ya sisterhood should offer a free ticket ( second prize , of course , two free tickets ) to anyone who can locate a genuinely honest moment in their movie . \ngod help the poor woman if attal is this insecure in real life : his fictional yvan's neuroses are aggravating enough to exhaust the patience of even the most understanding spouse . \nlet's cut to the consumer-advice bottom line : stay home . \nundercover brother doesn't go far enough . it's just a silly black genre spoof . \nthe film's implicit premise is that the faith of the tonga people is in every way inferior to that of john . \nrates an 'e' for effort -- and a 'b' for boring . \nyou've already seen heartbreak if you've watched the far superior nurse betty or sunset boulevard . even the unwatchable soapdish is more original . \nplays like one of those conversations that comic book guy on \" the simpsons \" has . \na journey that's too random and inconclusive to be compelling , but which hoffman's brilliance almost makes worth taking . \nthe movie bounces all over the map . \nbut buying into sham truths and routine \" indie \" filmmaking , freundlich has made just another safe movie . it's not horrible , just horribly mediocre . \nnot always too whimsical for its own good ( but enough to do harm ) , this strange hybrid of crime thriller , quirky character study , third-rate romance and female empowerment fantasy never really finds the tonal or thematic glue it needs . \n \" juwanna mann ? \" no thanks . wewannour money back , actually . \nit's a sometimes interesting remake that doesn't compare to the brilliant original . \nyou're too conscious of the effort it takes to be this spontaneous . \n . . . salaciously simplistic . \nfeel bad for king , who's honestly trying , and schwartzman , who's shot himself in the foot . \nsome actors steal scenes . tom green just gives them a bad odor . this self-infatuated goofball is far from the only thing wrong with the clumsy comedy stealing harvard , but he's the most obvious one . \nsometimes , fond memories should stay in the past : a lesson this film teaches all too well . \nthe enormous comic potential of an oafish idiot impersonating an aristocrat remains sadly unrealized . \nshallow . \nbegins as a promising meditation on one of america's most durable obsessions but winds up as a slender cinematic stunt . \na monster combat thriller as impersonal in its relentlessness as the videogame series that inspired it . \nproof that a thriller can be sleekly shot , expertly cast , paced with crisp professionalism . . . and still be a letdown if its twists and turns hold no more surprise than yesterday's weather report . \nthe reason we keep seeing the same movie with roughly the same people every year is because so many of us keep going and then , out of embarrassment or stupidity , not warning anyone . \nthere's not much going on in this movie unless you simply decide to buy into the notion that something inexplicably strange once happened in point pleasant . \nin the second half of the film , frei's control loosens in direct proportion to the amount of screen time he gives nachtwey for self-analysis . \ndirector barry skolnick and his screenwriters glibly tick off every point of \" the longest yard \" playbook like a checklist . \nthe furious coherence that [deniro] brings to this part only underscores the fuzzy sentimentality of the movie itself , which feels , as it plods toward the end , less like a movie than like the filmed reading of a script in need of polishing . \noh , it's extreme , all right . extremely dumb . extremely confusing . extremely boring . \nwe never really feel involved with the story , as all of its ideas remain just that : abstract ideas . \nfor a shoot-'em-up , ballistic is oddly lifeless . \none minute , you think you're watching a serious actioner ; the next , it's as though clips from the pink panther strikes again and/or sailor moon have been spliced in . \nwhat happened with pluto nash ? how did it ever get made ? \nwe may never think of band camp as a geeky or nerdy thing again . \nopens as promising as any war/adventure film you'll ever see and dissolves into a routine courtroom drama , better suited for a movie titled \" glory : a soldier's story . \" \nthe result is solemn and horrifying , yet strangely detached . \nthese spiders can outrun a motorcycle and wrap a person in a sticky cocoon in seconds , but they fall short of being interesting or entertaining . \na less-than-thrilling thriller . \na long slog for anyone but the most committed pokemon fan . \nmatthew mcconaughey tries , and fails , to control the screen with swaggering machismo and over-the-top lunacy . \nhow on earth , or anywhere else , did director ron underwood manage to blow $100 million on this ? \neveryone connected to this movie seems to be part of an insider clique , which tends to breed formulaic films rather than fresh ones . \nwith a story inspired by the tumultuous surroundings of los angeles , where feelings of marginalization loom for every dreamer with a burst bubble , the dogwalker has a few characters and ideas , but it never manages to put them on the same path . \nits lack of quality earns it a place alongside those other two recent dumas botch-jobs , the man in the iron mask and the musketeer . \na benign but forgettable sci-fi diversion . \nthe plot grinds on with yawn-provoking dullness . \nit's never a good sign when a film's star spends the entirety of the film in a coma . it's a worse sign when you begin to envy her condition . \nit doesn't help that the director and cinematographer stephen kazmierski shoot on grungy video , giving the whole thing a dirty , tasteless feel . \ndesperately unfunny when it tries to makes us laugh and desperately unsuspenseful when it tries to make us jump out of our seats . \nthis film's relationship to actual tension is the same as what christmas-tree flocking in a spray can is to actual snow : a poor -- if durable -- imitation . \njohn mctiernan's botched remake may be subtler than norman jewison's 1975 ultraviolent futuristic corporate-sports saga . it's also stupider . \ndirector dirk shafer and co-writer greg hinton ride the dubious divide where gay porn reaches for serious drama . \nborrows from so many literary and cinematic sources that this future world feels absolutely deja vu . \nas [the characters] get more depressed , the story gets more tiresome , especially as it continues to mount a conspicuous effort to be profound . \nthe acting by the over-25s lacks spark , with csokas particularly unconnected . \nthough howard demonstrates a great eye as a director , this southern gothic drama is sadly a tough sit , with an undeveloped narrative and enough flashbacks and heavy-handed metaphors to choke a horse -- or at least slow him down to a canter . \nfalters when it takes itself too seriously and when it depends too heavily on its otherwise talented cast to clown in situations that aren't funny . \nthe fight scenes are fun , but it grows tedious . \nanyone who can count to five ( the film's target market ? ) can see where this dumbed-down concoction is going . \nall this turns out to be neither funny nor provocative - only dull . \ndemme's loose approach kills the suspense . \nthis idea has lost its originality . . . and neither star appears very excited at rehashing what was basically a one-joke picture . \n[plays] in broad outline as pandering middle-age buddy-comedy . \nthe film is itself a sort of cinematic high crime , one that brings military courtroom dramas down very , very low . \nnothing more than a mediocre trifle . \na turgid little history lesson , humourless and dull . \ntoo silly to be frightening , too stolid to be funny , it projects the same lazy affability as its nominal star , david arquette . \nif kaufman kept cameron diaz a prisoner in a cage with her ape , in his latest , he'd have them mate . \ntrivial where it should be profound , and hyper-cliched where it should be sincere . \nit would be hard to think of a recent movie that has worked this hard to achieve this little fun . \ntheology aside , why put someone who ultimately doesn't learn at the center of a kids' story ? \nall that ( powerpuff girls ) charm is present in the movie , but it's spread too thin . \nsometimes smart but more often sophomoric . \nthe ill-conceived modern-day ending falls flat where it should deliver a moral punch . \ncollateral damage is , despite its alleged provocation post-9/11 , an antique , in the end . as are its star , its attitude and its obliviousness . \ndawdles and drags when it should pop ; it doesn't even have the virtue of enough mindless violence to break up the tedium of all its generational bonding . \nits save-the-planet message clashes with its crass marketing . \na great idea becomes a not-great movie . \n . . . watching this film nearly provoked me to take my own life . and if the hours wins 'best picture' i just might . \nby the end , i was looking for something hard with which to bludgeon myself unconscious . \nnot a movie but a live-action agitprop cartoon so shameless and coarse , it's almost funny . \nfeels like six different movies fighting each other for attention . \nlife is a crock -- or something like it . \nmade by jackasses for jackasses . \ndespite a powerful portrayal by binoche , it's a period romance that suffers from an overly deliberate pace and uneven narrative momentum . \nthe picture is a primer on what happens when lack of know-how mixes with lack of give-a-damn . \nbartleby is a one-joke movie , and a bad joke at that . \ndisjointed parody . \ngiven that both movies expect us to root for convicted violent felons over those assigned to protect us from same , we need every bit of sympathy the cons can muster ; this time , there isn't much . \na bravura exercise in emptiness . \nphilip k . dick must be turning in his grave , along with my stomach . \ndespite the premise of a good story . . . it wastes all its star power on cliched or meaningless roles . \nall prints of this film should be sent to and buried on pluto . \nnot only does the movie fail to make us part of its reality , it fails the most basic relevancy test as well . \nfirst good , then bothersome . excellent acting and direction . \nthis goofy gangster yarn never really elevates itself from being yet another earnestly generic crime-busting comic vehicle -- a well-intentioned remake that shows some spunk and promise but fails to register as anything distinctive or daring\nit's difficult to say whether the tuxedo is more boring or embarrassing--i'm prepared to call it a draw . \neven as lame horror flicks go , this is lame . \nso routine , familiar and predictable , it raises the possibility that it wrote itself as a newly automated final draft computer program . \nsunshine state lacks the kind of dynamic that limbo offers , and in some ways is a rather indulgent piece . \neven legends like alfred hitchcock and john huston occasionally directed trifles . . . so it's no surprise to see a world-class filmmaker like zhang yimou behind the camera for a yarn that's ultimately rather inconsequential . \na long-winded , predictable scenario . \ntoo close to phantom menace for comfort . \n . . . silly humbuggery . . . \nalthough trying to balance self-referential humor and a normal ol' slasher plot seemed like a decent endeavor , the result doesn't fully satisfy either the die-hard jason fans or those who can take a good joke . \ntoo clunky and too busy ribbing itself to be truly entertaining . \nthis movie is about lying , cheating , but loving the friends you betray . \nit smacks of purely commercial motivation , with no great love for the original . \nmelanie eventually slugs the yankee . too bad the former murphy brown doesn't pop reese back . \nat the bottom rung of the series' entries . \na real clunker . a well-made , thoughtful , well-acted clunker , but a clunker nonetheless . \na dreary rip-off of goodfellas that serves as a muddled and offensive cautionary tale for hispanic americans . \nthe picture , scored by a perversely cheerful marcus miller accordion/harmonica/banjo abomination , is a monument to bad in all its florid variety . \ngood for a few unintentional laughs , \" extreme ops \" was obviously made for the \" xxx \" crowd , people who enjoy mindless action without the benefit of decent acting , writing , and direction . \na big , baggy , sprawling carnival of a movie , stretching out before us with little rhyme or reason . \na frustrating combination of strained humor and heavy-handed sentimentality . \nlawrence preaches strictly to the converted . \nas written by michael berg and michael j . wilson from a story by wilson , this relentless , all-wise-guys-all-the-time approach tries way too hard and gets tiring in no time at all . \nwill probably stay in the shadow of its two older , more accessible qatsi siblings . \njust when the movie seems confident enough to handle subtlety , it dives into soapy bathos . \nchoppy editing and too many repetitive scenes spoil what could have been an important documentary about stand-up comedy . \n . . . the film falls back on the same old formula of teen sex , outrageous pranks and scenes designed to push the envelope of bad taste for laughs . \nthe only thing in pauline and paulette that you haven't seen before is a scene featuring a football field-sized oriental rug crafted out of millions of vibrant flowers . \nif you're looking for comedy to be served up , better look elsewhere . \nit virtually defines a comedy that's strongly mediocre , with funny bits surfacing every once in a while . \nan instant candidate for worst movie of the year . \ndespite its title , amy's orgasm is not a porno , though it is as tedious as one . \nlike a tone-deaf singer at a benefit concert , john q . is a bad movie appearing on behalf of a good cause . \nadam sandler is to gary cooper what a gnat is to a racehorse . \nwhat kids will discover is a new collectible . what parents will suspect is that they're watching a 76-minute commercial . \nbeers , who , when she's given the right lines , can charm the paint off the wall . . . [but] the script goes wrong at several key junctures . \nwithout september 11 , collateral damage would have been just another bad movie . now it's a bad , embarrassing movie . \ndrags along in a dazed and enervated , drenched-in-the- past numbness . \nthere's a disturbing 'great white hope' undertone to the other side of heaven that subtly undermines its message of christian love and compassion . \nunless you are in dire need of a diesel fix , there is no real reason to see it . wait for video -- and then don't rent it . \nthe attempt is courageous , even if the result is wildly uneven . \nthere's something fundamental missing from this story : something or someone to care about . \ntoo much of this well-acted but dangerously slow thriller feels like a preamble to a bigger , more complicated story , one that never materializes . \nwhen a film is created solely because it's a marketable product , soulless and ugly movies like this are the result . let your silly childhood nostalgia slumber unmolested . \nunfortunately , heartbreak hospital wants to convey the same kind of haughtiness in its own sketchy material but this territory has already been explored previously with better aplomb and sardonic wit . \nthe more kevin costner rests on his pretty-boy laurels , the public is , regrettably , going to have tepid films like dragonfly tossed at them . \n[it's] difficult to get beyond the overall blandness of american chai , despite its likable performances and refreshingly naive point of view . \nthe latest installment in the pokemon canon , pokemon 4ever is surprising less moldy and trite than the last two , likely because much of the japanese anime is set in a scenic forest where pokemon graze in peace . \ntaken purely as an exercise in style , this oppressively gloomy techno-horror clambake is impossible to ignore . but as a movie , it's a humorless , disjointed mess . \nif myers decides to make another austin powers movie , maybe he should just stick with austin and dr evil . \nthe backyard battles you staged with your green plastic army men were more exciting and almost certainly made more sense . \ntoo stupid to be satire , too obviously hateful to be classified otherwise , frank novak's irritating slice of lumpen life is as reliably soul-killing as its title is nearly meaningless . \noverly stylized with lots of flash black-&-white freeze frames reminiscent of a pseudo-hip luxury car commercial , ( it's ) at its worst when it's actually inside the ring . \nwhat is captured during the conceptual process doesn't add up to a sufficient explanation of what the final dance work , the selection , became in its final form . \nif this is satire , it's the smug and self-congratulatory kind that lets the audience completely off the hook . \nhad the film boasted a clearer , more memorable , the creepiness would have gotten under the skin . \nthese people wouldn't know subtle characterization if it put on a giant furry monster costume and then gave them a lapdance . \nimagine if you will a tony hawk skating video interspliced with footage from behind enemy lines and set to jersey shore techno . \nit doesn't quite work , but there's enough here to make us look forward to the russos' next offering . \nany film featuring young children threatened by a terrorist bomb can no longer pass as mere entertainment . \nthe director's twitchy sketchbook style and adroit perspective shifts grow wearisome amid leaden pacing and indifferent craftsmanship ( most notably wretched sound design ) . \nabout as satisfying and predictable as the fare at your local drive through . \napparently kissing leads to suicide attempts and tragic deaths . marisa tomei is good , but just a kiss is just a mess . \nopens at a funeral , ends on the protagonist's death bed and doesn't get much livelier in the three hours in between . \nthe noble tradition of men in drag hits an all-time low in sorority boys , whose makers apparently believe that women's clothing can cover up any deficiency in acting , writing or direction . \nto portray modern women the way director davis has done is just unthinkable . \ntoo simple for its own good . \nflaccid drama and exasperatingly slow journey . \na chaotic panorama that's too busy flying a lot of metaphoric flags . \nsuffers from rambling , repetitive dialogue and the visual drabness endemic to digital video . \nthe screenplay sabotages the movie's strengths at almost every juncture . all the characters are stereotypes , and their interaction is numbingly predictable . \na trashy , exploitative , thoroughly unpleasant experience . \nnewcomer helmer kevin donovan is hamstrung by a badly handled screenplay of what is really an amusing concept  a high-tech tux that transforms its wearer into a superman . \nshouldn't have been allowed to use the word \" new \" in its title , because there's not an original character , siuation or joke in the entire movie . \noften silly  and gross  but it's rarely as moronic as some campus gross-out films . \nthe advantage of a postapocalyptic setting is that it can be made on the cheap . any rock pile will do for a set . reign of fire has the disadvantage of also looking cheap . \nit's exactly what you'd expect . \nthe performances are so leaden , michael rymer's direction is so bloodless and the dialogue is so corny that the audience laughs out loud . \na subtle variation on i spit on your grave in which our purported heroine pathologically avenges a hatred for men . \nreggio's trippy , ambitious downer can also sometimes come across like nothing more than a glorified nike ad . \nas tweedy talks about canning his stockbroker and repairing his pool , you yearn for a few airborne tv sets or nude groupies on the nod to liven things up . \nif somebody was bored and . . . decided to make a dull , pretentious version of jesus' son , they'd come up with something like bart freundlich's world traveler . \nthe best you can say about it is it's so uninspired , it barely gives one pause when considering some of the other dreck out there right now . \na tough go , but leigh's depth and rigor , and his skill at inspiring accomplished portrayals that are all the more impressive for their lack of showiness , offsets to a notable degree the film's often-mined and despairing milieu . \nan overstuffed compendium of teen-catholic-movie dogma . \ntoo leisurely paced and visually drab for its own good , it succeeds in being only sporadically amusing . \nben affleck as jack ryan , tom clancy's intrepid hero ? ridiculous . what's next ? d . j . qualls as indiana jones ? or tom green as han solo ? \neh . \nthe movie's biggest offense is its complete and utter lack of tension . \nthe film is directed by wally wolodarsky from a script by joe jarvis and greg coolidge . these are names to remember , in order to avoid them in the future . \nif this is the resurrection of the halloween franchise , it would have been better off dead . \nthis formulaic chiller will do little to boost stallone's career . \ni saw knockaround guys yesterday , and already the details have faded like photographs from the spanish-american war . . . it's so unmemorable that it turned my ballpoint notes to invisible ink . \nthough avary has done his best to make something out of ellis' nothing novel , in the end , his rules is barely worth following . \nthe average local news columnist has a bigger rant on the war between modern landscape architecture and small-town america . \nno worse than a lot of the crap we've been offered this summer , and slightly better than men in black 2 as far as slapdash extraterrestrial comedies go . \nthe plot is so predictable and sentimental that viewers are likely to lose interest before sandrine and her goats walk off into the sunset . \nat first , the sight of a blind man directing a film is hilarious , but as the film goes on , the joke wears thin . \nnever again swings between false sentiment and unfunny madcap comedy and , along the way , expects the audience to invest in the central relationship as some kind of marriage of true minds . \nthe story itself is uninteresting , and the songs are painfully undistinguished : they might be giants' so to be one of us may be the most tuneless tune ever composed . \neste  apenas mais um ( longo ) episdio do programa da mtv . a nica diferena  que , desta vez , a paramount teve o mau gosto de exibi-lo nos cinemas . \ntechnically , the film is about as interesting as an insurance commercial . \nthe title's lameness should clue you in on how bad the movie is . \nthe parts are better than the whole ( bizarre , funny , tragic - like love in new york ) . \nwhile the film shuns the glamour or glitz that an american movie might demand , scherfig tosses us a romantic scenario that is just as simplistic as a hollywood production . \nthe humor is hinged on the belief that knees in the crotch , elbows in the face and spit in the eye are inherently funny . \nit's a movie forged in the fires of chick flick hell . \nits characters are thinner than cardboard -- or even comic-book paper . \nany one episode of the sopranos would send this ill-conceived folly to sleep with the fishes . \nfeels like the grittiest movie that was ever made for the lifetime cable television network . \nlanie's professional success means she must be a failure at life , because she's driven by ambition and doesn't know how to have fun . \ncredit must be given to harland williams , michael rosenbaum and barry watson , who inject far more good-natured spirit and talent into this project than it deserves\ndenzel washington's efforts are sunk by all the sanctimony . \nif this holiday movie is supposed to be a gift , somebody unwrapped it early , took out all the good stuff , and left behind the crap ( literally ) . \nso faithful to the doldrums of the not-quite-urban , not-quite-suburban milieu as to have viewers recoiling from the reality check . \nrambles on in a disjointed , substandard fashion from one poorly executed action sequence to the next . \nthere's an audience for it , but it could have been funnier and more innocent . \ni can only imagine one thing worse than kevin spacey trying on an irish accent , and that's sultry linda fiorentino doing the same thing . \nan awkward and indigestible movie . \nthe movie has very little to offer besides unintentional laughs . \nhaneke keeps us at arm's length . guided more by intellect than heart , his story flattens instead of sharpens . \nall the well-meaningness in the world can't erase the fact that the believer feels like a 12-step program for the jewish nazi . \nlike most sequels , it takes what worked last time , repeats it and adds more characters , more stunts , more stuff in attempt to camouflage its sameness . \nthere's no way to sort out the mess in our heads and deconstruct where it all went wrong . this is an hour and a half of daydreaming . \nmessage movie or an action-packed submarine spectacular ? alas , it's neither . \nno movement , no yuks , not much of anything . \n'dragonfly' is a movie about a bus wreck that turns into a film wreck . \nthirty years ago , it would have been groundbreaking . now it's just tired . \nheavy-handed exercise in time-vaulting literary pretension . \nthe movie is too cute to take itself too seriously , but it still feels like it was made by some very stoned college students . \na thinly veiled excuse for wilson to play his self-deprecating act against murphy's well-honed prima donna shtick . \na minor picture with a major identity crisis -- it's sort of true and it's sort of bogus and it's ho-hum all the way through . \nthe movie succumbs to being nothing more than a formulaic chase in the dark . \nplays as hollow catharsis , with lots of tears but very little in the way of insights . \nit will come as no surprise that the movie isn't scary . but here's the real damn : it isn't funny , either . \nquick : who wants to see a comedy about shoddy airport security ? \nthe reason i found myself finally unmoved by this film , which is immaculately produced and has serious things to say , is that it comes across rather too plainly as allegory . \nwhat you get with empire is a movie you've seen many times before , repackaged as new material because there is a latino in the lead . \ncho's fans are sure to be entertained ; it's only fair in the interest of full disclosure to say that -- on the basis of this film alone -- i'm not one of them . \nlooks awfully like one long tourist spot for a mississippi that may never have existed outside of a scriptwriter's imagination . \nthe period -- swinging london in the time of the mods and the rockers -- gets the once-over once again in gangster no . 1 , but falls apart long before the end . \nhigh crimes miscasts nearly every leading character . \nthe overall feel of the film is pretty cheesy , but there's still a real sense that the star trek tradition has been honored as best it can , given the embarrassing script and weak direction . \nde niro looks bored , murphy recycles murphy , and you mentally add showtime to the pile of hollywood dreck that represents nothing more than the art of the deal . \ni didn't believe for a moment in these villains or their plot . \nanother rent installment for the ian fleming estate . \nwith danilo donati's witty designs and dante spinotti's luscious cinematography , this might have made a decent children's movie -- if only benigni hadn't insisted on casting himself in the title role . \nso lazy and slipshod it confuses the mere flashing of kinky soft-core imagery with naughty fun . \nthough it draws several decent laughs , it's low-cal woody at best . \nhas little on its mind aside from scoring points with drag gags . \nbritney's performance cannot be faulted . lucy's a dull girl , that's all . \n \" the kid stays in the picture \" is a great story , terrifically told by the man who wrote it but this cliff notes edition is a cheat . \nwell-shot but badly written tale set in a future ravaged by dragons . \nsadly , hewitt's forte is leaning forward while wearing low-cut gowns , not making snappy comebacks . \nif melville is creatively a great whale , this film is canned tuna . \nthis film is so slick , superficial and trend-hoppy , that it's easy to imagine that a new software program spit out the screenplay . \njust one more collection of penis , breast and flatulence gags in search of a story . or a profit . or some damn thing . \nthe acting is stiff , the story lacks all trace of wit , the sets look like they were borrowed from gilligan's island -- and the cgi scooby might well be the worst special-effects creation of the year . \nenough trivializes an important crisis , reduces it to an almost comic embarrassment . \nthe makers have forsaken the entertaining elements of the original and , instead , rehash old jokes and leave any life at the doorstep . i like frank the pug , though . \na restrained ribisi convinces as an italian , though if ever a movie needed one of the actor's whiny jags to pump it up , this has to be among the rare ones . \ngee , a second assassin shot kennedy ? moot point . \na hysterical yet humorless disquisition on the thin line between sucking face and literally sucking face . \nneedless to say , the dramatics that follow are utter hooey . \nthe problem is that rather than dramatizing this premise , mr . desplechin is content to state it . \nmadonna still can't act a lick . \neven the imaginative gore can't hide the musty scent of todd farmer's screenplay , which is a simple retread of the 1979 alien , with a plucky heroine battling a monster loose in a spaceship . \nif you pitch your expectations at an all time low , you could do worse than this oddly cheerful -- but not particularly funny -- body-switching farce . \nthe episodic film makes valid points about the depersonalization of modern life . but the characters tend to be cliches whose lives are never fully explored . \nserry does a fine job of capturing the climate of the times and , perhaps unwittingly , relating it to what is happening in america in 2002 . but hard-to-believe plot twists force the movie off track in its final half hour . \ndon't let your festive spirit go this far . \nthough the book runs only about 300 pages , it is so densely packed . . . that even an ambitious adaptation and elaborate production like mr . schepisi's seems skimpy and unclear . \nhey , at least the title of this film lets you know exactly where it's heading . \nintended to be a comedy about relationships , this wretched work falls flat in just about every conceivable area . \nsensitive though not quite revelatory documentary . \ndirector brian levant , who never strays far from his sitcom roots , skates blithely from one implausible situation to another , pausing only to tie up loose ends with more bows than you'll find on a french poodle . \n . . . perhaps the heaviest , most joyless movie ever made about giant dragons taking over the world . \nsuggests puns about ingredients and soup and somebody being off their noodle , but let's just say the ingredients don't quite add up to a meal . \ni'd give real money to see the perpetrators of chicago torn apart by dingoes . \nmovies like this are selling the old european candor , the old wink of 'bold' revelation . but in 2002 , such revelations wilt . \nthe timing in nearly every scene seems a half beat off . \ni admire it and yet cannot recommend it , because it overstays its natural running time . \na fairly by-the-books blend of action and romance with sprinklings of intentional and unintentional comedy . \nflounders due to the general sense that no two people working on the production had exactly the same thing in mind . \neven if you feel like you've seen this movie a thousand times before , it is kind of enjoyable thanks mainly to belushi's easy-going likableness . \nthe story bogs down in a mess of purposeless violence . \ndespite some gulps the film is a fuzzy huggy . \nmckay deflates his piece of puffery with a sour cliche and heavy doses of mean-spiritedness\na recipe for cinematic disaster . . . part quentin tarantino , part guy ritchie , and part 1960s spy spoof , it's all bad . \nthe cumulative effect of the relentless horror on parade numbs the movie's power as a work of drama . \nanother big , dumb action movie in the vein of xxx , the transporter is riddled with plot holes big enough for its titular hero to drive his sleek black bmw through . \naaliyah rarely dampens her diva persona enough to spark genuine chemistry with townsend . when she speaks , her creepy egyptian demigod voice is as computer processed and overproduced as it was in her music . \noutrageousness is all plympton seemed to be going for this time . we miss the quirky amazement that used to come along for an integral part of the ride . \nmuch of what is meant to be 'inspirational' and 'uplifting' is simply distasteful to audiences not already sharing [the movie's] mindset . \nwell before it's over , beijing bicycle begins spinning its wheels . \nhome alone goes hollywood , a funny premise until the kids start pulling off stunts not even steven spielberg would know how to do . besides , real movie producers aren't this nice . \n[nelson's] movie about morally compromised figures leaves viewers feeling compromised , unable to find their way out of the fog and the ashes . \npassion , lip-synching , tragedy , and lots of really really high notes . for me , this opera isn't a favorite , so it's a long time before the fat lady sings . \nlooks like a high school film project completed the day before it was due . \ntruth to tell , if you've seen more than half-a-dozen horror films , there's nothing here you haven't seen before . \nabandons all pretense of creating historical context and waltzes off into a hectic soap about the ups and downs of the heavy breathing between the two artists . \neven die-hard fans of japanese animation . . . will find this one a challenge . \nfilmmakers have to dig deep to sink this low . fortunately for all involved , this movie is likely to disappear as quickly as an ice cube thrown into a pot of boiling water . \nthe movie gets muted and routine . \nthe issue of faith is not explored very deeply\nit's a bad sign when you're rooting for the film to hurry up and get to its subjects' deaths just so the documentary will be over , but it's indicative of how uncompelling the movie is unless it happens to cover your particular area of interest . \nthe situations and jokes are as predictable and as lowbrow as the endless pratfalls the boys take in their high heels . \nit's frustrating to see these guys -- who are obviously pretty clever -- waste their talent on parodies of things they probably thought were funniest when they were high . \ni was perplexed to watch it unfold with an astonishing lack of passion or uniqueness . \nk-19 may not hold a lot of water as a submarine epic , but it holds even less when it turns into an elegiacally soggy saving private ryanovich . \na very stylish but ultimately extremely silly tale . . . a slick piece of nonsense but nothing more . \nautomatically pegs itself for the straight-to-video sci-fi rental shelf . \nblack-and-white and unrealistic . \nthe film is like a series of beginnings and middles that never take off . \nfeels less like it's about teenagers , than it was written by teenagers . \nja rule and kurupt should have gotten to rap . it would have benefitted the dialogue . \na standard haunted house tale transplanted to the high seas . \nelicits more groans from the audience than jar jar binks , scrappy doo and scooby dumb , all wrapped up into one . \nit's badly acted , blandly directed , and could have been scripted by someone who just graduated from elementary school . \n in the end , white oleander isn't an adaptation of a novel . it's a flashy , star-splashed reduction . \nthis film , starring anthony hopkins and chris rock , is your typical 'fish out of water' story . you've seen them a million times . just one problem : fish out of water usually die . this one does . \nthe angst-ridden , affluent slacker characters are more grating than engaging . \na soggy , shapeless mess . . . just a dumb excuse for a waterlogged equivalent of a haunted-house movie . \nmen in black ii has sequel-itis something fierce . an ungainly , comedy-deficient , b-movie rush job . . . \na combination of standard , stiff tv-style animation and snazzy-looking digital effects that do little to disguise the fact that the characters barely move . \nan unsatisfying hybrid of blair witch and typical stalk-and-slash fare , where the most conservative protagonist is always the last one living . \nsay this for the soundtrack , it drowns out the lousy dialogue . \nafter seeing swept away , i feel sorry for madonna . \ninstead of kicking off the intrigue and suspense and mystery of the whole thing , hart's war , like the st . louis rams in the super bowl , waits until after halftime to get started . \ntwo-bit potboiler . \na gob of drivel so sickly sweet , even the eager consumers of moore's pasteurized ditties will retch it up like rancid crme brle . \nmaudlin and melodramatic we expected . boring we didn't . \nnever quite transcends jokester status . . . and the punchline doesn't live up to barry's dead-eyed , perfectly chilled delivery . \nthe film's bathos often overwhelms what could have been a more multifaceted look at this interesting time and place . \nit almost plays like solaris , but with guns and jokes . \na baffling misfire , and possibly the weakest movie [woody allen] has made in the last twenty years . \nit won't be long before you'll spy i spy at a video store near you . \nthis film looks like it was produced in 1954 , shelved for 48 years , and repackaged for a 2002 audience . \npropelled not by characters but by caricatures . \nthere is not an ounce of honesty in the entire production . \nthis extremely unfunny film clocks in at 80 minutes , but feels twice as long . \nearnest but earthbound . . . a slow , soggy , soporific , visually dank crime melodrama/character study that would be more at home on the small screen but for its stellar cast . \nthe pivotal narrative point is so ripe the film can't help but go soft and stinky . \nfor all its alleged youthful fire , xxx is no less subservient to bond's tired formula of guns , girls and gadgets while brandishing a new action hero . \na predictable and stereotypical little b-movie . \nif i spy were funny ( enough ) or exciting ( enough ) then it would be fairly simple to forgive the financial extortion it's trying to reap from the moviegoing public . \nfrustratingly , dridi tells us nothing about el gallo other than what emerges through his music . \nplaces a slightly believable love triangle in a difficult-to-swallow setting , and then disappointingly moves the story into the realm of an improbable thriller . \nstephen earnhart's documentary is a decomposition of healthy eccentric inspiration and ambition  wearing a cloak of unsentimental , straightforward text  when it's really an exercise in gross romanticization of the delusional personality type . \na very average science fiction film . \nundone by its overly complicated and derivative screenplay , the glacier-paced direction and the stereotypical characters . \nhow anyone over the age of 2 can stomach the touchy-feely message this preachy produce promotes is beyond us . \nthe sort of movie that gives tastelessness a bad rap . \nthe cold and dreary weather is a perfect metaphor for the movie itself , which contains few laughs and not much drama . \nthe plot is straight off the shelf , the performances are television- caliber and the message of providing solace through deception is a little creepy . \nordinary melodrama that is heavy on religious symbols but wafer-thin on dramatic substance\na whimsical if predictable time-travel fable marred by a willful single-mindedness . \nthose who managed to avoid the deconstructionist theorizing of french philosopher jacques derrida in college can now take an 85-minute brush-up course with the documentary derrida . or , you can do something fun tonight . \njolie's performance vanishes somewhere between her hair and her lips . \nas with too many studio pics , plot mechanics get in the way of what should be the lighter-than-air adventure . \nstatic , repetitive , muddy and blurry , hey arnold ! would seem to have a lock on the title of ugliest movie of the year . \nfor all of the contemporary post-colonialist consciousness that kapur tries to bring to the four feathers , the oddest thing about the movie is how it winds up affirming the same damn moldy values the material has always held dear . \nun thriller manqu qui tombe sur les nerfs presque ds la premire image . \nwhen it comes to entertainment , children deserve better than pokemon 4ever . \nwhat the four feathers lacks is genuine sweep or feeling or even a character worth caring about . \nwhile benigni ( who stars and co-wrote ) seems to be having a wonderful time , he might be alone in that . \nyes , 4ever is harmless in the extreme and it'll mute your kids for nearly 80 minutes , but why not just treat the little yard apes to the real deal and take them to spirited away ? \npreposterous and tedious , sonny is spiked with unintentional laughter that , unfortunately , occurs too infrequently to make the film even a guilty pleasure . \ncalling this movie brainless would be paying it a compliment : it's more like entertainment for trolls . \nnone of these characters resembles anyone you've ever met in real life , unless you happen to know annoyingly self-involved people who speak in glib sentences that could have only come from the pen of a screenwriter . \n . . . just a big mess of a movie , full of images and events , but no tension or surprise . \nas elegantly crafted as it often is , anderson's movie is essentially a one-trick pony that , hampered by an undeveloped script , ultimately pulls up lame . \nsplashes its drama all over the screen , subjecting its audience and characters to action that feels not only manufactured , but also so false you can see the filmmakers' puppet strings . \n . . . has virtually no script at all . . . \nwill only satisfy those who can't tell the difference between the good , the bad and the ugly . \nthis kind of dark comedy requires a delicate , surgical touch . but director danny devito and screenwriter adam resnick ( remember cabin boy ? ) just pound away . \nat times , however , dogtown and z-boys lapses into an insider's lingo and mindset that the uninitiated may find hard to follow , or care about . \nrather quickly , the film falls into a soothing formula of brotherly conflict and reconciliation . \nscreenwriters scott abbott and michael petroni have turned rice's complex akasha into a cartoon monster . \nthe writers , director wally wolodarsky , and all the actors should start their own coeducational fraternity : kappa rho alpha phi . \nbad beyond belief and ridiculous beyond description . \nthe new faces are interesting , but the old story isn't , especially when it starts to seem more improvised than scripted . \nmost of the action setups are incoherent . \nliman , of swingers and go , makes his big-budget action film debut something of a clunker as he delivers a long , low-heat chase , interrupted by a middling car chase . \n . . . surprisingly inert for a movie in which the main character travels back and forth between epochs . \nthe problem isn't that the movie hits so close to home so much as that it hits close to home while engaging in such silliness as that snake-down-the-throat business and the inevitable shot of schwarzenegger outrunning a fireball . \ndreary , highly annoying . . . 'some body' will appeal to no one . \na prison comedy that never really busts out of its comfy little cell . \nthere's something deeply creepy about never again , a new arrow in schaeffer's quiver of ineptitudes . \nthe problem with concept films is that if the concept is a poor one , there's no saving the movie . sorry , charlie\na painfully leaden film destined for pre-dawn cable television slots . \nblessed with immense physical prowess he may well be , but ahola is simply not an actor . and in truth , cruel as it may sound , he makes arnold schwarzenegger look like spencer tracy . \nthe cartoon that isn't really good enough to be on afternoon tv is now a movie that isn't really good enough to be in theaters . \nshocking only in that it reveals the filmmaker's bottomless pit of self-absorption . \nthis pep-talk for faith , hope and charity does little to offend , but if saccharine earnestness were a crime , the film's producers would be in the clink for life . \nall ends well , sort of , but the frenzied comic moments never click . \nif this is the danish idea of a good time , prospective tourists might want to consider a different destination -- some jolly country embroiled in a bloody civil war , perhaps . \nformula 51 is so trite that even yu's high-energy action stylings can't break through the stupor . \nonly a few minutes elapse before the daddy of all slashers arrives , still with the boiler suit and white mask , which look remarkably clean for a guy who has been mass-murdering since 1978 but has never been seen doing laundry . \nwhen the painted backdrops in a movie are more alive than its characters , you know you're in trouble . \n[two] fairly dull -- contrasting and interlocking stories about miserable scandinavian settlers in 18th-century canada , and yuppie sailboaters in the here and now . \nthe film is really not so much bad as bland . \nthe central story lacks punch . \nthough ganesh is successful in a midlevel sort of way , there's nothing so striking or fascinating or metaphorically significant about his career as to rate two hours of our attention . \nthis may be the dumbest , sketchiest movie on record about an aspiring writer's coming-of-age . \nsuccumbs to the same kind of maudlin , sentimental mysticism that mars the touched by an angel school of non-god spiritual-uplift movies . \na harmless and mildly amusing family comedy . \nwhat was subtle and mystifying in the novella is now broad and farcical . \nthe kids often appear to be reading the lines and are incapable of conveying any emotion . \n \" men in black ii , \" has all the earmarks of a sequel . the story is less vibrant , the jokes are a little lukewarm , but will anyone really care ? \nsucking all the 'classic' out of robert louis stevenson's treasure island and filling the void with sci-fi video game graphics and disney-fied adolescent angst . . . \nthis 72-minute film does have some exciting scenes , but it's a tad slow . \nwhile super troopers is above academy standards , its quintet of writers could still use some more schooling . \na mix of velocity and idiocy , this ruinous remake lacks the brawn -- and the brains -- of the 1970s original . \nthe low-budget full frontal was one of the year's murkiest , intentionally obscure and self-indulgent pictures , and solaris is its big-budget brother . \nthe plot is very clever , but boyd weighs it down with too many characters and events , all intertwined and far too complicated to keep track of . \nthe film seems all but destined to pop up on a television screen in the background of a scene in a future quentin tarantino picture\na free-for-all of half-baked thoughts , clumsily used visual tricks and self-indulgent actor moments . \napallingly absurd . . . the chemistry or lack thereof between newton and wahlberg could turn an imax theater into a 9 \" black and white portable tv . \na well acted and well intentioned snoozer . \nthe smug , oily demeanor that donovan adopts throughout the stupidly named pipe dream is just repulsive . \nmust-see viewing for anyone involved in the high-tech industry . others may find it migraine-inducing , despite moore's attempts at whimsy and spoon feeding . \ndespite its good nature and some genuinely funny moments , super troopers suffers from a bad case of arrested development . \nit's hard not to feel you've just watched a feature-length video game with some really heavy back story . \ni watched the brainless insanity of no such thing with mounting disbelief . \nthis limp gender-bender-baller from a first-time director and rookie screenwriter steals wholesale from that 1982's tootsie , forgetting only to retain a single laugh . \nkwan makes the mix-and- match metaphors intriguing , while lulling us into torpor with his cultivated allergy to action . \nwhile obviously an extremely personal work , it remains inextricably stuck in an emotionally unavailable rut . \nany movie this boring should be required to have ushers in the theater that hand you a cup of coffee every few minutes . like a marathon runner trying to finish a race , you need a constant influx of liquid just to get through it . \ni loved looking at this movie . i just didn't care as much for the story . \nhas all the poignancy of a hallmark card and all the comedy of a gallagher stand-up act . \nit doesn't do the original any particular dishonor , but neither does it exude any charm or personality . \nfear dot com is so rambling and disconnected it never builds any suspense . \na gorgeous , somnolent show that is splendidly mummified and thoroughly unsurprising . \na semi-autobiographical film that's so sloppily written and cast that you cannot believe anyone more central to the creation of bugsy than the caterer had anything to do with it . \nit feels like a community theater production of a great broadway play : even at its best , it will never hold a candle to the original . \nthe film apparently takes place in a fantasy world where people in hotel hallways recite poetry in voice-over instead of speaking to each other . \nthe element of surprise might be the only thing femme fatale has going for it . \nit's the kind of movie you can't quite recommend because it is all windup and not much of a pitch , yet you can't bring yourself to dislike it . \nmaybe it's asking too much , but if a movie is truly going to inspire me , i want a little more than this . \na graceless , witless attempt at mating some like it hot with the wwii espionage thriller . \nthe story and characters are nowhere near gripping enough . \nbased on a david leavitt story , the film shares that writer's usual blend of observant cleverness , too-facile coincidence and slightly noxious preciousness . \njust like every other seagal movie , only louder and without that silly ponytail . \nto enjoy this movie's sharp dialogue and delightful performance by jolie and burns , you have to gloss over the no sense ending . \nnational lampoon's van wilder could be the worst thing to come out of national lampoon since class reunion\nthis is a great subject for a movie , but hollywood has squandered the opportunity , using it as a prop for warmed-over melodrama and the kind of choreographed mayhem that director john woo has built his career on . \necks this one off your must-see list . \noverall , the film misses the brilliance of jelinek's novel by some way . it settles for being merely grim . \nthe irwins emerge unscathed , but the fictional footage is unconvincing and criminally badly acted . \nit's not thirsty , consuming passion which drives this movie . no , it's the repetition of said behavior , and so children of the century is more mindless love than mad , more grating and boring than anything else . \njust because it really happened to you , honey , doesn't mean that it's interesting to anyone else . \njust like the deli sandwich : lots of ham , lots of cheese , with a sickly sweet coating to disguise its excrescence until just after ( or during ) consumption of its second half . \nevery so often a movie comes along that confirms one's worse fears about civilization as we know it . the new guy is one of them . \nthis isn't a \" friday \" worth waiting for . \neverything that's worthwhile about collision course can already be seen on television . \nif this movie belonged to a sorority , it would be called beta alpha delta . \nnot a cheap slasher flick , as the subject matter would suggest , but is a little like a nature film , showing a patient predator and his foolish prey . \nuneasy mishmash of styles and genres . \nherzog is obviously looking for a moral to his fable , but the notion that a strong , unified showing among germany and eastern european jews might have changed 20th-century history is undermined by ahola's inadequate performance . \nall in all , there's only one thing to root for : expulsion for everyone . \nbeyond a handful of mildly amusing lines . . . there just isn't much to laugh at . \nsecret ballot is too contemplative to be really funny . \nthe film's center will not hold . \nmyers never knows when to let a gag die ; thus , we're subjected to one mind-numbingly lengthy riff on poo and pee jokes after another . \ntoo lazy to take advantage of its semi-humorous premise . \na great ending doesn't make up for a weak movie , and crazy as hell doesn't even have a great ending . \ndialogue-heavy and too cerebral for its own good -- or , at any rate , too cerebral for its racy subject matter . \nits over-reliance on genre conventions , character types and formulaic conflict resolutions crushes all the goodwill it otherwise develops . \nas an actor , the rock is aptly named . \na mostly tired retread of several other mob tales . \ni wish i could say \" thank god it's friday \" , but the truth of the matter is i was glad when it was over . \nnothing about it fits . \nas it abruptly crosscuts among the five friends , it fails to lend the characters' individual stories enough dramatic resonance to make us care about them . \nsomehow we're meant to buy that this doting mother would shun her kids , travel to one of the most dangerous parts of the world , don fatigues and become g . i . jane . \nthe cast is so low-wattage that none of the characters comes off as big . . . and the setting remains indistinct . \nconsider the title's clunk-on-the-head that suggests the overtime someone put in to come up with an irritatingly unimaginative retread concept . \nthe movie quickly drags on becoming boring and predictable . i tried to read the time on my watch . \nthe film makes a tragic error by going on for too long , trying to mirror every subsequent event in chinese history : war , revolution , communism , etc . \njohnson has , in his first film , set himself a task he is not nearly up to . \nultimately the project comes across as clinical , detached , uninvolving , possibly prompting audience members to wonder , 'what's the point ? '\nthe two leads are almost good enough to camouflage the dopey plot , but so much naturalistic small talk , delivered in almost muffled exchanges , eventually has a lulling effect . \nthe film meant well in its horse tale about freedom , but wasn't able to reach the heart because it was too overbearing . \nthe movie is as far as you can get from racy , to the point where it almost stops the blood flow to your brain ; it has a dull , costumey feel . \nonce the audience figure out what's being said , the filmmaker's relative passivity will make it tough for them to really care . \nthere's nothing provocative about this film save for the ways in which it studiously avoids provoking thought . \nit seems just a long , convoluted ploy to get men into drag -- period drag , no less . \nthe premise for this kegger comedy probably sounded brilliant four six-packs and a pitcher of margaritas in , but the film must have been written . . . in the thrall of a vicious hangover . \neven by dumb action-movie standards , ballistic : ecks vs . sever is a dumb action movie . \nthe film equivalent of a toy chest whose contents get scattered over the course of 80 minutes . \njust a bloody mess . \ncreepy but ultimately unsatisfying thriller . \nmartin scorsese cria um espetculo visual que no possui alma - um filme esteticamente belo , mas emocionalmente frio . \nyou would be better off investing in the worthy emi recording that serves as the soundtrack , or the home video of the 1992 malfitano-domingo production . \nhas something to say . . . but it is a statement and issue worthy of a much more thoughtfulness and insight than a melodramatic and wholly predictable thriller . \nbears is bad . not 'terrible filmmaking' bad , but more like , 'i once had a nightmare like this , and it's now coming true' bad . \nas is most commonly case with projects such noble and lofty ambitions , the film is less poetic than simply pretentious . \ngeorge , hire a real director and good writers for the next installment , please . \nfor a film about two mismatched buddies , crystal and de niro share little screen time and even less chemistry . \nhowever clever nelson has been in providing variation within the confines of her structure and staging , the question remains whether this should , indeed , have been presented as a theatrical release . \nextreme oops - oops , ops , no matter how you spell it , it's still a mistake to go see it . \nwhat could and should have been biting and droll is instead a tepid waste of time and talent . \nwhat will , most likely , turn out to be the most repellent movie of 2002 . \n . . . too dull to enjoy . \na morality tale whose thought-provoking potential is hampered by a made-for-tv look , rigid performances and an asinine 'twist' that brazenly rips off the sixth sense . \nhere's a self-congratulatory 3d imax rah-rah . \neastwood is an icon of moviemaking , one of the best actors , directors and producers around , responsible for some excellent work . but even a hero can stumble sometimes . \na sophomoric exploration of 'life problems' most people solved long ago -- or at least got tired of hearing people kvetch about . \nit's all very cute , though not terribly funny if you're more than six years old . \nthe impact of the armenian genocide is diluted by too much stage business in the modern day . \n[u]nrelentingly stupid . \ngoing to the website may be just as fun ( and scary ) as going to the film . \nlacking gravitas , macdowell is a placeholder for grief , and ergo this sloppy drama is an empty vessel . leave these flowers unpicked -- they're dead on the vine . \nadmirable , certainly , but not much fun to watch . for caine lovers only . \na shambles of a movie--visually unattractive , unbearably loud and utterly silly . . . its hilarity is completely unintentional . \nde niro may enjoy the same free ride from critics afforded to clint eastwood in the lazy bloodwork . but like bruce springsteen's gone-to-pot asbury park , new jersey , this sad-sack waste of a movie is a city of ruins . \nno , it's not nearly as good as any of its influences . \na reasonably efficient mechanism , but it offers few surprises and finds its stars slumming in territory they should have avoided . \nthe rest of the plot is impossible to explain without blowing whatever tension there is , although it's more comedy than suspense de palma creates . \nhuman nature talks the talk , but it fails to walk the silly walk that distinguishes the merely quirky from the surreal . \ncity by the sea is the cinematic equivalent of defensive driving : it's careful , conscientious and makes no major mistakes . but what saves lives on the freeway does not necessarily make for persuasive viewing . \nthe marquis de sade couldn't have been as dull a person as this film makes him out to be . \nwhat could have been a neat little story about believing in yourself is swamped by heavy-handed melodrama . \nthe cast is uniformly excellent . . . but the film itself is merely mildly charming . \ndrives for the same kind of bittersweet , conciliatory tone that three seasons achieved but loses its way in rhetorical excess and blatant sentimentality . \na bigger holiday downer than your end-of-year 401 ( k ) statement . \nthe whole thing plays out with the drowsy heaviness of synchronized swimmer wearing a wool wetsuit . \nfairly successful at faking some pretty cool stunts but a complete failure at trying to create some pretty cool characters . and forget about any attempt at a plot ! \nit will probably prove interesting to ram dass fans , but to others it may feel like a parody of the mellow , peace-and-love side of the '60s counterculture . \ntake away all the cliches and the carbon copy scenes from every drug movie we've seen and all you have left are john leguizamo's cool jackets . \nit's so full of wrong choices that all you can do is shake your head in disbelief -- and worry about what classic oliver parker intends to mangle next time . \nshowtime is closer to slowtime . \nit may be an easy swipe to take , but this barbershop just doesn't make the cut . \nthe weight of water uses water as a metaphor for subconscious desire , but this leaky script barely stays afloat . \n'how many more voyages can this limping but dearly-loved franchise survive ? '\ndespite a blue-chip cast and a provocative title , writer-director peter mattei's first feature microwaves dull leftover romantic motifs basted in faux-contemporary gravy . \nfans of the tv series will be disappointed , and everyone else will be slightly bored . \nthe only element of suspense is whether the movie will change titles or distributors again before the closing credits roll . \nbarely goes beyond comic book status . \nit's disappointing when filmmakers throw a few big-name actors and cameos at a hokey script . \ni spy is an embarrassment , a monotonous , disjointed jumble of borrowed plot points and situations . it's as flat as an open can of pop left sitting in the sun . \nan eccentric little comic/thriller deeply in love with its own quirky personality . \nafraid to pitch into farce , yet only half-hearted in its spy mechanics , all the queen's men is finally just one long drag . \npainfully padded . \nmaybe it's the star power of the cast or the redundant messages , but something aboul \" full frontal \" seems , well , contrived . \n[morgan] , judd and franklin can't save the script , rooted in a novel by joseph finder , from some opportunism . \nthe movie is obviously a labour of love so howard appears to have had free rein to be as pretentious as he wanted . \npassably entertaining but also mechanical and joyless . \nsafe conduct , however ambitious and well-intentioned , fails to hit the entertainment bull's-eye . \nmy response to the film is best described as lukewarm . maybe i found the proceedings a little bit too conventional . \ntoo timid to bring a sense of closure to an ugly chapter of the twentieth century . \nit's push-the-limits teen comedy , the type written by people who can't come up with legitimate funny , and it's used so extensively that good bits are hopelessly overshadowed . \nit's too long , too repetitive , and takes way too many years to resolve to be a total winner . \na sudsy cautionary tale . \na movie that tries to fuse the two 'woods' but winds up a bolly-holly masala mess . \nmr . wedge and mr . saldanha handle the mix of verbal jokes and slapstick well . their film falters , however , in its adherence to the disney philosophy of required poignancy , a salute that i'd hoped the movie would avoid . \nleaves you with a knot in your stomach , its power is undercut by its own head-banging obviousness . \nwatching it is rather like an overlong visit from a large group of your relatives . as your relatives swap one mundane story after another , you begin to wonder if they are ever going to depart . \nunfortunately , as a writer , mr . montias isn't nearly as good to his crew as he is as a director or actor . \non the right track to something that's creepy and effective . . . it's just going to take more than a man in a bullwinkle costume to get there . \nthe only thing that could possibly make them less interesting than they already are is for them to get full montied into a scrappy , jovial team . \none of those movies where you walk out of the theater not feeling cheated exactly , but feeling pandered to , which , in the end , might be all the more infuriating . \n . . . this movie has a glossy coat of action movie excess while remaining heartless at its core . \nmurder by numbers is like a couple of mediocre tv-movie -of-the-week films clumsily stuck together . \nthe film is surprisingly well-directed by brett ratner , who keeps things moving well -- at least until the problematic third act . \nwarmed-over tarantino by way of wannabe elmore leonard . \n[sen's] soap opera-ish approach undermines his good intentions . \nshowtime is one of the hapless victims of the arrogant \" if we put together a wry white man and a chatty black man and give them guns , the movie will be funny \" syndrome . \nsushi for the connoisseurs of the macabre . \ndon't waste your money . \nthough certainly original in form , altar boys requires a taste for swamp thing-type animation , doubled with a deafening score . \nthere aren't many laughs in this interesting study of the cultural mores of georgian jews in tel aviv . \nthere's not enough to sustain the comedy . \nlike those to rome , all roads in the banger sisters inevitably lead to a joke about hawn's breasts , which constantly threaten to upstage the woman sporting them . \nwe may get the full visceral impact of a ruthless army on the warpath but no sense of the devilish complexity of the balkans conflict . \nyou're better off staying home and watching the x-files . \nchaotic , self-indulgent and remarkably ugly to look at , it's . . . like a series of pretentiously awful student films strung together into one feature-length horror . \nbears resemblance to , and shares the weaknesses of , too many recent action-fantasy extravaganzas in which special effects overpower cogent story-telling and visual clarity during the big action sequences . \nthis is the type of movie best enjoyed by frat boys and college kids while sucking on the bong and downing one alcoholic beverage after another . \nfriday after next has the same problem that next friday did -- it's called where's chris tucker when you need him ? \nthis film is full of rabbits . brimful . but like most rabbits , it seems to lack substance . \ni weep for the future when a good portion of the respected critical community in this country consider blue crush to be an intelligent film about young women . \ni kept wishing i was watching a documentary about the wartime navajos and what they accomplished instead of all this specious hollywood hoo-ha . \nno number of fantastic sets , extras , costumes and spectacular locales can disguise the emptiness at the center of the story . \na movie far more cynical and lazy than anything a fictitious charlie kaufman might object to . \ncould the country bears really be as bad as its trailers ? in a word -- yes . \nif high crimes were any more generic it would have a universal product code instead of a title . \nreggio falls victim to relying on the very digital technology that he fervently scorns , creating a meandering , inarticulate and ultimately disappointing film . \nthe movie makes absolutely no sense . its underlying mythology is a hodgepodge of inconsistencies that pose the question : since when did dumb entertainment have to be this dumb ? \nthe problem with this film is that it's forced to make its characters idiots in order to advance the plot . had anyone here done anything remotely intelligent , we all could have stopped watching long ago . \ndespite the authenticity of the trappings , the film is overblown in its plotting , hackneyed in its dialogue and anachronistic in its style . \nmurder and mayhem of this sort quickly becomes monotonous . \nthe journey toward redemption feels more like a cinematic experiment than a full-blown movie . \nthat zhang would make such a strainingly cute film -- with a blind orphan at its center , no less -- indicates where his ambitions have wandered . \nthe gantzes' interviews tend to let the guys off the hook . \nthe streets , shot by cinematographer michael ballhaus , may be as authentic as they are mean , but it is nearly impossible to care about what happens on them . \nthis is a good movie in spurts , but when it doesn't work , it's at important times . \nparker probably thinks he's shaking up a classic the way kenneth branagh and baz luhrmann have , but this half-hearted messing-about just makes us miss wilde's still-contemporary play . \nflotsam in the sea of moviemaking , not big enough for us to worry about it causing significant harm and not smelly enough to bother despising . \na tv episode inflated past its natural length . \ninvolving at times , but lapses quite casually into the absurd . \nall these developments and challenges facing santa weigh down the plot so heavily that they drain all the film of its energy and needlessly strain credibility . \nanemic , pretentious . \nthere are weird resonances between actor and role here , and they're not exactly flattering . \nthe unceasing sadism is so graphically excessive , the director just ends up exposing his own obsession . \nthe story . . . is moldy and obvious . \nit's drained of life in an attempt to be sober and educational , and yet it's so devoid of realism that its lack of whistles and bells just makes it obnoxious and stiff . \nsuffocated at conception by its munchausen-by-proxy mum . punish the vehicle to adore the star . \neven if britney spears is really cute , her movie is really bad . \nbig fat liar is just futile silliness looking to tap into the kiddie sensibilities . \nusually when i get this much syrup , i like pancakes to go with it . \nall the necessary exposition prevents the picture from rising above your generic sand 'n' sandal adventure . \nglib , satirical documentary that fudges facts , makes facile points and engages in the cinematic equivalent of tabloid journalism . \ngangs of new york is an unapologetic mess , whose only saving grace is that it ends by blowing just about everything up . \nan overblown clunker full of bad jokes , howling cliches and by-the-numbers action sequences . \nwithout a strong script and energetic acting , dogma films can produce the same sleep-inducing effects as watching your neighbor's home videos . \na mild , reluctant , thumbs down . \nstrong setup and ambitious goals fade as the film descends into unsophisticated scare tactics and b-film thuggery . \nschindler's list it ain't . \na cinematic sleeping pill of impressive potency . \nit's an awfully derivative story . \nthe movie barely makes sense , with its unbelievable navet and arbitrary flashbacks . \nit's an earnest debut full of heartfelt performances , but is ultimately let down by a story that is all too predictable . \nabout as cutting-edge as pet rock : the movie . \nwith generic sets and b-grade special effects , jason is about as convincing on the sci-fi front as tv's defunct cleopatra 2525 . \nnot sweet enough to liven up its predictable story and will leave even fans of hip-hop sorely disappointed . \nwinds up feeling like lots of other quirky movies that try to score hipness points with young adults . \noft-described as the antidote to american pie-type sex comedies , it actually has a bundle in common with them , as the film diffuses every opportunity for a breakthrough\nthe pacing is glacial , the screenplay is stiff as a board , and things heat up only in the movie's final scenes . \nthe premise is overshadowed by the uberviolence of the clericks as this becomes just another kung-fu sci-fi movie with silly action sequences . \nwith its hints of a greater intelligence lurking somewhere , the ring makes its stupidity more than obvious . it's painful . \npumpkin struts about with \" courage \" pinned to its huckster lapel while a yellow streak a mile wide decorates its back . \nan uneven film dealing with too many problems to be taken seriously . \nsheridan . . . smoothes over sources of conflict that could have lent the film a bit more depth . \nan atonal estrogen opera that demonizes feminism while gifting the most sympathetic male of the piece with a nice vomit bath at his wedding . \nhalf of it is composed of snappy patter and pseudo-sophisticated cultural observations , while the remainder . . . would be more at home on a daytime television serial . \nwriter/director john mckay ignites some charming chemistry between kate and jed but , when he veers into sodden melodrama , punctuated by violins , it's disastrous and kate's jealous female friends become downright despicable . \n[newton]wanders through charlie completely unaware she needs to show some presence and star quality . \nunfortunately , the picture failed to capture me . i found it slow , drab , and bordering on melodramatic . \na lousy movie that's not merely unwatchable , but also unlistenable . \nthe best way to hope for any chance of enjoying this film is by lowering your expectations . then lower them a bit more . \nwhen not obscured by the booming bass-heavy soundtrack , the conversation presents the kind of linguistic fumbling not heard since macy gray's game of chinese whispers with mr bean . \ncliches are as thick as the cigarette smoke . \na woozy , roisterous , exhausting mess , and the off-beat casting of its two leads turns out to be as ill-starred as you might expect . \n'abandon all hope , ye who enter here' . . . you should definitely let dante's gloomy words be your guide . \nwhat begins as a seemingly brainless , bubbly romantic comedy becomes a cliche-drenched melodrama by mid-film and , by film's end , a feminist action fantasy . \na grim , flat and boring werewolf movie that refuses to develop an energy level . \nbirot's directorial debut ( she co-wrote the script with christophe honor ) isn't so much bad as it is bland . \nwatching the film is like reading a times portrait of grief that keeps shifting focus to the journalist who wrote it . \nenough similarities to gymkata and howie long's firestorm that my fingernails instinctively crawled towards my long-suffering eyeballs . \nsucceeds in providing a disquiet world the long-dreaded completion of the police academy series . \nchristians sensitive to a reductionist view of their lord as a luv-spreading dr . feelgood or omnipotent slacker will feel vastly more affronted than secularists , who might even praise god for delivering such an instant camp classic . \nif this dud had been made in the '70s , it would have been called the hills have antlers and played for about three weeks in drive-ins . \na frantic search for laughs , with a hit-to-miss ratio that doesn't exactly favour the audience . \nlike a documentary version of fight club , shorn of social insight , intellectual pretension and cinematic interest . \n \" an entire film about researchers quietly reading dusty old letters . \" \npryor lite , with half the demons , half the daring , much less talent , many fewer laughs . \nwhat's at stake in this film is nothing more than an obsolete , if irritating , notion of class . \nthe stories here suffer from the chosen format . \nwhile the mystery surrounding the nature of the boat's malediction remains intriguing enough to sustain mild interest , the picture refuses to offer much accompanying sustenance in the way of characterization , humor or plain old popcorn fun . \njust how extreme are these ops ? i regret to report that these ops are just not extreme enough . \nthe actors are forced to grapple with hazy motivations that never come into focus . \njohn leguizamo may be a dramatic actor -- just not in this movie . \nthe sequel has turned completely and irrevocably bizarre to the point of utter nonsense . \nhardly makes the kind of points egoyan wanted to make , nor does it exist as the kind of monument he wanted to build , to victims whose voices have never gained the ears of the world . \nbuild some robots , haul 'em to the theatre with you for the late show , and put on your own mystery science theatre 3000 tribute to what is almost certainly going to go down as the worst -- and only -- killer website movie of this or any other year . \nrainy days and movies about the disintegration of families always get me down . \n . . . has about 3/4th the fun of its spry 2001 predecessor  but it's a rushed , slapdash , sequel-for-the-sake- of-a-sequel with less than half the plot and ingenuity . \na incompetncia de marcus adams como roteirista s  superada por sua pssima direo . \nthe master of disaster - it's a piece of dreck disguised as comedy . \nthis isn't a movie ; it's a symptom . \nthe film has a few cute ideas and several modest chuckles but it isn't exactly kiddie-friendly alas , santa is more ho-hum than ho-ho-ho and the snowman ( who never gets to play that flute ) has all the charm of a meltdown . \nthe stupidest , most insulting movie of 2002's first quarter . \nit's so underwritten that you can't figure out just where the other characters , including ana's father and grandfather , come down on the issue of ana's future . \na film so tedious that it is impossible to care whether that boast is true or not . \nnone of this violates the letter of behan's book , but missing is its spirit , its ribald , full-throated humor . \nbad company leaves a bad taste , not only because of its bad-luck timing , but also the staleness of its script . \neven if it ultimately disappoints , the picture does have about a matinee admission's worth of funny to keep it afloat . \nfails to bring as much to the table . \na film made with as little wit , interest , and professionalism as artistically possible for a slummy hollywood caper flick . \ndisturbingly superficial in its approach to the material . \nif you're not the target demographic . . . this movie is one long chick-flick slog . \ni hate this movie\nit's tough to tell which is in more abundant supply in this woefully hackneyed movie , directed by scott kalvert , about street gangs and turf wars in 1958 brooklyn -- stale cliches , gratuitous violence , or empty machismo . \ngooding and coburn are both oscar winners , a fact which , as you watch them clumsily mugging their way through snow dogs , seems inconceivable . \nat every opportunity to do something clever , the film goes right over the edge and kills every sense of believability . . . all you have left is a no-surprise series of explosions and violence while banderas looks like he's not trying to laugh at how bad it\n . . . pitiful , slapdash disaster . a doa dud from frame one . \njaw-droppingly superficial , straining to get by on humor that is not even as daring as john ritter's glory days on three's company . \nthere are plenty of scenes in frida that do work , but rarely do they involve the title character herself . \nalthough god is great addresses interesting matters of identity and heritage , it's hard to shake the feeling that it was intended to be a different kind of film . \nthe dark and bittersweet twist feels strange as things turn nasty and tragic during the final third of the film . first-timer john mckay is never able to pull it back on course . \ndistances you by throwing out so many red herrings , so many false scares , that the genuine ones barely register . \ngodard uses his characters -- if that's not too glorified a term -- as art things , mouthpieces , visual motifs , blanks . \nthe movie generates plot points with a degree of randomness usually achieved only by lottery drawing . \na predictable , manipulative stinker . the story passes time until it's time for an absurd finale of twisted metal , fireballs and revenge . \neastwood winces , clutches his chest and gasps for breath . it's a spectacular performance - ahem , we hope it's only acting . \nthe movie suffers from two fatal ailments -- a dearth of vitality and a story that's shapeless and uninflected . \nit's just weirdness for the sake of weirdness , and where human nature should be ingratiating , it's just grating . \nan ambitiously naturalistic , albeit half-baked , drama about an abused , inner-city autistic teen . \nonce she lets her love depraved leads meet , [denis'] story becomes a hopeless , unsatisfying muddle\nthe dose is strong and funny , for the first 15 minutes anyway ; after that , the potency wanes dramatically . \nthe people in abc africa are treated as docile , mostly wordless ethnographic extras . \nwhat's really sad is to see two academy award winning actresses ( and one academy award winning actor ) succumb to appearing in this junk that's tv sitcom material at best . \nno doubt the star and everyone else involved had their hearts in the right place . where their heads were is anyone's guess . \ncall me a cynic , but there's something awfully deadly about any movie with a life-affirming message . \na gratingly unfunny groaner littered with zero-dimensional , unlikable characters and hackneyed , threadbare comic setups . \ngot some good , organic character work , lots of obvious political insights and little room for engaging , imaginative filmmaking in its nearly 2 1/2-hour , dissipated length . \na painfully slow cliche-ridden film filled with more holes than clyde barrow's car . \nlike leafing through an album of photos accompanied by the sketchiest of captions . \ni guess it just goes to show that if you give a filmmaker an unlimited amount of phony blood , nothing good can happen . \ngaghan captures the half-lit , sometimes creepy intimacy of college dorm rooms , a subtlety that makes the silly , over-the-top coda especially disappointing . \nfeels less like a change in [herzog's] personal policy than a half-hearted fluke . \none of the most unpleasant things the studio has ever produced . \nwill anyone who isn't a fangoria subscriber be excited that it hasn't gone straight to video ? \na selection of scenes in search of a movie . \n[janey] forgets about her other obligations , leading to a tragedy which is somehow guessable from the first few minutes , maybe because it echoes the by now intolerable morbidity of so many recent movies . \nwill undoubtedly play well in european markets , where mr . besson is a brand name , and in asia , where ms . shu is an institution , but american audiences will probably find it familiar and insufficiently cathartic . \ntrue to its animatronic roots : . . . as stiff , ponderous and charmless as a mechanical apparatus . . . 'the country bears' should never have been brought out of hibernation . \n[evans is] a fascinating character , and deserves a better vehicle than this facetious smirk of a movie . \nthe script's judgment and sense of weight is way , way off . \nyou come away wishing , though , that the movie spent a lot less time trying to make a credible case for reports from the afterlife and a lot more time on the romantic urgency that's at the center of the story . \nevelyn may be based on a true and historically significant story , but the filmmakers have made every effort to disguise it as an unimaginative screenwriter's invention . \na derivative collection of horror and sci-fi cliches . \nloosely speaking , we're in all of me territory again , and , strictly speaking , schneider is no steve martin . \nas aimless as an old pickup skidding completely out of control on a long patch of black ice , the movie makes two hours feel like four . \nlimps along on a squirm-inducing fish-out-of-water formula that goes nowhere and goes there very , very slowly . \njust the sort of lazy tearjerker that gives movies about ordinary folk a bad name . \nthe redeeming feature of chan's films has always been the action , but the stunts in the tuxedo seem tired and , what's worse , routine . \nwhile the importance of being earnest offers opportunities for occasional smiles and chuckles , it doesn't give us a reason to be in the theater beyond wilde's wit and the actors' performances . \nhas all the scenic appeal of a cesspool . \na rambling ensemble piece with loosely connected characters and plots that never quite gel . \na lifetime movie about men . \nthe balkans provide the obstacle course for the love of a good woman . \nit's hard to say who might enjoy this , are there tolstoy groupies out there ? it's dark and tragic , and lets the business of the greedy talent agents get in the way of saying something meaningful about facing death\nthis is a movie that starts out like heathers , then becomes bring it on , then becomes unwatchable . \nthe screenplay flounders under the weight of too many story lines . \ni think it was plato who said , 'i think , therefore i know better than to rush to the theatre for this one . '\nif damon and affleck attempt another project greenlight , next time out they might try paying less attention to the miniseries and more attention to the film it is about . \nthis is rote drivel aimed at mom and dad's wallet . \ncontrived , maudlin and cliche-ridden . . . if this sappy script was the best the contest received , those rejected must have been astronomically bad . \ni hate the feeling of having been slimed in the name of high art . \nthis is more a case of 'sacre bleu ! ' than 'magnifique' . \nthe kids in the audience at the preview screening seemed bored , cheering the pratfalls but little else ; their parents , wise folks that they are , read books . \nwhether jason x is this bad on purpose is never clear . but one thing's for sure : it never comes close to being either funny or scary . \nyet another weepy southern bore-athon . \ni like all four of the lead actors a lot and they manage to squeeze a few laughs out of the material , but they're treading water at best in this forgettable effort . \nperhaps the most annoying thing about who is cletis tout ? is that it's a crime movie made by someone who obviously knows nothing about crime . \nexcept as an acting exercise or an exceptionally dark joke , you wonder what anyone saw in this film that allowed it to get made . \nthis insufferable movie is meant to make you think about existential suffering . instead , it'll only put you to sleep . \nwho is this movie for ? not kids , who don't need the lesson in repugnance . it's also not smart or barbed enough for older viewers -- not everyone thinks poo-poo jokes are 'edgy . '\na sleep-inducingly slow-paced crime drama with clumsy dialogue , heavy-handed phoney-feeling sentiment , and an overly-familiar set of plot devices . \nit's so tedious that it makes you forgive every fake , dishonest , entertaining and , ultimately , more perceptive moment in bridget jones's diary . \nalmost as offensive as \" freddy got fingered . \" \nthe gifted crudup has the perfect face to play a handsome blank yearning to find himself , and his cipherlike personality and bad behavior would play fine if the movie knew what to do with him . \nit is depressing , ruthlessly pained and depraved , the movie equivalent of staring into an open wound . \nponderous , plodding soap opera disguised as a feature film . \ncold , pretentious , thoroughly dislikable study in sociopathy . \nfor the future , one hopes mr . plympton will find room for one more member of his little band , a professional screenwriter . \na cheap scam put together by some cynical creeps at revolution studios and imagine entertainment to make the suckers out there surrender $9 and 93 minutes of unrecoverable life . \nreign of fire never comes close to recovering from its demented premise , but it does sustain an enjoyable level of ridiculousness . \nhuman nature is a goofball movie , in the way that malkovich was , but it tries too hard . \noriginality is sorely lacking . \nthe plot's clearly mythic structure may owe more to disney's strong sense of formula than to the original story . but while the highly predictable narrative falls short , treasure planet is truly gorgeous to behold . \nthough there are entertaining and audacious moments , the movie's wildly careening tone and an extremely flat lead performance do little to salvage this filmmaker's flailing reputation . \nram dass fierce grace moulds itself as an example to up-and-coming documentarians , of the overlooked pitfalls of such an endeavour . \nlucky break is perfectly inoffensive and harmless , but it's also drab and inert . \nfor a story set at sea , ghost ship is pretty landbound , with its leaden acting , dull exposition and telegraphed 'surprises . '\nthere might be some sort of credible gender-provoking philosophy submerged here , but who the hell cares ? \nwhat we have is a character faced with the possibility that her life is meaningless , vapid and devoid of substance , in a movie that is definitely meaningless , vapid and devoid of substance . \nnothing more than a stifling morality tale dressed up in peekaboo clothing . \ni like my christmas movies with more elves and snow and less pimps and ho's . \nit all seemed wasted like deniro's once promising career and the once grand long beach boardwalk . \n[i]t's certainly laudable that the movie deals with hot-button issues in a comedic context , but barbershop isn't as funny as it should be . \nunfortunately , a cast of competent performers from movies , television and the theater are cast adrift in various new york city locations with no unifying rhythm or visual style . \njust entertaining enough not to hate , too mediocre to love . \n'sophisticated' viewers who refuse to admit that they don't like it will likely call it 'challenging' to their fellow sophisticates . \nequilibrium the movie , as opposed to the manifesto , is really , really stupid . \nexcruciatingly unfunny and pitifully unromantic . \nthe film's thoroughly recycled plot and tiresome jokes . . . drag the movie down . \nit doesn't offer audiences any way of gripping what its point is , or even its attitude toward its subject . \nthis kiddie-oriented stinker is so bad that i even caught the gum stuck under my seat trying to sneak out of the theater\nthough impostor deviously adopts the guise of a modern motion picture , it too is a bomb . \ncox offers plenty of glimpses at existing photos , but there are no movies of nijinsky , so instead the director treats us to an aimless hodgepodge . \nevery note rings false . \nwhen the screenwriter responsible for one of the worst movies of one year directs an equally miserable film the following year , you'd have a hard time believing it was just coincidence . \nit never rises to its clever what-if concept . \nadmirably ambitious but self-indulgent . \nthis story of unrequited love doesn't sustain interest beyond the first half-hour . \nthis angst-ridden territory was covered earlier and much better in ordinary people . \nthe entire film is one big excuse to play one lewd scene after another . about half of them are funny , a few are sexy and none are useful in telling the story , which is paper-thin and decidedly unoriginal . \na big , loud , bang-the-drum bore . \ngrating and tedious . \n[less a movie than] an appalling , odoriferous thing . . . so rotten in almost every single facet of production that you'll want to crawl up your own * * * in embarrassment . \nthe concept behind kung pow : enter the fist is hilarious . it's too bad nothing else is . \nhardman is a grating , mannered onscreen presence , which is especially unfortunate in light of the fine work done by most of the rest of her cast . \nel crimen del padre amaro would likely be most effective if used as a tool to rally anti-catholic protestors . \ninteresting and thoroughly unfaithful version of carmen\na serious movie with serious ideas . but seriously , folks , it doesn't work . \nthere's nothing exactly wrong here , but there's not nearly enough that's right . \nthe action here is unusually tame , the characters are too simplistic to maintain interest , and the plot offers few surprises . \ni couldn't help but feel the wasted potential of this slapstick comedy . \nwhat madonna does here can't properly be called acting -- more accurately , it's moving and it's talking and it's occasionally gesturing , sometimes all at once . \n[a] painfully flat gross-out comedy . . . \neven if you're an elvis person , you won't find anything to get excited about on this dvd . \nthe movie certainly has its share of clever moments and biting dialogue , but there's just not much lurking below its abstract surface . \nit's bedeviled by labored writing and slack direction . \ni'm sure there's a teenage boy out there somewhere who's dying for this kind of entertainment . \nthe tuxedo miscalculates badly by forcing the star to play second fiddle to the dull effects that allow the suit to come to life . \nthe film is a confusing melange of tones and styles , one moment a romantic trifle and the next a turgid drama . \nobviously , a lot of people wasted a lot of their time ( including mine ) on something very inconsequential . \nthere's a little violence and lots of sex in a bid to hold our attention , but it grows monotonous after a while , as do joan and philip's repetitive arguments , schemes and treachery . \nit drowns in sap . \ndeliberately and devotedly constructed , far from heaven is too picture postcard perfect , too neat and new pin-like , too obviously a recreation to resonate . \nit's rare that a movie can be as intelligent as this one is in every regard except its storyline ; everything that's good is ultimately scuttled by a plot that's just too boring and obvious . \ni'm giving it thumbs down due to the endlessly repetitive scenes of embarrassment . there's got to be a more graceful way of portraying the devastation of this disease . \nthe good thing -- the only good thing -- about extreme ops is that it's so inane that it gave me plenty of time to ponder my thanksgiving to-do list . \nthe modern-day characters are nowhere near as vivid as the 19th-century ones . \nblessed with a searing lead performance by ryan gosling ( murder by numbers ) , the movie is powerful and provocative . it's also built on a faulty premise , one it follows into melodrama and silliness . \nuneven performances and a spotty script add up to a biting satire that has no teeth . \ndirector jay russell stomps in hobnail boots over natalie babbitt's gentle , endearing 1975 children's novel . \nbenigni's pinocchio is extremely straight and mind-numbingly stilted , its episodic pacing keeping the film from developing any storytelling flow . \nthe troubling thing about clockstoppers is that it doesn't make any sense . \nwith its paint fights , motorized scooter chases and dewy-eyed sentiment , it's a pretty listless collection of kid-movie clichs . \nmostly the film is just hectic and homiletic : two parts exhausting men in black mayhem to one part family values . \nkicks off with an inauspicious premise , mopes through a dreary tract of virtually plotless meanderings and then ends with a whimper . \na rote exercise in both animation and storytelling . \nthe material and the production itself are little more than routine . \nthe movie's major and most devastating flaw is its reliance on formula , though , and it's quite enough to lessen the overall impact the movie could have had . \nnot even steven spielberg has dreamed up such blatant and sickening product placement in a movie . \nit's all surface psychodramatics . \nthe mothman prophecies , which is mostly a bore , seems to exist only for its climactic setpiece . \nthat frenetic spectacle [on the tv show] has usually been leavened by a charm that's conspicuously missing from the girls' big-screen blowout . \nkitschy , flashy , overlong soap opera . \nfor all the time we spend with these people , we never really get inside of them . \nyet another arnold vehicle that fails to make adequate use of his particular talents . \nsandra bullock , despite downplaying her good looks , carries a little too much ain't- she-cute baggage into her lead role as a troubled and determined homicide cop to quite pull off the heavy stuff . \nan undistinguished attempt to make a classic theater piece cinematic . \ntoo many scenarios in which the hero might have an opportunity to triumphantly sermonize , and too few that allow us to wonder for ourselves if things will turn out okay . \nthere is simply not enough of interest onscreen to sustain its seventy-minute running time . \na wordy wisp of a comedy . \nbroomfield's style of journalism is hardly journalism at all , and even those with an avid interest in the subject will grow impatient . \n[seagal's] strenuous attempt at a change in expression could very well clinch him this year's razzie . \nhas the disjointed feel of a bunch of strung-together tv episodes . \na series of escapades demonstrating the adage that what is good for the goose is also good for the gander , some of which occasionally amuses but none of which amounts to much of a story . \nozpetek offers an aids subtext , skims over the realities of gay sex , and presents yet another tired old vision of the gay community as an all-inclusive world where uptight , middle class bores like antonia can feel good about themselves . \na dopey movie clothed in excess layers of hipness . \nthe sweetest thing is expressly for idiots who don't care what kind of sewage they shovel into their mental gullets to simulate sustenance . \nsinks so low in a poorly played game of absurd plot twists , idiotic court maneuvers and stupid characters that even freeman can't save it . \ni realized that no matter how fantastic reign of fire looked , its story was making no sense at all . \nit made me realize that we really haven't had a good cheesy b-movie playing in theaters since . . . well . . . since last week's reign of fire . \nsome movies were made for the big screen , some for the small screen , and some , like ballistic : ecks vs . sever , were made for the palm screen . \nsc2 is an autopilot hollywood concoction lacking in imagination and authentic christmas spirit , yet it's geared toward an audience full of masters of both . \nafter all the big build-up , the payoff for the audience , as well as the characters , is messy , murky , unsatisfying . \nseems content to dog-paddle in the mediocre end of the pool , and it's a sad , sick sight . \nit's refreshing that someone understands the need for the bad boy ; diesel , with his brawny frame and cool , composed delivery , fits the bill perfectly . \nif all of eight legged freaks was as entertaining as the final hour , i would have no problem giving it an unqualified recommendation . \nsuffers from a flat script and a low budget . \nthere are deeply religious and spiritual people in this world who would argue that entering a church , synagogue or temple doesn't mean you have to check your brain at the door . the same should go for movie theaters . \nseemingly a vehicle to showcase the canadian's inane ramblings , stealing harvard is a smorgasbord of soliloquies about nothing delivered by the former mr . drew barrymore . \nthe film tries to touch on spousal abuse but veers off course and becomes just another revenge film . \nas it stands it's an opera movie for the buffs . \nla cinta comienza intentando ser un drama , rpidamente se transforma en una comedia y termina por ser una parodia absolutamente predecible\nthis franchise has not spawned a single good film . the crap continues . \nif only it were , well , funnier . \nits inescapable absurdities are tantamount to insulting the intelligence of anyone who hasn't been living under a rock ( since sept . 11 ) . \nhigh drama , disney-style - a wing and a prayer and a hunky has-been pursuing his castle in the sky . \nlike its script , which nurses plot holes gaping enough to pilot an entire olympic swim team through , the characters in swimfan seem motivated by nothing short of dull , brain-deadening hangover . \none big blustery movie where nothing really happens . when it comes out on video , then it's the perfect cure for insomnia . \nlike a comedian who starts off promisingly but then proceeds to flop , comedian runs out of steam after a half hour . \nthe pairing does sound promising in theory . . . but their lack of chemistry makes eddie murphy and robert deniro in showtime look like old , familiar vaudeville partners . \ndirector chris eyre is going through the paces again with his usual high melodramatic style of filmmaking . \nas it stands , there's some fine sex onscreen , and some tense arguing , but not a whole lot more . \ni could just feel the screenwriter at every moment 'tap , tap , tap , tap , tapping away' on this screenplay . \nthe picture doesn't know it's a comedy . \na stupid , derivative horror film that substitutes extreme gore for suspense . \nthe rollerball sequences feel sanitised and stagey . \nroman polanski directs the pianist like a surgeon mends a broken heart ; very meticulously but without any passion . \nnothing more than a run-of-the-mill action flick . \nlan yu is certainly a serviceable melodrama , but it doesn't even try for the greatness that happy together shoots for ( and misses ) . \nthis is an action movie with an action icon who's been all but decommissioned . \neven if it is generally amusing from time to time , i spy has all the same problems the majority of action comedies have . \nmuch like robin williams , death to smoochy has already reached its expiration date . \nan annoying orgy of excess and exploitation that has no point and goes nowhere . \na tired , unnecessary retread . . . a stale copy of a picture that wasn't all that great to begin with . \nan often unfunny romp . \na worthy idea , but the uninspired scripts , acting and direction never rise above the level of an after-school tv special . \ntwenty years later , reggio still knows how to make a point with poetic imagery , but his ability to startle has been stifled by the very prevalence of the fast-forward technology that he so stringently takes to task . \nit bites hard . \near-splitting exercise in formula crash-and-bash action . \nwe have an actor who is great fun to watch performing in a film that is only mildly diverting . \ndespite hoffman's best efforts , wilson remains a silent , lumpish cipher ; his encounters reveal nothing about who he is or who he was before . \nthere's a thin line between likably old-fashioned and fuddy-duddy , and the count of monte cristo . . . never quite settles on either side . \nthe emotional overload of female angst irreparably drags the film down . \nschaefer's . . . determination to inject farcical raunch . . . drowns out the promise of the romantic angle . \nlike showgirls and glitter , the most entertaining moments here are unintentional . \nwhile some of the camera work is interesting , the film's mid-to-low budget is betrayed by the surprisingly shoddy makeup work . \nthe origin story is well told , and the characters will not disappoint anyone who values the original comic books . it's in the action scenes that things fall apart . \nimpostor is a step down for director gary fleder . \nseagal , who looks more like danny aiello these days , mumbles his way through the movie . \nthe movie is a negligible work of manipulation , an exploitation piece doing its usual worst to guilt-trip parents . \nlacks dramatic punch and depth . \nthere are moments of real pleasure to be found in sara sugarman's whimsical comedy very annie-mary but not enough to sustain the film . \ni can analyze this movie in three words : thumbs friggin' down . \nsadly , 'garth' hasn't progressed as nicely as 'wayne . '\nmake like the title and dodge this one . \nthis is not one of the movies you'd want to watch if you only had a week to live . \nthe first hour is tedious though ford and neeson capably hold our interest , but its just not a thrilling movie . \nhere's a case of two actors who do everything humanly possible to create characters who are sweet and believable , and are defeated by a screenplay that forces them into bizarre , implausible behavior . \nit's obvious [je-gyu is] trying for poetry ; what he gets instead has all the lyricism of a limerick scrawled in a public restroom . \nsweet home alabama is one dumb movie , but its stupidity is so relentlessly harmless that it almost wins you over in the end . \n[green is] the comedy equivalent of saddam hussein , and i'm just about ready to go to the u . n . and ask permission for a preemptive strike . \nno amount of good acting is enough to save oleander's uninspired story . \na vile , incoherent mess . . . a scummy ripoff of david cronenberg's brilliant 'videodrome . '\nmurphy and wilson actually make a pretty good team . . . but the project surrounding them is distressingly rote . \ndespite its raucous intent , xxx is as conventional as a nike ad and as rebellious as spring break . \nthink the lion king redone for horses , with fewer deliberate laughs , more inadvertent ones and stunningly trite songs by bryan adams , the world's most generic rock star . \nit is one more celluloid testimonial to the cruelties experienced by southern blacks as distilled through a caucasian perspective . \nit's tough being a black man in america , especially when the man has taken away your car , your work-hours and denied you health insurance . \na small fortune in salaries and stunt cars might have been saved if the director , tom dey , had spliced together bits and pieces of midnight run and 48 hours ( and , for that matter , shrek ) . \nan amateurish , quasi-improvised acting exercise shot on ugly digital video . \nthe holes in this film remain agape -- holes punched through by an inconsistent , meandering , and sometimes dry plot . \nwould benigni's italian pinocchio have been any easier to sit through than this hastily dubbed disaster ? \nunofficially , national lampoon's van wilder is son of animal house . officially , it is twice as bestial but half as funny . \nthis is a fragmented film , once a good idea that was followed by the bad idea to turn it into a movie . \nmindless yet impressively lean spinoff of last summer's bloated effects fest the mummy returns . \nhalfway through the movie , the humor dwindles . it's replaced by some dramatic scenes that are jarring and deeply out of place in what could have ( and probably should have ) been a lighthearted comedy . \nwill be far more interesting to the soderbergh faithful than it will be to the casual moviegoer who might be lured in by julia roberts . . . \nauthentic , and at times endearing , humorous , spooky , educational , but at other times as bland as a block of snow . \n[a] mess . \ncontrol-alt-delete simone as quickly as possible\nfollows the original film virtually scene for scene and yet manages to bleed it almost completely dry of humor , verve and fun . \nthe filmmaker ascends , literally , to the olympus of the art world , but he would have done well to end this flawed , dazzling series with the raising of something other than his own cremaster . \nthe screenplay comes across , rather unintentionally , as hip-hop scooby-doo . \nhas lost some of the dramatic conviction that underlies the best of comedies . . . \nvaguely interesting , but it's just too too much . \n . . . no charm , no laughs , no fun , no reason to watch . \na generic family comedy unlikely to be appreciated by anyone outside the under-10 set . \nkung pow seems like some futile concoction that was developed hastily after oedekerk and his fellow moviemakers got through crashing a college keg party . \nkurys seems intimidated by both her subject matter and the period trappings of this debut venture into the heritage business . \nthe film virtually chokes on its own self-consciousness . \na manipulative feminist empowerment tale thinly posing as a serious drama about spousal abuse . \neverything in maid in manhattan is exceedingly pleasant , designed not to offend . it goes down easy , leaving virtually no aftertaste . \na profoundly stupid affair , populating its hackneyed and meanspirited storyline with cardboard characters and performers who value cash above credibility . \n . . . pays tribute to heroes the way julia roberts hands out awards--with phony humility barely camouflaging grotesque narcissism . \ntime stands still in more ways that one in clockstoppers , a sci-fi thriller as lazy as it is interminable . \nas a director , eastwood is off his game -- there's no real sense of suspense , and none of the plot 'surprises' are really surprising . \neccentric enough to stave off doldrums , caruso's self-conscious debut is also eminently forgettable . \nto work , love stories require the full emotional involvement and support of a viewer . that is made almost impossible by events that set the plot in motion . \nalthough barbershop boasts some of today's hottest and hippest acts from the world of television , music and stand-up comedy , this movie strangely enough has the outdated swagger of a shameless 70s blaxploitation shuck-and-jive sitcom . \na puzzle whose pieces do not fit . some are fascinating and others are not , and in the end , it is almost a good movie . \nwould that greengrass had gone a tad less for grit and a lot more for intelligibility . \nthe good is very , very good . . . the rest runs from mildly unimpressive to despairingly awful . \n'butterfingered' is the word for the big-fisted direction of jez butterworth , who manages to blast even the smallest sensitivities from the romance with his clamorous approach . \nbe forewarned , if you're depressed about anything before watching this film , you may just end up trying to drown yourself in a lake afterwards . \na terrible adaptation of a play that only ever walked the delicate tightrope between farcical and loathsome . in the wrong hands , i . e . peploe's , it's simply unbearable\nan inexperienced director , mehta has much to learn . \na limp eddie murphy vehicle that even he seems embarrassed to be part of . \ndramatically lackluster . \nso muddled , repetitive and ragged that it says far less about the horrifying historical reality than about the filmmaker's characteristic style . \na gushy episode of \" m * a * s * h \" only this time from an asian perspective . \n \" looking for leonard \" just seems to kinda sit in neutral , hoping for a stiff wind to blow it uphill or something . \nnothing more than four or five mild chuckles surrounded by 86 minutes of overly-familiar and poorly-constructed comedy . \ndefinitely in the guilty pleasure b-movie category , reign of fire is so incredibly inane that it is laughingly enjoyable . \ngood-looking but relentlessly lowbrow outing plays like clueless does south fork . \nentertaining enough , but nothing new\n . . . one resurrection too many . \nthis is a film about the irksome , tiresome nature of complacency that remains utterly satisfied to remain the same throughout . even as the hero of the story rediscovers his passion in life , the mood remains oddly detached . \ndemeo is not without talent ; he just needs better material . \nin spite of featuring a script credited to no fewer than five writers , apparently nobody here bothered to check it twice . \nno one involved , save dash , shows the slightest aptitude for acting , and the script , credited to director abdul malik abbott and ernest 'tron' anderson , seems entirely improvised . \ninitially gripping , eventually cloying pow drama . \na timid , soggy near miss . \nworks better in the conception than it does in the execution . . . winds up seeming just a little too clever . \nto the vast majority of more casual filmgoers , it will probably be a talky bore . \nobservant intelligence constantly vies with pretension -- and sometimes plain wacky implausibility -- throughout maelstrom . \nthis version of h . g . wells' time machine was directed by h . g . wells' great-grandson . they should have found orson welles' great-grandson . \nshunji iwai's all about lily chou chou is a beautifully shot , but ultimately flawed film about growing up in japan . \nwith more character development this might have been an eerie thriller ; with better payoffs , it could have been a thinking man's monster movie . \nthriller directorial debut for traffic scribe gaghan has all the right parts , but the pieces don't quite fit together . \n . . . would be a total loss if not for two supporting performances taking place at the movie's edges . \nthere's not a single jump-in-your-seat moment and believe it or not , jason actually takes a backseat in his own film to special effects . \ngoldbacher draws on an elegant visual sense and a talent for easy , seductive pacing . . . but she and writing partner laurence coriat don't manage an equally assured narrative coinage . \nthough harris is affecting at times , he cannot overcome the sense that pumpkin is a mere plot pawn for two directors with far less endearing disabilities . \nthe documentary is much too conventional -- lots of boring talking heads , etc . -- to do the subject matter justice . \nthe movie itself appears to be running on hypertime in reverse as the truly funny bits get further and further apart . \nthis is not a jackie chan movie . it's just a movie that happens to have jackie chan in it . and that makes all the difference . \nfar too clever by half , howard's film is really a series of strung-together moments , with all the spaces in between filled with fantasies , daydreams , memories and one fantastic visual trope after another . \nthe problem with movies about angels is they have a tendency to slip into hokum . a rumor of angels doesn't just slip -- it avalanches into forced fuzziness . \nno big whoop , nothing new to see , zero thrills , too many flashbacks and a choppy ending make for a bad film . \ni don't think this movie loves women at all . \nshankman . . . and screenwriter karen janszen bungle their way through the narrative as if it were a series of bible parables and not an actual story . \na negligible british comedy . \nfails to convince the audience that these brats will ever be anything more than losers . \nstay away . far away . \nslack and uninspired , and peopled mainly by characters so unsympathetic that you're left with a sour taste in your mouth . \nskip this turd and pick your nose instead because you're sure to get more out of the latter experience . \nwhat can one say about a balding 50-year-old actor playing an innocent boy carved from a log ? \ntrailer trash cinema so uncool the only thing missing is the \" gadzooks ! \" \nher film is like a beautiful food entre that isn't heated properly , so that it ends up a bit cold and relatively flavorless . \nlike the world of his film , hartley created a monster but didn't know how to handle it . \nno new plot conceptions or environmental changes , just different bodies for sharp objects to rip through . \nneeds more impressionistic cinematography and exhilarating point-of-view shots and fewer slow-motion 'grandeur' shots and quick-cut edits that often detract from the athleticism . \nin the end , there isn't much to it . \na waste of fearless purity in the acting craft . \nthe film is ultimately about as inspiring as a hallmark card . \nanyone not into high-tech splatterfests is advised to take the warning literally , and log on to something more user-friendly . \ndisreputable doings and exquisite trappings are dampened by a lackluster script and substandard performances . \nyou could easily mistake it for a sketchy work-in-progress that was inexplicably rushed to the megaplexes before its time . \ndirectors harry gantz and joe gantz have chosen a fascinating subject matter , but the couples exposing themselves aren't all that interesting . \nyet another entry in the sentimental oh-those-wacky-brits genre that was ushered in by the full monty and is still straining to produce another smash hit . \nfor those for whom the name woody allen was once a guarantee of something fresh , sometimes funny , and usually genuinely worthwhile , hollywood ending is a depressing experience\nfemme fatale offers nothing more than a bait-and-switch that is beyond playing fair with the audience . are we dealing with dreams , visions or being told what actually happened as if it were the third ending of clue ? \nit could have been something special , but two things drag it down to mediocrity -- director clare peploe's misunderstanding of marivaux's rhythms , and mira sorvino's limitations as a classical actress . \nfluffy neo-noir hiding behind cutesy film references . \nimagine susan sontag falling in love with howard stern . \nlike being trapped inside a huge video game , where exciting , inane images keep popping past your head and the same illogical things keep happening over and over again . \nshould have been worth cheering as a breakthrough but is devoid of wit and humor . \nthe best thing about the movie is its personable , amusing cast . \nthese guys seem great to knock back a beer with but they're simply not funny performers . \neverything was as superficial as the forced new jersey lowbrow accent uma had . \no timo esforo do diretor acaba sendo frustrado pelo roteiro , que , depois de levar um bom tempo para colocar a trama em andamento , perde-se de vez a partir do instante em que os estranhos acontecimentos so explicados . \ndirector david fincher and writer david koepp can't sustain it . \nfinally coming down off of miramax's deep shelves after a couple of aborted attempts , waking up in reno makes a strong case for letting sleeping dogs lie . \na movie that feels like the pilot episode of a new teen-targeted action tv series . \none of the most highly-praised disappointments i've had the misfortune to watch in quite some time . \nthe animation and backdrops are lush and inventive , yet return to neverland never manages to take us to that elusive , lovely place where we suspend our disbelief . \ndirector shekhar kapur and screenwriters michael schiffer and hossein amini have tried hard to modernize and reconceptualize things , but the barriers finally prove to be too great . \nstrong filmmaking requires a clear sense of purpose , and in that oh-so-important category , the four feathers comes up short . \nthe thought of watching this film with an audience full of teenagers fixating on its body humour and reinforcement of stereotypes ( of which they'll get plenty ) fills me with revulsion . \ndevolves into the derivative , leaning on badly-rendered cgi effects . \nanyone who gets chills from movies with giant plot holes will find plenty to shake and shiver about in 'the ring . '\na grand fart coming from a director beginning to resemble someone's crazy french grandfather . \nthe script is a disaster , with cloying messages and irksome characters . \nboth overstuffed and undernourished . . . the film can't be called a solid success , although there's plenty of evidence here to indicate clooney might have better luck next time . \nplods along , minus the twisted humor and eye-popping visuals that have made miike . . . a cult hero . \nhollywood has taken quite a nosedive from alfred hitchcock's imaginative flight to shyamalan's self-important summer fluff . \nthe film's maudlin focus on the young woman's infirmity and her naive dreams play like the worst kind of hollywood heart-string plucking . \na pretentious mess . \npredictably soulless techno-tripe . \ni firmly believe that a good video game movie is going to show up soon . i also believe that resident evil is not it . \nit has the air of a surprisingly juvenile lark , a pop-influenced prank whose charms are immediately apparent and wear thin with repetition . \nthe plot meanders from gripping to plodding and back . \nthis is cruel , misanthropic stuff with only weak claims to surrealism and black comedy . \nno amount of nostalgia for carvey's glory days can disguise the fact that the new film is a lame kiddie flick and that carvey's considerable talents are wasted in it . \nbest described as i know what you did last winter . \n[taylor] takes us on a ride that's consistently surprising , easy to watch -- but , oh , so dumb . \nit's difficult for a longtime admirer of his work to not be swept up in invincible and overlook its drawbacks . \nlazily directed by charles stone iii . . . from a leaden script by matthew cirulnick and novelist thulani davis . \nthough jones and snipes are enthralling , the movie bogs down in rhetoric and clich . \nthe most remarkable ( and frustrating ) thing about world traveler , which opens today in manhattan , is that its protagonist , after being an object of intense scrutiny for 104 minutes , remains a complete blank . \nan artsploitation movie with too much exploitation and too little art . \nthe pacing is often way off and there are too many bona fide groaners among too few laughs . \nwith lines that feel like long soliloquies -- even as they are being framed in conversation -- max is static , stilted . \nbarely manages for but a few seconds over its seemingly eternal running time to pique your interest , your imagination , your empathy or anything , really , save your disgust and your indifference . \nwriter/director burr steers emphasizes the q in quirky , with mixed results . \none senses in world traveler and in his earlier film that freundlich bears a grievous but obscure complaint against fathers , and circles it obsessively , without making contact . \nin between the icy stunts , the actors spout hilarious dialogue about following your dream and 'just letting the mountain tell you what to do . '\nthe obligatory break-ups and hook-ups don't seem to have much emotional impact on the characters . \nmake no mistake , ivans xtc . is a mess . \nhypnotically dull , relentlessly downbeat , laughably predictable wail pitched to the cadence of a depressed fifteen-year-old's suicidal poetry . \nthe concept is a hoot . the trailer is a riot . the movie is a dud . \nit's a boring movie about a boring man , made watchable by a bravura performance from a consummate actor incapable of being boring . \nbecause the intelligence level of the characters must be low , very low , very very low , for the masquerade to work , the movie contains no wit , only labored gags . \nit's hard to imagine another director ever making his wife look so bad in a major movie . \nsome stunning visuals -- and some staggeringly boring cinema . \nthese characters become wearisome . \na hit- and-miss affair , consistently amusing but not as outrageous or funny as cho may have intended or as imaginative as one might have hoped . \nthis may be the first cartoon ever to look as if it were being shown on the projection television screen of a sports bar . \nkim ki-deok seems to have in mind an ( emotionally at least ) adolescent audience demanding regular shocks and bouts of barely defensible sexual violence to keep it interested . \na sterling film - a cross between boys don't cry , deliverance , and ode to billy joe - lies somewhere in the story of matthew shepard , but that film is yet to be made . \nafter sitting through this sloppy , made-for-movie comedy special , it makes me wonder if lawrence hates criticism so much that he refuses to evaluate his own work . \ncontrived pastiche of caper clichs . \nmany shallower movies these days seem too long , but this one is egregiously short . \njust a kiss wants desperately to come off as a fanciful film about the typical problems of average people . but it is set in a world that is very , very far from the one most of us inhabit . \nthe most ill-conceived animated comedy since the 1991 dog rover dangerfield . \nlike shave ice without the topping , this cinematic snow cone is as innocuous as it is flavorless . \ndespite its sincere acting , signs is just another unoriginal run of the mill sci-fi film with a flimsy ending and lots of hype . \nyet another movie which presumes that high school social groups are at war , let alone conscious of each other's existence . \nloud , chaotic and largely unfunny . \ni can't remember the last time i saw an audience laugh so much during a movie , but there's only one problemit's supposed to be a drama . \nqualities that were once amusing are becoming irritating . \nwell , jason's gone to manhattan and hell , i guess a space station in the year 2455 can be crossed off the list of ideas for the inevitable future sequels ( hey , don't shoot the messenger ) . \ndonovan . . . squanders his main asset , jackie chan , and fumbles the vital action sequences . \nthere is no psychology here , and no real narrative logic -- just a series of carefully choreographed atrocities , which become strangely impersonal and abstract . \nbread , my sweet has so many flaws it would be easy for critics to shred it . it may even fall into the category of films you love to hate . i admit it , i hate to like it . \nfrida is certainly no disaster , but neither is it the kahlo movie frida fans have been looking for . \nleaks treacle from every pore . \nthe characters are so generic and the plot so bland that even as rogue cia assassins working for chris cooper's agency boss close in on the resourceful amnesiac , we don't feel much for damon/bourne or his predicament . \nkapur weighs down the tale with bogus profundities . \nwhile we want macdowell's character to retrieve her husband , we have to ask whether her personal odyssey trumps the carnage that claims so many lives around her . \nblue crush is as predictable as the tides . . . . the movie feels stitched together from stock situations and characters from other movies . \nif you enjoy being rewarded by a script that assumes you aren't very bright , then blood work is for you . \ntrouble every day is a success in some sense , but it's hard to like a film so cold and dead . \nthe film's stagecrafts are intimate and therefore bolder than the otherwise calculated artifice that defines and overwhelms the film's production design . \na well-intentioned effort that's still too burdened by the actor's offbeat sensibilities for the earnest emotional core to emerge with any degree of accessibility . \na family-friendly fantasy that ends up doing very little with its imaginative premise . \na plodding look at the french revolution through the eyes of aristocrats . \ntom shadyac has learned a bit more craft since directing adams , but he still lingers over every point until the slowest viewer grasps it . \nunspools like a highbrow , low-key , 102-minute infomercial , blending entrepreneurial zeal with the testimony of satisfied customers . \na fast-paced , glitzy but extremely silly piece . \nany reasonably creative eighth-grader could have written a more credible script , though with the same number of continuity errors . \n . . . while the humor aspects of 'jason x' were far more entertaining than i had expected , everything else about the film tanks . \nyour taste for jonah - a veggie tales movie may well depend on your threshold for pop manifestations of the holy spirit . \nlike an afterschool special with costumes by gianni versace , mad love looks better than it feels . \nwhile certain cues , like the happy music , suggest that this movie is supposed to warm our hearts , jeong-hyang lee's film is just as likely to blacken that organ with cold vengefulness . \nthe script , the gags , the characters are all direct-to-video stuff , and that's where this film should have remained . \na thriller without thrills and a mystery devoid of urgent questions . \na collage of clichs and a dim echo of allusions to other films . \nthe film is hampered by its predictable plot and paper-thin supporting characters . \narty gay film . \njonah is only so-so . . . the addition of a biblical message will either improve the film for you , or it will lessen it . \nan excruciating demonstration of the unsalvageability of a movie saddled with an amateurish screenplay . \nhow many more times will indie filmmakers subject us to boring , self-important stories of how horrible we are to ourselves and each other ? \nthere are some laughs in this movie , but williams' anarchy gets tiresome , the satire is weak . \nas steamy as last week's pork dumplings . \nincoherence reigns . \nthe somber pacing and lack of dramatic fireworks make green dragon seem more like medicine than entertainment . \nthe filmmakers needed more emphasis on the storytelling and less on the glamorous machine that thrusts the audience into a future they won't much care about . \nanother wholly unnecessary addition to the growing , moldering pile of , well , extreme stunt pictures . \nthis strenuously unfunny showtime deserves the hook . \nthe whole thing's fairly lame , making it par for the course for disney sequels . \nits solemn pretension prevents us from sharing the awe in which it holds itself . \n . . . the good and different idea [of middle-aged romance] is not handled well and , except for the fine star performances , there is little else to recommend \" never again . \" \nif disney's cinderella proved that 'a dream is a wish your heart makes , ' then cinderella ii proves that a nightmare is a wish a studio's wallet makes . \nfeatures nonsensical and laughable plotting , wooden performances , ineptly directed action sequences and some of the worst dialogue in recent memory . \nwith rare birds , as with the shipping news before it , an attempt is made to transplant a hollywood star into newfoundland's wild soil -- and the rock once again resists the intrusion . \nnothing about this movie works . \nif the idea of the white man arriving on foreign shores to show wary natives the true light is abhorrent to you , the simplistic heaven will quite likely be more like hell . \na spooky yarn of demonic doings on the high seas that works better the less the brain is engaged . \nnone of birthday girl's calculated events take us by surprise . . . \nare monsters born , or made ? \nlisa rinzler's cinematography may be lovely , but love liza's tale itself virtually collapses into an inhalant blackout , maintaining consciousness just long enough to achieve callow pretension . \nthe narrator and the other characters try to convince us that acting transfigures esther , but she's never seen speaking on stage ; one feels cheated , and esther seems to remain an unchanged dullard . \nit's exactly the kind of movie toback's detractors always accuse him of making . \nwith the dog days of august upon us , think of this dog of a movie as the cinematic equivalent of high humidity . \nless about shakespeare than the spawn of fools who saw quentin tarantino's handful of raucous gangster films and branched out into their own pseudo-witty copycat interpretations . \nthe film is like sitting in a downtown caf , overhearing a bunch of typical late-twenty-somethings natter on about nothing , and desperately wishing you could change tables . \nthis rather unfocused , all-over-the-map movie would be a lot better if it pared down its plots and characters to a few rather than dozens . . . or if it were subtler . . . or if it had a sense of humor . \ntakes a clunky tv-movie approach to detailing a chapter in the life of the celebrated irish playwright , poet and drinker . \nnot only does the thoroughly formulaic film represent totally exemplify middle-of-the-road mainstream , it also represents glossy hollywood at its laziest . \na shame that stealing harvard is too busy getting in its own way to be anything but frustrating , boring , and forgettable . \nnearly every attempt at humor here is doa . \ncollapses under its own meager weight . \nthis is mild-mannered , been-there material given a pedestrian spin by a director who needed a touch of the flamboyant , the outrageous . \nif you adored the full monty so resoundingly that you're dying to see the same old thing in a tired old setting , then this should keep you reasonably entertained . \ntechnically and artistically inept . \nthose who are only mildly curious , i fear , will be put to sleep or bewildered by the artsy and often pointless visuals . \nthough tom shadyac's film kicks off spookily enough , around the halfway mark it takes an abrupt turn into glucose sentimentality and laughable contrivance . \na long , dull procession of despair , set to cello music culled from a minimalist funeral . \ncall me a cold-hearted curmudgeon for not being able to enjoy a mindless action movie , but i believe a movie can be mindless without being the peak of all things insipid . \ndeath might be a release . \n'[hopkins]doesn't so much phone in his performance as fax it . no , even that's too committed . he gets his secretary to fax it . \" \nsodden and glum , even in those moments where it's supposed to feel funny and light . \npriggish , lethargically paced parable of renewal . \na beautifully shot but dull and ankle-deep 'epic . '\neven with its $50-million us budget , pinocchio never quite achieves the feel of a fanciful motion picture . \nthis is a third-person story now , told by hollywood , and much more ordinary for it . \nthe filmmakers know how to please the eye , but it is not always the prettiest pictures that tell the best story . \nwritten , flatly , by david kendall and directed , barely , by there's something about mary co-writer ed decter . \nthe characters are interesting and the relationship between yosuke and saeko is worth watching as it develops , but there's not enough to the story to fill two hours . \nvery well made , but doesn't generate a lot of tension . \nlike being invited to a classy dinner soiree and not knowing anyone . you leave the same way you came -- a few tasty morsels under your belt , but no new friends . \nit's depressing to see how far herzog has fallen . \nthe question hanging over the time machine is not , as the main character suggests , 'what if ? ' but rather , 'how can you charge money for this ? '\nmillions of dollars heaped upon a project of such vast proportions need to reap more rewards than spiffy bluescreen technique and stylish weaponry . \n \" freaky friday , \" it's not . \nperhaps a better celebration of these unfairly dismissed heroes would be a film that isn't this painfully forced , false and fabricated . \nalthough no pastry is violated , this nasty comedy pokes fun at the same easy targets as other rowdy raunch-fests -- farts , boobs , unmentionables -- without much success . \nin this film , aussie david caesar channels the not-quite-dead career of guy ritchie . \nmaybe you'll be lucky , and there'll be a power outage during your screening so you can get your money back . \nthe characterizations and dialogue lack depth or complexity , with the ironic exception of scooter . \nthis film was made by and for those folks who collect the serial killer cards and are fascinated by the mere suggestion of serial killers . for the rest of us , sitting through dahmer's two hours amounts to little more than punishment . \nnarc can only remind us of brilliant crime dramas without becoming one itself . \nsomewhere inside the mess that is world traveler , there is a mediocre movie trying to get out . \na tedious parable about honesty and good sportsmanship . \nits strengths and weaknesses play off each other virtually to a stand-off , with the unfortunate trump card being the dreary mid-section of the film . \nan artful yet depressing film that makes a melodramatic mountain out of the molehill of a missing bike . \nthe movie's ultimate point -- that everyone should be themselves -- is trite , but the screenwriter and director michel gondry restate it to the point of ridiculousness . \na glossy knock-off of a b-movie revenge flick . \n . . . expands the horizons of boredom to the point of collapse , turning into a black hole of dullness , from which no interesting concept can escape . \nit's just plain boring . \nsad nonsense , this . but not without cheesy fun factor . \none of those decades-spanning historical epics that strives to be intimate and socially encompassing but fails to do justice to either effort in three hours of screen time . \nreally dumb but occasionally really funny . \nthe movie wavers between hallmark card sentimentality and goofy , life-affirming moments straight out of a cellular phone commercial . \na half-assed film . \nthe director's many dodges and turns add up to little more than a screenful of gamesmanship that's low on both suspense and payoff . \nwhile the transgressive trappings ( especially the frank sex scenes ) ensure that the film is never dull , rodrigues's beast-within metaphor is ultimately rather silly and overwrought , making the ambiguous ending seem goofy rather than provocative . \nthe satire is unfocused , while the story goes nowhere . \nthey threw loads of money at an idea that should've been so much more even if it was only made for teenage boys and wrestling fans . \nit's mired in a shabby script that piles layer upon layer of action man clich atop wooden dialogue and a shifting tone that falls far short of the peculiarly moral amorality of [woo's] best work . \nreyes' directorial debut has good things to offer , but ultimately it's undone by a sloppy script\nif you're over 25 , have an iq over 90 , and have a driver's license , you should be able to find better entertainment . \nthe darker elements of misogyny and unprovoked violence suffocate the illumination created by the two daughters and the sparse instances of humor meant to shine through the gloomy film noir veil . \n . . . the picture's cleverness is ironically muted by the very people who are intended to make it shine . \nnever does \" lilo & stitch \" reach the emotion or timelessness of disney's great past , or even that of more recent successes such as \" mulan \" or \" tarzan . \" \none of those so-so films that could have been much better . \ncrossroads feels like a teenybopper ed wood film , replete with the pubescent scandalous innuendo and the high-strung but flaccid drama . \nfails to satisfactorily exploit its gender politics , genre thrills or inherent humor . \ninterview with the assassin is structured less as a documentary and more as a found relic , and as such the film has a difficult time shaking its blair witch project real-time roots . \ncacoyannis' vision is far less mature , interpreting the play as a call for pity and sympathy for anachronistic phantasms haunting the imagined glory of their own pasts . \nit has more in common with a fireworks display than a movie , which normally is expected to have characters and a storyline . \nit appears to have been made by people to whom the idea of narrative logic or cohesion is an entirely foreign concept . \nless a heartfelt appeal for the handicapped than a nice belgian waffle . \nit's not helpful to listen to extremist name-calling , regardless of whether you think kissinger was a calculating fiend or just a slippery self-promoter . \nabandon spends 90 minutes trying figure out whether or not some cocky pseudo-intellectual kid has intentionally left college or was killed . the only problem is that , by the end , no one in the audience or the film seems to really care . \nno such thing is sort of a minimalist beauty and the beast , but in this case the beast should definitely get top billing . robert john burke as the monster horns in and steals the show . \ndue to stodgy , soap opera-ish dialogue , the rest of the cast comes across as stick figures reading lines from a teleprompter . \n[t]he film is never sure to make a clear point  even if it seeks to rely on an ambiguous presentation . \nwhile it may not add up to the sum of its parts , holofcener's film offers just enough insight to keep it from being simpleminded , and the ensemble cast is engaging enough to keep you from shifting in your chair too often . \nan overwrought taiwanese soaper about three people and their mixed-up relationship . \nnobody seems to have cared much about any aspect of it , from its cheesy screenplay to the grayish quality of its lighting to its last-minute , haphazard theatrical release . \na thoroughly awful movie--dumb , narratively chaotic , visually sloppy . . . a weird amalgam of 'the thing' and a geriatric 'scream . '\n . . . another example of how sandler is losing his touch . \nnothing sticks , really , except a lingering creepiness one feels from being dragged through a sad , sordid universe of guns , drugs , avarice and damaged dreams . \nwhat goes on for the 110 minutes of \" panic room \" is a battle of witlessness between a not-so-bright mother and daughter and an even less capable trio of criminals . \nthe plot's contrivances are uncomfortably strained . \nguilty of the worst sin of attributable to a movie like this : it's not scary in the slightest . \nschnieder bounces around with limp wrists , wearing tight tummy tops and hip huggers , twirling his hair on his finger and assuming that's enough to sustain laughs . . . \nits simplicity puts an exclamation point on the fact that this isn't something to be taken seriously , but it also wrecks any chance of the movie rising above similar fare . \nby the final whistle you're convinced that this mean machine was a decent tv outing that just doesn't have big screen magic . \nto say that this vapid vehicle is downright doltish and uneventful is just as obvious as telling a country skunk that he has severe body odor . \na film of empty , fetishistic violence in which murder is casual and fun . \npretend it's a werewolf itself by avoiding eye contact and walking slowly away . it's fun , but it's a real howler . \nsome fine acting , but ultimately a movie with no reason for being . \nit's difficult to feel anything much while watching this movie , beyond mild disturbance or detached pleasure at the acting . \na waterlogged version of 'fatal attraction' for the teeny-bopper set . . . a sad , soggy potboiler that wastes the talents of its attractive young leads . \nit tells its story in a flat manner and leaves you with the impression that you should have gotten more out of it than you did . \nsweet gentle jesus , did the screenwriters just do a cut-and-paste of every bad action-movie line in history ? \nit's not the worst comedy of the year , but it certainly won't win any honors . \nthis is for the most part a useless movie , even with a great director at the helm . \na loud , witless mess that has none of the charm and little of the intrigue from the tv series . \neven on its own ludicrous terms , the sum of all fears generates little narrative momentum , and invites unflattering comparisons to other installments in the ryan series . \nthough it inspires some ( out-of-field ) creative thought , the film is -- to its own detriment -- much more a cinematic collage than a polemical tract . \nas predictable as the outcome of a globetrotters-generals game , juwanna mann is even more ludicrous than you'd expect from the guy-in-a-dress genre , and a personal low for everyone involved . \nsinks into the usual cafeteria goulash of fart jokes , masturbation jokes , and racist japanese jokes . \nwhere tom green stages his gags as assaults on america's knee-jerk moral sanctimony , jackass lacks aspirations of social upheaval . \nmore of an intriguing curiosity than a gripping thriller . \nthe april 2002 instalment of the american war for independence , complete with loads of cgi and bushels of violence , but not a drop of human blood . \ncontains all the substance of a twinkie -- easy to swallow , but scarcely nourishing . \nreturn to neverland manages to straddle the line between another classic for the company and just another run-of-the-mill disney sequel intended for the home video market . \nrarely does a film so graceless and devoid of merit as this one come along . \nit's a thin notion , repetitively stretched out to feature length , awash in self-consciously flashy camera effects , droning house music and flat , flat dialogue . \non a certain base level , blue crush delivers what it promises , just not well enough to recommend it . \nthe colorful masseur wastes its time on mood rather than riding with the inherent absurdity of ganesh's rise up the social ladder . \n . . . an incredibly heavy-handed , manipulative dud that feels all too familiar . \nwimps out by going for that pg-13 rating , so the more graphic violence is mostly off-screen and the sexuality is muted . \ntrapped presents a frightening and compelling 'what if ? ' scenario that will give most parents pause . . . . then , something terrible happens . \nmadonna has made herself over so often now , there's apparently nothing left to work with , sort of like michael jackson's nose . \nnever having seen the first two films in the series , i can't compare friday after next to them , but nothing would change the fact that what we have here is a load of clams left in the broiling sun for a good three days . \nthe story is lacking any real emotional impact , and the plot is both contrived and cliched . \na depraved , incoherent , instantly disposable piece of hackery . \nit's a bad action movie because there's no rooting interest and the spectacle is grotesque and boring . \n[soderbergh] tends to place most of the psychological and philosophical material in italics rather than trust an audience's intelligence , and he creates an overall sense of brusqueness . \nhandsome and sincere but slightly awkward in its combination of entertainment and evangelical boosterism . \nso aggressively cheery that pollyana would reach for a barf bag . \nscooby-doo doesn't know if it wants to be a retro-refitting exercise in campy recall for older fans or a silly , nickelodeon-esque kiddie flick . \nrussell lacks the visual panache , the comic touch , and perhaps the budget of sommers's title-bout features . \nhighly uneven and inconsistent . . . margarita happy hour kinda resembles the el cheapo margaritas served within . \nvery stupid and annoying . \nthe sum of all fears pretends to be a serious exploration of nuclear terrorism , but it's really nothing more than warmed-over cold war paranoia . \na listless and desultory affair . \nrepresents the depths to which the girls-behaving-badly film has fallen . \nhow inept is serving sara ? it makes even elizabeth hurley seem graceless and ugly . \njam-packed with literally bruising jokes . every five minutes or so , someone gets clocked . \nwins my vote for 'the 2002 enemy of cinema' award . \nany chekhov is better than no chekhov , but it would be a shame if this was your introduction to one of the greatest plays of the last 100 years . \nhelmer hudlin tries to make a hip comedy , but his dependence on slapstick defeats the possibility of creating a more darkly edged tome . \nlazy , miserable and smug . this is one of the biggest disappointments of the year . \nformula 51 has dulled your senses faster and deeper than any recreational drug on the market . \nevery visual joke is milked , every set-up obvious and lengthy , every punchline predictable . there's no energy . \napparently writer-director attal thought he need only cast himself and his movie-star wife sitting around in their drawers to justify a film . \nafter the setup , the air leaks out of the movie , flattening its momentum with about an hour to go . \nthis is a poster movie , a mediocre tribute to films like them ! \nat three hours and with very little story or character development , there is plenty of room for editing , and a much shorter cut surely would have resulted in a smoother , more focused narrative without sacrificing any of the cultural intrigue . \na bit too derivative to stand on its own as the psychological thriller it purports to be . \na crude teen-oriented variation on a theme that the playwright craig lucas explored with infinitely more grace and eloquence in his prelude to a kiss . \nthe film's darker moments become smoothed over by an overwhelming need to tender inspirational tidings , especially in the last few cloying moments . \nif you recognize zeus ( the dog from snatch ) it will make you wish you were at home watching that movie instead of in the theater watching this one . \nabysmally pathetic\nthis is the kind of movie that you only need to watch for about thirty seconds before you say to yourself , 'ah , yes , here we have a bad , bad , bad movie . '\nshanghai ghetto should be applauded for finding a new angle on a tireless story , but you might want to think twice before booking passage . \nplays like a checklist of everything rob reiner and his cast were sending up . \nthere's too much forced drama in this wildly uneven movie , about a young man's battle with his inescapable past and uncertain future in a very shapable but largely unfulfilling present . \nit's at once laughable and compulsively watchable , in its committed dumbness . \nall the sensuality , all the eroticism of a good vampire tale has been , pardon the pun , sucked out and replaced by goth goofiness . \na cross between blow and boyz n the hood , this movie strives to be more , but doesn't quite get there . good performances keep it from being a total rehash . \nthe screenplay is hugely overwritten , with tons and tons of dialogue -- most of it given to children . \ntroll the cult section of your local video store for the real deal . \nat times , the movie looks genuinely pretty . your nightmares , on the other hand , will be anything but . not even felinni would know what to make of this italian freakshow . \nelmo touts his drug as being 51 times stronger than coke . if you're looking for a tale of brits behaving badly , watch snatch again . it's 51 times better than this . \nit's difficult to conceive of anyone who has reached puberty actually finding the characters in slackers or their antics amusing , let alone funny . \ndespite its promising cast of characters , big trouble remains a loosely tied series of vignettes which only prove that 'zany' doesn't necessarily mean 'funny . '\nboth shrill and soporific , and because everything is repeated five or six times , it can seem tiresomely simpleminded . \ndoes not go far enough in its humor or stock ideas to stand out as particularly memorable or even all that funny . \nneither revelatory nor truly edgy--merely crassly flamboyant and comedically labored . \njust about everyone involved here seems to be coasting . there are a few modest laughs , but certainly no thrills . \nfails so fundamentally on every conventional level that it achieves some kind of goofy grandeur . \nthere's a persistent theatrical sentiment and a woozy quality to the manner of the storytelling , which undercuts the devastatingly telling impact of utter loss personified in the film's simple title . \nwhile howard's appreciation of brown and his writing is clearly well-meaning and sincere , the movie would be impossible to sit through were it not for the supporting cast . \na preposterous , prurient whodunit . \ngo , girls , right down the reality drain . \nboasting some of the most poorly staged and lit action in memory , impostor is as close as you can get to an imitation movie . \ncan be classified as one of those 'alternate reality' movies . . . except that it would have worked so much better dealing in only one reality . \npredictable and cloying , though brown sugar is so earnest in its yearning for the days before rap went nihilistic that it summons more spirit and bite than your average formulaic romantic quadrangle . \n . . . unlikable , uninteresting , unfunny , and completely , utterly inept . \nthe film is so busy making reference to other films and trying to be other films that it fails to have a heart , mind or humor of its own . \nan imponderably stilted and self-consciously arty movie . \nmuddled , melodramatic paranormal romance is an all-time low for kevin costner . \ntoo clumsy in key moments . . . to make a big splash . \njust a bunch of good actors flailing around in a caper that's neither original nor terribly funny . \n`matrix'-style massacres erupt throughout . . . but the movie has a tougher time balancing its violence with kafka-inspired philosophy . \nat least it's a fairly impressive debut from the director , charles stone iii . \nit all unfolds predictably , and the adventures that happen along the way seem repetitive and designed to fill time , providing no real sense of suspense . \nwanker goths are on the loose ! run for your lives ! \nwhy would anyone cast the magnificent jackie chan in a movie full of stunt doubles and special effects ? \na grating , emaciated flick . \nunambitious writing emerges in the movie , using a plot that could have come from an animated-movie screenwriting textbook . \npresents a good case while failing to provide a reason for us to care beyond the very basic dictums of human decency . \nwe have poignancy jostling against farce , thoughtful dialogue elbowed aside by one-liners , and a visual style that incorporates rotoscope animation for no apparent reason except , maybe , that it looks neat . \n . . . unbearably lame . \naccording to the script , grant and bullock's characters are made for each other . but you'd never guess that from the performances . \nthe animation merely serves up a predictable , maudlin story that swipes heavily from bambi and the lion king , yet lacks the emotional resonance of either of those movies . \nararat feels like a book report\nsteve oedekerk is , alas , no woody allen . \na lot like the imaginary sport it projects onto the screen -- loud , violent and mindless . \nan amalgam of the fugitive , blade runner , and total recall , only without much energy or tension . \nthe acting is amateurish , the cinematography is atrocious , the direction is clumsy , the writing is insipid and the violence is at once luridly graphic and laughably unconvincing . \nshows that jackie chan is getting older , and that's something i would rather live in denial about\nwith miscast leads , banal dialogue and an absurdly overblown climax , killing me softly belongs firmly in the so-bad-it's-good camp . \nalas , the black-and-white archival footage of their act showcases pretty mediocre shtick . \nthe slapstick is labored , and the bigger setpieces flat . \nthis is the kind of movie where people who have never picked a lock do so easily after a few tries and become expert fighters after a few weeks . \nthe problem with the mayhem in formula 51 is not that it's offensive , but that it's boring . \nmuch of the digitally altered footage appears jagged , as if filmed directly from a television monitor , while the extensive use of stock footage quickly becomes a tiresome clich . \nthe film never rises above a conventional , two dimension tale\nmark wahlberg . . . may look classy in a '60s-homage pokepie hat , but as a character he's dry , dry , dry . \ntold in scattered fashion , the movie only intermittently lives up to the stories and faces and music of the men who are its subject . \nthe irony is that this film's cast is uniformly superb ; their performances could have -- should have -- been allowed to stand on their own . \nnow i can see why people thought i was too hard on \" the mothman prophecies \" . \nif ever a concept came handed down from the movie gods on a silver platter , this is it . if ever such a dependable concept was botched in execution , this is it . \nwith an unusual protagonist ( a kilt-wearing jackson ) and subject matter , the improbable \" formula 51 \" is somewhat entertaining , but it could have been much stronger . \nsandra bullock's best dramatic performance to date ( is ) almost enough to lift ( this ) thrill-kill cat-and-mouser . . . above its paint-by-numbers plot . \na feel-good movie that doesn't give you enough to feel good about . \nadolescents will be adequately served by the movie's sophomoric blend of shenanigans and slapstick , although the more lascivious-minded might be disappointed in the relative modesty of a movie that sports a 'topless tutorial service . '\nthis mistaken-identity picture is so film-culture referential that the final product is a ghost . \nthe picture emerges as a surprisingly anemic disappointment . \nde niro cries . you'll cry for your money back . \nslap me , i saw this movie . \n[the kid's] just too bratty for sympathy , and as the film grows to its finale , his little changes ring hollow . \nbehind the glitz , hollywood is sordid and disgusting . quelle surprise ! \nscherfig , who has had a successful career in tv , tackles more than she can handle . \njust consider what new best friend does not have , beginning with the minor omission of a screenplay . \noscar caliber cast doesn't live up to material\nthe problems of the people in love in the time of money are hardly specific to their era . they just have problems , which are neither original nor are presented in convincing way . \ncarrying this wafer-thin movie on his nimble shoulders , chan wades through putrid writing , direction and timing with a smile that says , 'if i stay positive , maybe i can channel one of my greatest pictures , drunken master . '\nso putrid it is not worth the price of the match that should be used to burn every print of the film . \nin the end , the movie bogs down in insignificance , saying nothing about kennedy's assassination and revealing nothing about the pathology it pretends to investigate . \nstarts out ballsy and stylish but fails to keep it up and settles into clichs . \nsometimes makes less sense than the bruckheimeresque american action flicks it emulates . \none of those films where the characters inhabit that special annex of hell where adults behave like kids , children behave like adults and everyone screams at the top of their lungs no matter what the situation . \nthere's only one way to kill michael myers for good : stop buying tickets to these movies . \nbland but harmless . \n'rare birds' tries to force its quirkiness upon the audience . \nthe movie is about as humorous as watching your favorite pet get buried alive . \nresident evil is what comes from taking john carpenter's ghosts of mars and eliminating the beheadings . in other words , about as bad a film you're likely to see all year . \nfive screenwriters are credited with the clich-laden screenplay ; it seems as if each watered down the version of the one before . \nthe whole thing comes off like a particularly amateurish episode of bewitched that takes place during spring break . \nwell made but uninvolving , bloodwork isn't a terrible movie , just a stultifyingly obvious one -- an unrewarding collar for a murder mystery . \nso we got ten little indians meets friday the 13th by way of clean and sober , filmed on the set of carpenter's the thing and loaded with actors you're most likely to find on the next inevitable incarnation of the love boat . \nthe movie's blatant derivativeness is one reason it's so lackluster . \nla de salma es una versin de frida superficial , preciosista y sin ningn contenido . \nkids don't mind crappy movies as much as adults , provided there's lots of cute animals and clumsy people . 'snow dogs' has both . \nit's almost as if it's an elaborate dare more than a full-blooded film . \nwobbly senegalese updating of \" carmen \" which is best for the stunning star turn by djeinaba diop gai\nit's the humanizing stuff that will probably sink the film for anyone who doesn't think about percentages all day long . \nken russell would love this . in one scene , we get a stab at soccer hooliganism , a double-barreled rip-off of quentin tarantino's climactic shootout  and meat loaf explodes . \nbella is the picture of health with boundless energy until a few days before she dies . this is absolutely and completely ridiculous and an insult to every family whose mother has suffered through the horrible pains of a death by cancer . \nthe premise of \" abandon \" holds promise , . . . but its delivery is a complete mess . \nwhat could have been a pointed little chiller about the frightening seductiveness of new technology loses faith in its own viability and succumbs to joyless special-effects excess . \na little too ponderous to work as shallow entertainment , not remotely incisive enough to qualify as drama , monsoon wedding serves mostly to whet one's appetite for the bollywood films . \nunless bob crane is someone of particular interest to you , this film's impressive performances and adept direction aren't likely to leave a lasting impression . \nthe rock has a great presence but one battle after another is not the same as one battle followed by killer cgi effects . \nthe bottom line with nemesis is the same as it has been with all the films in the series : fans will undoubtedly enjoy it , and the uncommitted needn't waste their time on it . \nthe lousy john q all but spits out denzel washington's fine performance in the title role . \nthe whole thing feels like a ruse , a tactic to cover up the fact that the picture is constructed around a core of flimsy -- or , worse yet , nonexistent -- ideas . \nwhat a stiflingly unfunny and unoriginal mess this is ! \nthe film is so packed with subplots involving the various silbersteins that it feels more like the pilot episode of a tv series than a feature film . \nopera on film is never satisfactory . the art demands live viewing . the innate theatrics that provide its thrills and extreme emotions lose their luster when flattened onscreen . \ndespite all the closed-door hanky-panky , the film is essentially juiceless . \nit is parochial , accessible to a chosen few , standoffish to everyone else , and smugly suggests a superior moral tone is more important than filmmaking skill\nthe sweetest thing leaves an awful sour taste . \nit's lost the politics and the social observation and become just another situation romance about a couple of saps stuck in an inarticulate screenplay . \nterminally bland , painfully slow and needlessly confusing . . . the movie , shot on digital videotape rather than film , is frequently indecipherable . \nas dumb and cheesy as they may be , the cartoons look almost shakespearean -- both in depth and breadth -- after watching this digital-effects-heavy , supposed family-friendly comedy . \naloof and lacks any real raw emotion , which is fatal for a film that relies on personal relationships . \na low-rent retread of the alien pictures . \nserviceable at best , slightly less than serviceable at worst . \nits initial excitement settles into a warmed over pastiche . \na big meal of cliches that the talented cast generally chokes on . \nthe story has little wit and no surprises . \nthe merchant-ivory team continues to systematically destroy everything we hold dear about cinema , only now it's begun to split up so that it can do even more damage . \nwhat should have been a cutting hollywood satire is instead about as fresh as last week's issue of variety . \nhey everybody , wanna watch a movie in which a guy dressed as a children's party clown gets violently gang-raped ? i didn't think so . \na little more intensity and a little less charm would have saved this film a world of hurt . \ndense and enigmatic . . . elusive . . . stagy and stilted\n[t]his slop doesn't even have potential as a cult film , as it's too loud to shout insults at the screen . \nthe movie's plot is almost entirely witless and inane , carrying every gag two or three times beyond its limit to sustain a laugh . \n . . . may work as an addictive guilty pleasure but the material never overcomes its questionable satirical ambivalence . this scarlet's letter is a . . . as in aimless , arduous , and arbitrary . \nplays like a glossy melodrama that occasionally verges on camp . \nthe central character isn't complex enough to hold our interest . \na modestly comic , modestly action-oriented world war ii adventure that , in terms of authenticity , is one of those films that requires the enemy to never shoot straight . \na puppy dog so desperate for attention it nearly breaks its little neck trying to perform entertaining tricks . \njust about all of the film is confusing on one level or another , making ararat far more demanding than it needs to be . \na little less extreme than in the past , with longer exposition sequences between them , and with fewer gags to break the tedium . \nthere's a heavy stench of 'been there , done that' hanging over the film . it's everything you'd expect -- but nothing more . \nthe biggest problem with satin rouge is lilia herself . she's a cipher , played by an actress who smiles and frowns but doesn't reveal an inner life . \na quaint , romanticized rendering . \nwhat with the incessant lounge music playing in the film's background , you may mistake love liza for an adam sandler chanukah song . \nthe movie's heavy-handed screenplay navigates a fast fade into pomposity and pretentiousness . \na potentially good comic premise and excellent cast are terribly wasted . \nwoody allen used to ridicule movies like hollywood ending . now he makes them . \nshe's not yet an actress , not quite a singer . . . \nnot a bad premise , but the execution is lackluster at best . \nbeen there done that . \nthere is only so much baked cardboard i need to chew . \na movie like the guys is why film criticism can be considered work . \nschnitzler's film has a great hook , some clever bits and well-drawn , if standard issue , characters , but is still only partly satisfying . \neven if it made its original release date last fall , it would've reeked of a been-there , done-that sameness . \nonly two-fifths of a satisfying movie experience . \na loud , ugly , irritating movie without any of its satirical salvos hitting a discernible target . \n[l]ame and unnecessary . \na movie version of a paint-by-numbers picture . we can tell what it is supposed to be , but can't really call it a work of art . \nit's a brilliant , honest performance by nicholson , but the film is an agonizing bore except when the fantastic kathy bates turns up . bravado kathy ! \n . . . liotta is put in an impossible spot because his character's deceptions ultimately undo him and the believability of the entire scenario . too bad . \nyou can thank me for this . i saw juwanna mann so you don't have to . \nunfunny and lacking any sense of commitment to or affection for its characters , the reginald hudlin comedy relies on toilet humor , ethnic slurs . \nbasically , it's pretty but dumb . \nthis romantic/comedy asks the question how much souvlaki can you take before indigestion sets in . \nsquandering his opportunity to make absurdist observations , burns gets caught up in the rush of slapstick thoroughfare . \nthere's a neat twist , subtly rendered , that could have wrapped things up at 80 minutes , but kang tacks on three or four more endings . \nreeboir varies between a sweet smile and an angry bark , while said attempts to wear down possible pupils through repetition . it has no affect on the kurds , but it wore me down . \nthe actors improvise and scream their way around this movie directionless , lacking any of the rollicking dark humor so necessary to make this kind of idea work on screen . \nco-writer/director jonathan parker's attempts to fashion a brazil-like , hyper-real satire fall dreadfully short . \nif this silly little cartoon can inspire a few kids not to grow up to be greedy bastards , more power to it . \na superfluous sequel . . . plagued by that old familiar feeling of 'let's get this thing over with' : everyone has shown up at the appointed time and place , but visible enthusiasm is mighty hard to find . \nif there's a heaven for bad movies , deuces wild is on its way . \ncomes off like a bad imitation of the bard . \nwhat's missing in murder by numbers is any real psychological grounding for the teens' deviant behaviour . being latently gay and liking to read are hardly enough . \nan uninspired preachy and clichd war film . \nhorrendously amateurish filmmaking that is plainly dull and visually ugly when it isn't incomprehensible . \na movie that harps on media-constructed 'issues' like whether compromise is the death of self this orgasm [won't be an] exceedingly memorable one for most people . \nslackers' jokey approach to college education is disappointingly simplistic -- the film's biggest problem -- and there are no unforgettably stupid stunts or uproariously rude lines of dialogue to remember it by . \nif festival in cannes nails hard- boiled hollywood argot with a bracingly nasty accuracy , much about the film , including some of its casting , is frustratingly unconvincing . \nthe movie is too impressed with its own solemn insights to work up much entertainment value . \ni haven't seen such self-amused trash since freddy got fingered . \nlittle more than a well-mounted history lesson . \nrob schneider's infantile cross-dressing routines fill the hot chick , the latest gimmick from this unimaginative comedian . \na horrible , 99-minute stink bomb . \nthe film is weighed down by supporting characters who are either too goodly , wise and knowing or downright comically evil . \nher fans walked out muttering words like \" horrible \" and \" terrible , \" but had so much fun dissing the film that they didn't mind the ticket cost . in this case zero . \nthe film is so bad it doesn't improve upon the experience of staring at a blank screen . \nsheridan's take on the author's schoolboy memoir . . . is a rather toothless take on a hard young life . \nit jumps around with little logic or continuity , presenting backstage bytes of information that never amount to a satisfying complete picture of this particular , anciently demanding mtier . \nhow i killed my father is one of those art house films that makes you feel like you're watching an iceberg melt -- only it never melts . \nwhen it comes to the battle of hollywood vs . woo , it looks like woo's a p . o . w . \nthere are a few chuckles , but not a single gag sequence that really scores , and the stars seem to be in two different movies . \nthe chateau has one very funny joke and a few other decent ones , but all it amounts to is a mildly funny , sometimes tedious , ultimately insignificant film . \nit's dull , spiritless , silly and monotonous : an ultra-loud blast of pointless mayhem , going nowhere fast . \nthe mushy finale turns john q into a movie-of-the-week tearjerker . \ncontent merely to lionize its title character and exploit his anger - all for easy sanctimony , formulaic thrills and a ham-fisted sermon on the need for national health insurance . \nthe movie turns out to be [assayas'] homage to the gallic 'tradition of quality , ' in all its fusty squareness . \nits message has merit and , in the hands of a brutally honest individual like prophet jack , might have made a point or two regarding life . \n[seems] even more uselessly redundant and shamelessly money-grubbing than most third-rate horror sequels . \nit's hard to imagine that even very small children will be impressed by this tired retread . \nneither as scary-funny as tremors nor demented-funny as starship troopers , the movie isn't tough to take as long as you've paid a matinee price . \nif swimfan does catch on , it may be because teens are looking for something to make them laugh . \nwhat might've been an exhilarating exploration of an odd love triangle becomes a sprawl of uncoordinated vectors . \nthe master of disguise may have made a great saturday night live sketch , but a great movie it is not . \nit's quite an achievement to set and shoot a movie at the cannes film festival and yet fail to capture its visual appeal or its atmosphere . \nboll uses a lot of quick cutting and blurry step-printing to goose things up , but dopey dialogue and sometimes inadequate performances kill the effect . \nit's always disappointing when a documentary fails to live up to -- or offer any new insight into -- its chosen topic . unfortunately , that's precisely what arthur dong's family fundamentals does . \nhas the marks of a septuagenarian ; it's a crusty treatment of a clever gimmick . \nlike a medium-grade network sitcom--mostly inoffensive , fitfully amusing , but ultimately so weightless that a decent draft in the auditorium might blow it off the screen . \nsomething must have been lost in the translation . \nbecomes the last thing you would expect from a film with this title or indeed from any plympton film : boring . \nin the end , the film feels homogenized and a bit contrived , as if we're looking back at a tattered and ugly past with rose-tinted glasses . \nchan's stunts are limited and so embellished by editing that there's really not much of a sense of action or even action-comedy . \nrock's stand-up magic wanes . hopkins , squarely fills the screen . action - mechanical . \n \" the tuxedo \" should have been the vehicle for chan that \" the mask \" was for jim carrey . alas , it's the man that makes the clothes . \nfor casual moviegoers who stumble into rules expecting a slice of american pie hijinks starring the kid from dawson's creek , they'll probably run out screaming . \nthe biggest problem i have ( other than the very sluggish pace ) is we never really see her esther blossom as an actress , even though her talent is supposed to be growing . \nwhat puzzles me is the lack of emphasis on music in britney spears' first movie . \nplot , characters , drama , emotions , ideas -- all are irrelevant to the experience of seeing the scorpion king . \ncity by the sea is a gritty police thriller with all the dysfunctional family dynamics one could wish for . but how it washed out despite all of that is the project's prime mystery . \nwhatever the movie's sentimental , hypocritical lessons about sexism , its true colors come out in various wet t-shirt and shower scenes . \nas a hybrid teen thriller and murder mystery , murder by numbers fits the profile too closely . \nthere ain't a lot more painful than an unfunny movie that thinks it's hilarious . \ni enjoyed the movie in a superficial way , while never sure what its purpose was . \nwhat a pity . . . that the material is so second-rate . \ndoesn't deliver a great story , nor is the action as gripping as in past seagal films . \nthe kind of film that leaves you scratching your head in amazement over the fact that so many talented people could participate in such an ill-advised and poorly executed idea . \nnicks refuses to let slackers be seen as just another teen movie , which means he can be forgiven for frequently pandering to fans of the gross-out comedy . \nnothing about the film -- with the possible exception of elizabeth hurley's breasts -- is authentic . \namid the clich and foreshadowing , cage manages a degree of casual realism . . . that is routinely dynamited by blethyn . \nmostly , shafer and co-writer gregory hinton lack a strong-minded viewpoint , or a sense of humor . \nno cliche escapes the perfervid treatment of gang warfare called ces wild . \neddie murphy and owen wilson have a cute partnership in i spy , but the movie around them is so often nearly nothing that their charm doesn't do a load of good . \nstrictly a 'guy's film' in the worst sense of the expression . \nthere's some good material in their story about a retail clerk wanting more out of life , but the movie too often spins its wheels with familiar situations and repetitive scenes . \nit's a lot to ask people to sit still for two hours and change watching such a character , especially when rendered in as flat and impassive a manner as phoenix's . \nthere's something fishy about a seasonal holiday kids' movie . . . that derives its moment of most convincing emotional gravity from a scene where santa gives gifts to grownups . \nwe're left with a story that tries to grab us , only to keep letting go at all the wrong moments . \nlike many such biographical melodramas , it suffers from the awkwardness that results from adhering to the messiness of true stories . \nthere is nothing redeeming about this movie . \nthe film has [its] moments , but they are few and far between . \na dreary indulgence . \ni was trying to decide what annoyed me most about god is great . . . i'm not , and then i realized that i just didn't care . \nderailed by bad writing and possibly also by some of that extensive post-production reworking to aim the film at young males in the throes of their first full flush of testosterone . \ndeserves high marks for political courage but barely gets by on its artistic merits . \n . . . comes alive only when it switches gears to the sentimental . \nbrosnan's finest non-bondish performance yet fails to overcome the film's manipulative sentimentality and annoying stereotypes . \na film that will be best appreciated by those willing to endure its extremely languorous rhythms , waiting for happiness is ultimately thoughtful without having much dramatic impact . \nto me , it sounds like a cruel deception carried out by men of marginal intelligence , with reactionary ideas about women and a total lack of empathy . \ntsai may be ploughing the same furrow once too often . \nflashy gadgets and whirling fight sequences may look cool , but they can't distract from the flawed support structure holding equilibrium up . \nzigzag might have been richer and more observant if it were less densely plotted . \n[a] crushing disappointment . \nhow can such a cold movie claim to express warmth and longing ? in truth , it has all the heart of a porno flick ( but none of the sheer lust ) . \nnicks and steinberg match their own creations for pure venality -- that's giving it the old college try . \nepisode ii-- attack of the clones is a technological exercise that lacks juice and delight . \nthe problem with all of this : it's not really funny . \n[denis'] bare-bones narrative more closely resembles an outline for a '70s exploitation picture than the finished product . \nwanders all over the map thematically and stylistically , and borrows heavily from lynch , jeunet , and von trier while failing to find a spark of its own . \nviewing this underdramatized but overstated film is like watching a transcript of a therapy session brought to humdrum life by some freudian puppet . \noverall tomfoolery like this is a matter of taste . \nthe mantra behind the project seems to have been 'it's just a kids' flick . ' translation : 'we don't need to try very hard . '\nin all the annals of the movies , few films have been this odd , inexplicable and unpleasant . \nit takes a really long , slow and dreary time to dope out what tuck everlasting is about . so here it is : it's about a family of sour immortals . \nan essentially awkward version of the lightweight female empowerment picture we've been watching for decades\nthe author's devotees will probably find it fascinating ; others may find it baffling . \nwriter-director walter hill and co-writer david giler try to create characters out of the obvious cliches , but wind up using them as punching bags . \nthere's a scientific law to be discerned here that producers would be well to heed : mediocre movies start to drag as soon as the action speeds up ; when the explosions start , they fall to pieces . \na cockeyed shot all the way . \nlush and beautifully photographed ( somebody suggested the stills might make a nice coffee table book ) , but ultimately you'll leave the theater wondering why these people mattered . \nunfortunately , one hour photo lives down to its title . thanks largely to williams , all the interesting developments are processed in 60 minutes -- the rest is just an overexposed waste of film . \ncold , sterile and lacking any color or warmth . \nthe film is undone by anachronistic quick edits and occasional jarring glimpses of a modern theater audience watching the events unfold . \nseems like someone going through the motions . \nfor a film about explosions and death and spies , \" ballistic : ecks vs . sever \" seems as safe as a children's film . well , in some of those , the mother deer even dies . \nwallace gets a bit heavy handed with his message at times , and has a visual flair that waxes poetic far too much for our taste . \nimpostor doesn't do much with its template , despite a remarkably strong cast . \nwraps itself in the guise of a dark and quirky comedy , but it isn't as quirky as it thinks it is and its comedy is generally mean-spirited . \nchoppy , overlong documentary about 'the lifestyle . '\none sloughs one's way through the mire of this alleged psychological thriller in search of purpose or even a plot . \na film which presses familiar herzog tropes into the service of a limpid and conventional historical fiction , when really what we demand of the director is to be mesmerised . \nit's a fanboy 'what if ? ' brought to life on the big screen . \nthe story itself is actually quite vapid . \nit's a hellish , numbing experience to watch , and it doesn't offer any insights that haven't been thoroughly debated in the media already , back in the dahmer heyday of the mid-'90s . \nwait for pay per view or rental but don't dismiss barbershop out of hand . \na few zingers aside , the writing is indifferent , and jordan brady's direction is prosaic . \neach scene drags , underscoring the obvious , and sentiment is slathered on top . \nwould've been nice if the screenwriters had trusted audiences to understand a complex story , and left off the film's predictable denouement . then nadia's birthday might not have been such a bad day after all . \none of those staggeringly well-produced , joylessly extravagant pictures that keep whooshing you from one visual marvel to the next , hastily , emptily . \nnair just doesn't have the necessary self-control to guide a loose , poorly structured film through the pitfalls of incoherence and redundancy . \nenthusiastically taking up the current teen movie concern with bodily functions , walt becker's film pushes all the demographically appropriate comic buttons . \nit's the funniest american comedy since graffiti bridge . \nthat neither protagonist has a distinguishable condition hardly matters because both are just actory concoctions , defined by childlike dimness and a handful of quirks . \nwhat starts off as a possible argentine american beauty reeks like a room stacked with pungent flowers . \nthe project's filmmakers forgot to include anything even halfway scary as they poorly rejigger fatal attraction into a high school setting . \nin old-fashioned screenwriting parlance , ms . shreve's novel proved too difficult a text to 'lick , ' despite the efforts of a first-rate cast . \nsolondz may well be the only one laughing at his own joke\nstitch is a bad mannered , ugly and destructive little * * * * . no cute factor here . not that i mind ugly ; the problem is he has no character , loveable or otherwise . \ndeep down , i realized the harsh reality of my situation : i would leave the theater with a lower i . q . than when i had entered . \na really funny fifteen-minute short stretched beyond its limits to fill an almost feature-length film . \naside from the fact that the film idiotically uses the website feardotcom . com or the improperly hammy performance from poor stephen rea , the film gets added disdain for the fact that it is nearly impossible to look at or understand . \nit is bad , but certainly not without merit as entertainment . \nfor its 100 minutes running time , you'll wait in vain for a movie to happen . \na work that lacks both a purpose and a strong pulse . \na faster paced family flick . upper teens may get cynical . smaller numbered kidlets will enjoy . \nwhile this film has an 'a' list cast and some strong supporting players , the tale -- like its central figure , vivi -- is just a little bit hard to love . \nit's a road-trip drama with too many wrong turns . \nmost fish stories are a little peculiar , but this is one that should be thrown back in the river . \nit's all gratuitous before long , as if schwentke were fulfilling a gross-out quota for an anticipated audience demographic instead of shaping the material to fit the story . \n \" i blame all men for war , \" [the warden's daughter] tells her father . the movie is about as deep as that sentiment . \nit's fitfully funny but never really takes off . \ni've seen some bad singer-turned actors , but lil bow wow takes the cake . \nby halfway through this picture i was beginning to hate it , and , of course , feeling guilty for it . . . . then , miracle of miracles , the movie does a flip-flop . \nfor all the complications , it's all surprisingly predictable . \nit's been 20 years since 48 hrs . made eddie murphy a movie star and the man hasn't aged a day . but his showboating wise-cracker stock persona sure is getting old . \nif deuces wild had been tweaked up a notch it would have become a camp adventure , one of those movies that's so bad it starts to become good . but it wasn't . \nfor a film about action , ultimate x is the gabbiest giant-screen movie ever , bogging down in a barrage of hype . \n . . . a low rate annie featuring some kid who can't act , only echoes of jordan , and weirdo actor crispin glover screwing things up old school . \nit might not be 1970s animation , but everything else about it is straight from the saturday morning cartoons  a retread story , bad writing , and the same old silliness . \nthe picture seems uncertain whether it wants to be an acidic all-male all about eve or a lush , swooning melodrama in the intermezzo strain . \ntends to plod . \na nearly 21/2 hours , the film is way too indulgent . \ngorgeous to look at but insufferably tedious and turgid . . . a curiously constricted epic . \nit looks much more like a cartoon in the end than the simpsons ever has . \nwith a tighter editorial process and firmer direction this material could work , especially since the actresses in the lead roles are all more than competent , but as is , personal velocity seems to be idling in neutral . \ndoesn't really add up to much . \nit's better suited for the history or biography channel , but there's no arguing the tone of the movie - it leaves a bad taste in your mouth and questions on your mind . \nan entertainment so in love with its overinflated mythology that it no longer recognizes the needs of moviegoers for real characters and compelling plots . \na prolonged extrusion of psychopathic pulp . \nborrows from other movies like it in the most ordinary and obvious fashion . \nit's surprisingly bland despite the heavy doses of weird performances and direction . \na chilly , remote , emotionally distant piece . . . so dull that its tagline should be : 'in space , no one can hear you snore . '\nthe characters seem one-dimensional , and the film is superficial and will probably be of interest primarily to its target audience . \nsorvino makes the princess seem smug and cartoonish , and the film only really comes alive when poor hermocrates and leontine pathetically compare notes about their budding amours . \nit's like a drive-by . you can drive right by it without noticing anything special , save for a few comic turns , intended and otherwise . \neverything -- even life on an aircraft carrier -- is sentimentalized . \nthis would-be 'james bond for the extreme generation' pic is one big , dumb action movie . stress 'dumb . '\nthe movie has generic virtues , and despite a lot of involved talent , seems done by the numbers . \nwhen your subject is illusion versus reality , shouldn't the reality seem at least passably real ? \nit's a terrible movie in every regard , and utterly painful to watch . \nthis is rote spookiness , with nary an original idea ( or role , or edit , or score , or anything , really ) in sight , and the whole of the proceedings beg the question 'why ? '\na fan film that for the uninitiated plays better on video with the sound turned down . \ntoo infuriatingly quirky and taken with its own style . \nthere's a whole heap of nothing at the core of this slight coming-of-age/coming-out tale . \nas much as i laughed throughout the movie , i cannot mount a cogent defense of the film as entertainment , or even performance art , although the movie does leave you marveling at these guys' superhuman capacity to withstand pain . \nthe type of dumbed-down exercise in stereotypes that gives the [teen comedy] genre a bad name . \ndistinctly sub-par . . . more likely to drown a viewer in boredom than to send any shivers down his spine . \nplays like a bad blend of an overripe episode of tv's dawson's creek and a recycled and dumbed-down version of love story . \nunless you come in to the film with a skateboard under your arm , you're going to feel like you weren't invited to the party . \nwhen the casting call for this movie went out , it must have read 'seeking anyone with acting ambition but no sense of pride or shame . '\njust isn't as weird as it ought to be . \na \" home alone \" film that is staged like \" rosemary's baby , \" but is not as well-conceived as either of those films . \n[siegel] and co-writers lisa bazadona and grace woodard have relied too much on convention in creating the characters who surround frankie . \nno film could possibly be more contemptuous of the single female population . \n`hey arnold ! ' has some visual wit . . . but little imagination elsewhere . \nthey're going through the motions , but the zip is gone . \na sluggish pace and lack of genuine narrative hem the movie in every bit as much as life hems in the spirits of these young women . \na low-budget affair , tadpole was shot on digital video , and the images often look smeary and blurry , to the point of distraction . then again , in a better movie , you might not have noticed . \nit's mindless junk like this that makes you appreciate original romantic comedies like punch-drunk love . \nthe movie is like a year late for tapping into our reality tv obsession , and even tardier for exploiting the novelty of the \" webcast . \" \ntale will be all too familiar for anyone who's seen george roy hill's 1973 film , \" the sting . \" \ngets the look and the period trappings right , but it otherwise drowns in a sea of visual and verbal clichs . \nit's hard to quibble with a flick boasting this many genuine cackles , but notorious c . h . o . still feels like a promising work-in-progress . \nanyone who wants to start writing screenplays can just follow the same blueprint from hundreds of other films , sell it to the highest bidder and walk away without anyone truly knowing your identity . \na major waste . . . generic . \nthe problem with the bread , my sweet is that it's far too sentimental . \na late-night cable sexploitation romp masquerading as a thriller about the ruthless social order that governs college cliques . \nfalls short in explaining the music and its roots . \nnever inspires more than an interested detachment . \nwhat might have emerged as hilarious lunacy in the hands of woody allen or mel brooks ( at least during their '70s heyday ) comes across as lame and sophomoric in this debut indie feature . \ndespite slick production values and director roger michell's tick-tock pacing , the final effect is like having two guys yelling in your face for two hours . \npretty much sucks , but has a funny moment or two . \nthey do a good job of painting this family dynamic for the audience but they tried to squeeze too many elements into the film . \na supernatural mystery that doesn't know whether it wants to be a suspenseful horror movie or a weepy melodrama . it ends up being neither , and fails at both endeavors . \ntwo badly interlocked stories drowned by all too clever complexity . \nit is so earnest , so overwrought and so wildly implausible that it begs to be parodied . \nthese are textbook lives of quiet desperation . \nswimfan , like fatal attraction , eventually goes overboard with a loony melodramatic denouement in which a high school swimming pool substitutes for a bathtub . \nclaims to sort the bad guys from the good , which is its essential problem . \npurposefully shocking in its eroticized gore , if unintentionally dull in its lack of poetic frissons . \nfeels like pieces a bunch of other , better movies slapped together . \nalmost everything about the film is unsettling , from the preposterous hairpiece worn by lai's villainous father to the endless action sequences . \nwriter-director randall wallace has bitten off more than he or anyone else could chew , and his movie veers like a drunken driver through heavy traffic . \nit follows the blair witch formula for an hour , in which we're told something creepy and vague is in the works , and then it goes awry in the final 30 minutes . \none can't shake the feeling that crossroads is nothing more than an hour-and-a-half-long commercial for britney's latest album . \nphoned-in business as usual . \nthere's an epic here , but you have to put it together yourself . \nwhat little atmosphere is generated by the shadowy lighting , macabre sets , and endless rain is offset by the sheer ugliness of everything else . \ndirector-chef gabriele muccino keeps it fast -- zippy , comin' at ya -- as if fearing that his film is molto superficiale . \ntartakovsky's team has some freakish powers of visual charm , but the five writers slip into the modern rut of narrative banality . \nthe most horrific movie experience i've had since \" can't stop the music . \" it may as well be called \" jar-jar binks : the movie . \" it's that painful . \ngod is great , the movie's not . \nlike a three-ring circus , there are side stories aplenty -- none of them memorable . \nwhen in doubt , the film ratchets up the stirring soundtrack , throws in a fish-out-of-water gag and lets the cliched dialogue rip . or else a doggie winks . \na 'girls gone wild' video for the boho art-house crowd , the burning sensation isn't a definitive counter-cultural document -- its makers aren't removed and inquisitive enough for that . \nas original and insightful as last week's episode of behind the music . \nplays like john le carr with a couple of burnt-out cylinders . \nyou may be galled that you've wasted nearly two hours of your own precious life with this silly little puddle of a movie . \nit's neither as sappy as big daddy nor as anarchic as happy gilmore or the waterboy , but it has its moments . \ndespite the surface attractions -- conrad l . hall's cinematography will likely be nominated for an oscar next year -- there's something impressive and yet lacking about everything . \na smug and convoluted action-comedy that doesn't allow an earnest moment to pass without reminding audiences that it's only a movie . \n[crystal and de niro] manage to squeeze out some good laughs but not enough to make this silly con job sing . \nworthless , from its pseudo-rock-video opening to the idiocy of its last frames . \nthe christ allegory doesn't work because there is no foundation for it\ngo for la salle's performance , and make do as best you can with a stuttering script . \nit's hard to care about a film that proposes as epic tragedy the plight of a callow rich boy who is forced to choose between his beautiful , self-satisfied 22-year-old girlfriend and an equally beautiful , self-satisfied 18-year-old mistress . \ntries too hard to be funny in a way that's too loud , too goofy and too short of an attention span . \ni didn't find much fascination in the swinging . what they're doing is a matter of plumbing arrangements and mind games , of no erotic or sensuous charge . but that they are doing it is thought-provoking . \nthe acting is just fine , but there's not enough substance here to sustain interest for the full 90 minutes , especially with the weak payoff . \nafter collateral damage , you might imagine that most every aggrieved father clich has been unturned . but no . \nultimately the , yes , snail-like pacing and lack of thematic resonance make the film more silly than scary , like some sort of martha stewart decorating program run amok . \nreleasing a film with the word 'dog' in its title in january lends itself to easy jokes and insults , and snow dogs deserves every single one of them . \ntedious norwegian offering which somehow snagged an oscar nomination . \nit was a dark and stormy night . . . \na dark-as-pitch comedy that frequently veers into corny sentimentality , probably would not improve much after a therapeutic zap of shock treatment . \nthis sort of cute and cloying material is far from zhang's forte and it shows . \nbray is completely at sea ; with nothing but a savage garden music video on his resume , he has no clue about making a movie . \nfreundlich's made [crudup] a suburban architect , and a cipher . \na huge disappointment coming , as it does , from filmmakers and performers of this calibre\nthough it pretends to expose the life of male hustlers , it's exploitive without being insightful . \naimed squarely at the least demanding of demographic groups : very small children who will be delighted simply to spend more time with familiar cartoon characters . \nwhat starts off as a satisfying kids flck becomes increasingly implausible as it races through contrived plot points . \nexhibits the shallow sensationalism characteristic of soap opera . . . more salacious telenovela than serious drama . \nseagal is painfully foolish in trying to hold onto what's left of his passe' chopsocky glory . \neven with harris's strong effort , the script gives him little to effectively probe lear's soul-stripping breakdown . \nthe story is bogus and its characters tissue-thin . \nwhereas the extremely competent hitman films such as pulp fiction and get shorty resonate a sardonic verve to their caustic purpose for existing , who is cletis tout ? is an inexpressible and drab wannabe looking for that exact niche . \nwhile american adobo has its heart ( and its palate ) in the right place , its brain is a little scattered -- ditsy , even . \n . . . a confusing drudgery . \nimagine a film that begins as a seven rip-off , only to switch to a mix of the shining , the thing , and any naked teenagers horror flick from the 1980s . \nmost of the dialogue made me want to pack raw dough in my ears . \ncostner's warm-milk persona is just as ill-fitting as shadyac's perfunctory directing chops , and some of the more overtly silly dialogue would sink laurence olivier . \nit's coherent , well shot , and tartly acted , but it wears you down like a dinner guest showing off his doctorate . \ndirected by kevin bray , whose crisp framing , edgy camera work , and wholesale ineptitude with acting , tone and pace very obviously mark him as a video helmer making his feature debut . \nturns a potentially interesting idea into an excruciating film school experience that plays better only for the film's publicists or for people who take as many drugs as the film's characters\nrobin williams departs from his fun friendly demeanor in exchange for a darker unnerving role . \nhigh crimes is a cinematic misdemeanor , a routine crime thriller remarkable only for its lack of logic and misuse of two fine actors , morgan freeman and ashley judd . \nset in a 1986 harlem that doesn't look much like anywhere in new york . \nthe chocolate factory without charlie . \nlong on twinkly-eyed close-ups and short on shame . \nhip-hop rarely comes alive as its own fire-breathing entity in this picture . \na well-crafted letdown . \na dull , somnambulant exercise in pretension whose pervasive quiet is broken by frequent outbursts of violence and noise . \ndeserving of its critical backlash and more . \nboring and meandering . \nneither a rousing success nor a blinding embarrassment . still , it just sits there like a side dish no one ordered . \nthe sum of all fears is remarkably fuddled about motives and context , which drains it of the dramatic substance that would shake us in our boots ( or cinema seats ) . \nthe movie spends more time with schneider than with newcomer mcadams , even though her performance is more interesting ( and funnier ) than his . \nthis low-rent -- and even lower-wit -- rip-off of the farrelly brothers' oeuvre gets way too mushy -- and in a relatively short amount of time . \nit recycles every clich about gays in what is essentially an extended soap opera . \ni'm all for the mentally challenged getting their fair shot in the movie business , but surely it doesn't have to be as a collection of keening and self-mutilating sideshow geeks . \nmay offend viewers not amused by the sick sense of humor . \nmany of benjamins' elements feel like they've been patched in from an episode of miami vice . \nit aimlessly and unsuccessfully attempts to fuse at least three dull plots into one good one . \nmost folks with a real stake in the american sexual landscape will find it either moderately amusing or just plain irrelevant . \nif you're not fans of the adventues of steve and terri , you should avoid this like the dreaded king brown snake . personally , i'd rather watch them on the animal planet . \ncherish is a dud -- a romantic comedy that's not the least bit romantic and only mildly funny . \nfeels as if the inmates have actually taken over the asylum . \nall of the filmmakers' calculations can't rescue brown sugar from the curse of blandness . \nthe movie's gloomy atmosphere is fascinating , though , even if the movie itself doesn't stand a ghost of a chance . \n . . . post-september 11 , \" the sum of all fears \" seems more tacky and reprehensible , manipulating our collective fear without bestowing the subject with the intelligence or sincerity it unequivocally deserves . \nthe exclamation point seems to be the only bit of glee you'll find in this dreary mess . \nno matter how you slice it , mark wahlberg and thandie newton are not hepburn and grant , two cinematic icons with chemistry galore . \ngodard's ode to tackling life's wonderment is a rambling and incoherent manifesto about the vagueness of topical excess . . . in praise of love remains a ponderous and pretentious endeavor that's unfocused and tediously exasperating . \nhumorless , self-conscious art drivel , made without a glimmer of intelligence or invention . \nthe movie's progression into rambling incoherence gives new meaning to the phrase 'fatal script error . '\nsolondz may be convinced that he has something significant to say , but he isn't talking a talk that appeals to me . \nmore tiring than anything . \nnelson's intentions are good , but the end result does no justice to the story itself . it's horribly depressing and not very well done . \n . . . the efforts of its star , kline , to lend some dignity to a dumb story are for naught . \na good-natured ensemble comedy that tries hard to make the most of a bumper cast , but never quite gets off the ground . \nisn't it a bit early in his career for director barry sonnenfeld to do a homage to himself ? and it's a lousy one at that . \noverly long and worshipful bio-doc . \ni'll go out on a limb . it isn't quite one of the worst movies of the year . it's just merely very bad . \nwriter-director ritchie reduces wertmuller's social mores and politics to tiresome jargon . \nabout amy's cuteness , amy's career success ( she's a best-selling writer of self-help books who can't help herself ) , and amy's neuroses when it comes to men . \neverything about girls can't swim , even its passages of sensitive observation , feels secondhand , familiar -- and not in a good way . \nfeels aimless for much of its running time , until late in the film when a tidal wave of plot arrives , leaving questions in its wake . \nless than fresh . \nin my own very humble opinion , in praise of love lacks even the most fragmented charms i have found in almost all of his previous works . \nthe script is too mainstream and the psychology too textbook to intrigue . \nmuddled , simplistic and more than a little pretentious . \nmeandering and glacially paced , and often just plain dull . \na disaster of a drama , saved only by its winged assailants . \na road trip that will get you thinking , 'are we there yet ? '\ndirector elie chouraqui , who co-wrote the script , catches the chaotic horror of war , but why bother if you're going to subjugate truth to the tear-jerking demands of soap opera ? \ndong never pushes for insights beyond the superficial tensions of the dynamic he's dissecting , and the film settles too easily along the contours of expectation . \nif there was any doubt that peter o'fallon didn't have an original bone in his body , a rumor of angels should dispel it . \nan occasionally interesting but mostly repetitive look at a slice of counterculture that might be best forgotten . \nwhat could have been right at home as a nifty plot line in steven soderbergh's traffic fails to arrive at any satisfying destination . \nthe movie is like scorsese's mean streets redone by someone who ignored it in favor of old 'juvenile delinquent' paperbacks with titles like leather warriors and switchblade sexpot . \nthis pathetic junk is barely an hour long . nevertheless , it still seems endless . \nit isn't that stealing harvard is a horrible movieif only it were that grand a failure ! it's just that it's so not-at-all-good . and i expect much more from a talent as outstanding as director bruce mcculloch . \ndolman confines himself to shtick and sentimentality -- the one bald and the other sloppy . \nis it possible for a documentary to be utterly entranced by its subject and still show virtually no understanding of it ? \nit's supposed to be a romantic comedy - it suffers from too much norma rae and not enough pretty woman . \nthe leads are so unmemorable , despite several attempts at lengthy dialogue scenes , that one eventually resents having to inhale this gutter romancer's secondhand material . \nthe script ? please . \nstaggers between flaccid satire and what is supposed to be madcap farce . \nnot that any of us should be complaining when a film clocks in around 90 minutes these days , but the plotting here leaves a lot to be desired . \nbrainy , artistic and muted , almost to the point of suffocation . \nplays like the old disease-of-the-week small-screen melodramas . \nlike life on the island , the movie grows boring despite the scenery . \nthe truth about charlie is that it's a brazenly misguided project . \ncaso voc sinta necessidade de sair da sala antes do trmino da projeo , no se preocupe : ningum lhe enviar penas simbolizando covardia . \ndisplays the potential for a better movie than what bailly manages to deliver\nso exaggerated and broad that it comes off as annoying rather than charming . \nan awkward hybrid of genres that just doesn't work . \nthe latest vapid actor's exercise to appropriate the structure of arthur schnitzler's reigen . \nsnipes is both a snore and utter tripe . \nritchie's film is easier to swallow than wertmuller's polemical allegory , but it's self-defeatingly decorous . \nchalk it up as the worst kind of hubristic folly . \nit's the kind of under-inspired , overblown enterprise that gives hollywood sequels a bad name . \nrosenthal ( halloween ii ) seems to have forgotten everything he ever knew about generating suspense . \neven murphy's expert comic timing and famed charisma can't rescue this effort . \nrodriguez . . . was unable to reproduce the special spark between the characters that made the first film such a delight . \na sleek advert for youthful anomie that never quite equals the sum of its pretensions . \nsome body smacks of exhibitionism more than it does cathartic truth telling . \nthis isn't a terrible film by any means , but it's also far from being a realized work . \napparently , romantic comedy with a fresh point of view just doesn't figure in the present hollywood program . \na lame comedy . \ndepressingly thin and exhaustingly contrived . only masochistic moviegoers need apply . \na movie that's held captive by mediocrity . not bad , but not all that good . bacon keeps things interesting , but don't go out of your way to pay full price . \nwhat's next ? rob schneider , dana carvey and sarah michelle gellar in the philadelphia story ? david spade as citizen kane ? \ncan't seem to get anywhere near the story's center . \nthe problem , amazingly enough , is the screenplay . \nit's a frankenstein-monster of a film that doesn't know what it wants to be . \nupper west sidey exercise in narcissism and self-congratulation disguised as a tribute . \non its icy face , the new film is a subzero version of monsters , inc . , without the latter's imagination , visual charm or texture . \ni can't say this enough : this movie is about an adult male dressed in pink jammies . \nit's a mindless action flick with a twist -- far better suited to video-viewing than the multiplex . \nafter a while , the only way for a reasonably intelligent person to get through the country bears is to ponder how a whole segment of pop-music history has been allowed to get wet , fuzzy and sticky . \nwe get light showers of emotion a couple of times , but then -- strangely -- these wane to an inconsistent and ultimately unsatisfying drizzle . \nsummer's far too fleeting to squander on offal like this . \nthe film is grossly contradictory in conveying its social message , if indeed there is one . \noften lingers just as long on the irrelevant as on the engaging , which gradually turns what time is it there ? into how long is this movie ? \ntoo bad kramer couldn't make a guest appearance to liven things up . \ndeuces wild is an encyclopedia of cliches that shoplifts shamelessly from farewell-to-innocence movies like the wanderers and a bronx tale without cribbing any of their intelligence . \nit's a barely tolerable slog over well-trod ground . \nepps has neither the charisma nor the natural affability that has made tucker a star . \nit's sweet . . . but just a little bit too precious at the start and a little too familiar at the end . \na dull , dumb and derivative horror film . \nan awkwardly contrived exercise in magic realism . \ndemme gets a lot of flavor and spice into his charade remake , but he can't disguise that he's spiffing up leftovers that aren't so substantial or fresh . \nthis is a heartfelt story . . . it just isn't a very involving one . \nthese self-styled athletes have banged their brains into the ground so frequently and furiously , their capacity to explain themselves has gone the same way as their natural instinct for self-preservation . \nthe fact that the 'best part' of the movie comes from a 60-second homage to one of demme's good films doesn't bode well for the rest of it . \nrichard pryor mined his personal horrors and came up with a treasure chest of material , but lawrence gives us mostly fool's gold . \nthe band performances featured in drumline are red hot . . . [but] from a mere story point of view , the film's ice cold . \n . . . built on the premise that middle-class arkansas consists of monster truck-loving good ol' boys and peroxide blond honeys whose worldly knowledge comes from tv reruns and supermarket tabloids . \na laughable -- or rather , unlaughable -- excuse for a film . \nthe sequel is everything the original was not : contrived , overblown and tie-in ready . \nlike a grinning jack o' lantern , its apparent glee is derived from a lobotomy , having had all its vital essence scooped out and discarded . \na sentimental hybrid that could benefit from the spice of specificity . \n . . . familiar and predictable , and 4/5ths of it might as well have come from a xerox machine rather than ( writer-director ) franc . reyes' word processor . \ngive shapiro , goldman , and bolado credit for good intentions , but there's nothing here that they couldn't have done in half an hour . \nit's so devoid of joy and energy it makes even jason x . . . look positively shakesperean by comparison . \na little objectivity could have gone a long way . \none of the worst films of 2002 . \ni believe silberling had the best intentions here , but he just doesn't have the restraint to fully realize them . \nplays like an unbalanced mixture of graphic combat footage and almost saccharine domestic interludes that are pure hollywood . \nmctiernan's remake may be lighter on its feet -- the sober-minded original was as graceful as a tap-dancing rhino -- but it is just as boring and as obvious . \nhigh crimes carries almost no organic intrigue as a government/ marine/legal mystery , and that's because the movie serves up all of that stuff , nearly subliminally , as the old-hat province of male intrigue . \nthis movie is about the worst thing chan has done in the united states . \nthe explosion essentially ruined -- or , rather , overpowered -- the fiction of the movie for me . \nthis ludicrous film is predictable at every turn . \nan incredibly irritating comedy about thoroughly vacuous people . . . manages to embody the worst excesses of nouvelle vague without any of its sense of fun or energy . \nthe film desperately sinks further and further into comedy futility . \ninstead of a balanced film that explains the zeitgeist that is the x games , we get a cinematic postcard that's superficial and unrealized . \nthe crassness of this reactionary thriller is matched only by the ridiculousness of its premise . \ni wish it would have just gone more over-the-top instead of trying to have it both ways . \nthe superior plotline isn't quite enough to drag along the dead ( water ) weight of the other . \nthe film doesn't really care about the thousands of americans who die hideously , it cares about how ryan meets his future wife and makes his start at the cia . \nadrift , bentley and hudson stare and sniffle , respectively , as ledger attempts , in vain , to prove that movie-star intensity can overcome bad hair design . \nafter an hour and a half of wondering -- sometimes amusedly , sometimes impatiently -- just what this strenuously unconventional movie is supposed to be , you discover that the answer is as conventional as can be . \n'linklater fans , or pretentious types who want to appear avant-garde will suck up to this project . . . '\na woefully dull , redundant concept that bears more than a whiff of exploitation , despite iwai's vaunted empathy . \nscreenwriter chris ver weil's directing debut is good-natured and never dull , but its virtues are small and easily overshadowed by its predictability . \nif you really want to understand what this story is really all about , you're far better served by the source material . \nit's mildly sentimental , unabashedly consumerist . . . studiously inoffensive and completely disposable . \nlike its title character , esther kahn is unusual but unfortunately also irritating . \nthe star who helped give a spark to \" chasing amy \" and \" changing lanes \" falls flat as thinking man cia agent jack ryan in this summer's new action film , \" the sum of all fears . \" \na summary of the plot doesn't quite do justice to the awfulness of the movie , for that comes through all too painfully in the execution . \nevery conceivable mistake a director could make in filming opera has been perpetrated here . \nsnoots will no doubt rally to its cause , trotting out threadbare standbys like 'masterpiece' and 'triumph' and all that malarkey , but rarely does an established filmmaker so ardently waste viewers' time with a gobbler like this . \n[the film's] taste for \" shock humor \" will wear thin on all but those weaned on the comedy of tom green and the farrelly brothers . \nany enjoyment will be hinge from a personal threshold of watching sad but endearing characters do extremely unconventional things . \nif legendary shlockmeister ed wood had ever made a movie about a vampire , it probably would look a lot like this alarming production , adapted from anne rice's novel the vampire chronicles . \nhardly a nuanced portrait of a young woman's breakdown , the film nevertheless works up a few scares . \ninterminably bleak , to say nothing of boring . \nthings really get weird , though not particularly scary : the movie is all portent and no content . \nit's difficult to discern if this is a crazy work of disturbed genius or merely 90 minutes of post-adolescent electra rebellion . \nbogs down badly as we absorb jia's moody , bad-boy behavior which he portrays himself in a one-note performance . \nthe camera whirls ! the camera twirls ! oh , look at that clever angle ! wow , a jump cut ! \ndemme finally succeeds in diminishing his stature from oscar-winning master to lowly studio hack . \nthe action scenes have all the suspense of a 20-car pileup , while the plot holes are big enough for a train car to drive through -- if kaos hadn't blown them all up . \nit almost feels as if the movie is more interested in entertaining itself than in amusing us . \nit puts washington , as honest working man john q . archibald , on a pedestal , then keeps lifting the pedestal higher . \nultimately , the film amounts to being lectured to by tech-geeks , if you're up for that sort of thing . \nfar more enjoyable than its predecessor . \n[gayton's script] telegraphs every discovery and layers on the gloss of convenience . \nfull frontal , which opens today nationwide , could almost be classified as a movie-industry satire , but it lacks the generous inclusiveness that is the genre's definitive , if disingenuous , feature . \na ragbag of cliches . \nthis rough trade punch-and-judy act didn't play well then and it plays worse now . \na reality-snubbing hodgepodge . \nthe three leads produce adequate performances , but what's missing from this material is any depth of feeling . \nit's possible that something hip and transgressive was being attempted here that stubbornly refused to gel , but the result is more puzzling than unsettling . \nthis painfully unfunny farce traffics in tired stereotypes and encumbers itself with complications . . . that have no bearing on the story . \nshort and sweet , but also more than anything else slight tadpole pulls back from the consequences of its own actions and revelations . \nhas its moments , but it's pretty far from a treasure . \nwhat more can be expected from a college comedy that's target audience hasn't graduated from junior high school ? \ncollateral damage offers formula payback and the big payoff , but the explosions tend to simply hit their marks , pyro-correctly . \nthe plan to make enough into an inspiring tale of survival wrapped in the heart-pounding suspense of a stylish psychological thriller' has flopped as surely as a souffl gone wrong . \ninstead of letting the laughs come as they may , lawrence unleashes his trademark misogyny -- er , comedy -- like a human volcano or an overflowing septic tank , take your pick . \nyou know that ten bucks you'd spend on a ticket ? just send it to cranky . we don't get paid enough to sit through crap like this . \nan even more predictable , cliche-ridden endeavor than its predecessor . \nthe whole thing plays like a tired tyco ad . \nthe film doesn't show enough of the creative process or even of what was created for the non-fan to figure out what makes wilco a big deal . \nthe soupy end result has the odd distinction of being playful without being fun , too . \nno , i don't know why steven seagal is considered a star , nor why he keeps being cast in action films when none of them are ever any good or make any money . \neven by the intentionally low standards of frat-boy humor , sorority boys is a bowser . \none well-timed explosion in a movie can be a knockout , but a hundred of them can be numbing . proof of this is ballistic : ecks vs . sever . \nhalfway through , however , having sucked dry the undead action flick formula , blade ii mutates into a gross-out monster movie with effects that are more silly than scary . \nweighted down with slow , uninvolving storytelling and flat acting . \nwe can't accuse kung pow for misfiring , since it is exactly what it wants to be : an atrociously , mind-numbingly , indescribably bad movie . unfortunately , we'd prefer a simple misfire . \nthere isn't one moment in the film that surprises or delights . \n'wouldn't it be nice if all guys got a taste of what it's like on the other side of the bra ? '\nthe movie is essentially a series of fleetingly interesting actors' moments . \nmost of the information has already appeared in one forum or another and , no matter how broomfield dresses it up , it tends to speculation , conspiracy theories or , at best , circumstantial evidence . \nthis movie , a certain scene in particular , brought me uncomfortably close to losing my lunch . \nthe secrets of time travel will have been discovered , indulged in and rejected as boring before i see this piece of crap again . \nsmug , artificial , ill-constructed and fatally overlong . . . it never finds a consistent tone and lacks bite , degenerating into a pious , preachy soap opera . \nchelsea walls is a case of too many chefs fussing over too weak a recipe . \nevery joke is repeated at least four times . every joke is repeated at least four times . every joke is repeated at least--annoying , isn't it ? \ncomes across as a fairly weak retooling . \nthe lousy lead performances . . . keep the movie from ever reaching the comic heights it obviously desired . \nits and pieces of the hot chick are so hilarious , and schneider's performance is so fine , it's a real shame that so much of the movie -- again , as in the animal -- is a slapdash mess . \n[creates] the worst kind of mythologizing , the kind that sacrifices real heroism and abject suffering for melodrama . \nthe movie resolutely avoids all the comic possibilities of its situation , and becomes one more dumb high school comedy about sex gags and prom dates . \nearnest and heartfelt but undernourished and plodding . \na sugar-coated rocky whose valuable messages are forgotten 10 minutes after the last trombone honks . \nromanek keeps adding flourishes -- artsy fantasy sequences -- that simply feel wrong . they cheapen the overall effect . \nhas all the complexity and realistic human behavior of an episode of general hospital . \nan acceptable way to pass a little over an hour with moviegoers ages 8-10 , but it's unlikely to inspire anything more than a visit to mcdonald's , let alone some savvy street activism . \n[allen's] been making piffle for a long while , and hollywood ending may be his way of saying that piffle is all that the airhead movie business deserves from him right now . \nan exercise in cynicism every bit as ugly as the shabby digital photography and muddy sound . \nmildly amusing . \nnot good enough to pass for a litmus test of the generation gap and not bad enough to repulse any generation of its fans . \nthe movie is silly beyond comprehension , and even if it weren't silly , it would still be beyond comprehension . \nwatchable up until the point where the situations and the dialogue spin hopelessly out of control -- that is to say , when carol kane appears on the screen . \nthe scriptwriters are no less a menace to society than the film's characters . \nfairly run-of-the-mill . \nmerchant hasn't directed this movie so much as produced it -- like sausage . \nthe film has a nearly terminal case of the cutes , and it's neither as funny nor as charming as it thinks it is . \nmore a gunfest than a rock concert . \nit's a frightful vanity film that , no doubt , pays off what debt miramax felt they owed to benigni . \na muddy psychological thriller rife with miscalculations . it makes me say the obvious : abandon all hope of a good movie ye who enter here . \nmildly entertaining . \nit's not original enough . \na listless sci-fi comedy in which eddie murphy deploys two guises and elaborate futuristic sets to no particularly memorable effect . \nterrible . \nlittle more than a super-sized infomercial for the cable-sports channel and its summer x games . \ndegenerates into hogwash . \na generic bloodbath that often becomes laughably unbearable when it isn't merely offensive . \njulie davis is the kathie lee gifford of film directors , sadly proving once again ego doesn't always go hand in hand with talent . \nan unholy mess , driven by the pathetic idea that if you shoot something on crummy-looking videotape , it must be labelled 'hip' , 'innovative' and 'realistic' . \nthe story's pathetic and the gags are puerile . \n . curiously , super troopers suffers because it doesn't have enough vices to merit its 103-minute length . \nso bland and utterly forgettable that it might as well have been titled generic jennifer lopez romantic comedy . \ni was sent a copyof this film to review on dvd . for free . i still want my money back . \nit plods along methodically , somehow under the assumption that its \" dead wife communicating from beyond the grave \" framework is even remotely new or interesting . \nit's hard to believe that a relationship like holly and marina's could survive the hothouse emotions of teendom , and its longevity gets more inexplicable as the characterizations turn more crassly reductive . \nall too familiar . . . basically the sort of cautionary tale that was old when 'angels with dirty faces' appeared in 1938 . \npassable enough for a shoot-out in the o . k . court house of life type of flick . strictly middle of the road . \nalthough purportedly a study in modern alienation , it's really little more than a particularly slanted , gay s/m fantasy , enervating and deadeningly drawn-out . \nafter the first 10 minutes , which is worth seeing , the movie sinks into an abyss of clichs , depression and bad alternative music . \nno one can doubt the filmmakers' motives , but the guys still feels counterproductive . \na very slow , uneventful ride around a pretty tattered old carousel . \nwith little visible talent and no energy , colin hanks is in bad need of major acting lessons and maybe a little coffee . \n \" feardotcom \" has the makings of an interesting meditation on the ethereal nature of the internet and the otherworldly energies it could channel , but it simply becomes a routine shocker . \na meatballs for the bare-midriff generation . \nwell-meaning to a fault , antwone fisher manages the dubious feat of turning one man's triumph of will into everyman's romance comedy . \nseemingly disgusted with the lazy material and the finished product's unshapely look , director fisher stevens inexplicably dips key moments from the film in waking life water colors . \nformula 51 promises a new kind of high but delivers the same old bad trip . \neverything that was right about blade is wrong in its sequel . \na few energetic stunt sequences briefly enliven the film , but the wheezing terrorist subplot hasn't the stamina for the 100-minute running time , and the protagonists' bohemian boorishness mars the spirit of good clean fun . \nthe film was produced by jerry bruckheimer and directed by joel schumacher , and reflects the worst of their shallow styles : wildly overproduced , inadequately motivated every step of the way and demographically targeted to please every one ( and no one ) . \ndisney again ransacks its archives for a quick-buck sequel . \n coarse , cliched and clunky , this trifling romantic comedy in which opposites attract for no better reason than that the screenplay demands it squanders the charms of stars hugh grant and sandra bullock . \nanyone who suffers through this film deserves , at the very least , a big box of consolation candy . \nhow much you are moved by the emotional tumult of [franois and michle's] relationship depends a lot on how interesting and likable you find them . \nthey presume their audience won't sit still for a sociology lesson , however entertainingly presented , so they trot out the conventional science-fiction elements of bug-eyed monsters and futuristic women in skimpy clothes . \ncollapses after 30 minutes into a slap-happy series of adolescent violence . \nthe following things are not at all entertaining : the bad sound , the lack of climax and , worst of all , watching seinfeld ( who is also one of the film's producers ) do everything he can to look like a good guy . \nattal's hang-ups surrounding infidelity are so old-fashioned and , dare i say , outdated , it's a wonder that he couldn't have brought something fresher to the proceedings simply by accident . \nobvious , obnoxious and didactic burlesque . \nthe most surprising thing about this film is that they are actually releasing it into theaters . \nmichele is a such a brainless flibbertigibbet that it's hard to take her spiritual quest at all seriously . \nultimately , clarity matters , both in breaking codes and making movies . enigma lacks it . \npotty-mouthed enough for pg-13 , yet not as hilariously raunchy as south park , this strangely schizo cartoon seems suited neither to kids or adults . \n . . . has its moments , but ultimately , its curmudgeon doesn't quite make the cut of being placed on any list of favorites . \na distinctly minor effort that will be seen to better advantage on cable , especially considering its barely feature-length running time of one hour . \nmost of the movie is so deadly dull that watching the proverbial paint dry would be a welcome improvement . \nin the end , tuck everlasting falls victim to that everlasting conundrum experienced by every human who ever lived : too much to do , too little time to do it in . \nrather less than the sum of its underventilated pre-fils confrontations . \nmckay shows crushingly little curiosity about , or is ill-equipped to examine , the interior lives of the characters in his film , much less incorporate them into his narrative . \nplays like a series of vignettes -- clips of a film that are still looking for a common through-line . \nnew yorkers always seem to find the oddest places to dwell . . . \namid the shock and curiosity factors , the film is just a corny examination of a young actress trying to find her way . \nyes , spirited away is a triumph of imagination , but it's also a failure of storytelling . \na characteristically engorged and sloppy coming-of-age movie . \na somewhat disappointing and meandering saga . \nwhenever you think you've seen the end of the movie , we cut to a new scene , which also appears to be the end . but , no , we get another scene , and then another . you begin to long for the end credits as the desert does for rain . \nan empty , ugly exercise in druggy trance-noir and trumped-up street credibility . \nthe screenplay , co-written by director imogen kimmel , lacks the wit necessary to fully exploit the comic elements of the premise , making the proceedings more bizarre than actually amusing . \nthe milieu is wholly unconvincing . . . and the histrionics reach a truly annoying pitch . \nunfunny comedy with a lot of static set ups , not much camera movement , and most of the scenes take place indoors in formal settings with motionless characters . \neach story is built on a potentially interesting idea , but the first two are ruined by amateurish writing and acting , while the third feels limited by its short running time . \nexcept for paymer as the boss who ultimately expresses empathy for bartleby's pain , the performances are so stylized as to be drained of human emotion . \nwill no doubt delight plympton's legion of fans ; others may find 80 minutes of these shenanigans exhausting . \nthe laughs are as rare as snake foo yung . \nfor a film that celebrates radical , nonconformist values , what to do in case of fire ? lazily and glumly settles into a most traditional , reserved kind of filmmaking . \nknockaround guys plays like a student film by two guys who desperately want to be quentin tarantino when they grow up . but they lack their idol's energy and passion for detail . \nmattei so completely loses himself to the film's circular structure to ever offer any insightful discourse on , well , love in the time of money . \nit briefly flirts with player masochism , but the point of real interest - audience sadism -- is evaded completely . \nholland lets things peter out midway , but it's notably better acted -- and far less crass - than some other recent efforts in the burgeoning genre of films about black urban professionals . \nfor every articulate player , such as skateboarder tony hawk or bmx rider mat hoffman , are about a half dozen young turks angling to see how many times they can work the words \" radical \" or \" suck \" into a sentence . \nthere's not a fresh idea at the core of this tale . \nan impenetrable and insufferable ball of pseudo-philosophic twaddle . \nit's unfortunate that wallace , who wrote gibson's braveheart as well as the recent pearl harbor , has such an irrepressible passion for sappy situations and dialogue . \ni liked the movie , but i know i would have liked it more if it had just gone that one step further . i'm left slightly disappointed that it didn't . \ndreary tale of middle-class angst\nfor a movie about the power of poetry and passion , there is precious little of either . \n[jackson and bledel] seem to have been picked not for their acting chops , but for their looks and appeal to the pre-teen crowd . \nlillard and cardellini earn their scooby snacks , but not anyone else . \nlike schindler's list , the grey zone attempts to be grandiloquent , but ends up merely pretentious -- in a grisly sort of way . \nan unremittingly ugly movie to look at , listen to , and think about , it is quite possibly the sturdiest example yet of why the dv revolution has cheapened the artistry of making a film . \n[screenwriter] pimental took the farrelly brothers comedy and feminized it , but it is a rather poor imitation . \nit's kind of sad that so many people put so much time and energy into this turkey . \nfriday after next is a lot more bluster than bite . \nits juxtaposition of overwrought existentialism and stomach-churning gore will have you forever on the verge of either cracking up or throwing up . \na decidedly mixed bag . \nmeandering and confusing . \nthere are cheesy backdrops , ridiculous action sequences , and many tired jokes about men in heels . \nice cube isn't quite out of ripe screwball ideas , but friday after next spreads them pretty thin . \nnot everything in the film works , including its somewhat convenient ending . \nthe characters , cast in impossibly contrived situations , are totally estranged from reality . \neverything else about high crimes is , like the military system of justice it portrays , tiresomely regimented . \njust dreadful . i don't blame eddie murphy but shouldn't owen wilson know a movie must have a story and a script ? \nsweet home alabama certainly won't be remembered as one of [witherspoon's] better films . \nhard as this may be to believe , here on earth , a surprisingly similar teen drama , was a better film . \nthis is just lazy writing . even kids deserve better . \nthe pretensions -- and disposable story -- sink the movie . and diesel isn't the actor to save it . \nbravo reveals the true intent of her film by carefully selecting interview subjects who will construct a portrait of castro so predominantly charitable it can only be seen as propaganda . \n . . . a preachy parable stylized with a touch of john woo bullet ballet . \nfrank capra played this story straight . but the 2002 film doesn't really believe in it , and breaks the mood with absurdly inappropriate 'comedy' scenes . \nhow about starting with a more original story instead of just slapping extreme humor and gross-out gags on top of the same old crap ? \nthe problem is that for the most part , the film is deadly dull . \nhandled correctly , wilde's play is a masterpiece of elegant wit and artifice . here , alas , it collapses like an overcooked souffl . \n \" sorority boys \" was funnier , and that movie was pretty bad . \na bizarre piece of work , with premise and dialogue at the level of kids' television and plot threads as morose as teen pregnancy , rape and suspected murder\npaul bettany is good at being the ultra-violent gangster wannabe , but the movie is certainly not number 1 . \nit's a gag that's worn a bit thin over the years , though don't ask still finds a few chuckles . \nan uplifting drama . . . what antwone fisher isn't , however , is original . \noften likable , but just as often it's meandering , low on energy , and too eager to be quirky at moments when a little old-fashioned storytelling would come in handy . \ncertain to be distasteful to children and adults alike , eight crazy nights is a total misfire . \nelaborate special effects take centre screen , so that the human story is pushed to one side . \nshowtime isn't particularly assaultive , but it can still make you feel that you never want to see another car chase , explosion or gunfight again . \nall the characters are clinically depressed and have abandoned their slim hopes and dreams . \nthis tuxedo . . . should have been sent back to the tailor for some major alterations . \ni have no problem with \" difficult \" movies , or movies that ask the audience to meet them halfway and connect the dots instead of having things all spelled out . but first , you have to give the audience a reason to <b>want</b> to put for that effort , and \" i\nbeen there , done that . . . a thousand times already , and better . \nwhat's most offensive isn't the waste of a good cast , but the film's denial of sincere grief and mourning in favor of bogus spiritualism . \nsunk by way too much indulgence of scene-chewing , teeth-gnashing actorliness . \nfans of plympton's shorts may marginally enjoy the film , but it is doubtful this listless feature will win him any new viewers . \nbarrels along at the start before becoming mired in sentimentality . \nnone of this sounds promising and , indeed , the first half of sorority boys is as appalling as any 'comedy' to ever spill from a projector's lens . \nthe kind of movie that leaves vague impressions and a nasty aftertaste but little clear memory of its operational mechanics . \n'punch-drunk love is so convinced of its own brilliance that , if it were a person , you'd want to smash its face in . '\nat once overly old-fashioned in its sudsy plotting and heavy-handed in its effort to modernize it with encomia to diversity and tolerance . \nthe trashy teen-sleaze equivalent of showgirls . \nwhile the production details are lavish , film has little insight into the historical period and its artists , particularly in how sand developed a notorious reputation . \na crass and insulting homage to great films like some like it hot and the john wayne classics . \nwhat's the most positive thing that can be said about the new rob schneider vehicle ? well , it's not as pathetic as the animal . \nwith all the sympathy , empathy and pity fogging up the screen . . . his secret life enters the land of unintentional melodrama and tiresome love triangles . \nthe problematic characters and overly convenient plot twists foul up shum's good intentions . \nwhat 'blade runner' would've looked like as a low-budget series on a uhf channel . \ndoesn't add up to much . \nhas all the values of a straight-to-video movie , but because it has a bigger-name cast , it gets a full theatrical release . \nwith its lackadaisical plotting and mindless action , all about the benjamins evokes the bottom tier of blaxploitation flicks from the 1970s . \nit never quite makes it to the boiling point , but manages to sustain a good simmer for most of its running time . \nloud , silly , stupid and pointless . \nmandel holland's direction is uninspired , and his scripting unsurprising , but the performances by phifer and black are ultimately winning . you'll find yourself wishing that you and they were in another movie . \na yawn-provoking little farm melodrama . \ndid no one on the set have a sense of humor , or did they not have the nerve to speak up ? \ncrummy\nseriously , rent the disney version . \nas david letterman and the onion have proven , the worst of tragedies can be fertile sources of humor , but lawrence has only a fleeting grasp of how to develop them . \nlike its parade of predecessors , this halloween is a gory slash-fest . it can't escape its past , and it doesn't want to . \n \" abandon \" will leave you wanting to abandon the theater . \nproblem is , we have no idea what in creation is going on . \na live-action cartoon , a fast-moving and cheerfully simplistic 88 minutes of exaggerated action put together with the preteen boy in mind . \na loquacious and dreary piece of business . \nwhat the audience feels is exhaustion , from watching a movie that is dark ( dark green , to be exact ) , sour , bloody and mean . \ndirector hoffman , his writer and kline's agent should serve detention\ndodgy mixture of cutesy romance , dark satire and murder mystery . \nmeticulously mounted , exasperatingly well-behaved film , which ticks off kahlo's lifetime milestones with the dutiful precision of a tax accountant . \ntime of favor could have given audiences the time of day by concentrating on the elements of a revealing alienation among a culture of people who sadly are at hostile odds with one another through recklessness and retaliation . \ni'm not sure which will take longer to heal : the welt on johnny knoxville's stomach from a riot-control projectile or my own tortured psyche . \nwhile serving sara does have a long way to go before it reaches the level of crudity in the latest austin powers extravaganza , there's nothing here to match that movie's intermittent moments of inspiration . \ni'm not sure which is worse : the poor acting by the ensemble cast , the flat dialogue by vincent r . nebrida or the gutless direction by laurice guillen . \nthe only reason you should see this movie is if you have a case of masochism and an hour and a half to blow . \nwhatever about warning kids about the dangers of ouija boards , someone should dispense the same advice to film directors . \nas with so many merchandised-to-the-max movies of this type , more time appears to have gone into recruiting the right bands for the playlist and the costuming of the stars than into the script , which has a handful of smart jokes and not much else . \nthe irwins' scenes are fascinating ; the movie as a whole is cheap junk and an insult to their death-defying efforts . \nif routine action and jokes like this are your cup of tea , then pay your $8 and get ready for the big shear . this is one baaaaaaaaad movie . \na man leaving the screening said the film was better than saving private ryan . he may have meant the internet short saving ryan's privates . but windtalkers doesn't beat that one , either . \nmay puzzle his most ardent fans . \nstarts as a tart little lemon drop of a movie and ends up as a bitter pill . \nwe never feel anything for these characters , and as a result the film is basically just a curiosity . \nthose unfamiliar with mormon traditions may find the singles ward occasionally bewildering . \nritchie may not have a novel thought in his head , but he knows how to pose madonna . \nthe story , touching though it is , does not quite have enough emotional resonance or variety of incident to sustain a feature , and even at 85 minutes it feels a bit long . \nfeels like the work of an artist who is simply tired -- of fighting the same fights , of putting the weight of the world on his shoulders , of playing with narrative form . \nwhile you have to admit it's semi-amusing to watch robert deniro belt out \" when you're a jet , you're a jet all the way , \" it's equally distasteful to watch him sing the lyrics to \" tonight . \" \nan opportunity missed . \nthe whole mess boils down to a transparently hypocritical work that feels as though it's trying to set the women's liberation movement back 20 years . \n' . . . the cast portrays their cartoon counterparts well . . . but quite frankly , scoob and shag don't eat enough during the film . '\nmore of the same old garbage hollywood has been trying to pass off as acceptable teen entertainment for some time now . \ntv skit-com material fervently deposited on the big screen . \n[johnnie to and wai ka fai are] sure to find an enthusiastic audience among american action-adventure buffs , but the film's interests may be too narrow to attract crossover viewers . \nif there was ever a movie where the upbeat ending feels like a copout , this is the one . \nit's as sorry a mess as its director's diabolical debut , mad cows . \nany attempts at nuance given by the capable cast is drowned out by director jon purdy's sledgehammer sap . \nits audacious ambitions sabotaged by pomposity , steven soderbergh's space opera emerges as a numbingly dull experience . \ndespite some strong performances , never rises above the level of a telanovela . \nthis is a picture that maik , the firebrand turned savvy ad man , would be envious of : it hijacks the heat of revolution and turns it into a sales tool . \nfeels slight , as if it were an extended short , albeit one made by the smartest kids in class . \nunspeakable , of course , barely begins to describe the plot and its complications . vulgar is too optimistic a title . \nthe actors pull out all the stops in nearly every scene , but to diminishing effect . the characters never change . \nif the last man were the last movie left on earth , there would be a toss-up between presiding over the end of cinema as we know it and another night of delightful hand shadows . \nwelles groupie/scholar peter bogdanovich took a long time to do it , but he's finally provided his own broadside at publishing giant william randolph hearst . \nmakes the same mistake as the music industry it criticizes , becoming so slick and watered-down it almost loses what made you love it in the first place . \neven as i valiantly struggled to remain interested , or at least conscious , i could feel my eyelids . . . getting . . . very . . . heavy . . . \na bad movie that happened to good actors . \nwishy-washy . \nboasts eye-catching art direction but has a forcefully quirky tone that quickly wears out its limited welcome . \nscreenwriter dan schneider and director shawn levy substitute volume and primary colors for humor and bite . \noversexed , at times overwrought comedy/drama that offers little insight into the experience of being forty , female and single . \nthat such a horrible movie could have sprung from such a great one is one of the year's worst cinematic tragedies . \nit all starts to smack of a hallmark hall of fame , with a few four letter words thrown in that are generally not heard on television . \nrarely has a film's title served such dire warning . \nif you saw benigni's pinocchio at a public park , you'd grab your kids and run and then probably call the police . \nthe animation is competent , and some of the gags are quite funny , but jonah . . . never shakes the oppressive , morally superior good-for-you quality that almost automatically accompanies didactic entertainment . \nthe pace of the film is very slow ( for obvious reasons ) and that too becomes off-putting . \nmr . wollter and ms . seldhal give strong and convincing performances , but neither reaches into the deepest recesses of the character to unearth the quaking essence of passion , grief and fear . \nshafer's feature doesn't offer much in terms of plot or acting . \nin his role of observer of the scene , lawrence sounds whiny and defensive , as if his life-altering experiences made him bitter and less mature . \n[t]he ideas of revolution #9 are more compelling than the execution\nthe film didn't convince me that calvin jr . 's barbershop represents some sort of beacon of hope in the middle of chicago's south side . \nwhat happens when something goes bump in the night and nobody cares ? \ndespite some comic sparks , welcome to collinwood never catches fire . \ndirector george hickenlooper has had some success with documentaries , but here his sense of story and his juvenile camera movements smack of a film school undergrad , and his maudlin ending might not have gotten him into film school in the first place . \nshows moments of promise but ultimately succumbs to cliches and pat storytelling . \neven accepting this in the right frame of mind can only provide it with so much leniency . \nsome body is a shaky , uncertain film that nevertheless touches a few raw nerves . \nall the small moments and flashbacks don't add up to much more than trite observations on the human condition . \n[a] stale retread of the '53 original . \none thing's for sureif george romero had directed this movie , it wouldn't have taken the protagonists a full hour to determine that in order to kill a zombie you must shoot it in the head . \nfor dance completists only . \none of the worst movies of the year . \nspreads itself too thin , leaving these actors , as well as the members of the commune , short of profound characterizations\nit wouldn't matter so much that this arrogant richard pryor wannabe's routine is offensive , puerile and unimaginatively foul-mouthed if it was at least funny . \nthe locale . . . remains far more interesting than the story at hand . \nyo , it's the days of our lives meets electric boogaloo . \ninconsequential road-and-buddy pic . \ni liked the original short story but this movie , even at an hour and twenty-some minutes , it's too long and it goes nowhere . \nthis little film is so slovenly done , so primitive in technique , that it can't really be called animation . \nmakes 98 minutes feel like three hours . \nhawke's film , a boring , pretentious waste of nearly two hours , doesn't tell you anything except that the chelsea hotel today is populated by whiny , pathetic , starving and untalented artistes . \naspires to the cracked lunacy of the adventures of buckaroo banzai , but thanks to an astonishingly witless script ends up more like the adventures of ford fairlane . \nreal-life strongman ahola lacks the charisma and ability to carry the film on his admittedly broad shoulders . \nthe title , alone , should scare any sane person away . \nlow comedy doesn't come much lower . \nappropriately cynical social commentary aside , #9 never quite ignites . \nit's crap on a leash--far too polite to scale the lunatic heights of joe dante's similarly styled gremlins . \none of the most depressing movie-going experiences i can think of is to sit through about 90 minutes of a so-called 'comedy' and not laugh once . \nthis is the kind of movie where the big scene is a man shot out of a cannon into a vat of ice cream . \nlet's face it -- there aren't many reasons anyone would want to see crossroads if they're not big fans of teen pop kitten britney spears . \na loud , brash and mainly unfunny high school comedy . \nan exceptionally dreary and overwrought bit of work , every bit as imperious as katzenberg's the prince of egypt from 1998 . \ninsufferably naive . \nthe movie is so resolutely cobbled together out of older movies that it even uses a totally unnecessary prologue , just because it seems obligatory . \nthe movie's vision of a white american zealously spreading a puritanical brand of christianity to south seas islanders is one only a true believer could relish . \nmaid in manhattan proves that it's easier to change the sheets than to change hackneyed concepts when it comes to dreaming up romantic comedies . \na fairly harmless but ultimately lifeless feature-length afterschool special . \ni can't remember the last time i saw a movie where i wanted so badly for the protagonist to fail . \nill-considered , unholy hokum . \namazingly lame . \n . . . the whole thing succeeded only in making me groggy . \nlike most of jaglom's films , some of it is honestly affecting , but more of it seems contrived and secondhand . \none long , numbing action sequence made up mostly of routine stuff yuen has given us before . \nforgettable , if good-hearted , movie . \nthe film's most improbable feat ? it didn't go straight to video . \n . . . about as exciting to watch as two last-place basketball teams playing one another on the final day of the season . \nthe chateau . . . is less concerned with cultural and political issues than doting on its eccentric characters . \ncruel and inhuman cinematic punishment . . . simultaneously degrades its characters , its stars and its audience . \nit's not too fast and not too slow . it's not too racy and it's not too offensive . it's not too much of anything . \nthe great pity is that those responsible didn't cut their losses  and ours  and retitle it the adventures of direct-to-video nash , and send it to its proper home . \nabout as original as a gangster sweating bullets while worrying about a contract on his life . \nan empty shell of an epic rather than the real deal . \nwe could have expected a little more human being , and a little less product . \ninstead of using george and lucy's most obvious differences to ignite sparks , lawrence desperately looks elsewhere , seizing on george's haplessness and lucy's personality tics . \nwhether quitting will prove absorbing to american audiences is debatable . \nbecomes a bit of a mishmash : a tearjerker that doesn't and a thriller that won't . \nfamily togetherness takes a back seat to inter-family rivalry and workplace ambition&#133 ; whole subplots have no explanation or even plot relevance . \ngrant isn't cary and bullock isn't katherine . \nlike a fish that's lived too long , austin powers in goldmember has some unnecessary parts and is kinda wrong in places . \ntwo tedious acts light on great scares and a good surprise ending . \nshyamalan should stop trying to please his mom . \nthe entire movie is in need of a scented bath . \ni'm sorry to say that this should seal the deal - arnold is not , nor will he be , back . \nthe story of trouble every day . . . is so sketchy it amounts to little more than preliminary notes for a science-fiction horror film , and the movie's fragmentary narrative style makes piecing the story together frustrating difficult . \na movie to forget\nfor all of its insights into the dream world of teen life , and its electronic expression through cyber culture , the film gives no quarter to anyone seeking to pull a cohesive story out of its 2 1/2-hour running time . \nenough is not a bad movie , just mediocre . the performances are so overstated , the effect comes off as self-parody . \nit looks good , but it is essentially empty . \nthe film never finds its tone and several scenes run too long . \nthe idea is more interesting than the screenplay , which lags badly in the middle and lurches between not-very-funny comedy , unconvincing dramatics and some last-minute action strongly reminiscent of run lola run . \nvan wilder has a built-in audience , but only among those who are drying out from spring break and are still unconcerned about what they ingest . \na complete waste of time . \nit's hard to believe that something so short could be so flabby . \ndo we really need another film that praises female self-sacrifice ? \nthe major problem with windtalkers is that the bulk of the movie centers on the wrong character . \ntennessee williams by way of oprah's book club . \nso verbally flatfooted and so emotionally predictable or bland that it plays like the standard made-for-tv movie . \nthe entire point of a shaggy dog story , of course , is that it goes nowhere , and this is classic nowheresville in every sense . \nstale and clichd to a fault . \nthis film is too busy hitting all of its assigned marks to take on any life of its own . \nwatching junk like this induces a kind of abstract guilt , as if you were paying dues for good books unread , fine music never heard . \nthe script feels as if it started to explore the obvious voyeuristic potential of 'hypertime' but then backed off when the producers saw the grosses for spy kids . \nstarts off witty and sophisticated and you want to love it -- but filmmaker yvan attal quickly writes himself into a corner . \nsome like it hot on the hardwood proves once again that a man in drag is not in and of himself funny . \nunfortunately , contrived plotting , stereotyped characters and woo's over-the-top instincts as a director undermine the moral dilemma at the movie's heart . \nwitless and utterly pointless . \nwhen 'science fiction' takes advantage of the fact that its intended audience hasn't yet had much science , it does a disservice to the audience and to the genre . \nshow me the mugging . \nrepresents something very close to the nadir of the thriller/horror genre . \nvisually sumptuous but intellectually stultifying . \nas a feature-length film , it wears out its welcome as tryingly as the title character . \na guilty pleasure at best , and not worth seeing unless you want to laugh at it . \na sleep-inducing thriller with a single twist that everyone except the characters in it can see coming a mile away . \nwith a \" spy kids \" sequel opening next week , why bother with a contemptible imitator starring a \" snl \" has-been acting like an 8-year-old channeling roberto benigni ? \nit's just rather leaden and dull . \nlacks the visual flair and bouncing bravado that characterizes better hip-hop clips and is content to recycle images and characters that were already tired 10 years ago . \n'pocas ideas interesantes , un final pseudo mstico que no corresponde al tono general del filme y que deja una sensacin de inconformidad que hace pensar ms de una vez si vale la pena ir a la taquilla y reclamar el precio del boleto . '\nstatham employs an accent that i think is supposed to be an attempt at hardass american but sometimes just lapses into unhidden british . \ninstead of trying to bust some blondes , [diggs] should be probing why a guy with his talent ended up in a movie this bad . \ninitial strangeness inexorably gives way to rote sentimentality and mystical tenderness becomes narrative expedience . \nde ayala is required to supply too much of the energy in a film that is , overall , far too staid for its subject matter . \ndismally dull sci-fi comedy . \nthere's surely something wrong with a comedy where the only belly laughs come from the selection of outtakes tacked onto the end credits . \nwhen one hears harry shearer is going to make his debut as a film director , one would hope for the best\nthe leads we are given here are simply too bland to be interesting . \n[toback's] fondness for fancy split-screen , stuttering editing and pompous references to wittgenstein and kirkegaard . . . blends uneasily with the titillating material . \nadam sandler's 8 crazy nights is 75 wasted minutes of sandler as the voice-over hero in columbia pictures' perverse idea of an animated holiday movie . \nessentially \" fatal attraction \" remade for viewers who were in diapers when the original was released in 1987 . . . . this story gets sillier , not scarier , as it goes along . . . \neven a hardened voyeur would require the patience of job to get through this interminable , shapeless documentary about the swinging subculture . \nthe film's hero is a bore and his innocence soon becomes a questionable kind of inexcusable dumb innocence . \na singularly off-putting romantic comedy . \nthis is an exercise not in biography but in hero worship . \nit all comes down to whether you can tolerate leon barlow . i can't . \nin the spirit of the season , i assign one bright shining star to roberto benigni's pinocchio -- but i guarantee that no wise men will be following after it . \ncheck your brain and your secret agent decoder ring at the door because you don't want to think too much about what's going on . the movie does has some entertainment value - how much depends on how well you like chris rock . \na movie that seems motivated more by a desire to match mortarboards with dead poets society and good will hunting than by its own story . \na culture clash comedy only half as clever as it thinks it is . \nthe logic of it all will be greek to anyone not predisposed to the movie's rude and crude humor . \nas self-aware movies go , who is cletis tout ? is clever enough , though thin writing proves its undoing . \nstarts out strongly before quickly losing its focus , point and purpose in a mess of mixed messages , over-blown drama and bruce willis with a scar . \n . . . a fascinating curiosity piece -- fascinating , that is , for about ten minutes . after that it becomes long and tedious like a classroom play in a college history course . \ndirector jay russell weighs down his capricious fairy-tale with heavy sentiment and lightweight meaning . \nthere are many things that solid acting can do for a movie , but crafting something promising from a mediocre screenplay is not one of them . \nits screenplay serves as auto-critique , and its clumsiness as its own most damning censure . \nat times , it actually hurts to watch . \nnemesis suffers from a paunchy midsection , several plodding action sequences and a wickedly undramatic central theme . \nthe jokes are telegraphed so far in advance they must have been lost in the mail . \n[tries] to parody a genre that's already a joke in the united states . the movie is the equivalent of french hip-hop , which also seems to play on a 10-year delay . \na beyond-lame satire , teddy bears' picnic ranks among the most pitiful directing debuts by an esteemed writer-actor . \nlong before it's over , you'll be thinking of 51 ways to leave this loser . \ni've never seen ( a remake ) do anything as stomach-turning as the way adam sandler's new movie rapes , pillages and incinerates frank capra's classic . . . \nhollywood's answer to an air ball . \nand people make fun of me for liking showgirls</> . \nsuch a wildly uneven hit-and-miss enterprise , you can't help suspecting that it was improvised on a day-to-day basis during production . \na weird little movie that's amusing enough while you watch it , offering fine acting moments and pungent insights into modern l . a . 's show-biz and media subcultures . but it doesn't leave you with much . \ni'm convinced i could keep a family of five blind , crippled , amish people alive in this situation better than these british soldiers do at keeping themselves kicking . \nlike mike is a slight and uninventive movie : like the exalted michael jordan referred to in the title , many can aspire but none can equal . \nthere is nothing funny in this every-joke-has- been-told-a- thousand-times- before movie . \nalways destined to be measured against anthony asquith's acclaimed 1952 screen adaptation . \nthis is standard crime drama fare . . . instantly forgettable and thoroughly dull . \nthere's some outrageously creative action in the transporter . . . [b]ut by the time frank parachutes down onto a moving truck , it's just another cartoon with an unstoppable superman . \none of those based-on-truth stories that persuades you , with every scene , that it could never really have happened this way . \nfrom its nauseating spinning credits sequence to a very talented but underutilized supporting cast , bartleby squanders as much as it gives out . \nyet another genre exercise , gangster no . 1 is as generic as its title . \ndespite the holes in the story and the somewhat predictable plot , moments of the movie caused me to jump in my chair . . . \nthere's an admirable rigor to jimmy's relentless anger , and to the script's refusal of a happy ending , but as those monologues stretch on and on , you realize there's no place for this story to go but down . \nonce again , the intelligence of gay audiences has been grossly underestimated , and a meaty plot and well-developed characters have been sacrificed for skin and flash that barely fizzle . \na lightweight , uneven action comedy that freely mingles french , japanese and hollywood cultures . \n[a] slummer . \nsuch a fine idea for a film , and such a stultifying , lifeless execution . \n[allen's] best works understand why snobbery is a better satiric target than middle-america diversions could ever be . \nthis overlong infomercial , due out on video before month's end , is tepid and tedious . \nan ambitious , guilt-suffused melodrama crippled by poor casting . \nrarely has sex on screen been so aggressively anti-erotic . \na dull , inconsistent , dishonest female bonding picture . \nso much about the film is loopy and ludicrous . . . that it could have been a hoot in a bad-movie way if the laborious pacing and endless exposition had been tightened . \na disappointment for a movie that should have been the ultimate imax trip . \ndoes little to elaborate the conceit of setting this blood-soaked tragedy of murderous ambition in the era of richard nixon . \nthis sade is hardly a perverse , dangerous libertine and agitator -- which would have made for better drama . he's just a sad aristocrat in tattered finery , and the film seems as deflated as he does . \nthe film's needlessly opaque intro takes its doe-eyed crudup out of pre-9/11 new york and onto a cross-country road trip of the homeric kind . \nit's as if a bored cage spent the duration of the film's shooting schedule waiting to scream : \" got aids yet ? \" \nin a strange way , egoyan has done too much . he's worked too hard on this movie . \nthe film has the thrown-together feel of a summer-camp talent show : hastily written , underrehearsed , arbitrarily plotted and filled with crude humor and vulgar innuendo . \nthe last three narcissists left on earth compete for each others' affections . \na clash between the artificial structure of the story and the more contemporary , naturalistic tone of the film . . . \n[a] poorly executed comedy . \nthe movie's messages are quite admirable , but the story is just too clichd and too often strains credulity . \nwhat we have here isn't a disaster , exactly , but a very handsomely produced let-down . \nthe script was reportedly rewritten a dozen times -- either 11 times too many or else too few . \na shoddy male hip hop fantasy filled with guns , expensive cars , lots of naked women and rocawear clothing . \nto the filmmakers , ivan is a prince of a fellow , but he comes across as shallow and glib though not mean-spirited , and there's no indication that he's been responsible for putting together any movies of particular value or merit . \nthis is a movie filled with unlikable , spiteful idiots ; whether or not their friendship is salvaged makes no difference in the least . \nit's as if allen , at 66 , has stopped challenging himself . \nscotland , pa is entirely too straight-faced to transcend its clever concept . \na movie that the less charitable might describe as a castrated cross between highlander and lolita . \nnot only does leblanc make one spectacularly ugly-looking broad , but he appears miserable throughout as he swaggers through his scenes . \nthere's little to recommend snow dogs , unless one considers cliched dialogue and perverse escapism a source of high hilarity . \nit's deep-sixed by a compulsion to catalog every bodily fluids gag in there's something about mary and devise a parallel clone-gag . \nthe film rehashes several old themes and is capped with pointless extremes -- it's insanely violent and very graphic . \nsorority boys , which is as bad at it is cruel , takes every potential laugh and stiletto-stomps the life out of it . \nhere the love scenes all end in someone screaming . maybe there's a metaphor here , but figuring it out wouldn't make trouble every day any better . \nthis is the first film i've ever seen that had no obvious directing involved . \nfans of so-bad-they're-good cinema may find some fun in this jumbled mess . \nweiss and speck never make a convincing case for the relevance of these two 20th-century footnotes . \nsheridan is painfully bad , a fourth-rate jim carrey who doesn't understand the difference between dumb fun and just plain dumb . \npresents nothing special and , until the final act , nothing overtly disagreeable . \nthe most excruciating 86 minutes one might sit through this summer that do not involve a dentist drill . \nwas that movie nothing more than a tepid exercise in trotting out a formula that worked five years ago but has since lost its fizz ? \nit goes on for too long and bogs down in a surfeit of characters and unnecessary subplots . \nit's absolutely amazing how first-time director kevin donovan managed to find something new to add to the canon of chan . make chan's action sequences boring . \nyou . . . get a sense of good intentions derailed by a failure to seek and strike just the right tone . \n'in this poor remake of such a well loved classic , parker exposes the limitations of his skill and the basic flaws in his vision . '\nit's the movie equivalent of a sweaty old guy in a rain coat shopping for cheap porn . \nthe film's final hour , where nearly all the previous unseen material resides , is unconvincing soap opera that tornatore was right to cut . \nthe movie does such an excellent job of critiquing itself at every faltering half-step of its development that criticizing feels more like commiserating . \ni found it slow , predictable and not very amusing . \ndirector yu seems far more interested in gross-out humor than in showing us well-thought stunts or a car chase that we haven't seen 10 , 000 times . \nviewers will need all the luck they can muster just figuring out who's who in this pretentious mess . \na pint-sized 'goodfellas' designed to appeal to the younger set , it's not a very good movie in any objective sense , but it does mostly hold one's interest . \nget out your pooper-scoopers . \nwhile the material is slight , the movie is better than you might think . \nit's definitely not made for kids or their parents , for that matter , and i think even fans of sandler's comic taste may find it uninteresting . \nsheridan seems terrified of the book's irreverent energy , and scotches most of its lan , humor , bile , and irony . \nmore busy than exciting , more frantic than involving , more chaotic than entertaining . \nthere are more shots of children smiling for the camera than typical documentary footage which hurts the overall impact of the film . it's makes a better travelogue than movie . \n . . . really horrible drek . \nit's as if solondz had two ideas for two movies , couldn't really figure out how to flesh either out , so he just slopped em together here . \nthe fourth in a series that i'll bet most parents had thought --hoped ! -- was a fad that had long since vanished . \nit's a long way from orwell's dark , intelligent warning cry [1984] to the empty stud knockabout of equilibrium , and what once was conviction is now affectation . \nits premise is smart , but the execution is pretty weary . \nthe holiday message of the 37-minute santa vs . the snowman leaves a lot to be desired . \nmore precious than perspicacious\nif you saw it on tv , you'd probably turn it off , convinced that you had already seen that movie . \n[t]he script isn't up to the level of the direction , nor are the uneven performances by the cast members , who seem bound and determined to duplicate bela lugosi's now-cliched vampire accent . \nif this is cinema , i pledge allegiance to cagney and lacey . \nenigma looks great , has solid acting and a neat premise . yet why it fails is a riddle wrapped in a mystery inside an enigma . \nmost of the characters come off as pantomimesque sterotypes . \nstarts promisingly but disintegrates into a dreary , humorless soap opera . \nwhile there's likely very little crossover appeal to those without much interest in the elizabethans ( as well as rank frustration from those in the know about rubbo's dumbed-down tactics ) , much ado about something is an amicable endeavor . \nit's actually too sincere -- the crime movie equivalent of a chick flick . \nmost of the film feels conceived and shot on the fly -- like between lunch breaks for shearer's radio show and his simpson voice-overs . \nperry's good and his is an interesting character , but \" serving sara \" hasn't much more to serve than silly fluff . nor is it a romantic comedy . \nculkin turns his character into what is basically an anti-harry potter -- right down to the gryffindor scarf . \nmemorable for a peculiar malaise that renders its tension flaccid and , by extension , its surprises limp and its resolutions ritual . \nit's a documentary that says that the alternate sexuality meant to set you free may require so much relationship maintenance that celibacy can start looking good . \nin the not-too-distant future , movies like ghost ship will be used as analgesic balm for overstimulated minds . right now , they're merely signposts marking the slow , lingering death of imagination . \nan intriguing near-miss . \nthe movie's biggest shocks come from seeing former nymphette juliette lewis playing a salt-of-the-earth mommy named minnie and watching slim travel incognito in a ridiculous wig no respectable halloween costume shop would ever try to sell . \nlike most movies about the pitfalls of bad behavior . . . circuit gets drawn into the party . \nit appears as if even the filmmakers didn't know what kind of movie they were making . \nbeneath the uncanny , inevitable and seemingly shrewd facade of movie-biz farce . . . lies a plot cobbled together from largely flat and uncreative moments . \nsnipes relies too much on a scorchingly plotted dramatic scenario for its own good . \npiccoli's performance is amazing , yes , but the symbols of loss and denial and life-at-arm's-length in the film seem irritatingly transparent . \nstarts out mediocre , spirals downward , and thuds to the bottom of the pool with an utterly incompetent conclusion . \nnicolas cage isn't the first actor to lead a group of talented friends astray , and this movie won't create a ruffle in what is already an erratic career . \nit lacks the compassion , good-natured humor and the level of insight that made [eyre's] first film something of a sleeper success . \nthe result is good gossip , entertainingly delivered , yet with a distinctly musty odour , its expiry date long gone . \na sustained fest of self-congratulation between actor and director that leaves scant place for the viewer . \nall analyze that proves is that there is really only one movie's worth of decent gags to be gleaned from the premise . \ngreen ruins every single scene he's in , and the film , while it's not completely wreaked , is seriously compromised by that . \nthere's not a comedic moment in this romantic comedy . \nthe story is predictable , the jokes are typical sandler fare , and the romance with ryder is puzzling . \nwallace directs with such patronising reverence , it turns the stomach . \nresurrection has the dubious distinction of being a really bad imitation of the really bad blair witch project . \npoor ben bratt couldn't find stardom if mapquest emailed him point-to-point driving directions . \npretend like your sat scores are below 120 and you might not notice the flaws . \nunlike trey parker , sandler doesn't understand that the idea of exploiting molestation for laughs is funny , not actually exploiting it yourself . \na fake street drama that keeps telling you things instead of showing them . \nan empty , purposeless exercise . \nearnest and tentative even when it aims to shock . \nhaneke's script ( from elfriede jelinek's novel ) is contrived , unmotivated , and psychologically unpersuasive , with an inconclusive ending . \na sometimes incisive and sensitive portrait that is undercut by its awkward structure and a final veering toward melodrama . \nthose 24-and-unders looking for their own caddyshack to adopt as a generational signpost may have to keep on looking . \na distinctly mixed bag , the occasional bursts of sharp writing alternating with lots of sloppiness and the obligatory moments of sentimental ooze . \nwhat begins brightly gets bogged down over 140 minutes . \nultimately , jane learns her place as a girl , softens up and loses some of the intensity that made her an interesting character to begin with . \nah-nuld's action hero days might be over . \nit's clear why deuces wild , which was shot two years ago , has been gathering dust on mgm's shelf . \nfeels like nothing quite so much as a middle-aged moviemaker's attempt to surround himself with beautiful , half-naked women . \nwhen the precise nature of matthew's predicament finally comes into sharp focus , the revelation fails to justify the build-up . \nthis picture is murder by numbers , and as easy to be bored by as your abc's , despite a few whopping shootouts . \nhilarious musical comedy though stymied by accents thick as mud . \nif you are into splatter movies , then you will probably have a reasonably good time with the salton sea . \na dull , simple-minded and stereotypical tale of drugs , death and mind-numbing indifference on the inner-city streets . \nthe feature-length stretch . . . strains the show's concept . \nthis slender plot feels especially thin stretched over the nearly 80-minute running time . \na film that will probably please people already fascinated by behan but leave everyone else yawning with admiration . \ndavis the performer is plenty fetching enough , but she needs to shake up the mix , and work in something that doesn't feel like a half-baked stand-up routine . \nthe densest distillation of roberts' movies ever made . \nultimately , the film never recovers from the clumsy clich of the ugly american abroad , and the too-frosty exterior ms . paltrow employs to authenticate her british persona is another liability . \na handsome but unfulfilling suspense drama more suited to a quiet evening on pbs than a night out at an amc . \ntom green and an ivy league college should never appear together on a marquee , especially when the payoff is an unschooled comedy like stealing harvard , which fails to keep 80 minutes from seeming like 800 . \n ( it ) highlights not so much the crime lord's messianic bent , but spacey's . \nmaster of disguise runs for only 71 minutes and feels like three hours . \na reworking of die hard and cliffhanger but it's nowhere near as exciting as either . \nsuffers from unlikable characters and a self-conscious sense of its own quirky hipness . \na film without surprise geared toward maximum comfort and familiarity . \nfessenden continues to do interesting work , and it would be nice to see what he could make with a decent budget . but the problem with wendigo , for all its effective moments , isn't really one of resources . \nspirit is a visual treat , and it takes chances that are bold by studio standards , but it lacks a strong narrative . \nit stars schticky chris rock and stolid anthony hopkins , who seem barely in the same movie . their contrast is neither dramatic nor comic -- it's just a weird fizzle . \nthis is a children's film in the truest sense . it's packed with adventure and a worthwhile environmental message , so it's great for the kids . parents , on the other hand , will be ahead of the plot at all times , and there isn't enough clever innuendo to fil\nthe niftiest trick perpetrated by the importance of being earnest is the alchemical transmogrification of wilde into austen--and a hollywood-ized austen at that . \ntykwer's surface flash isn't just a poor fit with kieslowski's lyrical pessimism ; it completely contradicts everything kieslowski's work aspired to , including the condition of art . \nice age is the first computer-generated feature cartoon to feel like other movies , and that makes for some glacial pacing early on . \ntoo slick and manufactured to claim street credibility . \ncherry orchard is badly edited , often awkwardly directed and suffers from the addition of a wholly unnecessary pre-credit sequence designed to give some of the characters a 'back story . '\nwhat ensues are much blood-splattering , mass drug-induced bowel evacuations , and none-too-funny commentary on the cultural distinctions between americans and brits . \na dark comedy that goes for sick and demented humor simply to do so . the movie is without intent . \nvisually exciting sci-fi film which suffers from a lackluster screenplay . \nwhile hollywood ending has its share of belly laughs ( including a knockout of a closing line ) , the movie winds up feeling like a great missed opportunity . \nif the full monty was a freshman fluke , lucky break is [cattaneo] sophomore slump . \n sandra bullock and hugh grant make a great team , but this predictable romantic comedy should get a pink slip . \nallegiance to chekhov , which director michael cacoyannis displays with somber earnestness in the new adaptation of the cherry orchard , is a particularly vexing handicap . \nyou expect more from director michael apted ( enigma ) and screenwriter nicholas kazan ( reversal of fortune ) than this cliche pileup . \nthe first mistake , i suspect , is casting shatner as a legendary professor and kunis as a brilliant college student--where's pauly shore as the rocket scientist ? \nthe dramatic scenes are frequently unintentionally funny , and the action sequences -- clearly the main event -- are surprisingly uninvolving . \nreplacing john carpenter's stylish tracking shots is degraded , handheld blair witch video-cam footage . of all the halloween's , this is the most visually unappealing . \nit has the requisite faux-urban vibe and hotter-two-years-ago rap and r&b names and references . \ndespite its dry wit and compassion , the film suffers from a philosophical emptiness and maddeningly sedate pacing . \n . . . feels as if ( there's ) a choke leash around your neck so director nick cassavetes can give it a good , hard yank whenever he wants you to feel something . \nattal pushes too hard to make this a comedy or serious drama . he seems to want both , but succeeds in making neither . \ni could have used my two hours better watching being john malkovich again . \nit's not a bad plot ; but , unfortunately , the movie is nowhere near as refined as all the classic dramas it borrows from . \nflat , misguided comedy . \ngirlfriends are bad , wives are worse and babies are the kiss of death in this bitter italian comedy . \nthe only young people who possibly will enjoy it are infants . . . who might be distracted by the movie's quick movements and sounds . \nthe film boasts at least a few good ideas and features some decent performances , but the result is disappointing . \nno such thing breaks no new ground and treads old turf like a hippopotamus ballerina . \nunfortunately , neither sendak nor the directors are particularly engaging or articulate . \na wishy-washy melodramatic movie that shows us plenty of sturm und drung , but explains its characters' decisions only unsatisfactorily . \nbang ! zoom ! it's actually pretty funny , but in all the wrong places . \nlurid and less than lucid work . \na wannabe comedy of manners about a brainy prep-school kid with a mrs . robinson complex founders on its own preciousness -- and squanders its beautiful women . \nat a brief 42 minutes , we need more x and less blab . \nif anything , see it for karen black , who camps up a storm as a fringe feminist conspiracy theorist named dirty dick . \nthis 90-minute dud could pass for mike tyson's e ! true hollywood story . \nthis is surely one of the most frantic , virulent and foul-natured christmas season pics ever delivered by a hollywood studio . \nonce the expectation of laughter has been quashed by whatever obscenity is at hand , even the funniest idea isn't funny . \na porn film without the sex scenes . \nthe connected stories of breitbart and hanussen are actually fascinating , but the filmmaking in invincible is such that the movie does not do them justice . \na depressingly retrograde , 'post-feminist' romantic comedy that takes an astonishingly condescending attitude toward women . \nreturn to never land is much more p . c . than the original version ( no more racist portraits of indians , for instance ) , but the excitement is missing . \nby the end , you just don't care whether that cold-hearted snake petrovich ( that would be reno ) gets his comeuppance . just bring on the battle bots , please ! \nwhile it's all quite tasteful to look at , the attention process tends to do a little fleeing of its own . \nbroder's screenplay is shallow , offensive and redundant , with pitifully few real laughs . \nyes they can swim , the title is merely anne-sophie birot's off-handed way of saying girls find adolescence difficult to wade through . \ndon michael paul uses quick-cuts , ( very ) large shadows and wide-angle shots taken from a distance to hide the liberal use of a body double ( for seagal ) . \nslow , silly and unintentionally hilarious . \nthe sweetest thing leaves a bitter taste . \nin a big corner office in hell , satan is throwing up his hands in surrender , is firing his r&d people , and has decided he will just screen the master of disguise 24/7 . \nfor something as splendid-looking as this particular film , the viewer expects something special but instead gets [sci-fi] rehash . \na thriller without a lot of thrills . \nthis stuck pig of a movie flails limply between bizarre comedy and pallid horror . \nah , the travails of metropolitan life ! alas , another breathless movie about same ! \nin moonlight mile , no one gets shut out of the hug cycle . \nthough uniformly well acted , especially by young ballesta and galan ( a first-time actor ) , writer/director achero manas's film is schematic and obvious . \ndone in mostly by a weak script that can't support the epic treatment . \ndespite its visual virtuosity , 'naqoyqatsi' is banal in its message and the choice of material to convey it . \nslap her - she's not funny ! no french people were harmed during the making of this movie , but they were insulted and the audience was put through torture for an hour and a half . \nthough its rather routine script is loaded with familiar situations , the movie has a cinematic fluidity and sense of intelligence that makes it work more than it probably should . \n \" one look at a girl in tight pants and big tits and you turn stupid ? \" um . . isn't that the basis for the entire plot ? \n \" not really as bad as you might think ! \" \nstrident and inelegant in its 'message-movie' posturing . \none regards reign of fire with awe . what a vast enterprise has been marshaled in the service of such a minute idea . \nit has the right approach and the right opening premise , but it lacks the zest and it goes for a plot twist instead of trusting the material . \nits impressive images of crematorium chimney fires and stacks of dead bodies are undermined by the movie's presentation , which is way too stagy . \nseeing as the film lacks momentum and its position remains mostly undeterminable , the director's experiment is a successful one . \nthe plot is romantic comedy boilerplate from start to finish . \ni suspect this is the kind of production that would have been funnier if the director had released the outtakes theatrically and used the film as a bonus feature on the dvd . \nan unfortunate title for a film that has nothing endearing about it . \nninety minutes of viva castro ! can be as tiresome as 9 seconds of jesse helms' anti- castro rhetoric , which are included\ncomes off as a long , laborious whine , the bellyaching of a paranoid and unlikable man . \nit just goes to show , an intelligent person isn't necessarily an admirable storyteller . \nin a 102-minute film , aaliyah gets at most 20 minutes of screen time . . . . most viewers will wish there had been more of the \" queen \" and less of the \" damned . \" \nhopelessly inane , humorless and under-inspired . \nkapur fails to give his audience a single character worth rooting for ( or worth rooting against , for that matter ) . \nit reduces the complexities to bromides and slogans and it gets so preachy-keen and so tub-thumpingly loud it makes you feel like a chump just for sitting through it . \nnone of this has the suavity or classical familiarity of bond , but much of it is good for a laugh . the problem with \" xxx \" is that its own action isn't very effective . \na great script brought down by lousy direction . same guy with both hats . big mistake . \na mediocre exercise in target demographics , unaware that it's the butt of its own joke . \ndirector kevin bray excels in breaking glass and marking off the \" miami vice \" checklist of power boats , latin music and dog tracks . he doesn't , however , deliver nearly enough of the show's trademark style and flash . \nin gleefully , thumpingly hyperbolic terms , it covers just about every cliche in the compendium about crass , jaded movie types and the phony baloney movie biz . \nthe spalding gray equivalent of a teen gross-out comedy . \nperhaps even the slc high command found writer-director mitch davis's wall of kitsch hard going . \naccording to wendigo , 'nature' loves the members of the upper class almost as much as they love themselves . \nan encouraging effort from mccrudden\nthe romance between the leads isn't as compelling or as believable as it should be . \nif i could have looked into my future and saw how bad this movie was , i would go back and choose to skip it . fortunately , you still have that option . \nsupposedly authentic account of a historical event that's far too tragic to merit such superficial treatment . \nadroit but finally a trifle flat , mad love doesn't galvanize its outrage the way , say , jane campion might have done , but at least it possesses some . \nto blandly go where we went 8 movies ago . . . \na slow-moving police-procedural thriller that takes its title all too literally . \nthis u-boat doesn't have a captain . \nwith nary a glimmer of self-knowledge , [crane] becomes more specimen than character -- and auto focus remains a chilly , clinical lab report . \nthis one aims for the toilet and scores a direct hit . \ndull , a road-trip movie that's surprisingly short of both adventure and song . \ni walked away not really know who \" they \" were , what \" they \" looked like . why \" they \" were here and what \" they \" wanted and quite honestly , i didn't care . \npredictably melodramatic . \nafter several scenes of this tacky nonsense , you'll be wistful for the testosterone-charged wizardry of jerry bruckheimer productions , especially because half past dead is like the rock on a wal-mart budget . \na relatively effective little potboiler until its absurd , contrived , overblown , and entirely implausible finale . \nthe country bears wastes an exceptionally good idea . but the movie that doesn't really deliver for country music fans or for family audiences\nadults will certainly want to spend their time in the theater thinking up grocery lists and ways to tell their kids how not to act like pinocchio . as for children , they won't enjoy the movie at all . \n . . . you can be forgiven for realizing that you've spent the past 20 minutes looking at your watch and waiting for frida to just die already . \ntoo bad writer-director adam rifkin situates it all in a plot as musty as one of the golden eagle's carpets . \nit's lazy for a movie to avoid solving one problem by trying to distract us with the solution to another . \nthe movie is genial but never inspired , and little about it will stay with you . \nthe movie obviously seeks to re-create the excitement of such '50s flicks as jules verne's '20 , 000 leagues under the sea' and the george pal version of h . g . wells' 'the time machine . ' but its storytelling prowess and special effects are both listless . \ndespite the opulent lushness of every scene , the characters never seem to match the power of their surroundings . \neven after 90 minutes of playing opposite each other bullock and grant still look ill at ease sharing the same scene . what should have been a painless time-killer becomes instead a grating endurance test . \na bland , obnoxious 88-minute infomercial for universal studios and its ancillary products . \n . . little action , almost no suspense or believable tension , one-dimensional characters up the wazoo and sets that can only be described as sci-fi generic . \nthe movie strains to stay on the light , comic side of the issue , despite the difficulty of doing so when dealing with the destruction of property and , potentially , of life itself . \nthe master of disguise is awful . it's pauly shore awful . don't say you weren't warned . \ndisappointing in comparison to other recent war moviesor any other john woo flick for that matter . \nthe entire movie is filled with deja vu moments . \n'opening up' the play more has partly closed it down . \nwhat [frei] gives us . . . is a man who uses the damage of war -- far more often than the warfare itself -- to create the kind of art shots that fill gallery shows . \nan ugly , revolting movie . \nthe film is way too full of itself ; it's stuffy and pretentious in a give-me-an-oscar kind of way . \nthe movie is concocted and carried out by folks worthy of scorn , and the nicest thing i can say is that i can't remember a single name responsible for it . \nwatching \" ending \" is too often like looking over the outdated clothes and plastic knickknacks at your neighbor's garage sale . you can't believe anyone would really buy this stuff . \ncertainly beautiful to look at , but its not very informative about its titular character and no more challenging than your average television biopic . \nit desperately wants to be a wacky , screwball comedy , but the most screwy thing here is how so many talented people were convinced to waste their time . \nthe skills of a calculus major at m . i . t . are required to balance all the formulaic equations in the long-winded heist comedy who is cletis tout ? \nfrom the choppy editing to the annoying score to 'special effects' by way of replacing objects in a character's hands below the camera line , \" besotted \" is misbegotten\nmy advice is to skip the film and pick up the soundtrack . \na film that presents an interesting , even sexy premise then ruins itself with too many contrivances and goofy situations . \nfilled with low-brow humor , gratuitous violence and a disturbing disregard for life . \ndirected in a flashy , empty sub-music video style by a director so self-possessed he actually adds a period to his first name\nthe 70-year-old godard has become , to judge from in praise of love , the sort of bitter old crank who sits behind his light meter and harangues the supposed injustices of the artistic world-at-large without doing all that much to correct them . \nan unsophisticated sci-fi drama that takes itself all too seriously . \nsolondz is without doubt an artist of uncompromising vision , but that vision is beginning to feel , if not morally bankrupt , at least terribly monotonous . \nharvard man is a semi-throwback , a reminiscence without nostalgia or sentimentality . \nsupposedly based upon real , or at least soberly reported incidents , the film ends with a large human tragedy . alas , getting there is not even half the interest . \nwhile hoffman's performance is great , the subject matter goes nowhere . \nthe smash 'em-up , crash 'em-up , shoot 'em-up ending comes out of nowhere substituting mayhem for suspense . \ndeuces wild treads heavily into romeo and juliet/west side story territory , where it plainly has no business going . \nhart's war seems to want to be a character study , but apparently can't quite decide which character . \ntheological matters aside , the movie is so clumsily sentimental and ineptly directed it may leave you speaking in tongues . \nthis latest installment of the horror film franchise that is apparently as invulnerable as its trademark villain has arrived for an incongruous summer playoff , demonstrating yet again that the era of the intelligent , well-made b movie is long gone . \nnovak contemplates a heartland so overwhelmed by its lack of purpose that it seeks excitement in manufactured high drama . \nbeen there , done that , liked it much better the first time around - when it was called the professional . \nthe film is all over the place , really . it dabbles all around , never gaining much momentum . \nthe beautiful , unusual music is this film's chief draw , but its dreaminess may lull you to sleep . \nthe action quickly sinks into by-the-numbers territory . \nforages for audience sympathy like a temperamental child begging for attention , giving audiences no reason to truly care for its decrepit freaks beyond the promise of a reprieve from their incessant whining . \nwhen [reno] lets her radical flag fly , taking angry potshots at george w . bush , henry kissinger , larry king , et al . , reno devolves into a laugh-free lecture . \nsuch a premise is ripe for all manner of lunacy , but kaufman and gondry rarely seem sure of where it should go . \nburns' fifth beer-soaked film feels in almost every possible way -- from the writing and direction to the soggy performances -- tossed off . \n'es en verdad una pena que mandoki est realizando cintas tan malas desde hace algn tiempo , pues talento tiene , pero quin sabe dnde lo tiene escondido . '\nwhile this one gets off with a good natured warning , future lizard endeavors will need to adhere more closely to the laws of laughter\nanother boorish movie from the i-heard-a-joke- at-a-frat-party school of screenwriting . \ntoo much of the movie feels contrived , as if the filmmakers were worried the story wouldn't work without all those gimmicks . \nit's hard to understand why anyone in his right mind would even think to make the attraction a movie . and it's harder still to believe that anyone in his right mind would want to see the it . \nthe ethos of the chelsea hotel may shape hawke's artistic aspirations , but he hasn't yet coordinated his own dv poetry with the beat he hears in his soul . \nthe sight of the name bruce willis brings to mind images of a violent battlefield action picture , but the film has a lot more on its mind--maybe too much . \nwhy sit through a crummy , wannabe-hip crime comedy that refers incessantly to old movies , when you could just rent those movies instead , let alone seek out a respectable new one ? \nthe obnoxious special effects , the obligatory outbursts of flatulence and the incessant , so-five-minutes-ago pop music on the soundtrack overwhelm what is left of the scruffy , dopey old hanna-barbera charm . \nexploring value choices is a worthwhile topic for a film -- but here the choices are as contrived and artificial as kerrigan's platinum-blonde hair . \nthe movie's downfall is to substitute plot for personality . it doesn't really know or care about the characters , and uses them as markers for a series of preordained events . \nall mood and no movie . \npress the delete key . \nsimone is not a bad film . it just doesn't have anything really interesting to say . \nonce he starts learning to compromise with reality enough to become comparatively sane and healthy , the film becomes predictably conventional . \n . . . hopefully it'll be at the dollar theatres by the time christmas rolls around . wait to see it then . \nthere's no disguising this as one of the worst films of the summer . or for the year , for that matter . \nlacks the spirit of the previous two , and makes all those jokes about hos and even more unmentionable subjects seem like mere splashing around in the muck . \nthis hastily mounted production exists only to capitalize on hopkins' inclination to play hannibal lecter again , even though harris has no immediate inclination to provide a fourth book . \ndeath to smoochy tells a moldy-oldie , not-nearly -as-nasty -as-it- thinks-it-is joke . over and over again . \nthe threat implied in the title pokmon 4ever is terrifying  like locusts in a horde these things will keep coming . \nthe film never gets over its own investment in conventional arrangements , in terms of love , age , gender , race , and class . \nto call this film a lump of coal would only be to flatter it . \nentertainment more disposable than hanna-barbera's half-hour cartoons ever were . \nthe film falls short on tension , eloquence , spiritual challenge -- things that have made the original new testament stories so compelling for 20 centuries . \nby the end of it all i sort of loved the people onscreen , even though i could not stand them . perhaps the film should be seen as a conversation starter . it's not an easy one to review . \nat best this is a film for the under-7 crowd . but it would be better to wait for the video . and a very rainy day . \nthe whole talking-animal thing is grisly . \nnever again , while nothing special , is pleasant , diverting and modest -- definitely a step in the right direction . \nwouldn't it be funny if a bunch of allied soldiers went undercover as women in a german factory during world war ii ? um , no . but here's a movie about it anyway . \nhas not so much been written as assembled , frankenstein-like , out of other , marginally better shoot-em-ups . \nthe punch lines that miss , unfortunately , outnumber the hits by three-to-one . but death to smoochy keeps firing until the bitter end . \nmushes the college-friends genre ( the big chill ) together with the contrivances and overwrought emotion of soap operas . \nshowtime's starry cast could be both an asset and a detriment . those who trek to the 'plex predisposed to like it probably will enjoy themselves . but ticket-buyers with great expectations will wind up as glum as mr . de niro . \na determined , ennui-hobbled slog that really doesn't have much to say beyond the news flash that loneliness can make people act weird . \ntoo daft by half . . . but supremely good natured . \nfails in making this character understandable , in getting under her skin , in exploring motivation . . . well before the end , the film grows as dull as its characters , about whose fate it is hard to care . \nit's a shame that the storyline and its underlying themes . . . finally seem so impersonal or even shallow . \nwoody , what happened ? \njuliette binoche's sand is vivacious , but it's hard to sense that powerhouse of 19th-century prose behind her childlike smile . \nit's supposed to be post-feminist breezy but ends up as tedious as the chatter of parrots raised on oprah . \nyou can tell almost immediately that welcome to collinwood isn't going to jell . \nthroughout all the tumult , a question comes to mind : so why is this so boring ? \ncattaneo reworks the formula that made the full monty a smashing success . . . but neglects to add the magic that made it all work . \nroutine and rather silly . \na rip-off twice removed , modeled after [seagal's] earlier copycat under siege , sometimes referred to as die hard on a boat . \ntotally overwrought , deeply biased , and wholly designed to make you feel guilty about ignoring what the filmmakers clearly believe are the greatest musicians of all time . \nyou can practically hear george orwell turning over . \nbehan's memoir is great material for a film -- rowdy , brawny and lyrical in the best irish sense -- but sheridan has settled for a lugubrious romance . \nwhile holm is terrific as both men and hjejle quite appealing , the film fails to make the most out of the intriguing premise . \nlazy filmmaking , with the director taking a hands-off approach when he should have shaped the story to show us why it's compelling . \nif it were any more of a turkey , it would gobble in dolby digital stereo . if nothing else , \" rollerball \" 2002 may go down in cinema history as the only movie ever in which the rest of the cast was outshined by ll cool j . \na movie that falls victim to frazzled wackiness and frayed satire . \nhow do you make a movie with depth about a man who lacked any ? on the evidence before us , the answer is clear : not easily and , in the end , not well enough . \nthe film's trailer also looked like crap , so crap is what i was expecting . \nmore trifle than triumph . \nthe movie is almost completely lacking in suspense , surprise and consistent emotional conviction . \nfesters in just such a dungpile that you'd swear you were watching monkeys flinging their feces at you . \nlyne's latest , the erotic thriller unfaithful , further demonstrates just how far his storytelling skills have eroded . \nit sounds like another clever if pointless excursion into the abyss , and that's more or less how it plays out . \nrumor , a muddled drama about coming to terms with death , feels impersonal , almost generic . \nreport card : doesn't live up to the exalted tagline - there's definite room for improvement . doesn't deserve a passing grade ( even on a curve ) . \nthe pacing is deadly , the narration helps little and naipaul , a juicy writer , is negated . \nas his circle of friends keeps getting smaller one of the characters in long time dead says 'i'm telling you , this is f * * * ed' . maybe he was reading the minds of the audience . \n . . . if it had been only half-an-hour long or a tv special , the humor would have been fast and furious-- at ninety minutes , it drags . \nbean drops the ball too many times . . . hoping the nifty premise will create enough interest to make up for an unfocused screenplay . \na well-acted , but one-note film . \nblood work is laughable in the solemnity with which it tries to pump life into overworked elements from eastwood's dirty harry period . \nthe movie is too amateurishly square to make the most of its own ironic implications . \n[lee] treats his audience the same way that jim brown treats his women -- as dumb , credulous , unassuming , subordinate subjects . and lee seems just as expectant of an adoring , wide-smiling reception . \nthere's not one decent performance from the cast and not one clever line of dialogue . \none of the worst movies of the year . . . . watching it was painful . \na era do gelo diverte , mas no convence .  um passatempo descompromissado  e s . \nno amount of burning , blasting , stabbing , and shooting can hide a weak script . \nit's an odd show , pregnant with moods , stillborn except as a harsh conceptual exercise . \nnearly all the fundamentals you take for granted in most films are mishandled here . \nthe armenian genocide deserves a more engaged and honest treatment . \nearnest yet curiously tepid and choppy recycling in which predictability is the only winner . \nultimately this is a frustrating patchwork : an uneasy marriage of louis begley's source novel ( about schmidt ) and an old payne screenplay . \nthe exploitative , clumsily staged violence overshadows everything , including most of the actors . \nwe started to wonder if  some unpaid intern had just typed 'chris rock , ' 'anthony hopkins' and 'terrorists' into some univac-like script machine . \neven when crush departs from the 4w formula . . . it feels like a glossy rehash . \nmore likely to have you scratching your head than hiding under your seat . \nbears is even worse than i imagined a movie ever could be . \nwhen you find yourself rooting for the monsters in a horror movie , you know the picture is in trouble . \nthis is very much of a mixed bag , with enough negatives to outweigh the positives . \nmarinated in clichs and mawkish dialogue . \nwhether it's the worst movie of 2002 , i can't say for sure : memories of rollerball have faded , and i skipped country bears . but this new jangle of noise , mayhem and stupidity must be a serious contender for the title . \n[a] boldly stroked , luridly coloured , uni-dimensional nonsense machine that strokes the eyeballs while it evaporates like so much crypt mist in the brain . \nnot once in the rush to save the day did i become very involved in the proceedings ; to me , it was just a matter of 'eh . '\nrollerball is as bad as you think , and worse than you can imagine . \nthe first question to ask about bad company is why anthony hopkins is in it . we assume he had a bad run in the market or a costly divorce , because there is no earthly reason other than money why this distinguished actor would stoop so low . \nnot exaggerated enough to be a parody of gross-out flicks , college flicks , or even flicks in general . it merely indulges in the worst elements of all of them . \nshame on writer/director vicente aranda for making a florid biopic about mad queens , obsessive relationships , and rampant adultery so dull . \nsuffers from a decided lack of creative storytelling . \nviolent , vulgar and forgettably entertaining . \nnothing happens , and it happens to flat characters . \nwith a completely predictable plot , you'll swear that you've seen it all before , even if you've never come within a mile of the longest yard . \nremember back when thrillers actually thrilled ? when the twist endings were actually surprising ? when the violence actually shocked ? when the heroes were actually under 40 ? sadly , as blood work proves , that was a long , long time ago . \nblue crush has all the trappings of an energetic , extreme-sports adventure , but ends up more of a creaky \" pretty woman \" retread , with the emphasis on self-empowering schmaltz and big-wave surfing that gives pic its title an afterthought . \nthis movie plays like an extended dialogue exercise in retard 101 . \nwhat we get in feardotcom is more like something from a bad clive barker movie . in other words , it's badder than bad . \nif they broke out into elaborate choreography , singing and finger snapping it might have held my attention , but as it stands i kept looking for the last exit from brooklyn . \na sloppy slapstick throwback to long gone bottom-of-the-bill fare like the ghost and mr . chicken . \na small independent film suffering from a severe case of hollywood-itis . \nwhere the film falters is in its tone . \nthe story alone could force you to scratch a hole in your head . \nultimately , sarah's dedication to finding her husband seems more psychotic than romantic , and nothing in the movie makes a convincing case that one woman's broken heart outweighs all the loss we witness . \nit's supposed to be a humorous , all-too-human look at how hope can breed a certain kind of madness -- and strength -- but it never quite adds up . \nfeels more like a rejected x-files episode than a credible account of a puzzling real-life happening . \nsome motion pictures portray ultimate passion ; others create ultimate thrills . men in black ii achieves ultimate insignificance -- it's the sci-fi comedy spectacle as whiffle-ball epic . \nan enigmatic film that's too clever for its own good , it's a conundrum not worth solving . \na zombie movie in every sense of the word--mindless , lifeless , meandering , loud , painful , obnoxious . \nrashomon-for-dipsticks tale . \na film that clearly means to preach exclusively to the converted . \nit doesn't take a rocket scientist to figure out that this is a mormon family movie , and a sappy , preachy one at that . \ndefinitely a crowd-pleaser , but then , so was the roman colosseum . \ncertainly not a good movie , but it wasn't horrible either . \nalthough it starts off so bad that you feel like running out screaming , it eventually works its way up to merely bad rather than painfully awful . \nthe result is so tame that even slightly wised-up kids would quickly change the channel . \nit appears to have been modeled on the worst revenge-of-the-nerds clichs the filmmakers could dredge up . \nnothing but an episode of smackdown ! in period costume and with a bigger budget . \nit takes you somewhere you're not likely to have seen before , but beneath the exotic surface ( and exotic dancing ) it's surprisingly old-fashioned . \nwhile the story is better-focused than the incomprehensible anne rice novel it's based upon , queen of the damned is a pointless , meandering celebration of the goth-vampire , tortured woe-is-me lifestyle . \nit should be interesting , it should be poignant , it turns out to be affected and boring . \na good-looking but ultimately pointless political thriller with plenty of action and almost no substance . \na tired , predictable , bordering on offensive , waste of time , money and celluloid . \nif hill isn't quite his generation's don siegel ( or robert aldrich ) , it's because there's no discernible feeling beneath the chest hair ; it's all bluster and clich . \nstealing harvard will dip into your wallet , swipe 90 minutes of your time , and offer you precisely this in recompense : a few early laughs scattered around a plot as thin as it is repetitious . \nthis is an insultingly inept and artificial examination of grief and its impacts upon the relationships of the survivors . \ndoes anyone much think the central story of brendan behan is that he was a bisexual sweetheart before he took to drink ? \n`martin lawrence live' is so self-pitying , i almost expected there to be a collection taken for the comedian at the end of the show . \nthe dialogue is cumbersome , the simpering soundtrack and editing more so . \nnever decides whether it wants to be a black comedy , drama , melodrama or some combination of the three . \nit has become apparent that the franchise's best years are long past . \ndoes what should seem impossible : it makes serial killer jeffrey dahmer boring . \ndon't hate el crimen del padre amaro because it's anti-catholic . hate it because it's lousy . \n . . . better described as a ghost story gone badly awry . \nlike a bad improvisation exercise , the superficially written characters ramble on tediously about their lives , loves and the art they're struggling to create . \nthe filmmakers are playing to the big boys in new york and l . a . to that end , they mock the kind of folks they don't understand , ones they figure the power-lunchers don't care to understand , either . \ncompetently directed but terminally cute drama . \nthe big finish is a bit like getting all excited about a chocolate eclair and then biting into it and finding the filling missing . \nnot just unlikable . disturbing . disgusting . without any redeeming value whatsoever . \nthis thing is virtually unwatchable . \nthose eternally devoted to the insanity of black will have an intermittently good time . feel free to go get popcorn whenever he's not onscreen . \nthe self-serious equilibrium makes its point too well ; a movie , like life , isn't much fun without the highs and lows . \nthe work of an exhausted , desiccated talent who can't get out of his own way . \nthe main characters are simply named the husband , the wife and the kidnapper , emphasizing the disappointingly generic nature of the entire effort . \nin terms of execution this movie is careless and unfocused . \nswims in mediocrity , sticking its head up for a breath of fresh air now and then . \nthe only type of lives this glossy comedy-drama resembles are ones in formulaic mainstream movies . \nthe characters . . . are paper-thin , and their personalities undergo radical changes when it suits the script . \na sha-na-na sketch punctuated with graphic violence . \nthe trouble is , its filmmakers run out of clever ideas and visual gags about halfway through . \nspy-vs . -spy action flick with antonio banderas and lucy liu never comes together . \na so-so , made-for-tv something posing as a real movie . \nthe only upside to all of this unpleasantness is , given its labor day weekend upload , feardotcom should log a minimal number of hits . \nwhether this is art imitating life or life imitating art , it's an unhappy situation all around . \nan uneasy mix of run-of-the-mill raunchy humor and seemingly sincere personal reflection . \na formula family tearjerker told with a heavy irish brogue . . . accentuating , rather than muting , the plot's saccharine thrust . \nthis is sandler running on empty , repeating what he's already done way too often . \nthis is as lax and limp a comedy as i've seen in a while , a meander through worn-out material . \ntime literally stops on a dime in the tries-so-hard-to-be-cool \" clockstoppers , \" but that doesn't mean it still won't feel like the longest 90 minutes of your movie-going life . \nthe sort of picture in which , whenever one of the characters has some serious soul searching to do , they go to a picture-perfect beach during sunset . \naptly named , this shimmering , beautifully costumed and filmed production doesn't work for me . \na preposterously melodramatic paean to gang-member teens in brooklyn circa 1958 . \nhas none of the crackle of \" fatal attraction \" , \" 9  weeks \" , or even \" indecent proposal \" , and feels more like lyne's stolid remake of \" lolita \" . \neverything its title implies , a standard-issue crime drama spat out from the tinseltown assembly line . \nan extraordinarily silly thriller . \na rehash of every gangster movie from the past decade . \ngaping plot holes sink this 'sub'-standard thriller and drag audience enthusiasm to crush depth . \ntalkiness isn't necessarily bad , but the dialogue frequently misses the mark . \nthe beautiful images and solemn words cannot disguise the slack complacency of [godard's] vision , any more than the gorgeous piano and strings on the soundtrack can drown out the tinny self-righteousness of his voice . \nthe stunt work is top-notch ; the dialogue and drama often food-spittingly funny . \nthe movie isn't painfully bad , something to be 'fully experienced' ; it's just tediously bad , something to be fully forgotten . \ncharly comes off as emotionally manipulative and sadly imitative of innumerable past love story derisions . \nwhat a great shame that such a talented director as chen kaige has chosen to make his english-language debut with a film so poorly plotted and scripted . \nno amount of good intentions is able to overcome the triviality of the story . \nthe film . . . presents classic moral-condundrum drama : what would you have done to survive ? the problem with the film is whether these ambitions , laudable in themselves , justify a theatrical simulation of the death camp of auschwitz ii-birkenau . \n . . . for all its social and political potential , state property doesn't end up being very inspiring or insightful . \na film really has to be exceptional to justify a three hour running time , and this isn't . \nlittle more than a stylish exercise in revisionism whose point . . . is no doubt true , but serves as a rather thin moral to such a knowing fable . \nthe nonstop artifice ultimately proves tiresome , with the surface histrionics failing to compensate for the paper-thin characterizations and facile situations . \nthis is a monumental achievement in practically every facet of inept filmmaking : joyless , idiotic , annoying , heavy-handed , visually atrocious , and often downright creepy . \nthis off-putting french romantic comedy is sure to test severely the indulgence of fans of amlie . \noverburdened with complicated plotting and banal dialogue\nensemble movies , like soap operas , depend on empathy . if there ain't none , you have a problem . \nthe master of disguise falls under the category of 'should have been a sketch on saturday night live . '\nyet another self-consciously overwritten story about a rag-tag bunch of would-be characters that team up for a can't-miss heist -- only to have it all go wrong . \nkoepp's screenplay isn't nearly surprising or clever enough to sustain a reasonable degree of suspense on its own . \nis it really an advantage to invest such subtlety and warmth in an animatronic bear when the humans are acting like puppets ? \nmore successful at relating history than in creating an emotionally complex , dramatically satisfying heroine\nclumsy , obvious , preposterous , the movie will likely set the cause of woman warriors back decades . \nit's hard to pity the 'plain' girl who becomes a ravishing waif after applying a smear of lip-gloss . rather , pity anyone who sees this mishmash . \na banal , virulently unpleasant excuse for a romantic comedy . \nthe drama discloses almost nothing . \na minor-league soccer remake of the longest yard . \nbelongs in the too-hot-for-tv direct-to-video/dvd category , and this is why i have given it a one-star rating . \nas earnest as a community-college advertisement , american chai is enough to make you put away the guitar , sell the amp , and apply to medical school . \na dim-witted and lazy spin-off of the animal planet documentary series , crocodile hunter is entertainment opportunism at its most glaring . \nthere is more than one joke about putting the toilet seat down . and that should tell you everything you need to know about all the queen's men . \neven fans of ismail merchant's work , i suspect , would have a hard time sitting through this one . \nit's really just another silly hollywood action film , one among a multitude of simple-minded , yahoo-ing death shows . \nit's not a particularly good film , but neither is it a monsterous one . \nthe world needs more filmmakers with passionate enthusiasms like martin scorsese . but it doesn't need gangs of new york . \nenchanted with low-life tragedy and liberally seasoned with emotional outbursts . . . what is sorely missing , however , is the edge of wild , lunatic invention that we associate with cage's best acting . \nharry potter and the chamber of secrets is deja vu all over again , and while that is a cliche , nothing could be more appropriate . it's likely that whatever you thought of the first production -- pro or con -- you'll likely think of this one . \nsade achieves the near-impossible : it turns the marquis de sade into a dullard . \n[lin chung's] voice is rather unexceptional , even irritating ( at least to this western ear ) , making it awfully hard to buy the impetus for the complicated love triangle that develops between the three central characters . \none of the most plain , unimaginative romantic comedies i've ever seen . \nthough there's a clarity of purpose and even-handedness to the film's direction , the drama feels rigged and sluggish . \nunfortunately , the experience of actually watching the movie is less compelling than the circumstances of its making . \nunless there are zoning ordinances to protect your community from the dullest science fiction , impostor is opening today at a theater near you . \nit should be doing a lot of things , but doesn't . \nchen films the resolutely downbeat smokers only with every indulgent , indie trick in the book . \n . . . a rather bland affair . \nfar-fetched premise , convoluted plot , and thematic mumbo jumbo about destiny and redemptive love . \nthe movie tries to be ethereal , but ends up seeming goofy . \ni was hoping that it would be sleazy and fun , but it was neither . \nharris is supposed to be the star of the story , but comes across as pretty dull and wooden . \nsoulless and -- even more damning -- virtually joyless , xxx achieves near virtuosity in its crapulence . \na boring masquerade ball where normally good actors , even kingsley , are made to look bad . \nall the queen's men is a throwback war movie that fails on so many levels , it should pay reparations to viewers . \nthe filmmakers keep pushing the jokes at the expense of character until things fall apart . \nrather than real figures , elling and kjell bjarne become symbolic characters whose actions are supposed to relate something about the naf's encounter with the world . \nmariah carey gives us another peek at some of the magic we saw in glitter here in wisegirls . \nit's all arty and jazzy and people sit and stare and turn away from one another instead of talking and it's all about the silences and if you're into that , have at it . \ni suspect that you'll be as bored watching morvern callar as the characters are in it . if you go , pack your knitting needles . \nthe lead actors share no chemistry or engaging charisma . we don't even like their characters . \nsome writer dude , i think his name was , uh , michael zaidan , was supposed to have like written the screenplay or something , but , dude , the only thing that i ever saw that was written down were the zeroes on my paycheck . \nthe movie doesn't generate a lot of energy . it is dark , brooding and slow , and takes its central idea way too seriously . \nthis feature is about as necessary as a hole in the head\nthe cinematic equivalent of patronizing a bar favored by pretentious , untalented artistes who enjoy moaning about their cruel fate . \nspectators will indeed sit open-mouthed before the screen , not screaming but yawning . \nit feels like very light errol morris , focusing on eccentricity but failing , ultimately , to make something bigger out of its scrapbook of oddballs . \na period story about a catholic boy who tries to help a jewish friend get into heaven by sending the audience straight to hell . \nthe premise itself is just sooooo tired . pair that with really poor comedic writing . . . and you've got a huge mess . \nproves a lovely trifle that , unfortunately , is a little too in love with its own cuteness . \ndid we really need a remake of \" charade ? \" \nsome movies can get by without being funny simply by structuring the scenes as if they were jokes : a setup , delivery and payoff . stealing harvard can't even do that much . each scene immediately succumbs to gravity and plummets to earth . \nthe only fun part of the movie is playing the obvious game . you try to guess the order in which the kids in the house will be gored . \ni spied with my little eye . . . a mediocre collection of cookie-cutter action scenes and occasionally inspired dialogue bits\nentertains not so much because of its music or comic antics , but through the perverse pleasure of watching disney scrape the bottom of its own cracker barrel . \nthe satire is just too easy to be genuinely satisfying . \nbearable . barely . \nless funny than it should be and less funny than it thinks it is . \nan \" o bruin , where art thou ? \" -style cross-country adventure . . . it has sporadic bursts of liveliness , some so-so slapstick and a few ear-pleasing songs on its soundtrack . \na feeble tootsie knockoff . \nan awful movie that will only satisfy the most emotionally malleable of filmgoers . \nthe story is far-flung , illogical , and plain stupid . \nthe very simple story seems too simple and the working out of the plot almost arbitrary . \nan allegory concerning the chronically mixed signals african american professionals get about overachieving could be intriguing , but the supernatural trappings only obscure the message . \na very familiar tale , one that's been told by countless filmmakers about italian- , chinese- , irish- , latin- , indian- , russian- and other hyphenate american young men struggling to balance conflicting cultural messages . \none key problem with these ardently christian storylines is that there is never any question of how things will turn out . \nessentially , the film is weak on detail and strong on personality\na relentless , bombastic and ultimately empty world war ii action flick . \n[hell is] looking down at your watch and realizing serving sara isn't even halfway through . \ntoo long , and larded with exposition , this somber cop drama ultimately feels as flat as the scruffy sands of its titular community . \nleaves viewers out in the cold and undermines some phenomenal performances . \n . . . a ho-hum affair , always watchable yet hardly memorable . \nswiftly deteriorates into a terribly obvious melodrama and rough-hewn vanity project for lead actress andie macdowell . \nthe histrionic muse still eludes madonna and , playing a charmless witch , she is merely a charmless witch . \nyou have no affinity for most of the characters . nothing about them is attractive . what they see in each other also is difficult to fathom . \ndiaz , applegate , blair and posey are suitably kooky which should appeal to women and they strip down often enough to keep men alert , if not amused . \na technically well-made suspenser . . . but its abrupt drop in iq points as it races to the finish line proves simply too discouraging to let slide . \nan inept , tedious spoof of '70s kung fu pictures , it contains almost enough chuckles for a three-minute sketch , and no more . \nit's a mystery how the movie could be released in this condition . \nabsolutely ( and unintentionally ) terrifying . \neckstraordinarily lame and severely boring . \neight legged freaks falls flat as a spoof . \nno matter how much he runs around and acts like a doofus , accepting a 50-year-old in the role is creepy in a michael jackson sort of way . \nyou'll just have your head in your hands wondering why lee's character didn't just go to a bank manager and save everyone the misery . \n'dragonfly' dwells on crossing-over mumbo jumbo , manipulative sentimentality , and sappy dialogue . \nin his determination to lighten the heavy subject matter , silberling also , to a certain extent , trivializes the movie with too many nervous gags and pratfalls . \nblade ii has a brilliant director and charismatic star , but it suffers from rampant vampire devaluation . \nveers uncomfortably close to pro-serb propaganda . \nstaggeringly dreadful romance . \nmovies like high crimes flog the dead horse of surprise as if it were an obligation . how about surprising us by trying something new ? \nfinal verdict : you've seen it all before . \nthrowing in everything except someone pulling the pin from a grenade with his teeth , windtalkers seems to have ransacked every old world war ii movie for overly familiar material . \nif a few good men told us that we \" can't handle the truth \" than high crimes poetically states at one point in this movie that we \" don't care about the truth . \" \nfurther sad evidence that tom tykwer , director of the resonant and sense-spinning run lola run , has turned out to be a one-trick pony -- a maker of softheaded metaphysical claptrap . \nyou'll trudge out of the theater feeling as though you rode the zipper after eating a corn dog and an extra-large cotton candy . \nthe movie is a little tired ; maybe the original inspiration has run its course . \nthis will go on so long as there are moviegoers anxious to see strange young guys doing strange guy things . \na full-frontal attack on audience patience . \nany intellectual arguments being made about the nature of god are framed in a drama so clumsy , there is a real danger less sophisticated audiences will mistake it for an endorsement of the very things that bean abhors . \nit's a big idea , but the film itself is small and shriveled . \ndebut effort by \" project greenlight \" winner is sappy and amateurish . \none gets the impression the creators of don't ask don't tell laughed a hell of a lot at their own jokes . too bad none of it is funny . \nthe cast has a high time , but de broca has little enthusiasm for such antique pulp . \nthe film , like jimmy's routines , could use a few good laughs . \nthe film has too many spots where it's on slippery footing , but is acceptable entertainment for the entire family and one that's especially fit for the kiddies . \npurports to be a hollywood satire but winds up as the kind of film that should be the target of something deeper and more engaging . oh , and more entertaining , too . \n . . . in the pile of useless actioners from mtv schmucks who don't know how to tell a story for more than four minutes . \nthough it was made with careful attention to detail and is well-acted by james spader and maggie gyllenhaal , i felt disrespected . \nwell-made but mush-hearted . \nhumor in i spy is so anemic . \nthe film is strictly routine . \na real snooze . \nskillful as he is , mr . shyamalan is undone by his pretensions . \nwhile the new film is much more eye-catching than its blood-drenched stephen norrington-directed predecessor , the new script by the returning david s . goyer is much sillier . \nin addition to sporting one of the worst titles in recent cinematic history , ballistic : ecks vs . sever also features terrible , banal dialogue ; convenient , hole-ridden plotting ; superficial characters and a rather dull , unimaginative car chase . \nit shares the first two films' loose-jointed structure , but laugh-out-loud bits are few and far between . \nthe santa clause 2 is a barely adequate babysitter for older kids , but i've got to give it thumbs down . \nyou cannot guess why the cast and crew didn't sign a pact to burn the negative and the script and pretend the whole thing never existed . \nbarney throws away the goodwill the first half of his movie generates by orchestrating a finale that is impenetrable and dull . \nif you're really renting this you're not interested in discretion in your entertainment choices , you're interested in anne geddes , john grisham , and thomas kincaid . \nwe get the comedy we settle for . \nthe uneven movie does have its charms and its funny moments but not quite enough of them . \ntwo hours of sepia-tinted heavy metal images and surround sound effects of people moaning . \na word of advice to the makers of the singles ward : celebrity cameos do not automatically equal laughs . and neither do cliches , no matter how 'inside' they are . \nthe campy results make mel brooks' borscht belt schtick look sophisticated . \nits appeal will probably limited to lds church members and undemanding armchair tourists . \nthe hanukkah spirit seems fried in pork . \ncherish would've worked a lot better had it been a short film . \nmanipulative claptrap , a period-piece movie-of-the-week , plain old blarney . . . take your pick . all three descriptions suit evelyn , a besotted and obvious drama that tells us nothing new . \nhey arnold ! is now stretched to barely feature length , with a little more attention paid to the animation . still , the updated dickensian sensibility of writer craig bartlett's story is appealing . \ntrue to its title , it traps audiences in a series of relentlessly nasty situations that we would pay a considerable ransom not to be looking at . \ndoesn't come close to justifying the hype that surrounded its debut at the sundance film festival two years ago . \nthe plot is paper-thin and the characters aren't interesting enough to watch them go about their daily activities for two whole hours . \nkaufman's script is never especially clever and often is rather pretentious . \nthe film didn't move me one way or the other , but it was an honest effort and if you want to see a flick about telemarketers this one will due . \nqueen of the damned is too long with too little going on . \nit collapses when mr . taylor tries to shift the tone to a thriller's rush . \nany film that doesn't even in passing mention political prisoners , poverty and the boat loads of people who try to escape the country is less a documentary and more propaganda by way of a valentine sealed with a kiss . \n . . . blade ii is still top-heavy with blazing guns , cheatfully filmed martial arts , disintegrating bloodsucker computer effects and jagged camera moves that serve no other purpose than to call attention to themselves . \nthe rules of attraction gets us too drunk on the party favors to sober us up with the transparent attempts at moralizing . \nthough there are many tense scenes in trapped , they prove more distressing than suspenseful . \nin this film we at least see a study in contrasts ; the wide range of one actor , and the limited range of a comedian . \nfeels strangely hollow at its emotional core . \nno surprises . \nyou have once again entered the bizarre realm where director adrian lyne holds sway , where all relationships are simultaneously broadly metaphorical , oddly abstract , and excruciatingly literal . \nthe high-concept scenario soon proves preposterous , the acting is robotically italicized , and truth-in-advertising hounds take note : there's very little hustling on view . \nthis director's cut -- which adds 51 minutes -- takes a great film and turns it into a mundane soap opera . \ncharacterisation has been sacrificed for the sake of spectacle . \nthe venezuelans say things like \" si , pretty much \" and \" por favor , go home \" when talking to americans . that's muy loco , but no more ridiculous than most of the rest of \" dragonfly . \" \nit's a movie that ends with truckzilla , for cryin' out loud . if that doesn't clue you in that something's horribly wrong , nothing will . \ndirector tom shadyac and star kevin costner glumly mishandle the story's promising premise of a physician who needs to heal himself . \nit's difficult to imagine that a more confused , less interesting and more sloppily made film could possibly come down the road in 2002 . \nlike the tuck family themselves , this movie just goes on and on and on and on\nas pedestrian as they come . \na film that plays things so nice 'n safe as to often play like a milquetoast movie of the week blown up for the big screen . \nit's a feel-bad ending for a depressing story that throws a bunch of hot-button items in the viewer's face and asks to be seen as hip , winking social commentary . \nput it somewhere between sling blade and south of heaven , west of hell in the pantheon of billy bob's body of work . \nmore intellectually scary than dramatically involving . \nan inconsequential , barely there bit of piffle . \nthe abiding impression , despite the mild hallucinogenic buzz , is of overwhelming waste -- the acres of haute couture can't quite conceal that there's nothing resembling a spine here . \nas saccharine as it is disposable . \nyou come away thinking not only that kate isn't very bright , but that she hasn't been worth caring about and that maybe she , janine and molly -- an all-woman dysfunctional family -- deserve one another . \nthe metaphors are provocative , but too often , the viewer is left puzzled by the mechanics of the delivery . \nvery much a home video , and so devoid of artifice and purpose that it appears not to have been edited at all . \ntoo much power , not enough puff . \nthe attempt to build up a pressure cooker of horrified awe emerges from the simple fact that the movie has virtually nothing to show . \nit's provocative stuff , but the speculative effort is hampered by taylor's cartoonish performance and the film's ill-considered notion that hitler's destiny was shaped by the most random of chances . \na cellophane-pop remake of the punk classic ladies and gentlemen , the fabulous stains . . . crossroads is never much worse than bland or better than inconsequential . \nmuddled , trashy and incompetent\nfor this sort of thing to work , we need agile performers , but the proficient , dull sorvino has no light touch , and rodan is out of his league . \nnarc is all menace and atmosphere . \nthough excessively tiresome , the uncertainty principle , as verbally pretentious as the title may be , has its handful of redeeming features , as long as you discount its ability to bore . \ndespite juliet stevenon's attempt to bring cohesion to pamela's emotional roller coaster life , it is not enough to give the film the substance it so desperately needs . \nit's tough to be startled when you're almost dozing . \nhis [nelson's] screenplay needs some serious re-working to show more of the dilemma , rather than have his characters stage shouting matches about it . \nit's so downbeat and nearly humorless that it becomes a chore to sit through -- despite some first-rate performances by its lead . \na terrible movie that some people will nevertheless find moving . \nthere are many definitions of 'time waster' but this movie must surely be one of them . \nas it stands , crocodile hunter has the hurried , badly cobbled look of the 1959 godzilla , which combined scenes of a japanese monster flick with canned shots of raymond burr commenting on the monster's path of destruction . \nthe thing looks like a made-for-home-video quickie . \nenigma is well-made , but it's just too dry and too placid . \n"
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    "content": "the rock is destined to be the 21st century's new \" conan \" and that he's going to make a splash even greater than arnold schwarzenegger , jean-claud van damme or steven segal . \nthe gorgeously elaborate continuation of \" the lord of the rings \" trilogy is so huge that a column of words cannot adequately describe co-writer/director peter jackson's expanded vision of j . r . r . tolkien's middle-earth . \neffective but too-tepid biopic\nif you sometimes like to go to the movies to have fun , wasabi is a good place to start . \nemerges as something rare , an issue movie that's so honest and keenly observed that it doesn't feel like one . \nthe film provides some great insight into the neurotic mindset of all comics -- even those who have reached the absolute top of the game . \noffers that rare combination of entertainment and education . \nperhaps no picture ever made has more literally showed that the road to hell is paved with good intentions . \nsteers turns in a snappy screenplay that curls at the edges ; it's so clever you want to hate it . but he somehow pulls it off . \ntake care of my cat offers a refreshingly different slice of asian cinema . \nthis is a film well worth seeing , talking and singing heads and all . \nwhat really surprises about wisegirls is its low-key quality and genuine tenderness . \n ( wendigo is ) why we go to the cinema : to be fed through the eye , the heart , the mind . \none of the greatest family-oriented , fantasy-adventure movies ever . \nultimately , it ponders the reasons we need stories so much . \nan utterly compelling 'who wrote it' in which the reputation of the most famous author who ever lived comes into question . \nilluminating if overly talky documentary . \na masterpiece four years in the making . \nthe movie's ripe , enrapturing beauty will tempt those willing to probe its inscrutable mysteries . \noffers a breath of the fresh air of true sophistication . \na thoughtful , provocative , insistently humanizing film . \nwith a cast that includes some of the top actors working in independent film , lovely & amazing involves us because it is so incisive , so bleakly amusing about how we go about our lives . \na disturbing and frighteningly evocative assembly of imagery and hypnotic music composed by philip glass . \nnot for everyone , but for those with whom it will connect , it's a nice departure from standard moviegoing fare . \nscores a few points for doing what it does with a dedicated and good-hearted professionalism . \noccasionally melodramatic , it's also extremely effective . \nspiderman rocks\nan idealistic love story that brings out the latent 15-year-old romantic in everyone . \nat about 95 minutes , treasure planet maintains a brisk pace as it races through the familiar story . however , it lacks grandeur and that epic quality often associated with stevenson's tale as well as with earlier disney efforts . \nit helps that lil bow wow . . . tones down his pint-sized gangsta act to play someone who resembles a real kid . \nguaranteed to move anyone who ever shook , rattled , or rolled . \na masterful film from a master filmmaker , unique in its deceptive grimness , compelling in its fatalist worldview . \nlight , cute and forgettable . \nif there's a way to effectively teach kids about the dangers of drugs , i think it's in projects like the ( unfortunately r-rated ) paid . \nwhile it would be easy to give crush the new title of two weddings and a funeral , it's a far more thoughtful film than any slice of hugh grant whimsy . \nthough everything might be literate and smart , it never took off and always seemed static . \ncantet perfectly captures the hotel lobbies , two-lane highways , and roadside cafes that permeate vincent's days\nms . fulford-wierzbicki is almost spooky in her sulky , calculating lolita turn . \nthough it is by no means his best work , laissez-passer is a distinguished and distinctive effort by a bona-fide master , a fascinating film replete with rewards to be had by all willing to make the effort to reap them . \nlike most bond outings in recent years , some of the stunts are so outlandish that they border on being cartoonlike . a heavy reliance on cgi technology is beginning to creep into the series . \nnewton draws our attention like a magnet , and acts circles around her better known co-star , mark wahlberg . \nthe story loses its bite in a last-minute happy ending that's even less plausible than the rest of the picture . much of the way , though , this is a refreshingly novel ride . \nfuller would surely have called this gutsy and at times exhilarating movie a great yarn . \n'compleja e intelectualmente retadora , el ladrn de orqudeas es uno de esos filmes que vale la pena ver precisamente por su originalidad . '\nthe film makes a strong case for the importance of the musicians in creating the motown sound . \nkarmen moves like rhythm itself , her lips chanting to the beat , her long , braided hair doing little to wipe away the jeweled beads of sweat . \ngosling provides an amazing performance that dwarfs everything else in the film . \na real movie , about real people , that gives us a rare glimpse into a culture most of us don't know . \ntender yet lacerating and darkly funny fable . \nmay be spoofing an easy target -- those old '50's giant creature features -- but . . . it acknowledges and celebrates their cheesiness as the reason why people get a kick out of watching them today . \nan engaging overview of johnson's eccentric career . \nin its ragged , cheap and unassuming way , the movie works . \nsome actors have so much charisma that you'd be happy to listen to them reading the phone book . hugh grant and sandra bullock are two such likeable actors . \nsandra nettelbeck beautifully orchestrates the transformation of the chilly , neurotic , and self-absorbed martha as her heart begins to open . \nbehind the snow games and lovable siberian huskies ( plus one sheep dog ) , the picture hosts a parka-wrapped dose of heart . \neverytime you think undercover brother has run out of steam , it finds a new way to surprise and amuse . \nmanages to be original , even though it rips off many of its ideas . \nsinger/composer bryan adams contributes a slew of songs  a few potential hits , a few more simply intrusive to the story  but the whole package certainly captures the intended , er , spirit of the piece . \nyou'd think by now america would have had enough of plucky british eccentrics with hearts of gold . yet the act is still charming here . \nwhether or not you're enlightened by any of derrida's lectures on \" the other \" and \" the self , \" derrida is an undeniably fascinating and playful fellow . \na pleasant enough movie , held together by skilled ensemble actors . \nthis is the best american movie about troubled teens since 1998's whatever . \ndisney has always been hit-or-miss when bringing beloved kids' books to the screen . . . tuck everlasting is a little of both . \njust the labour involved in creating the layered richness of the imagery in this chiaroscuro of madness and light is astonishing . \nthe animated subplot keenly depicts the inner struggles of our adolescent heroes - insecure , uncontrolled , and intense . \nthe invincible werner herzog is alive and well and living in la\nmorton is a great actress portraying a complex character , but morvern callar grows less compelling the farther it meanders from its shocking start . \npart of the charm of satin rouge is that it avoids the obvious with humour and lightness . \nson of the bride may be a good half-hour too long but comes replete with a flattering sense of mystery and quietness . \na simmering psychological drama in which the bursts of sudden violence are all the more startling for the slow buildup that has preceded them . \na taut , intelligent psychological drama . \na compelling coming-of-age drama about the arduous journey of a sensitive young girl through a series of foster homes and a fierce struggle to pull free from her dangerous and domineering mother's hold over her . \na truly moving experience , and a perfect example of how art -- when done right -- can help heal , clarify , and comfort . \nthis delicately observed story , deeply felt and masterfully stylized , is a triumph for its maverick director . \nat heart the movie is a deftly wrought suspense yarn whose richer shadings work as coloring rather than substance . \nthe appearance of treebeard and gollum's expanded role will either have you loving what you're seeing , or rolling your eyes . i loved it ! gollum's 'performance' is incredible ! \na screenplay more ingeniously constructed than \" memento \" \nif this movie were a book , it would be a page-turner , you can't wait to see what happens next . \nhaneke challenges us to confront the reality of sexual aberration . \nabsorbing and disturbing -- perhaps more disturbing than originally intended -- but a little clarity would have gone a long way . \nit's the best film of the year so far , the benchmark against which all other best picture contenders should be measured . \npainful to watch , but viewers willing to take a chance will be rewarded with two of the year's most accomplished and riveting film performances . \nthis is a startling film that gives you a fascinating , albeit depressing view of iranian rural life close to the iraqi border . \nan imaginative comedy/thriller . \na few artsy flourishes aside , narc is as gritty as a movie gets these days . \nwhile the isle is both preposterous and thoroughly misogynistic , its vistas are incredibly beautiful to look at . \ntogether , tok and o orchestrate a buoyant , darkly funny dance of death . in the process , they demonstrate that there's still a lot of life in hong kong cinema . \ndirector kapur is a filmmaker with a real flair for epic landscapes and adventure , and this is a better film than his earlier english-language movie , the overpraised elizabeth . \nthe movie is a blast of educational energy , as bouncy animation and catchy songs escort you through the entire 85 minutes . \na sports movie with action that's exciting on the field and a story you care about off it . \ndoug liman , the director of bourne , directs the traffic well , gets a nice wintry look from his locations , absorbs us with the movie's spycraft and uses damon's ability to be focused and sincere . \nthe tenderness of the piece is still intact . \nkatz uses archival footage , horrifying documents of lynchings , still photographs and charming old reel-to-reel recordings of meeropol entertaining his children to create his song history , but most powerful of all is the song itself\nlike the film's almost anthropologically detailed realization of early-'80s suburbia , it's significant without being overstated . \nwhile mcfarlane's animation lifts the film firmly above the level of other coming-of-age films . . . it's also so jarring that it's hard to get back into the boys' story . \nif nothing else , this movie introduces a promising , unusual kind of psychological horror . \nin a normal screen process , these bromides would be barely enough to sustain an interstitial program on the discovery channel . but in imax 3-d , the clichs disappear into the vertiginous perspectives opened up by the photography . \nwriter-director burger imaginatively fans the embers of a dormant national grief and curiosity that has calcified into chronic cynicism and fear . \n . . . a roller-coaster ride of a movie\ni enjoyed time of favor while i was watching it , but i was surprised at how quickly it faded from my memory . \nchicago is sophisticated , brash , sardonic , completely joyful in its execution . \nsteve irwin's method is ernest hemmingway at accelerated speed and volume . \na refreshing korean film about five female high school friends who face an uphill battle when they try to take their relationships into deeper waters . \non the surface , it's a lovers-on-the-run crime flick , but it has a lot in common with piesiewicz's and kieslowski's earlier work , films like the double life of veronique . \nthe values that have held the enterprise crew together through previous adventures and perils do so again-courage , self-sacrifice and patience under pressure . \nif it's possible for a sequel to outshine the original , then sl2 does just that . \na romantic comedy that operates by the rules of its own self-contained universe . \n4 friends , 2 couples , 2000 miles , and all the pabst blue ribbon beer they can drink - it's the ultimate redneck road-trip . \nthe film is often filled with a sense of pure wonderment and excitement not often seen in today's cinema du sarcasm\nit might be tempting to regard mr . andrew and his collaborators as oddballs , but mr . earnhart's quizzical , charming movie allows us to see them , finally , as artists . \na feel-good picture in the best sense of the term . \nedited and shot with a syncopated style mimicking the work of his subjects , pray turns the idea of the documentary on its head , making it rousing , invigorating fun lacking any mtv puffery . \na mostly intelligent , engrossing and psychologically resonant suspenser . \nit's this memory-as-identity obviation that gives secret life its intermittent unease , reaffirming that long-held illusions are indeed reality , and that erasing them recasts the self . \nhip-hop has a history , and it's a metaphor for this love story . \nin scope , ambition and accomplishment , children of the century . . . takes kurys' career to a whole new level . \nthis may not have the dramatic gut-wrenching impact of other holocaust films , but it's a compelling story , mainly because of the way it's told by the people who were there . \nbetween the drama of cube ? s personal revelations regarding what the shop means in the big picture , iconic characters gambol fluidly through the story , with charming results . \na gentle , compassionate drama about grief and healing . \nsomewhere short of tremors on the modern b-scene : neither as funny nor as clever , though an agreeably unpretentious way to spend ninety minutes . \ndigital-video documentary about stand-up comedians is a great glimpse into a very different world . \nunlike most teen flicks , swimming takes its time to tell its story , casts mostly little-known performers in key roles , and introduces some intriguing ambiguity . \nan enthralling , playful film that constantly frustrates our desire to know the 'truth' about this man , while deconstructing the very format of the biography in a manner that derrida would doubtless give his blessing to . \n \" extreme ops \" exceeds expectations . good fun , good action , good acting , good dialogue , good pace , good cinematography . \nyou should pay nine bucks for this : because you can hear about suffering afghan refugees on the news and still be unaffected . dramas like this make it human . \na thunderous ride at first , quiet cadences of pure finesse are few and far between ; their shortage dilutes the potency of otherwise respectable action . still , this flick is fun , and host to some truly excellent sequences . \nit's obviously struck a responsive chord with many south koreans , and should work its magic in other parts of the world . \nrun , don't walk , to see this barbed and bracing comedy on the big screen . \na classy item by a legend who may have nothing left to prove but still has the chops and drive to show how its done . \nit is nature against progress . in fessenden's horror trilogy , this theme has proved important to him and is especially so in the finale . \nit's not exactly a gourmet meal but the fare is fair , even coming from the drive-thru . \nthis is what imax was made for : strap on a pair of 3-d goggles , shut out the real world , and take a vicarious voyage to the last frontier -- space . \nmerely as a technical , logistical feat , russian ark marks a cinematic milestone . \n[schweiger is] talented and terribly charismatic , qualities essential to both movie stars and social anarchists . \nit's a great deal of sizzle and very little steak . but what spectacular sizzle it is ! . . . in this incarnation its fizz is infectious . \nan original gem about an obsession with time . \nit will delight newcomers to the story and those who know it from bygone days . \ngloriously goofy ( and gory ) midnight movie stuff . \nthe film overcomes the regular minefield of coming-of-age cliches with potent doses of honesty and sensitivity . \nif your senses haven't been dulled by slasher films and gorefests , if you're a connoisseur of psychological horror , this is your ticket . \nit's a minor comedy that tries to balance sweetness with coarseness , while it paints a sad picture of the singles scene . \nit is intensely personal and yet -- unlike quills -- deftly shows us the temper of the times . \nas lo-fi as the special effects are , the folks who cobbled nemesis together indulge the force of humanity over hardware in a way that george lucas has long forgotten . \nlike mike doesn't win any points for originality . it does succeed by following a feel-good formula with a winning style , and by offering its target audience of urban kids some welcome role models and optimism . \nit's a hoot and a half , and a great way for the american people to see what a candidate is like when he's not giving the same 15-cent stump speech . \nfar from perfect , but its heart is in the right place . . . innocent and well-meaning . \na sad , superior human comedy played out on the back roads of life . \nwaydowntown is by no means a perfect film , but its boasts a huge charm factor and smacks of originality . \ntim allen is great in his role but never hogs the scenes from his fellow cast , as there are plenty of laughs and good lines for everyone in this comedy . \nmore a load of enjoyable , conan-esque claptrap than the punishing , special-effects soul assaults the mummy pictures represent . \nenormously likable , partly because it is aware of its own grasp of the absurd . \nhere's a british flick gleefully unconcerned with plausibility , yet just as determined to entertain you . \nit's an old story , but a lively script , sharp acting and partially animated interludes make just a kiss seem minty fresh . \nmust be seen to be believed . \nray liotta and jason patric do some of their best work in their underwritten roles , but don't be fooled : nobody deserves any prizes here . \neverything that has to do with yvan and charlotte , and everything that has to do with yvan's rambunctious , jewish sister and her non-jew husband , feels funny and true . \nsweet home alabama \" is what it is  a nice , harmless date film . . . \nthe year's happiest surprise , a movie that deals with a real subject in an always surprising way . \nfans of behan's work and of irish movies in general will be rewarded by borstal boy . \nits mysteries are transparently obvious , and it's too slowly paced to be a thriller . [but it's] worth recommending because of two marvelous performances by michael caine and brendan fraser . \nthe film is faithful to what one presumes are the book's twin premises -- that we become who we are on the backs of our parents , but we have no idea who they were at our age ; and that time is a fleeting and precious commodity no matter how old you are . \nstephen earnhart's homespun documentary mule skinner blues has nothing but love for its posse of trailer park denizens . \na solidly seaworthy chiller . \nif you can get past the fantastical aspects and harsh realities of \" the isle \" you'll get a sock-you-in-the-eye flick that is a visual tour-de-force and a story that is unlike any you will likely see anywhere else . \nthere are as many misses as hits , but ultimately , it finds humor in the foibles of human behavior , and it's a welcome return to the roots of a genre that should depend on surprises . \na well-made thriller with a certain level of intelligence and non-reactionary morality . \nthere's enough science to make it count as educational , and enough beauty to make it unforgettable . \nremains a solid , if somewhat heavy-handed , account of the near-disaster . . . done up by howard with a steady , if not very imaginative , hand . \nmakmalbaf follows a resolutely realistic path in this uncompromising insight into the harsh existence of the kurdish refugees of iran's borderlands . \nfor a good chunk of its running time , trapped is an effective and claustrophobic thriller . \nmost of crush is a clever and captivating romantic comedy with a welcome pinch of tartness . \nnair does capture the complexity of a big family and its trials and tribulations . . . \nthe seaside splendor and shallow , beautiful people are nice to look at while you wait for the story to get going . \nrare is the 'urban comedy' that even attempts the insight and honesty of this disarming indie . \nranks among willams' best screen work . \nengagingly captures the maddening and magnetic ebb and flow of friendship . \nan experience so engrossing it is like being buried in a new environment . \nit's traditional moviemaking all the way , but it's done with a lot of careful period attention as well as some very welcome wit . \nthoroughly enjoyable . \nmaybe it's just because this past year has seen the release of some of the worst film comedies in decades . . . but honestly , analyze that really isn't all that bad . \na droll , well-acted , character-driven comedy with unexpected deposits of feeling . \nthis is simply the most fun you'll ever have with a documentary ! \na very funny movie . \nwatching haneke's film is , aptly enough , a challenge and a punishment . but watching huppert , a great actress tearing into a landmark role , is riveting . \na cop story that understands the medium amazingly well . \none of the best , most understated performances of [jack nicholson's] career . \nbritney has been delivered to the big screen safe and sound , the way we like our 20-year-old superstar girls to travel on the fame freeway . \nthose outside show business will enjoy a close look at people they don't really want to know . \nthe kind of nervous film that will either give you a mild headache or exhilarate you . \nwatching beanie and his gang put together his slasher video from spare parts and borrowed materials is as much fun as it must have been for them to make it . \nchildren may not understand everything that happens -- i'm not sure even miyazaki himself does -- but they will almost certainly be fascinated , and undoubtedly delighted . \na fascinating and fun film . \ntadpole is a sophisticated , funny and good-natured treat , slight but a pleasure . \nthis insightful , oscar-nominated documentary , in which children on both sides of the ever-escalating conflict have their say away from watchful parental eyes , gives peace yet another chance . \ni admired this work a lot . \nwhether you're moved and love it , or bored or frustrated by the film , you'll still feel something . \n . . . there are enough moments of heartbreaking honesty to keep one glued to the screen . \nmy goodness , queen latifah has a lot to offer and she seemed to have no problem flaunting her natural gifts . she must have a very strong back . \na smart , sweet and playful romantic comedy . \naustralian actor/director john polson and award-winning english cinematographer giles nuttgens make a terrific effort at disguising the obvious with energy and innovation . \nwithout heavy-handedness , dong provides perspective with his intelligent grasp of human foibles and contradictions . \nsolid , lump-in-the-throat family entertainment that derives its power by sticking to the facts . \nas an entertainment , the movie keeps you diverted and best of all , it lightens your wallet without leaving a sting . \nit is interesting and fun to see goodall and her chimpanzees on the bigger-than-life screen . \nit won't bust your gut -- and it's not intended to -- it's merely a blandly cinematic surgical examination of what makes a joke a joke . \na somewhat crudely constructed but gripping , questing look at a person so racked with self-loathing , he becomes an enemy to his own race . \nit extends the writings of jean genet and john rechy , the films of fassbinder , perhaps even the nocturnal works of goya . \nnarc may not get an 'a' for originality , but it wears its b-movie heritage like a badge of honor . \nwith the film's striking ending , one realizes that we have a long way to go before we fully understand all the sexual permutations involved . \n[drumline] is entertaining for what it does , and admirable for what it doesn't do . \nat its best early on as it plays the culture clashes between the brothers . \n[a] rare , beautiful film . \nan unabashedly schmaltzy and thoroughly enjoyable true story . \na thoughtful look at a painful incident that made headlines in 1995 . \nyou walk out of the good girl with mixed emotions  disapproval of justine combined with a tinge of understanding for her actions . \ntsai ming-liang has taken his trademark style and refined it to a crystalline point . \npurely propaganda , a work of unabashed hero worship , it is nonetheless -- and likely inadvertently -- a timely and invaluable implicit reminder of the role that u . s . foreign policy has played in the rise of castro . \nnow trimmed by about 20 minutes , this lavish three-year-old production has enough grandeur and scale to satisfy as grown-up escapism . \nwe get some truly unique character studies and a cross-section of americana that hollywood couldn't possibly fictionalize and be believed . \nthough this film can be clumsy , its ambitions are equally -- and admirably -- uncommercial . \ndaring , mesmerizing and exceedingly hard to forget . \nthe dangerous lives of altar boys' take on adolescence feels painfully true . \nmoore's performance impresses almost as much as her work with haynes in 1995's safe . \nvisits spy-movie territory like a novel you can't put down , examines a footnote to history seldom brought to light on the screen , and keeps you guessing from first frame to last . \nan absorbing , slice-of-depression life that touches nerves and rings true . \nmr . parker has brilliantly updated his source and grasped its essence , composing a sorrowful and hilarious tone poem about alienated labor , or an absurdist workplace sitcom . \nthe result is something quite fresh and delightful . \nall but the most persnickety preteens should enjoy this nonthreatening but thrilling adventure . \ndespite its many infuriating flaws -- not the least of which is amy's self-absorbed personality -- amy's o's honesty will win you over . \nthis is one of polanski's best films . \nday is not a great bond movie , but it is a good bond movie , which still makes it much better than your typical bond knock-offs . \npolished korean political-action film is just as good -- and bad -- as hollywood action epics . is this progress ? \nelling , portrayed with quiet fastidiousness by per christian ellefsen , is a truly singular character , one whose frailties are only slightly magnified versions of the ones that vex nearly everyone . \ndenis and co-writer michele petin's impeccable screenplay penetrates with a rawness that that is both unflinching and tantalizing . lead provocatuers testud and parmentier give superlative performances\nan absorbing trip into the minds and motivations of people under stress as well as a keen , unsentimental look at variations on the theme of motherhood . \ni admired it , particularly that unexpected downer of an ending . \nthe passions aroused by the discord between old and new cultures are set against the strange , stark beauty of the mideast desert , so lovingly and perceptively filmed that you can almost taste the desiccated air . \nremarkably accessible and affecting . \nnever mind whether you buy the stuff about barris being a cia hit man . the kooky yet shadowy vision clooney sustains throughout is daring , inventive and impressive . \na triumph of art direction over narrative , but what art direction ! \nbehan himself knew how to spin a tale and one can't help but think he'd appreciate this attempt to turn his life into art . \njir hubac's script is a gem . his characters are engaging , intimate and the dialogue is realistic and greatly moving . the scope of the silberstein family is large and we grow attached to their lives , full of strength , warmth and vitality . . \nmoore's complex and important film is also , believe it or not , immensely entertaining , a david and goliath story that's still very much playing itself out . \nthe additional storyline is interesting and entertaining , but it doesn't have the same magical quality as the beginning of the story . i like the new footage and still love the old stuff . \nthough mama takes a bit too long to find its rhythm and a third-act plot development is somewhat melodramatic , its ribald humor and touching nostalgia are sure to please anyone in search of a jules and jim for the new millennium . \nyou might not buy the ideas . but you'll definitely want the t-shirt . \nprovides an intriguing window into the imagination and hermetic analysis of todd solondz . \nwindtalkers is shapelessly gratifying , the kind of movie that invites you to pick apart its faults even as you have to admit that somehow it hit you where you live . \npresents an astute appraisal of middle american musical torpor and the desperate struggle to escape it . \njust what makes us happy , anyway ? \na thoughtful , moving piece that faces difficult issues with honesty and beauty . \none of the greatest romantic comedies of the past decade . \nyou wouldn't call the good girl a date movie ( an anti-date movie is more like it ) , but when it's good , it's good and horrid . \nbenefits from a strong performance from zhao , but it's dong jie's face you remember at the end . \nthis is a film brimming with detail and nuance and one that speaks volumes about the ability of the human spirit to find solace in events that could easily crush it forever . \nthe director , steven shainberg , has succeeded by focusing intently on his characters , making them quirky individuals rather than figures of fun . \nit ultimately stands forth as an important chronicle of the abuses of one of latin america's most oppressive regimes . \nthe movie has a soft , percolating magic , a deadpan suspense . \na well-made and often lovely depiction of the mysteries of friendship . \nusing his audience as a figurative port-of-call , dong pulls his even-handed ideological ship to their dock for unloading , before he continues his longer journey still ahead . \n . . . understands that a generation defines its music as much as the music defines a generation . \nthe transporter is as lively and as fun as it is unapologetically dumb\nas a witness to several greek-american weddings -- but , happily , a victim of none -- i can testify to the comparative accuracy of ms . vardalos' memories and insights . \nhas it ever been possible to say that williams has truly inhabited a character ? it is now . \nby presenting an impossible romance in an impossible world , pumpkin dares us to say why either is impossible -- which forces us to confront what's possible and what we might do to make it so . \nan impressive debut for first-time writer-director mark romanek , especially considering his background is in music video . \nan incendiary , deeply thought-provoking look at one of the most peculiar ( and peculiarly venomous ) bigotries in our increasingly frightening theocracy\nall the performances are top notch and , once you get through the accents , all or nothing becomes an emotional , though still positive , wrench of a sit . \n \" its successes are also tempered with elements which prove the direct antithesis of what it gets right . \" \nit's solid and affecting and exactly as thought-provoking as it should be . \nthis is such a dazzlingly self-assured directorial debut that it's hard to know what to praise first . \nparker holds true to wilde's own vision of a pure comedy with absolutely no meaning , and no desire to be anything but a polished , sophisticated entertainment that is in love with its own cleverness . \nmnch's genuine insight makes the film's occasional overindulgence forgivable . \nthankfully , the film , which skirts that rapidly deteriorating line between fantasy and reality . . . takes a tongue-in-cheek attitude even as it pushes the croc hunter agenda . \nultimately , the message of trouble every day seems to be that all sexual desire disrupts life's stasis . \nif you're like me , a sucker for a good old fashion romance and someone who shamelessly loves to eat , then mostly martha offers all the perfect ingredients to more than satisfy your appetite . \nthe film has just enough of everything -- re-enactments , archival footage , talking-head interviews -- and the music is simply sublime . \nthere are a few stabs at absurdist comedy . . . but mostly the humor is of the sweet , gentle and occasionally cloying kind that has become an iranian specialty . \na wonderful character-based comedy . \nit would be interesting to hear from the other side , but in talk to her , the women are down for the count . \nan endearingly offbeat romantic comedy with a great meet-cute gimmick . \nthe unique tug-of-war with viewer expectations is undeniable , if not a pleasure in its own right . \nit uses an old-time formula , it's not terribly original and it's rather messy -- but you just have to love the big , dumb , happy movie my big fat greek wedding . \nit's almost impossible not to be moved by the movie's depiction of sacrifice and its stirring epilogue in post-soviet russia . \nwho knows what exactly godard is on about in this film , but his words and images don't have to add up to mesmerize you . \nthe tone is balanced , reflective and reasonable . \nthe principals in this cast are all fine , but bishop and stevenson are standouts . \nit could change america , not only because it is full of necessary discussion points , but because it is so accessible that it makes complex politics understandable to viewers looking for nothing but energetic entertainment . \nwhat's most striking about this largely celebratory film . . . is the sense of isolation that permeates these bastions of individuality in an ikea world . \n . . . if you're in a mind set for goofy comedy , the troopers will entertain with their gross outs , bawdy comedy and head games . \nsomewhat blurred , but kinnear's performance is razor sharp . \nas a director , mr . ratliff wisely rejects the temptation to make fun of his subjects . \nfor anyone who remembers the '60s or is interested in one man's response to stroke , ram dass : fierce grace is worth seeking out . \nintriguing and beautiful film , but those of you who read the book are likely to be disappointed . \nthe new guy does have a heart . now , if it only had a brain . \na savvy exploration of paranoia and insecurity in america's culture of fear . \nlegendary irish writer brendan behan's memoir , borstal boy , has been given a loving screen transferral . \nthe film's greatest asset is how much it's not just another connect-the-dots , spy-on-the-run picture . \nthis clever caper movie has twists worthy of david mamet and is enormous fun for thinking audiences . \nit's one of the saddest films i have ever seen that still manages to be uplifting but not overly sentimental . \nmorton is , as usual , brilliant . \neven with all those rough edges safely sanded down , the american insomnia is still pretty darned good . \ni don't know precisely what to make of steven soderbergh's full frontal , though that didn't stop me from enjoying much of it . \nthe tug of war that ensues is as much a snapshot of modern china in microcosm as it is a crash course in movie mythology . \nnearly surreal , dabbling in french , this is no simple movie , and you'll be taking a risk if you choose to see it . i enjoyed the ride ( bumps and all ) , creamy depth , and ultimate theme . \nyou could say that it's slow at times , you could say that a few of the characters act in ways that real people wouldn't , but one thing you couldn't say is that alias betty is predictable . \nasia authors herself as anna battista , an italian superstar and aspiring directress who just happens to be her own worst enemy . \nroman coppola may never become the filmmaker his dad was , but heck  few filmmakers will . but based on cq , i'll certainly be keeping an eye out for his next project . \nan amusing , breezily apolitical documentary about life on the campaign trail . \nhigh on melodrama . but it's emotionally engrossing , too , thanks to strong , credible performances from the whole cast . \nfinally , a genre movie that delivers -- in a couple of genres , no less . \nit's not so much enjoyable to watch as it is enlightening to listen to new sides of a previous reality , and to visit with some of the people who were able to make an impact in the theater world . \nspielberg is the rare director who does not want to invite viewers to gawk at or applaud his special effects . he just wants them to be part of the action , the wallpaper of his chosen reality . here , thankfully , they are . \npost 9/11 the philosophical message of \" personal freedom first \" might not be as palatable as intended . \nhu and liu offer natural , matter-of-fact performances that glint with sorrow , longing and love . \nthis bold and lyrical first feature from raja amari expands the pat notion that middle-aged women just wanna have fun into a rousing treatise of sensual empowerment . \neasier to respect than enthuse over , andersson's rigorous personal vision is not only distanced but distancing . \ngirls gone wild and gone civil again\n . . . tunney is allowed to build an uncommonly human character , an almost real-live girl complete with trouble and hope . \nwhile this film is not in the least surprising , it is still ultimately very satisfying . think of it as a sort of comfort food for the mind . \nclever , brutal and strangely soulful movie . \n . . . always remains movingly genuine . \nan intelligent fiction about learning through cultural clash . \nwill grab your children by the imagination and amaze them and amuse them . \na remarkable 179-minute meditation on the nature of revolution . \nthose who would follow haneke on his creepy explorations . . . are rewarded by brutal , committed performances from huppert and magimel . \nan involving true story of a chinese actor who takes up drugs and winds up in an institution--acted mostly by the actual people involved . \nhands down the year's most thought-provoking film . but it pays a price for its intricate intellectual gamesmanship . \nit's a terrific american sports movie and dennis quaid is its athletic heart . \nthis is such a high-energy movie where the drumming and the marching are so excellent , who cares if the story's a little weak . \ncompelling revenge thriller , though somewhat weakened by a miscast leading lady . \nit's amazingly perceptive in its subtle , supportive but unsentimental look at the marks family . \na whole lot foul , freaky and funny . \nfamily fare . \nattal mixes comedy with a serious exploration of ego and jealousy within a seemingly serene marriage . \nthe diversity of the artists represented , both in terms of style and ethnicity , prevents the proceedings from feeling repetitious , as does the appropriately brief 40-minute running time . \nthe pianist is a fine valedictory work for polanski , made richer by his own experiences , making his other movies somehow richer in the bargain . \nfoster nails the role , giving a tight , focused performance illuminated by shards of feeling . \neven if you can't pronounce \" gyro \" correctly , you'll appreciate much of vardalos' humor , which transcends ethnic boundaries . \nis office work really as alienating as 'bartleby' so effectively makes it ? \nfarrell . . . thankfully manages to outshine the role and successfully plays the foil to willis's world-weary colonel . \naudiences conditioned to getting weepy over saucer-eyed , downy-cheeked moppets and their empathetic caretakers will probably feel emotionally cheated by the film's tart , sugar-free wit . \nbennett's dramatization of her personal descent into post-breakup perdition has a morbid appeal that's tough to shake . \nan intriguing and entertaining introduction to johnson . \nas expected , sayles' smart wordplay and clever plot contrivances are as sharp as ever , though they may be overshadowed by some strong performances . \na model of what films like this should be like . \nas weber and weissman demonstrate with such insight and celebratory verve , the cockettes weren't as much about gender , sexual preference or political agitprop as they were simply a triumph of the indomitable human will to rebel , connect and create . \nyeah , these flicks are just that damn good . isn't it great ? \nan unbelievably fun film just a leading man away from perfection . \nover-the-top and a bit ostentatious , this is a movie that's got oodles of style and substance . \n . . . a poignant and powerful narrative that reveals that reading writing and arithmetic are not the only subjects to learn in life . \nnicely serves as an examination of a society in transition . \nboisterous , heartfelt comedy . \na tender and touching drama , based on the true story of a troubled african-american's quest to come to terms with his origins , reveals the yearning we all have in our hearts for acceptance within the family circle . \nas a randy film about sexy people in gorgeous places being pushed and pulled ( literally and figuratively ) by desire . . . [sex and luca] makes for an arousing good time . \nabsorbing character study by andr turpin . \ncelebrated at sundance , this slight comedy of manners has winning performances and a glossy , glib charm that's hard to beat . \nrenner's performance as dahmer is unforgettable , deeply absorbing . \nif no one singles out any of these performances as award-worthy , it's only because we would expect nothing less from this bunch . \nif you love reading and/or poetry , then by all means check it out . you'll probably love it . \nthough of particular interest to students and enthusiast of international dance and world music , the film is designed to make viewers of all ages , cultural backgrounds and rhythmic ability want to get up and dance . \nenergetic and boldly provocative . \nstar wars is back in a major way . \nit's a movie -- and an album -- you won't want to miss . \nit's rare to find a film that dazzles the eye , challenges the brain , and satisfies our lust for fast-paced action , but minority report delivers all that and a whole lot more . \nwhile not all transitions to adulthood are so fraught , there's much truth and no small amount of poetry in girls can't swim . \nif there's nothing fresh about wannabes , which was written by mr . demeo , who produced and directed the film with charles a . addessi , much of the time the movie feels authentic . \njacquot's tosca is a treat . \nby the end of no such thing the audience , like beatrice , has a watchful affection for the monster . \nif you liked such movies as notting hill , four weddings and a funeral , bridget jones' diary or high fidelity , then you won't want to miss about a boy . \n . . . the gentle melding of drama and comedy makes \" what time is it there ? \" something the true film buff will enjoy . \nromanek keeps the film constantly taut . . . reflecting the character's instability with a metaphorical visual style and an unnerving , heartbeat-like score . \ni whole-heartedly recommend that everyone see this movie-- for its historical significance alone . \nhey , who else needs a shower ? \nlongley has constructed a remarkably coherent , horrifically vivid snapshot of those turbulent days . \nalthough it bangs a very cliched drum at times , this crowd-pleaser's fresh dialogue , energetic music , and good-natured spunk are often infectious . \noften gruelling and heartbreaking to witness , but seldahl and wollter's sterling performances raise this far above the level of the usual maudlin disease movie . \ngo see it and enjoy . \nthe stunning , dreamlike visuals will impress even those viewers who have little patience for euro-film pretension . \ngeorge clooney proves he's quite a talented director and sam rockwell shows us he's a world-class actor with confessions of a dangerous mind . \nthere's a vastness implied in metropolis that is just breathtaking . \nmurderous maids may well be the most comprehensive of these films and also strike closest to the truth . \nthe people in dogtown and z-boys are so funny , aggressive and alive , you have to watch them because you can't wait to see what they do next . \nas green-guts monster movies go , it's a beaut . \nas bundy , michael reilly burke ( octopus 2 : river of fear ) has just the right amount of charisma and menace . \na deceivingly simple film , one that grows in power in retrospect . \nana is a vivid , vibrant individual and the movie's focus upon her makes it successful and accessible . \na slick , skillful little horror film . \na very witty take on change , risk and romance , and the film uses humour to make its points about acceptance and growth . \n[anderson] uses a hit-or-miss aesthetic that hits often enough to keep the film entertaining even if none of it makes a lick of sense . \nbubba ho-tep is a wonderful film with a bravura lead performance by bruce campbell that doesn't deserve to leave the building until everyone is aware of it . \ndespite the long running time , the pace never feels slack -- there's no scene that screams \" bathroom break ! \" \nbullock does a good job here of working against her natural likability . \na film of precious increments artfully camouflaged as everyday activities . \nkinnear gives a tremendous performance . \nthe best movie of its kind since 'brazil . ' lucas , take notes . this is how you use special effects . \n \" frailty \" has been written so well , that even a simple \" goddammit ! \" near the end takes on a whole other meaning . \none hour photo is an intriguing snapshot of one man and his delusions ; it's just too bad it doesn't have more flashes of insight . \nkaufman creates an eerie sense of not only being there at the time of these events but the very night matthew was killed . \nchalk it up to my adoration for both de niro and murphy , but i had a pretty good time with this movie - despite its myriad flaws . \nits scenes and sensibility are all more than familiar , but it exudes a kind of nostalgic spy-movie charm and , at the same time , is so fresh and free of the usual thriller nonsense that it all seems to be happening for the first time . \nit represents better-than-average movie-making that doesn't demand a dumb , distracted audience . \na charming yet poignant tale of the irrevocable ties that bind . \nan enchanting spectacular for potter fans anxious to ride the hogwarts express toward a new year of magic and mischief . \nthe talents of the actors helps \" moonlight mile \" rise above its heart-on-its-sleeve writing . \nit's a humble effort , but spiced with wry humor and genuine pathos , especially between morgan and redgrave . \nthis examination of aquatic life off the shores of the baja california peninsula of mexico offers an engrossing way to demonstrate the virtues of the imax format . \ndark and disturbing , but also surprisingly funny . \nthe movie has an avalanche of eye-popping visual effects . \nstarts off with a bang , but then fizzles like a wet stick of dynamite at the very end . it's still worth a look . \nmost impressive , though , is the film's open-ended finale that refuses to entirely close its characters' emotional wounds . \na hip ride into hyper-time , clockstoppers is a lively and enjoyable adventure for all ages at any time . \ngrenier is terrific , bringing an unforced , rapid-fire delivery to toback's heidegger- and nietzsche-referencing dialogue . \n . . . a polished and relatively sincere piece of escapism . \nthe story wraps back around on itself in the kind of elegant symmetry that's rare in film today , but be warned : it's a slow slog to get there . \nthe whole cast looks to be having so much fun with the slapstick antics and silly street patois , tossing around obscure expressions like bellini and mullinski , that the compact 86 minutes breezes by . \n . . . has freaky scenes where the crew wonder if they're ghosts imagining themselves as alive . it's a sly wink to the others without becoming a postmodern joke , made creepy by its \" men in a sardine can \" warped logic . \nlong after you leave justine , you'll be wondering what will happen to her and wishing her the best -- whatever that might mean . \nstill pretentious and filled with subtext , but entertaining enough at 'face value' to recommend to anyone looking for something different . \ncall me a wimp , but i cried , not once , but three times in this animated sweet film . \nnotorious c . h . o . has oodles of vulgar highlights . \nan inspiring and heart-affecting film about the desperate attempts of vietnamese refugees living in u . s . relocation camps to keep their hopes alive in 1975 . \nthe level of maturity displayed by this 33-year-old first-time feature director is astonishing , considering her inexperience and her subject matter . \na splendid entertainment , young in spirit but accomplished in all aspects with the fullness of spirit and sense of ease that comes only with experience . \ndisney's live-action division has a history of releasing cinematic flotsam , but this is one occasion when they have unearthed a rare gem . \nif the message seems more facile than the earlier films , the images have such a terrible beauty you may not care . \nwhether kiss is a future cult classic or destined to be completely forgotten is open to question , but the risk-takers in the crowd should check it out and form their own opinion . \nthere are moments in this account of the life of artist frida kahlo that are among cinema's finest this year . unfortunately , they're sandwiched in between the most impossibly dry account of kahlo's life imaginable . \nthere are moments it can be heart-rending in an honest and unaffected ( and gentle ) way . \nstay clear of reminding yourself that it's a \" true story \" and you're likely to have one helluva time at the movies . \nthere are just enough twists in the tale to make it far more satisfying than almost any horror film in recent memory . \nthe sundance film festival has become so buzz-obsessed that fans and producers descend upon utah each january to ferret out the next great thing . 'tadpole' was one of the films so declared this year , but it's really more of the next pretty good thing . \nworking from elliott's memoir , rohmer fashions the sort of delicate , articulate character- and- relationship study he's favored for decades . \nthe story feels more like a serious read , filled with heavy doses of always enticing sayles dialogue . \nwhen it really counts . . . bloody sunday connects on a visceral level that transcends language . \nthe crime matters less than the characters , although the filmmakers supply enough complications , close calls and double-crosses to satisfy us . \n[an] hilarious romantic comedy . \nthe actors are fantastic . they are what makes it worth the trip to the theatre . \nranging from funny to shattering and featuring some of the year's best acting , personal velocity gathers plenty of dramatic momentum . \ni complain all the time about seeing the same ideas repeated in films over and over again , but the bourne identity proves that a fresh take is always possible . \nrecalls quiet freak-outs like l'avventura and repulsion . \nonly an epic documentary could get it all down , and spike lee's jim brown : all american at long last gives its subject a movie worthy of his talents . \n . . . as the story congeals you feel the pieces of the star wars saga falling into place in a way that makes your spine tingle with revelation and excitement . \na great comedy filmmaker knows great comedy needn't always make us laugh . tim story's not there yet - but 'barbershop' shows he's on his way . \nthe movie is one of the best examples of artful large format filmmaking you are likely to see anytime soon . \nlends itself to the narcotizing bland ( sinister , though not nearly so sinister as the biennial disney girl movie ) machinations of the biennial disney boy movie . \nwell-written , nicely acted and beautifully shot and scored , the film works on several levels , openly questioning social mores while ensnaring the audience with its emotional pull . \njason x has cheesy effects and a hoary plot , but its macabre , self-deprecating sense of humor makes up for a lot . \n[taymor] utilizes the idea of making kahlo's art a living , breathing part of the movie , often catapulting the artist into her own work . this isn't a new idea . it's been done before but never so vividly or with so much passion . \nan impressive if flawed effort that indicates real talent . \ntwo generations within one family test boundaries in this intelligent and restrained coming-of-age drama . \nit sounds sick and twisted , but the miracle of shainberg's film is that it truly is romance\ndisturbing and brilliant documentary . \n . . . mesmerizing , an eye-opening tour of modern beijing culture in a journey of rebellion , retreat into oblivion and return . \none of the best examples of how to treat a subject , you're not fully aware is being examined , much like a photo of yourself you didn't know was being taken . \nnot too far below the gloss you can still feel director denis villeneuve's beating heart and the fondness he has for his characters . \nas if to prove a female director can make a movie with no soft edges , kathryn bigelow offers no sugar-coating or interludes of lightness . her film is unrelentingly claustrophobic and unpleasant . \n[villeneuve] seems to realize intuitively that even morality is reduced to an option by the ultimate mysteries of life and death . \nthe result is mesmerizing -- filled with menace and squalor . \nfisher has bared his soul and confronted his own shortcomings here in a way . . . that feels very human and very true to life . \nit's fun , but the code-talk will fly right over everyone's head\nbourne , jason bourne . he can scale a building like a super hero , he can out-stealth any agent , he'll get the girl . he's super spy ! \nwhat makes the movie a comedy is the way it avoids the more serious emotions involved . \nan exhilarating experience . \nthis cuddly sequel to the 1999 hit is a little more visually polished , a little funnier , and a little more madcap . \nthe pleasures of super troopers may be fleeting , but they'll register strongly with anybody who still retains a soft spot for precollegiate humor . \nthe film is exhilarating to watch because sandler , liberated from the constraints of formula , reveals unexpected depths as an actor . \na distant , even sterile , yet compulsively watchable look at the sordid life of hogan's heroes star bob crane . \nthe film delivers not just the full assault of reno's immense wit and insight , but a time travel back to what it felt like during those unforgettably uncertain days . \nwhat might have been a predictably heartwarming tale is suffused with complexity . \nsound the trumpets : for the first time since desperately seeking susan , madonna doesn't suck as an actress . \nalthough very much like the first movie based on j . k . rowling's phenomenal fantasy best sellers , this second go-round possesses a quite pleasing , headlong thrust and a likably delinquent attitude . \n[ \" take care of my cat \" ] is an honestly nice little film that takes us on an examination of young adult life in urban south korea through the hearts and minds of the five principals . \nas the story moves inexorably through its seven day timeframe , the picture becomes increasingly mesmerizing . \nmaguire is a surprisingly effective peter/spider-man . \nnot a cozy or ingratiating work , but it's challenging , sometimes clever , and always interesting , and those are reasons enough to see it . \nthe film runs on equal parts of innocence and wisdom -- wisdom that comes with experience . it has fun being grown up . \nlike old myths and wonder tales spun afresh . \nrarely do films come along that are as intelligent , exuberant , and moving as monsoon wedding . \none scarcely needs the subtitles to enjoy this colorful action farce . \nquite funny for the type of movie it is . . . \nit's often infuriatingly glib and posturing , and yet it has been made with great evident care and manages to deliver up the man in a way to arouse further curiosity in even the most unknowing viewer . \none of [herzog's] least inspired works . \nthis boisterous comedy serves up a cruel reminder of the fate of hundreds of thousands of chinese , one which can only qualify as a terrible tragedy . \nelling really is about a couple of crazy guys , and it's therapeutic to laugh along with them . \nnever [sinks] into exploitation . \nan irresistible combination of a rousing good story set on a truly grand scale . \nthere's no denying the physically spectacular qualities of the film . . . or the emotional integrity of the performances . \nfew films this year have been as resolute in their emotional nakedness . \nexquisitely acted and masterfully if preciously interwoven [the film] addresses in a fascinating , intelligent manner the intermingling of race , politics and local commerce . \nstevenson's performance is at once clueless and fiercely committed , a volatile combination . \nthis is a very fine movie -- go see it . \nas shaky as the plot is , kaufman's script is still memorable for some great one-liners . \ndespite its flaws , secretary stays in your head and makes you question your own firmly held positions . \none of those rare , exhilarating cinematic delights that gets even better in hindsight , as you mull over its every nuance in your mind . \nnot everything works , but the average is higher than in mary and most other recent comedies . \na byzantine melodrama that stimulates the higher brain functions as well as the libido . \na sensitive and expertly acted crowd-pleaser that isn't above a little broad comedy and a few unabashedly sentimental tears . \nthe film's sharp , often mischievous sense of humor will catch some off guard . . . \ndoes what a fine documentary does best : it extends a warm invitation into an unfamiliar world , then illuminates it fully and allows the larger implications of the journey to sink in unobtrusively . \nalmost every scene in this film is a gem that could stand alone , a perfectly realized observation of mood , behavior and intent . \na psychologically rich and suspenseful moral thriller with a stellar performance by al pacino . \nyou won't believe much of it , but you will laugh at the audacity , at the who's who casting and the sheer insanity of it all . \nthis version's no classic like its predecessor , but its pleasures are still plentiful . \nthe bourne identity is what summer screen escapism used to be in the decades when it was geared more to grownups . \nprovide[s] nail-biting suspense and credible characters without relying on technology-of-the-moment technique or pretentious dialogue . \nif it tried to do anything more , it would fail and perhaps explode , but at this level of manic whimsy , it is just about right . \ntoo sincere to exploit its subjects and too honest to manipulate its audience . \nthe saturation bombing of reggio's images and glass' evocative music . . . ultimately leaves viewers with the task of divining meaning . \nfor all its serious sense of purpose . . . [it] finds a way to lay bare the tragedies of its setting with a good deal of warmth and humor . \na depressing confirmation of everything those of us who don't object to the description \" unelected \" have suspected all along : george w . bush is an incurious , uncharismatic , overgrown frat boy with a mean streak a mile wide . \nthis road movie gives you emotional whiplash , and you'll be glad you went along for the ride . \nsure , it's more of the same , but as the film proves , that's not always a bad thing . \na lighthearted , feel-good film that embraces the time-honored truth that the most powerful thing in life is love . \na bowel-curdling , heart-stopping recipe for terror . \ndaughter from danang is a film that should be seen by all , especially those who aren't aware of , or have forgotten about the unmentioned victims of war . \nzhang yimou delivers warm , genuine characters who lie not through dishonesty , but because they genuinely believe it's the only way to bring happiness to their loved ones . \n . . . breathes surprising new life into the familiar by amalgamating genres and adding true human complexity to its not-so-stock characters . \n' . . . both hokey and super-cool , and definitely not in a hurry , so sit back , relax and have a few laughs while the little ones get a fuzzy treat . '\na pleasant romantic comedy . \nit's a count for our times . \ngreengrass has delivered an undoubted stylistic tour-de-force , and has managed elements such as sound and cinematography with skill\nsmith's point is simple and obvious -- people's homes are extensions of themselves , and particularly eccentric people have particularly eccentric living spaces -- but his subjects are charmers . \na romantic comedy , yes , but one with characters who think and talk about their goals , and are working on hard decisions . \nvividly conveys both the pitfalls and the pleasures of over-the-top love . \n . . . a weak , manipulative , pencil-thin story that is miraculously able to entertain anyway . \na pro-fat farce that overcomes much of its excessive moral baggage thanks to two appealing lead performances . \nfor the first two-thirds of this sparklingly inventive and artful , always fast and furious tale , kids will go happily along for the ride . \nmajidi's poetic love story is a ravishing consciousness-raiser , if a bit draggy at times . \nthe smartest bonehead comedy of the summer . \neffectively feeds our senses with the chilling sights and sounds from within the camp to create a completely numbing experience . \ni love the way that it took chances and really asks you to take these great leaps of faith and pays off . \nin his debut as a film director , denzel washington delivers a lean and engaging work . \nonly two words will tell you what you know when deciding to see it : anthony . hopkins . \nthe movie's quiet affirmation of neighborhood values gives it an honest , lived-in glow . \na teasing drama whose relentless good-deed/bad-deed reversals are just interesting enough to make a sinner like me pray for an even more interesting , less symmetrical , less obviously cross-shaped creation . \nhayek is stunning as frida and . . . a star-making project . \nit's both a necessary political work and a fascinating documentary . . . \nhilarious , acidic brit comedy . \nas a revenge thriller , the movie is serviceable , but it doesn't really deliver the delicious guilty pleasure of the better film versions . \nan ironic speculation on democracy in a culture unaccustomed to it . \nit's not life-affirming  its vulgar and mean , but i liked it . \nseveral degrees shy of the gross-out contests one expects from current teen fare . \nthe inherent strength of the material as well as the integrity of the filmmakers gives this coming-of-age story restraint as well as warmth . \nled by griffin's smartly nuanced performance and enthusiasm , the cast has a lot of fun with the material . \ntuck everlasting achieves a delicate balance of romantic innocence and philosophical depth . \na gentle blend of present day testimonials , surviving footage of burstein and his family performing , historical archives , and telling stills . \na generation x artifact , capturing a brief era of insanity in the sports arena that surely cannot last . \npossession is elizabeth barrett browning meets nancy drew , and it's directed by . . . neil labute . hmm . \nan uneven but intriguing drama that is part homage and part remake of the italian masterpiece . \nwindtalkers celebrates the human spirit and packs an emotional wallop . \nhaving never been a huge fan of dickens' 800-page novel , it surprised me how much pleasure i had watching mcgrath's version . \nthe best thing the film does is to show us not only what that mind looks like , but how the creative process itself operates . \nfor all its failed connections , divine secrets of the ya-ya sisterhood is nurturing , in a gauzy , dithering way . \nthis is pretty dicey material . but some unexpected zigs and zags help . \ncompellingly watchable . \nthe filmmakers skillfully evoke the sense of menace that nature holds for many urban dwellers . \nthe laser-projected paintings provide a spell-casting beauty , while russell and dreyfus are a romantic pairing of hearts , preciously exposed as history corners them . \nyou don't have to be an especially tough grader to give a charitable b-minus to the emperor's club . \nthis romantic thriller is steeped in the atmosphere of wartime england , and ably captures the speech patterns , moral codes and ideals of the 1940s . \ndivine secrets of the ya-ya sisterhood may not be exactly divine , but it's definitely -- defiantly -- ya ya , what with all of those terrific songs and spirited performances . \nviewed on its own terms , treasure planet is better-than-average family entertainment , but true fans of the stevenson's novel will likely prefer disney's more faithful 1950 live-action swashbuckling classic . \na journey through memory , a celebration of living , and a sobering rumination on fatality , classism , and ignorance . \nresourceful and ingenious entertainment . \n \" antwone fisher \" is an earnest , by-the-numbers effort by washington . it won't rock any boats but is solid meat-and-potatoes filmmaking . \na historical epic with the courage of its convictions about both scope and detail . \nwe need [moore's] noisy , cocky energy , his passion and class consciousness ; we need his shticks , we need his stones . \nalthough the editing might have been tighter , hush ! sympathetically captures the often futile lifestyle of young people in modern japan . \n[gai] comes closer to any actress i can remember to personifying independence in its purest and , yes , most intimidating form . \nthese are lives worth watching , paths worth following . \nit's rather like a lifetime special -- pleasant , sweet and forgettable . \na moody horror/thriller elevated by deft staging and the director's well-known narrative gamesmanship . \nas a singular character study , it's perfect . it's also the year's sweetest movie . \na graceful , contemplative film that gradually and artfully draws us into a world where the personal and the political get fatally intertwined . \nwhile not as aggressively impressive as its american counterpart , \" in the bedroom , \" moretti's film makes its own , quieter observations\nthe experience of watching blobby old-school cgi animation in this superlarge format is just surreal enough to be diverting . \ntime changer may not be the most memorable cinema session but its profound self-evaluation message about our fragile existence and the absence of spiritual guidance should at least invade an abundance of mindsets\n \" the emperor's new clothes \" begins with a simple plan . . . . well , at least that's the plan . \nhaynes has so fanatically fetishized every bizarre old-movie idiosyncrasy with such monastic devotion you're not sure if you should applaud or look into having him committed . \n[director peter] jackson and his crew have so steeped themselves in the majesty of tolkien's writing that every frame produces new joys , whether you're a fan of the books or not . \nwhile the glass slipper doesn't quite fit , pumpkin is definitely a unique modern fairytale . \nthe drama is played out with such aching beauty and truth that it brings tears to your eyes . \nan exciting and involving rock music doc , a smart and satisfying look inside that tumultuous world . \nan offbeat , sometimes gross and surprisingly appealing animated film about the true meaning of the holidays . \nthis version incarnates the prophetic book in a way even its exacting author might admire . \nsometimes , nothing satisfies like old-fashioned swashbuckling . and in this regard , on guard delivers . \n . . . ambition is in short supply in the cinema , and egoyan tackles his themes and explores his characters' crises with seriousness and compassion . \nan impossible romance , but we root for the patronized iranian lad . \nlike dickens with his passages , mcgrath crafts quite moving scenes throughout his resolutely dramatic variation on the novel . \nthere's a disreputable air about the whole thing , and that's what makes it irresistible . \nan exceedingly clever piece of cinema . another great what you don't see' is much more terrifying than what you do see thriller , coupled with some arresting effects , incandescent tones and stupendous performances\na carefully structured scream of consciousness that is tortured and unsettling--but unquestionably alive . \na quietly reflective and melancholy new zealand film about an eventful summer in a 13-year-old girl's life . \ncute , funny , heartwarming digitally animated feature film with plenty of slapstick humor for the kids , lots of in-jokes for the adults and heart enough for everyone . \nvery solid , very watchable first feature for director peter sheridan\na budget affair that exposes the generally sad existence of the bedouins while providing a precious twinkle of insight into their lives . \nit suggests the wide-ranging effects of media manipulation , from the kind of reporting that is done by the supposedly liberal media . . . to the intimate and ultimately tragic heartache of maverick individuals like hatfield and hicks . \nworkmanlike , maybe , but still a film with all the elements that made the other three great , scary times at the movies . \na pleasant enough comedy that should have found a summer place . \nbranagh , in his most forceful non-shakespeare screen performance , grounds even the softest moments in the angry revolt of his wit . \nthough the violence is far less sadistic than usual , the film is typical miike : fast , furious and full of off-the-cuff imaginative flourishes . \ncompelling as it is exotic , fast runner has a plot that rivals shakespeare for intrigue , treachery and murder . \nwhat it lacks in originality it makes up for in intelligence and b-grade stylishness . \nthe warm presence of zhao benshan makes the preposterous lying hero into something more than he reasonably should be . \nthis is as powerful a set of evidence as you'll ever find of why art matters , and how it can resonate far beyond museum walls and through to the most painfully marginal lives . \ndirector rob marshall went out gunning to make a great one . \nskip work to see it at the first opportunity . \nbow's best moments are when he's getting busy on the basketball court because that's when he really scores . \noffers enough playful fun to entertain the preschool set while embracing a wholesome attitude . \nin the end , punch-drunk love is one of those films that i wanted to like much more than i actually did . sometimes , that's enough . \nan intimate , good-humored ethnic comedy like numerous others but cuts deeper than expected . \nice cube holds the film together with an engaging and warm performance . . . \nboth deeply weird and charmingly dear . \nas blunt as it is in depicting child abuse , el bola is a movie steeped in an ambiguity that lends its conflicts a symbolic resonance . \ndespite a story predictable enough to make the sound of music play like a nail-biting thriller , its heart is so much in the right place it is difficult to get really peeved at it . \nit's a masterpiece . \nan incredibly low-rent danish film , it brings a group of people together in a sweet and charming way , if a little convenient\nit's the cinematic equivalent of a good page-turner , and even if it's nonsense , its claws dig surprisingly deep . \ndirector nalin pan doesn't do much to weigh any arguments one way or the other . he simply presents his point of view that ayurveda works . no question . \nwhat \" empire \" lacks in depth it makes up for with its heart . \nclaude miller airs out a tight plot with an easy pace and a focus on character drama over crime-film complications . \nwhat full frontal lacks in thematic coherence it largely makes up for as loosey-goosey , experimental entertainment . still , i'm not quite sure what the point is\nrich in detail , gorgeously shot and beautifully acted , les destinees is , in its quiet , epic way , daring , inventive and refreshingly unusual . \n ( a ) hollywood sheen bedevils the film from the very beginning . . . ( but ) lohman's moist , deeply emotional eyes shine through this bogus veneer . . . \ndo we really need a 77-minute film to tell us exactly why a romantic relationship between a 15-year-old boy and a 40-year-old woman doesn't work ? \nford deserves to be remembered at oscar time for crafting this wonderful portrait of a conflicted soldier . \nthe film's 45-minute running time stops shy of overkill , though viewers may be more exhausted than the athletes onscreen . \ndon't expect any surprises in this checklist of teamwork cliches . . . \nas adapted by kevin molony from simon leys' novel \" the death of napoleon \" and directed by alan taylor , napoleon's journey is interesting but his parisian rebirth is stillborn\nthe movie addresses a hungry need for pg-rated , nonthreatening family movies , but it doesn't go too much further . \nthis warm and gentle romantic comedy has enough interesting characters to fill several movies , and its ample charms should win over the most hard-hearted cynics . \na yarn that respects the marvel version without becoming ensnared by it . \nthis is a happy throwback to the time when cartoons were cinema's most idiosyncratic form instead of one of its most predictable . \ncomplex , affecting and uniquely almodvar , the film evokes strong emotions and pushes viewers to question their deepest notions of moral right and wrong . \ngood ol' urban legend stuff . \nnot so much a movie as a picture book for the big screen . this isn't my favorite in the series , still i enjoyed it enough to recommend . \nit's one of the most honest films ever made about hollywood . \nit is a film that will have people walking out halfway through , will encourage others to stand up and applaud , and will , undoubtedly , leave both camps engaged in a ferocious debate for years to come . \non its own cinematic terms , it successfully showcases the passions of both the director and novelist byatt . \nlight , silly , photographed with colour and depth , and rather a good time . \npray's film works well and will appeal even to those who aren't too familiar with turntablism . \ntroubling and powerful . \ngood movie . good actress . but if you expect light romantic comedy , good gosh , will you be shocked . \nit has the courage to wonder about big questions with sincerity and devotion . it risks seeming slow and pretentious , because it thinks the gamble is worth the promise . \nwith youthful high spirits , tautou remains captivating throughout michele's religious and romantic quests , and she is backed by a likable cast . \nit's an example of sophisticated , challenging filmmaking that stands , despite its noticeable lack of emotional heft , in welcome contrast to the indulgent dead-end experimentation of the director's previous full frontal . \na very funny look at how another culture handles the process of courting and marriage . \nbut tongue-in-cheek preposterousness has always been part of for the most part wilde's droll whimsy helps \" being earnest \" overcome its weaknesses and parker's creative interference . . . \nmuch of the movie's charm lies in the utter cuteness of stuart and margolo . their computer-animated faces are very expressive . \nthe path ice age follows most closely , though , is the one established by warner bros . giant chuck jones , who died a matter of weeks before the movie's release . \nanchored by a terrific performance by abbass , satin rouge shows that the idea of women's self-actualization knows few continental divides . \nawkward but sincere and , ultimately , it wins you over . \nsmith profiles five extraordinary american homes , and because the owners seem fully aware of the uses and abuses of fame , it's a pleasure to enjoy their eccentricities . \nthough the plot is predictable , the movie never feels formulaic , because the attention is on the nuances of the emotional development of the delicate characters . \nsam jones became a very lucky filmmaker the day wilco got dropped from their record label , proving that one man's ruin may be another's fortune . \ngoyer's screenplay and direction are thankfully understated , and he has drawn excellent performances from his cast . \nbinoche and magimel are perfect in these roles . \nwhen your leading ladies are a couple of screen-eating dominatrixes like goldie hawn and susan sarandon at their raunchy best , even hokum goes down easily . \nwhile undercover brother is definitely one for the masses , it's also full of sharp , smart satire . \ngets under the skin of a man who has just lost his wife . \nit may not be \" last tango in paris \" but . . . \nno wonder they're talking about \" talk to her . \" it's astonishing . \nfor its seriousness , high literary aspirations and stunning acting , the film can only be applauded . \nlook , this is a terrific flick replete with dazzling camera-work , dancing and music . \nit is inspirational in characterizing how people from such diverse cultures share the same human and spiritual needs . \nit's fairly self-aware in its dumbness . \na triumph , relentless and beautiful in its downbeat darkness . \ntailored to entertain ! \na compelling , moving film that respects its audience and its source material . \nhas a plot full of twists upon knots . . . and a nonstop parade of mock-tarantino scuzbag types that starts out clever but veers into overkill . \na work of astonishing delicacy and force . \nthe film benefits greatly from a less manic tone than its predecessor , as cho appears to have settled comfortably into her skin . \nfor the first time in several years , mr . allen has surpassed himself with the magic he's spun with the hollywood empress of ms . leoni's ellie . \nisn't quite the equal of woo's best earlier work , but it's easily his finest american film . . . comes close to recapturing the brilliance of his hong kong films . \nthe film hinges on its performances , and both leads are up to the task . \nan intelligent , earnest , intimate film that drops the ball only when it pauses for blunt exposition to make sure you're getting its metaphysical point . \na modest pleasure that accomplishes its goals with ease and confidence . \na breezy , diverting , conventional , well-acted tale of two men locked in an ongoing game of cat-and-cat . \nwhat jackson has accomplished here is amazing on a technical level . \nas teen movies go , \" orange county \" is a refreshing change\nmakes s&m seem very romantic , and maggie gyllenhaal is a delight . \na deliciously mordant , bitter black comedy . \nalthough life or something like it is very much in the mold of feel-good movies , the cast and director stephen herek's polished direction pour delightfully piquant wine from aged bottles . \nit is risky , intelligent , romantic and rapturous from start to finish . \nthe movie sticks much closer to hornby's drop-dead confessional tone than the film version of high fidelity did . \na pleasant ramble through the sort of idoosyncratic terrain that errol morris has often dealt with . . . it does possess a loose , lackadaisical charm . \n . . . spiced with humor ( 'i speak fluent flatula , ' advises denlopp after a rather , er , bubbly exchange with an alien deckhand ) and witty updatings ( silver's parrot has been replaced with morph , a cute alien creature who mimics everyone and everything around ) \nthis is a raw and disturbing tale that took five years to make , and the trio's absorbing narrative is a heart-wrenching showcase indeed . \na beautiful and haunting examination of the stories we tell ourselves to make sense of the mundane horrors of the world . \naside from being the funniest movie of the year , simone , andrew niccol's brilliant anti-hollywood satire , has a wickedly eccentric enchantment to it . \nwatstein handily directs and edits around his screenplay's sappier elements . . . and sustains off the hook's buildup with remarkable assuredness for a first-timer . \njust another fish-out-of-water story that barely stays afloat . \nthere's an energy to y tu mam tambin . much of it comes from the brave , uninhibited performances by its lead actors . \nit's the kind of pigeonhole-resisting romp that hollywood too rarely provides . \nreinforces the often forgotten fact of the world's remarkably varying human population and mindset , and its capacity to heal using creative , natural and ancient antidotes . \nyou can feel the heat that ignites this gripping tale , and the humor and humanity that root it in feeling . \nit's hard not to be seduced by [witherspoon's] charisma , even in this run-of-the-mill vehicle , because this girl knows how to drive it to the max . \na movie for 11-year-old boys with sports dreams of their own and the preteen girls who worship lil' bow wow . \na refreshingly authentic coming-of-age tale . \nif you're not into the pokemon franchise , this fourth animated movie in four years won't convert you -- or even keep your eyes open . but fans should have fun meeting a brand-new pokemon called celebi . \nfrom the big giant titles of the opening credits to elmer bernstein's perfectly melodic score , haynes gets just about everything right . \nwhether seen on a 10-inch television screen or at your local multiplex , the edge-of-your-seat , educational antics of steve irwin are priceless entertainment . \nhas a shambling charm . . . a cheerfully inconsequential diversion . \nferrara directs the entire film with the kind of detachment that makes any given frame look like a family's custom-made christmas card . \nthe movie has lots of dancing and fabulous music . there are slow and repetitive parts , but it has just enough spice to keep it interesting . \nan incredibly clever and superbly paced caper filled with scams within scams within scams . \nthere's not much more to this adaptation of the nick hornby novel than charm -- effortless , pleasurable , featherweight charm . \nas a belated nod to some neglected all-stars , standing in the shadows of motown is cultural history of the best kind : informative , revealing and richly entertaining . \neven if the ride's a little bumpy , with a final lap that's all too suspiciously smooth , you gotta give director roger michell , best known for the superfluous notting hill , credit for trying . \nnot as distinctive or even as humorous as its needs to be to stand out , but it has clearly been made with affection and care . \nthis is carion's debut feature but his script and direction hums with a confidence that many spend entire careers trying to reach . \nan intelligent , moving and invigorating film . \nofrece una buena oportunidad de cultura ( aunque sea condensada ) que bien vale la pena aprovechar . \n . . . one of the most ingenious and entertaining thrillers i've seen in quite a long time . \na clever blend of fact and fiction . \na vivid cinematic portrait . \nhilarious , touching and wonderfully dyspeptic . \nes divertida , visualmente espectacular y muy entretenida . simple y sencillamente te sorprender . \ntheirs is a simple and heart-warming story , full of mirth that should charm all but the most cynical . \nthe film is an enjoyable family film -- pretty much aimed at any youngster who loves horses . \na frisky and fresh romantic comedy exporing sexual politics and the challenges of friendships between women . \nit's a good film -- not a classic , but odd , entertaining and authentic . \nflavorful and romantic , you could call this how martha got her groove back -- assuming , that is , she ever had one to begin with . \nhappily for mr . chin -- though unhappily for his subjects -- the invisible hand of the marketplace wrote a script that no human screenwriter could have hoped to match . \nthurman and lewis are hilarious throughout . \nthe plot is so amusingly contrived and outlandish in its coincidences that no one could ever mistake it for anything resembling reality\nhits one out of the park for the 'they don't make 'em like that anymore' department . \nit dares to be a little different , and that shading is what makes it worthwhile . \n[fessenden] is much more into ambiguity and creating mood than he is for on screen thrills\nthe comic performances are all spot on , especially lee ross's turn as ken . \na compelling journey . . . and \" his best friend remembers \" is up there with the finest of specials . \nat nearly three hours , the whole of safe conduct is less than the sum of its parts . \nthe hours makes you examine your own life in much the same way its characters do , and the experience is profound . the hours is what movies are supposed to be . . . \na bold and subversive film that cuts across the grain of what is popular and powerful in this high-tech age , speaking its truths with spellbinding imagery and the entrancing music of philip glass . \npretty darn good , despite its smarty-pants aura . \nso young , so smart , such talent , such a wise * * * . \nwoo's fights have a distinct flair . his warriors collide in balletic explosion that implies an underlying order throughout the chaos . \nbarney has created a tour de force that is weird , wacky and wonderful . \nthe ending does leave you unfulfilled , but these are performances to enjoy in a memorable ensemble piece . \n . . . an agreeable time-wasting device -- but george pal's low-tech 1960 version still rules the epochs . \nit's a brave attempt to tap into the heartbeat of the world , a salute to the universal language of rhythm and a zippy sampling of sounds . \noffers an unusual opportunity to observe the inequities in the death penalty , not just the inherent immorality but also the haphazard administration of it and public misperception of how the whole thing works . \ni don't think i've been as entranced and appalled by an asian film since shinya tsukamoto's iron man . \nit is so refreshing to see robin williams turn 180 degrees from the string of insultingly innocuous and sappy fiascoes he's been making for the last several years . \ndirector benoit jacquot , making his first opera-to-film translation with tosca , conveys the heaving passion of puccini's famous love-jealousy- murder-suicide fandango with great cinematic innovation . \nlilia's transformation from strict mother to sensual siren is superficially preposterous , but abbas infuses the role with an unimpeachable core of emotional truth . \nfrida's artistic brilliance is undeniable -- it's among the most breathtakingly designed films i've ever seen . \nthe perfect film for those who like sick comedies that can be snide . \n'charly' will divide its audience in two separate groups , those reaching for more tissues and those begging for mercy . . . \nnervy and sensitive , it taps into genuine artistic befuddlement , and at the same time presents a scathing indictment of what drives hollywood . \na marvellous journey from childhood idealism to adolescent self-absorption . \nthe film is just a big , gorgeous , mind-blowing , breath-taking mess . \nsharp , lively , funny and ultimately sobering film . \nthough the film's scenario is certainly not earthshaking , this depiction of fluctuating female sexuality has two winning lead performances and charm to spare . \na worthy tribute to a great humanitarian and her vibrant 'co-stars . '\na recent favourite at sundance , this white-trash satire will inspire the affection of even those unlucky people who never owned a cassette of def leppard's pyromania . \nthe recording session is the only part of the film that is enlightening -- and how appreciative you are of this depends on your level of fandom . \noccasionally funny and consistently odd , and it works reasonably well as a star vehicle for zhao . \nbright seems alternately amused and disgusted with this material , and he can't help throwing in a few of his own touches . \nthe 3d images only enhance the film's otherworldly quality , giving it a strange combo of you-are-there closeness with the disorienting unreality of the seemingly broken-down fourth wall of the movie screen . \nandersson creates a world that's at once surreal and disturbingly familiar ; absurd , yet tremendously sad . \nit's predictable , but it jumps through the expected hoops with style and even some depth . \noften hilarious , well-shot and , importantly , entertaining , hell house is a fascinating document of an event that has to be seen to be believed . \nde oliveira creates an emotionally rich , poetically plump and visually fulsome , but never showy , film whose bittersweet themes are reinforced and brilliantly personified by michel piccoli . \n . . . an inviting piece of film . \nthe film's real appeal won't be to clooney fans or adventure buffs , but to moviegoers who enjoy thinking about compelling questions with no easy answers . \nthe fact that the rookie is a nearly impeccable cinematic experience -- and a wonderful all-ages triumph besides -- is a miracle akin to the story the film portrays . \na deviant topical comedy which is funny from start to finish . \na startling and fresh examination of how the bike still remains an ambiguous icon in chinese society . \na highly intriguing thriller , coupled with some ingenious plot devices and some lavishly built settings . . it's a worthwhile tutorial in quantum physics and slash-dash\nas hugh grant says repeatedly throughout the movie , 'lovely ! brilliant ! '\ncho's fearless in picking apart human foibles , not afraid to lay her life bare in front of an audience . her delivery and timing are flawless . \nworks because , for the most part , it avoids the stupid cliches and formulaic potholes that befall its brethren . \nat its best , the good girl is a refreshingly adult take on adultery . . . \nan amazing and incendiary movie that dives straight into the rough waters of contradiction . \nabout nowhere kids who appropriated turfs as they found them and become self-made celebrity athletes -- a low-down version of the american dream . \noccasionally , in the course of reviewing art-house obscurities and slam-bam action flicks , a jaded critic smacks into something truly new . \na miniscule little bleep on the film radar , but one that many more people should check out\ndesta vez , columbus capturou o pomo de ouro . \n \" 13 conversations \" holds its goodwill close , but is relatively slow to come to the point . \na slick , well-oiled machine , exquisitely polished and upholstered . \ndon't plan on the perfect ending , but sweet home alabama hits the mark with critics who escaped from a small town life . \nit has a subtle way of getting under your skin and sticking with you long after it's over . \nthe movie stays afloat thanks to its hallucinatory production design . \nit helps that the central performers are experienced actors , and that they know their roles so well . \na provocative movie about loss , anger , greed , jealousy , sickness and love . \nworth the effort to watch . \nthat rara avis : the intelligent romantic comedy with actual ideas on its mind . \nboisterous and daft documentary . \nhawke draws out the best from his large cast in beautifully articulated portrayals that are subtle and so expressive they can sustain the poetic flights in burdette's dialogue . \na work of the utmost subtlety and perception , it marks the outstanding feature debut of writer-director eric byler , who understands the power of the implicit and the virtues of simplicity and economy . \nfull frontal is the antidote for soderbergh fans who think he's gone too commercial since his two oscar nominated films in 2000\nit turns out to be a cut above the norm , thanks to some clever writing and sprightly acting . \nyou might not want to hang out with samantha , but you'll probably see a bit of yourself in her unfinished story . \na work of intricate elegance , literary lyricism and profound common sense . \nit's as close as we'll ever come to looking through a photographer's viewfinder as he works . \nthoughtful , provocative and entertaining . \nwitty , touching and well paced . \nlee jeong-hyang tells it so lovingly and films it so beautifully that i couldn't help being captivated by it . \nyou have to pay attention to follow all the stories , but they're each interesting . the movie is well shot and very tragic , and one to ponder after the credits roll . \nenjoy it for what it is ; you can hate yourself later . \na map of the inner rhythms of love and jealousy and sacrifice drawn with a master's steady stroke . \nms sarcstica , divertida y demencial que su predecesora , es un buen ejemplo de lo que es el cine de entretenimiento puro y sin complejos . \na psychological thriller with a smart script and an obsessive-compulsive's attention to detail . \noften hilarious . \ngrant gets to display his cadness to perfection , but also to show acting range that may surprise some who thought light-hearted comedy was his forte . \nat times funny and at other times candidly revealing , it's an intriguing look at two performers who put themselves out there because they love what they do . \nwestfeldt and juergensen exude a chemistry and comfort level that's both saucy and endearing . \nharsh , effective documentary on life in the israeli-occupied palestinian territories . \nthe film is all a little lit crit 101 , but it's extremely well played and often very funny . \nearns its laughs from stock redneck 'types' and from the many , many moments when we recognize even without the elizabethan prose , the play behind the thing . \na real story about real people living their lives concerned about the future of an elderly , mentally handicapped family member . \nit's absolutely spooky how lillard channels the shagster right down to the original casey kasem-furnished voice . \na dream cast of solid female talent who build a seamless ensemble . there isn't a weak or careless performance amongst them . \nsmart science fiction for grown-ups , with only a few false steps along the way . \nit's a refreshing change from the self-interest and paranoia that shape most american representations of castro . \noften moving and explores the discomfort inherent in the contacts between the american 'hosts' and their 'guests . '\nthough the controversial korean filmmaker's latest effort is not for all tastes , it offers gorgeous imagery , effective performances , and an increasingly unsettling sense of foreboding . \nlathan and diggs have considerable personal charm , and their screen rapport makes the old story seem new . \nthe story may not be new , but australian director john polson , making his american feature debut , jazzes it up adroitly . \nit's endearing to hear madame d . refer to her husband as 'jackie' -- and he does make for excellent company , not least as a self-conscious performer . \nthe film often achieves a mesmerizing poetry . \nmore than makes up for its mawkish posing by offering rousing spates of genuine feeling . \nit's neither as romantic nor as thrilling as it should be . but it offers plenty to ponder and chew on as its unusual relationship slowly unfolds . \noccasionally funny , always very colorful and enjoyably overblown in the traditional almodvar style . \nmerchant effectively translates naipaul's lively mix of characters from the page to screen . \nsome movies are like a tasty hors-d'oeuvre ; this one is a feast . \nwhat could have become just another cautionary fable is allowed to play out as a clever , charming tale  as pleasantly in its own way as its self-dramatizing characters . \ndavis has filled out his cast with appealing fresh faces . \nachieves a sort of filmic epiphany that revels in the true potential of the medium . \nonce you get into its rhythm . . . the movie becomes a heady experience . \n \" auto focus \" works as an unusual biopic and document of male swingers in the playboy era\nif mr . zhang's subject matter is , to some degree at least , quintessentially american , his approach to storytelling might be called iranian . \na fast-moving and remarkable film that appears destined to become a landmark in japanese animation . \n . . . a sour little movie at its core ; an exploration of the emptiness that underlay the relentless gaiety of the 1920's . . . the film's ending has a \" what was it all for ? \" feeling to it , but like the 1920's , the trip there is a great deal of fun . \na worthy entry into a very difficult genre . \n[broomfield] uncovers a story powerful enough to leave the screen sizzling with intrigue . \neight crazy nights is a showcase for sandler's many talents . \na sweet-natured reconsideration of one of san francisco's most vital , if least widely recognized , creative fountainheads . \nthis is one of the most visually stunning and thematically moving epics in recent memory , and in spite of numerous minor flaws , scorsese's best in more than a decade . \neverywhere the camera looks there is something worth seeing . \na richly imagined and admirably mature work from a gifted director who definitely has something on his mind . \nit's a nicely detailed world of pawns , bishops and kings , of wagers in dingy backrooms or pristine forests . \na charming , quirky and leisurely paced scottish comedy -- except with an outrageous central gimmick that could have been a reject from monty python's meaning of life . \nit never fails to engage us . \nits direction , its script , and weaver's performance as a vaguely discontented woman of substance make for a mildly entertaining 77 minutes , if that's what you're in the mood for . \na charming romantic comedy that is by far the lightest dogme film and among the most enjoyable . \nthis is the kind of movie that used to be right at home at the saturday matinee , and it still is . \nthe spark of special anime magic here is unmistakable and hard to resist . \nlike its two predecessors , 1983's koyaanisqatsi and 1988's powaqqatsi , the cinematic collage naqoyqatsi could be the most navel-gazing film ever . \nbaran isn't the most transporting or gripping film from iran -- or , indeed , by its director -- but it's a worthy companion to the many fine , focused films emerging from that most surprising of nations . \nthe visuals alone make metropolis worth seeing . \ndark , resonant , inventively detailed and packed with fleet turns of plot and a feast of visual amazement . \na picture that extols the virtues of comradeship and community in a spunky , spirited fashion . \na resonant tale of racism , revenge and retribution . \nnoyce's film is contemplative and mournfully reflective . \nhere , adrian lyne comes as close to profundity as he is likely to get . \nevokes a little of the fear that parents have for the possible futures of their children--and the sometimes bad choices mothers and fathers make in the interests of doing them good . \nuno de los policiales ms interesantes de los ltimos tiempos . \nrain is a small treasure , enveloping the viewer in a literal and spiritual torpor that is anything but cathartic . \nan elegant , exquisitely modulated psychological thriller . \nthis concoction , so bizarre to the adult mind , is actually a charming triumph where its intended under-12 audience is concerned . \ndroll caper-comedy remake of \" big deal on madonna street \" that's a sly , amusing , laugh-filled little gem in which the ultimate \" bellini \" begins to look like a \" real kaputschnik . \" \nit's a beautifully accomplished lyrical meditation on a bunch of despondent and vulnerable characters living in the renown chelsea hotel . . . \nis it a total success ? no . is it something any true film addict will want to check out ? you bet . \nzany , exuberantly irreverent animated space adventure . \ndolgin and franco fashion a fascinating portrait of a vietnamese-born youngster who eagerly and easily assimilated as an all-american girl with a brand new name in southern tennessee . \nthe disarming cornball atmosphere has a way of infecting the entire crowd as the film rolls on . \na refreshingly honest and ultimately touching tale of the sort of people usually ignored in contemporary american film . search it out . \nengrossing and affecting , if ultimately not quite satisfying . \nthe story , like life , refuses to be simple , and the result is a compelling slice of awkward emotions . \na sly game of cat and mouse that's intense and thrilling at times , but occasionally stretches believability to its limits and relies on predictable plot contrivances . \nfunny and , at times , poignant , the film from director george hickenlooper all takes place in pasadena , \" a city where people still read . \" \nthis horror-comedy doesn't go for the usual obvious laughs at the expense of cheap-looking monsters -- unless you count elvira's hooters . \nthe movie's eventual success should be credited to dennis quaid , in fighting trim shape as an athlete as well as an actor\nnot a bad journey at all . \nsits uneasily as a horror picture . . . but finds surprising depth in its look at the binds of a small family . \nwindtalkers blows this way and that , but there's no mistaking the filmmaker in the tall grass , true to himself . \nthere is a refreshing absence of cynicism in stuart little 2--quite a rarity , even in the family film market . eventually , it wins you over . \nnoyce films it more as a shocking history lesson than as drama . \nlike a south-of-the-border melrose place . \nthose with an interest in new or singular sorts of film experiences will find what time is it there ? well worth the time . \na wildly funny prison caper . \nhuppert gives erika a persona that is so intriguing that you find yourself staring hypnotically at her , trying to understand her and wondering if she'll crack . \ndespite what anyone believes about the goal of its makers , the show . . . represents a spectacular piece of theater , and there's no denying the talent of the creative forces behind it . \nyou'll be left with the sensation of having just witnessed a great performance and , perhaps , give in to the urge to get on your feet and shake it . \nthe actors are so terrific at conveying their young angst , we do indeed feel for them . \nthe reason this picture works better than its predecessors is that myers is no longer simply spoofing the mini-mod-madness of '60s spy movies . \nit is a kickass , dense sci-fi action thriller hybrid that delivers and then some . i haven't seen one in so long , no wonder i didn't recognize it at first . \na compelling portrait of moral emptiness\nin adobo , ethnicity is not just the spice , but at the heart of more universal concerns . \nit is ridiculous , of course . . . but it is also refreshing , disarming , and just outright enjoyable despite its ridiculousness . \n . . . blade ii is more enjoyable than the original . \na film that takes you inside the rhythms of its subject : you experience it as you watch . \nthe movie exists for its soccer action and its fine acting . \nthe movie is saved from unbearable lightness by the simplicity of the storytelling and the authenticity of the performances . \nthe film starts out as competent but unremarkable . . . and gradually grows into something of considerable power . \nnothing denis has made before , like beau travil and nenette et boni , could prepare us for this gory , perverted , sex-soaked riff on the cannibal genre . \nreinforces the talents of screenwriter charlie kaufman , creator of adaptation and being john malkovich . \ngreene delivers a typically solid performance in a role that is a bit of a departure from the noble characters he has played in the past , and he is matched by schweig , who carries the film on his broad , handsome shoulders . \nfinds a way to tell a simple story , perhaps the simplest story of all , in a way that seems compelling and even original . \na stunning piece of visual poetry that will , hopefully , be remembered as one of the most important stories to be told in australia's film history . \nthis is art paying homage to art . \n . . . a joke at once flaky and resonant , lightweight and bizarrely original . \ninvincible is a wonderful movie . \n . . . a cute and sometimes side-splittingly funny blend of legally blonde and drop dead gorgeous , starring piper perabo in what could be her breakthrough role . \ndazzling and sugar-sweet , a blast of shallow magnificence that only sex , scandal , and a chorus line of dangerous damsels can deliver . \noccasionally amateurishly made but a winsome cast and nice dialogue keeps it going . \njapan's premier stylist of sex and blood hits audiences with what may be his most demented film to date . \nculkin , who's in virtually every scene , shines as a young man who uses sarcastic lies like a shield . \ncuts right through the b . s . giving a big middle-fingered \" shut up \" to those who talk up what is nothing more than two guys beating the hell outta one another . \nthe am-radio soundtrack and game cast -- tierney and the inimitable walken especially -- keep this unusual comedy from choking on its own conceit . \n . . . does such a fine job of engulfing you in its world and allying you with its characters' choices , good and ill , that its shortcomings are remembered only as an afterthought . \nmarvelous , merry and , yes , melancholy film . \nfrom spiritual rebirth to bruising defeat , vincent's odyssey resonates in a profound way , comparable to the classic films of jean renoir . \nnovak manages to capture a cruelly hilarious vein of black comedy in the situation with his cast of non-actors and a gritty , no-budget approach . \ninsomnia is involving . still , i thought it could have been more . \nthere was time on that second round to see the subtleties of ramsay's portrait of grief . \nwe can see the wheels turning , and we might resent it sometimes , but this is still a nice little picture , made by bright and friendly souls with a lot of good cheer . \na comprehensive and provocative film -- one that pushes the boundaries of biography , and challenges its audience . \nthe way coppola professes his love for movies -- both colorful pop junk and the classics that unequivocally qualify as art -- is giddily entertaining . \na modest masterpiece . \na worthwhile way to spend two hours . \nfrancophiles will snicker knowingly and you'll want to slap them . \nsensitive , insightful and beautifully rendered film . one of the best of the year . \na love for films shines through each frame and the era is recreated with obvious affection , scored to perfection with some tasty boogaloo beats . \nthrowing caution to the wind with an invitation to the hedonist in us all , nair has constructed this motion picture in such a way that even the most cynical curmudgeon with find himself or herself smiling at one time or another . \nmakes an aborbing if arguable case for the man's greatness . \nan endlessly fascinating , landmark movie that is as bold as anything the cinema has seen in years . \n . . . a haunting vision , with images that seem more like disturbing hallucinations . \nthey crush each other under cars , throw each other out windows , electrocute and dismember their victims in full consciousness . and we don't avert our eyes for a moment . \nit is not a mass-market entertainment but an uncompromising attempt by one artist to think about another . \nfrailty isn't as gory or explicit . but in its child-centered , claustrophobic context , it can be just as frightening and disturbing -- even punishing . \nmixes likeable personalities , inventive photography and cutting , and wall-to-wall toe-tapping music to paint a picture of a subculture that is at once exhilarating , silly , perverse , hopeful and always fun . \nthe long-range appeal of \" minority report \" should transcend any awards it bags . this is one for the ages . \n[a] superbly controlled , passionate adaptation of graham greene's 1955 novel . \nmuch monkeyfun for all . \nan enchanting film that presents an audacious tour of the past and takes within its warm embrace the bounties of cultural artifacts inside st . petersburg's hermitage museum . \n[hawn's character]is so bluntly written , without a trace of sentimentality , and so blisteringly defined , that every other character seems overlooked and underwritten . \nthe heightened symmetry of this new/old cinema paradiso makes the film a fuller experience , like an old friend haunted by the exigencies of time . \nthe powers team has fashioned a comedy with more laughs than many , no question . but this time there's some mold on the gold . \nwhile surprisingly sincere , this average little story is adorned with some awesome action photography and surfing . \nit is far from the worst , thanks to the topical issues it raises , the performances of stewart and hardy , and that essential feature -- a decent full-on space battle . \na film that is a portrait of grace in an imperfect world . \na pleasurably jacked-up piece of action moviemaking . \nnicolas philibert observes life inside a one-room schoolhouse in northern france in his documentary to be and to have , easily one of the best films of the year . \na perverse little truffle , dainty psychological terror on the outside with a creamy filling of familial jealousy and unrepentant domestic psychopathy . \nthis ecologically minded , wildlife friendly film teaches good ethics while entertaining with its unconventionally wacky but loving family\nan enjoyably half-wit remake of the venerable italian comedy big deal on madonna street . \nit takes this never-ending confusion and hatred , puts a human face on it , evokes shame among all who are party to it and even promotes understanding . \nreign of fire may be little more than another platter of reheated aliens , but it's still pretty tasty . \nthere are times when a rumor of angels plays like an extended episode of touched by an angel -- a little too much dancing , a few too many weeping scenes -- but i liked its heart and its spirit . \ntwo hours of melodramatic musical married to two hours of underdog sports intrigue , if the picture also shares the weaknesses of both genres , more's the pity . \nthis cheery , down-to-earth film is warm with the cozy feeling of relaxing around old friends . \nthrilling , provocative and darkly funny , this timely sci-fi mystery works on so many different levels that it not only invites , it demands repeated viewings . \na tale of horror and revenge that is nearly perfect in its relentless descent to the depths of one man's tortured soul . \nan epic of grandeur and scale that's been decades gone from the popcorn pushing sound stages of hollywood . \ngenuinely touching because it's realistic about all kinds of love . \nlauren ambrose comes alive under the attention from two strangers in town - with honest performances and realistic interaction between the characters , this is a coming-of-age story with a twist . \nthere has been much puzzlement among critics about what the election symbolizes . i believe the message is in the messenger : the agent is a woman . \nan enjoyable film for the family , amusing and cute for both adults and kids . \n \" the mothman prophecies \" is a difficult film to shake from your conscience when night falls . \nthe second chapter of the harry potter series is even more magical than the first and simply the best family film of the year . \nmore honest about alzheimer's disease , i think , than iris . \nthe acting alone is worth the price of admission . \nan excellent romp that boasts both a heart and a mind . \ninteracting eyeball-to-eyeball and toe-to-toe , hopkins and norton are a winning combination -- but fiennes steals 'red dragon' right from under their noses . \nthis is a terrific character study , a probe into the life of a complex man . \nimpresses you with its open-endedness and surprises . \nthis isn't a narrative film -- i don't know if it's possible to make a narrative film about september 11th , though i'm sure some will try -- but it's as close as anyone has dared to come . \nmy oh my , is this an invigorating , electric movie . \nthe two leads chomp considerably more scenery with their acting than fire-breathing monsters barbecue with their breath . . . \ncedar takes a very open-minded approach to this sensitive material , showing impressive control , both visually and in the writing . \nnever once predictable . \nbiggie and tupac is so single-mindedly daring , it puts far more polished documentaries to shame . \nso many documentaries like this presuppose religious bigotry or zealous nuttiness of its antagonists , but family fundamentals displays a rare gift for unflinching impartiality . \nthe cast is uniformly excellent and relaxed . \nafter making several adaptations of other writers' work , armenian-canadian director atom egoyan broached an original treatment of a deeply personal subject . \nthe film is painfully authentic , and the performances of the young players are utterly convincing . \nif it seems like a minor miracle that its septuagenarian star is young enough to be the nonagenarian filmmaker's son , more incredible still are the clear-eyed boldness and quiet irony with which actor and director take on life's urgent questions . \na candid and often fascinating documentary about a pentecostal church in dallas that assembles an elaborate haunted house each year to scare teenagers into attending services . \nfans of the animated wildlife adventure show will be in warthog heaven ; others need not necessarily apply . \nwithout resorting to hyperbole , i can state that kissing jessica stein may be the best same-sex romance i have seen . \nnolan bravely treads where few american films dare to delve -- into the world of ambivalence and ambiguity . . . \nunlike the nauseating fictions peddled by such 'have-yourself-a-happy-little-holocaust' movies as life is beautiful and jakob the liar , the grey zone is honest enough to deny the possibility of hope in auschwitz . \na potent allegorical love story . \neven those who would like to dismiss the film outright should find much to mull and debate . \nthis is cool , slick stuff , ready to quench the thirst of an audience that misses the summer blockbusters . \nthe movie is full of fine performances , led by josef bierbichler as brecht and monica bleibtreu as helene weigel , his wife . \na captivating cross-cultural comedy of manners . \nandy garcia enjoys one of his richest roles in years and mick jagger gives his best movie performance since , well , performance . \nthe movie isn't always easy to look at . but if it is indeed a duty of art to reflect life , than leigh has created a masterful piece of artistry right here . \nit's [ricci's] best work yet , this girl-woman who sincerely believes she can thwart the world's misery with blind good will . \nhighlights are the terrific performances by christopher plummer , as the prime villain , and nathan lane as vincent crummles , the eccentric theater company manager . \n[howard] so good as leon barlow . . . that he hardly seems to be acting . \nan uplifting , near-masterpiece . \nsuperior genre storytelling , which gets under our skin simply by crossing the nuclear line . \nby taking entertainment tonight subject matter and giving it humor and poignancy , auto focus becomes both gut-bustingly funny and crushingly depressing . \nit's a bittersweet and lyrical mix of elements . \nsubversive , meditative , clinical and poetic , the piano teacher is a daring work of genius . \nthe weakest of the four harry potter books has been transformed into the stronger of the two films by the thinnest of margins . \nits gross-out gags and colorful set pieces . . . are of course stultifyingly contrived and too stylized by half . still , it gets the job done -- a sleepy afternoon rental . \nit further declares its director , zhang yang of shower , as a boldly experimental , contemporary stylist with a bright future . \nsmith's approach is never to tease , except gently and in that way that makes us consider our own eccentricities and how they are expressed through our homes . \nfull of profound , real-life moments that anyone can relate to , it deserves a wide audience . \na movie that will touch the hearts of both children and adults , as well as bring audiences to the edge of their seats . \nleave it to rohmer , now 82 , to find a way to bend current technique to the service of a vision of the past that is faithful to both architectural glories and commanding open spaces of the city as it was more than two centuries ago . \nfine acting but there is no sense of connecting the dots , just dots . \nan extraordinary swedish film about the soul adventure of marriage -- the kind of intimate and character-driven film that bille august does best . \na blessed gift to film geeks and historians . if the '70's were your idea of a good time at the movies , this will make you very happy . \nit took 19 predecessors to get this ? \nthoughtful , even stinging at times , and lots of fun . \none of the most haunting , viciously honest coming-of-age films in recent memory . \nthe wwii drama is well plotted , visually striking and filled with enjoyably complex characters who are never what they first appear . \nit's a pleasure to see seinfeld griping about the biz with buddies chris rock , garry shandling and colin quinn . \nif you love motown music , you'll love this documentary . \nthis time out , [sade] is an unsettlingly familiar figure -- in turns loyal and deceitful , responsible and reckless , idealistically selfless and coldly self-interested . \nhuman resources was a good , straightforward tale , but time out is better . it's haunting . it's like a poem . \nto the film's credit , the acting is fresh and unselfconscious , and munch is a marvel of reality versus sappy sentiment . \nchicago is , in many ways , an admirable achievement . \nshainberg weaves a carefully balanced scenario that is controlled by neither character , is weirdly sympathetic to both and manages to be tender and darkly comic . \neven when foreign directors . . . borrow stuff from hollywood , they invariably shake up the formula and make it more interesting . \na cockamamie tone poem pitched precipitously between swoony lyricism and violent catastrophe . . . the most aggressively nerve-wracking and screamingly neurotic romantic comedy in cinema history . \nsturdy , entertaining period drama . . . both caine and fraser have their moments . \nwhether [binoche and magimel] are being charming or angst-ridden , they easily fill their scenes and , fine judges both , never overcook the hysteria . \na spunky , original take on a theme that will resonate with singles of many ages . \nit's a glorious groove that leaves you wanting more . \nmajidi gets uniformly engaging performances from his largely amateur cast . \n . . . a well-observed and disturbing little movie\nfans of nijinsky will savor every minute of cox's work . \neverything you loved about it in 1982 is still there , for everybody who wants to be a kid again , or show it to their own kids . \njagger the actor is someone you want to see again . \nwarm and exotic . \nescapism in its purest form . \nthere is a kind of attentive concern that hoffman brings to his characters , as if he has been giving them private lessons , and now it is time for their first public recital . \na comic gem with some serious sparkles . \nu . s . audiences may find [attal and gainsbourg's] unfamiliar personas give the film an intimate and quaint reality that is a little closer to human nature than what hollywood typically concocts . \n \" cremaster 3 \" should come with the warning \" for serious film buffs only ! \" \nonce again , director jackson strikes a rewarding balance between emotion on the human scale and action/effects on the spectacular scale . \na loving little film of considerable appeal . \nalthough it's a bit smug and repetitive , this documentary engages your brain in a way few current films do . \nflawed but worthy look at life in u . s . relocation camps . \nit's a lovely film with lovely performances by buy and accorsi . \nno one goes unindicted here , which is probably for the best . and if you're not nearly moved to tears by a couple of scenes , you've got ice water in your veins . \na warm , funny , engaging film . \nuses sharp humor and insight into human nature to examine class conflict , adolescent yearning , the roots of friendship and sexual identity . \nhalf submarine flick , half ghost story , all in one criminally neglected film\nentertains by providing good , lively company . \ndazzles with its fully-written characters , its determined stylishness ( which always relates to characters and story ) and johnny dankworth's best soundtrack in years . \nvisually imaginative , thematically instructive and thoroughly delightful , it takes us on a roller-coaster ride from innocence to experience without even a hint of that typical kiddie-flick sentimentality . \nnothing's at stake , just a twisty double-cross you can smell a mile away--still , the derivative nine queens is lots of fun . \nunlike the speedy wham-bam effect of most hollywood offerings , character development -- and more importantly , character empathy -- is at the heart of italian for beginners . \nyou'll gasp appalled and laugh outraged and possibly , watching the spectacle of a promising young lad treading desperately in a nasty sea , shed an errant tear . \nthe band's courage in the face of official repression is inspiring , especially for aging hippies ( this one included ) . \nalthough german cooking does not come readily to mind when considering the world's best cuisine , mostly martha could make deutchland a popular destination for hungry tourists . \na beguiling splash of pastel colors and prankish comedy from disney . \nas surreal as a dream and as detailed as a photograph , as visually dexterous as it is at times imaginatively overwhelming . \n[lawrence bounces] all over the stage , dancing , running , sweating , mopping his face and generally displaying the wacky talent that brought him fame in the first place . \nthe film serves as a valuable time capsule to remind us of the devastating horror suffered by an entire people . \nwhat's surprising about full frontal is that despite its overt self-awareness , parts of the movie still manage to break past the artifice and thoroughly engage you . \nwhether you like rap music or loathe it , you can't deny either the tragic loss of two young men in the prime of their talent or the power of this movie . \n . . . an otherwise intense , twist-and-turn thriller that certainly shouldn't hurt talented young gaghan's resume . \nit provides the grand , intelligent entertainment of a superior cast playing smart people amid a compelling plot . \ncharming and funny ( but ultimately silly ) movie . \nthere's . . . tremendous energy from the cast , a sense of playfulness and excitement that seems appropriate . \nit moves quickly , adroitly , and without fuss ; it doesn't give you time to reflect on the inanity -- and the cold war datedness -- of its premise . \na deep and meaningful film . \nthe film's welcome breeziness and some unbelievably hilarious moments -- most portraying the idiocy of the film industry -- make it mostly worth the trip . \nit's a remarkably solid and subtly satirical tour de force . \nenormously entertaining for moviegoers of any age . \na poignant , artfully crafted meditation on mortality . \na rarity among recent iranian films : it's a comedy full of gentle humor that chides the absurdity of its protagonist's plight . \nnot only is undercover brother as funny , if not more so , than both austin powers films , but it's also one of the smarter , savvier spoofs to come along in some time . \nin a way , the film feels like a breath of fresh air , but only to those that allow it in . \nwoody allen's latest is an ambling , broad comedy about all there is to love -- and hate -- about the movie biz . \nit's a stunning lyrical work of considerable force and truth . \nthe inhospitability of the land emphasizes the spare precision of the narratives and helps to give them an atavistic power , as if they were tales that had been handed down since the beginning of time . \n[ns] directed the stage version of elling , and gets fine performances from his two leads who originated the characters on stage . \nmade me unintentionally famous  as the queasy-stomached critic who staggered from the theater and blacked out in the lobby . but believe it or not , it's one of the most beautiful , evocative works i've seen . \na coda in every sense , the pinochet case splits time between a minute-by-minute account of the british court's extradition chess game and the regime's talking-head survivors . \nlike mike is a winner for kids , and no doubt a winner for lil bow wow , who can now add movies to the list of things he does well . \n[t]his beguiling belgian fable , very much its own droll and delicate little film , has some touching things to say about what is important in life and why . \nhere's yet another studio horror franchise mucking up its storyline with glitches casual fans could correct in their sleep . but taken as a stylish and energetic one-shot , the queen of the damned cannot be said to suck . \nyou won't like roger , but you will quickly recognize him . and that's a big part of why we go to the movies . \nwhile the stoically delivered hokum of hart's war is never fun , it's still a worthy addition to the growing canon of post-saving private ryan tributes to the greatest generation . \nwe know the plot's a little crazy , but it held my interest from start to finish . \na sober and affecting chronicle of the leveling effect of loss . \na fast , funny , highly enjoyable movie . \na celebration of quirkiness , eccentricity , and certain individuals' tendency to let it all hang out , and damn the consequences . \nwriter/director joe carnahan's grimy crime drama is a manual of precinct cliches , but it moves fast enough to cover its clunky dialogue and lapses in logic . \na smart , witty follow-up . \nwhile the ideas about techno-saturation are far from novel , they're presented with a wry dark humor . \nan infectious cultural fable with a tasty balance of family drama and frenetic comedy . \nalthough occasionally static to the point of resembling a stage play , the film delivers a solid mixture of sweetness and laughs . \nit provides an honest look at a community striving to anchor itself in new grounds . \nadd yet another hat to a talented head , clooney's a good director . \nbuilding slowly and subtly , the film , sporting a breezy spontaneity and realistically drawn characterizations , develops into a significant character study that is both moving and wise . \nultimately feels empty and unsatisfying , like swallowing a communion wafer without the wine . \nchilling , well-acted , and finely directed : david jacobson's dahmer . \na swashbuckling tale of love , betrayal , revenge and above all , faith . \n . . . a true delight . \nwithout ever becoming didactic , director carlos carrera expertly weaves this novelistic story of entangled interrelationships and complex morality . \nit's a coming-of-age story we've all seen bits of in other films -- but it's rarely been told with such affecting grace and cultural specificity . \na literate presentation that wonderfully weaves a murderous event in 1873 with murderous rage in 2002 . \nmakes even the claustrophobic on-board quarters seem fun . \nthis is as respectful a film as byatt fans could hope for , though lovers of the book may wonder why it's necessary . \none of the best films of the year with its exploration of the obstacles to happiness faced by five contemporary individuals . . . a psychological masterpiece . \nnot far beneath the surface , this reconfigured tale asks disturbing questions about those things we expect from military epics . \nfor the most part stevens glides through on some solid performances and witty dialogue . \nbroomfield turns his distinctive 'blundering' style into something that could really help clear up the case . \nagainst all odds in heaven and hell , it creeped me out just fine . \nit's refreshing to see a girl-power movie that doesn't feel it has to prove anything . \nit's worth seeing just on the basis of the wisdom , and at times , the startling optimism , of the children . \na rigorously structured and exquisitely filmed drama about a father and son connection that is a brief shooting star of love . \nthis surreal gilliam-esque film is also a troubling interpretation of ecclesiastes . a rewarding work of art for only the most patient and challenge-hungry moviegoers . \na quiet treasure -- a film to be savored . \nmay be far from the best of the series , but it's assured , wonderfully respectful of its past and thrilling enough to make it abundantly clear that this movie phenomenon has once again reinvented itself for a new generation . \na compelling spanish film about the withering effects of jealousy in the life of a young monarch whose sexual passion for her husband becomes an obsession . \nhuston nails both the glad-handing and the choking sense of hollow despair . \nmay not have generated many sparks , but with his affection for astoria and its people he has given his tale a warm glow . \na delirious celebration of the female orgasm . \nexquisitely nuanced in mood tics and dialogue , this chamber drama is superbly acted by the deeply appealing veteran bouquet and the chilling but quite human berling . \nit's fascinating to see how bettany and mcdowell play off each other . \nthe film is beautifully mounted , but , more to the point , the issues are subtly presented , managing to walk a fine line with regard to the question of joan's madness . \nleigh's film is full of memorable performances from top to bottom . \none of the most significant moviegoing pleasures of the year . \njose campanella delivers a loosely autobiographical story brushed with sentimentality but brimming with gentle humor , bittersweet pathos , and lyric moments that linger like snapshots of memory . \ngenerally , clockstoppers will fulfill your wildest fantasies about being a different kind of time traveler , while happily killing 94 minutes . \nthe movie is beautiful to behold and engages one in a sense of epic struggle -- inner and outer -- that's all too rare in hollywood's hastier productions . \nao sair do cinema , eu estava feliz e com saudades de um tempo em que , para mim , a existncia de papai noel era um fato inquestionvel . \nneither parker nor donovan is a typical romantic lead , but they bring a fresh , quirky charm to the formula . \nit's a much more emotional journey than what shyamalan has given us in his past two movies , and gibson , stepping in for bruce willis , is the perfect actor to take us on the trip . \nnot only are the special effects and narrative flow much improved , and daniel radcliffe more emotionally assertive this time around as harry , but the film conjures the magic of author j . k . rowling's books . \njaglom . . . put[s] the audience in the privileged position of eavesdropping on his characters\nbeautifully observed , miraculously unsentimental comedy-drama . \na must-see for the david mamet enthusiast and for anyone who appreciates intelligent , stylish moviemaking . \ncrackerjack entertainment -- nonstop romance , music , suspense and action . \nthe acting , costumes , music , cinematography and sound are all astounding given the production's austere locales . \ngarca bernal and talancn are an immensely appealing couple , and even though their story is predictable , you'll want things to work out . \nfar more imaginative and ambitious than the trivial , cash-in features nickelodeon has made from its other animated tv series . \nthe very definition of the 'small' movie , but it is a good stepping stone for director sprecher . \na gripping , searing portrait of a lost soul trying to find her way through life . \nsuffers from the lack of a compelling or comprehensible narrative . still , as a visual treat , the film is almost unsurpassed . \nso unassuming and pure of heart , you can't help but warmly extend your arms and yell 'safe ! '\nan intriguing cinematic omnibus and round-robin that occasionally is more interesting in concept than in execution . \nso refreshingly incisive is grant that for the first time he'll probably appeal more to guys than to their girlfriends who drag them to this movie for the hugh factor . \nat a time when half the so-called real movies are little more than live-action cartoons , it's refreshing to see a cartoon that knows what it is , and knows the form's history . \nthe magic of the film lies not in the mysterious spring but in the richness of its performances . \nhoffman notches in the nuances of pain , but his smart , edgy voice and waddling profile ( emphasized here ) accent the humor of wilson's plight , and that saves his pathos from drippiness . \nwhat better message than 'love thyself' could young women of any size receive ? \nthe second coming of harry potter is a film far superior to its predecessor . a movie that successfully crushes a best selling novel into a timeframe that mandates that you avoid the godzilla sized soda . \n84 minutes of rolling musical back beat and supercharged cartoon warfare . it's also , clearly , great fun . \n . . . takes the beauty of baseball and melds it with a story that could touch anyone regardless of their familiarity with the sport\nseldahl's barbara is a precise and moving portrait of someone whose world is turned upside down , first by passion and then by illness . \na warm but realistic meditation on friendship , family and affection . \nbyler reveals his characters in a way that intrigues and even fascinates us , and he never reduces the situation to simple melodrama . \nturns potentially forgettable formula into something strangely diverting . \nbogdanovich tantalizes by offering a peep show into the lives of the era's creme de la celluloid . \npeople cinema at its finest . \nthe performances take the movie to a higher level . \nwhat really makes it special is that it pulls us into its world , gives us a hero whose suffering and triumphs we can share , surrounds him with interesting characters and sends us out of the theater feeling we've shared a great adventure . \n . . . a spoof comedy that carries its share of laughs  sometimes a chuckle , sometimes a guffaw and , to my great pleasure , the occasional belly laugh . \nmanages to transcend the sex , drugs and show-tunes plot into something far richer . \ndense with characters and contains some thrilling moments . \na true pleasure . \nlapaglia's ability to convey grief and hope works with weaver's sensitive reactions to make this a two-actor master class . \nreign of fire looks as if it was made without much thought -- and is best watched that way . \naltogether , this is successful as a film , while at the same time being a most touching reconsideration of the familiar masterpiece . \nwe root for [clara and paul] , even like them , though perhaps it's an emotion closer to pity . \nthe best film about baseball to hit theaters since field of dreams . \ninstead of a hyperbolic beat-charged urban western , it's an unpretentious , sociologically pointed slice of life . \nthe film tunes into a grief that could lead a man across centuries . \nif the count of monte cristo doesn't transform caviezel into a movie star , then the game is even more rigged than it was two centuries ago . \n[d]oesn't bother being as cloying or preachy as equivalent evangelical christian movies -- maybe the filmmakers know that the likely audience will already be among the faithful . \nas a tolerable diversion , the film suffices ; a triumph , however , it is not . \nif director michael dowse only superficially understands his characters , he doesn't hold them in contempt . \nif your taste runs to 'difficult' films you absolutely can't miss it . \n[city] reminds us how realistically nuanced a robert de niro performance can be when he is not more lucratively engaged in the shameless self-caricature of 'analyze this' ( 1999 ) and 'analyze that , ' promised ( or threatened ) for later this year . \n . . . a story we haven't seen on the big screen before , and it's a story that we as americans , and human beings , should know . \nlike leon , it's frustrating and still oddly likable . \nall in all , the count of monte cristo is okay , but it is surely no classic , like the novel upon which it is based . \nif you can stomach the rough content , it's worth checking out for the performances alone . \nlooking aristocratic , luminous yet careworn in jane hamilton's exemplary costumes , rampling gives a performance that could not be improved upon . \n' . . . mafia , rap stars and hood rats butt their ugly heads in a regurgitation of cinematic violence that gives brutal birth to an unlikely , but likable , hero . '\non this tricky topic , tadpole is very much a step in the right direction , with its blend of frankness , civility and compassion . \nfun , flip and terribly hip bit of cinematic entertainment . \nmontias . . . pumps a lot of energy into his nicely nuanced narrative and surrounds himself with a cast of quirky -- but not stereotyped -- street characters . \nfalls neatly into the category of good stupid fun . \nthe film's performances are thrilling . \neven in its most tedious scenes , russian ark is mesmerizing . \nthe continued good chemistry between carmen and juni is what keeps this slightly disappointing sequel going , with enough amusing banter -- blessedly curse-free -- to keep both kids and parents entertained . \nreggio's continual visual barrage is absorbing as well as thought-provoking . \nunfortunately , it appears that [jackie] chan's us influence is starting to show in his hong kong films . \nit all adds up to good fun . \na big , gorgeous , sprawling swashbuckler that delivers its diversions in grand , uncomplicated fashion . \nthe wanton slipperiness of * corpus and its amiable jerking and reshaping of physical time and space would make it a great piece to watch with kids and use to introduce video as art . \na stunning and overwhelmingly cogent case for kissinger as a calculating war criminal . \nsade is an engaging look at the controversial eponymous and fiercely atheistic hero . \na quiet , pure , elliptical film\nkinnear doesn't aim for our sympathy , but rather delivers a performance of striking skill and depth . \nthe subtle strength of \" elling \" is that it never loses touch with the reality of the grim situation . \na study in shades of gray , offering itself up in subtle plot maneuvers . . . \nthe format gets used best . . . to capture the dizzying heights achieved by motocross and bmx riders , whose balletic hotdogging occasionally ends in bone-crushing screwups . \nhas a lot of the virtues of eastwood at his best . \nchilling but uncommercial look into the mind of jeffrey dahmer , serial killer . \nthough it's become almost redundant to say so , major kudos go to leigh for actually casting people who look working-class . \nit deserves to be seen by anyone with even a passing interest in the events shaping the world beyond their own horizons . \na movie that reminds us of just how exciting and satisfying the fantasy cinema can be when it's approached with imagination and flair . \nthanks to scott's charismatic roger and eisenberg's sweet nephew , roger dodger is one of the most compelling variations on in the company of men . \nnine queens is not only than a frighteningly capable debut and genre piece , but also a snapshot of a dangerous political situation on the verge of coming to a head . \nit's the chemistry between the women and the droll scene-stealing wit and wolfish pessimism of anna chancellor that makes this \" two weddings and a funeral \" fun . \nwill amuse and provoke adventurous adults in specialty venues . \nyou don't have to know about music to appreciate the film's easygoing blend of comedy and romance . \nthird time's the charm . . . yeah , baby ! \na film about a young man finding god that is accessible and touching to the marrow . \nfor the first time in years , de niro digs deep emotionally , perhaps because he's been stirred by the powerful work of his co-stars . \nthe film's snags and stumblings are more than compensated for by its wryly subversive tone . \ninside the film's conflict-powered plot there is a decent moral trying to get out , but it's not that , it's the tension that keeps you in your seat . affleck and jackson are good sparring partners . \nthe old-world- meets-new mesh is incarnated in the movie's soundtrack , a joyful effusion of disco bollywood that , by the end of monsoon wedding , sent my spirit soaring out of the theater . \nan effectively creepy , fear-inducing ( not fear-reducing ) film from japanese director hideo nakata , who takes the superstitious curse on chain letters and actually applies it . \nhaving had the good sense to cast actors who are , generally speaking , adored by the movie-going public , khouri then gets terrific performances from them all . \na subtle and well-crafted ( for the most part ) chiller . \nwarm water under a red bridge is a quirky and poignant japanese film that explores the fascinating connections between women , water , nature , and sexuality . \nalthough laced with humor and a few fanciful touches , the film is a refreshingly serious look at young women . \nthe best revenge may just be living well because this film , unlike other dumas adaptations , is far more likened to a treasure than a lengthy jail sentence . \na delectable and intriguing thriller filled with surprises , read my lips is an original . this is a story of two misfits who don't stand a chance alone , but together they are magnificent . \nhighbrow self-appointed guardians of culture need not apply , but those who loved cool as ice have at last found a worthy follow-up . \none of creepiest , scariest movies to come along in a long , long time , easily rivaling blair witch or the others . \nmaud and roland's search for an unknowable past makes for a haunting literary detective story , but labute pulls off a neater trick in possession : he makes language sexy . \npacino is brilliant as the sleep-deprived dormer , his increasing weariness as much existential as it is physical . \nrare birds has more than enough charm to make it memorable . \nmanages to be sweet and wickedly satisfying at the same time . \nthis nickleby thing might have more homosexual undertones than an eddie murphy film . and just when you think it can't get any more gay , in pops nathan lane . \nno sophomore slump for director sam mendes , who segues from oscar winner to oscar-winning potential with a smooth sleight of hand . \nthe movie isn't just hilarious : it's witty and inventive , too , and in hindsight , it isn't even all that dumb . \nold-form moviemaking at its best . \nahhhh . . . revenge is sweet ! \nyakusho and shimizu . . . create engaging characterizations in imamura's lively and enjoyable cultural mix . \nyou will emerge with a clearer view of how the gears of justice grind on and the death report comes to share airtime alongside the farm report . \nruzowitzky has taken this mothball-y stuff and made a rather sturdy , old-fashioned entertainment out of it . \nin spite of good housekeeping's unsavory characters and wwf mentality , this white trash war of the roses is a surprisingly engaging film . \ncollateral damage finally delivers the goods for schwarzenegger fans . \nthere has always been something likable about the marquis de sade . \nas a first-time director , paxton has tapped something in himself as an actor that provides frailty with its dark soul . \nfor the most part , director anne-sophie birot's first feature is a sensitive , extraordinarily well-acted drama . \nby the time we learn that andrew's turnabout is fair play is every bit as awful as borchardt's coven , we can enjoy it anyway . \nthis riveting world war ii moral suspense story deals with the shadow side of american culture : racial prejudice in its ugly and diverse forms . \na tender , heartfelt family drama . \na difficult , absorbing film that manages to convey more substance despite its repetitions and inconsistencies than do most films than are far more pointed and clear . \nfor the most part , it's a work of incendiary genius , steering clear of knee-jerk reactions and quick solutions . \nit has the charm of the original american road movies , feasting on the gorgeous , ramshackle landscape of the filmmaker's motherland . \n[chaiken's] talent lies in an evocative , accurate observation of a distinctive milieu and in the lively , convincing dialogue she creates for her characters . \nin all , this is a watchable movie that's not quite the memorable experience it might have been . \nhuppert's superbly controlled display of murderous vulnerability ensures that malice has a very human face . \nmy thoughts were focused on the characters . that is a compliment to kuras and miller . if i had been thinking about the visual medium , they would have been doing something wrong . \none of the more intelligent children's movies to hit theaters this year . \nremember the kind of movie we were hoping \" ecks vs . sever \" or \" xxx \" was going to be ? this is it . \nnot for the prurient or squeamish , it's a daring if overlong examination of an idolized culture , self-loathing and sexual politics . \na cartoon that's truly cinematic in scope , and a story that's compelling and heartfelt -- even if the heart belongs to a big , four-legged herbivore . \nthe film's almost unbearable portrait of sadness and grief transcends its specific story to speak to the ways in which need , history and presumption tangle , and sometimes destroy , blood ties . \ntravels a fascinating arc from hope and euphoria to reality and disillusionment . \npolished , well-structured film . \nthere's something auspicious , and daring , too , about the artistic instinct that pushes a majority-oriented director like steven spielberg to follow a . i . with this challenging report so liable to unnerve the majority . \nfor anyone unfamiliar with pentacostal practices in general and theatrical phenomenon of hell houses in particular , it's an eye-opener . \nit seems like i have been waiting my whole life for this movie and now i can't wait for the sequel . \nit's a bit disappointing that it only manages to be decent instead of dead brilliant . \napesar de seus graves problemas , o filme consegue entreter . \nan operatic , sprawling picture that's entertainingly acted , magnificently shot and gripping enough to sustain most of its 170-minute length . \nthe far future may be awesome to consider , but from period detail to matters of the heart , this film is most transporting when it stays put in the past . \nit inspires a continuing and deeply satisfying awareness of the best movies as monumental 'picture shows . '\nawesome creatures , breathtaking scenery , and epic battle scenes add up to another 'spectacular spectacle . '\nby candidly detailing the politics involved in the creation of an extraordinary piece of music , [jones] calls our attention to the inherent conflict between commerce and creativity . \nit's unnerving to see recoing's bizzarre reaction to his unemployment . good film , but very glum . \nmuch as we might be interested in gratuitous sexualization , haneke has a different objective in mind--namely the implications of our craving for fake stimulation . \ndazzling in its complexity , disturbing for its extraordinary themes , the piano teacher is a film that defies categorisation . it haunts , horrifies , startles and fascinates ; it is impossible to look away . ah yes , and then there's the music . . . \nit has charm to spare , and unlike many romantic comedies , it does not alienate either gender in the audience . \nalthough jackson is doubtless reserving the darkest hours for the return of the king , we long for a greater sense of urgency in the here and now of the two towers . \nit is great summer fun to watch arnold and his buddy gerald bounce off a quirky cast of characters . \nbleakly funny , its characters all the more touching for refusing to pity or memorialize themselves . \nit will not appeal to the impatient , but those who like long books and movies will admire the way it accumulates power and depth . \nthis flick is about as cool and crowd-pleasing as a documentary can get . \n \" the ring \" is pretty much an english-language copy of the film that inspired it , and it carries the same strengths and flaws . \nthe wild thornberrys movie is a jolly surprise . \ngriffiths proves she's that rare luminary who continually raises the standard of her profession . \nprancing his way through the tailor-made part of a male hooker approaching the end of his vitality , jagger obviously relishes every self-mocking moment . \nsexy and romantic . \noffers much to enjoy . . . and a lot to mull over in terms of love , loyalty and the nature of staying friends . \nan important movie , a reminder of the power of film to move us and to make us examine our values . \nis this love or is it masochism ? binoche makes it interesting trying to find out . \na pleasant , if forgettable , romp of a film . \nthe mesmerizing performances of the leads keep the film grounded and keep the audience riveted . \nworth watching for dong jie's performance -- and for the way it documents a culture in the throes of rapid change . \ntwo hours fly by -- opera's a pleasure when you don't have to endure intermissions -- and even a novice to the form comes away exhilarated . \nit's one heck of a character study -- not of hearst or davies but of the unique relationship between them . \ncandid camera on methamphetamines . \na subject like this should inspire reaction in its audience ; the pianist does not . \nequilibrium is what george orwell might have imagined had today's mood-altering drug therapy been envisioned by chemists in 1949 . \ncreepy , authentic and dark . this disturbing bio-pic is hard to forget . \nmartin and barbara are complex characters -- sometimes tender , sometimes angry -- and the delicate performances by sven wollter and viveka seldahl make their hopes and frustrations vivid . \na twisty , moody slice of southern gothic . . . \nit's so good that you can practically see the hollywood 'suits' trying to put together the cast and filmmaking team for the all-too -inevitable american remake . \nthe weight of the piece , the unerring professionalism of the chilly production , and the fascination embedded in the lurid topic prove recommendation enough . \nan absurdist comedy about alienation , separation and loss . \n'they' begins and ends with scenes so terrifying i'm still stunned . and i've decided to leave a light on every night from now on . \nthis tenth feature is a big deal , indeed -- at least the third-best , and maybe even a notch above the previous runner-up , nicholas meyer's star trek vi : the undiscovered country . \n . . . with \" the bourne identity \" we return to the more traditional action genre . \nbeneath clouds is a succinct low-budget film whose compelling characters and intelligent script are exactly what was missing from rabbit-proof fence . \nthe film is a contrivance , as artificial as the video games japanese teens play in a nightclub sequence , but it's an enjoyable one . \nholm . . . embodies the character with an effortlessly regal charisma . \nit is amusing , and that's all it needs to be . \namong the year's most intriguing explorations of alientation . \na full world has been presented onscreen , not some series of carefully structured plot points building to a pat resolution . \nseldom has a movie so closely matched the spirit of a man and his work . \naudrey tatou has a knack for picking roles that magnify her outrageous charm , and in this literate french comedy , she's as morning-glory exuberant as she was in amlie . \nthe movie has an infectious exuberance that will engage anyone with a passing interest in the skate/surf culture , the l . a . beach scene and the imaginative ( and sometimes illegal ) ways kids can make a playground out of the refuse of adults . \neven if you don't think [kissinger's] any more guilty of criminal activity than most contemporary statesmen , he'd sure make a courtroom trial great fun to watch . \nthe story and structure are well-honed . fresnadillo's dark and jolting images have a way of plying into your subconscious like the nightmare you had a week ago that won't go away . \nit's made with deftly unsettling genre flair . \nit just may inspire a few younger moviegoers to read stevenson's book , which is a treasure in and of itself . \nfunny but perilously slight . \noverall very good for what it's trying to do . \nforgettable horror -- more gory than psychological -- with a highly satisfying quotient of friday-night excitement and milla power . \nramsay , as in ratcatcher , remains a filmmaker with an acid viewpoint and a real gift for teasing chilly poetry out of lives and settings that might otherwise seem drab and sordid . \nit may seem long at 110 minutes if you're not a fan , because it includes segments of 12 songs at a reunion concert . \na lean , deftly shot , well-acted , weirdly retro thriller that recalls a raft of '60s and '70s european-set spy pictures . \nit proves quite compelling as an intense , brooding character study . \nthe son's room is a triumph of gentility that earns its moments of pathos . \nmorton uses her face and her body language to bring us morvern's soul , even though the character is almost completely deadpan . \nthe film may appear naked in its narrative form . . . but it goes deeper than that , to fundamental choices that include the complexity of the catholic doctrine\na superbly acted and funny/gritty fable of the humanizing of one woman at the hands of the unseen forces of fate . \none of the smartest takes on singles culture i've seen in a long time . \nthere is a fabric of complex ideas here , and feelings that profoundly deepen them . \ncq's reflection of artists and the love of cinema-and-self suggests nothing less than a new voice that deserves to be considered as a possible successor to the best european directors . \nthe emotions are raw and will strike a nerve with anyone who's ever had family trauma . \nholy mad maniac in a mask , splat-man ! good old-fashioned slash-and-hack is back ! \nas unseemly as its title suggests . \nby the end of the movie , you're definitely convinced that these women are spectacular . \nthe french are rather good at this kind of thing , unlike the americans , who have a passion for musketeers , only to spoof them . \nthe fly-on-the-wall method used to document rural french school life is a refreshing departure from the now more prevalent technique of the docu-makers being a visible part of their work . \nit's an offbeat treat that pokes fun at the democratic exercise while also examining its significance for those who take part . \nallows us to hope that nolan is poised to embark a major career as a commercial yet inventive filmmaker . \nmaneuvers skillfully through the plot's hot brine -- until it's undone by the sogginess of its contemporary characters , and actors . \nit has the ability to offend and put off everyone , but it holds you with its outrageousness . \nanchored by friel and williams's exceptional performances , the film's power lies in its complexity . nothing is black and white . \nit's a charming and often affecting journey . \npsychologically savvy . \nno screen fantasy-adventure in recent memory has the showmanship of clones' last 45 minutes . \na poignant and compelling story about relationships , food of love takes us on a bumpy but satisfying journey of the heart . \nbirot creates a drama with such a well-defined sense of place and age -- as in , 15 years old -- that the torments and angst become almost as operatic to us as they are to her characters . \nthe chateau cleverly probes the cross-cultural differences between gauls and yanks . \nnot since tom cruise in risky business has an actor made such a strong impression in his underwear . \naside from minor tinkering , this is the same movie you probably loved in 1994 , except that it looks even better . \nuses high comedy to evoke surprising poignance . \nit confirms fincher's status as a film maker who artfully bends technical know-how to the service of psychological insight . \nvera's three actors -- moll , gil and bardem -- excel in insightful , empathetic performances . \na marvel like none you've seen . \nwith tightly organized efficiency , numerous flashbacks and a constant edge of tension , miller's film is one of 2002's involvingly adult surprises . \nmr . tsai is a very original artist in his medium , and what time is it there ? should be seen at the very least for its spasms of absurdist humor . \nwriter/director mark romanek spotlights the underlying caste system in america . it's a scathing portrayal . \nthis is a good script , good dialogue , funny even for adults . the characters are interesting and often very creatively constructed from figure to backstory . the film will play equally well on both the standard and giant screens . \nmoody , heartbreaking , and filmed in a natural , unforced style that makes its characters seem entirely convincing even when its script is not . \nnot a film to rival to live , but a fine little amuse-bouche to keep your appetite whetted . \ntrue tale of courage -- and complicity -- at auschwitz is a harrowing drama that tries to tell of the unspeakable . \ngives you the steady pulse of life in a beautiful city viewed through the eyes of a character who , in spite of tragic loss and increasing decrepitude , knows in his bones that he is one of the luckiest men alive . \nmacdowell , whose wifty southern charm has anchored lighter affairs . . . brings an absolutely riveting conviction to her role . \nwhat time is it there ? is not easy . it haunts you , you can't forget it , you admire its conception and are able to resolve some of the confusions you had while watching it . \nif you are an actor who can relate to the search for inner peace by dramatically depicting the lives of others onstage , then esther's story is a compelling quest for truth . \nalthough the level of the comedy declines as the movie proceeds , there's no denying the fun of watching de niro and crystal having fun . \nclaude chabrol has here a thriller without thrills , but that's okay . \nfor movie lovers as well as opera lovers , tosca is a real treat . \nunflinchingly bleak and desperate\nmoretti's compelling anatomy of grief and the difficult process of adapting to loss . \nchallenging , intermittently engrossing and unflaggingly creative . but it's too long and too convoluted and it ends in a muddle . \nthe vivid lead performances sustain interest and empathy , but the journey is far more interesting than the final destination . \na painfully funny ode to bad behavior . \n'easily my choice for one of the year's best films . '\ncharles' entertaining film chronicles seinfeld's return to stand-up comedy after the wrap of his legendary sitcom , alongside wannabe comic adams' attempts to get his shot at the big time . \nthat dogged good will of the parents and 'vain' jia's defoliation of ego , make the film touching despite some doldrums . \nthe movie is for fans who can't stop loving anime , and the fanatical excess built into it . \nthe volatile dynamics of female friendship is the subject of this unhurried , low-key film that is so off-hollywood that it seems positively french in its rhythms and resonance . \na densely constructed , highly referential film , and an audacious return to form that can comfortably sit among jean-luc godard's finest work . \nmichael gerbosi's script is economically packed with telling scenes . \na strangely compelling and brilliantly acted psychological drama . \ncandid and comfortable ; a film that deftly balances action and reflection as it lets you grasp and feel the passion others have for their work . \nopen-minded kids -- kids who read , kids who dream -- will be comforted by the way it deals with big issues like death and destiny . \nbennett's naturalistic performance speaks volumes more truth than any 'reality' show , and anybody contemplating their own drastic life changes should watch some body first . \n . . . a good , if not entirely fresh , look at war . \n the film is powerful , accessible and funny . you won't miss its messages , but you'll be entertained as well . \n'frailty \" starts out like a typical bible killer story , but it turns out to be significantly different ( and better ) than most films with this theme . \nif you dig on david mamet's mind tricks . . . rent this movie and enjoy ! \nthe primitive force of this film seems to bubble up from the vast collective memory of the combatants . it's like watching a nightmare made flesh . \nit is the sheer , selfish , wound-licking , bar-scrapping doggedness of leon's struggle to face and transmute his demons that makes the movie a spirited and touching occasion , despite its patchy construction . \na gorgeous , high-spirited musical from india that exquisitely blends music , dance , song , and high drama . \nit's hard to imagine alan arkin being better than he is in this performance . \nfor those who pride themselves on sophisticated , discerning taste , this might not seem like the proper cup of tea , however it is almost guaranteed that even the stuffiest cinema goers will laugh their * * * off for an hour-and-a-half . \ndespite the 2-d animation , the wild thornberrys movie makes for a surprisingly cinematic experience . \n . . . a fun little timewaster , helped especially by the cool presence of jean reno . \n[majidi] makes us think twice about immigrants we see around us every day . \nthough only 60 minutes long , the film is packed with information and impressions . \ni have no way of knowing exactly how much is exaggeration , but i've got a creepy feeling that the film is closer to the mark than i want to believe . \nimmersing us in the endlessly inventive , fiercely competitive world of hip-hop djs , the project is sensational and revelatory , even if scratching makes you itch . \nfurther proof that the epicenter of cool , beautiful , thought-provoking foreign cinema is smack-dab in the middle of dubya's axis of evil . \nthere's really only one good idea in this movie , but the director runs with it and presents it with an unforgettable visual panache . \na simple , but gritty and well-acted ensemble drama that encompasses a potent metaphor for a country still dealing with its fascist past . \nlovely and poignant . puts a human face on a land most westerners are unfamiliar with . \ni can't say that i liked homeboy ; it'd be more accurate to say that i found it intriguing , bizarre , dogma-like in spots - and quite truthful , in its way . \ndisplaying about equal amounts of naivet , passion and talent , beneath clouds establishes sen as a filmmaker of considerable potential . \nthe vitality of the actors keeps the intensity of the film high , even as the strafings blend together . \nnot since japanese filmmaker akira kurosawa's ran have the savagery of combat and the specter of death been visualized with such operatic grandeur . \nwe learn a lot about dying coral and see a lot of life on the reef . \nif the first men in black was money , the second is small change . but it still jingles in the pocket . it's fun lite . \npassable entertainment , but it's the kind of motion picture that won't make much of a splash when it's released , and will not be remembered long afterwards . \ni just loved every minute of this film . \nthis is a winning ensemble comedy that shows canadians can put gentle laughs and equally gentle sentiments on the button , just as easily as their counterparts anywhere else in the world . \njust as moving , uplifting and funny as ever . \nmy wife is an actress is an utterly charming french comedy that feels so american in sensibility and style it's virtually its own hollywood remake . \nit will grip even viewers who aren't interested in rap , as it cuts to the heart of american society in an unnerving way . \na muckraking job , the cinematic equivalent of a legal indictment , and a fairly effective one at that . \na tender , witty , captivating film about friendship , love , memory , trust and loyalty . \nbelongs to daniel day-lewis as much as it belongs to martin scorsese ; it's a memorable performance in a big , brassy , disturbing , unusual and highly successful film . \nan exhilarating futuristic thriller-noir , minority report twists the best of technology around a gripping story , delivering a riveting , pulse intensifying escapist adventure of the first order\na psychological thriller with a genuinely spooky premise and an above-average cast , actor bill paxton's directing debut is a creepy slice of gothic rural americana . \nwhile locals will get a kick out of spotting cleveland sites , the rest of the world will enjoy a fast-paced comedy with quirks that might make the award-winning coen brothers envious . \npumpkin takes an admirable look at the hypocrisy of political correctness , but it does so with such an uneven tone that you never know when humor ends and tragedy begins . \nif you're hard up for raunchy college humor , this is your ticket right here . \nfew films capture so perfectly the hopes and dreams of little boys on baseball fields as well as the grown men who sit in the stands . \ncorny , schmaltzy and predictable , but still manages to be kind of heartwarming , nonetheless . it's the perfect kind of film to see when you don't want to use your brain . at all . \nwhile it regards 1967 as the key turning point of the 20th century , and returns again and again to images of dissidents in the streets , it's alarmingly current . \nfeature debuter d . j . caruso directs a crack ensemble cast , bringing screenwriter tony gayton's narcotics noir to life . \nevery dance becomes about seduction , where backstabbing and betrayals are celebrated , and sex is currency . \nharris commands the screen , using his frailty to suggest the ravages of a life of corruption and ruthlessness . \nstephen rea , aidan quinn , and alan bates play desmond's legal eagles , and when joined by brosnan , the sight of this grandiloquent quartet lolling in pretty irish settings is a pleasant enough thing , 'tis . \ndirector of photography benoit delhomme shot the movie in delicious colors , and the costumes and sets are grand . \nthe movie's relatively simple plot and uncomplicated morality play well with the affable cast . \nthe film is quiet , threatening and unforgettable . \nthis illuminating documentary transcends our preconceived vision of the holy land and its inhabitants , revealing the human complexities beneath . \ndeliriously funny , fast and loose , accessible to the uninitiated , and full of surprises\ntrademark american triteness and simplicity are tossed out the window with the intelligent french drama that deftly explores the difficult relationship between a father and son . \none from the heart . \nmore concerned with sade's ideas than with his actions . the movie achieves as great an impact by keeping these thoughts hidden as . . . [quills] did by showing them . \nan entertaining , colorful , action-filled crime story with an intimate heart . \nwhile undisputed isn't exactly a high , it is a gripping , tidy little movie that takes mr . hill higher than he's been in a while . \nthe most compelling wiseman epic of recent years . \nthe socio-histo-political treatise is told in earnest strides . . . [and] personal illusion is deconstructed with poignancy . \nit's great escapist fun that recreates a place and time that will never happen again . \ngood car chases , great fight scenes , and a distinctive blend of european , american and asian influences . \nliotta put on 30 pounds for the role , and has completely transformed himself from his smooth , goodfellas image . \na woman's pic directed with resonance by ilya chaiken . \ncontando com uma premissa curiosa , o filme mergulha o espectador em um clima de forte suspense , culminando em um desfecho que certamente fica na memria . \n[grant's] bumbling magic takes over the film , and it turns out to be another winning star vehicle . \n . . . brian de palma is utterly mad : cinema mad , set-piece mad , style mad . it's a beautiful madness . \ngenerally provides its target audience of youngsters enough stimulating eye and ear candy to make its moral medicine go down . \nthere are some wonderfully fresh moments that smooth the moral stiffness with human kindness and hopefulness . \na grimly competent and stolid and earnest military courtroom drama . \nescaping the studio , piccoli is warmly affecting and so is this adroitly minimalist movie . \nvery psychoanalytical -- provocatively so -- and also refreshingly literary . \na gorgeous , witty , seductive movie . \nthe special effects and many scenes of weightlessness look as good or better than in the original , while the oscar-winning sound and james horner's rousing score make good use of the hefty audio system . \non the heels of the ring comes a similarly morose and humorless horror movie that , although flawed , is to be commended for its straight-ahead approach to creepiness . \nwith rabbit-proof fence , noyce has tailored an epic tale into a lean , economical movie . \n[a]n utterly charming and hilarious film that reminded me of the best of the disney comedies from the 60s . \npreaches to two completely different choirs at the same time , which is a pretty amazing accomplishment . \nthanks to haynes' absolute control of the film's mood , and buoyed by three terrific performances , far from heaven actually pulls off this stylistic juggling act . \nbirthday girl is an amusing joy ride , with some surprisingly violent moments . \nmore romantic , more emotional and ultimately more satisfying than the teary-eyed original . \nan appealingly juvenile trifle that delivers its share of laughs and smiles . \nwriter-director's mehta's effort has tons of charm and the whimsy is in the mixture , the intoxicating masala , of cultures and film genres . \nthe draw [for \" big bad love \" ] is a solid performance by arliss howard . \nit gets onto the screen just about as much of the novella as one could reasonably expect , and is engrossing and moving in its own right . \nthe terrific and bewilderingly underrated campbell scott gives a star performance that is nothing short of mesmerizing . \ncool ? this movie is a snow emergency . \nlike mike isn't interested in recycling old cliches . it wants to tweak them with a taste of tangy new humor . \nsmith is careful not to make fun of these curious owners of architectural oddities . instead , he shows them the respect they are due . \na mess when it comes to the characters and writing . . . but works its way underneath the skin like few movies have in recent memory . \ndrops you into a dizzying , volatile , pressure-cooker of a situation that quickly snowballs out of control , while focusing on the what much more than the why . \nzhang . . . has done an amazing job of getting realistic performances from his mainly nonprofessional cast . \na solid examination of the male midlife crisis . \nif you're in the mood for a bollywood film , here's one for you . \nas the two leads , lathan and diggs are charming and have chemistry both as friends and lovers . \na beguiling , slow-moving parable about the collision of past and present on a remote seacoast in iran . \nmy big fat greek wedding uses stereotypes in a delightful blend of sweet romance and lovingly dished out humor . \nkept aloft largely by a comically adept ensemble . \nthe sort of film that makes me miss hitchcock , but also feel optimistic that there's hope for popular cinema yet . \nfirst-time writer-director serry shows a remarkable gift for storytelling with this moving , effective little film . \nit cuts to the core of what it actually means to face your fears , to be a girl in a world of boys , to be a boy truly in love with a girl , and to ride the big metaphorical wave that is life -- wherever it takes you . \natom egoyan has conjured up a multilayered work that tackles any number of fascinating issues\nessentially an exceptionally well-written , well-edited , well-directed , well-acted , bald rip-off of aliens . \n'de niro . . . is a veritable source of sincere passion that this hollywood contrivance orbits around . '\nthe whole is quite entertaining , but despite its virtues , there is an unsettled feeling to the film . \nwhile its careful pace and seemingly opaque story may not satisfy every moviegoer's appetite , the film's final scene is soaringly , transparently moving . \nhardly a masterpiece , but it introduces viewers to a good charitable enterprise and some interesting real people . \nbased on a devilishly witty script by heather mcgowan and niels mueller , the film gets great laughs , but never at the expense of its characters\nit's somewhat clumsy and too lethargically paced -- but its story about a mysterious creature with psychic abilities offers a solid build-up , a terrific climax , and some nice chills along the way . \nif you've ever wondered what an ending without the input of studio executives or test audiences would look like , here it is . \nexciting and direct , with ghost imagery that shows just enough to keep us on our toes . \nwhether writer-director anne fontaine's film is a ghost story , an account of a nervous breakdown , a trip down memory lane , all three or none of the above , it is as seductive as it is haunting . \nwhat the film lacks in general focus it makes up for in compassion , as corcuera manages to find the seeds of hope in the form of collective action . \nif you enjoy more thoughtful comedies with interesting conflicted characters ; this one is for you . \nthe quality of the art combined with the humor and intelligence of the script allow the filmmakers to present the biblical message of forgiveness without it ever becoming preachy or syrupy . \nthis film seems thirsty for reflection , itself taking on adolescent qualities . \nanother one of those estrogen overdose movies like \" divine secrets of the ya ya sisterhood , \" except that the writing , acting and character development are a lot better . \na breezy romantic comedy that has the punch of a good sitcom , while offering exceptionally well-detailed characters . \na romantic comedy enriched by a sharp eye for manners and mores . \nviewers of \" the ring \" are more likely to remember the haunting images than the plot holes . \na delightful coming-of-age story . \none of those energetic surprises , an original that pleases almost everyone who sees it . \nan exquisitely crafted and acted tale . \na taut psychological thriller that doesn't waste a moment of its two-hour running time . \njones . . . does offer a brutal form of charisma . \ndespite its title , punch-drunk love is never heavy-handed . the jabs it employs are short , carefully placed and dead-center . \nthere's a wickedly subversive bent to the best parts of birthday girl . \nlikely to expertly drum up repressed teenage memories in any viewer . \nblanchett's performance confirms her power once again . \n . . . a magnificent drama well worth tracking down . \na good piece of work more often than not . \nthe movie understands like few others how the depth and breadth of emotional intimacy give the physical act all of its meaning and most of its pleasure . \nwhat distinguishes time of favor from countless other thrillers is its underlying concern with the consequences of words and with the complicated emotions fueling terrorist acts . \nsmart , provocative and blisteringly funny . \nnothing is sacred in this gut-buster . \nthe movie occasionally threatens to become didactic , but it's too grounded in the reality of its characters to go over the edge . a touch of humor or an unexpected plot twist always pulls it back . \nfilmmakers who can deftly change moods are treasures and even marvels . so , too , is this comedy about mild culture clashing in today's new delhi . \nif steven soderbergh's 'solaris' is a failure it is a glorious failure . \n \" mostly martha \" is a bright , light modern day family parable that wears its heart on its sleeve for all to see . \nit's a scattershot affair , but when it hits its mark it's brilliant . \na pleasant enough romance with intellectual underpinnings , the kind of movie that entertains even as it turns maddeningly predictable . \nfeaturing a dangerously seductive performance from the great daniel auteuil , \" sade \" covers the same period as kaufmann's \" quills \" with more unsettlingly realistic results . \na spellbinding african film about the modern condition of rootlessness , a state experienced by millions around the globe . \nit's a work by an artist so in control of both his medium and his message that he can improvise like a jazzman . \na moody , multi-dimensional love story and sci-fi mystery , solaris is a thought-provoking , haunting film that allows the seeds of the imagination to germinate . \na very well-made , funny and entertaining picture . \na giggle-inducing comedy with snappy dialogue and winning performances by an unlikely team of oscar-winners : susan sarandon and goldie hawn . \nits maker , steven spielberg , hasn't had so much fun in two decades , since he was schlepping indiana jones around the globe in search of a giant misplaced ashtray . \noscar wilde's masterpiece , the importance of being earnest , may be the best play of the 19th century . it's so good that its relentless , polished wit can withstand not only inept school productions , but even oliver parker's movie adaptation . \nthe movie does a good job of laying out some of the major issues that we encounter as we journey through life . \nbrilliantly explores the conflict between following one's heart and following the demands of tradition . \nthis remake gets all there is to get out of a peculiar premise with promise : al pacino loathing robin williams . \nthe next generation of mob movie . part low rent godfather . part three stooges . \nlan yu is at times too restrained , yet there are moments it captures the erotics of intimacy in a way that makes most american love stories look downright unfree . \na thinly veiled look at different aspects of chinese life clashing with each other . \nlight years/ several warp speeds/ levels and levels of dilithium crystals better than the pitiful insurrection . which isn't to say that it's the equal of some of its predecessors . \ndelightfully rendered\nif this story must be told and retold -- and indeed it must -- then the grey zone is to be lauded for finding a new and ingenious angle . \nthe lion king was a roaring success when it was released eight years ago , but on imax it seems better , not just bigger . \na gripping movie , played with performances that are all understated and touching . \nthe piece plays as well as it does thanks in large measure to anspaugh's three lead actresses . \nthe inspirational screenplay by mike rich covers a lot of ground , perhaps too much , but ties things together , neatly , by the end . \nnot the kind of film that will appeal to a mainstream american audience , but there is a certain charm about the film that makes it a suitable entry into the fest circuit . \ndirector andrew niccol . . . demonstrates a wry understanding of the quirks of fame . his healthy sense of satire is light and fun . . . . \nabout a manga-like heroine who fights back at her abusers , it's energetic and satisfying if not deep and psychological . \nthis is human comedy at its most amusing , interesting and confirming . \nan artful , intelligent film that stays within the confines of a well-established genre . \nmajidi is an unconventional storyteller , capable of finding beauty in the most depressing places . \nrichard gere and diane lane put in fine performances as does french actor oliver martinez . \nthe minor figures surrounding [bobby] . . . form a gritty urban mosaic . \nthis is wild surreal stuff , but brilliant and the camera just kind of sits there and lets you look at this and its like you're going from one room to the next and none of them have any relation to the other . \nit's a demented kitsch mess ( although the smeary digital video does match the muddled narrative ) , but it's savvy about celebrity and has more guts and energy than much of what will open this year . \nthere is nothing outstanding about this film , but it is good enough and will likely be appreciated most by sailors and folks who know their way around a submarine . \nall-in-all , the film is an enjoyable and frankly told tale of a people who live among us , but not necessarily with us . \nan interesting story with a pertinent ( cinematically unique ) message , told fairly well and scored to perfection , i found myself struggling to put my finger on that elusive \" missing thing . \" \na movie with a real anarchic flair . \na welcome relief from baseball movies that try too hard to be mythic , this one is a sweet and modest and ultimately winning story . \na crisp psychological drama [and] a fascinating little thriller that would have been perfect for an old \" twilight zone \" episode . \nit has more than a few moments that are insightful enough to be fondly remembered in the endlessly challenging maze of moviegoing . \nopening with some contrived banter , cliches and some loose ends , the screenplay only comes into its own in the second half . \nan uncluttered , resonant gem that relays its universal points without lectures or confrontations . \n'[the cockettes] provides a window into a subculture hell-bent on expressing itself in every way imaginable . '\na smart , steamy mix of road movie , coming-of-age story and political satire . \nthe modern-day royals have nothing on these guys when it comes to scandals . it's only in fairy tales that princesses that are married for political reason live happily ever after . \na terrific b movie -- in fact , the best in recent memory . \n \" birthday girl \" is an actor's movie first and foremost . \ni walked away from this new version of e . t . just as i hoped i would -- with moist eyes . \nfor devotees of french cinema , safe conduct is so rich with period minutiae it's like dying and going to celluloid heaven . \nwhat's really so appealing about the characters is their resemblance to everyday children . \nshamelessly resorting to pee-related sight gags that might even cause tom green a grimace ; still , myer's energy and the silliness of it all eventually prevail\nan absurdist spider web . \nif you're as happy listening to movies as you are watching them , and the slow parade of human frailty fascinates you , then you're at the right film . \nthis version moves beyond the original's nostalgia for the communal film experiences of yesteryear to a deeper realization of cinema's inability to stand in for true , lived experience . \nsome movies blend together as they become distant memories . mention \" solaris \" five years from now and i'm sure those who saw it will have an opinion to share . \nallen's funniest and most likeable movie in years . \nit's a glorious spectacle like those d . w . griffith made in the early days of silent film . \nthis comic gem is as delightful as it is derivative . \nmore timely than its director could ever have dreamed , this quietly lyrical tale probes the ambiguous welcome extended by iran to the afghani refugees who streamed across its borders , desperate for work and food . \nthe leaping story line , shaped by director peter kosminsky into sharp slivers and cutting impressions , shows all the signs of rich detail condensed into a few evocative images and striking character traits . \nwith three excellent principal singers , a youthful and good-looking diva and tenor and richly handsome locations , it's enough to make you wish jacquot had left well enough alone and just filmed the opera without all these distortions of perspective . \nthe production has been made with an enormous amount of affection , so we believe these characters love each other . \ncertainly the performances are worthwhile . \n'no es la mejor cinta de la serie , ni la mejor con brosnan a la cabeza , pero de que entretiene ni duda cabe . '\nwinds up being both revelatory and narcissistic , achieving some honest insight into relationships that most high-concept films candy-coat with pat storylines , precious circumstances and beautiful stars . \nwatching these eccentrics is both inspiring and pure joy . \nsteven spielberg brings us another masterpiece\nfinally , the french-produced \" read my lips \" is a movie that understands characters must come first . \nms . seigner and mr . serrault bring fresh , unforced naturalism to their characters . \nallen shows he can outgag any of those young whippersnappers making moving pictures today . \na good film with a solid pedigree both in front of and , more specifically , behind the camera . \nby no means a slam-dunk and sure to ultimately disappoint the action fans who will be moved to the edge of their seats by the dynamic first act , it still comes off as a touching , transcendent love story . \ni encourage young and old alike to go see this unique and entertaining twist on the classic whale's tale -- you won't be sorry ! \na literary detective story is still a detective story and aficionados of the whodunit won't be disappointed . \nhigh crimes steals so freely from other movies and combines enough disparate types of films that it can't help but engage an audience . \nif you're a fan of the series you'll love it and probably want to see it twice . i will be . \nit celebrates the group's playful spark of nonconformity , glancing vividly back at what hibiscus grandly called his 'angels of light . '\nthe story . . . is inspiring , ironic , and revelatory of just how ridiculous and money-oriented the record industry really is . it is also a testament to the integrity and vision of the band . \nlaced with liberal doses of dark humor , gorgeous exterior photography , and a stable-full of solid performances , no such thing is a fascinating little tale . \nhuppert's show to steal and she makes a meal of it , channeling kathy baker's creepy turn as the repressed mother on boston public just as much as 8 women's augustine . \nnair doesn't treat the issues lightly . she allows each character to confront their problems openly and honestly . \none of the best silly horror movies of recent memory , with some real shocks in store for unwary viewers . \nthe work of a filmmaker who has secrets buried at the heart of his story and knows how to take time revealing them . strange occurrences build in the mind of the viewer and take on extreme urgency . \nhas a certain ghoulish fascination , and generates a fair amount of b-movie excitement . \nfamiliar but utterly delightful . \na fascinating , dark thriller that keeps you hooked on the delicious pulpiness of its lurid fiction . \nthe film aims to be funny , uplifting and moving , sometimes all at once . the extent to which it succeeds is impressive . \nthe film brilliantly shines on all the characters , as the direction is intelligently accomplished . \nwhile not for every taste , this often very funny collegiate gross-out comedy goes a long way toward restoring the luster of the national lampoon film franchise , too long reduced to direct-to-video irrelevancy . \nas broad and cartoonish as the screenplay is , there is an accuracy of observation in the work of the director , frank novak , that keeps the film grounded in an undeniable social realism . \nin addition to hoffman's powerful acting clinic , this is that rare drama that offers a thoughtful and rewarding glimpse into the sort of heartache everyone has felt , or will feel someday . \njeffrey tambor's performance as the intelligent jazz-playing exterminator is oscar-worthy . \nfrom the opening strains of the average white band's \" pick up the pieces \" , you can feel the love . \nstevens' vibrant creative instincts are the difference between this and countless other flicks about guys and dolls . \nthat it'll probably be the best and most mature comedy of the 2002 summer season speaks more of the season than the picture\nold people will love this movie , and i mean that in the nicest possible way : last orders will touch the heart of anyone old enough to have earned a 50-year friendship . \nmeyjes' provocative film might be called an example of the haphazardness of evil . \ntian emphasizes the isolation of these characters by confining color to liyan's backyard . \nthe movie is pretty funny now and then without in any way demeaning its subjects . \nimagine a scenario where bergman approaches swedish fatalism using gary larson's far side humor\ntoo damn weird to pass up , and for the blacklight crowd , way cheaper ( and better ) than pink floyd tickets . \nit is most remarkable not because of its epic scope , but because of the startling intimacy it achieves despite that breadth . \nit's not a great monster movie . but if you've paid a matinee price and bought a big tub of popcorn , there's guilty fun to be had here . chomp chomp ! \nthe grey zone gives voice to a story that needs to be heard in the sea of holocaust movies . . . but the film suffers from its own difficulties . \nfun and nimble . \nothers , more attuned to the anarchist maxim that 'the urge to destroy is also a creative urge' , or more willing to see with their own eyes , will find morrison's iconoclastic uses of technology to be liberating . \nmiller tells this very compelling tale with little fuss or noise , expertly plucking tension from quiet . \ntime out is existential drama without any of the pretension associated with the term . \nit's a sweet , laugh-a-minute crowd pleaser that lifts your spirits as well as the corners of your mouth . \nwriter/director alexander payne ( election ) and his co-writer jim taylor brilliantly employ their quirky and fearless ability to look american angst in the eye and end up laughing . \na movie that at its best doesn't just make the most out of its characters' flaws but insists on the virtue of imperfection . \nit's tough to watch , but it's a fantastic movie . \nthe best animated feature to hit theaters since beauty and the beast 11 years ago . \nwhat saves this deeply affecting film from being merely a collection of wrenching cases is corcuera's attention to detail . \nfunny and touching . \npacino is the best he's been in years and keener is marvelous . \na solid , spooky entertainment worthy of the price of a ticket . \nby turns fanciful , grisly and engagingly quixotic . \n . . . very funny , very enjoyable . . . \nadaptation is intricately constructed and in a strange way nails all of orlean's themes without being a true adaptation of her book . \nso purely enjoyable that you might not even notice it's a fairly straightforward remake of hollywood comedies such as father of the bride . \nmoonlight mile gives itself the freedom to feel contradictory things . it is sentimental but feels free to offend , is analytical and then surrenders to the illogic of its characters , is about grief and yet permits laughter . \nthe real triumphs in igby come from philippe , who makes oliver far more interesting than the character's lines would suggest , and sarandon , who couldn't be better as a cruel but weirdly likable wasp matron . \nrobin williams has thankfully ditched the saccharine sentimentality of bicentennial man in favour of an altogether darker side . \nif you're willing to have fun with it , you won't feel cheated by the high infidelity of unfaithful . \naustralia : land beyond time is an enjoyable big movie primarily because australia is a weirdly beautiful place . \nhoffman's performance is authentic to the core of his being . \ntold just proficiently enough to trounce its overly comfortable trappings . \nan enthralling aesthetic experience , one that's steeped in mystery and a ravishing , baroque beauty . \nthe quirky drama touches the heart and the funnybone thanks to the energetic and always surprising performance by rachel griffiths . \na captivating coming-of-age story that may also be the first narrative film to be truly informed by the wireless age . \nwhat could have been a daytime soap opera is actually a compelling look at a young woman's tragic odyssey . \nduvall is strong as always . \na no-holds-barred cinematic treat . \nyou'd have to be a most hard-hearted person not to be moved by this drama . \nallen's underestimated charm delivers more goodies than lumps of coal . \nmeasured against practically any like-themed film other than its oscar-sweeping franchise predecessor the silence of the lambs , red dragon rates as an exceptional thriller . \nan exhilarating serving of movie fluff . \nmaelstrom is strange and compelling , engrossing and different , a moral tale with a twisted sense of humor . \nit makes you believe the cast and crew thoroughly enjoyed themselves and believed in their small-budget film . \ndark and disturbing , yet compelling to watch . \ntoo often , son of the bride becomes an exercise in trying to predict when a preordained \" big moment \" will occur and not \" if . \" \nthe picture uses humor and a heartfelt conviction to tell a story about discovering your destination in life , but also acknowledging the places , and the people , from whence you came . \na solid piece of journalistic work that draws a picture of a man for whom political expedience became a deadly foreign policy . \na terrific insider look at the star-making machinery of tinseltown . \nit's a diverting enough hour-and-a-half for the family audience . \na party-hearty teen flick that scalds like acid . \nas giddy and whimsical and relevant today as it was 270 years ago . \nthe film offers an intriguing what-if premise . \nthe pianist is the film roman polanski may have been born to make . \nthis version does justice both to stevenson and to the sci-fi genre . \npoignant and delicately complex . \nenough may pander to our basest desires for payback , but unlike many revenge fantasies , it ultimately delivers . \ncho's latest comic set isn't as sharp or as fresh as i'm the one that i want . . . but it's still damn funny stuff . \nin the pianist , polanski is saying what he has long wanted to say , confronting the roots of his own preoccupations and obsessions , and he allows nothing to get in the way . \ndespite the film's shortcomings , the stories are quietly moving . \nthose who love cinema paradiso will find the new scenes interesting , but few will find the movie improved . \nif you come from a family that eats , meddles , argues , laughs , kibbitzes and fights together , then go see this delightful comedy . \nthis bracingly truthful antidote to hollywood teenage movies that slather clearasil over the blemishes of youth captures the combustible mixture of a chafing inner loneliness and desperate grandiosity that tend to characterize puberty . \nthe reason to see \" sade \" lay with the chemistry and complex relationship between the marquis ( auteil ) and emilie ( le besco ) . \nit's the filmmakers' post-camp comprehension of what made old-time b movies good-bad that makes eight legged freaks a perfectly entertaining summer diversion . \nserious and thoughtful . \nit strikes hardest when it reminds you how pertinent its dynamics remain . fifty years after the fact , the world's political situation seems little different , and [director phillip] noyce brings out the allegory with remarkable skill . \nthe film's strength isn't in its details , but in the larger picture it paints - of a culture in conflict with itself , with the thin veneer of nationalism that covers our deepest , media-soaked fears . \n . . . best seen as speculative history , as much an exploration of the paranoid impulse as a creative sequel to the warren report . \nit has its faults , but it is a kind , unapologetic , sweetheart of a movie , and mandy moore leaves a positive impression . \nthe saigon of 1952 is an uneasy mix of sensual delights and simmering violence , and the quiet american brings us right into the center of that world . \ndespite its shortcomings , girls can't swim represents an engaging and intimate first feature by a talented director to watch , and it's a worthy entry in the french coming-of-age genre . \nflawed , but worth seeing for ambrose's performance . \nwith dirty deeds , david caesar has stepped into the mainstream of filmmaking with an assurance worthy of international acclaim and with every cinematic tool well under his control -- driven by a natural sense for what works on screen . \nthe humor and humanity of monsoon wedding are in perfect balance . \nlookin' for sin , american-style ? try hell house , which documents the cautionary christian spook-a-rama of the same name . \na compelling motion picture that illustrates an american tragedy . \nas comedic spotlights go , notorious c . h . o . hits all the verbal marks it should . \nit's a day at the beach -- with air conditioning and popcorn . \nfrida isn't that much different from many a hollywood romance . what sets it apart is the vision that taymor , the avant garde director of broadway's the lion king and the film titus , brings . \nstevens has a flair for dialogue comedy , the film operates nicely off the element of surprise , and the large cast is solid . \nextremely well acted by the four primary actors , this is a seriously intended movie that is not easily forgotten . \nthe film exudes the urbane sweetness that woody allen seems to have bitterly forsaken . \nk-19 : the widowmaker is derivative , overlong , and bombastic -- yet surprisingly entertaining . \nit's good , hard-edged stuff , violent and a bit exploitative but also nicely done , morally alert and street-smart . \ncineasts will revel in those visual in-jokes , as in the film's verbal pokes at everything from the likes of miramax chief harvey weinstein's bluff personal style to the stylistic rigors of denmark's dogma movement . \nit's a rare window on an artistic collaboration . \n . . . begins with promise , but runs aground after being snared in its own tangled plot . \nperhaps the best sports movie i've ever seen . \ncho's timing is priceless . \n . . . creates a visceral sense of its characters' lives and conflicted emotions that carries it far above . . . what could have been a melodramatic , lifetime channel-style anthology . \na sensitive , moving , brilliantly constructed work . \nan edgy thriller that delivers a surprising punch . \na reasonably entertaining sequel to 1994's surprise family hit that may strain adult credibility . \n[reno] delivers a monologue that manages to incorporate both the horror and the absurdity of the situation in a well-balanced fashion . \nthere is truth here\na confident , richly acted , emotionally devastating piece of work and 2002's first great film\na touching , small-scale story of family responsibility and care in the community . \narteta directs one of the best ensemble casts of the year\nthe casting of von sydow . . . is itself intacto's luckiest stroke . \nno , it's not as single-minded as john carpenter's original , but it's sure a lot smarter and more unnerving than the sequels . \na gem of a romantic crime comedy that turns out to be clever , amusing and unpredictable . \nstands as one of the year's most intriguing movie experiences , letting its imagery speak for it while it forces you to ponder anew what a movie can be . \n . . . the first 2/3 of the film are incredibly captivating and insanely funny , thanks in part to interesting cinematic devices ( cool visual backmasking ) , a solid cast , and some wickedly sick and twisted humor . . . \nthis movie got me grinning . there's a part of us that cannot help be entertained by the sight of someone getting away with something . \nan old-fashioned drama of substance about a teacher's slide down the slippery slope of dishonesty after an encounter with the rich and the powerful who have nothing but disdain for virtue . \nwhat's not to like about a movie with a 'children's' song that includes the line 'my stepdad's not mean , he's just adjusting' ? \nthis english-language version . . . does full honor to miyazaki's teeming and often unsettling landscape , and to the conflicted complexity of his characters . \nthe pleasures that it does afford may be enough to keep many moviegoers occupied amidst some of the more serious-minded concerns of other year-end movies . \nnot everyone will welcome or accept the trials of henry kissinger as faithful portraiture , but few can argue that the debate it joins is a necessary and timely one . \nthere are no special effects , and no hollywood endings . \nlike the original , this version is raised a few notches above kiddie fantasy pablum by allen's astringent wit . \ndespite its hawaiian setting , the science-fiction trimmings and some moments of rowdy slapstick , the basic plot of \" lilo \" could have been pulled from a tear-stained vintage shirley temple script . \na brutally honest documentary about a much anticipated family reunion that goes wrong thanks to culture shock and a refusal to empathize with others . \nfilled with honest performances and exceptional detail , baran is a gentle film with dramatic punch , a haunting ode to humanity . \nsparkles in its deft portrait of tinseltown's seasoned veterans of gossip , wealth , paranoia , and celebrityhood . \nin its dry and forceful way , it delivers the same message as jiri menzel's closely watched trains and danis tanovic's no man's land . \none-of-a-kind near-masterpiece . \n . . . a triumph of emotionally and narratively complex filmmaking . \n[haynes'] homage to such films as \" all that heaven allows \" and \" imitation of life \" transcends them . simply put , \" far from heaven \" is a masterpiece . \nan intense and effective film about loneliness and the chilly anonymity of the environments where so many of us spend so much of our time . \nalthough fairly involving as far as it goes , the film doesn't end up having much that is fresh to say about growing up catholic or , really , anything . \nproves mainly that south korean filmmakers can make undemanding action movies with all the alacrity of their hollywood counterparts . \na very funny romantic comedy about two skittish new york middle-agers who stumble into a relationship and then struggle furiously with their fears and foibles . \ntop-notch action powers this romantic drama . \nberesford nicely mixes in as much humor as pathos to take us on his sentimental journey of the heart . it really is a shame that more won't get an opportunity to embrace small , sweet 'evelyn . '\ni stopped thinking about how good it all was , and started doing nothing but reacting to it - feeling a part of its grand locations , thinking urgently as the protagonists struggled , feeling at the mercy of its inventiveness , gasping at its visual delights . \nprobably the best case for christianity since chesterton and lewis . \na gently funny , sweetly adventurous film that makes you feel genuinely good , that is to say , entirely unconned by false sentiment or sharp , overmanipulative hollywood practices . \nwould be an unendurable viewing experience for this ultra-provincial new yorker if 26-year-old reese witherspoon were not on hand to inject her pure fantasy character , melanie carmichael , with a massive infusion of old-fashioned hollywood magic . \nvisually fascinating . . . an often intense character study about fathers and sons , loyalty and duty . \na lyrical metaphor for cultural and personal self-discovery and a picaresque view of a little-remembered world . \nschtte's dramatic snapshot of the artist three days before his death offers an interesting bit of speculation as to the issues brecht faced as his life drew to a close . \na slick , engrossing melodrama . \ns1m0ne's satire is not subtle , but it is effective . it's a quirky , off-beat project . . . . \nwhile some will object to the idea of a vietnam picture with such a rah-rah , patriotic tone , soldiers ultimately achieves its main strategic objective : dramatizing the human cost of the conflict that came to define a generation . \neven if you don't know the band or the album's songs by heart , you will enjoy seeing how both evolve , and you will also learn a good deal about the state of the music business in the 21st century . \nthe solid filmmaking and convincing characters makes this a high water mark for this genre . \nfilms about loss , grief and recovery are pretty valuable these days . seen in that light , moonlight mile should strike a nerve in many . \nit's endlessly inventive , consistently intelligent and sickeningly savage . \nit is definitely worth seeing . \nan impeccable study in perversity . \nfar from heaven is a dazzling conceptual feat , but more than that , it's a work of enthralling drama . \na movie that both thrills the eye and , in its over-the-top way , touches the heart . \nstuffed to the brim with ideas , american instigator michael moore's film is a rambling examination of american gun culture that uses his usual modus operandi of crucifixion through juxtaposition . \naffectionately reminds us that , in any language , the huge stuff in life can usually be traced back to the little things . \na drama of great power , yet some members of the audience will leave the theater believing they have seen a comedy . \nthe large-frame imax camera lends itself beautifully to filming the teeming life on the reefs , making this gorgeous film a must for everyone from junior scientists to grown-up fish lovers . \nthe result is more depressing than liberating , but it's never boring . \na story about intelligent high school students that deals with first love sweetly but also seriously . it is also beautifully acted . \nit isn't that the picture is unfamiliar , but that it manages to find new avenues of discourse on old problems . \nsame song , second verse , coulda been better , but it coulda been worse . \nit's a technically superb film , shining with all the usual spielberg flair , expertly utilizing the talents of his top-notch creative team . \nwilco fans will have a great time , and the movie should win the band a few new converts , too . \ntsai has a well-deserved reputation as one of the cinema world's great visual stylists , and in this film , every shot enhances the excellent performances . \nthe date movie that franz kafka would have made . \nthe fact is that the screen is most alive when it seems most likely that broomfield's interviewees , or even himself , will not be for much longer . \nleguizamo and jones are both excellent and the rest of the cast is uniformly superb . \ni liked this film a lot . . . \n . . . there is enough originality in 'life' to distance it from the pack of paint-by-number romantic comedies that so often end up on cinema screens . \na solid and refined piece of moviemaking imbued with passion and attitude . \nnettelbeck has crafted an engaging fantasy of flavours and emotions , one part romance novel , one part recipe book . \nwith or without the sex , a wonderful tale of love and destiny , told well by a master storyteller\non the surface a silly comedy , scotland , pa would be forgettable if it weren't such a clever adaptation of the bard's tragic play . \na weird , arresting little ride . \na fine film , but it would be a lot better if it stuck to betty fisher and left out the other stories . \na first-class road movie that proves you can run away from home , but your ego and all your problems go with you . \nyou might want to take a reality check before you pay the full ticket price to see \" simone , \" and consider a dvd rental instead . \nwell cast and well directed - a powerful drama with enough sardonic wit to keep it from being maudlin . \na backstage must-see for true fans of comedy . \nthere's back-stabbing , inter-racial desire and , most importantly , singing and dancing . \nthe film sounds like the stuff of lurid melodrama , but what makes it interesting as a character study is the fact that the story is told from paul's perspective . \njones . . . makes a great impression as the writer-director of this little $1 . 8 million charmer , which may not be cutting-edge indie filmmaking but has a huge heart . \nin the disturbingly involving family dysfunctional drama how i killed my father , french director anne fontaine delivers an inspired portrait of male-ridden angst and the emotional blockage that accompanies this human condition\nbelow may not mark mr . twohy's emergence into the mainstream , but his promise remains undiminished . \nthere's no reason to miss interview with the assassin\nhappily stays close to the ground in a spare and simple manner and doesn't pummel us with phony imagery or music . \nits sheer dynamism is infectious . \nfor his first attempt at film noir , spielberg presents a fascinating but flawed look at the near future . \nlightweight but appealing . \nit somehow managed to make its way past my crappola radar and find a small place in my heart\nperhaps it's cliche to call the film 'refreshing , ' but it is . 'drumline' shows a level of young , black manhood that is funny , touching , smart and complicated . \nit does give a taste of the burning man ethos , an appealing blend of counter-cultural idealism and hedonistic creativity . \nthe limited sets and small confined and dark spaces also are homages to a classic low-budget film noir movie . \nthe movie is well done , but slow . \n[a] wonderfully loopy tale of love , longing , and voting . \nthe fascination comes in the power of the huston performance , which seems so larger than life and yet so fragile , and in the way the ivan character accepts the news of his illness so quickly but still finds himself unable to react . \nthe last scenes of the film are anguished , bitter and truthful . mr . koshashvili is a director to watch . \npredictable storyline and by-the-book scripting is all but washed away by sumptuous ocean visuals and the cinematic stylings of director john stockwell . \nantwone fisher certainly does the trick of making us care about its protagonist and celebrate his victories but , with few exceptions , it rarely stoops to cheap manipulation or corny conventions to do it . \none feels the dimming of a certain ambition , but in its place a sweetness , clarity and emotional openness that recalls the classics of early italian neorealism . \nit challenges , this nervy oddity , like modern art should . \nwhenever you think you've figured out late marriage , it throws you for a loop . \nthe pianist is polanski's best film . \nit is a testament of quiet endurance , of common concern , of reconciled survival . \nthis orange has some juice , but it's far from fresh-squeezed . \na sensitive , modest comic tragedy that works as both character study and symbolic examination of the huge economic changes sweeping modern china . \nhighly engaging . \nhigh crimes knows the mistakes that bad movies make and is determined not to make them , and maybe that is nobility of a sort . \ncusack's just brilliant in this . \nknows how to make our imagination wonder . \njae-eun jeong's take care of my cat brings a beguiling freshness to a coming-of-age story with such a buoyant , expressive flow of images that it emerges as another key contribution to the flowering of the south korean cinema . \nthe overall fabric is hypnotic , and mr . mattei fosters moments of spontaneous intimacy . \nevokes a palpable sense of disconnection , made all the more poignant by the incessant use of cell phones . \nmalcolm mcdowell is cool . paul bettany is cool . paul bettany playing malcolm mcdowell ? cool . \na touching , sophisticated film that almost seems like a documentary in the way it captures an italian immigrant family on the brink of major changes . \n . . . a trashy little bit of fluff stuffed with enjoyable performances and a bewildering sense of self-importance\nan inventive , absorbing movie that's as hard to classify as it is hard to resist . \nit made me want to get made-up and go see this movie with my sisters . i thought the relationships were wonderful , the comedy was funny , and the love 'real' . \n ( caine ) proves once again he hasn't lost his touch , bringing off a superb performance in an admittedly middling film . \nbogdanovich puts history in perspective and , via kirsten dunst's remarkable performance , he showcases davies as a young woman of great charm , generosity and diplomacy . \nthis breezy caper movie becomes a soulful , incisive meditation on the way we were , and the way we are . \na captivating new film . \nthose who aren't put off by the film's austerity will find it more than capable of rewarding them . \nit's a clear-eyed portrait of an intensely lived time , filled with nervous energy , moral ambiguity and great uncertainties . \nreveals how important our special talents can be when put in service of of others . it also shows how deeply felt emotions can draw people together across the walls that might otherwise separate them . \nwith the same sort of good-natured fun found in films like tremors , eight legged freaks is prime escapist fare . \na sharp , amusing study of the cult of celebrity . \nthe sentimental cliches mar an otherwise excellent film . a powerful performance from mel gibson and a brutal 90-minute battle sequence that does everything but issue you a dog-tag and an m-16 . \na graceful , moving tribute to the courage of new york's finest and a nicely understated expression of the grief shared by the nation at their sacrifice . \na coming-of-age tale from new zealand whose boozy , languid air is balanced by a rich visual clarity and deeply felt performances across the board . \nmade to be jaglomized is the cannes film festival , the annual riviera spree of flesh , buzz , blab and money . the charming result is festival in cannes . \nif you're looking for something new and hoping for something entertaining , you're in luck . \na hugely rewarding experience that's every bit as enlightening , insightful and entertaining as grant's two best films -- four weddings and a funeral and bridget jones's diary . \na rip-roaring comedy action fest that'll put hairs on your chest . \nif there's no art here , it's still a good yarn -- which is nothing to sneeze at these days . \nsimultaneously heart-breaking and very funny , the last kiss is really all about performances . \nthere is a subversive element to this disney cartoon , providing unexpected fizzability . \nan unforgettable look at morality , family , and social expectation through the prism of that omnibus tradition called marriage . \nan enjoyable , if occasionally flawed , experiment . \nmiyazaki is one of world cinema's most wondrously gifted artists and storytellers . \nif ayurveda can help us return to a sane regimen of eating , sleeping and stress-reducing contemplation , it is clearly a good thing . \nmeeting , even exceeding expectations , it's the best sequel since the empire strikes back . . . a majestic achievement , an epic of astonishing grandeur and surprising emotional depth . \nleigh is one of the rare directors who feels acting is the heart and soul of cinema . he allows his cast members to make creative contributions to the story and dialogue . this method almost never fails him , and it works superbly here . \npoetry in motion captured on film . while it can be a bit repetitive , overall it's an entertaining and informative documentary . \ndirecting with a sure and measured hand , [haneke] steers clear of the sensational and offers instead an unflinching and objective look at a decidedly perverse pathology . \nthe entire movie establishes a wonderfully creepy mood . \ni found the ring moderately absorbing , largely for its elegantly colorful look and sound . \nthe filmmakers want nothing else than to show us a good time , and in their cheap , b movie way , they succeed . \namari has dressed up this little parable in a fairly irresistible package full of privileged moments and memorable performances . \nrabbit-proof fence will probably make you angry . but it will just as likely make you weep , and it will do so in a way that doesn't make you feel like a sucker . \nboth heartbreaking and heartwarming . . . just a simple fable done in an artless sytle , but it's tremendously moving . \nthis masterfully calibrated psychological thriller thrives on its taut performances and creepy atmosphere even if the screenplay falls somewhat short . \nthe film's sense of imagery gives it a terrible strength , but it's propelled by the acting . \nthe pianist [is] a supremely hopeful cautionary tale of war's madness remembered that we , today , can prevent its tragic waste of life . here is a divine monument to a single man's struggle to regain his life , his dignity and his music . \nstrange it is , but delightfully so . \nelegant , mannered and teasing . \nan average coming-of-age tale elevated by the wholesome twist of a pesky mother interfering during her son's discovery of his homosexuality . \nthe ingenuity that parker displays in freshening the play is almost in a class with that of wilde himself . \ndecasia is what has happened already to so many silent movies , newsreels and the like . the unexpected thing is that its dying , in this shower of black-and-white psychedelia , is quite beautiful . \na droll , bitchy frolic which pokes fun at the price of popularity and small-town pretension in the lone star state . \nwith each of her three protagonists , miller eloquently captures the moment when a woman's life , out of a deep-seated , emotional need , is about to turn onto a different path . \nryan gosling . . . is at 22 a powerful young actor . \na minor work yet there's no denying the potency of miller's strange , fleeting brew of hopeful perseverance and hopeless closure . \nas an introduction to the man's theories and influence , derrida is all but useless ; as a portrait of the artist as an endlessly inquisitive old man , however , it's invaluable . \nthe film is a verbal duel between two gifted performers . \nimperfect ? yes , but also intriguing and honorable , a worthwhile addition to a distinguished film legacy . \nyou'll get the enjoyable basic minimum . but not a whit more . \nwhat a great way to spend 4 units of your day . \nthe movie is hardly a masterpiece , but it does mark ms . bullock's best work in some time . \nas simple and innocent a movie as you can imagine . this is a movie you can trust . \npassionate , irrational , long-suffering but cruel as a tarantula , helga figures prominently in this movie , and helps keep the proceedings as funny for grown-ups as for rugrats . \n \" it's all about the image . \" \nferal and uncomfortable . \nvividly conveys the passion , creativity , and fearlessness of one of mexico's most colorful and controversial artists -- a captivating drama that will speak to the nonconformist in us all . \nhollywood ending is not show-stoppingly hilarious , but scathingly witty nonetheless . \nmaybe thomas wolfe was right : you can't go home again . \na compelling yarn , but not quite a ripping one . \non the granger movie gauge of 1 to 10 , the powerpuff girls is a fast , frenetic , funny , even punny 6 -- aimed specifically at a grade-school audience . \nthe film has several strong performances . \ni've never bought from telemarketers , but i bought this movie . \nperfectly pitched between comedy and tragedy , hope and despair , about schmidt instead comes far closer than many movies to expressing the way many of us live -- someplace between consuming self-absorption and insistently demanding otherness . \nthe funny thing is , i didn't mind all this contrived nonsense a bit . \n[shyamalan] turns the goose-pimple genre on its empty head and fills it with spirit , purpose and emotionally bruised characters who add up to more than body count . \na sexy , peculiar and always entertaining costume drama set in renaissance spain , and the fact that it's based on true events somehow makes it all the more compelling . \nan entertaining documentary that freshly considers arguments the bard's immortal plays were written by somebody else . \na highly spirited , imaginative kid's movie that broaches neo-augustinian theology : is god stuck in heaven because he's afraid of his best-known creation ? \ncall it magic realism or surrealism , but miss wonton floats beyond reality with a certain degree of wit and dignity . \nraimi and his team couldn't have done any better in bringing the story of spider-man to the big screen . \nthe director explores all three sides of his story with a sensitivity and an inquisitiveness reminiscent of truffaut . \nwell-acted , well-directed and , for all its moodiness , not too pretentious . \nit's a satisfying summer blockbuster and worth a look . \nboomers and their kids will have a barrie good time . \nreal women have curves wears its empowerment on its sleeve but even its worst harangues are easy to swallow thanks to remarkable performances by ferrera and ontiveros . \nultimately , \" mib ii \" succeeds due to its rapid-fire delivery and enough inspired levity that it can't be dismissed as mindless . \nstage director sam mendes showcases tom hanks as a depression era hit-man in this dark tale of revenge . \nsitting in the third row of the imax cinema at sydney's darling harbour , but i sometimes felt as though i was in the tiny two seater plane that carried the giant camera around australia , sweeping and gliding , banking and hovering over some of the most not\nthe real charm of this trifle is the deadpan comic face of its star , jean reno , who resembles sly stallone in a hot sake half-sleep . \nwhat's so fun about this silly , outrageous , ingenious thriller is the director's talent . watching a brian depalma movie is like watching an alfred hitchcock movie after drinking twelve beers . \nstrip it of all its excess debris , and you'd have a 90-minute , four-star movie . as it is , it's too long and unfocused . \nan immensely entertaining look at some of the unsung heroes of 20th century pop music . \nthis familiar rise-and-fall tale is long on glamour and short on larger moralistic consequences , though it's told with sharp ears and eyes for the tenor of the times . \nthis beautifully animated epic is never dull . \nbrian tufano's handsome widescreen photography and paul grabowsky's excellent music turn this fairly parochial melodrama into something really rather special . \nit makes compelling , provocative and prescient viewing . \na thoroughly entertaining comedy that uses grant's own twist of acidity to prevent itself from succumbing to its own bathos . \nusing a stock plot , about a boy injects just enough freshness into the proceedings to provide an enjoyable 100 minutes in a movie theater . \nwhat eric schaeffer has accomplished with never again may not , strictly speaking , qualify as revolutionary . but it's defiantly and delightfully against the grain . \nthe hard-to-predict and absolutely essential chemistry between the down-to-earth bullock and the nonchalant grant proves to be sensational , and everything meshes in this elegant entertainment . \na positively thrilling combination of ethnography and all the intrigue , betrayal , deceit and murder of a shakespearean tragedy or a juicy soap opera . \nmr . clooney , mr . kaufman and all their collaborators are entitled to take a deep bow for fashioning an engrossing entertainment out of an almost sure-fire prescription for a critical and commercial disaster . \ndefinitely funny stuff , but it's more of the 'laughing at' variety than the 'laughing with . '\neasily the most thoughtful fictional examination of the root causes of anti-semitism ever seen on screen . \na gripping drama . \na real winner -- smart , funny , subtle , and resonant . \nfamily portrait of need , neurosis and nervy negativity is a rare treat that shows the promise of digital filmmaking . \nthe pitch must have read like a discarded house beautiful spread . \nuplifting as only a document of the worst possibilities of mankind can be , and among the best films of the year . \ndirector david jacobson gives dahmer a consideration that the murderer never game his victims . \nthe film has a terrific look and salma hayek has a feel for the character at all stages of her life . \na decided lack of spontaneity in its execution and a dearth of real poignancy in its epiphanies . \nthe performances are remarkable . \nit's burns' visuals , characters and his punchy dialogue , not his plot , that carry waydowntown . \nas literary desecrations go , this makes for perfectly acceptable , occasionally very enjoyable children's entertainment . you'll forget about it by monday , though , and if they're old enough to have developed some taste , so will your kids . \nwhile i can't say it's on par with the first one , stuart little 2 is a light , fun cheese puff of a movie . \nstrange , funny , twisted , brilliant and macabre . \na genuinely moving and wisely unsentimental drama . \nheaven is a haunting dramatization of a couple's moral ascension . \nthe mothman prophecies is best when illustrating the demons bedevilling the modern masculine journey . \n'stock up on silver bullets for director neil marshall's intense freight train of a film . '\nplays out with a dogged and eventually winning squareness that would make it the darling of many a kids-and-family-oriented cable channel . \nan entertaining british hybrid of comedy , caper thrills and quirky romance . \nalain choquart's camera barely stops moving , portraying both the turmoil of the time and giving conduct a perpetual sense of urgency , which , for a film that takes nearly three hours to unspool , is both funny and irritating . \nmostly martha could have used a little trimming -- 10 or 15 minutes could be cut and no one would notice -- but it's a pleasurable trifle . the only pain you'll feel as the credits roll is your stomach grumbling for some tasty grub . \nhardly an objective documentary , but it's great cinematic polemic . . . love moore or loathe him , you've got to admire . . . the intensity with which he's willing to express his convictions . \nthe mark of a respectable summer blockbuster is one of two things : unadulterated thrills or genuine laughs . \nthe film is visually dazzling , the depicted events dramatic , funny and poignant . \na directorial tour de force by bernard rose , ivans xtc . is one of this year's very best pictures . \nwhat makes the movie work  to an admittedly limited extent  is the commitment of two genuinely engaging performers . weaver and lapaglia are both excellent , in the kind of low-key way that allows us to forget that they are actually movie folk . \neven the digressions are funny . \nmr . spielberg and his company just want you to enjoy yourselves without feeling conned . and they succeed merrily at their noble endeavor . \nmelodrama with a message . \nen s mismo el rey len es un espectculo digno de contemplarse en cine , dvd o en su soberbio montaje teatral ; pero el hacerlo en la pantalla imax es una experiencia colosal . \na perfectly pleasant if slightly pokey comedy . \ncoppola's directorial debut is an incredibly layered and stylistic film that , despite a fairly slow paced , almost humdrum approach to character development , still manages at least a decent attempt at meaningful cinema . \nat the end , when the now computerized yoda finally reveals his martial artistry , the film ascends to a kinetic life so teeming that even cranky adults may rediscover the quivering kid inside . \nwang xiaoshuai directs this intricately structured and well-realized drama that presents a fascinating glimpse of urban life and the class warfare that embroils two young men . \nit's hard to imagine anybody ever being \" in the mood \" to view a movie as harrowing and painful as the grey zone , but it's equally hard to imagine anybody being able to tear their eyes away from the screen once it's started . \nbogdanovich taps deep into the hearst mystique , entertainingly reenacting a historic scandal . \na moving tale of love and destruction in unexpected places , unexamined lives . \nclooney directs this film always keeping the balance between the fantastic and the believable . . . \nbeautifully produced . \nsmart and taut . \neven if you don't understand what on earth is going on , this is a movie that will stimulate hours of post viewing discussion , if only to be reminded of who did what to whom and why . \n . . . a lesson in prehistoric hilarity . \na fantastically vital movie that manages to invest real humor , sensuality , and sympathy into a story about two adolescent boys . \nlawrence plumbs personal tragedy and also the human comedy . \nthough a capable thriller , somewhere along the way k-19 jettisoned some crucial drama . \njust about the surest bet for an all-around good time at the movies this summer . \nit would be disingenuous to call reno a great film , but you can say that about most of the flicks moving in and out of the multiplex . this is a movie that is what it is : a pleasant distraction , a friday night diversion , an excuse to eat popcorn . \nos universos de chuck barris e charlie kaufman so complementares . e igualmente fascinantes . \nthere is a certain sense of experimentation and improvisation to this film that may not always work , but it is nevertheless compelling . \nthe four feathers has rewards , from the exoticism of its seas of sand to the fierce grandeur of its sweeping battle scenes . \na delicious , quirky movie with a terrific screenplay and fanciful direction by michael gondry . \nthis story still seems timely and important . and there's an element of heartbreak to watching it now , with older and wiser eyes , because we know what will happen after greene's story ends . \nthe bodily function jokes are about what you'd expect , but there are rich veins of funny stuff in this movie . \nthe performances are amiable and committed , and the comedy more often than not hits the bullseye . \nthis time , the hype is quieter , and while the movie is slightly less successful than the first , it's still a rollicking good time for the most part . \nthere's plenty to enjoy -- in no small part thanks to lau . \nwith a romantic comedy plotline straight from the ages , this cinderella story doesn't have a single surprise up its sleeve . but it does somehow manage to get you under its spell . \nthough few will argue that it ranks with the best of herzog's works , invincible shows he's back in form , with an astoundingly rich film . \n \" catch me \" feels capable of charming the masses with star power , a pop-induced score and sentimental moments that have become a spielberg trademark . \nby no means a great movie , but it is a refreshingly forthright one . \nthe casting of raymond j . barry as the 'assassin' greatly enhances the quality of neil burger's impressive fake documentary . \ndespite besson's high-profile name being wasabi's big selling point , there is no doubt that krawczyk deserves a huge amount of the credit for the film's thoroughly winning tone . \nthis documentary is a dazzling , remarkably unpretentious reminder of what [evans] had , lost , and got back . \na thoughtful movie , a movie that is concerned with souls and risk and schemes and the consequences of one's actions . \nas satisfyingly odd and intriguing a tale as it was a century and a half ago . . . has a delightfully dour , deadpan tone and stylistic consistency . \nmethodical , measured , and gently tedious in its comedy , secret ballot is a purposefully reductive movie -- which may be why it's so successful at lodging itself in the brain . \na witty , trenchant , wildly unsentimental but flawed look at the ins and outs of modern moviemaking . \nfor most of the distance the picture provides a satisfyingly unsettling ride into the dark places of our national psyche . \nby the standards of knucklehead swill , the hot chick is pretty damned funny . \none of the most gloriously unsubtle and adrenalized extreme shockers since the evil dead . \n[reaches] wholly believable and heart-wrenching depths of despair . \nan absorbing and unsettling psychological drama . \nthis movie may not have the highest production values you've ever seen , but it's the work of an artist , one whose view of america , history and the awkwardness of human life is generous and deep . \nthough it's not very well shot or composed or edited , the score is too insistent and the dialogue is frequently overwrought and crudely literal , the film shatters you in waves . \nthe entire cast is extraordinarily good . \nyakusho , as always , is wonderful as the long-faced sad sack . . . and his chemistry with shimizu is very believable . \nthe film delivers what it promises : a look at the \" wild ride \" that ensues when brash young men set out to conquer the online world with laptops , cell phones and sketchy business plans . \nyoung hanks and fisk , who vaguely resemble their celebrity parents , bring fresh good looks and an ease in front of the camera to the work . \na captivatingly quirky hybrid of character portrait , romantic comedy and beat-the-clock thriller . \nthe film sparkles with the the wisdom and humor of its subjects . \nif [jaglom's] latest effort is not the director at his most sparkling , some of its repartee is still worth hearing . \nlike the english patient and the unbearable lightness of being , the hours is one of those reputedly \" unfilmable \" novels that has bucked the odds to emerge as an exquisite motion picture in its own right . \njust about the best straight-up , old-school horror film of the last 15 years . \na chilling tale of one of the great crimes of 20th century france : the murder of two rich women by their servants in 1933 . \nan oddity , to be sure , but one that you might wind up remembering with a degree of affection rather than revulsion . \nwhile the film is not entirely successful , it still manages to string together enough charming moments to work . \na winning piece of work filled with love for the movies of the 1960s . \ne . t . works because its flabbergasting principals , 14-year-old robert macnaughton , 6-year-old drew barrymore and 10-year-old henry thomas , convince us of the existence of the wise , wizened visitor from a faraway planet . \nhelps to remind the first world that hiv/aids is far from being yesterday's news . \na heartening tale of small victories and enduring hope . \nthe vistas are sweeping and the acting is far from painful . \njackson and co have brought back the value and respect for the term epic cinema . \nit may be a somewhat backhanded compliment to say that the film makes the viewer feel like the movie's various victimized audience members after a while , but it also happens to be the movie's most admirable quality\ncharlotte sometimes is a brilliant movie . it is about irrational , unexplainable life and it seems so real because it does not attempt to filter out the complexity . \na delightful stimulus for the optic nerves , so much that it's forgivable that the plot feels like every other tale of a totalitarian tomorrow . \ndefies logic , the laws of physics and almost anyone's willingness to believe in it . but darned if it doesn't also keep us riveted to our seats . \na complex psychological drama about a father who returns to his son's home after decades away . \nwriter and director otar iosseliani's pleasant tale about a factory worker who escapes for a holiday in venice reveals how we all need a playful respite from the grind to refresh our souls . \nthis is not a retread of \" dead poets' society . \" \nsweet and memorable film . \na smart , arch and rather cold-blooded comedy . \nkeenly observed and refreshingly natural , swimming gets the details right , from its promenade of barely clad bodies in myrtle beach , s . c . , to the adrenaline jolt of a sudden lunch rush at the diner . \nhighly watchable stuff . \n . . . begins on a high note and sustains it beautifully . \ndavis . . . gets vivid performances from her cast and pulls off some deft ally mcbeal-style fantasy sequences . \n'it's better to go in knowing full well what's going to happen , but willing to let the earnestness of its execution and skill of its cast take you down a familiar road with a few twists . cynics need not apply . '\nfunny , somber , absurd , and , finally , achingly sad , bartleby is a fine , understated piece of filmmaking . \n \" red dragon \" is entertaining . an obvious copy of one of the best films ever made , how could it not be ? but it is entertaining on an inferior level . it is a popcorn film , not a must-own , or even a must-see . \nsucceeds only because bullock and grant were made to share the silver screen . \nboth flawed and delayed , martin scorcese's gangs of new york still emerges as his most vital work since goodfellas . \nas any creature-feature fan knows , when you cross toxic chemicals with a bunch of exotic creatures , you get a lot of running around , screaming and death . on that score , the film certainly doesn't disappoint . \nas the movie traces mr . brown's athletic exploits , it is impossible not to be awed by the power and grace of one of the greatest natural sportsmen of modern times . \na moving and solidly entertaining comedy/drama that should bolster director and co-writer juan jos campanella's reputation in the united states . \nthanks to confident filmmaking and a pair of fascinating performances , the way to that destination is a really special walk in the woods . \nbeautifully shot , delicately scored and powered by a set of heartfelt performances , it's a lyrical endeavour . \na macabre and very stylized swedish fillm about a modern city where all the religious and civic virtues that hold society in place are in tatters . \na stylistic romp that's always fun to watch . \ninformative , intriguing , observant , often touching . . . gives a human face to what's often discussed in purely abstract terms . \n . . . once the true impact of the day unfolds , the power of this movie is undeniable . \nan honest , sensitive story from a vietnamese point of view . \n a buoyant romantic comedy about friendship , love , and the truth that we're all in this together . \nthe film's intimate camera work and searing performances pull us deep into the girls' confusion and pain as they struggle tragically to comprehend the chasm of knowledge that's opened between them . \nit's the perfect star vehicle for grant , allowing him to finally move away from his usual bumbling , tongue-tied screen persona . \ngaunt , silver-haired and leonine , [harris] brings a tragic dimension and savage full-bodied wit and cunning to the aging sandeman . \na disturbing examination of what appears to be the definition of a 'bad' police shooting . \nit's been made with an innocent yet fervid conviction that our hollywood has all but lost . \nnot only a reminder of how they used to make movies , but also how they sometimes still can be made . \na three-hour cinema master class . \neyre is on his way to becoming the american indian spike lee . \npsychologically revealing . \na witty , whimsical feature debut . \nwarm in its loving yet unforgivingly inconsistent depiction of everyday people , relaxed in its perfect quiet pace and proud in its message . i loved this film . \nit provides a grim , upsetting glimpse at the lives of some of the 1 . 2 million palestinians who live in the crowded cities and refugee camps of gaza . \nclint eastwood's blood work is a lot like a well-made pb& j sandwich : familiar , fairly uneventful and boasting no real surprises  but still quite tasty and inviting all the same . \na movie that will surely be profane , politically charged music to the ears of cho's fans . \nmuch of this slick and sprightly cgi feature is sufficiently funny to amuse even the most resolutely unreligious parents who escort their little ones to megaplex screenings . \nrarely , a movie is more than a movie . go . \njacquot's strategy allows his cast the benefit of being able to give full performances . . . while demonstrating vividly that the beauty and power of the opera reside primarily in the music itself . \nquitting delivers a sucker-punch , and its impact is all the greater beause director zhang's last film , the cuddly shower , was a non-threatening multi-character piece centered around a public bath house . \nby not averting his eyes , solondz forces us to consider the unthinkable , the unacceptable , the unmentionable . \none hour photo may seem disappointing in its generalities , but it's the little nuances that perhaps had to escape from director mark romanek's self-conscious scrutiny to happen , that finally get under your skin . \nwhile general audiences might not come away with a greater knowledge of the facts of cuban music , they'll be treated to an impressive and highly entertaining celebration of its sounds . \na fascinating documentary that provides a rounded and revealing overview of this ancient holistic healing system\nbirthday girl lucks out with chaplin and kidman , who are capable of anteing up some movie star charisma when they need it to sell us on this twisted love story , but who can also negotiate the movie's darker turns . \nan interesting look behind the scenes of chicago-based rock group wilco . . . \nsharp edges and a deep vein of sadness run through its otherwise comic narrative . \nthere's lots of cool stuff packed into espn's ultimate x . \nrock solid family fun out of the gates , extremely imaginative through out , but wanes in the middle\nas a film director , labute continues to improve . \nthe ya-ya's have many secrets and one is - the books are better . translating complex characters from novels to the big screen is an impossible task but they are true to the essence of what it is to be ya-ya . \nthe touch is generally light enough and the performances , for the most part , credible . \ni liked about schmidt a lot , but i have a feeling that i would have liked it much more if harry & tonto never existed . \nsteers has an unexpectedly adamant streak of warm-blooded empathy for all his disparate manhattan denizens--especially the a * * holes . \nthat storytelling has value cannot be denied . not even solondz's thirst for controversy , sketchy characters and immature provocations can fully succeed at cheapening it . \nonce the downward spiral comes to pass , auto focus bears out as your typical junkie opera . . . \na knowing sense of humor and a lot of warmth ignite son of the bride . \na rich tale of our times , very well told with an appropriate minimum of means . \nthe characters are complex and quirky , but entirely believable as the remarkable ensemble cast brings them to life . \nin all fairness , i must report that the children of varying ages in my audience never coughed , fidgeted or romped up and down the aisles for bathroom breaks . \nas gory as the scenes of torture and self-mutilation may be , they are pitted against shimmering cinematography that lends the setting the ethereal beauty of an asian landscape painting . \nefficient , suitably anonymous chiller . \ngorgeous scenes , masterful performances , but the sickly sweet gender normative narrative left an acrid test in this gourmet's mouth . \nthe hot topics of the plot are relegated to the background -- a welcome step forward from the sally jesse raphael atmosphere of films like philadelphia and american beauty . \nit's usually a bad sign when directors abandon their scripts and go where the moment takes them , but olympia , wash . , based filmmakers anne de marcken and marilyn freeman did just that and it's what makes their project so interesting . \na memorable experience that , like many of his works , presents weighty issues colorfully wrapped up in his own idiosyncratic strain of kitschy goodwill . \nexecuted with such gentle but insistent sincerity , with such good humor and appreciation of the daily grind that only the most hardhearted scrooge could fail to respond . \nthe gentle comic treatment of adolescent sturm und drang should please fans of chris fuhrman's posthumously published cult novel . \ndirector claude chabrol has become the master of innuendo . it is not what you see , it is what you think you see . \na deftly entertaining film , smartly played and smartly directed . \na documentary to make the stones weep -- as shameful as it is scary . \ni hope the movie is widely seen and debated with appropriate ferocity and thoughtfulness . \n a thought-provoking look at how western foreign policy - however well intentioned - can wreak havoc in other cultures . \nasks what truth can be discerned from non-firsthand experience , and specifically questions cinema's capability for recording truth . \nthe journey to the secret's eventual discovery is a separate adventure , and thrill enough . \na quiet , disquieting triumph . \ndarkly funny and frequently insightful . \n . . . the tale of her passionate , tumultuous affair with musset unfolds as sand's masculine persona , with its love of life and beauty , takes form . \nif you want to see a train wreck that you can't look away from , then look no further , because here it is . \nthere's so much to look at in metropolis you hate to tear your eyes away from the images long enough to read the subtitles . \nthe search for redemption makes for a touching love story , mainly because blanchett and ribisi compellingly tap into a spiritual aspect of their characters' suffering . \na fast paced and suspenseful argentinian thriller about the shadow side of play . \na film of ideas and wry comic mayhem . \nat its worst the screenplay is callow , but at its best it is a young artist's thoughtful consideration of fatherhood . \na worthwhile documentary , whether you're into rap or not , even if it may still leave you wanting more answers as the credits roll . \nfessenden's narrative is just as much about the ownership and redefinition of myth as it is about a domestic unit finding their way to joy . \nthat the film opens with maggots crawling on a dead dog is not an out of place metaphor . \nstanley kwan has directed not only one of the best gay love stories ever made , but one of the best love stories of any stripe . \nthe concert footage is stirring , the recording sessions are intriguing , and -- on the way to striking a blow for artistic integrity -- this quality band may pick up new admirers . \nnorton holds the film together . \n[there's] quite a bit of heart , as you would expect from the directors of the little mermaid and aladdin . \nyou won't have any trouble getting kids to eat up these veggies . \na creaky staircase gothic . \nenjoyably dumb , sweet , and intermittently hilarious -- if you've a taste for the quirky , steal a glimpse . \na movie that sends you out of the theater feeling like you've actually spent time living in another community . \nlight-years ahead of paint-by-number american blockbusters like pearl harbor , at least artistically . \na fascinating documentary about the long and eventful spiritual journey of the guru who helped launch the new age . \nisabelle huppert excels as the enigmatic mika and anna mouglalis is a stunning new young talent in one of chabrol's most intense psychological mysteries . \nperhaps not since nelson eddy crooned his indian love call to jeanette macdonald has there been a movie so unabashedly canadian , not afraid to risk american scorn or disinterest . \nwedding feels a bit anachronistic . still , not every low-budget movie must be quirky or bleak , and a happy ending is no cinematic sin . \nit's still a comic book , but maguire makes it a comic book with soul . \nbrings to a spectacular completion one of the most complex , generous and subversive artworks of the last decade . \nan amusing and unexpectedly insightful examination of sexual jealousy , resentment and the fine line between passion and pretence . \na fascinating , bombshell documentary that should shame americans , regardless of whether or not ultimate blame finally lies with kissinger . should be required viewing for civics classes and would-be public servants alike . \nadaptation's success in engaging the audience in the travails of creating a screenplay is extraordinary . \na polished and vastly entertaining caper film that puts the sting back into the con . \nit's no surprise that as a director washington demands and receives excellent performances , from himself and from newcomer derek luke . \n . . . while each moment of this broken character study is rich in emotional texture , the journey doesn't really go anywhere . \nthe film gets close to the chimps the same way goodall did , with a serious minded patience , respect and affection . \nit's an often-cute film but either needs more substance to fill the time or some judicious editing . \nthis may be burns's strongest film since the brothers mcmullen . \nwhat makes this film special is serry's ability to take what is essentially a contained family conflict and put it into a much larger historical context . \nit's quaid who anchors the film with his effortless performance and that trademark grin of his -- so perfect for a ballplayer . \nit is ok for a movie to be something of a sitcom apparatus , if the lines work , the humor has point and the actors are humanly engaged . \nthough not for everyone , the guys is a somber trip worth taking . \nwill warm your heart without making you feel guilty about it . \na sly female empowerment movie , although not in a way anyone would expect . \nyou really have to salute writer-director haneke ( he adapted elfriede jelinek's novel ) for making a film that isn't nearly as graphic but much more powerful , brutally shocking and difficult to watch . \nit's a wonderful , sobering , heart-felt drama . \nruns on the pure adrenalin of pacino's performance . \nthe paradiso's rusted-out ruin and ultimate collapse during the film's final ( restored ) thirdemotionally belittle a cinema classic . sometimes shorter is better . \nphillip noyce and all of his actors -- as well as his cinematographer , christopher doyle -- understand the delicate forcefulness of greene's prose , and it's there on the screen in their version of the quiet american . \nthe film just might turn on many people to opera , in general , an art form at once visceral and spiritual , wonderfully vulgar and sublimely lofty -- and as emotionally grand as life . \nas a vehicle to savour binoche's skill , the film is well worthwhile . \n . . . wise and elegiac . . . \nthe huskies are beautiful , the border collie is funny and the overall feeling is genial and decent . \nwhatever complaints i might have , i'd take [its] earnest errors and hard-won rewards over the bombastic self-glorification of other feel-good fiascos like antwone fisher or the emperor's club any time . \nmastering its formidable arithmetic of cameras and souls , group articulates a flood of emotion . \na pretty decent kid-pleasing , tolerable-to-adults lark of a movie . \neven during the climactic hourlong cricket match , boredom never takes hold . \ncombine the paranoid claustrophobia of a submarine movie with the unsettling spookiness of the supernatural -- why didn't hollywood think of this sooner ? \nlike kubrick , soderbergh isn't afraid to try any genre and to do it his own way . \nnothing can detract from the affection of that moral favorite : friends will be friends through thick and thin . \nif the film has a problem , its shortness disappoints : you want the story to go on and on . \nunlike most anime , whose most ardent fans outside japan seem to be introverted young men with fantasy fetishes , metropolis never seems hopelessly juvenile . \nthe plot twists give i am trying to break your heart an attraction it desperately needed . \nthe most brilliant and brutal uk crime film since jack carter went back to newcastle , the first half of gangster no . 1 drips with style and , at times , blood . \nlike its new england characters , most of whom wander about in thick clouds of denial , the movie eventually gets around to its real emotional business , striking deep chords of sadness . \nthe bai brothers have taken an small slice of history and opened it up for all of us to understand , and they've told a nice little story in the process . \nflamboyant in some movies and artfully restrained in others , 65-year-old jack nicholson could be looking at his 12th oscar nomination by proving that he's now , more than ever , choosing his roles with the precision of the insurance actuary . \n . . . is there a deeper , more direct connection between these women , one that spans time and reveals meaning ? you bet there is and it's what makes this rather convoluted journey worth taking . \nthe most amazing super-sized dosage of goofball stunts any \" jackass \" fan could want . \nreal women may have many agendas , but it also will win you over , in a big way . \nyoung everlyn sampi , as the courageous molly craig , simply radiates star-power potential in this remarkable and memorable film . \nsurprisingly powerful and universal . \napart from its own considerable achievement , metropolis confirms tezuka's status as both the primary visual influence on the anim tradition and its defining philosophical conscience . \ni'll put it this way : if you're in the mood for a melodrama narrated by talking fish , this is the movie for you . \nmorvern callar confirms lynne ramsay as an important , original talent in international cinema . \nwell-done supernatural thriller with keen insights into parapsychological phenomena and the soulful nuances of the grieving process . \na plethora of engaging diatribes on the meaning of 'home , ' delivered in grand passion by the members of the various households . \nit's technically sumptuous but also almost wildly alive . \nthis film puts wang at the forefront of china's sixth generation of film makers . \nit's refreshing to see a movie that embraces its old-fashioned themes and in the process comes out looking like something wholly original . \nwiseman is patient and uncompromising , letting his camera observe and record the lives of women torn apart by a legacy of abuse . \nthere's none of the happily-ever -after spangle of monsoon wedding in late marriage -- and that's part of what makes dover kosashvili's outstanding feature debut so potent . \nan ingenious and often harrowing look at damaged people and how families can offer either despair or consolation . \narguably the best script that besson has written in years . \nit's no lie -- big fat liar is a real charmer . \ninvigorating , surreal , and resonant with a rainbow of emotion . \ndirector alfonso cuaron gets vivid , convincing performances from a fine cast , and generally keeps things going at a rapid pace , occasionally using an omniscient voice-over narrator in the manner of french new wave films . \npray has really done his subject justice . \nan unexpectedly sweet story of sisterhood . \nmaintains your sympathy for this otherwise challenging soul by letting you share her one-room world for a while . \na subtle , humorous , illuminating study of politics , power and social mobility . \neven if you have no interest in the gang-infested , east-vs . -west coast rap wars , this modern mob music drama never fails to fascinate . \nnair's attention to detail creates an impeccable sense of place , while thurman and lewis give what can easily be considered career-best performances . \nberry's saucy , full-bodied performance gives this aging series a much needed kick , making \" die another day \" one of the most entertaining bonds in years\nred dragon is less baroque and showy than hannibal , and less emotionally affecting than silence . but , like silence , it's a movie that gets under your skin . \ncaviezel embodies the transformation of his character completely . \na creepy , intermittently powerful study of a self-destructive man . . . about as unsettling to watch as an exploratory medical procedure or an autopsy . \npacino and williams seem to keep upping the ante on each other , just as their characters do in the film . what results is the best performance from either in years . \nthe cast is top-notch and i predict there will be plenty of female audience members drooling over michael idemoto as michael . \nbart and berling are both superb , while huppert . . . is magnificent . \nall the actors are good in pauline & paulette but van der groen , described as 'belgium's national treasure , ' is especially terrific as pauline . \nmiyazaki has created such a vibrant , colorful world , it's almost impossible not to be swept away by the sheer beauty of his images . \nmuccino seems to be exploring the idea of why human beings long for what they don't have , and how this gets us in trouble . but even while his characters are acting horribly , he is always sympathetic . \nwhether or not you buy mr . broomfield's findings , the film acquires an undeniable entertainment value as the slight , pale mr . broomfield continues to force himself on people and into situations that would make lesser men run for cover . \nozpetek joins the ranks of those gay filmmakers who have used the emigre experience to explore same-sex culture in ways that elude the more nationally settled . \nan eerily suspenseful , deeply absorbing piece that works as a treatise on spirituality as well as a solid sci-fi thriller . \ni've never seen or heard anything quite like this film , and i recommend it for its originality alone . \nnicole kidman makes it a party worth attending . \ncatch it . . . if you can ! \nthe direction has a fluid , no-nonsense authority , and the performances by harris , phifer and cam'ron seal the deal . \nthe komediant is a tale worth catching . \nthe writing is clever and the cast is appealing . \nthe simplicity of the way home has few equals this side of aesop\nlife on the rez is no picnic : this picture shows you why . \nspielberg has managed to marry science fiction with film noir and action flicks with philosophical inquiry . \nit's the type of film about growing up that we don't see often enough these days : realistic , urgent , and not sugarcoated in the least . \na taut , sobering film . \nexudes the fizz of a busby berkeley musical and the visceral excitement of a sports extravaganza . \nit's full of cheesy dialogue , but great trashy fun that finally returns de palma to his pulpy thrillers of the early '80s . \nthe results , if not memorable , are at least interesting . \na quietly moving look back at what it was to be iranian-american in 1979 . \nlike a veteran head cutter , barbershop is tuned in to its community . \ni'm sure mainstream audiences will be baffled , but , for those with at least a minimal appreciation of woolf and clarissa dalloway , the hours represents two of those well spent . \nyou live the mood rather than savour the story . \nangela gheorghiu as famous prima donna floria tosca , roberto alagna as her lover mario cavaradossi , and ruggero as the villainous , lecherous police chief scarpia , all sing beautifully and act adequately . \nwhile there are times when the film's reach exceeds its grasp , the production works more often than it doesn't . \nwhile scorsese's bold images and generally smart casting ensure that \" gangs \" is never lethargic , the movie is hindered by a central plot that's peppered with false starts and populated by characters who are nearly impossible to care about . \nwatching this gentle , mesmerizing portrait of a man coming to terms with time , you barely realize your mind is being blown . \nthe beautifully choreographed kitchen ballet is simple but absorbing . \nthere's . . . an underlying old world sexism to monday morning that undercuts its charm . \n \" the best disney movie since the lion king \" \ntranscends its agenda to deliver awe-inspiring , at times sublime , visuals and offer a fascinating glimpse into the subculture of extreme athletes whose derring-do puts the x into the games . \nspare yet audacious . . . \nthink of it as gidget , only with muscles and a lot more smarts , but just as endearing and easy to watch . \nthere is no solace here , no entertainment value , merely a fierce lesson in where filmmaking can take us . \ngiggling at the absurdities and inconsistencies is part of the fun . but the talented cast alone will keep you watching , as will the fight scenes . \narteta paints a picture of lives lived in a state of quiet desperation . \ndrug abuse , infidelity and death aren't usually comedy fare , but turpin's film allows us to chuckle through the angst . \nwhile insomnia is in many ways a conventional , even predictable remake , nolan's penetrating undercurrent of cerebral and cinemantic flair lends ( it ) stimulating depth . \nefteriades gives the neighborhood -- scenery , vibe and all -- the cinematic equivalent of a big , tender hug . \nthis is a nicely handled affair , a film about human darkness but etched with a light ( yet unsentimental ) touch . \namazing ! a college story that works even without vulgarity , sex scenes , and cussing ! \nthe amazing film work is so convincing that by movies' end you'll swear you are wet in some places and feel sand creeping in others . \na raunchy and frequently hilarious follow-up to the gifted korean american stand-up's i'm the one that i want . \nif you ever wanted to be an astronaut , this is the ultimate movie experience - it's informative and breathtakingly spectacular . \nwhile parker and co-writer catherine di napoli are faithful to melville's plotline , they and a fully engaged supporting cast . . . have made the old boy's characters more quick-witted than any english lit major would have thought possible . \na smart , sassy and exceptionally charming romantic comedy . \nsurprisingly insightful\nthere are flaws , but also stretches of impact and moments of awe ; we're wrapped up in the characters , how they make their choices , and why . \na gift to anyone who loves both dance and cinema\nit seems grant doesn't need the floppy hair and the self-deprecating stammers after all . \na reminder that beyond all the hype and recent digital glitz , spielberg knows how to tell us about people . \none of the finest , most humane and important holocaust movies ever made . \nan engrossing and infectiously enthusiastic documentary . \na beautiful , timeless and universal tale of heated passions -- jealousy , betrayal , forgiveness and murder . \na culture-clash comedy that , in addition to being very funny , captures some of the discomfort and embarrassment of being a bumbling american in europe . \nshattering , devastating documentary on two maladjusted teens in a downward narcotized spiral . extraordinary debut from josh koury . \nthe most compelling performance of the year adds substantial depth to this shocking testament to anti-semitism and neo-fascism . \nfor those who are intrigued by politics of the '70s , the film is every bit as fascinating as it is flawed . \nall right , so it's not a brilliant piece of filmmaking , but it is a funny ( sometimes hilarious ) comedy with a deft sense of humor about itself , a playful spirit and a game cast . \ndouglas mcgrath's nicholas nickleby does dickens as it should be done cinematically . \nit's a lovely , eerie film that casts an odd , rapt spell . \nthe quirky and recessive charms of co-stars martin donovan and mary-louise parker help overcome the problematic script . \nit's good to see michael caine whipping out the dirty words and punching people in the stomach again . \nyou just know something terrible is going to happen . but when it does , you're entirely unprepared . \nit's fun , wispy , wise and surprisingly inoffensive for a film about a teen in love with his stepmom . \nable to provide insight into a fascinating part of theater history . \nan unflinching , complex portrait of a modern israel that is rarely seen on-screen . \na jewish ww ii doc that isn't trying simply to out-shock , out-outrage or out-depress its potential audience ! who knew . . . \nit's a familiar story , but one that is presented with great sympathy and intelligence . \ngently humorous and touching . \nit won't hold up over the long haul , but in the moment , finch's tale provides the forgettable pleasures of a saturday matinee . \nkinnear's performance is a career-defining revelation . \nthe film is predictable in the reassuring manner of a beautifully sung holiday carol . \n . . . hits every cliche we've come to expect , including the assumption that \" crazy \" people are innocent , childlike and inherently funny . \nthe strong subject matter continues to shock throughout the film . not everyone will play the dark , challenging tune taught by the piano teacher . \na certain sexiness underlines even the dullest tangents . \nyou may be captivated , as i was , by its moods , and by its subtly transformed star , and still wonder why paul thomas anderson ever had the inclination to make the most sincere and artful movie in which adam sandler will probably ever appear . \nthere is no substitute for on-screen chemistry , and when friel pulls the strings that make williams sink into melancholia , the reaction in williams is as visceral as a gut punch . \nthat old adage about women being unknowable gets an exhilarating new interpretation in morvern callar . \na mix of gritty realism , crisp storytelling and radiant compassion that effortlessly draws you in . \nafter watching it , you can only love the players it brings to the fore for the gifted but no-nonsense human beings they are and for the still-inestimable contribution they have made to our shared history . \nin his u . s . debut , mr . schnitzler proves himself a deft pace master and stylist . \nultimate x is a ride , basically the kind of greatest-hits reel that might come with a subscription to espn the magazine . \nrich in shadowy metaphor and as sharp as a samurai sword , jiang wen's devils on the doorstep is a wartime farce in the alternately comic and gut-wrenching style of joseph heller or kurt vonnegut . \noffers a clear-eyed chronicle of a female friendship that is more complex and honest than anything represented in a hollywood film . \na winning comedy with its wry observations about long-lived friendships and the ways in which we all lose track of ourselves by trying to please others . \nits cast full of caffeinated comedy performances more than make up for its logical loopholes , which fly by so fast there's no time to think about them anyway . \nlohman adapts to the changes required of her , but the actress and director peter kosminsky never get the audience to break through the wall her character erects\nalthough it includes a fair share of dumb drug jokes and predictable slapstick , \" orange county \" is far funnier than it would seem to have any right to be . \nfor a movie audience , the hours doesn't connect in a neat way , but introduces characters who illuminate mysteries of sex , duty and love . \na bright , inventive , thoroughly winning flight of revisionist fancy . \nozpetek's effort has the scope and shape of an especially well-executed television movie . \ngraas s interaes entre seus personagens , o filme torna-se no apenas uma histria divertida sobre uma curiosa perseguio , mas tambm um belo estudo de personagens . \naffirms the gifts of all involved , starting with spielberg and going right through the ranks of the players -- on-camera and off -- that he brings together . \na delightful little film that revels in its own simplicity , mostly martha will leave you with a smile on your face and a grumble in your stomach . \nmakes one thing abundantly clear . american musical comedy as we know it wouldn't exist without the precedent of yiddish theater , whose jolly , fun-for-fun's-sake communal spirit goes to the essence of broadway . \ndeepa mehta provides an accessible introduction as well as some intelligent observations on the success of bollywood in the western world . \nif anything , the film is doing something of a public service -- shedding light on a group of extremely talented musicians who might otherwise go unnoticed and underappreciated by music fans . \nin addition to gluing you to the edge of your seat , changing lanes is also a film of freshness , imagination and insight . \npan nalin's exposition is beautiful and mysterious , and the interviews that follow , with the practitioners of this ancient indian practice , are as subtle and as enigmatic . \nthe mood , look and tone of the film fit the incredible storyline to a t . \nit's crafty , energetic and smart -- the kid is sort of like a fourteen-year old ferris bueller . \na work of extraordinary journalism , but it is also a work of deft and subtle poetry . \nit's funny and human and really pretty damned wonderful , all at once . \nat 78 minutes it just zings along with vibrance and warmth . \na strangely stirring experience that finds warmth in the coldest environment and makes each crumb of emotional comfort feel like a 10-course banquet . \nsometimes this 'blood' seems as tired as its protagonist . . . still , the pulse never disappears entirely , and the picture crosses the finish line winded but still game . \nthe stripped-down dramatic constructs , austere imagery and abstract characters are equal parts poetry and politics , obvious at times but evocative and heartfelt . \ndogtown and z-boys more than exposes the roots of the skateboarding boom that would become \" the punk kids' revolution . \" \n . . . plenty of warmth to go around , with music and laughter and the love of family . \nit'll keep you wide awake and . . . very tense . \ncould use a little more humanity , but it never lacks in eye-popping visuals . \n ( danny huston gives ) an astounding performance that deftly , gradually reveals a real human soul buried beneath a spellbinding serpent's smirk . \nthese three films form a remarkably cohesive whole , both visually and thematically , through their consistently sensitive and often exciting treatment of an ignored people . \na funny and well-contructed black comedy where the old adage \" be careful what you wish for \" is given a full workout . \nit reaffirms life as it looks in the face of death . \nthe film is reasonably entertaining , though it begins to drag two-thirds through , when the melodramatic aspects start to overtake the comedy . \nthis is more fascinating -- being real -- than anything seen on jerry springer . \na different movie -- sometimes tedious -- by a director many viewers would like to skip but film buffs should get to know . \nwilliams plays sy , another of his open-faced , smiling madmen , like the killer in insomnia . he does this so well you don't have the slightest difficulty accepting him in the role . \ntwist open the ouzo ! it's time to let your hair down  greek style . a vibrant whirlwind of love , family and all that goes with it , my big fat greek wedding is a non-stop funny feast of warmth , colour and cringe . \nthought-provoking and stylish , if also somewhat hermetic . \nbroomfield is energized by volletta wallace's maternal fury , her fearlessness , and because of that , his film crackles . \nwhile it has definite weaknesses -- like a rather unbelievable love interest and a meandering ending -- this '60s caper film is a riveting , brisk delight . \nfunny in a sick , twisted sort of way . \nif cinema had been around to capture the chaos of france in the 1790's , one imagines the result would look like something like this . \nit's a talking head documentary , but a great one . \nthe fast runner' transports the viewer into an unusual space\nultimately engages less for its story of actorly existential despair than for its boundary-hopping formal innovations and glimpse into another kind of chinese 'cultural revolution . '\n . . . a solid , well-formed satire . \nas part of mr . dong's continuing exploration of homosexuality in america , family fundamentals is an earnest study in despair . \nmost consumers of lo mein and general tso's chicken barely give a thought to the folks who prepare and deliver it , so , hopefully , this film will attach a human face to all those little steaming cartons . \nhatosy . . . portrays young brendan with his usual intelligence and subtlety , not to mention a convincing brogue . \nthe filmmakers' eye for detail and the high standards of performance convey a strong sense of the girls' environment . \nuneven , self-conscious but often hilarious spoof . \neven bigger and more ambitious than the first installment , spy kids 2 looks as if it were made by a highly gifted 12-year-old instead of a grown man . \nthanks to the chteau's balance of whimsicality , narrative discipline and serious improvisation , almost every relationship and personality in the film yields surprises . \nalan and his fellow survivors are idiosyncratic enough to lift the movie above its playwriting 101 premise . \nfresh and raw like a blown-out vein , narc takes a walking-dead , cop-flick subgenre and beats new life into it . \nthe premise of jason x is silly but strangely believable . \nit's a wise and powerful tale of race and culture forcefully told , with superb performances throughout . \nan awfully good , achingly human picture . \nthe cast comes through even when the movie doesn't . \nyou'll laugh at either the obviousness of it all or its stupidity or maybe even its inventiveness , but the point is , you'll laugh . \ndefinitely worth 95 minutes of your time . \nthe film jolts the laughs from the audience--as if by cattle prod . \na sexy , surprising romance . . . idemoto and kim make a gorgeous pair . . . their scenes brim with sexual possibility and emotional danger . \ntoes the fine line between cheese and earnestness remarkably well ; everything is delivered with such conviction that it's hard not to be carried away . \nwhereas oliver stone's conspiracy thriller jfk was long , intricate , star-studded and visually flashy , interview with the assassin draws its considerable power from simplicity . \nfunny , sexy , devastating and incurably romantic . \ntriple x is a double agent , and he's one bad dude . when you've got the wildly popular vin diesel in the equation , it adds up to big box office bucks all but guaranteed . \nvery well-written and very well-acted . \na powerful and telling story that examines forbidden love , racial tension , and other issues that are as valid today as they were in the 1950s . \nyou emerge dazed , confused as to whether you've seen pornography or documentary . \nit ain't art , by a long shot , but unlike last year's lame musketeer , this dumas adaptation entertains . \nlikeable thanks to its cast , its cuisine and its quirky tunes . \nchilling in its objective portrait of dreary , lost twenty-first century america . \nhighly recommended as an engrossing story about a horrifying historical event and the elements which contributed to it . \n . . . there's enough cool fun here to warm the hearts of animation enthusiasts of all ages . \nit manages to squeeze by on angelina jolie's surprising flair for self-deprecating comedy . \nsecretary manages a neat trick , bundling the flowers of perversity , comedy and romance into a strangely tempting bouquet of a movie . \njudith and zaza's extended bedroom sequence . . . is so intimate and sensual and funny and psychologically self-revealing that it makes most of what passes for sex in the movies look like cheap hysterics . \nphotographed with melancholy richness and eloquently performed yet also decidedly uncinematic . \na knowing look at female friendship , spiked with raw urban humor . \nas i settled into my world war ii memories , i found myself strangely moved by even the corniest and most hackneyed contrivances . \nthe overall effect is awe and affection -- and a strange urge to get on a board and , uh , shred , dude . \nit's that rare family movie -- genuine and sweet without relying on animation or dumb humor . \nthe trinity assembly approaches the endeavor with a shocking lack of irony , and george ratliff's documentary , hell house , reflects their earnestness  which makes for a terrifying film . \nconfessions may not be a straightforward bio , nor does it offer much in the way of barris' motivations , but the film is an oddly fascinating depiction of an architect of pop culture . \nan intoxicating experience . \na special kind of movie , this melancholic film noir reminded me a lot of memento . . . \nsimple , poignant and leavened with humor , it's a film that affirms the nourishing aspects of love and companionship . \ntogether , miller , kuras and the actresses make personal velocity into an intricate , intimate and intelligent journey . \nthe wonder of mostly martha is the performance of gedeck , who makes martha enormously endearing . \nwith notorious c . h . o . cho proves she has the stuff to stand tall with pryor , carlin and murphy . \nless front-loaded and more shapely than the two-hour version released here in 1990 . \nwatching war photographer , you come to believe that nachtwey hates the wars he shows and empathizes with the victims he reveals . \n[a] real pleasure in its laid-back way . \nsome may choose to interpret the film's end as hopeful or optimistic but i think payne is after something darker . \nthough it runs 163 minutes , safe conduct is anything but languorous . it's packed to bursting with incident , and with scores of characters , some fictional , some from history . \na much better documentary -- more revealing , more emotional and more surprising -- than its pedestrian english title would have you believe . \nnotwithstanding my problem with the movie's final half hour , i'm going to recommend secretary , based on the wonderful acting clinic put on by spader and gyllenhaal , and also the unique way shainberg goes about telling what at heart is a sweet little girl-\na well-crafted film that is all the more remarkable because it achieves its emotional power and moments of revelation with restraint and a delicate ambiguity . \nthe film has the uncanny ability to right itself precisely when you think it's in danger of going wrong . \nmy big fat greek wedding is that rare animal known as 'a perfect family film , ' because it's about family . \nwould make an excellent companion piece to the similarly themed 'the french lieutenant's woman . '\n . . . with the gifted pearce on hand to keep things on semi-stable ground dramatically , this retooled machine is ultimately effective enough at achieving the modest , crowd-pleasing goals it sets for itself . \na movie that's just plain awful but still manages to entertain on a guilty-pleasure , so-bad-it's-funny level . \na disoriented but occasionally disarming saga packed with moments out of an alice in wonderland adventure , a stalker thriller , and a condensed season of tv's big brother . \nfunctions as both a revealing look at the collaborative process and a timely , tongue-in-cheek profile of the corporate circus that is the recording industry in the current climate of mergers and downsizing . \nwith a confrontational stance , todd solondz takes aim on political correctness and suburban families . \na mess , but it's a sincere mess . \nthis odd , distant portuguese import more or less borrows from bad lieutenant and les vampires , and comes up with a kind of art-house gay porn film . \nfor a debut film , skin of man , heart of beast feels unusually assured . \na photographic marvel of sorts , and it's certainly an invaluable record of that special fishy community . \nit's soulful and unslick , and that's apparently just what [aniston] has always needed to grow into a movie career . \nalthough olivier assayas' elegantly appointed period drama seems , at times , padded with incident in the way of a too-conscientious adaptation . . . its three-hour running time plays closer to two . \na jaw-droppingly beautiful work that upends nearly every clich of japanese animation while delivering a more than satisfactory amount of carnage . \nterry is a sort of geriatric dirty harry , which will please eastwood's loyal fans -- and suits the story , wherein our hero must ride roughshod over incompetent cops to get his man . \nparts seem like they were lifted from terry gilliam's subconscious , pressed through kafka's meat grinder and into buuel's casings\n'like a child with an important message to tell . . . [skins'] faults are easy to forgive because the intentions are lofty . '\na delightful entree in the tradition of food movies . \nan escapist confection that's pure entertainment . \nthe ring is worth a look , if you don't demand much more than a few cheap thrills from your halloween entertainment . \nthe movie ultimately relies a bit too heavily on grandstanding , emotional , rocky-like moments . . . but it's such a warm and charming package that you'll feel too happy to argue much . \nreassuring , retro uplifter . \nthrowing it all away for the fleeting joys of love's brief moment . \narmed with a game supporting cast , from the pitch-perfect forster to the always hilarious meara and levy , like mike shoots and scores , doing its namesake proud . \na decent-enough nail-biter that stands a good chance of being the big hit franklin needs to stay afloat in hollywood . \nbegins like a docu-drama but builds its multi-character story with a flourish . \none of the most genuinely sweet films to come along in quite some time . \nafter an uncertain start , murder hits and generally sustains a higher plateau with bullock's memorable first interrogation of gosling . \nthe story ultimately takes hold and grips hard . \na bit of a downer and a little over-dramatic at times , but this is a beautiful film for people who like their romances to have that french realism . \nan emotionally strong and politically potent piece of cinema . \nenticing and often funny documentary . \ngoing to this movie is a little like chewing whale blubber - it's an acquired taste that takes time to enjoy , but it's worth it , even if it does take 3 hours to get through . \na portrait of hell so shattering it's impossible to shake . \nalmodovar is an imaginative teacher of emotional intelligence in this engaging film about two men who discover what william james once called 'the gift of tears . '\nbetter than the tepid star trek : insurrection ; falls short of first contact because the villain couldn't pick the lint off borg queen alice krige's cape ; and finishes half a parsec ( a nose ) ahead of generations . \nat times a bit melodramatic and even a little dated ( depending upon where you live ) , ignorant fairies is still quite good-natured and not a bad way to spend an hour or two . \ntense , terrific , sweaty-palmed fun . \nmajidi's direction has never been smoother or more confident . \nvisually captivating . \nwhat a bewilderingly brilliant and entertaining movie this is . \nhard , endearing , caring , warm . bring tissues . \na thriller with an edge -- which is to say that it doesn't follow the stale , standard , connect-the-dots storyline which has become commonplace in movies that explore the seamy underbelly of the criminal world . \n \" me without you \" is a probing examination of a female friendship set against a few dynamic decades . \ninherently caustic and oddly whimsical , the film chimes in on the grieving process and strangely draws the audience into the unexplainable pain and eccentricities that are attached to the concept of loss . \nthough frodo's quest remains unfulfilled , a hardy group of determined new zealanders has proved its creative mettle . \nit's a square , sentimental drama that satisfies , as comfort food often can . \npure cinematic intoxication , a wildly inventive mixture of comedy and melodrama , tastelessness and swooning elegance . \nramsay is clearly extraordinarily talented , and based on three short films and two features , here's betting her third feature will be something to behold . \ni was impressed by how many tit-for-tat retaliatory responses the filmmakers allow before pulling the plug on the conspirators and averting an american-russian armageddon . \na classy , sprightly spin on film . \nfast , frantic and fun , but also soon forgotten\na spiffy animated feature about an unruly adolescent boy who is yearning for adventure and a chance to prove his worth . \ndevos and cassel have tremendous chemistry -- their sexual and romantic tension , while never really vocalized , is palpable . \nfulfills the minimum requirement of disney animation . \na moving , if uneven , success . \n with one exception , every blighter in this particular south london housing project digs into dysfunction like it's a big , comforting jar of marmite , to be slathered on crackers and served as a feast of bleakness . \nwickedly funny , visually engrossing , never boring , this movie challenges us to think about the ways we consume pop culture . \nthere's plenty to impress about e . t . \na chronicle not only of one man's quest to be president , but of how that man single-handedly turned a plane full of hard-bitten , cynical journalists into what was essentially , by campaign's end , an extended publicity department . \nuntil it goes off the rails in its final 10 or 15 minutes , wendigo , larry fessenden's spooky new thriller , is a refreshingly smart and newfangled variation on several themes derived from far less sophisticated and knowing horror films . \n[woo's] most resonant film since the killer . \ncollateral damage is trash , but it earns extra points by acting as if it weren't . \na whole lot of fun and funny in the middle , though somewhat less hard-hitting at the start and finish . \nmaybe it is formula filmmaking , but there's nothing wrong with that if the film is well-crafted and this one is . \n[fincher's] camera sense and assured pacing make it an above-average thriller . \nthe film is insightful about kissinger's background and history . \nan engrossing portrait of a man whose engaging manner and flamboyant style made him a truly larger-than-life character . \na lot of the credit for the film's winning tone must go to grant , who hasn't lost a bit of the dry humor that first made audiences on both sides of the atlantic love him . \nexploits [headbanger] stereotypes in good fun , while adding a bit of heart and unsettling subject matter . \na journey that is as difficult for the audience to take as it is for the protagonist -- yet it's potentially just as rewarding . \nratliff's two previous titles , plutonium circus and purgatory county show his penchant for wry , contentious configurations , and this film is part of that delicate canon . \nfrom its invitingly upbeat overture to its pathos-filled but ultimately life-affirming finale , martin is a masterfully conducted work . \npassions , obsessions , and loneliest dark spots are pushed to their most virtuous limits , lending the narrative an unusually surreal tone . \na comedy that swings and jostles to the rhythms of life . \nat times auto focus feels so distant you might as well be watching it through a telescope . yet in its own aloof , unreachable way it's so fascinating you won't be able to look away for a second . \nif you're part of her targeted audience , you'll cheer . otherwise , maybe . \nas animation increasingly emphasizes the computer and the cool , this is a film that takes a stand in favor of tradition and warmth . \nblade ii merges bits and pieces from fighting games , wire fu , horror movies , mystery , james bond , wrestling , sci-fi and anime into one big bloody stew . \ninstead of hitting the audience over the head with a moral , schrader relies on subtle ironies and visual devices to convey point of view . \nk-19 will not go down in the annals of cinema as one of the great submarine stories , but it is an engaging and exciting narrative of man confronting the demons of his own fear and paranoia . \ncontrived as this may sound , mr . rose's updating works surprisingly well . \na glib but bouncy bit of sixties-style slickness in which the hero might wind up caught but the audience gets pure escapism . \nlavishly , exhilaratingly tasteless . \nyou don't need to be a hip-hop fan to appreciate scratch , and that's the mark of a documentary that works . \nbetween bursts of automatic gunfire , the story offers a trenchant critique of capitalism . \ncombines improbable melodrama ( gored bullfighters , comatose ballerinas ) with subtly kinky bedside vigils and sensational denouements , and yet at the end , we are undeniably touched . \nwhile the story's undeniably hard to follow , iwai's gorgeous visuals seduce . \nif you can get past the taboo subject matter , it will be well worth your time . \na lovely film . . . elegant , witty and beneath a prim exterior unabashedly romantic . . . hugely enjoyable in its own right though not really faithful to its source's complexity . \nscooby doo is surely everything its fans are hoping it will be , and in that sense is a movie that deserves recommendation . \n[a] devastatingly powerful and astonishingly vivid holocaust drama . \na solid cast , assured direction and complete lack of modern day irony . \nthese characters are so well established that the gang feels comfortable with taking insane liberties and doing the goofiest stuff out of left field , and i'm all for that . \na sun-drenched masterpiece , part parlor game , part psychological case study , part droll social satire . \nworth a look as a curiosity . \nyou watch for that sense of openness , the little surprises . \ndirector peter kosminsky gives these women a forum to demonstrate their acting 'chops' and they take full advantage . \nauto focus is not your standard hollywood bio-pic . schrader aims to present an unflinching look at one man's downfall , brought about by his lack of self-awareness . \nthe bourne identity shouldn't be half as entertaining as it is , but director doug liman and his colleagues have managed to pack it with enough action to satisfy the boom-bam crowd without a huge sacrifice of character and mood . \nfor veggietales fans , this is more appetizing than a side dish of asparagus . if you're not a fan , it might be like trying to eat brussels sprouts . \nremove spider-man the movie from its red herring surroundings and it's apparent that this is one summer film that satisfies . \nthe whole mildly pleasant outing -- the r rating is for brief nudity and a grisly corpse -- remains aloft not on its own self-referential hot air , but on the inspired performance of tim allen . \na gorgeously strange movie , heaven is deeply concerned with morality , but it refuses to spell things out for viewers . \nthe emperor's club , ruthless in its own placid way , finds one of our most conservative and hidebound movie-making traditions and gives it new texture , new relevance , new reality . \nit's truly awful and heartbreaking subject matter , but one whose lessons are well worth revisiting as many times as possible . \nthough intrepid in exploring an attraction that crosses sexual identity , ozpetek falls short in showing us antonia's true emotions . . . but at the very least , his secret life will leave you thinking . \nthere is little question that this is a serious work by an important director who has something new to say about how , in the flip-flop of courtship , we often reel in when we should be playing out . \nthe message of such reflections--intentional or not--is that while no art grows from a vacuum , many artists exist in one . \ngooding is the energetic frontman , and it's hard to resist his enthusiasm , even if the filmmakers come up with nothing original in the way of slapstick sequences . \nthe otherwise good-naturedness of mr . deeds , with its embrace of sheer goofiness and cameos of less- than-likely new york celebrities . . . certainly raises the film above anything sandler's been attached to before . \nthe movie is brilliant , really . it is philosophy , illustrated through everyday events . \nit's stylishly directed with verve . . . \ngives an intriguing twist to the french coming-of-age genre . \noffers an interesting look at the rapidly changing face of beijing . \na solid , psychological action film from hong kong . \nsee it now , before the inevitable hollywood remake flattens out all its odd , intriguing wrinkles . \nholm does his sly , intricate magic , and iben hjelje is entirely appealing as pumpkin . \nan enjoyable feel-good family comedy regardless of race . \nfeatures what is surely the funniest and most accurate depiction of writer's block ever . \nit would take a complete moron to foul up a screen adaptation of oscar wilde's classic satire . \nit's bright , pristine style and bold colors make it as much fun as reading an oversized picture book before bedtime . \nin the long , dishonorable history of quickie teen-pop exploitation , like mike stands out for its only partly synthetic decency . \nbravo for history rewritten , and for the uncompromising knowledge that the highest power of all is the power of love . \nlead actress ga , she of the impossibly long limbs and sweetly conspiratorial smile , is a towering siren . \neven if you've seen \" stomp \" ( the stage show ) , you still have to see this ! \n . . . a light , yet engrossing piece . lux , now in her eighties , does a great combination act as narrator , jewish grandmother and subject  taking us through a film that is part biography , part entertainment and part history . \nit's a setup so easy it borders on facile , but keeping the film from cheap-shot mediocrity is its crack cast . \nrife with the rueful , wry humor springing out of yiddish culture and language . \na time machine , a journey back to your childhood , when cares melted away in the dark theater , and films had the ability to mesmerize , astonish and entertain . \nrubbo's humorously tendentious intervention into the who-wrote-shakespeare controversy . \ncantet beautifully illuminates what it means sometimes to be inside looking out , and at other times outside looking in . \nk-19 : the widowmaker is a great yarn . \nit's as raw and action-packed an experience as a ringside seat at a tough-man contest . \nevokes the frustration , the awkwardness and the euphoria of growing up , without relying on the usual tropes . \na brilliant gag at the expense of those who paid for it and those who pay to see it . \nvisually striking and viscerally repellent . \novercomes its visual hideousness with a sharp script and strong performances . \ntouch ! \nastonishingly skillful and moving . . . it could become a historically significant work as well as a masterfully made one . \nbeautifully crafted and cooly unsettling . . . recreates the atmosphere of the crime expertly . \nthe year 2002 has conjured up more coming-of-age stories than seem possible , but take care of my cat emerges as the very best of them . \nalthough it doesn't always hang together -- violence and whimsy don't combine easily -- \" cherish \" certainly isn't dull . \nthe sight of the spaceship on the launching pad is duly impressive in imax dimensions , as are shots of the astronauts floating in their cabins . \ntime is a beautiful film to watch , an interesting and at times captivating take on loss and loneliness . \nan intriguing look at the french film industry during the german occupation ; its most delightful moments come when various characters express their quirky inner selves . \na fine documentary can be distinguished from a mediocre one by the better film's ability to make its subject interesting to those who aren't part of its supposed target audience . judging by those standards , 'scratch' is a pretty decent little documentary . \nfubar is very funny , but not always in a laugh-out-loud way . \na diverse and astonishingly articulate cast of palestinian and israeli children . \nslight but enjoyable documentary . \n'the film is stark , straightforward and deadly . . . an unnatural calm that's occasionally shaken by . . . blasts of rage , and later , violent jealousy . '\nan impressive hybrid . \ncall this the full monty on ice , the underdog sports team formula redux . \nunfolds in a low-key , organic way that encourages you to accept it as life and go with its flow . \na beguiling evocation of the quality that keeps dickens evergreen : the exuberant openness with which he expresses our most basic emotions . \nthe heat of the moment prevails . it cooks conduct in a low , smoky and inviting sizzle . \na riveting story well told . \ndenis forges out of the theories of class- based rage and sisterly obsession a razor-sided tuning fork that rings with cultural , sexual and social discord . \na compelling pre-wwii drama with vivid characters and a warm , moving message . \nthe stars may be college kids , but the subject matter is as adult as you can get : the temptations of the flesh are unleashed by a slightly crazed , overtly determined young woman and a one-night swim turns into an ocean of trouble . \npretty good little movie . \nby turns touching , raucously amusing , uncomfortable , and , yes , even sexy , never again is a welcome and heartwarming addition to the romantic comedy genre . \nworse than 'silence of the lambs' better than 'hannibal'\nif you haven't seen the film lately , you may be surprised at the variety of tones in spielberg's work . much of it is funny , but there are also some startling , surrealistic moments . . . \n[the digital effects] reminded me of terry gilliam's rudimentary old monty python cartoons , in which he would cut out figures from drawings and photographs and paste them together . \nan entertaining mix of period drama and flat-out farce that should please history fans . \ncanada's arctic light shines bright on this frozen tundra soap opera that breathes extraordinary life into the private existence of the inuit people . \nthe fluid motion is astounding on any number of levels -- including the physical demands made on bttner -- and it implies in its wake the intractable , irreversible flow of history . \nalternately hilarious and sad , aggravating and soulful , scathing and joyous . it's a masterpeice . \nthe film's messages of tolerance and diversity aren't particularly original , but one can't help but be drawn in by the sympathetic characters . \nthough it lacks the utter authority of a genre gem , there's a certain robustness to this engaging mix of love and bloodletting . \na conventional , but well-crafted film about a historic legal battle in ireland over a man's right to raise his own children . \nyes , it's as good as you remember . in fact , even better . \nhartley adds enough quirky and satirical touches in the screenplay to keep the film entertaining . \nan uncomfortable movie , suffocating and sometimes almost senseless , the grey zone does have a center , though a morbid one . \nthis is a harrowing movie about how parents know where all the buttons are , and how to push them . \na stirring road movie . \none of the best films i have ever seen , constantly pulling the rug from underneath us , seeing things from new sides , plunging deeper , getting more intense . \ninsanely hilarious ! i haven't laughed that hard in years ! \nanyone who's ever suffered under a martinet music instructor has no doubt fantasized about what an unhappy , repressed and twisted personal life their tormentor deserved . these people are really going to love the piano teacher . \nit's a tour de force , written and directed so quietly that it's implosion rather than explosion you fear . \nit may not be history  but then again , what if it is ?  but it makes for one of the most purely enjoyable and satisfying evenings at the movies i've had in a while . \nif \" lilo & stitch \" isn't the most edgy piece of disney animation to hit the silver screen , then this first film to use a watercolor background since \" dumbo \" certainly ranks as the most original in years . \nthis may be dover kosashvili's feature directing debut , but it looks an awful lot like life -- gritty , awkward and ironic . \nthis ready-made midnight movie probably won't stand the cold light of day , but under the right conditions , it's goofy ( if not entirely wholesome ) fun . \nsee scratch for the history , see scratch for the music , see scratch for a lesson in scratching , but , most of all , see it for the passion . \n . . . \" bowling for columbine \" remains a disquieting and thought-provoking film . . . \neven though it is infused with the sensibility of a video director , it doesn't make for completely empty entertainment\nbut even with the two-wrongs-make-a-right chemistry between jolie and burns . . . this otherwise appealing picture loses its soul to screenwriting for dummies conformity . \ntalk to her is so darned assured , we have absolutely no idea who the main characters are until the film is well under way -- and yet it's hard to stop watching . \nstar/producer salma hayek and director julie taymor have infused frida with a visual style unique and inherent to the titular character's paintings and in the process created a masterful work of art of their own . \na tasty masala . \na truly wonderful tale combined with stunning animation . \na low-key labor of love that strikes a very resonant chord . \nan average kid-empowerment fantasy with slightly above-average brains . \nconfessions isn't always coherent , but it's sharply comic and surprisingly touching , so hold the gong . \nwhile guzmn frustratingly refuses to give pinochet's crimes a political context , his distance from the material is mostly admirable . \n . . . a story , an old and scary one , about the monsters we make , and the vengeance they take . \na sentimental but entirely irresistible portrait of three aging sisters . \nwhite oleander may leave you rolling your eyes in the dark , but that doesn't mean you won't like looking at it . \nin painting an unabashedly romantic picture of a nation whose songs spring directly from the lives of the people , the movie exalts the marxian dream of honest working folk , with little to show for their labor , living harmoniously , joined in song . \nthe most brilliant work in this genre since the 1984 uncut version of sergio leone's flawed but staggering once upon a time in america . \nit looks closely , insightfully at fragile , complex relationships . \nnot a bad choice here , assuming that . . . the air-conditioning in the theater is working properly . \na fine effort , an interesting topic , some intriguing characters and a sad ending . certainly the big finish wasn't something galinsky and hawley could have planned for . . . but part of being a good documentarian is being there when the rope snaps . \nit must be the end of the world : the best film so far this year is a franchise sequel starring wesley snipes . \nthere are moments of hilarity to be had . \nrefreshing . \na hypnotic portrait of this sad , compulsive life . \n[while the last metro] was more melodramatic , confined to a single theater company and its strategies and deceptions , while tavernier is more concerned with the entire period of history . \none of the best films of the year with its exquisite acting , inventive screenplay , mesmerizing music , and many inimitable scenes of tenderness , loss , discontent , and yearning . \nreturn to never land is reliable , standard disney animated fare , with enough creative energy and wit to entertain all ages . \nmichael moore's latest documentary about america's thirst for violence is his best film yet . . . \nsuffice to say that after seeing this movie in imax form , you'll be more acquainted with the tiniest details of tom hanks' face than his wife is . \nlike a tarantino movie with heart , alias betty is richly detailed , deftly executed and utterly absorbing . \nmarvelously entertaining and deliriously joyous documentary . \na brisk , reverent , and subtly different sequel . \na movie i loved on first sight and , even more important , love in remembrance . \n'divertida , enternecedora , universal y profundamente sincera , es una de las mejores comedias romnticas en mucho tiempo . una verdadera delicia . '\ndeserves a place of honor next to nanook as a landmark in film history . \nmurderous maids pulls no punches in its depiction of the lives of the papin sister and the events that led to their notorious rise to infamy . . . \nthis is an undeniably intriguing film from an adventurous young talent who finds his inspiration on the fringes of the american underground . \nthe sweetest thing , a romantic comedy with outrageous tendencies , may be a mess in a lot of ways . but it does have one saving grace . a lot of its gags and observations reflect a woman's point-of-view . \nthis is lightweight filmmaking , to be sure , but it's pleasant enough -- and oozing with attractive men . \nat its most basic , this cartoon adventure is that wind-in-the-hair exhilarating . \nfans of critics' darling band wilco will marvel at the sometimes murky , always brooding look of i am trying to break your heart . \nthe film presents visceral and dangerously honest revelations about the men and machines behind the curtains of our planet . \n[gosling's] combination of explosive physical energy and convincing intelligence helps create a complex , unpredictable character . \nconfounding because it solemnly advances a daringly preposterous thesis . acting cannot be acted . \nfulford-wierzbicki . . . deftly captures the wise-beyond-her-years teen . \na wild ride juiced with enough energy and excitement for at least three films . \nit's a cool event for the whole family . maybe not a classic , but a movie the kids will want to see over and over again . \nfeatherweight romantic comedy has a few nice twists in a standard plot and the charisma of hugh grant and sandra bullock . \nthe movie is not as terrible as the synergistic impulse that created it . \na typically observant , carefully nuanced and intimate french coming-of-age film that is an encouraging debut feature but has a needlessly downbeat ending that is too heavy for all that has preceded it . \nless an examination of neo-nazism than a probe into the nature of faith itself . \na moving and weighty depiction of one family's attempts to heal after the death of a child . \ni don't think most of the people who loved the 1989 paradiso will prefer this new version . but i do . \na zinger-filled crowd-pleaser that open-minded elvis fans ( but by no means all ) will have fun with . \ndiggs and lathan are among the chief reasons brown sugar is such a sweet and sexy film . \nentirely suspenseful , extremely well-paced and ultimately . . . dare i say , entertaining ! \nthe riveting performances by the incredibly flexible cast make love a joy to behold . \nterrific as nadia , a russian mail-order bride who comes to america speaking not a word of english , it's kidman who holds the film together with a supremely kittenish performance that gradually accumulates more layers . \nwith an unflappable air of decadent urbanity , everett remains a perfect wildean actor , and a relaxed firth displays impeccable comic skill . \nthe re-release of ron howard's apollo 13 in the imax format proves absolutely that really , really , really good things can come in enormous packages . \nvery well written and directed with brutal honesty and respect for its audience . \nwonderful fencing scenes and an exciting plot make this an eminently engrossing film . \nit's pretty linear and only makeup-deep , but bogdanovich ties it together with efficiency and an affection for the period . \na surprisingly charming and even witty match for the best of hollywood's comic-book adaptations . \nthis is a superior horror flick . \nadaptation is simply brilliant . \nsmart and alert , thirteen conversations about one thing is a small gem . \nthe pleasure of read my lips is like seeing a series of perfect black pearls clicking together to form a string . we're drawn in by the dark luster . \na haunting tale of murder and mayhem . \ni love the opening scenes of a wintry new york city in 1899 . cinematic poetry showcases the city's old-world charm before machines change nearly everything . \nit's hard to imagine anyone managing to steal a movie not only from charismatic rising star jake gyllenhaal but also from accomplished oscar winners susan sarandon , dustin hoffman and holly hunter , yet newcomer ellen pompeo pulls off the feat with aplomb . \none of the best rock documentaries ever . wilco is a phenomenal band with such an engrossing story that will capture the minds and hearts of many . \nian holm conquers france as an earthy napoleon\noffers big , fat , dumb laughs that may make you hate yourself for giving in . ah , what the hell . \n[sports] admirable energy , full-bodied characterizations and narrative urgency . \na portrait of an artist . \ndirectors brett morgen and nanette burstein have put together a bold biographical fantasia . \nthe subtitled costume drama is set in a remote african empire before cell phones , guns , and the internal combustion engine , but the politics that thump through it are as timely as tomorrow . \na tremendous piece of work . \na delightful , if minor , pastry of a movie . \nwhile obviously aimed at kids , the country bears . . . should keep parents amused with its low groan-to-guffaw ratio . \nlabute masterfully balances both traditional or modern stories together in a manner that one never overwhelms the other . something for everyone . \nirwin is so earnest that it's hard to resist his pleas to spare wildlife and respect their environs . there are far worse messages to teach a young audience , which will probably be perfectly happy with the sloppy slapstick comedy . \nleigh succeeds in delivering a dramatic slap in the face that's simultaneously painful and refreshing . \nnot about scares but a mood in which an ominous , pervasive , and unknown threat lurks just below the proceedings and adds an almost constant mindset of suspense . \n'film aficionados cannot help but love cinema paradiso , whether the original version or new director's cut . '\na fascinating glimpse into an insular world that gives the lie to many clichs and showcases a group of dedicated artists . \nit's one thing to read about or rail against the ongoing - and unprecedented - construction project going on over our heads . it's quite another to feel physically caught up in the process . \ncontradicts everything we've come to expect from movies nowadays . instead of simply handling conventional material in a conventional way , secretary takes the most unexpected material and handles it in the most unexpected way . \ncould i have been more geeked when i heard that apollo 13 was going to be released in imax format ? in a word : no . \nmurderous maids has a lot going for it , not least the brilliant performances by testud . . . and parmentier . \nfilmmaker stacy peralta has a flashy editing style that doesn't always jell with sean penn's monotone narration , but he respects the material without sentimentalizing it . \nthere are a couple of things that elevate \" glory \" above most of its ilk , most notably the mere presence of duvall . \nit's light on the chills and heavy on the atmospheric weirdness , and there are moments of jaw-droppingly odd behavior -- yet i found it weirdly appealing . \n ( rises ) above its oh-so-hollywood rejiggering and its conventional direction to give the film a soul and an unabashed sense of good old-fashioned escapism . \na breezy blend of art , history , esoteric musings and philosophy . \nkids will love its fantasy and adventure , and grownups should appreciate its whimsical humor . \ntsai ming-liang's ghosts are painfully aware of their not-being . \nleaping from one arresting image to another , songs from the second floor has all the enjoyable randomness of a very lively dream and so manages to be compelling , amusing and unsettling at the same time . \nsean penn , you owe nicolas cage an apology . \nthe performances are uniformly good . \nshe's all-powerful , a voice for a pop-cyber culture that feeds on her bjorkness . \nit's a perfect show of respect to just one of those underrated professionals who deserve but rarely receive it . \nfor all its plot twists , and some of them verge on the bizarre as the film winds down , blood work is a strong , character-oriented piece . \nthe story line may be 127 years old , but el crimen del padre amaro . . . couldn't be more timely in its despairing vision of corruption within the catholic establishment . \nthis in-depth study of important developments of the computer industry should make it required viewing in university computer science departments for years to come . \nit shows us a slice of life that's very different from our own and yet instantly recognizable . \na wonderfully speculative character study that made up for its rather slow beginning by drawing me into the picture . \nhas its share of arresting images . \nleave it to john sayles to take on developers , the chamber of commerce , tourism , historical pageants , and commercialism all in the same movie . . . without neglecting character development for even one minute . \nreign of fire just might go down as one of the all-time great apocalypse movies . \npoignant and funny . \na smart little indie . \npayne has created a beautiful canvas , and nicholson proves once again that he's the best brush in the business . \nsee it . debate it . remember it . \ntry as you might to resist , if you've got a place in your heart for smokey robinson , this movie will worm its way there . \na riveting profile of law enforcement , and a visceral , nasty journey into an urban hades . \ndirector douglas mcgrath takes on nickleby with all the halfhearted zeal of an 8th grade boy delving into required reading . \nstands as a document of what it felt like to be a new yorker -- or , really , to be a human being -- in the weeks after 9/11 . \ni am not generally a huge fan of cartoons derived from tv shows , but hey arnold ! the movie is clever , offbeat and even gritty enough to overcome my resistance . \nwith not a lot of help from the screenplay ( proficient , but singularly cursory ) , [testud] acts with the feral intensity of the young bette davis . \nit's a film that's destined to win a wide summer audience through word-of-mouth reviews and , not far down the line , to find a place among the studio's animated classics . \nslow and ponderous , but rohmer's drama builds to an intense indoor drama about compassion , sacrifice , and christian love in the face of political corruption . \nif you're not totally weirded- out by the notion of cinema as community-therapy spectacle , quitting hits home with disorienting force . \naustin powers for the most part is extremely funny , the first part making up for any flaws that come later . \nwhile tattoo borrows heavily from both seven and the silence of the lambs , it manages to maintain both a level of sophisticated intrigue and human-scale characters that suck the audience in . \ncho continues her exploration of the outer limits of raunch with considerable brio . \nelvira fans could hardly ask for more . \na canny , derivative , wildly gruesome portrait of a london sociopath who's the scariest of sadists . \nthe movie should be credited with remembering his victims . \nfast-paced and wonderfully edited , the film is extremely thorough . \na bracing , unblinking work that serves as a painful elegy and sobering cautionary tale . \nhashiguchi uses the situation to evoke a japan bustling atop an undercurrent of loneliness and isolation . \nas if trying to grab a lump of play-doh , the harder that liman tries to squeeze his story , the more details slip out between his fingers . \nmy big fat greek wedding is not only the best date movie of the year , it's also a -- dare i say it twice -- delightfully charming -- and totally american , i might add -- slice of comedic bliss . \nfew films have captured the chaos of an urban conflagration with such fury , and audience members will leave feeling as shaken as nesbitt's cooper looks when the bullets stop flying . \nanother love story in 2002's remarkable procession of sweeping pictures that have reinvigorated the romance genre . \nit's another retelling of alexandre dumas' classic . why ? who knows , but it works under the direction of kevin reynolds . \n[f]rom the performances and the cinematography to the outstanding soundtrack and unconventional narrative , the film is blazingly alive and admirable on many levels . \nshiri is an action film that delivers on the promise of excitement , but it also has a strong dramatic and emotional pull that gradually sneaks up on the audience . \nprovides the kind of 'laugh therapy' i need from movie comedies -- offbeat humor , amusing characters , and a happy ending . after seeing 'analyze that , ' i feel better already . \na penetrating , potent exploration of sanctimony , self-awareness , self-hatred and self-determination . \nthis isn't a retooled genre piece , the tale of a guy and his gun , but an amiably idiosyncratic work . \noverall , it's a very entertaining , thought-provoking film with a simple message : god is love . \nit may not be a great piece of filmmaking , but its power comes from its soul's-eye view of how well-meaning patronizing masked a social injustice , at least as represented by this case . \nalthough mainstream american movies tend to exploit the familiar , every once in a while a film arrives from the margin that gives viewers a chance to learn , to grow , to travel . \njeong-hyang lee's film is deceptively simple , deeply satisfying . \nthe film is a hoot , and is just as good , if not better than much of what's on saturday morning tv especially the pseudo-educational stuff we all can't stand . \ngeorge clooney , in his first directorial effort , presents this utterly ridiculous shaggy dog story as one of the most creative , energetic and original comedies to hit the screen in years . \neven when it drags , we are forced to reflect that its visual imagination is breathtaking\nalthough commentary on nachtwey is provided . . . it's the image that really tells the tale . \na life-size reenactment of those jack chick cartoon tracts that always ended with some hippie getting tossed into the lake of fire . \ngrainy photography mars an otherwise delightful comedy of errors . \nthis film is not a love letter for the slain rappers , it's a taunt -a call for justice for two crimes from which many of us have not yet recovered . \nthe film is impressive for the sights and sounds of the wondrous beats the world has to offer . \ndaily struggles and simple pleasures usurp the preaching message so that , by the time the credits roll across the pat ending , a warm , fuzzy feeling prevails . \n . . . in no way original , or even all that memorable , but as downtown saturday matinee brain candy , it doesn't disappoint . \nclever and unflinching in its comic barbs , slap her is a small but rewarding comedy that takes aim at contemporary southern adolescence and never lets up . \ncremaster 3 is at once a tough pill to swallow and a minor miracle of self-expression . \nsex is one of those films that aims to confuse . \ncompared to his series of spectacular belly flops both on and off the screen , runteldat is something of a triumph . \n[moore's] better at fingering problems than finding solutions . but though he only scratches the surface , at least he provides a strong itch to explore more . \nthe powerful success of read my lips with such provocative material shows why , after only three films , director/co-writer jacques audiard , though little known in this country , belongs in the very top rank of french filmmakers . \nin his debut as a director , washington has a sure hand . his work with actors is particularly impressive . \na generous , inspiring film that unfolds with grace and humor and gradually becomes a testament to faith . \ndelivers the sexy razzle-dazzle that everyone , especially movie musical fans , has been hoping for . \nmorvern rocks . \nvincent gallo is right at home in this french shocker playing his usual bad boy weirdo role . \nfierce , glaring and unforgettable . \ncletis is playful but highly studied and dependent for its success on a patient viewer . \nlike its predecessor , it's no classic , but it provides a reasonably attractive holiday contraption , one that families looking for a clean , kid-friendly outing should investigate . \ncampanella gets the tone just right -- funny in the middle of sad in the middle of hopeful . \neither a fascinating study of the relationship between mothers and their children or a disturbing story about sociopaths and their marks . \n . . . gripping and handsome execution , ( but ) there isn't much about k-19 that's unique or memorable . \neffective in all its aspects , margarita happy hour represents an auspicious feature debut for chaiken . \nthe delicious trimmingsarrive early and stay late , filling nearly every minutewith a lighthearted glow , some impudent snickers , and a glorious dose of humankind's liberating ability to triumph over a scrooge or two . \nstanding by yourself is haunting . . . [it's] what punk rock music used to be , and what the video medium could use more of : spirit , perception , conviction . \nnot the best herzog perhaps , but unmistakably herzog . \nenjoyably fast-moving , hard-hitting documentary . \nrehearsals are frequently more fascinating than the results . last dance , whatever its flaws , fulfills one facet of its mission in making me want to find out whether , in this case , that's true . \nthe film's constant mood of melancholy and its unhurried narrative are masterfully controlled . but  in trying to capture the novel's deeper intimate resonances , the film has  ironically - distanced us from the characters . \nthis is a stunning film , a one-of-a-kind tour de force . \n[cho's face is] an amazing slapstick instrument , creating a scrapbook of living mug shots . \nit's about as convincing as any other arnie musclefest , but has a little too much resonance with real world events and ultimately comes off as insultingly simplistic . \nwhile not quite a comedy , the film tackles its relatively serious subject with an open mind and considerable good cheer , and is never less than engaging . \nan extremely funny , ultimately heartbreaking look at life in contemporary china . \nyour response to its new sequel , analyze that , may hinge on what you thought of the first film . \ndavis is funny , charming and quirky in her feature film acting debut as amy . \nbloody sunday has the grace to call for prevention rather than to place blame , making it one of the best war movies ever made . it's a movie that accomplishes so much that one viewing can't possibly be enough . \na lively and engaging examination of how similar obsessions can dominate a family . \nin the new release of cinema paradiso , the tale has turned from sweet to bittersweet , and when the tears come during that final , beautiful scene , they finally feel absolutely earned . \nfaithful without being forceful , sad without being shrill , \" a walk to remember \" succeeds through sincerity . \nthe film is a masterpiece of nuance and characterization , marred only by an inexplicable , utterly distracting blunder at the very end . \nthe film is full of charm . \nthe movie is well crafted , and well executed . if you're paying attention , the \" big twists \" are pretty easy to guess - but that doesn't make the movie any less entertaining . \none of those unassuming films that sneaks up on you and stays with you long after you have left the theatre . \n . . . pray doesn't have a passion for the material . he nonetheless appreciates the art and reveals a music scene that transcends culture and race . \nthe one-liners are snappy , the situations volatile and the comic opportunities richly rewarded . \nit's anchored by splendid performances from an honored screen veteran and a sparkling newcomer who instantly transform themselves into a believable mother/daughter pair . \nintelligent and moving . \nfathers and sons , and the uneasy bonds between them , rarely have received such a sophisticated and unsentimental treatment on the big screen as they do in this marvelous film . \nthis sci-fi techno-sex thriller starts out bizarre and just keeps getting weirder . \nlast orders nurtures the multi-layers of its characters , allowing us to remember that life's ultimately a gamble and last orders are to be embraced . it's affecting , amusing , sad and reflective . \na slight but sweet film . \nel peso de un lquido incoloro , el peso del lquido vital , del amor como elemento de vida , y de muerte . . . \nwriter/director walter hill is in his hypermasculine element here , once again able to inject some real vitality and even art into a pulpy concept that , in many other hands would be completely forgettable . \nit is a happy , heady jumble of thought and storytelling , an insane comic undertaking that ultimately coheres into a sane and breathtakingly creative film . \nthis new time machine is hardly perfect yet it proves surprisingly serviceable . even at its worst , it's not half-bad . \nalmost everyone growing up believes their family must look like \" the addams family \" to everyone looking in . . . \" my big fat greek wedding \" comes from the heart . . . \nonce folks started hanging out at the barbershop , they never wanted to leave . chances are you won't , either . \ngeorge lucas returns as a visionary with a tale full of nuance and character dimension . \ncan be viewed as pure composition and form -- film as music\nan extraordinary dramatic experience . \nevery individual will see the movie through the prism of his or her own beliefs and prejudices , but the one thing most will take away is the sense that peace is possible . that , in itself , is extraordinary . \nif you can tolerate the redneck-versus-blueblood cliches that the film trades in , sweet home alabama is diverting in the manner of jeff foxworthy's stand-up act . \nit's a treat watching shaw , a british stage icon , melting under the heat of phocion's attentions . \nall in all , an interesting look at the life of the campaign-trail press , especially ones that don't really care for the candidate they're forced to follow . \nnarc is a no-bull throwback to 1970s action films . it zips along with b-movie verve while adding the rich details and go-for-broke acting that heralds something special . \nme without you has a bracing truth that's refreshing after the phoniness of female-bonding pictures like divine secrets of the ya-ya sisterhood . \nit's a strange film , one that was hard for me to warm up to . \ngoes a long way on hedonistic gusto . \nthe result puts a human face on derrida , and makes one of the great minds of our times interesting and accessible to people who normally couldn't care less . \nthe scorpion king is more fun than conan the barbarian . \nif there's one big point to promises , it's that nothing can change while physical and psychological barriers keep the sides from speaking even one word to each other . \nunexpected moments of authentically impulsive humor are the hallmark of this bittersweet , uncommonly sincere movie that portrays the frank humanity ofemotional recovery . \njacquot has filmed the opera exactly as the libretto directs , ideally capturing the opera's drama and lyricism . \nthis is a sincerely crafted picture that deserves to emerge from the traffic jam of holiday movies . \ni liked it because it was so endlessly , grotesquely , inventive . \naudiard successfully maintains suspense on different levels throughout a film that is both gripping and compelling . \ncredit director ramsay for taking the sometimes improbable story and making it feel realistic . \nthis is dicaprio's best performance in anything ever , and easily the most watchable film of the year . \nwitherspoon puts to rest her valley-girl image , but it's dench who really steals the show . \neven when there are lulls , the emotions seem authentic , and the picture is so lovely toward the end . . . you almost don't notice the 129-minute running time . \nwhile dutifully pulling on heartstrings , directors dean deblois and chris sanders valiantly keep punching up the mix . \nambitious , unsettling psychodrama that takes full , chilling advantage of its rough-around-the-edges , low-budget constraints . \neric byler's nuanced pic avoids easy sentiments and explanations . . . \nmanages to be wholesome and subversive at the same time . \nwhen it's not wallowing in hormonal melodrama , \" real women have curves \" is a sweet , honest , and enjoyable comedy-drama about a young woman who wants many things in life , but fears she'll become her mother before she gets to fulfill her dreams . \nthe film runs on a little longer than it needs to -- muccino either doesn't notice when his story ends or just can't tear himself away from the characters -- but it's smooth and professional . \nblithely anachronistic and slyly achronological . \nthis starts off with a 1950's doris day feel and it gets very ugly , very fast . the first five minutes will have you talking 'til the end of the year ! \ntriumph of love is a very silly movie , but the silliness has a pedigree . \ndiscursive but oddly riveting documentary . \nthe movie has no respect for laws , political correctness or common decency , but it displays something more important : respect for its flawed , crazy people . \non its own , big trouble could be considered a funny little film . \nan undeniably gorgeous , terminally smitten document of a troubadour , his acolytes , and the triumph of his band . \nthis cinema verite speculation on the assassination of john f . kennedy may have been inspired by blair witch , but it takes its techniques into such fresh territory that the film never feels derivative . \na beautifully observed character piece . \na coming-of-age movie that hollywood wouldn't have the guts to make . \nit is quite a vision . \nthere are laughs aplenty , and , as a bonus , viewers don't have to worry about being subjected to farts , urine , feces , semen , or any of the other foul substances that have overrun modern-day comedies . \na bittersweet drama about the limbo of grief and how truth-telling can open the door to liberation . \na strong and confident work which works so well for the first 89 minutes , but ends so horrendously confusing in the final two\nsalma goes native and she's never been better in this colorful bio-pic of a mexican icon . \nfilled with alexandre desplat's haunting and sublime music , the movie completely transfixes the audience . \nas chilling and fascinating as philippe mora's modern hitler-study , snide and prejudice . \nan hour and a half of joyful solo performance . \nstrange and beautiful film . \nno worse a film than breaking out , and breaking out was utterly charming . \nparker cannot sustain the buoyant energy level of the film's city beginnings into its country conclusion\n' . . . despite lagging near the finish line , the movie runs a good race , one that will have you at the edge of your seat for long stretches . '\n . . . a guiltless film for nice evening out . \ndeflated ending aside , there's much to recommend the film . \nit's a treat  a delightful , witty , improbable romantic comedy with a zippy jazzy score grant and bullock make it look as though they are having so much fun . \nperformances all around are tops , with the two leads delivering oscar-caliber performances . \neverything about the quiet american is good , except its timing . \na savage john waters-like humor that dances on the edge of tastelessness without ever quite falling over . \nat once a testament to the divine calling of education and a demonstration of the painstaking process of imparting knowledge . \nmay seriously impair your ability to ever again maintain a straight face while speaking to a highway patrolman . \nit's an interesting effort ( particularly for jfk conspiracy nuts ) , and barry's cold-fish act makes the experience worthwhile . \nthey're just a couple of cops in copmovieland , these two , but in narc , they find new routes through a familiar neighborhood . \nbrings awareness to an issue often overlooked -- women's depression . \nit's a shame the marvelous first 101 minutes have to be combined with the misconceived final 5 . \nit has a caffeinated , sloppy brilliance , sparkling with ideas you wish had been developed with more care , but animated by an energy that puts the dutiful efforts of more disciplined grade-grubbers to shame . \nyou can almost see mendes and company getting together before a single frame had been shot and collectively vowing , 'this is going to be something really good . ' and it is . \nfoster and whitaker are especially fine . she is a lioness , protecting her cub , and he a reluctant villain , incapable of controlling his crew . \nundoubtedly the scariest movie ever made about tattoos . \na movie that will wear you out and make you misty even when you don't want to be . \nnot only better than its predecessor , it may rate as the most magical and most fun family fare of this or any recent holiday season . \nthough the story . . . is hackneyed , the characters have a freshness and modesty that transcends their predicament . \nalthough frailty fits into a classic genre , in its script and execution it is a remarkably original work . \nif this movie leaves you cool , it also leaves you intriguingly contemplative . \nthe climactic events are so well realized that you may forget all about the original conflict , just like the movie does\na rude black comedy about the catalytic effect a holy fool has upon those around him in the cutthroat world of children's television . \nall comedy is subversive , but this unrelenting bleak insistence on opting out of any opportunity for finding meaning in relationships or work just becomes sad . \nif a horror movie's primary goal is to frighten and disturb , then they works spectacularly well . . . a shiver-inducing , nerve-rattling ride . \na playful iranian parable about openness , particularly the need for people of diverse political perspectives to get along despite their ideological differences . \nbrilliantly written and well-acted , yellow asphalt is an uncompromising film . \nthat 'alabama' manages to be pleasant in spite of its predictability and occasional slowness is due primarily to the perkiness of witherspoon ( who is always a joy to watch , even when her material is not first-rate ) . . . \npersonal velocity has a no-frills docu-dogma plainness , yet miller lingers on invisible , nearly psychic nuances , leaping into digressions of memory and desire . she boxes these women's souls right open for us . \na fascinating literary mystery story with multiple strands about the controversy of who really wrote shakespeare's plays . \nthroughout , mr . audiard's direction is fluid and quick . \na dashing and absorbing outing with one of france's most inventive directors . \nit's a fine , old-fashioned-movie movie , which is to say it's unburdened by pretensions to great artistic significance . \n . . . flat-out amusing , sometimes endearing and often fabulous , with a solid cast , noteworthy characters , delicious dialogue and a wide supply of effective sight gags . \nthe trials of henry kissinger is a remarkable piece of filmmaking  because you get it . \nnachtwey clears the cynicism right out of you . he makes you realize that deep inside righteousness can be found a tough beauty . \nwhat it lacks in substance it makes up for in heart . \nrobert harmon's less-is-more approach delivers real bump-in -the-night chills -- his greatest triumph is keeping the creepy crawlies hidden in the film's thick shadows . \ngenuinely unnerving . \namerican and european cinema has amassed a vast holocaust literature , but it is impossible to think of any film more challenging or depressing than the grey zone . \na compelling film . \nwith its hint of an awkward hitchcockian theme in tact , harmon's daunting narrative promotes a reasonable landscape of conflict and pathos to support the scattershot terrorizing tone\nin auteil's less dramatic but equally incisive performance , he's a charismatic charmer likely to seduce and conquer . \nthe heart of the film is a touching reflection on aging , suffering and the prospect of death . \nwill you go ape over this movie ? well , it probably won't have you swinging from the trees hooting it's praises , but it's definitely worth taking a look . \nits director's most substantial feature for some time . \nfontaine's direction , especially her agreeably startling use of close-ups and her grace with a moving camera , creates sheerly cinematic appeal . \nthe son of the bride's humour is born out of an engaging storyline , which also isn't embarrassed to make you reach for the tissues . this movie is to be cherished . \n . . . a visually seductive , unrepentantly trashy take on rice's second installment of her vampire chronicles . \nthe story's scope and pageantry are mesmerizing , and mr . day-lewis roars with leonine power . \np . t . anderson understands the grandness of romance and how love is the great equalizer that can calm us of our daily ills and bring out joys in our lives that we never knew were possible . \ntwenty years later , e . t . is still a cinematic touchstone . \nthis fascinating experiment plays as more of a poetic than a strict reality , creating an intriguing species of artifice that gives the lady and the duke something of a theatrical air . \nit's virtually impossible to like any of these despicable characters . \nthis is mostly well-constructed fluff , which is all it seems intended to be . \neven through its flaws , revolution #9 proves to be a compelling , interestingly told film . \nthe best way to describe it is as a cross between paul thomas anderson's magnolia and david lynch's mulholland dr . \nschepisi , aided by a cast that seems to include every top-notch british actor who did not appear in gosford park ( as well as one , ms . mirren , who did ) , has succeeded beyond all expectation . \nlejos del paraso es al mismo tiempo una fiesta para los ojos y odos y un movilizador cuadro de personajes enfrentados a sus propios deseos , miedos y prejuicios . \nwatching this film , one is left with the inescapable conclusion that hitchens' obsession with kissinger is , at bottom , a sophisticated flower child's desire to purge the world of the tooth and claw of human power . \nthere is no denying the power of polanski's film . . . \nthe movie is amateurish , but it's a minor treat . \nthis charming but slight tale has warmth , wit and interesting characters compassionately portrayed . \nel ttulo no engaa : la pelcula narra una historia donde una mujer enfrentar cierta realidad , y donde el sexo es . . . \noffers a persuasive look at a defeated but defiant nation in flux . \n'interesante y disfrutable trabajo gracias a que prescinde del clsico elemento estadounidense patriotero y manipulador . '\na return to pure disney magic and is enjoyable family fare . \ntakes a fresh and absorbing look at a figure whose legacy had begun to bronze . \na triumph of pure craft and passionate heart . \ngosling creates a staggeringly compelling character , a young man whose sharp intellect is at the very root of his contradictory , self-hating , self-destructive ways . \nwitty and often surprising , a dark little morality tale disguised as a romantic comedy . \neven as it pays earnest homage to turntablists and beat jugglers , old schoolers and current innovators , scratch is great fun , full of the kind of energy it's documenting . \ngot a david lynch jones ? then you'd do well to check this one out because it's straight up twin peaks action . . . \nastonishing . . . [frames] profound ethical and philosophical questions in the form of dazzling pop entertainment . \ntake care is nicely performed by a quintet of actresses , but nonetheless it drags during its 112-minute length . \nit's hard to fairly judge a film like ringu when you've seen the remake first . many of the effective horror elements are dampened through familiarity , [yet] are worthwhile . \none of the very best movies ever made about the life of moviemaking . \nrarely does such high-profile talent serve such literate material . \nan elegant and sly deadpan comedy . \nthe way the roundelay of partners functions , and the interplay within partnerships and among partnerships and the general air of gator-bashing are consistently delightful . \nland , people and narrative flow together in a stark portrait of motherhood deferred and desire explored . \n'blue crush' swims away with the sleeper movie of the summer award . \nyou're not merely watching history , you're engulfed by it . \na chick flick for guys . \nit's mildly entertaining , especially if you find comfort in familiarity . but it's hardly a necessary enterprise . \nthe quiet american isn't a bad film , it's just one that could easily wait for your pay per view dollar . \nas home movie gone haywire , it's pretty enjoyable , but as sexual manifesto , i'd rather listen to old tori amos records . \nin its treatment of the dehumanizing and ego-destroying process of unemployment , time out offers an exploration that is more accurate than anything i have seen in an american film . \nlike an episode of mtv's undressed , with 20 times the creativity but without any more substance . . . indulgently entertaining but could have and should have been deeper . \ndeliciously slow . \na sensitive , cultivated treatment of greene's work as well as a remarkably faithful one . \nit's not just a feel-good movie , it's a feel movie . you feel good , you feel sad , you feel pissed off , but in the end , you feel alive - which is what they did . \nit's a piece of handiwork that shows its indie tatters and self-conscious seams in places , but has some quietly moving moments and an intelligent subtlety . \nwhat makes barbershop so likable , with all its flaws , is that it has none of the pushiness and decibel volume of most contemporary comedies . \nwatching these two actors play against each other so intensely , but with restraint , is a treat . \nan example of quiet , confident craftsmanship that tells a sweet , charming tale of intergalactic friendship . \na meditation on faith and madness , frailty is blood-curdling stuff . \nthe production design , score and choreography are simply intoxicating . \na comedy that is warm , inviting , and surprising . \nso vivid a portrait of a woman consumed by lust and love and crushed by betrayal that it conjures up the intoxicating fumes and emotional ghosts of a freshly painted rembrandt . \nsuspend your disbelief here and now , or you'll be shaking your head all the way to the credits . \ntrades run-of-the-mill revulsion for extreme unease . \n . . . one of the more influential works of the 'korean new wave' . \nimplicitly acknowledges and celebrates the glorious chicanery and self-delusion of this most american of businesses , and for that reason it may be the most oddly honest hollywood document of all . \na beautifully tooled action thriller about love and terrorism in korea . \ndirector-writer bille august . . . depicts this relationship with economical grace , letting his superb actors convey martin's deterioration and barbara's sadness -- and , occasionally , anger . \nvictor rosa is leguizamo's best movie work so far , a subtle and richly internalized performance . \nbirthday girl doesn't try to surprise us with plot twists , but rather seems to enjoy its own transparency . \nsmart , funny and just honest enough to provide the pleasures of a slightly naughty , just-above-average off- broadway play . \ntopics that could make a sailor blush - but lots of laughs . \nmichael moore has perfected the art of highly entertaining , self-aggrandizing , politically motivated documentary-making , and he's got as potent a topic as ever here . \na fine production with splendid singing by angela gheorghiu , ruggero raimondi , and roberto alagna . \nabout a boy vividly recalls the cary grant of room for one more , houseboat and father goose in its affectionate depiction of the gentle war between a reluctant , irresponsible man and the kid who latches onto him . \nnone of this is meaningful or memorable , but frosting isn't , either , and you wouldn't turn down a big bowl of that , would you ? \nthe film is a fierce dance of destruction . its flame-like , roiling black-and-white inspires trembling and gratitude . \na riveting documentary . \nmay lack the pungent bite of its title , but it's an enjoyable trifle nonetheless . \n . . . manages to fall closer in quality to silence than to the abysmal hannibal . \nyou may think you have figured out the con and the players in this debut film by argentine director fabian bielinsky , but while you were thinking someone made off with your wallet . \ndiane lane works nothing short of a minor miracle in unfaithful . \ntakashi miike keeps pushing the envelope : ichi the killer\na fantastic premise anchors this movie , but what it needs is either a more rigid , blair witch-style commitment to its mockumentary format , or a more straightforward , dramatic treatment , with all the grandiosity that that implies . \nexhilarating but blatantly biased . \nmuch of what we see is horrible but it's also undeniably exceedingly clever . \nit understands , in a way that speaks forcefully enough about the mechanisms of poverty to transcend the rather simplistic filmmaking . \nramsay succeeds primarily with her typical blend of unsettling atmospherics , delivering a series of abrasive , stylized sequences that burn themselves upon the viewer's memory . \n . . . a thoughtful what-if for the heart as well as the mind . \nlike its bizarre heroine , it irrigates our souls . \nhawn and sarandon form an acting bond that makes the banger sisters a fascinating character study with laughs to spare . \nit's a fun adventure movie for kids ( of all ages ) that like adventure . \na very capable nailbiter . \nbecause the genre is well established , what makes the movie fresh is smart writing , skewed characters , and the title performance by kieran culkin . \n \" white oleander , \" the movie , is akin to a reader's digest condensed version of the source material . \nit's like going to a house party and watching the host defend himself against a frothing ex-girlfriend . you don't want to call the cops . you want to call domino's . \nwhat's most refreshing about real women have curves is its unforced comedy-drama and its relaxed , natural-seeming actors . \nthe low-key direction is pleasingly emphatic in this properly intense , claustrophobic tale of obsessive love . \nsecretary is just too original to be ignored . \nthat rare film whose real-life basis is , in fact , so interesting that no embellishment is needed . \nsmart and fun , but far more witty than it is wise . \nthis isn't a stand up and cheer flick ; it's a sit down and ponder affair . and thanks to kline's superbly nuanced performance , that pondering is highly pleasurable . \noriginality ain't on the menu , but there's never a dull moment in the giant spider invasion comic chiller . \nwalter hill's undisputed is like a 1940s warner bros . b picture , and i mean that as a compliment . \nthis one is not nearly as dreadful as expected . in fact , it's quite fun in places . \nwith elements cribbed from lang's metropolis , welles' kane , and eisenstein's potemkin , the true wonder of rintar's metropolis is the number of lasting images all its own . \na biopic about crane's life in the classic tradition but evolves into what has become of us all in the era of video . \nthe best of the pierce brosnan james bond films to date . \nthanks to a small star with big heart , this family film sequel is plenty of fun for all . \nwhile the now 72-year-old robert evans been slowed down by a stroke , he has at least one more story to tell : his own . \nit's about individual moments of mood , and an aimlessness that's actually sort of amazing . \nthe people in jessica are so recognizable and true that , as in real life , we're never sure how things will work out . \na tone poem of transgression . \ncreeps you out in high style , even if nakata did it better . \nrubbo runs through a remarkable amount of material in the film's short 90 minutes . \nvisually engrossing , seldom hammy , honorably mexican and burns its kahlories with conviction . \nthis is christmas future for a lot of baby boomers . \ndespite a quieter middle section , involving aragorn's dreams of arwen , this is even better than the fellowship . there are scenes of cinematic perfection that steal your heart away . \nspider-man is in the same category as x-men - occasionally brilliant but mostly average , showing signs of potential for the sequels , but not giving us much this time around . \nthe obnoxious title character provides the drama that gives added clout to this doc . \nan enjoyable experience . \nanyone who welcomes a dash of the avant-garde fused with their humor should take pleasure in this crazed , joyous romp of a film . \nthe fun of the movie is the chance it affords to watch jackson , who also served as executive producer , take his smooth , shrewd , powerful act abroad . \n'si bien no logra desarrollarse como un gran drama , tampoco es tan superficial como muchas cintas que pecan de pretenciosas y que resultan totalmente banales . '\nsaddled with an unwieldy cast of characters and angles , but the payoff is powerful and revelatory . \nit's something of the ultimate scorsese film , with all the stomach-turning violence , colorful new york gang lore and other hallmarks of his personal cinema painted on their largest-ever historical canvas . \nmr . caine and mr . fraser are the whole show here , with their memorable and resourceful performances . \na frustrating yet deeply watchable melodrama that makes you think it's a tougher picture than it is . \na giddy and provocative sexual romp that has something to say . \n[russell] makes good b movies ( the mask , the blob ) , and the scorpion king more than ably meets those standards . \notto-sallies has a real filmmaker's eye . \nthis is a smart movie that knows its classical music , knows its freud and knows its sade . \nthe film has an infectious enthusiasm and we're touched by the film's conviction that all life centered on that place , that time and that sport . \nbeautifully reclaiming the story of carmen and recreating it an in an african idiom . \nthe camera soars above the globe in dazzling panoramic shots that make the most of the large-screen format , before swooping down on a string of exotic locales , scooping the whole world up in a joyous communal festival of rhythm . \na flawed but engrossing thriller . \ndemonstrates the unusual power of thoughtful , subjective filmmaking . \nexpect no major discoveries , nor any stylish sizzle , but the film sits with square conviction and touching good sense on the experience of its women . \nthe success of undercover brother is found in its ability to spoof both black and white stereotypes equally . \nthis is the kind of subject matter that could so easily have been fumbled by a lesser filmmaker , but ayres makes the right choices at every turn . \ncox creates a fluid and mesmerizing sequence of images to match the words of nijinsky's diaries . \nwhat bloody sunday lacks in clarity , it makes up for with a great , fiery passion . \nits adult themes of familial separation and societal betrayal are head and shoulders above much of the director's previous popcorn work . \ndirector nancy savoca's no-frills record of a show forged in still-raw emotions captures the unsettled tenor of that post 9-11 period far better than a more measured or polished production ever could . \nthe film grows on you . and how . \none thing you have to give them credit for : the message of the movie is consistent with the messages espoused in the company's previous video work . \nhalloween : resurrection isn't exactly quality cinema , but it isn't nearly as terrible as it cold have been . \nas banal as the telling may be -- and at times , all my loved ones more than flirts with kitsch -- the tale commands attention . \nromantic comedy and dogme 95 filmmaking may seem odd bedfellows , but they turn out to be delightfully compatible here . \nthe most wondrous love story in years , it is a great film . \nsome movies suck you in despite their flaws , and heaven is one such beast . \nmy wife is an actress works as well as it does because [the leads] are such a companionable couple . \nwith spy kids 2 : the island of lost dreams , however , robert rodriguez adorns his family-film plot with an elegance and maturity that even most contemporary adult movies are lacking . \nbased on dave barry's popular book of the same name , the movie benefits from having a real writer plot out all of the characters' moves and overlapping story . \nbouquet gives a performance that is masterly . \na poignant comedy that offers food for thought . \n . . . a series of tales told with the intricate preciseness of the best short story writing . \nif you're content with a clever pseudo-bio that manages to have a good time as it doles out pieces of the famous director's life , eisenstein delivers . \nthis filmed tosca -- not the first , by the way -- is a pretty good job , if it's filmed tosca that you want . i'll stay with the stage versions , however , which bite cleaner , and deeper . \nwhile the path may be familiar , first-time director denzel washington and a top-notch cast manage to keep things interesting . \nan engaging criminal romp that will have viewers guessing just who's being conned right up to the finale . \nthe picture runs a mere 84 minutes , but it's no glance . it's a head-turner -- thoughtfully written , beautifully read and , finally , deeply humanizing . \nit asks nothing of the audience other than to sit back and enjoy a couple of great actors hamming it up . \nit is as uncompromising as it is nonjudgmental , and makes clear that a prostitute can be as lonely and needy as any of the clients . \n'barbershop \" is a good-hearted ensemble comedy with a variety of quirky characters and an engaging story . \na good thriller . \ntully is in many ways the perfect festival film : a calm , self-assured portrait of small town regret , love , duty and friendship that appeals to the storytelling instincts of a slightly more literate filmgoing audience . \ni like this movie a lot . i like that smith , he's not making fun of these people , he's not laughing at them . \na glorious mess . \n . . . the implication is kissinger may have decided that  when it comes to truncheoning  it's better to give than to receive . \n'what's the russian word for wow ! ? '\nkiarostami has crafted a deceptively casual ode to children and managed to convey a tiny sense of hope . \ni had more fun watching spy than i had with most of the big summer movies . \nwhat lee does so marvelously compelling is present brown as a catalyst for the struggle of black manhood in restrictive and chaotic america . . . sketchy but nevertheless gripping portrait of jim brown , a celebrated wonder in the spotlight\nmurder by numbers' isn't a great movie , but it's a perfectly acceptable widget . \nfor those of an indulgent , slightly sunbaked and summery mind , sex and lucia may well prove diverting enough . \nwhat [denis] accomplishes in his chilling , unnerving film is a double portrait of two young women whose lives were as claustrophic , suffocating and chilly as the attics to which they were inevitably consigned . \na well-done film of a self-reflexive , philosophical nature . \ntexan director george ratliff had unlimited access to families and church meetings , and he delivers fascinating psychological fare . \nthe rich performances by friel -- and especially williams , an american actress who becomes fully english -- round out the square edges . \nthe new insomnia is a surprisingly faithful remake of its chilly predecessor , and when it does elect to head off in its own direction , it employs changes that fit it well rather than ones that were imposed for the sake of commercial sensibilities . \na film in a class with spike lee's masterful do the right thing . \njagger , stoppard and director michael apted . . . deliver a riveting and surprisingly romantic ride . \ngreengrass ( working from don mullan's script ) forgoes the larger socio-political picture of the situation in northern ireland in favour of an approach that throws one in the pulsating thick of a truly frightening situation . \na thought-provoking and often-funny drama about isolation . \nwhatever one makes of its political edge , this is beautiful filmmaking from one of french cinema's master craftsmen . \nmama africa pretty much delivers on that promise . it does give you a peek . the main problem being that it's only a peek . \nroman polanski's autobiographical gesture at redemption is better than 'shindler's list' - it is more than merely a holocaust movie . \na perfectly respectable , perfectly inoffensive , easily forgettable film . \nromanek's themes are every bit as distinctive as his visuals . beyond the cleverness , the weirdness and the pristine camerawork , one hour photo is a sobering meditation on why we take pictures . \nseeing seinfeld at home as he watches his own appearance on letterman with a clinical eye reminds you that the key to stand-up is to always make it look easy , even though the reality is anything but . \nspeaks eloquently about the symbiotic relationship between art and life . \nthe work of an artist tormented by his heritage , using his storytelling ability to honor the many faceless victims . \nthe audacity to view one of shakespeare's better known tragedies as a dark comedy is , by itself , deserving of discussion . \nthis is an exercise in chilling style , and twohy films the sub , inside and out , with an eye on preserving a sense of mystery . \nan uncomfortable experience , but one as brave and challenging as you could possibly expect these days from american cinema . \nhailed as a clever exercise in neo-hitchcockianism , this clever and very satisfying picture is more accurately chabrolian . \nfunny and also heartwarming without stooping to gooeyness . \nat the film's centre is a precisely layered performance by an actor in his mid-seventies , michel piccoli . \nthe viewer takes great pleasure in watching the resourceful molly stay a step ahead of her pursuers . \nwith amazing finesse , the film shadows heidi's trip back to vietnam and the city where her mother , mai thi kim , still lives . \ndirector charles stone iii applies more detail to the film's music than to the story line ; what's best about drumline is its energy . \na heroic tale of persistence that is sure to win viewers' hearts . \nit's all a rather shapeless good time . . . \nhas far more energy , wit and warmth than should be expected from any movie with a \" 2 \" at the end of its title . \na little better than sorcerer's stone . \na chilling movie without oppressive gore . \na uniquely sensual metaphorical dramatization of sexual obsession that spends a bit too much time on its fairly ludicrous plot . \nif you like peace , you'll like promises . \nbe prepared to cling to the edge of your seat , tense with suspense . the ring never lets you off the hook . \nthumbs up to paxton for not falling into the hollywood trap and making a vanity project with nothing new to offer . \nat once disarmingly straightforward and strikingly devious . \nif you like quirky , odd movies and/or the ironic , here's a fun one . \nsensitive ensemble performances and good period reconstruction add up to a moving tragedy with some buoyant human moments . \nit's not the least of afghan tragedies that this noble warlord would be consigned to the dustbin of history . \nit's a lovely , sad dance highlighted by kwan's unique directing style . \nthe script by david koepp is perfectly serviceable and because he gives the story some soul . . . he elevates the experience to a more mythic level . \nthis is a visually stunning rumination on love , memory , history and the war between art and commerce . \nshort-story quaint , touchingly mending a child's pain for his dead mother via communication with an old woman straight out of eudora welty . \nit's always fascinating to watch marker the essayist at work . \na quiet family drama with a little bit of romance and a dose of darkness . \nthe tasteful little revision works wonders , enhancing the cultural and economic subtext , bringing richer meaning to the story's morals . \nkosminsky . . . puts enough salt into the wounds of the tortured and self-conscious material to make it sting . \none of the greatest films i've ever seen . \nits gentle , touching story creeps into your heart . \nabout as big a crowdpleaser as they possibly come . \nbound to appeal to women looking for a howlingly trashy time . \neven these tales of just seven children seem at times too many , although in reality they are not enough . every child's story is what matters . this film can only point the way -- but thank goodness for this signpost . \na poignant and gently humorous parable that loves its characters and communicates something rather beautiful about human nature . \nreal women have curves doesn't offer any easy answers . \nvampire epic succeeds as spooky action-packed trash of the highest order . \none of the funniest motion pictures of the year , but . . . also one of the most curiously depressing . \nwhile somewhat less than it might have been , the film is a good one , and you've got to hand it to director george clooney for biting off such a big job the first time out . \nlike the chilled breath of oral storytelling frozen onto film . \na charmer from belgium . \na wild , endearing , masterful documentary . \njackie chan movies are a guilty pleasure - he's easy to like and always leaves us laughing . \nbrown sugar signals director rick famuyiwa's emergence as an articulate , grown-up voice in african-american cinema . \nwith exquisite craftsmanship . . . olivier assayas has fashioned an absorbing look at provincial bourgeois french society . \nit's rare for any movie to be as subtle and touching as the son's room . \nit has a way of seeping into your consciousness , with lingering questions about what the film is really getting at . \nmaelstrom is a deliberately unsteady mixture of stylistic elements . \n[leigh] has a true talent for drawing wrenching performances from his actors ( improvised over many months ) and for conveying the way tiny acts of kindness make ordinary life survivable . \n[d]espite its familiar subject matter , ice age is consistently amusing and engrossing . . . \nthe ingenious construction ( adapted by david hare from michael cunningham's novel ) constantly flows forwards and back , weaving themes among three strands which allow us to view events as if through a prism\nassured , glossy and shot through with brittle desperation . \nthe bottom line is the piece works brilliantly . \nit was only a matter of time before some savvy producer saw the potential success inherent in the mixture of bullock bubble and hugh goo . \nit is scott's convincing portrayal of roger the sad cad that really gives the film its oomph . \nwhile this movie , by necessity , lacks fellowship's heart , two towers outdoes its spectacle . \nmeyjes . . . has done his homework and soaked up some jazzy new revisionist theories about the origins of nazi politics and aesthetics . \npara hitler , o mundo era sua tela ; e o horror , seu pincel . e max retrata este fato com elegante abandono , numa triste constatao da realidade histrica . \naside from rohmer's bold choices regarding point of view , the lady and the duke represents the filmmaker's lifelong concern with formalist experimentation in cinematic art . \nwhat 'dumb and dumber' would have been without the vulgarity and with an intelligent , life-affirming script . \n . . . a vivid , thoughtful , unapologetically raw coming-of-age tale full of sex , drugs and rock 'n' roll . \nyou wouldn't want to live waydowntown , but it is a hilarious place to visit . \nfilms are made of little moments . changing lanes tries for more . it doesn't reach them , but the effort is gratefully received . \nwhen the movie mixes the cornpone and the cosa nostra , it finds a nice rhythm . \nthe story is a rather simplistic one : grief drives her , love drives him , and a second chance to find love in the most unlikely place - it struck a chord in me . \nterrific casting and solid execution give all three stories life . \na hard look at one man's occupational angst and its subsequent reinvention , a terrifying study of bourgeois desperation worthy of claude chabrol . \nneatly constructed thriller . \n[ramsay] visually transforms the dreary expanse of dead-end distaste the characters inhabit into a poem of art , music and metaphor . \nfrequent flurries of creative belly laughs and genuinely enthusiastic performances . . . keep the movie slaloming through its hackneyed elements with enjoyable ease . \nat its best , this is grand-scale moviemaking for a larger-than-life figure , an artist who has been awarded mythic status in contemporary culture . \n'possession , ' based on the book by a . s . byatt , demands that labute deal with the subject of love head-on ; trading in his cynicism for reverence and a little wit\nthe plot of the comeback curlers isn't very interesting actually , but what i like about men with brooms and what is kind of special is how the film knows what's unique and quirky about canadians . \n10 minutes into the film you'll be white-knuckled and unable to look away . \nit's a beautiful film , full of elaborate and twisted characters - and it's also pretty funny . \ncould this be the first major studio production shot on video tape instead of film ? \nnot since ghostbusters has a film used manhattan's architecture in such a gloriously goofy way . \nas tricky and satisfying as any of david mamet's airless cinematic shell games . \nthe universal theme of becoming a better person through love has never been filmed more irresistibly than in 'baran . '\ncube's charisma and chemistry compensate for corniness and cliche . \nwith lesser talents , high crimes would be entertaining , but forgettable . with freeman and judd , i'll at least remember their characters . \nas a director , paxton is surprisingly brilliant , deftly sewing together what could have been a confusing and horrifying vision into an intense and engrossing head-trip . \nthe film is filled with humorous observations about the general absurdity of modern life as seen through the eyes outsiders , but deftly manages to avoid many of the condescending stereotypes that so often plague films dealing with the mentally ill . \ndaughter from danang sticks with its subjects a little longer and tells a deeper story\na coming-of-age film that avoids the cartoonish clichs and sneering humor of the genre as it provides a fresh view of an old type -- the uncertain girl on the brink of womanhood . \nthe faithful will enjoy this sometimes wry adaptation of v . s . naipaul's novel , but newcomers may find themselves stifling a yawn or two during the first hour . \na distinguished and thoughtful film , marked by acute writing and a host of splendid performances . \nbarry convinces us he's a dangerous , secretly unhinged guy who could easily have killed a president because it made him feel powerful . \ntakes you by the face , strokes your cheeks and coos beseechingly at you : slow down , shake off your tensions and take this picture at its own breezy , distracted rhythms . \ni don't feel the least bit ashamed in admitting that my enjoyment came at the expense of seeing justice served , even if it's a dish that's best served cold . \nit's a smart , solid , kinetically-charged spy flick worthy of a couple hours of summertime and a bucket of popcorn . nothing overly original , mind you , but solidly entertaining . \nchanging lanes is an anomaly for a hollywood movie ; it's a well-written and occasionally challenging social drama that actually has something interesting to say . \nborrows a bit from the classics \" wait until dark \" and \" extremities \" . . . but in terms of its style , the movie is in a class by itself . \nbecause eight legged freaks is partly an homage to them , tarantula and other low- budget b-movie thrillers of the 1950s and '60s , the movie is a silly ( but not sophomoric ) romp through horror and hellish conditions . \nputs a refreshing and comical spin on the all-too-familiar saga of the contemporary single woman . \nif you grew up on scooby -- you'll love this movie . matthew lillard is born to play shaggy ! \nintimate and panoramic . \nthough filmed partly in canada , paid in full has clever ways of capturing inner-city life during the reagan years . \n \" spider-man is better than any summer blockbuster we had to endure last summer , and hopefully , sets the tone for a summer of good stuff . if you're a comic fan , you can't miss it . if you're not , you'll still have a good time . \" \nexciting documentary . \nthis movie has a strong message about never giving up on a loved one , but it's not an easy movie to watch and will probably disturb many who see it . \nthe movie is a trove of delights . \nexcellent performances from jacqueline bisset and martha plimpton grace this deeply touching melodrama . \nin a summer of clones , harvard man is something rare and riveting : a wild ride that relies on more than special effects . \nwhile the humor is recognizably plympton , he has actually bothered to construct a real story this time . \njolting into charleston rhythms , the story has the sizzle of old news that has finally found the right vent ( accurate ? who cares ? ) . \nan overly melodramatic but somewhat insightful french coming-of-age film . . . \nmost thrillers send audiences out talking about specific scary scenes or startling moments ; \" frailty \" leaves us with the terrifying message that the real horror may be waiting for us at home . \nclose enough in spirit to its freewheeling trash-cinema roots to be a breath of fresh air . \nskillfully weaves both the elements of the plot and a powerfully evocative mood combining heated sexuality with a haunting sense of malaise . \ndamon brings the proper conviction to his role as [jason bourne] . \nfor the most part , it works beautifully as a movie without sacrificing the integrity of the opera . \nas played by ryan gosling , danny is a frighteningly fascinating contradiction . \nthis is not chabrol's best , but even his lesser works outshine the best some directors can offer . \ndespite its flaws , crazy as hell marks an encouraging new direction for la salle . \nyou'll end up moved . \nif you ever wondered what it would be like to be smack in the middle of a war zone armed with nothing but a camera , this oscar-nominated documentary takes you there . \nthe woodman seems to have directly influenced this girl-meets-girl love story , but even more reassuring is how its makers actually seem to understand what made allen's romantic comedies so pertinent and enduring . \ni loved the look of this film . \nit's the kind of movie that , aside from robert altman , spike lee , the coen brothers and a few others , our moviemakers don't make often enough . \nthose with a modicum of patience will find in these characters' foibles a timeless and unique perspective . \nbeautiful to watch and holds a certain charm . \n13 conversations may be a bit too enigmatic and overly ambitious to be fully successful , but sprecher and her screenwriting partner and sister , karen sprecher , don't seem ever to run out of ideas . \nthe movie is our story as much as it is schmidt's , no matter if it's viewed as a self-reflection or cautionary tale . \nfoster breathes life into a roll that could have otherwise been bland and run of the mill . \nquitting offers piercing domestic drama with spikes of sly humor . \nsome people want the ol' ball-and-chain and then there are those who just want the ball and chain . \n[barry] gives assassin a disquieting authority . \nit's refreshing to see a romance this smart . \nat its best ( and it does have some very funny sequences ) looking for leonard reminds you just how comically subversive silence can be . \nas improbable as this premise may seem , abbass's understated , shining performance offers us the sense that on some elemental level , lilia deeply wants to break free of her old life . \nanyone who ever fantasized about space travel but can't afford the $20 million ticket to ride a russian rocket should catch this imax offering . \n \" the turntable is now outselling the electric guitar . . . \" \ntransforms one of [shakespeare's] deepest tragedies into a smart new comedy . \nan intelligent and deeply felt work about impossible , irrevocable choices and the price of making them . \nit may sound like a mere disease-of- the-week tv movie , but a song for martin is made infinitely more wrenching by the performances of real-life spouses seldahl and wollter . \nsayles is making a statement about the inability of dreams and aspirations to carry forward into the next generation . \nas antonia is assimilated into this newfangled community , the film settles in and becomes compulsively watchable in a guilty-pleasure , daytime-drama sort of fashion . \nevery once in a while , a movie will come along that turns me into that annoying specimen of humanity that i usually dread encountering the most - the fanboy\non its own staggeringly unoriginal terms , this gender-bending comedy is generally quite funny . \nit's never dull and always looks good . \nthe tonal shifts are jolting , and though wen's messages are profound and thoughtfully delivered , more thorough transitions would have made the film more cohesive . \nchicago pode at ser um filme divertido e cativante ( como  ) , mas acaba representando um passo que vai na direo contrria  evoluo dos musicais . \nas commander-in-chief of this film , bigelow demonstrates a breadth of vision and an attention to detail that propels her into the upper echelons of the directing world . \nwith wit and empathy to spare , waydowntown acknowledges the silent screams of workaday inertia but stops short of indulging its characters' striving solipsism . \nall of it works smoothly under the direction of spielberg , who does a convincing impersonation here of a director enjoying himself immensely . \nthe kind of sweet-and-sour insider movie that film buffs will eat up like so much gelati . \nwith 'bowling for columbine , ' michael moore gives us the perfect starting point for a national conversation about guns , violence , and fear . \none of the year's most weirdly engaging and unpredictable character pieces . \none of the best inside-show-biz yarns ever . \nnone of his actors stand out , but that's less of a problem here than it would be in another film : characterization matters less than atmosphere . \na terrifically entertaining specimen of spielbergian sci-fi . \na rare and lightly entertaining look behind the curtain that separates comics from the people laughing in the crowd . \nsolondz is so intent on hammering home his message that he forgets to make it entertaining . \nwhatever heartwarming scene the impressively discreet filmmakers may have expected to record with their mini dv , they show a remarkable ability to document both sides of this emotional car-wreck . \nit establishes its ominous mood and tension swiftly , and if the suspense never rises to a higher level , it is nevertheless maintained throughout . \nalthough what time offers tsai's usual style and themes , it has a more colorful , more playful tone than his other films . \na moving and stark reminder that the casualties of war reach much further than we imagine . \na thoroughly engaging , surprisingly touching british comedy . \na sloppy , amusing comedy that proceeds from a stunningly unoriginal premise . \n . . . a rich and intelligent film that uses its pulpy core conceit to probe questions of attraction and interdependence and how the heart accomodates practical needs . it is an unstinting look at a collaboration between damaged people that may or may not qual\ncaptures that perverse element of the kafkaesque where identity , overnight , is robbed and replaced with a persecuted \" other . \" \nthe actors are simply too good , and the story too intriguing , for technical flaws to get in the way . \nan estrogen opera so intensely feminine that it serves as the antidote ( and cannier doppelganger ) to diesel's xxx flex-a-thon . \nimamura has said that warm water under a red bridge is a poem to the enduring strengths of women . it may also be the best sex comedy about environmental pollution ever made . \nit's a ripper of a yarn and i for one enjoyed the thrill of the chill . naomi watts is terrific as rachel ; her petite frame and vulnerable persona emphasising her plight and isolation . \na family film that contains some hefty thematic material on time , death , eternity , and what is needed to live a rich and full life . \nwith dickens' words and writer-director douglas mcgrath's even-toned direction , a ripping good yarn is told . \nexactly what its title implies : lusty , boisterous and utterly charming . \nthe film is darkly funny in its observation of just how much more grueling and time-consuming the illusion of work is than actual work . \na smart , compelling drama . \na must-see for fans of thoughtful war films and those interested in the sights and sounds of battle . \ni found myself liking the film , though in this case one man's treasure could prove to be another man's garbage . \n . . . rogers's mouth never stops shut about the war between the sexes and how to win the battle . \ndeliberately and skillfully uses ambiguity to suggest possibilities which imbue the theme with added depth and resonance . \nit's all entertaining enough , but don't look for any hefty anti-establishment message in what is essentially a whip-crack of a buddy movie that ends with a whimper . \nnicholson's understated performance is wonderful . as warren he stumbles in search of all the emotions and life experiences he's neglected over the years . \ndespite its old-hat set-up and predictable plot , empire still has enough moments to keep it entertaining . \nanother entertaining romp from robert rodriguez . \nit's not a classic spy-action or buddy movie , but it's entertaining enough and worth a look . \ni am more offended by his lack of faith in his audience than by anything on display here . \na sharp satire of desperation and cinematic deception . \ntsai convincingly paints a specifically urban sense of disassociation here . \nif you can swallow its absurdities and crudities lagaan really is enormously good fun . \nan unorthodox little film noir organized crime story that includes one of the strangest love stories you will ever see . \na pleasing , often-funny comedy . \n[a] rare movie that makes us re-assess the basis for our lives and evaluate what is truly ours in a world of meaningless activity . \nall three actresses are simply dazzling , particularly balk , who's finally been given a part worthy of her considerable talents . \nan asian neo-realist treasure . \nplummer steals the show without resorting to camp as nicholas' wounded and wounding uncle ralph . it's a great performance and a reminder of dickens' grandeur . \n . . . less a story than an inexplicable nightmare , right down to the population's shrugging acceptance to each new horror . \ni am highly amused by the idea that we have come to a point in society where it has been deemed important enough to make a film in which someone has to be hired to portray richard dawson . \na compassionate , moving portrait of an american ( and an america ) always reaching for something just outside his grasp . \n'estupendamente actuada , sumamente emotiva y profundamente humana , es una experiencia flmica imposible de olvidar'\nan original little film about one young woman's education . \nthe film is about the relationships rather than about the outcome . and it sees those relationships , including that between the son and his wife , and the wife and the father , and between the two brothers , with incredible subtlety and acumen . \none of those terrific documentaries that collect a bunch of people who are enthusiastic about something and then figures out how to make us share their enthusiasm . \nan instance of an old dog not only learning but inventing a remarkable new trick . \nrodriguez has the chops of a smart-aleck film school brat and the imagination of a big kid . . . \namy and matthew have a bit of a phony relationship , but the film works in spite of it . \ngarcia and the other actors help make the wobbly premise work . \nit's surprisingly decent , particularly for a tenth installment in a series . \na fascinating , unnerving examination of the delusions of one unstable man . \ngood , solid storytelling . \nit's no accident that the accidental spy is a solid action pic that returns the martial arts master to top form . \nleave it to the french to truly capture the terrifying angst of the modern working man without turning the film into a cheap thriller , a dumb comedy or a sappy melodrama . \nthe director , mark pellington , does a terrific job conjuring up a sinister , menacing atmosphere though unfortunately all the story gives us is flashing red lights , a rattling noise , and a bump on the head . \nheartwarming here relies less on forced air than on petter nss' delicate , clever direction . . . and a wonderful , imaginative script by axel hellstenius . \nmakes the case for a strong education and good teachers being more valuable in the way they help increase an average student's self-esteem , and not strictly in the knowledge imparted . \nsteers refreshingly clear of the usual cliches . \n \" home movie \" is a sweet treasure and something well worth your time . \nhighly recommended viewing for its courage , ideas , technical proficiency and great acting . \nthe movie's thesis -- elegant technology for the masses -- is surprisingly refreshing . \nscott delivers a terrific performance in this fascinating portrait of a modern lothario . \n . . . wallace is smart to vary the pitch of his movie , balancing deafening battle scenes with quieter domestic scenes of women back home receiving war department telegrams . \ncombines sharp comedy , old-fashioned monster movie atmospherics , and genuine heart to create a film that's not merely about kicking undead * * * , but also about dealing with regret and , ultimately , finding redemption . \nwhile most films these days are about nothing , this film seems to be about everything that's plaguing the human spirit in a relentlessly globalizing world . \nmarshall puts a suspenseful spin on standard horror flick formula . \nas lively an account as seinfeld is deadpan . \nthough lan yu lacks a sense of dramatic urgency , the film makes up for it with a pleasing verisimilitude . \nyou may leave the theater with more questions than answers , but darned if your toes won't still be tapping . \ntake any 12-year-old boy to see this picture , and he'll be your slave for a year . \nbut this is not a movie about an inhuman monster ; it's about a very human one . \nat times the guys taps into some powerful emotions , but this kind of material is more effective on stage . it's not a motion picture ; it's an utterly static picture . \nwhat makes it worth watching is quaid's performance . \nsoderbergh skims the fat from the 1972 film . what's left is a rich stew of longing . \nit's the brilliant surfing photography bringing you right inside the massive waves that lifts blue crush into one of the summer's most pleasurable movies . \nmore of the same from taiwanese auteur tsai ming-liang , which is good news to anyone who's fallen under the sweet , melancholy spell of this unique director's previous films . \nhatfield and hicks make the oddest of couples , and in this sense the movie becomes a study of the gambles of the publishing world , offering a case study that exists apart from all the movie's political ramifications . \ninfidelity drama is nicely shot , well-edited and features a standout performance by diane lane . \nbest of all is garcia , who perfectly portrays the desperation of a very insecure man . \nthe filmmakers try to balance pointed , often incisive satire and unabashed sweetness , with results that are sometimes bracing , sometimes baffling and quite often , and in unexpected ways , touching . \na sobering and powerful documentary about the most severe kind of personal loss : rejection by one's mother . \naudacious-impossible yet compelling . . . \noften overwrought and at times positively irritating , the film turns into an engrossing thriller almost in spite of itself . \nhumorous and heartfelt , douglas mcgrath's version of 'nicholas nickleby' left me feeling refreshed and hopeful . not many movies have that kind of impact on me these days . \na poignant lyricism runs through balzac and the little chinese seamstress that transforms this story about love and culture into a cinematic poem . \nthis is so de palma . if you love him , you'll like it . if you don't . . . well , skip to another review . \nrouge is less about a superficial midlife crisis than it is about the need to stay in touch with your own skin , at 18 or 80 . \nthe moral shrapnel and mental shellshock will linger long after this film has ended . \no ltimo suspeito ganha fora ao tambm funcionar em uma esfera adicional : a do drama familiar . \nunfolds in a series of achronological vignettes whose cumulative effect is chilling . \nthe movie enters a realm where few non-porn films venture , and comes across as darkly funny , energetic , and surprisingly gentle . \nalthough the subject matter may still be too close to recent national events , the film works - mostly due to its superior cast of characters . \nit's not going to be everyone's bag of popcorn , but it definitely gives you something to chew on . \nhuppert and girardot give performances of exceptional honesty . \nit has that rare quality of being able to creep the living hell out of you . . . \na cautionary tale about the grandiosity of a college student who sees himself as impervious to a fall . \nan infinitely wittier version of the home alone formula . \nfeardotcom's thrills are all cheap , but they mostly work . \n[hayek] throws herself into this dream hispanic role with a teeth-clenching gusto , she strikes a potent chemistry with molina and she gradually makes us believe she is kahlo . \nmr . deeds is , as comedy goes , very silly -- and in the best way . \nyou could love safe conduct ( laissez passer ) for being a subtitled french movie that is 170 minutes long . you could hate it for the same reason . \nwith we were soldiers , hollywood makes a valiant attempt to tell a story about the vietnam war before the pathology set in . \n'moore is like a progressive bull in a china shop , a provocateur crashing into ideas and special-interest groups as he slaps together his own brand of liberalism . '\nbroomfield reveals an ironic manifestation of institutionalized slavery that ties a black-owned record label with a white-empowered police force . \nat just over an hour , home movie will leave you wanting more , not to mention leaving you with some laughs and a smile on your face . \nstuart's poor-me persona needs a whole bunch of snowball's cynicism to cut through the sugar coating . but once the falcon arrives in the skies above manhattan , the adventure is on red alert . \nthere is greatness here . \nboasts enough funny dialogue and sharp characterizations to be mildly amusing . \ndirector juan jose campanella could have turned this into an argentine retread of \" iris \" or \" american beauty , \" but instead pulls a little from each film and creates something more beautiful than either of those films . \nif you love the music , and i do , its hard to imagine having more fun watching a documentary . . . \nnakata's technique is to imply terror by suggestion , rather than the overuse of special effects . \n \" 13 conversations about one thing \" is an intelligent flick that examines many different ideas from happiness to guilt in an intriguing bit of storytelling . \nsatin rouge is not a new , or inventive , journey , but it's encouraging to see a three-dimensional , average , middle-aged woman's experience of self-discovery handled with such sensitivity . \nthough an important political documentary , this does not really make the case the kissinger should be tried as a war criminal . \ncannon's confidence and laid-back good spirits are , with the drumming routines , among the film's saving graces . \nin its understanding , often funny way , it tells a story whose restatement is validated by the changing composition of the nation . \nshe may not be real , but the laughs are . \na fiercely clever and subtle film , capturing the precarious balance between the extravagant confidence of the exiled aristocracy and the cruel earnestness of the victorious revolutionaries . \nok arthouse . the power of this script , and the performances that come with it , is that the whole damned thing didn't get our moral hackles up . \nthe movie itself is far from disappointing , offering an original take on courtroom movies , a few nifty twists that are so crucial to the genre and another first-rate performance by top-billed star bruce willis . \nabout schmidt is undoubtedly one of the finest films of the year . if you're not deeply touched by this movie , check your pulse . \nthe charm of revolution os is rather the way it introduces you to new , fervently held ideas and fanciful thinkers . \nuntil its final minutes this is a perceptive study of two families in crisis -- and of two girls whose friendship is severely tested by bad luck and their own immaturity . \noffers the flash of rock videos fused with solid performances and eerie atmosphere . \nfilmmakers dana janklowicz-mann and amir mann area headed east , far east , in retelling a historically significant , and personal , episode detailing how one international city welcomed tens of thousands of german jewish refugees while the world's democracie\nfor all its problems . . . the lady and the duke surprisingly manages never to grow boring . . . which proves that rohmer still has a sense of his audience . \nan edifying glimpse into the wit and revolutionary spirit of these performers and their era . \ncraig bartlett and director tuck tucker should be commended for illustrating the merits of fighting hard for something that really matters . \nthe film is saved from aren't-kids-cute sentimentality by a warmth that isn't faked and a stately sense of composition . \nthis is one of the year's best films . \na fleet-footed and pleasingly upbeat family diversion . \nsorvino is delightful in the central role . she nearly glows with enthusiasm , sensuality and a conniving wit . \nit's immensely ambitious , different than anything that's been done before and amazingly successful in terms of what it's trying to do . \nthe story , once it gets rolling , is nothing short of a great one . \ngreat performances , stylish cinematography and a gritty feel help make gangster no . 1 a worthwhile moviegoing experience . \n \" mr . deeds \" is suitable summer entertainment that offers escapism without requiring a great deal of thought . \nit's an ambitious film , and as with all ambitious films , it has some problems . but on the whole , you're gonna like this movie . \nchaiken ably balances real-time rhythms with propulsive incident . \nthis is an extraordinary film , not least because it is japanese and yet feels universal . \nin a summer overrun with movies dominated by cgi aliens and super heroes , it revigorates the mind to see a feature that concentrates on people , a project in which the script and characters hold sway . \nthere's just something about watching a squad of psychopathic underdogs whale the tar out of unsuspecting lawmen that reaches across time and distance . \na funny and touching film that is gorgeously acted by a british cast to rival gosford park's . \nthere's nothing more satisfying during a summer of event movies than a spy thriller like the bourne identity that's packed with just as much intelligence as action . \ni'm not generally a fan of vegetables but this batch is pretty cute . \nqutting may be a flawed film , but it is nothing if not sincere . \nbeautifully crafted , engaging filmmaking that should attract upscale audiences hungry for quality and a nostalgic , twisty yarn that will keep them guessing . \na thoughtful and surprisingly affecting portrait of a screwed-up man who dared to mess with some powerful people , seen through the eyes of the idealistic kid who chooses to champion his ultimately losing cause . \na cultural wildcard experience : wacky , different , unusual , even nutty . \ndaughter from danang reveals that efforts toward closure only open new wounds . it doesn't flinch from its unsettling prognosis , namely , that the legacy of war is a kind of perpetual pain . \nfor most of its footage , the new thriller proves that director m . night shyamalan can weave an eerie spell and that mel gibson can gasp , shudder and even tremble without losing his machismo . \nthis is not an easy film . but it could be , by its art and heart , a necessary one . \na very good film sits in the place where a masterpiece should be . \n . . . spellbinding fun and deliciously exploitative . \nit's jagger's bone-dry , mournfully brittle delivery that gives the film its bittersweet bite . \nimpossible as it may sound , this film's heart is even more embracing than monty , if only because it accepts nasty behavior and severe flaws as part of the human condition . \ndespite the predictable parent vs . child coming-of-age theme , first-class , natural acting and a look at \" the real americans \" make this a charmer . \none of the smarter offerings the horror genre has produced in recent memory , even if it's far tamer than advertised . \none of recent memory's most thoughtful films about art , ethics , and the cost of moral compromise . \nthe film doesn't sustain its initial promise with a jarring , new-agey tone creeping into the second half\nblade ii is as estrogen-free as movies get , so you might want to leave your date behind for this one , or she's gonna make you feel like you owe her big-time . \nthe message is that even the most unlikely can link together to conquer all kinds of obstacles , whether they be of nature , of man or of one another . \nmany a parent and their teen ( or preteen ) kid could bond while watching a walk to remember . so could young romantics out on a date . \nall leather pants & augmented boobs , hawn is hilarious as she tries to resuscitate the fun-loving libertine lost somewhere inside the conservative , handbag-clutching sarandon . \nthe members manage to pronounce kok exactly as you think they might , thus giving the cast ample opportunity to use that term as often as possible . it's very beavis and butthead , yet always seems to elicit a chuckle . \nwhile this gentle and affecting melodrama will have luvvies in raptures , it's far too slight and introspective to appeal to anything wider than a niche audience . \nchicago offers much colorful eye candy , including the spectacle of gere in his dancing shoes , hoofing and crooning with the best of them . \na difficult but worthy film that bites off more than it can chew by linking the massacre of armenians in 1915 with some difficult relationships in the present . \nby and large this is mr . kilmer's movie , and it's his strongest performance since the doors . \nsome of the most ravaging , gut-wrenching , frightening war scenes since \" saving private ryan \" have been recreated by john woo in this little-known story of native americans and their role in the second great war . \na charming but slight comedy . \nhenry bean's thoughtful screenplay provides no easy answers , but offers a compelling investigation of faith versus intellect\na great cast and a wonderful but sometimes confusing flashback movie about growing up in a dysfunctional family . \nplaying a role of almost bergmanesque intensity . . . bisset is both convincing and radiant . \na smart , provocative drama that does the nearly impossible : it gets under the skin of a man we only know as an evil , monstrous lunatic . \nan alternately fascinating and frustrating documentary . \ngriffin & co . manage to be spectacularly outrageous . \nnair's cast is so large it's altman-esque , but she deftly spins the multiple stories in a vibrant and intoxicating fashion . \nthe movie plays up the cartoon's more obvious strength of snazziness while neglecting its less conspicuous writing strength . \npoignant japanese epic about adolescent anomie and heartbreak . \nwe've seen it all before in one form or another , but director hoffman , with great help from kevin kline , makes us care about this latest reincarnation of the world's greatest teacher . \nsecretary is not a movie about fetishism . it is a movie about passion . \neven though it's common knowledge that park and his founding partner , yong kang , lost kozmo in the end , you can't help but get caught up in the thrill of the company's astonishing growth . \nalthough some viewers will not be able to stomach so much tongue-in-cheek weirdness , those who do will have found a cult favorite to enjoy for a lifetime . \nwhat could have easily become a cold , calculated exercise in postmodern pastiche winds up a powerful and deeply moving example of melodramatic moviemaking . \na delightful surprise because despite all the backstage drama , this is a movie that tells stories that work -- is charming , is moving , is funny and looks professional . \nthe imax screen enhances the personal touch of manual animation . \ndoes an impressive job of relating the complicated history of the war and of filling in the background . \nit's all about anakin . . . and the lustrous polished visuals rich in color and creativity and , of course , special effect . \nlacks the inspiration of the original and has a bloated plot that stretches the running time about 10 minutes past a child's interest and an adult's patience . but it also has many of the things that made the first one charming . \nit's funny , touching , dramatically forceful , and beautifully shot . \nits rawness and vitality give it considerable punch . \na live-wire film that never loses its ability to shock and amaze . \nthe year's greatest adventure , and jackson's limited but enthusiastic adaptation has made literature literal without killing its soul -- a feat any thinking person is bound to appreciate . \nit's fairly solid--not to mention well edited so that it certainly doesn't feel like a film that strays past the two and a half mark . \nbrims with passion : for words , for its eccentric , accident-prone characters , and for the crazy things that keep people going in this crazy life . \nit's secondary to american psycho but still has claws enough to get inside you and stay there for a couple of hours . \nthe hours , a delicately crafted film , is an impressive achievement in spite of a river of sadness that pours into every frame . \nfudges fact and fancy with such confidence that we feel as if we're seeing something purer than the real thing . \nthis is unusual , food-for-thought cinema that's as entertaining as it is instructive . \nwith an expressive face reminiscent of gong li and a vivid personality like zhang ziyi's , dong stakes out the emotional heart of happy . \nnohe's documentary about the event is sympathetic without being gullible : he isn't blind to the silliness , but also captures moments of spontaneous creativity and authentic co-operative interaction . \nit may not be as cutting , as witty or as true as back in the glory days of weekend and two or three things i know about her , but who else engaged in filmmaking today is so cognizant of the cultural and moral issues involved in the process ? \nsecret ballot is a funny , puzzling movie ambiguous enough to be engaging and oddly moving . \nalthough devoid of objectivity and full of nostalgic comments from the now middle-aged participants , dogtown and z-boys has a compelling story to tell . \nit's got some pretentious eye-rolling moments and it didn't entirely grab me , but there's stuff here to like . \nbirthday girl walks a tricky tightrope between being wickedly funny and just plain wicked . \na muted freak-out\nthe enjoyable undercover brother , a zany mix of saturday night live-style parody , '70s blaxploitation films and goofball action comedy gone wild , dishes out a ton of laughs that everyone can enjoy . \nbrings an irresistible blend of warmth and humor and a consistent embracing humanity in the face of life's harshness . \njackson is always watchable . \nto the degree that ivans xtc . works , it's thanks to huston's revelatory performance . \na wild ride of a movie that keeps throwing fastballs . \nconfessions is without a doubt a memorable directorial debut from king hunk . \nweird , vulgar comedy that's definitely an acquired taste . \na . . . cynical and serious look at teenage boys doing what they do best - being teenagers . \nthe film is a very good viewing alternative for young women . \naustralian filmmaker david flatman uses the huge-screen format to make an old-fashioned nature film that educates viewers with words and pictures while entertaining them . \na dazzling dream of a documentary . \na keep-'em-guessing plot and an affectionate take on its screwed-up characters . \nmoving and vibrant . \nbrave and sweetly rendered love story . \nthe film proves unrelentingly grim -- and equally engrossing . \na hallmark film in an increasingly important film industry and worth the look . \nthe last kiss will probably never achieve the popularity of my big fat greek wedding , but its provocative central wedding sequence has far more impact . \nif you like blood , guts and crazy beasts stalking men with guns though . . . you will likely enjoy this monster . \nthe difference between cho and most comics is that her confidence in her material is merited . \nsad to say -- it accurately reflects the rage and alienation that fuels the self-destructiveness of many young people . \nthere is a strong directorial stamp on every frame of this stylish film that is able to visualize schizophrenia but is still confident enough to step back and look at the sick character with a sane eye . \n'anyone with a passion for cinema , and indeed sex , should see it as soon as possible . '\nseeks to transcend its genre with a curiously stylized , quasi-shakespearean portrait of pure misogynist evil . \nmordantly funny and intimately knowing . . . \nwhat makes the movie special is its utter sincerity . \nfast and funny , an action cartoon that's suspenseful enough for older kids but not too scary for the school-age crowd . \none of those rare films that come by once in a while with flawless amounts of acting , direction , story and pace . \nthe aaa of action , xxx is a blast of adrenalin , rated eee for excitement . and vin diesel is the man . \nearnest , unsubtle and hollywood-predictable , green dragon is still a deeply moving effort to put a human face on the travail of thousands of vietnamese . \nan ambitious movie that , like shiner's organizing of the big fight , pulls off enough of its effects to make up for the ones that don't come off . \nnair and writer laura cahill dare to build a movie around some flawed but rather unexceptional women , emerging with a fine character study that's short on plot but rich in the tiny revelations of real life . \nthe film's unhurried pace is actually one of its strengths . entirely appropriately , the tale unfolds like a lazy summer afternoon and concludes with the crisp clarity of a fall dawn . \ndespite its floating narrative , this is a remarkably accessible and haunting film . \nvibrantly colored and beautifully designed , metropolis is a feast for the eyes . \nsweetly sexy , funny and touching . \n . . . while dark water isn't a complete wash ( no pun intended ) , watched side-by-side with ringu , it ultimately comes off as a pale successor . \nis truth stranger than fiction ? in [screenwriter] charlie kaufman's world , truth and fiction are equally strange , and his for the taking . \nfor decades we've marveled at disney's rendering of water , snow , flames and shadows in a hand-drawn animated world . prepare to marvel again . \na witty , low-key romantic comedy . \nmore good than great but freeman and judd make it work . \nif you're looking for a smart , nuanced look at de sade and what might have happened at picpus , sade is your film . \ncould have been crisper and punchier , but it's likely to please audiences who like movies that demand four hankies . \ntogether writer-director danny verete's three tales comprise a powerful and reasonably fulfilling gestalt . \nbursting through the constraints of its source , this is one adapted- from-television movie that actually looks as if it belongs on the big screen . \n . . . quite endearing . \nits almost too-spectacular coastal setting distracts slightly from an eccentric and good-naturedly aimless story . \nin other words , it's just another sports drama/character study . yet this one makes up for in heart what it lacks in outright newness . plus , like i already mentioned . . . it's robert duvall ! c'mon ! \nthis story of a determined woman's courage to find her husband in a war zone offers winning performances and some effecting moments . \nlike shrek , spirit's visual imagination reminds you of why animation is such a perfect medium for children , because of the way it allows the mind to enter and accept another world . \na modestly made but profoundly moving documentary . \nit irritates and saddens me that martin lawrence's latest vehicle can explode obnoxiously into 2 , 500 screens while something of bubba ho-tep's clearly evident quality may end up languishing on a shelf somewhere . \nnot everything in this ambitious comic escapade works , but coppola , along with his sister , sofia , is a real filmmaker . it must be in the genes . \nthe performers are so spot on , it is hard to conceive anyone else in their roles . \nthis slight premise . . . works because of the ideal casting of the masterful british actor ian holm as the aged napoleon . \nhashiguchi covers this territory with wit and originality , suggesting that with his fourth feature -- the first to be released in the u . s . -- a major director is emerging in world cinema . \nalthough the film boils down to a lightweight story about matchmaking , the characters make italian for beginners worth the journey\nthe dragons are the real stars of reign of fire and you won't be disappointed . \nkudos to the most enchanting film of the year . \nit works well enough , since the thrills pop up frequently , and the dispatching of the cast is as often imaginative as it is gory . \ncolorful and deceptively buoyant until it suddenly pulls the rug out from under you , burkinabe filmmaker dani kouyate's reworking of a folk story whose roots go back to 7th-century oral traditions is also a pointed political allegory . \nit's a powerful though flawed movie , guaranteed to put a lump in your throat while reaffirming washington as possibly the best actor working in movies today . \ndirector paul cox's unorthodox , abstract approach to visualizing nijinsky's diaries is both stimulating and demanding . \nfor 95 often hilarious minutes , [cho] riffs on the diciness of colonics , on straight versus gay personal ads , on how men would act if they had periods , and on the perils of a certain outr sexual practice . \nmost of the things that made the original men in black such a pleasure are still there . \nmostly honest , this somber picture reveals itself slowly , intelligently , artfully . \nbest enjoyed as a work of fiction inspired by real-life events . those seeking a definitive account of eisenstein's life would do better elsewhere . \n[westbrook] makes a wonderful subject for the camera . \na film that's flawed and brilliant in equal measure . \neven if invincible is not quite the career peak that the pianist is for roman polanski , it demonstrates that werner herzog can still leave us with a sense of wonder at the diverse , marvelously twisted shapes history has taken . \nultimately too repellent to fully endear itself to american art house audiences , but it is notable for its stylistic austerity and forcefulness . \nhardly a film that comes along every day . \nharmless fun . \na wild ride with eight boarders from venice beach that was a deserved co-winner of the audience award for documentaries at the sundance film festival . \nthe film's only missteps come from the script's insistence on providing deep emotional motivation for each and every one of abagnale's antics . \na sweet , tender sermon about a 12-year-old welsh boy more curious about god than girls , who learns that believing in something does matter . \nthe film belongs to the marvelous verdu , a sexy slip of an earth mother who mourns her tragedies in private and embraces life in public\nmore intimate than spectacular , e . t . is carried less by wow factors than by its funny , moving yarn that holds up well after two decades . \nfor once , a movie does not proclaim the truth about two love-struck somebodies , but permits them time and space to convince us of that all on their own . \nif you're burnt out on it's a wonderful life marathons and bored with a christmas carol , it might just be the movie you're looking for . it depends on how well flatulence gags fit into your holiday concept . \nmoonlight mile doesn't quite go the distance but the cast is impressive and they all give life to these broken characters who are trying to make their way through this tragedy . \nit is an indelible epic american story about two families , one black and one white , facing change in both their inner and outer lives . \nnot as well-written as sexy beast , not as gloriously flippant as lock , stock and two smoking barrels , but stylish and moody and exceptionally well-acted . \nquite simply , a joy to watch and--especially--to listen to . \na flawed film but an admirable one that tries to immerse us in a world of artistic abandon and political madness and very nearly succeeds . \nthe filmmakers wisely decided to let crocodile hunter steve irwin do what he does best , and fashion a story around him . \na winning and wildly fascinating work . \nwe do get the distinct impression that this franchise is drawing to a close . \nworth catching for griffiths' warm and winning central performance . \nthe tone errs on the shrill side , tempered by a soft southern gentility that speaks of beauty , grace and a closet full of skeletons . \nan interesting psychological game of cat-and-mouse , three-dimensional characters and believable performances all add up to a satisfying crime drama . \na meatier deeper beginning and/or ending would have easily tipped this film into the \" a \" range , as is , it's a very very strong \" b+ . \" i love the robust middle of this picture . \nthe power of shanghai ghetto , a documentary by dana janklowicz-mann and amir mann , rests in the voices of men and women , now in their 70s , who lived there in the 1940s . \nmaintains your interest until the end and even leaves you with a few lingering animated thoughts . \nthere is a beautiful , aching sadness to it all . paul cox needed to show it . it is up to you to decide if you need to see it . \nif divine secrets of the ya-ya sisterhood suffers from a ploddingly melodramatic structure , it comes to life in the performances . \nif you ignore the cliches and concentrate on city by the sea's interpersonal drama , it ain't half-bad . \nalternates between deadpan comedy and heartbreaking loneliness and isn't afraid to provoke introspection in both its characters and its audience . \nthere aren't too many films that can be as simultaneously funny , offbeat and heartwarming ( without a thick shmear of the goo , at least ) , but \" elling \" manages to do all three quite well , making it one of the year's most enjoyable releases . \nreign of fire is hardly the most original fantasy film ever made -- beyond road warrior , it owes enormous debts to aliens and every previous dragon drama -- but that barely makes it any less entertaining . \nan earnest , roughshod document , it serves as a workable primer for the region's recent history , and would make a terrific 10th-grade learning tool . \nsamuel beckett applied to the iranian voting process . \nthe bard as black comedy -- willie would have loved it . \nanother trumpet blast that there may be a new mexican cinema a-bornin' . \n' . . . the film's considered approach to its subject matter is too calm and thoughtful for agitprop , and the thinness of its characterizations makes it a failure as straight drama . '\ntadpole may be one of the most appealing movies ever made about an otherwise appalling , and downright creepy , subject -- a teenage boy in love with his stepmother . \nthis is a story that zings all the way through with originality , humour and pathos . \nas underwater ghost stories go , below casts its spooky net out into the atlantic ocean and spits it back , grizzled and charred , somewhere northwest of the bermuda triangle . \nit is a challenging film , if not always a narratively cohesive one . \ntrapped won't score points for political correctness , but it may cause parents a few sleepless hours -- a sign of its effectiveness . \na rock-solid gangster movie with a fair amount of suspense , intriguing characters and bizarre bank robberies , plus a heavy dose of father-and-son dynamics . \nit's incredible the number of stories the holocaust has generated . just when you think that every possible angle has been exhausted by documentarians , another new film emerges with yet another remarkable yet shockingly little-known perspective . \nas they used to say in the 1950s sci-fi movies , signs is a tribute to shyamalan's gifts , which are such that we'll keep watching the skies for his next project . \nthere's no conversion effort , much of the writing is genuinely witty and both stars are appealing enough to probably have a good shot at a hollywood career , if they want one . \nlike a skillful fisher , the director uses the last act to reel in the audience since its poignancy hooks us completely . \na film with contemporary political resonance illustrated by a winning family story . \nkids five and up will be delighted with the fast , funny , and even touching story . parents may even find that it goes by quickly , because it has some of the funniest jokes of any movie this year , including those intended for adults . \nan unsettling , memorable cinematic experience that does its predecessors proud . \nmaid in manhattan might not look so appealing on third or fourth viewing down the road . . . but as a high concept vehicle for two bright stars of the moment who can rise to fans' lofty expectations , the movie passes inspection . \nmuch of all about lily chou-chou is mesmerizing : some of its plaintiveness could make you weep . \nferrara's strongest and most touching movie of recent years . \nspielberg's first real masterpiece , it deserved all the hearts it won -- and wins still , 20 years later . \nthe screenwriters dig themselves in deeper every time they toss logic and science into what is essentially a \" dungeons and dragons \" fantasy with modern military weaponry . . . \nmore than simply a portrait of early extreme sports , this peek into the 1970s skateboard revolution is a skateboard film as social anthropology . . . \nwhat i saw , i enjoyed . \ngood-naturedly cornball sequel . \nthe level of acting elevates the material above pat inspirational status and gives it a sturdiness and solidity that we've long associated with washington the actor . \na deft , delightful mix of sulky teen drama and overcoming-obstacles sports-movie triumph . \ndaringly perceptive , taut , piercing and feisty , biggie and tupac is undeniably subversive and involving in its bold presentation . \ndelivers more than its fair share of saucy hilarity . \na fairly enjoyable mixture of longest yard . . . and the 1999 guy ritchie caper lock stock and two smoking barrels . \nhappily , some things are immune to the folly of changing taste and attitude . for proof of that on the cinematic front , look no further than this 20th anniversary edition of the film that spielberg calls , retrospectively , his most personal work yet . \nhugely entertaining from start to finish , featuring a fall from grace that still leaves shockwaves , it will gratify anyone who has ever suspected hollywood of being overrun by corrupt and hedonistic weasels . \nit's not like having a real film of nijinsky , but at least it's better than that eponymous 1980 biopic that used soap in the places where the mysteries lingered . \nit's probably worth catching solely on its visual merits . if only it had the story to match . \nlike other great documentaries . . . this goes after one truth ( the ford administration's complicity in tearing 'orphans' from their mothers ) and stumbles upon others even more compelling . \n . . . only bond can save us from the latest eccentric , super-wealthy megalomaniac bent on world domination and destruction . \nthe first half bursts with a goofy energy previous disney films only used for a few minutes here and there . \nit's quite diverting nonsense . \nan old-fashioned scary movie , one that relies on lingering terror punctuated by sudden shocks and not constant bloodshed punctuated by flying guts . \nfor all the wit and hoopla , festival in cannes offers rare insight into the structure of relationships . \nwhat makes how i killed my father compelling , besides its terrific performances , is fontaine's willingness to wander into the dark areas of parent-child relationships without flinching . \nrenner ? s face is chillingly unemotive , yet he communicates a great deal in his performance . see it for his performance if nothing else . \n . . . the kind of entertainment that parents love to have their kids see . \nit's a fine , focused piece of work that reopens an interesting controversy and never succumbs to sensationalism . \nits engaging simplicity is driven by appealing leads . \nswimming is above all about a young woman's face , and by casting an actress whose face projects that woman's doubts and yearnings , it succeeds . \na respectable venture on its own terms , lacking the broader vision that has seen certain trek films . . . cross over to a more mainstream audience . \nit's weird , wonderful , and not necessarily for kids . \nan elegant film with often surprising twists and an intermingling of naivet and sophistication . \nblessed with two fine , nuanced lead performances . \nwhile this has the making of melodrama , the filmmaker cuts against this natural grain , producing a work that's more interested in asking questions than in answering them . \nas a girl-meets-girl romantic comedy , kissing jessica steinis quirky , charming and often hilarious . yet it's not quite the genre-busting film it's been hyped to be because it plays everything too safe . \nyou don't need to know your ice-t's from your cool-j's to realize that as far as these shootings are concerned , something is rotten in the state of california . \nturturro is fabulously funny and over the top as a 'very sneaky' butler who excels in the art of impossible disappearing/reappearing acts\nmeant for star wars fans . it is there to give them a good time . \nfrom a deceptively simple premise , this deeply moving french drama develops a startling story that works both as a detailed personal portrait and as a rather frightening examination of modern times . \nsimply and eloquently articulates the tangled feelings of particular new yorkers deeply touched by an unprecedented tragedy . \nprovides a very moving and revelatory footnote to the holocaust . \nterrific performances , great to look at , and funny . a little uneven to be the cat's meow , but it's good enough to be the purr . \nit's a compelling and horrifying story , and the laramie project is worthwhile for reminding us that this sort of thing does , in fact , still happen in america . \ni like it . there is a freedom to watching stunts that are this crude , this fast-paced and this insane . \nthat rare documentary that incorporates so much of human experience -- drama , conflict , tears and surprise -- that it transcends the normal divisions between fiction and nonfiction film . \nthat rare movie that works on any number of levels -- as a film of magic and whimsy for children , a heartfelt romance for teenagers and a compelling argument about death , both pro and con , for adults . \nit's both degrading and strangely liberating to see people working so hard at leading lives of sexy intrigue , only to be revealed by the dispassionate gantz brothers as ordinary , pasty lumpen . \na sharp and quick documentary that is funny and pithy , while illuminating an era of theatrical comedy that , while past , really isn't . \nthe script is smart , not cloying . \nthe film does a solid job of slowly , steadily building up to the climactic burst of violence . \nfred schepisi's tale of four englishmen facing the prospect of their own mortality views youthful affluence not as a lost ideal but a starting point . \nthe directive to protect the code at all costs also begins to blur as the importance of the man and the code merge\noverall , cletis tout is a winning comedy that excites the imagination and tickles the funny bone . \neasily one of the best and most exciting movies of the year . \nthe script manages the rare trick of seeming at once both refreshingly different and reassuringly familiar . \nan engaging , formulaic sports drama that carries a charge of genuine excitement . \ninsomnia is one of the year's best films and pacino gives one of his most daring , and complicated , performances . \nlike vardalos and corbett , who play their roles with vibrant charm , the film , directed by joel zwick , is heartfelt and hilarious in ways you can't fake . \ni don't know if frailty will turn bill paxton into an a-list director , but he can rest contentedly with the knowledge that he's made at least one damn fine horror movie . \ndespite its flaws . . . belinsky is still able to create an engaging story that keeps you guessing at almost every turn . \neach punch seen through prison bars , the fights become not so much a struggle of man vs . man as brother-man vs . the man . \nthe evocative imagery and gentle , lapping rhythms of this film are infectious -- it gets under our skin and draws us in long before the plot kicks into gear . \nlike the best of godard's movies . . . it is visually ravishing , penetrating , impenetrable . \nhorns and halos benefits from serendipity but also reminds us of our own responsibility to question what is told as the truth . \noddly compelling . \n[has] an immediacy and an intimacy that sucks you in and dares you not to believe it's all true . \nit treats ana's journey with honesty that is tragically rare in the depiction of young women in film . \ncaptivates as it shows excess in business and pleasure , allowing us to find the small , human moments , and leaving off with a grand whimper . \na refreshingly realistic , affectation-free coming-of-age tale . \nhow good this film might be , depends if you believe that the shocking conclusion is too much of a plunge or not . \ngreat fun both for sports aficionados and for ordinary louts whose idea of exercise is climbing the steps of a stadium-seat megaplex . \nfeatures one of the most affecting depictions of a love affair ever committed to film . \nto honestly address the flaws inherent in how medical aid is made available to american workers , a more balanced or fair portrayal of both sides will be needed . \none of the best movies of the year . \nthe usual movie rah-rah , pleasantly and predictably delivered in low-key style by director michael apted and writer tom stoppard . \na superlative b movie -- funny , sexy , and rousing . \nthose prone to indignation need not apply ; those susceptible to blue hilarity , step right up . \nwitless but watchable . \nlike mike isn't going to make box office money that makes michael jordan jealous , but it has some cute moments , funny scenes , and hits the target audience ( young bow wow fans ) - with nothing but net . \n[dong] makes a valiant effort to understand everyone's point of view , and he does such a good job of it that family fundamentals gets you riled up . \nthe trick when watching godard is to catch the pitch of his poetics , savor the pleasure of his sounds and images , and ponder the historical , philosophical , and ethical issues that intersect with them . \nat its best , which occurs often , michael moore's bowling for columbine rekindles the muckraking , soul-searching spirit of the 'are we a sick society ? ' journalism of the 1960s . \na modestly surprising movie . \na headline-fresh thriller set among orthodox jews on the west bank , joseph cedar's time of favor manages not only to find a compelling dramatic means of addressing a complex situation , it does so without compromising that complexity . \nthere's a spontaneity to the chateau , a sense of light-heartedness , that makes it attractive throughout . \nthe first tunisian film i have ever seen , and it's also probably the most good-hearted yet sensual entertainment i'm likely to see all year . \nlike any good romance , son of the bride , proves it's never too late to learn . \n[kline's] utterly convincing -- and deeply appealing -- as a noble teacher who embraces a strict moral code , and as a flawed human being who can't quite live up to it . \nthe film , while not exactly assured in its execution , is notable for its sheer audacity and openness . \na thoroughly enjoyable , heartfelt coming-of-age comedy . \nfeeling like a dope has rarely been more fun than it is in nine queens . \nleigh makes these lives count . and he allows a gawky actor like spall -- who could too easily become comic relief in any other film -- to reveal his impressively delicate range . \na lot of fun , with an undeniable energy sparked by two actresses in their 50s working at the peak of their powers . \npromises is one film that's truly deserving of its oscar nomination . \nwhat bubbles up out of john c . walsh's pipe dream is the distinct and very welcome sense of watching intelligent people making a movie they might actually want to watch . \nif reno is to the left of liberal on the political spectrum , her tough , funny , rather chaotic show isn't subversive so much as it is nit-picky about the hypocrisies of our time . \nbeautiful , angry and sad , with a curious sick poetry , as if the marquis de sade had gone in for pastel landscapes . \nms . hutchins is talented enough and charismatic enough to make us care about zelda's ultimate fate . \nmonte cristo smartly emphasizes the well-wrought story and omits needless chase scenes and swordfights as the revenge unfolds . \na mesmerizing cinematic poem from the first frame to the last . \n[it's] a clever thriller with enough unexpected twists to keep our interest . \nan undeniably moving film to experience , and ultimately that's what makes it worth a recommendation . \nnicole kidman evolved from star to superstar some time over the past year , which means that birthday girl is the kind of quirkily appealing minor movie she might not make for a while . \nvividly conveys the shadow side of the 30-year friendship between two english women . \nthe story has some nice twists but the ending and some of the back-story is a little tired . the performances are all solid ; it merely lacks originality to make it a great movie . \nmanages to please its intended audience -- children -- without placing their parents in a coma-like state . \nmr . deeds is sure to give you a lot of laughs in this simple , sweet and romantic comedy . \nwhen you think you've figured out bielinsky's great game , that's when you're in the most trouble : he's the con , and you're just the mark . \na strong first act and absolutely , inescapably gorgeous , skyscraper-trapeze motion of the amazing spider-man . \ndriven by a fantastic dual performance from ian holm . . . the film is funny , insightfully human and a delightful lark for history buffs . \na well-put-together piece of urban satire . \nit's the sweet cinderella story that \" pretty woman \" wanted to be . \nit will make you think twice about what might be going on inside each trailer park you drive past -- even if it chiefly inspires you to drive a little faster . \nwhat doesn't this film have that an impressionable kid couldn't stand to hear ? \nwhat saves it . . . and makes it one of the better video-game-based flicks , is that the film acknowledges upfront that the plot makes no sense , such that the lack of linearity is the point of emotional and moral departure for protagonist alice . \na deeply felt and vividly detailed story about newcomers in a strange new world . \nit's a visual delight and a decent popcorn adventure , as long as you don't try to look too deep into the story\nit's a feel-good movie about which you can actually feel good . \na full experience , a love story and a murder mystery that expands into a meditation on the deep deceptions of innocence . \nvisually , 'santa clause 2' is wondrously creative . \nthe powerpuff girls arrive on the big screen with their super-powers , their super-simple animation and their super-dooper-adorability intact . \n[raimi's] matured quite a bit with spider-man , even though it's one of the most plain white toast comic book films you'll ever see . \na new film from bill plympton , the animation master , is always welcome . \na devastating indictment of unbridled greed and materalism . \nwhat makes the film special is the refreshingly unhibited enthusiasm that the people , in spite of clearly evident poverty and hardship , bring to their music . \nthe film has a kind of hard , cold effect . \nthe gags are often a stitch . \nthe asylum material is gripping , as are the scenes of jia with his family . \na bonanza of wacky sight gags , outlandish color schemes , and corny visual puns that can be appreciated equally as an abstract frank tashlin comedy and as a playful recapitulation of the artist's career . \none can't deny its seriousness and quality . \ngood performances and a realistic , non-exploitive approach make paid in full worth seeing . \nthis engrossing , characteristically complex tom clancy thriller is shifty in the manner in which it addresses current terrorism anxieties and sidesteps them at the same time . \nryan gosling is , in a word , brilliant as the conflicted daniel . \n . . . somehow manages to escape the shackles of its own clichs to be the best espionage picture to come out in weeks . \nmuch of the lady and the duke is about quiet , decisive moments between members of the cultural elite as they determine how to proceed as the world implodes . \ntakes a simple premise and carries it to unexpected heights . \nwith few respites , marshall keeps the energy humming , and his edits , unlike those in moulin rouge , are crisp and purposeful without overdoing it . \nits metaphors are opaque enough to avoid didacticism , and the film succeeds as an emotionally accessible , almost mystical work . \nprovides a satisfactory overview of the bizarre world of extreme athletes as several daredevils express their own views . \ninventive , fun , intoxicatingly sexy , violent , self-indulgent and maddening . \nhard to resist . \ncomedian , like its subjects , delivers the goods and audiences will have a fun , no-frills ride . \na naturally funny film , home movie makes you crave chris smith's next movie . \npipe dream does have its charms . the leads are natural and lovely , the pace is serene , the humor wry and sprightly . \nthose who want to be jolted out of their gourd should drop everything and run to ichi . \na bittersweet contemporary comedy about benevolent deception , which , while it may not rival the filmmaker's period pieces , is still very much worth seeing . \n . . . enthusiastically invokes the percussion rhythm , the brass soul and the sense of fierce competition that helps make great marching bands half the fun of college football games . \nsheds light on a subject few are familiar with , and makes you care about music you may not have heard before . \ndespite the film's bizarre developments , hoffman keeps us riveted with every painful nuance , unexpected flashes of dark comedy and the character's gripping humanity . \nto get at the root psychology of this film would require many sessions on the couch of dr . freud . \ngreat over-the-top moviemaking if you're in a slap-happy mood . \nviveka seldahl and sven wollter will touch you to the core in a film you will never forget -- that you should never forget . \nthe magic ( and original running time ) of ace japanimator hayao miyazaki's spirited away survives intact in bv's re-voiced version . \nfrom the dull , surreal ache of mortal awareness emerges a radiant character portrait . \ncaptures the raw comic energy of one of our most flamboyant female comics . \nit's not particularly subtle . . . however , it still manages to build to a terrifying , if obvious , conclusion . \nthe auteur's ear for the way fears and slights are telegraphed in the most blithe exchanges gives the film its lingering tug . \nbolstered by exceptional performances and a clear-eyed take on the economics of dealing and the pathology of ghetto fabulousness . \nthis enthralling documentary . . . is at once playful and haunting , an in-depth portrait of an iconoclastic artist who was fundamentally unknowable even to his closest friends . \nsome remarkable achival film about how shanghai ( of all places ) served jews who escaped the holocaust . \nin a movie full of surprises , the biggest is that secret ballot is a comedy , both gentle and biting . \nthe urban landscapes are detailed down to the signs on the kiosks , and the color palette , with lots of somber blues and pinks , is dreamy and evocative . \na manically generous christmas vaudeville . \ntony gayton's script doesn't give us anything we haven't seen before , but director d . j . caruso's grimy visual veneer and kilmer's absorbing performance increase the gravitational pull considerably . \na psychic journey deep into the very fabric of iranian . . . life . \nit's a smartly directed , grown-up film of ideas . \na true-blue delight . \nwhile puerile men dominate the story , the women shine . \nunlike lots of hollywood fluff , this has layered , well-developed characters and some surprises . \nfor a film that's being advertised as a comedy , sweet home alabama isn't as funny as you'd hoped . for a film that's being advertised as a comedy , sweet home alabama isn't as funny as you'd hoped . \nvera has created a provocative , absorbing drama that reveals the curse of a self-hatred instilled by rigid social mores . \na french film with a more down-home flavor . \na fun ride . \ndepending upon your reaction to this movie , you may never again be able to look at a red felt sharpie pen without disgust , a thrill , or the giggles . \nwhile bollywood/hollywood will undoubtedly provide its keenest pleasures to those familiar with bombay musicals , it also has plenty for those ( like me ) who aren't . \nthere are times when you wish that the movie had worked a little harder to conceal its contrivances , but brown sugar turns out to be a sweet and enjoyable fantasy . \nfontaine masterfully creates a portrait of two strong men in conflict , inextricably entwined through family history , each seeing himself in the other , neither liking what he sees . \none fantastic ( and educational ) documentary . \nas janice , eileen walsh , an engaging , wide-eyed actress whose teeth are a little too big for her mouth , infuses the movie with much of its slender , glinting charm . \nsure , it's contrived and predictable , but its performances are so well tuned that the film comes off winningly , even though it's never as solid as you want it to be . \ndong shows how intolerance has the power to deform families , then tear them apart . \nthe chateau belongs to rudd , whose portrait of a therapy-dependent flakeball spouting french malapropisms . . . is a nonstop hoot . \nthe cast , collectively a successful example of the lovable-loser protagonist , shows deft comic timing . \nit trusts the story it sets out to tell . \ni couldn't recommend this film more . \nas a good old-fashioned adventure for kids , spirit : stallion of the cimarron is a winner . \nan effective portrait of a life in stasis -- of the power of inertia to arrest development in a dead-end existence . \nsucceeds as a well-made evocation of a subculture . \n . . . an interesting slice of history . \nme no lika da accents so good , but i thoroughly enjoyed the love story . scott baio is turning in some delightful work on indie projects . \nit's an experience in understanding a unique culture that is presented with universal appeal . \nwhat's surprising is how well it holds up in an era in which computer-generated images are the norm . \nbrings together some of the biggest names in japanese anime , with impressive results . \nwonder , hope and magic can never escape the heart of the boy when the right movie comes along , especially if it begins with the name of star wars\na flick about our infantilized culture that isn't entirely infantile . \nan exceptionally acted , quietly affecting cop drama . \nsensual , funny and , in the end , very touching . \nangel presents events partly from the perspective of aurelie and christelle , and infuses the film with the sensibility of a particularly nightmarish fairytale . \nwho needs mind-bending drugs when they can see this , the final part of the 'qatsi' trilogy , directed by godfrey reggio , with music by philip glass ? \n[a] smarter and much funnier version of the old police academy flicks . \nproof once again that if the filmmakers just follow the books , they can't go wrong . better effects , better acting and a hilarious kenneth branagh . an excellent sequel . \nboth a grand tour through 300 hundred years of russian cultural identity and a stunning technical achievement . \njust how these families interact may surprise you . \nallen se atreve a atacar , a atacarse y nos ofrece gags que van de la sonrisa a la risa de larga duracin\nproves that some movie formulas don't need messing with -- like the big-bug movie . \na surprisingly funny movie . \nthis new movie version of the alexandre dumas classic is the stuff of high romance , brought off with considerable wit . \nlike all of egoyan's work , ararat is fiercely intelligent and uncommonly ambitious . \nif a big musical number like 'praise the lord , he's the god of second chances' doesn't put you off , this will be an enjoyable choice for younger kids . \n . . . fuses the events of her life with the imagery in her paintings so vividly that the artist's work may take on a striking new significance for anyone who sees the film . \n[clooney's] debut can be accused of being a bit undisciplined , but it has a tremendous , offbeat sense of style and humor that suggests he was influenced by some of the filmmakers who have directed him , especially the coen brothers and steven soderbergh . \nalthough made on a shoestring and unevenly acted , conjures a lynch-like vision of the rotting underbelly of middle america . \na piquant meditation on the things that prevent people from reaching happiness . \na timely look back at civil disobedience , anti-war movements and the power of strong voices . \nrifkin's references are . . . impeccable throughout . \ni'd be lying if i said my ribcage didn't ache by the end of kung pow . \nmore than their unique residences , home movie is about the people who live in them , who have carved their own comfortable niche in the world and have been kind enough to share it . \nthe movie is ingenious fun . see it . \nthe combination of lightness and strictness in this instance gives italian for beginners an amiable aimlessness that keeps it from seeming predictably formulaic . \nuna pelcula oscura , precisa , por momentos grandiosa y casi siempre conmovedora . \nthe script is smart and dark - hallelujah for small favors . \nan intelligent , multi-layered and profoundly humanist ( not to mention gently political ) meditation on the values of knowledge , education , and the affects of cultural and geographical displacement . \nmr . polanski is in his element here : alone , abandoned , but still consoled by his art , which is more than he has ever revealed before about the source of his spiritual survival . \nspectacular in every sense of the word , even if you don' t know an orc from a uruk-hai . \nthis isn't exactly profound cinema , but it's good-natured and sometimes quite funny . \nthis is a finely written , superbly acted offbeat thriller . \ntres greek writer and star nia vardalos has crafted here a worldly-wise and very funny script . \nos problemas tm incio a partir do momento em que samos do cinema e comeamos a pensar sobre o que acabamos de ver .  ento que sinais realmente desaponta . \na tasty appetizer that leaves you wanting more . \nit gives devastating testimony to both people's capacity for evil and their heroic capacity for good . \nthe film reminds me of a vastly improved germanic version of my big fat greek wedding -- with better characters , some genuine quirkiness and at least a measure of style . the difference is that i truly enjoyed most of mostly martha while i ne\nmorton deserves an oscar nomination . \na colorful , vibrant introduction to a universal human impulse , lushly photographed and beautifully recorded . \nthe screenplay never lets us forget that bourne was once an amoral assassin just like the ones who are pursuing him . . . there is never really a true \" us \" versus \" them \" . \nthe history is fascinating ; the action is dazzling . they just don't work in concert . \nfor those in search of something different , wendigo is a genuinely bone-chilling tale . \na lovely film for the holiday season . \nit remains to be seen whether statham can move beyond the crime-land action genre , but then again , who says he has to ? \na hypnotic cyber hymn and a cruel story of youth culture . \nit's a fairy tale that comes from a renowned indian film culture that allows americans to finally revel in its splendor . \nat once subtle and visceral , the film never succumbs to the trap of the maudlin or tearful , offering instead with its unflinching gaze a measure of faith in the future . \nthe performances of the children , untrained in acting , have an honesty and dignity that breaks your heart . \ndespite its lavish formalism and intellectual austerity , the film manages to keep you at the edge of your seat with its shape-shifting perils , political intrigue and brushes with calamity . \nthis rush to profits has created a predictably efficient piece of business notable largely for its overwhelming creepiness , for an eagerness to create images you wish you hadn't seen , which , in this day and age , is of course the point . \nadams , with four scriptwriters , takes care with the characters , who are so believable that you feel what they feel . \na completely spooky piece of business that gets under your skin and , some plot blips aside , stays there for the duration . \nsuperbly photographed and staged by mendes with a series of riveting set pieces the likes of which mainstream audiences have rarely seen . \nthe ensemble cast turns in a collectively stellar performance , and the writing is tight and truthful , full of funny situations and honest observations . \nnot quite as miraculous as its dreamworks makers would have you believe , but it more than adequately fills the eyes and stirs the emotions . \na properly spooky film about the power of spirits to influence us whether we believe in them or not . \nthe lightest , most breezy movie steven spielberg has made in more than a decade . and the positive change in tone here seems to have recharged him . \nlike edward norton in american history x , ryan gosling ( murder by numbers ) delivers a magnetic performance . \nthis is a very funny , heartwarming film . it has fun with the quirks of family life , but it also treats the subject with fondness and respect . \nrarely , indeed almost never , is such high-wattage brainpower coupled with pitch-perfect acting and an exquisite , unfakable sense of cinema . \nthe leanest and meanest of solondz's misanthropic comedies . \na dark , quirky road movie that constantly defies expectation . \nthere are some movies that hit you from the first scene and you know it's going to be a trip . igby goes down is one of those movies . \noften messy and frustrating , but very pleasing at its best moments , it's very much like life itself . \na burst of color , music , and dance that only the most practiced curmudgeon could fail to crack a smile at . \nan energetic , violent movie with a momentum that never lets up . \nlasker's canny , meditative script distances sex and love , as byron and luther . . . realize they can't get no satisfaction without the latter . \nit turns out to be smarter and more diabolical than you could have guessed at the beginning . \ncage makes an unusual but pleasantly haunting debut behind the camera . \nnoyce has worked wonders with the material . \nit's mostly a pleasure to watch . and the reason for that is a self-aware , often self-mocking , intelligence . \nthe chateau is a risky venture that never quite goes where you expect and often surprises you with unexpected comedy . \na very well-meaning movie , and it will stand in future years as an eloquent memorial to the world trade center tragedy . \nthere aren't many conclusive answers in the film , but there is an interesting story of pointed personalities , courage , tragedy and the little guys vs . the big guys . \nvividly demonstrates that the director of such hollywood blockbusters as patriot games can still turn out a small , personal film with an emotional wallop . \na four star performance from kevin kline who unfortunately works with a two star script . \ndogtown & z-boys evokes the blithe rebel fantasy with the kind of insouciance embedded in the sexy demise of james dean . \nif you don't flee , you might be seduced . if you don't laugh , flee . \npayne constructs a hilarious ode to middle america and middle age with this unlikely odyssey , featuring a pathetic , endearing hero who is all too human . \nkoury frighteningly and honestly exposes one teenager's uncomfortable class resentment and , in turn , his self-inflicted retaliation . \nthe santa clause 2 proves itself a more streamlined and thought out encounter than the original could ever have hoped to be . \nnow as a former gong show addict , i'll admit it , my only complaint is that we didn't get more re-creations of all those famous moments from the show . \nsucceeds where its recent predecessor miserably fails because it demands that you suffer the dreadfulness of war from both sides . \nthe first bond movie in ages that isn't fake fun . \nthis odd , poetic road movie , spiked by jolts of pop music , pretty much takes place in morton's ever-watchful gaze -- and it's a tribute to the actress , and to her inventive director , that the journey is such a mesmerizing one . \na film centering on a traditional indian wedding in contemporary new delhi may not sound like specialized fare , but mira nair's film is an absolute delight for all audiences . \na weird and wonderful comedy . \nthe movie should jolt you out of your seat a couple of times , give you a few laughs , and leave you feeling like it was worth your seven bucks , even though it does turn out to be a bit of a cheat in the end . \nhas the capability of effecting change and inspiring hope . \na first-class , thoroughly involving b movie that effectively combines two surefire , beloved genres -- the prison flick and the fight film . \nlabute's careful handling makes the material seem genuine rather than pandering . \nin between all the emotional seesawing , it's hard to figure the depth of these two literary figures , and even the times in which they lived . but they fascinate in their recklessness . \ndeath to smoochy is often very funny , but what's even more remarkable is the integrity of devito's misanthropic vision . \na beautiful , entertaining two hours . you get the idea , though , that kapur intended the film to be more than that . \na wonderful , ghastly film . \namid the new populist comedies that underscore the importance of family tradition and familial community , one would be hard-pressed to find a movie with a bigger , fatter heart than barbershop . \nparris' performance is credible and remarkably mature . \n'enigma' is the kind of engaging historical drama that hollywood appears to have given up on in favor of sentimental war movies in the vein of 'we were soldiers . '\nmunch's screenplay is tenderly observant of his characters . he watches them as they float within the seas of their personalities . his scenes are short and often unexpected . \nit grabs you in the dark and shakes you vigorously for its duration . \nleigh's daring here is that without once denying the hardscrabble lives of people on the economic fringes of margaret thatcher's ruinous legacy , he insists on the importance of those moments when people can connect and express their love for each other . \nhashiguchi vividly captures the way young japanese live now , chafing against their culture's manic mix of millennial brusqueness and undying , traditional politesse . \nuneven but a lot of fun . \ni know that i'll never listen to marvin gaye or the supremes the same way again\nthe two leads , nearly perfect in their roles , bring a heart and reality that buoy the film , and at times , elevate it to a superior crime movie . \nnot as good as the full monty , but a really strong second effort . \nwhenever it threatens to get bogged down in earnest dramaturgy , a stirring visual sequence like a surge through swirling rapids or a leap from pinnacle to pinnacle rouses us . if horses could fly , this is surely what they'd look like . \nunfolds as one of the most politically audacious films of recent decades from any country , but especially from france . \nthis real-life hollywood fairy-tale is more engaging than the usual fantasies hollywood produces . \nthe graphic carnage and re-creation of war-torn croatia is uncomfortably timely , relevant , and sickeningly real . \nleft me with the visceral sensation of longing , lasting traces of charlotte's web of desire and desperation . \nthe characters are more deeply thought through than in most 'right-thinking' films . \ncrammed with incident , and bristles with passion and energy . \nit's fun , splashy and entertainingly nasty . \na simple tale of an unlikely friendship , but thanks to the gorgeous locales and exceptional lead performances , it has considerable charm . \nit might be 'easier' to watch on video at home , but that shouldn't stop die-hard french film connoisseurs from going out and enjoying the big-screen experience . \nthere's very little sense to what's going on here , but the makers serve up the cliches with considerable dash . \nwitty , contemplative , and sublimely beautiful . \na surprisingly 'solid' achievement by director malcolm d . lee and writer john ridley . \nwoven together handsomely , recalling sixties' rockumentary milestones from lonely boy to don't look back . \nthis is pure , exciting moviemaking . you won't exactly know what's happening but you'll be blissfully exhausted . \nthe 1960s rebellion was misdirected : you can't fight your culture . \nworks because reno doesn't become smug or sanctimonious towards the audience . \nnettelbeck . . . has a pleasing way with a metaphor . \na pure participatory event that malnourished intellectuals will gulp down in a frenzy . \nthe cast delivers without sham the raw-nerved story . \nsteven soderbergh's digital video experiment is a clever and cutting , quick and dirty look at modern living and movie life . \nthe film's highlight is definitely its screenplay , both for the rhapsodic dialogue that jumps off the page , and for the memorable character creations . \nit lets you brush up against the humanity of a psycho , without making him any less psycho . \nsillier , cuter , and shorter than the first ( as best i remember ) , but still a very good time at the cinema . \nthe film is bright and flashy in all the right ways . \nelegant and eloquent [meditation] on death and that most elusive of passions , love . \ncut through the layers of soap-opera emotion and you find a scathing portrayal of a powerful entity strangling the life out of the people who want to believe in it the most . \nfilmmaker tian zhuangzhuang triumphantly returns to narrative filmmaking with a visually masterful work of quiet power . \nit excels because , unlike so many other hollywood movies of its ilk , it offers hope . \nshot in rich , shadowy black-and-white , devils chronicles , with increasingly amused irony , the relationship between reluctant captors and befuddled captives . \nthere's no clear picture of who killed bob crane . but here's a glimpse at his life . \nspectacularly beautiful , not to mention mysterious , sensual , emotionally intense , and replete with virtuoso throat-singing . \nweird . rewarding . \na summer entertainment adults can see without feeling embarrassed , but it could have been more . \nsparse but oddly compelling . \na stirring , funny and finally transporting re-imagining of beauty and the beast and 1930s horror films\nthe pinochet case is a searing album of remembrance from those who , having survived , suffered most . \na sweet-tempered comedy that forgoes the knee-jerk misogyny that passes for humor in so many teenage comedies . \nargento , at only 26 , brings a youthful , out-to-change-the-world aggressiveness to the project , as if she's cut open a vein and bled the raw film stock . \nsleek and arty . \nwith so many bad romances out there , this is the kind of movie that deserves a chance to shine . \nbrash , intelligent and erotically perplexing , haneke's portrait of an upper class austrian society and the suppression of its tucked away demons is uniquely felt with a sardonic jolt . \nthough jackson doesn't always succeed in integrating the characters in the foreground into the extraordinarily rich landscape , it must be said that he is an imaginative filmmaker who can see the forest for the trees . \n \" the quiet american \" begins in saigon in 1952 . that's its first sign of trouble . \na dazzling thing to behold -- as long as you're wearing the somewhat cumbersome 3d goggles the theater provides . \nbe patient with the lovely hush ! and your reward will be a thoughtful , emotional movie experience . \nthe large-format film is well suited to capture these musicians in full regalia and the incredible imax sound system lets you feel the beat down to your toes . \ngodard has never made a more sheerly beautiful film than this unexpectedly moving meditation on love , history , memory , resistance and artistic transcendence . \nthe kind of movie that comes along only occasionally , one so unconventional , gutsy and perfectly executed it takes your breath away . \nunlike most surf movies , blue crush thrillingly uses modern technology to take the viewer inside the wave . by the end you can't help but feel 'stoked . '\nthe off-center humor is a constant , and the ensemble gives it a buoyant delivery . \na tasty slice of droll whimsy . \nmike leigh populates his movie with a wonderful ensemble cast of characters that bring the routine day to day struggles of the working class to life\nawesome work : ineffable , elusive , yet inexplicably powerful\nsparkling , often hilarious romantic jealousy comedy . . . attal looks so much like a young robert deniro that it seems the film should instead be called 'my husband is travis bickle' . \neven if you're an agnostic carnivore , you can enjoy much of jonah simply , and gratefully , as laugh-out-loud lunacy with a pronounced monty pythonesque flavor . \nwhere bowling for columbine is at its most valuable is in its examination of america's culture of fear as a root cause of gun violence . \nthe result is somewhat satisfying -- it still comes from spielberg , who has never made anything that wasn't at least watchable . but it's also disappointing to a certain degree . \nthe all-french cast is marveilleux . \nthere's a lot to recommend read my lips . \na minor film with major pleasures from portuguese master manoel de oliviera . . . \nbrosnan gives a portrayal as solid and as perfect as his outstanding performance as bond in die another day . \naudiences are advised to sit near the back and squint to avoid noticing some truly egregious lip-non-synching , but otherwise the production is suitably elegant . \nthe movie is . . . very funny as you peek at it through the fingers in front of your eyes . \nnicks sustains the level of exaggerated , stylized humor throughout by taking your expectations and twisting them just a bit . \na refreshing change from the usual whoopee-cushion effort aimed at the youth market . \nit finds its moviegoing pleasures in the tiny events that could make a person who has lived her life half-asleep suddenly wake up and take notice . \n . . . an enjoyably frothy 'date movie' . . . \nthe genius of the work speaks volumes , offering up a hallucinatory dreamscape that frustrates and captivates . \ntwo weeks notice has appeal beyond being a sandra bullock vehicle or a standard romantic comedy . \nthe movie's seams may show . . . but pellington gives \" mothman \" an irresistibly uncanny ambience that goes a long way toward keeping the picture compelling . \nif mostly martha is mostly unsurprising , it's still a sweet , even delectable diversion . \na wild comedy that could only spring from the demented mind of the writer of being john malkovich . \nschnitzler does a fine job contrasting the sleekness of the film's present with the playful paranoia of the film's past . \n'a fresh-faced , big-hearted and frequently funny thrill ride for the kiddies , with enough eye candy and cheeky wit to keep parents away from the concession stand . '\nmana gives us compelling , damaged characters who we want to help -- or hurt . \nthe sentimental script has problems , but the actors pick up the slack . \na good documentary can make interesting a subject you thought would leave you cold . a case in point : doug pray's scratch . \nabderrahmane sissako's heremakono ( waiting for happiness ) is an elegiac portrait of a transit city on the west african coast struggling against foreign influences . \nin xxx , diesel is that rare creature -- an action hero with table manners , and one who proves that elegance is more than tattoo deep . \nan engrossing and grim portrait of hookers : what they think of themselves and their clients . \nit all plays out . . . like a high-end john hughes comedy , a kind of elder bueller's time out . \nthe film is enriched by an imaginatively mixed cast of antic spirits , headed by christopher plummer as the subtlest and most complexly evil uncle ralph i've ever seen in the many film and stage adaptations of the work . \nthis is one of the rarest kinds of films : a family-oriented non-disney film that is actually funny without hitting below the belt . \nit is refreshingly undogmatic about its characters . \na moving and important film . \ndeep intelligence and a warm , enveloping affection breathe out of every frame . \nfamuyiwa's feature deals with its subject matter in a tasteful , intelligent manner , rather than forcing us to endure every plot contrivance that the clich-riddled genre can offer . \nshowtime is a fine-looking film with a bouncy score and a clutch of lively songs for deft punctuation . \nsweet home alabama isn't going to win any academy awards , but this date-night diversion will definitely win some hearts . \na cruelly funny twist on teen comedy packed with inventive cinematic tricks and an ironically killer soundtrack\na gracious , eloquent film that by its end offers a ray of hope to the refugees able to look ahead and resist living in a past forever lost . \neven though many of these guys are less than adorable ( their lamentations are pretty much self-centered ) , there's something vital about the movie . \na tour de force drama about the astonishingly pivotal role of imagination in the soulful development of two rowdy teenagers . \nit is a strength of a documentary to disregard available bias , especially as temptingly easy as it would have been with this premise . \nwhen twentysomething hotsies make movies about their lives , hard-driving narcissism is a given , but what a world we'd live in if argento's hollywood counterparts . . . had this much imagination and nerve . \nmaryam is more timely now than ever . \nan eloquent , reflective and beautifully acted meditation on both the profoundly devastating events of one year ago and the slow , painful healing process that has followed in their wake . \npiccoli gives a superb performance full of deep feeling . \nwhat a concept , what an idea , what a thrill ride . this is a more fascinating look at the future than \" bladerunner \" and one of the most high-concept sci fi adventures attempted for the screen . \nthe rare movie that's as crisp and to the point as the novel on which it's based . \na film of epic scale with an intimate feeling , a saga of the ups and downs of friendships . \nsayles has an eye for the ways people of different ethnicities talk to and about others outside the group . \n \" nicholas nickleby \" is a perfect family film to take everyone to since there's no new \" a christmas carol \" out in the theaters this year . charlie hunnam has the twinkling eyes , repressed smile and determined face needed to carry out a dickensian hero . \nniccol the filmmaker merges his collaborators' symbolic images with his words , insinuating , for example , that in hollywood , only god speaks to the press\nkhouri manages , with terrific flair , to keep the extremes of screwball farce and blood-curdling family intensity on one continuum . \nimpresses as a skillfully assembled , highly polished and professional adaptation . . . just about as chilling and unsettling as 'manhunter' was . \nit's a solid movie about people whose lives are anything but . \nthough a touch too arthouse 101 in its poetic symbolism , heaven proves to be a good match of the sensibilities of two directors . \ni simply can't recommend it enough . \nwiseman reveals the victims of domestic abuse in all of their pity and terror . \nmuccino , who directed from his own screenplay , is a canny crowd pleaser , and the last kiss . . . provides more than enough sentimental catharsis for a satisfying evening at the multiplex . \nwe want the funk - and this movie's got it . \nwow , so who knew charles dickens could be so light-hearted ? \nmany went to see the attraction for the sole reason that it was hot outside and there was air conditioning inside , and i don't think that a . c . will help this movie one bit . \nsem se preocupar em criar momentos melodramticos para arrancar lgrimas do espectador , o pianista  um filme triste sem que , para isso , precise ser emocionante . \nthe storylines are woven together skilfully , the magnificent swooping aerial shots are breathtaking , and the overall experience is awesome . \na miraculous movie , i'm going home is so slight , yet overflows with wisdom and emotion . \nbaran is shockingly devoid of your typical majid majidi shoe-loving , crippled children . \nan ambitious 'what if ? ' that works . \nevery moment crackles with tension , and by the end of the flick , you're on the edge of your seat . \na fine , rousing , g-rated family film , aimed mainly at little kids but with plenty of entertainment value to keep grown-ups from squirming in their seats . \nthe series' message about making the right choice in the face of tempting alternatives remains prominent , as do the girls' amusing personalities . \nrichly entertaining and suggestive of any number of metaphorical readings . \na compelling allegory about the last days of germany's democratic weimar republic . \noffers a guilt-free trip into feel-good territory . \na b-movie you can sit through , enjoy on a certain level and then forget . \ndevos delivers a perfect performance that captures the innocence and budding demons within a wallflower . \ndisappointingly , the characters are too strange and dysfunctional , tom included , to ever get under the skin , but this is compensated in large part by the off-the-wall dialogue , visual playfulness and the outlandishness of the idea itself . \ndirector todd solondz has made a movie about critical reaction to his two previous movies , and about his responsibility to the characters that he creates . \nthe word that comes to mind , while watching eric rohmer's tribute to a courageous scottish lady , is painterly . \na fascinating case study of flower-power liberation -- and the price that was paid for it . \nbluer than the atlantic and more biologically detailed than an autopsy , the movie . . . is , also , frequently hilarious . \nreally is a pan-american movie , with moments of genuine insight into the urban heart . \nobligada para impotentes , daneses , camareras italianas , profesores de idiomas , y todo aquel que desee una lengua para expresar su amor . \nan overly familiar scenario is made fresh by an intelligent screenplay and gripping performances in this low-budget , video-shot , debut indie effort . \npeppering this urban study with references to norwegian folktales , villeneuve creates in maelstrom a world where the bizarre is credible and the real turns magical . \nong's promising debut is a warm and well-told tale of one recent chinese immigrant's experiences in new york city . \nfantastic ! \nthat the real antwone fisher was able to overcome his personal obstacles and become a good man is a wonderful thing ; that he has been able to share his story so compellingly with us is a minor miracle . \nthere's not much to fatale , outside of its stylish surprises . . . but that's ok . \nwhat redeems the film is the cast , particularly the ya-yas themselves . \nbeautiful , cold , oddly colorful and just plain otherworldly , a freaky bit of art that's there to scare while we delight in the images . \nit's up to [watts] to lend credibility to this strange scenario , and her presence succeeds in making us believe . \nthe film is darkly atmospheric , with herrmann quietly suggesting the sadness and obsession beneath hearst's forced avuncular chortles . \nshyamalan takes a potentially trite and overused concept ( aliens come to earth ) and infuses it into a rustic , realistic , and altogether creepy tale of hidden invasion . \n . . . the story , like ravel's bolero , builds to a crescendo that encompasses many more paths than we started with . \nit's plotless , shapeless -- and yet , it must be admitted , not entirely humorless . indeed , the more outrageous bits achieve a shock-you-into-laughter intensity of almost dadaist proportions . \ngondry's direction is adequate . . . but what gives human nature its unique feel is kaufman's script . \nthe film's plot may be shallow , but you've never seen the deep like you see it in these harrowing surf shots . \nwith a large cast representing a broad cross-section , tavernier's film bounds along with the rat-a-tat energy of \" his girl friday , \" maintaining a light touch while tackling serious themes . \nthe observations of this social/economic/urban environment are canny and spiced with irony . \nrenner carries much of the film with a creepy and dead-on performance . \njarecki and gibney do find enough material to bring kissinger's record into question and explain how the diplomat's tweaked version of statecraft may have cost thousands and possibly millions of lives . \nthe spaniel-eyed jean reno infuses hubert with a mixture of deadpan cool , wry humor and just the measure of tenderness required to give this comic slugfest some heart . \naniston has at last decisively broken with her friends image in an independent film of satiric fire and emotional turmoil . \na mildly enjoyable if toothless adaptation of a much better book . \n \" the dangerous lives of altar boys \" has flaws , but it also has humor and heart and very talented young actors\nunexpected , and often contradictory , truths emerge . \n300 years of russian history and culture compressed into an evanescent , seamless and sumptuous stream of consciousness . \nintelligent , caustic take on a great writer and dubious human being . \nmay take its sweet time to get wherever it's going , but if you have the patience for it , you won't feel like it's wasted yours . \nless the sensational true-crime hell-jaunt purists might like and more experimental in its storytelling ( though no less horrifying for it ) . \nthe film is one of the year's best . \neerily accurate depiction of depression . \n . . . a delicious crime drama on par with the slickest of mamet . \ncharming and witty , it's also somewhat clumsy . \ndirected with purpose and finesse by england's roger mitchell , who handily makes the move from pleasing , relatively lightweight commercial fare such as notting hill to commercial fare with real thematic heft . \nescapes the precious trappings of most romantic comedies , infusing into the story very real , complicated emotions . \nthis big screen caper has a good bark , far from being a bow-wow . \n[allen] manages to breathe life into this somewhat tired premise . \ni have two words to say about reign of fire . great dragons ! \nby surrounding us with hyper-artificiality , haynes makes us see familiar issues , like racism and homophobia , in a fresh way . \na deliberative account of a lifestyle characterized by its surface-obsession  one that typifies the delirium of post , pre , and extant stardom . \nsuperb production values & christian bale's charisma make up for a derivative plot . \nthe film has the courage of its convictions and excellent performances on its side . \ni know i shouldn't have laughed , but hey , those farts got to my inner nine-year-old . \na movie that will thrill you , touch you and make you laugh as well . \nit's a smart , funny look at an arcane area of popular culture , and if it isn't entirely persuasive , it does give exposure to some talented performers . \nmore vaudeville show than well-constructed narrative , but on those terms it's inoffensive and actually rather sweet . \nthe case is a convincing one , and should give anyone with a conscience reason to pause . \nthe actresses find their own rhythm and protect each other from the script's bad ideas and awkwardness . \ndiverting french comedy in which a husband has to cope with the pesky moods of jealousy . \ncaptivates and shows how a skillful filmmaker can impart a message without bludgeoning the audience over the head . \nthere is a welcome lack of pretension about the film , which very simply sets out to entertain and ends up delivering in good measure . \ncoy but exhilarating , with really solid performances by ving rhames and wesley snipes . \nit is a likable story , told with competence . \nnot only does spider-man deliver , but i suspect it might deliver again and again . \ntackles the difficult subject of grief and loss with such life-embracing spirit that the theme doesn't drag an audience down . \na small movie with a big impact . \nthe movie , despite its rough edges and a tendency to sag in certain places , is wry and engrossing . \n i admire the closing scenes of the film , which seem to ask whether our civilization offers a cure for vincent's complaint . \nlike rudy yellow lodge , eyre needs to take a good sweat to clarify his cinematic vision before his next creation and remember the lessons of the trickster spider . \na delightful romantic comedy with plenty of bite . it's far from a frothy piece , and the characters are complex , laden with plenty of baggage and tinged with tragic undertones . \nusing an endearing cast , writer/director dover kosashvili takes a slightly dark look at relationships , both sexual and kindred . \nwhen a movie has stuck around for this long , you know there's something there . it's that good . \nsmart , sassy interpretation of the oscar wilde play . \nforget about one oscar nomination for julianne moore this year - she should get all five . \njapanese director shohei imamura's latest film is an odd but ultimately satisfying blend of the sophomoric and the sublime . \nkwan is a master of shadow , quietude , and room noise , and lan yu is a disarmingly lived-in movie . \nwhile the plot follows a predictable connect-the-dots course . . . director john schultz colors the picture in some evocative shades . \nnice piece of work . \nkatz's documentary doesn't have much panache , but with material this rich it doesn't need it . \nwe get an image of big papa spanning history , rather than suspending it . \nevelyn's strong cast and surehanded direction make for a winning , heartwarming yarn . \n'aunque recurre a ciertos clichs del gnero , la poderosa actuacin de robin williams perdona las fallas del guin . '\na conventional but heartwarming tale . \na thought-provoking picture . \nthis is one of the outstanding thrillers of recent years . \nskins has a desolate air , but eyre , a native american raised by white parents , manages to infuse the rocky path to sibling reconciliation with flashes of warmth and gentle humor . \na film of quiet power . \nmore concerned with overall feelings , broader ideas , and open-ended questions than concrete story and definitive answers , soderbergh's solaris is a gorgeous and deceptively minimalist cinematic tone poem . \nan intelligent romantic thriller of a very old-school kind of quality . \nthe sword fighting is well done and auteuil is a goofy pleasure . \nyes , mibii is rote work and predictable , but with a philosophical visual coming right at the end that extravagantly redeems it . \nfilm can't quite maintain its initial momentum , but remains sporadically funny throughout . \no fantasma is boldly , confidently orchestrated , aesthetically and sexually , and its impact is deeply and rightly disturbing . \nit's still adam sandler , and it's not little nicky . and for many of us , that's good enough . \nhere's yet another cool crime movie that actually manages to bring something new into the mix . \nlee's achievement extends to his supple understanding of the role that brown played in american culture as an athlete , a movie star , and an image of black indomitability . \nkaufman and jonze take huge risks to ponder the whole notion of passion -- our desire as human beings for passion in our lives and the emptiness one feels when it is missing . \nit tends to remind one of a really solid woody allen film , with its excellent use of new york locales and sharp writing\nwhile centered on the life experiences of a particular theatrical family , this marvelous documentary touches -- ever so gracefully -- on the entire history of the yiddish theater , both in america and israel . \nthe film , despite the gratuitous cinematic distractions impressed upon it , is still good fun . \nthe immersive powers of the giant screen and its hyper-realistic images are put to perfect use in the breathtakingly beautiful outer-space documentary space station 3d . \nhas an unmistakable , easy joie de vivre . \nmore than anything else , kissing jessica stein injects freshness and spirit into the romantic comedy genre , which has been held hostage by generic scripts that seek to remake sleepless in seattle again and again . \nthis movie has the usual impossible stunts . . . but it has just as many scenes that are lean and tough enough to fit in any modern action movie . \nmostly works because of the universal themes , earnest performances . . . and excellent use of music by india's popular gulzar and jagjit singh . \n . . . the one thing this wild film has that other imax films don't : chimps , lots of chimps , all blown up to the size of a house . that's fun for kids of any age . \nwriter/director david caesar ladles on the local flavour with a hugely enjoyable film about changing times , clashing cultures and the pleasures of a well-made pizza . \nrarely have i seen a film so willing to champion the fallibility of the human heart . \nholofcener rejects patent solutions to dramatize life's messiness from inside out , in all its strange quirks . \nlike the full monty , this is sure to raise audience's spirits and leave them singing long after the credits roll . \n . . . a gleefully grungy , hilariously wicked black comedy . . . \nkinnear and dafoe give what may be the performances of their careers . \nall in all , a great party . \na moving story of determination and the human spirit . \n \" brown sugar \" admirably aspires to be more than another \" best man \" clone by weaving a theme throughout this funny film . \n[gulpilil] is a commanding screen presence , and his character's abundant humanism makes him the film's moral compass . \na stylish thriller . \nan effortlessly accomplished and richly resonant work . \nin some ways , lagaan is quintessential bollywood . except it's much , much better . \nthough it never rises to its full potential as a film , still offers a great deal of insight into the female condition and the timeless danger of emotions repressed . \nscotland looks wonderful , the fans are often funny fanatics , the showdown sure beats a bad day of golf . \nwhat enlivens this film , beyond the astute direction of cardoso and beautifully detailed performances by all of the actors , is a note of defiance over social dictates . \nthe emotion is impressively true for being so hot-blooded , and both leads are up to the task . \nalthough it lacks the detail of the book , the film does pack some serious suspense . \ni'd watch these two together again in a new york minute . \nthere's nothing like love to give a movie a b-12 shot , and cq shimmers with it . \na moving essay about the specter of death , especially suicide . \nthis film is so different from the apple and so striking that it can only encourage us to see samira makhmalbaf as a very distinctive sensibility , working to develop her own film language with conspicuous success . \nlike a less dizzily gorgeous companion to mr . wong's in the mood for love -- very much a hong kong movie despite its mainland setting . \n . . . a somber film , almost completely unrelieved by any comedy beyond the wistful everyday ironies of the working poor . \ncoral reef adventure is a heavyweight film that fights a good fight on behalf of the world's endangered reefs -- and it lets the pictures do the punching . \nthe overall result is an intelligent , realistic portrayal of testing boundaries . \npoignant and moving , a walk to remember is an inspirational love story , capturing the innocence and idealism of that first encounter . \nworth a salute just for trying to be more complex than your average film . \nhandsome and sophisticated approach to the workplace romantic comedy . \na shimmeringly lovely coming-of-age portrait , shot in artful , watery tones of blue , green and brown . \nwhile cherish doesn't completely survive its tonal transformation from dark comedy to suspense thriller , it's got just enough charm and appealing character quirks to forgive that still serious problem . \nin many ways , reminiscent of 1992's unforgiven which also utilized the scintillating force of its actors to draw out the menace of its sparse dialogue . \nwe admire this film for its harsh objectivity and refusal to seek our tears , our sympathies . \nan often watchable , though goofy and lurid , blast of a costume drama set in the late 15th century . \nthe entire cast is first-rate , especially sorvino . \nthe cat's meow marks a return to form for director peter bogdanovich . . . \nthis one is strictly a lightweight escapist film . \nthis sensitive , smart , savvy , compelling coming-of-age drama delves into the passive-aggressive psychology of co-dependence and the struggle for self-esteem . \nthe culmination of everyone's efforts is given life when a selection appears in its final form [in \" last dance \" ] . \nin questioning the election process , payami graphically illustrates the problems of fledgling democracies , but also the strength and sense of freedom the iranian people already possess , with or without access to the ballot box . \na very charming and funny movie . \nthis is a film that manages to find greatness in the hue of its drastic iconography . \nstreamlined to a tight , brisk 85-minute screwball thriller , \" big trouble \" is funny , harmless and as substantial as a tub of popcorn with extra butter . \nconsummate actor barry has done excellent work here . \nthe biggest problem with this movie is that it's not nearly long enough . \nwhile not all that bad of a movie , it's nowhere near as good as the original . \nali's graduation from little screen to big is far less painful than his opening scene encounter with an over-amorous terrier . \ni have always appreciated a smartly written motion picture , and , whatever flaws igby goes down may possess , it is undeniably that . \nyou can sip your vintage wines and watch your merchant ivory productions ; i'll settle for a nice cool glass of iced tea and a jerry bruckheimer flick any day of the week . \nmay be the most undeserving victim of critical overkill since town and country . \na chilly , brooding but quietly resonant psychological study of domestic tension and unhappiness . \nthe movie does its best to work us over , with second helpings of love , romance , tragedy , false dawns , real dawns , comic relief , two separate crises during marriage ceremonies , and the lush scenery of the cotswolds . \ncold , nervy and memorable . \nbecomes a fascinating study of isolation and frustration that successfully recreates both the physical setting and emotional tensions of the papin sisters . \nspend your benjamins on a matinee . \nall in all , it's a pretty good execution of a story that's a lot richer than the ones hollywood action screenwriters usually come up with on their own . \nworth seeing just for weaver and lapaglia . \na pleasant piece of escapist entertainment . \namong the many pleasures are the lively intelligence of the artists and their perceptiveness about their own situations . \nit's consistently funny , in an irresistible junior-high way , and consistently free of any gag that would force you to give it a millisecond of thought . \nit's the cute frissons of discovery and humor between chaplin and kidman that keep this nicely wound clock not just ticking , but humming . \nthe storytelling may be ordinary , but the cast is one of those all-star reunions that fans of gosford park have come to assume is just another day of brit cinema . \nthere's something about a marching band that gets me where i live . \ncuaron repeatedly , perversely undercuts the joie de vivre even as he creates it , giving the movie a mournful undercurrent that places the good-time shenanigans in welcome perspective . \nit's definitely an improvement on the first blade , since it doesn't take itself so deadly seriously . \na slam-bang extravaganza that is all about a wild-and-woolly , wall-to-wall good time . \nwhat's infuriating about full frontal is that it's too close to real life to make sense . what's invigorating about it is that it doesn't give a damn . \nis red dragon worthy of a place alongside the other hannibal movies ? as hannibal would say , yes , 'it's like having an old friend for dinner' . \nwriter-director juan carlos fresnadillo makes a feature debut that is fully formed and remarkably assured . \ninsightfully written , delicately performed\nperhaps the grossest movie ever made . funny , though . \nthis 90-minute postmodern voyage was more diverting and thought-provoking than i'd expected it to be . \none of those exceedingly rare films in which the talk alone is enough to keep us involved . \na heartbreakingly thoughtful minor classic , the work of a genuine and singular artist . \nan affectionately goofy satire that's unafraid to throw elbows when necessary . . . \nbetween them , de niro and murphy make showtime the most savory and hilarious guilty pleasure of many a recent movie season . \njackson tries to keep the plates spinning as best he can , but all the bouncing back and forth can't help but become a bit tedious -- even with the breathtaking landscapes and villainous varmints there to distract you from the ricocheting . \nfilmmakers david weissman and bill weber benefit enormously from the cockettes' camera craziness -- not only did they film performances , but they did the same at home . \ninteresting both as a historical study and as a tragic love story . \na stylish but steady , and ultimately very satisfying , piece of character-driven storytelling . \nit picked me up , swung me around , and dropped me back in my seat with more emotional force than any other recent film . \ngraham greene's novel of colonialism and empire is elevated by michael caine's performance as a weary journalist in a changing world . \nthough it's equally solipsistic in tone , the movie has enough vitality to justify the notion of creating a screen adaptation of evans' saga of hollywood excess . \ncompulsively watchable , no matter how degraded things get . \ndelivers roughly equal amounts of beautiful movement and inside information . \nbon apptit ! just like a splendid meal , red dragon satisfies  from its ripe recipe , inspiring ingredients , certified cuisine and palatable presentation . \nthe structure is simple , but in its own way , rabbit-proof fence is a quest story as grand as the lord of the rings . \nthis charming , thought-provoking new york fest of life and love has its rewards . \nsome people march to the beat of a different drum , and if you ever wondered what kind of houses those people live in , this documentary takes a look at 5 alternative housing options . \nplayfully profound . . . and crazier than michael jackson on the top floor of a skyscraper nursery surrounded by open windows . \na film that will enthrall the whole family . \nthe charm of the first movie is still there , and the story feels like the logical , unforced continuation of the careers of a pair of spy kids . \nk 19 stays afloat as decent drama/action flick\nit sends you away a believer again and quite cheered at just that . \nlike the best 60 minutes expos , the film ( at 80 minutes ) is actually quite entertaining . \nan 83 minute document of a project which started in a muddle , seesawed back and forth between controlling interests multiple times , then found its sweet spot\nan emotionally and spiritually compelling journey seen through the right eyes , with the right actors and with the kind of visual flair that shows what great cinema can really do . \nnair doesn't use [monsoon wedding] to lament the loss of culture . instead , she sees it as a chance to revitalize what is and always has been remarkable about clung-to traditions . \nboth grant and hoult carry the movie because they are believable as people -- flawed , assured of the wrong things , and scared to admit how much they may really need the company of others . \nleading a double life in an american film only comes to no good , but not here . matters play out realistically if not always fairly . \nin the affable maid in manhattan , jennifer lopez's most aggressive and most sincere attempt to take movies by storm , the diva shrewdly surrounds herself with a company of strictly a-list players . \nlike mike is a harmlessly nave slice of b-ball fantasy , fit for filling in during the real nba's off-season . \nthough writer/director bart freundlich's film ultimately becomes a simplistic story about a dysfunctional parent-child relationship , it has some special qualities and the soulful gravity of crudup's anchoring performance . \ny tu mam tambin es un buen filme gracias a lo poco convencional de su narrativa , y es quiz el proyecto ms arriesgado en la carrera de alfonso cuarn\nwhat the movie lacks in action it more than makes up for in drama , suspense , revenge , and romance . \njust offbeat enough to keep you interested without coming close to bowling you over . \nprobes in a light-hearted way the romantic problems of individuals for whom the yearning for passion spells discontent . \nwhat elevates the movie above the run-of-the-mill singles blender is its surreal sense of humor and technological finish . \na film about female friendship that men can embrace and women will talk about for hours . \nthe directing and story are disjointed , flaws that have to be laid squarely on taylor's doorstep . but the actors make this worth a peek . \n uma pena que , mais tarde , o prprio filme abandone o tom de pardia e passe a utilizar os mesmos clichs que havia satirizado . \nlight the candles , bring out the cake and don't fret about the calories because there's precious little substance in birthday girl -- it's simply , and surprisingly , a nice , light treat . \nit may be about drug dealers , kidnapping , and unsavory folks , but the tone and pacing are shockingly intimate . \nmassoud's story is an epic , but also a tragedy , the record of a tenacious , humane fighter who was also the prisoner ( and ultimately the victim ) of history . \nif villainous vampires are your cup of blood , blade 2 is definitely a cut above the rest . \ndrumline ably captures the complicated relationships in a marching band . \nbecause the film deliberately lacks irony , it has a genuine dramatic impact ; it plays like a powerful 1957 drama we've somehow never seen before . \ndoes point the way for adventurous indian filmmakers toward a crossover into nonethnic markets . \nseems based on ugly ideas instead of ugly behavior , as happiness was . . . hence , storytelling is far more appealing . \nsum \" is jack ryan's \" do-over . \" give credit to everyone from robinson down to the key grip that this bold move works . especially give credit to affleck . \nan intelligently made ( and beautifully edited ) picture that at the very least has a spark of life to it -- more than you can say for plenty of movies that flow through the hollywood pipeline without a hitch . \na terrific date movie , whatever your orientation . \nnot all of the stories work and the ones that do are thin and scattered , but the film works well enough to make it worth watching . \nwhat it lacks in originality it makes up for in effective if cheap moments of fright and dread . \nthe pain , loneliness and insecurity of the screenwriting process are vividly and painfully brought to slovenly life in this self-deprecating , biting and witty feature written by charlie kaufman and his twin brother , donald , and directed by spike jonze . \na gem of a movie . \nwitty , vibrant , and intelligent . \nit's all stitched together with energy , intelligence and verve , enhanced by a surplus of vintage archive footage . \nmiller comes at film with bracing intelligence and a vision both painterly and literary . \nthe film is moody , oozing , chilling and heart-warming all at once . . . a twisting , unpredictable , cat-and-mouse thriller . \neight legged freaks is clever and funny , is amused by its special effects , and leaves you feeling like you've seen a movie instead of an endless trailer . \nthis is historical filmmaking without the balm of right-thinking ideology , either liberal or conservative . mr . scorsese's bravery and integrity in advancing this vision can hardly be underestimated . \na thriller whose style , structure and rhythms are so integrated with the story , you cannot separate them . \nit's a hoot watching the rock chomp on jumbo ants , pull an arrow out of his back , and leap unscathed through raging fire ! \nreturning director rob minkoff . . . and screenwriter bruce joel rubin . . . have done a fine job of updating white's dry wit to a new age . \nunfolds with such a wallop of you-are-there immediacy that when the bullets start to fly , your first instinct is to duck . \na strong script , powerful direction and splendid production design allows us to be transported into the life of wladyslaw szpilman , who is not only a pianist , but a good human being . \nan unflinching look at the world's dispossessed . \nif the film fails to fulfill its own ambitious goals , it nonetheless sustains interest during the long build-up of expository material . \npolanski has found the perfect material with which to address his own world war ii experience in his signature style . \nit is life affirming and heartbreaking , sweet without the decay factor , funny and sad . \nan off-beat and fanciful film about the human need for monsters to blame for all that is amiss in the world . \na colorful , joyous celebration of life ; a tapestry woven of romance , dancing , singing , and unforgettable characters . \nfrei assembles a fascinating profile of a deeply humanistic artist who , in spite of all that he's witnessed , remains surprisingly idealistic , and retains an extraordinary faith in the ability of images to communicate the truth of the world around him . \nnicely combines the enigmatic features of 'memento' with the hallucinatory drug culture of 'requiem for a dream . '\na well paced and satisfying little drama that deserved better than a direct-to-video' release . \nthe best part about \" gangs \" was daniel day-lewis . \na treat for its depiction on not giving up on dreams when you're a struggling nobody . \none of those rare films that seems as though it was written for no one , but somehow manages to convince almost everyone that it was put on the screen , just for them . \na gripping documentary that reveals how deep the antagonism lies in war-torn jerusalem . \ndirector chris wedge and screenwriters michael berg , michael j . wilson and peter ackerman create some episodes that rival vintage looney tunes for the most creative mayhem in a brief amount of time . \none of the film's most effective aspects is its tchaikovsky soundtrack of neurasthenic regret . \nsolondz creates some effective moments of discomfort for character and viewer alike . \nthe film's appeal has a lot to do with the casting of juliette binoche as sand , who brings to the role her pale , dark beauty and characteristic warmth . \ni was amused and entertained by the unfolding of bielinsky's cleverly constructed scenario , and greatly impressed by the skill of the actors involved in the enterprise . \nsomehow ms . griffiths and mr . pryce bring off this wild welsh whimsy . \nmore mature than fatal attraction , more complete than indecent proposal and more relevant than 9  weeks , unfaithful is at once intimate and universal cinema . \nfor all the dolorous trim , secretary is a genial romance that maintains a surprisingly buoyant tone throughout , notwithstanding some of the writers' sporadic dips into pop freudianism . \na fanciful drama about napoleon's last years and his surprising discovery of love and humility . \na highly personal look at the effects of living a dysfunctionally privileged lifestyle , and by the end , we only wish we could have spent more time in its world . \neric schweig and graham greene both exude an air of dignity that's perfect for the proud warrior that still lingers in the souls of these characters . \nlovely and amazing is holofcener's deep , uncompromising curtsy to women she knows , and very likely is . when all is said and done , she loves them to pieces -- and so , i trust , will you . \ncampbell scott finds the ideal outlet for his flick-knife diction in the role of roger swanson . \n[fiji diver rusi vulakoro and the married couple howard and michelle hall] show us the world they love and make us love it , too . \nrussian ark is a new treasure of the hermitage . \nthe animated sequences are well done and perfectly constructed to convey a sense of childhood imagination and creating adventure out of angst . \nit's definitely a step in the right direction . \nas the princess , sorvino glides gracefully from male persona to female without missing a beat . ben kingsley is truly funny , playing a kind of ghandi gone bad . \nourside the theatre roger might be intolerable company , but inside it he's well worth spending some time with . \na gem , captured in the unhurried , low-key style favored by many directors of the iranian new wave . \nin an era where big stars and high production values are standard procedure , narc strikes a defiantly retro chord , and outpaces its contemporaries with daring and verve . \nalmost peerlessly unsettling . \nranges from laugh-out-loud hilarious to wonder-what- time-it-is tedious . \nthe film's gamble to occasionally break up the live-action scenes with animated sequences pays off , as does its sensitive handling of some delicate subject matter . \ntalk to her is not the perfect movie many have made it out to be , but it's still quite worth seeing . \nbeating the austin powers films at their own game , this blaxploitation spoof downplays the raunch in favor of gags that rely on the strength of their own cleverness as opposed to the extent of their outrageousness . \nthis is a dark , gritty , sometimes funny little gem . \nfor all its visual panache and compelling supporting characters , the heart of the film rests in the relationship between sullivan and his son . \nwhat makes salton sea surprisingly engrossing is that caruso takes an atypically hypnotic approach to a world that's often handled in fast-edit , hopped-up fashion . \na hidden-agenda drama that shouts classic french nuance . \nwith spy kids 2 : the island of lost dreams , the spy kids franchise establishes itself as a durable part of the movie landscape : a james bond series for kids . \nan invaluable historical document thanks to the filmmaker's extraordinary access to massoud , whose charm , cultivation and devotion to his people are readily apparent . \nthe performances of the four main actresses bring their characters to life . a little melodramatic , but with enough hope to keep you engaged . \nlan yu seems altogether too slight to be called any kind of masterpiece . it is , however , a completely honest , open-hearted film that should appeal to anyone willing to succumb to it . \neveryone should be able to appreciate the wonderful cinematography and naturalistic acting . \nthis often-hilarious farce manages to generate the belly laughs of lowbrow comedy without sacrificing its high-minded appeal . \nstock up on silver bullets for director neil marshall's intense freight train of a film . \nexpands the limits of what a film can be , taking us into the lives of women to whom we might not give a second look if we passed them on the street . \nthe farcical elements seemed too pat and familiar to hold my interest , yet its diverting grim message is a good one . \nshanghai ghetto may not be as dramatic as roman polanski's the pianist , but its compassionate spirit soars every bit as high . \ndespite these annoyances , the capable clayburgh and tambor really do a great job of anchoring the characters in the emotional realities of middle age . \nthe underworld urban angst is derivative of martin scorsese's taxi driver and goodfellas , but this film speaks for itself . \nthe film's heady yet far from impenetrable theory suggests that russians take comfort in their closed-off nationalist reality . \ndespite modest aspirations its occasional charms are not to be dismissed . \nconstantly touching , surprisingly funny , semi-surrealist exploration of the creative act . \nthe journey is worth your time , especially if you have ellen pompeo sitting next to you for the ride . \nmerci pour le movie . \nfor every cheesy scene , though , there is a really cool bit -- the movie's conception of a future-world holographic librarian ( orlando jones ) who knows everything and answers all questions , is visually smart , cleverly written , and nicely realized . \nwhat sets ms . birot's film apart from others in the genre is a greater attention to the parents -- and particularly the fateful fathers -- in the emotional evolution of the two bewitched adolescents . \nall three women deliver remarkable performances . \nclaire is a terrific role for someone like judd , who really ought to be playing villains . \nit's clear that mehta simply wanted to update her beloved genre for the thousands of indians who fancy themselves too sophisticated for the cheese-laced spectacles that pack 'em in on the subcontinent . \ncompassionately explores the seemingly irreconcilable situation between conservative christian parents and their estranged gay and lesbian children . \nthe soundtrack alone is worth the price of admission . \nrodriguez does a splendid job of racial profiling hollywood style--casting excellent latin actors of all ages--a trend long overdue . \nbeneath the film's obvious determination to shock at any cost lies considerable skill and determination , backed by sheer nerve . \nbielinsky is a filmmaker of impressive talent . \nso beautifully acted and directed , it's clear that washington most certainly has a new career ahead of him if he so chooses . \na visual spectacle full of stunning images and effects . \na gentle and engrossing character study . \nit's enough to watch huppert scheming , with her small , intelligent eyes as steady as any noir villain , and to enjoy the perfectly pitched web of tension that chabrol spins . \nan engrossing portrait of uncompromising artists trying to create something original against the backdrop of a corporate music industry that only seems to care about the bottom line . \na mischievous visual style and oodles of charm make 'cherish' a very good ( but not great ) movie . \njust as the recent argentine film son of the bride reminded us that a feel-good movie can still show real heart , time of favor presents us with an action movie that actually has a brain . \n[a] strong piece of work . \na stirring tribute to the bravery and dedication of the world's reporters who willingly walk into the nightmare of war not only to record the events for posterity , but to help us clearly see the world of our making . \nthe importance of being earnest , so thick with wit it plays like a reading from bartlett's familiar quotations\ndaring and beautifully made . \nmade for teens and reviewed as such , this is recommended only for those under 20 years of age . . . and then only as a very mild rental . \nimagine o . henry's <b>the gift of the magi</b> relocated to the scuzzy underbelly of nyc's drug scene . merry friggin' christmas ! \nthe film does give a pretty good overall picture of the situation in laramie following the murder of matthew shepard . \nboth lead performances are oscar-size . quaid is utterly fearless as the tortured husband living a painful lie , and moore wonderfully underplays the long-suffering heroine with an unflappable '50s dignity somewhere between jane wyman and june cleaver . \nferrara's best film in years . \na remarkably insightful look at the backstage angst of the stand-up comic . \nnothing short of wonderful with its ten-year-old female protagonist and its steadfast refusal to set up a dualistic battle between good and evil . \ndavis' candid , archly funny and deeply authentic take on intimate relationships comes to fruition in her sophomore effort . \nit's more enjoyable than i expected , though , and that's because the laughs come from fairly basic comedic constructs . cinematic pratfalls given a working over . the cast is spot on and the mood is laid back . \nmatches neorealism's impact by showing the humanity of a war-torn land filled with people who just want to live their lives . \nthose moviegoers who would automatically bypass a hip-hop documentary should give \" scratch \" a second look . \nbaby-faced renner is eerily convincing as this bland blank of a man with unimaginable demons within . \nromantic , riveting and handsomely animated . \na competent , unpretentious entertainment destined to fill the after-school slot at shopping mall theaters across the country . \nshot largely in small rooms , the film has a gentle , unforced intimacy that never becomes claustrophobic . \nwhere janice beard falters in its recycled aspects , implausibility , and sags in pace , it rises in its courageousness , and comedic employment . \nbyler is too savvy a filmmaker to let this morph into a typical romantic triangle . instead , he focuses on the anguish that can develop when one mulls leaving the familiar to traverse uncharted ground . \nmcgrath has deftly trimmed dickens' wonderfully sprawling soap opera , the better to focus on the hero's odyssey from cowering poverty to courage and happiness . \na chance to see three splendid actors turn a larky chase movie into an emotionally satisfying exploration of the very human need to be somebody , and to belong to somebody . \nmetaphors abound , but it is easy to take this film at face value and enjoy its slightly humorous and tender story . \nas directed by dani kouyate of burkina faso , sia lacks visual flair . but kouyate elicits strong performances from his cast , and he delivers a powerful commentary on how governments lie , no matter who runs them . \nthe best comedy concert movie i've seen since cho's previous concert comedy film , i'm the one that i want , in 2000 . \nbroomfield reminds us that beneath the hype , the celebrity , the high life , the conspiracies and the mystery there were once a couple of bright young men -- promising , talented , charismatic and tragically doomed . \noffers laughs and insight into one of the toughest ages a kid can go through . \na perceptive , good-natured movie . \nan amused indictment of jaglom's own profession . \na small movie with a big heart . \nhugely accomplished slice of hitchcockian suspense . \nthe formula is familiar but enjoyable . \ntells a fascinating , compelling story . \na triumph , a film that hews out a world and carries us effortlessly from darkness to light . \nwhat begins as a conventional thriller evolves into a gorgeously atmospheric meditation on life-changing chance encounters . \nthe lady and the duke is a smart , romantic drama that dares to depict the french revolution from the aristocrats' perspective . \nmost haunting about \" fence \" is its conclusion , when we hear the ultimate fate of these girls and realize , much to our dismay , that this really did happen . noyce's greatest mistake is thinking that we needed sweeping , dramatic , hollywood moments to keep us\nworld traveler might not go anywhere new , or arrive anyplace special , but it's certainly an honest attempt to get at something . \nthere's much tongue in cheek in the film and there's no doubt the filmmaker is having fun with it all . \nthere's absolutely no reason why blue crush , a late-summer surfer girl entry , should be as entertaining as it is\nan action/thriller of the finest kind , evoking memories of day of the jackal , the french connection , and heat . \nthe best movie in many a moon about the passions that sometimes fuel our best achievements and other times leave us stranded with nothing more than our lesser appetites . \nin capturing the understated comedic agony of an ever-ruminating , genteel yet decadent aristocracy that can no longer pay its bills , the film could just as well be addressing the turn of the 20th century into the 21st . \ninsomnia does not become one of those rare remakes to eclipse the original , but it doesn't disgrace it , either . \nclassic cinema served up with heart and humor\n[stephen] earnhart's film is more about the optimism of a group of people who are struggling to give themselves a better lot in life than the ones they currently have . \nthe events of the film are just so weird that i honestly never knew what the hell was coming next . \nnicole holofcener's lovely and amazing , from her own screenplay , jumps to the head of the class of women's films that manage to avoid the ghetto of sentimental chick-flicks by treating female follies with a satirical style . \nthat jack nicholson makes this man so watchable is a tribute not only to his craft , but to his legend . \nhas a solid emotional impact . \nsuccessfully blended satire , high camp and yet another sexual taboo into a really funny movie . \nmark pellington's latest pop thriller is as kooky and overeager as it is spooky and subtly in love with myth . \nwhile maintaining the appearance of clinical objectivity , this sad , occasionally horrifying but often inspiring film is among wiseman's warmest . \nraimi crafted a complicated hero who is a welcome relief from the usual two-dimensional offerings . \nan enjoyable above average summer diversion . \nthere is simply no doubt that this film asks the right questions at the right time in the history of our country . \nif you've the patience , there are great rewards here . \nas a science fiction movie , \" minority report \" astounds . \nwatching e . t now , in an era dominated by cold , loud special-effects-laden extravaganzas , one is struck less by its lavish grandeur than by its intimacy and precision . \nvisually breathtaking , viscerally exciting , and dramatically moving , it's the very definition of epic adventure . \nchris columbus' sequel is faster , livelier and a good deal funnier than his original . \nwatching this film , what we feel isn't mainly suspense or excitement . the dominant feeling is something like nostalgia . \n' . . . a great , participatory spectator sport . '\na rather brilliant little cult item : a pastiche of children's entertainment , superhero comics , and japanese animation . \nbelieves so fervently in humanity that it feels almost anachronistic , and it is too cute by half . but arriving at a particularly dark moment in history , it offers flickering reminders of the ties that bind us . \nadam sandler ! in an art film ! \nas averse as i usually am to feel-good , follow-your-dream hollywood fantasies , this one got to me . \nstone seems to have a knack for wrapping the theater in a cold blanket of urban desperation . \n . . . a funny yet dark and seedy clash of cultures and generations . \nthe hook is the drama within the drama , as an unsolved murder and an unresolved moral conflict jockey for the spotlight . \nover the years , hollywood has crafted a solid formula for successful animated movies , and ice age only improves on it , with terrific computer graphics , inventive action sequences and a droll sense of humor . \nlike smoke signals , the film is also imbued with strong themes of familial ties and spirituality that are powerful and moving without stooping to base melodrama\none of those movies that make us pause and think of what we have given up to acquire the fast-paced contemporary society . \none of the most original american productions this year , you'll find yourself remembering this refreshing visit to a sunshine state . \nmelds derivative elements into something that is often quite rich and exciting , and always a beauty to behold . \ngives everyone something to shout about . \nthe entire movie has a truncated feeling , but what's available is lovely and lovable . \n[a] thoughtful , visually graceful work . \nadmirers of director abel ferrara may be relieved that his latest feature , r xmas , marks a modest if encouraging return to form . \nthe slam-bang superheroics are kinetic enough to engross even the most antsy youngsters . \na worthy addition to the cinematic canon , which , at last count , numbered 52 different versions . \ndeliciously mean-spirited and wryly observant . \nthe kind of primal storytelling that george lucas can only dream of . \neven if the ring has a familiar ring , it's still unusually crafty and intelligent for hollywood horror . \nthe sheer joy and pride they took in their work -- and in each other -- shines through every frame . \na solidly constructed , entertaining thriller that stops short of true inspiration . \nthe cast . . . keeps this pretty watchable , and casting mick jagger as director of the escort service was inspired . \nan entertaining , if somewhat standardized , action movie . \nit has a dashing and resourceful hero ; a lisping , reptilian villain ; big fights ; big hair ; lavish period scenery ; and a story just complicated enough to let you bask in your own cleverness as you figure it out . \nan enjoyable comedy of lingual and cultural differences the chteau is a film -- full of life and small delights -- that has all the wiggling energy of young kitten . \nintriguing and downright intoxicating . \nan incredibly thoughtful , deeply meditative picture that neatly and effectively captures the debilitating grief felt in the immediate aftermath of the terrorist attacks . \nwith an obvious rapport with her actors and a striking style behind the camera , hlne angel is definitely a director to watch . \n . . . could easily be called the best korean film of 2002 . \nfull of detail about the man and his country , and is well worth seeing . \nthe banter between calvin and his fellow barbers feels like a streetwise mclaughlin group . . . and never fails to entertain . \nthoroughly engrossing and ultimately tragic . \npeter jackson and company once again dazzle and delight us , fulfilling practically every expectation either a longtime tolkien fan or a movie-going neophyte could want . \nbill morrison's decasia is uncompromising , difficult and unbearably beautiful . \nfull of bland hotels , highways , parking lots , with some glimpses of nature and family warmth , time out is a discreet moan of despair about entrapment in the maze of modern life . \neven with all its botches , enigma offers all the pleasure of a handsome and well-made entertainment . \nhis work transcends the boy-meets-girl posturing of typical love stories . \nif the real-life story is genuinely inspirational , the movie stirs us as well . \nan ebullient tunisian film about the startling transformation of a tradition-bound widow who is drawn into the exotic world of belly dancing . \nthe dramatic crisis doesn't always succeed in its quest to be taken seriously , but huppert's volatile performance makes for a riveting movie experience . \nhighly irritating at first , mr . koury's passive technique eventually begins to yield some interesting results . \nabout schmidt belongs to nicholson . gone are the flamboyant mannerisms that are the trademark of several of his performances . as schmidt , nicholson walks with a slow , deliberate gait , chooses his words carefully and subdues his natural exuberance . \nthe powder blues and sun-splashed whites of tunis make an alluring backdrop for this sensuous and spirited tale of a prim widow who finds an unlikely release in belly-dancing clubs . \nit doesn't make for great cinema , but it is interesting to see where one's imagination will lead when given the opportunity . \nit's sobering , particularly if anyone still thinks this conflict can be resolved easily , or soon . \nif it's not entirely memorable , the movie is certainly easy to watch . \n . . . by the time it's done with us , mira nair's new movie has its audience giddy with the delight of discovery , of having been immersed in a foreign culture only to find that human nature is pretty much the same all over . \nbest indie of the year , so far . \n[ferrera] has the charisma of a young woman who knows how to hold the screen . \n . . . the plot weaves us into a complex web . \ndon't judge this one too soon - it's a dark , gritty story but it takes off in totally unexpected directions and keeps on going . \nit's funny , as the old saying goes , because it's true . \nin death to smoochy , we don't get williams' usual tear and a smile , just sneers and bile , and the spectacle is nothing short of refreshing . \na serviceable euro-trash action extravaganza , with a decent sense of humor and plenty of things that go boom  handguns , bmws and seaside chateaus . \nfortunately , elling never gets too cloying thanks to the actors' perfect comic timing and sweet , genuine chemistry . \nif you've grown tired of going where no man has gone before , but several movies have - take heart . this is the best star trek movie in a long time . \ngreg kinnear gives a mesmerizing performance as a full-fledged sex addict who is in complete denial about his obsessive behavior . \nnot only a coming-of-age story and cautionary parable , but also a perfectly rendered period piece . \nou've got to love a disney pic with as little cleavage as this one has , and a heroine as feisty and principled as jane . \na funny , triumphant , and moving documentary . \ndelirious fun . \nlathan and diggs carry the film with their charisma , and both exhibit sharp comic timing that makes the more hackneyed elements of the film easier to digest . \nabout schmidt is nicholson's goofy , heartfelt , mesmerizing king lear . \na confluence of kiddie entertainment , sophisticated wit and symbolic graphic design . \ngay or straight , kissing jessica stein is one of the greatest date movies in years . \nthis is a movie full of grace and , ultimately , hope . \nexciting and well-paced . \neven better than the first one ! \nits compelling mix of trial movie , escape movie and unexpected fable ensures the film never feels draggy . \na must see for all sides of the political spectrum\n[reynolds] takes a classic story , casts attractive and talented actors and uses a magnificent landscape to create a feature film that is wickedly fun to watch . \nthere are problems with this film that even 3 oscar winners can't overcome , but it's a nice girl-buddy movie once it gets rock-n-rolling . \nrich in atmosphere of the post-war art world , it manages to instruct without reeking of research library dust . \nhas the rare capability to soothe and break your heart with a single stroke . \nit rapidly develops into a gut-wrenching examination of the way cultural differences and emotional expectations collide . \nthough it flirts with bathos and pathos and the further oprahfication of the world as we know it , it still cuts all the way down to broken bone . \nthis humbling little film , fueled by the light comedic work of zhao benshan and the delicate ways of dong jie , is just the sort for those moviegoers who complain that 'they don't make movies like they used to anymore . '\nes una de esas pelculas de las que uno sale reconfortado , agradecido , genuinamente sorprendido . \nit will break your heart many times over . \na straight-shooting family film which awards animals the respect they've rarely been given . \noverall , interesting as a documentary -- but not very imaxy . \nthis is one of those war movies that focuses on human interaction rather than battle and action sequences . . . and it's all the stronger because of it . \nsecretary \" is owned by its costars , spader and gyllenhaal . maggie g . makes an amazing breakthrough in her first starring role and eats up the screen . \nthe film fits into a genre that has been overexposed , redolent of a thousand cliches , and yet remains uniquely itself , vibrant with originality . \nnot only is it a charming , funny and beautifully crafted import , it uses very little dialogue , making it relatively effortless to read and follow the action at the same time . \n'almodvar logra un filme entraable , lleno de compasin , comprensin , amor , amistad , esperanza y humanidad que es sencillamente inolvidable . '\nthe kind of sense of humor that derives from a workman's grasp of pun and entendre and its attendant need to constantly draw attention to itself . \ntoo much of storytelling moves away from solondz's social critique , casting its audience as that of intellectual lector in contemplation of the auteur's professional injuries . \nthe story is virtually impossible to follow here , but there's a certain style and wit to the dialogue . \nthe music makes a nice album , the food is enticing and italy beckons us all . \nthe film is an earnest try at beachcombing verismo , but it would be even more indistinct than it is were it not for the striking , quietly vulnerable personality of ms . ambrose . \nthe film is small in scope , yet perfectly formed . \njones has delivered a solidly entertaining and moving family drama . \nhappy times maintains an appealing veneer without becoming too cute about it . \noliveira seems to pursue silent film representation with every mournful composition . \none of the pleasures in walter's documentary . . . is the parade of veteran painters , confounded dealers , and miscellaneous bohos who expound upon the subject's mysterious personality without ever explaining him . \ncaptures all the longing , anguish and ache , the confusing sexual messages and the wish to be a part of that elusive adult world . \nhe's the scariest guy you'll see all summer . \n \" frailty \" offers chills much like those that you get when sitting around a campfire around midnight , telling creepy stories to give each other the willies . and , there's no way you won't be talking about the film once you exit the theater . \nreally quite funny . \nif i have to choose between gorgeous animation and a lame story ( like , say , treasure planet ) or so-so animation and an exciting , clever story with a batch of appealing characters , i'll take the latter every time . \nquiet , adult and just about more stately than any contemporary movie this year . . . a true study , a film with a questioning heart and mind that isn't afraid to admit it doesn't have all the answers . \nin the end , the film is less the cheap thriller you'd expect than it is a fairly revealing study of its two main characters  damaged-goods people whose orbits will inevitably and dangerously collide . \nsome of the visual flourishes are a little too obvious , but restrained and subtle storytelling , and fine performances make this delicate coming-of-age tale a treat . \nit is hard not to be especially grateful for freedom after a film like this . \nthe dirty jokes provide the funniest moments in this oddly sweet comedy about jokester highway patrolmen . \ny tu mam tambin is hilariously , gloriously alive , and quite often hotter than georgia asphalt . \n . . . works on some levels and is certainly worth seeing at least once . \nyou come away from his film overwhelmed , hopeful and , perhaps paradoxically , illuminated . \nif the material is slight and admittedly manipulative , jacquot preserves tosca's intoxicating ardor through his use of the camera . \nthirteen conversations about one thing lays out a narrative puzzle that interweaves individual stories , and , like a mobius strip , elliptically loops back to where it began . \noverall , it's a wacky and inspired little film that works effortlessly at delivering genuine , acerbic laughs . \nmais um momento inspirado de david fincher . \na must for fans of british cinema , if only because so many titans of the industry are along for the ride . \ntsai has managed to create an underplayed melodrama about family dynamics and dysfunction that harks back to the spare , unchecked heartache of yasujiro ozu . \nuntil ( the ) superfluous . . . epilogue that leaks suspension of disbelief like a sieve , die another day is as stimulating & heart-rate-raising as any james bond thriller . \nit's a good film , but it falls short of its aspiration to be a true 'epic' . \nall the pieces fall together without much surprise , but little moments give it a boost . \nthe beauty of alexander payne's ode to the everyman is in the details . \na touching drama about old age and grief with a tour de force performance by michel piccoli . \nthe ending feels at odds with the rest of the film . \na tone of rueful compassion . . . reverberates throughout this film , whose meaning and impact is sadly heightened by current world events . \na beautiful paean to a time long past . \ndense and thoughtful and brimming with ideas that are too complex to be rapidly absorbed . \nif you thought tom hanks was just an ordinary big-screen star , wait until you've seen him eight stories tall . \nwith this masterful , flawless film , [wang] emerges in the front ranks of china's now numerous , world-renowned filmmakers . \nshyamalan offers copious hints along the way -- myriad signs , if you will -- that beneath the familiar , funny surface is a far bigger , far more meaningful story than one in which little green men come to earth for harvesting purposes . \nthis film is an act of spiritual faith -- an eloquent , deeply felt meditation on the nature of compassion . \na different kind of love story - one that is dark , disturbing , painful to watch , yet compelling . \nsplendidly illustrates the ability of the human spirit to overcome adversity . \na compelling , gut-clutching piece of advocacy cinema that carries you along in a torrent of emotion as it explores the awful complications of one terrifying day . \nshe's as rude and profane as ever , always hilarious and , most of the time , absolutely right in her stinging social observations . \nto those who have not read the book , the film is a much better mother-daughter tale than last summer's 'divine secrets of the ya-ya sisterhood , ' but that's not saying much . \neven before it builds up to its insanely staged ballroom scene , in which 3000 actors appear in full regalia , it's waltzed itself into the art film pantheon . \na thoughtful , reverent portrait of what is essentially a subculture , with its own rules regarding love and family , governance and hierarchy . \nit seems impossible that an epic four-hour indian musical about a cricket game could be this good , but it is . \nwill certainly appeal to asian cult cinema fans and asiaphiles interested to see what all the fuss is about . \ntouches smartly and wistfully on a number of themes , not least the notion that the marginal members of society . . . might benefit from a helping hand and a friendly kick in the pants . \na wildly entertaining scan of evans' career . \na mature , deeply felt fantasy of a director's travel through 300 years of russian history . \nboldly engineering a collision between tawdry b-movie flamboyance and grandiose spiritual anomie , rose's film , true to its source material , provides a tenacious demonstration of death as the great equalizer . \na finely tuned mood piece , a model of menacing atmosphere . \nthe salton sea has moments of inspired humour , though every scrap is of the darkest variety . \nboth a beautifully made nature film and a tribute to a woman whose passion for this region and its inhabitants still shines in her quiet blue eyes . \nalthough shot with little style , skins is heartfelt and achingly real . \nharks back to a time when movies had more to do with imagination than market research . \nupsetting and thought-provoking , the film has an odd purity that doesn't bring you into the characters so much as it has you study them . \na well-executed spy-thriller . \na very pretty after-school special . it's an effort to watch this movie , but it eventually pays off and is effective if you stick with it . \na harrowing account of a psychological breakdown . \ncontinually challenges perceptions of guilt and innocence , of good guys and bad , and asks us whether a noble end can justify evil means . \nit certainly won't win any awards in the plot department but it sets out with no pretensions and delivers big time . \ndog soldiers doesn't transcend genre -- it embraces it , energizes it and takes big bloody chomps out of it . \nat once emotional and richly analytical , the cosby-seinfeld encounter alone confirms the serious weight behind this superficially loose , larky documentary . \nit may scream low budget , but this charmer has a spirit that cannot be denied . \n'alice's adventure through the looking glass and into zombie-land' is filled with strange and wonderful creatures . \nwithout [de niro] , city by the sea would slip under the waves . he drags it back , single-handed . \na good music documentary , probably one of the best since the last waltz . \nif the plot seems a bit on the skinny side , that's because panic room is interested in nothing more than sucking you inand making you sweat . \n . . . [the film] works , due mostly to the tongue-in-cheek attitude of the screenplay . \nthe film becomes an overwhelming pleasure , and you find yourself rooting for gai's character to avoid the fate that has befallen every other carmen before her . \nbroomfield has a rather unique approach to documentary . he thinks the film is just as much a document about him as it is about the subject . \nat its best when the guarded , resentful betty and the manipulative yet needy margot are front and center . \ngloriously straight from the vagina . \nit's excessively quirky and a little underconfident in its delivery , but otherwise this is the best 'old neighborhood' project since christopher walken kinda romanced cyndi lauper in the opportunists . \nthe film oozes craft . \nrobinson's web of suspense matches the page-turning frenzy that clancy creates . \nmanages to be both hugely entertaining and uplifting . \na classic fairy tale that perfectly captures the wonders and worries of childhood in a way that few movies have ever approached . \nit's the unsettling images of a war-ravaged land that prove more potent and riveting than the unlikely story of sarah and harrison . \na wonderfully warm human drama that remains vividly in memory long after viewing\njaunty fun , with its celeb-strewn backdrop well used . \nrecoing's fantastic performance doesn't exactly reveal what makes vincent tick , but perhaps any definitive explanation for it would have felt like a cheat . \nwashington overcomes the script's flaws and envelops the audience in his character's anguish , anger and frustration . \nthe film fearlessly gets under the skin of the people involved . . . this makes it not only a detailed historical document , but an engaging and moving portrait of a subculture . \na searing , epic treatment of a nationwide blight that seems to be , horrifyingly , ever on the rise . \nnot a film for the faint of heart or conservative of spirit , but for the rest of us -- especially san francisco lovers -- it's a spirited film and a must-see . \nread my lips is to be viewed and treasured for its extraordinary intelligence and originality as well as its lyrical variations on the game of love . \nthe color sense of stuart little 2 is its most immediate and most obvious pleasure , but it would count for very little if the movie weren't as beautifully shaped and as delicately calibrated in tone as it is . \nwhile [roman coppola] scores points for style , he staggers in terms of story . \nidiotic and ugly . \nany movie that makes hard work seem heroic deserves a look . \nit may not be a huge cut of above the rest , but i enjoyed barbershop . it's a funny little movie with clever dialogue and likeable characters . \na different and emotionally reserved type of survival story -- a film less about refracting all of world war ii through the specific conditions of one man , and more about that man lost in its midst . \nit's sweet , funny , charming , and completely delightful . \na perfectly competent and often imaginative film that lacks what little lilo & stitch had in spades -- charisma . \nbeautifully shot against the frozen winter landscapes of grenoble and geneva , the film unfolds with all the mounting tension of an expert thriller , until the tragedy beneath it all gradually reveals itself . \nmedem may have disrobed most of the cast , leaving their bodies exposed , but the plot remains as guarded as a virgin with a chastity belt . that's why sex and lucia is so alluring . \nan elegant work , food of love is as consistently engaging as it is revealing . \nalthough largely a heavy-handed indictment of parental failings and the indifference of spanish social workers and legal system towards child abuse , the film retains ambiguities that make it well worth watching . \na behind the scenes look at the training and dedication that goes into becoming a world-class fencer and the champion that's made a difference to nyc inner-city youth . \na brain twister , less a movie-movie than a funny and weird meditation on hollywood , success , artistic integrity and intellectual bankruptcy . \na powerful , inflammatory film about religion that dares to question an ancient faith , and about hatred that offers no easy , comfortable resolution . \nin its own floundering way , it gets to you . just like igby . \nreturn to never land may be another shameless attempt by disney to rake in dough from baby boomer families , but it's not half-bad . \nwise and deadpan humorous . \ngod bless crudup and his aversion to taking the easy hollywood road and cashing in on his movie-star gorgeousness . \nif signs is a good film , and it is , the essence of a great one is in there somewhere . \nveterans of the dating wars will smirk uneasily at the film's nightmare versions of everyday sex-in-the-city misadventures . \nschrader examines crane's decline with unblinking candor . \nyou can watch , giggle and get an adrenaline boost without feeling like you've completely lowered your entertainment standards . \nit thankfully goes easy on the reel/real world dichotomy that [jaglom] pursued with such enervating determination in venice/venice . \nthis rich , bittersweet israeli documentary , about the life of song-and-dance-man pasach'ke burstein and his family , transcends ethnic lines . \nsensitively examines general issues of race and justice among the poor , and specifically raises serious questions about the death penalty and asks what good the execution of a mentally challenged woman could possibly do . \ncool gadgets and creatures keep this fresh . not as good as the original , but what is . . . \npresents a side of contemporary chinese life that many outsiders will be surprised to know exists , and does so with an artistry that also smacks of revelation . \n[jeff's] gorgeous , fluid compositions , underlined by neil finn and edmund mcwilliams's melancholy music , are charged with metaphor , but rarely easy , obvious or self-indulgent . \nengages us in constant fits of laughter , until we find ourselves surprised at how much we care about the story , and end up walking out not only satisfied but also somewhat touched . \na bilingual charmer , just like the woman who inspired it\nblisteringly rude , scarily funny , sorrowfully sympathetic to the damage it surveys , the film has in kieran culkin a pitch-perfect holden . \napuestas fuertes para el futuro del director , y apuestas bien fundadas , pues la suerte ya la tiene , y la cinta lo comprueba . . . . \nthe fourth \" pokemon \" is a diverting--if predictable--adventure suitable for a matinee , with a message that cautions children about disturbing the world's delicate ecological balance . \nwhat one is left with , even after the most awful acts are committed , is an overwhelming sadness that feels as if it has made its way into your very bloodstream . \n[it] has the feel of a summer popcorn movie . nothing too deep or substantial . explosions , jokes , and sexual innuendoes abound . \nmiyazaki's nonstop images are so stunning , and his imagination so vivid , that the only possible complaint you could have about spirited away is that there is no rest period , no timeout . \n . . . a delightfully unpredictable , hilarious comedy with wonderful performances that tug at your heart in ways that utterly transcend gender labels . \nassured , vital and well wrought , the film is , arguably , the most accomplished work to date from hong kong's versatile stanley kwan . \ndelia , greta , and paula rank as three of the most multilayered and sympathetic female characters of the year . as each of them searches for their place in the world , miller digs into their very minds to find an unblinking , flawed humanity . \na surprisingly sweet and gentle comedy . \nshanghai ghetto , much stranger than any fiction , brings this unknown slice of history affectingly to life . \nit's not particularly well made , but since i found myself howling more than cringing , i'd say the film works . \nbut this is lohman's film . her performance moves between heartbreak and rebellion as she continually tries to accommodate to fit in and gain the unconditional love she seeks . \nthough its story is only surface deep , the visuals and enveloping sounds of blue crush make this surprisingly decent flick worth a summertime look-see . \nryosuke has created a wry , winning , if languidly paced , meditation on the meaning and value of family . \nsometimes charming , sometimes infuriating , this argentinean 'dramedy' succeeds mainly on the shoulders of its actors . \nyou may feel compelled to watch the film twice or pick up a book on the subject . \noften shocking but ultimately worthwhile exploration of motherhood and desperate mothers . \na venturesome , beautifully realized psychological mood piece that reveals its first-time feature director's understanding of the expressive power of the camera . \nlike the rugrats movies , the wild thornberrys movie doesn't offer much more than the series , but its emphasis on caring for animals and respecting other cultures is particularly welcome . \ntaken outside the context of the current political climate ( see : terrorists are more evil than ever ! ) , the sum of all fears is simply a well-made and satisfying thriller . \nthe setting is so cool that it chills the characters , reducing our emotional stake in the outcome of \" intacto's \" dangerous and seductively stylish game . \na lovely and beautifully photographed romance . \none of the most splendid entertainments to emerge from the french film industry in years . \nits vision of that awkward age when sex threatens to overwhelm everything else is acute enough to make everyone who has been there squirm with recognition . \nfor almost the first two-thirds of martin scorsese's 168-minute gangs of new york , i was entranced . \nopen-ended and composed of layer upon layer , talk to her is a cinephile's feast , an invitation to countless interpretations . \none of the most slyly exquisite anti-adult movies ever made . \nwhat makes esther kahn so demanding is that it progresses in such a low-key manner that it risks monotony . but it's worth the concentration . \nneither the funniest film that eddie murphy nor robert de niro has ever made , showtime is nevertheless efficiently amusing for a good while . before it collapses into exactly the kind of buddy cop comedy it set out to lampoon , anyway . \na clever script and skilled actors bring new energy to the familiar topic of office politics . \nthe determination of pinochet's victims to seek justice , and their often heartbreaking testimony , spoken directly into director patricio guzman's camera , pack a powerful emotional wallop . \ndisney aficionados will notice distinct parallels between this story and the 1971 musical \" bedknobs and broomsticks , \" which also dealt with british children rediscovering the power of fantasy during wartime . \nit's . . . worth the extra effort to see an artist , still committed to growth in his ninth decade , change while remaining true to his principles with a film whose very subject is , quite pointedly , about the peril of such efforts . \ndark and unrepentant , this excursion into the epicenter of percolating mental instability is not easily dismissed or forgotten . \nit's a rollicking adventure for you and all your mateys , regardless of their ages . \nboasts a handful of virtuosic set pieces and offers a fair amount of trashy , kinky fun . \n . . . myers has turned his franchise into the movie version of an adolescent dirty-joke book done up in post-tarantino pop-culture riffs . . . \nif you're down for a silly hack-and-slash flick , you can do no wrong with jason x . \nthis is a very ambitious project for a fairly inexperienced filmmaker , but good actors , good poetry and good music help sustain it . \nthe modern master of the chase sequence returns with a chase to end all chases\nthe messy emotions raging throughout this three-hour effort are instantly recognizable , allowing the film to paradoxically feel familiar and foreign at the same time . \n . . . either you're willing to go with this claustrophobic concept or you're not . \njust watch bettany strut his stuff . you'll know a star when you see one . \naustin powers in goldmember is a cinematic car wreck , a catastrophic collision of tastelessness and gall that nevertheless will leave fans clamoring for another ride . \nyou can fire a torpedo through some of clancy's holes , and the scripters don't deserve any oscars . but the nerve-raked acting , the crackle of lines , the impressive stagings of hardware , make for some robust and scary entertainment . \ncontrasting the original ringu with the current americanized adaptation is akin to comparing the evil dead with evil dead ii\na small gem of a movie that defies classification and is as thought-provoking as it is funny , scary and sad . \nfor a long time the film succeeds with its dark , delicate treatment of these characters and its unerring respect for them . \nit's the kind of effectively creepy-scary thriller that has you fixating on a far corner of the screen at times because your nerves just can't take it any more . \nlate marriage is an in-your-face family drama and black comedy that is filled with raw emotions conveying despair and love . \nan ambitious and moving but bleak film . \nit's too harsh to work as a piece of storytelling , but as an intellectual exercise -- an unpleasant debate that's been given the drive of a narrative and that's been acted out -- the believer is nothing less than a provocative piece of work . \nit's sweet . it's funny . it wears its heart on the sleeve of its gaudy hawaiian shirt . and , thanks to the presence of 'the king , ' it also rocks . \nit's never laugh-out-loud funny , but it is frequently amusing . \na bittersweet film , simple in form but rich with human events . \nthe unexplored story opportunities of \" punch-drunk love \" may have worked against the maker's minimalist intent but it is an interesting exercise by talented writer/director anderson . \n \" punch-drunk love \" is a little like a chocolate milk moustache . . . \n . . . digs beyond the usual portrayals of good kids and bad seeds to reveal a more ambivalent set of characters and motivations . \nthe beauty of the piece is that it counts heart as important as humor . \npiercingly affecting . . . while clearly a manipulative film , emerges as powerful rather than cloying . \nvery amusing , not the usual route in a thriller , and the performances are odd and pixilated and sometimes both . \nwhile the frequent allusions to gurus and doshas will strike some westerners as verging on mumbo-jumbo . . . broad streaks of common sense emerge with unimpeachable clarity . \nthe cast is phenomenal , especially the women . \na marvel of production design . \nthe byplay and bickering between the now spy-savvy siblings , carmen ( vega ) and juni ( sabara ) cortez , anchor the film in a very real and amusing give-and-take . \ngood actors have a radar for juicy roles -- there's a plethora of characters in this picture , and not one of them is flat . \nthough in some ways similar to catherine breillat's fat girl , rain is the far superior film . \nis not so much a work of entertainment as it is a unique , well-crafted psychological study of grief . \nremarkable for its excellent storytelling , its economical , compressed characterisations and for its profound humanity , it's an adventure story and history lesson all in one . \ncolorful , energetic and sweetly whimsical . . . the rare sequel that's better than its predecessor . \nreno himself can take credit for most of the movie's success . he's one of the few 'cool' actors who never seems aware of his own coolness . \nsignificantly better than its 2002 children's-movie competition . \nub equally spoofs and celebrates the more outre aspects of 'black culture' and the dorkier aspects of 'white culture , ' even as it points out how inseparable the two are . \na lot smarter than your average bond . \n . . . bright , intelligent , and humanly funny film . \npainful , horrifying and oppressively tragic , this film should not be missed . \npart of the film's cheeky charm comes from its vintage schmaltz . \nso unique and stubborn and charismatic that you want it to be better and more successful than it is . \ni won't argue with anyone who calls 'slackers' dumb , insulting , or childish . . . but i laughed so much that i didn't mind . \nit arrives with an impeccable pedigree , mongrel pep , and almost indecipherable plot complications . \nso fiendishly cunning that even the most jaded cinema audiences will leave the auditorium feeling dizzy , confused , and totally disorientated . not to mention absolutely refreshed . \na vibrant , colorful , semimusical rendition . \nthe film sometimes flags . . . but there is enough secondary action to keep things moving along at a brisk , amusing pace . \nit's a drawling , slobbering , lovable run-on sentence of a film , a southern gothic with the emotional arc of its raw blues soundtrack . \nns gosta muito de as duas torres . \nnolan proves that he can cross swords with the best of them and helm a more traditionally plotted popcorn thriller while surrendering little of his intellectual rigor or creative composure . \nit is different from others in its genre in that it is does not rely on dumb gags , anatomical humor , or character cliches ; it primarily relies on character to tell its story . \nboth a successful adaptation and an enjoyable film in its own right . \nall the filmmakers are asking of us , is to believe in something that is improbable . \nif the very concept makes you nervous . . . you'll have an idea of the film's creepy , scary effectiveness . \nworth a look by those on both sides of the issues , if only for the perspective it offers , one the public rarely sees . \na mostly believable , refreshingly low-key and quietly inspirational little sports drama . \nmay be more genial than ingenious , but it gets the job done . \na stylish cast and some clever scripting solutions help chicago make the transition from stage to screen with considerable appeal intact . \nexhilarating , funny and fun . \nwhile not quite \" shrek \" or monsters , inc . \" , it's not too bad . it's worth taking the kids to . \nin the end there is one word that best describes this film : honest . \nwriter-director david jacobson and his star , jeremy renner , have made a remarkable film that explores the monster's psychology not in order to excuse him but rather to demonstrate that his pathology evolved from human impulses that grew hideously twisted . \nthe action sequences are fun and reminiscent of combat scenes from the star wars series . \nnorton is magnetic as graham . \nsavvy director robert j . siegel and his co-writers keep the story subtle and us in suspense . \nit pulls the rug out from under you , just when you're ready to hate one character , or really sympathize with another character , something happens to send you off in different direction . \ntwenty years after its first release , e . t . remains the most wondrous of all hollywood fantasies -- and the apex of steven spielberg's misunderstood career . \nit says a lot about a filmmaker when he can be wacky without clobbering the audience over the head and still maintain a sense of urgency and suspense . \ngives us a lot to chew on , but not all of it has been properly digested . \nit's an exhilarating place to visit , this laboratory of laughter . \n \" simone \" is a fun and funky look into an artificial creation in a world that thrives on artificiality . \na great companion piece to other napoleon films . \nto some eyes this will seem like a recycling of clichs , an assassin's greatest hits . to others , it will remind them that hong kong action cinema is still alive and kicking . \ngran historia sobre el amor , la familia , la lealtad y la traicin que seguramente se convertir en un nuevo clsico del gnero . \nat the end of the movie , my 6-year-old nephew said , \" i guess i come from a broken family , and my uncles are all aliens , too . \" congrats disney on a job well done , i enjoyed it just as much ! \na remarkably alluring film set in the constrictive eisenhower era about one suburban woman's yearning in the face of a loss that shatters her cheery and tranquil suburban life . \nberling and bart . . . continue to impress , and isabelle huppert . . . again shows uncanny skill in getting under the skin of her characters . \nuplifting , funny and wise . \nremarkable for its intelligence and intensity . \nthe hypnotic imagery and fragmentary tale explore the connections between place and personal identity . \nbrosnan is more feral in this film than i've seen him before and halle berry does her best to keep up with him . \na film that begins with the everyday lives of naval personnel in san diego and ends with scenes so true and heartbreaking that tears welled up in my eyes both times i saw the film . \na funny film . \n \" on guard ! \" won't be placed in the pantheon of the best of the swashbucklers but it is a whole lot of fun and you get to see the one of the world's best actors , daniel auteuil , have a whale of a good time . \nthe movie starts with a legend and ends with a story that is so far-fetched it would be impossible to believe if it weren't true . this is the stuff that disney movies are made of . \nlike all great films about a life you never knew existed , it offers much to absorb and even more to think about after the final frame . \nthat the e-graveyard holds as many good ideas as bad is the cold comfort that chin's film serves up with style and empathy . \nwhile we no longer possess the lack-of-attention span that we did at seventeen , we had no trouble sitting for blade ii . \nlike a poor man's you can count on me\n . . . a solid , unassuming drama . \na seriocomic debut of extravagant promise by georgian-israeli director dover kosashvili . \nthanks to ice cube , benjamins feels an awful lot like friday in miami . \nthough the film is static , its writer-director's heart is in the right place , his plea for democracy and civic action laudable . \nthe real star of this movie is the score , as in the songs translate well to film , and it's really well directed . \nit's rare to find a film to which the adjective 'gentle' applies , but the word perfectly describes pauline & paulette . \nmy wife is an actress has its moments in looking at the comic effects of jealousy . in the end , though , it is only mildly amusing when it could have been so much more . \nboth garcia and jagger turn in perfectly executed and wonderfully sympathetic characters , who are alternately touching and funny . \nhumorous , artsy , and even cute , in an off-kilter , dark , vaguely disturbing way . \nthe more you think about the movie , the more you will probably like it . \n . . . a powerful sequel and one of the best films of the year . \nfor the most part , the film does hold up pretty well . \ntogether [time out and human resources] establish mr . cantet as france's foremost cinematic poet of the workplace . \nthe filmmaker's heart is in the right place . . . \nyou can take the grandkids or the grandparents and never worry about anyone being bored . . . audience is a sea of constant smiles and frequent laughter . \nlike these russo guys lookin' for their mamet instead found their sturges . \n[a] satisfying niblet . \nthere has been a string of ensemble cast romances recently . . . but peter mattei's love in the time of money sets itself apart by forming a chain of relationships that come full circle to end on a positive ( if tragic ) note . \nby applying definition to both sides of the man , the picture realizes a fullness that does not negate the subject . \nwho is the audience for cletis tout ? anybody who enjoys quirky , fun , popcorn movies with a touch of silliness and a little bloodshed . \n[cuarn has] created a substantive movie out of several cliched movie structures : the road movie , the coming-of-age movie , and the teenage sex comedy . \nputs to rest any thought that the german film industry cannot make a delightful comedy centering on food . \npoetic , heartbreaking . \nwitty dialog between realistic characters showing honest emotions . it's touching and tender and proves that even in sorrow you can find humor . like blended shades of lipstick , these components combine into one terrific story with lots of laughs . \n<em>ash wednesday</em> is not edward burns' best film , but it is a good and ambitious film . and it marks him as one of the most interesting writer/directors working today . \nafter one gets the feeling that the typical hollywood disregard for historical truth and realism is at work here , it's a matter of finding entertainment in the experiences of zishe and the fiery presence of hanussen . \nthe footage of the rappers at play and the prison interview with suge knight are just two of the elements that will grab you . \n . . . it's as comprehensible as any dummies guide , something even non-techies can enjoy . \ndon't wait to see this terrific film with your kids -- if you don't have kids borrow some . \nmoretti . . . is the rare common-man artist who's wise enough to recognize that there are few things in this world more complex -- and , as it turns out , more fragile -- than happiness . \nthe movie's captivating details are all in the performances , from foreman's barking-mad taylor to thewlis's smoothly sinister freddie and bettany/mcdowell's hard-eyed gangster . \nfeatures fincher's characteristically startling visual style and an almost palpable sense of intensity . \nprecocious smarter-than-thou wayward teen struggles to rebel against his oppressive , right-wing , propriety-obsessed family . anyone else seen this before ? \nmoore provides an invaluable service by sparking debate and encouraging thought . better still , he does all of this , and more , while remaining one of the most savagely hilarious social critics this side of jonathan swift . \nalternating between facetious comic parody and pulp melodrama , this smart-aleck movie . . . tosses around some intriguing questions about the difference between human and android life . \na cutesy romantic tale with a twist . \nthis is a gorgeous film - vivid with color , music and life . delight your senses and crash this wedding ! \na brutally dry satire of middle american numbness . \nmore sophisticated and literate than such pictures usually are . . . an amusing little catch . \nsmith examines the intimate , unguarded moments of folks who live in unusual homes -- which pop up in nearly every corner of the country . \nwith an admirably dark first script by brent hanley , paxton , making his directorial feature debut , does strong , measured work . \na compelling french psychological drama examining the encounter of an aloof father and his chilly son after 20 years apart . \n . . . even if you've never heard of chaplin , you'll still be glued to the screen . \nyou have enough finely tuned acting to compensate for the movie's failings . \nas the dominant christine , sylvie testud is icily brilliant . \nalthough tender and touching , the movie would have benefited from a little more dramatic tension and some more editing . \nthe story that emerges has elements of romance , tragedy and even silent-movie comedy . \n[ \" safe conduct \" ] is a long movie at 163 minutes but it fills the time with drama , romance , tragedy , bravery , political intrigue , partisans and sabotage . viva le resistance ! \nit offers a glimpse of the solomonic decision facing jewish parents in those turbulent times : to save their children and yet to lose them . \nthe film is delicately narrated by martin landau and directed with sensitivity and skill by dana janklowicz-mann . \nmartyr gets royally screwed and comes back for more . \na virtual roller-coaster ride of glamour and sleaze . \nan admirable , sometimes exceptional film\nif you like an extreme action-packed film with a hint of humor , then triple x marks the spot . \nfrom blushing to gushing---imamura squirts the screen in warm water under a red bridge'\nif you're the kind of parent who enjoys intentionally introducing your kids to films which will cause loads of irreparable damage that years and years of costly analysis could never fix , i have just one word for you - decasia\nmay not be a breakthrough in filmmaking , but it is unwavering and arresting . \nthe film's images give a backbone to the company and provide an emotional edge to its ultimate demise . \na bodice-ripper for intellectuals . \nthe locations go from stark desert to gorgeous beaches . the story plays out slowly , but the characters are intriguing and realistic . \ncount on his movie to work at the back of your neck long after you leave the theater . \nneil burger here succeeded in . . . making the mystery of four decades back the springboard for a more immediate mystery in the present . \nthe complex , politically charged tapestry of contemporary chinese life this exciting new filmmaker has brought to the screen is like nothing we westerners have seen before . \na thriller made from a completist's checklist rather than with a cultist's passion . \ntry as you might to scrutinize the ethics of kaufman's approach , somehow it all comes together to create a very compelling , sensitive , intelligent and almost cohesive piece of film entertainment . \nas quiet , patient and tenacious as mr . lopez himself , who approaches his difficult , endless work with remarkable serenity and discipline . \nthough the film never veers from its comic course , its unintentional parallels might inadvertently evoke memories and emotions which are anything but humorous . \nevokes the style and flash of the double-cross that made mamet's \" house of games \" and last fall's \" heist \" so much fun . \nso original in its base concept that you cannot help but get caught up . \nit may be a no-brainer , but at least it's a funny no-brainer . \na lot more dimensional and complex than its sunny disposition would lead you to believe . \njeffs has created a breathtakingly assured and stylish work of spare dialogue and acute expressiveness . \nunderachieves only in not taking the shakespeare parallels quite far enough . \nthe most audacious , outrageous , sexually explicit , psychologically probing , pure libido film of the year has arrived from portugal . \nthe creative animation work may not look as fully 'rendered' as pixar's industry standard , but it uses lighting effects and innovative backgrounds to an equally impressive degree . \nart-house to the core , read my lips is a genre-curling crime story that revives the free-wheeling noir spirit of old french cinema . \ngrant is certainly amusing , but the very hollowness of the character he plays keeps him at arms length\nconceptually brilliant . . . plays like a living-room war of the worlds , gaining most of its unsettling force from the suggested and the unknown . \n . . . manages to deliver a fair bit of vampire fun . \ndrama of temptation , salvation and good intentions is a thoughtful examination of faith , love and power . \nthe strength of the film comes not from any cinematic razzle-dazzle but from its recovery of an historical episode that , in the simple telling , proves simultaneously harrowing and uplifting . \nthe performances are strong , though the subject matter demands acting that borders on hammy at times . \na damn fine and a truly distinctive and a deeply pertinent film . \nstill rapturous after all these years , cinema paradiso stands as one of the great films about movie love . \nreggio and glass put on an intoxicating show . \nmacdowell . . . gives give a solid , anguished performance that eclipses nearly everything else she's ever done . \nthe thing about guys like evans is this : you're never quite sure where self-promotion ends and the truth begins . but as you watch the movie , you're too interested to care . \ni liked a lot of the smaller scenes . \nthe film will appeal to discovery channel fans and will surely widen the perspective of those of us who see the continent through rose-colored glasses . \nan eye-boggling blend of psychedelic devices , special effects and backgrounds , 'spy kids 2' is a visual treat for all audiences . \nformuliac , but fun . \nstraightforward and old-fashioned in the best possible senses of both those words , possession is a movie that puts itself squarely in the service of the lovers who inhabit it . \nit may . . . work as a jaunt down memory lane for teens and young adults who grew up on televised scooby-doo shows or reruns . \none of those movies that catches you up in something bigger than yourself , namely , an archetypal desire to enjoy good trash every now and then . \nthis harrowing journey into combat hell vividly captures the chaotic insanity and personal tragedies that are all too abundant when human hatred spews forth unchecked . \nfar more successful , if considerably less ambitious , than last year's kubrick-meets-spielberg exercise . \napart from anything else , this is one of the best-sustained ideas i have ever seen on the screen . \n'pocas veces es posible ver un elenco tan compenetrado con la historia , donde todos y cada uno de los actores ofrecen actuaciones verdaderamente memorables . '\nelling builds gradually until you feel fully embraced by this gentle comedy . \na fascinating examination of the joyous , turbulent self-discovery made by a proper , middle-aged woman . \nhere is a vh1 behind the music special that has something a little more special behind it : music that didn't sell many records but helped change a nation . \nbuy popcorn . take nothing seriously and enjoy the ride . \ncarrying off a spot-on scottish burr , duvall ( also a producer ) peels layers from this character that may well not have existed on paper . \nthe acting , for the most part , is terrific , although the actors must struggle with the fact that they're playing characters who sometimes feel more like literary conceits than flesh-and-blood humans . \nsome body will take you places you haven't been , and also places you have . \nveret has a whip-smart sense of narrative bluffs . \nparts of the film feel a bit too much like an infomercial for ram dass's latest book aimed at the boomer demographic . but mostly it's a work that , with humor , warmth , and intelligence , captures a life interestingly lived . \nwere it not for a sentimental resolution that explains way more about cal than does the movie or the character any good , freundlich's world traveler might have been one of the more daring and surprising american movies of the year . \n \" home movie \" is the film equivalent of a lovingly rendered coffee table book . \ngraphic sex may be what's attracting audiences to unfaithful , but gripping performances by lane and gere are what will keep them awake . \nwhen compared to the usual , more somber festival entries , davis' highly personal brand of romantic comedy is a tart , smart breath of fresh air that stands out from the pack even if the picture itself is somewhat problematic . \nfeels untidily honest . \nboth damning and damned compelling . \nmuch has been written about those years when the psychedelic '60s grooved over into the gay '70s , but words don't really do the era justice . you have to see it . \neven if it pushes its agenda too forcefully , this remains a film about something , one that attempts and often achieves a level of connection and concern . \nwhat lifts the film high above run-of-the-filth gangster flicks is its refusal to recognise any of the signposts , as if discovering a way through to the bitter end without a map . \nwe've seen the hippie-turned-yuppie plot before , but there's an enthusiastic charm in <i ? fire that makes the formula fresh again . \nboth an admirable reconstruction of terrible events , and a fitting memorial to the dead of that day , and of the thousands thereafter . \na sly dissection of the inanities of the contemporary music business and a rather sad story of the difficulties of artistic collaboration . \nthe unique niche of self-critical , behind-the-scenes navel-gazing kaufman has carved from orleans' story and his own infinite insecurity is a work of outstanding originality . \nlovingly photographed in the manner of a golden book sprung to life , stuart little 2 manages sweetness largely without stickiness . \nconsistently clever and suspenseful . \nit's like a \" big chill \" reunion of the baader-meinhof gang , only these guys are more harmless pranksters than political activists . \nthe story gives ample opportunity for large-scale action and suspense , which director shekhar kapur supplies with tremendous skill . \nred dragon \" never cuts corners . \nfresnadillo has something serious to say about the ways in which extravagant chance can distort our perspective and throw us off the path of good sense . \nthrows in enough clever and unexpected twists to make the formula feel fresh . \nweighty and ponderous but every bit as filling as the treat of the title . \na real audience-pleaser that will strike a chord with anyone who's ever waited in a doctor's office , emergency room , hospital bed or insurance company office . \ngenerates an enormous feeling of empathy for its characters . \nexposing the ways we fool ourselves is one hour photo's real strength . \nit's up to you to decide whether to admire these people's dedication to their cause or be repelled by their dogmatism , manipulativeness and narrow , fearful view of american life . \nmostly , [goldbacher] just lets her complicated characters be unruly , confusing and , through it all , human . \n . . . quite good at providing some good old fashioned spooks . \nat its worst , the movie is pretty diverting ; the pity is that it rarely achieves its best . \nscherfig's light-hearted profile of emotional desperation is achingly honest and delightfully cheeky . \na journey spanning nearly three decades of bittersweet camaraderie and history , in which we feel that we truly know what makes holly and marina tick , and our hearts go out to them as both continue to negotiate their imperfect , love-hate relationship . \nthe wonderfully lush morvern callar is pure punk existentialism , and ms . ramsay and her co-writer , liana dognini , have dramatized the alan warner novel , which itself felt like an answer to irvine welsh's book trainspotting . \nas it turns out , you can go home again . \nyou've already seen city by the sea under a variety of titles , but it's worth yet another visit . \nthis kind of hands-on storytelling is ultimately what makes shanghai ghetto move beyond a good , dry , reliable textbook and what allows it to rank with its worthy predecessors . \nmaking such a tragedy the backdrop to a love story risks trivializing it , though chouraqui no doubt intended the film to affirm love's power to help people endure almost unimaginable horror . \ngrown-up quibbles are beside the point here . the little girls understand , and mccracken knows that's all that matters . \na powerful , chilling , and affecting study of one man's dying fall . \nthis is a fascinating film because there is no clear-cut hero and no all-out villain . \na dreadful day in irish history is given passionate , if somewhat flawed , treatment . \n . . . a good film that must have baffled the folks in the marketing department . \n . . . is funny in the way that makes you ache with sadness ( the way chekhov is funny ) , profound without ever being self-important , warm without ever succumbing to sentimentality . \ndevotees of star trek ii : the wrath of khan will feel a nagging sense of deja vu , and the grandeur of the best next generation episodes is lacking . \na soul-stirring documentary about the israeli/palestinian conflict as revealed through the eyes of some children who remain curious about each other against all odds . \nwhat's so striking about jolie's performance is that she never lets her character become a caricature -- not even with that radioactive hair . \nthe main story . . . is compelling enough , but it's difficult to shrug off the annoyance of that chatty fish . \nthe performances are immaculate , with roussillon providing comic relief . \nkinnear . . . gives his best screen performance with an oddly winning portrayal of one of life's ultimate losers . \nhugh grant , who has a good line in charm , has never been more charming than in about a boy . \nthere's a lot of tooth in roger dodger . but what's nice is that there's a casual intelligence that permeates the script . \nreminiscent of alfred hitchcock's thrillers , most of the scary parts in 'signs' occur while waiting for things to happen . \none of the best looking and stylish animated movies in quite a while . . . \nits use of the thriller form to examine the labyrinthine ways in which people's lives cross and change , buffeted by events seemingly out of their control , is intriguing , provocative stuff . \ndenver should not get the first and last look at one of the most triumphant performances of vanessa redgrave's career . it deserves to be seen everywhere . \nyou needn't be steeped in '50s sociology , pop culture or movie lore to appreciate the emotional depth of haynes' work . though haynes' style apes films from the period . . . its message is not rooted in that decade . \nwaiting for godard can be fruitful : 'in praise of love' is the director's epitaph for himself . \na gangster movie with the capacity to surprise . \nthe film has a laundry list of minor shortcomings , but the numerous scenes of gory mayhem are worth the price of admission . . . if \" gory mayhem \" is your idea of a good time . \nif not a home run , then at least a solid base hit . \ngoldmember is funny enough to justify the embarrassment of bringing a barf bag to the moviehouse . \n . . . a fairly disposable yet still entertaining b picture . \nit may not be particularly innovative , but the film's crisp , unaffected style and air of gentle longing make it unexpectedly rewarding . \nthe film truly does rescue [the funk brothers] from motown's shadows . it's about time . \ndrawing on an irresistible , languid romanticism , byler reveals the ways in which a sultry evening or a beer-fueled afternoon in the sun can inspire even the most retiring heart to venture forth . \nworks because we're never sure if ohlinger's on the level or merely a dying , delusional man trying to get into the history books before he croaks . \n[scherfig] has made a movie that will leave you wondering about the characters' lives after the clever credits roll . \na heady , biting , be-bop ride through nighttime manhattan , a loquacious videologue of the modern male and the lengths to which he'll go to weave a protective cocoon around his own ego . \nskin of man gets a few cheap shocks from its kids-in-peril theatrics , but it also taps into the primal fears of young people trying to cope with the mysterious and brutal nature of adults . \nthe piano teacher is not an easy film . it forces you to watch people doing unpleasant things to each other and themselves , and it maintains a cool distance from its material that is deliberately unsettling . \nas refreshing as a drink from a woodland stream . \nwilliams absolutely nails sy's queasy infatuation and overall strangeness . \ncan i admit xxx is as deep as a petri dish and as well-characterized as a telephone book but still say it was a guilty pleasure ? \nwhile it's nothing we haven't seen before from murphy , i spy is still fun and enjoyable and so aggressively silly that it's more than a worthwhile effort . \nby the time it ends in a rush of sequins , flashbulbs , blaring brass and back-stabbing babes , it has said plenty about how show business has infiltrated every corner of society -- and not always for the better . \nan intimate contemplation of two marvelously messy lives . \nrarely has skin looked as beautiful , desirable , even delectable , as it does in trouble every day . \nthis is one of those rare docs that paints a grand picture of an era and makes the journey feel like a party . \npoignant if familiar story of a young person suspended between two cultures . \na metaphor for a modern-day urban china searching for its identity . \nfor all its brooding quality , ash wednesday is suspenseful and ultimately unpredictable , with a sterling ensemble cast . \nan odd drama set in the world of lingerie models and bar dancers in the midwest that held my interest precisely because it didn't try to . \nthe film feels uncomfortably real , its language and locations bearing the unmistakable stamp of authority . \ndespite its faults , gangs excels in spectacle and pacing . \nentertaining despite its one-joke premise with the thesis that women from venus and men from mars can indeed get together . \na tightly directed , highly professional film that's old-fashioned in all the best possible ways . \nit's dark but has wonderfully funny moments ; you care about the characters ; and the action and special effects are first-rate . \nin visual fertility treasure planet rivals the top japanese animations of recent vintage . \nenormously enjoyable , high-adrenaline documentary . \nbuy is an accomplished actress , and this is a big , juicy role . \nit works its magic with such exuberance and passion that the film's length becomes a part of its fun . \nbeautifully crafted and brutally honest , promises offers an unexpected window into the complexities of the middle east struggle and into the humanity of its people . \nan old-fashioned but emotionally stirring adventure tale of the kind they rarely make anymore . \ncharlotte sometimes is a gem . it's always enthralling . \nin my opinion , analyze that is not as funny or entertaining as analyze this , but it is a respectable sequel . \na remarkable film by bernard rose . \nzhuangzhuang creates delicate balance of style , text , and subtext that's so simple and precise that anything discordant would topple the balance , but against all odds , nothing does . \na much more successful translation than its most famous previous film adaptation , writer-director anthony friedman's similarly updated 1970 british production . \nan original and highly cerebral examination of the psychopathic mind\nmichel piccoli's moving performance is this films reason for being . \na captivating and intimate study about dying and loving . . . \nthis is an elegantly balanced movie -- every member of the ensemble has something fascinating to do -- that doesn't reveal even a hint of artifice . \n[grant] goes beyond his usual fluttering and stammering and captures the soul of a man in pain who gradually comes to recognize it and deal with it . \na high-spirited buddy movie about the reunion of berlin anarchists who face arrest 15 years after their crime . \nabout the best thing you could say about narc is that it's a rock-solid little genre picture . whether you like it or not is basically a matter of taste . \nan involving , inspirational drama that sometimes falls prey to its sob-story trappings . \nsome of the most inventive silliness you are likely to witness in a movie theatre for some time . \ncanadian filmmaker gary burns' inventive and mordantly humorous take on the soullessness of work in the city . \na rollicking ride , with jaw-dropping action sequences , striking villains , a gorgeous color palette , astounding technology , stirring music and a boffo last hour that leads up to a strangely sinister happy ending . \neveryone's insecure in lovely and amazing , a poignant and wryly amusing film about mothers , daughters and their relationships . \nthe closest thing to the experience of space travel\nfull of surprises . \nconnoisseurs of chinese film will be pleased to discover that tian's meticulous talent has not withered during his enforced hiatus . \nif you can push on through the slow spots , you'll be rewarded with some fine acting . \nan unusually dry-eyed , even analytical approach to material that is generally played for maximum moisture . \nsymbolically , warm water under a red bridge is a celebration of feminine energy , a tribute to the power of women to heal . \nspy kids 2 also happens to be that rarity among sequels : it actually improves upon the original hit movie . \nexceptionally well acted by diane lane and richard gere . \nlike a precious and finely cut diamond , magnificent to behold in its sparkling beauty yet in reality it's one tough rock . \nin addition to scoring high for originality of plot -- putting together familiar themes of family , forgiveness and love in a new way -- lilo & stitch has a number of other assets to commend it to movie audiences both innocent and jaded . \nmiller has crafted an intriguing story of maternal instincts and misguided acts of affection . \none of the most exciting action films to come out of china in recent years . \nthis is a nervy , risky film , and villeneuve has inspired croze to give herself over completely to the tormented persona of bibi . \nmy little eye is the best little \" horror \" movie i've seen in years . \ntunney , brimming with coltish , neurotic energy , holds the screen like a true star . \neven if the naipaul original remains the real masterpiece , the movie possesses its own languorous charm . \n[the film] tackles the topic of relationships in such a straightforward , emotionally honest manner that by the end , it's impossible to ascertain whether the film is , at its core , deeply pessimistic or quietly hopeful . \nsometimes we feel as if the film careens from one colorful event to another without respite , but sometimes it must have seemed to frida kahlo as if her life did , too . \nthe strength of the film lies in its two central performances by sven wollter as the stricken composer and viveka seldahl as his desperate violinist wife . \nlike the series , the movie is funny , smart , visually inventive , and most of all , alive . \nit was filled with shootings , beatings , and more cussing than you could shake a stick at . \nyou don't know whether to admire the film's stately nature and call it classicism or be exasperated by a noticeable lack of pace . or both . \nsure , i hated myself in the morning . but then again , i hate myself most mornings . i still like moonlight mile , better judgment be damned . \ntime out is as serious as a pink slip . and more than that , it's an observant , unfussily poetic meditation about identity and alienation . \nwill assuredly rank as one of the cleverest , most deceptively amusing comedies of the year . \nmaryam is a small film , but it offers large rewards . \na highly watchable , giggly little story with a sweet edge to it . \nthe most consistently funny of the austin powers films . \nana's journey is not a stereotypical one of self-discovery , as she's already comfortable enough in her own skin to be proud of her rubenesque physique . . . \ncockettes has the glorious , gaudy benefit of much stock footage of those days , featuring all manner of drag queen , bearded lady and lactating hippie . \nthere's something poignant about an artist of 90-plus years taking the effort to share his impressions of life and loss and time and art with us . \nthe comedy makes social commentary more palatable . \nan ideal love story for those intolerant of the more common saccharine genre . \none funny popcorn flick . \nthis new zealand coming-of-age movie isn't really about anything . when it's this rich and luscious , who cares ? \ntully is worth a look for its true-to-life characters , its sensitive acting , its unadorned view of rural life and the subtle direction of first-timer hilary birmingham . \nthis gorgeous epic is guaranteed to lift the spirits of the whole family . \nthe wild thornberrys movie is pleasant enough and the message of our close ties with animals can certainly not be emphasized enough . \nwilliams creates a stunning , taxi driver-esque portrayal of a man teetering on the edge of sanity . \nif you're in the right b-movie frame of mind , it may just scare the pants off you . \na movie of riveting power and sadness . \nboth a detective story and a romance spiced with the intrigue of academic skullduggery and politics . \nquietly engaging . \nludicrous , but director carl franklin adds enough flourishes and freak-outs to make it entertaining . \ndirector roger kumble offers just enough sweet and traditional romantic comedy to counter the crudity . and there's the inimitable diaz , holding it all together . \nspielberg's picture is smarter and subtler than [total recall and blade runner] , although its plot may prove too convoluted for fun-seeking summer audiences . \nit's got all the familiar bruckheimer elements , and schumacher does probably as good a job as anyone at bringing off the hopkins/rock collision of acting styles and onscreen personas . \na grittily beautiful film that looks , sounds , and feels more like an extended , open-ended poem than a traditionally structured story . \ndense , exhilarating documentary . \nthe production values are of the highest and the performances attractive without being memorable . \na well-rounded tribute to a man whose achievements -- and complexities -- reached far beyond the end zone . \nfinely crafted , finely written , exquisitely performed\nramsay and morton fill this character study with poetic force and buoyant feeling . \nthis submarine drama earns the right to be favorably compared to das boot . \nclaude chabrol's camera has a way of gently swaying back and forth as it cradles its characters , veiling tension beneath otherwise tender movements . \nthere's a great deal of corny dialogue and preposterous moments . and yet , it still works . \nthe film was immensely enjoyable thanks to great performances by both steve buscemi and rosario dawson . . . \nlike many western action films , this thriller is too loud and thoroughly overbearing , but its heartfelt concern about north korea's recent past and south korea's future adds a much needed moral weight . \nspecial p . o . v . camera mounts on bikes , skateboards , and motorcycles provide an intense experience when splashed across the immense imax screen . \na joyous occasion\nmike white's deft combination of serious subject matter and dark , funny humor make \" \" the good girl \" a film worth watching . \nthis is a shrewd and effective film from a director who understands how to create and sustain a mood . \nmeant to reduce blake's philosophy into a tragic coming-of-age saga punctuated by bursts of animator todd mcfarlane's superhero dystopia . \nassayas' ambitious , sometimes beautiful adaptation of jacques chardonne's novel . \nas ex-marine walter , who may or may not have shot kennedy , actor raymond j . barry is perfectly creepy and believable . \nthose who don't entirely 'get' godard's distinctive discourse will still come away with a sense of his reserved but existential poignancy . \npete's screenplay manages to find that real natural , even-flowing tone that few movies are able to accomplish . \nlike brosnan's performance , evelyn comes from the heart . \nit uses some of the figures from the real-life story to portray themselves in the film . the result is a powerful , naturally dramatic piece of low-budget filmmaking . \nits spirit of iconoclastic abandon -- however canned -- makes for unexpectedly giddy viewing . \nthe early and middle passages are surprising in how much they engage and even touch us . this is not a classical dramatic animated feature , nor a hip , contemporary , in-jokey one . it's sort of in-between , and it works . \nthis quiet , introspective and entertaining independent is worth seeking . \nwhether our action-and-popcorn obsessed culture will embrace this engaging and literate psychodrama isn't much of a mystery , unfortunately . \nwhether or not ram dass proves as clear and reliable an authority on that as he was about inner consciousness , fierce grace reassures us that he will once again be an honest and loving one . \nsly , sophisticated and surprising . \nspare but quietly effective retelling . \ndemonstrates a vivid imagination and an impressive style that result in some terrific setpieces . \nby its modest , straight-ahead standards , undisputed scores a direct hit . \nits story about a young chinese woman , ah na , who has come to new york city to replace past tragedy with the american dream is one that any art-house moviegoer is likely to find compelling . \nfor those who like quirky , slightly strange french films , this is a must ! \nthere are so few films about the plight of american indians in modern america that skins comes as a welcome , if downbeat , missive from a forgotten front . \n[shyamalan] continues to cut a swathe through mainstream hollywood , while retaining an integrity and refusing to compromise his vision . \na whale of a good time for both children and parents seeking christian-themed fun . \nwhat begins as a film in the tradition of the graduate quickly switches into something more recyclable than significant . \nmuch smarter and more attentive than it first sets out to be . \nthe story is smart and entirely charming in intent and execution . \na movie of technical skill and rare depth of intellect and feeling . \nrepresents a worthy departure from the culture clash comedies that have marked an emerging indian american cinema . \ndoesn't do more than expand a tv show to movie length . however , it's pleasant enough and its ecological , pro-wildlife sentiments are certainly welcome . \nif you're looking for an intelligent movie in which you can release your pent up anger , enough is just the ticket you need . \na pointed , often tender , examination of the pros and cons of unconditional love and familial duties . \nas well-acted and well-intentioned as all or nothing is , however , the film comes perilously close to being too bleak , too pessimistic and too unflinching for its own good . \na comedy-drama of nearly epic proportions rooted in a sincere performance by the title character undergoing midlife crisis . \nit's about issues most adults have to face in marriage and i think that's what i liked about it -- the real issues tucked between the silly and crude storyline . \nelegantly produced and expressively performed , the six musical numbers crystallize key plot moments into minutely detailed wonders of dreamlike ecstasy . \nenriched by a strong and unforced supporting cast . \nwriter/ director m . night shyamalan's ability to pull together easily accessible stories that resonate with profundity is undeniable . \nif you can keep your eyes open amid all the blood and gore , you'll see del toro has brought unexpected gravity to blade ii . \nnot a strike against yang's similarly themed yi yi , but i found what time ? to be more engaging on an emotional level , funnier , and on the whole less detached . \na breathtaking adventure for all ages , spirit tells its poignant and uplifting story in a stunning fusion of music and images . \na charming and funny story of clashing cultures and a clashing mother/daughter relationship . \nnever lets go your emotions , taking them to surprising highs , sorrowful lows and hidden impulsive niches . . . gorgeous , passionate , and at times uncommonly moving . \n \" . . . something appears to have been lost in the translation this time . the importance of being earnest movie seems to be missing a great deal of the acerbic repartee of the play . \" \n[washington's] strong hand , keen eye , sweet spirit and good taste are reflected in almost every scene . \nshiner can certainly go the distance , but isn't world championship material\nthe film's desire to be liked sometimes undermines the possibility for an exploration of the thornier aspects of the nature/nurture argument in regards to homosexuality . \n . . . a quietly introspective portrait of the self-esteem of employment and the shame of losing a job . . . \naffable if not timeless , like mike raises some worthwhile themes while delivering a wholesome fantasy for kids . \na film of delicate interpersonal dances . caine makes us watch as his character awakens to the notion that to be human is eventually to have to choose . it's a sight to behold . \nit's an unusual , thoughtful bio-drama with a rich subject and some fantastic moments and scenes . \nsaved from being merely way-cool by a basic , credible compassion . \nthe increasingly diverse french director has created a film that one can honestly describe as looking , sounding and simply feeling like no other film in recent history . \ngangs , despite the gravity of its subject matter , is often as fun to watch as a good spaghetti western . \npeter jackson has done the nearly impossible . he has improved upon the first and taken it a step further , richer and deeper . what jackson has done is proven that no amount of imagination , no creature , no fantasy story and no incredibly outlandish scenery\nthere has to be a few advantages to never growing old . like being able to hit on a 15-year old when you're over 100 . \nice age won't drop your jaw , but it will warm your heart , and i'm giving it a strong thumbs up . \nlike kissing jessica stein , amy's orgasm has a key strength in its willingness to explore its principal characters with honesty , insight and humor . \nthe lady and the duke is eric rohmer's economical antidote to the bloated costume drama\none of the year's best films , featuring an oscar-worthy performance by julianne moore . \na small gem from belgium . \ncombines a comically dismal social realism with a farcically bawdy fantasy of redemption and regeneration . \na soap-opera quality twist in the last 20 minutes . . . almost puts the kibosh on what is otherwise a sumptuous work of b-movie imagination . \nthe most ingenious film comedy since being john malkovich . \nthere's something to be said for a studio-produced film that never bothers to hand viewers a suitcase full of easy answers . \na movie where story is almost an afterthought amidst a swirl of colors and inexplicable events . \nmanages to accomplish what few sequels can -- it equals the original and in some ways even betters it . \nto call this one an eventual cult classic would be an understatement , and woe is the horror fan who opts to overlook this goofily endearing and well-lensed gorefest . \njolie gives it that extra little something that makes it worth checking out at theaters , especially if you're in the mood for something more comfortable than challenging . \nalthough melodramatic and predictable , this romantic comedy explores the friendship between five filipino-americans and their frantic efforts to find love . \ni have a new favorite musical -- and i'm not even a fan of the genre\nit's unlikely we'll see a better thriller this year . \nthere is a real subject here , and it is handled with intelligence and care . \njason patric and ray liotta make for one splendidly cast pair . \nnoyce creates a film of near-hypnotic physical beauty even as he tells a story as horrifying as any in the heart-breakingly extensive annals of white-on-black racism . \nstarts slowly , but adrien brody  in the title role  helps make the film's conclusion powerful and satisfying . \nvery predictable but still entertaining\nnothing short of a masterpiece -- and a challenging one . \npratfalls aside , barbershop gets its greatest play from the timeless spectacle of people really talking to each other . \nthis amiable picture talks tough , but it's all bluster -- in the end it's as sweet as greenfingers . . . \nthis is one of mr . chabrol's subtlest works , but also one of his most uncanny . \nan engrossing iranian film about two itinerant teachers and some lost and desolate people they encounter in a place where war has savaged the lives and liberties of the poor and the dispossessed . \neven though we know the outcome , the seesawing of the general's fate in the arguments of competing lawyers has the stomach-knotting suspense of a legal thriller , while the testimony of witnesses lends the film a resonant undertone of tragedy . \nwatching spirited away is like watching an eastern imagination explode . \nas relationships shift , director robert j . siegel allows the characters to inhabit their world without cleaving to a narrative arc . \ntwohy knows how to inflate the mundane into the scarifying , and gets full mileage out of the rolling of a stray barrel or the unexpected blast of a phonograph record . \nwhile the story does seem pretty unbelievable at times , it's awfully entertaining to watch . \na smart and funny , albeit sometimes superficial , cautionary tale of a technology in search of an artist . \nexamines its explosive subject matter as nonjudgmentally as wiseman's previous studies of inner-city high schools , hospitals , courts and welfare centers . \ni prefer soderbergh's concentration on his two lovers over tarkovsky's mostly male , mostly patriarchal debating societies . \n'if you are in the mood for an intelligent weepy , it can easily worm its way into your heart . '\nin imax in short , it's just as wonderful on the big screen . \ndoes a good job of establishing a time and place , and of telling a fascinating character's story . \ni'm going to give it a marginal thumbs up . i liked it just enough . \nthose of you who don't believe in santa claus probably also think that sequels can never capture the magic of the original . well , this movie proves you wrong on both counts . \na deliciously nonsensical comedy about a city coming apart at its seams . \nthe rare imax movie that you'll wish was longer than an hour . \nmy wife's plotting is nothing special ; it's the delivery that matters here . \ni've yet to find an actual vietnam war combat movie actually produced by either the north or south vietnamese , but at least now we've got something pretty damn close . \na moving and not infrequently breathtaking film . \nit's a sharp movie about otherwise dull subjects . \n[an] absorbing documentary . \nit's like rocky and bullwinkle on speed , but that's neither completely enlightening , nor does it catch the intensity of the movie's strangeness . \nas action-adventure , this space-based homage to robert louis stevenson's treasure island fires on all plasma conduits . \na melancholy , emotional film . \nwhile the filmmaking may be a bit disjointed , the subject matter is so fascinating that you won't care . \nintensely romantic , thought-provoking and even an engaging mystery . \ngoofy , nutty , consistently funny . and educational ! \nnot a schlocky creature feature but something far more stylish and cerebral--and , hence , more chillingly effective . \nanother in a long line of ultra-violent war movies , this one is not quite what it could have been as a film , but the story and theme make up for it . \nit leaves little doubt that kidman has become one of our best actors . \nthe film boasts dry humor and jarring shocks , plus moments of breathtaking mystery . \nbeautifully directed and convincingly acted . \ngambling and throwing a basketball game for money isn't a new plot -- in fact toback himself used it in black and white . but toback's deranged immediacy makes it seem fresh again . \nin the director's cut , the film is not only a love song to the movies but it also is more fully an example of the kind of lush , all-enveloping movie experience it rhapsodizes . \nbring on the sequel . \ngraced with the kind of social texture and realism that would be foreign in american teen comedies . \nif we sometimes need comforting fantasies about mental illness , we also need movies like tim mccann's revolution no . 9 . \nthe film occasionally tries the viewer's patience with slow pacing and a main character who sometimes defies sympathy , but it ultimately satisfies with its moving story . \na big-budget/all-star movie as unblinkingly pure as the hours is a distinct rarity , and an event . \n . . . certainly an entertaining ride , despite many talky , slow scenes . but something seems to be missing . a sense of real magic , perhaps . \nthat haynes can both maintain and dismantle the facades that his genre and his character construct is a wonderous accomplishment of veracity and narrative grace . \nthe movie worked for me right up to the final scene , and then it caved in . \n . . . one of the most entertaining monster movies in ages . . . \nplunges you into a reality that is , more often then not , difficult and sad , and then , without sentimentalizing it or denying its brutality , transforms that reality into a lyrical and celebratory vision . \nwould you laugh if a tuba-playing dwarf rolled down a hill in a trash can ? do you chuckle at the thought of an ancient librarian whacking a certain part of a man's body ? if you answered yes , by all means enjoy the new guy . \nthe film is . . . determined to treat its characters , weak and strong , as fallible human beings , not caricatures , and to carefully delineate the cost of the inevitable conflicts between human urges and an institution concerned with self-preservation . \nmissteps take what was otherwise a fascinating , riveting story and send it down the path of the mundane . \nan indispensable peek at the art and the agony of making people laugh . \nsteadfastly uncinematic but powerfully dramatic . \nthe engagingly primitive animated special effects contribute to a mood that's sustained through the surprisingly somber conclusion . \nmade-up lampoons the moviemaking process itself , while shining a not particularly flattering spotlight on america's skin-deep notions of pulchritude . \nevokes the 19th century with a subtlety that is an object lesson in period filmmaking . \nya-yas everywhere will forgive the flaws and love the film . \nthe film's best trick is the way that it treats conspiracy as a kind of political blair witch , a monstrous murk that haunts us precisely because it can never be seen . \nthe artwork is spectacular and unlike most animaton from japan , the characters move with grace and panache . \nthe picture's fascinating byways are littered with trenchant satirical jabs at the peculiar egocentricities of the acting breed . \nthe modern remake of dumas's story is long on narrative and ( too ) short on action . \nfred schepisi's film is paced at a speed that is slow to those of us in middle age and deathly slow to any teen . with a cast of a-list brit actors , it is worth searching out . \nsuffers from its timid parsing of the barn-side target of sons trying to breach gaps in their relationships with their fathers . \nnonchalantly freaky and uncommonly pleasurable , warm water may well be the year's best and most unpredictable comedy . \nit's like an old warner bros . costumer jived with sex -- this could be the movie errol flynn always wanted to make , though bette davis , cast as joan , would have killed him . \nit's a great american adventure and a wonderful film to bring to imax . \nsatisfyingly scarifying , fresh and old-fashioned at the same time . \noh , james ! your 20th outing shows off a lot of stamina and vitality , and get this , madonna's cameo doesn't suck ! \na genuine mind-bender . \nthat death is merely a transition is a common tenet in the world's religions . this deeply spiritual film taps into the meaning and consolation in afterlife communications . \nthere is something that is so meditative and lyrical about babak payami's boldly quirky iranian drama secret ballot . . . a charming and evoking little ditty that manages to show the gentle and humane side of middle eastern world politics\na huge box-office hit in korea , shiri is a must for genre fans . \n . . . planos fijos , tomas largas , un ritmo pausado y una sutil observacin de sus personajes , sin estridencias ni grandes revelaciones . \ni'm not a fan of the phrase 'life affirming' because it usually means 'schmaltzy , ' but real women have curves truly is life affirming . \nthe symbols float like butterflies and the spinning styx sting like bees . i wanted more . \nif it's unnerving suspense you're after -- you'll find it with ring , an indisputably spooky film ; with a screenplay to die for . \nthe art direction and costumes are gorgeous and finely detailed , and kurys' direction is clever and insightful . \nred dragon makes one appreciate silence of the lambs . \nproves a servicable world war ii drama that can't totally hide its contrivances , but it at least calls attention to a problem hollywood too long has ignored . \nleigh isn't breaking new ground , but he knows how a daily grind can kill love . \nwhile broomfield's film doesn't capture the effect of these tragic deaths on hip-hop culture , it succeeds as a powerful look at a failure of our justice system . \n . . . strips bible stores of the potential for sanctimoniousness , making them meaningful for both kids and church-wary adults . \nlaugh-out-loud lines , adorably ditsy but heartfelt performances , and sparkling , bittersweet dialogue that cuts to the chase of the modern girl's dilemma . \ntends to pile too many \" serious issues \" on its plate at times , yet remains fairly light , always entertaining , and smartly written . \na solidly entertaining little film . \nit's an entertaining movie , and the effects , boosted to the size of a downtown hotel , will all but take you to outer space . \nsayles has a knack for casting , often resurrecting performers who rarely work in movies now . . . and drawing flavorful performances from bland actors . \ndespite an overwrought ending , the film works as well as it does because of the performances . \na passionately inquisitive film determined to uncover the truth and hopefully inspire action . \nthough nijinsky's words grow increasingly disturbed , the film maintains a beguiling serenity and poise that make it accessible for a non-narrative feature . \na muddle splashed with bloody beauty as vivid as any scorsese has ever given us . \nfrom both a great and a terrible story , mr . nelson has made a film that is an undeniably worthy and devastating experience . \nspider-man is about growing strange hairs , getting a more mature body , and finding it necessary to hide new secretions from the parental units . \nthe first shocking thing about sorority boys is that it's actually watchable . even more baffling is that it's funny . \nhighlighted by a gritty style and an excellent cast , it's better than one might expect when you look at the list of movies starring ice-t in a major role . \nneither quite a comedy nor a romance , more of an impish divertissement of themes that interest attal and gainsbourg -- they live together -- the film has a lot of charm . \nfirst and foremost . . . the reason to go see \" blue crush \" is the phenomenal , water-born cinematography by david hennings . \na visionary marvel , but it's lacking a depth in storytelling usually found in anime like this . \nthe problems and characters it reveals are universal and involving , and the film itself -- as well its delightful cast -- is so breezy , pretty and gifted , it really won my heart . \nin his latest effort , storytelling , solondz has finally made a movie that isn't just offensive -- it also happens to be good . \nhow i killed my father would be a rarity in hollywood . it's an actor's showcase that accomplishes its primary goal without the use of special effects , but rather by emphasizing the characters -- including the supporting ones . \ni just saw this movie . . . well , it's probably not accurate to call it a movie . \nwhat's most memorable about circuit is that it's shot on digital video , whose tiny camera enables shafer to navigate spaces both large . . . and small . . . with considerable aplomb . \nscherfig , the writer-director , has made a film so unabashedly hopeful that it actually makes the heart soar . yes , soar . \na delicious and delicately funny look at the residents of a copenhagen neighborhood coping with the befuddling complications life tosses at them . \n \" what really happened ? \" is a question for philosophers , not filmmakers ; all the filmmakers need to do is engage an audience . \nsoderbergh , like kubrick before him , may not touch the planet's skin , but understands the workings of its spirit . \nmuch credit must be given to the water-camera operating team of don king , sonny miller , and michael stewart . their work is fantastic . \ncrush is so warm and fuzzy you might be able to forgive its mean-spirited second half . \nfranco is an excellent choice for the walled-off but combustible hustler , but he does not give the transcendent performance sonny needs to overcome gaps in character development and story logic . \ntsai ming-liang's witty , wistful new film , what time is it there ? , is a temporal inquiry that shoulders its philosophical burden lightly . \nthe pianist lacks the quick emotional connections of steven spielberg's schindler's list . but mr . polanski creates images even more haunting than those in mr . spielberg's 1993 classic . \nsteers , in his feature film debut , has created a brilliant motion picture . \na brilliant , absurd collection of vignettes that , in their own idiosyncratic way , sum up the strange horror of life in the new millennium . \nas warm as it is wise , deftly setting off uproarious humor with an underlying seriousness that sneaks up on the viewer , providing an experience that is richer than anticipated . \nthe film may not hit as hard as some of the better drug-related pictures , but it still manages to get a few punches in . \nold-fashioned but thoroughly satisfying entertainment . \nan energizing , intoxicating documentary charting the rise of hip-hop culture in general and the art of scratching ( or turntablism ) in particular . \na fun family movie that's suitable for all ages -- a movie that will make you laugh , cry and realize , 'it's never too late to believe in your dreams . '\nif you open yourself up to mr . reggio's theory of this imagery as the movie's set . . . it can impart an almost visceral sense of dislocation and change . \ni had a dream that a smart comedy would come along to rescue me from a summer of teen-driven , toilet-humor codswallop , and its name was earnest . \neven though the film doesn't manage to hit all of its marks , it's still entertaining to watch the target practice . \nwhere this was lazy but enjoyable , a formula comedy redeemed by its stars , that is even lazier and far less enjoyable . \nthe 3-d vistas from orbit , with the space station suspended like a huge set of wind chimes over the great blue globe , are stanzas of breathtaking , awe-inspiring visual poetry . \nthe attraction between these two marginal characters is complex from the start -- and , refreshingly , stays that way . \nfans of the modern day hong kong action film finally have the worthy successor to a better tomorrow and the killer which they have been patiently waiting for . \neven when he's not at his most critically insightful , godard can still be smarter than any 50 other filmmakers still at work . \nwhat sets this romantic comedy apart from most hollywood romantic comedies is its low-key way of tackling what seems like done-to-death material . \nhas enough wit , energy and geniality to please not only the fanatical adherents on either side , but also people who know nothing about the subject and think they're not interested . \nthis seductive tease of a thriller gets the job done . it's a scorcher . \nbittersweet comedy/drama full of life , hand gestures , and some really adorable italian guys . \nworks as pretty contagious fun . \nthe best didacticism is one carried by a strong sense of humanism , and bertrand tavernier's oft-brilliant safe conduct ( \" laissez-passer \" ) wears its heart on its sleeve . \na realistically terrifying movie that puts another notch in the belt of the long list of renegade-cop tales . \na charming , banter-filled comedy . . . one of those airy cinematic bon bons whose aims -- and by extension , accomplishments -- seem deceptively slight on the surface . \na film with almost as many delights for adults as there are for children and dog lovers . \nserious movie-goers embarking upon this journey will find that the road to perdition leads to a satisfying destination . \nheartwarming and gently comic even as the film breaks your heart . \ncaruso sometimes descends into sub-tarantino cuteness . . . but for the most part he makes sure the salton sea works the way a good noir should , keeping it tight and nasty . \na \" black austin powers ? \" i prefer to think of it as \" pootie tang with a budget . \" sa da tay ! \noddly , the film isn't nearly as downbeat as it sounds , but strikes a tone that's alternately melancholic , hopeful and strangely funny . \ni would be shocked if there was actually one correct interpretation , but that shouldn't make the movie or the discussion any less enjoyable . \nchouraqui brings documentary-like credibility to the horrors of the killing field and the barbarism of 'ethnic cleansing . '\nthe best thing i can say about this film is that i can't wait to see what the director does next . \nsmarter than its commercials make it seem . \ngreat character interaction . \none of the funnier movies in town . \ncampanella's competent direction and his excellent cast overcome the obstacles of a predictable outcome and a screenplay that glosses over rafael's evolution . \nby turns very dark and very funny . \nsteven soderbergh doesn't remake andrei tarkovsky's solaris so much as distill it . \nfor more than two decades mr . nachtwey has traveled to places in the world devastated by war , famine and poverty and documented the cruelty and suffering he has found with an devastating , eloquent clarity . \nsimultaneously heartbreakingly beautiful and exquisitely sad . \nthough overall an overwhelmingly positive portrayal , the film doesn't ignore the more problematic aspects of brown's life . \nthe philosophical musings of the dialogue jar against the tawdry soap opera antics of the film's action in a way that is surprisingly enjoyable . \nnot too fancy , not too filling , not too fluffy , but definitely tasty and sweet . \nquando tiros em columbine acerta o alvo ( com o perdo do trocadilho ) , no h como negar o brilhantismo da argumentao de seu diretor . \ndirector lee has a true cinematic knack , but it's also nice to see a movie with its heart so thoroughly , unabashedly on its sleeve . \nas allen's execution date closes in , the documentary gives an especially poignant portrait of her friendship with the never flagging legal investigator david presson . \njones has tackled a meaty subject and drawn engaging characters while peppering the pages with memorable zingers . \na vivid , spicy footnote to history , and a movie that grips and holds you in rapt attention from start to finish . \nif s&m seems like a strange route to true love , maybe it is , but it's to this film's ( and its makers' ) credit that we believe that that's exactly what these two people need to find each other -- and themselves . \nif the film's vision of sport as a secular religion is a bit cloying , its through-line of family and community is heartening in the same way that each season marks a new start . \none of the best of a growing strain of daring films . . . that argue that any sexual relationship that doesn't hurt anyone and works for its participants is a relationship that is worthy of our respect . \nan adorably whimsical comedy that deserves more than a passing twinkle . \nan engrossing story that combines psychological drama , sociological reflection , and high-octane thriller . \nit's easy to be cynical about documentaries in which underdogs beat the odds and the human spirit triumphs , but westbrook's foundation and dalrymple's film earn their uplift . \nmel gibson fights the good fight in vietnam in director randall wallace's flag-waving war flick with a core of decency . \nthere's real visual charge to the filmmaking , and a strong erotic spark to the most crucial lip-reading sequence . \na brutal and funny work . nicole holofcenter , the insightful writer/director responsible for this illuminating comedy doesn't wrap the proceedings up neatly but the ideas tie together beautifully . \nthe film is a blunt indictment , part of a perhaps surreal campaign to bring kissinger to trial for crimes against humanity . \none of the most important and exhilarating forms of animated filmmaking since old walt doodled steamboat willie . \nmove over bond ; this girl deserves a sequel . \nthe kind of trifle that date nights were invented for . \n . it's a testament to the film's considerable charm that it succeeds in entertaining , despite playing out like a feature-length sitcom replete with stereotypical familial quandaries . there's a sheer unbridled delight in the way the story unfurls . . . \ntells ( the story ) with such atmospheric ballast that shrugging off the plot's persnickety problems is simply a matter of ( being ) in a shrugging mood . \nthe film is hard to dismiss -- moody , thoughtful , and lit by flashes of mordant humor . \nif the man from elysian fields is doomed by its smallness , it is also elevated by it--the kind of movie that you enjoy more because you're one of the lucky few who sought it out . \nwhat emerges is an unsettling picture of childhood innocence combined with indoctrinated prejudice . promises is a compelling piece that demonstrates just how well children can be trained to live out and carry on their parents' anguish . \nmeticulously uncovers a trail of outrageous force and craven concealment . \nhey , happy ! is many things -- stoner midnight flick , sci-fi deconstruction , gay fantasia -- but above all it's a love story as sanguine as its title . \nyou won't look at religious fanatics -- or backyard sheds -- the same way again . \nat its best . . . festival in cannes bubbles with the excitement of the festival in cannes . \nthere is a general air of exuberance in all about the benjamins that's hard to resist . \na lovably old-school hollywood confection . \ni'm happy to have seen it -- not as an alternate version , but as the ultimate exercise in viewing deleted scenes . \nby turns gripping , amusing , tender and heart-wrenching , laissez-passer has all the earmarks of french cinema at its best . \nthe warnings to resist temptation in this film . . . are blunt and challenging and offer no easy rewards for staying clean . \nwonder of wonders -- a teen movie with a humanistic message . \na quirky comedy set in newfoundland that cleverly captures the dry wit that's so prevalent on the rock . \npeppered with witty dialogue and inventive moments . \ni'd rather watch a rerun of the powerpuff girls\nwith the prospect of films like kangaroo jack about to burst across america's winter movie screens it's a pleasure to have a film like the hours as an alternative . \nthe wonderful combination of the sweetness and the extraordinary technical accomplishments of the first film are maintained , but its overall impact falls a little flat with a storyline that never quite delivers the original magic . \nlike its title character , this nicholas nickleby finds itself in reduced circumstances -- and , also like its hero , it remains brightly optimistic , coming through in the end . \nas a thoughtful and unflinching examination of an alternative lifestyle , sex with strangers is a success . \nunpretentious , charming , quirky , original\nspinning a web of dazzling entertainment may be overstating it , but \" spider-man \" certainly delivers the goods . \nother than the slightly flawed ( and fairly unbelievable ) finale , everything else is top shelf . \nthis fascinating look at israel in ferment feels as immediate as the latest news footage from gaza and , because of its heightened , well-shaped dramas , twice as powerful . \nmanages to delight without much of a story . \nthere's no denying that burns is a filmmaker with a bright future ahead of him . \ni have a confession to make : i didn't particularly like e . t . the first time i saw it as a young boy . that is because - damn it ! - i also wanted a little alien as a friend ! \nfairy-tale formula , serves as a paper skeleton for some very good acting , dialogue , comedy , direction and especially charm . \na genuinely funny ensemble comedy that also asks its audience -- in a heartwarming , nonjudgmental kind of way -- to consider what we value in our daily lives . \nthough the aboriginal aspect lends the ending an extraordinary poignancy , and the story itself could be played out in any working class community in the nation . \nan energetic and engaging film that never pretends to be something it isn't . \na violent initiation rite for the audience , as much as it is for angelique , the [opening] dance guarantees karmen's enthronement among the cinema's memorable women . \nan animation landmark as monumental as disney's 1937 breakthrough snow white and the seven dwarfs . \nan entertaining , if ultimately minor , thriller . \nsex with strangers is fascinating . . . \na subtle , poignant picture of goodness that is flawed , compromised and sad . \na wry , affectionate delight . \nthe acting in pauline and paulette is good all round , but what really sets the film apart is debrauwer's refusal to push the easy emotional buttons . \none of those joyous films that leaps over national boundaries and celebrates universal human nature . \na penetrating glimpse into the tissue-thin ego of the stand-up comic . \nkids should have a stirring time at this beautifully drawn movie . and adults will at least have a dream image of the west to savor whenever the film's lamer instincts are in the saddle . \npaid in full is remarkably engaging despite being noticeably derivative of goodfellas and at least a half dozen other trouble-in-the-ghetto flicks . \nless cinematically powerful than quietly and deeply moving , which is powerful in itself . \nwaydowntown manages to nail the spirit-crushing ennui of denuded urban living without giving in to it . \neach of these stories has the potential for touched by an angel simplicity and sappiness , but thirteen conversations about one thing , for all its generosity and optimism , never resorts to easy feel-good sentiments . \nif borstal boy isn't especially realistic , it is an engaging nostalgia piece . \noften demented in a good way , but it is an uneven film for the most part . \nthe script's snazzy dialogue establishes a realistic atmosphere that involves us in the unfolding crisis , but the lazy plotting ensures that little of our emotional investment pays off . \nmaggie smith as the ya-ya member with the o2-tank will absolutely crack you up with her crass , then gasp for gas , verbal deportment . \nthis is a movie that refreshes the mind and spirit along with the body , so original is its content , look , and style . \nalthough i didn't hate this one , it's not very good either . it can be safely recommended as a video/dvd babysitter . \nanother best of the year selection . \nthe film has the high-buffed gloss and high-octane jolts you expect of de palma , but what makes it transporting is that it's also one of the smartest , most pleasurable expressions of pure movie love to come from an american director in years . \nit's a very valuable film . . . \nmax pokes , provokes , takes expressionistic license and hits a nerve . . . as far as art is concerned , it's mission accomplished . \nliterary purists may not be pleased , but as far as mainstream matinee-style entertainment goes , it does a bang-up job of pleasing the crowds . \nhere polanski looks back on those places he saw at childhood , and captures them by freeing them from artefact , and by showing them heartbreakingly drably . \nintriguing and stylish . \nthe story itself it mostly told through on-camera interviews with several survivors , whose riveting memories are rendered with such clarity that it's as if it all happened only yesterday . \na compelling story of musical passion against governmental odds . \nwith \" ichi the killer \" , takashi miike , japan's wildest filmmaker gives us a crime fighter carrying more emotional baggage than batman . . . \nyou never know where changing lanes is going to take you but it's a heck of a ride . samuel l . jackson is one of the best actors there is . \n[breheny's] lensing of the new zealand and cook island locations captures both the beauty of the land and the people . \nan almost unbearably morbid love story . \nthe wild thornberrys movie has all the sibling rivalry and general family chaos to which anyone can relate . \na forceful drama of an alienated executive who re-invents himself . \nspielberg's realization of a near-future america is masterful . this makes minority report necessary viewing for sci-fi fans , as the film has some of the best special effects ever . \nthe gags that fly at such a furiously funny pace that the only rip off that we were aware of was the one we felt when the movie ended so damned soon . \nthe best film of the year 2002 . \nan enthralling , entertaining feature . \nstripped almost entirely of such tools as nudity , profanity and violence , labute does manage to make a few points about modern man and his problematic quest for human connection . \na remarkable movie with an unsatisfying ending , which is just the point . \nall in all , brown sugar is a satisfying well-made romantic comedy that's both charming and well acted . it will guarantee to have you leaving the theater with a smile on your face . \nsmith finds amusing juxtapositions that justify his exercise . \nworking from a surprisingly sensitive script co-written by gianni romoli . . . ozpetek avoids most of the pitfalls you'd expect in such a potentially sudsy set-up . \nan older cad instructs a younger lad in zen and the art of getting laid in this prickly indie comedy of manners and misanthropy . \n \" austin powers in goldmember \" has the right stuff for silly summer entertainment and has enough laughs to sustain interest to the end . \none of [jaglom's] better efforts -- a wry and sometime bitter movie about love . \nschaeffer isn't in this film , which may be why it works as well as it does . \na fresh , entertaining comedy that looks at relationships minus traditional gender roles . \nalthough estela bravo's documentary is cloyingly hagiographic in its portrait of cuban leader fidel castro , it's still a guilty pleasure to watch . \nsurprisingly , the film is a hilarious adventure and i shamelessly enjoyed it . \nthe way home is an ode to unconditional love and compassion garnered from years of seeing it all , a condition only the old are privy to , and . . . often misconstrued as weakness . \nbrutally honest and told with humor and poignancy , which makes its message resonate . \nif you can read the subtitles ( the opera is sung in italian ) and you like 'masterpiece theatre' type costumes , you'll enjoy this movie . \na pretty funny movie , with most of the humor coming , as before , from the incongruous but chemically perfect teaming of crystal and de niro . \ngangster no . 1 is solid , satisfying fare for adults . \nthis chicago has hugely imaginative and successful casting to its great credit , as well as one terrific score and attitude to spare . \nhas enough gun battles and throwaway humor to cover up the yawning chasm where the plot should be . \nwith its jerky hand-held camera and documentary feel , bloody sunday is a sobering recount of a very bleak day in derry . \nyou will likely prefer to keep on watching . \ninsomnia loses points when it surrenders to a formulaic bang-bang , shoot-em-up scene at the conclusion . but the performances of pacino , williams , and swank keep the viewer wide-awake all the way through . \nwhat might have been readily dismissed as the tiresome rant of an aging filmmaker still thumbing his nose at convention takes a surprising , subtle turn at the midway point . \nat a time when commercialism has squeezed the life out of whatever idealism american moviemaking ever had , godfrey reggio's career shines like a lonely beacon . \nan inuit masterpiece that will give you goosebumps as its uncanny tale of love , communal discord , and justice unfolds . \nthis is popcorn movie fun with equal doses of action , cheese , ham and cheek ( as well as a serious debt to the road warrior ) , but it feels like unrealized potential\nit's a testament to de niro and director michael caton-jones that by movie's end , we accept the characters and the film , flaws and all . \nperformances are potent , and the women's stories are ably intercut and involving . \nan enormously entertaining movie , like nothing we've ever seen before , and yet completely familiar . \nlan yu is a genuine love story , full of traditional layers of awakening and ripening and separation and recovery . \nyour children will be occupied for 72 minutes . \npull[s] off the rare trick of recreating not only the look of a certain era , but also the feel . \ntwohy's a good yarn-spinner , and ultimately the story compels . \n'tobey maguire is a poster boy for the geek generation . '\n . . . a sweetly affecting story about four sisters who are coping , in one way or another , with life's endgame . \npassion , melodrama , sorrow , laugther , and tears cascade over the screen effortlessly . . . \nroad to perdition does display greatness , and it's worth seeing . but it also comes with the laziness and arrogance of a thing that already knows it's won . \na marvelous performance by allison lohman as an identity-seeking foster child . \narliss howard's ambitious , moving , and adventurous directorial debut , big bad love , meets so many of the challenges it poses for itself that one can forgive the film its flaws . \ncritics need a good laugh , too , and this too-extreme-for-tv rendition of the notorious mtv show delivers the outrageous , sickening , sidesplitting goods in steaming , visceral heaps . \nwhat a dumb , fun , curiously adolescent movie this is . \nmany insightful moments . \nthe charms of the lead performances allow us to forget most of the film's problems . \na vivid , sometimes surreal , glimpse into the mysteries of human behavior . \na tour de force of modern cinema . \nperalta captures , in luminous interviews and amazingly evocative film from three decades ago , the essence of the dogtown experience . \nthe lively appeal of the last kiss lies in the ease with which it integrates thoughtfulness and pasta-fagioli comedy . \nwithout resorting to camp or parody , haynes ( like sirk , but differently ) has transformed the rhetoric of hollywood melodrama into something provocative , rich , and strange . \nthe performances are an absolute joy . \na quasi-documentary by french filmmaker karim dridi that celebrates the hardy spirit of cuban music . \ngrant carries the day with impeccable comic timing , raffish charm and piercing intellect . \na sensitive and astute first feature by anne-sophie birot . \nboth exuberantly romantic and serenely melancholy , what time is it there ? may prove to be [tsai's] masterpiece . \nmazel tov to a film about a family's joyous life acting on the yiddish stage . \nstanding in the shadows of motown is the best kind of documentary , one that makes a depleted yesterday feel very much like a brand-new tomorrow . \nit's nice to see piscopo again after all these years , and chaykin and headly are priceless . \nprovides a porthole into that noble , trembling incoherence that defines us all . \n"
  },
  {
    "path": "deep-learning/keras-tutorial/data_helpers.py",
    "content": "import numpy as np\nimport re\nimport itertools\nfrom collections import Counter\n\"\"\"\nOriginal taken from https://github.com/dennybritz/cnn-text-classification-tf\n\"\"\"\n\ndef clean_str(string):\n    \"\"\"\n    Tokenization/string cleaning for all datasets except for SST.\n    Original taken from https://github.com/yoonkim/CNN_sentence/blob/master/process_data.py\n    \"\"\"\n    string = re.sub(r\"[^A-Za-z0-9(),!?\\'\\`]\", \" \", string)\n    string = re.sub(r\"\\'s\", \" \\'s\", string)\n    string = re.sub(r\"\\'ve\", \" \\'ve\", string)\n    string = re.sub(r\"n\\'t\", \" n\\'t\", string)\n    string = re.sub(r\"\\'re\", \" \\'re\", string)\n    string = re.sub(r\"\\'d\", \" \\'d\", string)\n    string = re.sub(r\"\\'ll\", \" \\'ll\", string)\n    string = re.sub(r\",\", \" , \", string)\n    string = re.sub(r\"!\", \" ! \", string)\n    string = re.sub(r\"\\(\", \" \\( \", string)\n    string = re.sub(r\"\\)\", \" \\) \", string)\n    string = re.sub(r\"\\?\", \" \\? \", string)\n    string = re.sub(r\"\\s{2,}\", \" \", string)\n    return string.strip().lower()\n\n\ndef load_data_and_labels():\n    \"\"\"\n    Loads MR polarity data from files, splits the data into words and generates labels.\n    Returns split sentences and labels.\n    \"\"\"\n    # Load data from files\n    positive_examples = list(open(\"./data/rt-polarity.pos\", encoding='ISO-8859-1').readlines())\n    positive_examples = [s.strip() for s in positive_examples]\n    negative_examples = list(open(\"./data/rt-polarity.neg\", encoding='ISO-8859-1').readlines())\n    negative_examples = [s.strip() for s in negative_examples]\n    # Split by words\n    x_text = positive_examples + negative_examples\n    x_text = [clean_str(sent) for sent in x_text]\n    x_text = [s.split(\" \") for s in x_text]\n    # Generate labels\n    positive_labels = [[0, 1] for _ in positive_examples]\n    negative_labels = [[1, 0] for _ in negative_examples]\n    y = np.concatenate([positive_labels, negative_labels], 0)\n    return [x_text, y]\n\n\ndef pad_sentences(sentences, padding_word=\"<PAD/>\"):\n    \"\"\"\n    Pads all sentences to the same length. The length is defined by the longest sentence.\n    Returns padded sentences.\n    \"\"\"\n    sequence_length = max(len(x) for x in sentences)\n    padded_sentences = []\n    for i in range(len(sentences)):\n        sentence = sentences[i]\n        num_padding = sequence_length - len(sentence)\n        new_sentence = sentence + [padding_word] * num_padding\n        padded_sentences.append(new_sentence)\n    return padded_sentences\n\n\ndef build_vocab(sentences):\n    \"\"\"\n    Builds a vocabulary mapping from word to index based on the sentences.\n    Returns vocabulary mapping and inverse vocabulary mapping.\n    \"\"\"\n    # Build vocabulary\n    word_counts = Counter(itertools.chain(*sentences))\n    # Mapping from index to word\n    vocabulary_inv = [x[0] for x in word_counts.most_common()]\n    # Mapping from word to index\n    vocabulary = {x: i for i, x in enumerate(vocabulary_inv)}\n    return [vocabulary, vocabulary_inv]\n\n\ndef build_input_data(sentences, labels, vocabulary):\n    \"\"\"\n    Maps sentencs and labels to vectors based on a vocabulary.\n    \"\"\"\n    x = np.array([[vocabulary[word] for word in sentence] for sentence in sentences])\n    y = np.array(labels)\n    return [x, y]\n\n\ndef load_data():\n    \"\"\"\n    Loads and preprocessed data for the MR dataset.\n    Returns input vectors, labels, vocabulary, and inverse vocabulary.\n    \"\"\"\n    # Load and preprocess data\n    sentences, labels = load_data_and_labels()\n    sentences_padded = pad_sentences(sentences)\n    vocabulary, vocabulary_inv = build_vocab(sentences_padded)\n    x, y = build_input_data(sentences_padded, labels, vocabulary)\n    return [x, y, vocabulary, vocabulary_inv]\n\n\ndef batch_iter(data, batch_size, num_epochs):\n    \"\"\"\n    Generates a batch iterator for a dataset.\n    \"\"\"\n    data = np.array(data)\n    data_size = len(data)\n    num_batches_per_epoch = int(len(data)/batch_size) + 1\n    for epoch in range(num_epochs):\n        # Shuffle the data at each epoch\n        shuffle_indices = np.random.permutation(np.arange(data_size))\n        shuffled_data = data[shuffle_indices]\n        for batch_num in range(num_batches_per_epoch):\n            start_index = batch_num * batch_size\n            end_index = min((batch_num + 1) * batch_size, data_size)\n            yield shuffled_data[start_index:end_index]"
  },
  {
    "path": "deep-learning/keras-tutorial/deep-learning-osx.yml",
    "content": "name: deep-learning\nchannels:\n- conda-forge\n- defaults\ndependencies:\n- accelerate=2.3.0=np111py35_3\n- accelerate_cudalib=2.0=0\n- appnope=0.1.0=py35_0\n- bokeh=0.12.1=py35_0\n- cffi=1.6.0=py35_0\n- backports.shutil_get_terminal_size=1.0.0=py35_0\n- blas=1.1=openblas\n- ca-certificates=2016.8.2=3\n- certifi=2016.8.2=py35_0\n- cycler=0.10.0=py35_0\n- cython=0.24.1=py35_0\n- decorator=4.0.10=py35_0\n- entrypoints=0.2.2=py35_0\n- freetype=2.6.3=1\n- h5py=2.6.0=np111py35_6\n- hdf5=1.8.17=2\n- ipykernel=4.3.1=py35_1\n- ipython=5.1.0=py35_0\n- ipywidgets=5.2.2=py35_0\n- jinja2=2.8=py35_1\n- jsonschema=2.5.1=py35_0\n- jupyter_client=4.3.0=py35_0\n- jupyter_console=5.0.0=py35_0\n- jupyter_core=4.1.1=py35_1\n- libgfortran=3.0.0=0\n- libpng=1.6.24=0\n- libsodium=1.0.10=0\n- markupsafe=0.23=py35_0\n- matplotlib=1.5.2=np111py35_5\n- mistune=0.7.3=py35_0\n- nbconvert=4.2.0=py35_0\n- nbformat=4.0.1=py35_0\n- ncurses=5.9=8\n- nose=1.3.7=py35_1\n- notebook=4.2.2=py35_0\n- numpy=1.11.1=py35_blas_openblas_201\n- openblas=0.2.18=4\n- openssl=1.0.2h=2\n- pandas=0.18.1=np111py35_1\n- pexpect=4.2.0=py35_1\n- pickleshare=0.7.3=py35_0\n- pip=8.1.2=py35_0\n- prompt_toolkit=1.0.6=py35_0\n- ptyprocess=0.5.1=py35_0\n- pygments=2.1.3=py35_1\n- pyparsing=2.1.7=py35_0\n- python=3.5.2=2\n- python-dateutil=2.5.3=py35_0\n- pytz=2016.6.1=py35_0\n- pyyaml=3.11=py35_0\n- pyzmq=15.4.0=py35_0\n- qtconsole=4.2.1=py35_0\n- readline=6.2=0\n- requests=2.11.0=py35_0\n- scikit-learn=0.17.1=np111py35_blas_openblas_201\n- scipy=0.18.0=np111py35_blas_openblas_201\n- setuptools=25.1.6=py35_0\n- simplegeneric=0.8.1=py35_0\n- sip=4.18=py35_0\n- six=1.10.0=py35_0\n- sqlite=3.13.0=1\n- terminado=0.6=py35_0\n- tk=8.5.19=0\n- tornado=4.4.1=py35_1\n- traitlets=4.2.2=py35_0\n- wcwidth=0.1.7=py35_0\n- wheel=0.29.0=py35_0\n- widgetsnbextension=1.2.6=py35_3\n- xz=5.2.2=0\n- yaml=0.1.6=0\n- zeromq=4.1.5=0\n- zlib=1.2.8=3\n- cudatoolkit=7.5=0\n- ipython_genutils=0.1.0=py35_0\n- jupyter=1.0.0=py35_3\n- llvmlite=0.11.0=py35_0\n- mkl=11.3.3=0\n- mkl-service=1.1.2=py35_2\n- numba=0.26.0=np111py35_0\n- pycparser=2.14=py35_1\n- pyqt=4.11.4=py35_4\n- python.app=1.2=py35_4\n- qt=4.8.7=4\n- snakeviz=0.4.1=py35_0\n- pip:\n  - backports.shutil-get-terminal-size==1.0.0\n  - certifi==2016.8.2\n  - cycler==0.10.0\n  - cython==0.24.1\n  - decorator==4.0.10\n  - h5py==2.6.0\n  - ipykernel==4.3.1\n  - ipython==5.1.0\n  - ipython-genutils==0.1.0\n  - ipywidgets==5.2.2\n  - jinja2==2.8\n  - jsonschema==2.5.1\n  - jupyter-client==4.3.0\n  - jupyter-console==5.0.0\n  - jupyter-core==4.1.1\n  - keras==1.0.7\n  - markupsafe==0.23\n  - matplotlib==1.5.2\n  - mistune==0.7.3\n  - nbconvert==4.2.0\n  - nbformat==4.0.1\n  - nose==1.3.7\n  - notebook==4.2.2\n  - numpy==1.11.1\n  - pandas==0.18.1\n  - pexpect==4.2.0\n  - pickleshare==0.7.3\n  - pip==8.1.2\n  - prompt-toolkit==1.0.6\n  - ptyprocess==0.5.1\n  - pygments==2.1.3\n  - pyparsing==2.1.7\n  - python-dateutil==2.5.3\n  - pytz==2016.6.1\n  - pyyaml==3.11\n  - pyzmq==15.4.0\n  - qtconsole==4.2.1\n  - requests==2.11.0\n  - scikit-learn==0.17.1\n  - scipy==0.18.0\n  - setuptools==25.1.6\n  - simplegeneric==0.8.1\n  - six==1.10.0\n  - terminado==0.6\n  - theano==0.8.2\n  - tornado==4.4.1\n  - traitlets==4.2.2\n  - wcwidth==0.1.7\n  - wheel==0.29.0\n  - widgetsnbextension==1.2.6\nprefix: /Users/valerio/anaconda/envs/deep-learning\n\n"
  },
  {
    "path": "deep-learning/keras-tutorial/deep-learning.yml",
    "content": "name: deep-learning\nchannels:\n- conda-forge\n- defaults\ndependencies:\n- accelerate=2.3.0=np111py35_3\n- accelerate_cudalib=2.0=0\n- bokeh=0.12.1=py35_0\n- cffi=1.6.0=py35_0\n- backports.shutil_get_terminal_size=1.0.0=py35_0\n- blas=1.1=openblas\n- ca-certificates=2016.8.2=3\n- cairo=1.12.18=8\n- certifi=2016.8.2=py35_0\n- cycler=0.10.0=py35_0\n- cython=0.24.1=py35_0\n- decorator=4.0.10=py35_0\n- entrypoints=0.2.2=py35_0\n- fontconfig=2.11.1=3\n- freetype=2.6.3=1\n- gettext=0.19.7=1\n- glib=2.48.0=4\n- h5py=2.6.0=np111py35_6\n- harfbuzz=1.0.6=0\n- hdf5=1.8.17=2\n- icu=56.1=4\n- ipykernel=4.3.1=py35_1\n- ipython=5.1.0=py35_0\n- ipywidgets=5.2.2=py35_0\n- jinja2=2.8=py35_1\n- jpeg=9b=0\n- jsonschema=2.5.1=py35_0\n- jupyter_client=4.3.0=py35_0\n- jupyter_console=5.0.0=py35_0\n- jupyter_core=4.1.1=py35_1\n- libffi=3.2.1=2\n- libiconv=1.14=3\n- libpng=1.6.24=0\n- libsodium=1.0.10=0\n- libtiff=4.0.6=6\n- libxml2=2.9.4=0\n- markupsafe=0.23=py35_0\n- matplotlib=1.5.2=np111py35_6\n- mistune=0.7.3=py35_0\n- nbconvert=4.2.0=py35_0\n- nbformat=4.0.1=py35_0\n- ncurses=5.9=8\n- nose=1.3.7=py35_1\n- notebook=4.2.2=py35_0\n- numpy=1.11.1=py35_blas_openblas_201\n- openblas=0.2.18=4\n- openssl=1.0.2h=2\n- pandas=0.18.1=np111py35_1\n- pango=1.40.1=0\n- path.py=8.2.1=py35_0\n- pcre=8.38=1\n- pexpect=4.2.0=py35_1\n- pickleshare=0.7.3=py35_0\n- pip=8.1.2=py35_0\n- pixman=0.32.6=0\n- prompt_toolkit=1.0.6=py35_0\n- protobuf=3.0.0b3=py35_1\n- ptyprocess=0.5.1=py35_0\n- pygments=2.1.3=py35_1\n- pyparsing=2.1.7=py35_0\n- python=3.5.2=2\n- python-dateutil=2.5.3=py35_0\n- pytz=2016.6.1=py35_0\n- pyyaml=3.11=py35_0\n- pyzmq=15.4.0=py35_0\n- qt=4.8.7=0\n- qtconsole=4.2.1=py35_0\n- readline=6.2=0\n- requests=2.11.0=py35_0\n- scikit-learn=0.17.1=np111py35_blas_openblas_201\n- scipy=0.18.0=np111py35_blas_openblas_201\n- setuptools=25.1.6=py35_0\n- simplegeneric=0.8.1=py35_0\n- sip=4.18=py35_0\n- six=1.10.0=py35_0\n- sqlite=3.13.0=1\n- terminado=0.6=py35_0\n- tk=8.5.19=0\n- tornado=4.4.1=py35_1\n- traitlets=4.2.2=py35_0\n- wcwidth=0.1.7=py35_0\n- wheel=0.29.0=py35_0\n- widgetsnbextension=1.2.6=py35_3\n- xz=5.2.2=0\n- yaml=0.1.6=0\n- zeromq=4.1.5=0\n- zlib=1.2.8=3\n- cudatoolkit=7.5=0\n- ipython_genutils=0.1.0=py35_0\n- jupyter=1.0.0=py35_3\n- libgfortran=3.0.0=1\n- llvmlite=0.11.0=py35_0\n- mkl=11.3.3=0\n- mkl-service=1.1.2=py35_2\n- numba=0.26.0=np111py35_0\n- pycparser=2.14=py35_1\n- pyqt=4.11.4=py35_4\n- snakeviz=0.4.1=py35_0\n- pip:\n  - backports.shutil-get-terminal-size==1.0.0\n  - certifi==2016.8.2\n  - cycler==0.10.0\n  - cython==0.24.1\n  - decorator==4.0.10\n  - h5py==2.6.0\n  - ipykernel==4.3.1\n  - ipython==5.1.0\n  - ipython-genutils==0.1.0\n  - ipywidgets==5.2.2\n  - jinja2==2.8\n  - jsonschema==2.5.1\n  - jupyter-client==4.3.0\n  - jupyter-console==5.0.0\n  - jupyter-core==4.1.1\n  - keras==1.0.7\n  - mako==1.0.4\n  - markupsafe==0.23\n  - matplotlib==1.5.2\n  - mistune==0.7.3\n  - nbconvert==4.2.0\n  - nbformat==4.0.1\n  - nose==1.3.7\n  - notebook==4.2.2\n  - numpy==1.11.1\n  - pandas==0.18.1\n  - path.py==8.2.1\n  - pexpect==4.2.0\n  - pickleshare==0.7.3\n  - pip==8.1.2\n  - prompt-toolkit==1.0.6\n  - protobuf==3.0.0b2\n  - ptyprocess==0.5.1\n  - pygments==2.1.3\n  - pyparsing==2.1.7\n  - python-dateutil==2.5.3\n  - pytz==2016.6.1\n  - pyyaml==3.11\n  - pyzmq==15.4.0\n  - qtconsole==4.2.1\n  - requests==2.11.0\n  - scikit-learn==0.17.1\n  - scipy==0.18.0\n  - setuptools==25.1.4\n  - simplegeneric==0.8.1\n  - six==1.10.0\n  - terminado==0.6\n  - theano==0.8.2\n  - tornado==4.4.1\n  - traitlets==4.2.2\n  - wcwidth==0.1.7\n  - wheel==0.29.0\n  - widgetsnbextension==1.2.6\nprefix: /home/valerio/anaconda3/envs/deep-learning\n\n"
  },
  {
    "path": "deep-learning/keras-tutorial/deep_learning_models/LICENSE",
    "content": "MIT License\n\nCopyright (c) 2016 François Chollet\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n"
  },
  {
    "path": "deep-learning/keras-tutorial/deep_learning_models/README.md",
    "content": "# Trained image classification models for Keras\n\nThis repository contains code for the following Keras models:\n\n- VGG16\n- VGG19\n- ResNet50\n\nWe plan on adding Inception v3 soon.\n\nAll architectures are compatible with both TensorFlow and Theano, and upon instantiation the models will be built according to the image dimension ordering set in your Keras configuration file at `~/.keras/keras.json`. For instance, if you have set `image_dim_ordering=tf`, then any model loaded from this repository will get built according to the TensorFlow dimension ordering convention, \"Width-Height-Depth\".\n\nWeights can be automatically loaded upon instantiation (`weights='imagenet'` argument in model constructor). Weights are automatically downloaded if necessary, and cached locally in `~/.keras/models/`.\n\n**Note that using these models requires the latest version of Keras (from the Github repo, not PyPI).**\n\n## Examples\n\n### Classify images\n\n```python\nfrom resnet50 import ResNet50\nfrom keras.preprocessing import image\nfrom imagenet_utils import preprocess_input, decode_predictions\n\nmodel = ResNet50(weights='imagenet')\n\nimg_path = 'elephant.jpg'\nimg = image.load_img(img_path, target_size=(224, 224))\nx = image.img_to_array(img)\nx = np.expand_dims(x, axis=0)\nx = preprocess_input(x)\n\npreds = model.predict(x)\nprint('Predicted:', decode_predictions(preds))\n# print: [[u'n02504458', u'African_elephant']]\n```\n\n### Extract features from images\n\n```python\nfrom vgg16 import VGG16\nfrom keras.preprocessing import image\nfrom imagenet_utils import preprocess_input\n\nmodel = VGG16(weights='imagenet', include_top=False)\n\nimg_path = 'elephant.jpg'\nimg = image.load_img(img_path, target_size=(224, 224))\nx = image.img_to_array(img)\nx = np.expand_dims(x, axis=0)\nx = preprocess_input(x)\n\nfeatures = model.predict(x)\n```\n\n### Extract features from an arbitrary intermediate layer\n\n```python\nfrom vgg19 import VGG19\nfrom keras.preprocessing import image\nfrom imagenet_utils import preprocess_input\nfrom keras.models import Model\n\nbase_model = VGG19(weights='imagenet')\nmodel = Model(input=base_model.input, output=base_model.get_layer('block4_pool').output)\n\nimg_path = 'elephant.jpg'\nimg = image.load_img(img_path, target_size=(224, 224))\nx = image.img_to_array(img)\nx = np.expand_dims(x, axis=0)\nx = preprocess_input(x)\n\nblock4_pool_features = model.predict(x)\n```\n\n## References\n\n- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://arxiv.org/abs/1409.1556) - please cite this paper if you use the VGG models in your work.\n- [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) - please cite this paper if you use the ResNet model in your work.\n\nAdditionally, don't forget to [cite Keras](https://keras.io/getting-started/faq/#how-should-i-cite-keras) if you use these models.\n\n\n## License\n\n- All code in this repository is under the MIT license as specified by the LICENSE file.\n- The ResNet50 weights are ported from the ones [released by Kaiming He](https://github.com/KaimingHe/deep-residual-networks) under the [MIT license](https://github.com/KaimingHe/deep-residual-networks/blob/master/LICENSE).\n- The VGG16 and VGG19 weights are ported from the ones [released by VGG at Oxford](http://www.robots.ox.ac.uk/~vgg/research/very_deep/) under the [Creative Commons Attribution License](https://creativecommons.org/licenses/by/4.0/)."
  },
  {
    "path": "deep-learning/keras-tutorial/deep_learning_models/imagenet_utils.py",
    "content": "import numpy as np\nimport json\n\nfrom keras.utils.data_utils import get_file\nfrom keras import backend as K\n\nCLASS_INDEX = None\nCLASS_INDEX_PATH = 'https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json'\n\n\ndef preprocess_input(x, dim_ordering='default'):\n    if dim_ordering == 'default':\n        dim_ordering = K.image_dim_ordering()\n    assert dim_ordering in {'tf', 'th'}\n\n    if dim_ordering == 'th':\n        x[:, 0, :, :] -= 103.939\n        x[:, 1, :, :] -= 116.779\n        x[:, 2, :, :] -= 123.68\n        # 'RGB'->'BGR'\n        x = x[:, ::-1, :, :]\n    else:\n        x[:, :, :, 0] -= 103.939\n        x[:, :, :, 1] -= 116.779\n        x[:, :, :, 2] -= 123.68\n        # 'RGB'->'BGR'\n        x = x[:, :, :, ::-1]\n    return x\n\n\ndef decode_predictions(preds):\n    global CLASS_INDEX\n    assert len(preds.shape) == 2 and preds.shape[1] == 1000\n    if CLASS_INDEX is None:\n        fpath = get_file('imagenet_class_index.json',\n                         CLASS_INDEX_PATH,\n                         cache_subdir='models')\n        CLASS_INDEX = json.load(open(fpath))\n    indices = np.argmax(preds, axis=-1)\n    results = []\n    for i in indices:\n        results.append(CLASS_INDEX[str(i)])\n    return results\n"
  },
  {
    "path": "deep-learning/keras-tutorial/deep_learning_models/resnet50.py",
    "content": "# -*- coding: utf-8 -*-\n'''ResNet50 model for Keras.\n\n# Reference:\n\n- [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385)\n\nAdapted from code contributed by BigMoyan.\n'''\nfrom __future__ import print_function\n\nimport numpy as np\nimport warnings\n\nfrom keras.layers import merge, Input\nfrom keras.layers import Dense, Activation, Flatten\nfrom keras.layers import Convolution2D, MaxPooling2D, ZeroPadding2D, AveragePooling2D\nfrom keras.layers import BatchNormalization\nfrom keras.models import Model\nfrom keras.preprocessing import image\nimport keras.backend as K\nfrom keras.utils.layer_utils import convert_all_kernels_in_model\nfrom keras.utils.data_utils import get_file\n\n\nTH_WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/resnet50_weights_th_dim_ordering_th_kernels.h5'\nTF_WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/resnet50_weights_tf_dim_ordering_tf_kernels.h5'\nTH_WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/resnet50_weights_th_dim_ordering_th_kernels_notop.h5'\nTF_WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5'\n\n\ndef identity_block(input_tensor, kernel_size, filters, stage, block):\n    '''The identity_block is the block that has no conv layer at shortcut\n\n    # Arguments\n        input_tensor: input tensor\n        kernel_size: defualt 3, the kernel size of middle conv layer at main path\n        filters: list of integers, the nb_filters of 3 conv layer at main path\n        stage: integer, current stage label, used for generating layer names\n        block: 'a','b'..., current block label, used for generating layer names\n    '''\n    nb_filter1, nb_filter2, nb_filter3 = filters\n    if K.image_dim_ordering() == 'tf':\n        bn_axis = 3\n    else:\n        bn_axis = 1\n    conv_name_base = 'res' + str(stage) + block + '_branch'\n    bn_name_base = 'bn' + str(stage) + block + '_branch'\n\n    x = Convolution2D(nb_filter1, 1, 1, name=conv_name_base + '2a')(input_tensor)\n    x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x)\n    x = Activation('relu')(x)\n\n    x = Convolution2D(nb_filter2, kernel_size, kernel_size,\n                      border_mode='same', name=conv_name_base + '2b')(x)\n    x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x)\n    x = Activation('relu')(x)\n\n    x = Convolution2D(nb_filter3, 1, 1, name=conv_name_base + '2c')(x)\n    x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x)\n\n    x = merge([x, input_tensor], mode='sum')\n    x = Activation('relu')(x)\n    return x\n\n\ndef conv_block(input_tensor, kernel_size, filters, stage, block, strides=(2, 2)):\n    '''conv_block is the block that has a conv layer at shortcut\n\n    # Arguments\n        input_tensor: input tensor\n        kernel_size: defualt 3, the kernel size of middle conv layer at main path\n        filters: list of integers, the nb_filters of 3 conv layer at main path\n        stage: integer, current stage label, used for generating layer names\n        block: 'a','b'..., current block label, used for generating layer names\n\n    Note that from stage 3, the first conv layer at main path is with subsample=(2,2)\n    And the shortcut should have subsample=(2,2) as well\n    '''\n    nb_filter1, nb_filter2, nb_filter3 = filters\n    if K.image_dim_ordering() == 'tf':\n        bn_axis = 3\n    else:\n        bn_axis = 1\n    conv_name_base = 'res' + str(stage) + block + '_branch'\n    bn_name_base = 'bn' + str(stage) + block + '_branch'\n\n    x = Convolution2D(nb_filter1, 1, 1, subsample=strides,\n                      name=conv_name_base + '2a')(input_tensor)\n    x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x)\n    x = Activation('relu')(x)\n\n    x = Convolution2D(nb_filter2, kernel_size, kernel_size, border_mode='same',\n                      name=conv_name_base + '2b')(x)\n    x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x)\n    x = Activation('relu')(x)\n\n    x = Convolution2D(nb_filter3, 1, 1, name=conv_name_base + '2c')(x)\n    x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x)\n\n    shortcut = Convolution2D(nb_filter3, 1, 1, subsample=strides,\n                             name=conv_name_base + '1')(input_tensor)\n    shortcut = BatchNormalization(axis=bn_axis, name=bn_name_base + '1')(shortcut)\n\n    x = merge([x, shortcut], mode='sum')\n    x = Activation('relu')(x)\n    return x\n\n\ndef ResNet50(include_top=True, weights='imagenet',\n             input_tensor=None):\n    '''Instantiate the ResNet50 architecture,\n    optionally loading weights pre-trained\n    on ImageNet. Note that when using TensorFlow,\n    for best performance you should set\n    `image_dim_ordering=\"tf\"` in your Keras config\n    at ~/.keras/keras.json.\n\n    The model and the weights are compatible with both\n    TensorFlow and Theano. The dimension ordering\n    convention used by the model is the one\n    specified in your Keras config file.\n\n    # Arguments\n        include_top: whether to include the 3 fully-connected\n            layers at the top of the network.\n        weights: one of `None` (random initialization)\n            or \"imagenet\" (pre-training on ImageNet).\n        input_tensor: optional Keras tensor (i.e. xput of `layers.Input()`)\n            to use as image input for the model.\n\n    # Returns\n        A Keras model instance.\n    '''\n    if weights not in {'imagenet', None}:\n        raise ValueError('The `weights` argument should be either '\n                         '`None` (random initialization) or `imagenet` '\n                         '(pre-training on ImageNet).')\n    # Determine proper input shape\n    if K.image_dim_ordering() == 'th':\n        if include_top:\n            input_shape = (3, 224, 224)\n        else:\n            input_shape = (3, None, None)\n    else:\n        if include_top:\n            input_shape = (224, 224, 3)\n        else:\n            input_shape = (None, None, 3)\n\n    if input_tensor is None:\n        img_input = Input(shape=input_shape)\n    else:\n        if not K.is_keras_tensor(input_tensor):\n            img_input = Input(tensor=input_tensor)\n        else:\n            img_input = input_tensor\n    if K.image_dim_ordering() == 'tf':\n        bn_axis = 3\n    else:\n        bn_axis = 1\n\n    x = ZeroPadding2D((3, 3))(img_input)\n    x = Convolution2D(64, 7, 7, subsample=(2, 2), name='conv1')(x)\n    x = BatchNormalization(axis=bn_axis, name='bn_conv1')(x)\n    x = Activation('relu')(x)\n    x = MaxPooling2D((3, 3), strides=(2, 2))(x)\n\n    x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1))\n    x = identity_block(x, 3, [64, 64, 256], stage=2, block='b')\n    x = identity_block(x, 3, [64, 64, 256], stage=2, block='c')\n\n    x = conv_block(x, 3, [128, 128, 512], stage=3, block='a')\n    x = identity_block(x, 3, [128, 128, 512], stage=3, block='b')\n    x = identity_block(x, 3, [128, 128, 512], stage=3, block='c')\n    x = identity_block(x, 3, [128, 128, 512], stage=3, block='d')\n\n    x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a')\n    x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b')\n    x = identity_block(x, 3, [256, 256, 1024], stage=4, block='c')\n    x = identity_block(x, 3, [256, 256, 1024], stage=4, block='d')\n    x = identity_block(x, 3, [256, 256, 1024], stage=4, block='e')\n    x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f')\n\n    x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a')\n    x = identity_block(x, 3, [512, 512, 2048], stage=5, block='b')\n    x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c')\n\n    x = AveragePooling2D((7, 7), name='avg_pool')(x)\n\n    if include_top:\n        x = Flatten()(x)\n        x = Dense(1000, activation='softmax', name='fc1000')(x)\n\n    model = Model(img_input, x)\n\n    # load weights\n    if weights == 'imagenet':\n        print('K.image_dim_ordering:', K.image_dim_ordering())\n        if K.image_dim_ordering() == 'th':\n            if include_top:\n                weights_path = get_file('resnet50_weights_th_dim_ordering_th_kernels.h5',\n                                        TH_WEIGHTS_PATH,\n                                        cache_subdir='models')\n            else:\n                weights_path = get_file('resnet50_weights_th_dim_ordering_th_kernels_notop.h5',\n                                        TH_WEIGHTS_PATH_NO_TOP,\n                                        cache_subdir='models')\n            model.load_weights(weights_path)\n            if K.backend() == 'tensorflow':\n                warnings.warn('You are using the TensorFlow backend, yet you '\n                              'are using the Theano '\n                              'image dimension ordering convention '\n                              '(`image_dim_ordering=\"th\"`). '\n                              'For best performance, set '\n                              '`image_dim_ordering=\"tf\"` in '\n                              'your Keras config '\n                              'at ~/.keras/keras.json.')\n                convert_all_kernels_in_model(model)\n        else:\n            if include_top:\n                weights_path = get_file('resnet50_weights_tf_dim_ordering_tf_kernels.h5',\n                                        TF_WEIGHTS_PATH,\n                                        cache_subdir='models')\n            else:\n                weights_path = get_file('resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5',\n                                        TF_WEIGHTS_PATH_NO_TOP,\n                                        cache_subdir='models')\n            model.load_weights(weights_path)\n            if K.backend() == 'theano':\n                convert_all_kernels_in_model(model)\n    return model\n\n\nif __name__ == '__main__':\n    model = ResNet50(include_top=True, weights='imagenet')\n\n    img_path = 'elephant.jpg'\n    img = image.load_img(img_path, target_size=(224, 224))\n    x = image.img_to_array(img)\n    x = np.expand_dims(x, axis=0)\n    x = preprocess_input(x)\n    print('Input image shape:', x.shape)\n\n    preds = model.predict(x)\n    print('Predicted:', decode_predictions(preds))\n"
  },
  {
    "path": "deep-learning/keras-tutorial/deep_learning_models/vgg16.py",
    "content": "# -*- coding: utf-8 -*-\n'''VGG16 model for Keras.\n\n# Reference:\n\n- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://arxiv.org/abs/1409.1556)\n\n'''\nfrom __future__ import print_function\n\nimport numpy as np\nimport warnings\n\nfrom keras.models import Model\nfrom keras.layers import Flatten, Dense, Input\nfrom keras.layers import Convolution2D, MaxPooling2D\nfrom keras.preprocessing import image\nfrom keras.utils.layer_utils import convert_all_kernels_in_model\nfrom keras.utils.data_utils import get_file\nfrom keras import backend as K\n# from imagenet_utils import decode_predictions, preprocess_input\n\n\nTH_WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_th_dim_ordering_th_kernels.h5'\nTF_WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels.h5'\nTH_WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_th_dim_ordering_th_kernels_notop.h5'\nTF_WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5'\n\n\ndef VGG16(include_top=True, weights='imagenet',\n          input_tensor=None):\n    '''Instantiate the VGG16 architecture,\n    optionally loading weights pre-trained\n    on ImageNet. Note that when using TensorFlow,\n    for best performance you should set\n    `image_dim_ordering=\"tf\"` in your Keras config\n    at ~/.keras/keras.json.\n\n    The model and the weights are compatible with both\n    TensorFlow and Theano. The dimension ordering\n    convention used by the model is the one\n    specified in your Keras config file.\n\n    # Arguments\n        include_top: whether to include the 3 fully-connected\n            layers at the top of the network.\n        weights: one of `None` (random initialization)\n            or \"imagenet\" (pre-training on ImageNet).\n        input_tensor: optional Keras tensor (i.e. output of `layers.Input()`)\n            to use as image input for the model.\n\n    # Returns\n        A Keras model instance.\n    '''\n    if weights not in {'imagenet', None}:\n        raise ValueError('The `weights` argument should be either '\n                         '`None` (random initialization) or `imagenet` '\n                         '(pre-training on ImageNet).')\n    # Determine proper input shape\n    if K.image_dim_ordering() == 'th':\n        if include_top:\n            input_shape = (3, 224, 224)\n        else:\n            input_shape = (3, None, None)\n    else:\n        if include_top:\n            input_shape = (224, 224, 3)\n        else:\n            input_shape = (None, None, 3)\n\n    if input_tensor is None:\n        img_input = Input(shape=input_shape)\n    else:\n        if not K.is_keras_tensor(input_tensor):\n            img_input = Input(tensor=input_tensor)\n        else:\n            img_input = input_tensor\n    # Block 1\n    x = Convolution2D(64, 3, 3, activation='relu', border_mode='same', name='block1_conv1')(img_input)\n    x = Convolution2D(64, 3, 3, activation='relu', border_mode='same', name='block1_conv2')(x)\n    x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)\n\n    # Block 2\n    x = Convolution2D(128, 3, 3, activation='relu', border_mode='same', name='block2_conv1')(x)\n    x = Convolution2D(128, 3, 3, activation='relu', border_mode='same', name='block2_conv2')(x)\n    x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)\n\n    # Block 3\n    x = Convolution2D(256, 3, 3, activation='relu', border_mode='same', name='block3_conv1')(x)\n    x = Convolution2D(256, 3, 3, activation='relu', border_mode='same', name='block3_conv2')(x)\n    x = Convolution2D(256, 3, 3, activation='relu', border_mode='same', name='block3_conv3')(x)\n    x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x)\n\n    # Block 4\n    x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block4_conv1')(x)\n    x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block4_conv2')(x)\n    x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block4_conv3')(x)\n    x = MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)\n\n    # Block 5\n    x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block5_conv1')(x)\n    x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block5_conv2')(x)\n    x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block5_conv3')(x)\n    x = MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x)\n\n    if include_top:\n        # Classification block\n        x = Flatten(name='flatten')(x)\n        x = Dense(4096, activation='relu', name='fc1')(x)\n        x = Dense(4096, activation='relu', name='fc2')(x)\n        x = Dense(1000, activation='softmax', name='predictions')(x)\n\n    # Create model\n    model = Model(img_input, x)\n\n    # load weights\n    if weights == 'imagenet':\n        print('K.image_dim_ordering:', K.image_dim_ordering())\n        if K.image_dim_ordering() == 'th':\n            if include_top:\n                weights_path = get_file('vgg16_weights_th_dim_ordering_th_kernels.h5',\n                                        TH_WEIGHTS_PATH,\n                                        cache_subdir='models')\n            else:\n                weights_path = get_file('vgg16_weights_th_dim_ordering_th_kernels_notop.h5',\n                                        TH_WEIGHTS_PATH_NO_TOP,\n                                        cache_subdir='models')\n            model.load_weights(weights_path)\n            if K.backend() == 'tensorflow':\n                warnings.warn('You are using the TensorFlow backend, yet you '\n                              'are using the Theano '\n                              'image dimension ordering convention '\n                              '(`image_dim_ordering=\"th\"`). '\n                              'For best performance, set '\n                              '`image_dim_ordering=\"tf\"` in '\n                              'your Keras config '\n                              'at ~/.keras/keras.json.')\n                convert_all_kernels_in_model(model)\n        else:\n            if include_top:\n                weights_path = get_file('vgg16_weights_tf_dim_ordering_tf_kernels.h5',\n                                        TF_WEIGHTS_PATH,\n                                        cache_subdir='models')\n            else:\n                weights_path = get_file('vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5',\n                                        TF_WEIGHTS_PATH_NO_TOP,\n                                        cache_subdir='models')\n            model.load_weights(weights_path)\n            if K.backend() == 'theano':\n                convert_all_kernels_in_model(model)\n    return model\n\n\nif __name__ == '__main__':\n    model = VGG16(include_top=True, weights='imagenet')\n\n    img_path = 'elephant.jpg'\n    img = image.load_img(img_path, target_size=(224, 224))\n    x = image.img_to_array(img)\n    x = np.expand_dims(x, axis=0)\n    x = preprocess_input(x)\n    print('Input image shape:', x.shape)\n\n    preds = model.predict(x)\n    print('Predicted:', decode_predictions(preds))\n"
  },
  {
    "path": "deep-learning/keras-tutorial/deep_learning_models/vgg19.py",
    "content": "# -*- coding: utf-8 -*-\n'''VGG19 model for Keras.\n\n# Reference:\n\n- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://arxiv.org/abs/1409.1556)\n\n'''\nfrom __future__ import print_function\n\nimport numpy as np\nimport warnings\n\nfrom keras.models import Model\nfrom keras.layers import Flatten, Dense, Input\nfrom keras.layers import Convolution2D, MaxPooling2D\nfrom keras.preprocessing import image\nfrom keras.utils.layer_utils import convert_all_kernels_in_model\nfrom keras.utils.data_utils import get_file\nfrom keras import backend as K\n\n\nTH_WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg19_weights_th_dim_ordering_th_kernels.h5'\nTF_WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg19_weights_tf_dim_ordering_tf_kernels.h5'\nTH_WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg19_weights_th_dim_ordering_th_kernels_notop.h5'\nTF_WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5'\n\n\ndef VGG19(include_top=True, weights='imagenet',\n          input_tensor=None):\n    '''Instantiate the VGG19 architecture,\n    optionally loading weights pre-trained\n    on ImageNet. Note that when using TensorFlow,\n    for best performance you should set\n    `image_dim_ordering=\"tf\"` in your Keras config\n    at ~/.keras/keras.json.\n\n    The model and the weights are compatible with both\n    TensorFlow and Theano. The dimension ordering\n    convention used by the model is the one\n    specified in your Keras config file.\n\n    # Arguments\n        include_top: whether to include the 3 fully-connected\n            layers at the top of the network.\n        weights: one of `None` (random initialization)\n            or \"imagenet\" (pre-training on ImageNet).\n        input_tensor: optional Keras tensor (i.e. output of `layers.Input()`)\n            to use as image input for the model.\n\n    # Returns\n        A Keras model instance.\n    '''\n    if weights not in {'imagenet', None}:\n        raise ValueError('The `weights` argument should be either '\n                         '`None` (random initialization) or `imagenet` '\n                         '(pre-training on ImageNet).')\n    # Determine proper input shape\n    if K.image_dim_ordering() == 'th':\n        if include_top:\n            input_shape = (3, 224, 224)\n        else:\n            input_shape = (3, None, None)\n    else:\n        if include_top:\n            input_shape = (224, 224, 3)\n        else:\n            input_shape = (None, None, 3)\n\n    if input_tensor is None:\n        img_input = Input(shape=input_shape)\n    else:\n        if not K.is_keras_tensor(input_tensor):\n            img_input = Input(tensor=input_tensor)\n        else:\n            img_input = input_tensor\n    # Block 1\n    x = Convolution2D(64, 3, 3, activation='relu', border_mode='same', name='block1_conv1')(img_input)\n    x = Convolution2D(64, 3, 3, activation='relu', border_mode='same', name='block1_conv2')(x)\n    x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)\n\n    # Block 2\n    x = Convolution2D(128, 3, 3, activation='relu', border_mode='same', name='block2_conv1')(x)\n    x = Convolution2D(128, 3, 3, activation='relu', border_mode='same', name='block2_conv2')(x)\n    x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)\n\n    # Block 3\n    x = Convolution2D(256, 3, 3, activation='relu', border_mode='same', name='block3_conv1')(x)\n    x = Convolution2D(256, 3, 3, activation='relu', border_mode='same', name='block3_conv2')(x)\n    x = Convolution2D(256, 3, 3, activation='relu', border_mode='same', name='block3_conv3')(x)\n    x = Convolution2D(256, 3, 3, activation='relu', border_mode='same', name='block3_conv4')(x)\n    x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x)\n\n    # Block 4\n    x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block4_conv1')(x)\n    x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block4_conv2')(x)\n    x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block4_conv3')(x)\n    x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block4_conv4')(x)\n    x = MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)\n\n    # Block 5\n    x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block5_conv1')(x)\n    x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block5_conv2')(x)\n    x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block5_conv3')(x)\n    x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block5_conv4')(x)\n    x = MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x)\n\n    if include_top:\n        # Classification block\n        x = Flatten(name='flatten')(x)\n        x = Dense(4096, activation='relu', name='fc1')(x)\n        x = Dense(4096, activation='relu', name='fc2')(x)\n        x = Dense(1000, activation='softmax', name='predictions')(x)\n\n    # Create model\n    model = Model(img_input, x)\n\n    # load weights\n    if weights == 'imagenet':\n        print('K.image_dim_ordering:', K.image_dim_ordering())\n        if K.image_dim_ordering() == 'th':\n            if include_top:\n                weights_path = get_file('vgg19_weights_th_dim_ordering_th_kernels.h5',\n                                        TH_WEIGHTS_PATH,\n                                        cache_subdir='models')\n            else:\n                weights_path = get_file('vgg19_weights_th_dim_ordering_th_kernels_notop.h5',\n                                        TH_WEIGHTS_PATH_NO_TOP,\n                                        cache_subdir='models')\n            model.load_weights(weights_path)\n            if K.backend() == 'tensorflow':\n                warnings.warn('You are using the TensorFlow backend, yet you '\n                              'are using the Theano '\n                              'image dimension ordering convention '\n                              '(`image_dim_ordering=\"th\"`). '\n                              'For best performance, set '\n                              '`image_dim_ordering=\"tf\"` in '\n                              'your Keras config '\n                              'at ~/.keras/keras.json.')\n                convert_all_kernels_in_model(model)\n        else:\n            if include_top:\n                weights_path = get_file('vgg19_weights_tf_dim_ordering_tf_kernels.h5',\n                                        TF_WEIGHTS_PATH,\n                                        cache_subdir='models')\n            else:\n                weights_path = get_file('vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5',\n                                        TF_WEIGHTS_PATH_NO_TOP,\n                                        cache_subdir='models')\n            model.load_weights(weights_path)\n            if K.backend() == 'theano':\n                convert_all_kernels_in_model(model)\n    return model\n\n\nif __name__ == '__main__':\n    model = VGG19(include_top=True, weights='imagenet')\n\n    img_path = 'cat.jpg'\n    img = image.load_img(img_path, target_size=(224, 224))\n    x = image.img_to_array(img)\n    x = np.expand_dims(x, axis=0)\n    x = preprocess_input(x)\n    print('Input image shape:', x.shape)\n\n    preds = model.predict(x)\n    print('Predicted:', decode_predictions(preds))\n"
  },
  {
    "path": "deep-learning/keras-tutorial/outline.md",
    "content": "# Outline (Draft)\n\n- Part I: Introduction\n\n    - Intro to ANN \n        - (naive pure-Python implementation from `pybrain`)\n        - fast forward\n        - sgd + backprop\n    - Intro to Theano\n        - Model + SGD with Theano (simple logreg) \n        \n    - Introduction to Keras\n        - Overview and main features\n            - Theano backend\n            - Tensorflow backend\n        - Same LogReg with Keras\n\n- Part II: Supervised Learning + Keras Internals\n    - Intro: Focus on Image Classification\n    - Multi-Layer Perceptron and Fully Connected\n        - Examples with `keras.models.Sequential` and `Dense`\n        - HandsOn: MLP with keras \n        \n    - Intro to CNN\n        - meaning of convolutional filters\n            - examples from ImageNet\n        \n        - Meaning of dimensions of Conv filters (through an exmple of ConvNet) \n        - HandsOn: ConvNet with keras \n\n    - Advanced CNN\n        -  Dropout and MaxPooling\n    - Famous ANN in Keras (likely moved somewhere else)\n        - ref: https://github.com/fchollet/deep-learning-models\n        - VGG16\n        - VGG19\n        - LaNet\n        - Inception/GoogleNet\n        - ResNet\n        *Implementation and examples \n        - HandsOn: Fine tuning a network on new dataset \n        \n- Part III: Unsupervised Learning + Keras Internals\n    - AutoEncoders\n    - word2vec & doc2vec (gensim) + `keras.dataset` (i.e. `keras.dataset.imdb`)   \n    - HandsOn: _______\n\n*should we include embedding here? \n\n- Part IV: Advanced Materials\n    - RNN (LSTM)\n        -  RNN, LSTM, GRU  \n        -  Meaning of dimensions of rnn (backprop though time, etc)\n        -  HandsOn: IMDB (?)\n\n    - CNN-RNN\n    - Time Distributed Convolution \n    - Some of the recent advances in DL implemented in Keras\n        - e.g. https://github.com/snf/keras-fractalnet - Fractal Net Implementation with Keras\n\n\nNotes:\n\n1) Please, add more details in Part IV (i.e. /Advanced Materials/)\n2) As for Keras internals, I Would consider this: https://github.com/wuaalb/keras_extensions/blob/master/keras_extensions/rbm.py\nThis is just to show how easy it is to extend Keras ( in this case, properly creating a new `Layer`).\n"
  },
  {
    "path": "deep-learning/keras-tutorial/solutions/sol_111.py",
    "content": "ann = ANN(2, 10, 1)\n%timeit -n 1 -r 1 ann.train(zip(X,y), iterations=2)\nplot_decision_boundary(ann)\nplt.title(\"Our next model with 10 hidden units\")\n"
  },
  {
    "path": "deep-learning/keras-tutorial/solutions/sol_112.py",
    "content": "ann = ANN(2, 10, 1)\n%timeit -n 1 -r 1 ann.train(zip(X,y), iterations=100)\nplot_decision_boundary(ann)\nplt.title(\"Our model with 10 hidden units and 100 iterations\")\n"
  },
  {
    "path": "deep-learning/keras-tutorial/w2v.py",
    "content": "from gensim.models import word2vec\nfrom os.path import join, exists, split\nimport os\nimport numpy as np\n\ndef train_word2vec(sentence_matrix, vocabulary_inv,\n                   num_features=300, min_word_count=1, context=10):\n    \"\"\"\n    Trains, saves, loads Word2Vec model\n    Returns initial weights for embedding layer.\n   \n    inputs:\n    sentence_matrix # int matrix: num_sentences x max_sentence_len\n    vocabulary_inv  # dict {str:int}\n    num_features    # Word vector dimensionality                      \n    min_word_count  # Minimum word count                        \n    context         # Context window size \n    \"\"\"\n    model_dir = 'word2vec_models'\n    model_name = \"{:d}features_{:d}minwords_{:d}context\".format(num_features, min_word_count, context)\n    model_name = join(model_dir, model_name)\n    if exists(model_name):\n        embedding_model = word2vec.Word2Vec.load(model_name)\n        print('Loading existing Word2Vec model \\'%s\\'' % split(model_name)[-1])\n    else:\n        # Set values for various parameters\n        num_workers = 2       # Number of threads to run in parallel\n        downsampling = 1e-3   # Downsample setting for frequent words\n        \n        # Initialize and train the model\n        print(\"Training Word2Vec model...\")\n        sentences = [[vocabulary_inv[w] for w in s] for s in sentence_matrix]\n        embedding_model = word2vec.Word2Vec(sentences, workers=num_workers, \\\n                            size=num_features, min_count = min_word_count, \\\n                            window = context, sample = downsampling)\n        \n        # If we don't plan to train the model any further, calling \n        # init_sims will make the model much more memory-efficient.\n        embedding_model.init_sims(replace=True)\n        \n        # Saving the model for later use. You can load it later using Word2Vec.load()\n        if not exists(model_dir):\n            os.mkdir(model_dir)\n        print('Saving Word2Vec model \\'%s\\'' % split(model_name)[-1])\n        embedding_model.save(model_name)\n    \n    #  add unknown words\n    embedding_weights = [np.array([embedding_model[w] if w in embedding_model\\\n                                                        else np.random.uniform(-0.25,0.25,embedding_model.vector_size)\\\n                                                        for w in vocabulary_inv])]\n    return embedding_weights\n\nif __name__=='__main__':\n    import data_helpers\n    print(\"Loading data...\")\n    x, _, _, vocabulary_inv = data_helpers.load_data()\n    w = train_word2vec(x, vocabulary_inv)\n"
  },
  {
    "path": "deep-learning/tensor-flow-examples/Setup_TensorFlow.md",
    "content": "_From TensorFlow Official docs_\n\n# Download and Setup <a class=\"md-anchor\" id=\"AUTOGENERATED-download-and-setup\"></a>\n\nYou can install TensorFlow using our provided binary packages or from source.\n\n## Binary Installation <a class=\"md-anchor\" id=\"AUTOGENERATED-binary-installation\"></a>\n\nThe TensorFlow Python API currently requires Python 2.7: we are\n[working](https://github.com/tensorflow/tensorflow/issues/1) on adding support\nfor Python 3.\n\nThe simplest way to install TensorFlow is using\n[pip](https://pypi.python.org/pypi/pip) for both Linux and Mac.\n\nIf you encounter installation errors, see\n[common problems](#common_install_problems) for some solutions. To simplify\ninstallation, please consider using our virtualenv-based instructions\n[here](#virtualenv_install).\n\n### Ubuntu/Linux 64-bit <a class=\"md-anchor\" id=\"AUTOGENERATED-ubuntu-linux-64-bit\"></a>\n\n```bash\n# For CPU-only version\n$ pip install https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.5.0-cp27-none-linux_x86_64.whl\n\n# For GPU-enabled version (only install this version if you have the CUDA sdk installed)\n$ pip install https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.5.0-cp27-none-linux_x86_64.whl\n```\n\n### Mac OS X <a class=\"md-anchor\" id=\"AUTOGENERATED-mac-os-x\"></a>\n\nOn OS X, we recommend installing [homebrew](http://brew.sh) and `brew install\npython` before proceeding, or installing TensorFlow within [virtualenv](#virtualenv_install).\n\n```bash\n# Only CPU-version is available at the moment.\n$ pip install https://storage.googleapis.com/tensorflow/mac/tensorflow-0.5.0-py2-none-any.whl\n```\n\n## Docker-based installation <a class=\"md-anchor\" id=\"AUTOGENERATED-docker-based-installation\"></a>\n\nWe also support running TensorFlow via [Docker](http://docker.com/), which lets\nyou avoid worrying about setting up dependencies.\n\nFirst, [install Docker](http://docs.docker.com/engine/installation/). Once\nDocker is up and running, you can start a container with one command:\n\n```bash\n$ docker run -it b.gcr.io/tensorflow/tensorflow\n```\n\nThis will start a container with TensorFlow and all its dependencies already\ninstalled.\n\n### Additional images <a class=\"md-anchor\" id=\"AUTOGENERATED-additional-images\"></a>\n\nThe default Docker image above contains just a minimal set of libraries for\ngetting up and running with TensorFlow. We also have the following container,\nwhich you can use in the `docker run` command above:\n\n* `b.gcr.io/tensorflow/tensorflow-full`: Contains a complete TensorFlow source\n  installation, including all utilities needed to build and run TensorFlow. This\n  makes it easy to experiment directly with the source, without needing to\n  install any of the dependencies described above.\n\n## VirtualEnv-based installation <a class=\"md-anchor\" id=\"virtualenv_install\"></a>\n\nWe recommend using [virtualenv](https://pypi.python.org/pypi/virtualenv) to\ncreate an isolated container and install TensorFlow in that container -- it is\noptional but makes verifying installation issues easier.\n\nFirst, install all required tools:\n\n```bash\n# On Linux:\n$ sudo apt-get install python-pip python-dev python-virtualenv\n\n# On Mac:\n$ sudo easy_install pip  # If pip is not already installed\n$ sudo pip install --upgrade virtualenv\n```\n\nNext, set up a new virtualenv environment.  To set it up in the\ndirectory `~/tensorflow`, run:\n\n```bash\n$ virtualenv --system-site-packages ~/tensorflow\n$ cd ~/tensorflow\n```\n\nThen activate the virtualenv:\n\n```bash\n$ source bin/activate  # If using bash\n$ source bin/activate.csh  # If using csh\n(tensorflow)$  # Your prompt should change\n```\n\nInside the virtualenv, install TensorFlow:\n\n```bash\n# For CPU-only linux x86_64 version\n(tensorflow)$ pip install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.5.0-cp27-none-linux_x86_64.whl\n\n# For GPU-enabled linux x86_64 version\n(tensorflow)$ pip install --upgrade https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.5.0-cp27-none-linux_x86_64.whl\n\n# For Mac CPU-only version\n(tensorflow)$ pip install --upgrade https://storage.googleapis.com/tensorflow/mac/tensorflow-0.5.0-py2-none-any.whl\n```\n\nMake sure you have downloaded the source code for TensorFlow, and then you can\nthen run an example TensorFlow program like:\n\n```bash\n(tensorflow)$ cd tensorflow/models/image/mnist\n(tensorflow)$ python convolutional.py\n\n# When you are done using TensorFlow:\n(tensorflow)$ deactivate  # Deactivate the virtualenv\n\n$  # Your prompt should change back\n```\n\n## Try your first TensorFlow program <a class=\"md-anchor\" id=\"AUTOGENERATED-try-your-first-tensorflow-program\"></a>\n\n### (Optional) Enable GPU Support <a class=\"md-anchor\" id=\"AUTOGENERATED--optional--enable-gpu-support\"></a>\n\nIf you installed the GPU-enabled TensorFlow pip binary, you must have the\ncorrect versions of the CUDA SDK and CUDNN installed on your\nsystem.  Please see [the CUDA installation instructions](#install_cuda).\n\nYou also need to set the `LD_LIBRARY_PATH` and `CUDA_HOME` environment\nvariables.  Consider adding the commands below to your `~/.bash_profile`.  These\nassume your CUDA installation is in `/usr/local/cuda`:\n\n```bash\nexport LD_LIBRARY_PATH=\"$LD_LIBRARY_PATH:/usr/local/cuda/lib64\"\nexport CUDA_HOME=/usr/local/cuda\n```\n\n### Run TensorFlow <a class=\"md-anchor\" id=\"AUTOGENERATED-run-tensorflow\"></a>\n\nOpen a python terminal:\n\n```bash\n$ python\n\n>>> import tensorflow as tf\n>>> hello = tf.constant('Hello, TensorFlow!')\n>>> sess = tf.Session()\n>>> print sess.run(hello)\nHello, TensorFlow!\n>>> a = tf.constant(10)\n>>> b = tf.constant(32)\n>>> print sess.run(a+b)\n42\n>>>\n\n```\n\n## Installing from sources <a class=\"md-anchor\" id=\"source\"></a>\n\n### Clone the TensorFlow repository <a class=\"md-anchor\" id=\"AUTOGENERATED-clone-the-tensorflow-repository\"></a>\n\n```bash\n$ git clone --recurse-submodules https://github.com/tensorflow/tensorflow\n```\n\n`--recurse-submodules` is required to fetch the protobuf library that TensorFlow\ndepends on.\n\n### Installation for Linux <a class=\"md-anchor\" id=\"AUTOGENERATED-installation-for-linux\"></a>\n\n#### Install Bazel <a class=\"md-anchor\" id=\"AUTOGENERATED-install-bazel\"></a>\n\n\nFollow instructions [here](http://bazel.io/docs/install.html) to install the\ndependencies for Bazel. Then download bazel version 0.1.1 using the\n[installer for your system](https://github.com/bazelbuild/bazel/releases) and\nrun the installer as mentioned there:\n\n```bash\n$ chmod +x PATH_TO_INSTALL.SH\n$ ./PATH_TO_INSTALL.SH --user\n```\n\nRemember to replace `PATH_TO_INSTALL.SH` to point to the location where you\ndownloaded the installer.\n\nFinally, follow the instructions in that script to place bazel into your binary\npath.\n\n#### Install other dependencies <a class=\"md-anchor\" id=\"AUTOGENERATED-install-other-dependencies\"></a>\n\n```bash\n$ sudo apt-get install python-numpy swig python-dev\n```\n\n#### Optional: Install CUDA (GPUs on Linux) <a class=\"md-anchor\" id=\"install_cuda\"></a>\n\nIn order to build or run TensorFlow with GPU support, both Cuda Toolkit 7.0 and\nCUDNN 6.5 V2 from NVIDIA need to be installed.\n\nTensorFlow GPU support requires having a GPU card with NVidia Compute Capability >= 3.5.  Supported cards include but are not limited to:\n\n* NVidia Titan\n* NVidia Titan X\n* NVidia K20\n* NVidia K40\n\n##### Download and install Cuda Toolkit 7.0 <a class=\"md-anchor\" id=\"AUTOGENERATED-download-and-install-cuda-toolkit-7.0\"></a>\n\nhttps://developer.nvidia.com/cuda-toolkit-70\n\nInstall the toolkit into e.g. `/usr/local/cuda`\n\n##### Download and install CUDNN Toolkit 6.5 <a class=\"md-anchor\" id=\"AUTOGENERATED-download-and-install-cudnn-toolkit-6.5\"></a>\n\nhttps://developer.nvidia.com/rdp/cudnn-archive\n\nUncompress and copy the cudnn files into the toolkit directory.  Assuming the\ntoolkit is installed in `/usr/local/cuda`:\n\n``` bash\ntar xvzf cudnn-6.5-linux-x64-v2.tgz\nsudo cp cudnn-6.5-linux-x64-v2/cudnn.h /usr/local/cuda/include\nsudo cp cudnn-6.5-linux-x64-v2/libcudnn* /usr/local/cuda/lib64\n```\n\n##### Configure TensorFlow's canonical view of Cuda libraries <a class=\"md-anchor\" id=\"AUTOGENERATED-configure-tensorflow-s-canonical-view-of-cuda-libraries\"></a>\nFrom the root of your source tree, run:\n\n``` bash\n$ ./configure\nDo you wish to build TensorFlow with GPU support? [y/n] y\nGPU support will be enabled for TensorFlow\n\nPlease specify the location where CUDA 7.0 toolkit is installed. Refer to\nREADME.md for more details. [default is: /usr/local/cuda]: /usr/local/cuda\n\nPlease specify the location where CUDNN 6.5 V2 library is installed. Refer to\nREADME.md for more details. [default is: /usr/local/cuda]: /usr/local/cuda\n\nSetting up Cuda include\nSetting up Cuda lib64\nSetting up Cuda bin\nSetting up Cuda nvvm\nConfiguration finished\n```\n\nThis creates a canonical set of symbolic links to the Cuda libraries on your system.\nEvery time you change the Cuda library paths you need to run this step again before\nyou invoke the bazel build command.\n\n##### Build your target with GPU support. <a class=\"md-anchor\" id=\"AUTOGENERATED-build-your-target-with-gpu-support.\"></a>\nFrom the root of your source tree, run:\n\n```bash\n$ bazel build -c opt --config=cuda //tensorflow/cc:tutorials_example_trainer\n\n$ bazel-bin/tensorflow/cc/tutorials_example_trainer --use_gpu\n# Lots of output. This tutorial iteratively calculates the major eigenvalue of\n# a 2x2 matrix, on GPU. The last few lines look like this.\n000009/000005 lambda = 2.000000 x = [0.894427 -0.447214] y = [1.788854 -0.894427]\n000006/000001 lambda = 2.000000 x = [0.894427 -0.447214] y = [1.788854 -0.894427]\n000009/000009 lambda = 2.000000 x = [0.894427 -0.447214] y = [1.788854 -0.894427]\n```\n\nNote that \"--config=cuda\" is needed to enable the GPU support.\n\n##### Enabling Cuda 3.0. <a class=\"md-anchor\" id=\"AUTOGENERATED-enabling-cuda-3.0.\"></a>\nTensorFlow officially supports Cuda devices with 3.5 and 5.2 compute\ncapabilities. In order to enable earlier Cuda devices such as Grid K520, you\nneed to target Cuda 3.0. This can be done through TensorFlow unofficial\nsettings with \"configure\".\n\n```bash\n$ TF_UNOFFICIAL_SETTING=1 ./configure\n\n# Same as the official settings above\n\nWARNING: You are configuring unofficial settings in TensorFlow. Because some\nexternal libraries are not backward compatible, these settings are largely\nuntested and unsupported.\n\nPlease specify a list of comma-separated Cuda compute capabilities you want to\nbuild with. You can find the compute capability of your device at:\nhttps://developer.nvidia.com/cuda-gpus.\nPlease note that each additional compute capability significantly increases\nyour build time and binary size. [Default is: \"3.5,5.2\"]: 3.0\n\nSetting up Cuda include\nSetting up Cuda lib64\nSetting up Cuda bin\nSetting up Cuda nvvm\nConfiguration finished\n```\n\n##### Known issues <a class=\"md-anchor\" id=\"AUTOGENERATED-known-issues\"></a>\n\n* Although it is possible to build both Cuda and non-Cuda configs under the same\nsource tree, we recommend to run \"bazel clean\" when switching between these two\nconfigs in the same source tree.\n\n* You have to run configure before running bazel build. Otherwise, the build\nwill fail with a clear error message. In the future, we might consider making\nthis more conveninent by including the configure step in our build process,\ngiven necessary bazel new feature support.\n\n### Installation for Mac OS X <a class=\"md-anchor\" id=\"AUTOGENERATED-installation-for-mac-os-x\"></a>\n\nMac needs the same set of dependencies as Linux, however their installing those\ndependencies is different. Here is a set of useful links to help with installing\nthe dependencies on Mac OS X :\n\n#### Bazel <a class=\"md-anchor\" id=\"AUTOGENERATED-bazel\"></a>\n\nLook for installation instructions for Mac OS X on\n[this](http://bazel.io/docs/install.html) page.\n\n#### SWIG <a class=\"md-anchor\" id=\"AUTOGENERATED-swig\"></a>\n\n[Mac OS X installation](http://www.swig.org/Doc3.0/Preface.html#Preface_osx_installation).\n\nNotes : You need to install\n[PCRE](ftp://ftp.csx.cam.ac.uk/pub/software/programming/pcre/) and *NOT* PCRE2.\n\n#### Numpy <a class=\"md-anchor\" id=\"AUTOGENERATED-numpy\"></a>\n\nFollow installation instructions [here](http://docs.scipy.org/doc/numpy/user/install.html).\n\n\n### Create the pip package and install <a class=\"md-anchor\" id=\"create-pip\"></a>\n\n```bash\n$ bazel build -c opt //tensorflow/tools/pip_package:build_pip_package\n\n# To build with GPU support:\n$ bazel build -c opt --config=cuda //tensorflow/tools/pip_package:build_pip_package\n\n$ bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg\n\n# The name of the .whl file will depend on your platform.\n$ pip install /tmp/tensorflow_pkg/tensorflow-0.5.0-cp27-none-linux_x86_64.whl\n```\n\n## Train your first TensorFlow neural net model <a class=\"md-anchor\" id=\"AUTOGENERATED-train-your-first-tensorflow-neural-net-model\"></a>\n\nStarting from the root of your source tree, run:\n\n```python\n$ cd tensorflow/models/image/mnist\n$ python convolutional.py\nSuccesfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.\nSuccesfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.\nSuccesfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.\nSuccesfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.\nExtracting data/train-images-idx3-ubyte.gz\nExtracting data/train-labels-idx1-ubyte.gz\nExtracting data/t10k-images-idx3-ubyte.gz\nExtracting data/t10k-labels-idx1-ubyte.gz\nInitialized!\nEpoch 0.00\nMinibatch loss: 12.054, learning rate: 0.010000\nMinibatch error: 90.6%\nValidation error: 84.6%\nEpoch 0.12\nMinibatch loss: 3.285, learning rate: 0.010000\nMinibatch error: 6.2%\nValidation error: 7.0%\n...\n...\n```\n\n## Common Problems <a class=\"md-anchor\" id=\"common_install_problems\"></a>\n\n### GPU-related issues <a class=\"md-anchor\" id=\"AUTOGENERATED-gpu-related-issues\"></a>\n\nIf you encounter the following when trying to run a TensorFlow program:\n\n```python\nImportError: libcudart.so.7.0: cannot open shared object file: No such file or directory\n```\n\nMake sure you followed the the GPU installation [instructions](#install_cuda).\n\n### Pip installation issues <a class=\"md-anchor\" id=\"AUTOGENERATED-pip-installation-issues\"></a>\n\n#### Can't find setup.py <a class=\"md-anchor\" id=\"AUTOGENERATED-can-t-find-setup.py\"></a>\n\nIf, during `pip install`, you encounter an error like:\n\n```bash\n...\nIOError: [Errno 2] No such file or directory: '/tmp/pip-o6Tpui-build/setup.py'\n```\n\nSolution: upgrade your version of `pip`:\n\n```bash\npip install --upgrade pip\n```\n\nThis may require `sudo`, depending on how `pip` is installed.\n\n#### SSLError: SSL_VERIFY_FAILED <a class=\"md-anchor\" id=\"AUTOGENERATED-sslerror--ssl_verify_failed\"></a>\n\nIf, during pip install from a URL, you encounter an error like:\n\n```bash\n...\nSSLError: [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed\n```\n\nSolution: Download the wheel manually via curl or wget, and pip install locally.\n\n### On Linux <a class=\"md-anchor\" id=\"AUTOGENERATED-on-linux\"></a>\n\nIf you encounter:\n\n```python\n...\n \"__add__\", \"__radd__\",\n             ^\nSyntaxError: invalid syntax\n```\n\nSolution: make sure you are using Python 2.7.\n\n### On MacOSX <a class=\"md-anchor\" id=\"AUTOGENERATED-on-macosx\"></a>\n\n\nIf you encounter:\n\n```python\nimport six.moves.copyreg as copyreg\n\nImportError: No module named copyreg\n```\n\nSolution: TensorFlow depends on protobuf, which requires `six-1.10.0`. Apple's\ndefault python environment has `six-1.4.1` and may be difficult to upgrade.\nThere are several ways to fix this:\n\n1. Upgrade the system-wide copy of `six`:\n\n    ```bash\n    sudo easy_install -U six\n    ```\n\n2. Install a separate copy of python via homebrew:\n\n    ```bash\n    brew install python\n    ```\n\n3. Build or use TensorFlow\n   [within `virtualenv`](#virtualenv_install).\n\n\n\nIf you encounter:\n\n```\n>>> import tensorflow as tf\nTraceback (most recent call last):\n  File \"<stdin>\", line 1, in <module>\n  File \"/usr/local/lib/python2.7/site-packages/tensorflow/__init__.py\", line 4, in <module>\n    from tensorflow.python import *\n  File \"/usr/local/lib/python2.7/site-packages/tensorflow/python/__init__.py\", line 13, in <module>\n    from tensorflow.core.framework.graph_pb2 import *\n...\n  File \"/usr/local/lib/python2.7/site-packages/tensorflow/core/framework/tensor_shape_pb2.py\", line 22, in <module>\n    serialized_pb=_b('\\n,tensorflow/core/framework/tensor_shape.proto\\x12\\ntensorflow\\\"d\\n\\x10TensorShapeProto\\x12-\\n\\x03\\x64im\\x18\\x02 \\x03(\\x0b\\x32 .tensorflow.TensorShapeProto.Dim\\x1a!\\n\\x03\\x44im\\x12\\x0c\\n\\x04size\\x18\\x01 \\x01(\\x03\\x12\\x0c\\n\\x04name\\x18\\x02 \\x01(\\tb\\x06proto3')\nTypeError: __init__() got an unexpected keyword argument 'syntax'\n```\n\nThis is due to a conflict between protobuf versions (we require protobuf 3.0.0).\nThe best current solution is to make sure older versions of protobuf are not\ninstalled, such as:\n\n```bash\nbrew reinstall --devel protobuf\n```"
  },
  {
    "path": "deep-learning/tensor-flow-examples/input_data.py",
    "content": "\"\"\"Functions for downloading and reading MNIST data.\"\"\"\nfrom __future__ import print_function\nimport gzip\nimport os\nimport urllib\nimport numpy\nSOURCE_URL = 'http://yann.lecun.com/exdb/mnist/'\ndef maybe_download(filename, work_directory):\n  \"\"\"Download the data from Yann's website, unless it's already here.\"\"\"\n  if not os.path.exists(work_directory):\n    os.mkdir(work_directory)\n  filepath = os.path.join(work_directory, filename)\n  if not os.path.exists(filepath):\n    filepath, _ = urllib.urlretrieve(SOURCE_URL + filename, filepath)\n    statinfo = os.stat(filepath)\n    print('Succesfully downloaded', filename, statinfo.st_size, 'bytes.')\n  return filepath\ndef _read32(bytestream):\n  dt = numpy.dtype(numpy.uint32).newbyteorder('>')\n  return numpy.frombuffer(bytestream.read(4), dtype=dt)\ndef extract_images(filename):\n  \"\"\"Extract the images into a 4D uint8 numpy array [index, y, x, depth].\"\"\"\n  print('Extracting', filename)\n  with gzip.open(filename) as bytestream:\n    magic = _read32(bytestream)\n    if magic != 2051:\n      raise ValueError(\n          'Invalid magic number %d in MNIST image file: %s' %\n          (magic, filename))\n    num_images = _read32(bytestream)\n    rows = _read32(bytestream)\n    cols = _read32(bytestream)\n    buf = bytestream.read(rows * cols * num_images)\n    data = numpy.frombuffer(buf, dtype=numpy.uint8)\n    data = data.reshape(num_images, rows, cols, 1)\n    return data\ndef dense_to_one_hot(labels_dense, num_classes=10):\n  \"\"\"Convert class labels from scalars to one-hot vectors.\"\"\"\n  num_labels = labels_dense.shape[0]\n  index_offset = numpy.arange(num_labels) * num_classes\n  labels_one_hot = numpy.zeros((num_labels, num_classes))\n  labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1\n  return labels_one_hot\ndef extract_labels(filename, one_hot=False):\n  \"\"\"Extract the labels into a 1D uint8 numpy array [index].\"\"\"\n  print('Extracting', filename)\n  with gzip.open(filename) as bytestream:\n    magic = _read32(bytestream)\n    if magic != 2049:\n      raise ValueError(\n          'Invalid magic number %d in MNIST label file: %s' %\n          (magic, filename))\n    num_items = _read32(bytestream)\n    buf = bytestream.read(num_items)\n    labels = numpy.frombuffer(buf, dtype=numpy.uint8)\n    if one_hot:\n      return dense_to_one_hot(labels)\n    return labels\nclass DataSet(object):\n  def __init__(self, images, labels, fake_data=False):\n    if fake_data:\n      self._num_examples = 10000\n    else:\n      assert images.shape[0] == labels.shape[0], (\n          \"images.shape: %s labels.shape: %s\" % (images.shape,\n                                                 labels.shape))\n      self._num_examples = images.shape[0]\n      # Convert shape from [num examples, rows, columns, depth]\n      # to [num examples, rows*columns] (assuming depth == 1)\n      assert images.shape[3] == 1\n      images = images.reshape(images.shape[0],\n                              images.shape[1] * images.shape[2])\n      # Convert from [0, 255] -> [0.0, 1.0].\n      images = images.astype(numpy.float32)\n      images = numpy.multiply(images, 1.0 / 255.0)\n    self._images = images\n    self._labels = labels\n    self._epochs_completed = 0\n    self._index_in_epoch = 0\n  @property\n  def images(self):\n    return self._images\n  @property\n  def labels(self):\n    return self._labels\n  @property\n  def num_examples(self):\n    return self._num_examples\n  @property\n  def epochs_completed(self):\n    return self._epochs_completed\n  def next_batch(self, batch_size, fake_data=False):\n    \"\"\"Return the next `batch_size` examples from this data set.\"\"\"\n    if fake_data:\n      fake_image = [1.0 for _ in xrange(784)]\n      fake_label = 0\n      return [fake_image for _ in xrange(batch_size)], [\n          fake_label for _ in xrange(batch_size)]\n    start = self._index_in_epoch\n    self._index_in_epoch += batch_size\n    if self._index_in_epoch > self._num_examples:\n      # Finished epoch\n      self._epochs_completed += 1\n      # Shuffle the data\n      perm = numpy.arange(self._num_examples)\n      numpy.random.shuffle(perm)\n      self._images = self._images[perm]\n      self._labels = self._labels[perm]\n      # Start next epoch\n      start = 0\n      self._index_in_epoch = batch_size\n      assert batch_size <= self._num_examples\n    end = self._index_in_epoch\n    return self._images[start:end], self._labels[start:end]\ndef read_data_sets(train_dir, fake_data=False, one_hot=False):\n  class DataSets(object):\n    pass\n  data_sets = DataSets()\n  if fake_data:\n    data_sets.train = DataSet([], [], fake_data=True)\n    data_sets.validation = DataSet([], [], fake_data=True)\n    data_sets.test = DataSet([], [], fake_data=True)\n    return data_sets\n  TRAIN_IMAGES = 'train-images-idx3-ubyte.gz'\n  TRAIN_LABELS = 'train-labels-idx1-ubyte.gz'\n  TEST_IMAGES = 't10k-images-idx3-ubyte.gz'\n  TEST_LABELS = 't10k-labels-idx1-ubyte.gz'\n  VALIDATION_SIZE = 5000\n  local_file = maybe_download(TRAIN_IMAGES, train_dir)\n  train_images = extract_images(local_file)\n  local_file = maybe_download(TRAIN_LABELS, train_dir)\n  train_labels = extract_labels(local_file, one_hot=one_hot)\n  local_file = maybe_download(TEST_IMAGES, train_dir)\n  test_images = extract_images(local_file)\n  local_file = maybe_download(TEST_LABELS, train_dir)\n  test_labels = extract_labels(local_file, one_hot=one_hot)\n  validation_images = train_images[:VALIDATION_SIZE]\n  validation_labels = train_labels[:VALIDATION_SIZE]\n  train_images = train_images[VALIDATION_SIZE:]\n  train_labels = train_labels[VALIDATION_SIZE:]\n  data_sets.train = DataSet(train_images, train_labels)\n  data_sets.validation = DataSet(validation_images, validation_labels)\n  data_sets.test = DataSet(test_images, test_labels)\n  return data_sets"
  },
  {
    "path": "deep-learning/tensor-flow-examples/multigpu_basics.py",
    "content": "#Multi GPU Basic example\n'''\nThis tutorial requires your machine to have 2 GPUs\n\"/cpu:0\": The CPU of your machine.\n\"/gpu:0\": The first GPU of your machine\n\"/gpu:1\": The second GPU of your machine\n'''\n\nimport numpy as np\nimport tensorflow as tf\nimport datetime\n\n#Processing Units logs\nlog_device_placement = True\n\n#num of multiplications to perform\nn = 10\n\n'''\nExample: compute A^n + B^n on 2 GPUs\nResults on 8 cores with 2 GTX-980:\n * Single GPU computation time: 0:00:11.277449\n * Multi GPU computation time: 0:00:07.131701\n'''\n#Create random large matrix\nA = np.random.rand(1e4, 1e4).astype('float32')\nB = np.random.rand(1e4, 1e4).astype('float32')\n\n# Creates a graph to store results\nc1 = []\nc2 = []\n\ndef matpow(M, n):\n    if n < 1: #Abstract cases where n < 1\n        return M\n    else:\n        return tf.matmul(M, matpow(M, n-1))\n\n'''\nSingle GPU computing\n'''\nwith tf.device('/gpu:0'):\n    a = tf.constant(A)\n    b = tf.constant(B)\n    #compute A^n and B^n and store results in c1\n    c1.append(matpow(a, n))\n    c1.append(matpow(b, n))\n\nwith tf.device('/cpu:0'):\n  sum = tf.add_n(c1) #Addition of all elements in c1, i.e. A^n + B^n\n\nt1_1 = datetime.datetime.now()\nwith tf.Session(config=tf.ConfigProto(log_device_placement=log_device_placement)) as sess:\n    # Runs the op.\n    sess.run(sum)\nt2_1 = datetime.datetime.now()\n\n\n'''\nMulti GPU computing\n'''\n#GPU:0 computes A^n\nwith tf.device('/gpu:0'):\n    #compute A^n and store result in c2\n    a = tf.constant(A)\n    c2.append(matpow(a, n))\n\n#GPU:1 computes B^n\nwith tf.device('/gpu:1'):\n    #compute B^n and store result in c2\n    b = tf.constant(B)\n    c2.append(matpow(b, n))\n\nwith tf.device('/cpu:0'):\n  sum = tf.add_n(c2) #Addition of all elements in c2, i.e. A^n + B^n\n\nt1_2 = datetime.datetime.now()\nwith tf.Session(config=tf.ConfigProto(log_device_placement=log_device_placement)) as sess:\n    # Runs the op.\n    sess.run(sum)\nt2_2 = datetime.datetime.now()\n\n\nprint \"Single GPU computation time: \" + str(t2_1-t1_1)\nprint \"Multi GPU computation time: \" + str(t2_2-t1_2)"
  },
  {
    "path": "deep-learning/tensor-flow-examples/notebooks/1_intro/basic_operations.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Basic Operations in TensorFlow\\n\",\n    \"\\n\",\n    \"Credits: Forked from [TensorFlow-Examples](https://github.com/aymericdamien/TensorFlow-Examples) by Aymeric Damien\\n\",\n    \"\\n\",\n    \"## Setup\\n\",\n    \"\\n\",\n    \"Refer to the [setup instructions](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-examples/Setup_TensorFlow.md)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import tensorflow as tf\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Basic constant operations\\n\",\n    \"# The value returned by the constructor represents the output\\n\",\n    \"# of the Constant op.\\n\",\n    \"a = tf.constant(2)\\n\",\n    \"b = tf.constant(3)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"a=2, b=3\\n\",\n      \"Addition with constants: 5\\n\",\n      \"Multiplication with constants: 6\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Launch the default graph.\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    print \\\"a=2, b=3\\\"\\n\",\n    \"    print \\\"Addition with constants: %i\\\" % sess.run(a+b)\\n\",\n    \"    print \\\"Multiplication with constants: %i\\\" % sess.run(a*b)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Basic Operations with variable as graph input\\n\",\n    \"# The value returned by the constructor represents the output\\n\",\n    \"# of the Variable op. (define as input when running session)\\n\",\n    \"# tf Graph input\\n\",\n    \"a = tf.placeholder(tf.int16)\\n\",\n    \"b = tf.placeholder(tf.int16)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Define some operations\\n\",\n    \"add = tf.add(a, b)\\n\",\n    \"mul = tf.mul(a, b)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Addition with variables: 5\\n\",\n      \"Multiplication with variables: 6\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Launch the default graph.\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    # Run every operation with variable input\\n\",\n    \"    print \\\"Addition with variables: %i\\\" % sess.run(add, feed_dict={a: 2, b: 3})\\n\",\n    \"    print \\\"Multiplication with variables: %i\\\" % sess.run(mul, feed_dict={a: 2, b: 3})\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# ----------------\\n\",\n    \"# More in details:\\n\",\n    \"# Matrix Multiplication from TensorFlow official tutorial\\n\",\n    \"\\n\",\n    \"# Create a Constant op that produces a 1x2 matrix.  The op is\\n\",\n    \"# added as a node to the default graph.\\n\",\n    \"#\\n\",\n    \"# The value returned by the constructor represents the output\\n\",\n    \"# of the Constant op.\\n\",\n    \"matrix1 = tf.constant([[3., 3.]])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Create another Constant that produces a 2x1 matrix.\\n\",\n    \"matrix2 = tf.constant([[2.],[2.]])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Create a Matmul op that takes 'matrix1' and 'matrix2' as inputs.\\n\",\n    \"# The returned value, 'product', represents the result of the matrix\\n\",\n    \"# multiplication.\\n\",\n    \"product = tf.matmul(matrix1, matrix2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[ 12.]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# To run the matmul op we call the session 'run()' method, passing 'product'\\n\",\n    \"# which represents the output of the matmul op.  This indicates to the call\\n\",\n    \"# that we want to get the output of the matmul op back.\\n\",\n    \"#\\n\",\n    \"# All inputs needed by the op are run automatically by the session.  They\\n\",\n    \"# typically are run in parallel.\\n\",\n    \"#\\n\",\n    \"# The call 'run(product)' thus causes the execution of threes ops in the\\n\",\n    \"# graph: the two constants and matmul.\\n\",\n    \"#\\n\",\n    \"# The output of the op is returned in 'result' as a numpy `ndarray` object.\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    result = sess.run(product)\\n\",\n    \"    print result\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 2\",\n   \"language\": \"python\",\n   \"name\": \"python2\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 2\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython2\",\n   \"version\": \"2.7.5+\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "deep-learning/tensor-flow-examples/notebooks/2_basic_classifiers/linear_regression.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Linear Regression in TensorFlow\\n\",\n    \"\\n\",\n    \"Credits: Forked from [TensorFlow-Examples](https://github.com/aymericdamien/TensorFlow-Examples) by Aymeric Damien\\n\",\n    \"\\n\",\n    \"## Setup\\n\",\n    \"\\n\",\n    \"Refer to the [setup instructions](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-examples/Setup_TensorFlow.md)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import tensorflow as tf\\n\",\n    \"import numpy\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"rng = numpy.random\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Parameters\\n\",\n    \"learning_rate = 0.01\\n\",\n    \"training_epochs = 2000\\n\",\n    \"display_step = 50\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Training Data\\n\",\n    \"train_X = numpy.asarray([3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167,7.042,10.791,5.313,7.997,5.654,9.27,3.1])\\n\",\n    \"train_Y = numpy.asarray([1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221,2.827,3.465,1.65,2.904,2.42,2.94,1.3])\\n\",\n    \"n_samples = train_X.shape[0]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# tf Graph Input\\n\",\n    \"X = tf.placeholder(\\\"float\\\")\\n\",\n    \"Y = tf.placeholder(\\\"float\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Create Model\\n\",\n    \"\\n\",\n    \"# Set model weights\\n\",\n    \"W = tf.Variable(rng.randn(), name=\\\"weight\\\")\\n\",\n    \"b = tf.Variable(rng.randn(), name=\\\"bias\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Construct a linear model\\n\",\n    \"activation = tf.add(tf.mul(X, W), b)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Minimize the squared errors\\n\",\n    \"cost = tf.reduce_sum(tf.pow(activation-Y, 2))/(2*n_samples) #L2 loss\\n\",\n    \"optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) #Gradient descent\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Initializing the variables\\n\",\n    \"init = tf.initialize_all_variables()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch: 0001 cost= 3.389688730 W= 0.0198441 b= -0.273522\\n\",\n      \"Epoch: 0051 cost= 0.134034902 W= 0.383208 b= -0.159746\\n\",\n      \"Epoch: 0101 cost= 0.127440125 W= 0.375261 b= -0.102578\\n\",\n      \"Epoch: 0151 cost= 0.121607177 W= 0.367787 b= -0.0488099\\n\",\n      \"Epoch: 0201 cost= 0.116448022 W= 0.360758 b= 0.00175997\\n\",\n      \"Epoch: 0251 cost= 0.111884907 W= 0.354146 b= 0.0493223\\n\",\n      \"Epoch: 0301 cost= 0.107848980 W= 0.347928 b= 0.0940558\\n\",\n      \"Epoch: 0351 cost= 0.104279339 W= 0.34208 b= 0.136129\\n\",\n      \"Epoch: 0401 cost= 0.101122171 W= 0.336579 b= 0.1757\\n\",\n      \"Epoch: 0451 cost= 0.098329842 W= 0.331405 b= 0.212917\\n\",\n      \"Epoch: 0501 cost= 0.095860250 W= 0.32654 b= 0.247921\\n\",\n      \"Epoch: 0551 cost= 0.093676031 W= 0.321963 b= 0.280843\\n\",\n      \"Epoch: 0601 cost= 0.091744311 W= 0.317659 b= 0.311807\\n\",\n      \"Epoch: 0651 cost= 0.090035893 W= 0.313611 b= 0.340929\\n\",\n      \"Epoch: 0701 cost= 0.088524953 W= 0.309804 b= 0.36832\\n\",\n      \"Epoch: 0751 cost= 0.087188691 W= 0.306222 b= 0.394082\\n\",\n      \"Epoch: 0801 cost= 0.086007021 W= 0.302854 b= 0.418311\\n\",\n      \"Epoch: 0851 cost= 0.084961981 W= 0.299687 b= 0.441099\\n\",\n      \"Epoch: 0901 cost= 0.084037818 W= 0.296708 b= 0.462532\\n\",\n      \"Epoch: 0951 cost= 0.083220571 W= 0.293905 b= 0.48269\\n\",\n      \"Epoch: 1001 cost= 0.082497880 W= 0.29127 b= 0.50165\\n\",\n      \"Epoch: 1051 cost= 0.081858821 W= 0.288791 b= 0.519481\\n\",\n      \"Epoch: 1101 cost= 0.081293717 W= 0.28646 b= 0.536251\\n\",\n      \"Epoch: 1151 cost= 0.080794014 W= 0.284267 b= 0.552026\\n\",\n      \"Epoch: 1201 cost= 0.080352172 W= 0.282205 b= 0.566861\\n\",\n      \"Epoch: 1251 cost= 0.079961479 W= 0.280265 b= 0.580815\\n\",\n      \"Epoch: 1301 cost= 0.079616025 W= 0.278441 b= 0.593939\\n\",\n      \"Epoch: 1351 cost= 0.079310589 W= 0.276725 b= 0.606284\\n\",\n      \"Epoch: 1401 cost= 0.079040587 W= 0.275111 b= 0.617893\\n\",\n      \"Epoch: 1451 cost= 0.078801893 W= 0.273594 b= 0.62881\\n\",\n      \"Epoch: 1501 cost= 0.078590907 W= 0.272167 b= 0.639077\\n\",\n      \"Epoch: 1551 cost= 0.078404360 W= 0.270824 b= 0.648734\\n\",\n      \"Epoch: 1601 cost= 0.078239456 W= 0.269562 b= 0.657817\\n\",\n      \"Epoch: 1651 cost= 0.078093678 W= 0.268374 b= 0.66636\\n\",\n      \"Epoch: 1701 cost= 0.077964827 W= 0.267257 b= 0.674395\\n\",\n      \"Epoch: 1751 cost= 0.077850945 W= 0.266207 b= 0.681952\\n\",\n      \"Epoch: 1801 cost= 0.077750273 W= 0.265219 b= 0.68906\\n\",\n      \"Epoch: 1851 cost= 0.077661335 W= 0.264289 b= 0.695745\\n\",\n      \"Epoch: 1901 cost= 0.077582702 W= 0.263416 b= 0.702033\\n\",\n      \"Epoch: 1951 cost= 0.077513263 W= 0.262593 b= 0.707947\\n\",\n      \"Optimization Finished!\\n\",\n      \"cost= 0.077453 W= 0.261835 b= 0.713401\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Launch the graph\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    sess.run(init)\\n\",\n    \"\\n\",\n    \"    # Fit all training data\\n\",\n    \"    for epoch in range(training_epochs):\\n\",\n    \"        for (x, y) in zip(train_X, train_Y):\\n\",\n    \"            sess.run(optimizer, feed_dict={X: x, Y: y})\\n\",\n    \"\\n\",\n    \"        #Display logs per epoch step\\n\",\n    \"        if epoch % display_step == 0:\\n\",\n    \"            print \\\"Epoch:\\\", '%04d' % (epoch+1), \\\"cost=\\\", \\\\\\n\",\n    \"                \\\"{:.9f}\\\".format(sess.run(cost, feed_dict={X: train_X, Y:train_Y})), \\\\\\n\",\n    \"                \\\"W=\\\", sess.run(W), \\\"b=\\\", sess.run(b)\\n\",\n    \"\\n\",\n    \"    print \\\"Optimization Finished!\\\"\\n\",\n    \"    print \\\"cost=\\\", sess.run(cost, feed_dict={X: train_X, Y: train_Y}), \\\\\\n\",\n    \"          \\\"W=\\\", sess.run(W), \\\"b=\\\", sess.run(b)\\n\",\n    \"\\n\",\n    \"    #Graphic display\\n\",\n    \"    plt.plot(train_X, train_Y, 'ro', label='Original data')\\n\",\n    \"    plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line')\\n\",\n    \"    plt.legend()\\n\",\n    \"    plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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ggAAHBjt9u1PClJKydM0LCuXTW8c2cN69pVKydMqPVH8Hr79Tp27Kip\\nU6fqzJkzGj58uMd9MFatWqWHH35YwcHBWrBgQY1fs0GDBho7dqyOHj2qWbNmufTt2LFD8fHxNX6N\\nipTsObJt2zb9+uuvpe1nzpzRww8/rCNHjtT4NWJjYyVJ06ZNc5nm9PPPP7u93/OtqWXLlpLk9rjj\\nEh07dtSRI0f09ddfu7S/9dZb+ve//139N4U6wxQsAADgkd1u19zipxv52+s9++yz+vXXX/Xyyy/r\\nyiuv1ODBg9W1a1edOXNGX375pZKTkxUSEqJly5bVePSjxJw5c/Tpp5/K4XBoy5Yt6tu3r7KysrRi\\nxQoNHTpUq1atctt0UKr5zt82m00PPfSQ5syZo+7du2v48OE6ffq0PvvsMx09erT0iVM1MW7cOC1f\\nvlxr1qzR7373Ow0fPlxnzpzRBx98oMjISO3du7fGNTVr1kx9+vRRYmKixo8fr8suu0xBQUEaMWKE\\nunfvrkmTJmnjxo3q37+/Ro8erebNm2vbtm364osvdPvttyshIaFG7xG1hxEQAAAQcAzD0Isvvqgt\\nW7bojjvu0K5du/R///d/+uc//6m8vDw99thj2rNnj2677TaP157PlKWLLrpIX375pX7/+99r165d\\neuWVV7Rjxw4tWLBAd955p6SytSI1eS1P58+cOVMvvfSSmjRpojfeeEOrVq1SZGSkkpOT1b59+xpP\\nwZKkFStWaMaMGSoqKtLf/vY3rVu3Tn/4wx+0fPlyj+efT03vvPOOhg4dqo8++kjPPfecpk+fru3b\\nt0uSBg8erLVr16pr165avny5Fi5cqCZNmmjTpk0aOnRorbxH1A7DrGmsRq1JTU1VRESEUlJS1KtX\\nL6vLAQD4Gf6d8V3Tpk3TX//6V23cuLF0V3R4V1X/fPDnqOYYAQEAAPAST/tQfP3115o/f77CwsJq\\nbboX4MtYAwIAAOAlV199tS677DJ169ZNTZs21bfffqv169dLkt58802XXcQBf0UAAQAA8JI//elP\\nWrVqld577z0dP35cLVu2VExMjB577DGPj48F/BEBBAAAwEueeeYZPfPMM1aXAViKNSAAAAAAvIYA\\nAgAAAMBrCCAAAAAAvIYAAgAAAMBrCCAAAAAAvIYAAgAAAMBrCCAAAAAAvIYAAgAAAMBrCCAAAAAA\\nvIYAAgAA4EFmZqZsNptiY2OtLqVCmzZtks1m04wZM6p0/qJFi2Sz2bR48WKX9g4dOqhjx451USLg\\nhgACAAACjs1mq/S/+Ph4GYYhSaW/lpgwYYJsNpv279/v8d4DBgyQzebdr1jla6zu+YZhVPsewPkK\\ntroAAAAAKxiGoenTp3vs69mzp37zm98oPT1doaGhHq89173rk08//dTqEhBACCAAACBgPfPMM5X2\\nd+7c2WO7aZoyTbMuSrIE06/gTUzBAgAA8MDTGpCS6VmS80t7yZStjh07at++fbLZbNq8ebNM03SZ\\n0jVw4ECXe//444964IEHdOmll6px48Zq1aqVRowYoW3btnms5dChQ7rnnnt08cUXKyQkRFdddVVp\\nHbXB0xqQs9eLfPbZZxowYICaN2+u0NBQ3XLLLUpPT/d4r7y8PP31r39Vz5491axZM11wwQWKjo7W\\ne++9V2v1on5jBAQAAKASZ0+nmj59ulatWqUdO3Zo0qRJatGihSSpRYsWatGihaZPn65FixZp3759\\nevbZZ0uv69ChQ+n/p6am6qabbtIvv/yim2++WbfffrtycnK0atUq9e/fXx9++KFiYmJKzz98+LCi\\no6P1/fff65prrlH//v114MAB/elPf9KNN95YJ+/zbOvWrdPq1as1ZMgQ/fnPf9auXbu0YcMGbd26\\nVWlpaQoLCys99+jRoxo0aJC++uorRURE6J577lFRUZE++ugj3XHHHdq1a5dmzpxZazWjfiKAAACA\\ngGSapmbMmOE2lapjx466++67PV4zffp0ff/996UBpH379m79n332mfbv3+9xeldBQYFGjx6tvLw8\\nbdq0Sddcc01p3+zZs9W7d2/dc889yszMVMOGDSVJU6dO1ffff6/JkyfrpZdeKj3/gQceUN++fc/7\\n/VfV6tWrtXHjRpdRnKlTp2rOnDl6++23NWXKlNL2SZMm6auvvpLD4dBjjz1W2n7q1Cndeuutmj17\\ntm6//XZdeeWVdV43fBcBBAAAVCovT6pgtk2d6tJFCgmp29fw9PjaAQMGVBhAamr9+vXau3evpkyZ\\n4hI+JKlNmzaaMmWKJk+erP/85z+KiYnRmTNntHTpUjVv3txlREWSIiIidOedd7o9Ure2jR071m0K\\n2cSJEzVnzhxt3bq1tO3IkSNasmSJevfu7RI+JKlRo0aaM2eONm7cqHfffZcAEuAIIAAAoFLp6VJE\\nhPdfNyVF6tWr7u5vGIYKCwvr7gU8SEpKkuRcX1I+UEjSt99+K0navXu3YmJilJ6erpMnT6p37966\\n4IIL3M6/7rrr6jyAXH311W5t7dq1kyT98ssvpW1bt25VUVGRJHl8b2fOnJHkfG8IbAQQAABQqS5d\\nnGHAitf1N0eOHJEkrVixosJzDMPQr7/+KknKzc2VJF188cUez23dunUtV+iuZJ3L2YKDnV8hzw5w\\nJe9t69atLiMjZzv7vSFwEUAAAEClQkLqdiQikJTsKbJmzRrdcsstVT7/0KFDHvsPHjxYe8XVUEmt\\njzzyiF588UWLq4Ev4zG8AAAA1RAUFCRJFU7fKun3tE9IyaLxzZs3V+m1rrjiCjVp0kRfffWVjh07\\n5ta/adOmKt3HG6KiokofQwxUhgACAABQDSWPnd23b1+F/aZpeuwfMWKEwsPD9be//U3/+te/PF6f\\nlJSkkydPSnJOdRo/fryOHTvmtq5i27ZtWrp0aQ3eSe2y2+268847tW3bNs2aNat0PcjZMjIylJmZ\\n6f3i4FOYggUAAFANN9xwg1588UX98Y9/1MiRI3XBBReoZcuWuv/++0v7ExISNHLkSMXExKhJkybq\\n0KGDxo8fr+DgYK1cuVKDBw/W0KFDFR0drSuvvFIhISH64YcftHXrVn3//fc6ePCgmjRpIsn5eN7/\\n/Oc/euWVV7Rt2zb169dPWVlZev/99zV06FCtWbPGyo/DxWuvvaZvv/1WzzzzjN555x3169dPF198\\nsQ4cOKDdu3dr27Zteu+991z2RUHgIYAAAABUw0033aSXXnpJb775pl599VWdPn1aHTp0KA0g9957\\nr/bt26f33ntPc+fOVUFBgQYMGKDx48dLkrp3764dO3bo5Zdf1rp160p3HG/btq0iIiI0c+ZMl839\\nwsLC9MUXX2jq1Klau3attm3bpi5duujvf/+7LrnkkmoFEMMwPG44WFFbRZsTVuSCCy7Q559/rjfe\\neEPvvvuuVq5cqfz8fLVu3VqXXXaZXnnlFd1www3Vuif8j2F6mqAIS6SmpioiIkIpKSnqxWo/AEAt\\n498ZoGJV/fPBn6OaYw0IAAAAAK8hgAAAAADwGgIIAAAAAK8hgAAAAADwGgIIAAAAAK8hgAAAAADw\\nGgIIAAAAAK8hgAAAAADwGgIIAAAAAK8hgFRi165dGjVqlMLDw9W0aVOFhYUpOjpaS5cuPee1ixYt\\nks1m8/hfdna2F6oHAAAAfE+w1QX4sv379+vEiROaMGGC2rZtq7y8PCUkJOiuu+5SZmampk2bds57\\nzJw5Ux07dnRpCw0NrauSAQA4p927d1tdAuBz+HPhPQSQSsTExCgmJsal7f7771dERITeeOONKgWQ\\nmJgY9erVq65KBACg2saPH291CQACGAGkmmw2m9q1a6fjx49X6XzTNHX8+HGFhIQoKCiojqsDAKBi\\nXbp0UUpKitVlAF535Ih0001lx3ffLT30kOdzu3Tp4p2iAhgBpAry8vKUl5en3NxcrVmzRhs3btRr\\nr71WpWsHDhyoEydOqGHDhho8eLBeeuklderUqY4rBgDAXUhICKPyCCimKY0fL737rvO4aVMpK0u6\\n4AJr6wp0BJAqeOSRR/TGG29IkoKDgzV//nxNnDix0muaNm2q2NhYDRw4UM2bN9e2bdv08ssvKzo6\\nWqmpqWrXrp03SgcAAAhIGzdKN99cdvzpp9LAgdbVgzIEkCqYPHmyRo8erQMHDmjp0qV64IEH1KRJ\\nE919990VXjNq1CiNGjWq9Hj48OEaPHiwrr32Wj3//PNasGCBN0oHAAAIKEePSi1blh3fc4/0z39a\\nVw/cEUCq4PLLL9fll18uyblwb/DgwZo0aZJGjx6tJk2aVPk+/fr1U1RUlD755JO6KhUAACBgPfyw\\nNH9+2XFOjtSqlXX1wDMCyHm47bbb9PHHH+ubb75Rz549q3Vtu3bttGfPnkrPmTx5stujeseNG6dx\\n48ZVu1YAAAB/9+WXUr9+ZccffijdemvN77ts2TItW7bMpS03N7fmNw5wBJDzcPLkSUnOJ2JV1969\\ne2W32ys9Z968eSwSBAAAOIe8PKlDB+dIhySNGOEMH4ZRO/f39APg1NRURURE1M4LBCh2Qq9ETsnv\\n5rOcOXNG8fHxCgsLU7du3SRJWVlZSk9PV0FBQaXXbtiwQampqbr57BVRAAAAqLbnnnM+1arkK9f+\\n/dKqVecOHzk5OZoSG6uh3bpp+OWXa2i3bpoSG+vxuxvqBiMglZg4caKOHz+ua6+9Vm3bttXBgwe1\\ndOlS7dmzRwsXLizd1+PJJ59UfHy8MjMz1b59e0lSdHS0evXqpYiICIWGhio1NVVvv/222rdvr6lT\\np1r5tgAAAOqtnTulK68sO160yLmvR1VkZ2drbHS0ZmdkyCHJkFQkKTktTWMSE7U8KemcM1VQcwSQ\\nSowdO1ZvvfWWFixYoCNHjqh58+aKiorSa6+9puuvv770PMMwZJSL22PHjtX69ev173//W3l5eWrb\\ntq3uu+8+TZ8+nd/YAAAA1XT6tNSzp7R7t/M4OlravFmqzj7Pcx9/XLMzMtTnrDabpD6Sns/IkCMu\\nTnMXLqzFquGJYZqmaXURcCqZU5iSksIaEAAAgGJ/+5v0wANlx998I3XuXP37DO3WTevS0uRpllaR\\npGFdu2r9rl2V3oPvazXHGhAAAAD4pIwM55qOkvAxb55zd/PzCR+SFFRQ4DF8SM4vxUFnredF3WEK\\nFgAAAHxKYaE0aJBzipUkXXaZ9PXXUqNGNbxvcLBMqcIRkMJgvhp7AyMgAAAA8BlLl0rBwWXhIzVV\\n2rOn5uFDkrpGRmpLBX1bivtR9wggAAAAsNyBA87pVuPHO4+ffto53eqqq2rvNeIcDk0ND1eSnCMe\\nKv41SdK08HDFORy192KoEONMAAAAsIxpSqNGSR984Dxu2dK5p0ezZrX/Wna7XcuTkuSIi9Os5GQF\\nFRSoMDhYXSMjtdzh4EmlXkIAAQAAgCXWrZOGDSs73rxZuuaaun1Nu93Oo3YtRgABAACAVx05IrVq\\nVXb85z9Lr79uXT3wLgIIAAAAvOZPf5L+8Y+y4yNHpAsvtK4eeB+L0AEAAFDnNm92LjIvCR/r1zvX\\nfxA+Ag8jIAAAAKgzJ05IbdtKx487j0ePlt57zxlGEJgYAQEAAECdeOop6YILysLHgQPS8uWEj0DH\\nCAgAAIAfysnJkSMuTmnlHjcb54XHzaamShERZcfvviuNG1enL4l6hAACAADgZ7KzszU2OlqzMzLk\\nkGTIueFeclqaxiQmanlSUp2EkFOnpK5dpb17nccDB0qffCLZmHODs/DbAQAAwM/Mffxxzc7IUB85\\nw4fk/NLXR9LzGRlyxMXV+mu+/LLUuHFZ+MjIkD79lPABd/yWAAAA8DNpycmKqqAvqri/tnzzjXNN\\nx6OPOo//9jfn060uvbTWXgJ+hilYAAAAfiaooEAVrfO2FffXVEGB1L+/tGWL87h7dyklRWrQoMa3\\nhp9jBAQAAMDPFAYHy6ygr6i4vyYWLXIGjZLwsXOn8z/CB6qCAAIAAOBnukZGaksFfVuK+8/HDz84\\np1vFxjqPZ81yTrfq3v28bocARQABAADwM3EOh6aGhytJzhEPFf+aJGlaeLjiHI5q3c80peHDpfbt\\nncetW0u//ipNm1aLRSNgsAYEAADAz9jtdi1PSpIjLk6zyu0Dsrya+4B8+KE0cmTZcVKS1KdPHRSN\\ngEEAAQAA8EN2u11zFy487+tzcqSLLio7fvhh6ZVXaqEwBDwCCAAAAEqZpnTPPVJJdrHZpCNHpBYt\\nrK0L/oMjsRRyAAAgAElEQVQ1IAAAAJBUtnFgSfjYuFEqLCR8oHYxAgIAABDgjh1zTrc6dcp5PH68\\nFB/vfOIVUNsYAQEAAAhgjz8uhYaWhY+DB6V33iF8oO4wAgIAABCAtm6Vzt4O5P33pVGjrKsHgYMA\\nAgAAEEBOnpQ6d5Z+/NF5PHiwtGGDc+0H4A38VgMAAAgQL7wghYSUhY/MTOmjjwgf8C5GQAAAAPxc\\nWprUrVvZ8RtvSH/8o3X1ILARQAAAAPxUfr7UpEnZcUSE9L//ScF8A4SFGHADAADwQ5GRruFj1y5p\\n2zbCB6zHb0EAAAA/8vnn0oABZcd2u5SdbVk5gBsCCAAAgB8oLHQf3fjlF3Yxh+9hChYAAEA9N3q0\\na/hYsEAyTcIHfBMjIAAAAPXUzp3SlVe6tpmmNbUAVUUAAQAAqGdM033vjp9+ktq2taYeoDqYggUA\\nAFCPPPqoa/h4+mlnICF8oL5gBAQAAKAeyMyUOnZ0bWO6FeojRkAAAAB8nGG4ho/duwkfqL8IIAAA\\nAD7q5Zed4aNEbKwzeHTpYl1NQE0xBQsA4DNycnLkiItTWnKyggoKVBgcrK6RkYpzOGS3260uD/Ca\\nI0ekVq1c2woL3ReeA/URAQQA4BOys7M1NjpaszMy5JBkSCqSlJyWpjGJiVqelEQIQUBo1Eg6fbrs\\nOClJ6tPHunqA2kaOBgD4hLmPP67ZGRnqI2f4kJz/SPWR9HxGhhxxcdYVB3jBu+86p1uVhI9Bg5zT\\nrQgf8DeMgAAAfEJacrIcFfRFSZqVnOzNcgCv+fVXqVkz17ZTp6SGDa2pB6hrjIAAAHxCUEGBjAr6\\nbMX9gL/53e9cw8e6dc5RD8IH/BkjIAAAn1AYHCxT8hhCior7AX/x8cfSTTeVHXfsKO3da109gDfx\\ntzkAwCd0jYzUlrQ0eZruvqW4H6jvzpxxH904dky64AJr6gGswBQsAIBPiHM4NDU8XElyjnio+Nck\\nSdPCwxXnqGiFCFA/DBvmGj7efts53YrwgUDDCAgAwCfY7XYtT0qSIy5Os8rtA7KcfUBQj6WkSFdf\\n7drGLuYIZAQQAIDPsNvtmrtwodVlALXCNN03Djx4ULr4YmvqAXwFU7AAAABq2S23uIaP2bOdgYTw\\nATACAgAAUGu2bpXKPy+B6VaAKwIIAABALTDKPUM6JUXq1cuaWgBfxhQsAACAGhgyxDV8dOjgHPUg\\nfACeMQICAABwHvbulcLDXduKitxHQgC4YgQEAACgmgzDNXysXesc9SB8AOdGAAEAAKiihx5yDxmm\\n6XzqFYCqYQoWAADAORw+LJXfC/PUKdedzQFUDSMgAAAAlTAM1/Dx9787Rz0IH8D5IYAAAAB48PLL\\nnqdb3XefNfUA/oIpWAAAAGc5eVIKCXFtO3pUCg21ph7A3zACAgAAUMwwXMPHk086Rz0IH0DtYQQE\\nAAAEvBUrpNGjXdtM05paAH9HAAEAAAGrsFAKLvdtaP9+6be/taYeIBAwBQsAAASkCy90DR+jRjlH\\nPQgfQN1iBAQAAASU//5XuuYa1zamWwHewwhIBXbt2qVRo0YpPDxcTZs2VVhYmKKjo7V06dIqXX/0\\n6FFNnDhRdrtdzZo106BBg7R9+/Y6rhoAAFTENJ2LzM8OH199RfgAvI0RkArs379fJ06c0IQJE9S2\\nbVvl5eUpISFBd911lzIzMzVt2rQKry0qKtLQoUO1c+dOxcXFKSwsTK+//roGDBiglJQUderUyYvv\\nBAAAXHONc+SjRI8e0o4d1tUDBDLDNMn9VVVUVKSIiAj9/PPP2rdvX4Xnvf/++xo7dqwSEhI0cuRI\\nSdLhw4fVuXNnxcTEVDiKkpqaqoiICKWkpKhXr1518h4AAAgk6enSFVe4thUVuW8wCFQV39dqjilY\\n1WCz2dSuXTs1aNCg0vMSEhLUunXr0vAhSa1atdLo0aO1evVqnTlzpq5LBQAg4BmGa/j4+OOyaVgA\\nrEMAOYe8vDwdPnxYGRkZmjdvnjZu3Ki4uLhKr9m+fbvHRNy7d2/l5eVpz549dVUuAAAB7557XEOG\\nzeYMHjfcYF1NAMqwBuQcHnnkEb3xxhuSpODgYM2fP18TJ06s9JqsrCwNGDDArb1NmzaSpAMHDqhb\\nt261XisAAIHs4EGp+J/aUmfOuO/zAcBajICcw+TJk/XJJ58oPj5e119/vR544AEtXry40mvy8/PV\\nqFEjt/bGjRtLkk6ePFkntQIAEKgMwzV8xMc7Rz0IH4DvIYCcw+WXX65BgwZp/Pjx+te//qXrr79e\\nkyZNqjRENGnSRKdOnXJrz8/PL+0HAAA19/zz7ms6TFO66y5r6gFwbvxcoJpuu+02ffzxx/rmm2/U\\ns2dPj+e0adNGBw4ccGvPysqSJLVt27bS15g8ebJCQ0Nd2saNG6dx48adZ9UAAPiXEyekCy5wbTt+\\nXGrWzJp64J+WLVumZcuWubTl5uZaVI3/IIBUU8nIh81W8eBRz549lZiYKNM0ZZz1Y5ktW7aoadOm\\n6ty5c6WvMW/ePB7rBgBABcqPeMycKT31lDW1wL95+gFwyWN4cf6YglWBnJwct7YzZ84oPj5eYWFh\\npYvIs7KylJ6eroKCgtLzbr/9dh06dEgrV64sbTt8+LBWrFihYcOGnfMxvgAAwN0773iebkX4AOoX\\nRkAqMHHiRB0/flzXXnut2rZtq4MHD2rp0qXas2ePFi5cqKCgIEnSk08+qfj4eGVmZqp9+/aSnAGk\\nT58+io2NVVpaWulO6KZpasaMGVa+LQAA6p2CAqn8z+6ysqTWra2pB0DNEEAqMHbsWL311ltasGCB\\njhw5oubNmysqKkqvvfaarr/++tLzDMNwmWYlOadnbdiwQVOmTNH8+fN18uRJRUZGKj4+Xpdddpm3\\n3woAAPVWgwbOAFIiNlZ6+23r6gFQc4ZpmqbVRcCpZE5hSkoKa0AAAAHtP/9x3ziQbyzwBXxfqzlG\\nQAAAgM8wTefO5WfbvVvq0sWaeqorJydHjrg4pSUnK6igQIXBweoaGak4h0N2u93q8gCfQAABAAA+\\noWdPaceOsuN+/aT//te6eqorOztbY6OjNTsjQw5JhqQiSclpaRqTmKjlSUmEEEA8BQsAAFhs507n\\n063ODh9FRfUrfEjS3Mcf1+yMDPWRM3xIzi9afSQ9n5EhR1ycdcUBPoQAAgAALGMY0pVXlh0nJjqn\\nYZV/3G59kJacrKgK+qKK+wEQQAAAgAXGjHENGS1aOINH//7W1VRTQQUFqig32Yr7AbAGBACAgGL1\\nIukffpCKt80qVVAgFW+vVa8VBgfLlDyGkKLifgAEEAAAAobVi6TLT6t6/31p1Kg6ezmv6xoZqS1p\\naerjoW9LcT8ApmABABAwrFokPW2ae/gwTf8KH5IU53Boani4kuQMdir+NUnStPBwxTkc1hUH+BBG\\nQAAACBBpycmq6CtwlKRZtbxIOjfXubbjbHl5UpMmtfoyPsNut2t5UpIccXGaVW6K23L2AQFKEUAA\\nAAgQ3lwkXX7E46WXpEceqbXb+yy73a65CxdaXQbg0wggAAAECG8skn7jDem++1zbTLPGtwXgRwgg\\nAAAEiLpcJH36tNSokWtbTo7UqtV53xKAn2IROgAAAaKuFkkbhmv4ePBB56gH4QOAJ4yAAAAQIGp7\\nkfS6ddKwYa5tTLcCcC4EEAAAAkhtLJI2TclWbg7Fd99J4eE1ui2AAMEULAAAUGUdO7qGj5tvdgYS\\nwgeAqmIEBAAAnNPWrVL5NepMtwJwPgggAACgUuX39EhOlnr3tqYWAPUfU7AAAAEhJydHU2JjNbRb\\nNw2//HIN7dZNU2JjlZOTY3VpPmvIENfw0b69c9SD8AGgJhgBAQD4vezsbI2NjtbsjAw55NyIr0hS\\nclqaxiQmanlSUrWfAOXPvv9euvRS17bCQveF5wBwPvirBADg9+Y+/rhmZ2Soj8p2AbdJ6iPp+YwM\\nOeLirCvOxxiGa/hYs8bzU68A4Hzx1wkAwO+lJScrqoK+qOL+QDdpkvtaD9N03+cDAGqKKVgAAL8X\\nVFAgo4I+W3F/oDpyxH3H8vx8153NAaA2MQICAPB7hcHBquiJsUXF/YHIMFzDx4IFzlEPwgeAukQA\\nAQD4va6RkdpSQd+W4v5Acv/9nqdb/elP1tQDILAQQAAAfi/O4dDU8HAlyTnioeJfkyRNCw9XnMNh\\nXXFedOKEM3i8/npZ2y+/sKEgAO8KzDFnAEBAsdvtWp6UJEdcnGYlJyuooECFwcHqGhmp5Q5HQDyC\\nt/yIx5Ah0vr11tQCILARQAAAAcFut2vuwoVWl+F1r77qfMLV2RjxAGAlAggANzk5OXLExSmt3E+K\\n4wLkJ8WAPygslMqvrd+9W+rSxZp6AKAEAQSAC3aMBuq/8tOt2raVfvrJmloAoDwWoQNwwY7RQP21\\nerXnp1sRPgD4EgIIABfsGA3UT4Yh3Xpr2fGmTaz1AOCbmIIFwAU7RgP1S/kRD4ngAcC3MQICwAU7\\nRgP1w7Zt7uGjqIjwAcD3EUAAuGDHaMD3GYbUu3fZcXy8M3h4Gg0BAF9DAAHggh2jAd911VWeF5nf\\ndZc19QDA+WAuBQAX7BgN+J59+6QOHVzbTp2SGja0pBwAqBECCAA3gbpjNOCLyo94PPusNH26JaUA\\nQK0ggAAA4IPuuktassS1jQXmAPwBAQQAAB9y9KjUsqVr288/u7cBQH3FInQAAHyEYbgGjXHjnKMe\\nhA8A/oQAAgCAxSZO9Px0q3fftaYeAKhLTMECAMAip05JjRu7tu3dK3XsaE09AOANBBAAACzgadNA\\nFpkDCARMwQIAwItefdXzdCvCB4BAwQgIAABeYJqSrdyP/T7+WLrhBmvqAQCrEEAAAKhjTLcCgDJM\\nwQIAoI6sX+8ePoqKCB8AAhsBBACAOmAY0i23lB3//e/O4OFpNAQAAglTsAAAqEVMtwKAyjECAgBA\\nLdi50z18nD5N+ACA8gggAADUkGFIV15Zdvzww87g0aCBdTUBgK9iChYAAOepUycpI8O1jREPAKgc\\nIyAAAFRTVpZz1OPs8PHLL4QPAKgKAggAANVgGFLbtmXH117rDB4tWlhXEwDUJ0zBAoAAlpOTI0dc\\nnNKSkxVUUKDC4GB1jYxUnMMhu91udXk+ZcwY6f33XdsY8QCA6iOAAECAys7O1tjoaM3OyJBDkiGp\\nSFJyWprGJCZqeVISIURSXp7UtKlrW0aGdOml1tQDAPUdU7AAIEDNffxxzc7IUB85w4fk/Eehj6Tn\\nMzLkiIuzrjgfYRiu4aNRI+eoB+EDAM4fAQQAAlRacrKiKuiLKu4PVLNmue/pYZpSfr419QCAP2EK\\nFgAEqKCCAnnYtFuS86dTQQUF3izHJxQVSUFBrm2JiVL//tbUAwD+iAACAAGqMDhYpuQxhBQV9weS\\n8iMeEovMAaAuMAULAAJU18hIbamgb0txfyB4/33P060IHwBQNwggABCg4hwOTQ0PV5KcIx4q/jVJ\\n0rTwcMU5HNYV5yWG4Xy8boklSwgeAFDXAmt8HQBQym63a3lSkhxxcZpVbh+Q5X6+DwjTrQDAOgQQ\\nAAhgdrtdcxcutLoMr9myRerTx7WtoMB94TkAoO4wBQsAEBAMwzV8PP20c9TD6vCRk5OjKbGxGtqt\\nm4ZffrmGduumKbGxysnJsbYwAKgjBJBKbN26VQ888IC6deumZs2a6ZJLLtGYMWP07bffnvPaRYsW\\nyWazefwvOzvbC9UDACSpZUvPi8yfe86aes6WnZ2tMX376rZFi7QuLU1r9uzR2rQ03bZokcb07UsI\\nAeCXmIJViRdeeEFJSUkaNWqUevTooaysLL322mvq1auX/ve//6lbt27nvMfMmTPVsWNHl7bQ0NC6\\nKhkAUGzfPqlDB9e2Eydcdza32tm70Zcovxt9IE2RAxAYCCCVePTRR9W7d28Fn/Us/DFjxqh79+6a\\nM2eO3nnnnXPeIyYmRr169arLMgEA5ZQf8RgxQlq1yppaKpOWnKyKnjUWJWlWAO9GD8B/EUAq0bdv\\nX7e2Tp06qWvXrkpPT6/SPUzT1PHjxxUSEqIgqycaA4CfCw+X9u51bfPlp1uxGz2AQMQakGoyTVOH\\nDh1Sq1atqnT+wIEDFRoaqqZNm2rEiBH67rvv6rhCAAg8R486Rz3ODh8//ODb4UMq243ek0DcjR5A\\nYOBvtmpaunSpDhw4oFmzZlV6XtOmTRUbG6uBAweqefPm2rZtm15++WVFR0crNTVV7dq181LFAODf\\n6vOeHl0jI7UlLU19PPQF0m70AAKLYZr15a9p66WnpysqKkrdu3dXYmKiDE//6lXiiy++0LXXXquJ\\nEydqwYIFbv2pqamKiIhQSkoK60YA4BzuvFN6913Xtvr2L1pOTo7G9O2r5zMyFCXntIQiOcPHtPBw\\nLU9K8usNIYH6iO9rNccISBUdPHhQQ4cOVcuWLZWQkFDt8CFJ/fr1U1RUlD755JM6qBAAAkNBgdSg\\ngWtbYqLUv7819dREIO9GDyBwEUCqIDc3VzExMTp27JgSExPVunXr875Xu3bttGfPnkrPmTx5stuj\\neseNG6dx48ad9+sCgD+oz9OtKhJou9ED9cmyZcu0bNkyl7bc3FyLqvEfBJBzyM/P17Bhw/Tdd9/p\\nk08+UZcuXWp0v717957zJ1rz5s1jSA8AzvLii9KUKa5t9T14APB9nn4AXDIFC+ePp2BVorCwUGPG\\njNGWLVu0YsUKRUVFeTzv4MGDSk9PV8FZj0v0tHvthg0blJqaqptvvrnOagYAf2MYruFj0SLCBwDU\\nZ4yAVOLRRx/V2rVrNWzYMB0+fFhLlixx6R8/frwk6YknnlB8fLwyMzPVvn17SVJ0dLR69eqliIgI\\nhYaGKjU1VW+//bbat2+vqVOnev29AEB944/TrQAABJBK7dixQ4ZhaO3atVq7dq1Ln2EYpQHEMAy3\\nReljx47V+vXr9e9//1t5eXlq27at7rvvPk2fPp1FhQBQiXXrpGHDXNuKijwHEgBA/cNjeH0Ij3UD\\nEOjKh4xHHpFeesmaWgDAE76v1RwjIAAAyzHdCgACB4vQAQCW+fpr9/Dx66+EDwDwZwQQAIAlDEPq\\n0aPs+JprnMEjJMS6mgAAdY8pWADgRTk5OXLExSmt3K7XcQG063VoqHTsmGsbIx4AEDgIIADgJdnZ\\n2RobHa3ZGRlySDIkFUlKTkvTmMRELU9K8usQkp0tXXyxa9tPP0lt21pTDwDAGkzBAgAvmfv445qd\\nkaE+coYPyfmXcB9Jz2dkyBEXZ11xdcwwXMOHYThHPQgfABB4CCAA4CVpycmKqqAvqrjf3wwZ4r7I\\n3DSd+3oAAAITU7AAwEuCCgpU0V56tuJ+f3HqlNS4sWvbtm1SRIQ19QAAfAcBBAC8pDA4WKbkMYQU\\nFff7A/b0AABUhilYAOAlXSMjtaWCvi3F/fXZ0097nm5F+AAAnI0AAgBeEudwaGp4uJLkHPFQ8a9J\\nkqaFhyvO4bCuuBowTWfwmDWrrC0hgeABAPDMP8b7AaAesNvtWp6UJEdcnGaV2wdkeT3dB4TpVgCA\\n6iKAAIAX2e12zV240OoyamzZMumOO1zbioo8BxIAAM7GFCwAQLUYhmv4eO65smlYAACcCyMgAIAq\\nYboVAKA2MAICAKhUcrJ7+Dh1ivABADg/BBAAQIUMQ4o6a/v2W291Bo+GDa2rCQBQvzEFCwDghulW\\nAIC6wggIAKDUjz+6h4+cHMIHAKD2MAICAJDkHjwuukg6dMiaWgAA/osREAAIcOPGuYcP0yR8AADq\\nBiMgABCgTp6UQkJc23btkrp2taYeAEBgIIAAQAAqP+LRoIF0+rQ1tQAAAgtTsAAggMye7Xm6FeED\\nAOAtjIAAQAAoKpKCglzbNm+WrrnGmnoAAIGLAAIAfo49PQAAvoQpWADgp1as8DzdivABALASAQQA\\n/JBhSKNHlx0vXkzwAAD4BqZgAYAfYboVAMDXMQICAH4gOdk9fBQUED4AAL6HAAIA9ZxhSFFRZcdT\\npzqDR/mnXgEA4AuYggUA9VRYmPTzz65tjHgAAHwdIyAAUM/s3+8c9Tg7fBw/TvgAANQPBBAAqEcM\\nQ7rkkrLjW25xBo9mzayrCQCA6mAKFgDUA0OGSP/6l2sbIx4AgPqIAAIAPuz4cal5c9e2/ful3/7W\\nmnoAAKgpAggA+Kjyj9W96CLp0CFragEAoLawBgQAfMwTT7iHD9MkfAAA/AMjIADgIwoLpeByfytv\\n3SpdfbU19QAAUBcIIADgA8qPeEgsMgcA+CemYAGAhRYt8jzdivABAPBXjIAAgEXKB48PPpBGjrSm\\nFgAAvIUAAgBexnQrAEAgYwoWAHhJaqp7+CgqInwAAAILAQQAvMAwpIiIsuNFi5zBw9NoCAAA/owp\\nWABQh66+WkpJcW1jxAMAEMgIIABQB/bvly65xLUtP19q1MiaegAA8BVMwQKAWmYYruHjqaecox6E\\nDwAAGAEBgFoTG+tc23E2plsBAOCKAAIANXTsmBQa6tp25Ih04YXW1AMAgC9jChYA1IBhuIaP225z\\njnoQPgAA8IwAAgDnYc4c90fomqaUkGBNPQAA1BdMwQKAajhzRmrY0LXtu++k8HBr6gEAoL4hgABA\\nFZUf8ejcWfrmG2tqAQCgvmIKFgCcw/vve55uRfgAAKD6GAEBgAqYpmQr92OaL7+U+va1ph4AAPwB\\nAQQAPGjXTvrpJ9e2ivb0yMnJkSMuTmnJyQoqKFBhcLC6RkYqzuGQ3W6v+2IBAKhHCCAAcJZdu6Tf\\n/c61rajIfQpWiezsbI2NjtbsjAw5JBmSiiQlp6VpTGKiliclEUIAADgLa0AAoJhhuIaPzz5zjnpU\\nFD4kae7jj2t2Rob6yBk+JOdfrH0kPZ+RIUdcXN0VDABAPUQAARDw7rrLNWQ0buwMHgMGnPvatORk\\nRVXQF1XcDwAAyjAFC0DA+ukn51qPs505IwVX42/GoIICVTRAYivuBwAAZRgBARCQDMM1fCxd6hz1\\nqE74kKTC4GBVsDZdRcX9AACgDAEEQECZPt3znh533HF+9+saGaktFfRtKe4HAABl+NEcgIBw7JgU\\nGuraduKE1LRpze4b53BoTGKins/IUJScP9UpkjN8TAsP13KHo2YvAACAn2EEBIDfMwzX8DFnjnPU\\no6bhQ5LsdruWJyVp5YQJGta1q4Z37qxhXbtq5YQJPIIXAAAPGAEB4Lfeeku6917Xtoo2E6wJu92u\\nuQsX1v6NAQDwQ4yAVGDr1q164IEH1K1bNzVr1kyXXHKJxowZo2+//bZK1x89elQTJ06U3W5Xs2bN\\nNGjQIG3fvr2OqwYgOZ9kZRiu4ePQoboJHwAAoHoIIBV44YUX9OGHH+rGG2/U/PnzNXHiRG3evFm9\\nevXSrl27Kr22qKhIQ4cO1bJly/TQQw/J4XAoOztbAwYM0HfffeeldwAEJsOQGjYsO77vPmfwuOgi\\n62oCAABlmIJVgUcffVS9e/dW8FmP0BwzZoy6d++uOXPm6J133qnw2oSEBCUlJSkhIUEjR46UJI0e\\nPVqdO3fW9OnTtXTp0jqvHwg0H30kxcS4tjHiAQCA7yGAVKBv375ubZ06dVLXrl2Vnp5e6bUJCQlq\\n3bp1afiQpFatWmn06NFasmSJzpw5owYNGtR6zUAgMk3JVm4sd88e6bLLrKkHAABUjilY1WCapg4d\\nOqRWrVpVet727dvVq1cvt/bevXsrLy9Pe/bsqasSgYDSpYtr+Bg40BlICB8AAPguAkg1LF26VAcO\\nHNCYMWMqPS8rK0tt2rRxay9pO3DgQJ3UBwSK1FTnWo9vvilrM03p00+tqwkAAFQNAaSK0tPTdf/9\\n9ys6Olp33313pefm5+erUaNGbu2NGzeWJJ08ebJOagQCgWFIERFlx19+yVoPAADqEwJIFRw8eFBD\\nhw5Vy5YtlZCQIMMwKj2/SZMmOnXqlFt7fn5+aT+A6rn1Vmf4KHHxxc7g4WG5FgAA8GEsQj+H3Nxc\\nxcTE6NixY0pMTFTr1q3PeU2bNm08TrPKysqSJLVt27bS6ydPnqzQs7dtljRu3DiNGzeuGpUD/iEz\\nU+rY0bWtsNB94TkAALVt2bJlWrZsmUtbbm6uRdX4DwJIJfLz8zVs2DB99913+uSTT9SlS5cqXdez\\nZ08lJibKNE2X0ZItW7aoadOm6ty5c6XXz5s3z+MidiDQlB9sXLlS+n//z5paAACBx9MPgFNTUxVx\\n9lxgVBs/Q6xAYWGhxowZoy1btmjFihWKioryeN7BgweVnp6ugoKC0rbbb79dhw4d0sqVK0vbDh8+\\nrBUrVmjYsGE8ghc4h8cecw8fpkn4AADAHzACUoFHH31Ua9eu1bBhw3T48GEtWbLEpX/8+PGSpCee\\neELx8fHKzMxU+/btJTkDSJ8+fRQbG6u0tDSFhYXp9ddfl2mamjFjhtffC1Bf/PKLdOGFrm0nT0rF\\nz28AAAB+gABSgR07dsgwDK1du1Zr16516TMMozSAGIbhtijdZrNpw4YNmjJliubPn6+TJ08qMjJS\\n8fHxuowNCgCPyo94zJ8vPfigNbUAAIC6Y5gmD7D0FSVzClNSUlgDgoDx2mvuQYO/lQAAvorvazXH\\nCAgAS+TnS+WfSH3kiPsULAAA4F9YhA7A6wzDNXw88ohz1IPwAQCA/2MEBIDXrF7t3FDwbEy3AgAg\\nsBBAANS5oiIpKMi17fvvpQ4dLCkHAABYiClYAOrUb37jGj6GDXOOehA+AAAITIyAAKgT//uf1Lev\\naxvTrQAAAAEEQK0rv6fHtm1SRIQ1tQAAAN/CFCwAteaGG1zDR6dOzlEPwgcAACjBCAiAGvv2W6lz\\nZ9e2oiL3kRAAAABGQADUiGG4ho8NG5yjHoQPAADgCQEEwHn585/dQ4ZpSjEx1tQDAADqB6ZgAaiW\\nnBzpootc206dkho2tKYeAABQvzACAqDKDMM1fPzzn85RD8IHAACoKgIIgHOaO9fzdKt77rGmHgAA\\nUDBYugsAACAASURBVH8xBQtAhfLypKZNXdtyc6Xmza2pBwAA1H+MgADwyDBcw8dTTzlHPQgfAACg\\nJhgBAeDivfekceNc20zTmloAAID/IYAAkCQVFkrB5f5G+PFH6Te/saYeAADgn5iCBUDNmrmGjzvu\\ncI56ED4AAEBtYwQECGCbNkkDB7q2Md0KAADUJQIIEIBMU7KVG//8+v+3d+fRUZUHH8d/M0BYAqEk\\nhCVSFsMBJUJjkC0qL6htAAVsEdCKQsQNUNT2ECxaqbJYg1aq1Fo3lgo0soiI60uFF6whEUKVQ0Rw\\nNCBLyFAMAmHJct8/Lkk6TAJBM/eZyXw/5+Tk3Ocm8OMaZH7zPPc+26TLLjOTBwAAhA+WYAFhpndv\\n3/KRlGQXEsoHAABwAjMgQJjYvt2/ZJSV+W8wCAAAEEjMgABhwOXyLR8ffWTPelA+AACA0yggQB2W\\nnu5bMho2tIvH2TeeAwAAOIUlWEAd9N13UnS071hxsf8+HwAAAE5jBgSoY37yE9/ysWGDPetB+QAA\\nAMGAAgLUEcuW2cutjhyxj/v1s4vH1VebzQUAAPDfeE8UCHEnTkhNmviPNWpkJg8AAMC5MAMChLAr\\nrvAtHytX2rMelA8AABCsmAEBQtC6ddI111Qet24t5eebywMAAFBTFBAghJSUSA0a+I5995194zkA\\nAEAoYAkWECJGjPAtHy++aC+3onwAAIBQwgwIEOT+/W/p8st9xyzLTBYAAIAfiwICBCnLktxnzVHu\\n2yfFxZnJAwAAUBtYggUEoYce8i0f06fbhYTyAQAAQh0zIEAQycuTOnXyHWO5FQAAqEuYAQGChMvl\\nWz527KB8AACAuocCgrDh9Xo1JTVV1yckaFjXrro+IUFTUlPl9XqN5nr6abt8lBs/3i4eXbuaywQA\\nABAoLMFCWCgoKNDNycma7fEoXZJLUpmk7Nxcjd64URmZmYqNjXU006FD0tm/ZWmp/43nAGrO6/Uq\\nPS1NudnZqldSotL69dWtd2+lpac7/nccAFA1XuogLMyZOlWzPR71lV0+JPuHv6+kWR6P0tPSHM3T\\noIFv+di0qeqnXgGouYKCAo3u108jFizQmtxcrd65U2/n5mrEggUa3a+f8dlOAICNlzsIC7nZ2epT\\nzbk+Z847YfFie7lVSYl9fN11dvHoU104ADUWbG80AACqxhIshIV6JSVyVXPOfeZ8IB0/LjVt6jt2\\n6pQUERHQ3xYIK7nZ2Uqv5lwfSTMdeqMBAHBuzIAgLJTWr6/qHihVduZ8oFx6qW/5eOcde9aD8gHU\\nLtNvNAAAaoYCgrDQrXdvZVVzLuvM+dr24Yf2cqsdO+zjiy+2i8eQIbX+WwGQ2TcaAAA1RwFBWEhL\\nT9e0+Hhlyn4hojOfMyU9Eh+vtPTqFm5cuOJiu3ikpFSOff+95PHU2m8BoAom3mgAAFw4CgjCQmxs\\nrDIyM7Vy3DgN7dZNw7p00dBu3bRy3LhafQTvkCG+S6vmz7dnPZo1q5VfHg4I1v1icH5OvtEAAPjh\\nmI9G2IiNjdWc+fMD8mtv3iz16uU7xi7moScY94tBzZW/0ZCelqaZZ+0DksE+IAAQNCggwI9Q1d4d\\nBw9KrVqZyYMf578f41ru7Me4BqrEonYE8o0GAEDtYAkW8APde69v+Zg92y4klI/QFSz7xQAAUJcx\\nAwJcoF27pC5dfMdYblU38BhXAAACjwICXADXWa9Ov/pKio83kwW1r/wxrlWVEB7jCgBA7WAJFlAD\\nM2f6lo9Jk+xZD8pH3cJjXAEACDzezgPOIT9fatvWd6yszH8mBHVDWnq6Rm/cqFkej/rIfoemTHb5\\neCQ+Xhk8xhUAgB+NAgJU4+ySsWWLlJRkJgucwWNcAQAIPAoIcJZXX5XuvLPyeNgw6a23zOWBs3iM\\nKwAAgUUBAc74/nupeXPfseJiifuOAQAAag83oQOSfvpT3/Lxv/9r32RO+QAAAKhdvLxCWFu9Who+\\nvPK4e3fp88/N5aktXq9X6Wlpyj3rPoY07mMAAACGUUAQlk6dkho18h07flxq0sRMntpUUFCgm5OT\\nNdvjUbrsPS3KJGXn5mr0xo3KyMykhAAAAGNYgoWwM2CAb/lYutReblUXyockzZk6VbM9HvVV5YZ6\\nbkl9Jc3yeJSelmYuHAAACHvMgCBsbN8uXXZZ5XHjxlJRkbk8gZKbna3qdqvoI2lmdraTcQAAAHxQ\\nQFDnlZVJ9er5jh06JMXEmMkTaPVKSlTdPonuM+cBAABMYQkW6rQnn/QtH8uW2cut6mr5kKTS+vVl\\nVXOu7Mx5AAAAU3glgjpp926pY8fK427d7CVY4aBb797Kys1V3yrOZZ05DwAAYAozIKhTymc3/rt8\\n5OeHT/mQpLT0dE2Lj1em7BkPnfmcKemR+HilpVd3hwgAAEDgUUDO4fjx45o+fboGDRqk6Ohoud1u\\nLVy4sEbfu2DBArnd7io/CgoKApw8PL36quR2S4cP28d//atdSFq3NpvLabGxscrIzNTKceM0tFs3\\nDevSRUO7ddPKceN4BC8AADCOJVjn4PV6NWPGDHXo0EGJiYlav369XK7qbu+t2owZM9SpUyefseb/\\nveU2fjSvV2rVqvK4aVPp+++lC/xPVafExsZqzvz5pmMAAAD4oYCcQ1xcnPLz89WqVStt2bJFvXr1\\nuuBfY/DgwUpKSgpAOkhSUpK0dWvlsccjXXyxuTwAAAA4N5ZgnUNERIRanXlr3bKqe67QuVmWpaNH\\nj6q0tLQ2o4W9DRvsGY7y8vHEE/ZyK8oHAABAcGMGJMAGDhyoY8eOKSIiQikpKXrmmWfUuXNn07FC\\n1okTUpcu0t69lWOlpfa9HwAAAAh+FJAAiYyMVGpqqgYOHKioqCht3rxZf/rTn5ScnKycnBy1a9fO\\ndMSQ89RT0sMPVx7n5UkdOhiLAwAAgB+AAhIgI0eO1MiRIyuOhw0bppSUFPXv31+zZs3SX//6V4Pp\\nQkturpSQUHn88svSnXeaywMAAIAfjgLioCuvvFJ9+vTR2rVrTUcJCcXFUp8+lfd5XHGFlJkpsZE3\\nAABA6OKlnMPatWunnTt3nvNrHnroIb9H9d5yyy265ZZbAhktqLz0knTPPZXHubnSpZeaywMAAMLP\\n0qVLtXTpUp+xI0eOGEpTd1BAHPb111+fdyO4Z599Nmwf3ZuXJ/33tinp6dKUKcbiAACAMFbVG8A5\\nOTnq2bOnoUR1A88OqgX5+fnasWOHSkpKKsa8Xq/f17377rvKycnRoEGDnIwXEsrKpF/8orJ8dOhg\\nP/GK8gEAAFC3MANyHvPmzVNhYaH2798vSVq9erX27NkjSZo8ebKioqL08MMPa9GiRcrLy1P79u0l\\nScnJyUpKSlLPnj3VvHlz5eTk6LXXXlP79u01bdo0Y3+eYLRsmTRqVOXxp5/a93sAAACg7qGAnMcz\\nzzyj3bt3S5JcLpfefPNNrVy5Ui6XS7fffruioqLkcrnkcrl8vu/mm2/WO++8ow8//FBFRUWKi4vT\\nPffco+nTp593CVa4OHhQatOm8njqVOmPfzSXBwAAAIHnsn7oFt+odeVrCrds2VKn7wGxLGnMGGnJ\\nEvs4MlI6cEBq1sxsLgAAgPMJl9drgcQ9IHDUBx/Yu5aXl4+PPpKOHaN8AAAAhAuWYMERhYVSdLQ9\\n+yHZGwm+/LLZTAAAAHAeMyAIuIceklq0qCwfXi/lAwAAIFxRQBAwmZmSyyXNnWsfr1pll5CWLc3m\\nAgAAgDkswUKtKyqy9/MoKLCPhw+X3nzTLiMAAAAIb8yAoFbNnGk/1aq8fHz7rT3zQfkAAACAxAwI\\nasm2bVKPHpXHCxZIY8caiwMAAIAgRQHBj3L6tJSUJG3fbh9feaX0f/8n1atnNhcAAACCE0uw8IP9\\n5S9Sw4aV5ePLL6WPP6Z8AAAAoHrMgOCCeTxS586Vx3PnSg88YC4PAAAAQgcFBDVWWipde629xEqS\\nunSRPv/cngUBAAAAaoIlWKiRJUuk+vUry8fWrfaSK8oHAAAALgQFBOe0f7/9CN1bb7WPH3vM3kww\\nMdFsLgAAAIQmlmChSpYljRolLV9uH7doYe/pERlpNhcAAABCGzMg8PPOO5LbXVk+Nm6UDh+mfAAA\\nAODHYwYEFQ4flmJiKo8nTrQftQsAAADUFgoIJEkTJkgvvlh5/J//SNHR5vIAAACgbmIJVpjbsMG+\\nyby8fLzzjn3/B+UDAAAAgcAMSJg6dkxq1046csQ+HjVK+sc/7DICAAAABAozIGHo97+XmjWrLB/7\\n90sZGZQPAAAABB4zIGFk61YpKanyeMkS6ZZbzOUBAABA+KGAhIFTp6SEBMnjsY8HDpTWrrUftQsA\\nAAA4iZegddzcuVKjRpXlw+ORPvootMuH1+vVlNRUXZ+QoGFdu+r6hARNSU2V1+s1HQ0AAADnwQxI\\nHbVzp9S1a+XxX/5i7+sR6goKCnRzcrJmezxKl+SSVCYpOzdXozduVEZmpmJjYw2nBAAAQHVC+H1w\\nVKW0VEpOriwf3btLp0/XjfIhSXOmTtVsj0d9ZZcPyf4h7itplsej9LQ0c+EAAABwXhSQOmThQql+\\nfSkz0z7+/HP7o0EDs7lqU252tvpUc67PmfMAAAAIXhSQOmDvXvsRuuPG2cczZ9qbCXbvbjRWQNQr\\nKVF1Twt2nzkPAACA4MU9ICHMsqThw6W337aP27SxbzJv0sRsrkAqrV9fllRlCSk7cx4AAADBixmQ\\nEFVUZD/Jqrx8ZGZKBw7U7fIhSd1691ZWNeeyzpwHAABA8KKAhKijR+3PDz5oz4T07Ws2j1PS0tM1\\nLT5embJnPHTmc6akR+LjlZaebi4cAAAAzov1KiGqdWu7eISb2NhYZWRmKj0tTTOzs1WvpESl9eur\\nW+/eykhP5xG8AAAAQY4CgpATGxurOfPnm44BAACAH4AlWAAAAAAcQwEBAAAA4BgKCAAAAADHcA9I\\nCPJ6vUpPS1PuWTdhp3ETNgAAAIIcBSTEFBQU6ObkZM32eJQue0O+MknZubkavXGjMjIzKSEAAAAI\\nWizBCjFzpk7VbI9HfVW5G7hbUl9JszwepaelmQsHAAAAnAcFJMTkZmerTzXn+pw5DwAAAAQrCkiI\\nqVdSUjHzcTb3mfMAAABAsKKAhJjS+vVV3QboZWfOAwAAAMGKAhJiuvXuraxqzmWdOQ8AAAAEKwpI\\niElLT9e0+Hhlyp7x0JnPmZIeiY9XWnq6uXAAAADAebBeJ8TExsYqIzNT6WlpmnnWPiAZ7AMCAACA\\nIEcBCUGxsbGaM3++6RgAAADABWMJFgAAAADHUEAAAAAAOIYCAgAAAMAxFBAAAAAAjqGAAAAAAHAM\\nBQQAAACAYyggAAAAABxDAQEAAADgGAoIAAAAAMdQQAAAAAA4hgICAAAAwDEUEAAAAACOoYAAAAAA\\ncAwFBAAAAIBjKCAAAAAAHEMBAQAAAOAYCggAAAAAx1BAAAAAADiGAgIAAADAMRQQAAAAAI6hgAAA\\nAABwDAXkHI4fP67p06dr0KBBio6Oltvt1sKFC2v8/YWFhbr77rsVGxurpk2b6pprrtHWrVsDmBgA\\nAAAIbhSQc/B6vZoxY4a+/PJLJSYmSpJcLleNvresrEzXX3+9li5dqsmTJys9PV0FBQUaMGCAvvrq\\nq0DGBgAAAIIWBeQc4uLilJ+fr2+++UZz5sy5oO9dvny5MjMztXDhQv3+97/XxIkTtX79etWrV0/T\\np08PUOK6aenSpaYjBB2uiT+uiT+uiS+uhz+uiT+uiT+uCWobBeQcIiIi1KpVK0mSZVkX9L3Lly9X\\nmzZt9Ktf/apirGXLlho1apTeeustFRcX12rWuoz/8fnjmvjjmvjjmvjievjjmvjjmvjjmqC2UUAC\\nZOvWrUpKSvIb79Wrl4qKirRz504DqQAAAACzKCABcuDAAbVt29ZvvHxs//79TkcCAAAAjKOABMjJ\\nkyfVsGFDv/FGjRpJkk6cOOF0JAAAAMC4+qYD1FWNGzfWqVOn/MZPnjxZcf5s5aXkiy++CGy4EHPk\\nyBHl5OSYjhFUuCb+uCb+uCa+uB7+uCb+uCb+uCa+yl+n8WbyD0cBCZC2bdtWuczqwIEDkuwnbJ0t\\nLy9PkjRmzJiAZgtFPXv2NB0h6HBN/HFN/HFNfHE9/HFN/HFN/HFN/OXl5enKK680HSMkUUACJDEx\\nURs3bpRlWT57h2RlZSkyMlJdunTx+56UlBS9/vrr6tixY5UzJAAAADDrxIkTysvLU0pKiukoIYsC\\nUgvy8/NVWFiozp07q359+5LedNNNWr58uVauXKkRI0ZIkg4dOqRly5Zp6NChatCggd+v07JlS916\\n662OZgcAAMCFYebjx6GAnMe8efNUWFhYsZxq9erV2rNnjyRp8uTJioqK0sMPP6xFixYpLy9P7du3\\nl2QXkL59+yo1NVW5ubmKiYnRCy+8IMuy9Pjjjxv78wAAAAAmuawL3WEvzHTq1Em7d++WpIqlVOXL\\nqr755hu1b99eqampWrRoUcVxucLCQk2ZMkWrVq3SiRMn1Lt3bz399NNV7g8CAAAAhAMKCAAAAADH\\nsA8IAAAAAMdQQAz79NNPdd999ykhIUFNmzZVhw4dNHr0aO3atct0NGO2b9+ukSNHKj4+XpGRkYqJ\\niVFycrIWL15sOlrQmDVrltxut7p37246ijHr16+X2+2u8iM7O9t0PGNycnI0bNgwxcTEKDIyUt27\\nd9fzzz9vOpYx48aNq/bnxO12VzwaPZxs3rxZw4cPV1xcnCIjI3XppZdqxowZYb2nwZYtWzRo0CA1\\nb95cUVFRSklJ0WeffWY6liOOHz+u6dOna9CgQYqOjpbb7dbChQur/NovvvhCgwYNUrNmzRQTE6Pb\\nb79dhw4dcjhx4NX0mmRnZ2vixInq2bOnGjRoILebl9U1xU3ohj311FPKzMzUyJEj1aNHDx04cEDz\\n5s1TUlKSNm3apISEBNMRHbdnzx4dO3ZM48aNU1xcnIqKirR8+XLddtttysvL0yOPPGI6olF79+7V\\n7NmzFRkZ6fOI53D1wAMPqFevXj5j8fHxhtKY9eGHH2ro0KHq2bOnHnvsMTVt2lRfffWV9u3bZzqa\\nMffee69+8Ytf+IyVlZXp3nvvVadOndS2bVtDyczYtm2brrrqKsXFxenBBx9UdHS0PvnkE02fPl1b\\ntmzRqlWrTEd0XE5Ojq666ip16NBBf/jDH1RaWqoXXnhB//M//6Ps7OwqH5tfl3i9Xs2YMUMdOnRQ\\nYmKi1q9fX+W/LXv37lX//v3VokULPfnkkzp69Kiefvppbdu2TdnZ2VU+3TNU1fSavPvuu3r11Vf1\\ns5/9TPHx8WH95vEFs2DUJ598YhUXF/uM7dq1y2rUqJE1ZswYQ6mCT2lpqZWYmGi1b9/edBTjRo8e\\nbV133XXWgAEDrMsuu8x0HGPWrVtnuVwua8WKFaajBIUjR45YrVu3tkaMGGE6StDbuHGj5XK5rCef\\nfNJ0FMdNmzbNcrlcVm5urs/42LFjLZfLZRUWFhpKZs6QIUOsmJgY6/DhwxVjBw4csJo1axYWf59O\\nnTplHTx40LIsy9q8ebPlcrmshQsX+n3dhAkTrMjISOvbb7+tGFu7dq3lcrmsl156ybG8TqjpNTl4\\n8KB18uRJy7Isa9KkSZbL5XI0Zyhjrsiwfv36VewdUq5z587q1q2bduzYYShV8HG73WrXrl2deofl\\nh9iwYYNWrFihuXPn+m1yGa4sy9LRo0dVUlJiOopRS5YsUUFBgWbNmiXJXkJQVlZmOFVwWrJkiVwu\\nl37961+bjuK48k1uW7Vq5TPepk0b1atXTxERESZiGbVx40Zdd911atGiRcVYmzZt1L9/f61Zs0ZF\\nRUUG0wVeRERExc+DdY7nEq1YsUI33HCD2rVrVzF27bXXqkuXLnrjjTcCntNJNb0mrVq1UsOGDZ2K\\nVadQQIKQZVk6ePCgWrZsaTqKUUVFRTp06JA8Ho+effZZffDBB0pLSzMdy5jS0lLdf//9uuuuu8Jy\\naV51UlNT1bx5czVu3FjXXHONtmzZYjqSEWvXrlVUVJS+/fZbde3aVc2aNVPz5s01ceJEnTp1ynS8\\noFFcXKw33nhDV155pc9j08PFHXfcodatW2v8+PH67LPP9O233yojI0MvvviiJk+eXFFQwsnp06er\\n/HM3adJEp0+f1rZt2wykCi779u2T1+vVFVdc4XeuV69e2rp1q4FUCGXcAxKEFi9erP3792vmzJmm\\noxj1m9/8Ri+99JIkqX79+nruued09913G05lzosvvqg9e/boo48+Mh0lKDRs2FA33XSThgwZopYt\\nW2r79u16+umndfXVV+uTTz5RYmKi6YiO2rVrl0pKSnTjjTfqzjvv1FNPPaV169bp+eefV2FhoZYs\\nWWI6YlD44IMPdPjwYd16662moxgRFxenf/3rXxoyZIguv/zyivFHH31UTzzxhMFk5nTt2lWZmZkq\\nKyuruIn49OnTysrKkqSKjYjDWfnDGqq6Z6pt27Y6fPiwiouLw36VAmqOAhJkduzYoUmTJik5OVlj\\nx441Hceohx56SKNGjdL+/fu1ePFi3XfffWrcuHFYXpf//Oc/euyxx/TYY48pJibGdJyg0K9fP/Xr\\n16/i+IYbbtBNN92kHj166He/+53ee+89g+mcd+zYMRUVFWnChAmaO3euJOnGG2/U6dOn9be//U1P\\nPPGEOnfubDileUuWLFFERIRGjRplOooRBw8e1ODBgyVJL7/8smJiYrRmzRrNmjVLrVu31qRJkwwn\\ndN7EiRM1YcIEjR8/XmlpaSotLdXMmTOVn58vSWH9dLBy5degquVGjRo1qvgaCghqiiVYQSQ/P1/X\\nX3+9WrRooeXLl4f9+v6uXbvqmmuu0ZgxY/Tee+/p2muv1YMPPhiW/xg8+uijatmype6//37TUYJa\\nfHy8hg8frnXr1p1z3W5dVL6E5JZbbvEZLz/etGmT45mCzbFjx/TWW28pJSXFZ71/OJkxY4b27dun\\ndevWafz48brxxhv1yiuvaOzYsZo6daoOHz5sOqLj7rnnHk2bNk1LlixRQkKCevTooW+++aZiyW/T\\npk0NJzSv/P8vVS3nPHnypM/XADVBAQkSR44c0eDBg/X999/r/fffV5s2bUxHCjojRozQkSNH9OWX\\nX5qO4qhdu3bp5Zdf1v3336+9e/cqLy9PeXl5OnnypE6fPq3du3fru+++Mx0zaLRr106nT5/W8ePH\\nTUdxVFxcnCSpdevWPuPlN1LyMyKtWrVKJ06cCNvlV5L08ccf6/LLL6/4eSk3dOhQFRUV6d///reh\\nZGbNnDlTBw8e1Mcff6xt27YpKytLpaWlklTnH8NbE+VLr6raN+fAgQOKiYlh9gMXhAISBE6ePKmh\\nQ4fqq6++0po1a3TJJZeYjhSUymc+wm2jn3379qmsrEyTJ0/WxRdfXPGRnZ2tnTt3qlOnTpoxY4bp\\nmEHj66+/VuPGjcPuXcvym0P37t3rM16+fj02NtbxTMFm8eLFatasmYYNG2Y6ijHFxcUVL6zPHpcU\\n1k+T+8lPfqLk5OSKh3ysXbtWP/3pT/k3WdJFF12k2NhYffrpp37nsrOzw+6eO/x44fVKLgiVlpZq\\n9OjRysrK0rJly9SnTx/TkYzzer1+Y8XFxVq0aJFiYmLC7glQ3bt315tvvqlVq1ZVfLz55ptKSEhQ\\nhw4dtGrVKo0fP950TMdV9XPy2WefafXq1X4bz4WD8nsaXn31VZ/xV155RQ0aNNCAAQMMpAoeXq9X\\na9eu1S9/+cuKNevhKCkpSTk5OX4bpi1dulT16tVTjx49DCULLhkZGdq8ebMefPBB01GCxogRI7Rm\\nzRqfNzn++c9/ateuXRo5cqTBZAhF3IRu2G9/+1u9/fbbGjp0qA4dOqTXX3/d5/yYMWMMJTPn7rvv\\n1tGjR9W/f3/FxcUpPz9fixcv1s6dOzV//nzVq1fPdERHxcTEaPjw4X7jzz77rCSF7bu5o0ePVpMm\\nTdSvXz+1atVKubm5eumll9S0aVP98Y9/NB3PcYmJibrjjjv02muvqaSkRP3799f69eu1fPlyTZs2\\nLeyXdWZkZKi0tDSsl19J0pQpU7RixQpdffXVuu+++xQdHa01a9bo/fff11133RWWPycbNmzQE088\\noZSUFEVHR2vTpk1asGCBBg8erAceeMB0PEfMmzdPhYWFFTOmq1ev1p49eyRJkydPVlRUlKZNm6Zl\\ny5Zp4MCBeuCBB3T06FHNmTNHPXr0UGpqqsn4AVGTa7J79279/e9/lyRt3rxZkjRr1ixZlqWOHTuG\\n5Wu4GjO4CSIsyxowYIDldrstl8vl9+F2u03HM+If//iH9fOf/9xq06aN1aBBAysmJsYaMmSItXbt\\nWtPRgsqAAQOs7t27m45hzHPPPWf16dPHiomJsRo0aGBddNFF1u233255PB7T0YwpLi62Hn/8catj\\nx45WRESE1aVLF+vPf/6z6VhBoV+/flabNm2ssrIy01GMy8rKsgYNGmRFRUVZERER1iWXXGI9+eST\\nVmlpqeloRng8HislJcWKjY21GjVqZHXr1s166qmnrOLiYtPRHNOxY0ef1x7lr0vcbre1e/fuiq/b\\nvn27lZKSYkVGRlrR0dHWbbfdZhUUFBhMHjg1uSbr1q2r8mtcLpc1cOBAw3+C4OayrDB7VAwAAAAA\\nY7gHBAAAAIBjKCAAAAAAHEMBAQAAAOAYCggAAAAAx1BAAAAAADiGAgIAAADAMRQQAAAAAI6hgAAA\\nAABwDAUEAAAAgGMoIAAAAAAcQwEBAAAA4BgKCAAAAADHUEAAAAAAOIYCAgAAAMAxFBAAAAAAjqGA\\nAAAAAHAMBQQAAACAYyggAAAAABxDAQEAAADgGAoIAAAAAMdQQAAAAAA4hgICAAAAwDEUEAAAAACO\\noYAAAAAAcAwFBAAAAIBjKCAAAAAAHEMBAQAAAOAYCggAAAAAx1BAAAAAADiGAgIAAADAMRQQMuD6\\npgAAAEZJREFUAAAAAI6hgAAAAABwDAUEAAAAgGMoIAAAAAAcQwEBAAAA4BgKCAAAAADHUEAAAAAA\\nOIYCAgAAAMAxFBAAAAAAjvl/RxyPZhg3pnEAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<IPython.core.display.Image object>\"\n      ]\n     },\n     \"execution_count\": 23,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from IPython.display import Image\\n\",\n    \"Image(filename='linearreg.png')\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.4.3\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "deep-learning/tensor-flow-examples/notebooks/2_basic_classifiers/logistic_regression.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"# Logistic Regression in TensorFlow\\n\",\n    \"\\n\",\n    \"Credits: Forked from [TensorFlow-Examples](https://github.com/aymericdamien/TensorFlow-Examples) by Aymeric Damien\\n\",\n    \"\\n\",\n    \"## Setup\\n\",\n    \"\\n\",\n    \"Refer to the [setup instructions](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-examples/Setup_TensorFlow.md)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Extracting /tmp/data/train-images-idx3-ubyte.gz\\n\",\n      \"Extracting /tmp/data/train-labels-idx1-ubyte.gz\\n\",\n      \"Extracting /tmp/data/t10k-images-idx3-ubyte.gz\\n\",\n      \"Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Import MINST data\\n\",\n    \"import input_data\\n\",\n    \"mnist = input_data.read_data_sets(\\\"/tmp/data/\\\", one_hot=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import tensorflow as tf\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Parameters\\n\",\n    \"learning_rate = 0.01\\n\",\n    \"training_epochs = 25\\n\",\n    \"batch_size = 100\\n\",\n    \"display_step = 1\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# tf Graph Input\\n\",\n    \"x = tf.placeholder(\\\"float\\\", [None, 784]) # mnist data image of shape 28*28=784\\n\",\n    \"y = tf.placeholder(\\\"float\\\", [None, 10]) # 0-9 digits recognition => 10 classes\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Create model\\n\",\n    \"\\n\",\n    \"# Set model weights\\n\",\n    \"W = tf.Variable(tf.zeros([784, 10]))\\n\",\n    \"b = tf.Variable(tf.zeros([10]))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Construct model\\n\",\n    \"activation = tf.nn.softmax(tf.matmul(x, W) + b) # Softmax\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Minimize error using cross entropy\\n\",\n    \"# Cross entropy\\n\",\n    \"cost = -tf.reduce_sum(y*tf.log(activation)) \\n\",\n    \"# Gradient Descent\\n\",\n    \"optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Initializing the variables\\n\",\n    \"init = tf.initialize_all_variables()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch: 0001 cost= 29.860479714\\n\",\n      \"Epoch: 0002 cost= 22.080549484\\n\",\n      \"Epoch: 0003 cost= 21.237104595\\n\",\n      \"Epoch: 0004 cost= 20.460196280\\n\",\n      \"Epoch: 0005 cost= 20.185128237\\n\",\n      \"Epoch: 0006 cost= 19.940297202\\n\",\n      \"Epoch: 0007 cost= 19.645111119\\n\",\n      \"Epoch: 0008 cost= 19.507218031\\n\",\n      \"Epoch: 0009 cost= 19.389794492\\n\",\n      \"Epoch: 0010 cost= 19.177005816\\n\",\n      \"Epoch: 0011 cost= 19.082493615\\n\",\n      \"Epoch: 0012 cost= 19.072873598\\n\",\n      \"Epoch: 0013 cost= 18.938005402\\n\",\n      \"Epoch: 0014 cost= 18.891806430\\n\",\n      \"Epoch: 0015 cost= 18.839480221\\n\",\n      \"Epoch: 0016 cost= 18.769349510\\n\",\n      \"Epoch: 0017 cost= 18.590865587\\n\",\n      \"Epoch: 0018 cost= 18.623413677\\n\",\n      \"Epoch: 0019 cost= 18.546149085\\n\",\n      \"Epoch: 0020 cost= 18.432274895\\n\",\n      \"Epoch: 0021 cost= 18.358189004\\n\",\n      \"Epoch: 0022 cost= 18.380014628\\n\",\n      \"Epoch: 0023 cost= 18.499993471\\n\",\n      \"Epoch: 0024 cost= 18.386477311\\n\",\n      \"Epoch: 0025 cost= 18.258080609\\n\",\n      \"Optimization Finished!\\n\",\n      \"Accuracy: 0.9048\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Launch the graph\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    sess.run(init)\\n\",\n    \"\\n\",\n    \"    # Training cycle\\n\",\n    \"    for epoch in range(training_epochs):\\n\",\n    \"        avg_cost = 0.\\n\",\n    \"        total_batch = int(mnist.train.num_examples/batch_size)\\n\",\n    \"        # Loop over all batches\\n\",\n    \"        for i in range(total_batch):\\n\",\n    \"            batch_xs, batch_ys = mnist.train.next_batch(batch_size)\\n\",\n    \"            # Fit training using batch data\\n\",\n    \"            sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys})\\n\",\n    \"            # Compute average loss\\n\",\n    \"            avg_cost += sess.run(cost, feed_dict={x: batch_xs, y: batch_ys})/total_batch\\n\",\n    \"        # Display logs per epoch step\\n\",\n    \"        if epoch % display_step == 0:\\n\",\n    \"            print \\\"Epoch:\\\", '%04d' % (epoch+1), \\\"cost=\\\", \\\"{:.9f}\\\".format(avg_cost)\\n\",\n    \"\\n\",\n    \"    print \\\"Optimization Finished!\\\"\\n\",\n    \"\\n\",\n    \"    # Test model\\n\",\n    \"    correct_prediction = tf.equal(tf.argmax(activation, 1), tf.argmax(y, 1))\\n\",\n    \"    # Calculate accuracy\\n\",\n    \"    accuracy = tf.reduce_mean(tf.cast(correct_prediction, \\\"float\\\"))\\n\",\n    \"    print \\\"Accuracy:\\\", accuracy.eval({x: mnist.test.images, y: mnist.test.labels})\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.4.3\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "deep-learning/tensor-flow-examples/notebooks/2_basic_classifiers/nearest_neighbor.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Nearest Neighbor in TensorFlow\\n\",\n    \"\\n\",\n    \"Credits: Forked from [TensorFlow-Examples](https://github.com/aymericdamien/TensorFlow-Examples) by Aymeric Damien\\n\",\n    \"\\n\",\n    \"## Setup\\n\",\n    \"\\n\",\n    \"Refer to the [setup instructions](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-examples/Setup_TensorFlow.md)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import tensorflow as tf\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Extracting /tmp/data/train-images-idx3-ubyte.gz\\n\",\n      \"Extracting /tmp/data/train-labels-idx1-ubyte.gz\\n\",\n      \"Extracting /tmp/data/t10k-images-idx3-ubyte.gz\\n\",\n      \"Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Import MINST data\\n\",\n    \"import input_data\\n\",\n    \"mnist = input_data.read_data_sets(\\\"/tmp/data/\\\", one_hot=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# In this example, we limit mnist data\\n\",\n    \"Xtr, Ytr = mnist.train.next_batch(5000) #5000 for training (nn candidates)\\n\",\n    \"Xte, Yte = mnist.test.next_batch(200) #200 for testing\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Reshape images to 1D\\n\",\n    \"Xtr = np.reshape(Xtr, newshape=(-1, 28*28))\\n\",\n    \"Xte = np.reshape(Xte, newshape=(-1, 28*28))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# tf Graph Input\\n\",\n    \"xtr = tf.placeholder(\\\"float\\\", [None, 784])\\n\",\n    \"xte = tf.placeholder(\\\"float\\\", [784])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Nearest Neighbor calculation using L1 Distance\\n\",\n    \"# Calculate L1 Distance\\n\",\n    \"distance = tf.reduce_sum(tf.abs(tf.add(xtr, tf.neg(xte))), reduction_indices=1)\\n\",\n    \"# Predict: Get min distance index (Nearest neighbor)\\n\",\n    \"pred = tf.arg_min(distance, 0)\\n\",\n    \"\\n\",\n    \"accuracy = 0.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Initializing the variables\\n\",\n    \"init = tf.initialize_all_variables()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Test 0 Prediction: 7 True Class: 7\\n\",\n      \"Test 1 Prediction: 2 True Class: 2\\n\",\n      \"Test 2 Prediction: 1 True Class: 1\\n\",\n      \"Test 3 Prediction: 0 True Class: 0\\n\",\n      \"Test 4 Prediction: 4 True Class: 4\\n\",\n      \"Test 5 Prediction: 1 True Class: 1\\n\",\n      \"Test 6 Prediction: 4 True Class: 4\\n\",\n      \"Test 7 Prediction: 9 True Class: 9\\n\",\n      \"Test 8 Prediction: 8 True Class: 5\\n\",\n      \"Test 9 Prediction: 9 True Class: 9\\n\",\n      \"Test 10 Prediction: 0 True Class: 0\\n\",\n      \"Test 11 Prediction: 0 True Class: 6\\n\",\n      \"Test 12 Prediction: 9 True Class: 9\\n\",\n      \"Test 13 Prediction: 0 True Class: 0\\n\",\n      \"Test 14 Prediction: 1 True Class: 1\\n\",\n      \"Test 15 Prediction: 5 True Class: 5\\n\",\n      \"Test 16 Prediction: 4 True Class: 9\\n\",\n      \"Test 17 Prediction: 7 True Class: 7\\n\",\n      \"Test 18 Prediction: 3 True Class: 3\\n\",\n      \"Test 19 Prediction: 4 True Class: 4\\n\",\n      \"Test 20 Prediction: 9 True Class: 9\\n\",\n      \"Test 21 Prediction: 6 True Class: 6\\n\",\n      \"Test 22 Prediction: 6 True Class: 6\\n\",\n      \"Test 23 Prediction: 5 True Class: 5\\n\",\n      \"Test 24 Prediction: 4 True Class: 4\\n\",\n      \"Test 25 Prediction: 0 True Class: 0\\n\",\n      \"Test 26 Prediction: 7 True Class: 7\\n\",\n      \"Test 27 Prediction: 4 True Class: 4\\n\",\n      \"Test 28 Prediction: 0 True Class: 0\\n\",\n      \"Test 29 Prediction: 1 True Class: 1\\n\",\n      \"Test 30 Prediction: 3 True Class: 3\\n\",\n      \"Test 31 Prediction: 1 True Class: 1\\n\",\n      \"Test 32 Prediction: 3 True Class: 3\\n\",\n      \"Test 33 Prediction: 4 True Class: 4\\n\",\n      \"Test 34 Prediction: 7 True Class: 7\\n\",\n      \"Test 35 Prediction: 2 True Class: 2\\n\",\n      \"Test 36 Prediction: 7 True Class: 7\\n\",\n      \"Test 37 Prediction: 1 True Class: 1\\n\",\n      \"Test 38 Prediction: 2 True Class: 2\\n\",\n      \"Test 39 Prediction: 1 True Class: 1\\n\",\n      \"Test 40 Prediction: 1 True Class: 1\\n\",\n      \"Test 41 Prediction: 7 True Class: 7\\n\",\n      \"Test 42 Prediction: 4 True Class: 4\\n\",\n      \"Test 43 Prediction: 1 True Class: 2\\n\",\n      \"Test 44 Prediction: 3 True Class: 3\\n\",\n      \"Test 45 Prediction: 5 True Class: 5\\n\",\n      \"Test 46 Prediction: 1 True Class: 1\\n\",\n      \"Test 47 Prediction: 2 True Class: 2\\n\",\n      \"Test 48 Prediction: 4 True Class: 4\\n\",\n      \"Test 49 Prediction: 4 True Class: 4\\n\",\n      \"Test 50 Prediction: 6 True Class: 6\\n\",\n      \"Test 51 Prediction: 3 True Class: 3\\n\",\n      \"Test 52 Prediction: 5 True Class: 5\\n\",\n      \"Test 53 Prediction: 5 True Class: 5\\n\",\n      \"Test 54 Prediction: 6 True Class: 6\\n\",\n      \"Test 55 Prediction: 0 True Class: 0\\n\",\n      \"Test 56 Prediction: 4 True Class: 4\\n\",\n      \"Test 57 Prediction: 1 True Class: 1\\n\",\n      \"Test 58 Prediction: 9 True Class: 9\\n\",\n      \"Test 59 Prediction: 5 True Class: 5\\n\",\n      \"Test 60 Prediction: 7 True Class: 7\\n\",\n      \"Test 61 Prediction: 8 True Class: 8\\n\",\n      \"Test 62 Prediction: 9 True Class: 9\\n\",\n      \"Test 63 Prediction: 3 True Class: 3\\n\",\n      \"Test 64 Prediction: 7 True Class: 7\\n\",\n      \"Test 65 Prediction: 4 True Class: 4\\n\",\n      \"Test 66 Prediction: 6 True Class: 6\\n\",\n      \"Test 67 Prediction: 4 True Class: 4\\n\",\n      \"Test 68 Prediction: 3 True Class: 3\\n\",\n      \"Test 69 Prediction: 0 True Class: 0\\n\",\n      \"Test 70 Prediction: 7 True Class: 7\\n\",\n      \"Test 71 Prediction: 0 True Class: 0\\n\",\n      \"Test 72 Prediction: 2 True Class: 2\\n\",\n      \"Test 73 Prediction: 7 True Class: 9\\n\",\n      \"Test 74 Prediction: 1 True Class: 1\\n\",\n      \"Test 75 Prediction: 7 True Class: 7\\n\",\n      \"Test 76 Prediction: 3 True Class: 3\\n\",\n      \"Test 77 Prediction: 7 True Class: 2\\n\",\n      \"Test 78 Prediction: 9 True Class: 9\\n\",\n      \"Test 79 Prediction: 7 True Class: 7\\n\",\n      \"Test 80 Prediction: 7 True Class: 7\\n\",\n      \"Test 81 Prediction: 6 True Class: 6\\n\",\n      \"Test 82 Prediction: 2 True Class: 2\\n\",\n      \"Test 83 Prediction: 7 True Class: 7\\n\",\n      \"Test 84 Prediction: 8 True Class: 8\\n\",\n      \"Test 85 Prediction: 4 True Class: 4\\n\",\n      \"Test 86 Prediction: 7 True Class: 7\\n\",\n      \"Test 87 Prediction: 3 True Class: 3\\n\",\n      \"Test 88 Prediction: 6 True Class: 6\\n\",\n      \"Test 89 Prediction: 1 True Class: 1\\n\",\n      \"Test 90 Prediction: 3 True Class: 3\\n\",\n      \"Test 91 Prediction: 6 True Class: 6\\n\",\n      \"Test 92 Prediction: 9 True Class: 9\\n\",\n      \"Test 93 Prediction: 3 True Class: 3\\n\",\n      \"Test 94 Prediction: 1 True Class: 1\\n\",\n      \"Test 95 Prediction: 4 True Class: 4\\n\",\n      \"Test 96 Prediction: 1 True Class: 1\\n\",\n      \"Test 97 Prediction: 7 True Class: 7\\n\",\n      \"Test 98 Prediction: 6 True Class: 6\\n\",\n      \"Test 99 Prediction: 9 True Class: 9\\n\",\n      \"Test 100 Prediction: 6 True Class: 6\\n\",\n      \"Test 101 Prediction: 0 True Class: 0\\n\",\n      \"Test 102 Prediction: 5 True Class: 5\\n\",\n      \"Test 103 Prediction: 4 True Class: 4\\n\",\n      \"Test 104 Prediction: 9 True Class: 9\\n\",\n      \"Test 105 Prediction: 9 True Class: 9\\n\",\n      \"Test 106 Prediction: 2 True Class: 2\\n\",\n      \"Test 107 Prediction: 1 True Class: 1\\n\",\n      \"Test 108 Prediction: 9 True Class: 9\\n\",\n      \"Test 109 Prediction: 4 True Class: 4\\n\",\n      \"Test 110 Prediction: 8 True Class: 8\\n\",\n      \"Test 111 Prediction: 7 True Class: 7\\n\",\n      \"Test 112 Prediction: 3 True Class: 3\\n\",\n      \"Test 113 Prediction: 9 True Class: 9\\n\",\n      \"Test 114 Prediction: 7 True Class: 7\\n\",\n      \"Test 115 Prediction: 9 True Class: 4\\n\",\n      \"Test 116 Prediction: 9 True Class: 4\\n\",\n      \"Test 117 Prediction: 4 True Class: 4\\n\",\n      \"Test 118 Prediction: 9 True Class: 9\\n\",\n      \"Test 119 Prediction: 7 True Class: 2\\n\",\n      \"Test 120 Prediction: 5 True Class: 5\\n\",\n      \"Test 121 Prediction: 4 True Class: 4\\n\",\n      \"Test 122 Prediction: 7 True Class: 7\\n\",\n      \"Test 123 Prediction: 6 True Class: 6\\n\",\n      \"Test 124 Prediction: 7 True Class: 7\\n\",\n      \"Test 125 Prediction: 9 True Class: 9\\n\",\n      \"Test 126 Prediction: 0 True Class: 0\\n\",\n      \"Test 127 Prediction: 5 True Class: 5\\n\",\n      \"Test 128 Prediction: 8 True Class: 8\\n\",\n      \"Test 129 Prediction: 5 True Class: 5\\n\",\n      \"Test 130 Prediction: 6 True Class: 6\\n\",\n      \"Test 131 Prediction: 6 True Class: 6\\n\",\n      \"Test 132 Prediction: 5 True Class: 5\\n\",\n      \"Test 133 Prediction: 7 True Class: 7\\n\",\n      \"Test 134 Prediction: 8 True Class: 8\\n\",\n      \"Test 135 Prediction: 1 True Class: 1\\n\",\n      \"Test 136 Prediction: 0 True Class: 0\\n\",\n      \"Test 137 Prediction: 1 True Class: 1\\n\",\n      \"Test 138 Prediction: 6 True Class: 6\\n\",\n      \"Test 139 Prediction: 4 True Class: 4\\n\",\n      \"Test 140 Prediction: 6 True Class: 6\\n\",\n      \"Test 141 Prediction: 7 True Class: 7\\n\",\n      \"Test 142 Prediction: 2 True Class: 3\\n\",\n      \"Test 143 Prediction: 1 True Class: 1\\n\",\n      \"Test 144 Prediction: 7 True Class: 7\\n\",\n      \"Test 145 Prediction: 1 True Class: 1\\n\",\n      \"Test 146 Prediction: 8 True Class: 8\\n\",\n      \"Test 147 Prediction: 2 True Class: 2\\n\",\n      \"Test 148 Prediction: 0 True Class: 0\\n\",\n      \"Test 149 Prediction: 1 True Class: 2\\n\",\n      \"Test 150 Prediction: 9 True Class: 9\\n\",\n      \"Test 151 Prediction: 9 True Class: 9\\n\",\n      \"Test 152 Prediction: 5 True Class: 5\\n\",\n      \"Test 153 Prediction: 5 True Class: 5\\n\",\n      \"Test 154 Prediction: 1 True Class: 1\\n\",\n      \"Test 155 Prediction: 5 True Class: 5\\n\",\n      \"Test 156 Prediction: 6 True Class: 6\\n\",\n      \"Test 157 Prediction: 0 True Class: 0\\n\",\n      \"Test 158 Prediction: 3 True Class: 3\\n\",\n      \"Test 159 Prediction: 4 True Class: 4\\n\",\n      \"Test 160 Prediction: 4 True Class: 4\\n\",\n      \"Test 161 Prediction: 6 True Class: 6\\n\",\n      \"Test 162 Prediction: 5 True Class: 5\\n\",\n      \"Test 163 Prediction: 4 True Class: 4\\n\",\n      \"Test 164 Prediction: 6 True Class: 6\\n\",\n      \"Test 165 Prediction: 5 True Class: 5\\n\",\n      \"Test 166 Prediction: 4 True Class: 4\\n\",\n      \"Test 167 Prediction: 5 True Class: 5\\n\",\n      \"Test 168 Prediction: 1 True Class: 1\\n\",\n      \"Test 169 Prediction: 4 True Class: 4\\n\",\n      \"Test 170 Prediction: 9 True Class: 4\\n\",\n      \"Test 171 Prediction: 7 True Class: 7\\n\",\n      \"Test 172 Prediction: 2 True Class: 2\\n\",\n      \"Test 173 Prediction: 3 True Class: 3\\n\",\n      \"Test 174 Prediction: 2 True Class: 2\\n\",\n      \"Test 175 Prediction: 1 True Class: 7\\n\",\n      \"Test 176 Prediction: 1 True Class: 1\\n\",\n      \"Test 177 Prediction: 8 True Class: 8\\n\",\n      \"Test 178 Prediction: 1 True Class: 1\\n\",\n      \"Test 179 Prediction: 8 True Class: 8\\n\",\n      \"Test 180 Prediction: 1 True Class: 1\\n\",\n      \"Test 181 Prediction: 8 True Class: 8\\n\",\n      \"Test 182 Prediction: 5 True Class: 5\\n\",\n      \"Test 183 Prediction: 0 True Class: 0\\n\",\n      \"Test 184 Prediction: 2 True Class: 8\\n\",\n      \"Test 185 Prediction: 9 True Class: 9\\n\",\n      \"Test 186 Prediction: 2 True Class: 2\\n\",\n      \"Test 187 Prediction: 5 True Class: 5\\n\",\n      \"Test 188 Prediction: 0 True Class: 0\\n\",\n      \"Test 189 Prediction: 1 True Class: 1\\n\",\n      \"Test 190 Prediction: 1 True Class: 1\\n\",\n      \"Test 191 Prediction: 1 True Class: 1\\n\",\n      \"Test 192 Prediction: 0 True Class: 0\\n\",\n      \"Test 193 Prediction: 4 True Class: 9\\n\",\n      \"Test 194 Prediction: 0 True Class: 0\\n\",\n      \"Test 195 Prediction: 1 True Class: 3\\n\",\n      \"Test 196 Prediction: 1 True Class: 1\\n\",\n      \"Test 197 Prediction: 6 True Class: 6\\n\",\n      \"Test 198 Prediction: 4 True Class: 4\\n\",\n      \"Test 199 Prediction: 2 True Class: 2\\n\",\n      \"Done!\\n\",\n      \"Accuracy: 0.92\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Launch the graph\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    sess.run(init)\\n\",\n    \"\\n\",\n    \"    # loop over test data\\n\",\n    \"    for i in range(len(Xte)):\\n\",\n    \"        # Get nearest neighbor\\n\",\n    \"        nn_index = sess.run(pred, feed_dict={xtr: Xtr, xte: Xte[i,:]})\\n\",\n    \"        # Get nearest neighbor class label and compare it to its true label\\n\",\n    \"        print \\\"Test\\\", i, \\\"Prediction:\\\", np.argmax(Ytr[nn_index]), \\\\\\n\",\n    \"              \\\"True Class:\\\", np.argmax(Yte[i])\\n\",\n    \"        # Calculate accuracy\\n\",\n    \"        if np.argmax(Ytr[nn_index]) == np.argmax(Yte[i]):\\n\",\n    \"            accuracy += 1./len(Xte)\\n\",\n    \"    print \\\"Done!\\\"\\n\",\n    \"    print \\\"Accuracy:\\\", accuracy\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.4.3\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "deep-learning/tensor-flow-examples/notebooks/3_neural_networks/alexnet.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# AlexNet in TensorFlow\\n\",\n    \"\\n\",\n    \"Credits: Forked from [TensorFlow-Examples](https://github.com/aymericdamien/TensorFlow-Examples) by Aymeric Damien\\n\",\n    \"\\n\",\n    \"## Setup\\n\",\n    \"\\n\",\n    \"Refer to the [setup instructions](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-examples/Setup_TensorFlow.md)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Extracting /tmp/data/train-images-idx3-ubyte.gz\\n\",\n      \"Extracting /tmp/data/train-labels-idx1-ubyte.gz\\n\",\n      \"Extracting /tmp/data/t10k-images-idx3-ubyte.gz\\n\",\n      \"Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Import MINST data\\n\",\n    \"import input_data\\n\",\n    \"mnist = input_data.read_data_sets(\\\"/tmp/data/\\\", one_hot=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import tensorflow as tf\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Parameters\\n\",\n    \"learning_rate = 0.001\\n\",\n    \"training_iters = 300000\\n\",\n    \"batch_size = 64\\n\",\n    \"display_step = 100\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Network Parameters\\n\",\n    \"n_input = 784 # MNIST data input (img shape: 28*28)\\n\",\n    \"n_classes = 10 # MNIST total classes (0-9 digits)\\n\",\n    \"dropout = 0.8 # Dropout, probability to keep units\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# tf Graph input\\n\",\n    \"x = tf.placeholder(tf.float32, [None, n_input])\\n\",\n    \"y = tf.placeholder(tf.float32, [None, n_classes])\\n\",\n    \"keep_prob = tf.placeholder(tf.float32) # dropout (keep probability)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Create AlexNet model\\n\",\n    \"def conv2d(name, l_input, w, b):\\n\",\n    \"    return tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(l_input, w, strides=[1, 1, 1, 1], \\n\",\n    \"                                                  padding='SAME'),b), name=name)\\n\",\n    \"\\n\",\n    \"def max_pool(name, l_input, k):\\n\",\n    \"    return tf.nn.max_pool(l_input, ksize=[1, k, k, 1], strides=[1, k, k, 1], \\n\",\n    \"                          padding='SAME', name=name)\\n\",\n    \"\\n\",\n    \"def norm(name, l_input, lsize=4):\\n\",\n    \"    return tf.nn.lrn(l_input, lsize, bias=1.0, alpha=0.001 / 9.0, beta=0.75, name=name)\\n\",\n    \"\\n\",\n    \"def alex_net(_X, _weights, _biases, _dropout):\\n\",\n    \"    # Reshape input picture\\n\",\n    \"    _X = tf.reshape(_X, shape=[-1, 28, 28, 1])\\n\",\n    \"\\n\",\n    \"    # Convolution Layer\\n\",\n    \"    conv1 = conv2d('conv1', _X, _weights['wc1'], _biases['bc1'])\\n\",\n    \"    # Max Pooling (down-sampling)\\n\",\n    \"    pool1 = max_pool('pool1', conv1, k=2)\\n\",\n    \"    # Apply Normalization\\n\",\n    \"    norm1 = norm('norm1', pool1, lsize=4)\\n\",\n    \"    # Apply Dropout\\n\",\n    \"    norm1 = tf.nn.dropout(norm1, _dropout)\\n\",\n    \"\\n\",\n    \"    # Convolution Layer\\n\",\n    \"    conv2 = conv2d('conv2', norm1, _weights['wc2'], _biases['bc2'])\\n\",\n    \"    # Max Pooling (down-sampling)\\n\",\n    \"    pool2 = max_pool('pool2', conv2, k=2)\\n\",\n    \"    # Apply Normalization\\n\",\n    \"    norm2 = norm('norm2', pool2, lsize=4)\\n\",\n    \"    # Apply Dropout\\n\",\n    \"    norm2 = tf.nn.dropout(norm2, _dropout)\\n\",\n    \"\\n\",\n    \"    # Convolution Layer\\n\",\n    \"    conv3 = conv2d('conv3', norm2, _weights['wc3'], _biases['bc3'])\\n\",\n    \"    # Max Pooling (down-sampling)\\n\",\n    \"    pool3 = max_pool('pool3', conv3, k=2)\\n\",\n    \"    # Apply Normalization\\n\",\n    \"    norm3 = norm('norm3', pool3, lsize=4)\\n\",\n    \"    # Apply Dropout\\n\",\n    \"    norm3 = tf.nn.dropout(norm3, _dropout)\\n\",\n    \"\\n\",\n    \"    # Fully connected layer\\n\",\n    \"    # Reshape conv3 output to fit dense layer input\\n\",\n    \"    dense1 = tf.reshape(norm3, [-1, _weights['wd1'].get_shape().as_list()[0]]) \\n\",\n    \"    # Relu activation\\n\",\n    \"    dense1 = tf.nn.relu(tf.matmul(dense1, _weights['wd1']) + _biases['bd1'], name='fc1')\\n\",\n    \"    \\n\",\n    \"    # Relu activation\\n\",\n    \"    dense2 = tf.nn.relu(tf.matmul(dense1, _weights['wd2']) + _biases['bd2'], name='fc2') \\n\",\n    \"\\n\",\n    \"    # Output, class prediction\\n\",\n    \"    out = tf.matmul(dense2, _weights['out']) + _biases['out']\\n\",\n    \"    return out\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Store layers weight & bias\\n\",\n    \"weights = {\\n\",\n    \"    'wc1': tf.Variable(tf.random_normal([3, 3, 1, 64])),\\n\",\n    \"    'wc2': tf.Variable(tf.random_normal([3, 3, 64, 128])),\\n\",\n    \"    'wc3': tf.Variable(tf.random_normal([3, 3, 128, 256])),\\n\",\n    \"    'wd1': tf.Variable(tf.random_normal([4*4*256, 1024])),\\n\",\n    \"    'wd2': tf.Variable(tf.random_normal([1024, 1024])),\\n\",\n    \"    'out': tf.Variable(tf.random_normal([1024, 10]))\\n\",\n    \"}\\n\",\n    \"biases = {\\n\",\n    \"    'bc1': tf.Variable(tf.random_normal([64])),\\n\",\n    \"    'bc2': tf.Variable(tf.random_normal([128])),\\n\",\n    \"    'bc3': tf.Variable(tf.random_normal([256])),\\n\",\n    \"    'bd1': tf.Variable(tf.random_normal([1024])),\\n\",\n    \"    'bd2': tf.Variable(tf.random_normal([1024])),\\n\",\n    \"    'out': tf.Variable(tf.random_normal([n_classes]))\\n\",\n    \"}\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Construct model\\n\",\n    \"pred = alex_net(x, weights, biases, keep_prob)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Define loss and optimizer\\n\",\n    \"cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y))\\n\",\n    \"optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Evaluate model\\n\",\n    \"correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))\\n\",\n    \"accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Initializing the variables\\n\",\n    \"init = tf.global_variables_initializer()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Iter 6400, Minibatch Loss= 29666.185547, Training Accuracy= 0.59375\\n\",\n      \"Iter 12800, Minibatch Loss= 22125.562500, Training Accuracy= 0.60938\\n\",\n      \"Iter 19200, Minibatch Loss= 22631.134766, Training Accuracy= 0.59375\\n\",\n      \"Iter 25600, Minibatch Loss= 18498.414062, Training Accuracy= 0.62500\\n\",\n      \"Iter 32000, Minibatch Loss= 11318.283203, Training Accuracy= 0.70312\\n\",\n      \"Iter 38400, Minibatch Loss= 12076.280273, Training Accuracy= 0.70312\\n\",\n      \"Iter 44800, Minibatch Loss= 8195.520508, Training Accuracy= 0.82812\\n\",\n      \"Iter 51200, Minibatch Loss= 5176.181641, Training Accuracy= 0.84375\\n\",\n      \"Iter 57600, Minibatch Loss= 8951.896484, Training Accuracy= 0.81250\\n\",\n      \"Iter 64000, Minibatch Loss= 10096.946289, Training Accuracy= 0.78125\\n\",\n      \"Iter 70400, Minibatch Loss= 11466.641602, Training Accuracy= 0.68750\\n\",\n      \"Iter 76800, Minibatch Loss= 7469.824219, Training Accuracy= 0.78125\\n\",\n      \"Iter 83200, Minibatch Loss= 4147.449219, Training Accuracy= 0.89062\\n\",\n      \"Iter 89600, Minibatch Loss= 5904.782227, Training Accuracy= 0.82812\\n\",\n      \"Iter 96000, Minibatch Loss= 718.493713, Training Accuracy= 0.93750\\n\",\n      \"Iter 102400, Minibatch Loss= 2184.151367, Training Accuracy= 0.93750\\n\",\n      \"Iter 108800, Minibatch Loss= 2354.463135, Training Accuracy= 0.89062\\n\",\n      \"Iter 115200, Minibatch Loss= 8612.959961, Training Accuracy= 0.81250\\n\",\n      \"Iter 121600, Minibatch Loss= 2225.773926, Training Accuracy= 0.84375\\n\",\n      \"Iter 128000, Minibatch Loss= 160.583618, Training Accuracy= 0.96875\\n\",\n      \"Iter 134400, Minibatch Loss= 1524.846069, Training Accuracy= 0.93750\\n\",\n      \"Iter 140800, Minibatch Loss= 3501.871094, Training Accuracy= 0.89062\\n\",\n      \"Iter 147200, Minibatch Loss= 661.977051, Training Accuracy= 0.96875\\n\",\n      \"Iter 153600, Minibatch Loss= 367.857788, Training Accuracy= 0.98438\\n\",\n      \"Iter 160000, Minibatch Loss= 1735.458740, Training Accuracy= 0.90625\\n\",\n      \"Iter 166400, Minibatch Loss= 209.320374, Training Accuracy= 0.95312\\n\",\n      \"Iter 172800, Minibatch Loss= 1788.553955, Training Accuracy= 0.90625\\n\",\n      \"Iter 179200, Minibatch Loss= 912.995544, Training Accuracy= 0.93750\\n\",\n      \"Iter 185600, Minibatch Loss= 2534.074463, Training Accuracy= 0.87500\\n\",\n      \"Iter 192000, Minibatch Loss= 73.052612, Training Accuracy= 0.96875\\n\",\n      \"Iter 198400, Minibatch Loss= 1609.606323, Training Accuracy= 0.93750\\n\",\n      \"Iter 204800, Minibatch Loss= 1823.219727, Training Accuracy= 0.96875\\n\",\n      \"Iter 211200, Minibatch Loss= 578.051086, Training Accuracy= 0.96875\\n\",\n      \"Iter 217600, Minibatch Loss= 1532.326172, Training Accuracy= 0.89062\\n\",\n      \"Iter 224000, Minibatch Loss= 769.775269, Training Accuracy= 0.95312\\n\",\n      \"Iter 230400, Minibatch Loss= 2614.737793, Training Accuracy= 0.92188\\n\",\n      \"Iter 236800, Minibatch Loss= 938.664368, Training Accuracy= 0.95312\\n\",\n      \"Iter 243200, Minibatch Loss= 1520.495605, Training Accuracy= 0.93750\\n\",\n      \"Iter 249600, Minibatch Loss= 657.419739, Training Accuracy= 0.95312\\n\",\n      \"Iter 256000, Minibatch Loss= 522.802124, Training Accuracy= 0.90625\\n\",\n      \"Iter 262400, Minibatch Loss= 211.188477, Training Accuracy= 0.96875\\n\",\n      \"Iter 268800, Minibatch Loss= 520.451172, Training Accuracy= 0.92188\\n\",\n      \"Iter 275200, Minibatch Loss= 1418.759155, Training Accuracy= 0.89062\\n\",\n      \"Iter 281600, Minibatch Loss= 241.748596, Training Accuracy= 0.96875\\n\",\n      \"Iter 288000, Minibatch Loss= 0.000000, Training Accuracy= 1.00000\\n\",\n      \"Iter 294400, Minibatch Loss= 1535.772827, Training Accuracy= 0.92188\\n\",\n      \"Optimization Finished!\\n\",\n      \"Testing Accuracy: 0.980469\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Launch the graph\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    sess.run(init)\\n\",\n    \"    step = 1\\n\",\n    \"    # Keep training until reach max iterations\\n\",\n    \"    while step * batch_size < training_iters:\\n\",\n    \"        batch_xs, batch_ys = mnist.train.next_batch(batch_size)\\n\",\n    \"        # Fit training using batch data\\n\",\n    \"        sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys, keep_prob: dropout})\\n\",\n    \"        if step % display_step == 0:\\n\",\n    \"            # Calculate batch accuracy\\n\",\n    \"            acc = sess.run(accuracy, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.})\\n\",\n    \"            # Calculate batch loss\\n\",\n    \"            loss = sess.run(cost, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.})\\n\",\n    \"            print \\\"Iter \\\" + str(step*batch_size) + \\\", Minibatch Loss= \\\" \\\\\\n\",\n    \"                  + \\\"{:.6f}\\\".format(loss) + \\\", Training Accuracy= \\\" + \\\"{:.5f}\\\".format(acc)\\n\",\n    \"        step += 1\\n\",\n    \"    print \\\"Optimization Finished!\\\"\\n\",\n    \"    # Calculate accuracy for 256 mnist test images\\n\",\n    \"    print \\\"Testing Accuracy:\\\", sess.run(accuracy, feed_dict={x: mnist.test.images[:256], \\n\",\n    \"                                                             y: mnist.test.labels[:256], \\n\",\n    \"                                                             keep_prob: 1.})\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 2\",\n   \"language\": \"python\",\n   \"name\": \"python2\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 2\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython2\",\n   \"version\": \"2.7.12\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "deep-learning/tensor-flow-examples/notebooks/3_neural_networks/convolutional_network.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Convolutional Neural Network in TensorFlow\\n\",\n    \"\\n\",\n    \"Credits: Forked from [TensorFlow-Examples](https://github.com/aymericdamien/TensorFlow-Examples) by Aymeric Damien\\n\",\n    \"\\n\",\n    \"## Setup\\n\",\n    \"\\n\",\n    \"Refer to the [setup instructions](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-examples/Setup_TensorFlow.md)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Extracting /tmp/data/train-images-idx3-ubyte.gz\\n\",\n      \"Extracting /tmp/data/train-labels-idx1-ubyte.gz\\n\",\n      \"Extracting /tmp/data/t10k-images-idx3-ubyte.gz\\n\",\n      \"Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Import MINST data\\n\",\n    \"import input_data\\n\",\n    \"mnist = input_data.read_data_sets(\\\"/tmp/data/\\\", one_hot=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import tensorflow as tf\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Parameters\\n\",\n    \"learning_rate = 0.001\\n\",\n    \"training_iters = 100000\\n\",\n    \"batch_size = 128\\n\",\n    \"display_step = 20\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Network Parameters\\n\",\n    \"n_input = 784 # MNIST data input (img shape: 28*28)\\n\",\n    \"n_classes = 10 # MNIST total classes (0-9 digits)\\n\",\n    \"dropout = 0.75 # Dropout, probability to keep units\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# tf Graph input\\n\",\n    \"x = tf.placeholder(tf.float32, [None, n_input])\\n\",\n    \"y = tf.placeholder(tf.float32, [None, n_classes])\\n\",\n    \"keep_prob = tf.placeholder(tf.float32) #dropout (keep probability)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Create model\\n\",\n    \"def conv2d(img, w, b):\\n\",\n    \"    return tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(img, w, strides=[1, 1, 1, 1], \\n\",\n    \"                                                  padding='SAME'),b))\\n\",\n    \"\\n\",\n    \"def max_pool(img, k):\\n\",\n    \"    return tf.nn.max_pool(img, ksize=[1, k, k, 1], strides=[1, k, k, 1], padding='SAME')\\n\",\n    \"\\n\",\n    \"def conv_net(_X, _weights, _biases, _dropout):\\n\",\n    \"    # Reshape input picture\\n\",\n    \"    _X = tf.reshape(_X, shape=[-1, 28, 28, 1])\\n\",\n    \"\\n\",\n    \"    # Convolution Layer\\n\",\n    \"    conv1 = conv2d(_X, _weights['wc1'], _biases['bc1'])\\n\",\n    \"    # Max Pooling (down-sampling)\\n\",\n    \"    conv1 = max_pool(conv1, k=2)\\n\",\n    \"    # Apply Dropout\\n\",\n    \"    conv1 = tf.nn.dropout(conv1, _dropout)\\n\",\n    \"\\n\",\n    \"    # Convolution Layer\\n\",\n    \"    conv2 = conv2d(conv1, _weights['wc2'], _biases['bc2'])\\n\",\n    \"    # Max Pooling (down-sampling)\\n\",\n    \"    conv2 = max_pool(conv2, k=2)\\n\",\n    \"    # Apply Dropout\\n\",\n    \"    conv2 = tf.nn.dropout(conv2, _dropout)\\n\",\n    \"\\n\",\n    \"    # Fully connected layer\\n\",\n    \"    # Reshape conv2 output to fit dense layer input\\n\",\n    \"    dense1 = tf.reshape(conv2, [-1, _weights['wd1'].get_shape().as_list()[0]]) \\n\",\n    \"    # Relu activation\\n\",\n    \"    dense1 = tf.nn.relu(tf.add(tf.matmul(dense1, _weights['wd1']), _biases['bd1']))\\n\",\n    \"    # Apply Dropout\\n\",\n    \"    dense1 = tf.nn.dropout(dense1, _dropout) # Apply Dropout\\n\",\n    \"\\n\",\n    \"    # Output, class prediction\\n\",\n    \"    out = tf.add(tf.matmul(dense1, _weights['out']), _biases['out'])\\n\",\n    \"    return out\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Store layers weight & bias\\n\",\n    \"weights = {\\n\",\n    \"    # 5x5 conv, 1 input, 32 outputs\\n\",\n    \"    'wc1': tf.Variable(tf.random_normal([5, 5, 1, 32])), \\n\",\n    \"    # 5x5 conv, 32 inputs, 64 outputs\\n\",\n    \"    'wc2': tf.Variable(tf.random_normal([5, 5, 32, 64])), \\n\",\n    \"    # fully connected, 7*7*64 inputs, 1024 outputs\\n\",\n    \"    'wd1': tf.Variable(tf.random_normal([7*7*64, 1024])), \\n\",\n    \"    # 1024 inputs, 10 outputs (class prediction)\\n\",\n    \"    'out': tf.Variable(tf.random_normal([1024, n_classes])) \\n\",\n    \"}\\n\",\n    \"\\n\",\n    \"biases = {\\n\",\n    \"    'bc1': tf.Variable(tf.random_normal([32])),\\n\",\n    \"    'bc2': tf.Variable(tf.random_normal([64])),\\n\",\n    \"    'bd1': tf.Variable(tf.random_normal([1024])),\\n\",\n    \"    'out': tf.Variable(tf.random_normal([n_classes]))\\n\",\n    \"}\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Construct model\\n\",\n    \"pred = conv_net(x, weights, biases, keep_prob)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Define loss and optimizer\\n\",\n    \"cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y))\\n\",\n    \"optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Evaluate model\\n\",\n    \"correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))\\n\",\n    \"accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Initializing the variables\\n\",\n    \"init = tf.global_variables_initializer()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Iter 2560, Minibatch Loss= 26046.011719, Training Accuracy= 0.21094\\n\",\n      \"Iter 5120, Minibatch Loss= 10456.769531, Training Accuracy= 0.52344\\n\",\n      \"Iter 7680, Minibatch Loss= 6273.207520, Training Accuracy= 0.71875\\n\",\n      \"Iter 10240, Minibatch Loss= 6276.231445, Training Accuracy= 0.64062\\n\",\n      \"Iter 12800, Minibatch Loss= 4188.221680, Training Accuracy= 0.77344\\n\",\n      \"Iter 15360, Minibatch Loss= 2717.077637, Training Accuracy= 0.80469\\n\",\n      \"Iter 17920, Minibatch Loss= 4057.120361, Training Accuracy= 0.81250\\n\",\n      \"Iter 20480, Minibatch Loss= 1696.550415, Training Accuracy= 0.87500\\n\",\n      \"Iter 23040, Minibatch Loss= 2525.317627, Training Accuracy= 0.85938\\n\",\n      \"Iter 25600, Minibatch Loss= 2341.906738, Training Accuracy= 0.87500\\n\",\n      \"Iter 28160, Minibatch Loss= 4200.535156, Training Accuracy= 0.79688\\n\",\n      \"Iter 30720, Minibatch Loss= 1888.964355, Training Accuracy= 0.89062\\n\",\n      \"Iter 33280, Minibatch Loss= 2167.645996, Training Accuracy= 0.84375\\n\",\n      \"Iter 35840, Minibatch Loss= 1932.107544, Training Accuracy= 0.89844\\n\",\n      \"Iter 38400, Minibatch Loss= 1562.430054, Training Accuracy= 0.90625\\n\",\n      \"Iter 40960, Minibatch Loss= 1676.755249, Training Accuracy= 0.84375\\n\",\n      \"Iter 43520, Minibatch Loss= 1003.626099, Training Accuracy= 0.93750\\n\",\n      \"Iter 46080, Minibatch Loss= 1176.615479, Training Accuracy= 0.86719\\n\",\n      \"Iter 48640, Minibatch Loss= 1260.592651, Training Accuracy= 0.88281\\n\",\n      \"Iter 51200, Minibatch Loss= 1399.667969, Training Accuracy= 0.86719\\n\",\n      \"Iter 53760, Minibatch Loss= 1259.961426, Training Accuracy= 0.89844\\n\",\n      \"Iter 56320, Minibatch Loss= 1415.800781, Training Accuracy= 0.89062\\n\",\n      \"Iter 58880, Minibatch Loss= 1835.365967, Training Accuracy= 0.85156\\n\",\n      \"Iter 61440, Minibatch Loss= 1395.168823, Training Accuracy= 0.90625\\n\",\n      \"Iter 64000, Minibatch Loss= 973.283569, Training Accuracy= 0.88281\\n\",\n      \"Iter 66560, Minibatch Loss= 818.093811, Training Accuracy= 0.92969\\n\",\n      \"Iter 69120, Minibatch Loss= 1178.744263, Training Accuracy= 0.92188\\n\",\n      \"Iter 71680, Minibatch Loss= 845.889709, Training Accuracy= 0.89844\\n\",\n      \"Iter 74240, Minibatch Loss= 1259.505615, Training Accuracy= 0.90625\\n\",\n      \"Iter 76800, Minibatch Loss= 738.037109, Training Accuracy= 0.89844\\n\",\n      \"Iter 79360, Minibatch Loss= 862.499146, Training Accuracy= 0.93750\\n\",\n      \"Iter 81920, Minibatch Loss= 739.704041, Training Accuracy= 0.90625\\n\",\n      \"Iter 84480, Minibatch Loss= 652.880310, Training Accuracy= 0.95312\\n\",\n      \"Iter 87040, Minibatch Loss= 635.464600, Training Accuracy= 0.92969\\n\",\n      \"Iter 89600, Minibatch Loss= 933.166626, Training Accuracy= 0.90625\\n\",\n      \"Iter 92160, Minibatch Loss= 213.874893, Training Accuracy= 0.96094\\n\",\n      \"Iter 94720, Minibatch Loss= 609.575684, Training Accuracy= 0.91406\\n\",\n      \"Iter 97280, Minibatch Loss= 560.208008, Training Accuracy= 0.93750\\n\",\n      \"Iter 99840, Minibatch Loss= 963.577148, Training Accuracy= 0.90625\\n\",\n      \"Optimization Finished!\\n\",\n      \"Testing Accuracy: 0.960938\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Launch the graph\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    sess.run(init)\\n\",\n    \"    step = 1\\n\",\n    \"    # Keep training until reach max iterations\\n\",\n    \"    while step * batch_size < training_iters:\\n\",\n    \"        batch_xs, batch_ys = mnist.train.next_batch(batch_size)\\n\",\n    \"        # Fit training using batch data\\n\",\n    \"        sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys, keep_prob: dropout})\\n\",\n    \"        if step % display_step == 0:\\n\",\n    \"            # Calculate batch accuracy\\n\",\n    \"            acc = sess.run(accuracy, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.})\\n\",\n    \"            # Calculate batch loss\\n\",\n    \"            loss = sess.run(cost, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.})\\n\",\n    \"            print \\\"Iter \\\" + str(step*batch_size) + \\\", Minibatch Loss= \\\" + \\\\\\n\",\n    \"                  \\\"{:.6f}\\\".format(loss) + \\\", Training Accuracy= \\\" + \\\"{:.5f}\\\".format(acc)\\n\",\n    \"        step += 1\\n\",\n    \"    print \\\"Optimization Finished!\\\"\\n\",\n    \"    # Calculate accuracy for 256 mnist test images\\n\",\n    \"    print \\\"Testing Accuracy:\\\", sess.run(accuracy, feed_dict={x: mnist.test.images[:256], \\n\",\n    \"                                                             y: mnist.test.labels[:256], \\n\",\n    \"                                                             keep_prob: 1.})\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 2\",\n   \"language\": \"python\",\n   \"name\": \"python2\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 2\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython2\",\n   \"version\": \"2.7.12\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "deep-learning/tensor-flow-examples/notebooks/3_neural_networks/multilayer_perceptron.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Multilayer Perceptron in TensorFlow\\n\",\n    \"\\n\",\n    \"Credits: Forked from [TensorFlow-Examples](https://github.com/aymericdamien/TensorFlow-Examples) by Aymeric Damien\\n\",\n    \"\\n\",\n    \"## Setup\\n\",\n    \"\\n\",\n    \"Refer to the [setup instructions](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-examples/Setup_TensorFlow.md)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Extracting /tmp/data/train-images-idx3-ubyte.gz\\n\",\n      \"Extracting /tmp/data/train-labels-idx1-ubyte.gz\\n\",\n      \"Extracting /tmp/data/t10k-images-idx3-ubyte.gz\\n\",\n      \"Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Import MINST data\\n\",\n    \"import input_data\\n\",\n    \"mnist = input_data.read_data_sets(\\\"/tmp/data/\\\", one_hot=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import tensorflow as tf\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Parameters\\n\",\n    \"learning_rate = 0.001\\n\",\n    \"training_epochs = 15\\n\",\n    \"batch_size = 100\\n\",\n    \"display_step = 1\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Network Parameters\\n\",\n    \"n_hidden_1 = 256 # 1st layer num features\\n\",\n    \"n_hidden_2 = 256 # 2nd layer num features\\n\",\n    \"n_input = 784 # MNIST data input (img shape: 28*28)\\n\",\n    \"n_classes = 10 # MNIST total classes (0-9 digits)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# tf Graph input\\n\",\n    \"x = tf.placeholder(\\\"float\\\", [None, n_input])\\n\",\n    \"y = tf.placeholder(\\\"float\\\", [None, n_classes])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Create model\\n\",\n    \"def multilayer_perceptron(_X, _weights, _biases):\\n\",\n    \"    #Hidden layer with RELU activation\\n\",\n    \"    layer_1 = tf.nn.relu(tf.add(tf.matmul(_X, _weights['h1']), _biases['b1'])) \\n\",\n    \"    #Hidden layer with RELU activation\\n\",\n    \"    layer_2 = tf.nn.relu(tf.add(tf.matmul(layer_1, _weights['h2']), _biases['b2'])) \\n\",\n    \"    return tf.matmul(layer_2, weights['out']) + biases['out']\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Store layers weight & bias\\n\",\n    \"weights = {\\n\",\n    \"    'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])),\\n\",\n    \"    'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),\\n\",\n    \"    'out': tf.Variable(tf.random_normal([n_hidden_2, n_classes]))\\n\",\n    \"}\\n\",\n    \"biases = {\\n\",\n    \"    'b1': tf.Variable(tf.random_normal([n_hidden_1])),\\n\",\n    \"    'b2': tf.Variable(tf.random_normal([n_hidden_2])),\\n\",\n    \"    'out': tf.Variable(tf.random_normal([n_classes]))\\n\",\n    \"}\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Construct model\\n\",\n    \"pred = multilayer_perceptron(x, weights, biases)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Define loss and optimizer\\n\",\n    \"# Softmax loss\\n\",\n    \"cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y)) \\n\",\n    \"# Adam Optimizer\\n\",\n    \"optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Initializing the variables\\n\",\n    \"init = tf.global_variables_initializer()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch: 0001 cost= 160.113980416\\n\",\n      \"Epoch: 0002 cost= 38.665780694\\n\",\n      \"Epoch: 0003 cost= 24.118004577\\n\",\n      \"Epoch: 0004 cost= 16.440921303\\n\",\n      \"Epoch: 0005 cost= 11.689460141\\n\",\n      \"Epoch: 0006 cost= 8.469423468\\n\",\n      \"Epoch: 0007 cost= 6.223237230\\n\",\n      \"Epoch: 0008 cost= 4.560174118\\n\",\n      \"Epoch: 0009 cost= 3.250516910\\n\",\n      \"Epoch: 0010 cost= 2.359658795\\n\",\n      \"Epoch: 0011 cost= 1.694081847\\n\",\n      \"Epoch: 0012 cost= 1.167997509\\n\",\n      \"Epoch: 0013 cost= 0.872986831\\n\",\n      \"Epoch: 0014 cost= 0.630616366\\n\",\n      \"Epoch: 0015 cost= 0.487381571\\n\",\n      \"Optimization Finished!\\n\",\n      \"Accuracy: 0.9462\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Launch the graph\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    sess.run(init)\\n\",\n    \"\\n\",\n    \"    # Training cycle\\n\",\n    \"    for epoch in range(training_epochs):\\n\",\n    \"        avg_cost = 0.\\n\",\n    \"        total_batch = int(mnist.train.num_examples/batch_size)\\n\",\n    \"        # Loop over all batches\\n\",\n    \"        for i in range(total_batch):\\n\",\n    \"            batch_xs, batch_ys = mnist.train.next_batch(batch_size)\\n\",\n    \"            # Fit training using batch data\\n\",\n    \"            sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys})\\n\",\n    \"            # Compute average loss\\n\",\n    \"            avg_cost += sess.run(cost, feed_dict={x: batch_xs, y: batch_ys})/total_batch\\n\",\n    \"        # Display logs per epoch step\\n\",\n    \"        if epoch % display_step == 0:\\n\",\n    \"            print \\\"Epoch:\\\", '%04d' % (epoch+1), \\\"cost=\\\", \\\"{:.9f}\\\".format(avg_cost)\\n\",\n    \"\\n\",\n    \"    print \\\"Optimization Finished!\\\"\\n\",\n    \"\\n\",\n    \"    # Test model\\n\",\n    \"    correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))\\n\",\n    \"    # Calculate accuracy\\n\",\n    \"    accuracy = tf.reduce_mean(tf.cast(correct_prediction, \\\"float\\\"))\\n\",\n    \"    print \\\"Accuracy:\\\", accuracy.eval({x: mnist.test.images, y: mnist.test.labels})\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 2\",\n   \"language\": \"python\",\n   \"name\": \"python2\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 2\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython2\",\n   \"version\": \"2.7.12\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "deep-learning/tensor-flow-examples/notebooks/3_neural_networks/recurrent_network.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Recurrent Neural Network in TensorFlow\\n\",\n    \"\\n\",\n    \"Credits: Forked from [TensorFlow-Examples](https://github.com/aymericdamien/TensorFlow-Examples) by Aymeric Damien\\n\",\n    \"\\n\",\n    \"## Setup\\n\",\n    \"\\n\",\n    \"Refer to the [setup instructions](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-examples/Setup_TensorFlow.md)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Extracting /tmp/data/train-images-idx3-ubyte.gz\\n\",\n      \"Extracting /tmp/data/train-labels-idx1-ubyte.gz\\n\",\n      \"Extracting /tmp/data/t10k-images-idx3-ubyte.gz\\n\",\n      \"Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Import MINST data\\n\",\n    \"import input_data\\n\",\n    \"mnist = input_data.read_data_sets(\\\"/tmp/data/\\\", one_hot=True)\\n\",\n    \"\\n\",\n    \"import tensorflow as tf\\n\",\n    \"from tensorflow.models.rnn import rnn, rnn_cell\\n\",\n    \"import numpy as np\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"'''\\n\",\n    \"To classify images using a reccurent neural network, we consider every image row as a sequence of pixels.\\n\",\n    \"Because MNIST image shape is 28*28px, we will then handle 28 sequences of 28 steps for every sample.\\n\",\n    \"'''\\n\",\n    \"\\n\",\n    \"# Parameters\\n\",\n    \"learning_rate = 0.001\\n\",\n    \"training_iters = 100000\\n\",\n    \"batch_size = 128\\n\",\n    \"display_step = 10\\n\",\n    \"\\n\",\n    \"# Network Parameters\\n\",\n    \"n_input = 28 # MNIST data input (img shape: 28*28)\\n\",\n    \"n_steps = 28 # timesteps\\n\",\n    \"n_hidden = 128 # hidden layer num of features\\n\",\n    \"n_classes = 10 # MNIST total classes (0-9 digits)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# tf Graph input\\n\",\n    \"x = tf.placeholder(\\\"float\\\", [None, n_steps, n_input])\\n\",\n    \"istate = tf.placeholder(\\\"float\\\", [None, 2*n_hidden]) #state & cell => 2x n_hidden\\n\",\n    \"y = tf.placeholder(\\\"float\\\", [None, n_classes])\\n\",\n    \"\\n\",\n    \"# Define weights\\n\",\n    \"weights = {\\n\",\n    \"    'hidden': tf.Variable(tf.random_normal([n_input, n_hidden])), # Hidden layer weights\\n\",\n    \"    'out': tf.Variable(tf.random_normal([n_hidden, n_classes]))\\n\",\n    \"}\\n\",\n    \"biases = {\\n\",\n    \"    'hidden': tf.Variable(tf.random_normal([n_hidden])),\\n\",\n    \"    'out': tf.Variable(tf.random_normal([n_classes]))\\n\",\n    \"}\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def RNN(_X, _istate, _weights, _biases):\\n\",\n    \"\\n\",\n    \"    # input shape: (batch_size, n_steps, n_input)\\n\",\n    \"    _X = tf.transpose(_X, [1, 0, 2])  # permute n_steps and batch_size\\n\",\n    \"    # Reshape to prepare input to hidden activation\\n\",\n    \"    _X = tf.reshape(_X, [-1, n_input]) # (n_steps*batch_size, n_input)\\n\",\n    \"    # Linear activation\\n\",\n    \"    _X = tf.matmul(_X, _weights['hidden']) + _biases['hidden']\\n\",\n    \"\\n\",\n    \"    # Define a lstm cell with tensorflow\\n\",\n    \"    lstm_cell = rnn_cell.BasicLSTMCell(n_hidden, forget_bias=1.0)\\n\",\n    \"    # Split data because rnn cell needs a list of inputs for the RNN inner loop\\n\",\n    \"    _X = tf.split(0, n_steps, _X) # n_steps * (batch_size, n_hidden)\\n\",\n    \"\\n\",\n    \"    # Get lstm cell output\\n\",\n    \"    outputs, states = rnn.rnn(lstm_cell, _X, initial_state=_istate)\\n\",\n    \"\\n\",\n    \"    # Linear activation\\n\",\n    \"    # Get inner loop last output\\n\",\n    \"    return tf.matmul(outputs[-1], _weights['out']) + _biases['out']\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"pred = RNN(x, istate, weights, biases)\\n\",\n    \"\\n\",\n    \"# Define loss and optimizer\\n\",\n    \"cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y)) # Softmax loss\\n\",\n    \"optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) # Adam Optimizer\\n\",\n    \"\\n\",\n    \"# Evaluate model\\n\",\n    \"correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))\\n\",\n    \"accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Iter 1280, Minibatch Loss= 1.888242, Training Accuracy= 0.39844\\n\",\n      \"Iter 2560, Minibatch Loss= 1.519879, Training Accuracy= 0.47656\\n\",\n      \"Iter 3840, Minibatch Loss= 1.238005, Training Accuracy= 0.63281\\n\",\n      \"Iter 5120, Minibatch Loss= 0.933760, Training Accuracy= 0.71875\\n\",\n      \"Iter 6400, Minibatch Loss= 0.832130, Training Accuracy= 0.73438\\n\",\n      \"Iter 7680, Minibatch Loss= 0.979760, Training Accuracy= 0.70312\\n\",\n      \"Iter 8960, Minibatch Loss= 0.821921, Training Accuracy= 0.71875\\n\",\n      \"Iter 10240, Minibatch Loss= 0.710566, Training Accuracy= 0.79688\\n\",\n      \"Iter 11520, Minibatch Loss= 0.578501, Training Accuracy= 0.82812\\n\",\n      \"Iter 12800, Minibatch Loss= 0.765049, Training Accuracy= 0.75000\\n\",\n      \"Iter 14080, Minibatch Loss= 0.582995, Training Accuracy= 0.78125\\n\",\n      \"Iter 15360, Minibatch Loss= 0.575092, Training Accuracy= 0.79688\\n\",\n      \"Iter 16640, Minibatch Loss= 0.701214, Training Accuracy= 0.75781\\n\",\n      \"Iter 17920, Minibatch Loss= 0.561972, Training Accuracy= 0.78125\\n\",\n      \"Iter 19200, Minibatch Loss= 0.394480, Training Accuracy= 0.85938\\n\",\n      \"Iter 20480, Minibatch Loss= 0.356244, Training Accuracy= 0.91406\\n\",\n      \"Iter 21760, Minibatch Loss= 0.632163, Training Accuracy= 0.78125\\n\",\n      \"Iter 23040, Minibatch Loss= 0.269334, Training Accuracy= 0.90625\\n\",\n      \"Iter 24320, Minibatch Loss= 0.485007, Training Accuracy= 0.86719\\n\",\n      \"Iter 25600, Minibatch Loss= 0.569704, Training Accuracy= 0.78906\\n\",\n      \"Iter 26880, Minibatch Loss= 0.267697, Training Accuracy= 0.92188\\n\",\n      \"Iter 28160, Minibatch Loss= 0.381177, Training Accuracy= 0.90625\\n\",\n      \"Iter 29440, Minibatch Loss= 0.350800, Training Accuracy= 0.87500\\n\",\n      \"Iter 30720, Minibatch Loss= 0.356782, Training Accuracy= 0.90625\\n\",\n      \"Iter 32000, Minibatch Loss= 0.322511, Training Accuracy= 0.89062\\n\",\n      \"Iter 33280, Minibatch Loss= 0.309195, Training Accuracy= 0.90625\\n\",\n      \"Iter 34560, Minibatch Loss= 0.535408, Training Accuracy= 0.83594\\n\",\n      \"Iter 35840, Minibatch Loss= 0.281643, Training Accuracy= 0.92969\\n\",\n      \"Iter 37120, Minibatch Loss= 0.290962, Training Accuracy= 0.89844\\n\",\n      \"Iter 38400, Minibatch Loss= 0.204718, Training Accuracy= 0.93750\\n\",\n      \"Iter 39680, Minibatch Loss= 0.205882, Training Accuracy= 0.92969\\n\",\n      \"Iter 40960, Minibatch Loss= 0.481441, Training Accuracy= 0.84375\\n\",\n      \"Iter 42240, Minibatch Loss= 0.348245, Training Accuracy= 0.89844\\n\",\n      \"Iter 43520, Minibatch Loss= 0.274692, Training Accuracy= 0.90625\\n\",\n      \"Iter 44800, Minibatch Loss= 0.171815, Training Accuracy= 0.94531\\n\",\n      \"Iter 46080, Minibatch Loss= 0.171035, Training Accuracy= 0.93750\\n\",\n      \"Iter 47360, Minibatch Loss= 0.235800, Training Accuracy= 0.89844\\n\",\n      \"Iter 48640, Minibatch Loss= 0.235974, Training Accuracy= 0.93750\\n\",\n      \"Iter 49920, Minibatch Loss= 0.207323, Training Accuracy= 0.92188\\n\",\n      \"Iter 51200, Minibatch Loss= 0.212989, Training Accuracy= 0.91406\\n\",\n      \"Iter 52480, Minibatch Loss= 0.151774, Training Accuracy= 0.95312\\n\",\n      \"Iter 53760, Minibatch Loss= 0.090070, Training Accuracy= 0.96875\\n\",\n      \"Iter 55040, Minibatch Loss= 0.264714, Training Accuracy= 0.92969\\n\",\n      \"Iter 56320, Minibatch Loss= 0.235086, Training Accuracy= 0.92969\\n\",\n      \"Iter 57600, Minibatch Loss= 0.160302, Training Accuracy= 0.95312\\n\",\n      \"Iter 58880, Minibatch Loss= 0.106515, Training Accuracy= 0.96875\\n\",\n      \"Iter 60160, Minibatch Loss= 0.236039, Training Accuracy= 0.94531\\n\",\n      \"Iter 61440, Minibatch Loss= 0.279540, Training Accuracy= 0.90625\\n\",\n      \"Iter 62720, Minibatch Loss= 0.173585, Training Accuracy= 0.93750\\n\",\n      \"Iter 64000, Minibatch Loss= 0.191009, Training Accuracy= 0.92188\\n\",\n      \"Iter 65280, Minibatch Loss= 0.210331, Training Accuracy= 0.89844\\n\",\n      \"Iter 66560, Minibatch Loss= 0.223444, Training Accuracy= 0.94531\\n\",\n      \"Iter 67840, Minibatch Loss= 0.278210, Training Accuracy= 0.91406\\n\",\n      \"Iter 69120, Minibatch Loss= 0.174290, Training Accuracy= 0.95312\\n\",\n      \"Iter 70400, Minibatch Loss= 0.188701, Training Accuracy= 0.94531\\n\",\n      \"Iter 71680, Minibatch Loss= 0.210277, Training Accuracy= 0.94531\\n\",\n      \"Iter 72960, Minibatch Loss= 0.249951, Training Accuracy= 0.95312\\n\",\n      \"Iter 74240, Minibatch Loss= 0.209853, Training Accuracy= 0.92188\\n\",\n      \"Iter 75520, Minibatch Loss= 0.049742, Training Accuracy= 0.99219\\n\",\n      \"Iter 76800, Minibatch Loss= 0.250095, Training Accuracy= 0.92969\\n\",\n      \"Iter 78080, Minibatch Loss= 0.133853, Training Accuracy= 0.95312\\n\",\n      \"Iter 79360, Minibatch Loss= 0.110206, Training Accuracy= 0.97656\\n\",\n      \"Iter 80640, Minibatch Loss= 0.141906, Training Accuracy= 0.93750\\n\",\n      \"Iter 81920, Minibatch Loss= 0.126872, Training Accuracy= 0.94531\\n\",\n      \"Iter 83200, Minibatch Loss= 0.138925, Training Accuracy= 0.95312\\n\",\n      \"Iter 84480, Minibatch Loss= 0.128652, Training Accuracy= 0.96094\\n\",\n      \"Iter 85760, Minibatch Loss= 0.099837, Training Accuracy= 0.96094\\n\",\n      \"Iter 87040, Minibatch Loss= 0.119000, Training Accuracy= 0.95312\\n\",\n      \"Iter 88320, Minibatch Loss= 0.179807, Training Accuracy= 0.95312\\n\",\n      \"Iter 89600, Minibatch Loss= 0.141792, Training Accuracy= 0.96094\\n\",\n      \"Iter 90880, Minibatch Loss= 0.142424, Training Accuracy= 0.96094\\n\",\n      \"Iter 92160, Minibatch Loss= 0.159564, Training Accuracy= 0.96094\\n\",\n      \"Iter 93440, Minibatch Loss= 0.111984, Training Accuracy= 0.95312\\n\",\n      \"Iter 94720, Minibatch Loss= 0.238978, Training Accuracy= 0.92969\\n\",\n      \"Iter 96000, Minibatch Loss= 0.068002, Training Accuracy= 0.97656\\n\",\n      \"Iter 97280, Minibatch Loss= 0.191819, Training Accuracy= 0.94531\\n\",\n      \"Iter 98560, Minibatch Loss= 0.081197, Training Accuracy= 0.99219\\n\",\n      \"Iter 99840, Minibatch Loss= 0.206797, Training Accuracy= 0.95312\\n\",\n      \"Optimization Finished!\\n\",\n      \"Testing Accuracy: 0.941406\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Initializing the variables\\n\",\n    \"init = tf.global_variables_initializer()\\n\",\n    \"\\n\",\n    \"# Launch the graph\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    sess.run(init)\\n\",\n    \"    step = 1\\n\",\n    \"    # Keep training until reach max iterations\\n\",\n    \"    while step * batch_size < training_iters:\\n\",\n    \"        batch_xs, batch_ys = mnist.train.next_batch(batch_size)\\n\",\n    \"        # Reshape data to get 28 seq of 28 elements\\n\",\n    \"        batch_xs = batch_xs.reshape((batch_size, n_steps, n_input))\\n\",\n    \"        # Fit training using batch data\\n\",\n    \"        sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys,\\n\",\n    \"                                       istate: np.zeros((batch_size, 2*n_hidden))})\\n\",\n    \"        if step % display_step == 0:\\n\",\n    \"            # Calculate batch accuracy\\n\",\n    \"            acc = sess.run(accuracy, feed_dict={x: batch_xs, y: batch_ys,\\n\",\n    \"                                                istate: np.zeros((batch_size, 2*n_hidden))})\\n\",\n    \"            # Calculate batch loss\\n\",\n    \"            loss = sess.run(cost, feed_dict={x: batch_xs, y: batch_ys,\\n\",\n    \"                                             istate: np.zeros((batch_size, 2*n_hidden))})\\n\",\n    \"            print \\\"Iter \\\" + str(step*batch_size) + \\\", Minibatch Loss= \\\" + \\\"{:.6f}\\\".format(loss) + \\\\\\n\",\n    \"                  \\\", Training Accuracy= \\\" + \\\"{:.5f}\\\".format(acc)\\n\",\n    \"        step += 1\\n\",\n    \"    print \\\"Optimization Finished!\\\"\\n\",\n    \"    # Calculate accuracy for 256 mnist test images\\n\",\n    \"    test_len = 256\\n\",\n    \"    test_data = mnist.test.images[:test_len].reshape((-1, n_steps, n_input))\\n\",\n    \"    test_label = mnist.test.labels[:test_len]\\n\",\n    \"    print \\\"Testing Accuracy:\\\", sess.run(accuracy, feed_dict={x: test_data, y: test_label,\\n\",\n    \"                                                             istate: np.zeros((test_len, 2*n_hidden))})\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 2\",\n   \"language\": \"python\",\n   \"name\": \"python2\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 2\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython2\",\n   \"version\": \"2.7.12\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "deep-learning/tensor-flow-examples/notebooks/4_multi_gpu/multigpu_basics.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Basic Multi GPU Computation in TensorFlow\\n\",\n    \"\\n\",\n    \"Credits: Forked from [TensorFlow-Examples](https://github.com/aymericdamien/TensorFlow-Examples) by Aymeric Damien\\n\",\n    \"\\n\",\n    \"## Setup\\n\",\n    \"\\n\",\n    \"Refer to the [setup instructions](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-examples/Setup_TensorFlow.md)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"This tutorial requires your machine to have 2 GPUs\\n\",\n    \"* \\\"/cpu:0\\\": The CPU of your machine.\\n\",\n    \"* \\\"/gpu:0\\\": The first GPU of your machine\\n\",\n    \"* \\\"/gpu:1\\\": The second GPU of your machine\\n\",\n    \"* For this example, we are using 2 GTX-980\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import tensorflow as tf\\n\",\n    \"import datetime\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"#Processing Units logs\\n\",\n    \"log_device_placement = True\\n\",\n    \"\\n\",\n    \"#num of multiplications to perform\\n\",\n    \"n = 10\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Example: compute A^n + B^n on 2 GPUs\\n\",\n    \"\\n\",\n    \"# Create random large matrix\\n\",\n    \"A = np.random.rand(1e4, 1e4).astype('float32')\\n\",\n    \"B = np.random.rand(1e4, 1e4).astype('float32')\\n\",\n    \"\\n\",\n    \"# Creates a graph to store results\\n\",\n    \"c1 = []\\n\",\n    \"c2 = []\\n\",\n    \"\\n\",\n    \"# Define matrix power\\n\",\n    \"def matpow(M, n):\\n\",\n    \"    if n < 1: #Abstract cases where n < 1\\n\",\n    \"        return M\\n\",\n    \"    else:\\n\",\n    \"        return tf.matmul(M, matpow(M, n-1))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Single GPU computing\\n\",\n    \"\\n\",\n    \"with tf.device('/gpu:0'):\\n\",\n    \"    a = tf.constant(A)\\n\",\n    \"    b = tf.constant(B)\\n\",\n    \"    #compute A^n and B^n and store results in c1\\n\",\n    \"    c1.append(matpow(a, n))\\n\",\n    \"    c1.append(matpow(b, n))\\n\",\n    \"\\n\",\n    \"with tf.device('/cpu:0'):\\n\",\n    \"  sum = tf.add_n(c1) #Addition of all elements in c1, i.e. A^n + B^n\\n\",\n    \"\\n\",\n    \"t1_1 = datetime.datetime.now()\\n\",\n    \"with tf.Session(config=tf.ConfigProto(log_device_placement=log_device_placement)) as sess:\\n\",\n    \"    # Runs the op.\\n\",\n    \"    sess.run(sum)\\n\",\n    \"t2_1 = datetime.datetime.now()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Multi GPU computing\\n\",\n    \"# GPU:0 computes A^n\\n\",\n    \"with tf.device('/gpu:0'):\\n\",\n    \"    #compute A^n and store result in c2\\n\",\n    \"    a = tf.constant(A)\\n\",\n    \"    c2.append(matpow(a, n))\\n\",\n    \"\\n\",\n    \"#GPU:1 computes B^n\\n\",\n    \"with tf.device('/gpu:1'):\\n\",\n    \"    #compute B^n and store result in c2\\n\",\n    \"    b = tf.constant(B)\\n\",\n    \"    c2.append(matpow(b, n))\\n\",\n    \"\\n\",\n    \"with tf.device('/cpu:0'):\\n\",\n    \"  sum = tf.add_n(c2) #Addition of all elements in c2, i.e. A^n + B^n\\n\",\n    \"\\n\",\n    \"t1_2 = datetime.datetime.now()\\n\",\n    \"with tf.Session(config=tf.ConfigProto(log_device_placement=log_device_placement)) as sess:\\n\",\n    \"    # Runs the op.\\n\",\n    \"    sess.run(sum)\\n\",\n    \"t2_2 = datetime.datetime.now()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Single GPU computation time: 0:00:11.833497\\n\",\n      \"Multi GPU computation time: 0:00:07.085913\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print \\\"Single GPU computation time: \\\" + str(t2_1-t1_1)\\n\",\n    \"print \\\"Multi GPU computation time: \\\" + str(t2_2-t1_2)\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.4.3\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "deep-learning/tensor-flow-examples/notebooks/5_ui/graph_visualization.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Graph Visualization in TensorFlow\\n\",\n    \"\\n\",\n    \"Credits: Forked from [TensorFlow-Examples](https://github.com/aymericdamien/TensorFlow-Examples) by Aymeric Damien\\n\",\n    \"\\n\",\n    \"## Setup\\n\",\n    \"\\n\",\n    \"Refer to the [setup instructions](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-examples/Setup_TensorFlow.md)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 51,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Extracting /tmp/data/train-images-idx3-ubyte.gz\\n\",\n      \"Extracting /tmp/data/train-labels-idx1-ubyte.gz\\n\",\n      \"Extracting /tmp/data/t10k-images-idx3-ubyte.gz\\n\",\n      \"Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import tensorflow as tf\\n\",\n    \"import numpy\\n\",\n    \"\\n\",\n    \"# Import MINST data\\n\",\n    \"import input_data\\n\",\n    \"mnist = input_data.read_data_sets(\\\"/tmp/data/\\\", one_hot=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 52,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Use Logistic Regression from our previous example\\n\",\n    \"\\n\",\n    \"# Parameters\\n\",\n    \"learning_rate = 0.01\\n\",\n    \"training_epochs = 10\\n\",\n    \"batch_size = 100\\n\",\n    \"display_step = 1\\n\",\n    \"\\n\",\n    \"# tf Graph Input\\n\",\n    \"x = tf.placeholder(\\\"float\\\", [None, 784], name='x') # mnist data image of shape 28*28=784\\n\",\n    \"y = tf.placeholder(\\\"float\\\", [None, 10], name='y') # 0-9 digits recognition => 10 classes\\n\",\n    \"\\n\",\n    \"# Create model\\n\",\n    \"\\n\",\n    \"# Set model weights\\n\",\n    \"W = tf.Variable(tf.zeros([784, 10]), name=\\\"weights\\\")\\n\",\n    \"b = tf.Variable(tf.zeros([10]), name=\\\"bias\\\")\\n\",\n    \"\\n\",\n    \"# Construct model\\n\",\n    \"activation = tf.nn.softmax(tf.matmul(x, W) + b) # Softmax\\n\",\n    \"\\n\",\n    \"# Minimize error using cross entropy\\n\",\n    \"cost = -tf.reduce_sum(y*tf.log(activation)) # Cross entropy\\n\",\n    \"optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) # Gradient Descent\\n\",\n    \"\\n\",\n    \"# Initializing the variables\\n\",\n    \"init = tf.initialize_all_variables()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Launch the graph\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    sess.run(init)\\n\",\n    \"\\n\",\n    \"    # Set logs writer into folder /tmp/tensorflow_logs\\n\",\n    \"    summary_writer = tf.train.SummaryWriter('/tmp/tensorflow_logs', graph_def=sess.graph_def)\\n\",\n    \"\\n\",\n    \"    # Training cycle\\n\",\n    \"    for epoch in range(training_epochs):\\n\",\n    \"        avg_cost = 0.\\n\",\n    \"        total_batch = int(mnist.train.num_examples/batch_size)\\n\",\n    \"        # Loop over all batches\\n\",\n    \"        for i in range(total_batch):\\n\",\n    \"            batch_xs, batch_ys = mnist.train.next_batch(batch_size)\\n\",\n    \"            # Fit training using batch data\\n\",\n    \"            sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys})\\n\",\n    \"            # Compute average loss\\n\",\n    \"            avg_cost += sess.run(cost, feed_dict={x: batch_xs, y: batch_ys})/total_batch\\n\",\n    \"        # Display logs per epoch step\\n\",\n    \"        if epoch % display_step == 0:\\n\",\n    \"            print \\\"Epoch:\\\", '%04d' % (epoch+1), \\\"cost=\\\", \\\"{:.9f}\\\".format(avg_cost)\\n\",\n    \"\\n\",\n    \"    print \\\"Optimization Finished!\\\"\\n\",\n    \"\\n\",\n    \"    # Test model\\n\",\n    \"    correct_prediction = tf.equal(tf.argmax(activation, 1), tf.argmax(y, 1))\\n\",\n    \"    # Calculate accuracy\\n\",\n    \"    accuracy = tf.reduce_mean(tf.cast(correct_prediction, \\\"float\\\"))\\n\",\n    \"    print \\\"Accuracy:\\\", accuracy.eval({x: mnist.test.images, y: mnist.test.labels})\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Run the command line\\n\",\n    \"```\\n\",\n    \"tensorboard --logdir=/tmp/tensorflow_logs\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"### Open http://localhost:6006/ into your web browser\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 58,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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WxKlbhCu8+SHp6bWrKk0LzT83RF2g57H+5wO8f+nKm4bzTVeXUE4c2K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\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WUZQ/LS6gvhEdRjv8XGvMmTJ4eZM2cWF/laAQUUUEABBRRQQAEF2pkA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vs8KZlQgoILxHnjgAR+saNPnhm2wjgACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAAC\\nCCCAAAIIIIBArQQIxMspraCxDz/8sGhpAvCK8vidmtZ04sSJiQU19ewZZ5xRCDRTIU1tuuOOO7oZ\\nM2b4OrNnz3b6SUoKDlPQm5JGjuvWrZsfbU2BZrfffruf1vWcc85xCtoqp021mxQ0pvxi6aCDDvJB\\ndwq8U/0pU6b4n7COAtZOOeWUMMuva0Q5XVc6BwXeKYAvTNpWYKPatcC8cH+x9ZNOOsldeeWVvm21\\nr2C6OHXq1MntuuuuPluvhYLoHnzwQb+toEoF42Wl0aNH+5HwNCVw2vnHbehYhx9+uA9U1D4FDN5w\\nww1xMScfgvAasZCBAAIIIIAAAggg0EQCdk+re3R90adY0peG7D5e97e6X69G0pea9LlK7elzT7Fk\\nZVVGo47baNXF6oT79KWf73//+06foz796U+Hu1hHAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACB\\nSICpaSOQtM1io+HpIQxT0KbJOVdsqlQ9DOq/YXS2k08+2T/cSXowdNxxx/nR2OIHVwoS08hplq+R\\n0ywp79hjj210bCtbTptqWyPcKdmodH4j4R+bWtd2KcBw7733LvTV8rXU+X/pS1/yQYdhvtZ1PBsl\\nTw/6+vTp06CIRvfTQz2lrbbaKvNhYFi5R48e7oILLvCj6oX5tj5w4ED3xS9+0cnZ0l577eWOOOKI\\nRuev89VIheYbvo5aP//8812/fv2smcJSI/rFVrZT7Y0bN64wFa/la6n33J577ulOPfXUMJt1BBBA\\nAAEEEEAAAQSaTEBfKrnwwgvdiSee6MaOHeuKfUbUKNLnnnuuL6svwBQrW6zDCuZbs2ZNgyI33XST\\nb/eEE05wTzzxRIN98cYf/vAHX1Z9njp1arw7c/vFF1/0x7fRsjMrUAABBBBAAAEEEEAAAQQQQAAB\\nBBBAAAEEEEAAgVYs0GbZsmUfteLzz3XqGsXghRdeKEwhapUUDKTAKP1ondS0AhrNQcF2egCmoLSe\\nPXvmOqBGolPq2rWr6969e4M65bbZoJESN+bNm+fPQdeMguHCoLUSm6pacT3g08NBucpE09FmBRva\\neSj4zoIFszqk6YU1eoceTOr10/H0cFAPF7WugMSkwM3ly5f76YhVRinva5/VH/YjgEDpAvpdSkIA\\nAQQQQKA1CuhzyLe//W0/IrXO/8tf/rJTMFxSmjZtmvvGN77hd+m++rrrriv5HlYjVv/qV7/yQX+D\\nBw/2bQ0YMMA99dRT7tJLL/Xb+nLKRRddVPhCTNgXjd6nUex0r6/005/+1O2zzz5hkcz1G2+80R9L\\n56nzJSGAAAIIIIAAAggggAACCCCAAAIIIIAAAggggEC6ANFj6TaFPZqCU1PTWiIAzyRqu1SAVt6A\\nr7Bnffv2DTcbrJfbZoNGStwo5xxKPETJxRXgVswpqcG856HRMzQF7pAhQ9yRRx7pgw+tvfvuu68w\\nwodG9EsKwlNZBf4Q/GNqLBFAAAEEEEAAAQTqQUAj02kU7qQvZf3zn/+suItLly71bdio3EkNPv30\\n0/7LSrqXjtPjjz9eCMKL97GNAAIIIIAAAggggAACCCCAAAIIIIAAAggggAAC1RcgEC/DVAF4CsRT\\nIgAvA4vdCCQI3HrrrX4EPI0KMnfuXLf77rv7KXefeeYZt3DhwkINjeZBQgABBBBAAAEEEECgpQjM\\nnz/fvfTSS27XXXdt0OUlS5a4yZMnF/LikaY1st7EiRPdpEmTnEZ+1v4jjjjCHXXUUX5dI3rfcsst\\nflR2NaKAutmzZ7uk+2WNNK22zjzzzMLxtKJ8jWaXlNT+VVdd5Y8X9l35GrlPI76fccYZmSNkJ7VN\\nHgIIIIAAAggggAACCCCAAAIIIIAAAggggAACrVmAQLwir76mpH3llVd8iW222YYpaItYsQuBNIGx\\nY8e6a665xun99P7777sHHnigUdE99tjD9e/fv1E+GQgggAACCCCAAAII1JuAAudGjBjh9MUSBcyF\\nwWzq65QpU/yoz506dfIjqysozpKmif3c5z7nFMQXJgX03XXXXe53v/udW7x4sdOXWSy98cYbTj8f\\nfvihGzlypGW7UaNGuYceesj34bTTTnPt27cv7JszZ4579dVX/ZS1nTt3dpqm1pLaV/Ceyod9V/7d\\nd9/tNLqeprQlIYAAAggggAACCCCAAAIIIIAAAggggAACCCCAQGkCbUsr3rpKa/Sunj17ut12280p\\nEC9pyqHWJcLZIlC6wBZbbOEuuOACt9dee7lu3br5h4HWSu/evZ0eGh588MGWxRIBBBBAAAEEEEAA\\ngboW0Ih2GuVZgWyPPfaYe++99wr9VdCdpqxVOu+885yC4ML0j3/8wwfh6bPlr371K3f77be773zn\\nO370OX0J7Nlnn3U77rij/yLLqaee6qsOHz7cfelLX3KHHXZY2JTr1auX0/20vuyioMAwjR8/3m8e\\nd9xx/vNsuM8C9jp06BBmFwL52rRp0+CevUEhNhBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQCBV\\ngBHxUmg0JS3Bdyk4ZCNQooAe9h144IH+p8SqFEcAAQQQQAABBBBAoK4EFIi38847+2C8J554wt1/\\n//3u5JNP9n2cPn260xSvCnLTiHWXX355oe+qt27dOj+q3TnnnOOGDRvm940ePdqPGq0paNesWePr\\nKsBu88039/sHDx7sNttss0I7ttK1a1d/3D//+c9+VDx98UVBdBr9TiPeaf3YY491f/3rX60KSwQQ\\nQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEGhCAQLxUnDj0QFSipGNAAIIIIAAAggggAACCCDQygT0\\nRZPjjz/eKRDv+uuvd2PHjvUjqGuqWiXt69Gjh1PwnSUFxikAT+nll1/2AXJLlizxgXfvvvuuFWu0\\nVHBeUlq9erU79NBD3V/+8hf39NNPuwULFvhpZR9++GGnKXB32GEHt/322/vpcZPqk4cAAggggAAC\\nCCCAAAIIIIAAAggggAACCCCAAALVFSAQr7qetIYAAggggAACCCCAAAIIILCRC6xYscJpBDqNSqdg\\nOo2Ep8C3Rx55xI9Ed8wxxzgFyin4LkyaRvaCCy7wQXNhfjnrCtDTqHl77rmnDwi866673Lhx49x1\\n113nm9PUtjq+RuEjIYAAAggggAACCCCAAAIIIIAAAggggAACCCCAQNMLtG36Q3AEBBBAAAEEEEAA\\nAQQQQAABBDYugbZt2/qpX3VWEyZMcFOmTPFTyw4aNMhtvfXWjU5Wo+P99re/9UF4W265pfvlL3/p\\np5S99957CyPlNaqUI0MBd0qajlYj7c2dO9dpxL7999/f52+yySZ+Gf/Trh3fy4tN2EYAAQQQQAAB\\nBBBAAAEEEEAAAQQQQAABBBBAoBIBAvEq0aMuAggggAACCCCAAAIIIIBAqxU46qij/LlrNLpf//rX\\nfv2MM85wCtJLSosXL/bZX/va19zIkSP9iHoatU6j5ynFI+gpLytgbtddd3WbbbaZmz9/vlO7SupX\\nly5d/HraP1OnTnXr169P200+AggggAACCCCAAAIIIIAAAggggAACCCCAAAIIlCiQ/HSgxEYojgAC\\nCCCAAAIIIIAAAggggEBrE9DIdyNGjCicdocOHfxUsYWMaMUC7jRynUbIU5o8ebK7/vrr/bqmro2T\\nBe/F+batoL/TTjvNNn0w33HHHVfYjlc0pa2SRs5T8J7SypUr3Z/+9Ce/zj8IIIAAAggggAACCCCA\\nAAIIIIAAAggggAACCCBQngBz0ZTnRi0EEEAAAQQQQAABBBBAAIFWJrBu3boGZ6wR7DQ17HPPPefz\\nTznlFLfpppsWyliwnZYqq1HwZsyY4a655hp38803u1WrVjUYle6VV15xhx9+uK+/2267+eV9993n\\nJk2a5BRcN2DAgELb4cphhx3mLrvsMt9W//793XbbbVfYHfdZbXTq1MmtWLHCT4mr40ybNs1Pq2uV\\nrN+2zRIBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQSyBRgRL9uIEggggAACCCCAAAIIIIAAAgi4\\nzp07N1JQcJ2mhtXIdDZVrRVS8F379u0L9c477zynYD0lBcJpaliNqDdq1Cift3z5cr/UPzvuuKMP\\n3NO6AuM0gl2YevToUdjs2bOnO/jgg/32mWee2WBq3I4dO/ogwF69evn9CsK7+OKLnfKVnn32Wbd2\\n7Vp39NFH+3rdu3dvNEWuRvojIYAAAggggAACCCCAAAIIIIAAAggggAACCCCAQHGBNsuWLft4Ppzi\\n5diLAAIIIIAAAgi4rl27ooAAAggggAACFQpoKlgF3bVr185tvvnmRVtbtGiRe+ihh3ww38CBA1NH\\nxSvaSMJOBfe99957PshPf98tMC+hKFkIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCQQ4CpaXMg\\nUQQBBBBAAAEEEEAAAQQQQACBagko6C1v4JtGsuvSpUu1Dl1oR6P12Sh5hUxWEEAAAQQQQAABBBBA\\nAAEEEEAAAQQQQAABBBBAoGwBpqYtm46KCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCA\\nAAIIIIAAAggggAACCDhHIB5XAQIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggg\\ngAACCCCAAAIVCBCIVwEeVRFAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQ\\nQAABBBAgEI9rAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIEK\\nBAjEqwCPqggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgi0gwAB\\nBBBAAAEEEEAAAQQQQAABBJpXYM2aNW7p0qXOlmFv1q5d69avX+8WLlzotB6mXr16ufbt27vu3buH\\n2awjgAACCCCAAAIIIIAAAggggAACCCCAAAIIIIBAjQXaLFu27KMaH5PDIYAAAggggEALFejatWsL\\n7TndRgABBBBAoH4EFHD37rvv+sC7999/3y+r0bt27dq5zTbbzG2xxRY+MM+C9KrRNm0ggAACCCCA\\nAAIIIIAAAggggAACCCCAAAIIIIBAcQEC8Yr7sBcBBBBAAAEEAgEC8QIMVhFAAAEEEChB4J133nH6\\nmTdvXqNR7UpopuSiGilv2223dX369HGdO3cuuT4VEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBA\\nIJ8AgXj5nCiFQJMJaOqpiRMnutWrV7uddtrJDR06tKJjvfLKK+7ll192Gg3jsMMOcx07dqyovZZa\\n+ZFHHvFTd/Xo0cMdeOCBLfU0mr3fODb7S1B3HSAQr+5eEjqEAAIIIFDHAh9++KGbMWNGScF3CpzT\\nVLOW4m21uWLFCtvtNKJePF1tYWe0orYGDBjgA/OiXWwigAACCCCAAAIIIIAAAggggAACCCCAAAII\\nIIBAhQLtKqy/UVdXQFPPnj3dlltuuVGfZ61P7oknnnDTp093G6ZFLhxaxnvvvbfbYYcdCnmtZWXV\\nqlXe46OPPnJ6qFZpIJ4e9M2aNcu1adPG7bPPPq02EO+FF15wy5cv9w8xDzjgALfJJpu0lkuqqueJ\\nY1U5aQwBBBBAAIEmEVAQ1rRp09xWW23lRz1rkoOkNPrOgkXu3kmP+r1b9e7lxhy6X6Hkldf/s7B+\\nxIb8Phv2K92zofz8DfWURuyys//RetiWts/59LFa+JTW1utvznNL3l/mtt92a7f5Zt2seLMvLQDv\\nzTffTO2LguL0o6lk9RMH3KVWTNmh6W51XAXmLVq0yP/ERVXm2WefdfqsO2jQoLoNyLvvvvsKXR85\\ncqTTl2uUZs+e7V577TW/rs+OCipUWrx4sXv66af9uv7RF5IsKV/7ldLaUvvap6Syb731luvdu7f/\\n8Zl18o+NqNi/f/+CSZ10jW4ggAACCCCAAAIIIIAAAggggAACCCCAAAIIbBAgEK/IZTBw4ED/kGLu\\n3Llum222ISCviFWeXXPmzHG33nqrW7duXaPiMtZPv3793KmnntpsQVMrV670D3YUFKeHG7WYuqlt\\n27ZOP3IJR75ohJQzQyPhWVIwXmtN5mDL1upQ6Xmbny0rbY/6CCCAAAIIIJAt8H//93/usssuc3ff\\nfbfLMxKpvthxxBFHuAsvvNB961vfyj5AhSUUMLdu/UduyfKP3Nvz5jsFwykt+2CV22La24XWLV8Z\\nL7wyz721cJXfN+u1t917773n19t36OrWtunq15UX1pmao62Pv4Qy03XosKkP3LNgP99gM/2j4DsF\\nuyUlTQ9rP9W49w+PYYF9at+SAu/eeOMNPyVuOIqe1tVH9XWvvfaqyucQO2a5S30eVPCbrucFCxYU\\nmtE52L2orhHbp+BF+9Kcyli+KoZf+nr33XcLQYlpbemzmNVRGX0ZRWmXXXZxw4cP9+v18I8CFM86\\n6yynUav3228/V+rvino4B/qAAAIIIIAAAggggAACCCCAAAIIIIAAAghszAKfROxszGdZ5rnpP/uH\\nDBni/xP+1Vdf9YFiBOSVh6mHHzfffLNTgJuSAsQ0ekGXLl28qz2I08gDf//7392nP/3p8g5UYS2N\\nsHDXXXf5VkaNGuVHlKuwSao3s4Bdc83cjRZ/eBxb/EvICSCAAAIItCABBZfNnDnTrVmzJlevNfLv\\nFlts4Tp16lS0/G9+8xv34osvuksvvdR/EaRo4SI7b7vzAbd02Ydu9z0PcG3adXb7jfpk9LHFS1cW\\naob5yrR9g4buXigT5pfT1pZ9tnMdOnd3L0971j3z/CvuqNH7N2i7lht6vTQyYTwKnl4Xmw622sF3\\nWeen4DwFk+lHo+Tp845GVbOkvIkTJzqNIK2yzZU0Qp9GrtOI3hqd7sgjj2zQFZt6V1+W048ly1ff\\nwzqWr3IKNAyT7SvW1kEHHeTfg+qXfW4N22iu9Q4dOvhD23VU6u+KPP3WiIMHHnigu+OOO9yuu+6a\\npwplEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBP4lQCBexqWgQDF9y37hwoX+m/kE5GWApeweP358\\nIQhPIxeceeaZDR4UyvX2229369ev99MA6T//m2OaWnugodOwhxwpp0R2nQsQOFadFwjH6jjSCgII\\nIIAAAqUI/PSnP3U//OEPSx6dedNNNy16GN1j63NNJaMmazS8BQvfc/132KnosWq5s3v3zd3IvUe5\\noQO3quVhGx1LQVthEJ6+2KUAuG233bZR2ebI6NWrl9OPpq/VaHgKwlNSYNrDDz/sFHxWixHBk85d\\no+F169atbqZbVfDk0KFDfcCq/k+guZNeI11P4edV9anc3xVZ56PXg88BWUrsRwABBBBAAAEEEEAA\\nAQQQQAABBBBAAAEEGgu0bZxFTiyw/fbbF6bC0T5NlaPAsWeeecY/yIrLs91QQFPS2oh3Cm4bN25c\\ngyA8ldZoBCNGjChUfP755wvr4YoeHj700EPugQcecA8++KAP2gv327oeVGgKJv0oaeqlxx9/3NfT\\ndD4aqSJMH3zwgW9r3ryPp9XSPo0UoW3ru/I0Yp/a1HRFixcvdpMnT/Ztzpo1S7sbpLx9bVCpxI2X\\nX37ZH18eTzzxhJ/eNk8Aoa5f6/uUKVPckiVLGh05NtT5mr2Opzaykgz1UE/ldbzXX389sYpcNTqH\\n3ltKmgpKdfRj55VYcUOmHhBNnTq1UN6uHY0MUyxpxBKNCGMOamP16tWNquja0Gv+9ttv+30a3VHX\\nnvqmn7RzsoZs2i8rr5E+7DytjB7Y6hh6MJ6UdB2GfUgqE+ctX77cPfnkk76POnZ8zcfly3XUa2eG\\nWs6fP983rXPST5KpCuS9NuJ+so0AAggggEA9CaxcudKdcsop7j//8z8LQSu6T9Q0sRoN2pLuFRWM\\nZdNd6j7rf//3f91WW23lg+JOP/30BvdW9957r29Xf8+V9Hf6j3/8oy+rILof/ehH7k9/+pOfmjK8\\nj9Pf3j//+c+Fct/73vf8fYcd/4YbbvD3Bvvvv7+76qqrfNsKylL/1a5+jj76aP85x+9M+GfVqo/v\\nl7p0a77R0xK65dpt0m5DQNn6pF1Vy9P91K233upeeumlRqMVKqhN9/+WFPCmwLZ6CcKzfmmpYDtd\\nA8OGDStk65pMm063UKjCFY18rvtT3V/HSdPKxkFmcZlabyvwrW3b7P8y0fvrkEMO8e8fvad1nied\\ndJLT+03p5z//ufvsZz/rLrroIl9GI1Iq6XOF3qP23tPvAX2+C9P999/vf0/I5txzz3VXXHFFuNvF\\nvyu0U9enjq92NVLg5Zdf7r/wpn0a/X3vvff2S33+Vhn1WZ+VlX784x/73z1aP+ecc/zIePocSEIA\\nAQQQQAABBBBAAAEEEEAAAQQQQAABBBDIJ8CIeDmc9B/wffr08VOohsUtIE/fFmfK2lCm4fr06dML\\nGZraJu0Bix4EKbhRScE8Gh3PHnwo6OvOO+9sFNSjBzkasfDUU09tENynKXpsillN06VgOrUXJgWV\\n6eGCRjuYNGmSU50w6UGbfvr27evOOOMMpwehephiIwPooYWtK1Bqxx139NVL7Wt4zLzrCu669tpr\\nGwXQPfLII43OM2xTQWMaeTAOjlKwm0brGDNmTKF4aNivXz8fiGbnq0IKXJO9RjeMg97UvxtvvNE/\\nXCo0uGHlqaee8u8lTT1sdUJXvRbKt4fOVlev1dixYxtMQ6V9NpXxunXrrKhfKrhQD8bTks5XbYbn\\no7IKJDv88MPd8OHDC1XDa0MOOmaY5LDddtu5k08+uXC92v577rnHB/vZti0VGKcHYJr+WNemHtap\\nL7rev/KVrzR4j+i6laUejuqa+9KXvpQ5Usk///lPpxFZ4qTz03tFr1uYynW85ZZbfABl2JZeYz14\\nthFWNK1VOB1YKddG2C7rCCCAAAII1KNAx44d/b2igmO++c1vuq5du/q//RMmTHAanU73L7q30b2H\\nPjOovP7mq+zvf/97/wUVTcN58cUX+wC55557zt8r6V5I9xg2Ne0vf/lLH7Cjv6nnn3+++6//+i//\\nN7h3796F+xl9ZlFAnfI0Be1tt93mfvGLX/i/+woCUj0LvlPQkO6R1ZfPfOYzvuyFF17oy37729/2\\n99G6b9Q9Tpy233Zr95nTT3avvdX4ixxx2Vpvx/d2TXF8m35W98o777yz22mnnfy9W3jvpeAnvVZp\\nn3uaol/ltKkpV9VHC8DT/Zt+dC/XVElf0tJP//79/YhzNtqcAlrjzwBN1Ydqtqsvl+gaUFKArO7Z\\nTzvtNL89evRov9R7/29/+5tf1+ctvfcUsKtATQXNKfhNvzv+3//7fw1+D+je/dBDD/UjBep3hD7j\\n6P47TPHvCgXy6XO1fg9897vfdfqdove+Anb1e0dBkPoMrYBbBfbpd9RPfvITd/zxx/vfXbpu9dlE\\nac8993T6/FPv13HowToCCCCAAAIIIIAAAggggAACCCCAAAIIINDcAgTi5XwF9IBM/4GelAjIS1L5\\nJM9GElMQkab3SUt6WKCHEXqApodXFoSnICE9SAwfrClgSwE9ShpFTCOKfOELXygE4+lBpCWNNGBJ\\nD0ItaEsPLTSihYLsik3jZSPMqT/6sfphf+x45fTV+pZ3qcAsjWoQBpqp/3ooaH1LaksPif7xj380\\ncAzLaXQ4PTg65phjfLadkzYs+MyC5+w4steD5/POO6/QVFL/Cjs3rKgfehClB8Jmaq72mqp8+FrJ\\n+o477nD/9m//VngQpNc1DIxUHb1Wej+GNsoPkx6E6yFWUtJxNKqErlUFJiolOSg/DMTUaHWPPfaY\\nH1lE+5QUOBoGoeo1ko2MdRyNOqHz1mgkmq5ZD8e0Xw+9LahT7WikQNVR0mgVWdOF3XTTTamj9MlX\\nD+D1ME7HVCrXMT6OXi+ZqK8WhKf2lWep1GvD6rFEAAEEEECgngWOOuooH1Snv+EKgFEQnpKCXTTK\\nl77QoxGZFbS1ww47+KAnBeFpRDvd2yh98YtfdEOGDHF33323D47xmf/6R/dbCqw74IADfNu6Dz77\\n7LPdwQcf3OBvvv4GK7BKQfH67PL5z3/eB/7rSwVf//rXfbCPvgCgIKif/exn/m+0RsObOXOmD8ZR\\nsJ/SkUce6b+UoFGNkwLx/tWtult88MFyN+G+Z928wdu7QTs2DiCsRoflZckC8hSApy9lhfc/uo9s\\nKcFLGrFPIyla/3Ud656tqZOuQ/3o/aER6DUtbT0m3c8rgHbkyJGJ3bv++ut9/vjx492xxx7r1xXA\\nphHpLNlnKH1e2GeffXy2RtFTEJ6+QGOfv3bbbTen4D256LOxAvS0VD397vja177m/v3f/90Hzlnb\\n4VKfMX7wgx/461FfcLNgW33RR79D9DvB0nXXXef05SilwYMH+y9X6Qtl6ot+j9nxwlHrrS5LBBBA\\nAAEEEEAAAQQQQAABBBBAAAEEEEAAgXSBT6KV0suwZ4OAvqlvQT5pIATkpcl8kh8GNX2S+/GaAnb0\\n0CJMegikhxp6qKCkBzUaLUGvhQKINFKYgov04FEj4IUPPMJ2NEqBHioqGEpTl06cONG3qZHs3n//\\nfT8SnEYn0Ah4egCqpNHK7EFJ2JatK5BJD0B1bWjUiGr11dpPW2rUOws0k9lxxx3nR2HQw0AFFioo\\nLE7y00Mec5SHHhQpEEznrJHbtE8PEvWQd/PNN4+bcHvssYc/X+1QIJsC2pQ0VZHqDRo0yG/Lz/qn\\ndjQihB6s6SGyRlfQ66WgMx03HHnOV97wT8+ePd2JJ57o+6CgPQXb6fXV+ek4FiCnh9x2PhpBQsfR\\nQ2f1Rw/Ewgel1raCL/VQ2dKnPvUp/4Ba27omNGKEksooaFQ+YZK3rguNZqcUjninQMZ9993X19HD\\nMwvCU52DN1wn9vBOr58epqnvWuqBmx52Wb/kEgbihdNTqVyxpId5eniqpPfaCSec4B9syk5BmAqo\\n1HEf2DAin0a/UCrHUe3YcdRGeG1o2l4FHiSlSq+NpDbJQwABBBBAoLkFdt99d98FjWCngBbdn567\\nYaQpBeUrT/eY+puvYDv9fbZ7BH2xQVPW6++0BdGFU5vaeSlASgF9+iKGgvCUFJiv+47w77HyNc2l\\n7oeUdI+qexAFWel+JEy6b1VwkO511CcF6/3lL3/xo2/pfkP3C+pTS0rr1q11C99d5J5/YbmbN3d2\\nzbouJ70O9uUdHbgpR5RrihMLRzPWuSggq1ZJ9/v6UTCj3j/1lpYtW1b4AlhS3zTdtIJsw5HFdX8f\\nJ31mC0eJ1v2+3md6z+tLZ/p9IAclvSf1eUrve42GraA4Jb2Pdd+dlvQ5y36/qF29jgoIVaCjPvPq\\nM5iOqeA+jYhnyT6n6LhK9t7X7yYSAggggAACCCCAAAIIIIAAAggggAACCCCAQGkCBOKV4KWHWvaf\\n48WqEZCXrKORBEod6UBBZZo+R0lBXRq9zoKj9A1/PeTUaHh6cKHgJwVfxSOGaSQPTbVjSdPjauQP\\nlVcKHzCEI1fYyAW+UPSPHoJqRLewjNqrtK/RYRpt6sFJGJil87KgLfVd045eeeWVPugtrKxRCfXw\\nRUmjqoUeetiqB0wKBFP7Gp0hDoiUmR7kWlIAm9xsKmEFjykQTw91ZaukBzkarcVGG9R0qJq+9Zpr\\nrvHHUVBdHIgnV00XbK4KvNSIcQruUrIAPwXU2XtR18O4ceP8SBUqo/epti+99NLCQyTlK2naLfVR\\nSQ/CLKBO2xp9QsGCstLrqBFjdI2FKQzCU76msZWX+iUP+SkpKM+SHpLbwy3l6Xz0UE0PxlRe0zAr\\nuPDRRx/1fdPDcrWl11N9tQfsOk+NlFMs2bRiekingFVNJaWkthSo+Ic//MG3rdE99Z7Rw7pyHBXM\\naknXT3htaCpa9d/6YuUqvTasHZYIIIAAAgjUm4DurTR9pKa9V1CeAuNvv/12//dXAe+6V1Oe/kYq\\n2b3RV7/61Uanonsru1exnRYco+NkJX0OCZPdU4V5WrfAPN2fK1hfI2Up4MfSZZdd5u91bTtcvrNg\\nkZs46UHXZ5uBG4L9uoa76mK9f//+bq/di98zldtR3e/bZwhrQ6+Pgsjsnkr5Cp5sScF4Yd8VtKVR\\n8poi6csgcdJ1rddMAWT6ybrfjes397YCXnUO9hlV/dF9dlbS54Avf/nL7s9//nNiUWtPI8WHSffZ\\naUl1dD3qXj/8nGPl9fnGUthH+x1j+1gigAACCCCAAAIIIIAAAggggAACCCCAAAIIlC9AIF6KnQXU\\nhLv1ICspPywTris4R0E6CqzRwxkFIrXGZGZa2k9eBz20tKTAKT00tPaUr8CtgQMHuhkzZvh8BTcp\\nqCksY6MNWDtWT0srFy9tn+XH2/awJdxfSV+tHS1tXceMkwLQli5d6rP10EejI8Tl1TcFlFlbWoaB\\nYXq4pjwFYSnpwUv4oFCjJ2ikhbBdm9bIV/jXPxotUCPI6WGxHt5p5ATVtREU1KbaDkemU5CkHhDp\\nwY8C0FTW+qlmk1zDh09WVsFp9pBaBhqBJOyvrgv5KPjQ6mip68SS6un4q1ev9lmqo/eoAvFUVuei\\ncwjbja8lXY86tt7r4XHsYab2KwgvbEMHU6CjXktZqJ/60cgUqqcH6HrAq2OpLxZ8qKBEPbiP27Lz\\nkbNMlfS7SgGJob3OT6M4aiRJtamRA1W+HMdwmm495Iv7pOBXC8QzF703S7020oIH7JxZNo9A/Ho3\\nTy84KgIIIFBfAp/5zGfc97//ff/lBo2Opb+FGnFKo+ApIF6fBZRvfxfVe30Jwr7IoL/HuqfQT3i/\\nq/IWeKO/60m/g8M2w/VQKM4Pt3UvrRHxdP+o+9nf/e53fhp73Xto2t04rVy5ys1fsNBt0We7xP7E\\n5Wu13alzF3fYIQe6ITtu7TbfrGmmOdW9k+yU9LpqlGm9rlrXqMc2vatGSdOXL5Rf70mfVe0LO+qr\\nrt3ws0E1+292alOfSfSFDt0HKymQNbwufWYd/KP3qD6PhH0Pu2UBbXoPW/Cc7Y/PJ9zWaNwKwtOX\\nyvTlMn1u0ucPvR9VztpVW+Gx7f7Y2rJ9Vkf325py9o9//KO/91a/dB2qnj4L6MtQ1mZYN8yzfMvz\\nFfgHAQQQQAABBBBAAAEEEEAAAQQQQAABBBCosoCeh2yMqa4C8fSfzZoqNCkpqMT+k16jVSmIJClt\\nvfXWhdG0LDgoLqd21J6Sjqlgl6QUtqXjxSNMJNVJymOEvI9V9BBR3vbwIMmqWJ4eYCYlPfyyACsL\\nWgrLWfBPmFfpugUvpbVTbl/T2rN8Pdyxh7MKGosf9qhc+NDG6oV5mlLWppW1/VnLJEMF1WmEQz24\\nMw9bqj09qPzNb36T1XSD/WH9BjuijXDUBj3Ey5tCB00bV2rK2z9rVw/Tw75avkaqO/PMM23TLzU6\\noAXw2fS0WloaMWKErSYu1Td7aKbXK210jbBy2LdSHO0PooL7wkBJazvJKcwr59qwtlkigAACCCBQ\\njwI24u3111/vfv/73/u///rSgtL//M//OI1+17Xrx6PH2ejN+sLOfvvtVzid+++/3+nvvUapC5NG\\nhVbS33YdR/fS+ls/e3bp06/qXkjTU+rvsu4jNdLy0KFD/ah4J554ojvooIOcAvBuueUWPzpZUiBe\\n2Ld6Wm+3STu3Ve8eTRaEZ+caB+BZvgK2FIynpC/OPPnkk05TlNrrbeXqaalrKLzfVABeUwXh2XnH\\nAXiWX69L3eva/x0k9VGf+/70pz+5p556yk8XrTLhl1aS6ijP7sP1JTJbVxtKusb0BRwd9+KLL/aj\\nU1ofwtHRfeHgH9XT/3Xos57u1+2LgPoijoJsNbp3nmSf/cIATdXTZ+3w95P+n0Ofdyzp94s+j9j5\\nWD5LBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgScDiG8J9FosQ5rW09boKxNMDi7T/tNZ/Ausb80pz\\nNowWpak0k5IF2WmUK5VLSnropNHVlBSEl3ZM7beAKj0oC4N4tK/UZAF5CjbUVJ+tJdmbR3563cp9\\nuFOpfy29a9HXtGDUSs/TRl3JakcPcMMR17LKx/vtAU+cX+p2OMVSVt1SfmlX4zW0az+rX9qvh7ca\\njUbH1Yh/WmpUDCU9cNfoGMVSGKRZrJzti/tWiqNNqadrRddBucG11pd4Wa1rI26XbQQQQAABBJpK\\noP+GqTV1f6/p2y3YRSP9KtDutttuc6NHj/aBMTq+prY/4IAD3Lhx4/yoxUceeaS799573S9/+Ut3\\nySWXuC984QsNuqnAuO985zt+v4JezjrrLF/ujjvu8CPqqrAFvGfd66hPChpScOCpp57qRyMeMGCA\\nO3fDiFy6t9Rnn7/85S/++PZ5qUFnNmx06LCpGzpkZ9dh008CcOIyzbXdrl3bJj20Po/aZ9L4QPqM\\noxGX7f5No+NNnjzZj9bdVFO9xn3Iu617eI1ebCP4qZ6CpxQ42JRJ11xa0r2wffEurUxz5Cd98Sns\\nh0af02iYY8aM8aPQad8555wTFkn8fwR9oUXpK1/5iv9d8MYbb/j3pfL0/xMKyv3e977njj32WL+u\\n96WCO3/84x+rSGJSIN7PfvYzd8QRR7jBgwf73xO6X7dpsGfOnJlYL8604F/9TtKXIDW652OPPeY+\\n9alPuWuvvdaPuKcRNPVFotNPP90HC+qzi46r89C1ZYHHcdtsI4AAAggggAACCCCAAAIIIIAAAggg\\ngAACxQTiOIas5x7F2mqufXUViGdBdElBGOHDi759+yaOYqeHB/af9woU0XSa4Sh29oKFgWD6Znn8\\nTW97Mexb59rW8RcsWFBR4JH6pwdp+mlNSedr06kqCDH0Dx00Veell17qv0Uv+89+9rPh7tRgn7yB\\nYw0aa+KNtMCkSvuqh6x2HYfXZymnc/DBB/vREWxK1riu3jd5kn7h6QFS2jlppEJNy2pT4MZtqm6a\\nU1y22HbPnj2L7U7dp1FflOzBdVhQ52ZBuGF+qeul/FHQ7yyNEKKRSfQ7cOrUqYXfN/r9Y8Fvefqg\\nkTs0gk3SCJGqr4d0ep3D4Lu8jrr+LABTv9OyHk4m9bdW10bSsclDAAEEEECgKQQ0StRJJ53kp57X\\n3zkl3QdoytpHH33UT0tvx9Xf4TvvvNP98Ic/dP/93//tf7RPU1Sef/75vpjqhn/7f/KTn/j7posu\\nushdccUVTvdzYdLfY30OUttxss9Hyj/hhBPcv//7v/sAH/0d/9a3vuXuvvtuH/z3uc99zlfViMdX\\nXnllIaAwbq9P715u7FGfco89N9etXLU23t0s20uXLnHz337D7TviyGY5vh1UwWS6N7YveumeTkFJ\\nmg5UAY+6p0t6jax+Uy8VeKd7TRuF2Y6na0HBoc05ep99bki7f7W+1mqp106Ba/q/B03pmpb6bwjC\\n1Uh2ClazADy91uGIlXpPxUkjZuq9rIDcp59+2gfVfve733W/+MUv/JdyVF5tWpljjjnGl/mP//gP\\n96tf/crZvXv8u0JBv/fcc487++yz/e8ktaOg2htuuMGPZh7+PtC+pKSA3W9+85vu17/+tXvuuef8\\n6JhJ/0ejYDsLKFQ7edpOOh55CCCAAAIIIIAAAggggAACCCCAAAIIIIBAmoDFx5QSe5HWVq3y6yoQ\\nTw+b9PAiKykAKU8Q0o477uibshcmqV39Z3HaqAZheQWTqR2NVFVqCgPwtN7akn2jXuethwyaficp\\n6WGFvVbhf+hbWU0/a1N8WZ6WL7zwQmFT0wnXQ2qqvuohq3404oBGc9QyDmaLt2MP1Q8DW+P9SdtJ\\n161GTbEASz18jn/x6SGkHlw1RdI5WNL0THlH77DrS3V1Xeb5PWLHKWVpx9GDRI0CGb4H1I6mZn38\\n8cd9kxpZwvqh94Y9tHvooYcKh8w7gqYdV/b6nZXnWrCD5HXU66yHtBodQ+enAOX4fRe+PtZ+uGzK\\nmTF+HQAAPKtJREFUayM8DusIIIAAAgjUUuBHP/qR00+YNAJY0ihgCszRlJMKrNPfb/3tDO9/Tznl\\nFKcfS5pOVqPiKQhHATH63HTaaaf5v8NqS/dqdm9hdbT87W9/G276Eaz091tflLDgIn1m0qi8CrTX\\n32h9Pkq69wsbardJ2w1Bb9u4V157190+/g7fJ+3fZ9/9CgFCMzfcu8+a9fEIXAoa0j4lfRHg8cce\\n9ev656ijjymsK9++KJCnLVW86847fP0Om7bfEAj5gevaueHUvoXGa7CiILvdd9/df+FBAXiW5K0p\\nYPWjezT7qUVQnu7X33zzTacvRCV9QUZfklKgVi36Yh5pS/uijkaWtNHb1D+bwlnXhk3/qzY0Wpwl\\nBbzaCH877bRT4f8VFASZ1Jbq/fOf/7TqqW0lBdEVKv1rRQFpCm7Ue0ufi5555hm3xx57+GtBRX7+\\n85/HVfy2guX0PtbvAL3n9KPfCWGyMmrb3psK4rUU/65QvkbdfOutt3x/9PvC3uvap+C+OBBTvwPC\\nLyfpfl/Bfvp9pt81OidNWx2W0bS3mjXAkvo+fvx422SJAAIIIIAAAggggAACCCCAAAIIIIAAAghU\\nVcBiIeK4lKoepEqNbfRRYfZiVMNLQU+lJP1ntD1k0XprTXvvvbcf3Ut+7777rnvwwQfdgQce2IBD\\nr9MTTzxRyLMgSgUgTZ8+3edrOhwFXGlKLksKjFRAmpKMe/fubbsqXpb6MKoWfVUAlB566OGJRrTT\\nA9f999+/cK56uKYgwDgNGTKk4KgpsrQdOqr8Lbfc4vP0cCZOesgSB7vpdbTUr18//wBZI0Io+Euv\\ntablevXVVxtNqarXUUFfmsYofOBsbeVZapQJO45efz1oUh8s6bhJI12qnj0U1YM3PdgKk0yvvvpq\\nP/2THp6VmzQtmUaPUJLT8ccf36ApBdlZUK+uGwvEU//0uoQjeerhl9rLSro21I4eUOo6mDJlijs4\\nGi1HTjpvPbSUV7mO6o+mo1LSg/szzzyzQfeSAgFqdW006AgbCCCAAAII1LlAnhHIFEijkXwVTHT9\\n9df7zxdXXXWV+8c//uF++tOfZgbNxQS6t9BPnPL0JayjYLxhO/Z2L/bvt+HeZbXfteugrZ1GzFNq\\n/9Fyt2bVcr++1Ya8PYd9/AWNdxZ0cO+89cmXZyxfBRfN7+vmd/n4Xj9PW6rz1us7+WOOGD7Iddww\\nbW49JH3pRQFkCgKz0fGsX7qPt0AojWKsIKlwWepnEGtXSwXdKaBSS33ussC0sIyt6z5cX4Ir9Qs6\\nVr+plro2dT9sn+t0f2vBcHovWL6Ob/la12jP9iUUBX7aPq1bnbAt1bF8rVt5rastfdFEI2Tbfbry\\nk5I+Wx1yyCH+vaigW31u0lSwats+0ybVs7z4M5nlh0uVyVMurCOLSkeo03VJQgABBBBAAAEEEEAA\\nAQQQQAABBBBAAAEE6klAsUX1Hoy3UUeHVTMITxeWjc6QdZERgNdQSA9TFNRkgXaaukcPhhRApn0K\\nmlJwlkbgUNI37jWShJKChRR4pqAfBXdp6toxY8b4hyIaUSIMBhs+fHjFIzno4Y4ljd6nhzB6PTU9\\nT1aqVV/33HPPwugNctOIa/vuu6+/Pu+44w4foBf3VUFQesin4DQ5XnLJJe6www7zgVganU0PkOz6\\n1kPDeOTBN954w/397393RxxxhA+4mzBhgpszZ07hMPZ66fUcPHiwH+1DO2+77bbCaBAaxUEjWNiI\\nb7feeqs744wzCm2UsqJRYzTtmwVpaqqlUaNG+dEWlafzSUpy0ggbGs1B19Rll13mryeNWKfzuf/+\\n+/11qPqaJlavfzlJfjqOfgfpYZyCHBV8KvswCE/XVhhAqPNScJydl45tAWx5+qEpxWwkCl2/GrVQ\\nI+6p3RdffNFPm6V29FrqAaEe0JXjqKnDnnzySX9+un6uueYafz2pPU2FpVHy4lSrayM+LtsIIIAA\\nAgi0dAHdL/zxj3/000weeuihhdO58MIL/dSyhYxmWjn9xCMSjzxil52dfuKkQL1zPv3JSGbh/jGH\\nfjxqXpin9bS2tG/s0QdrUXdJgY26R1awmwLyFHwXftZQhxUwp584KQAqDMjTfXy4rWA7/VjSPX7c\\ntu2Ll2pb95v1FoAX9lOf65KSguL0GSYp6f40Kelc9ZOUSm0rqQ3df2ukyh/84Af+R2UUhKfPQfoc\\nS0IAAQQQQAABBBBAAAEEEEAAAQQQQAABBBCorkC9B+NttIF41Q7C0whV4cOOpMuEALwklY/zFCSl\\nqSwVPKekoCf9JCVNr2OjGWi/thUwpdHK9GOBRmFdBUxpupxyUnitbLfddj56VnkKYlKAl17XL3/5\\ny77psGzSscrta1a74bEUOKURyTTinJJNcxWWsfWw3ZNOOsldeeWVPhhMAWH33nuvFSssFQSZNg2q\\nprTS6xAnTe0cBioqWE/BWQq2VJo6dar/ieuFoyKG/YzLpW0rIFOjytn7UgFu4XSuSfX0QFRTNSlY\\nTEkPLW+88cZGRfVgMikIL6mfSXmankoP9iZOnOjb1mtlr1d4sLFjxzZ4qKp9eogYBuKNGDEirFJ0\\nXdNwaXpbBd0ppR1Xo1Tae6xcR72nNeqekl7va6+91q8X+6eca6NYe+xDAAEEEECgtQjo/kzB/Zpe\\nVPfU+hJB0r1Ka/FoSedpAXnqs4Lx9BomBeWF5xQH5xUb2S6sl7au4Dvd32q09lJHPkxrk/yPBfRZ\\nUVPFfv3rXy98sUkj6SmfhAACCCCAAAIIIIAAAggggAACCCCAAAIIINA0AorTqNeR8TbK/x1OCoyp\\n9KW10cKS2iEAL0mlcZ4CfjS6l4KTwqk3raSCiDQtqgUIWb6mTbrgggucRlCLg/c00pceTB68YQpO\\nrVsK18PRI2y/RueyFB5PAVRhgJHKhPvVroLYktpU2XL6qnr6BaHrNu9UrZqeTKPSvfDCC6peSDbd\\nkkawUwofAGmfHBXIGDuqrEaKOO644xqcr/KV+vbt6x8saVS7MCmgS15h0rmcc845frRCjX4YJ40M\\nceSRRzYYIaKYa+gfno/qnH/++X5aNpue2I6l60yBlAq0C+tovwUOykFlwqRjaQreMKgz7fhWz/bH\\nx9F1qdEFNRWsHpiHSa/FMccc02A6LNuvETT0gFQBhgqMlH0pScFuGmVPo/rFr5faO3jDe2Xo0KGF\\nJst13GuvvXz/9H4Of+fqvaWHjzbyYeFAG1bKuTbC+qwjgAACCCDQ2gU0XSap5QooEE4/SjZ9rO5X\\n00bGK+dMdU+qEfR0H6pgzXh0vXLapE62gJz1Q0IAAQQQQAABBBBAAAEEEEAAAQQQQAABBBCojUC9\\nBuO12TCt5Ue1IajNUcKAkGodUdP8KOApDh7TQw57mBIH4VTr2BtrOxotbcWKFT7oS3YKPsqTFFhk\\nQZEKhmuq6X7C4yhoKm+AXHgOYRtN1Vc7hoIDu3Tp4nr27Bl2IXXd6ikwS3X1oE4BWWGaMWNGYQpc\\nje6mkdk0natNIawHe2FAY1g3XNeoHx06dPDvH43eUo5l2F7SugLqdD3ZueQd6WP58uX+wacclPL6\\nJfWhWJ76p2MpaC/retJ7QyMXKmlaLo3gV25SW5qGVwFweg2yHg6W66jX2P7IKUDg8ccfdw8//LDv\\ntkY+1HTKSakW10bSccmrTEAByyQEEEAAAQQQqL6A7rMVlKelAvQsLViwwN9L2v2WRvG2z5/6nGFT\\n12bd61l7LBFAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQ2FgEFA9RT2mjGhGvKYLw9GJp6qAwCI8A\\nvMov4XKn0ipnhLByeluN41Sjjay+l3uMUutZ8F05gY+1GLlFwW36KTUpoKgWQUWl9C+cXlfTzFaS\\nSn2fldJPjSioKfLGjRvnwtdYAYePPfZYodsanTAthfXSypCPAAIIIIAAAgi0FgEF1ekLMko2cp6+\\nQPPmm282GpF70KBBrYWF80QAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAIFXAvsSeWqDGOzaaQLym\\nCsJTAJ4C8ZQIwKvx1cnhEGglAkuWLHGvvfaa0yiEb731lj9rjWxSr4Fqmtp45syZvp9/+9vf3ODB\\ng93AgQN935999tnCq6ZAwFKDAQuVWUEAAQQQQAABBBBwr776aiMFjSqsey99uYaEAAIIIIAAAggg\\ngAACCCCAAAIIIIAAAggggEBrF6inYLwWH4jXVAF4ukg1Je0rr7zir9dtttnGj0pgUwC19ouY80cA\\ngeoJTJkypRDYZq1qOuB6TRrlbtiwYW7atGm+iy+//LLTT5g0bfEpp5wSZrGOAAIIIIAAAgggUIKA\\nRsNT0F1SUoCe7sdICCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggg4JzFjzX3VLUtLhDP4GpxEc2d\\nO9f17NmTALxaYHOMuhNQINUmm2zi+9WtW7e669/G1CFZW9KUZKNHj3bFpnS1ss25HDNmjNOUaJqG\\ndv78+W7dunW+OzqXoUOHuoMPPti1bdu2ObvIsRFAAAEEEEAAgRYtkDQanp0Qo+KZBEsEEEAAAQQQ\\nQAABBBBAAAEEEEAAAQQQQAABBD4RCOPKmiMor82yZcs++qQ79bsWQtWil5qSVkFIjIBXC22OgQAC\\nK1as8MFsXbt2BQOBuhbgGq3rl4fOIYAAAghsJAIaDe+hhx4qejZbb701o+IVFWInAggggAACCCCA\\nAAIIIIAAAggggAACCCCAAAIfC9QqKK/uhitSwF3ST60vjA4dOhCEV2t0jodAKxbo1KmTI8CpFV8A\\nnDoCCCCAAAIIIBAIFBsNz4ppVDwF7JEQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEECguEBSLFpT\\nDArX7FPTNsVJFaet3t6W3PfqKdASAggggEBrEuBvX2t6tTlXBBBAAIHmEFBwnYLs8qRZs2YxKl4e\\nKMoggAACCCCAAAIIIIAAAggggAACCCCAAAIINIlArUaaa5LOb2g0fv5d6fk0y4h4Ogn7aSqoarRr\\nfUxbVuMYtIEAAggggAACCCCAAAIIIICACeQZDc/KvvPOO4yKZxgsEUAAAQQQQAABBBBAAAEEEEAA\\nAQQQQAABBGoukBZTZfk171CFB7R+a1lOqmkgnnW2nI7Woo71z5a1OCbHQAABBBBAAAEEEEAAAQQQ\\nQEACGg1PwXWlpFIC90ppl7IIIIAAAggggAACCCCAAAIIIIAAAggggAACCFQqYDFYtqy0vVrWL6fP\\nNQvEU+fqMRlavfavHs3oEwIIIIAAAggggAACCCCAQPUFygmqY1S86r8OtIgAAggggAACCCCAAAII\\nIIAAAggggAACCCDQNAItMU6rlJiymgTildKhpnkZG7baEl/UhmfAFgIIIIAAAggggAACCCCAwMYk\\nUM5oeHb+5QTwWV2WCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgg0h4DFbzXHsUs9Zt7YtyYPxMvb\\nkVJPsNTy9uLVS39K7T/lEUAAAQQQQAABBBBAAAEENl6BSoLpGBVv470uODMEEEAAAQQQQAABBBBA\\nAAEEEEAAAQQQQGBjF2gpMV15Ys6aNBAvTwea+mKxF6upj0P7CCCAAAIIIIAAAggggAACCJQjUMlo\\neHa8SgL5rA2WCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgg0p0C9x3llxcI1WSBe1oGb+kWr9xem\\nqc+f9hFAAAEEEEAAAQQQQAABBFqGQDWC6BgVr2W81vQSAQQQQAABBBBAAAEEEEAAAQQQQAABBBBA\\nIFugnuO+isXENUkgXrEDZlNWVqKeX4jKzozaCCCAAAIIIIAAAggggAACG5tA0mh4m2yyidtiiy3c\\nTjvt5EaOHFk45T59+rghQ4a4HXbYwXXp0qWQbyvVCOiztlgigAACCCCAAAIIIIAAAggggAACCCCA\\nAAIIINDcAs0Zg1bs3NP61a5YpXL2pR2onLZKqdNcxy2lj5RFAAEEEEAAAQQQQAABBBBAIBQIg+c2\\n22wzt/3227stt9wyLFJY79Spk+vbt6/fHjBggFMQ3+uvv+7mzZvn1q1b5zQq3sCBA13Hjh0LdVhB\\nAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQKAlC1hMWJs2berqNNSvuE9VDcSzE6/lWTfHMWt5fhwL\\nAQQQQAABBBBAAAEEEEBg4xSw0fA6dOjgFFhnQXZ5z1YBd4MGDfLBewroUyCelsOGDcvbBOUQQAAB\\nBBBAAAEEEEAAAQQQQAABBBBAAAEEEGgRAmGMWBwA11wnEAfjVS0QLzzZWpxcrY+X95zqtV95+085\\nBBBAAAEEignwd66YDvsQQAABBBAoTUBBc5puduedd3bt2rVzaX9nLV9LWw+PpEC+oUOH+pH0Xnrp\\nJbdixQpGxQuBWEcAAQQQQAABBBBAAAEEEEAAAQQQQAABBBComkA9BMGF/1fe3P1RX6wPbauhHJ5c\\nNdor1oaOVcvjJfXF+pC0TCpPHgIIIIAAAggggAACCCCAAAKhgEbD69q1qw+gUxBeNZKmtB05cqRb\\nsGBBNZqjDQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEGgnE8VKNCtQ4ox76oz4oVfS//dZILfxq\\neSw7n+Y4ph2bJQIIIIAAAggggAACCCCAwMYroGllt9tuu6qfYLdu3Zx+SAgggAACCCCAAAIIIIAA\\nAggggAACCCCAAAII1EIgjq+y0eFqceykY4T9qWVfdNyyA/HCTiedVLXyanUc62+tj2fHZYkAAggg\\ngAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIItGSBOPaqlsFwsZv1pVZ9KCsQ\\nzzoZd77a2xvbcartQ3sIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCA\\nQL0KxPFftQqKCz2sD0197JID8axjYWervb6xHMNcanE+diyWnwisWLHCTZo0yc2dO9e1bdvWaeqn\\nsWPHuu7du39SiLVmE1izZo1/fVatWuUGDBjgdtlll2brCwdGAIH8AvxNy29FSQQQQAABBKolEP79\\nDder1T7tIIAAAggggAACCCCAAAIIIIAAAggggAACCCCQJVCtILbw/7mr1WZW322/jt2UxywpEC+E\\nsA5We1nNY6xdu9a98847Tsv+/fv7rpbS/owZM1yPHj3clltumXmapbSb2dhGVmDRokVu8uTJiRfy\\nunXrXOfOnd3gwYN9MFa1Tl3HvPrqq9369esLTX7wwQdOwV+k+hBYuXKlmz59utN7R0GTBOLVx+tC\\nLxBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgVggjo2qRkCbtVmNtuL+pm03\\n5TFzB+JZJ9I6WY38ah3DAvAUhLfpppu64cOH+2CfUvuoUbqee+4599Zbb7l+/fr5gLxq9bHUvrTk\\n8gsWLHCvv/560VN45ZVXXKdOndyxxx7rttlmm6Jl8+ycOHFiIQhPb9btttvOaeS1du1yX/J5DkOZ\\nCgQ22WQTP1KhgjHbt29fQUtURQABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEE\\nEKilQBxDVUkwXdhWJe2Ucv46ZrWPVTdRSSFoKShh2TAAT8E9CvQZMmRIWKSkdQVtDRo0yL344ovu\\n1Vdf9VOcKkhsiy22KKmd1l44Dn7r1atXIUhOI6FpZDQlrd90003u3HPPdZtvvnnZbKtXr3YK/lPS\\nlLRqb7PNNiu7PSoigAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIBAukAY\\n+1VJgJu1U0kb6b1suKfawXi5AvHsBBt2pXpblbYfB+BZe9tuu21ZI6BZfZ1hly5d/Eh4Cxcu9COq\\nEZBX2euuKYJPPPHEBo28/fbb7tZbb/W+sn/wwQfd8ccf36BMKRsK/OvQoYOfklhBfwThlaJXu7Lh\\ntMG1OypHQgABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEGhKgTD2qtyAOmuj\\n3Pp5z0/HqdYxMgPx7KTydq7UcpW0Hwfg6djWngKxevfuXVJ3rG5cSdOaLl682Ad2aZ+mOCUgL1bK\\nt51k3LdvX3fCCSe4G264wTeiwDwb0TBsdf78+d59zZo1/g2goD69NmHSdMR6rT788MNCtkbH0yh5\\nuh40VXGY8rSp8tYnBfWpby+88IK/1jRC4sCBA8MmXSlt6lrS+et61ciLixYt8m117tzZjRw50o/q\\n2KDxYGPGjBlO5ytT1d9pp52cAg/TktxUR8dQnW7durlddtmlkUla/TA/7zlaneXLl/tpnvWeVZKb\\nAmWzkqYs1tTQGt1Sr93uu+/uOnbs6ObMmeODbNOmMZ49e7YfwVLnqbo6z0pGWczqJ/sRQAABBBBA\\nAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEGguEsULlBLxZ/XLqNu5Nco6OUY32iwbi\\n2Ykkd6Hy3HLbLxaAZ73afvvtbTVzmdUPjbDWp08fH9gTNpYWkJfVXthGa1iXh5mE6+G5b7XVVj6Y\\nTNPUaqS0sJxNWfvuu++GVdxTTz3lX5fTTjvNB1sp+O66664rHEuFFYR39dVX+3oHHHCA23vvvf16\\n3jZVWEFkChK0c9Abz9bnzZvnBgwYUFGbnTp18v3XccL00EMP+ZEBrX3bpyDASZMmFab3tfxHHnnE\\n9+W4447zU/JavpZPPvmke/jhhwv9tn2TJ092o0eP9oFqlldsWYqbtaPXacqUKbbpl1OnTnU6bwvM\\nC19vFdBxrrnmGrds2bIG9R577DHXtWtXn6/X4eyzz24QfPjGG2+48ePH+8DLsOITTzzhz/Hwww8P\\ns1lHAIEyBOz3XxlVqYIAAggggAACZQrY39/4vrnM5qiGAAIIIIAAAggggAACCCCAAAIIIIAAAggg\\ngEDZApUErNn/d+vgpbZjdUutl/dE1X6lbbdNO5h1Pm1/pfnltK+gnblz57pnn33Wj5KlkcmU4rYU\\nONejR4/MLqpeXDetUtpoWqqv4LFZs2a5Z555xmkKW1LpAjLUiG1K4WuioLy//e1vLg7CsyNoRLgr\\nr7yyEJSm0c/SkkZSUyq1zbZt2zYIbAv7p2ut0jYVdGZBeGH/dZw777yz4KLjTJ8+3U2cOLFwvvoF\\noNHwLGkkOBtZ0PIUhKegvrDftk95EyZMcNOmTbOs1GWpbmooKQjP+qvzTkp2nDAIT6Ph6VzV3zA/\\n/AWoUfpuvvnmRkF4dgyNOHjXXXfZJksEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAA\\nAQQQQACBEgUUuxH+lFi9UNzaKGTkXCm3Xp7m1XYlKXFEvEobzepQqe0njYCnY6S1kxWEl1avWL+7\\ndOnip8bUKHhpKW2EvLTyrTFfQW1J6f777/dTvmpf9+7d/QhxWr/33nt9oKPWFQx5yimn+ClVFZh3\\n0003+ZHTlixZ4l566SU/4tlXv/pVH6h1+eWX+2A1TX+qOmEqtc2wrtY1Pe1BBx3kdE307NnT7660\\nTbWj6XnVtgLKbrzxRj9anE0nO2zYMH+uCsKzNHjwYDdmzBgfJKipku+44w5vqOBEbWvKXAX4aaQ8\\nS6NGjXJ77bWX39Soes8995xfV5khQ4Y0CDi0OrYs9RwVUKcR7CxpGmGdo4INFTB4++23J76HH330\\n0cJrrkC7Y445xk+7KwvV0ah3cdJ7Wudv721N03v00Uf789G1ob5rn6a63X///b1z3AbbCCCAAAII\\nIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAqUJWKyGaoUDKuVtxeqXWrfceln9Urul\\n9sXabBQVZZ20As25TBsBT30q1s+0QDzVKVYv6VytjpZp7cb1LCBPI/eljeQW12kt2xox8PXXX3cz\\nZ870P5qiVEFzM2bMKBDst99+fl2BXCqnpJHnzjzzTB+Ep+0tttjCnXTSSYULP6yvQC97Q8SBf+W2\\nqWMqaTrVcePG+SA3TVeskdqq0eZZZ51VCA7TNL1moGNqtEClOXPmFKZy7devnzvqqKMKgXMKujvs\\nsMN8Of3z5ptv+nUF2ql/SnvuuWchCE/bhx56qOvbt69W3QcffOAWL17s15P+KeccFfRmoxwq0PDk\\nk08uBFhqut3jjz8+6VA+WM52KJhOQXVK7du3921sueWWtruw1BTB77//vt+W37HHHluwGTp0aMFT\\n72ONXklCAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQqK5AGGdVastWt5x6\\npdZpqvINRsTTCTV1ynOMtBHwrG9ZbYTTe6pOVnlrN1wm1bFpSMNyxdYtIE/T6W6zzTY+eKxY+daw\\nTyO0afrQtLTvvvv6IDftf+211wqBZwq8UyBWOJ1p586dfWCXrpcFCxb40eDi1z4+Tjlthm0o+C4+\\nRlO0qZHx4hSOBLf33nvHu52C23SdycOC1SzoTIGJ2q/pnFevXu3rKqiwd+/e7u233/bvEQX69erV\\nq1G7yijnHBVwaWmPPfaw1cLSLG2Kae348MMPC1PP6vVVgGGcVC+eAlqj3lnafvvt/apdK3rf6vqx\\npH4l9cf2s0QAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBCoTCCMvbIBtfK0\\nqHqllFeb5dQp1pdy2ysE4oUnX+xAlezLOoYChBQ4pNG5bBSv+HhZbai8RsZaunRpXDXXdrH2i01L\\nW6xxC8hToJNG1UsKLipWv7Xs0whpoU14DWi61YsvvrhiikrbDOtbZ8K8cvoZ1rc2iy31y0bT98ZJ\\ngXWnnnpqg2wLctN1reluy01hH/Oeo/1S1FIBgnmT1dPIdnHQo9qwcwrbC/OeeOIJpx8SAggggAAC\\nCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAQPMLWDyWxYRk9ajU8mpPdfK2n3X8ctvz\\ngXjW+TwHaaoyCr6bPn16YpCNnVzeY9sIX3nLl9p+Ke2GZRUspKlqNerX8OHDw12tZl2jmZ1++ulO\\nwYkKsrr22msLU6JqVLswEK8UFI0CV+3UUtosdt6l/IIJg9mKtZm1L8ktb9savc76vGTJkqxDlbXf\\npsstqzKVEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBMoSsBg1iw3JaqSp\\ny+c5ft6+qq121uGshivdn3WcLl26uMGDB7sZM2a4OFAmq27YN5Xt2rVrIZgn3Bevl9Ku6iqYSEF0\\n5Sa9MB07dvTnWeqxyz1mPdQLz3XTTTf1r40clA455JDCVLUaxWz33Xd3HTp08PvCejvvvLPfZ9ON\\n+gLBPxoNrm3bto2mIVYbYTvhet42g8P4tsI2tC/cboo27RjhcXQthdthH5PWVX7s2LF+Vzi6nZXV\\n/n79+qW2GR4r7zmGdXSceNuObfu0X6NiWv80PW9SnTBP6/Zj7R100EF+al6bgtfybalpasM2LJ8l\\nAgjkE+D9k8+JUggggAACCDSFQHzv2xTHoE0EEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBPIIKNak\\n3BQ+d87TjpXPU1Z9Uvm8ZbPOoZS2ClPTZjVa7n6DyFNfAXQjR470o6XNnTvXLVy4ME81XyY8zrbb\\nbps4dac1Fpa1vDxLTcn5+uuv5ynaoIxG+dI0m/rROukTge22285plDzZKgDrvvvuc0cfffQnBf61\\npuDMrbfeulF+JRktpc34HHX96v2x+eabN9il85kwYYIPGB06dKgfXTC81lU+rtOggZwbed1sWln1\\n4dVXX3U9e/ZscATbH2a2b9/eB1TqWnjrrbd8UK7ywpRUL9yvgMxSpsIN67KOAAIIIIAAAggggAAC\\nCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggkC0QxqSodLmBb9ZOnvpNVTbrbHXcPP1rm9VQJfvt\\n5EttQyOiDRgwwO22225+ZKus+vFx0qbBVLm4bFbb2m/1kqbcLFZfQXcaZWzXXXf1S4LwkrUOP/zw\\nwsU6c+ZM99577/mC22+/fSFwcc6cOW727NmNGtAoeldddZVLGykvrtBS2oz7re2ddtqpkP3YY481\\nmsb5+eef9yNKKuhNgXpKO+ywg1/qGr7jjjv8eviPRo274oor3NNPPx1mN1ovx03BgJaeeuopP9qd\\nbWuZdA4aMXHLLbf0xRTw9/jjj4dV/OusayROGk3T0pQpU3wwr23b8rbbbnN33323bbJEAAEEEEAA\\nAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQqJKAxVfZstRmrZ6WWcnKZpXT/jztVaud\\nTb73ve/9OE9jpZSp9ASsvgLXevTo4QNzkqaFtXJx3zSCVjjyV1q5uF64nVRHo+HlCcZTvzXKm4IJ\\nNb2mRuhqzWnRokU+QEwGel3CoCnlaVrZBQsW+AA8ub/77rtOQVwa+ez999/3+5SvaYsVOKZrQvka\\nPe+5557z0wW//fbbbtiwYWrOj6KmwDKNqhYfr5w2FRCW1p6O1xRthmYKgtNogDrv6dOnu5UrV3qH\\nV155xfXu3dtfkwpYU1CinBSBq+lZNcKkRmF85plnvMUHH3zgXn75Zf9+0jWpgLabb77ZLV++3I/0\\nqEA/vRZJqZxz7Natmz+e+qv3zYsvvuhfD70/HnjgAafAQUvh66RgPL3WSnpdly1b5jSd7Lx589zf\\n//73BkF2CnJVn/U+07npWHrddc46f/0oKHH8+PG+LV1bOpe+ffvaoVkigECJAnqPkhBAAAEEEECg\\ntgKvvfaaP6Dum/VDQgABBBBAAAEEEEAAAQQQQAABBBBAAAEEEECguQTyjAwX962cOmojb71ql4v7\\nH24XO1bV50lNCmALO5O1nlTfRsjT6HKarlJT1iaVs7YXL17sFLxUrIyVjZdpdVatWpU66prVUYAR\\nU9DGovm2DznkEKeHSzYlqYIe9RqOHj3aB+LZaz516lSnnzDpAh81alQhS23Ya1LIDFbKabNYe2q6\\nKdoMulxYPf744921117rR8NbsmSJu/HGGwv7bGWPPfbwgaDaVpCa+nbPPff43aqjYLY4aYrgXr16\\nxdkNtss5x6OOOspdf/31/vXQqIUKiMtKCgjUa2/TQE+bNs3pJyudcMIJ7uqrr/ZBfwr8u/feextV\\nkcfw4cMb5ZOBAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggkC0Qx9AUC0yz\\n1sI6ecrH9bLqqP2sMmozbzk7ftLSziXpeFUdqs0OlNSJPHlZ9RWQp6k2R4wYUZi+MqldBc0tXbo0\\naVdqno5d7PgK7rNkZW2pADymoDWdxkuNQGapY8eOttpgqZHLhgwZUsh79tln/bou2jPPPNPtueee\\niW8YjQin/eEIZxrtzUYhDI9tjZfbpurrtU5K1W4z7Hd4TAXLff7zn29wvtYfBZlpmt8wKFH75Hr2\\n2We7nj17WtHCUm0rcO+kk04q5KWtlHOOCkw966yz/Mh0YbtqSyMY2rnZ0sqceOKJiQFzGhlPr3lS\\n0sggX/ziFwvT8cZlBg4c6D73uc+5tGswLs82AggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAA\\nAggggAACCCCAQHEBi58qFncVtmDlw7ys9Txt5203T1tZ/dH+pHbabAhYy55YN0frSY3nqFYokqd+\\nXEYBdzZCnjVkZbp3794gsMv2x0srH+eH25qaVCNy6XhhUvBQnhHw8hwjbJf1dIF33nnHKSBTr4UC\\nr6oRVNVS2oxVPvzwQ6cAUQUddunSxemaz0qanlZBquam6W7LTaW6aVpYvZf0flAwXRx8l9QPvc7v\\nvfee36U+q78aBVDT1CqYT0F+SQGGVk91NK21ylhwZtJxyEMAgfwCCpwmIYAAAggggEBtBSZNmuQP\\nqC+m6YeEAAIIIIAAAggggAACCCCAAAIIIIAAAggggECtBRSnUUrKWz5vOTt2nvJ5yqi9vOXs2EnL\\nsI3k4b2SahXJqzTQLE/9pDLxlLULFiwo9FLBRsuWLXPdunUr5IUrSe2F+7VuZebPn98gCK9YAJ7V\\nidtiuzoCffr0qU5DQSstpc2gy361c+fOTj+lJAXs6acaqVQ3jWaXJ2lq4SuvvNIHzmm0w6233rpQ\\nbdasWT4ITxl6/2+22WaFfeGK9oX1wn2sI4AAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAA\\nAggggAACCJQmEMdEhQFoSS2F5YuVtXLFyoTt5ymvMnnay1suPH68HrZRcSCenVx8kLzbeepnldl0\\n0039qACanjQcIW/OnDl+VLxw5K2sttTvsMzq1audAvGUkgLwwrK+EP8ggEBFApMnT3bvv/++b+OS\\nSy7xU1EriG/mzJlu9uzZhbYHDx7swil8CztYQQABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQ\\nQAABBBBAAAEEEGhSgTBmKivoLU9ZK5PVlp2Uyhcrm7U/bztWrtjSjlVRIJ4BFDtQsX156meVCffH\\nI+QtXLjQvfHGG27AgAG+G2HZpH7F+9euXetmzJjhi/br189PQ2uBP3HZpPbIQwCB0gUOOuggH1C7\\naNEip/fg1KlTGzWioNtPfepTjfLJQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBA\\nAAEEEKitQBhHVSw4Tr2ysmnlsvaHZ6ayae3YsYrtt7ay2rFyxZa+jQ1TuH5UrFDaPjvptP1Z+Xnq\\nZ5XJ2r9q1Sof0NOpUydXbBrNtHZef/31wih4FoCXdV7sRwCB6gi88MIL7plnnnGaZnrdunW+0a5d\\nu7q99trL7brrrtU5CK0ggEDJAnofkhBAAAEEEECgtgKTJk3yB9xhhx38aPC1PTpHQwABBBBAAAEE\\nEEAAAQQQQAABBBBAAAEEEECgPIGsILhK94e9KtZWsX152wjLpa2XNSJeWuBa2kHi/Dz1s8pk7dcx\\nNUKeHlQoIC8pFWtDU9JqFLxKAvCKtZ/UH/IQQOATgV122cXpJynx3kpSIQ+B2gjw/quNM0dBAAEE\\nEEAgFLC/v1raerifdQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEaiGQN6DN+hL+n3ZSXduftE9t\\nZO2341jZarQTtlnqelmBeKUeJCxvQGFevJ5VJmu/2gvLKCAvTuH+pH3t27ePszO3i7WZWZkCCCCA\\nAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggECdCoSxUWlBb2ldV920OtZu\\nsf1p+8LjFTuGylW6PzxW0nrJgXh24kmNZeXlqZtVJmu/+lCsTLn70s6tWHtpdchHAAEEEEAAAQQQ\\nQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBFqqQBgzlSdITudpddLKa3+xfWojbb/2\\nKRVroxr7/UFS/ikpEM8wUtqqODur/az96kCxMuXui0+sWDtx2aw+JZUnDwEEEEAAgXoVKPVvYL2e\\nB/1CAAEEEECgJQro7zB/i1viK0efEUAAAQQQQAABBBBAAAEEEEAAAQQQQACBli9QLAAu/L/rYuVM\\nwconlS22T/W1P6metZ2nTFYbWfvDY4XrJQXihRVLXTektHqV7le7aW2k5RerE/azWP2wnK2XWt7q\\nsUQAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEE6k0gjIcqFgiXt5zO\\nz8omtad9SflWL22fuRWrn7cNayvvMncgnp143obDcll1i+0vts+OUaxM2r60fGtTyzxlSikXts06\\nAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIINDSBOKYqrSguLBcWhmd\\nu5WLy6TlF6sTWqp+3Gbe/Vl1w3ZsPXcgnlWo9tLAktotts/Kp5UpNd/a0zKtrpXJ2m/lWCKAAAII\\nILCxCfA3cGN7RTkfBBBAAIGWIGB/f7W09ZbQb/qIAAIIIIAAAggggAACCCCAAAIIIIAAAgggsPEI\\nZAW02ZmmlbP/307br/oqk7S/WN20OtafSvZn1bVj2DJXIJ6djFUqZVmsbrn77Php9UvNL7c9q8cS\\nAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEENhYBeJ4rKSAOZ17WC6p\\njO1P2hfWT9qvuqXk22uR55hJ7Vp/0vZZ+7bMFYhnhUtd2kkk1St3n9pKq1tqvvWr3HpWnyUCCCCA\\nAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggg0FoEwnirtEC1YmVsX1bdeH9a\\nvbT88PVQmbi9cH+l622zGrBOZpWL9zdVvbR2S81Xf1UnqV5afnyObCOAAAIIIIAAAggggAACCCCA\\nAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACrVnAYq2S4rDMxcrYti3T8sP9th4u046Vlm910/an\\n5atesX3WrpZFR8TL20jYoNaz6qXtT8u39tP2J+Un5Vk7aX3MqhPWz1qvZltZx2I/AggggAACtRLg\\n71utpDkOAggggAACnwiEf3/D9U9KsIYAAggggAACCCCAAAIIIIAAAggggAACCCCAQNML5BlNLvx/\\n7KTytj/ep/w4z86o1DrF2lKbafvT8q0fWcuigXhZlcvZbzBx3bR8K5e2Pyk/Ka+cdqxO3mWx4+Zt\\ng3IIIIAAAggggAACCCCAAAKtW2DlypXu6quvduvWrXNjxowpYOgz5w033OAWL17sRo0a5XbZZZfC\\nPlYQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEGhqgTA2Ki1oLuxDsfLaF7dh5eN8azOtTlL5pLLW\\njpZp+0vND9tMDcRTo+WkcusVO1Zam0n5SXnWdtK+pDwrn7XMW1flkl7wrPbZjwACCCCAAAIIIIAA\\nAggg0PoEOnTo4F566SU3a9Ys9+STT7ozzjjDtW3b1j3//PPummuu8SD77bdf64PhjBFAAAEEEEAA\\nAQQQQAABBBBAAAEEEEAAAQTqRiCMm8oTF2Xlw7JJeTrBYrFWSfvKaaccyKRjh+20Wbp0aWLEnXUw\\nLJy1nlUnbX9avh0vaX/evFLbsPJpy6TjxmXzlInrsI0AAggggEBLEOjSpUtL6CZ9RAABBBBAoMUL\\nvPfee+7zn/+8W7t2rRs9erQbNmyYu+SSS9yqVavc2LFj3Xnnndfiz5ETQAABBBBAAAEEEEAAAQQQ\\nQAABBBBAAAEEEGh5AmEgXVLvs/arTlqZpPykvGq1UWo7Kq+U2qekQLxyA8my6iXtT8r7uMsf/5u0\\nP2+eWiilbHjccD2pjXC/1vOUieuwjQACCCCAQEsTIBCvpb1i9BcBBBBAoCUL3Hfffe53v/udP4U+\\nffq4d955x3Xv3t1dfvnlrn379i351Og7AggggAACCCCAAAIIIIAAAggggAACCCCAwEYgkBaQZqdW\\nzv6kOkl5xY6RVD4pr9Q2rLyWSe2lTk0bVsyznhWIlrU/6RhJdfLmqb1SyuY9flguqf1wv9bzlInr\\nsI0AAggggEC9CvB3rV5fGfqFAAIIILAxChxyyCFuwoQJfpraefPm+VP87ne/69q1a8dnzY3xBeec\\nEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBZhZICi4r1qXw+XFS3Tz743qqkyfP+lVqeasXLstpI6lO\\n27BRratQqamcOlnHSmozb15a20n10841razy7adY3awyaXXJRwABBBBAAAEEEEAAAQQQQEAC+o+G\\nCy64oIAxZMgQN3To0MI2KwgggAACCCCAAAIIIIAAAggggAACCCCAAAIIVFPA4p2KLdOOZ3VK3Z9U\\nLy2vWNtp+yxfbZaasurE+xsF4pV6wDzl44OqTlKetZW0L29eWttJ9e144VLl4rKWF+dbvXB/Whkr\\nyxIBBBBAAAEEEEAAAQQQQACBvAL33HNPoejMmTPdwoULC9usIIAAAggggAACCCCAAAIIIIAAAggg\\ngAACCCBQa4EwTirp2Hn2p9WL8+M4rHg7LB/vi7dVNinP2ii2z8pkLSsOxKtGJ8JOJrWXN0/txGW1\\nHeeFxwvXk8ol5VmdUtq2OiwRQAABBBBAAAEEEEAAAQQQyCMwffp0N378+ELRtWvXut/85jdu/fr1\\nhTxWEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBoLgGLnUqLr0rbn1U+PJ+4rLUZlrH1pLK2z5Zx\\nGcvXMmlfUl5anXZpO8L8StaTOpOUV8ox0urH+fF2sWPEZeNtq5uWb/vDZSllw3qsI4AAAgggUK8C\\n/G2r11eGfiGAAAIIbGwCa9ascRdddJE/rREjRrh99tnH/fWvf3XTpk1zEyZMcEccccTGdsqcDwII\\nIIAAAggggAACCCCAAAIIIIAAAggggECdCrRp0yazZ+Gz5KTy2h/mW/kwzw6SVDYuF5exuvEyqVxS\\nXlyvlG1rr6IR8dRIsZS1P66bVD7Oi7etjTg/3rZy8VLl4rJJ20nl4ra0beXiNpLKkocAAggggAAC\\nCCCAAAIIIIBAksAVV1zhli5d6tq3b+/2228/17lzZ3fMMcf4opdccglT1CahkYcAAggggAACCCCA\\nAAIIIIAAAggggAACCCDQJAJhPFSemCgrH3cmKT+tvTg/3lbbleTFfbPtpDZtX9ayEIhXSSNZBwn3\\nl3KcvGXjcvF2ePxwPalcnBdvh/VtXWXsx/LCZZ42wvKsI4AAAggggAACCCCAAAIItF6BhQsXujvv\\nvNMDfOMb33AdO3b064cccojr06ePn5r2lltuab1AnDkCCCCAAAIIIIAAAggggAACCCCAAAIIIIBA\\nswoUi5MKO5ZWLo6liretjTg/3rZy8TJPuTxlrN08ZVWmzYZv2Pth7fJUsMZtWaxO2r5S8uOy8bb6\\nkTfP+mzLuF68nda21c/an9ReWJd1BBBAAAEEWqJAly5dWmK36TMCCCCAAAItWuCBBx7w/e/fv7/T\\nDwkBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgVoLxFPDxsfP2q/ycZmsbTtGVrl4f6X18vTVjhEu\\nCyPihZl51ssJNEurk5Qf58XbaX3MUy4uE2+r7aQ8y9e+rP1p/SMfAQQQQAABBBBAAAEEEEAAAQQQ\\nQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEGhJAhYvVUnMVFw3a9t8ssrF+61evIzLxdtx+XA7T1kf\\niJenYNhw1nql7eWtH5eLt5P6GZdJ2o7z1I7ykvLDfUn7rV64TOoXeQgggAACCCCAAAIIIIAAAggg\\ngAACCCCAAAIIIIAAAggggAACCCCAAAIIIIBAvQtYHFRSP4vtU3ntD1PWtpXNKhfvV72kPGvPlnnK\\n5C3bzgqWsiylA1nt5mkrqUycF28nHTcuk7VtbcTlsvK1P61O1j5rmyUCCCCAAAL1KFDs71s99pc+\\nIYAAAgggsDEJ6O8wf4s3pleUc0EAAQQQQAABBBBAAAEEEEAAAQQQQAABBFqWQDwFbPh/1mn74nyd\\nseqF+VnbppS3nJXPc6ywbLgeHyvcl7ZeViBeWmPKVyeSUlp+XDZvubBenjpxmaxtta8ycTnLD48f\\nrieVD/ezjgACCCCAQEsW4O9cS3716DsCCCCAQEsVsL+/Wtp6Sz0X+o0AAggggAACCCCAAAIIIIAA\\nAggggAACCCDQcgXs/6jDIDo7m7R9ylf5uI7lh/XDMvF+K1dsWU4dtVdKvWJl22lnPaek/iXlFTuH\\nuHyebZWxcvEy6VhWJt6Xlh+XYxsBBBBAAIGWILBu3bqW0E36iAACCCCAwEYlsH79en8+WvK3eKN6\\naTkZBKouEP5HZdUbp0EEEEAAAQQQQAABBBBAAAEEEEAAAQQigWL/H2X7tFT8lJb2Y81YXJWVtXLh\\nftuXlleNOtZ2vIzbjvfH2+1mzprltunXz3Xq1Cnel7htAEk7i+3LUz6uH2+rjTgv3k46TpgXlw+3\\nbV0PN7QeL8N2tG7l4/ysfUnlyUMAAQQQQKAlCKxZs6YldJM+IoAAAgggsFEJrF271p+P/g7zt3ij\\nemk5GQQQQAABBBBAAAEEEEAAAQQQQAABBBBAoEUJxEFx1vli+W3btvUBeOFS9cI6isGy7XBd5eLt\\npLykMipXSiqljbSybTp06LBh38ejv+mEPj4pjZLXRmfi1n+koLTmDTpT/3InnUOhsM6rsNFoxV5A\\nv2NDweSiG17otpv8q00F5yWXatR4lTLUR53DhsWGZW2PXaVToBkEEEAAAQQQQAABBBBAAAEEEEAA\\nAQQQQAABBBBAAAEEEEAAAQQQaJUCikMpErvRpq1r64M8ah+P0SpfDk4agWYUCOO0miIGKm4/3G7K\\n0w6P065Lly4+wMuC8XRgO1lbWmeKbds+W1qdsL0wL2k9rhtv56mTVKbUvIbHbes27bipa7MhCG79\\nmtVutft4SqBS26ykvILwlMIX7uMc/kUAAQQQQAABBBBAAAEEEPj/7d3BCoAgDADQoKj//9zoEoO6\\niDI6jeR5EabofHgcSoAAAQIECBAgQIAAAQIECBAgQIDAjALxelS0+M3v3y2K8fonWPdj2Z5CvOus\\nqMjo5yVaJzDPva8zrN45q2/Kxr/mn603Gh/F2/17895Y29+6I286giTBegAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<IPython.core.display.Image object>\"\n      ]\n     },\n     \"execution_count\": 58,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# Graph Visualization\\n\",\n    \"# Tensorflow makes it easy for you to visualize all computation graph, \\n\",\n    \"# you can click on any part of the graph for more in-depth details\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 59,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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1w0fcxdNVlTaFb+pxYQdPj7CpewV+wc+/OPYsdV9YNhkYWXExl1iYRP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\\nAAEEEEAAAQQQQAABBBBAAAEEEIg+AVXJG5eZYMX7zIXspg1q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VtFuN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\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HHFtHqrmRJmS40gWz6Ul+4bOnRomDRpUhyG6bPPPouemBGIGzNmTCCg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\\n\",\n      \"text/plain\": [\n       \"<IPython.core.display.Image object>\"\n      ]\n     },\n     \"execution_count\": 59,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# Weights details\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 60,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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Az/nGSv/LwA9q7PQUc9vx3+vNX1tNGbzfH9Z/TFQX2w6Pz0yjruCEhr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\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tyYZt0MCQmRmTNnxmn/ojFe8dJLL0nr1q2lRIkSUrduXfn5558lod7x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\\nIIAAAggggAACCCCAAAII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07d7aysjL7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\\n\",\n      \"text/plain\": [\n       \"<IPython.core.display.Image object>\"\n      ]\n     },\n     \"execution_count\": 60,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# Gradient descent details\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.4.3\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "deep-learning/tensor-flow-examples/notebooks/5_ui/loss_visualization.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Loss Visualization in TensorFlow\\n\",\n    \"\\n\",\n    \"Credits: Forked from [TensorFlow-Examples](https://github.com/aymericdamien/TensorFlow-Examples) by Aymeric Damien\\n\",\n    \"\\n\",\n    \"## Setup\\n\",\n    \"\\n\",\n    \"Refer to the [setup instructions](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-examples/Setup_TensorFlow.md)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 51,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Extracting /tmp/data/train-images-idx3-ubyte.gz\\n\",\n      \"Extracting /tmp/data/train-labels-idx1-ubyte.gz\\n\",\n      \"Extracting /tmp/data/t10k-images-idx3-ubyte.gz\\n\",\n      \"Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import tensorflow as tf\\n\",\n    \"import numpy\\n\",\n    \"\\n\",\n    \"# Import MINST data\\n\",\n    \"import input_data\\n\",\n    \"mnist = input_data.read_data_sets(\\\"/tmp/data/\\\", one_hot=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 52,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Use Logistic Regression from our previous example\\n\",\n    \"\\n\",\n    \"# Parameters\\n\",\n    \"learning_rate = 0.01\\n\",\n    \"training_epochs = 10\\n\",\n    \"batch_size = 100\\n\",\n    \"display_step = 1\\n\",\n    \"\\n\",\n    \"# tf Graph Input\\n\",\n    \"x = tf.placeholder(\\\"float\\\", [None, 784], name='x') # mnist data image of shape 28*28=784\\n\",\n    \"y = tf.placeholder(\\\"float\\\", [None, 10], name='y') # 0-9 digits recognition => 10 classes\\n\",\n    \"\\n\",\n    \"# Create model\\n\",\n    \"\\n\",\n    \"# Set model weights\\n\",\n    \"W = tf.Variable(tf.zeros([784, 10]), name=\\\"weights\\\")\\n\",\n    \"b = tf.Variable(tf.zeros([10]), name=\\\"bias\\\")\\n\",\n    \"\\n\",\n    \"# Construct model\\n\",\n    \"activation = tf.nn.softmax(tf.matmul(x, W) + b) # Softmax\\n\",\n    \"\\n\",\n    \"# Minimize error using cross entropy\\n\",\n    \"cost = -tf.reduce_sum(y*tf.log(activation)) # Cross entropy\\n\",\n    \"optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) # Gradient Descent\\n\",\n    \"\\n\",\n    \"# Initializing the variables\\n\",\n    \"init = tf.initialize_all_variables()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 53,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Create a summary to monitor cost function\\n\",\n    \"tf.scalar_summary(\\\"loss\\\", cost)\\n\",\n    \"\\n\",\n    \"# Merge all summaries to a single operator\\n\",\n    \"merged_summary_op = tf.merge_all_summaries()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Launch the graph\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    sess.run(init)\\n\",\n    \"\\n\",\n    \"    # Set logs writer into folder /tmp/tensorflow_logs\\n\",\n    \"    summary_writer = tf.train.SummaryWriter('/tmp/tensorflow_logs', graph_def=sess.graph_def)\\n\",\n    \"\\n\",\n    \"    # Training cycle\\n\",\n    \"    for epoch in range(training_epochs):\\n\",\n    \"        avg_cost = 0.\\n\",\n    \"        total_batch = int(mnist.train.num_examples/batch_size)\\n\",\n    \"        # Loop over all batches\\n\",\n    \"        for i in range(total_batch):\\n\",\n    \"            batch_xs, batch_ys = mnist.train.next_batch(batch_size)\\n\",\n    \"            # Fit training using batch data\\n\",\n    \"            sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys})\\n\",\n    \"            # Compute average loss\\n\",\n    \"            avg_cost += sess.run(cost, feed_dict={x: batch_xs, y: batch_ys})/total_batch\\n\",\n    \"            # Write logs at every iteration\\n\",\n    \"            summary_str = sess.run(merged_summary_op, feed_dict={x: batch_xs, y: batch_ys})\\n\",\n    \"            summary_writer.add_summary(summary_str, epoch*total_batch + i)\\n\",\n    \"        # Display logs per epoch step\\n\",\n    \"        if epoch % display_step == 0:\\n\",\n    \"            print \\\"Epoch:\\\", '%04d' % (epoch+1), \\\"cost=\\\", \\\"{:.9f}\\\".format(avg_cost)\\n\",\n    \"\\n\",\n    \"    print \\\"Optimization Finished!\\\"\\n\",\n    \"\\n\",\n    \"    # Test model\\n\",\n    \"    correct_prediction = tf.equal(tf.argmax(activation, 1), tf.argmax(y, 1))\\n\",\n    \"    # Calculate accuracy\\n\",\n    \"    accuracy = tf.reduce_mean(tf.cast(correct_prediction, \\\"float\\\"))\\n\",\n    \"    print \\\"Accuracy:\\\", accuracy.eval({x: mnist.test.images, y: mnist.test.labels})\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Run the command line\\n\",\n    \"```\\n\",\n    \"tensorboard --logdir=/tmp/tensorflow_logs\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"### Open http://localhost:6006/ into your web browser\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 57,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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/uTZ+5gU6rEFdp9lvTw3NSSJYXmnZ6nz1EGh9s59udMxX2jqc6rIwhv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\\n8c0GyCs4Tp9Gk3pNVq+7JAQ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VBbsoVXKdgOw0ryM5/fKCdcqUpU6aY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\\nUyYfW7F1mt8xxUZdJi/E75gy+diKrdP8e26xUZfZC/E7pugfn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x+Ta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\\nCCCAAAIIIIAAAggggAACCCCAAAIdKBD2gLsO7BuXRgABBBBAAAEE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W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\\nQ0bYAHvXSghbZwUBBBBAAAEEEEAAAQQQQAABBBBAAA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mlBFEvusXqO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\\nRNsIIIAAAggggAACCCCAAAIIIIAAAggg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nhIGgQAIRDC9E0YiJz+UZwMQQCPBCBJegRo3yohgTDRDzTNz+UfICmGAgicSFCGSe4hJkBMJDicyw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\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LVjsluc2Drm6BNTmU1bX2S3P+69d2nrFm93TVytJL50KhMvbtUSCVrX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M/U516ohZR4xOfbOMAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECA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\\nykeZIvGKlmtvv/31/wfSJn1/ECSR6QAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<IPython.core.display.Image object>\"\n      ]\n     },\n     \"execution_count\": 57,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# Loss per minibatch step\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.4.3\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "deep-learning/tensor-flow-exercises/1_notmnist.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"5hIbr52I7Z7U\"\n   },\n   \"source\": [\n    \"Deep Learning with TensorFlow\\n\",\n    \"=============\\n\",\n    \"\\n\",\n    \"Credits: Forked from [TensorFlow](https://github.com/tensorflow/tensorflow) by Google\\n\",\n    \"\\n\",\n    \"Setup\\n\",\n    \"------------\\n\",\n    \"\\n\",\n    \"Refer to the [setup instructions](https://github.com/donnemartin/data-science-ipython-notebooks/tree/feature/deep-learning/deep-learning/tensor-flow-exercises/README.md).\\n\",\n    \"\\n\",\n    \"Exercise 1\\n\",\n    \"------------\\n\",\n    \"\\n\",\n    \"The objective of this exercise is to learn about simple data curation practices, and familiarize you with some of the data we'll be reusing later.\\n\",\n    \"\\n\",\n    \"This notebook uses the [notMNIST](http://yaroslavvb.blogspot.com/2011/09/notmnist-dataset.html) dataset to be used with python experiments. This dataset is designed to look like the classic [MNIST](http://yann.lecun.com/exdb/mnist/) dataset, while looking a little more like real data: it's a harder task, and the data is a lot less 'clean' than MNIST.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"cellView\": \"both\",\n    \"colab\": {\n     \"autoexec\": {\n      \"startup\": false,\n      \"wait_interval\": 0\n     }\n    },\n    \"colab_type\": \"code\",\n    \"collapsed\": true,\n    \"id\": \"apJbCsBHl-2A\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# These are all the modules we'll be using later. Make sure you can import them\\n\",\n    \"# before proceeding further.\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import numpy as np\\n\",\n    \"import os\\n\",\n    \"import tarfile\\n\",\n    \"import urllib\\n\",\n    \"from IPython.display import display, Image\\n\",\n    \"from scipy import ndimage\\n\",\n    \"from sklearn.linear_model import LogisticRegression\\n\",\n    \"import cPickle as pickle\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"jNWGtZaXn-5j\"\n   },\n   \"source\": [\n    \"First, we'll download the dataset to our local machine. The data consists of characters rendered in a variety of fonts on a 28x28 image. The labels are limited to 'A' through 'J' (10 classes). The training set has about 500k and the testset 19000 labelled examples. Given these sizes, it should be possible to train models quickly on any machine.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"cellView\": \"both\",\n    \"colab\": {\n     \"autoexec\": {\n      \"startup\": false,\n      \"wait_interval\": 0\n     },\n     \"output_extras\": [\n      {\n       \"item_id\": 1\n      }\n     ]\n    },\n    \"colab_type\": \"code\",\n    \"collapsed\": false,\n    \"executionInfo\": {\n     \"elapsed\": 186058,\n     \"status\": \"ok\",\n     \"timestamp\": 1444485672507,\n     \"user\": {\n      \"color\": \"#1FA15D\",\n      \"displayName\": \"Vincent Vanhoucke\",\n      \"isAnonymous\": false,\n      \"isMe\": true,\n      \"permissionId\": \"05076109866853157986\",\n      \"photoUrl\": \"//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg\",\n      \"sessionId\": \"2a0a5e044bb03b66\",\n      \"userId\": \"102167687554210253930\"\n     },\n     \"user_tz\": 420\n    },\n    \"id\": \"EYRJ4ICW6-da\",\n    \"outputId\": \"0d0f85df-155f-4a89-8e7e-ee32df36ec8d\"\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Found and verified notMNIST_large.tar.gz\\n\",\n      \"Found and verified notMNIST_small.tar.gz\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"url = 'http://yaroslavvb.com/upload/notMNIST/'\\n\",\n    \"\\n\",\n    \"def maybe_download(filename, expected_bytes):\\n\",\n    \"  \\\"\\\"\\\"Download a file if not present, and make sure it's the right size.\\\"\\\"\\\"\\n\",\n    \"  if not os.path.exists(filename):\\n\",\n    \"    filename, _ = urllib.urlretrieve(url + filename, filename)\\n\",\n    \"  statinfo = os.stat(filename)\\n\",\n    \"  if statinfo.st_size == expected_bytes:\\n\",\n    \"    print 'Found and verified', filename\\n\",\n    \"  else:\\n\",\n    \"    raise Exception(\\n\",\n    \"      'Failed to verify' + filename + '. Can you get to it with a browser?')\\n\",\n    \"  return filename\\n\",\n    \"\\n\",\n    \"train_filename = maybe_download('notMNIST_large.tar.gz', 247336696)\\n\",\n    \"test_filename = maybe_download('notMNIST_small.tar.gz', 8458043)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"cC3p0oEyF8QT\"\n   },\n   \"source\": [\n    \"Extract the dataset from the compressed .tar.gz file.\\n\",\n    \"This should give you a set of directories, labelled A through J.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"cellView\": \"both\",\n    \"colab\": {\n     \"autoexec\": {\n      \"startup\": false,\n      \"wait_interval\": 0\n     },\n     \"output_extras\": [\n      {\n       \"item_id\": 1\n      }\n     ]\n    },\n    \"colab_type\": \"code\",\n    \"collapsed\": false,\n    \"executionInfo\": {\n     \"elapsed\": 186055,\n     \"status\": \"ok\",\n     \"timestamp\": 1444485672525,\n     \"user\": {\n      \"color\": \"#1FA15D\",\n      \"displayName\": \"Vincent Vanhoucke\",\n      \"isAnonymous\": false,\n      \"isMe\": true,\n      \"permissionId\": \"05076109866853157986\",\n      \"photoUrl\": \"//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg\",\n      \"sessionId\": \"2a0a5e044bb03b66\",\n      \"userId\": \"102167687554210253930\"\n     },\n     \"user_tz\": 420\n    },\n    \"id\": \"H8CBE-WZ8nmj\",\n    \"outputId\": \"ef6c790c-2513-4b09-962e-27c79390c762\"\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"['notMNIST_large/A', 'notMNIST_large/B', 'notMNIST_large/C', 'notMNIST_large/D', 'notMNIST_large/E', 'notMNIST_large/F', 'notMNIST_large/G', 'notMNIST_large/H', 'notMNIST_large/I', 'notMNIST_large/J']\\n\",\n      \"['notMNIST_small/A', 'notMNIST_small/B', 'notMNIST_small/C', 'notMNIST_small/D', 'notMNIST_small/E', 'notMNIST_small/F', 'notMNIST_small/G', 'notMNIST_small/H', 'notMNIST_small/I', 'notMNIST_small/J']\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"num_classes = 10\\n\",\n    \"\\n\",\n    \"def extract(filename):\\n\",\n    \"  tar = tarfile.open(filename)\\n\",\n    \"  tar.extractall()\\n\",\n    \"  tar.close()\\n\",\n    \"  root = os.path.splitext(os.path.splitext(filename)[0])[0]  # remove .tar.gz\\n\",\n    \"  data_folders = [os.path.join(root, d) for d in sorted(os.listdir(root))]\\n\",\n    \"  if len(data_folders) != num_classes:\\n\",\n    \"    raise Exception(\\n\",\n    \"      'Expected %d folders, one per class. Found %d instead.' % (\\n\",\n    \"        num_folders, len(data_folders)))\\n\",\n    \"  print data_folders\\n\",\n    \"  return data_folders\\n\",\n    \"  \\n\",\n    \"train_folders = extract(train_filename)\\n\",\n    \"test_folders = extract(test_filename)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"4riXK3IoHgx6\"\n   },\n   \"source\": [\n    \"---\\n\",\n    \"Problem 1\\n\",\n    \"---------\\n\",\n    \"\\n\",\n    \"Let's take a peek at some of the data to make sure it looks sensible. Each exemplar should be an image of a character A through J rendered in a different font. Display a sample of the images that we just downloaded. Hint: you can use the package IPython.display.\\n\",\n    \"\\n\",\n    \"---\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"PBdkjESPK8tw\"\n   },\n   \"source\": [\n    \"Now let's load the data in a more manageable format.\\n\",\n    \"\\n\",\n    \"We'll convert the entire dataset into a 3D array (image index, x, y) of floating point values, normalized to have approximately zero mean and standard deviation ~0.5 to make training easier down the road. The labels will be stored into a separate array of integers 0 through 9.\\n\",\n    \"\\n\",\n    \"A few images might not be readable, we'll just skip them.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"cellView\": \"both\",\n    \"colab\": {\n     \"autoexec\": {\n      \"startup\": false,\n      \"wait_interval\": 0\n     },\n     \"output_extras\": [\n      {\n       \"item_id\": 30\n      }\n     ]\n    },\n    \"colab_type\": \"code\",\n    \"collapsed\": false,\n    \"executionInfo\": {\n     \"elapsed\": 399874,\n     \"status\": \"ok\",\n     \"timestamp\": 1444485886378,\n     \"user\": {\n      \"color\": \"#1FA15D\",\n      \"displayName\": \"Vincent Vanhoucke\",\n      \"isAnonymous\": false,\n      \"isMe\": true,\n      \"permissionId\": \"05076109866853157986\",\n      \"photoUrl\": \"//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg\",\n      \"sessionId\": \"2a0a5e044bb03b66\",\n      \"userId\": \"102167687554210253930\"\n     },\n     \"user_tz\": 420\n    },\n    \"id\": \"h7q0XhG3MJdf\",\n    \"outputId\": \"92c391bb-86ff-431d-9ada-315568a19e59\"\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"notMNIST_large/A\\n\",\n      \"Could not read: notMNIST_large/A/SG90IE11c3RhcmQgQlROIFBvc3Rlci50dGY=.png : cannot identify image file - it's ok, skipping.\\n\",\n      \"Could not read: notMNIST_large/A/RnJlaWdodERpc3BCb29rSXRhbGljLnR0Zg==.png : cannot identify image file - it's ok, skipping.\\n\",\n      \"Could not read: notMNIST_large/A/Um9tYW5hIEJvbGQucGZi.png : cannot identify image file - it's ok, skipping.\\n\",\n      \"notMNIST_large/B\\n\",\n      \"Could not read: notMNIST_large/B/TmlraXNFRi1TZW1pQm9sZEl0YWxpYy5vdGY=.png : cannot identify image file - it's ok, skipping.\\n\",\n      \"notMNIST_large/C\\n\",\n      \"notMNIST_large/D\\n\",\n      \"Could not read: notMNIST_large/D/VHJhbnNpdCBCb2xkLnR0Zg==.png : cannot identify image file - it's ok, skipping.\\n\",\n      \"notMNIST_large/E\\n\",\n      \"notMNIST_large/F\\n\",\n      \"notMNIST_large/G\\n\",\n      \"notMNIST_large/H\\n\",\n      \"notMNIST_large/I\\n\",\n      \"notMNIST_large/J\\n\",\n      \"Full dataset tensor: (529114, 28, 28)\\n\",\n      \"Mean: -0.0816593\\n\",\n      \"Standard deviation: 0.454232\\n\",\n      \"Labels: (529114,)\\n\",\n      \"notMNIST_small/A\\n\",\n      \"Could not read: notMNIST_small/A/RGVtb2NyYXRpY2FCb2xkT2xkc3R5bGUgQm9sZC50dGY=.png : cannot identify image file - it's ok, skipping.\\n\",\n      \"notMNIST_small/B\\n\",\n      \"notMNIST_small/C\\n\",\n      \"notMNIST_small/D\\n\",\n      \"notMNIST_small/E\\n\",\n      \"notMNIST_small/F\\n\",\n      \"Could not read: notMNIST_small/F/Q3Jvc3NvdmVyIEJvbGRPYmxpcXVlLnR0Zg==.png : cannot identify image file - it's ok, skipping.\\n\",\n      \"notMNIST_small/G\\n\",\n      \"notMNIST_small/H\\n\",\n      \"notMNIST_small/I\\n\",\n      \"notMNIST_small/J\\n\",\n      \"Full dataset tensor: (18724, 28, 28)\\n\",\n      \"Mean: -0.0746364\\n\",\n      \"Standard deviation: 0.458622\\n\",\n      \"Labels: (18724,)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"image_size = 28  # Pixel width and height.\\n\",\n    \"pixel_depth = 255.0  # Number of levels per pixel.\\n\",\n    \"\\n\",\n    \"def load(data_folders, min_num_images, max_num_images):\\n\",\n    \"  dataset = np.ndarray(\\n\",\n    \"    shape=(max_num_images, image_size, image_size), dtype=np.float32)\\n\",\n    \"  labels = np.ndarray(shape=(max_num_images), dtype=np.int32)\\n\",\n    \"  label_index = 0\\n\",\n    \"  image_index = 0\\n\",\n    \"  for folder in data_folders:\\n\",\n    \"    print folder\\n\",\n    \"    for image in os.listdir(folder):\\n\",\n    \"      if image_index >= max_num_images:\\n\",\n    \"        raise Exception('More images than expected: %d >= %d' % (\\n\",\n    \"          num_images, max_num_images))\\n\",\n    \"      image_file = os.path.join(folder, image)\\n\",\n    \"      try:\\n\",\n    \"        image_data = (ndimage.imread(image_file).astype(float) -\\n\",\n    \"                      pixel_depth / 2) / pixel_depth\\n\",\n    \"        if image_data.shape != (image_size, image_size):\\n\",\n    \"          raise Exception('Unexpected image shape: %s' % str(image_data.shape))\\n\",\n    \"        dataset[image_index, :, :] = image_data\\n\",\n    \"        labels[image_index] = label_index\\n\",\n    \"        image_index += 1\\n\",\n    \"      except IOError as e:\\n\",\n    \"        print 'Could not read:', image_file, ':', e, '- it\\\\'s ok, skipping.'\\n\",\n    \"    label_index += 1\\n\",\n    \"  num_images = image_index\\n\",\n    \"  dataset = dataset[0:num_images, :, :]\\n\",\n    \"  labels = labels[0:num_images]\\n\",\n    \"  if num_images < min_num_images:\\n\",\n    \"    raise Exception('Many fewer images than expected: %d < %d' % (\\n\",\n    \"        num_images, min_num_images))\\n\",\n    \"  print 'Full dataset tensor:', dataset.shape\\n\",\n    \"  print 'Mean:', np.mean(dataset)\\n\",\n    \"  print 'Standard deviation:', np.std(dataset)\\n\",\n    \"  print 'Labels:', labels.shape\\n\",\n    \"  return dataset, labels\\n\",\n    \"train_dataset, train_labels = load(train_folders, 450000, 550000)\\n\",\n    \"test_dataset, test_labels = load(test_folders, 18000, 20000)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"vUdbskYE2d87\"\n   },\n   \"source\": [\n    \"---\\n\",\n    \"Problem 2\\n\",\n    \"---------\\n\",\n    \"\\n\",\n    \"Let's verify that the data still looks good. Displaying a sample of the labels and images from the ndarray. Hint: you can use matplotlib.pyplot.\\n\",\n    \"\\n\",\n    \"---\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"GPTCnjIcyuKN\"\n   },\n   \"source\": [\n    \"Next, we'll randomize the data. It's important to have the labels well shuffled for the training and test distributions to match.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"cellView\": \"both\",\n    \"colab\": {\n     \"autoexec\": {\n      \"startup\": false,\n      \"wait_interval\": 0\n     }\n    },\n    \"colab_type\": \"code\",\n    \"collapsed\": true,\n    \"id\": \"6WZ2l2tN2zOL\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"np.random.seed(133)\\n\",\n    \"def randomize(dataset, labels):\\n\",\n    \"  permutation = np.random.permutation(labels.shape[0])\\n\",\n    \"  shuffled_dataset = dataset[permutation,:,:]\\n\",\n    \"  shuffled_labels = labels[permutation]\\n\",\n    \"  return shuffled_dataset, shuffled_labels\\n\",\n    \"train_dataset, train_labels = randomize(train_dataset, train_labels)\\n\",\n    \"test_dataset, test_labels = randomize(test_dataset, test_labels)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"puDUTe6t6USl\"\n   },\n   \"source\": [\n    \"---\\n\",\n    \"Problem 3\\n\",\n    \"---------\\n\",\n    \"Convince yourself that the data is still good after shuffling!\\n\",\n    \"\\n\",\n    \"---\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"cYznx5jUwzoO\"\n   },\n   \"source\": [\n    \"---\\n\",\n    \"Problem 4\\n\",\n    \"---------\\n\",\n    \"Another check: we expect the data to be balanced across classes. Verify that.\\n\",\n    \"\\n\",\n    \"---\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"LA7M7K22ynCt\"\n   },\n   \"source\": [\n    \"Prune the training data as needed. Depending on your computer setup, you might not be able to fit it all in memory, and you can tune train_size as needed.\\n\",\n    \"\\n\",\n    \"Also create a validation dataset for hyperparameter tuning.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"cellView\": \"both\",\n    \"colab\": {\n     \"autoexec\": {\n      \"startup\": false,\n      \"wait_interval\": 0\n     },\n     \"output_extras\": [\n      {\n       \"item_id\": 1\n      }\n     ]\n    },\n    \"colab_type\": \"code\",\n    \"collapsed\": false,\n    \"executionInfo\": {\n     \"elapsed\": 411281,\n     \"status\": \"ok\",\n     \"timestamp\": 1444485897869,\n     \"user\": {\n      \"color\": \"#1FA15D\",\n      \"displayName\": \"Vincent Vanhoucke\",\n      \"isAnonymous\": false,\n      \"isMe\": true,\n      \"permissionId\": \"05076109866853157986\",\n      \"photoUrl\": \"//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg\",\n      \"sessionId\": \"2a0a5e044bb03b66\",\n      \"userId\": \"102167687554210253930\"\n     },\n     \"user_tz\": 420\n    },\n    \"id\": \"s3mWgZLpyuzq\",\n    \"outputId\": \"8af66da6-902d-4719-bedc-7c9fb7ae7948\"\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Training (200000, 28, 28) (200000,)\\n\",\n      \"Validation (10000, 28, 28) (10000,)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"train_size = 200000\\n\",\n    \"valid_size = 10000\\n\",\n    \"\\n\",\n    \"valid_dataset = train_dataset[:valid_size,:,:]\\n\",\n    \"valid_labels = train_labels[:valid_size]\\n\",\n    \"train_dataset = train_dataset[valid_size:valid_size+train_size,:,:]\\n\",\n    \"train_labels = train_labels[valid_size:valid_size+train_size]\\n\",\n    \"print 'Training', train_dataset.shape, train_labels.shape\\n\",\n    \"print 'Validation', valid_dataset.shape, valid_labels.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"tIQJaJuwg5Hw\"\n   },\n   \"source\": [\n    \"Finally, let's save the data for later reuse:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"cellView\": \"both\",\n    \"colab\": {\n     \"autoexec\": {\n      \"startup\": false,\n      \"wait_interval\": 0\n     }\n    },\n    \"colab_type\": \"code\",\n    \"collapsed\": true,\n    \"id\": \"QiR_rETzem6C\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"pickle_file = 'notMNIST.pickle'\\n\",\n    \"\\n\",\n    \"try:\\n\",\n    \"  f = open(pickle_file, 'wb')\\n\",\n    \"  save = {\\n\",\n    \"    'train_dataset': train_dataset,\\n\",\n    \"    'train_labels': train_labels,\\n\",\n    \"    'valid_dataset': valid_dataset,\\n\",\n    \"    'valid_labels': valid_labels,\\n\",\n    \"    'test_dataset': test_dataset,\\n\",\n    \"    'test_labels': test_labels,\\n\",\n    \"    }\\n\",\n    \"  pickle.dump(save, f, pickle.HIGHEST_PROTOCOL)\\n\",\n    \"  f.close()\\n\",\n    \"except Exception as e:\\n\",\n    \"  print 'Unable to save data to', pickle_file, ':', e\\n\",\n    \"  raise\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"cellView\": \"both\",\n    \"colab\": {\n     \"autoexec\": {\n      \"startup\": false,\n      \"wait_interval\": 0\n     },\n     \"output_extras\": [\n      {\n       \"item_id\": 1\n      }\n     ]\n    },\n    \"colab_type\": \"code\",\n    \"collapsed\": false,\n    \"executionInfo\": {\n     \"elapsed\": 413065,\n     \"status\": \"ok\",\n     \"timestamp\": 1444485899688,\n     \"user\": {\n      \"color\": \"#1FA15D\",\n      \"displayName\": \"Vincent Vanhoucke\",\n      \"isAnonymous\": false,\n      \"isMe\": true,\n      \"permissionId\": \"05076109866853157986\",\n      \"photoUrl\": \"//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg\",\n      \"sessionId\": \"2a0a5e044bb03b66\",\n      \"userId\": \"102167687554210253930\"\n     },\n     \"user_tz\": 420\n    },\n    \"id\": \"hQbLjrW_iT39\",\n    \"outputId\": \"b440efc6-5ee1-4cbc-d02d-93db44ebd956\"\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Compressed pickle size: 718193801\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"statinfo = os.stat(pickle_file)\\n\",\n    \"print 'Compressed pickle size:', statinfo.st_size\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"gE_cRAQB33lk\"\n   },\n   \"source\": [\n    \"---\\n\",\n    \"Problem 5\\n\",\n    \"---------\\n\",\n    \"\\n\",\n    \"By construction, this dataset might contain a lot of overlapping samples, including training data that's also contained in the validation and test set! Overlap between training and test can skew the results if you expect to use your model in an environment where there is never an overlap, but are actually ok if you expect to see training samples recur when you use it.\\n\",\n    \"Measure how much overlap there is between training, validation and test samples.\\n\",\n    \"Optional questions:\\n\",\n    \"- What about near duplicates between datasets? (images that are almost identical)\\n\",\n    \"- Create a sanitized validation and test set, and compare your accuracy on those in subsequent exercises.\\n\",\n    \"---\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"L8oww1s4JMQx\"\n   },\n   \"source\": [\n    \"---\\n\",\n    \"Problem 6\\n\",\n    \"---------\\n\",\n    \"\\n\",\n    \"Let's get an idea of what an off-the-shelf classifier can give you on this data. It's always good to check that there is something to learn, and that it's a problem that is not so trivial that a canned solution solves it.\\n\",\n    \"\\n\",\n    \"Train a simple model on this data using 50, 100, 1000 and 5000 training samples. Hint: you can use the LogisticRegression model from sklearn.linear_model.\\n\",\n    \"\\n\",\n    \"Optional question: train an off-the-shelf model on all the data!\\n\",\n    \"\\n\",\n    \"---\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"colabVersion\": \"0.3.2\",\n  \"colab_default_view\": {},\n  \"colab_views\": {},\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.4.3\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "deep-learning/tensor-flow-exercises/2_fullyconnected.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"kR-4eNdK6lYS\"\n   },\n   \"source\": [\n    \"Deep Learning with TensorFlow\\n\",\n    \"=============\\n\",\n    \"\\n\",\n    \"Credits: Forked from [TensorFlow](https://github.com/tensorflow/tensorflow) by Google\\n\",\n    \"\\n\",\n    \"Setup\\n\",\n    \"------------\\n\",\n    \"\\n\",\n    \"Refer to the [setup instructions](https://github.com/donnemartin/data-science-ipython-notebooks/tree/feature/deep-learning/deep-learning/tensor-flow-exercises/README.md).\\n\",\n    \"\\n\",\n    \"Exercise 2\\n\",\n    \"------------\\n\",\n    \"\\n\",\n    \"Previously in `1_notmnist.ipynb`, we created a pickle with formatted datasets for training, development and testing on the [notMNIST dataset](http://yaroslavvb.blogspot.com/2011/09/notmnist-dataset.html).\\n\",\n    \"\\n\",\n    \"The goal of this exercise is to progressively train deeper and more accurate models using TensorFlow.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"cellView\": \"both\",\n    \"colab\": {\n     \"autoexec\": {\n      \"startup\": false,\n      \"wait_interval\": 0\n     }\n    },\n    \"colab_type\": \"code\",\n    \"collapsed\": true,\n    \"id\": \"JLpLa8Jt7Vu4\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# These are all the modules we'll be using later. Make sure you can import them\\n\",\n    \"# before proceeding further.\\n\",\n    \"import cPickle as pickle\\n\",\n    \"import numpy as np\\n\",\n    \"import tensorflow as tf\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"1HrCK6e17WzV\"\n   },\n   \"source\": [\n    \"First reload the data we generated in `1_notmist.ipynb`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"cellView\": \"both\",\n    \"colab\": {\n     \"autoexec\": {\n      \"startup\": false,\n      \"wait_interval\": 0\n     },\n     \"output_extras\": [\n      {\n       \"item_id\": 1\n      }\n     ]\n    },\n    \"colab_type\": \"code\",\n    \"collapsed\": false,\n    \"executionInfo\": {\n     \"elapsed\": 19456,\n     \"status\": \"ok\",\n     \"timestamp\": 1449847956073,\n     \"user\": {\n      \"color\": \"\",\n      \"displayName\": \"\",\n      \"isAnonymous\": false,\n      \"isMe\": true,\n      \"permissionId\": \"\",\n      \"photoUrl\": \"\",\n      \"sessionId\": \"0\",\n      \"userId\": \"\"\n     },\n     \"user_tz\": 480\n    },\n    \"id\": \"y3-cj1bpmuxc\",\n    \"outputId\": \"0ddb1607-1fc4-4ddb-de28-6c7ab7fb0c33\"\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Training set (200000, 28, 28) (200000,)\\n\",\n      \"Validation set (10000, 28, 28) (10000,)\\n\",\n      \"Test set (18724, 28, 28) (18724,)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"pickle_file = 'notMNIST.pickle'\\n\",\n    \"\\n\",\n    \"with open(pickle_file, 'rb') as f:\\n\",\n    \"  save = pickle.load(f)\\n\",\n    \"  train_dataset = save['train_dataset']\\n\",\n    \"  train_labels = save['train_labels']\\n\",\n    \"  valid_dataset = save['valid_dataset']\\n\",\n    \"  valid_labels = save['valid_labels']\\n\",\n    \"  test_dataset = save['test_dataset']\\n\",\n    \"  test_labels = save['test_labels']\\n\",\n    \"  del save  # hint to help gc free up memory\\n\",\n    \"  print 'Training set', train_dataset.shape, train_labels.shape\\n\",\n    \"  print 'Validation set', valid_dataset.shape, valid_labels.shape\\n\",\n    \"  print 'Test set', test_dataset.shape, test_labels.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"L7aHrm6nGDMB\"\n   },\n   \"source\": [\n    \"Reformat into a shape that's more adapted to the models we're going to train:\\n\",\n    \"- data as a flat matrix,\\n\",\n    \"- labels as float 1-hot encodings.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"cellView\": \"both\",\n    \"colab\": {\n     \"autoexec\": {\n      \"startup\": false,\n      \"wait_interval\": 0\n     },\n     \"output_extras\": [\n      {\n       \"item_id\": 1\n      }\n     ]\n    },\n    \"colab_type\": \"code\",\n    \"collapsed\": false,\n    \"executionInfo\": {\n     \"elapsed\": 19723,\n     \"status\": \"ok\",\n     \"timestamp\": 1449847956364,\n     \"user\": {\n      \"color\": \"\",\n      \"displayName\": \"\",\n      \"isAnonymous\": false,\n      \"isMe\": true,\n      \"permissionId\": \"\",\n      \"photoUrl\": \"\",\n      \"sessionId\": \"0\",\n      \"userId\": \"\"\n     },\n     \"user_tz\": 480\n    },\n    \"id\": \"IRSyYiIIGIzS\",\n    \"outputId\": \"2ba0fc75-1487-4ace-a562-cf81cae82793\"\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Training set (200000, 784) (200000, 10)\\n\",\n      \"Validation set (10000, 784) (10000, 10)\\n\",\n      \"Test set (18724, 784) (18724, 10)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"image_size = 28\\n\",\n    \"num_labels = 10\\n\",\n    \"\\n\",\n    \"def reformat(dataset, labels):\\n\",\n    \"  dataset = dataset.reshape((-1, image_size * image_size)).astype(np.float32)\\n\",\n    \"  # Map 0 to [1.0, 0.0, 0.0 ...], 1 to [0.0, 1.0, 0.0 ...]\\n\",\n    \"  labels = (np.arange(num_labels) == labels[:,None]).astype(np.float32)\\n\",\n    \"  return dataset, labels\\n\",\n    \"train_dataset, train_labels = reformat(train_dataset, train_labels)\\n\",\n    \"valid_dataset, valid_labels = reformat(valid_dataset, valid_labels)\\n\",\n    \"test_dataset, test_labels = reformat(test_dataset, test_labels)\\n\",\n    \"print 'Training set', train_dataset.shape, train_labels.shape\\n\",\n    \"print 'Validation set', valid_dataset.shape, valid_labels.shape\\n\",\n    \"print 'Test set', test_dataset.shape, test_labels.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"nCLVqyQ5vPPH\"\n   },\n   \"source\": [\n    \"We're first going to train a multinomial logistic regression using simple gradient descent.\\n\",\n    \"\\n\",\n    \"TensorFlow works like this:\\n\",\n    \"* First you describe the computation that you want to see performed: what the inputs, the variables, and the operations look like. These get created as nodes over a computation graph. This description is all contained within the block below:\\n\",\n    \"\\n\",\n    \"      with graph.as_default():\\n\",\n    \"          ...\\n\",\n    \"\\n\",\n    \"* Then you can run the operations on this graph as many times as you want by calling `session.run()`, providing it outputs to fetch from the graph that get returned. This runtime operation is all contained in the block below:\\n\",\n    \"\\n\",\n    \"      with tf.Session(graph=graph) as session:\\n\",\n    \"          ...\\n\",\n    \"\\n\",\n    \"Let's load all the data into TensorFlow and build the computation graph corresponding to our training:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"cellView\": \"both\",\n    \"colab\": {\n     \"autoexec\": {\n      \"startup\": false,\n      \"wait_interval\": 0\n     }\n    },\n    \"colab_type\": \"code\",\n    \"collapsed\": true,\n    \"id\": \"Nfv39qvtvOl_\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# With gradient descent training, even this much data is prohibitive.\\n\",\n    \"# Subset the training data for faster turnaround.\\n\",\n    \"train_subset = 10000\\n\",\n    \"\\n\",\n    \"graph = tf.Graph()\\n\",\n    \"with graph.as_default():\\n\",\n    \"\\n\",\n    \"  # Input data.\\n\",\n    \"  # Load the training, validation and test data into constants that are\\n\",\n    \"  # attached to the graph.\\n\",\n    \"  tf_train_dataset = tf.constant(train_dataset[:train_subset, :])\\n\",\n    \"  tf_train_labels = tf.constant(train_labels[:train_subset])\\n\",\n    \"  tf_valid_dataset = tf.constant(valid_dataset)\\n\",\n    \"  tf_test_dataset = tf.constant(test_dataset)\\n\",\n    \"  \\n\",\n    \"  # Variables.\\n\",\n    \"  # These are the parameters that we are going to be training. The weight\\n\",\n    \"  # matrix will be initialized using random valued following a (truncated)\\n\",\n    \"  # normal distribution. The biases get initialized to zero.\\n\",\n    \"  weights = tf.Variable(\\n\",\n    \"    tf.truncated_normal([image_size * image_size, num_labels]))\\n\",\n    \"  biases = tf.Variable(tf.zeros([num_labels]))\\n\",\n    \"  \\n\",\n    \"  # Training computation.\\n\",\n    \"  # We multiply the inputs with the weight matrix, and add biases. We compute\\n\",\n    \"  # the softmax and cross-entropy (it's one operation in TensorFlow, because\\n\",\n    \"  # it's very common, and it can be optimized). We take the average of this\\n\",\n    \"  # cross-entropy across all training examples: that's our loss.\\n\",\n    \"  logits = tf.matmul(tf_train_dataset, weights) + biases\\n\",\n    \"  loss = tf.reduce_mean(\\n\",\n    \"    tf.nn.softmax_cross_entropy_with_logits(logits, tf_train_labels))\\n\",\n    \"  \\n\",\n    \"  # Optimizer.\\n\",\n    \"  # We are going to find the minimum of this loss using gradient descent.\\n\",\n    \"  optimizer = tf.train.GradientDescentOptimizer(0.5).minimize(loss)\\n\",\n    \"  \\n\",\n    \"  # Predictions for the training, validation, and test data.\\n\",\n    \"  # These are not part of training, but merely here so that we can report\\n\",\n    \"  # accuracy figures as we train.\\n\",\n    \"  train_prediction = tf.nn.softmax(logits)\\n\",\n    \"  valid_prediction = tf.nn.softmax(\\n\",\n    \"    tf.matmul(tf_valid_dataset, weights) + biases)\\n\",\n    \"  test_prediction = tf.nn.softmax(tf.matmul(tf_test_dataset, weights) + biases)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"KQcL4uqISHjP\"\n   },\n   \"source\": [\n    \"Let's run this computation and iterate:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"cellView\": \"both\",\n    \"colab\": {\n     \"autoexec\": {\n      \"startup\": false,\n      \"wait_interval\": 0\n     },\n     \"output_extras\": [\n      {\n       \"item_id\": 9\n      }\n     ]\n    },\n    \"colab_type\": \"code\",\n    \"collapsed\": false,\n    \"executionInfo\": {\n     \"elapsed\": 57454,\n     \"status\": \"ok\",\n     \"timestamp\": 1449847994134,\n     \"user\": {\n      \"color\": \"\",\n      \"displayName\": \"\",\n      \"isAnonymous\": false,\n      \"isMe\": true,\n      \"permissionId\": \"\",\n      \"photoUrl\": \"\",\n      \"sessionId\": \"0\",\n      \"userId\": \"\"\n     },\n     \"user_tz\": 480\n    },\n    \"id\": \"z2cjdenH869W\",\n    \"outputId\": \"4c037ba1-b526-4d8e-e632-91e2a0333267\"\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Initialized\\n\",\n      \"Loss at step 0 : 17.2939\\n\",\n      \"Training accuracy: 10.8%\\n\",\n      \"Validation accuracy: 13.8%\\n\",\n      \"Loss at step 100 : 2.26903\\n\",\n      \"Training accuracy: 72.3%\\n\",\n      \"Validation accuracy: 71.6%\\n\",\n      \"Loss at step 200 : 1.84895\\n\",\n      \"Training accuracy: 74.9%\\n\",\n      \"Validation accuracy: 73.9%\\n\",\n      \"Loss at step 300 : 1.60701\\n\",\n      \"Training accuracy: 76.0%\\n\",\n      \"Validation accuracy: 74.5%\\n\",\n      \"Loss at step 400 : 1.43912\\n\",\n      \"Training accuracy: 76.8%\\n\",\n      \"Validation accuracy: 74.8%\\n\",\n      \"Loss at step 500 : 1.31349\\n\",\n      \"Training accuracy: 77.5%\\n\",\n      \"Validation accuracy: 75.0%\\n\",\n      \"Loss at step 600 : 1.21501\\n\",\n      \"Training accuracy: 78.1%\\n\",\n      \"Validation accuracy: 75.4%\\n\",\n      \"Loss at step 700 : 1.13515\\n\",\n      \"Training accuracy: 78.6%\\n\",\n      \"Validation accuracy: 75.4%\\n\",\n      \"Loss at step 800 : 1.0687\\n\",\n      \"Training accuracy: 79.2%\\n\",\n      \"Validation accuracy: 75.6%\\n\",\n      \"Test accuracy: 82.9%\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"num_steps = 801\\n\",\n    \"\\n\",\n    \"def accuracy(predictions, labels):\\n\",\n    \"  return (100.0 * np.sum(np.argmax(predictions, 1) == np.argmax(labels, 1))\\n\",\n    \"          / predictions.shape[0])\\n\",\n    \"\\n\",\n    \"with tf.Session(graph=graph) as session:\\n\",\n    \"  # This is a one-time operation which ensures the parameters get initialized as\\n\",\n    \"  # we described in the graph: random weights for the matrix, zeros for the\\n\",\n    \"  # biases. \\n\",\n    \" tf.global_variables_initializer().run()\\n\",\n    \"  print 'Initialized'\\n\",\n    \"  for step in xrange(num_steps):\\n\",\n    \"    # Run the computations. We tell .run() that we want to run the optimizer,\\n\",\n    \"    # and get the loss value and the training predictions returned as numpy\\n\",\n    \"    # arrays.\\n\",\n    \"    _, l, predictions = session.run([optimizer, loss, train_prediction])\\n\",\n    \"    if (step % 100 == 0):\\n\",\n    \"      print 'Loss at step', step, ':', l\\n\",\n    \"      print 'Training accuracy: %.1f%%' % accuracy(\\n\",\n    \"        predictions, train_labels[:train_subset, :])\\n\",\n    \"      # Calling .eval() on valid_prediction is basically like calling run(), but\\n\",\n    \"      # just to get that one numpy array. Note that it recomputes all its graph\\n\",\n    \"      # dependencies.\\n\",\n    \"      print 'Validation accuracy: %.1f%%' % accuracy(\\n\",\n    \"        valid_prediction.eval(), valid_labels)\\n\",\n    \"  print 'Test accuracy: %.1f%%' % accuracy(test_prediction.eval(), test_labels)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"x68f-hxRGm3H\"\n   },\n   \"source\": [\n    \"Let's now switch to stochastic gradient descent training instead, which is much faster.\\n\",\n    \"\\n\",\n    \"The graph will be similar, except that instead of holding all the training data into a constant node, we create a `Placeholder` node which will be fed actual data at every call of `sesion.run()`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"cellView\": \"both\",\n    \"colab\": {\n     \"autoexec\": {\n      \"startup\": false,\n      \"wait_interval\": 0\n     }\n    },\n    \"colab_type\": \"code\",\n    \"collapsed\": true,\n    \"id\": \"qhPMzWYRGrzM\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"batch_size = 128\\n\",\n    \"\\n\",\n    \"graph = tf.Graph()\\n\",\n    \"with graph.as_default():\\n\",\n    \"\\n\",\n    \"  # Input data. For the training data, we use a placeholder that will be fed\\n\",\n    \"  # at run time with a training minibatch.\\n\",\n    \"  tf_train_dataset = tf.placeholder(tf.float32,\\n\",\n    \"                                    shape=(batch_size, image_size * image_size))\\n\",\n    \"  tf_train_labels = tf.placeholder(tf.float32, shape=(batch_size, num_labels))\\n\",\n    \"  tf_valid_dataset = tf.constant(valid_dataset)\\n\",\n    \"  tf_test_dataset = tf.constant(test_dataset)\\n\",\n    \"  \\n\",\n    \"  # Variables.\\n\",\n    \"  weights = tf.Variable(\\n\",\n    \"    tf.truncated_normal([image_size * image_size, num_labels]))\\n\",\n    \"  biases = tf.Variable(tf.zeros([num_labels]))\\n\",\n    \"  \\n\",\n    \"  # Training computation.\\n\",\n    \"  logits = tf.matmul(tf_train_dataset, weights) + biases\\n\",\n    \"  loss = tf.reduce_mean(\\n\",\n    \"    tf.nn.softmax_cross_entropy_with_logits(logits, tf_train_labels))\\n\",\n    \"  \\n\",\n    \"  # Optimizer.\\n\",\n    \"  optimizer = tf.train.GradientDescentOptimizer(0.5).minimize(loss)\\n\",\n    \"  \\n\",\n    \"  # Predictions for the training, validation, and test data.\\n\",\n    \"  train_prediction = tf.nn.softmax(logits)\\n\",\n    \"  valid_prediction = tf.nn.softmax(\\n\",\n    \"    tf.matmul(tf_valid_dataset, weights) + biases)\\n\",\n    \"  test_prediction = tf.nn.softmax(tf.matmul(tf_test_dataset, weights) + biases)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"XmVZESmtG4JH\"\n   },\n   \"source\": [\n    \"Let's run it:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"cellView\": \"both\",\n    \"colab\": {\n     \"autoexec\": {\n      \"startup\": false,\n      \"wait_interval\": 0\n     },\n     \"output_extras\": [\n      {\n       \"item_id\": 6\n      }\n     ]\n    },\n    \"colab_type\": \"code\",\n    \"collapsed\": false,\n    \"executionInfo\": {\n     \"elapsed\": 66292,\n     \"status\": \"ok\",\n     \"timestamp\": 1449848003013,\n     \"user\": {\n      \"color\": \"\",\n      \"displayName\": \"\",\n      \"isAnonymous\": false,\n      \"isMe\": true,\n      \"permissionId\": \"\",\n      \"photoUrl\": \"\",\n      \"sessionId\": \"0\",\n      \"userId\": \"\"\n     },\n     \"user_tz\": 480\n    },\n    \"id\": \"FoF91pknG_YW\",\n    \"outputId\": \"d255c80e-954d-4183-ca1c-c7333ce91d0a\"\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Initialized\\n\",\n      \"Minibatch loss at step 0 : 16.8091\\n\",\n      \"Minibatch accuracy: 12.5%\\n\",\n      \"Validation accuracy: 14.0%\\n\",\n      \"Minibatch loss at step 500 : 1.75256\\n\",\n      \"Minibatch accuracy: 77.3%\\n\",\n      \"Validation accuracy: 75.0%\\n\",\n      \"Minibatch loss at step 1000 : 1.32283\\n\",\n      \"Minibatch accuracy: 77.3%\\n\",\n      \"Validation accuracy: 76.6%\\n\",\n      \"Minibatch loss at step 1500 : 0.944533\\n\",\n      \"Minibatch accuracy: 83.6%\\n\",\n      \"Validation accuracy: 76.5%\\n\",\n      \"Minibatch loss at step 2000 : 1.03795\\n\",\n      \"Minibatch accuracy: 78.9%\\n\",\n      \"Validation accuracy: 77.8%\\n\",\n      \"Minibatch loss at step 2500 : 1.10219\\n\",\n      \"Minibatch accuracy: 80.5%\\n\",\n      \"Validation accuracy: 78.0%\\n\",\n      \"Minibatch loss at step 3000 : 0.758874\\n\",\n      \"Minibatch accuracy: 82.8%\\n\",\n      \"Validation accuracy: 78.8%\\n\",\n      \"Test accuracy: 86.1%\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"num_steps = 3001\\n\",\n    \"\\n\",\n    \"with tf.Session(graph=graph) as session:\\n\",\n    \"  tf.global_variables_initializer().run()\\n\",\n    \"  print \\\"Initialized\\\"\\n\",\n    \"  for step in xrange(num_steps):\\n\",\n    \"    # Pick an offset within the training data, which has been randomized.\\n\",\n    \"    # Note: we could use better randomization across epochs.\\n\",\n    \"    offset = (step * batch_size) % (train_labels.shape[0] - batch_size)\\n\",\n    \"    # Generate a minibatch.\\n\",\n    \"    batch_data = train_dataset[offset:(offset + batch_size), :]\\n\",\n    \"    batch_labels = train_labels[offset:(offset + batch_size), :]\\n\",\n    \"    # Prepare a dictionary telling the session where to feed the minibatch.\\n\",\n    \"    # The key of the dictionary is the placeholder node of the graph to be fed,\\n\",\n    \"    # and the value is the numpy array to feed to it.\\n\",\n    \"    feed_dict = {tf_train_dataset : batch_data, tf_train_labels : batch_labels}\\n\",\n    \"    _, l, predictions = session.run(\\n\",\n    \"      [optimizer, loss, train_prediction], feed_dict=feed_dict)\\n\",\n    \"    if (step % 500 == 0):\\n\",\n    \"      print \\\"Minibatch loss at step\\\", step, \\\":\\\", l\\n\",\n    \"      print \\\"Minibatch accuracy: %.1f%%\\\" % accuracy(predictions, batch_labels)\\n\",\n    \"      print \\\"Validation accuracy: %.1f%%\\\" % accuracy(\\n\",\n    \"        valid_prediction.eval(), valid_labels)\\n\",\n    \"  print \\\"Test accuracy: %.1f%%\\\" % accuracy(test_prediction.eval(), test_labels)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"7omWxtvLLxik\"\n   },\n   \"source\": [\n    \"---\\n\",\n    \"Problem\\n\",\n    \"-------\\n\",\n    \"\\n\",\n    \"Turn the logistic regression example with SGD into a 1-hidden layer neural network with rectified linear units (nn.relu()) and 1024 hidden nodes. This model should improve your validation / test accuracy.\\n\",\n    \"\\n\",\n    \"---\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"colabVersion\": \"0.3.2\",\n  \"colab_default_view\": {},\n  \"colab_views\": {},\n  \"kernelspec\": {\n   \"display_name\": \"Python 2\",\n   \"language\": \"python\",\n   \"name\": \"python2\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 2\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython2\",\n   \"version\": \"2.7.12\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "deep-learning/tensor-flow-exercises/3_regularization.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"kR-4eNdK6lYS\"\n   },\n   \"source\": [\n    \"Deep Learning with TensorFlow\\n\",\n    \"=============\\n\",\n    \"\\n\",\n    \"Credits: Forked from [TensorFlow](https://github.com/tensorflow/tensorflow) by Google\\n\",\n    \"\\n\",\n    \"Setup\\n\",\n    \"------------\\n\",\n    \"\\n\",\n    \"Refer to the [setup instructions](https://github.com/donnemartin/data-science-ipython-notebooks/tree/feature/deep-learning/deep-learning/tensor-flow-exercises/README.md).\\n\",\n    \"\\n\",\n    \"Exercise 3\\n\",\n    \"------------\\n\",\n    \"\\n\",\n    \"Previously in `2_fullyconnected.ipynb`, you trained a logistic regression and a neural network model.\\n\",\n    \"\\n\",\n    \"The goal of this exercise is to explore regularization techniques.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"cellView\": \"both\",\n    \"colab\": {\n     \"autoexec\": {\n      \"startup\": false,\n      \"wait_interval\": 0\n     }\n    },\n    \"colab_type\": \"code\",\n    \"collapsed\": true,\n    \"id\": \"JLpLa8Jt7Vu4\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# These are all the modules we'll be using later. Make sure you can import them\\n\",\n    \"# before proceeding further.\\n\",\n    \"import cPickle as pickle\\n\",\n    \"import numpy as np\\n\",\n    \"import tensorflow as tf\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"1HrCK6e17WzV\"\n   },\n   \"source\": [\n    \"First reload the data we generated in _notmist.ipynb_.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"cellView\": \"both\",\n    \"colab\": {\n     \"autoexec\": {\n      \"startup\": false,\n      \"wait_interval\": 0\n     },\n     \"output_extras\": [\n      {\n       \"item_id\": 1\n      }\n     ]\n    },\n    \"colab_type\": \"code\",\n    \"collapsed\": false,\n    \"executionInfo\": {\n     \"elapsed\": 11777,\n     \"status\": \"ok\",\n     \"timestamp\": 1449849322348,\n     \"user\": {\n      \"color\": \"\",\n      \"displayName\": \"\",\n      \"isAnonymous\": false,\n      \"isMe\": true,\n      \"permissionId\": \"\",\n      \"photoUrl\": \"\",\n      \"sessionId\": \"0\",\n      \"userId\": \"\"\n     },\n     \"user_tz\": 480\n    },\n    \"id\": \"y3-cj1bpmuxc\",\n    \"outputId\": \"e03576f1-ebbe-4838-c388-f1777bcc9873\"\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Training set (200000, 28, 28) (200000,)\\n\",\n      \"Validation set (10000, 28, 28) (10000,)\\n\",\n      \"Test set (18724, 28, 28) (18724,)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"pickle_file = 'notMNIST.pickle'\\n\",\n    \"\\n\",\n    \"with open(pickle_file, 'rb') as f:\\n\",\n    \"  save = pickle.load(f)\\n\",\n    \"  train_dataset = save['train_dataset']\\n\",\n    \"  train_labels = save['train_labels']\\n\",\n    \"  valid_dataset = save['valid_dataset']\\n\",\n    \"  valid_labels = save['valid_labels']\\n\",\n    \"  test_dataset = save['test_dataset']\\n\",\n    \"  test_labels = save['test_labels']\\n\",\n    \"  del save  # hint to help gc free up memory\\n\",\n    \"  print 'Training set', train_dataset.shape, train_labels.shape\\n\",\n    \"  print 'Validation set', valid_dataset.shape, valid_labels.shape\\n\",\n    \"  print 'Test set', test_dataset.shape, test_labels.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"L7aHrm6nGDMB\"\n   },\n   \"source\": [\n    \"Reformat into a shape that's more adapted to the models we're going to train:\\n\",\n    \"- data as a flat matrix,\\n\",\n    \"- labels as float 1-hot encodings.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"cellView\": \"both\",\n    \"colab\": {\n     \"autoexec\": {\n      \"startup\": false,\n      \"wait_interval\": 0\n     },\n     \"output_extras\": [\n      {\n       \"item_id\": 1\n      }\n     ]\n    },\n    \"colab_type\": \"code\",\n    \"collapsed\": false,\n    \"executionInfo\": {\n     \"elapsed\": 11728,\n     \"status\": \"ok\",\n     \"timestamp\": 1449849322356,\n     \"user\": {\n      \"color\": \"\",\n      \"displayName\": \"\",\n      \"isAnonymous\": false,\n      \"isMe\": true,\n      \"permissionId\": \"\",\n      \"photoUrl\": \"\",\n      \"sessionId\": \"0\",\n      \"userId\": \"\"\n     },\n     \"user_tz\": 480\n    },\n    \"id\": \"IRSyYiIIGIzS\",\n    \"outputId\": \"3f8996ee-3574-4f44-c953-5c8a04636582\"\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Training set (200000, 784) (200000, 10)\\n\",\n      \"Validation set (10000, 784) (10000, 10)\\n\",\n      \"Test set (18724, 784) (18724, 10)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"image_size = 28\\n\",\n    \"num_labels = 10\\n\",\n    \"\\n\",\n    \"def reformat(dataset, labels):\\n\",\n    \"  dataset = dataset.reshape((-1, image_size * image_size)).astype(np.float32)\\n\",\n    \"  # Map 2 to [0.0, 1.0, 0.0 ...], 3 to [0.0, 0.0, 1.0 ...]\\n\",\n    \"  labels = (np.arange(num_labels) == labels[:,None]).astype(np.float32)\\n\",\n    \"  return dataset, labels\\n\",\n    \"train_dataset, train_labels = reformat(train_dataset, train_labels)\\n\",\n    \"valid_dataset, valid_labels = reformat(valid_dataset, valid_labels)\\n\",\n    \"test_dataset, test_labels = reformat(test_dataset, test_labels)\\n\",\n    \"print 'Training set', train_dataset.shape, train_labels.shape\\n\",\n    \"print 'Validation set', valid_dataset.shape, valid_labels.shape\\n\",\n    \"print 'Test set', test_dataset.shape, test_labels.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"cellView\": \"both\",\n    \"colab\": {\n     \"autoexec\": {\n      \"startup\": false,\n      \"wait_interval\": 0\n     }\n    },\n    \"colab_type\": \"code\",\n    \"collapsed\": true,\n    \"id\": \"RajPLaL_ZW6w\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def accuracy(predictions, labels):\\n\",\n    \"  return (100.0 * np.sum(np.argmax(predictions, 1) == np.argmax(labels, 1))\\n\",\n    \"          / predictions.shape[0])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"sgLbUAQ1CW-1\"\n   },\n   \"source\": [\n    \"---\\n\",\n    \"Problem 1\\n\",\n    \"---------\\n\",\n    \"\\n\",\n    \"Introduce and tune L2 regularization for both logistic and neural network models. Remember that L2 amounts to adding a penalty on the norm of the weights to the loss. In TensorFlow, you can compue the L2 loss for a tensor `t` using `nn.l2_loss(t)`. The right amount of regularization should improve your validation / test accuracy.\\n\",\n    \"\\n\",\n    \"---\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"na8xX2yHZzNF\"\n   },\n   \"source\": [\n    \"---\\n\",\n    \"Problem 2\\n\",\n    \"---------\\n\",\n    \"Let's demonstrate an extreme case of overfitting. Restrict your training data to just a few batches. What happens?\\n\",\n    \"\\n\",\n    \"---\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"ww3SCBUdlkRc\"\n   },\n   \"source\": [\n    \"---\\n\",\n    \"Problem 3\\n\",\n    \"---------\\n\",\n    \"Introduce Dropout on the hidden layer of the neural network. Remember: Dropout should only be introduced during training, not evaluation, otherwise your evaluation results would be stochastic as well. TensorFlow provides `nn.dropout()` for that, but you have to make sure it's only inserted during training.\\n\",\n    \"\\n\",\n    \"What happens to our extreme overfitting case?\\n\",\n    \"\\n\",\n    \"---\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"-b1hTz3VWZjw\"\n   },\n   \"source\": [\n    \"---\\n\",\n    \"Problem 4\\n\",\n    \"---------\\n\",\n    \"\\n\",\n    \"Try to get the best performance you can using a multi-layer model! The best reported test accuracy using a deep network is [97.1%](http://yaroslavvb.blogspot.com/2011/09/notmnist-dataset.html?showComment=1391023266211#c8758720086795711595).\\n\",\n    \"\\n\",\n    \"One avenue you can explore is to add multiple layers.\\n\",\n    \"\\n\",\n    \"Another one is to use learning rate decay:\\n\",\n    \"\\n\",\n    \"    global_step = tf.Variable(0)  # count the number of steps taken.\\n\",\n    \"    learning_rate = tf.train.exponential_decay(0.5, step, ...)\\n\",\n    \"    optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss, global_step=global_step)\\n\",\n    \" \\n\",\n    \" ---\\n\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"colabVersion\": \"0.3.2\",\n  \"colab_default_view\": {},\n  \"colab_views\": {},\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.4.3\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "deep-learning/tensor-flow-exercises/4_convolutions.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"4embtkV0pNxM\"\n   },\n   \"source\": [\n    \"Deep Learning with TensorFlow\\n\",\n    \"=============\\n\",\n    \"\\n\",\n    \"Credits: Forked from [TensorFlow](https://github.com/tensorflow/tensorflow) by Google\\n\",\n    \"\\n\",\n    \"Setup\\n\",\n    \"------------\\n\",\n    \"\\n\",\n    \"Refer to the [setup instructions](https://github.com/donnemartin/data-science-ipython-notebooks/tree/feature/deep-learning/deep-learning/tensor-flow-exercises/README.md).\\n\",\n    \"\\n\",\n    \"Exercise 4\\n\",\n    \"------------\\n\",\n    \"\\n\",\n    \"Previously in `2_fullyconnected.ipynb` and `3_regularization.ipynb`, we trained fully connected networks to classify [notMNIST](http://yaroslavvb.blogspot.com/2011/09/notmnist-dataset.html) characters.\\n\",\n    \"\\n\",\n    \"The goal of this exercise is make the neural network convolutional.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"cellView\": \"both\",\n    \"colab\": {\n     \"autoexec\": {\n      \"startup\": false,\n      \"wait_interval\": 0\n     }\n    },\n    \"colab_type\": \"code\",\n    \"collapsed\": true,\n    \"id\": \"tm2CQN_Cpwj0\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# These are all the modules we'll be using later. Make sure you can import them\\n\",\n    \"# before proceeding further.\\n\",\n    \"import cPickle as pickle\\n\",\n    \"import numpy as np\\n\",\n    \"import tensorflow as tf\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"cellView\": \"both\",\n    \"colab\": {\n     \"autoexec\": {\n      \"startup\": false,\n      \"wait_interval\": 0\n     },\n     \"output_extras\": [\n      {\n       \"item_id\": 1\n      }\n     ]\n    },\n    \"colab_type\": \"code\",\n    \"collapsed\": false,\n    \"executionInfo\": {\n     \"elapsed\": 11948,\n     \"status\": \"ok\",\n     \"timestamp\": 1446658914837,\n     \"user\": {\n      \"color\": \"\",\n      \"displayName\": \"\",\n      \"isAnonymous\": false,\n      \"isMe\": true,\n      \"permissionId\": \"\",\n      \"photoUrl\": \"\",\n      \"sessionId\": \"0\",\n      \"userId\": \"\"\n     },\n     \"user_tz\": 480\n    },\n    \"id\": \"y3-cj1bpmuxc\",\n    \"outputId\": \"016b1a51-0290-4b08-efdb-8c95ffc3cd01\"\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Training set (200000, 28, 28) (200000,)\\n\",\n      \"Validation set (10000, 28, 28) (10000,)\\n\",\n      \"Test set (18724, 28, 28) (18724,)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"pickle_file = 'notMNIST.pickle'\\n\",\n    \"\\n\",\n    \"with open(pickle_file, 'rb') as f:\\n\",\n    \"  save = pickle.load(f)\\n\",\n    \"  train_dataset = save['train_dataset']\\n\",\n    \"  train_labels = save['train_labels']\\n\",\n    \"  valid_dataset = save['valid_dataset']\\n\",\n    \"  valid_labels = save['valid_labels']\\n\",\n    \"  test_dataset = save['test_dataset']\\n\",\n    \"  test_labels = save['test_labels']\\n\",\n    \"  del save  # hint to help gc free up memory\\n\",\n    \"  print 'Training set', train_dataset.shape, train_labels.shape\\n\",\n    \"  print 'Validation set', valid_dataset.shape, valid_labels.shape\\n\",\n    \"  print 'Test set', test_dataset.shape, test_labels.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"L7aHrm6nGDMB\"\n   },\n   \"source\": [\n    \"Reformat into a TensorFlow-friendly shape:\\n\",\n    \"- convolutions need the image data formatted as a cube (width by height by #channels)\\n\",\n    \"- labels as float 1-hot encodings.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"cellView\": \"both\",\n    \"colab\": {\n     \"autoexec\": {\n      \"startup\": false,\n      \"wait_interval\": 0\n     },\n     \"output_extras\": [\n      {\n       \"item_id\": 1\n      }\n     ]\n    },\n    \"colab_type\": \"code\",\n    \"collapsed\": false,\n    \"executionInfo\": {\n     \"elapsed\": 11952,\n     \"status\": \"ok\",\n     \"timestamp\": 1446658914857,\n     \"user\": {\n      \"color\": \"\",\n      \"displayName\": \"\",\n      \"isAnonymous\": false,\n      \"isMe\": true,\n      \"permissionId\": \"\",\n      \"photoUrl\": \"\",\n      \"sessionId\": \"0\",\n      \"userId\": \"\"\n     },\n     \"user_tz\": 480\n    },\n    \"id\": \"IRSyYiIIGIzS\",\n    \"outputId\": \"650a208c-8359-4852-f4f5-8bf10e80ef6c\"\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Training set (200000, 28, 28, 1) (200000, 10)\\n\",\n      \"Validation set (10000, 28, 28, 1) (10000, 10)\\n\",\n      \"Test set (18724, 28, 28, 1) (18724, 10)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"image_size = 28\\n\",\n    \"num_labels = 10\\n\",\n    \"num_channels = 1 # grayscale\\n\",\n    \"\\n\",\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"def reformat(dataset, labels):\\n\",\n    \"  dataset = dataset.reshape(\\n\",\n    \"    (-1, image_size, image_size, num_channels)).astype(np.float32)\\n\",\n    \"  labels = (np.arange(num_labels) == labels[:,None]).astype(np.float32)\\n\",\n    \"  return dataset, labels\\n\",\n    \"train_dataset, train_labels = reformat(train_dataset, train_labels)\\n\",\n    \"valid_dataset, valid_labels = reformat(valid_dataset, valid_labels)\\n\",\n    \"test_dataset, test_labels = reformat(test_dataset, test_labels)\\n\",\n    \"print 'Training set', train_dataset.shape, train_labels.shape\\n\",\n    \"print 'Validation set', valid_dataset.shape, valid_labels.shape\\n\",\n    \"print 'Test set', test_dataset.shape, test_labels.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"cellView\": \"both\",\n    \"colab\": {\n     \"autoexec\": {\n      \"startup\": false,\n      \"wait_interval\": 0\n     }\n    },\n    \"colab_type\": \"code\",\n    \"collapsed\": true,\n    \"id\": \"AgQDIREv02p1\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def accuracy(predictions, labels):\\n\",\n    \"  return (100.0 * np.sum(np.argmax(predictions, 1) == np.argmax(labels, 1))\\n\",\n    \"          / predictions.shape[0])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"5rhgjmROXu2O\"\n   },\n   \"source\": [\n    \"Let's build a small network with two convolutional layers, followed by one fully connected layer. Convolutional networks are more expensive computationally, so we'll limit its depth and number of fully connected nodes.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"cellView\": \"both\",\n    \"colab\": {\n     \"autoexec\": {\n      \"startup\": false,\n      \"wait_interval\": 0\n     }\n    },\n    \"colab_type\": \"code\",\n    \"collapsed\": true,\n    \"id\": \"IZYv70SvvOan\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"batch_size = 16\\n\",\n    \"patch_size = 5\\n\",\n    \"depth = 16\\n\",\n    \"num_hidden = 64\\n\",\n    \"\\n\",\n    \"graph = tf.Graph()\\n\",\n    \"\\n\",\n    \"with graph.as_default():\\n\",\n    \"\\n\",\n    \"  # Input data.\\n\",\n    \"  tf_train_dataset = tf.placeholder(\\n\",\n    \"    tf.float32, shape=(batch_size, image_size, image_size, num_channels))\\n\",\n    \"  tf_train_labels = tf.placeholder(tf.float32, shape=(batch_size, num_labels))\\n\",\n    \"  tf_valid_dataset = tf.constant(valid_dataset)\\n\",\n    \"  tf_test_dataset = tf.constant(test_dataset)\\n\",\n    \"  \\n\",\n    \"  # Variables.\\n\",\n    \"  layer1_weights = tf.Variable(tf.truncated_normal(\\n\",\n    \"      [patch_size, patch_size, num_channels, depth], stddev=0.1))\\n\",\n    \"  layer1_biases = tf.Variable(tf.zeros([depth]))\\n\",\n    \"  layer2_weights = tf.Variable(tf.truncated_normal(\\n\",\n    \"      [patch_size, patch_size, depth, depth], stddev=0.1))\\n\",\n    \"  layer2_biases = tf.Variable(tf.constant(1.0, shape=[depth]))\\n\",\n    \"  layer3_weights = tf.Variable(tf.truncated_normal(\\n\",\n    \"      [image_size / 4 * image_size / 4 * depth, num_hidden], stddev=0.1))\\n\",\n    \"  layer3_biases = tf.Variable(tf.constant(1.0, shape=[num_hidden]))\\n\",\n    \"  layer4_weights = tf.Variable(tf.truncated_normal(\\n\",\n    \"      [num_hidden, num_labels], stddev=0.1))\\n\",\n    \"  layer4_biases = tf.Variable(tf.constant(1.0, shape=[num_labels]))\\n\",\n    \"  \\n\",\n    \"  # Model.\\n\",\n    \"  def model(data):\\n\",\n    \"    conv = tf.nn.conv2d(data, layer1_weights, [1, 2, 2, 1], padding='SAME')\\n\",\n    \"    hidden = tf.nn.relu(conv + layer1_biases)\\n\",\n    \"    conv = tf.nn.conv2d(hidden, layer2_weights, [1, 2, 2, 1], padding='SAME')\\n\",\n    \"    hidden = tf.nn.relu(conv + layer2_biases)\\n\",\n    \"    shape = hidden.get_shape().as_list()\\n\",\n    \"    reshape = tf.reshape(hidden, [shape[0], shape[1] * shape[2] * shape[3]])\\n\",\n    \"    hidden = tf.nn.relu(tf.matmul(reshape, layer3_weights) + layer3_biases)\\n\",\n    \"    return tf.matmul(hidden, layer4_weights) + layer4_biases\\n\",\n    \"  \\n\",\n    \"  # Training computation.\\n\",\n    \"  logits = model(tf_train_dataset)\\n\",\n    \"  loss = tf.reduce_mean(\\n\",\n    \"    tf.nn.softmax_cross_entropy_with_logits(logits, tf_train_labels))\\n\",\n    \"    \\n\",\n    \"  # Optimizer.\\n\",\n    \"  optimizer = tf.train.GradientDescentOptimizer(0.05).minimize(loss)\\n\",\n    \"  \\n\",\n    \"  # Predictions for the training, validation, and test data.\\n\",\n    \"  train_prediction = tf.nn.softmax(logits)\\n\",\n    \"  valid_prediction = tf.nn.softmax(model(tf_valid_dataset))\\n\",\n    \"  test_prediction = tf.nn.softmax(model(tf_test_dataset))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"cellView\": \"both\",\n    \"colab\": {\n     \"autoexec\": {\n      \"startup\": false,\n      \"wait_interval\": 0\n     },\n     \"output_extras\": [\n      {\n       \"item_id\": 37\n      }\n     ]\n    },\n    \"colab_type\": \"code\",\n    \"collapsed\": false,\n    \"executionInfo\": {\n     \"elapsed\": 63292,\n     \"status\": \"ok\",\n     \"timestamp\": 1446658966251,\n     \"user\": {\n      \"color\": \"\",\n      \"displayName\": \"\",\n      \"isAnonymous\": false,\n      \"isMe\": true,\n      \"permissionId\": \"\",\n      \"photoUrl\": \"\",\n      \"sessionId\": \"0\",\n      \"userId\": \"\"\n     },\n     \"user_tz\": 480\n    },\n    \"id\": \"noKFb2UovVFR\",\n    \"outputId\": \"28941338-2ef9-4088-8bd1-44295661e628\"\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Initialized\\n\",\n      \"Minibatch loss at step 0 : 3.51275\\n\",\n      \"Minibatch accuracy: 6.2%\\n\",\n      \"Validation accuracy: 12.8%\\n\",\n      \"Minibatch loss at step 50 : 1.48703\\n\",\n      \"Minibatch accuracy: 43.8%\\n\",\n      \"Validation accuracy: 50.4%\\n\",\n      \"Minibatch loss at step 100 : 1.04377\\n\",\n      \"Minibatch accuracy: 68.8%\\n\",\n      \"Validation accuracy: 67.4%\\n\",\n      \"Minibatch loss at step 150 : 0.601682\\n\",\n      \"Minibatch accuracy: 68.8%\\n\",\n      \"Validation accuracy: 73.0%\\n\",\n      \"Minibatch loss at step 200 : 0.898649\\n\",\n      \"Minibatch accuracy: 75.0%\\n\",\n      \"Validation accuracy: 77.8%\\n\",\n      \"Minibatch loss at step 250 : 1.3637\\n\",\n      \"Minibatch accuracy: 56.2%\\n\",\n      \"Validation accuracy: 75.4%\\n\",\n      \"Minibatch loss at step 300 : 1.41968\\n\",\n      \"Minibatch accuracy: 62.5%\\n\",\n      \"Validation accuracy: 76.0%\\n\",\n      \"Minibatch loss at step 350 : 0.300648\\n\",\n      \"Minibatch accuracy: 81.2%\\n\",\n      \"Validation accuracy: 80.2%\\n\",\n      \"Minibatch loss at step 400 : 1.32092\\n\",\n      \"Minibatch accuracy: 56.2%\\n\",\n      \"Validation accuracy: 80.4%\\n\",\n      \"Minibatch loss at step 450 : 0.556701\\n\",\n      \"Minibatch accuracy: 81.2%\\n\",\n      \"Validation accuracy: 79.4%\\n\",\n      \"Minibatch loss at step 500 : 1.65595\\n\",\n      \"Minibatch accuracy: 43.8%\\n\",\n      \"Validation accuracy: 79.6%\\n\",\n      \"Minibatch loss at step 550 : 1.06995\\n\",\n      \"Minibatch accuracy: 75.0%\\n\",\n      \"Validation accuracy: 81.2%\\n\",\n      \"Minibatch loss at step 600 : 0.223684\\n\",\n      \"Minibatch accuracy: 100.0%\\n\",\n      \"Validation accuracy: 82.3%\\n\",\n      \"Minibatch loss at step 650 : 0.619602\\n\",\n      \"Minibatch accuracy: 87.5%\\n\",\n      \"Validation accuracy: 81.8%\\n\",\n      \"Minibatch loss at step 700 : 0.812091\\n\",\n      \"Minibatch accuracy: 75.0%\\n\",\n      \"Validation accuracy: 82.4%\\n\",\n      \"Minibatch loss at step 750 : 0.276302\\n\",\n      \"Minibatch accuracy: 87.5%\\n\",\n      \"Validation accuracy: 82.3%\\n\",\n      \"Minibatch loss at step 800 : 0.450241\\n\",\n      \"Minibatch accuracy: 81.2%\\n\",\n      \"Validation accuracy: 82.3%\\n\",\n      \"Minibatch loss at step 850 : 0.137139\\n\",\n      \"Minibatch accuracy: 93.8%\\n\",\n      \"Validation accuracy: 82.3%\\n\",\n      \"Minibatch loss at step 900 : 0.52664\\n\",\n      \"Minibatch accuracy: 75.0%\\n\",\n      \"Validation accuracy: 82.2%\\n\",\n      \"Minibatch loss at step 950 : 0.623835\\n\",\n      \"Minibatch accuracy: 87.5%\\n\",\n      \"Validation accuracy: 82.1%\\n\",\n      \"Minibatch loss at step 1000 : 0.243114\\n\",\n      \"Minibatch accuracy: 93.8%\\n\",\n      \"Validation accuracy: 82.9%\\n\",\n      \"Test accuracy: 90.0%\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"num_steps = 1001\\n\",\n    \"\\n\",\n    \"with tf.Session(graph=graph) as session:\\n\",\n    \"  tf.global_variables_initializer().run()\\n\",\n    \"  print \\\"Initialized\\\"\\n\",\n    \"  for step in xrange(num_steps):\\n\",\n    \"    offset = (step * batch_size) % (train_labels.shape[0] - batch_size)\\n\",\n    \"    batch_data = train_dataset[offset:(offset + batch_size), :, :, :]\\n\",\n    \"    batch_labels = train_labels[offset:(offset + batch_size), :]\\n\",\n    \"    feed_dict = {tf_train_dataset : batch_data, tf_train_labels : batch_labels}\\n\",\n    \"    _, l, predictions = session.run(\\n\",\n    \"      [optimizer, loss, train_prediction], feed_dict=feed_dict)\\n\",\n    \"    if (step % 50 == 0):\\n\",\n    \"      print \\\"Minibatch loss at step\\\", step, \\\":\\\", l\\n\",\n    \"      print \\\"Minibatch accuracy: %.1f%%\\\" % accuracy(predictions, batch_labels)\\n\",\n    \"      print \\\"Validation accuracy: %.1f%%\\\" % accuracy(\\n\",\n    \"        valid_prediction.eval(), valid_labels)\\n\",\n    \"  print \\\"Test accuracy: %.1f%%\\\" % accuracy(test_prediction.eval(), test_labels)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"KedKkn4EutIK\"\n   },\n   \"source\": [\n    \"---\\n\",\n    \"Problem 1\\n\",\n    \"---------\\n\",\n    \"\\n\",\n    \"The convolutional model above uses convolutions with stride 2 to reduce the dimensionality. Replace the strides a max pooling operation (`nn.max_pool()`) of stride 2 and kernel size 2.\\n\",\n    \"\\n\",\n    \"---\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"klf21gpbAgb-\"\n   },\n   \"source\": [\n    \"---\\n\",\n    \"Problem 2\\n\",\n    \"---------\\n\",\n    \"\\n\",\n    \"Try to get the best performance you can using a convolutional net. Look for example at the classic [LeNet5](http://yann.lecun.com/exdb/lenet/) architecture, adding Dropout, and/or adding learning rate decay.\\n\",\n    \"\\n\",\n    \"---\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"colabVersion\": \"0.3.2\",\n  \"colab_default_view\": {},\n  \"colab_views\": {},\n  \"kernelspec\": {\n   \"display_name\": \"Python 2\",\n   \"language\": \"python\",\n   \"name\": \"python2\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 2\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython2\",\n   \"version\": \"2.7.12\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "deep-learning/tensor-flow-exercises/5_word2vec.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"D7tqLMoKF6uq\"\n   },\n   \"source\": [\n    \"Deep Learning with TensorFlow\\n\",\n    \"=============\\n\",\n    \"\\n\",\n    \"Credits: Forked from [TensorFlow](https://github.com/tensorflow/tensorflow) by Google\\n\",\n    \"\\n\",\n    \"Setup\\n\",\n    \"------------\\n\",\n    \"\\n\",\n    \"Refer to the [setup instructions](https://github.com/donnemartin/data-science-ipython-notebooks/tree/feature/deep-learning/deep-learning/tensor-flow-exercises/README.md).\\n\",\n    \"\\n\",\n    \"Exercise 5\\n\",\n    \"------------\\n\",\n    \"\\n\",\n    \"The goal of this exercise is to train a skip-gram model over [Text8](http://mattmahoney.net/dc/textdata) data.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"cellView\": \"both\",\n    \"colab\": {\n     \"autoexec\": {\n      \"startup\": false,\n      \"wait_interval\": 0\n     }\n    },\n    \"colab_type\": \"code\",\n    \"collapsed\": true,\n    \"id\": \"0K1ZyLn04QZf\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# These are all the modules we'll be using later. Make sure you can import them\\n\",\n    \"# before proceeding further.\\n\",\n    \"import collections\\n\",\n    \"import math\\n\",\n    \"import numpy as np\\n\",\n    \"import os\\n\",\n    \"import random\\n\",\n    \"import tensorflow as tf\\n\",\n    \"import urllib\\n\",\n    \"import zipfile\\n\",\n    \"from matplotlib import pylab\\n\",\n    \"from sklearn.manifold import TSNE\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"aCjPJE944bkV\"\n   },\n   \"source\": [\n    \"Download the data from the source website if necessary.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"cellView\": \"both\",\n    \"colab\": {\n     \"autoexec\": {\n      \"startup\": false,\n      \"wait_interval\": 0\n     },\n     \"output_extras\": [\n      {\n       \"item_id\": 1\n      }\n     ]\n    },\n    \"colab_type\": \"code\",\n    \"collapsed\": false,\n    \"executionInfo\": {\n     \"elapsed\": 14640,\n     \"status\": \"ok\",\n     \"timestamp\": 1445964482948,\n     \"user\": {\n      \"color\": \"#1FA15D\",\n      \"displayName\": \"Vincent Vanhoucke\",\n      \"isAnonymous\": false,\n      \"isMe\": true,\n      \"permissionId\": \"05076109866853157986\",\n      \"photoUrl\": \"//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg\",\n      \"sessionId\": \"2f1ffade4c9f20de\",\n      \"userId\": \"102167687554210253930\"\n     },\n     \"user_tz\": 420\n    },\n    \"id\": \"RJ-o3UBUFtCw\",\n    \"outputId\": \"c4ec222c-80b5-4298-e635-93ca9f79c3b7\"\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Found and verified text8.zip\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"url = 'http://mattmahoney.net/dc/'\\n\",\n    \"\\n\",\n    \"def maybe_download(filename, expected_bytes):\\n\",\n    \"  \\\"\\\"\\\"Download a file if not present, and make sure it's the right size.\\\"\\\"\\\"\\n\",\n    \"  if not os.path.exists(filename):\\n\",\n    \"    filename, _ = urllib.urlretrieve(url + filename, filename)\\n\",\n    \"  statinfo = os.stat(filename)\\n\",\n    \"  if statinfo.st_size == expected_bytes:\\n\",\n    \"    print 'Found and verified', filename\\n\",\n    \"  else:\\n\",\n    \"    print statinfo.st_size\\n\",\n    \"    raise Exception(\\n\",\n    \"      'Failed to verify ' + filename + '. Can you get to it with a browser?')\\n\",\n    \"  return filename\\n\",\n    \"\\n\",\n    \"filename = maybe_download('text8.zip', 31344016)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"Zqz3XiqI4mZT\"\n   },\n   \"source\": [\n    \"Read the data into a string.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"cellView\": \"both\",\n    \"colab\": {\n     \"autoexec\": {\n      \"startup\": false,\n      \"wait_interval\": 0\n     },\n     \"output_extras\": [\n      {\n       \"item_id\": 1\n      }\n     ]\n    },\n    \"colab_type\": \"code\",\n    \"collapsed\": false,\n    \"executionInfo\": {\n     \"elapsed\": 28844,\n     \"status\": \"ok\",\n     \"timestamp\": 1445964497165,\n     \"user\": {\n      \"color\": \"#1FA15D\",\n      \"displayName\": \"Vincent Vanhoucke\",\n      \"isAnonymous\": false,\n      \"isMe\": true,\n      \"permissionId\": \"05076109866853157986\",\n      \"photoUrl\": \"//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg\",\n      \"sessionId\": \"2f1ffade4c9f20de\",\n      \"userId\": \"102167687554210253930\"\n     },\n     \"user_tz\": 420\n    },\n    \"id\": \"Mvf09fjugFU_\",\n    \"outputId\": \"e3a928b4-1645-4fe8-be17-fcf47de5716d\"\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Data size 17005207\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"def read_data(filename):\\n\",\n    \"  f = zipfile.ZipFile(filename)\\n\",\n    \"  for name in f.namelist():\\n\",\n    \"    return f.read(name).split()\\n\",\n    \"  f.close()\\n\",\n    \"  \\n\",\n    \"words = read_data(filename)\\n\",\n    \"print 'Data size', len(words)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"Zdw6i4F8glpp\"\n   },\n   \"source\": [\n    \"Build the dictionary and replace rare words with UNK token.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"cellView\": \"both\",\n    \"colab\": {\n     \"autoexec\": {\n      \"startup\": false,\n      \"wait_interval\": 0\n     },\n     \"output_extras\": [\n      {\n       \"item_id\": 1\n      }\n     ]\n    },\n    \"colab_type\": \"code\",\n    \"collapsed\": false,\n    \"executionInfo\": {\n     \"elapsed\": 28849,\n     \"status\": \"ok\",\n     \"timestamp\": 1445964497178,\n     \"user\": {\n      \"color\": \"#1FA15D\",\n      \"displayName\": \"Vincent Vanhoucke\",\n      \"isAnonymous\": false,\n      \"isMe\": true,\n      \"permissionId\": \"05076109866853157986\",\n      \"photoUrl\": \"//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg\",\n      \"sessionId\": \"2f1ffade4c9f20de\",\n      \"userId\": \"102167687554210253930\"\n     },\n     \"user_tz\": 420\n    },\n    \"id\": \"gAL1EECXeZsD\",\n    \"outputId\": \"3fb4ecd1-df67-44b6-a2dc-2291730970b2\"\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Most common words (+UNK) [['UNK', 418391], ('the', 1061396), ('of', 593677), ('and', 416629), ('one', 411764)]\\n\",\n      \"Sample data [5243, 3083, 12, 6, 195, 2, 3136, 46, 59, 156]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"vocabulary_size = 50000\\n\",\n    \"\\n\",\n    \"def build_dataset(words):\\n\",\n    \"  count = [['UNK', -1]]\\n\",\n    \"  count.extend(collections.Counter(words).most_common(vocabulary_size - 1))\\n\",\n    \"  dictionary = dict()\\n\",\n    \"  for word, _ in count:\\n\",\n    \"    dictionary[word] = len(dictionary)\\n\",\n    \"  data = list()\\n\",\n    \"  unk_count = 0\\n\",\n    \"  for word in words:\\n\",\n    \"    if word in dictionary:\\n\",\n    \"      index = dictionary[word]\\n\",\n    \"    else:\\n\",\n    \"      index = 0  # dictionary['UNK']\\n\",\n    \"      unk_count = unk_count + 1\\n\",\n    \"    data.append(index)\\n\",\n    \"  count[0][1] = unk_count\\n\",\n    \"  reverse_dictionary = dict(zip(dictionary.values(), dictionary.keys())) \\n\",\n    \"  return data, count, dictionary, reverse_dictionary\\n\",\n    \"\\n\",\n    \"data, count, dictionary, reverse_dictionary = build_dataset(words)\\n\",\n    \"print 'Most common words (+UNK)', count[:5]\\n\",\n    \"print 'Sample data', data[:10]\\n\",\n    \"del words  # Hint to reduce memory.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"lFwoyygOmWsL\"\n   },\n   \"source\": [\n    \"Function to generate a training batch for the skip-gram model.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"cellView\": \"both\",\n    \"colab\": {\n     \"autoexec\": {\n      \"startup\": false,\n      \"wait_interval\": 0\n     },\n     \"output_extras\": [\n      {\n       \"item_id\": 1\n      }\n     ]\n    },\n    \"colab_type\": \"code\",\n    \"collapsed\": false,\n    \"executionInfo\": {\n     \"elapsed\": 113,\n     \"status\": \"ok\",\n     \"timestamp\": 1445964901989,\n     \"user\": {\n      \"color\": \"#1FA15D\",\n      \"displayName\": \"Vincent Vanhoucke\",\n      \"isAnonymous\": false,\n      \"isMe\": true,\n      \"permissionId\": \"05076109866853157986\",\n      \"photoUrl\": \"//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg\",\n      \"sessionId\": \"2f1ffade4c9f20de\",\n      \"userId\": \"102167687554210253930\"\n     },\n     \"user_tz\": 420\n    },\n    \"id\": \"w9APjA-zmfjV\",\n    \"outputId\": \"67cccb02-cdaf-4e47-d489-43bcc8d57bb8\"\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \" 3083 -> 5243\\n\",\n      \"originated -> anarchism\\n\",\n      \"3083 -> 12\\n\",\n      \"originated -> as\\n\",\n      \"12 -> 3083\\n\",\n      \"as -> originated\\n\",\n      \"12 -> 6\\n\",\n      \"as -> a\\n\",\n      \"6 -> 12\\n\",\n      \"a -> as\\n\",\n      \"6 -> 195\\n\",\n      \"a -> term\\n\",\n      \"195 -> 6\\n\",\n      \"term -> a\\n\",\n      \"195 -> 2\\n\",\n      \"term -> of\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"data_index = 0\\n\",\n    \"\\n\",\n    \"def generate_batch(batch_size, num_skips, skip_window):\\n\",\n    \"  global data_index\\n\",\n    \"  assert batch_size % num_skips == 0\\n\",\n    \"  assert num_skips <= 2 * skip_window\\n\",\n    \"  batch = np.ndarray(shape=(batch_size), dtype=np.int32)\\n\",\n    \"  labels = np.ndarray(shape=(batch_size, 1), dtype=np.int32)\\n\",\n    \"  span = 2 * skip_window + 1 # [ skip_window target skip_window ]\\n\",\n    \"  buffer = collections.deque(maxlen=span)\\n\",\n    \"  for _ in range(span):\\n\",\n    \"    buffer.append(data[data_index])\\n\",\n    \"    data_index = (data_index + 1) % len(data)\\n\",\n    \"  for i in range(batch_size / num_skips):\\n\",\n    \"    target = skip_window  # target label at the center of the buffer\\n\",\n    \"    targets_to_avoid = [ skip_window ]\\n\",\n    \"    for j in range(num_skips):\\n\",\n    \"      while target in targets_to_avoid:\\n\",\n    \"        target = random.randint(0, span - 1)\\n\",\n    \"      targets_to_avoid.append(target)\\n\",\n    \"      batch[i * num_skips + j] = buffer[skip_window]\\n\",\n    \"      labels[i * num_skips + j, 0] = buffer[target]\\n\",\n    \"    buffer.append(data[data_index])\\n\",\n    \"    data_index = (data_index + 1) % len(data)\\n\",\n    \"  return batch, labels\\n\",\n    \"\\n\",\n    \"batch, labels = generate_batch(batch_size=8, num_skips=2, skip_window=1)\\n\",\n    \"for i in range(8):\\n\",\n    \"  print batch[i], '->', labels[i, 0]\\n\",\n    \"  print reverse_dictionary[batch[i]], '->', reverse_dictionary[labels[i, 0]]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"Ofd1MbBuwiva\"\n   },\n   \"source\": [\n    \"Train a skip-gram model.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"cellView\": \"both\",\n    \"colab\": {\n     \"autoexec\": {\n      \"startup\": false,\n      \"wait_interval\": 0\n     }\n    },\n    \"colab_type\": \"code\",\n    \"collapsed\": true,\n    \"id\": \"8pQKsV4Vwlzy\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"batch_size = 128\\n\",\n    \"embedding_size = 128 # Dimension of the embedding vector.\\n\",\n    \"skip_window = 1 # How many words to consider left and right.\\n\",\n    \"num_skips = 2 # How many times to reuse an input to generate a label.\\n\",\n    \"# We pick a random validation set to sample nearest neighbors. here we limit the\\n\",\n    \"# validation samples to the words that have a low numeric ID, which by\\n\",\n    \"# construction are also the most frequent. \\n\",\n    \"valid_size = 16 # Random set of words to evaluate similarity on.\\n\",\n    \"valid_window = 100 # Only pick dev samples in the head of the distribution.\\n\",\n    \"valid_examples = np.array(random.sample(xrange(valid_window), valid_size))\\n\",\n    \"num_sampled = 64 # Number of negative examples to sample.\\n\",\n    \"\\n\",\n    \"graph = tf.Graph()\\n\",\n    \"\\n\",\n    \"with graph.as_default():\\n\",\n    \"\\n\",\n    \"  # Input data.\\n\",\n    \"  train_dataset = tf.placeholder(tf.int32, shape=[batch_size])\\n\",\n    \"  train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1])\\n\",\n    \"  valid_dataset = tf.constant(valid_examples, dtype=tf.int32)\\n\",\n    \"  \\n\",\n    \"  # Variables.\\n\",\n    \"  embeddings = tf.Variable(\\n\",\n    \"    tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0))\\n\",\n    \"  softmax_weights = tf.Variable(\\n\",\n    \"    tf.truncated_normal([vocabulary_size, embedding_size],\\n\",\n    \"                         stddev=1.0 / math.sqrt(embedding_size)))\\n\",\n    \"  softmax_biases = tf.Variable(tf.zeros([vocabulary_size]))\\n\",\n    \"  \\n\",\n    \"  # Model.\\n\",\n    \"  # Look up embeddings for inputs.\\n\",\n    \"  embed = tf.nn.embedding_lookup(embeddings, train_dataset)\\n\",\n    \"  # Compute the softmax loss, using a sample of the negative labels each time.\\n\",\n    \"  loss = tf.reduce_mean(\\n\",\n    \"    tf.nn.sampled_softmax_loss(softmax_weights, softmax_biases, embed,\\n\",\n    \"                               train_labels, num_sampled, vocabulary_size))\\n\",\n    \"\\n\",\n    \"  # Optimizer.\\n\",\n    \"  optimizer = tf.train.AdagradOptimizer(1.0).minimize(loss)\\n\",\n    \"  \\n\",\n    \"  # Compute the similarity between minibatch examples and all embeddings.\\n\",\n    \"  # We use the cosine distance:\\n\",\n    \"  norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1, keep_dims=True))\\n\",\n    \"  normalized_embeddings = embeddings / norm\\n\",\n    \"  valid_embeddings = tf.nn.embedding_lookup(\\n\",\n    \"    normalized_embeddings, valid_dataset)\\n\",\n    \"  similarity = tf.matmul(valid_embeddings, tf.transpose(normalized_embeddings))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"cellView\": \"both\",\n    \"colab\": {\n     \"autoexec\": {\n      \"startup\": false,\n      \"wait_interval\": 0\n     },\n     \"output_extras\": [\n      {\n       \"item_id\": 23\n      },\n      {\n       \"item_id\": 48\n      },\n      {\n       \"item_id\": 61\n      }\n     ]\n    },\n    \"colab_type\": \"code\",\n    \"collapsed\": false,\n    \"executionInfo\": {\n     \"elapsed\": 436189,\n     \"status\": \"ok\",\n     \"timestamp\": 1445965429787,\n     \"user\": {\n      \"color\": \"#1FA15D\",\n      \"displayName\": \"Vincent Vanhoucke\",\n      \"isAnonymous\": false,\n      \"isMe\": true,\n      \"permissionId\": \"05076109866853157986\",\n      \"photoUrl\": \"//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg\",\n      \"sessionId\": \"2f1ffade4c9f20de\",\n      \"userId\": \"102167687554210253930\"\n     },\n     \"user_tz\": 420\n    },\n    \"id\": \"1bQFGceBxrWW\",\n    \"outputId\": \"5ebd6d9a-33c6-4bcd-bf6d-252b0b6055e4\"\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Initialized\\n\",\n      \"Average loss at step 0 : 8.58149623871\\n\",\n      \"Nearest to been: unfavourably, marmara, ancestral, legal, bogart, glossaries, worst, rooms,\\n\",\n      \"Nearest to time: conformist, strawberries, sindhi, waterfall, xia, nominates, psp, sensitivity,\\n\",\n      \"Nearest to over: overlord, panda, golden, semigroup, rawlings, involved, shreveport, handling,\\n\",\n      \"Nearest to not: hymenoptera, reintroducing, lamiaceae, because, davao, omnipotent, combustion, debilitating,\\n\",\n      \"Nearest to three: catalog, koza, gn, braque, holstein, postgresql, luddite, justine,\\n\",\n      \"Nearest to if: chilled, vince, fiddler, represented, sandinistas, happiness, lya, glands,\\n\",\n      \"Nearest to there: coast, photosynthetic, kimmei, legally, inner, illyricum, formats, fullmetal,\\n\",\n      \"Nearest to between: chuvash, prinz, suitability, wolfe, guideline, computability, diminutive, paulo,\\n\",\n      \"Nearest to from: tanganyika, workshop, elphinstone, spearhead, resurrected, kevlar, shangri, loves,\\n\",\n      \"Nearest to state: sextus, wuppertal, glaring, inches, unrounded, courageous, adler, connie,\\n\",\n      \"Nearest to on: gino, phocas, rhine, jg, macrocosm, jackass, jays, theorie,\\n\",\n      \"Nearest to and: standings, towed, reyes, willard, equality, juggling, wladislaus, faked,\\n\",\n      \"Nearest to eight: gresham, dogg, moko, tennis, superseded, telegraphy, scramble, vinod,\\n\",\n      \"Nearest to they: prisons, divisor, coder, ribeira, willingness, factional, nne, lotta,\\n\",\n      \"Nearest to more: blues, fur, sterling, tangier, khwarizmi, discouraged, cal, deicide,\\n\",\n      \"Nearest to other: enemies, bogged, brassicaceae, lascaux, dispense, alexandrians, crimea, dou,\\n\",\n      \"Average loss at step 2000 : 4.39983723116\\n\",\n      \"Average loss at step 4000 : 3.86921076906\\n\",\n      \"Average loss at step 6000 : 3.72542127335\\n\",\n      \"Average loss at step 8000 : 3.57835536212\\n\",\n      \"Average loss at step 10000 : 3.61056993055\\n\",\n      \"Nearest to been: glossaries, legal, unfavourably, be, hadad, wore, scarcity, were,\\n\",\n      \"Nearest to time: strawberries, conformist, gleichschaltung, waterfall, molality, nominates, baal, dole,\\n\",\n      \"Nearest to over: golden, semigroup, catus, motorways, brick, shehri, mussolini, overlord,\\n\",\n      \"Nearest to not: hinayana, it, often, they, boots, also, noaa, lindsey,\\n\",\n      \"Nearest to three: four, seven, six, five, nine, eight, two, zero,\\n\",\n      \"Nearest to if: glands, euros, wallpaper, redefine, toho, confuse, unsound, shepherd,\\n\",\n      \"Nearest to there: it, they, fullmetal, pace, legally, harpsichord, mma, bug,\\n\",\n      \"Nearest to between: chuvash, wandering, from, kirsch, pursuant, eurocents, suitability, jackie,\\n\",\n      \"Nearest to from: into, in, workshop, to, at, misogynist, elphinstone, spearhead,\\n\",\n      \"Nearest to state: sextus, glaring, connie, adler, esoteric, didactic, handedness, presidents,\\n\",\n      \"Nearest to on: in, at, for, ruminants, wakefulness, torrey, foley, gino,\\n\",\n      \"Nearest to and: or, who, but, zelda, of, for, thirst, chisel,\\n\",\n      \"Nearest to eight: nine, six, seven, five, four, three, zero, two,\\n\",\n      \"Nearest to they: he, prisons, there, we, hydrate, it, not, cumbersome,\\n\",\n      \"Nearest to more: skye, blues, trypomastigotes, deicide, most, readable, used, sterling,\\n\",\n      \"Nearest to other: trochaic, hush, surveyors, joachim, differentiation, attackers, reverence, attestation,\\n\",\n      \"Average loss at step 12000 : 3.66169466591\\n\",\n      \"Average loss at step 14000 : 3.60342905837\\n\",\n      \"Average loss at step 16000 : 3.57761328053\\n\",\n      \"Average loss at step 18000 : 3.57667332476\\n\",\n      \"Average loss at step 20000 : 3.53310145146\\n\",\n      \"Nearest to been: be, become, was, hadad, unfavourably, were, wore, partido,\\n\",\n      \"Nearest to time: gleichschaltung, strawberries, year, nominates, conformist, etch, admittedly, treasuries,\\n\",\n      \"Nearest to over: golden, semigroup, motorways, rawlings, triangle, trey, ustawa, mattingly,\\n\",\n      \"Nearest to not: they, boots, often, dieppe, still, hinayana, nearly, be,\\n\",\n      \"Nearest to three: two, four, five, seven, eight, six, nine, one,\\n\",\n      \"Nearest to if: wallpaper, euros, before, toho, unsound, so, bg, pfc,\\n\",\n      \"Nearest to there: they, it, he, usually, which, we, not, transactions,\\n\",\n      \"Nearest to between: from, with, about, near, reactance, eurocents, wandering, voltaire,\\n\",\n      \"Nearest to from: into, workshop, by, between, in, on, elphinstone, under,\\n\",\n      \"Nearest to state: glaring, esoteric, succeeding, sextus, vorarlberg, presidents, depends, connie,\\n\",\n      \"Nearest to on: in, at, upon, during, from, janis, foley, nubian,\\n\",\n      \"Nearest to and: or, thirst, but, where, s, who, pfaff, including,\\n\",\n      \"Nearest to eight: nine, seven, six, five, four, three, zero, one,\\n\",\n      \"Nearest to they: there, he, we, not, it, you, prisons, who,\\n\",\n      \"Nearest to more: less, most, deicide, skye, trypomastigotes, interventionism, toed, drummond,\\n\",\n      \"Nearest to other: such, joachim, hush, attackers, surveyors, trochaic, differentiation, reverence,\\n\",\n      \"Average loss at step 22000 : 3.59519316927\\n\",\n      \"Average loss at step 24000 : 3.55378576797\\n\",\n      \"Average loss at step 26000 : 3.56455037558\\n\",\n      \"Average loss at step 28000 : 3.5040882225\\n\",\n      \"Average loss at step 30000 : 3.39208897972\\n\",\n      \"Nearest to been: become, be, were, was, spotless, hadad, by, hausdorff,\\n\",\n      \"Nearest to time: gleichschaltung, year, day, nominates, jesus, strawberries, way, admittedly,\\n\",\n      \"Nearest to over: golden, semigroup, motorways, rawlings, interventionism, counternarcotics, adaption, brick,\\n\",\n      \"Nearest to not: often, they, it, never, still, nor, boots, pki,\\n\",\n      \"Nearest to three: four, six, two, eight, five, seven, nine, zero,\\n\",\n      \"Nearest to if: when, before, so, should, toho, where, bg, wallpaper,\\n\",\n      \"Nearest to there: they, it, which, usually, he, that, also, now,\\n\",\n      \"Nearest to between: with, from, in, panasonic, presupposes, churchmen, hijacking, where,\\n\",\n      \"Nearest to from: into, elphinstone, workshop, between, through, speculates, sosa, in,\\n\",\n      \"Nearest to state: esoteric, glaring, presidents, vorarlberg, atmosphere, succeeding, lute, connie,\\n\",\n      \"Nearest to on: upon, in, janis, during, torrey, against, infield, catalans,\\n\",\n      \"Nearest to and: or, thirst, in, but, of, sobib, cleaves, including,\\n\",\n      \"Nearest to eight: nine, six, four, seven, three, zero, five, one,\\n\",\n      \"Nearest to they: we, there, he, you, it, these, who, i,\\n\",\n      \"Nearest to more: less, most, deicide, faster, toed, very, skye, tonic,\\n\",\n      \"Nearest to other: different, attackers, joachim, various, such, many, differentiation, these,\\n\",\n      \"Average loss at step 32000 : 3.49501452419\\n\",\n      \"Average loss at step 34000 : 3.48593705952\\n\",\n      \"Average loss at step 36000 : 3.50112806576\\n\",\n      \"Average loss at step 38000 : 3.49244426501\\n\",\n      \"Average loss at step 40000 : 3.3890105716\\n\",\n      \"Nearest to been: become, be, were, was, jolie, hausdorff, spotless, had,\\n\",\n      \"Nearest to time: year, way, gleichschaltung, period, day, stanislav, stage, outcome,\\n\",\n      \"Nearest to over: through, semigroup, rawlings, golden, about, brick, on, motorways,\\n\",\n      \"Nearest to not: they, radiated, never, pki, still, omnipotent, hinayana, really,\\n\",\n      \"Nearest to three: four, six, five, two, seven, eight, one, nine,\\n\",\n      \"Nearest to if: when, before, where, then, bg, because, can, should,\\n\",\n      \"Nearest to there: they, it, he, usually, this, typically, still, often,\\n\",\n      \"Nearest to between: with, in, from, about, against, churchmen, johansen, presupposes,\\n\",\n      \"Nearest to from: into, through, elphinstone, in, workshop, between, suing, under,\\n\",\n      \"Nearest to state: esoteric, presidents, atmosphere, vorarlberg, lute, succeeding, glaring, didactic,\\n\",\n      \"Nearest to on: upon, at, in, during, unitarians, under, catalans, batavians,\\n\",\n      \"Nearest to and: or, but, s, incapacitation, including, while, of, which,\\n\",\n      \"Nearest to eight: nine, six, seven, four, five, three, one, two,\\n\",\n      \"Nearest to they: we, he, there, you, she, i, not, it,\\n\",\n      \"Nearest to more: less, most, deicide, toed, greater, faster, quite, longer,\\n\",\n      \"Nearest to other: various, different, attackers, joachim, clutter, nz, trochaic, apulia,\\n\",\n      \"Average loss at step 42000 : 3.45294014364\\n\",\n      \"Average loss at step 44000 : 3.47660055941\\n\",\n      \"Average loss at step 46000 : 3.47458503014\\n\",\n      \"Average loss at step 48000 : 3.47261548793\\n\",\n      \"Average loss at step 50000 : 3.45390708435\\n\",\n      \"Nearest to been: become, be, had, was, were, hausdorff, prem, remained,\\n\",\n      \"Nearest to time: way, year, period, stv, day, gleichschaltung, stage, outcome,\\n\",\n      \"Nearest to over: through, golden, semigroup, about, brick, counternarcotics, theremin, mattingly,\\n\",\n      \"Nearest to not: they, still, never, really, sometimes, it, kiwifruit, nearly,\\n\",\n      \"Nearest to three: five, four, six, seven, two, eight, one, nine,\\n\",\n      \"Nearest to if: when, before, where, because, connexion, though, so, whether,\\n\",\n      \"Nearest to there: they, it, he, this, now, often, usually, still,\\n\",\n      \"Nearest to between: with, from, fashioned, churchmen, panasonic, explores, within, racial,\\n\",\n      \"Nearest to from: into, through, under, elphinstone, between, workshop, circumpolar, idiom,\\n\",\n      \"Nearest to state: atmosphere, vorarlberg, esoteric, presidents, madhya, majority, moulin, bowmen,\\n\",\n      \"Nearest to on: upon, in, catalans, tezuka, minotaurs, wakefulness, batavians, guglielmo,\\n\",\n      \"Nearest to and: or, but, thirst, signifier, which, however, including, unattractive,\\n\",\n      \"Nearest to eight: six, nine, seven, five, four, three, zero, two,\\n\",\n      \"Nearest to they: we, there, he, you, it, she, these, not,\\n\",\n      \"Nearest to more: less, most, quite, very, further, faster, toed, deicide,\\n\",\n      \"Nearest to other: various, different, many, attackers, are, joachim, nihilo, reject,\\n\",\n      \"Average loss at step 52000 : 3.43597227755\\n\",\n      \"Average loss at step 54000 : 3.25126817495\\n\",\n      \"Average loss at step 56000 : 3.35102432287\\n\",\n      \"Average loss at step 58000 : 3.44654818082\\n\",\n      \"Average loss at step 60000 : 3.4287913968\\n\",\n      \"Nearest to been: become, be, was, prem, had, remained, hadad, stanislavsky,\\n\",\n      \"Nearest to time: year, way, period, stv, barely, name, stage, restoring,\\n\",\n      \"Nearest to over: about, through, golden, adaption, counternarcotics, up, mattingly, brick,\\n\",\n      \"Nearest to not: still, never, nor, kiwifruit, they, nearly, therefore, rarely,\\n\",\n      \"Nearest to three: two, five, four, six, seven, eight, one, nine,\\n\",\n      \"Nearest to if: when, though, before, where, although, because, can, could,\\n\",\n      \"Nearest to there: they, it, he, still, she, we, this, often,\\n\",\n      \"Nearest to between: with, from, churchmen, among, ethical, within, vma, panasonic,\\n\",\n      \"Nearest to from: through, into, under, during, between, in, suing, across,\\n\",\n      \"Nearest to state: atmosphere, infringe, madhya, vorarlberg, government, bowmen, vargas, republic,\\n\",\n      \"Nearest to on: upon, through, within, ridiculous, janis, in, under, over,\\n\",\n      \"Nearest to and: or, while, including, but, of, like, whose, bannister,\\n\",\n      \"Nearest to eight: nine, six, five, four, seven, zero, three, two,\\n\",\n      \"Nearest to they: we, there, you, he, it, these, she, prisons,\\n\",\n      \"Nearest to more: less, most, quite, further, toed, very, faster, rather,\\n\",\n      \"Nearest to other: different, various, many, nihilo, these, amour, including, screenplays,\\n\",\n      \"Average loss at step 62000 : 3.38358767056\\n\",\n      \"Average loss at step 64000 : 3.41693099326\\n\",\n      \"Average loss at step 66000 : 3.39588000977\\n\",\n      \"Average loss at step 68000 : 3.35567189544\\n\",\n      \"Average loss at step 70000 : 3.38878934443\\n\",\n      \"Nearest to been: become, be, was, prem, remained, were, being, discounts,\\n\",\n      \"Nearest to time: year, way, day, period, barely, ethos, stage, reason,\\n\",\n      \"Nearest to over: about, through, fortunately, semigroup, theremin, off, loudest, up,\\n\",\n      \"Nearest to not: still, nor, never, they, actually, nearly, unelected, therefore,\\n\",\n      \"Nearest to three: five, two, four, six, seven, eight, nine, zero,\\n\",\n      \"Nearest to if: when, though, before, where, because, then, after, since,\\n\",\n      \"Nearest to there: they, it, he, often, she, we, usually, still,\\n\",\n      \"Nearest to between: among, with, within, from, ethical, churchmen, racial, prentice,\\n\",\n      \"Nearest to from: through, into, within, during, under, until, between, across,\\n\",\n      \"Nearest to state: city, atmosphere, desks, surrounding, preservation, bohr, principal, republic,\\n\",\n      \"Nearest to on: upon, tezuka, through, within, wakefulness, catalans, at, ingeborg,\\n\",\n      \"Nearest to and: or, but, while, including, thirst, jerzy, massing, abadan,\\n\",\n      \"Nearest to eight: seven, six, nine, five, four, three, two, zero,\\n\",\n      \"Nearest to they: we, you, he, there, she, it, prisons, who,\\n\",\n      \"Nearest to more: less, most, quite, very, faster, smaller, further, larger,\\n\",\n      \"Nearest to other: various, different, some, screenplays, lab, many, including, debugging,\\n\",\n      \"Average loss at step 72000 : 3.41103189731\\n\",\n      \"Average loss at step 74000 : 3.44926435578\\n\",\n      \"Average loss at step 76000 : 3.4423020488\\n\",\n      \"Average loss at step 78000 : 3.41976813722\\n\",\n      \"Average loss at step 80000 : 3.39511853886\\n\",\n      \"Nearest to been: become, be, remained, was, grown, were, prem, already,\\n\",\n      \"Nearest to time: year, way, period, reason, barely, distance, stage, day,\\n\",\n      \"Nearest to over: about, fortunately, through, semigroup, further, mattingly, rawlings, golden,\\n\",\n      \"Nearest to not: still, they, nor, never, we, kiwifruit, noaa, really,\\n\",\n      \"Nearest to three: five, two, seven, four, eight, six, nine, zero,\\n\",\n      \"Nearest to if: when, where, though, before, since, because, although, follows,\\n\",\n      \"Nearest to there: they, it, he, we, she, still, typically, actually,\\n\",\n      \"Nearest to between: with, among, within, in, racial, around, from, serapeum,\\n\",\n      \"Nearest to from: into, through, in, within, under, using, during, towards,\\n\",\n      \"Nearest to state: city, atmosphere, ferro, vorarlberg, surrounding, republic, madhya, national,\\n\",\n      \"Nearest to on: upon, poll, in, from, tezuka, janis, through, within,\\n\",\n      \"Nearest to and: or, but, including, while, s, which, thirst, although,\\n\",\n      \"Nearest to eight: nine, seven, six, five, four, three, zero, two,\\n\",\n      \"Nearest to they: we, you, there, he, she, it, these, not,\\n\",\n      \"Nearest to more: less, most, smaller, very, faster, quite, rather, larger,\\n\",\n      \"Nearest to other: various, different, joachim, including, theos, smaller, individual, screenplays,\\n\",\n      \"Average loss at step 82000 : 3.40933967865\\n\",\n      \"Average loss at step 84000 : 3.41618054378\\n\",\n      \"Average loss at step 86000 : 3.31485116804\\n\",\n      \"Average loss at step 88000 : 3.37068593091\\n\",\n      \"Average loss at step 90000 : 3.2785516749\\n\",\n      \"Nearest to been: become, be, was, prem, remained, grown, recently, already,\\n\",\n      \"Nearest to time: year, way, period, day, barely, battle, buds, name,\\n\",\n      \"Nearest to over: through, about, fortunately, off, theremin, semigroup, extraterrestrial, mattingly,\\n\",\n      \"Nearest to not: nor, still, never, otherwise, generally, separately, gown, hydrate,\\n\",\n      \"Nearest to three: four, five, six, two, eight, seven, nine, zero,\\n\",\n      \"Nearest to if: when, where, before, though, because, since, then, while,\\n\",\n      \"Nearest to there: they, it, he, we, she, still, typically, fiorello,\\n\",\n      \"Nearest to between: with, among, within, from, churchmen, prentice, racial, panasonic,\\n\",\n      \"Nearest to from: through, into, across, during, towards, until, at, within,\\n\",\n      \"Nearest to state: bohr, city, atmosphere, ferro, bowmen, republic, retaliation, vorarlberg,\\n\",\n      \"Nearest to on: upon, in, tezuka, at, during, within, via, catalans,\\n\",\n      \"Nearest to and: or, including, but, while, like, thirst, with, schuman,\\n\",\n      \"Nearest to eight: seven, nine, six, five, four, three, zero, two,\\n\",\n      \"Nearest to they: we, there, he, you, she, it, prisons, these,\\n\",\n      \"Nearest to more: less, most, very, faster, larger, quite, smaller, better,\\n\",\n      \"Nearest to other: different, various, tamara, prosthetic, including, individual, failing, restaurants,\\n\",\n      \"Average loss at step 92000 : 3.40355363208\\n\",\n      \"Average loss at step 94000 : 3.35647508007\\n\",\n      \"Average loss at step 96000 : 3.34374570692\\n\",\n      \"Average loss at step 98000 : 3.4230104093\\n\",\n      \"Average loss at step 100000 : 3.36909827\\n\",\n      \"Nearest to been: become, be, grown, was, being, already, remained, prem,\\n\",\n      \"Nearest to time: way, year, day, period, years, days, mothersbaugh, separators,\\n\",\n      \"Nearest to over: through, about, semigroup, further, fortunately, off, into, theremin,\\n\",\n      \"Nearest to not: never, nor, still, dieppe, really, unelected, actually, now,\\n\",\n      \"Nearest to three: four, two, five, seven, six, eight, nine, zero,\\n\",\n      \"Nearest to if: when, though, where, before, is, abe, then, follows,\\n\",\n      \"Nearest to there: they, it, he, we, still, she, typically, often,\\n\",\n      \"Nearest to between: within, with, among, churchmen, around, explores, from, reactance,\\n\",\n      \"Nearest to from: into, through, within, across, in, between, using, workshop,\\n\",\n      \"Nearest to state: atmosphere, bohr, national, ferro, germ, desks, city, unpaid,\\n\",\n      \"Nearest to on: upon, in, within, tezuka, janis, batavians, about, macrocosm,\\n\",\n      \"Nearest to and: or, but, purview, thirst, sukkot, epr, including, honesty,\\n\",\n      \"Nearest to eight: seven, nine, six, four, five, three, zero, one,\\n\",\n      \"Nearest to they: we, there, you, he, she, prisons, it, these,\\n\",\n      \"Nearest to more: less, most, very, quite, faster, larger, rather, smaller,\\n\",\n      \"Nearest to other: various, different, tamara, theos, some, cope, many, others,\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"num_steps = 100001\\n\",\n    \"\\n\",\n    \"with tf.Session(graph=graph) as session:\\n\",\n    \"  tf.global_variables_initializer().run()\\n\",\n    \"  print \\\"Initialized\\\"\\n\",\n    \"  average_loss = 0\\n\",\n    \"  for step in xrange(num_steps):\\n\",\n    \"    batch_data, batch_labels = generate_batch(\\n\",\n    \"      batch_size, num_skips, skip_window)\\n\",\n    \"    feed_dict = {train_dataset : batch_data, train_labels : batch_labels}\\n\",\n    \"    _, l = session.run([optimizer, loss], feed_dict=feed_dict)\\n\",\n    \"    average_loss += l\\n\",\n    \"    if step % 2000 == 0:\\n\",\n    \"      if step > 0:\\n\",\n    \"        average_loss = average_loss / 2000\\n\",\n    \"      # The average loss is an estimate of the loss over the last 2000 batches.\\n\",\n    \"      print \\\"Average loss at step\\\", step, \\\":\\\", average_loss\\n\",\n    \"      average_loss = 0\\n\",\n    \"    # note that this is expensive (~20% slowdown if computed every 500 steps)\\n\",\n    \"    if step % 10000 == 0:\\n\",\n    \"      sim = similarity.eval()\\n\",\n    \"      for i in xrange(valid_size):\\n\",\n    \"        valid_word = reverse_dictionary[valid_examples[i]]\\n\",\n    \"        top_k = 8 # number of nearest neighbors\\n\",\n    \"        nearest = (-sim[i, :]).argsort()[1:top_k+1]\\n\",\n    \"        log = \\\"Nearest to %s:\\\" % valid_word\\n\",\n    \"        for k in xrange(top_k):\\n\",\n    \"          close_word = reverse_dictionary[nearest[k]]\\n\",\n    \"          log = \\\"%s %s,\\\" % (log, close_word)\\n\",\n    \"        print log\\n\",\n    \"  final_embeddings = normalized_embeddings.eval()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"cellView\": \"both\",\n    \"colab\": {\n     \"autoexec\": {\n      \"startup\": false,\n      \"wait_interval\": 0\n     }\n    },\n    \"colab_type\": \"code\",\n    \"collapsed\": true,\n    \"id\": \"jjJXYA_XzV79\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"num_points = 400\\n\",\n    \"\\n\",\n    \"tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000)\\n\",\n    \"two_d_embeddings = tsne.fit_transform(final_embeddings[1:num_points+1, :])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"cellView\": \"both\",\n    \"colab\": {\n     \"autoexec\": {\n      \"startup\": false,\n      \"wait_interval\": 0\n     },\n     \"output_extras\": [\n      {\n       \"item_id\": 1\n      }\n     ]\n    },\n    \"colab_type\": \"code\",\n    \"collapsed\": false,\n    \"executionInfo\": {\n     \"elapsed\": 4763,\n     \"status\": \"ok\",\n     \"timestamp\": 1445965465525,\n     \"user\": {\n      \"color\": \"#1FA15D\",\n      \"displayName\": \"Vincent Vanhoucke\",\n      \"isAnonymous\": false,\n      \"isMe\": true,\n      \"permissionId\": \"05076109866853157986\",\n      \"photoUrl\": \"//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg\",\n      \"sessionId\": \"2f1ffade4c9f20de\",\n      \"userId\": \"102167687554210253930\"\n     },\n     \"user_tz\": 420\n    },\n    \"id\": \"o_e0D_UezcDe\",\n    \"outputId\": \"df22e4a5-e8ec-4e5e-d384-c6cf37c68c34\"\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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yMyMpLIyAgSExNxdnYmWzYLGjZshJ/fcl59tRKHD//BX3/9xfTp3zB9\\n+uf89dcZWrR4m65d36dgwUIPuALDC/usREReZgpzIiLy0jMajakeFytWHDc3dwA8PYsSEhL8QsPc\\n48LlsWMBlCxZGoPBgJOTM76+S7l16xajRg1h+vRvsLKyZunShYwePYzg4CvcunWLZctW4eTkzNat\\nm9m//3eKFy9OaGgEX3/98MVKPvpoIJA86jdt2nTs7R0eePzMmXNTHjs7O+Pntzb9PxQREbmPwpyI\\niMh/3L3/C8Dc3OyF3wP2uHAZHBxMyZKlAahduy4Ax48f4+LF8/Ts2QWA+PgEwsKuMWHCVEaMGES/\\nfr2B5GDm6ur+xLVs3nyETz4JJCzMHR+fv5k3rw5ubtn/aSOedet2YzQaady4CpaWlo85m4iIpCeF\\nOREReaiFC79l8+aNODu7kCNHTry8itGmzXsZXVa6s7W1JSoqKqPLeKj7w2VCynMbG5uUxxUqVOLj\\njycAMHXqRDZsWM/UqZNwcHAkVy4PgoKCuHPnDtHR0VSsWJEiRbyoVKlySp+2b9+SqVNnYDQmMWBA\\nH4oXL8mmTfs5f96PhAQPdu0yMn7893z11TvEx8fToYMfW7e2BQysWLGM779/FysrqxfzoYiISPos\\ngJKYmEiTJk3o2bMnALdu3aJz587Ur1+fLl26EB4enh7NiIgIEBwcRLt2zZk8eQLt27dkwIA+xMbG\\npns7R48eZfv2bSxatIJp02Zw6tRJDFn0VignJ2dKlixNhw6tmD17RoZf57OESx+fEhw7FsCVK4EA\\n9OnTH2dnF775Zj4RERE4O2dn0aLlJCUlYWZmxv79+++bOmq458KvXAnknXfe5fbtYSQkeNw9gtu3\\nbQHw89vO1q0dAAfAnh07OrFs2fZnvWQREXkG6TIyt3jxYgoXLsydO3cAmDdvHpUrV6Z79+7MmzeP\\nefPmMWjQoPRoSkREgMDAy4wbN4mhQ0cyZsxwtm/fRr16DdO1jUOHDvHGGzWwsLDAwsKCKlXe4J7Z\\nf1nO2LHjH/h6//5DXnAlqcOllZUV2bO7PvY9Li4ujBz5MR9/PIK4uHgAEhISMDc3x9XVjbNnz/Dm\\nm7UJD7+NmZkZc+fOZe/e3VStWo3mzd9m1ar1AMTGxtC3b09y5sxF9uyu5Mo1HisrX5KSbLh+/UMq\\nVkwOfPHxRlL/M8Kc+PhHr6gpIiLpK81hLiQkhO3bt9OzZ08WLlwIwLZt21i6dCkATZs2pX379gpz\\nIiLpyMMjD56eRYDkTZqDg4PSvQ2DwZDq3i3IwknuH7Nnb+P77+NJTDTn3XcTGDy4QYbV8iTh0s9v\\nXaqvlStXgfnzF6c8b9GiMVFRUZiZGRgz5lPy5s1HixaNSUhIwMnJCTMzAxYWFhQpUpRDhw4SFxfH\\nwYP7KVOmHBcunGPKlAnMmjWeRYv+4tKlq0RHf0rPnsv/OXcV/PwWs39/J8BAuXILadv2zXT/HERE\\n5OHSPM1y4sSJDBkyBDOzf091/fp13NySNxN1c3Pj+vXraW1GRETuYWlpkfL4eW3SXK5cOXbv3klc\\nXBxRUVHs2bMrw6cfPk8HDhxn6tTCnDnTnHPnmjJjxqts3Lgvo8t6qMGDP+LOnchHHnPt2jUaNVrP\\n0aNedOz4FZcvhxAbG4u1tTUXLlzA1taO06dPUatWXVavXklwcBC7du2gcuU3SEpK4tixo0yaNI6g\\noGVky7YVa+uklKmYtra2/PDDW3z88Y+MHbsKP7+G2Nvbv4hLFxGRf6RpZO7XX3/F1dUVHx8f9u17\\n8C88g8GQag7+w7i7O6SlFMnk1L9Zm/r3xYqNtSNbNvOUz93e3gozs8R07wd395LUr1+XLl3a4ubm\\nho9PMXLlcsuy/X3hQiiRkZVSnsfGFiAo6Gimvd6FC30f+XWj0UhiIoSFVSE21gczsxF07dqNhIQI\\nmjdvxrFjx/D0LMTly5dZunQBwcHB5MuXj7//Pk/16q+zfPkinJwc+emn9Q9tw93dgbFjW6b3pUk6\\nyKz/30r6UP/KXWkKc4cPH2bbtm1s376duLg4IiMjGTx4MK6uroSGhuLu7s61a9fInj37Y88VGhqR\\nllIkE3N3d1D/ZmHq3xfvxo07JCYmpXzukZGxxMTEpXs/uLs70LhxS1q16khMTAx9+vSgdeuCWba/\\ny5cvjIfHbwQH1wTA1XU/ZcrkzhTX+7//bWDVqh9ISIjHx6cEAwYMpVWrJvj6LsXR0emBq462aNGa\\nhIT82Nntxs3tC8zNw8mevTEJCetZvdqf6OgoTpw4ycCBw6hVqw6jRw/D0tICOzt7rK2dWbhwBb16\\ndWHlSn9q1qyD0Wjk3LmzKdN7JfPSz+WsTf2bdT1LSE9TmBswYAADBgwAYP/+/fj6+jJ16lSmTJmC\\nv78/PXr0YM2aNdSpUyctzYiICBAXF8fhwydwdXVk0aIVKa+n91YBcXFx/PzzXlxd7dm2bTV//32B\\nuLg4GjZsRJEiXunaVmZSuHBepk+/yXff+WE0GmjdOjvly7+a0WVx8eIFtm3bwpw5vpibm/P555PZ\\nvHljyqyXkyePp6w6Gh8fT5cu7+HtXYxs2bLh6BhHeHgcly/74ez8A0lJSzAYzOjQoTO//76L3Lnz\\nUqtW8u/omjVrM2bMCF55pRtjxqxn5Mh6jBkznmnTPmPRIl8SEhKoU6eewpyISCbyXPaZ69GjB/36\\n9ePHH38kT548fPXVV8+jGRGRl0ZERAStWn1HaOgerl79mM6d/Tl/fjmffPIZBQsWSrd2YmJiaNvW\\nn1272gLxNGhwmgULPsHc3Dzd2sjMatQoRY0apTK6jFT++GM/p0+folu39kDyfXDR0cnbFhiNRo4d\\nC7hv1dG7ihZ1p2xZC+LjV1GqlBmbNsWwYsVaNmxYT6FChejZs1/Ksb/9FsOZM39w5owd27bFcePG\\nMr7+uhmffz7jxV6wiIg8sXQLcxUrVqRixYoAODs7p6xsKSIiaTdjxg4OHhyJq+sM7O1/Y+3aS3Tv\\nXjVdgxzAokW/sWtXZyB5gZVNm1qxfv0umjSpnq7tyNNp2LAR77//QarXWrRo/M+jh686ajAY6NKl\\nBl5e3ty6dYuff/7moW0cOWIN2P3zzJJDh+wYMuQn4uKy8e67r1CtWon0uBQREUlH6bJpuIiIPF8x\\nMeaAGdevf4Cd3W6yZbtMzZr10r2d+/cOsyImJiHd25HHu7s5/OHDh1i+fCkffvg+sbGxjB07An//\\nVQB07tyOCxfOsWzZYjp0aMWZMyfZs2cX8fHxTJw4jtOnTzJu3Eh27Uq9mbednV3K3rB3ubqm3qQ8\\nOPgKCxe2YdmyFvTuncDBg6ef7wWLiMhTU5gTETEB775bmNy5t2BufhODIQp7+zDy5Xsl3dtp1+51\\nSpVaTPLoTgKvv76UJk2qpHs7mcXKlcuIjY3J6DIeKjDwMp06dWP06E/4668ztG37LgcP7vtnS4Lk\\n1aI9PYvSrl1Hbt68yaBB/Shc2JOAgCNUqFARL69iDB48glmzpv9zncn32ZUtW4GzZ8/SuXNbtm37\\nBYCxY1/l9dcX4e6+gcKF53DnzuspdVy79gb/+9+FDPgERETkUZ7LPXMiIpK+ypYtwqJF8PHH3cmf\\n/zVKlnRm/vxvUm0gnR5cXJzx86vFkiWrcHKyokWLxlhbW6drG5mJn98K6td/EyurzHmNdzeH9/Qs\\nQkhIEAkJCVy5EkjevPnw81tLixaNqV69FnZ2dlSqVJk5c2YSEhJCdHQ0S5cuxNzcnBkzPic+Pp7Y\\n2Bj8/NYC4OjoyKpVq1KtiFegQG7Wrn2XuLg4LlzITf36iUSlDNZF4eqqv/+KiGQ2CnMiIiYiKOg0\\nxYvnYfz4oSQlJdGzZxcOHTpIuXIV0rUdFxdn+vZtkOWWv46OjmbMmGGEhoaSlJRIzZp1CAsLpW/f\\nnjg7uzB9+uyMLvE+928OH/vAY6ZMmcCpUye4du0anTt349dft/LxxxPImzcfAElJSQwe7M/Oneew\\ns4ulf393unat9ZA2LfHyKkyfPv9jwYJQYmIcqFnzBN26NX8+FykiIs9MYU5ExEQ0bNiIhg0bAWBm\\nZsa8eQsztiATs2/fHtzccjB16nQA7tyJZMOG9cycORdHR6cMri5txo4dz6lTJ5g1azrvvdeJO3fu\\nsGrVipSR2wkTFrNkSUfAEYAxY36mWbNbwMNXKR00qD49e0YQGxtH9uwlU7ZCEBGRzENzJkREMrmp\\nUzdRpcoWqlX7H99++1tGl2OyChcuwsGD+5g9eyYBAUews7PP6JIe6+kClCHl+E6dupGQkEDHjq1p\\n374lBw78wt0gBxAUVIRLl4Iee0Z7ewdcXV0V5EREMimDMfV6xhkmK03lkdSy2lQtSU39+3z9/PM+\\nevb0JDa2AAAODkdYtSqesmW9n3vbaenbjRt/YsWK7zEYDBQu7Mno0Z88cx11677Bli07n/n994qI\\niGDv3l2sW+dP+fKvsmHDer77bonJj8w9zurVe+nXz5OYmOStLHx8fuDgwcZERmql0qxIP5ezNvVv\\n1uXu7vDU79E0SxGRTOzMmZspQQ4gIqIkR4+ufiFh7lmdP3+OxYt9mTt3AY6OToSHh6fxjOkzKhQW\\nFoaDgwP16jXEzs6en35ai61t8hL9WSXMHT78FzNmnCEmxoIGDazo2DF5f8B3332d69e3sm3bYWxt\\n4xg4sDg2NjZERuofhCIipkxhTkQkE6tcOR8uLge5eTN5kZPcuX/L9Js3Hzp0gFq16qYEJEdHx8e8\\n48U4f/4ss2ZNx8zMQLZsFgwaNJw//wxg4MAPcXfPkSkXQHmQNWt+ZO3aHwGIjIzEwyM37dt3Yt68\\nbzhx4haRkaUICZnE3r2XWbmyHo0avc2BA/to164DBQoksXTpUj77zMiBA7Xo2PH9DL4aERFJC4U5\\nEZFMrFIlHz777Hf8/FZhZmaka1cPChZM//3l0pPBYCCTzOBPpWLF16hY8TWMRmPKKJ2XlzfNmrXK\\n6NKeSpMmzWjSpBkJCQl89FEv3nqrMYsW+dK2bS9atSqBi8tPuLgs4MaND4iNTcLJyRlf36WEhYXy\\n/vud8fVdir29A0OHfsTOnb/xxhs1MvqSRETkGWkBFBGRTK5p09dYtqw+S5c2oGbN0mk6V3BwEB06\\nPN/wUq7cq/z66y+Eh98GSPlvZhAREUHLlj9QqVIIlSvv5vvvd2d0Sc/sq6+mUb78qzg4OHLx4nl8\\nfb+kUKH2ODquxcIiGIPhOtmyGahduy4AJ08ep1y5Cjg5OWNubs7bb7/NkSOHM/gqREQkLRTmREQk\\nXRUsWIgOHbrQp08POnVqy9dff5XRJaWYMmU727d3JTKyMoGBTZg2LYbo6OiMLuupbdiwnmvXrtKl\\nSw+MRiMVKlRiyZKVDBw4DHv7jtjbV6Br1w04ONhgY2MD3D9imhlHT0VE5OlomqWIyEvqypVARo8e\\nypAho/D2Lpau5753TzyAFSuWsmHDegAaNWpCy5Zt0rW9J3X7tgX3/h3z5s1cREREpAQeU3Dq1ElW\\nrFjKrFnfAuDjU4IvvpjMlSuBtGjxGo0aRRMWFkrevPlo0cI35X3e3sX56qtp3L59C3t7BzZs2EDj\\nxtoIXETElCnMiYi8hC5dusjHH49k5MhxFC7smebzrVq1h3XrIrGwSKBPnyKULVsk5WunTp1k48af\\nmD9/EUlJRnr06EjZsuUoUsTric6dnnuc1azpxLp1J4iK8gGSKF/+GO7upnXP3OrVK4mIiKBv3+TF\\nS7y9fRg58mM+/ngEcXHxAPTo0Zu8efOlep+bmxs9e/ahb9+eGI1G6tSpTdWq1V54/SIikn60z5w8\\nd9oPJWtT/5oGP78VrF37I/ny5ePYsWM4OjoyceI08ucv8ND3PGnf/vbbUbp1syM8vAwAhQqtYePG\\ncri4uACwcuVyIiLC6do1OXx8++0cnJ2dad68ddov7Bn4+//Otm23cXSMY+jQ6plmtc0XTd+7WZf6\\nNmtT/2Zd2mdOROQldvdvcw8ayVqzZhXTp88mPj6eAQP6kDOnBwEBhx8Z5p7U7t1BhIe3SHl+/nx1\\nfv99Hw0bVnlgPUaj8YE1nj79N7NnHyc+PhvNmuWkVq20LfbyME2bvkbTps/l1JnW5s2HmTs3hPh4\\ncxo3tqBbt5oZXZKIiKQDLYAiImLCgoODaNPmXcaPH0uHDq24du3qfcdMnTqRoKArDBz4IT//vBYL\\nCwsmTpzXjaYmAAAgAElEQVTKpk0/s2XLpjTXkC+fNWZmoSnPnZ1PUKxY3pTnpUuXYceO34iNTV5s\\nZOfO3yhVqmyqc9y6dYuuXU+ybFkr/Pya8eGHBg4ePJ3m2gQCA4MZPDiOnTtb8vvvzZgwwZv//e+P\\njC5LRETSgUbmRERMXPJCJp/g4/PgzcQHDx7B/v2/M3PmXO7cucPOnduxtrZmypSv6N+/N7a2dlSp\\n8sYzt//ee9U5eXINmzc7YmWVQI8eNhQoUCrl60WLevPmm43o3r0jAG+/3ZQiRYqmOseOHcc4c6ZB\\nyvPQ0Cps3epHhQpPdl+dJNu1awcXL57nvfc68d13c7G1tcPK6hWSkrZjb59IZGR97O0Xs3OnB++9\\nVyOjyxURkTRSmBMRMXE5c3o8NMjd6/btW5iZmbNgwTIA7O3tmT9/cZrbNxgMTJzYlAkTHjx9EqBV\\nq3a0atXuoecoVCgXtrbniIoq8885b5Ejh0Waa3vZVK1aLWVRE4PBgMEA5cp5Ym29mjt3kvvm9u1u\\nvPba2YwsU0RE0onCnIiIibOxsX7sMeHh0dSvf5bIyLxUqfIDCxY0xdr68e97Gg+6N27t2l1cu3aH\\nd96pQM6cbg99b4kSRejXbwsLFvxNfLwN9epdoWPHd9O1vsfp1asLs2f7PvTrdeu+wZYtO19gRakF\\nBwcxcOCHlChRimPHAvD29qFhw0YsWDCPmzdvMXbsp1y4cJ7Tp0/Sv/8QAIxGKFDgFcqVS+L8+d0Y\\njZE4OMzD0/MTALZs2cTSpQsxGo28/npVevX6MOVaW7Row549u7CysuKzzz7HxSV7hl27iIg8mO6Z\\nExHJ4kJCgrl924wbN2oQE1OerVs7M336tufaptFopF+/H+nZsyKjRjWnWbODnD8f+Mj39OtXlwMH\\nqnLgQCm++qo5ZmYv9lfUo4JcsvTbIuFZXbkSSOvW77Fs2Y9cuvQ3W7duZvZsX/r0+YjFixc8dGQ0\\nf353Ro0qxy+/1OWVV7JjMBi4evUqc+Z8zYwZc1iwYBmnTp1g587fAIiJiaFEiVIsXLiM0qXLsm6d\\n/wu8ShEReVIKcyIiJu5x+7CFhd0iKeneiRgWREQ83x//gYGB+PuXIinJDTBw5kwLvvsu4LHvs7S0\\nxM7O7rnW9jB16ybfNxgWFsYHH3Snc+e2dOjQiqNHj6QcM3PmF7Rv35KPPurNrVu3AOjTpwezZ8+k\\ne/eOtGnzLgEBRx54/vTg4ZGHQoUKYzAYKFiwEBUqVASgYMHChIQEPfF5jEYjx44do2zZ8jg5OWNu\\nbk7dug04cuQwABYWFlSuXBUAL69ihIQEp//FiIhIminMiYiYmIiIcE6dOk1UVBQeHrlZtGjFA4+7\\ndu0aoaGhFC1amBw5OpKU5ASAq+teGjTI+8D3pJcHbWFqNGb8yNajJde3ZcsmKlV6nQULlrFw4XI8\\nPZMXa4mJicbb24clS1ZStmw5FiyYl/wug4GkpCTmz19E374DU15/Hiwt/72P0MzMDAsLi5THiYmJ\\n91/RIz7y+/8I8O89j+bm/4Z/MzPDA88tIiIZT2FORMSEbN58hJo1/6BaNUfq19/B4cN/3XeM0Wik\\nf/9VVKp0lddeC2LUqJ9ZtKgO77+/gg4dVjF7dhJVqxZ/rnXmzZuXd94JwGC4ARjx9PyRrl1LPtc2\\n04uPT3E2bFiPr+88zp07i62tLZAcmGrXrgdAvXoNU43YVa+evG+bl5d3phnFMhqNPCBTA8lBrlSp\\nUhw5cojbt2+RmJjIL79spkyZci+2SBERSRMtgCIiYkK+/PIKly61BuD06aJMm7aC778vkuqY1at3\\nsHx5Y5KSXAFYssSTGjUC+PTTRg88Z3BwEEOH9mfx4h/SrU6DwcCMGc2pXn0H169H8/bb5cidO0e6\\nnf95Kl26LLNmzWfPnl1MnPgxrVq1o0GDt1Id89+Nzy0sLAEwMzN/rqNY/x1Ne9AU27uv3V3N8mHc\\n3d3p2bMPffv2xGg0UrnyG6lWwnxUGyIikjkozImImJA7d6xSPY+KsrzvmKtXo1KCHEBiYg6CgyOe\\ne23/ZTAYaN68+gtvN61CQkJwd3fn7bebEBcXy19/naZBg7dISkri119/oXbtemzZsum+jc+ft/9O\\nqR0xYmyqr90N4w0bJof2Ll16PPDYmTPnpjyuU6c+derUT9XO7du3+PbbxSQmJmJubk6NGrWpUaN2\\n+l6MiIikC02zFBExIVWrRmIw3ATA0jKQGjXun0fXqFEZChZcl/Lc03Mtb7316OlzSUlJTJ48gfbt\\nWzJgQB9iY2PTt3ATcHcE6vDhg3Tu3JYuXdrx669badGiDQDW1jacOHGcDh1acfjwITp37vawMz1V\\nu8HBQXTo0CotpaebefN+o3LlE1SunEiLFquIiHjxfwQQEZEnZzA+6C71DBAaql8YWZW7u4P6NwtT\\n/75YRqORefO2cvFiEqVK2dKmTdUHHnfixAUWLjwFGOnWrQRFi+Z76DmDg4No3bop3323FE/PIowZ\\nM5yqVavRrl1L9e0L8DymuT6J/37vhoff5vXX/yQ0tME/ryTRq9cPjBv34Om5knnp53LWpv7Nutzd\\nHZ76PZpmKSJiQgwGA++/X+exx/n4FGTKlIJPfF4Pjzx4eibfe+fl5U1w8JMvc/8y+vvvYEaO3E9Q\\nkB1Fitzm88/rY29vn+bzXrkSyOjRQ6lTpwHHjh0hJiaGwMDLtG7djtjYOH75ZRMWFpZMnTodR0fH\\ndLiSf0VERBAefu99jWZERlo89HgREcl4mmYpIiL/WfL++S7ikRUMGbKPzZvf488/m+Lv34FRo35J\\n8zkvXbrI6NFDGTlyHM7Ozly4cJ6JE6cxf/5i5s37Bjs7O3x9v6dEiZJs2vRzOlxFah4eualU6Q8g\\nue+dnf+gfn33dG9HXi6DB3/EnTuRjzxm8WLfF1SNSNajMCciIvKULl26dyqMGZcvp21U7ubNmwwf\\nPoixYydQuLAnAGXLVsDGxgZnZ2fs7R2oUiV5pclChTyfaoPwJ2VmZsbChY3o08ePjh1/ZNasKOrV\\n01YFkjZTp07Hzu7R3x9Llix8McWIZEGaZikiIk+05L38q0CBcM6dM5K82EkCBQs+euThcezt7cmZ\\n04OAgMPkz18Ag8Fw3wbhd58/bIPw9GBvb8+YMW89/kB5KURHRzNmzDBCQ0NJSkqkY8duODk58c03\\n00lMTMTb24dBg4bzxx8H+PnndXz66WcAHDp0kBUrvmfKlC9p3vxtfH2X4ujoxP/+t4FVq34gISEe\\nH58SDBw4jLlzZxEXF0vnzm0pVKgwo0d/msFXLWJaFOZERF5y/13yvk2b915Y2w/6x52ZWeafNPLF\\nF1UZOXIpwcH2FCkSzqefNkzT+SwsLJg4cSoDBvTBxsbmkcdmknXL5CWwb98e3NxyMHXqdBYu/Jb5\\n87/h6tUQXn21EmXLVuDYsQA6dWqDlZU1Fy6c4+zZ03h6ejF16iRy5MhBjx6diIgI54svpmA0JrF/\\n/+/Y2zswfPgYpk6dyLvvvknFiq9jaWnFggXLmDbtM7p160BsbAw1atSma9f3AWje/G0aNmzE7t07\\nSUxM4NNPPyNfvgIZ++GIZBKZ/zemiIg8F3fu3GH9+p0cOHAsQ9q/ePEC27ZtYc4cXxYsWIbBYMbm\\nzRszpJan5eHhjq9vEzZurMOMGe8+NoA9jsFgwNramilTvmLlymXcuRP5n9HR1Jt4a+RUXoTChYtw\\n8OA+xo8fy6ZNPzN27AS8vX24dOkSAMHBweTMmQtf36W89loVxo0bTUJCAqGh17CwsGDu3AU4OjoB\\nEBh4GSsrawA++qg3CQkJNG78LufOncVoTAKgR4/efPvtYhYuXM6RI4c4f/4skPz/vLOzC76+S2nS\\npDnLly/NgE9DJHPSyJyIyEvo2rXrtGu3nYCAd7G0DKFTp3WMH9/4hdbwxx/7OX36FN26tQcgNjYW\\nV1fXx7wr67l3ZNTe3p758xffd4yf39qUxw0bNkrZGFzkecqbNx++vt/z5ZdTSEhI4Pffd2Nubk6V\\nKm8QFxfLxYvnCAqyonPntkRFRXHz5g0OHz6Ik5MTderUT/VHh0KFPKlY8XUaNXqHgQP7smLFagCC\\ngq5w4cJ5ALZt28y6dWtITEzk+vUwLly4QKFCyfeQVq9eC4CiRb3Zvn3bC/4kRDIvhTkRkZfQrFl7\\nCQjoABiIi3NgyZLr9O59hdy587zQOho2bMT773/wQts0Fdu2HWXSpEvcvGlD+fI3mTHjbaysrDK6\\nLHmJhIWF4eDggLe3D0lJifz55zFCQoLJk+cVHBwcMDMzo3v3njRv3prExERat27KunVryJ07D9bW\\n1qnOVaRIUVavXkX16rWwtLQgPPw2UVHRmJmZYW5uxuXLl1ix4nu+/XYJ9vb2TJw4jri42JT3371n\\n1Nz8+d0zKmKKNM1SRMTEzJnzNatX+6U8/+67uU897Sg+3px7p+7Fx9sRHR2TXiU+kfLlK/Lrr1u5\\nefMmkLxpdUhIyAutIbNKSEhg1KhAAgLacOlSE/z92zFlypaMLuup1K37RkaXIGl0/vxZevTohL+/\\nH7t27aBz5+707z+EzZs34u+/CltbW5ydXYDkhXl8fEqwb99ecuTIec9Zkn/O5MiRk+7dezF+/BgC\\nAy/Tv38fbtwIA6BChUoMHPght2/fws7Ojhs3rvP773te9OWKmCSFORERE1O7dl22bfv3H/a//rqV\\nOnXqPdU52rQpQt68d+9Pi6Zu3T0ULPjkm4ynhwIFCtK9ey8GDPiAjh3bpPrH3cvu9u3bXL36yj2v\\nWBISYplh9Twb3ddn6ipWfI1Fi5azfPlq2rbtwIQJY/nss08pWtSL7t17Mm/eIjZu/JlOndrSvn0r\\nChYsxObN2zE3N0+ZYunntxZLS0sMBgO1a9dl6tTp5M2bj+++W4KPTwkAGjR4k5Ur1/LGGzVo27YZ\\n48aNplSp0g+pSveMitzLYMwky2KFhkZkdAnynLi7O6h/szD1b8Z4770WfPXVbG7evMEXX0xm9uzv\\nnvocp0//zdq1J3F2NqNLl1pky5Z65n16922vXl2YPfvBmwPfu5S5JK9Y2ajRjxw40BkAc/MQPvlk\\nH92710q3Np73927dutXYsmUHUVFRDB8+iIiIcBITE+jevRdVq1YnODiIQYP6UqpUWf78MwB39xxM\\nmvQ5VlZWnDx5nM8++xQzMzMqVKjEvn17WLz4BzZsWM/p0yfp338IAEOG9KNNm/aULVueadM+49Sp\\nE/ethLh37y6+/vorrK1tKFmyFEFBQUyZ8iXR0dF8+eUULlw4T2JiAl269KBq1eqcP3+OSZM+ISEh\\nnqQkIxMmTOGVV/I+t8/peXjSvk1MTMTc3PyJzhkdHY2NjQ2ffjqGY8eOMmHCZIoU8Xqm+vbsOcGm\\nTZdwdjbywQe1NH34Ken3btbl7u7w+IP+Q/fMiYiYoJo16/Dbb79w/fr1px6Vu8vLKz9DhuRP58oe\\n7m6QMxqNLFmyg2PHYihUyIyePeu8sBpMhcFgYPbsKkyc+D3h4da89hp061Y3o8t6JlZWVkyaNBVb\\nWztu3bpFz56dqVq1OpC8wuG4cZMYOnQkY8YMZ/v2bdSr15CJE8cxbNgYihcvwZw5Xz9iJObfUZoe\\nPXrj6OhIYmIi/fr15ty5s7zySl6mTp3EN998S65cHnz88UjunmrxYl8qVKjIiBFjiYiIoEePjlSo\\nUIl161bTokUb6tVrQEJCQqa8P+vu3+EfN0K1cOG3bN68EWdnF3LkyImXVzH27NlJkSJFOXo0gLp1\\n61O6dDm+/jo53Do5OTNy5FhcXd24ciWQL76Ywq1bN7G2tsbOzo7Q0GsEBQVRpcobFCnixfz5swkN\\nvcawYaOfeEuR3347Su/eBsLCWgBxHDq0gCVL2jzwWuLi4pgyZTOBgZYUL26gT586GpUT+Q+FORER\\nE1SrVl0mTx7P7du3mDVrfkaX80Tq1n2DLVt20rlzf06cuILRaMHq1R0IDFxLs2avEB0dxahRQ7lw\\n4RxeXsUYMyZ58+CXdY+pfPk8mDPnxa4w+jwYjUbmzPmagIAjmJkZCAsL5ebNGwB4eOTB07MIAF5e\\n3gQHBxEZGUl0dDTFiydPwatbtwF79ux8bDv/XQnx4sXzJCUlkjt3HnLl8gCgTp36rFvnD8D+/b+z\\ne/cOli9fAkB8fDxXr4ZQvHhJFi/2JTT0KtWr18o0o3LBwUEMGNCH4sVLcvr0SYoVK86pUycwGAx0\\n6NCV2rXrcujQQXx95+Hq6kJAwFESExPp2fMDVq/2Y/v2bSmfw+XLlzAzM2PTpp/x9Z3PvHkLyZ+/\\nACNHDuGDD7rj7p6D48f/pG3b9+jWrRfHj//JvHmzWLBgGRMnjqNy5arMmjWd6OhoRowY+1TXsX59\\nMGFhzf95ZsnOnWW5ejUkpbZ7DRiwjpUr2wDW+Ptf586djQwb9mYaP0mRrEVhTkTEBBUsWIjo6Chy\\n5MhJ9uymspy/ge3bt3HxYggXL27E3PwG+fI1Z9euzjRrBn/9dZqlS/1wdXWjV6+uHDsWQMmSpVPt\\nMeXvv4rly5cydOiojL4YeUKbN2/k9u1b+PouxdzcnBYtGhMbGwf8u0IhgJmZOYmJsfe9/967QczN\\nzUlK+vf53dUOg4KuPGAlxDjuv28v9Z0lEyZMJW/efKley5+/AMWLl2TPnp0MGvQRQ4aMoFy5Cs9y\\n6enuypVARo/+hNDQa6xZ8yOLFq3g1q2bdOvWgTJlygJw9uxfzJq1iWXLVrJgwXxCQkL47rulfPjh\\n+xw7dgQzM3Pefbclr79ehfPnz9KtW8d/Apw7YWFhxMfHM3/+Yt55pz6LFy9g166dGAwQH58AJPfH\\nwoXf4eNTnCFDRj71NVhaxpPcD8l9Y2d3E1vb3A889vBhZ8D6n3ZdOXDA1O4bFXn+tACKiIiJWrRo\\nBdOnz87oMh4rODiIDh1aAXD06BGcnEoABhITXYmOfhVr6wsYDAaKFSuOm5s7BoMBT8+iBAcHp5zj\\n3j2mgoODMuIy5BnduXMHF5fsmJubc+jQQUJCgh95vL29Pba2tpw48ScAW7duTvlarly5OXv2NEaj\\nkatXQzh58jgAUVFRWFvb3LcSYr58+QkKupLS5tatW1KmWVas+BqrVq1IOfeZM6eA5GCYO3cemjdv\\nzRtvVOfcubPp80Gkg5w5PfDxKUFAwGHq1m2AwWDAxSU7ZcqU4+TJE/98H/ng5uaGuXk2HBwcqVTp\\ndQCcnJwJD0++zyoqKor+/T9gxIjBAHh7F2PBgmU0bdqcdu06YGZmwMHBkXz58jNt2nQWLFjG0qUr\\nAVLaOH36FOHh4U99DQMGvE758guBSzg47OH99++kbCz+Xy4uqVfYdXKKfur2RLI6jcyJiJgIo9HI\\n6tU7CQmJon794nh6Zo7pX0/HQP36Obl504/Tp0tgb3+ZJk2qAmBh8e9f3ZP3kkpIea49pkzP3Xub\\n6tVrwNChA+jYsTVeXsXIn7/gfcf89/mwYaOZPHkCZmYGypQpj52dPQClS5fBwyMP773Xgvz5C+Ll\\nVQwAT88iFC3qRdu2zciRI1fKSohWVlYMHDiMgQM/xNrahmLFfFLa6NSpGzNmfE7Hjq1JSkoid+48\\nTJ78Jdu2beF//9tAtmzZcHV1o0OHLs/3g3oKNjbJo1QGg4H/rl9397rufh+VKlWa+fNnAwaioqI4\\nceJPbGxsAfj++0V07fo+FSu+RsuW73DjxnUAkpKSCA8Px87Onty5c3P16lUSEhIxGo2cO3c2ZUps\\npUqvU7HiawwZ0o8vvvgaW1vbJ74Gd3dX1qx5m+PHz5AzZ3by5Cn50GNHjCjM8OHLCArKQ5EiFxk5\\nsuITtyPyslCYExExEYMHr2bp0rdISnLH13cj8+dHU65c0Ywu64kkJiYSFxfLtm1bSEhIYM2aFVy8\\neIEJE4Jo3boRFy6cz+gSJZ1t3rwdSB4RGjt2PAMHfghAYmICc+d+TcOGjbC1taV163cZO/ZTmjRp\\nxpdfTqF7947Ex8fRvXtPqlatzqhRQ7lx4zoDB/blypVAqlWrkXI/5b0edu9WuXIV+P77VQB8/vlk\\nvL19gOSgN3jwiJTjbt68we7df/DWW415771O6flRpLtSpcqydu1qGjZsxO3btwkIOEyfPv1SfR95\\ne/vg5OTE2LHDyZXLAw+P3ERGRmIwGIiJicbNzR0LCwu8vLw5cuQQnTq1JSwslLJlywMwZsx4OnRo\\nxaBBHwIG6tSplxLmDAYDNWrUJioqimHDBjBt2gwsLZ98CqSVlRXlyj08xN1VuXIxfv3Vi/Dw2zg5\\nldXiJyIPoDAnImICwsNvs25dXpKS3AG4fLkhixevxMPDnoEDP8Tb24czZ05RoEAhRo8eh5WVdQZX\\nnNqlS39jZWWFv/8GOnRoRadObXBxceGDD/rh4pKdixcv8GT/TtMeU8/Kz28Fa9f+iJeXN6NH3x+G\\nnrcrVwIZP34Kw4ePoVu3DmzdupnZs33ZtWs7ixcvoECBgimrS/700zpGjx5GnjyvkC2bBYmJiXz6\\n6SSyZbPgnXca0Lhx04cuTDJ58nhatWpHgQLJI4Dr1/uzceNPxMcn4OXlxTvvvHvfe7ZtC2DQoNsE\\nBpYlb97DTJ3qRK1aD9vnLOPc/X+/evWaHD9+lE6dkleB7N37owd+H+XIkZOPPhpE/vwF6NKlHS4u\\n2ZkxYw67dm1n9OihODg4Ur58BaKiopgxYw6+vvNSRtk8PHLj4ZGbKVOmkytXrpRz3hua33qrMW+9\\n9eSL9AQHBzF0aH8WL/6BU6dOsGnTBvr1G/TAY+/druTuxuQicj+FORERE2AwGDAzS0r12t2VwC9f\\nvsSIEWMpUaIUkyZ9wurVq2jT5r0MqPLh3NzcU/az6tdvMH5+K5g0aVrK18uWLZ8yIgCk7CMG4Oe3\\nLuWxt3cxZsyY8wIqznrWrFnF9OmzcXNzT3ktISHhvv0FnxcPjzwUKlQYSF7Ap0KFiv88LkxISBCh\\noddSrS7p6urGhAlTOXHiT44eDcDW1g5IXvTk8uW/HxjmkpKS7lscp2XLtrRs2faRtc2YcYXAwOT7\\nOi9fzs3MmT9kujDn4ZGbRYv+vcevd++P6N37o1TH/Pf7KGfOXHz22SfExcXx5ptvp4w4Vq1aPWV7\\niHt16dIj1fPFi39IeZyYmMjnn2/mr7+ykS9fLMOG1cfCwuK/p3hi3t4+KaOkIvLsFOZEREyAg4Mj\\nLVtew9f3EnFxr1Co0Dq6dfMGkv/6XqJEKQDq138TP78VmSrM3bhxnRs3rtO370Ag+d6/Jxld+/HH\\nPaxZE4GlZSK9exemfPln26BYYOrUiQQFXWHgwA+5ejWEKlWqERoagqtrDt5//wM+/XQM0dHJi0sM\\nGDCEEiVKpSxz7+zsct92ESdPHmfGjM+Jjo7BwsKCGTPmYGlpyZw5X3PkyB9cv34do9GIk5Mznp5F\\naNy4KWFhoXTs2AZnZxccHR2xsLBgwoSP8fEp/s/m1dm4ciWQrVt3p7Q9f/5sjh37N8j5+a0gLi6O\\nL7+cyooV3zN9+mzq1n2Dd95pxsGD+xkwYAjz5n1Dnz798fYuxv79v+PrO4+4uDjy5HmFESPGYmNj\\nw+zZM9m9eyfm5uZUrPgaMTGpQ0VMzLOHlMxk7NjxT3zsnj0n+PXXS3h4WNCpU8379o3r1WsSf/zx\\nFwZDEnv3Fuf69RiOH59OixZt2LNnF1ZWVnz22ee4uGTnypVAxo0bRWxsDFWqVMPPbwVbtuxIdb57\\nR94OH/6DGTM+B5L/cPX118nbrTxsuxIR+ZfCnIiIifjkk8ZUq7afS5f28+ab5ciVy53g4KBUwehJ\\ng9Kz6tWrS8rm308qe3ZXkpKSUhar2LJlE6VLl3nke3bsOMawYTm4fbs+AH/+uZYNG9xwdTWVbRgy\\nl8GDR7B//+/MnDmXVat+YM+eXfj5/cDt27HExsbw5ZezsLS05PLlS4wbN4pvv10MwNmzZ+7bLsLb\\n24exY0fwySef4e1djKioKCwtLfnpp7XY29szfPhYRowYhI2NLRMnTsXOzp5Ro4bi4ODAokXL+fnn\\ndfj6zqN27bqp/l+tWPE1/vrrTMrz06dPsnz5avbt28vcubM4diyAFi1aM2fOTD76aCBVqlQDICYm\\nhuLFS9CnTz8gOQwYDAZu3brF4sX/Z+8+A5o6uwCO/zNI2MsFooKigoLgrnsWt7Yqjrq1asW6xf1q\\nnbgHWndFcSuu2rp3XXWhOHHjYIlMIRAgyfshEkGw1bo6nt8ncnPHc29Sm3Ofc88JwN9/CUqlMevX\\nr2HLlg20adOOkyePs3HjdgBSUpJJTT3F9evhpKc7oFA85csv/1tFdvbvv8SQIabExbUDErh6dQcL\\nFngb3g8Le8idO7d58mQ7IKNgwUlcvnwfrTYNd3cP+vbtz5IlC9m9eyfdu3+Lv/8cOnToRMOGjdi1\\na/ufHn/z5vUMHz4ad3cP0tLSDDN+r7cruXr1Ch4ef/xvhyD814hgThAE4R/kyy9zV3OLjo7i+vVr\\nuLuXe6tA6X28ayAH+h/XxYo5snPnVmbMmIyTUwm+/tr7D7c5dSqcxMR2htcPH9bj7NkztGhR652P\\nL7ySVQGxVq06LwtWqMnIyGT+/Jncu3cXqVTK06dPDOtntYsAXraLiMDU1Ix8+fLj6qoPzrOesbpw\\n4Xfu37/Hrl3b0Wgy0Wq1PH36hCpVvuDu3VAKFCgE6GeP586dkSOQk0gk9OjRmw0b1tK9e0dUKhVK\\npZL8+QsglUqxtrYhMjKScuWyUh9fbSuVSqlXr2Gu87xx4xphYQ/o109fjTIjI5Ny5TwwMzNHoVAy\\nffpkatSoTc2atfH1bULRoqe4ceMM7u4WtG/f5MNd9H+AnTtjiYur9/KVNYcP26FWq1EqlQBcunQe\\nne4pxYq1BUAiUWNsXJSMDCNq1ND/N+niUoaLF88BcOPGNWbMmAeAl1djFi/2/8PjlyvnycKF82jU\\nqKibZbUAACAASURBVAl16zagQIGCQO7vX1RUpAjmBOE1IpgTBEH4h3vXQOl9eHnV5tChk++0jZ2d\\nvaGa4NtydDRBKo0xFHyxsrpJmTJF3mkfwptlL5CzZcsG8uXLz/jxU9BoNDRoUMPwXu52EZo/LFQz\\nbNhIHj9+RGxsLH379s+2rYyAgPWG16amptSt24Dffz+DlZUNgYGb0Wq1SKUSAgM3G1LwALy8mhAa\\netPQqsLWNh/lynkY9qVQKN84G1258hdMnDgt1/KVKwO5ePE8x48fYceOrfj7L6VDh9w3CqZNm0jN\\nmrVzBYvva9Wq5Ziamv1t0qGNjDJyvFYq03I9S9mkiRcnT5bhwYPCFC0aycyZbowYccHwvlQq+ctt\\nQ7p06UGNGrU5e/YUPj7fMm/eopfjyv39EwQhJ9E0XBAE4R9OJpMxfvwU1q8PYurUmYa76R/H26dw\\nqtVqZs3ay6hRB9m79+I7HaVTpzr07r2fYsV2UapUEOPGxeHs7PSOY/37yN44HWDjxnUEBKz4jCN6\\n5dixw8TFxQGwf/8etFp9oZ3ExARu374F6J9vOn1aH8QXK+ZEbOxzQkNvAtC2bQvi4+OoWrU6O3Zs\\nw9OzIseOHebmzeukpaWRlJSIu7uHofn3wYP78PSsAOgD/axjnDr1G5mZr3oLJieraN58J+XLn+bX\\nXx8QHa0fo6mpKSkpKX94ThKJBDe3cly7FkJ4+FMAUlNTefLkMampqSQnv6B69ZoMHDiMe/fu/OF+\\n3tW0aRM5fvwIgOFavmm/Pj5/3MNu7dqcM+F/tv5fNWCAG6VLBwHxWFhcoHdvqaFgEUClSlUJCbnI\\n+vV1uXjRjW3b6mFnZ/7G/bm5lePYMf01OHz44BvXyxIe/pQSJZzp3Lk7rq5lefz4kahaKwhvSczM\\nCYIg/AMdP36V48cjMDdP5F0CrE/pu+92sHdvD0BBUNB15sw5S5s21d9qW4lEwtSpXzNlysd9BvBz\\n+TznlD2t8dVSR8fiXL58iR49OvHFF9UNjaWtrKwNqZTZyeVyJk+ezvz5s1Gr1cTFxZKRkUnLll8T\\nGRnBlCnjSU1NZeDAfjg4OODqWpYhQ0YyffokNm5ch42NjaG8fatWrRk9eniuYwPcufOCq1e7AaDT\\nXWbnzkf06KHfZvjwgRQoUBB//6VvvJbW1taMGzeRiRPHkp6un3nq27c/pqamjB49nPT0dEDHwIHD\\nDNvs2/crmzdvQCKR4OxcEplMxpUrl9myZQOxsbH07z+IevUa5ijeATBv3kzKlHGjadMWHD9+hOTk\\nZNauXU3nzt0wMzNnxYolaLVarK2tWbBA/3dY2APkciPat/+K9u2/wdu7Y65zWLduTY6m5X8lzflt\\nuLo6sXevDWfOnMfZ2Z5SpRrkeN/JqTh9+vjw7bedSUhIxMTEhNmz/XOlymYZNGg4kyePZ9261VSt\\nWg1zc/M818v6MyhoE8HBF4mJeUbx4s5Uq1aTa9dC3rJdiSD8t0l0WQn0n1lMzIvPPQThIylQwEJ8\\nvv9i4vP99H7++Ry+vvlITKwAJNGhwzYWLWr3p9u9q7w+Wy+vOrmq0uUlOTmZypWvExfnZVjWtu12\\nli5t9MHH+U+Qvb8WwKZN60lNVeUqBf8pZAUsRkYyHB1LIJPJMDU14/btmzkCluxjzh68JCYmMHHi\\nOJ4/j8Hd3YMLF84RELCe6OgoRo8eTrlynty/f5fZsxdy9OhBjh07THp6BnXq1OPbb78jMjICX99B\\neHhU4Pr1EAoUKMj06XNzzSh7eR0mJKS14XXFijvYv9/r9dP5YB48uM+4cSNYvnw1lpZWJCUl8eOP\\n80lLS2Py5OmEhT1k9OhhbN68M8f12LfvV378cT5KpTEVKlTit9+OUbKkC6AlJiYGlUpFQMB6IiLC\\nWb58MTY2Nly7FkKxYo7cvXuX7dt/oWPH1hQv7kxqqgqNRsPw4WM4c+Ykmzevp0QJZ0qUcGb8+CmG\\nNGeVSsWYMb68eJGERpNJnz4+1KpV13Btv/iiKhcuXHzjtf2rOnf2fqv2Fmp1miGV9/DhAxw5cihH\\nK5I38fObRI0atT54Wuu/jfj/7r9XgQIW77yNmJkTBEH4h/nllwQSE798+cqS48cLkZ6e/rKgxd+D\\nsbEx5uYveJm9B+gwNVV/ziF9VjKZDK321b1TtTrts4zjwYP7rF0bwPLlq3F2LsL9++H8+ON84uJi\\nWbo0wBCw/NGP6dWrV+LpWYEePXpz9uwpfv31Z44evcbUqc9QKp8RFVUcf/9uPH4cxtOnT2jduh2h\\noTe5fTuUkJDLFCxYiKdPnzBp0nRGjRrHhAljOHHiKI0aNc1xHHf3JEJC1IASSMPD449TK9/GihVH\\n2b1bg1yuoU+f/DRvXtnwXnDwBRo08MLS0goAS0tLAGrX1vdjc3IqbkhHff161qlTDw+PCtSsWYff\\nfjuGhYUFs2bNZ8eOIJYtW4SdnT0REeE8eHCPdeu2snfvLxgZGXHv3j2srKyRy40oV84TH5+BaLVa\\n0tLS8PQsz44dQaxevTHbEfVTVUqlkunTZ2NqakZCQgL9+vU09I17+vQJCxf6M2jQyDde278ir/YW\\nERHh2NnZM3iwL3Pm+BEdHQVAs2at+PnnHcTGPkcikWBnZ59rBjL7LGjJkqX43/8mAXDlSjBz5/5I\\nYmIKtrZfMnlyGzw8Sr73+AXh30oEc4IgCP8wSmXOYgXGxrmLFXwsb5seKJfLGTzYhBkzDhAbW5SK\\nFS8wYkTtjzy6vy9b23wkJMSRlJSIsbEJZ86colq1Gn++4Qf2VwKW14WEXMbPTz/LUr16LSwsLFm6\\nNJKIiEYUKbKWsLChzJ+/merV73LhwjnOnTtLWloaFhaWPH36hIIFC2Fv70DJkqUAcHFxJTIyItdx\\nZs5sgaXlDsLCFDg7pzNmTPP3OvcDBy7i51cWlUp/3Hv3juHuHo6jowOg/27nlayUvTF21vsymRyd\\nTmu4nrGxz4FX17NWLX3bhEKF9FUhs5Qp44adnT0Acvmr/RobG3PkyEGUSiW1a9ejVKnSf3guOp2O\\nZct+JCTkClKphOfPY4iP139u9vYOuLq6EhPz4o3X9q/Iq73FkiU/oVAomDhxHO3bd8LDozxRUVH4\\n+g5k/fogVq1azsWL51m0aDkpKcl06tSW1q3b8ehRmOGmgqWlFS9evDCc1/nztzh/fgtGRgmkpfkw\\nZEgZDh50+mT/xgnCP434L0MQBOEfZsgQD65f38KtW7WxsrpHv37KXA1+35dOp0OlUuVYlpiYYPix\\n+ja6dq1FixZxPH8ei6Nji7/VzOGnJpfL6dGjN336dKdAgYI4ORX/LM/N5RWwqFQqFi9ewNmzp7lz\\nJxS1Og21Oo379+8RERHOt992RSKRYGGhT/9Rq9WMGeOLTqfDwaHIy++KEnv7YchkyRQr9jVPn8YR\\nF1eBLl16oFAoCA29ydChI4mPj2fq1AnExETTp083Bg0ajlQqQ6PJPWurUCiYNKnFBzv3K1eeo1LV\\nN7x+9qwa588fNARzFStWYexYXzp27PwyzTLxjfuys7MjLOwhVap8gVqt5tKli4aiLoAh8Chb1h2t\\nVmsIqN4UkJiYmDBmzETu3buNn99EOnToTJMmbw5eDx7cR2JiAgEB65HJZLRr1wq1Oh0AheJVkPim\\na/s+cre3gIsXz/Po0UPDOiqVitTUVCQSCTVq1EIul2NlZY2NjS1xcbG5bipkfbckEglSqSs6XT7S\\n0/Mhkz3n3r1SPH8eYwiCBUHISQRzgiAI/zClShVjzx5bQkJu4+hoR5EiFf58o3cQHHyXUaNuEhFh\\nR/Hi4fj7V8XKSsnAgd/xzTdd32lfNja22NjYftDx/VN5e3fMs8jFp5Q9YClQwMIQsDx//pw2bdrh\\n7u5B3bpfsH37Vo4cOUihQnasWrWOn35axt69vwD6oL5OnfqMGvU/Jk36Hy9eJFGrlopDhzIBHVFR\\ni+jVaytXr/7C06dPadasJQAxMc+YP382LVp8RUzMM6ZMmYWv70CaN//qk5y7h0c+jI3vk5bmDEDB\\nguepUuXVDFjx4iXo1q0XAwb0RSqVUbq0C/B6wQ7934UK2VG//pds2bKJxMR4KlWqApArALSxsUGh\\nUDBu3AiSk1NQqZKz7evVepmZGVhaWtKy5dekp6u5e/c2TZo0Ry6X5/lMWkpKCjY2tshkMoKDLxIV\\nFfkBrtC7yd7eAnSsWBGYYxYzS/YZSKk0q71F3rOgAHZ2MuAFYIFEosPR8QH58n28ZyUF4Z9OBHOC\\nIAj/QObm5tSsWemj7HvKlFBCQvT9r2JiYMqUDaxZ04pNm3Z8lOP9W92794S5c0NQqRR4eRnTpUud\\nzz2kHAGLQmFEiRKlkEj0lSvd3fW92+RyI86d+53Hjx+h0Wjo2bMTKSnJpKenk5KSjEKhJDo6iq5d\\n21OihDNyuRFjxjQhNHQLyclS5sy5TseOfWnbdjd16tQnMHAVarWaO3dCCQsL4/HjMCIiwhkzZhgq\\nlYrMzIxPMkvZtGkVRo06xC+/hGBkpKF3b1ucnIq8tk4LmjZ982zgwYMnDH/37z+I/v0HsW/fr2za\\ntI4tWzZy+fIl6tf/EjMzM8N6MpmcgIANBAdfZMsWfe+86tXrk5qaZgjounbtxZgxw5DL5Ziamhme\\nH2vVqjU9enyDi4sr48dPMVynRo2aMGrUMLp374iLSxkcHYsbjvf6tfwU17ZKlWoEBW2mUyf9zZ67\\nd+/8QaqoJI9Z0CTDrH+zZp7IZDs5f96CjIxMZs4snmeQKAiCngjmBEEQhBxiY01yvI6LM3nDmsKb\\npKWl8d13l7l2rTMAx4/fwcLiHF999cVnHtmrgCWrIl5kZAQDB35neH/WrPls374VZ+dSLFuWsxR+\\ncnIyUqmUefN+BPT9wZ48eYKVlTVFixakZ89xVKz4qqhImzbe2Nracvv2LYYOHUnz5l8yfPgEbGws\\ncXJy/DQnnM3333vx/fcfdp9vGwBWrFiZChUq4eu7nY0bK5OZaU3Dhv1IT09/4z58fAbi4zMw176s\\nrKxzfTZZAgM3G/7+8E3J825vMWSIL/PmzaR792/QaDSUL18RX9/RudbLktcsaFa7CplMysyZ+iqm\\njRpNoUaN3O0xBEF4RQRzgiAIQg6enomEhmZVEUyiQoXPU3nxn+zhw0dcu/YqqElNLc3Zs1f56tNk\\nFL6z6Ogorl+/hrt7OQ4d2o+bmzu//LLLsCwzM5MnTx5TvHgJLCwsCQm5gqdnefbv30OFCvoZYp1O\\nx9Gjh6hYsTIhIVcwN7fA1PTVDFVaWhrp6fZ07HiS1NSG9Oz5K126lKJUKZfPddqf3Jkzl9m4sS6Z\\nmfqZtCNHerBq1W58fJq8975TU1NZvvw46eng41MTC4u3f771bQUF/QyQq6WGlZU1kyZNz7X+6+tl\\nteaAvIPg778fTGamxvA6+0yoIAh5E8GcIAiCkMPcuS3Jn38Hz56Z4+SUyrBhzT73kP5x7O0LUqjQ\\nTaKjswIVFfZ/4/oNxYo5snPnVmbMmIyTUwm8vTtStWp1/P3nkJycjEaTSYcOnShevATjxk1kzpzp\\npKWl4eBQxDCjIpFIUCgU9OrVGY1Gw5gxEwzLJRIJS5Yc49q1lRQs6Iel5WT27UtBo3Fl6tQZn/PU\\nP6m4uBQyM/NnW6JEpXr/NMiMjAw6d/6ZU6d6AnJ27/6ZdetcKF7c4b33/alMnvwrGzYUIDNTQbNm\\nR/H3b/vBCzsJwr+RaBoufHSiueW/m/h8/51WrVpOwYK2tGz54ZuR/1cEBf2Ov38cycnG1KwZy8KF\\nbZDJZJ97WAbZ0yyzNzT/qwYO/I4BA4bi4uKa5/tTpux/rbl9JDt33v5oz37+HalUKtq23cOlSz0A\\nKSVK7GLTJtf3DrqOHz9P+/buQCHDsqFDtzJmzPv1l/Px6cXSpXmnc35IZ85coX17O9LTS71cksDc\\nuSfo2rXBRz/2P5H4/+6/l2gaLgiCIHwQn6Ns/r9Nu3bV8PbWodVq/1ZBXF4+9uedmJiIVhuDtfXP\\nJCR8BWipWnUvlSt/uLzTXbu2Y2xs/Icl/T83U1NTNm1qxJIlW8nMlPLNN27vHMidPHmcokUdcXJ6\\nVfTEzMwYmewFGk1WMKdBofjr9+qzKmh+ikAO4NGj56SnV822xJqYmPRPcmxB+KcTwZwgCIIAQGDg\\nKvbv34ONjS0FCxaiQAGbzz2kfzyJRPK3D+Ts7QvnKJrxVy1atDzP5bGxcXTocIyrV/sBV3F0nEKr\\nViUYPLgJSqXyvY8LoNFoOHBgzycLPt6HtbUVY8f+9YDzt9+OU7NmbZYvX8yzZ9Gkp6vx9u5Ihw53\\nOH++LQkJbSlY8ABhYY5cv16UZcsWER0dxeDBvtSqVQeNRsOyZT9y5col0tMzaNOmHV991Ybg4Iv8\\n9NMyLC0tefz4ERs3bsfLqzaHDp0EYP36NRw6tB+JREr16jX57rvv2b17J7/8spOMjEyKFCnC+PGT\\nUSqNmTZtImZm5ty+fZPY2Fj69x9EvXoN33hOjRtXwsXlZ27fbg9AkSKHaNYs7xleQRByEsGcIAiC\\nQGjoLY4ePcSaNZvQaDLp1asLlSt/2P51wn9TQMDvXL3aHX0lxPI8elSI2rVv5tmAPjU1lQkTRhMT\\nE4NWq6F79944OBThxx/nk5qaipWVNePG/UC+fPkZMKAvpUu7EBJyBS+vxlSpUo1Nm9bzzTddCA9/\\nyrx5s0hIiMfY2JhRo8ZRrJgTR48eZs2alUilMszNzfnxxxWf5Bps3LgWhUKBt3dHFi6cy/379/D3\\nX8qlSxfYs2c3TZs2Z9WqFaSnpxueQzQxMWHp0kWcPn0SmUxG1arVqFu3PqdPn+TKlcuYmJgwY8Zc\\n1Oo0+vbtgYNDUaTSNLp31zJt2lG8vBoxfvwoChQogIdHeaZNm0jjxk05c+Y0L14kMWrUOGrUqE3/\\n/r2pWrUaAHfv3mbduq3ZGnTrZ2zPnj3N6dO/sWJFIEqlkqSkJADq1WtAq1b6ypMrVy7l119/pm3b\\nDgDExcWydGkAYWEPGT162B8Gc7a2NgQGlmPp0s1oNDI6dXLC1dXp43wYgvAvI4I5QRAEgatXL1On\\nTv2XMyVKatas88amvoLwrgoXHoBcHolEkk5CQkugJF5etWnd2puzZ0+TL19+evf2YcaMyTx79owJ\\nE6ZQq1YdkpIS6datA7a2+dBotBQqVIgVK5bQuHEz7t69Q3R0FEZGRnTs2IV69arRr98AAIYNG4BM\\nJkOhUGJv78DcuTNp2LAR/v5zsLd3oFixIgwbNuqTnb+nZ0U2b16Pt3dHQkNvkZmZSWZmJiEhl3F2\\nLklgYAALFizB2NiY9evXsGXLBtq0acfJk8fZuHE7ACkpyZiZmVOrVh1q1qzNvXt3GTt2BOHhT5BI\\npIwYMZbvv+9NRMQ9lEolFhYWZGRksHz5GnQ6HQ0a1CAuLo7SpUsTGnqLSZP+h5NTcVJSUnj69Aky\\nmYwyZdyyBXKvXLx4nubNWxlmUrMC8fv377Fy5VJSUpJRqVL54ovqgH5GunbtugA4ORUnLi7uT69R\\niRJFmD27yJ+uJwhCTqJMkCAIgkD2/lF6IpATPoxvv61OvnzuPH68jcePAyladC3ly5ciLS2NSpWq\\nsm7dVkxNzVi1ahnTps3C1NSUmTOnEhJyhaCgTSQmJr68saDj4MF9PH36BIDUVBU+PoMMwU7Wd/jE\\niWNERIRjZGSERAJ37twiNjaWevUavOyvVwC1Op19+379ZNfAxcWV27dvoVKloFAocHcvR2joLa5e\\nvYJSqSQs7AE+Pr3o2bMT+/fvJTo6CjMzcxQKJdOnT+bEiWMolcaG/V25cplNm9axYMFitFotOp2W\\nKVPGo9FoiI2N1V8NiQSpVEpw8EWkUikajYbq1WsAMGrUOLRaLT/+uIKtW3+mShV9/0Nj41c9Jb29\\nWxpu6EgkEvK6t+PnN4nhw0cTGLiZXr36kJ6uNryXvdG3uDEkCB+PmJkTBEEQKF++AtOmTaJLlx5o\\nNJmcPn2K4sU7fe5hCf8CtrY2tG0bh6lpfaRSCWq1mvBwfbCVNZPj7FwShUKBo2NxAgM307Ztc1au\\nXEJCQryhOItUKiVfvvz06NEbAFNTMxwccs/kBAdfwNTULNdzgJcvX+LBg/vExj4nMTGRkJBgWrb8\\nGktLq498BUAul2Nv78Devb9Qrpwnzs4lCQ6+QHj4U+ztHahc+QsmTpyWa7uVKwO5ePE8x48fYceO\\nrfj7LwUgIyMdqVSKkZERJiamqFQqRo36HyNHDmX9+q0A6HTg4lKGSpWqvHytQ6fTUbVqdXbs2IZC\\nocTMzJzHjx9RsGChXMfOXhSnSpUvWLNmJY0aNUGpNCYpKQlLS0tSU1XY2uYjMzOTAwf25rkfQRA+\\nLhHMCYIgCJQu7UrDhl706PENNja2lC3r9rmH9LcTGRnB8OEDcXf34Nq1EFxdy9K0aQtWr15BfHwC\\nP/wwhTJlxHV7XXDwRa5fv8q2bdtRKpUMHPgd6elqZLJXP0EkEglyuRHPnz/HwsICiUTKN990ZebM\\nqVhaWjF8+Jgczcvj4+Py7EGm04GRkQJrayuOHTtM/fpfotPpuH//Hn5+kxg2bCTVq9di375f+fHH\\n+Tx79uyTBHMAnp7l2bRpPWPH/kCJEs4sXDiPMmXK4uZWjnnzZhIe/hQHhyKkpqby/HkM+fMXIC0t\\nlerVa1KunCcdOugrf5qampI/fwG0Wh1t2jQnNTUVY2MT0tPTSU1V4ec3iUePHpCc/IKrV0M4fvwI\\nz5/HALBq1QocHIrg5laOkyeP07mzNzY2tigUCsLDnxIfH8fRo4dp0OBLQF/VslevLmg0mVSp8gXf\\nftsNIyM51avXom/f/vTu3Y++fXtgbW2Nm5s7KpXKcL7Zg0FRHVcQPh4RzAmCIAgAdOvWi27dehle\\ni15GuYWHP2Xq1FmMGTOB3r27ceTIQZYuDeDUqROsXbua6dPnfO4h/u2oVClYWFi8TCd8yI0b19+4\\n7oMH91i82J+0tFTWrPmJFi2+5vr1EJYuXUhKSgppaSo6dOiMo2PxPLeXSPSzSJcvX2T37p0EBgag\\nVqtp3LgpqakqduwIYunSRURGRlCokB0lS5bKcz8fg6dnBdatW427ezmUSmOUSiWenhWwtrZm3LiJ\\nTJw4lvT0DAD69u2Pqakpo0cPJz09HdAxcOAwABo2bMS0aRNJS0tl6tSZuLiUoX//3vj5TUIul/P8\\n+XN27NjB0KG+REVFIpFI8PbuyPLli+ndux9Nm7YA9NUply5dRXDwRc6d+515834E9M/mZRk4cCht\\n27Zn585t3LkTapj1y/L11958/bV3rnPNaiSf5eDBEx/sOgqCkJMI5gRBEP7Drl9/wPjx14mONqNs\\n2Xj8/ZtiZmb2uYf1t2Vv70CJEs4AFC9egsqVq77825moqIjPObS/rS++qMGuXdvp0qUdRYs64u5e\\nDsg9WyORQNWq1ahatRqNGtVl5cpAdDodK1Ys4cyZk+h0OgoVsqdRo6bcuXObcuU8cjQoVygUdOzY\\nBdBXZdy/fy9GRnLq129Ijx69sba2ZsOGdVhbW9O8eascs0ifQqVKVTh27Kzh9aZNOwx/V6xYmZUr\\n1+baZuXKwFzLypXzZP78xQwY0NdQIXL8+MkEBW3i3r27jBo1DtAHVH5+kwzbWVvbULNmbcPrrEIn\\nzs6lWLzYn6VLF1GjRm08Pcsb1qlbV9+0u3RpV06cOPqn5xgWFs66dVeRy7X061cTGxvrP91GEIT3\\nI4I5QRCE/7DRo69x/rz+B/C9exqsrTcxZ86Ha+T8b6NQvCrqkPXMUtbfGo3mcw3rb83IyIg5cxbm\\nWp59tqZXr755vieRSPjuu+/57rvvc7xfoUIlKlSo9Mb9denSgy5degDw/HkcPj47iYqywtW1F5Mm\\nNUWhULzXOf0dZA+GdTodEok+7dTExCTXujduPCAhQUVg4DF8fFogl7/6+Ve0aDECAjZw9uwpVq5c\\nQuXKVQ3PJWZ932WyP/9+P3kSRefON7h7tz2g48SJNWzfLm4OCcLHJoI5QRCE/yidTkd4uHm2JTIi\\nInL/EBT+fsaM8TU0jG7X7htatWrNr7/uYsOGtZibW1CyZCkUCgVDh44kPj6euXOnEx0dBcCgQcMp\\nV87zM5/Bp9O6dSC3b3sC6Zw+XQ2p9ADTprX83MN6b9HRUVy/fg1393IcOrQfDw9P7t69nWu9+/cj\\nGDLEFLm8INOnV+fy5SBWrepoeD/rOcVGjZpiZmbOnj27/9J4tm27wt277V6+khAc3JF9+w7h7V3/\\nL+1PEIS3I4I5QRCE/yiJREKpUgmEh+vQl3VX4eKS/rmH9beWOzXw8xR5GDNmApaWlqjVafTp050a\\nNWoRGBhAQMAGTExMGDzYh1KlSgPg7z+H9u074eFRnqioKHx9B7J+fdAnG+vrIiMjGDVqKGvXbnnv\\nfQUHX2Tz5g3MmjU/z/c3bz7N7dudAeeXSzYRGip77+N+bhKJhGLFHNm5cyszZkzGyakErVt7s337\\n1lzrnj4dTXi4D9bW0RQpMoCrV02JiHiVbpn1nKJUKkEul+PrOzavI/7p99vMTAKkAfoWClJpHDY2\\npu9xloIgvA0RzAmCIPyHLVpUlwkTNhATY4qbWyrjxjX73EP627K3L5yj3H32Ig+vv/exBQVt4uRJ\\nfVrhs2fR7N+/hwoVKmFhYQFA/foNefLkMaBv+Pzo0UPDtiqVirS0NMDik433c7lwIZlXgRxAWayt\\nj3yu4Xwwdnb2rF27BZksZ2AaFJRzVm3s2B8YN+5XQEdCQhcSErpgaXkChUJpWDfrOcXXZd+Xq2sZ\\nFi5c9odj6tmzASdOBHLoUCPk8jS8vc/SoEHu4iiCIHxYIpgTBEH4DytUKD/Ll4tn5N6WTqdjzZrj\\nPH6cTrVq+WncuNKfb/SBBQdf5NKlCyxfvtpQ6t/R0YlHj8KyjTP7TKGOFSsCczRx/qsOHNjL1Vgz\\nsQAAIABJREFUtm1byMzMoGxZd4YNG0WTJvVo1+4bzpw5hVKpZMaMudjY2BIe/pRJk/6HWp1GzZp1\\nCArazKFDv+XYX2RkBFOn/kBqaioAw4aNxN3dg+DgiwQErMDa2oaHD+/j4lKGCROmAPD772dYtGge\\nSqUxHh7lc40xO3t7LZAK6NOHTUxuMHVqi/e+Dh/Sm67poUMnATh27DBnz55m7NgfmDZtIgqFgrt3\\n7+DhUZ7GjZsye/Z01Go1Dg5FGDNmAhYWFnTt2hVHR2euXLmEWp2Ou/sTrl//DmPj+1SsuISxYzPQ\\naDLp1asvtWrVzfE5xMa+4MWLlsjlxahX7wkREefy/BxeZ2RkxNq1Hbh8+QbGxgrc3LxFSwJB+ARy\\nN2kRBEEQBCFPY8bsYvToWixe7E2/foVYv/63P98om8jICLp16/BeY8he6v/RozBu3LhOamoaV64E\\n8+LFCzIzM3NUHqxSpRpBQa9mDfN6rupthIU95OjRQyxbFsDq1RuRSmUcPLiPtLQ03N09WLNmI56e\\nFdi9eyegT+/s0KETgYGb39hM2tbWlvnzFxMQsJ5Jk/xYsOBVa4d79+4wZIgv69cHERERzrVrIajV\\nambNmsasWQsICFhPXFwsfxQvDB78Jd7emyhadBdly27mxx/tsbe3/0vn/2d8fHr96Tpbt25ErU4z\\nvH7TNdWnPeu9HhA9fx7D8uWrGTBgCFOn/sD33w8mMHATzs4lWb16BaCffX38+BGrV29k1KhxWFv/\\nSt++k/n224307t2WlSsD8fdf9rINRJrhc+jVawQ3b44lKekUV660Zf36QoSGhub4HK5evWIYi5dX\\n7Rxjk8lkVK7sgbu7qwjkBOETETNzgiAIgvCWjh61QKezBSAlpQz79t2kS5e32zYzM/ODjCGvUv8F\\nCxaka9ee9OnTHUtLSxwdnTA11VcRHDLEl3nzZtK9+zdoNBrKl6+Ir+/odz7upUvnuX07lN69uwKQ\\nnp6OjY0NRkZG1KhRCwAXlzJcvHgOgBs3rjFjxjwAvLwas3ixf659ZmRkMn/+TO7du4tUKuXp0yeG\\n98qUcSN//gIAlCxZmsjICIyNjSlc2AEHhyIANGrU1BA85sXIyIglS9q9rPb4cYOLpUsD/nSdoKDN\\nNG7cDKVS/1zZm65pdlqtzvC3RCKhfv0vkUgkJCcnk5ycjKdnBQCaNGnO+PH6z1WlUmFrmx/Q97eT\\nSCSMHu3L4ME+3L9/k02b1gGQkZHBs2dR2NrmZ/78mfz+ezBWVrYoFI8ASEkpgY2NfY7PISoqMtuM\\nqAjYBOFzE8GcIAiCILwlE5MM8uVbiEZjRUJCd5TKDJYvX4ytbT6ePYvm3LkzSCQSunX7loYNvQgO\\nvshPPy3D0tKSx48fGRozg74B+fjxoxg58n+4upZ56zG8qdS/i0sZWrVqTWZmJuPGjaBOnXoAWFlZ\\nM2nS9Pc+d4CmTVvkahOwadN6w99SqeSdWjRs2bKBfPnyM378FDQaDQ0a1DC8Z2T0qn3Aq9L4rwcP\\nOt7Gp5gl8vKqzaFDJ9+YIhoUtJnnz2MYNKgf1tY2+PsvJSws7OX4pDg4FGHs2B8wMTFh9eqVLF26\\niAsXzuHuXo5jxw5TqJAdp079xqVLFyhb1g1b2/xkZGTQr18v0tPVAKSnZ5CRkcHTp0+Jjn5Gz56d\\n6NKlJ6mpqSxZog+mhwwZwZo1P5GYmIiDQ1GUSmO2bNnAnTu38fT05PDhh0gkKszND2BmFoWNjTmD\\nB/fnxYskoqIiMTKS06hR049+PQVBeDsizVIQBEEQ3pKPjxUyWWEsLbfh7LyTAQNKcvToIQoWLMi9\\ne3cIDNzMggVLWLLEn9jY54A+rXHIkBFs3LgdnU4ffDx+HMb48aMYN27SOwVyfyQgYAU9e3aie/eO\\nFC5cBDc3T+bN28f8+ftITEx87/1XqlSVY8eOEB8fD0BSUiJRUZFvXN/NrRzHjumLjRw+fDDPdVSq\\nFGxt8wGwf/8etFrtH47B0dGJyMgIwsOfAnDo0IF3Po+P51XAmFeKaLt2HcmfvwCLFi3H338pCQkJ\\n3Lp1HaVSydy5i3BxcSUwcBVRUZFIpVK0Wi0//bTW8D2ytrahVq06VK1ajU2b1mNubo61tQ3fffc9\\nAQEbKF7cmczMDIyMjHBwcKBQoUKsXr2R/PkLYGxsjJGREVWrVsPPbxLNmrUkMHATHh6eLFgwB5Uq\\nBaXSGLlcR7NmVZBIoGjRSfTunYyVlQXTp88mIGA9derUe+NnKQjC5yFm5gRBEAThLXXsWIN69aIY\\nPVrG4ME2qNWxlCrlwtWrV/DyaoJEIsHGxpby5Sty69ZNzMzMKFPGDTu7V89pxcfHM2aML35+c3B0\\ndPpgY/v++8GGvxMTE2nb9jBXr3YHdBw4sJpt25phbm7+5h38CSen4vTp48OwYd+j1eowMjJi6NCR\\nb2zPMGjQcCZPHs+6daupWrVajmNnrde6dTvGjRvJ/v17+eKL6piYmGZbJ/cYFAoFI0eOY+TIISiV\\nxnh6ViAi4mme49VoNLmqPX4quVNEI3P19rtx4xpRUZEYG5vg7d0CrVaHqakptWvXw8LCkmPHDnP1\\n6hVDsF+3bgNu375F4cJFuHTpPADffz+IsWNHoFanYWRkRL58+tRKiUSCVCqlV6/OaDQamjdvRVJS\\nIj169GbLlg1s3LiODRsCsbcvzM2b1/n++8Hs3fsr8fFxfPll45cpumnUrl2WzZuDWbbsR0JCrvD8\\neQwqVQrx8XHY2Nh+ugsqCMIbiWBOEARBEN6BnZ0dXbt248SJY8THx9K8eSsuXjxnmHXLkhWwGBvn\\nbMRubm5OoUL2hIRc/qDBXHabN5/h6tVu6GeLJAQHdyco6Gd69mz0Xvtt2NCLhg29ciw7ePCE4e96\\n9RpSr15DAAoUKMCKFWsAOHz4gKFVQvY2DkWKFCUwcJNhex+fgQBUrFiZihUrA/pqjzdv3uDq1Stc\\nv36VYcNG8exZdI5qj35+k96p2uOAAX0pVcqFK1cuodFoGDNmAmXKuJGamsr8+bN4+PBBjmqP7yor\\nRTQoaDNHjhzk3r07NGrUJNd6lSt/wcSJ03ItNzExYdWqdVhaWgFw5swpFAojxo79gdDQm5w/f/bl\\ndT1I797f0bZtB6KiIhk48DvDPooVc2LyZH167b59v5KUlIhSqcTU1JSAgPXI5XIyMzP5+usmFClS\\nlNq161KjRi3q1WuIj89AvLzqULFiZaKiIjl37gwBAeuRyWS0a9cKtVr0oxSEvwuRZikIgiAI76hu\\n3fqcO3eG0NBbVKtWAw+PChw5cgitVkt8fDwhIZcpW9YtV4AH+mfe/Pxms3//Hg4d2v9RxmdsLAey\\n/+BOw8Tk085ShYaG0qNHJ7p3/4Zdu7YzYMCQd95H9mqPP/20jjt3IvD1nZXjuv6Vao8SiQS1Oo3V\\nqzcyfPhopk+fDMDatQFUrlw1V7XHv2rXrm3Url2Xr75qA4CpqSkpKSkAlC3rzrVrIYaU0eTkF4aA\\n922lpKQYZgD37HnVF04mk+UYd/br5e7uwZEj+lTJgwf3GQqovC4jI5PmzQ+ycOEFXrzQz3IGB1/8\\nw9RaQRA+PTEzJwiCIPyrZRWmeF/BwRfZvHkDs2bNRy6XU6lSFSwsLJFIJNStW58bN67So8c3SCQS\\n+vcfjI2NLWFhD3OlC0okEoyNjZk1awFDh/bH1NSMmjVr53nMrVs38tVXbQzVD99Wp071OHAgkMOH\\nvQEtTZrsoF2792uJ8K48PcuzZs3G99pH9mqPT54kkJhoyosXVciXT8eNGw9wcyuRY/23rfYI8OWX\\njV+OswIpKSkkJydz/vzvnD79W65qj8WKOf3pWHOmm8Ls2X5ERIQTHx9Perqa3347RmJiIp07e1Oy\\nZClWrAikQoVK9OnTnfR0NXK5EUOHjmDJkoXExDxjwIC+jBz5P9zdy6FSpTJkyPfodFoKFy5iOE6n\\nTt2YNu0HAgNXUb16LbKe29uwYQPdu/cwFECRSCSG8Q0ZMpLp0yexceM6bGxsGDv2h1znsGvXWdLT\\n5dy82RaptAGJiR3p3NkbN7dyODoWz/OcBUH4PEQwJwiCIOQQGnqL/fv3MG3aJFatWo6pqRnffPOW\\n9fffko9PL5YuDSAqKpJr10Lw8sqdgvbhfPgfnFqtlhs3rjF16izDsv79B9O//+Ac61WoUIkKFV41\\nFs+eYmhubs7KlWv/8Divl7J/fQxSad4JNvoGzu05fPg8UqmEhg07fLbnx95X06YtaNq0JTVrpqNW\\n6wMzG5sANmy4jZ9fCdRqdY71jY3fLfDNkhWXTJs2m6JFixmWBwVtZuzYEbi4uDJ+fN4Ns+FVumn2\\nFNHz539n1ap1rFq1HBsbW6ZPn0tw8EUWLdK3bLC3L0zhwg4sWfITCoWCCRPGULFiZaZPn4NOp0Ol\\nSiEs7CHu7u74+c1BJpMxZ84MatfWp366u5dj06YdhjH06eMDgJWVVa7vVtOm+mbpdnZ2+PsvzTX+\\n7EHdtWuJ3Lt3GQCt1ob79zcyadIlGjWqnuc5C4Lw+YhgThAEQcjB1bWMoejC+9x5z8zMRC7P+38z\\nWT25IiLCOXTowEcO5vR0Oh1LlizMs31AXqXkAX7//QyLFs1DqTQ29NZ6+PABI0YMRiaTMXbsCNLS\\nUpFIpBgbG6PT6ShWzJELF86RlpaGvb09CxYsoVAhO/r2/Z6wsOJIpR589ZWMHTsmvnMpey+v2nz1\\nVVsuXjxPvXoNuH07lOnT9Y22L1z4nZ07t+PnNxsAuVxOkyY18r4Y/xCVKlVl9Ojh1KvXEJlMg1Sa\\ngFSagkaTn9TUaLRaLb/9dgwzs9yFXczNzbGwsCQk5AqenuXZv3+PIbDW6XQcPXqIihUrExJyBXNz\\nC8zMzKlatRrbtm1m6NCRANy5E8quXdvw919qSGf8I3l953U6HdeuhTBtmv5zqVixMomJiahUKUgk\\nEmrVqoNCoX/GLjj4ouG7J5FIMDMzZ//+PTl60anVavLly/cXr+jbcXOzRKl8jFqtD2rz5TvPyZPh\\nPHmSQs+eDd54E0EQhE9PBHOCIAj/cpGREYwaNZS1a7cAsHHjOtLSUrl8+RJly7oTHHyR5OQXjB49\\nAU/P8kyfPpkjRw7h5laWGzdu0rVrD8LDnzJv3iyCgy9QurQrAwYMZcuW9URHRwH6yoXlynmyatVy\\nIiKeEhERgZ2dPV279mT69ElkZmai1erw85uNg0MRQ+rjsmU/8vhxGD17dqJp0xb89ttxBg/2pVSp\\n0gD4+HyLr+8YnJ1Lvvd1OHHiqKF9QEJCPL17d6N8ef1Mz717d1i/Poh8+fLj4/Mt166FULq0K7Nm\\nTWPRouU4OBRhwoQxSCRQvHgJatasjY2NLXXq1Gf48IFYWFiyZs1GlixZyC+/7GLgwKHUqlUHb++W\\nzJ8/m169BnL5spzY2BokJzcmNPQuJUq8KsOf1/HbtevI1q0bWbRouaEQRlpaGm5u7obnzzp39iYx\\nMQErK2v27PmFFi2+eu/r9HeSVUFzxozJlC6dQHy8FdHRP2BsXJXHj7fj43MCV9cypKamGrbJfgNi\\n3LiJzJkznbS0NEMft6x1FAqFodrjmDETAOjRozcLF86le/eOaLVaVCoVcXGxDB8+kKZNWxAScpmI\\nCH3z8pEjx+HsXDLXd37QoGHMmuVHZGQEMTHPCA29CcCJE8c4cuQgmZkZJCUlodVq0Wq1nDlzkqNH\\nDyGRSEhLS8vzOcu8+vt9TG3a1CAs7AAHDlwiNTWeiAhYvrw3kERwcBCLF7f/ZGMRBOGPiWBOEATh\\nPyb7j12tVsvKlYGcPXua1atX0K/fQC5fvkT58hVZvHghdevW5cGDe8yadYmvv26LVquhd28fRo8e\\nxrRps/DwKE9UVBS+vgNZvz4IgEePHhnSxhYsmE27dp1o1KgJmZmZ2RpK68fg4zOQTZvWM2vWfAAs\\nLCzZt+8XSpUazuPHj8jIyPgggRzwp+0DcpaS1/9gL1zYAQcH/TNKjRo1ZffunQCGmZbTp3+jWbOW\\n7Nv3KypVCsbGxmRkpNOkSXNkMhkFCxbi6tXLnD9/h7S0VzM7KlUptNpXP9rfppQ9gFQqNVSLBGjc\\nuBkHDuyladOW3Lhx3TCr80/wtq0DslfQPH78ApGRT2natB/W1qNyrZs9VRCgVKnSLF++Os/9Nm7c\\nnEGDhhteJye/ICzsCT4+g3K0UWjXrhWLFi1n1arluLiUMaRKTp06gdWr9c8EZv/OZ6VKtmvXEW/v\\nlhQr5oiTUwl2797BunVbCQm5zPjxozl16jdiY2NJSUkxpEqOGzeCnTu30b79N2g0GtLSUg2zk+3b\\nd8LGxoakpERUqlTs7Oz+9Nq9j2HDGjNsGAwatJ/Q0HYvl1py8GAJww2E1w0Y0JeBA4fh4uLKiBGD\\nmThxGjodHDq0n9atvQF9gZoFC+YwderMdx7TtGkTqVmzdo7/BgThv04Ec4IgCP9hdevWB8DFxZWo\\nqEiuXr2Mh0d5kpKSMDc3p1q1GoSGhhITE839+3dQKJTMmeNHYmIC8+e/el5MpVKRmpqaK23Mza0c\\na9cGEBMTTd26DShSpGiO478+C1G//pcEBq6if//B7Nmzm2bNWn6wc5VIJG9sH5BVSh5AJpO+DDpf\\nTzHNua1Op8tzn1nvAUil+mClenVXjI33kZKin40zM7uFTvdqZi738TPzPAeFQpkjGG/WrBWjRg1F\\noVDQoMGXnyX9bc2anzh4cB/W1jYULFgIF5cy1KlTj3nzZpGQEI+xsTGjRo2jWDGnHK0DypXz5MWL\\nJMPr+Pg4Ro8ez969vxAaepOyZd0NwdmcOTMIDb2JWp1GvXoNsbb+EgBv75Y0bdqC06dPotFkMmXK\\nDIoUKUanTt4sWxaAtbU1Wq2WTp3asnz56jwDEIDjx68xcmQMYWHuODufY84ce2rWLGt4P69UybCw\\nh2+RKinFzMwcZ+eSHD9+BC+vOkil+psJkZERWFpakpSUyIIFs6levRbDh49m9mw/9uz5GalUiq/v\\nWNzc3HP095PL5QwfPuqjB3NZjIw0OV4rFKnI5UZ5rpv9uzl7tj+gzwzYuTPIEMzlz1/gLwVyWfsX\\nRVcEIScRzAmCIPzLyWSyHLNA6emvCkZk/SiTSmV5BjD58uUnKSkBMzNzlErjl72vLGnR4ktWrAjE\\nyCj3j7rsxTq8vJrg5laOM2dO4us7mJEjxxqKQ+TF2NiYypW/4OTJ4xw7dpiAgA1/9bRz8fCowM8/\\n76Bp0xYkJiYSEnKZAQOG8PDhgzzXd3R0IjIygvDwpzg4FOHQoQM59nXw4D7q1m3AsGEDsLKywtTU\\njLS0NOzsCnPkyEEaN25GcvILypRxw9m5GF9+ac2FC9tRKlVUrHib48c1eR43u6xS9llplq/Lnz8/\\n+fPnJzAwAH//JX/twryHW7ducOLEUQIDN5ORkUGvXl1wcSnDrFl+jBgxhiJFinLjxnXmzp1pKLqR\\n1TpAIpHg5zeJ5ORkli9fzalTJxg9ejjLlgVQvHgJevfuxt27dyhVqjR9+/bH0tISjUbDkCH9efDg\\nHiVKlEQikWBtbUNAwHp27tzGpk3rGTXqfzRu3JSDB/fRvv03XLx4npIlS+cI5BYtWp7jPObPf0xY\\nWEcA7t93ZsGCzTmCuSzZA/fsTbOzvvOZmZk51gsK+hnQf687dOicZ6pkz559OHfuDLt2bcfS0pLp\\n0+fmeF+tVlOyZCmWLFmFiYlJru3/iqwiR0OG+L5xneDgiwQGriIiIoLSpbejVr9AoylEhw5NuHXr\\nBkuW+KPRaHB1LYuv75hc/xZ4e7dk1ap1LFu2iPDwp/Ts2YkqVarRpk07RowYzLp1W9FoNCxduojz\\n588ikUhp1ao1bdu2Z/XqlZw5cxK1Wo27uwcjR44z7DevmyeC8F8mgjlBEIR/OVvbfCQkxJGUlIix\\nsQlnzpziiy+q57lu+fIV2LZtM8WKOZGcnMzp06dwdi7Fw4f3sbCwwNLSEp1Oh6urG0FBm+nUSV+U\\nIetH9+siIsIpXNgBb++OREdHc//+vRzBnKmpGSpVSo5tWrb8mpEjh1C+fMUc6W5/Vdad/HdpHwCg\\nUCgYOXIcI0cOQak0xtOzAhER+p5gvXr1Zfr0yRw7dgSFQkFaWio9enRCItEfZ+/eX9i4cR3JyS/o\\n1asvACNHDmT06OGo1WsoXLg6Jiam2caY99hbtWrN8OEDKVCgIP7+S/OclfDyakJiYuJblc//0K5d\\nC6F27XoYGRlhZGREzZq1SU9Xc/16COPHv0qDzMjQBznZWwdkyWrLULy4M7a2+ShRwvnl6xJERUVQ\\nqlRpjh49yO7du9BoNMTGPufhw4eUKKFPv61btwEApUu7cuLEUQCaNWvJmDG+tG//DXv2/IydnT19\\n+nQnMzODsmXdGTZsFE2a1KN1a2/Onj1NfLwUY2NX8uefg1weRWKifsZ6795fiIuLZfTo4UREhDNj\\nxlQWLlxKcPBFYmKeYWpqRkREOCEhlwkJCebx40dUqlSF4cMHkpqqQq1Op0WLr6hatVqeqZImJsbI\\n5XLq1m1A0aLFmDJlQo7re/nyXYYMucPdu2VwcjrJjBkO1Knj9t6fW/YiR38mKiqCuXNHExOj4bff\\ndlGoUBR+fktZuHAZRYoUZerUHwypodllzaL5+Azi4cMHhpTUyMgIw+e/e/dOoqOjWLNmE1KplKSk\\nJADatu1Az559AJgyZQKnT598Y/sOQfivE8GcIAjCv5xcLqdHj9706dOdAgUK4ujoBOSVsiShdGlX\\nKlaszOHDB+nbty9ly7phbW3D5cuXyJcvPz16dCIzM5Patetw+/ZNunfXP9tTvnxFfH1Hv9zvqz0e\\nPXqIAwf2IpfLyZcvP9269TIcG6BkyVLIZDJ69OhEs2Ytad/+G1xcXDE3N6d581Yf5Pyzl09/m/YB\\nWZUMAb74ojobNmzLtU/9DMqcHMtUKhXPnkVjb18YpVKZaxsbG9scz2/5+AwEcpayf/34bdt2oG3b\\nV/3hsp+LRqMhKiqS4OCLtGz5dR5n/inkTjPV6XSYm1sYfry/7vXWAVkzOlKpFIXi1eyOVCpFq9US\\nERHO5s0b+OmndZibm+PnNynH7HLWNq/SY6FQITtsbW25dOkCV6+GULq0C8uWBSCTyZg7dyYHD+4j\\nLS2NSpWq0r//YNq27UJy8hyePg3E1PQs+fOPAwYB+l5z//vfJJRKJZ06edOxYxusra1RKPSfsUQi\\nISbmGYsWLcfOzp6NG9exZ8/PyOVypFIJP/+8ndq16+aZKqlQKPHzm2RIue3Xb2COazN79m1u3dIH\\nSffueTJnzmbq1HEjNTWVCRNGExMTg1aroXv33lhZWRlmy8qX92TAAF+MjIy4desGCxfOJTU1DSMj\\nI/z9lxIaetPQM/HmzessXDiP9HQ1SqWSMWN+oFgxR8MYChYsZLj5U7iwMWvW/EThwg6GlOmmTVuw\\nY8fWXMFc9u/Dm1y6dJ6vv/Y2pAdbWloCEBx8gY0b16FWp5GUlESJEs4imBOENxDBnCAIwn+At3dH\\nvL07vvF9a2trQ0rY6NHjGT16PAUKWBAT84L09HR8fAbmmVL5uqxZqCxduvSgS5ceudbLCkrkcnmO\\nnlcZGRnEx8eh1WqpWrXa25za38KxY1cZOzaaR49KUrr0YRYudMHD48MUbslLRMQzevf+jefPNyOT\\nSXF2bvDRjvVHPDw8mTXLj65de5KZmcmZMydp1aoNhQsX5tixw9Sv/yU6nY779+9RsmSpd96/vtea\\nCmNjE8zMzIiLi+X338/kCL7fpGXLr5k8eTwlSjjnKO2fnp6OjY0NRkZGhiCladNaXL8ejonJTsqU\\nUbBx46vZ4saNmxmK4HTo0AkLC0vat/8GL686L7dtQXR0FHZ29gDcvHktRw/AjIwMnj59gpubO506\\ndcvVhiMgYP0bz+HFC2Wer8+dO0P+/AUNz6UlJyfTrVsHw2zZnDlT2blzG61be/PDD2OZPHkGrq5l\\nUKlUuW40ODkVZ/HilchkMi5cOMeKFYtz9E/MfsMnK1BPSkrMsex9vL69Wq1m3rxZrFq1jgIFChIQ\\nsIL09PT3OoYg/JuJYE74P3vnHRbF1cXhd5elSK8qikRABBVFECtW7L0XiNIixoLGFmvsBaMkdgVR\\nEMUSNcZesNdYsUQFKwQQLKj0ust+f6xsqJaoMeab93l8dGbuvXPv7IL3zDnndwQEBARKRS6XM336\\nHn77TQ+xWIaraw6TJnX8JPfKz89n9OidnD37J+XKHaBVq8/lafp7+PvH8vChwjNx504dFi7cQljY\\npzPmFiy4wJUrnoAXAIsXb6VXL/k7iUMUFIIfNWrYB8/D1rYmTZs2x8NjAIaGRlhZVUNHR5vp0+fi\\n77+A0NBgpFIpbdq0UxpzxedY+Li0a9WqWVO9ug1ubr0pX74ideqUVPl83bpY+GZz5s+fRc2adtja\\n1iySryaTydiy5S8jSiwW07ChDa6u7QHYuLGoKmYBcrkcsbjkM9bQKJrLNnbsBOrXL/oyIiLiirKm\\nYlLSS/z8zpGWpk6LFuX4+uvSvU5Nm+Zx5cozZLLyQDJNmiiMTCsra1auXMrq1ctp0qQZmpqaRbxl\\nPXr0ICQkFCen+hgZGStDKjU1NUvcIy0tjTlzZvD4cRwikUiZ91fA06dPuHXrD+zsanPkyCFsbWuw\\ne/dOZS7p4cMH3mhca2pqkpmZWeo1J6eG7N69E0dHJ1RUVEhNTVV+hrq6emRmZnLixFFcXNqWOb6A\\nwP87gjEnICAgIFAq27efZu1aF6RShcdh1aqHNG58lRYt3u4VeV/Wrj3G1q29AV2MjIzYu/cpQ4c+\\nJjQ06IuQIk9LKxo6mJFRMszy49/vL6MiJUUbqVT6Tt7Tj60G6Oo6CG/vIWRnZ+PrOwQbmxqYmlbi\\np5+WlWhbvHRA4WNT00q0atUGN7feSmXMhIQE7t+/S0xMNGpq6mhpaTFx4jRevnyBj4/FVRY3AAAg\\nAElEQVQH27fvARR5WH5+swgN3cqdO7fw85tHRkYaYrGY+vUbsmDBXK5du0LNmnZcuxZB48bO5ORk\\ns3r1ciIirvD4cTytW7cjKiqSgIDl5ObmMGbMCO7du4tUKsXUNIjTp48TFxfHokVLAJDL8xk3bhQJ\\nCfGkpCQTGxuDuXlVkpKes2iRH4aGhrx8+ZJ+/Vzp0qVHkZqKjx+bcf36KkDEoUMPUFU9T79+JQu8\\nT5jQESOjE9y5k4elpZjhwxXqrlWqmBMcvInffz9LUNAq6tWr/7c/v7VrA3Byqo+fnz9PniQycuS3\\nRa6bm3/Fb79tY8GC2VStakn//l9Tq1Ztpk2biEwmo0aNWvTo0afM8fX09Kld2x539/40auRMr159\\nld/Brl17EBcXi4eHKxKJhG7detKrV1+6du2Bu3t/DA2NqFnTrsh4gpqlgEBRBGNOQEBAQKBUoqNT\\nlIYcQHa2BQ8eRNCixce/V2KiDFDky8jlIrKyTIiOTvxipMgbN04jKioV0EUieULTpm9XqvwQWrZU\\n59ixR2RnWwK5NGjw5I2GXGjoOg4d2o+BgaGyfMD27dvZtGkzeXlSzMzMmDZtNjKZDA8PN7Zs+RWJ\\nREJGRjqenl+zZcuv/PbbDnbv3omKigpVq1owa9Z8ABYunEdMzCNyc3Pp2LEL1tY2f2tNZSljzp07\\nk7FjJ2Bv78C6dYGEhKxh1KhxSKV5JCYmYGqqUA9t3bod2dlZjBw5mceP26CnF06lSnU4cGCvsvB4\\nXFwspqaVcXZuTljYemWdxVmzfuDy5QuMGzeRdu06cvXqZebP92f//j0sX/4zR48eIj9fjrW1NQ8e\\n3MfR0Ync3FzGjPmeZ8+esnZtgFKx08zMnLS0dLKyshCLxaxevZyOHbsoayqOGzeJhg1fUGCMZ2dX\\n4+zZ6/QrpQ63SCRi8OCSIbRJSUno6OjQrl1HtLS02blzO0+eJCq9Zbt378bBoR7m5lV58SKJqKg7\\n2NrWJDMzo4jaLEBGRoayxuH+/XtK3EtFRYVp04rWL6xXr36pSrOFVUILDG2AGTPmFmkXGrpVOfbI\\nkWMYOXJMkes+PsPw8SnpOS7+MkBAQEAw5gQEBAQEyqBzZzuWLj1BYqJC2c/c/CDt2tX92+MdPLiP\\nrVs3KUPnBg8eyvz5s0hJSUEul2BoqM/Ll4pwqgoVHuHg0JczZ/Yrc2qioiJZsWIxWVlZ6OnpM3Xq\\nDIyMjImMvM2CBXMQi8U4OTXk4sXzbNjwCzKZjICAFVy/fpXc3Dx69epL9+69PvzBlIKfX3fMzI4Q\\nEyOndm0NPDzaf5L7FODh0QJ19XNcuBBB+fJSxo0rOyw1KiqS48ePsH79FmQyKd7eA7G1rUHbtm1p\\n2VKRvxUUtJp9+3bTu3d/HBwc+f33szRr1pKjR8Np2dIFiUTCpk2h7NixV2nkFVB8o/53KU0ZMzs7\\ni/T0NOztHQDo0KEz06YphHZcXNpy7Fg4Awd6cvz4UebMWcCKFdvJzExDW/sKMpkh8fFxJCdfYcqU\\nGezatYOqVS0wMDAkIGA5xsYm/P77Wc6ePcWIEd8RGXmbiIgrbNu2hS5depCXl8uBA3vIz8+nXLly\\nTJkyk6ioO+zevZO7dyORy+UMGtQPfX199PUNSE1VhCdKpVIGDPhaKUrTrl0LtLS0ld9jXV1djIxu\\n8VfkoRQDg7z3elaPHj1g5cqliMUiJBJVxo+fTHp6mtJb5uBQlx49+iCRSJg924/FixeRk5ODhoYG\\nixevfP2SRDGWm5s78+bNIDR0HY0bN6VoeZLP8zIlJSWFn38+Q1aWKl26VKZ5c7u3dxIQ+D9FMOYE\\nBAQEBErFzs6SlSufEha2HbFYzuDBVlSp8vcKFT969JANG4IJDAxBV1eP1NRU5s6dQadOXenQoTP7\\n9+9BLA5CVTWVly//oEWLr9DW1gFQ5vEsWbKIH3/8GT09fY4dC2fNmlVMnjyd+fNnMWnSdGrVsiMg\\nYIVy87lv3260tbUJCtpAbm4uw4cPpkGDRpiaVvpoz6gAsVjMyJHtPvq4b2LAAGcGlK1po+TmzWs0\\nb97qtfCFOs7OzZHL4d69eyxa9BMZGelkZmYpxUC6du3B5s0baNasJQcP7mPixB8ARZ7WzJlTad68\\nJc2atfwEKyq9AHtZuLi0Zdq0SbRo4YJIJKJyZTOysmTk5lYjLu4XACSSaJycPJR9ChdnB5g2bQ4v\\nX75g06ZQpRImKBQy160LpGLFSsTHx+PpOZi5c6fTr58bcnk+8fFxGBgYsnHjL7i59Wbt2o2IxWLG\\njfuVkydT+O23RCIi9jFjRpcSaypXrhxTp+qwcOF2UlL0qFfvTyZOfD/l1gYNGpUqEFTgLSsQLwJF\\nXmNhFVUoquBqZ1ebLVt2Kq8VeMSKq6z+U+Tl5TFo0GEuXPACxOzbd46goDul1v0TEBAA8eeegICA\\ngIDAv5emTe0ICOjAqlUdcXQsWUfuXYmIuIyLS1tl8WtdXV3u3PlDqezXvn0nkpL+JCioHZ07V8fE\\nxEDZVy6XExsbQ3T0Q0aPHo6XlxsbNgTz/Plz0tMV4Wy1aine3Ldt20G5eb58+QKHDu3Hy8uNb7/1\\nJDU1hfj4uL+9hi+X0j0rkydPZty4SYSGbsXb20cp91+7tj2JiYqSBzKZDAsLSwAWLVpCr159uXs3\\nCh8f9yLGz8egTh17zp07Q25uLpmZmZw/fwYNjXLo6Ohy48Z1AA4d2q80QipXNkNFRcz69Wtp3Vph\\nSHt5daRcuVg0NK4B+dSqtRMNjb/CTwsbVvr6iiLiNja2PH36tMR8/vjjBiNHjkFPTw97ewdSUlLI\\nzs4GRDRt2pzKlSsTEXEFAwNDXr58werVW9m0qSu5uZVIS7MnKKgRJ09eUY5XuKZir14NOH++HVeu\\n1CEsbECpwiT/JHK5nLCwU8yZc4jDh69+1rncvfuACxdaULBFTUpy5uDB2M86JwGBfzOCZ05AQEBA\\n4JMjEpXudSnLE1NaZJeFhRUBAcFFzqWlpb1xvNJUBf/fqFvXgXnzZjFwoCcymZRz587QvXsvMjIy\\nMDQ0QiqVcvjwAcqXr6Ds06FDJ2bPnoan52BA8VyfPn2Co6MTderU5dixcLKzs9DS+vCi7gWUpYw5\\ndepM/P39yM7OpnJlsyJ5Uy4u7Vi9ehk+PsMBMDOriL//HObPn4hUmoOOjoScnGxl+8JKjQUeXLFY\\nhfx8GWKxSolriu9TUbVNkQgkElWlYmdCwmNGjhyKnp41+fkFpTlE5Oaa8+efV99YU1FLS+ujPb8P\\nYdasfQQGtkEmK4+WVhSzZp3G3b35Z5mLsbE+OjqJpKUVqMFK0db+tDmoAgJfMoIxJyAgICDwyXF0\\nrM+UKeMZMODr12GWKdjZ1eHYsXDat+9EePhBZV6UXC6nsE0mEokwN69KcvIrpUS6VColLi4WCwtL\\nNDU1uXPnFjVr2nHsWLiyX4MGjdm5cwcODk5IJBJiY/+kfPkKJYpWv4n09HSOHDlEz55lq/X9E6xb\\nF6isbwYQGLgSQ0Mj8vJyOXHiKLm5eTRv3pJvvlEoEU6ePJ5nz56Sm5tD376utG7dFk9PVx4/jqdK\\nFXO2bt1Ez549cXXtSW5uHuXKafDixQvl/dq27UBQ0GratlXk/slkMubMmU5GRjpyuZy+fQd8VEOu\\ngNKUMa2tq5cIE/yr/UBcXQcWOdeoUUP27PkVUBhvPXp0IDU1hZ9+Ws7Ikd8qw0m//34qNja2JCcn\\nIxaL2b59NxERVyhfvgKjR3/PkiX+hIcfVJ7X1zegR4/evHiRBKBU7HR378/ChUtJTc3l8uVw4uP9\\nALCy2kWHDvXw8Ci9puK/ifBwzdflDyAjw5YDB+7g7v555lKxoikjR95i9epjZGQY4+x8ke+++7JK\\nlQgI/JMIxpyAgICAwCfHwsISd3dvfH2HIBarUL26DaNHT8DPbxabN2/EwMBA6XEpLM5QgEQiYc6c\\nH1m61J/09HRkMin9+7thYWHJpEnT+PHHeYjFIurWrac0Mrp27UFiYgLffDMQuVyOgYEh8+cveuc5\\nS6VS0tJS+e237Z/dmOvcuRtTpnxPv36u5Ofnc/z4EYYMGcHVq5cICtpAfn4+kyaN48aNa9jbOzB5\\n8nR0dXXJycnGx8eDFSuCcHf3plmz+gwePJRWrdogkUg5evQYmzcrDJ/CoiY3b16nVas2ymcpkUhY\\ntWrtJ1/nx1LGLEAikeDpORgfHw9MTMrz1VdVAUpRSS3sfVP87e09BD+/2Xh4uFKuXDl++GFmob4l\\n71W9ujlr1mSxceM2VFTk+PjUQE1Nwvff7yUlRYOmTdU/m7frbWhoFBVgUVOTltHyn2H06La4u78g\\nPT0dM7P+ygLsAgICJRHJ3yfb+BNSkKgr8N+jcCK2wH8P4fP97/KlfLZZWVmUK6co2jxt2kQiIq5i\\nYlK+hGKmvr4BU6ZMp0KFisybN7NI/bq2bZtx5MgZIiKusHZtALq6uvz5ZwzVq9ty9uwpzM2/on79\\nRgwfPuqzrXPMmBEMHz6KFy9esG/fbkxNK3Hy5DG0tbVfP4dsBg3ypHPnbqxbF8iZMwqP0JMnCfz8\\n8wpq1rSjRYuGnDx5AZFIhIFBObp374mNjS1NmjTD2bkZEomExYsXcvHiBfz9l2JmVoWwsDOcOpWF\\nnl42kyc3xcjI8LM9gy8JuVxO797bOHv2G0CEmlo08+ff+UcMuvf92d227XdmzlQlKckOC4vzLF9u\\nSoMGNT7hDAU+hC/ld7PA+2NiovPefQTPnICAgIDAF83582cJCwshKyuLpKTnTJmykGXLnrN/v5gz\\nZ0YzbFg/evTozf79e1iyxB8/P/9S5Nb/Or5//y4bN26jYkVTnjxJJDr6ISEhm//ZRZVCly492L9/\\nL69evaBz525cvXqZgQM9S5RbiIi4wtWrlwkMDEFdXZ2RI78lNzcXADU1deXaJRIJQUGhXLlyiZMn\\nj7Fz5zaWLl3NmDETlGNt3XqOKVNsyM62AuQ8eBDMb7/1/SJq/30KQkNPcfZsDrq6CsPW2LhswzY5\\n+RV//GFNwXcrN9eCCxciPlv44pvo168xTZsmEhl5A0fHuhgYCAa7gMCXguC3FhAQEBD4omndui0h\\nIZvp06c//ft/zeLFzzh/fiAPH7qRkvKSkycVm+n27Tvxxx/X3zpejRq1qFhRUSz9nw5e8fUdQlRU\\nZInzBw7s5fr1q1y8eJ6oqEgaNWpCw4aN2L9/D1lZWQA8f/6MV69ekZmZgY6ODurq6vz5Zwy3b98q\\n9V6ZmZmkp6fRuLEzI0eO5cGDeyXanD2b8dqQAxBx40ZNkpKSPtp6vyQ2bTrDDz/UYvfu3mzc6Mbg\\nwcfe+P3Q1tbB0PD566N8QIaBQc4/Mte/Q6VKprRu3VAw5AQEvjAEz5yAgICAwH+CAsXMhISiYSqJ\\nieVKtFVRUSE/X7ERz8/PRyr9K2dIQ6Nk+38CmUxWSi7XX4jFYurVq4+Oji4ikYj69RsRExPD0KFe\\nAGhqajJt2hwaNmzCrl2/MnBgX6pU+Qo7u9rKMQqPnZGRwYQJY1577eSMHDm2xD2NjHIBKQXbBWPj\\nRHR1rT/amr8kfv89i5wci9dHIv74w5YXL15gbGxcQnCmW7eedOrkQpMmzkgky0lNdUVffwUmJr0Z\\nNGgjRkbGDB48jICA5Tx79pRRo8bRtGlzfH2H8N1347G2VpQBGTbsG8aPn4yVVbWyJyYgIPB/jWDM\\nCQgICAj8JyhQzLSy0iUuTo5YnEJ2tj36+hFAxyKKmRUrmnL3biQuLm04e/Z0Ecn6wmhqapKZmfnW\\ne2/evAE1NTX69BnAsmU/8fDhA5YuXc3Vq5fZv38PjRs7Exa2HrlcTuPGTRk2bCSgyNXr3r03V65c\\nYuzYCUXG3L9/D2Fh69HW1qFateqoqkq4ffsP5s5dqGzTt+8A+vYtWTnc339ZqfMMDz+l/LeJiQlB\\nQaFvXNfEiS48eBBCRMRX6OklM3Gi0evi4/9/GBrmUNiwNTJKRFdXIdBSXHCmZUsXsrOz6datDT/9\\nNJ+srCw6dFhC48bOjB49nilTvmfdugCWLl1NdPQj5s2bQdOmzencuRsHD+7F2nocsbF/kpeXJxhy\\nAgICb0Qw5gQEBAQE/hMUKGZu3LieOnW2AGY4OjYjO/ssHh6uRRQzu3XryaRJ4/D0dKNhw8aUK/dX\\n0ebCjjE9PX1q17bH3b0/jRo5lymAYm/vyNatYfTpM4CoqEikUilSqZQbN65RpYo5AQErCA4OQ1tb\\nh7FjfTlz5iTNmrUkOzubWrXs8PUdXWS8pKQkgoPXEBwchpaWNj4+HiQkxNOtWy8qVzb7oOckl8vZ\\nvv0UmZlyWra0oWrVSmW21dTUZNOmAWRmZqKhofHZVAWLC9Z8DiZNas2jRyFcu2aOnl4ykyaZoKam\\nBsD27VuUgjPPnj0jLi4OsVhMy5atEYlEaGlpoaqqqiyLYGVVDTU1NVRUVLC0tCIxMRGAVq3aEBq6\\njuHDv2P//j106tT18yxWQEDgi0Ew5gQEBAQE/jN07NiFjh27FDs7sEQ7AwPDIrXLCjxljo5OODo6\\nFWk7Y8bct97XxsaWu3cjyczMQE1NDVvbGkRFRXLz5nWcnZvj6OiEnp4+oKjhdv36NZo1a6nc8BdG\\nLpdz584tHBzqKft07tyVuLhYRoz47q1zeRNyuZzRo39l69buyOWGWFjsJTg4m1q1LN/YT1NT843X\\nPyVvCz/9p9DU1CQsbABZWVloaGgo51O64ExOEbEZABWVv7ZcIpEIiUQVUITPymSKotgaGho4OTXk\\nzJmTnDhxlODgTf/gCgUEBL5EBAEUAQEBAQGBQty8+YAffjjIrFn7ePny1Tv1kUgkmJpW5sCBvdSu\\nbU+dOnWJiLjM48fxmJqaFhPKkCs3+cU3/AUUP/WxdFiePXvGnj22yOUKkYvo6K5s2FBS+ORTcPjw\\nAXx8PPDycmPRovnk5+fj7+/H4MHuDBrUj3XrApVt+/TpyurVy/H2HsjJk8cAhSEaEXGFyZPHK9td\\nvnyBKVO+/0fmX0C5cuWKfGaFBWdiYqLLFJx5V7p27cGSJf7UqFFLWXZCQEBAoCwEY05AQEBAQOA1\\nkZExeHk9Zc2afqxcOQA3t2PvlDMHYG9fly1bwqhb1xF7ewd27fqV6tVtqFGjFtevR5CSkoxMJuPo\\n0XDq1nUscxyRSETNmnZcvx5BamoKUqmUEyeOfpT1icVixOL8Yvf79IqdMTHRHD9+hICAYEJCNiMS\\niQkPP8iQISNYu3YD69dv4fr1CB49evB6TiL09PQJDg6jdet2ynOOjk7ExsaQkpIMwP79e+nSpfsn\\nn/+baNiwCTKZjIED+xIYuFIpOFPcSC95XPo1GxtbtLW16dy526ebtICAwH8GIcxSQEBA4F/I/fv3\\nSEp6TuPGzp97Kv9X7N4dRVxc39dHIiIiunP27BXatWv81r729g5s3BiCnV1t1NU1UFdXx97eASMj\\nY4YO9WXUqKHI5XKaNGlG06aKwtFlhQ4aGRnj7T2Eb7/1Qltbh+rVbT4ozDA/Px+xWIyJiQl9+pxh\\n40YLpNKKWFvvZPBgu7897rty9eol7t6NYvDgQQDk5uZiZGTE8ePh7NmzC5lMxosXSURHR2NpqRD8\\naN26baljtW/ficOHD9CxY1du377F9OlzPvn834SqqmqpgjOFxWaKH3t7Dylx7eHDP4mMjMPaujz5\\n+fk0aNDo00xYQEDgP4VgzAkICAj8C7l//y5370a+lzEnlUqRSIRf6x+Cjg5ADqBQbFRTe4qJid47\\n9a1Xrz4nTvyuPN6yZafy323atKdNm/Yl+hTf8C9fHsi6dYHcuHGNfv1c6dSpK4GBKzE0NCIvLxcf\\nH3dyc/No3rwl33zzLUCpsvhQVClz3LiJ1K5tD8CCBT1p2fIC6ekRNG/uRIUKRu/6eD6Ijh278O23\\nI5THCQmPGTvWl7VrN6Ktrc38+bPIzf2rDlu5ckVLRBSEqnbq1I2JE8egpqaGi0ubzybK8jHZsOEM\\nc+fqI5MlU7HiTLy8/oWVxQUEBP6VCP/rCwgICJTCwYP72Lp1EyKRiGrVrBk8eCjz588iJSUFfX0D\\npkyZToUKFZk3bybq6hrcv3+XV69eMmnSNA4c2EtU1B1q1rRTqie2bduMbt16cunSBQwNjZk1az76\\n+vr4+g7B13cMtrY1SE5OxsfHnS1bdrJ2bQC5ubncvHmdQYO8adzYmcWLFxId/QiZTIq39xCaNm3B\\ngQN7OXXqONnZ2eTn57N8eeBbVibwJnx8XDh/PoTjx5ujppaGh8cDHBz+2XC3jh27MGzYcB4+NKB1\\na3OOHz/CkCEjuHr1EkFBG8jPz2fSpHHcuHENe3uHUmTxW6Orq1umUqZIJKJjx8aYmOjw/HnaP7Km\\nevUaMGnSOPr1c8PAwIDU1BSePn2ChkY5tLS0ePnyBRcunMfBod5bxzI2NsbY2JjQ0GCWLl31D8z+\\n03L27GnWrDlEcvJyjIyukpT0LWfPaiISBVK3riP16tVn27bNdO/eC3V1jc89XQEBgX8ZgjEnICAg\\nUIxHjx6yYUMwgYEh6OrqkZqayty5M+jUqSsdOnRm//49LFnij5+fPwDp6WkEBoZw9uwpJk0aR0BA\\nMBYWlgwe7M6DB/epVs2a7OxsbG1rMnLkWNavX0tIyBrGjJlQqkqfRCLBx2cYd+9GMnq0QtwhMHAl\\nTk4NmDJlBmlpaQwZ4oGTU0NAEZIZGroVHZ2ixbIF3h81NTU2bnTlwYOHlCunQ5Uq/3ze0oIFvxMb\\na0ZEhB1bt56mUaMKREXd4fLli3h5uQGQlZVNfHwc9vYObNoUSnj4QfT09Hn27Cnx8bHUrGlXqlLm\\n56JqVQt8fIYxduwI8vPlqKqqMmbMBKpXt8HNrTfly1ekTh37N45R+OekbdsOpKSkYG5e9RPP/NPT\\ntGlz8vJyAZDLFWvMy5MoPa8A27dvpX37Tu9lzBWE1goICPy3EYw5AQEBgWJERFzGxaUturqK8Dpd\\nXV3u3PlDaby1b9+J1asVOTIikQhn52YAWFhYYWhohKWl1etjS548SaBaNWvEYrFSyKFdu45Mnfpm\\nBT65XF5EAfHSpQucO3eaLVs2ApCXl8fTp08QiUQ4OTUQDLmPiFgspnp1689y7/T0dMLDzcjNHYCu\\n7q/I5S/IzKyFXJ7PwIGedO/eq0j7All8LS0t1q/f/FoWX2EYlKWUWUB+fn6Z1z4FrVu3LZEHV6tW\\n6fl627fvKXJc4OEu4ObN63Tt2uPjTvADWLcuEE1NLVxdi5bBuHHjGqNHD6dt2w5cvXoJNTUNxoz5\\nnpCQNbx6lcyMGXOIjn6EhcUBYmObAKCunkD37pWUtfWSkp6TlPScUaOGoq9vwNKlq/H39yMqKpKc\\nnGxatmytNPz69OlK69btuH79Cs7OLTh58jjBwWEAxMXFMmPGFOWxgIDAfwPBmBMQEBAohkgkKiYl\\nr6C0c6AQQACFEaCmpqo8X7h+VPFxCjbZKioqyOWKTXXhfKHSmDdvEVWqmBc5d+fOrRK5RQJfFoVD\\nei0sLFFXb4S6+jm0tBT5dKqqX9OwYQPmz5/NjRvXeP78GQkJj+nevReWllYkJT0jLS2Nr7/uQ3x8\\nHPfv32Xz5o3K8X/++Udq1KhFx45dlJv9y5cv0rlzR/bvP/jFbPZlMhlz5x7k5Mk1qKlJGDjQ+4PG\\nK/h5/hj16940Rl5eHgMGDGTy5OkMHuzOsWPhrF4dzNmzp9iwIYTmzVvSoIEFPXpc4fDh21hZGdOv\\nnzPz5x9FJBLRp88AfvllM8uXBypfMA0ZMgJdXV1kMhmjRw/n0aMHWFpWU6qA7ty5k+fP07hy5RL3\\n79/D2ro6Bw7sFRQyBQT+gwj+dwEBAYFiODrW58SJo6SmpgCQmpqCnV0djh0LByA8/CD29g7vNWZ+\\nfr5SXv7IkUPUqaPob2paiaioOwDKeloAWlpaRSTxGzRoxI4dW5XH9+5FAWUbmAJfBgUhvcuXB7B+\\n/WbGjJlAzZq/kZ3tTGpqF8TiuqSnH6F+/UZYWlpx+vQJUlKSMTQ0YsuWjTg5NaRKla/Iz8/H3Lwq\\n9vYOypp0BQZGYUOjsOT/0KFD0dbW5v59RZ2599nsr1sXyJYtH8/oe5fx5s8/yMqV3bh9+wTXroUz\\nevTp975PYmICrq69mDt3Bv36dWfhwnn4+Ljj4eGKp6crJ08eIzExATe33owf/x29enXmhx8mkpOT\\nDSg8XwW/F6Ki7jBy5F+hkA8e3GPoUG8GDOjF3r27lOclElUsLa24du0qL1++wMmpAZmZmRw8uJ+L\\nF88TFLSahIR4BgxoTrNm1bCyqvTWdRw/Ho6390C8vQcSHf2I6Oho5bXC3s8uXXpw4MBe8vPzOX78\\nCG3bdnjvZyYgIPDvRjDmBAQEBIphYWGJu7s3vr5D8PR0Y8WKJYwePYEDB/bi4eFKePhBvvvur8LF\\nxTfLpaGhUY47d27j7t6fa9ci8PIaDICr60B+++1XvL2/JiUlBVD0d3BwIibmEV5ebhw/fhRPz8FI\\npVI8PAYUKbBcWs7d52LdukCuXLn0uafxRVFaSG9aWiwODsEYG++lSpWnSKV5ZGVlUatWbTw8vmHD\\nhl8ICgrFyMiY9PQ0pkyZQZUq5vj5+bNsWQDVqilCRIsrZRbwMTb7H/s79y7j3bmjBmgV9ODePf2/\\nda/Hj+Pp1asvtWvb8/DhA4KCNhASsonU1FRiYhRGUVxcLNWr29CsWQu0tLTYuXPHG+cpl8t5+PAB\\ny5YFEBgYTEhIEC9eJJXoIxKJUFVVZf36tWhr62BmVgUfn2GYmJQv1ObN809IeMzWrZtYtiyA0NAt\\nNGnStEwV0JYtXbhw4Rznz5/B1rYGurq67/ewBAQE/vUIYZYCAgICpdCxYxc6duxS5NzSpatLtCuc\\ny2NqWonQ0K2lXgMYOXJMif7m5lUJDd2iPPbxGQYoNvVBQRuKtP3++ynvNM9PidoUXuUAACAASURB\\nVEwmQ0VFpdRrhQUbBN6N0kJ6ZTIp+fky+vYdwIgR3xW5JpEUDeOVSouG8W7deo6tW2+TkfGU06dv\\n0by5HTk5RcN3i2/2Q0LWUK+e01s3+6Gh6zh0aD8GBoaUL18BG5saPH4cz88/LyQ5+RUaGhpMnDgV\\nQ0NjPD1d2bFjLwBZWVl8/XUftm/fw5MniSXaFxcxuX//LosW+ZGTk0PlymZMnjwdHR0dXr0KwMQk\\nknLlriAS5aGpmY2n5zqSkp7z1VdVSUtLIzY2BiMjYzQ1tRCJwMSkAjExjwgJ2URCQgLz5s1ALpez\\natUyIiNvk5eXR7Nm9dHW1iYjI4ONG9dz+PABxGIxu3f/ikgkRktLi0ePHpTIhyv+OTZr1gI1NTXU\\n1NRwdHTizp1bSiO9OFevXsbXdzSRkbcAhfAOFOTKlmyvqalJRkYGurp6ZGRkvLMKqJqaGg0bNsbf\\nfwGTJ08vc/4CAgJfLoJnTkBAQOAf4GN6MtauPUn79ofp0OEQmzad/VtjZGVl8f333+Hp6Ya7e3+O\\nHTtCVFQkvr5D+OabQYwdO5Lnz58D4Os7hGXLfmLwYHc2bAimT5+uSgMkKyuLXr06I5VKmTdvpjJU\\nNDLyNsOGeePp6YaPjwdZWVnIZDJWrlyqDGvbvXtnmfP7J0hMTMDdvf87tz94cB9JSUnK423bNivD\\n76BoCN674uBQr0RIb6NGzvTo0UdpyBWEQRZQPCRRU1OTzMxMTp/+gx9+MOXSJTdSUzMYPfoF9+8/\\n5OrVK2Xev/Bmv1OnskMso6IiOX78COvXb8Hff6kyNHjhwvmMGfM969ZtZPjw7/jppx/R1tbG2ro6\\nERGK+54/f4aGDZugoqLCwoXzSrQvoOBHZO7cGYwY8R2hoVuwsqpGSMgaACwtDTE3v4lY7ImeXmXU\\n1V+xfv1mevbsg0wmIy0tlXnzFpGc/ApDQyO6dOmBuro6ubk5SKVSlixZxMCBnqirq9OzZx/KldOk\\nefNW1K3rSJcuPWjfvhO2tjXQ1zfAwMAQsViFNm3aMWHCVExMKgCKHNf8fMV3Pycn942frUgkLrKu\\nv84rThQ24guHxJb2q6Jbt56MGzeS774bhrV1daUK6KxZ096qAtqmTQfEYrFQhFxA4D+K4JkTEBAQ\\n+AcoK+TtTWRlZTF9+iSeP39Ofr4MD4/BLF7sT1xcc9TUbpOfr8HcuV7UqnWX9PSnbNgQjFSah66u\\nHjNmzMXAwJDMzEyWLFnE3buRgAhvbx9atHAhLGw9UVGRmJiUp0oVc+ztHZg2bQILFvyMnp4+x46F\\ns3jxYsaMmYxIJEIqlbJ2rcJTeO9eFNeuXcXR0Um5UZdIJMqQz7y8PGbMmMLs2Qt49OgBt2/fRE1N\\njX37dqOtrU1Q0AZyc3MZPnwwDRo0wtT07TlCoDCWgoPDyvR2fGoOHNiLhYUVxsbGAISErCUnJ4dB\\ng7xYtuwnXr58ASi8Lvv370FTU4uoqDtlKg5evnyRr7/2UIb0isUqVK9uw+jR4/n55x/x8HBFJpNR\\nt64j48dPAhSGQfEXA3p6+tSubY+f3yTU1LqSmvo9aWkd0dZeyowZGtjY2LxxXW3adOD06ZNv3Ozf\\nvHmN5s1boa6uDqjj7Nyc3Nwcbt26wbRpE5Xt8vKkALi4tOX48SM4Ojpx9Gg4vXv3IzMzkz/+uFlq\\n+wIyMtJJT09X5qR26NCZadMUa1dRUWHePB8cHZ2Ii7Nm4MC+LF36ExkZGdjY1EAikdC4sTNyuRx3\\ndy927tyGlVU1rl+/yuPH8URHP2Tt2gBycnLYsCEYFRUVIiNvU6FCRVq0aMW2bZtp0qQZv/22nRcv\\nkl6LE8k5cuQQ9vZ1AahY0ZSoqDs0atSEU6f+ynGVy+WcPXuKQYO8yMrK5Nq1qwwbNpLc3FzMzKoo\\n21WrVp0WLVyIjLzzWgDlFwCaNm0BgLf3EGXbEyeOKr37vXv3p3fvv148FPf6F1BcBRQUdexyc3P/\\nNeHYpSGUURAQ+PsIxpyAgIDAv5SLF89jbFyeRYuWAoqNbl6ejOxsCxITF6Cjswsdnd1cvtyaAQOa\\nsmbNegD27t3Fpk0b8PUdzfr1a9HR0VGGf6alpZGcnMylS7+jrq5O/foNSU9PY8OGYB49esjo0cMB\\nxebK1LSici4FZRWg9I16AXK5nNjYPzEyMsbWtgbR0Q+RSFRRUVHh8uULPHz4QOm9y8jIID4+7p2N\\nuU+xGZXJZMyePY1796KoWtWSadNmsXnzRs6fP0NOTg52dnWYMGEqJ04cJSoqktmzf0BdXZ1OnbqR\\nlZVJWFgoV65cIi8vD7lcjlQq5caNa6iqqnLnzi3k8nxq17bn2rWrJRQHC6tGFg+VnTXLr8hxaOg6\\njhw5VCTEsW9fV6ZNm0BenhQzMzO8vL5n3DgbqlZtTUzMYaTSZkycCHPnTlGOV9pm/+bN63Tu3O0t\\nz7fkNblcjra2DiEhm0tcc3Zuzpo1q0hNTeXevSjq1atPZmYGOjqlt39fqlQxx8jIGEtLS0JDgzE3\\n/wpQhJ6qqEiUirEFf2SyfCwsrOjXz43582cRGrqV4OA13Lt3lytXLjF37gwyMtKxsamBTCbD3Pwr\\nYmNjCQ8/RL169enRow8AXl5DWLBgNmvXauPgUK+IR83KyppRo4aSnJyMl9dgjIyMSUxMKJYzp/jb\\nw+Mbfv75R9zd+yMWq+DtPYTmzVu+9Zm/D1FR0cyaNZ3s7GT09P5efuHHYvLk8Tx79pTc3Bz69nWl\\nW7eetG3bjO7de3PlyiXGjp1AYmICO3b8glSaR82adowbNwmxWIy//4JSX4oICAgoEIw5AQEBgX8p\\nVlbWrFy5lNWrl9OkSTPs7euipiYBLAFIS+tMhQpzaNjwW549e8r06ZN4+fIFeXl5VKpUGVB4iWbP\\n/ssw0NHR4dy5MyQmJmBoaMTRo4dJTk7mq6+qYmFhRUBAsLKtiYkOz5+nAQoBlwKcnZsTGLiSMWNG\\ncO3aVeLjY/H09CE5+RWBgasASEp6rlTjTEp6zrhxo7h58xqNGjkzZ84CQKHquWLFYuRyOY0bN2XY\\nsJHK82Fh60uc/xTExv7J5MnTsbOrg5/fbHbu3EHv3v3x8vIBYM6c6Zw7d4ZWrdqwc+d2fH3HYGNj\\nC8Avv2wCwM/PnylTvkdNTY379+9x8eLvqKmp0aVLN/bt28PJk8eRSvOIjo7G0rIaQIl6a2+icIij\\nTCbF23sgtrY1aNGilbLWWlDQalRUnjJ8eAb791egRo35fPONEzExz2nZ0qVEnqNMJmP58gPs2LEG\\niSSTNWuCS7u1krp1HZg3bxYDB3oik0k5d+4M3bv3olKlSpw4cZRWrdogl8t58OA+1tbV0dTUxNa2\\nJkuXLsLZuRkikQgtLe0S7R8+fKAUbJHLQUtLGx0dXW7cuI69fV0OHdqvzAeTy+XKlwinT59EW1uH\\nrl17cvXqFe7diyI3N5fHj+MBOHz4AHXrOpKamoqOji6XLv3O8+fPOHXqGDVq1GTECB8qVjTF2ro6\\nGRnp+PqO4ddff0Ff3wBQeAFVVSU0bdqcqVNnKp+DvX1dtmwpGR5c2KNWmMJ5tI6OTjg6OgGKvMXC\\n476JzMxMJk8eT1paKjKZFB+fYTRt2oLExATGjx9FnToO3Lp1AxOT8vj5/YS6ujoBAdvx9w8gL0+b\\nvLxGmJmdf6d7fSomT56Orq4uOTnZ+Ph40LKlC9nZ2dSqZYev72hiYqLZtCmUgACFx9TffwHh4Qfp\\n0KEzQ4YML1KG4eHDB1hZVfus6xEQ+DchGHMCAgIC/1KqVDEnOHgTv/9+lqCgVdSrVx8NDTVGj37K\\nwYM7AClZWVCnjjW+vkNwdR2Es3Mzrl27SnDwGuU4pZUvqF27LjNnzkNdXZ1z586wa9cO4uLiuHXr\\nD+zsaiOVSnnw4AF6ehVK9NXU1MTY2JiEhAS6devJ2LETychIZ8GCOXh6DqZ/fzdcXXsRHf0IuVzO\\n3btRhIRs4siRw6xcuYQnTxKRSCSsWLGENWvWY2xswtixvpw5c5IaNWoRELCC4OAwtLV1lOebNWv5\\nSZ5x+fIVsLOrAyiKwW/fvhVTU1M2bdpAbm4OqampWFpaKQvDF89zKl++IgcO7KV2bXsePLjPjRvX\\niIuLRUVFheXLF2Nu/hX6+vqoq2uUqTj4NkoLcZTLea3EuJqMjHQyM7No2LAxkydPoksXMzZv3oC7\\n+xiGDvVm4sQfiownl8sZOnQ7u3e7AZ2oVOkI9+49wcmp7PDV6tVtad26LZ6erhgYGFKzZi1EIpg+\\nfS7+/gsIDQ1GKpXSpk07rK2rAwqDdfr0ySxfHqgc5969e+zbt6dI+wJjTiRSFE1v3NiZVauWkp2d\\nTeXKZsqQQpFIhJqaGt7eX5OWloZYrIKXlxuvXr3CxaUtzs7NmDZtItnZWaioqNCjRx82bAjGyakh\\nO3b8gpaWFjdv3iA9PQ25XI6lZTVOnz5JQkI89+7dVd4DFN48FRWJUlF20CBvXFzavPNnVhY7d/7O\\nlSspmJmJGTq0zTuFFqqrq+PntwhNTS2Sk5MZOtRLGZYZHx/HrFl+TJw4lenTJ3Pq1HHatevIypUr\\nePz4J7KznTA2Xkhy8gdP/YPYvn0LZ84oQs2fPXtGXFwcYrGYli1bA3D16iXu3o1i8OBBAOTk5GBk\\nZAQoyjDs2bMLmUzGixdJxMQ8Eow5AYFCCMacgICAwL+UpKQkdHR0aNeuI1pa2uzbtxsAHZ1X7Nnj\\nyeHDBzhxwhGAzMwMjI1NAIVQRwH16zdk585tjBo1DlCEWdaqVRs/v9l4eX2NuroaKioquLt7Y2pa\\nmaVL/UlPT0cmk/LNN960bFm6VH2bNh1YsmQRaWm1uHHjOtra2mhoqGNmZoZEImHOnAUsXryI58+f\\nkZeXh6qqGj179mHr1jBGjRqKTCZ7HaanjYqKCm3bduD69WuIRCIcHOopw8IKzn8qY65wCFxBaN7P\\nPy9k3bqNmJiUJzh4Dbm5uaW2B7Czs2PLljCmTJnB/v17OHBgL+XLl8fWtiZRUZGEhGzi1auXeHq6\\nfcgsSz07f/5sFiz4CSurahw8uI9r164CULu2PYmJiUREXEEmk2FhYVmk39OnTwgPt6dA5j8hoS1b\\nt27HyenNuXXu7t64u5cs1P3TT8tKbd+yZWtOny5aqkIsFpfavsCzlZiYwLlzp5W5ZAUkJiZw585t\\ntLV1ycvLe12K4SdiY2NYtMiPq1cv8+RJIkuXBqCjo4Ov7xBWrVrKzZs3aN68JRKJBIlEFR0dbVas\\nWMOCBXPQ1NRETU0NY2MTzM2/omvX7oAi5PVThPSuXXuC2bNrkZ1tBaQQHb2LRYt6vbWfXC4nIGAF\\nN25cRywWkZT0nFevXgJgalpZaQzb2NiSmJhAeno6+fnZZGcrvICpqd0xMNhX5vifmoiIK1y9epnA\\nwBDU1dUZOfJbcnNzUFNTL/KcO3bswrffjijSt6AMw9q1G9HW1mb+/FlFfh4FBAQENUsBAQGBfy2P\\nHj1gyBBPvLzcWL9+LR4e3wAKg8zDw5UdO35h5MixgGIzPG3aRL75ZhD6+vrKTZKHxzekpaXh7t4f\\nT083rl27ir6+PrNn+6GlpUl+vpy8PCkqKhKsrauzYsUa1q/fzMaN2+jbty8Ay5cHKkMLC+jTpz8H\\nD56gcWNngoJWcerUcczMzGnRwgUAW9uaBAaG4OMzDBeXNmhoaCASiaha1ZLJk6czZsz3NGjQCC0t\\n7dcjllX8XP5JhRuePn3CrVt/AAXF3BXKgLq6emRmZioLvUOBPHx6kWMrq+q8fPkCO7vaqKiooKam\\nRqNGTbh+/RpffVUVN7feTJs2merV32wovYm6dR04ffokOTk5ZGZmcO7cGQCysjIwNDRCKpVy+PCB\\nIn06dOjE7NnTSi0CrpDPzyh0Ro6qqrREu09JZmYm3303HG/vgXh4DODsWYXXJiBgOY8fx+Pl5caq\\nVQqjb/PmDUyaNI6cnGzEYhEbN25DW1uHU6eOM3fuzFKVLwuL9ri7e9O0aXN8fb8jOHgTlSubAYq8\\n0KCgUEaMGM20afMZNOggTZv+jKPjcZo1O8D27Rc+6pqPHs17bcgB6HHmzLsJ+YSHHyQlJZng4DBC\\nQjZjYGCoVNJUUytcqkIFmUxRqkJbWwUTk98BUFePQ+/zaAYBKHMl1dXViYmJ5vbtWyXa1KvXgBMn\\njvHq1StAoer65MkTMjMzS5RhEBAQKIrgmRMQEPi/4ODBfdSv30ipRPgl0KBBo1IVBr/+2r1EHlnT\\npi2UoVeFKSs3x9HRqUQdu/ehuNdw164dvHz5gqioO9ja1iQzMwN1dQ1lWGJQ0AmOH5fy8uVTmjR5\\nQrNmDVmyxJ+UlGS0tXU4ejScPn0GUKNGzVLPfwpEIhHm5l/x22/bWLBgNlWrWtKzZx+l8WtoaETN\\nmnbK9p06dcXf3w8NDQ1Wrw6mW7eerF8fRJ06dVFX1wBg7doN6OrqYW1tS1hYCGpq6mRnZzFixCjl\\nWKWJkLyJskIcBw8eypAhnujr61Orlp0yRxEUHs2goNW0bdu+xHiGhka4u//OmjVR5ORUplatPfj6\\nNvw7j/BvU1bo4LBho4iOfqQUSbl06QLx8XEsWPATo0ePIC8vjxs3rmFjY8vjx/Gkp6eVqnwJRUV7\\noGS4cYsWrQDYuTOOJ0/yuHBBExgHKF4wzJ59iDZtXmJgYPhR1qypmVfkWEvr3TxMGRkZGBgYoqKi\\nQkTEFZ48SXxje21tbSpUMOabb2JISIgjNvYMiYnab+zzKWnYsAm7dv3KwIF9qVLlK+zsagNFvdxV\\nq1rg4zOMsWNHkJ8vRyKRMG7cRGrWtFOWYShfvuJbyzAICPw/IhhzAgIC/xcUl5X/cvlwL9Xu3eeI\\niUnDxaUatWv/vdyTR48esHLlUsRiERKJKuPHT0Yuz2fx4kXk5OSgoaHB4sUrEYlEPHz4hN27a5OT\\nY0GlSuEsXhxN+/YuDB3qy6hRQ5HL5TRp0oymTZsDlHn+Y6y9MBUrmrJp044S5318himLtxemRQsX\\npecRSsrFFzbSWrduW0Lk5NSpCB48eEbr1nWoWvXdFDwLKCvEsUBlsTg3b16nVas2hTyfRZk2rQtf\\nfx3D7dtnaNmyFTo6Ou81nw+lrNDB4gbXpUsXuHz5IjdvXuf586eIRCLi4+MQi1VIT0974z0Ki/ZA\\nyRBZVVVFoe6oKG1EonwgnwJDDuDpUysSEp5+NGNu7FhbHj7cRmRkPUxN7/Ldd2/+XVQw33btOjBx\\n4lg8PAZgY1ODr76yKHNNBcd+fn5MmDAJkQjq12/EkyePPsoa/g6qqqr4+5cMrS1erqW0nxkouwyD\\ngICAgg8y5hITE5kwYQIvX75EJBLRr18/3N3dSU5OZsyYMSQkJFC5cmWWLFmCrq7ux5qzgIDA/yGl\\n1Vw7evQwfn7+AFy+fIHffvuVuXN/xM9vNnfvRiISiejcuRvly1dQysoXeFWiox+xYsVisrKy0NPT\\nZ+rUGRgZGePrOwQbG1tu3LhOVlYmP/wwiw0bQoiOfkTr1m3x8RlW6lzeR53wQ9i+ffcH9Z8xYy9B\\nQa2QSk1Zu/YMy5bdoFWr93/bXZbXMDAwpMhxx45dOHlSlZwcxQY0ISGAhIS7xMbG0aZNe9q0Kek5\\natOmPS1auJCYmKDMA4QPX/s/wfbtv7NxYyoAbm7aDBjgDMDChQdZscKB7OxmVKkSzurVKTRoUOOj\\n3lsul7NmzTHCw3eRlfWAwMA1b2zfsGFtLC2rftQ5vCuFQwdVVFTo27dbmUW4Bw70pEGDRkycOEaZ\\nS7dlSxhaWtro6paufAlFPXGKENm/QktzcnKYPn0Sv/yyCyOjLF68ADAG7gOKHLRata5gadn6o63Z\\nzs6KgwdNefgwhipV7JTKmWVRYOzo6ekXUZktTIFSJoCr60Dlv2vVqsX69X+VgBg+fNSHTP2zcPz4\\nTVaufExOjoQOHcDX95/5HSsg8KXxQcacRCJhypQp1KhRg4yMDHr16oWzszO//vorTZo0wcfHhzVr\\n1rBmzRrGjx//seYsICDwf0hpNdeCgwNJSVHUUNq/fy9dunTn/v17JCU9V276MjLS0dLS5tdftyll\\n5aVSKUuWLOLHH/8qkL1mzSomT56OSCRCVVWNtWs3sH37ViZNGkdIyCZ0dHTp378H/fu7ERFxpcRc\\nvgSkUim7d2shlZoC8PRpMzZt2v63jLn3wcxMBKQBCu9P5cr3MDV1KLP93bt/MmLETSIja2Fmdp45\\nc4xp167uJ53jx+Datbv88IMRr14pwvuioq5hZXUHR0cbtm5VIztbYSTExXVg7dptH92YmzlzLwEB\\nHZDLe6Ki8oywsONMnmz+3uMkJiYUMZxAUR7h0KH9jB79cf4vLyt0UFNTs0i4aMOGjQgKCqBOnbqI\\nRCKeP3+GRKLIExOJREyZMhN/f78SypcF1wto3bodP/44jx07flGWxijg++/t8fVdS4UKSWhoHKdK\\nlaqULy9hzJja76U6+i5oampSu3bNjzpmYY4cucrNm89p1coSR8fqn+w+n5qkpBeMH59KfLyihuXN\\nm9FUqXKB7t3LLmwvIPD/ygcJoJiYmFCjhuI/Iy0tLaysrHj69CnHjx+nZ8+eAPTs2ZOjR4++aRgB\\nAQGBt2JlZc2VKxdZvXo5N25cR0tLm/btO3H48AHS0tK4ffsWjRo1wdS0EgkJj1myZBEXL/6OpqaW\\ncoyCN/WxsTFERysKZHt5ubFhQzDPnz9XtisI67O0tMLS0gpDQyNUVVWpVKkyz549K3UuXwIikQix\\nOL/IueLHn4IRI9owcOCvWFjsws7uF2bN0kJXt2xFhh9//IObN93Iy7MnOron/v7xn3yOH4OLFx/x\\n6tVfnqHkZAcuX/4TuVxOfrHHnJ//8UVdzp3TRC5XyLnLZOU5c0bto41ta1vjoxhyhUMHo6Ii8fAY\\nwKFD+5Whg3p6+tSubY+7e39WrVpG/fqNaNu2AzNnTgFg+vRJZGVl4uo6EC8vH6ytqxMYGEJo6Bbm\\nz1+EtrbiZ7G4aE/t2vaEhW0jODiMypXNmDVrPqqqqvz44zwWLZqBk5MtFy82ZPPmDhgb/0ZWVhih\\noStIS1OEcvr6DiEqKhKA5ORk+vZVCMs8evQQHx8PvLzc8PBwLVLnruD8okXzyS/+BfgErF59DB+f\\nSvz4Y1/69NEmOPjkJ7/np6Jv367Ex9dHReUppqajyM624ObNlM89LQGBfyUfLWcuPj6eyMhI6tSp\\nw4sXL5R5KcbGxrxQxC8ICAgI/G2K11xzcmpAly49mDhxDGpqari4KGo26erqEhq6lYsXz7Nr168c\\nP36EyZOnA39tJOVyShTILkxBLk2Bl64AkUiETCYrdS6enoM/8RP4cFRUVPj6axnLlt0lK8sac/ND\\n+PhYvr3jByIWi/n5597v3D49vagRkpb2cb0jnwpHR3P09G6QklKgiPkHDg6KUg09emQSFPSYvLzK\\nVKx4mkGDKn/0++vo5BQ51tb+cAn3x4/jmTZtIm3adOD69QgWLlzMunWBPH36hMTEBJ4+fUK/fq5K\\nkZr169cSHn4QfX0DypevgI1NjSLhf+8SOjhjxlwA0tJSefXqJX37DqBv348jgpOdnc2aNSd58eIV\\ncXGxzJw5X1mj7ezZU2zatIGxYydgb+/AunWBhISsYdSocYhEolJVVXfv/pW+fV1p164DUqkUmUxG\\nTEw0x48fKbUA9qdk1y4ZmZkKb1x6ug07d97Cu2Sa5Wfj7NnTxMQ8YuBAz7e2VVFRoWLFazx50pbE\\nxGWoqcVRo8aX8dJMQOCf5qMYcxkZGYwaNYqpU6cq34oVUNYvQAEBAYH3obh64v79ezA2NsbY2JjQ\\n0GCWLl0FQEpKMhKJhBYtXKhSxZy5cxVhV4Vl5c3NvyI5+VWRAtlxcbEl6nGVhlwuL7P+25fAuHHt\\nadz4Bvfv36J16zqYmVX83FMqQbNmIs6dUxg+kEHDhq8+95TeiQYNajFt2mk2b76HXC7C1VWDxo0V\\nCqMzZ3bF0fEcf/55DhcXG2rV+vhG9LhxFjx5spPoaBuqVYtk/Hirt3d6A7GxMcycOZWpU2eRmprC\\n9esRymtxcbEsXx5IRkY6bm696dmzL/fuRXHq1HFCQ7eSl5eHt/dAbG3/XijpvHn7CQszJC9Pnfbt\\nj/6PvfMOqKn/4/jrdttLQ0h2KDREZkY/hOxRZBXx8JgP2VtWZh57RzYRHnvzGI+RyCoy00BG2rfu\\n7f7+uE+XFIoQz3n9wznne77ne86593Y+5/P5vt8sXuySK4PtTyGVSunRYzdnzvRCVfU5Zcv6I5Mp\\nXhzkRh0zJ6ysbP7N7D+nYcNGlChR8pMG2N8SVdWs2T+xWPbNj5kX6tVr8J6Y0acRiUTMmqXG4sUr\\nSEzcQps2g9DWljJu3EgkEglRUZE0aOConAt4+fJFpSdkZrltfpfICggUVL46mEtPT2fIkCG0adOG\\nJk2aAGBsbExsbCwmJia8ePECI6PPK0GZmHxfJS2B74twf39tvsf9vXs3hNGj56CiooKqqire3t6Y\\nmOjRsWN7Nm7cSPXqCrnrV6+iGDVqnLKsadSokZiY6OHm1glf39loaWmxbds2li5dwvTp00lISEAm\\nk+Hh4UHNmraoqYkxNNTGxEQPQ0MdNDRUleenpibGyEiHV6+ilGNRU1NjypQpP9VnvG3berlu+yPO\\na+rUDpQseZIrV4IpVUrO2LHuiMXi7z6OL2H48JYMH57ztt69czZgz4lt27ahqalJu3btctU+MjIS\\nP7/p3LixnaioaEqUaJmrh9mc7q9EosPbt3FMmDCKJUuWYG5uzqVLl5TfBV1dTZycGmNqaggYUrhw\\nYUQiCQ8fhtG8eTOKF1f8zXdyaoyOjkaeP0MXLoSwcqUdqakKb76AADsaN75Av365v345cf58MGfO\\nNAcUc+7S0ozYt+8B9epZoa+vzfPnCYjFKsrxpqTooKYmxsREDy0tDQoVEIHtwgAAIABJREFU0sTE\\nRA+ZLAkVFREmJnp07epK/fq1OX36NGPGDMPb2xtdXU06duyAl5fXV403r/zxhynDhl0kNTUGE5NV\\nqKtLWbz4FlOmTOHcuXP8+eefyGQyDA0NWb9+PXFxcYwbN47IyEi0tLSYOnUqFhYWLF68mOjoaCIj\\nI4mJicHDw4MePRSB6bp16wgMDATAxcUFDw8PIiMj6dOnD3Z2dgQHB2NlZUX79u1ZsmQJb968Ye7c\\nudjY2BAYGMjt27eZOHEiL1++ZPLkyURGKspSp0yZgp3du3m0IhF4eDjSuHF5+vc/wJw5nQgMDOTR\\no/vs2bMHdXV1mjdvzu+/90FNTY2tW/3ZvHkjmpqarFq1in37Ahg4cGD2i/QL8TP9zRH4tnxVMCeX\\nyxk/fjzm5ub07NlTub5Ro0bs3r2bvn37smfPHmWQ9yliYz8tMSzw82Jioifc31+Y73V/LSxsWbt2\\nc5Z1sbEJnDv3D82bt1aOwdjYjJUr/bO1s7Orw8aNAQDEx6dhbGzGggXLs7Xz9V2m/H/ZspWYNm2u\\nsu/MbUWKlMpxLL8aP/K7265dDTLjmNevkz/d+BdDJpPRuLGiJC+31//16ySkUhlJSTIMDIqSmCj9\\nrHT/x+7v69dJaGvrULhwUU6fPo++fhHi4pKRSKTExiaQlCRBS0us3Fcuhxcv3pKUlEZiYqpyfXKy\\nhMRESZ4/Q9evPyY19X2POH0ePkz46s+iVCpHVTUeqdIfXY5UmkJsbAKJiRJUVNTR0dHl+PGz2NpW\\nZcuWHVhZVSU2NgFj4yJcvBhEsWJl2LVrLxkZcmJjE4iKisTMrATNm7fjwYMnBAffpEaNWqxbN5xW\\nrVwwNDQkPv4tyckpFCv2bbPgTZvasmTJWdasWc+ffy7BxKQI8+fPZtOm7axevZxly9ZQrJgpCQmK\\na7lgwXzKlq2At/dsgoODGD58BOvWbSEpSUJ4+IMsmVcnp9aEh98jIGAnq1atJyNDTt++HlSoUAVd\\nXT0iIiLw9p7FsGFj6dPHncDAvSxevJpz586waNFSfHzmkZCQSkpKGrGxCUyaNAVr66pMmTKLjIwM\\nUlKSs9xfuVzx2c/8XMfGJpCQkErVqvakpMhJSZFQsmRpbt26R0JCAuHh4bi4uAKQni7F2trml/xN\\nzkR4rvp1+ZIg/auCuatXr/LXX39hYWGhfHvo5eVF3759GTp0KLt27VJaEwgICAjkN56e3dHW1mbI\\nkI+kQvIZuVzO/v0XiI6Ox9m5KqVKmX6X434JP6NJ+q9ETEw0w4cPxtKyMvfuhVGmTDkmTvTm0aNH\\nH7XEqFjRghs3QmjSpCnJycloaWnTpUt3wsPvMneuDxKJBDOzEowdOwk9PT3CwkLx8ZmKSCSiZs38\\nNf1WU1Nj5sy5eHkNQktLC2Pjd5+jD73gFIiwsbFlzpyZ9OjRC6lUyoUL52jbtkOej92kSTUsLfcR\\nFqZQMixe/CTNm1f40lNRUrlyRbp1C2TLFg3kcglaWnEMHPiu7O9T6phdunRn4sSx/PXXburUqUem\\n5+HJk8c5evQgqqqqGBsXxt3dEz09vRwNsL91MAfw+nU0SUmvGDduOFKpDIlEwp07t7Czq0axYorf\\nq0xPwZs3Q5gxYy4A1arZ8/btW5KTkxCJRNStWw9VVVUKFTLA0NCI169fcePGdRo0+B8aGpqAwncx\\nJOQa9eo1xNTUjHLlFGW9ZcuWw96+5r//N+fZs+hs4wwODmLSpGmAYk5tbkWk1NXVlP9XUREjkylK\\nSe3tazFlyow8Xy8BgV+Brwrm7O3tCQsLy3Hb+vXrv6ZrAQEBgc/i57fpux5v3Li9+Ps7IZUWZe3a\\nA6xZk4SNzZeZbn9rfh2T9J+Xp08jGDduMlZWNvj4TGXXrh2cPXsaHx9fDAyyW2JIpVLWrNkAgJ/f\\nKjKnm0+fPhkvr9HZRDl8fLzx8hqDrW1Vli1b+Nnx7NmzC01NzWxCHDlZEYhEIjQ1NZkz50+GDRuA\\nh0cf5XgUc+Hf7f/2bRwSiQRLy8rUq9cADw83jIyMMTcvn20efW4wNDRg/Xprli3bjkymQufOpbCy\\n+rr5f5nMnduBzp1vEReXSL16u9HUVAQm74u0fOiVCFCqVBn8/bcqlzNN5Xv06EmPHj2ztf+YAfb3\\nwNm5FRMmjFFmbs6fP8uJE0dzbJtzYI7S/gEUwZZMJsumfyCXy5XrsgZZivLz9/fNy7HzgkgkokoV\\na3x9ZyuzpCkpKbx8GUvJknm35RAQ+BnJNzVLAQEBgV+Z+Pi3BAaaIpUq3q4/ftyK9et34Ov7bYK5\\ngmCS3rp1S7p16/1Nzu+/QJEiRbGysgGgWbMW+Pv78fDhA4YNGwBARkYGxsbvTNEbN26arY+kpEQS\\nExOziXIkJmaur/pv/y25ePHCJ8fTrl3uFEVNTYsrzah1dXVZvVoRYGaKV3h69s3SXl1dAwMDhdVE\\nly498PTsS2pq6r+frS8TQClXrgTz5pX4on0/h7291TfpFxSCcNu3n0NVVYSbmyPq6vlnD5Ebqlev\\nyZgxwxkwoC+gRnz8W8zNyzN//ixiYqIxNS1OfPxb9PULYWNjx9Gjh+jZsw/BwUEYGBiira2DXC4n\\nIeEt7u6d3wvwRdjaVmXGDG+6d/cgI0POqVPHcXZupQzKMr0IczfOGuzevZNOnbogk8lITU3Jkp17\\nP3DM/P/HBPUMDAwYP34KU6aMIy0tHYC+fQcIwZzAfwYhmBMQEBAogBQEk/QuXdrTurUr+vr6P/JS\\n/LS8/+Apl8vR0dH5pCWGpuY7wZLExAQCAwO4c+cWL1/GMmHCaCZO9GbgwN/IyJAxcOBvpKamKlX8\\nEhLiefkylpSUFLS0tFi+fDHnz59FLBZTq1ZtBgz4g7VrV6KtrUOXLt0JCwuld+/pyGTyLCWaMpmM\\nFSuWcP36VdLS0unQwZW2bTsQHByEn98qDAwMefToARYWlZg0aRoBAduIjY2lc+euiMW6lC9fjPj4\\nl6SlpeHs3IoKFSy+3QUuYCQmJtKp00GCgjwAKfv3+7N5s6syS/U9KFOmLL/91h9PT0/S0qSoqqri\\n5TWaUaPGM378SDIy5BgZGeHruwRPz774+EzFw6MLWlpaTJgwBcj83GYPmipWtKRFi1b89psHADVq\\n1OLmzRs0adIMkUiEpWUlLC0rMXOm92eDsaFDRzBnzgwOHNiLiooKI0aMo0qVd0F2poXF+y8WnJ1b\\n4ezcStlmzpwFyv9Xq2avfOkgIPBfQwjmBAQEBHKBvn4hOnaMYf36GKTSYpQuvZ+ePb9+Hs/HMDev\\nwNKlC1m+fDF169bH1raq0iTd2bk1t2/fYtKkaSQmJipN0uvUqUfNmrWVfeRkkg7ZM0I5maQDlCxZ\\nkufPnxWYYM7JqT7Hjp390cPINc+fP1PaXxw7dpgqVazYt29Pri0x4uLe0KlTVyIiIpBIUtm1K4DU\\n1JR/Pxur6d69E8uWLWLlSj/Wrl3F4cP72b59Mx06uHL27Gm2bNkFoLTkeL880sfHm6lTvSld2iJL\\nieb+/XuV2bi0tDQGDOij/Ezdv3+PTZsCMDYuTP/+vbl5M4TWrduxaNFqrl//i4wMQyIjT7JhgwG2\\ntt/uu1FQ2bDhLEFBPQExoMrp093Yu/ckLi7/y/djfWxO5s2bN9i8eT0gx9KyEiNGjEVNTQ0Xl9Y0\\nauTEpUsXSEh4J9yiq6tLr159cHRsDLz7jsXERHPunCKgmj17AdOmTSQlJQWAUaPGY2VlQ9++PYmI\\neMy4cSNo2bINwcFBbNu2mTlzFhAf/5axY4cTHR2NpqYWDx7cx9m5FdHRUcyc6Z2jR+GX8PBhJMuX\\nh5CRIaZLl7LY22d9efCz/WYICHwJX2faIiAgIPAfYsaMtqxaFcK0aTvZtcvim86XyzQmNzcvz+rV\\ny1i/fg0tWrThyJFDnDhxJJtJup1ddfbs2cWsWdOUfXxokr5u3RbWrduCv/82fH0XK9t9yiQ90+Kh\\nYPBzeZaWKlWa3bt30L27K4mJibi4uDFt2mxWrFhMz55d6dWrK7dv3/jo/rq6elhZ2TB+/BRiYqLx\\n919DerqU4cMV3mdt2rTj4cP7tGjRmEOH9pGQkMjz58/Q0dFFXV0DH5+pnDlzSilYkUlmiaa9vT2g\\nKNHM5MqVixw+fIBevbrSr19P4uPfEhn5FJFIRKVKVShc2ASRSET58hWJiYnh8uWbpKVpkXlvYmIa\\nsW/fgxzPJzExkd27d37RtXRxaU18/Nsv2vdX5enTCDp0cGXTpgB0dHTYunUTM2d6M3XqLPbt24dM\\nJlNeb5FIhJ6eHv7+2+jYsRMLF85Xrs9K9u+YkZERCxYsxc9vE97eM/nzT0Wpd//+g7GxsWPdui10\\n6tQ1yz5r167EwqIS/v5b6ddvINOnTwIUnnuXLoVQpkxr5s5dxLp1qz86p+5zvH79hp49b+Lv78bG\\nja707fuasLDHnz0fAYFfDSEzJyAgIJBLRCIRrVo5fJdjFRST9IJIcnIyY8eOICEhHplMym+/9ade\\nvYZs2bIBdXV1XFzcWLRoPg8e3GfhwuVcvXqFAwf+UqrnfS/EYjETJ2Y9ZoUKFVmyZFW2tosXr8yy\\n7Orahb//Pq3cZ9iwUezatYPw8LtKURszs5I0auSUo4rf6tX+BAVd5vTpEwQG7mDhwuXZ2mTyoRCF\\nl9coatSonWVdcHBQlkBfLFZBJpNSpIghItH7D+MS9D6irJ2QEM/u3QG0b++SbZtUqigJ/BCZTIZY\\nLM5xrlRBw929Pvv3r/+3zFKGo+MW2rbNfq75xYdzMtevX0Px4maUKFESUJQlBgbuoFOnLgA0adJM\\n+e/ixb65Pk56upQFC2Zz/344KioqREY+BT4tYPKhUmZMTAxubu2JjIwhMbE2s2e7cfjwegoXLsSb\\nN68pXPhdpcDBg/u4ezeUYcNGAXDkyEF27tyOVJrOw4cPOHnyAs2bO1K5cm2Sk6MpWXIX0dHLiIxs\\nSkDASiIjvUlNTcHBIXcG5QICPztCMCcgICBQAHn48D5Lly5ERUWEqqoqI0aMA8DJqTlv376lVKky\\nAMTGxjJzpjdyuSKD9vvvgwFo0aI18+b5KAVQpk2bzcKF80hMTEQmk9K5c9dswdyHKoUFFQ0NDXx8\\n5qKtrUNcXBy//96LevUaYmtbjW3bNuHi4kZYWChSqRSpVEpIyDWqVq32RccaOfIPpkyZ8Unp9EGD\\n+jJo0DAsLbOKfaSlpfHPP+epU+fLXgB8WKZpY2NLePhdbt9+xMaNMaSkpPDixZVsKn6FC5uQmppC\\nnToOWFvb0rlzW0Dx8C2XK0RNdHX1uHr1KqVKVeTo0UPKY9asWYfAwJ3Y2dmjqqpKRMQTihQpCsCL\\nF8/x8OiCSCQiLU1CqVKl2bRpFerqbyhduiOxsb9Tt+4btLTeZCmla9asBSdOHCUtTcLz589p3tyR\\nFi1aU7p0WZYtW6hU8ty+fQ/z5vkQFHQFNTVVtLV1cHHpTJEixYiNfcGgQX3R1y/EkiWrkEgkzJ8/\\ni7t3QxGLxQwaNIxq1ew5eHAf5879jUQiISoqkgYNHBkwYMgXXf+8oqurS0BAS7Zt24Oamgg3t46o\\nqalx9uxpSpYsTZkyZfP1eB/OydTV1cuSvXxfbfJj+4rFYjIyFEFZRkYGUml6trbbt2/G2LgwEydO\\nQyaT0ahR3VyN7/1gLyUlmebNf2PFCglyuS4gIiTEg5o11yGVZs3MvT/mx48fcfLkMVas8EMsFuPo\\nWJujRw+RmpqKjY01u3aNQF9/N4UK7eD16y6EhR3C3b0LzZq1IDAwIFfjFBD42RGCOQEBAYECSM2a\\ntbPMf8vkxo3rtG7dTrlcvnyFHC0aGjZsRMOGjZTLuckI2dlVx86uunJ548aNBdKYVi6Xs2LFEkJC\\nrqOiIuLly1jevHmNhYUld++GkpychLq6OpaWlQgLC+XGjevKt/x5Pc6cOX9+NiuU03ZT0+K4u3ty\\n8eKXB3OZZZqzZk2lTJlytG/vwo4dW/HyesqDB4qytqJFZYwc6YWamhhQqPhpa2szZsxw0tLSADmD\\nB3spx5k51HHjJjN16lRksgxq1KitPIfWrdsRExNN797dkcvlGBoaMXPmXJ49iyEy8imBgfvR1y/E\\n7NnTOXr0EAMHDsXOrjqbN29ER2caGzacZd261Tx9GqE0nXZza09iYiLTp89h7doVynLNnTu3IZPJ\\n2LQpgNu3b/4ryjOZ8eNHUqpUaW7eDKFFizYMHtwXIyNjlixZhVisOM/AwABUVFTw999GRMRjhg0b\\nxNatgYBibt/69VtQVVWja9eOuLq6YWJS5IvuQV7R0dGhd+9mWdb9/fdpHBzq53sw92Gwb2lZib17\\nA4mKisTEpBJHjhzM8hLjxImjdO/ekxMnjiozesWKmXL3biiNGjXh3Lm/kb5zVFeSnJykvH6HDx9Q\\nll5ra+uQnJyU49jeV8ocPXoYGRkZnDq1BS2tsqioSHjzxhOx+BlJSXGMGTMMNTU1hgwZjrW1bZZ+\\nTp48yqVL/+DkVB8dHV1kMhkxMdGoqanRu7c7UVH72bkzA1XVqzRurM7jx9HKDGSzZs4sX744p+EJ\\nCPxSCMGcgICAwE/CtzRJT09PZ8uW06SmyujUqTaGhgb5foz84ujRQ7x9G4ef3ybEYjGurm2QSNIw\\nNFTF1NSMgwf3YW1ti7l5eYKDFZmr0qXL5KrvmJhovLwGUaWKNXfvhvL48SMOHDiOvn4h1q9fw9Gj\\nhzAwMKRIkaJYWFRS+pOdOnWc+fNnkZiYwJgxk6hSxYo1a1aQlpbGjRvX6dHDk0aNmuTpPHMq0+ze\\nfSS//95Yufz8uSfu7vqMHJk1gFi92j9bf+9bClhYWLJ3715lsJ6ZvRKJRPTrN5B+/QZm2Tc5OYnO\\nnbuir6+wIBg9egKtWjmxYMEcAAoV0gfkSCSSbKbT+vqF0NTUomJFhThFpk1DTEw0IpGIsWOHK0V5\\nAgK2EhZ2hxcvnvP2bRyRkRFYW9ty8OB+Dh8+oPTIu3kzBBeXzoDCA65YMVOePo1AJBJRvXpNtLV1\\nAIW6Y0xMdJ6Duc+V7Do7t2Tt2lWkpaUpzcU/VBGtWbM2DRv+j/Pnz3L9+jX8/dcyffoczMzyx3Lh\\nw2C/c+duVKlizcSJowE5FStWol27d2WeCQkJeHh0QV1dXVma26ZNe8aMGU7Pnl2pVasOWlrayvaZ\\nAX779q6MHz+Kw4cPZmlTvnwFxGIxPXt2pUULhXJp5suCD5UyjY0L4+fnj5vbAF6+1AFeY239O4UK\\nGTNrlkKVcsSIwWzaFJAlo3f69Elq1arL7Nm+BAYGsHz5Yjw9+7J1q+IF1qRJrahe/S8uXXrK1Kmd\\naNkya7mygMB/ASGYExAQEPhJ+FYm6VKpFA+PAI4f9wDUCQjYyI4d/+Ps2WMEBV37oqzWtyQpKQlD\\nQyPEYjHBwUE8exaj3GZrW5WtWzcxbtxkypUzZ9EiXypVqpyn/qOiIpk4cSqVK1vh6toGgNDQ25w5\\ncxJ//22kp6fj6dk9S1llRkYGq1f7888/51m3bhV//rmM337rz927oQwdOvKLzjOnjF+FCsXR1b1H\\nYqLCX05F5SVmZprZ2uU3IpFI+ZB94EAQhw69JjFRwqpVvhQvXixb+w9Np98n06ahWLHiFC9uppSY\\nDw4OYs2aFVSsaMmQIcNZsmQBaWlpjBgxlrNnz/DyZSy9e/dg7dqNnxxrVgNr8ReJ+HyqZNfcvDz+\\n/n78+ecyNDU12bRp/UdVRHV0dKlXrwEODvWzZMrzg5yC/erVa+DntxkTE71sWfVu3dzp339wlnWG\\nhkZZTNIzt79vCVCiRMkshumZbVRVVbPNxczM7Ovr6ys9MQFcXdsgFosZMqQrx4+foVmzyyxd+gJd\\nXRPGjlVkjpOTk5WKmZm8ePECiSSNN2/e/JtpW5Tl+w6K+cGZ5u/W1racOHGUpk2dOXr08EevnYDA\\nr4SgZikgICDwH+fUqSCOH+8AqAMq3Ljhjp/fPz96WNnIDG6aNm1OWFgoHh5uHD58gNKl35Wv2dhU\\n5fXrV1hZWWNoaISGhobScDu3FC1qSuXK7zyv5HI5N2+GUL++I2pqamhra+PgUD/LPg0bKuTnLSws\\nlQ+bijlqHxeJ+BTvP0y/j7V1Rby8HlGy5G6KFduPu/sBunT59kIP1arV4NSp4+zde46hQw3YubMx\\nr183wt19PunpinlW4eH3Prr/y5exREQ8Jjk5WWnTkJSUQEJCPKB4ofDo0QP09PRQUVEhJiaa27dv\\nAYrgWl1dne7de2JgYMDz58+xta2qnOsXEfGE58+fUbp0mRyv95fcgw9Ldq2srJUluxoaGjx+/JD+\\n/T3p1asrhw8f/KyK6Jd+Dj5F3kRhfvxk2MOHgwkMvM3bt2k0bVobkLNqlb9SZTcw8ABaWlpZzkss\\nVqFPn9/x8hrI7797IpFIePXqVY4+dgB//DGCwMAAPDzcePky9qcQzhEQ+FqEzJyAgIDAL8DnysLq\\n1HFg06b1yOVy6tSpp3y77uRUn6pV61Kq1J+8eDEVdfXHGBmt4syZdNTVv49yZ27JNBIuVMjgo8bb\\n9vY1OXXqXSCaOY8qL2hp5ZTpEn3wQJ714TxT6VFFRfzFUuu5ITExETOzOC5fbk9s7AuWLPkTkajD\\nNzteJmXLlsPd3ZMFC3woVMgQTc3KvHgxAZFoBO7unRGLValatRojRihsEz58hi5e3IyjRw+RkBDP\\nqVPHadWqHZ6e/Vi+fBE9e3ZFJpPSsWNnZDIZoaG32bVrO1ZW1gAsW7aQ2NgX9O/fh1q1alOhQkVK\\nly7DvHk+eHi4IRaLGT9+CqqqqlmMqTP5kgd6VdWPl+yampphb18rTyqi+R1UfCzY/xgBAXvz9fh5\\nJTExlbFji6OiUgMNjet4ee2mRo3aBARso2vXHgCEh9+lQgWLLN8za2tb5PIM1q3bwu7dO1m2bBFV\\nqlgpfwsAHB0bK33yTE2LZ/lt+O23/t/pDAUEfhxCMCcgICDwC/CpsrCSJUuxYsUS/Pw2oaurh5fX\\nIM6ePU39+o6kpqbi7NyEFy9iiYoywtR0OAYGrqxf3xJv77GUK/dzmT9HRT1nzZqrZGRAr162lClj\\n9tV9ikQibGxsmTNnJj169EIqlXLhwjnatv10EKWjo0NycvJXH/993pf3L1bMlOnTZ+dr/5/C2bkV\\nISEifH07AopSxrQ0N5YsqYCxsbGy3ftz8wB8fZcwevSwbCWBQBYxH4B27Tpma5OTEqm6ujrjxk3O\\ncYzOzq2Uy5klnF/Cx0p2q1Sxxtd3dq5VRBU2ITkLhfxXSEmRk5xcAV3dSECds2cNOXHCiz//nIuH\\nRxdkMpnyZcD7Afkff4zA23sCmzf7U69ew08GxSdPhrBnzzM0NaX88Yc9ZmZFv9PZCQj8WIRgTkBA\\nQOAX4FNKjg4ODahWzZ5ChRSiJk5Ozbl+/Rr16zuioqLC//7XhIYNM5g7dxl375qyeHEHdHV1adGi\\nBaGhHy+dK2i8fv2Gbt0uc+dOF0DE8eMBBASoUbx43sQvsj4wKv5vaVmZevUa4OHhhpGRMebm5dHV\\n/ZhdgWIfOzt7Nm1aT69eXb9IACUnVqxYTFRUJL16daVEiVI8efKIDRu2c/DgPs6ePU1qaiqRkU9x\\nc+uGRJLG8eOHUVNTZ+7chejr6xMVFYmv7xzi4t6gp6eDl9cYpc1Fbhg6tDG3bq3j4kVLdHVfM2iQ\\napZA7mPkJTPl53eaPXvSUFWV0aePCS1a2Odqv4sXb3Ht2lNq1CiDvX2lz+/wGWxt7di4cR1WVtZo\\naGgqS3YNDAwYP34KU6aMIy1NUWL6KRXRxo2bMnv2DHbu3M60abPyTQDlZ8LC4g/u3DEgPr490J6K\\nFbdjaGiEt7dPtrbvB+S5zbT9808oAweKefXKBZATHLyRv/5qhra2do7tBQR+JYRgTkBAQOAX4NNl\\nYQr58Xe8859SV9dAJBIhFotxcLBGKn2pDFK+xTyfb8nevZe5c6czmcFUeLgLu3cHMHCgc677+LB8\\n7f3ytC5deuDp2ZfU1FQGDeqLhYUiYHjf3qFQoUKsWOGHRCJBX1+f1as3fOVZZaV//yE8evSQdeu2\\n8OxZDKNGDVVuy1wvkUjo3LktAwb8gZ/fZhYv9uXw4QN06tSFOXNmMHLkOEqUKEl09ENmz579SUPx\\nD9HU1GTjRjfi4t6gpVVJKTzxKfJSEnjy5DWmTatAUpIlAOHhp6lcOZIyZT4dAPn7n2HatNLEx3fC\\nwCCYqVPP4+b2dWXC1avX+GjJbrVq9jne25xURK2tbdm0acdXjeVnx8vLmnv3tnH7dk2KFLnPkCGG\\nn93n9u1wbt9+SsOG1hQtavLJtseOPeHVK9d/l0TcuOHEtWuhODhU/+R+AgK/AoIAioCAgMAvQmZZ\\nWNWq1bC1tWPPnl1UrGhBpUpVuH49mLdv45DJZBw/fjTH0rVKlay4fj2Y+Pi3SKVSDh/+udTgChfW\\nQSR6/d6aBAwN1fOt/zlzZtCrV1d69+6Oo2MjKlSwyLI9Pj4eV9cd1KwZhYPD3wQEXMy3Y2fyfoD9\\nYbBtZ2ePlpYWBgYG6Orq4eCgEEYpV648z55Fk5KSws2bN5g4cTS9enVl8uTJvHr1Ks9jEIlEGBoa\\n5SqQyyvBwS+UgRzA8+d1uHQp7LP7bd2aSny8Yo5dXFw1tmxJzPex5YV//gmlY8cDODkdZ/Lkv366\\nFyP5jYVFaQ4ebMSxYy84c6YinTrV+WT71atP07ZtBoMGNaNVqztcvhz6yfaGhgDvlDB1dSMwMyuc\\nDyMXECj4CJk5AQEBgV+Ej5WFGRsX5vffBzFkyO/I5XLq1q1PvXqKB/33y98KFy6Mp2df+vXrha6u\\nHjY2VnxDLY98p1UrBzp33smuXfbI5WJatbqAm1unfOt/8uTpn9zu43OGv//2BFRISIDZswNp2zYN\\ndfX8Cyg/RVZJfhXlsoqKCjKZDLk8Az09Pdat2wKQo3z9j8bKygiT9TckAAAgAElEQVRNzQekppoD\\nULhwENWrf8m8zR8XPKWmpjJy5BPu3XMD4MaN1xQrdoL+/b+8zDYxMZFjxw7Tvr3LR9s8exbDzZsh\\nODk1/2RfMTHRjB49jA0btn/xeL4ELS0tbG2rfLadXC7Hz09CfLyivPbJk1asWLGdmjU/Xjrbv38T\\nQkI2ceaMBRoaifTtm0qZMk75NnYBgYKMEMwJCAgI/CJ8qiysSZNmNGnSLNs+76vCAbRo0ZoWLVoD\\nBfNh/1OIRCIWLnRhyJCHZGRIqVCh83eVJo+P1+D9gpe4uMIkJSWirm6Ub8fQ1tbOs6hKZlZIW1uH\\n4sWLc+rUcf73vybI5XLu3w+nfPmCI3LTvHkNRo48xt69IaiqSunTx5Dy5W0+u1/Xrlrcv3+D+Hgb\\nDAyu0q2b/ncYbc48exbDw4fvsrZyuRH37+fd6+593he++RjR0VEcO3bks8Hcz4BUKs6ynJ4u/khL\\nBaqqqqxe3Zk3b16jqaklzJUT+E8hBHMCAgIC/2Ey3/gXK1aF06efYGqqjofH/34qf6ZDh/azbdtm\\nRCIR5ubladTICX//tUil6ejrF2Ly5OkYGhpx7dpVFi2aDygCv6VL16ClpcWWLRs4deo4aWnpNGjg\\nSO/e/b5oHI6Oeuzff4+UlIpABtWq3cPAoGo+nqnClsHa2hZ3986ULl1WeZ+yS/Jn9eHK3DZp0nTm\\nzZuFv78fkIGjY5MCFcwBDB7sxODBn2/3Pu7uDbC0vM3VqzuoWbMs1avX/TaDywXFiplSvvxpwsIU\\nQaiKykssLD4djHyO94VvatSohVwOly5dQCQS4e7em8aNnVixYgkREY/p1asrrq4uVKtWh2nTJimN\\nuL28RmFl9fnA+EcjEolo2TKFVaueIZUWw8AgCBeXQrnaz8jo82I8AgK/GiJ5ASnk/pne/grkjZ/t\\n7b5A3hDu789NTEw0Awf+TmjoLF69qoVI9IZu3fbg6+uS473N/JNRUIK9hw8fMH78SFauXIe+fiHi\\n4+MRiUTo6ekBsG/fHp48ecygQUMZPXoYPXr0wsrKhtTUVNTU1Lh69QqnT59g1KjxZGRkMGbMcLp1\\nc8+z0XgmAQH/cOZMAoUKSRgzxlE5joKI8N39dly5cpc5c+6TmKhO3bqpTJjQ6qu+M5liNxs2bOf0\\n6RPs3RuIr+8S4uLe0KePO6tWrSci4glbt25izpwFmJjoERkZi0ikgrq6Ok+fRuDtPYE1azb8sDLL\\nvCCXy9m58zyPHiVSv34p6tSp/KOHVKAQvru/LiYmef+bIWTmBAQEBP7DrFixmNjYWHR0fBCJ6iKT\\nGXPhwjbc3XfSokVz3Nx6EhMTjZfXIKpUseb27ZukpaWRmJiISARSqRQHh/oYGRVm//49SKVSrK1t\\nmTv3TzQ0NJkxYwoaGpqEh9/lzZvXjBkzkYMH9xEWdofKla2UXmGXL1/Ez28VaWlpmJmVYNy4yWhp\\naX12/MHBV2jUyAl9fcWbe319fR48uM+kSWN4/foV6enpFC+u8JqztrZl0SJfmjZtTsOGjTAxKcLl\\nyxe5cuUSvXp1BSAlRSHt/6XBnKtrHVxdP9/ueyOVSvH1PcajR2IqVJAzdKgwn+hbUqOGBQEBFp9v\\nmEvef+9+48Z1nJyaK4VoqlatRmjoHXR0dLLsk54uZcGC2dy/H46KigpPn0bk23i+NSKRCFfXej96\\nGAICPwWCmqWAgIDAf5j+/YegoWFIRMQekpProqb2hPT0AaxZs4Hbt28TEnINgKioSDp0cMXXdwmx\\nsS9ITU1h2bK11KlTjzt3bvP27RuOHTvLtGmziI2NZf9+haS/SCQiMTGBlSvXMWSIF2PGDKdrV3c2\\nbtzBgwf3CQ+/R1xcHBs2+LFw4TL8/DZhYWHJ9u2bczV+kUiUTSlwwYI5uLi44e+/jZEjxyGRSADo\\n3r0nY8ZMRCKR0L9/byIiHivXr1u3hXXrtrBtWyAtW7bJp6tbcBg7dh/z5rVi166OzJrlxJQp+3/0\\nkAS+kJw+8zll/bZv34yxcWH8/bexZs1G0tPTv9cQBQQEviNCMCcgICDwH0Yul2NsrEGFCjvR1j6J\\nru5JTE0X0a9fTx49ekRk5FMAihY1pXJlKwCKFCmGqakZ5cqZY2lZCV1dXUxNzRgwoA/Lli0iJiaa\\nR48eKY/h4FAfgLJlzTEyMqZcOXNEIhFly5bj2bNobt++yePHD/n9d0969erK4cMHef78Wa7GX61a\\nDU6dOk58/FsA4uPfkpycROHCCl+qQ4feBS1RUZGUK2dOt24eWFpWJiLiCbVq1ebAgb+U84piY1/w\\n5s2br7yqBY/gYD0gUxSiEEFBn896ChQc3he+sbGpyokTx8jIyODNmzeEhFyjcuUqaGlpk5ycpNwn\\nOTlJOYfs8OEDZGR8nQiLgIBAwUQosxQQEBD4j6Ohoc6BA7WYOvU4Fhbt6NdPIQCSOS8jJiYaLa13\\nnmJqaqqoqWXK3iuEHfbu3c3ChcvQ1tZmwIA+pKVJ3mv/TiL/Q/l8mUyGiooYe/taTJky47NjXbt2\\nJdraOnTp0h2AsmXL4e7uyaBBfVFREVOxogWenn2ZOHE0enr6VK9uz7NnMQAEBGwlODgIkUiFcuXM\\nqV3bAVVVVR4/fszvv/cCFA/NEydOw9Dw86bGPxMGBikfLKd+t2P/DHO0voSzZ09TsmRpypQp+82P\\n9b7wTe3adSlfvjw9e3ZBJBIxYMAfGBoaoaenj1gspmfPrnTq5EL79q6MHz+Kw4cPUqtWHbS03ik8\\nFpQ5rwICAl+PEMwJCAgI/IfJfONvYGCAm1tH1qxZQUqKO1paWjx//py3byWf7wRIS5NgZGRMYmIC\\niYm5n5gvEomoUsUaX9/ZREVFYmZWgpSUFF6+jKVEiZLKNu+3/xBn51Y4O7fKsq5evYYAyGQyxGJF\\nwDl06Mhs+6anp9OmTXtcXd0+O9bZs6fj5tad0qXLsGGDH+7unkDuPMB+NOPHWzJy5GYiIkpStuwT\\nxo8v+KqGBZ2//z6Ng0P97xLMQXafwwED/siyrKqqysKFy4F3L2L8/bcqt/fvr5AINTUtjr//tm88\\nWgEBge+FEMwJCAgI/If58I2/k1NzZZZKX1+PsWOnZJO9zy6DD02aNKVv355oa2tnM8n+VDB27tzf\\nrF27EhUVFYYM6YeGhhYxMVFYW9vy5s1r5s5dxJEjBzh8+ACGhkYUKVIUCwuFeXBUVCS+vnOIi3uD\\npqYmo0ePp1SpMsyYMQV1dXXCw+9hY1OVQYOG5njuvr5H2bBBjFSqRsuWL5k1q/1HMxYZGRmMHj1B\\nubxx43plMJcbD7AfTbVqFTh2zJz4+LcUKlT1izIz69ev4ejRQxgYGCrvg719DebO9UEikWBmVoKx\\nYyehp6dHWFgoPj5TEYlE1KxZ6xuc0ddz5MhBdu7cjlSaTuXKVgwfPgZf39mEhYUikaTi6NhYaVOx\\nfPlizp8/i1gspmbN2jRs+D/Onz/L9evX8Pdfy/TpczAzK/GDzyhnNm8+x7p1ichkYtq1gz/+EMRv\\nBAR+JQRrAoFvjiCh+2sj3N9fl299bxUP/N7Mn78YVVU1Bg/uy6RJ0+jduwcrVvhRubKVss2qVf7I\\nZFI8PbvTrl1H3Ny688cf/Rk5chwlSpTk9u1brFq1lIULlzNjxhTi498ya5bvR4OWoKDb9Ox5grS0\\nUsTF9aBIkYlYWQWzbds2rl69wv79ezl37m/atu1AUNBlvLxGsWrVMgYNGsapU8fZtm0T5cqZU7as\\nOTKZjHPnzlCqVGlq1KjNgAFDcvSui4mJZsSIIdjY2HHrVggmJkXw8ZmPhobGN7vGnyIv9zc09DZz\\n5sxg1Sp/0tPT8fTsTtu2HTh8+ABeXqOwtbVj7dqVJCUlMmTIcDw83PDyGoOtbVWWLVvIxYsXClSZ\\n5ePHj1i+fBEzZ85DLBYzb94srKysqVu3Pvr6+shkMoYOHcDQoSMpXLgw/fv3ZsuWXQAkJSWio6PL\\nzJneODjUp2HDRj/4bLKTeW9DQx/Qtm0qcXG1AdDUfMTKlQ9xdq75g0co8DUIf3d/XQRrAgEBAQGB\\n78alS6EsXPiIlBRVmjRRYeDAJnna//r1YOLji+Dg8AhNzQTq1ClLSMi1LGIrN25co0GD//0b8Gjg\\n4NAAgJSUFG7evMHEiaOV/aWnSwFF9u9//2vyyezTvXvRxMc3w9BwI3FxPVBXf0BSUipSqZQbN65T\\ntWo1jh8/QpUqVsrMXmZGsn//wQQGBrBu3RZA4QH26NED5fLlyxeJjHzK6tUblN51ISHXKFKkKJGR\\nT/H29mH06PFMmjSWM2dO0rSpc56u24/g5s0Q6td3RE1NDTU1NRwc6pOamkJiYoLSxqF585ZMnDiG\\nxMREEhMTsbVVGKY3a9aSixcv/MjhZ+Pq1cvcvRtGnz49AEhLS8PY2JiTJ4/y1197kMlkvHr1kseP\\nH1GmTFnU1TXw8ZlK3br1lYI+QDZVyYLG1asPiIt7p86amlqWO3eCcC74HzkBAYFcIgRzAgICAgJ5\\nJiEhnj/+iObhw84ABAU9plixC3TsWDfXfVy4cI+wMCvevGkMwD//nKN69bgsYivwYUCmeHiWyzPQ\\n09NTBlAfoqmpmeP6TBo3tsPM7Boy2W1EokTU1FKoWtWasLBQQkKuMXToSFRUVHB0bPzZ8/jwgf5j\\n3nVFihTF1NSM8uUrAGBhYUlMTPRn+y8YZJfDzy0FNeBxdm5Fv34DlcvR0VF4eQ1izZqN6OoqMm9p\\naRLEYjGrV/sTFHSZ06dPEBi4Qzk3raALidSrV5lixc7x7Nn/ANDXv0WNGmY/eFQCAgL5iWBNICAg\\nICCQZ0JDH/LwYXXlskRShpCQvJX9qKmVQVf3DCJRKiJRMurqtzA0NM3SpmpVO/7++zQSiYTk5CTO\\nnz8HgLa2DsWLF+fUqeOAImC4fz8818cuWrQwK1eWxchIjQYNJuPkZE7jxo4EB18hKipKmY350of1\\nj3nXva/mee/eXRISfo5SKRsbW86fP0taWhrJyclcuHAWTU0t9PT0CQm5Dijk7+3sqqOrq4uurh43\\nbijWHz166EcOPUeqV6/JqVMnlDYU8fFvef78GZqaWujo6PD69StlNjElRZGBrFPHgcGDvbh//x6g\\nEA9KSkr66DEKAmXKlGDePBUcHQOoV28X3t4RNGhg/aOHJSAgkI8ImTkBAQEBgTxTvnxJihe/RXS0\\nQnFSRSWWsmXVP7NXVtq1q8mJE28oVcoVAG3tilSvbs2+fe8CqIoVLWnc2ImePbtgaGhE5cpVlNsm\\nTZrOvHmz8Pf3QyqV0qRJU2XWKzdBmI1NeTp1asKBA3/Rvv1kypUzZ9EiXypVqvzZfVVVVZFKpaiq\\nqmbxAAOoVas2q1evoGlTZ7S0tIiNfYGqqlq2PkJD76Cjo/PZYxUELC0rU69eAzw83DAyMsbcvDx6\\nerqMHz+FefN8SE1NxcysBOPGTQZg3LjJ/wqgQI0atQtcBqtMmbL89lt/vLwGkpEhR01NjWHDRlGx\\nogVdu3akSJFi2NjYAgq/tjFjhpOWlgbIGTzYC4DGjZsye/YMdu7czrRps76bAEr//p4sX+730e0u\\nLq3Zu3cPoFBxbdq0Gk2bftmxnJzqc+zY2S/b+V/27NmFpqYmzZu35ODBfdSsWYfChQt/VZ8CAgLv\\nEARQBL45wkTdXxvh/v66fO7eHjx4lUWLnpOSoo6jYzJTprTO80P74cNX2b//FWpqaQwbVp1SpUw/\\nv1M+cvXqFUaMGMLhw6fQ0NCkS5cOtG/vQqdOXWnatCFHj55Rth08uB+DBg3DwsLyX3XDv7GwsGTi\\nxGl4e0/gwYNw7O1r8fTpE8LD7xEf/xZDQyN0dfVQV1dHIkklJiaGbdsCuXHjOt7eE9HR0aZo0WIs\\nX+733YVQ8vrdTUlJQUtLi9TUVAYN6svo0eOpUMEiWzu5XK4UCSloQdx/AVfXNuzZs5v0dPFX9+Xk\\n1IBjx/7Oh1EpGDy4HwMHDsXSslK+9flfRPi7++vyJQIoQjAn8M0RfnR+bYT7++vys93bK1duEhn5\\nmiZNqqGnp/iDGBYWyuHDBxg6dMRH9wsPv8fLl7HUqePw1WM4ffoEly5dZPTo8YBC+XDEiCHMmuVL\\noUIGnDhxlMuXLzJ27KQsweGPIK/319t7Ao8fPyQtLQ1n51Z0794zW5t79yIYOjSIBw/MKFXqGXPm\\nVMbOrkI+jvrH8ezZS0aO/JsnT/QpUyaeuXMbUrSo8XcfR2a27OXLl0yePJbk5CRkMhkjRozFxqZq\\nlmBu7NgRvHjxnLQ0Ca6uXWjTpr2yD1fXLly4cA4NDQ1mzZqPoaER0dFReHtPIDU1BQeHBgQEbMtz\\nMHfo0H62bduMSCTC3Lw8ZmYl0NLSxtTUlBkzvDExMUFDQ4O+fQfw11978PGZB8CVKxfZvXsXM2fO\\nzfdr9qvxs/02C+QeQc1SQEBAQOA/yZQp+1mzphppabZYWe1h48ZamJkVxdKy0mezAOHhd7l7NzRf\\ngjlz8wosXbqQ5csXU7duffT0dHn48AEDB/YlKiqJtDQRGhradO4cBRRccZCc+NC0OiemT79OUJAH\\nAG/ewPTpW9i169cI5saMOcuRI+6AiLAwOaqqG/Hza//Njjdy5B9MmTIDHR3dD7Yosp3Hjh2mVq06\\nuLt7kpGRQWpqarY+xo6dhL6+PhJJKr/95oGjY2P09fVJTU3FysqGvn0HsGzZIv76azceHr1ZuHAe\\nHTq40qxZCwIDA/I85ocPH7Bhgx8rV65DX78Q8fHx7Ny5DZEIHB0bs2vXjiwvMJYs+ZO3b+MoVMiA\\nAwf20apV2zwfU0Dgv44ggCIgICAgUGBJSUlh5Mg/6NmzK+7unTlx4hhBQZfx9OyGh4cbPj5TiYmJ\\nYdMmM0QiCSVL9iY+fgd9+vQlOTmZ4OAgRo0apuxr5kxvfvvNA0/Pbpw7dwapVMqaNSs4ceIYnp7d\\nOHHiGG5uHYiLiwMUZuFubu15+zYuV+MtWbIUfn6bMTcvz+rVyzh9+iRly5qjodGV27fPEB5+hlu3\\nDjFhQhBQ8NUQ88rr11pZll+90vpIy5+PqCg93qmriv5d/nbMnbswWyAnl8uVLwAqV67CwYP78PNb\\nxYMH99HW1s7WR0DAVnr27Eq/fp68ePGcyMgIANTU1Khbtx4AFhaVePYsBoBbt27QpEkzAJo1y7t/\\nQXDwFRo1ckJfvxAA+vr6PHnymNevXyvb7Nmzk6Cgy/8eowVHjhwkISGB27dvUbt27tVwsx733fdc\\nQOC/hpCZExAQEMhHZDIZYvHXz1URUHDp0gUKFy7C3LkLAUhMTMTdvTOLFq2gRImSTJ8+mQMH9pKW\\nVh9TUy9iYv5EIrHC0XFztjloGzb4YW9fk3HjJpOQkEDfvh7Y29fit9/6c/duKEOHjgQgIuIxR48e\\nolOnLgQFXaZ8+YoUKmSQq/G+fPkSPT09mjZ1RkdHlz17dhIXF8fr169QBALpqKs/ISZGl5IltUlK\\nSszPy/XDqV49lcuX4wF9IBU7u/gfPaR8o2zZeEJCMlC8B8+gbNm3+dZ3TuWQLi6t8fPbRFJSEl5e\\ng6hSxZq7d0OVwZytrR1Ll67mwoVzzJw5hc6du9G8eUtln8HBQVy9eoWVK9ehoaHB4MH9/hVxAbH4\\n3eOfiooImUyWZTw7dmxRBnWfYseOLbRt2wENDYUViEiU3cLiyZPHqKm9EwBq185FmZlr0aINo0cP\\nQ11dnUaNmqCiIuQYBATyihDMCQgICOSB9evXcPToIQwMDClSpCgWFpW4cOEsFSpU5MaNkH8VFSuy\\nbNlCZDIZlpaVGTFiLGpqasqHM339QoSF3WHp0oUsXryStWtXEh0dSVRUFHFxcXTr5k7r1u1+9KkW\\nCD4sW9TW1qZ4cTNKlFCoaDo7tyIwcAcNG6Zz544xEokVJUocoWtXy2xB9eXLFzl//m+2bt0IQHp6\\nOs+fP8uS7QBo2bINY8YMp1OnLhw4sJeWLVvnerwPH95n6dKFqKiIUFVVY8SIsaioqDBkyBhKlTqJ\\nSJTBmzfuVKiQQosWrZk3zwdNTc0fIoDyLZg0qQV6eke4e1eF0qXTGT0699euoDN/vhNqapuIiNCl\\ndOkEfHy+UCIyB7KXQzbKkrWNiopk4sSpVK5shZNTAwCePXuGiYkJrVu3Iy1NQnj43SzBXHJyEnp6\\nemhoaPDkyWNu37712XFYW9ty4sRRAgK2IZVKc2wTExPNiBFDsLGx49ChfZw7dwYrK1siIp4QEfGY\\np08jePAgnKlTfThx4hgPHoTz4sUzunbtSGpqCitXLqFNm/Y4Ojbm8eOHREZGsGDBXOrXb0h6erry\\nt9LZuRXnz59FJpMybdosSpUqw507t1i0yJe0NAkaGhqMHTuZUqVKf+XVFxD4uRGCOQEBAYFcEhp6\\nmzNnTuLvv4309HQ8PbtjYaGYj6Uo19uARCKhS5cOWTJHu3fvpFOnLp8sqXv48AErV64nJSWZXr26\\nUadOvWzy3R+TCf+Vpb8zyxb/+eccq1cvo3r1Glm2ZwZhkyY1Y/To07i5BdCypQWVK5fNsb8ZM+ZS\\nsmSpLOvu3Mn6kFukSFGMjIy4evUKoaF3mDJlZq7HW7NmbWrWrJ1t/e7dW5g06QiPHmlRr14y06Y1\\nRVdXl4YNG+XYz6BBfRk0aNhPp/onFosZMaL5jx7GN0FPT4+lS7/NHLmAgK2cPatQTn3x4gVPnz7N\\nsr1oUVMqV7YC3pXmXrsWxNatG/+1x9BhwgTvLPvUqlWXPXt20b27KyVLlsbK6p2/3Pu/Renp6Vy5\\ncomePbsikaSyYsUSXrx4ztatG5FIFPPw5s3zISwsFIkklerVaxIZ+RQHh4aIRCLu3r3Lw4cPcXHp\\nRJs27Zk/fzb//HMBZ+dG1KpVBzU1NRwcGjB+/BTOnDnJzJnePH0agb19TWbO9MbTsx+nTh1HU1Mr\\ny2+lgYEhfn6b2L17J1u3bmL06AmUKVOWpUtXIxaLuXLlEqtWLWX69Dn5f0MEBH4ihGBOQEBAIJfc\\nvBlC/fqOqKmp/fuAUl+5rXFjxVv6iIgnOWaOOnXq8tF+RSIR9eo1RF1dHXV1dapVsyc09Bb16zt+\\n2DLH/du166j8/6FD+ylXrvwPCeYSExM5duww7du7EBwcxLZtm5kzZ8FX9flh2WJgYADPnsUQFRWJ\\nmVkJjhw5iJ1ddcqWLYeqqpTWrUthaVmW5OQkZelXJjVr1mbnzm0MGzYKgHv3wqhY0TKbTxxA69bt\\nmDp1Is7OrfJlXpumpiY+Pq1YtuwEkZFaHDlyg44dPz4/SCQS/XLz6QRyJudySEmWNlpa7z7LmXYZ\\nzs6tcHZula2/gIC/MDDQIz09gXnzFuV4zPctN9TV1ald2yGLAmv37p3Q0NCgRo3adO/uSokSJVmy\\nZBXdu7ty8+Z1Chc2oVGjxuzcuZVOnboQEnKNq1eD8PNbjYqKGD09PczMSlC8uBnq6ho4ONQnODiI\\nAwf20bBhI6pXr8H06ZOJj3/Lhg1rady4KQ0bNmLYsEHK38rMFx0VK1py5sxJABISEpg2bTJRUU8R\\niUQfzR5msnbtSrS1dejSpfsn2wkI/MwIwZyAgIBArsk+HyQTTc2chR7kcrnyoVwsFpORodhfIknL\\n1nbLlg2oqyuMt/fv/4udO7ezcOFyrl69wv79ewFYtWpZNjnxzAcWU1NTwsJCmTp1grJ079GjhyxZ\\nsoCUlBQKFTJg/PjJGBt/m0AvISGe3bsDaN/eJd/6/LBs0ctrFMnJyUycOBqZTEalSlVo184FVVVV\\npk71YcGCuUgkEjQ1NVmwYOm/QZGir549+7Bo0Xw8PNzIyMigeHEzZs9egJ2dPZs2radXr650796L\\nxo2dcHBowMyZ3rRokX9lgsOH72bLFldAl61bH/D27WmcnSsyfPhgLC0rc+9eGGXKlGPixKwZlnnz\\nZhEWdgeJJBVHx8b07t0PUGSKFy2aT0pKKmpqaixatAJ1dXVWrFjC9etXSUtLp0MHV9q27ZBv5yCQ\\n/7xfDvn48aNclUPmJ+bmFfD1nUvnzl4UKWLOpElugKK0c8IEb6ysbPj9d0/c3NoTF/eGxMTELGIr\\nYrEKGRkZ3LwZgra2NkWKFOXx40c8fvwQU1OFb+SHLyZOnTpOZGQ86ekZFC9uSq9efXnwIJz3m6mr\\nqyn7z5zTt2bNCuzta+DjM49nz2IYPLjfJ89NeCEi8F9ACOYEBAQEcomNjS1z5sykR49eSKVSLlw4\\nS5s2igflzCCvVKnSxMREZ8kcVa1aDYBixUwJC7tD7dp1OXPmhLJfuVzOuXNnGDp0JFu3biQ8/B7G\\nxsaA4s3zjRvXqVq1GsePH8lRTjwzYPlQ+lsqlfLnn3OZPfudx9mqVcsYO3bSN7k+K1YsJioqkl69\\nuqKqqoqmphYTJozm0aMHWFhUYtKkaYDC+y2nADM8/C5z5/ogkUgwMyvB2LGTqFmzNhs2+FGxogU3\\nboRw8eIFDh7cz9atu1BVVSUpKZEuXTqybVsglpaVWblyXZYx2dlVx86uOgAaGhqMHDku27j19fVZ\\nvXoDoBCwCQq6QUzMU8qXr5iv83HOnzcEFOqEqanmHD9+HWdnePo0gnHjJmNlZcP/2TvzgBjzP46/\\npmu6iwodEklFypH7vuW2ZLGI3NbNSm65rXWvYyMikZy5WbfcVO4rQqdC0TXVzPz+mF+jUVjk2n1e\\n/3iO7/U8M43n83w+n/dnzhwfduzYptJvwIAhGBoaIpVKGTlyCJGRD7C2LsXUqRPw8ZmLg4Mj6enp\\naGlpsXfvbvT19fH13UBWVhZDhvSjevWamJtbFNp1CBQu7w6HfGOIfEmjJClJQlTURNLScjAy2kqX\\nLjMwMpJjamqGk5MzsbExxMfHYWdXjqioR5QpU5Z79+6ojCESiVBTU6NKlWr4+MyhU6fW2NiUYdCg\\nYVy6dJG0tDQMDAyV7S9cuMb9+2uxtBzG1auehIU95ty5/TYq9kAAACAASURBVEoBFLlcztq1qwkL\\nu4JEkoWmpuJxNS0tjVu3bhASspOXL18oX4qdPXuaa9fC6N27O1ZWVkye7JPPMy8g8G9FMOYEBAQE\\n/iEODuWpW7c+Hh5dKVrUBFvbsujr66uExInFYiZMmJrPcwTQp88A5s71Yc0afSpXrqrsoyiua8eK\\nFUu5c+c2w4eP4vTpk5QpY8udO7eJiAhj5Mjf8smJX758ocB15hqWT55E8ehRJCNHDgEUMvsmJmZf\\n7P4MHjycR48esm5dIGFhV/D2HkNAQDAmJqYMHtyXa9fCKV/e6Z0G5syZUxk92gsXl8qsXbuadev+\\nYvjwMcpwqjVrFAZXXFws586doV69hvz992EaNmxcKAqi2dnZ9OkTzKVLLzE23ouTU3MVz+rnoqcn\\neWtf8SBarFhxnJycAYVUe3DwFpV2x44dJiRkF1KplOfPk4iKegiAiYmpMqcu11Ny6dJ5IiMfcOKE\\n4mVBWloa0dFPBWPuO0ZTU7PAcMh16wLIzs7G3NwCf/8tBfQsHLZvDych4RfkcjEymQEyWQBGRmqA\\n4nckLS0NLS2xUpHy2rVwdHS0kUiyUFNTVypkurhUJjT0FP369URf3wCZTEZcXCz6+voEBm4kJyeb\\nYsWKk5WlS0aGBnK5AfHxszEzm8/s2c9p2rQmGhqKOTIzJTx8GIm//xYuXbrA+PGjef48CWfnSvz1\\n159YW5eibduOHDy4//9zV+Hp0yfMm7cIX9+V7N27m06dfv5i90xA4HtCMOYEBAQEPoJu3Xri6TmA\\nzMxMhg4dgIODYz7lyapVq+HntylfXxeXSmzevKPAcW1t7Zg0aTojRgxBLpdTsaILtrZluXr1EjEx\\nMdjYlP6gnHguucaHXA6lS9uyapXfp17uR5E3BFUul+PoWAFTU4XxWLZsOeLj49DX1y/QwExLSyU1\\nNRUXl8oAtGzZmsmTxyvHy81JBEU+W2DgBurVa8iBA3vx8ppUKOtft+4Yhw97ANq8fDmex49jOHbs\\nIk2a1CgUQZJRo0yZOnUfsbFlqVAhjDFjVAUtgHzGY2xsDFu2bGLNmo3o6+sze/Z0srKyeJ99OXr0\\nOKpVyy/CIvDjMGfOfjZsMCYnR0yLFn+zdGnnLybbL5XGY23dGblcHblck+TkrtSpc5Xdu7fRt29P\\n1q7diJqaGteuhSOVyrCxKU27dh05efIopqamnDhxjOzsbPT19Zk/f7HSQ6+oxReDtrY2GzYEKfNo\\nvbwmsXfvVIyNN5GYOIEnT3ZSp44f48e7c/ToEQCaN29B2bLlEIlEVK9ek0aNmnL79i2SkhIZOfI3\\nqlRxxdDQkP79BwOgr6/P69ev8fDoSnp6BjVq1Poi90pA4HtEKOghICAg8BHMnz+LPn2607dvDxo2\\nbIydnf1njbd790X8/e+zbNl9Jk3a/X+DL4BKlarg4lKZXbu2U65cufeOoZDWV2zr6r6pXWZtXYrk\\n5JfcuHEdUChuPnr08LPW+zFoamopt/PmvZQubcu6dYGsWxeIv/8WFi5cxjtSEZXkzUmsWNGFuLg4\\nrl69jFQqpXTpMoWy3rQ0OfAmNEsmK8rLlwphlM/xzuWKNLRvX52TJ505efI1+/c3xsHBBoCEhHjl\\nZ3TkyEGcnV0AxeealpaGtrYOenp6vHjxnPPnzwJgbW3D8+dJ3LlzC1DkXUmlUqpXr8WOHduUcz55\\n8pjMzMxPXvv3wODBnu8937lzW169Kpyab82a1ftwoy/MpUs3WLnShefPW5CS0pCtWzsTEHDii803\\nffpAHBzakpAwlVevfqV/fyk9evSiVCkbbGxs6NHDnbJl7di+fR9z5y4kOfkl27dvRV1dg2LFihMY\\nuB03tzbY2tqxbp0vGRmZjB07nk2bgnF1rabytyMSgY6ODt27N0BLK5JSpdywt69P/fqqnvWC6tUB\\nZGVlsWrVJWrVklCr1k3WrFEIucyePZ0xY8bj778FT8/++QRkBAT+zQieOQEBAYGPYOrUmYU21vPn\\nz5k0KYeEhFUAREY+Z9iw9bx48Rwnp4qIxdqIxWKlt0r1oUh1O3f37dplM2bMY8mSBaSmpiKV5vDz\\nz90Lzfh5m4JUId/G2tpGaWA6OVUkJyeHp0+fULp0GQwMDImICMfFpRIHD+5T5roVRMuWrfDxmUzv\\n3v1UjuetgXXjRgRmZsWYM+cPxowZpvSsJScn079/L4KDQ9i/fw+nT58gMzOTqKgo7OwukJTkiIHB\\nXnR1k6lb9y/l2IcO7WfevBlIpVK8vafg6FiBjIwMFi2az6NHD5FKc/D0HEDdug3Yv38PJ08eIzMz\\nE5lMxrJlqwEwMjLOV4Dc2roUO3duZe5cH2xsytCxY2dCQ08jEomwsytHuXL2dO/eiWLFSigNvYIE\\nXxYvXkHbth2Ii4ulb98eyOVyihQpyuzZv3/U5/i9sXLl+z3LhZtP9u0FM6KinpGZWSnPESOePcsv\\nmFRYaGlpsWFDV2JiotHRscHEpCpxcbGoq6szefIMlbbvii6oWbMZCxfeJDOzDi1bimnTpgGASoho\\nlSquVKniCsDo0a3p0KECT548o1q1Cujp6amM5+xcmd27d+Dm1oaUlBQiIsIYOnQkQUFXSUhIJCvL\\nkcTEyixevIMuXVLIyEinaFETcnJyOHRoP8WKFQd4p2CVgMC/CcGYExAQEPhGREY+JSGhvHJfLjch\\nM9Oa48fPKY/lfXDKKyfesGETGjZsAoCn5wDl8QYNGqvULrOzK8fy5W8Mki+JkZExFSu60KvXz4jF\\nYooWNcnXRkND450G5sSJ01iwYA6ZmZlYWloxYcLUd87VrFlLfH1X0qxZi3znoqOfMn36HLy8JjJl\\nijcnTx57r9R/bp6fRCLB3b0dDRqIKF36ZzQ0rnHq1HG6dOmGXC5HIslk3bpAIiLCmDPHhw0bgtiw\\nwQ9X1+pMmDCV169fM2CAB66uNQC4f/8e/v5bMDAweO99K+ihOdf4A955H94WfJHL5aSkJNO370AG\\nDvz1vXP+SOTWV0xKSmLqVG+lF3LsWG+cnSuptPX2HsuzZwlkZUlwd+9Gu3YdlWO4u3dTUYJNSEhg\\n+/Ygnjx5zMuXL5QvTYB8c40Z442Li+pcoPAK+vkFYGhoVGjX27RpFRwc9nLnjjsA5ubHaNnSrtDG\\nLwiRSKQsp5L32D8hPT2dwYNvcfu2oqTAqVP3MTa+QIcONfK1vXs3ipUrbyKTqdOtmzUNG1YvcM4G\\nDRpx8+Y1evdW1JwbMmQERYoURUvLkdTU4lhbd0Iu10QisefVKwf69RvEgAG9MTY2pkIFJ+VLpbwv\\nugQE/q0IxpyAgIDAN6J8eVvKlj3PgweKhyht7UhcXY0/0OufExQUyvnzqRQvLmX06KbKsgdfknd5\\nLnNru8G7DUw7u3L51ChB1bDJ5dq1cBo1aoqenn6+c+bmlpQtq3j4tbd3IC4u9r1rrlzZFR0dHXR0\\ndDA0NGT27CGYmpqyb5+UyMj7gOKhsGlTheHo4lKZtLQ0UlNTuXjxPKGhp9i8eSOgEFFJSIhHJBLh\\n6lr9g4Zc7tify/PnL+jf/2+uX7fFxOQZkycXo3Xrd3s2fywU9+fIkYPUqFGLXr08kclkBYaPentP\\nwdDQEIkkk/79PWjYsAmGhoZkZmYWqAT76lUKP/3kTnT0UyIjHyjHyTuXXC4nIyOj4JV9AUuhSBFj\\n1q934s8/t5CTozB6nJxsC32e9/ExoiuRkVHcvv3GKMvMtOPixXA6qKYSk5T0Ak/Pu9y/ryh9cPLk\\ncTZtilS5trwvrIYMGcGQISNUxmjRwow9e0rz+PEAQEq9en5YWFjSoUNnOnTonC/nNO+LLgGBfyuC\\nMScgICDwkUil0kJRT9TXN2D58lIsXryZzEwtmjfXpEOHRoWwQli37iRTppRHIikDSHjwIIA1a7oU\\nytjfkjNnIli79i9evoxi+fJVBbbJrU8FoKamjlQq+X+NP0XO3tv5NKrt1ZT7ampq7xSZAZRv/GfN\\n+p2SJa1Vzt26dQMdnYJrD+alsJQKZ806w5kznoCIlBSYM2czrVoVnhLn90D58hWYM8eHnJwc6tVr\\niJ2dai5pXFws/ft7KEV3oqOfsnLlUqKjFQWm163zZdmyhTRr5kZ8vCLn8sKFcwwfPoYVK5YAIjIz\\nM7h2LZySJa2ZPn0iO3YEY2BgwLhxE6lY0YWUlGSmTZtIUlIiTk7OXyyMr0wZK/74w+qLjF3YWFmV\\noHjx2yQk5IZvv8LSMr8kw5EjV7l/v51yPy6uEYcPB3+Uodq8eRWWL7/KoUPbMDDIZuzYNqipqXH7\\ndhReXuFERxtgZ5fC4sX1MDf/csq9AgLfE4IxJyAgIPAW69ev4fDhAxgbF6FYseLY2zty9uxp7OzK\\nce1aBE2bNqds2XKsWLEEqVSKg0N5xo71RlNTUyXs6s6dW/z55xKWLVvN2rWriY2NJiYmhuTkZH75\\npRdt23bA2rooBga7UVdP49QpKdWqFSkwnOtjOXlS8n9DDkDMpUtm5OTkoKHx4/7sr1t3kpkzy/D6\\n9QaMjcM5deox3buX/HBHFEbT3bu3cXSsoJTt/xBvq3MeO3aEKlVciYgIR1/fAD09fapXr8m2bVuU\\nnsd79+5QrpwDERFh3L59E4BTp05gbV0KG5vSH3nF/5yUFG3y5nu9fGlEVlYWYrH4i835tXFxqcyf\\nf/py9uwZZs+exs8//0LLlq2V52/evE5mZgarV69DLBbz888dlMa4mpoavr7+nDsXysqVy1RUSUuU\\nMKd9+05oaKizcaM/zs6VmDZtIlOmzCQ5+SVBQYFMnuzFrl0HWbfOFxeXyvTu3Y9z586wd+/ur34f\\nvjeKFCmKj48GixcHkZ4upn79ZAYP7pivXenSxdDWfkRmpkI0SiR6QYkSH//9bN68Cs2bqx6bNCmC\\n8+d7AhAdLWfKlE34+rb/+IsREPgB+XH/VxcQEBD4Aty+fZOTJ4/h77+F7OxsPD17YG+vePDLrXUm\\nkUjo1u0nli5dhZVVSWbOnMrOndvo0qXbez0hDx9Gsnr1ejIy0unT5xdq1ar7j8O5PhZ9fdUQNEPD\\n9ELxJn5LtmzJ5PVrRY5hcnIltmx5QPfu+du9/RmIRCK6devB5MnehITspFatuuQaPm/n0snlb4y4\\nvOdEIhFaWlp4ev6iFEAB6N27H0uX/oGHR1dkMhkWFpbMm7dIZczTp09Qp069L2rM1a6txaFDT8jK\\nsgakVKoU+68y5ADi4+MxMzOjbdsOZGVJuH//rooxl56ejpqaGmKxmMePo4iPj1PmweV+9+3tHUhO\\nfqnsY2BgyNGjhwG4e/dNIeyLF88TFfUQkUhEWloqaWnpZGRkEBERxuzZCwCoVauuSiHs/zIdO9ag\\nY8f8pTXyUrOmM4MHHyAgIJKcHDGtWj2hW7dOhTJ/QkJeARURz57pFsq4AgI/AoIxJyAgIJCH69cj\\nqFevIZqammhqalKnzhup8txaZ0+ePMbCwlIpGODm1oYdO7bSpUu3d44rEomoW7cBWlpaaGlpUaWK\\nK7dv3/hg6Nin4uVVncjI9dy44UyxYk8YO9bshw+5e3v5IlH+ELe3wxa7deuh3Pb336zczq1P5ebW\\nhkqVqtCt209UqFARHR1tduwI5uzZ02RlZVO/fkMA5s9fzJQp45HJ5MjlcmJjY3FwKM8vv3TO54kF\\nRfkELS0tbty4RmjoacLDw/D3X8vMmfOxtCz88Lm+fRuioXGSCxcuUaSIhAkT2hT6HAXxJQRA3ib3\\nexsWdpnNmzeioaGBrq4ekyZNV2lXtWo15HI5PXq4U7JkKaWiYd4x1NTUkclkyuOlS9uyY0cwMTHR\\n2NqWVbbLyclGKpWiqamJpaUVkyZNV4bNCgqJ7+ZDvzHe3m4MH56GTCbFwKD6e9t+DI6OKdy7JwXU\\ngUwqVCicl2ICAj8CgjEnICAgoELB9Y1AtdZZXvK+jVbkZin6SyTvlxMXidQ+GDr2qZQsWYI9e9oT\\nHx9H0aK10NX98d9U9+ihy8OHYSQnV8LE5DK9ehWeARETE83kyT6kpaVy/PhRfH03IJPJGD9+DBER\\nYSQnv8TUtBi//64w1tLT0wDVh9f7959y40YSder8jYXFZapXF+Pk5EzduvWpU6eeisrol8DDowEe\\nHl90iny8qx5YYZIriuHm1gY3t/xGanBwCAC6unqIxWJWrFiDtrYOw4YNpEQJc+LiYlm+3FfZXkdH\\nhwkTpnL16mW0tbVZunQlW7YEkJaWxuLFKwCoU6c+dnb2dO+uCN27f/8eJUqY4+JShSNHDuLh0Zdz\\n50J5/frVF732fyNvlyEoDBYvbomR0WZiY3Wws8ti0iS3Qp9DQOB7RSgaLiAgIJAHZ2cXQkNPk5WV\\nRXp6OmfPnlaey31otbYuRVxcLDEx0YCi/lilSlUARf5NbiHnkyePqvQ9c+YkWVlZpKQkExZ2BUfH\\n8sTHx2NsXIS2bTvQpk0H7t+/W2jXoqGhgZVVyX+FIQfQo0c9tmzJYcaMYIKC1OnUqVahjV28uDnl\\nyztx4cJ5Ll26oCwM/+TJY6Kjn1KmTFkuX77AypXLiIgIR1c3/wPp8uWRpKQU5/79joSF1eLixafK\\nc/8Gb05GRga//TaC3r2706vXzxw9egSAbduC8PTsgYdHV548iQLg1asUvL3H4OHRjYED+yiVIj08\\nupKWlopcLqdVqyYcPLgPgBkzpnDp0oXPWp+Ghga9e/ejf38PRo8eSqlSNkD+UNq8uYW5h+vUqc/J\\nk8dp3botw4atxtW1FXfv3sLDoxs9enRh925FiRBPz/5ERITRs2cXTp06QYkS5p+1ZoHCQU9PjwUL\\n2hMY2Jzp09ugqan54U4CAv8SBM+cgICAQB4cHMpTt259PDy6UrSoCba2ZdHX11d5IBSLxUyYMJXJ\\nk72QSqU4OlagQ4fOAPTpM4C5c31Ys0afypWrquRc2draMXz4IJKTk+nTpx8mJqYcOLD3vaFjPyJx\\ncbF4eY1iw4agTx4jLOwKmpqaODk5qxyvUsWBKlUcPneJ+dDR0VZu9+jRm/btf8rXxs9vE+fOncHX\\ndwWurtXp3buf0hMrk8lITMybo6ZOWtqbHMUfPcQV4MKFsyreybS0VFatWoaxcRH8/ALYuXMbmzcH\\n4OU1ibVrV2Nv78icOX9w9eplZs6cwrp1gVSs6MK1a+EUL14CS0tLrl0Lp2XL1ty8eYNx4yZ89ho7\\nd+5K585d33ne2NiY4GCFaEneItYlS1qjqdmBS5d6cumSNocOXWHx4k5Mn+6q0t/Q0IiFC5d/9joF\\nBAQECgvBmBMQEBB4i27deuLpOYDMzEyGDh2Ag4MjbduqFk2qWrUafn6b8vV1camkUug7L7a2dvmM\\ntXeFjv3XuXr1Mrq6evmMuS9NjRo18fVdRfPmbujo6JCY+AwNDU2kUikGBgY0b+6Gnp4++/YpQvty\\nPbE1a9bG1PQySUm5RpuEIkUUuVm6urqkpaV91ev4Etja2vHnn0tYuXIZtWvXU6qu5oaPlivnwMmT\\nxwBF7umsWb8DCqMpJSWF9PQ0nJ0rEx4eRokS5nTo0JmQkJ0kJSViYGCAWKxd8MRfgdevX3HmjC2g\\nWENyclX27dtOq1Zv2oSEnOPu3RRq1LCgfv2v+70UEBAQeBeCMScgICDwFvPnzyIq6iFZWVm4ubXB\\nzs6+UMbN65yZO3c/e/ZooaEhpW9fXXr1qvfujj8gUqkUH5/J3Lt3BxubMkyePJ1Hjx6xfPkiMjIy\\nMDIyZuLEqZiYmBIcvIXdu3egrq5O6dJlGDRoKCEhO1BTU+fw4f2MHDmuUMo1vI9cz1m1ajWJiopi\\n0KA+gMIQmzTJh5iYaP78cwlqaiI0NDQYO1bhRcrriW3SxJGjR69QqtQOzMzCcHCwARTCOfPmzWLb\\ntiBmzJj7RQRQvgYlS1qreCerVq0GvKnTp66uWpcvf2ipiEqVKrNjx1YSEuIZMGAIp04d5/jxo8ow\\n5W+FWKyNru4rXiqFLuVoa7/JeV2w4CBLllRHIimFoeF1fHxO0737v+tvVkBA4MdEMOYEBAQE3mLq\\n1JmFPqan5wDl9s6doSxfXoesLMVD/YwZF6hWLRJHx39ePPd758mTx3h7T8HJyZk5c3zYvn0rp0+f\\nYM6chRgbG3P06GH++msF3t5T2LTJn23b9qChoUFaWip6evq0b98JXV1dunbt8eHJPpO3FTDd3bvi\\n7q4aqmdpaUX16jXz9X3bE+vllbvVjPT0dORyORUruhAQsPWz1zlv3kx+/vmX95Y4OH36BCVLfpma\\ndklJSUrvpL6+AXv27HpnW2fnyhw+fIDevftx9epljI2LoKuri66uLsnJyUilOVhYWOLsXInNmzcy\\nerTXO8f6GmhpaTF4sDoLFpwgObkUlSqdZsyYOsrzISHqSCSlAHj1qiK7dt0tsCyGwIc5cGAv1arV\\nxNTUFPg6iqgCAv9mBAEUAQEBga/M/fuvlYYcQEqKC9euRX27BX0BihUrrgyRbNGiFRcunOfhw0hG\\njRpCnz7d2bDBj8TEREARvjdt2kQOHz6AmtqbPLMfVTPk8eM42rbdQdWqETRtuodLlwpH1MbLa9IH\\njbRTp04QFfWwUOZ7m4cPHzBgQG/69OnOunW+eHj0Ja+YCLzJK/X0HMDdu3fw8OjGX3+tYNKkacpW\\nFSo4UbKkwjBydq7E8+dJODt/Wc/rP2HAgIacPm3F4cPxhIS4YWFRTHlOU1Om0lZdXfZ29/8cv/02\\ngrS01I/qI5VK2b9/D0lJicpjX0MRVUDg34xI/p38BSUmvv7WSxD4QpiZGQif778Y4fP9eE6fvoGn\\npw4pKYoHWCurQ+zZUwZLyxLfeGWqfOpnGxcXy7BhA9m2bQ8AV65cYvv2rbx48ZxVq/zytZfJZISH\\nXyU09DQXLpzF338L/v5r0dHRVakT96l8qrdKEf65HXt7ByZPnvGP+/XtG8KePb8o92vWDCQkpG2+\\ndnFxsYwZMwwHh/Iq4ajXr19jxYolSKVSHBzKM3asN5qamgwdOoBhw0Zjb+9As2b1cHfvxtmzZxCL\\nxcyd+wfR0U/x8hqNnp4++vp6H6xpJ/zt/nMCAs7g41OE5OTKWFicYuFCHRo3dvnWy3onX/qzPXhw\\nH9u2BSGV5lC+vBNjxoxn4cJ53LlzG4kkk4YNm9C370BA4Xlr0qQ5ly5doGvXX/j99zmYmZmhra3N\\nihVr6dHDHTe3NoSGnkYqzWHGjLlYW9t8sbX/GxD+dv+9mJkZfHQfwTMnICAg8JWpV8+J2bMTaNx4\\nGy1aBLN4seF3Z8h9LgkJ8dy4cR2AI0cOUr58BZKTXyqP5eTk8OjRQ+RyOQkJ8VSp4srgwcNITU0l\\nIyMDXV1dZS23zyEnJ+eTvVW7dm1j8eIV/8iQy8nJUW6/eKFaj/D584LrEwI8ffqEn35yJyAgGD09\\nPTZvDmD27On4+MzF338LUqmUnTu3AaqKmJmZmTg5ObN+fSAuLpUJCdlJxYou1K1bn6FDR7BuXeB3\\nmZuXlPSCfv124uZ2hF9/3U5q6o8hDNOjR11CQrRZunQ/e/aU+q4NuS9FXFws3br9xPjxY1i4cB53\\n795m8eIV3Lp1k9mzpzNgwK+sWbOBBg0ac+jQAR4+fEBg4AaeP0/i4MF91KlTj+bN3ShdugwSiYQy\\nZcrSv38vZDKZUhG1Q4fObN4c8K0vVUDgh0LImRMQEBD4Bri718bd/VuvonB428NUooQFVlYlGTly\\nCFpaWshkMkaOHEvr1u0ZOXIwMpkMHR0dBg8eRsmS1owbN5KYmBhAjplZMfT19alatTrDhw8iMHAj\\nRYsWJTs7mypVXLlx4xqvX7+mePHivHjxnCJFiiKVSklPT8fQ0AiZTIpUKqNGjZpcuxZB/foNCQ09\\nTXh4GP7+az/orcrl999nExsbw5gxw3Bza0NERBixsbFoa2szbtxEbG3LsnbtajZs8KN8eSeMjY15\\n+vQJDg7lycm5TunSS0lMnIC29mV0dPYzYsQO6tdvQKdOP6vM83Y46vr1a7CwsMTKqiSgUDvdsWMr\\nXbp0U+mnqalJ7dp1AbC3d+Ty5Tc12r6TgJsCGTPmBAcO9AJEXLkiRV19E0uXdvzWy/pHODjY4uDw\\n78lr/RRiYqKpW7c+d+7cIisri2HDBpKRkU54+FWOHTtMSMguHj16iI6ODkeOHCIlJRlTUzOWLl3F\\nokW/ExERBsCzZwn89JM75cs74e7erkBFVAEBgX+G4JkTEBAQEPhs8nqYTExMaNu2I0WKFKFHj94c\\nPHgcV9fq7Nq1je3b93LkyGnKli2HkZExr1+/Ji0tjcDAbRw7dpY1azYCcOzYEUaN+o2jR88wZ84C\\nkpISadOmHbVr18XWtiwtWrTG3z+Iv/7yp1+/QWhpadG//2A2bAjCyMiInJwc1qzZQK9enp/krfrt\\ntwmYmpqxbNlq4uJisbd3xN9/MwMH/srMmVOU7eRyOUuWrGTOnD9o1KgpcXGx7NgRRMuWnbG0HE3j\\nxlL27duFuroagYEb882T19sml8vR11cNsXmXYaau/uZdrJqaSEVF8nuuaRcVZcibPDt1Hj36+JAi\\ngW9H8eLmlChhjptbG8zMirF8+V8EB+9BXV2dTZs2MHLkWBwcHKlbtz737t3h0qULJCY+w8trNE+e\\nPCY6+ikAJiamlC/vpBz3XYqoAgICH0bwzAkICAgIfDZve5iCgzcD0KRJMwBu375JlSquGBkZA9Cs\\nWUvCw8NQU1OnUqUqGBoa0bdvMNeuFcXUNA0joxOEhp5i8+aNZGdno6amhomJGRUqVCQ09DT79oWg\\npaXJwYP7SU19TWxsDFu2bERf3wB1dXWaNGmusr5P9VbJ5fJ31kwTiUSoq6ujpaVFXFwsu3Zto0uX\\n7jx9+oTY2GNADqmpF0lM7ERi4jOeP0+iT5/uVKtWkyFDhgNvwlGdnCpy5MhBHBwc2b17BzEx0Vha\\nWnHo0H4qV676j9erra3zXde0s7Z+ze3buXtySpYUnY+NGwAAIABJREFU8n5+JHR0tKlatTrjx49B\\nKlWIwLx6lULFii5cvnyRc+dCqVWrDtu2BWFnV44ePXoTELCe5ctXK9UqDx8+gKam5re8DAGBfxWC\\nMScgICAg8Nm87WESiRSBHzo6OsrzqgaVqnE1a9ZR9uzpBWjw+DHY2/uyadNyrK1LKQVVSpWyoVQp\\nG+RyOZs2bWDJkj+YNm0WjRs3Y+XKZURHP8XXdwUJCfFoa6vmqX2ut+qfGoMaGprs3r0dd/duzJs3\\nE4lEgq/vSp4/T0IkUmPVKj+uX7+Gp+cvZGZmoqurx/btQUyfPpHs7GyCgnYhk0np2rUjpUuXwd7e\\nkaCgQDp37opEImHRovlkZ2cjkWTy5EkU1tY27NgRzPPnSQwY0BsLC0sCAzcWWNOuIAn4jIwMpkwZ\\nT2JiIjKZFA+PfhgZGRUowNK5c9vPFqqYN68O4E9MjCFlyqQwe3bTj+ov8O2xsSlN//6DmTFjMkOG\\n9EdbW5uff/6F8+fPsnnzRsqXr4izswvm5pbs2xeCTKYw+hITn6GhoUmjRk1YsuQPPD1/YeXKtwWR\\nRN+1Z1lA4HtECLMUEBAQEPhs3hY8cXZWFYhwcKhAePhVUlKSkUql/P33YSpXrkqFChUJD7/K06cZ\\ngAZqaskApKc7ERQUqOwfHx/HjRvXiY2N4erVy7i5tUZLS4v4+HiePn3CmTMnKVvWjm7deirru+Wi\\nq6v7Wd6q3JppQJ6aaXrvNPCcnJzZuNGPnJwcoqOf4O7elRYtWiMSiVQETv74YxkikQhHxwps2bIT\\nLS0txGIxqalpODiU57ffJtCqVVulx1NHR4dJk6azdu1GVq70448/5gFgbFwEI6MiLFmykmnTZhEQ\\nsBU/vwAVQ04qlRb4kHzhwllMTYuxfn0gGzYEUaNGrfcKsHyuUIWFRTE2bvyJY8easmZNJ4yNv//a\\nYnFxsfTq9fOHGxbA1auXGTduVCGv6NuR+x1q0qQZZmbFWbHClzVrNtCsWQtMTExwcanMihW+zJw5\\nn19/HUGzZi3R19dn2LCBTJkynoyMdGrUqI21dSn8/DYhFosJDg5RvmBwcHBk6dJV3/ISBQR+OATP\\nnICAgIDAZ2NtXYqdO7cyd64PNjZl6NixM9u3vymUbWpqyqBBQxk+fBByuZzatetRt259AMaNm8jM\\nmXMpVWonOTnFiYlZg7V1JSASD4+uZGVloaOjw86dW7l48TwSSRYWFhaYmJiyc2cw+/fv4cWLF+zc\\nuY3ixUtQooS5iuHSpElz5s2bVaC36v0ovASengOYM8cHD49u/zeopinOFmAciUSKENIKFSrSvXtn\\n1NTUyMrKAkAsFnP16mWlwElcXCwGBoZERFylS5duWFpa8fhxFHfu3KJr118IDw9DJpMikUjYvHkD\\n169fY9CgPkgkWVhZWfH69Wt69x7A7duvgGSaN/+JNm3q4+XlDUCzZvVo374Tly9fZPTocco1SiSZ\\nTJgwjrZtW2FrW54//1zCypXLqF27Hrq6uu8VYBGEKv67mJtb4O+/RbkfHLxb5Xzec7m4u3fF3b1r\\nvuP+/lt4/vwFq1adQyZTo2dPF2xsLAp/0QIC/wEEY05AQEBA4LNRV1fPJ+EfHByist+0aQuaNm2R\\nr2/NmrXZs2c38+cf4OpVDWrWDGDq1MaUKKHwhsTFxeLlNeqjar3lpWJFFwICtn644VvkfVidM2dB\\nvvOengNUvFNFihSla9cexMREk5j4il9/nYSv7zwiIx/Qp08//v77EPr6Brx6lQIoHo69vCayc2cw\\nAC4ulTl37gzq6hpUrVqdgwenIpPJadmyNceOHcHAwABLSytycnJYsWIN/v5r8fV9iLr6bRISJvP6\\ndQf09VtTu/YJ6tVrSGZmJhUqODF06EjlGtPT05kyxRs3tza4u7uTmPgaP79NnDt3Bl/fFVStWk3l\\nGhUhs2+M1v+qUIVUKsXHZ/I/qgd4/vxZli1biFisjbNzJUQixX3s1q0Tq1b5YWxsjEwmo3v3Tqxe\\nvU6ZR/pvRiaTsXDhYW7dUsfCIpORI2vSrdspIiI8ABEHDwYTFKSOlVXxb71UAYEfDsGYExAQEBD4\\nbD43z0UkEuHl1eqjxt+37yJhYS9wdDSgU6c6yuPJySnMnHmS58+1cXUVMWRI0y+Wh5N33Nzt8eOX\\ncvfufeRydcTidExMzDA0NEJHR5fr1yOQSCQFCpy4uFRmxowptGrVFmNjY1JSUkhOfkmjRk3w9V1B\\n8eIlSE1NxdW1Grdv3+LcuVCyshzR0DBDLtcH1DEwqEJ4eBj16jVETU2Nhg2bKNcnl8sZP34Mv/zS\\ni2bNWgKQlJSEgYEBzZu7oaenz44dwcTHx6msr1KlKl/k3v1IPHnyGG/vKTg5OTNnjg+bNwcQErKT\\npUtXYWVVkpkzp7Jz5zbat/+J+fNnsWzZaiwtrZgyReElFYlEtGjhxuHDB+jSpRuXL19UKrr+F5g1\\naz/LlrUCjIBsLl/+g4iIUeQqm96/78727cGMGNHyWy5TQOCHRDDmBAQEBAQ+i7fDr77G+H/9dYyZ\\nMyuQmdkELa1oHj06yNixigfBgQMPcfx4H0CNgwfjEYmOMmTIlxHaOHz4pMoaHz9+wsmTfcnMdEVD\\nIxpLy/6sXLkJP79VlCtnz+TJPty4cY3Jk72QSqU4OlagQ4fOAMrC6i4ulQEoW9aOly9foKGhgbm5\\nJc7OLhw+fIBTp05w5MhBsrNzsLe3JzJSBogwNLyGk5NYaVRqaYnzGZvOzi6cP39Wacw9fPiAqVO9\\n0dc3wNi4CGPHepOa+rrA9b0pKaDY/lQD+f79eyQlJVKrVp0PN/5O+Kf1ACtXroqFhaUylLd5czdC\\nQnYC0Lp1O8aPH0OXLt3Yt283rVu3/TYX8w2IiNBGYcgBaBIXZ42a2ktkshL/P5aBvr4g4yAg8CkI\\nxpyAgICAwA/H3r1SMjPLApCVZcWBAxqMHQsSiYQbNyzI1feSSktw5crXK6Kdnp5BVpYBkADsB9IR\\niRoREDBY2aZq1Wr4+W3K11cs1ubYsbPK/XHjJiq3XVwqsW9fCBMmTKVMGVv69u2Js3Mlhg/vR8+e\\nx2nYMI7GjTU5ePABjRvnz1HKpV+/Qfj5+fLHH/OYO3cm1avXpF69htSuXVfFi1fQ+vKGneYKVeSK\\nwHyMYXf//l3u3r39UcZcTk4OGhrf7pGloHqAueGyuccK5s3xYsWKU7RoUa5cucTt27eYNm32l1ru\\nd0eRIqoCRNbWUL/+frZvr0VOjpiWLY/g4dHlG61OQODHRjDmBAQEBAR+OMTinLf2swHQ0tLC1DSZ\\nxMTcMzJMTDK+2rrKlStLw4aBHDumD7gBezh/vg27dl2gQ4canzyui0tlNm5ch5NTRcRibcRiMWlp\\nOmzceJXevfty6NBWAgNVhWXyG1iKfXt7B5YuXUitWkepVq0m6urqhIeHERS0iefPnzNkyHAaNmxC\\neno63t5jSUh4RlJSBmZmzRg8uAl2diaMHj2UChUqcvfubX7/fSkBAeu5c+cWEkkmDRs2oW/fgYCi\\nvuDSpX+QkZGJlpYWixYtZ82aVWRlZXHtWjg9e3pSq1YdFi2az6NHD5FKc/D0HEDdug3Yv38PJ08e\\nIzMzE5lMxrJlqz/5/n0ub9cDPHv2NCYmply/fo2goE3o6OhQuXJVSpWyIS4uVhmmeuTIIUAhRnPk\\nyGnatu2Aj89k3NzaIBKJOH36BCVLlsLGpvQ3u7avwdSpNUlMXM/9+8WxsHjO1KmOVK1qx8CBd8nK\\nekmlSl1RUxM8cwICn4JgzAkICAh8BbZuDaR9+58Qi7ULZbyCaoZ9DPv37+Hu3duMGjXuw42/QwYP\\ntuDBgwPExNSkWLEwBg0yBRQGzNSp1vj4BJKUZISTUzyTJn29PBx1dXV8fKpz/LgmOTklefx4DwBn\\nztyiQ4dPH/fu3dv8+usIxGJtlixZQGKinKNH56CjE4al5Q4aN7YlPv4poaGn0NTUpG/fgcyZswBv\\n77FK8ZZx47yZPNmbZ8/iCQraia2tFZGRMSxfvogXL56zcqUfUVGPGD9+NA0bNkEsFjNkyBi6d48l\\nJqYm1tZdGTq0PkuXPiYmJprJk30oX94JgAEDhmBoaIhUKmXkyCFERj7A2roUU6dOwMdnLg4OjqSn\\npyMWi+nffzB3795m5MjfAFi9+k9cXaszYcJUXr9+zYABHri6Kgzf+/fv4e+/BQMDg8/4VD4PkUiU\\nT61VU1OTCROmsmjRPJVwVA0NDcaNm8i4cSMRi7VxcalMbGw0uYZ0nTr1mT17Oq1aKUIsT506QZ06\\n9f71xpyVVXF27eqERCJBLBYrjzs5OXzDVQkI/DsQjDkBAQGBr0Bw8BZatGhVaMbcPwlrmzdvJj//\\n/As2NqWRyWQf9eY7Li4WT8+x+PkFfrjxN6BxYxcOHkwkLOwizs5lsLAokeecM40aVSQ7OxstLa2v\\nvjZzc3NKlIggLi631p4EM7PPU350canCli0BdO7clbCwq7x8qQtooKNzhcTEn5DLM1izZpbSmHr4\\n8AFVq1Zj4cJ5vHjxggULThMaGoJYrEGDBlWVLwEMDQ0BqFevAaAoCP3ixQtAETq4YMFC1NSSsbL6\\nCw2NZyQmOnHu3HaKFzdXGnIAx44dJiRkF1KplOfPk4iKegiAiYkpDg6OgKLeH8CyZYto0KCRsu/F\\ni+cJDT3F5s0bAcjOziYhIR6RSISra/VvasgBlChhzqZN21SONWtWn6pVqzFr1u94eY1i/PjJZGZm\\nMn36JB49ekipUqVJSkqkZcvW2Ns70KxZff76awXHj/+NXA4GBgZcvx5BaOhpwsPD8Pdfy8yZ8z+i\\nbMaPSV5DTkBAoHAQjDkBAQGBQiYjI4MpU8aTmJiITCalUaOmJCUlMnz4IIyNFcWdFyyYw507t/OF\\npXXu3BY3tzaEhp5GKs1hxoy5WFvbkJKSzLRpE0lKSsTJyVklR8fbeyzPniWQlSXB3b0b7dp1BODv\\nvw+hp6evrDP29OkTAgLWo69vQNmy5ZQy8z8qxYub0bKlWYHnRCLRNzHkAAwMDPH21mDx4mBev9an\\nZs0YRo3q+Flj2ts7cPfubdLT0xCLxWRllURb+8b/jbmJJCauxNNzh9KYevToEWXKlKVFi1ZMmLCI\\nXbu8sLZeR1xcbySSE3h7y1TG19R8813I/W4dPnwATU0ZCQnzycx0oHTpxmhqPsHSUo/bt9+8lIiN\\njWHLlk2sWbMRfX19Zs+eTlZWFgW9b3hX8fJZs36nZElrlWO3bt1AR0fnc27bV2XHjmCMjIwICNjK\\nw4eR9OnTHYArV+6RkZHBrl3hiESp1K1bj5CQnXh49KVu3frUqVNPWb/veye3TMiGDUHfeikCAgL/\\nRzDmBAQEBAqZCxfOYmpajN9/XwJAWloq+/fvYdmy1UqPyIABv6qEpT18+IAyZcoiEokwNi6Cn18A\\nO3duY/PmALy8JrFunS8uLpXp3bsf586dYe/eN2IU3t5T0NTUZOLE31i8+HeCgjbh6TmQjIwMihQp\\nyvr1gTRtWhd1dXWKFSuBSCTiwYN7VKjgRExMNNOnT0IiyaROnfoEB2/hyJFTKtcjlUpZtWo54eFX\\nyMrK5qef3Gnf/qevd0N/QLp2rUWXLjKysrLQ1q772ePlKlru37+H6tVrkpWVxIULB9DSisLRcS8v\\nX15l3brAPMaUBIBWrdqxcWNfDAxOkJrqRnp6LSQSX54+fUzx4s4qIh5vk5aWRrlyZRg69Dpbt+5D\\nQyOGBg1W0qzZGIKCljBixGCWLFnJ5csXSUlJ5uzZ0/j7ryU6+ikvX76kWbOWPH+eRJMmdejY0Z2L\\nF88zevQ4RCIRGRnpyuLlRYsWZcuWTTx7Fk9iYiIZGekMGPDre0RFvk+uX49QFlcvU8YWW1s7Xrx4\\nyYgRmWhoaHL58maKFr1I+/YXiI+PVvb70a5TQEDg+0LINhUQEPhPM3ToAO7cuV2oY9ra2nH58gVW\\nrlxGREQ4enr6+docO3YYT88eeHr24NGjhzx69Eh5LvctfblyDsTFxQIQERFGixaKOmy1atXFwMBQ\\n2T44eDM9e3bhzp3baGlpMXHiNGrWrAWgLAKdmZmJg0N5Nm4MolKlKhgbF0Eul7NkyQJ+/rk7/v5b\\nKFas4IK9e/fuRl9fH1/fDfj6+rNnzy7lugTejZqaGtrahRNWCwpFy82bA6hUqQp//DGI0qV34+ho\\nwsKF1dDT00dPT48XL55z/vwbRUxTU1OMjAwoWnQFKSk/kZVVFh2dmkyaNJ727duzfPlioOB6ec2b\\nt+TOndvcvLmejh1jsbCwoEQJRbioRCIhIyODnJwckpISKVHCnFmzpmFsXIS6desTFxfDuXNn8PGZ\\ng0Qi4cSJo+jq6uLgUB6xWExU1CPatGmOlZUVM2fOJzY2mhs3riOV5mBjU5qaNWshEn16+YNvxduG\\n2dWr94mKak7uu/MXL6pz61aSSsH1s2fPIJFkfs1lfhYymYx582bRs2cXRo8eikQiUfkdTU5Oxt29\\nHaDIzfX2HsOoUb/i7t6O7duDCAzciKfnLwwc2IdXr14BEBKyk/79e9G7d3cmTRqnvB+zZk1j8eIF\\nDB7sSZcu7Tlx4ui3uWgBge8YwTMnICDwn+ZLPDCWLGmNn98mzp07g6/vCqVBlUvBYWkS5fnc8Ed1\\ndTWVh76C3uBfvXqZK1cuMW/eIsaPH41MJuPWrVuUL++Empqa8to0NDQwMysGgL29IxERYVhYWHDz\\n5nXmzl0IQLNmLfjzzyX55rh06TyRkQ+UD1JpaWlERz/F3Nzic26TwEfytqKloaEBrVo1o2JFZ8qV\\ns6d7904UK1YCZ2cXlX4DB/Zg0aJVFCkSjqHheSZN6oazc1nMzAxITHydb57c2nlGRsasWuWnPJ6T\\nk0P37p0wMjLCycmZMmVsuXPnNteuhdOqVTvu3bvDxInTAMULgPDwMIYNG4W6ujrBwSHK76LiXxHj\\nxk1Q1rsbPdqL0aOHUrt2PSwsSnHp0i0aNWqKm1ubL3AnvwwVK7pw7NjfVKniyqNHD3n48AGdOvVC\\nX//NyyJ19QTMzMSAQqpfV1eXI0cOKcVt/ikfmwNbmDx9+oRp02bj5TWRKVO8OXny2Ht/Rx89esi6\\ndYFIJBJ+/rk9Q4aMwM9vE8uWLeTgwX106dKNhg0bK8PDfX1Xsnfvbjp1+hmgQHEeAQGBNwjGnICA\\nwH+CuLhYxowZhoNDee7du4ONTRkmT56u0mbBgrn55NWvXLnEtm1BSkXAS5fOs3PndmbP/p2LF8/j\\n5/cXWVlZWFpaMWHCVHR0dOjYsRVNmjTn6tXLuLpW5969u+jq6pGWloahoRFpaWloa+uoeFIqV676\\n3vW7uFThyJGDeHj05dy5UF6/VrzRTk9Pw8DAAFvbskyfPochQ/qyd+9OXr9WDZ/T0NAkPPwqr16l\\nIJfLiI2NUQpT/BNGjx5HtWo1/3F7gcKnatVqHD9+Trm/efMO5faECVPf2e/69Qh+/dWT1q2bfdb8\\neUM9K1Z0wda2LFevXiImJhpzc3Pu3s3r4ZZ/VPHy3BcgY8cuY+3abaSm1qJ06ccEBjajSBHjz1r3\\nl6AgT2Z2dhY3b16jR48uZGdnoampRaVK5enZcz9//51J6dK9KVo0iitXpBgZGf+/rxopKcl07Nia\\ncuXKsWrVunf+rnTu3JYmTZpz6dIFfvnFgyZNPu/z/FTMzS0pW9YOUORyfshLX7myKzo6Oujo6KCv\\nb0CdOorSGWXKlCUy8j4AkZEP8PVdSVpaKunpGdSooYgsEIlEBYrzCAgIvEEIsxQQEPjP8PTpE376\\nyZ2AgGD09PTYsUNVoW7AgCGsWbOB9es3Ex5+VakI+ORJFCkpyQDs27eHNm3ak5yczIYNfixZsgI/\\nvwDs7R0IClIUWpZKczhy5CByuYywsCv07t2Pdu06MGbMMEaMGIydXTmlJ2X69Mn5PClvePO229Oz\\nPxERYfTs2YVTp05QooQ5ADVq1EYqldK160/4+6/F2bkSTZq04N69u6ojiUR4eg5g4MA+rFmzCn19\\nA0QiERUqVOT4cYXH7e+/Dxe4iurVa7FjxzZychS13Z48eUxm5o8TFvZfZevWUFq2bEdo6BWaN3cr\\nlDHzhnq6uFRm167tlCtnj6NjBcLDr5KSkoxUKuXvvw9TqVKVd47Tr98gDAwM+eOPeQAkJSWRmJjI\\noUMdeP58ONraj7lypQ9Ll54plHUXNrneS3NzC/z9twBQpUo1LCysCAjYir6+ATk52ZiYmFKqFIwb\\nN57Dh2dw4MAegoP3oK9vwMOHDxgxYgzm5hbs2rWfVavWvfd3RSQSYWRkjJ9fwDcz5AAV4SQ1NXWk\\nUinq6urIZIoogrxRBvnbqyn3RSKRMvJg9uzpjBkzHn//LXh69lcZoyBxHgEBgTcInjkBAYH/DMWK\\nFcfJyRmAFi1aERy8ReX82/LqeRUBDx3aj5tbW27evMGUKTM4dy6UqKiHDBrkCUB2dg4VKyrGFou1\\nWb78L4oXfyOXb2/voAwbgnd7UoKDQ5TbDg6OLF26CgBDQyMWLlxeYJ8FC5Zy8eJ5/vxzCWpqIk6d\\nOs6YMeNJTX0TQicSiWjVqi2tWrXlxImjnD17hpEjfyM6+ik+PpPZuHEd1avXRF8/f35f27YdiIuL\\npW/fHsjlcooUKcrs2b+/+0YLfHP+/PMIc+dWRSI5jkj0gmnT9jNrVvvPHreg4uUuLpUxMTFl0KCh\\nDB8+CLn8nxUvHzlyLLNnT2fFiqW4ulZn4cL5mJmpI5MZ8OzZNEBEdvaP85hSqlQpzp49Ta9eXYmL\\ni6FWrTo8eHCfa9fCGTnyN44f/5tdu3YQG5uCRJLKypW7mT9/tMoYN29ef+fvCvBNjbj3YW5uwd27\\nt3F0rPBJeW0ZGekULWpCTk4Ohw7tf2f+roCAQH5+nF9JAQEBgc8k70OlXC5X2X9fHlurVu3w8hqF\\nlpYWjRs3VeaquLrWYNq0WQXO9bUl1atXr0n16qphkMuWrQYgMDCUnBxv6tU7QqtWWXh7t1bmnZiZ\\nmfHXX+sBRSmDp0+fAIqHsz179pCY+BqRSMTAgb8ycOCvX++CBD6LY8dESCSlAJDLi3LqlF6hjPu+\\nUM+mTVvQtGmLfH1yvVi5BAe/UWLN+1Jj06ZgevYM4u+/+wBaWFoeonNn20JZ99fA0NAIZ+fK1KtX\\nn5SUFJUwVLFYzJYtm9DQ+Inw8IEULz6JXbvKYGGR3xv+Pf2uFMTbxrlIJKJbtx5MnuxNSMhOatWq\\nS67Bnj+XTjU8Nfdcv36DGDCgN8bGxlSo4ER6enqB8/1ogjgCAl8DwZgTEBD4z5CQEM+NG9dxcqrI\\nkSMHcXZ2ITT0NHK5/L15bKamppiamuLvrwh/Aihf3omFC+cRExONpaUVGRkZJCUl5quV9a2JjHyM\\nj48+L14ocpMePYrBzi6Uzp3rAHDnzh0WLZqPXC7HwMAAb+8pKv3XrDlBQIAEmUxEp04iRoz4Pj0D\\nAqro6KiGuunqSt7R8tsSFRXL1KmXePZMFyenVFavbouf325SU6F9ezucnH4cYw7ehKFOmDCVMmVs\\nWbp0IY6O5f//+6JNRIQ56uov0dM7RXp6Da5ckaKrq6vMp/0Svytv14YLDNxIZmYGBgaG7N69A3V1\\ndWxsSjN9+uwPjpU3rBSgW7ceym1//83K7f79BwPg5tZGRcQmryGf91yHDp3p0KFzvvnejmB4+8WA\\ngICAYMwJCAj8h7C2LsXOnVuZO9cHG5sydOzYmdDQ04hEIpU8toIUAZs1a0lKSgrW1jYAFClShIkT\\npzFt2gSysrIBRc7d92bMXb8exYsXDZX7WVmW3L//RrrexaUS69cHFtj3woWbzJ1bilevFGFef/wR\\nSfnyl2nWzPWLrvlH4F3Fk9euXY2LS2VcXasX2O/06ROULFkKG5vSX3R9o0bZERUVzL17VbCyus3I\\nkeZfdL5PZdSoC4SG9gLgyhUJuro7mD79x1GwfJt3haGWLWtHuXIOREb+TokSu8nIqArIKVo0gwYN\\nOjJmzDDMzIqxZMnKd/6uyGQypk6dwMuXL5HJpHh49MPS0orlyxeRkZGBkZExEydOxcTElK1btxIY\\nuJns7BxMTEyRyd4Uic/1bm3a5M+2bXvQ0NAgLS31W9yud3L0aDhr1sQjlYpwdzfA3b32t16SgMB3\\ni0j+nWSTFiSPLPDv4F3y1wL/Dn6Uz/ddD9//lIUL52Fv70jr1u0KPJ+c/BKZTEbRoiafs8xCJyEh\\nkVat7vL0qUIAw8DgJmvWvKRRo3eJrrxh69YTDB3ahryhUZMmbWP48PyhdP81PvX7NGvWNOrUqfdR\\n8uq5AhMfS1paGlFRTyhZ0kJZrD4v3/pvVyaTUbnyceLiOiiPNW++nYCA5t9sTV+aU6duMmXKQxIT\\ni+LgEMeqVY0wM3v3b4ZcLufRo0doamoSGXmPCxfO4+U1EYC0tFTGjh3O3LkLMTIy5ujRw1y8eB5v\\n7yloakrJzlZ8ZxYtms+JE8fYvfsgAJs3B5CRkc7NmzfQ0dGhfv2G1KvX8LsI4QR49Cia9u3jiY9v\\nBICxcRj+/hJq1arwjVf2/fCt/3YFvhxmZgYf3UfwzAkICPxn+NR8C0/PHujq6jJ8+JgCz0+cuJut\\nWy0ANdq1O86CBZ2+m9yO4sXNWLQogdWrt5KdrUa7djo0alT/H/Vt0qTi/9g784CcsjeOf963fZUt\\nS0mpVKRIYxiyJcY2zNjXMBh+1rGHoiyhLNmXlLJkZDD2nRFZxhZmZEmJNor29X17f3+8ekkhS9b7\\n+Wfee+65555z79Wc55zn+T5UqhRCQoK8ftmyF2nU6PPaefyUFCRPvnEjjIoV9fH0XIi3t6fCWFu1\\nahlnzoSgpKREgwYNadasBWfOhHD16hUCAtYze/YCMjMz8PKSJ9Y2MDDExcUNHR0dRo4cSs2aFly7\\nFkbjxg7s37+XoKA/FbsoAwb0YevWHa818rStySyMAAAgAElEQVS0tKhdu+TpJ96XtzVwxWIxRkap\\nxMUVlORhZJRN164d8fPbVKwB+qXTtGltTpyoRU5OzhsTykulUoYO3caBAw1RUsqgc+co4uPPs2rV\\nMn74wQEdHW3u3Ytg7Nj/AfLvsXz5igDcvn0bL6+FZGSkk56eVkgdsiAht7e3D1euXMLffx2enh60\\naOGIm9vsdx7b+vVrqFvXjvr1v2PkyKGMHPn7W6U/KeDUqRvEx/+iOE5OrsfZs8GCMScg8AoEY05A\\nQOCb4OVYj7fBz2/TK88dPHgOf/8WSCQGAGzebM0PP4TQpUvJDKaPQdOm1jRtav3W11laGrNw4X38\\n/ILJz4eePXX57rsv390pPPwmBw/uY+zYCe/VzuuSJ6ekJBMScpItW/4E5LsoWlraNGnSlMaNHWjW\\nrCUAzs49GTduMra29Vi/fg3+/msZPXo8IpEIiUSCr28gIDeUzp49jYNDc44ePUzz5i3fabfuc8Pb\\n244ZMzbz+LEmtWql4ObWnn79fD91t0oVkUj0RkMOIDDwBHv29AG0kEjgzz8r4etrhrJyJuvWrcTO\\nzh4TE9NCid0LmDJlCnPnLsTU1Iy9e/9i0aL5pKamoK6uQWjoab7/vhEJCfHY2dnj7e2Jjo4u48dP\\nea9x/frrb4XG+K4LWnXr1qBMmaukpMhjltXVI7GyKvdefRMQ+JoRjDkBAQGB9+Dhw2QkkufxSPn5\\nFYiPz/iEPfqwtG5tR+uvzOvN0tLqnXYMXuZ1yZO1tXVQVVXD09ODH35woHFjB8W5guiG9PR00tPT\\nsbWtB8CPP7bH1fX5hNrR8fmD79ixM1u2BOLg0JwDB/YyefL09+5/aSCVSvHwcOX27XCMjWvg6urO\\n9evXWLnSB6lUiqVlLSZMcEFFRYWLFy+wcqUPampSWrV6Xl5ATk42U6dOokWLlrRq9SOurpN5/Pix\\nIl7sc5Xp/1CkpEiA5yqkMpmYpKRM+vVri5aWNrt2bSc5OVkh6iSRSHjwIBoTkxpkZj6X+j969BBm\\nZuYMGeJMxYr6GBubKN5TVNQ9UlNTKVeuPLt2/UlIyN/k5uagpqaGi8sMjIyqs3//HkJCTpKdnc3D\\nhw/o2bMPOTm5HD16EBUVVby8fNDV1S3iQiyTydi3bzcREXcUXg27d+/k/v1IRo0aV9yQAbC1rcm0\\naX8TELAdqVRM5875tG0ruHYLCLwKwZgTEBAQeA86dKjP+vW7iYiQx/0YGe2jXTubN1wlUBpkZWXh\\n5jal0IS/atWq+PgsJDs7GxUVFXx8VhEe/h9bt25mwQK5cMTixQuIjLyHVCph0KChNGnSjP3793D6\\n9ClycnKIiXlI06bN+d//RhMXF8v//jeYrKws0tPT+OmnNkyf7s6OHcE8eZKEqqoaly79w44dwSgr\\nK3P16mUSEuLZsWMbU6a4cvbsGa5evUxgoD/Dho0C4PLli/j5rUVNTZ2oqHt4eLgCoK6uUchVMykp\\nkcuXLyKVSjExqfEpH/UriY6+j4uLG9bWNnh6ehAUtIndu3eydOlqDA2rMXv2DHbu3E6nTr8wd657\\nkfLu3XsBkJmZiZubC23bdqBNm3acPHmMChX08fLyAfjsBDtKg06d6hAUtJvIyJ8AGRYWfhw+fJbj\\nxzeirKzChAkuiMVifHy8SU9PRyqV0KNHb0xMajBmzJgiUv8FypDx8XEkJaUwcOAQVFRU6NbtJ9av\\n34iysjI9e/ZFSUmJf/45z9q1K5g9ewEAkZH3WL58LYcOHWDt2pV06NCZChX0qVatGgcP7qN7915F\\nduNEIhEtWzoRGOjHiBFj2bDBl5Mnj+Hu7vnGsQ8Y0IwBA0rjqQoIfH0IxpyAgIDAe1C5ckX8/U3x\\n9d1Gfj4MGGCGiYnBp+7WN8n586FFJvwDB/bBw2MelpZWZGZmoqamVuiawEA/7O0bMHXqDNLS0hg6\\n1Bl7++8BuHv3Nhs2bEFZWYXevbvQrVtPUlJSePz4EdOnu7N5cwDKysocO3YYA4NqVKhQkcTEx5ia\\nmhMe/i9r1mxAW1uHwYP7k5aWQrly5WjevCVWVrWpU8cWd/fp6OjoEhFxl7t3b9O+/U9Ur27Mf//d\\nICcnh/T0tEKumoGB6/HwcGXAgMEf98G+Bfr6lbC2li9mtGnTjg0bfKla1QBDw2qAXI5+x45t2NnZ\\nF1vevXsvZDIZU6aMp0+f/jg5yVNqmJqas2KFjyJezNa27qcZ4EfExMSADRty2bRpG0pK+fzvf32p\\nXPn3IvWWL19bpKxXr160alVUFdTb+xCrVlUhPb0ijRr9ycaN7RXn0tLSmDVrBjExDxCJREilUsW5\\nevXsycvL49ChfWhr62BtbcODB9HUqGFGRMSdV45BQ0MDO7vvOHMmBFvbemzZEkiNGoXTTbxrfN3+\\n/Xu4desmv/8+6a2uExD42hCMOQEBAYH3xNLSGG9v40/djW+elyf82tralC9fQTFJ1NTULHLNhQvn\\nOHPmFEFBGwHIy8sjISEekUhE/foN0NSUu7kZG5sQFxdLVFQUmpqa2NrWIyhoI6am5tjbN2DlyqXk\\n5GSTnp7OkydJ5Ofn06dPV0BEfr6UIUOGk5cnISbmIXv37kYsFgEiVq/2w919GlJpPnFxcUydOoPV\\nq5dz5colNDW1CrlqtmnTnoAAP5ycPl+Xsxd3ZmQyGdraOqSmphQqK44Xy0UiETY2tpw7F6ow5qpV\\nM8LPbzNnz55m3bqV2Ns3+KyN2g+FlZUJc+aUPI3F7t0X2Lz5CaqqKnTrpsNPPz1PkZGQkMDq1fqk\\npclzTIaGmuHjsw2QP39f39XY23+Hp6c38fFxjBr1PAZOVVWF1auXERPzEKlUysaNfmhpabNz53Zi\\nYh6QnJyMsrJ8ShkefpM7d27h4TGdSpWq0K1bT/7660/u349CVVW+mNK1a0ccHVvzzz/nyc3Nfaf4\\nus9FZEpA4FMj/tQdEBAQEBD4Nrh8+SKTJhXdWfhQFEz4TU3NWLduJX//fbxE182Z44W//xb8/bew\\nffseqlc3BuQT2ALEYiWkUikikQixWKwQ1BGLxaioqFCuXHlmz16AsbEJpqbmbNoUzPHjoRw/foaT\\nJ8/Rp48zf/yxGTMzc06cCOXw4VNIJHmYm9dk3LjJ1KtXn7lzvdDW1kZJSUzfvs5YWdVi3boAmjd3\\nJDQ0hIkTx9CiRSu0tLRL4/F9EBIS4rlx4zoAR44cxNLSiri4WGJiHgJw6NB+6tWrj5FR9WLLCxg8\\neBg6OrosXDgfgMTERFRVVWndui29evXj1q3wjzyyz59r1+4wZYoWJ05049ChzkyZos21a893zVJT\\nU8nIqPDCFWKysp6v6WdkZFChglwNc9++3UXaHz58NAYGhlSsqM+gQb9x584tnJx+xMmpLbGxMTx5\\nkoRUKmXJEi9MTExxdZ1F+/YdOXnyGI8ePSIhIR5VVVU8PFxJTHzMuXNnWLXKl7Jlyyru4e09j8GD\\n+9OvX3fWr1+jKL9581+GDx/EgAG9GTp0AJmZmYUWAEJDTzNs2KBCCwcCAt8KgjEnICAgIPBV8PKE\\n/+bNf3nyJInw8P8AyMzMKOQ6BtCgQUO2b3+ucnr7ttxIKG4HSSQSUbOmBVlZ2Qqxk9zcXEU7+/fv\\nUfz29V1VpM3MzAxFHsKDB/cVSuRcHAVxeeXKGfLvv7FERt7D2fnXkj+Qj4xIJMLIqDo7d26jb99u\\npKen06NHH6ZOnYGr62ScnXuipKRE585dUVVVLbZ8/vzZSCQSAMaOnUBOTjYrVy7l3r27DB06gIED\\ne7Nhg+8H35ULDt5K377daNu2JZs3BwByqf2goFcr2X5unDlzl8TEhorjxMTvOXPmruK4Ro0aNG58\\nBpD/G9DXD6FDByNAHuvWu3d/Vq9ezqBBfZ59m/Kdr4JYuOf/JuS/raxqo6uri1gswsysJllZWTx+\\n/JjIyAju3r2Nu/s0AgP9ePz4MS1btqJMmbI8eZLEL790o2JFfapXN2HHju2FxjB06P/w9Q1kw4Yg\\nrl69TETEXfLy8pgxYypjxkxkw4YtLFmyEjU1NcXO3N9/n2Dz5gC8vZd+leksBATehOBmKSAgIPCN\\nUJxAiIGBIcuXy4VAypTRY9q0GZQvX4GHDx8wceICHj9OQiwWM3v2fKpWNWDFCh/Onw9FJBLRv/+v\\nODo6KQQ89PTKEhkZgYWFFW5uswA4dy6UZcsWoaamjo1N6cY53bt3lxUrfBCLRQqBCJksn8WLvRR5\\nvRYvXvFsciq/ZsCAwSxduhBn557k5+dTtaoB8+cvfqW0uq6uLvr6+kybNpH8fBnJyU9p0cKRAQMG\\nM2+eBw8fPuDUqRNkZWUVafPnn7sxbdokDh7cz/ffN0JD47nbZ3EeY5mZGYwaNYKoqCzy8rR5+tSV\\nRYsusHjx55nrr3LlKmzevL1Ief363+Hnt7lE5S+rdBaIdoDcSC4tdu3ajo/PKsXOFHx5bnx16lRF\\nSyucjAxLALS0wrG2fq60q6SkREBAR5YuDSYjQ4n27avTqJEVwcF/AWBtXYegoB2K+kOGDAfk8Yxt\\n23ZQLGAEB//F5csXUVFRVZxbvHgBHTp0wsLCkhMnjhZJlzBp0u8YGFQlLy9HEVPp5NSG/fv3Fqp3\\n/Phhdu/ehVQqJSkpkaioewDFukvLZDIuXbpIePhNFi9eUawbtYDAt4BgzAkICAh8IxQnEDJhwmjm\\nzVtEmTJ6HDt2mLVrV+Li4oa7+3RGjvwftrbfk5eXR36+lJMnj3H37m0CAraSnPyUwYP7U7euXFb/\\n7t3bbNoUTPnyFRg+/FeuXw+jZk1LFiyYw7JlazAwMMTNzaVYo+VD0aBBw2In/GvW+Bc6rlevvsKl\\nT01NjYkTpxa5pmCSWsCCBYsVv4ODi7qgAcyYMee1/TM0rEZAQJDiePhwuZqlnZ09dnb2ivLRo8cT\\nEXGP3NxcKlUayNGj3RTn9u49iZvbE8qW/fR5t+LiYhk/fhTW1jZcvx6GpWUt2rbtgJ/fWpKTk5kx\\nYxahoafR1NSiV6++APTr1x0vr6WUKVOm0MLCgAFDaNmyVSExjHPnQlmzZgUJCSmAFr/9No5Onb7/\\noGNwcnKgdeu2xMQ8pE+fbvz661BiYh6SnJzM5csXqV27Dr169WXkyKFYWFgSFnaVrKxMpk93JzDQ\\nn8jIezg6OikMn09Jkya2TJ58nKCgf1FRUaJLFxEODi0L1dHS0sLFpf0rWng9mpqaZGZmvraOkZEx\\nyclPuXHjOmZm5vj47OXo0dXUqVMbPb1yQJSirkxW2GCOjY1h69bN+PpuRFtbm7lz3Z/F0xV/L5FI\\nhIGBAXFxsURH3/8g6UYEBL5EBGNOQEBA4BvhZYEQHR1t7t2LYOzY/yGVSklNTaVGDTPOnj1DRMQd\\nWrVqxePHac9yf6lw/XoYeXm53L8fhbGxCXXr2nHz5n9oaWlhZVVbsathZlaTAwf2sn37H1StaoCB\\ngSEArVu3ZffunZ/wCZQOoaFh3LwZR8uW1piYGL5XW9nZ2Tg77+TUqSaoqz/B2PhWofNKSnmfVbLw\\nmJiHzJ69ABcXNwYP7s+xY4dZvdqP06f/JjDQH3PzmoXqyyfvsmIXFgrOi0Qinj59yoIFc9DQ+ImL\\nF0cjFmczZkwMOTmhdO/+IRPXi5g4cSoXLpxj/fqNnDkTQnZ2Nrdu3eTnn7sqdntEIhEqKqr4+gYS\\nHLyVKVPG4++/GR0dXXr06EyPHn3Q1dX9gP16N4YNa8mwYVCxog6PH6d90LbLlNGjTh1b+vfvgZqa\\nmsJl+EWUlZWZNWs+ixcv4L//YsjIKMPTpxNITVWlUaMdpKQkK2IqT548ho2NLWfOhCCTycjIyEBd\\nXQMtLS2ePEni3LnQZ/GVxiQlJRIe/h+WlrXIzMxATU0dmUxG5cpVGDFiDFOnTmLWrHmfbcoOAYHS\\nRDDmBAQEBL4RXlYEtLOzx8TElNWr/YiLi2Xy5N9ZtGgZZ8+efmUbbdt2xNj4ubpewcq6svJzsRAl\\nJTESSV4xVxevZPgl4+NzhMWLrcjMbIyBwVGWLk3GwcH6ndtbufIEJ04MBFTIyIC7d1UwMlpOdPRv\\nqKo+oFevxM8qLqhKFQOF1LyJSQ3s7Rs8+21KfHxsEWNOjui1qQZkMhn//nsda2sbgoPrAKrk56uS\\nmanL0aP/0b37hx+HRCJh+PBf6dPHmZCQv8nJyWbXrj9p3tyRmJiH3L17h6SkRK5fD6NDh5+oUcNU\\nYcxUrWpAQkL8Z2HMlTYzZswutvzF9ADm5jXp128EnTrVAOSLG6mp0KpVAkZG8ezcuQ01NTVyc3P5\\n+eeunDkTgkgkwty8JjVrWtC7dxf09StjY2MLyA1EDw/PV7hLizAyMmbGjFm4uk5hwYLFVK0qpIYR\\n+LYQjDkBAQGBb4TExER0dHRo3botWlra7Nq1neRk+Up5cPCWZ65m8t0IZWUVevbsyZMnTzE3r4mL\\nixs2NvWYN28WNWqYUblyFQ4fPoCGhib//HOOsmXLsW/fbjZt2kB6ejrVqxtjbFyDGzeuERPzEAMD\\nQ44cOfSpH8EHRSaTERQkIzNT7t4VE+OEn9+29zLmMjLEwHPDOCenCh4e1XnyZDfVqpWnWbOiucM+\\nJYUVP8XPdnHlv6VSKUpKSshkz4VeCgRj3pRqQCQSoaSkhI5OGklJBaUytLSyS31MjRs7cOvWTZo3\\nd0RTU5MFC+ZQrZoR48dPQSKRsHChJxUq6Bfq65vEbL41dHQ0UVFJJU+xpiOlTJkydO8+kfPnn9Kw\\nYROGD2+FkpISy5Y9V618MUbyRSwtaxVxl37RFdrc3IJNm7aVxlAEBD57BGNOQEBA4BuhOIEQsViM\\nj483T58+JT8/n169+mJgUI3Jk39/NqFW5vTpEM6ePUPz5o74+Hgzc+ZU1NXVAahf3x5HRycCA/3w\\n81uLn98mfH1X888/5zEzM2fSpGlMmjQWNTV1bG3rERv78BM/hQ+LVFpYFDo///2CAn/6qQZ//nmM\\n2FhHIJ8GDQ7i6NipSLLzL4UqVapy5kwIALduhStENF5eWHhRCl8kElG7dh0WLpzHr79+z8qV+0hK\\nKouNzS0mT25W6n0uUG2UyWTk5uZy48Y1xGKx4rtPTU0rZMwJFMXa2oLevXewebMyEkk5vv9+F3p6\\neoweXZ2srFZAOnfvBrNkSdd3av/gwUv88UciSkoyfv1VLuQiIPCtIhhzAgIC3zzp6ekcOXKQn3/u\\nyuXLF9m6dXMhwYuvhVcJhCxfvlbhZtmhQ2eF8MOmTYE8fpyGt/c8cnPlS+wGBoaMHPk7165dxcfH\\nm/nz59Cv3wB+/rkbISEnKVNGj/Hjp7B9+1aio6Np0KBhIYXD+Pg4jhw5qEgG/SUjEon45ZdcVq6M\\nJifHiIoVz9Knz/tN8m1tzfH1vcXOncGoqkoYO7bNZ23Ivaz4WHD86FECDx8+oFmzlhw8uI9+/bpT\\nq5Y11apVB15eWFBmwoTCIjR6enpMmjSNKVPGYW1tiK5uGZYtW6NITF1a43hRxbTgv9raOhgZVWfk\\nyN+xsLDkypVLbN365aQs+FR4ef1C9+7XSUm5j4NDZ3799W+yssyfndXm9OmyyGSyt1YNvXz5NuPG\\nqZGYKDcEr1w5wF9/xWNoWPkDj+DjUvA3ODDwj0/dFYEvDMGYExAQ+OZJS0tl585gfv753VaJv0ZU\\nVFQVv5WUxEilkkLnd+3ajrq6BgcPngAgJORkodxsf/8dzqVLiWzZcoxOnbJwde0IyBXrjhw59FUY\\ncwAuLu2wtT3H3bvnad7cFBubd3exLMDe3gJ7e4sP0LvSpSBxegEvusjp61fC0LAaampqLFq0vMi1\\nlStXLnZh4UWXu4YNf6BiRX1WrVpf6nGCK1asY/Lk32nbtgN169oxefLvDBo0FICzZ0/zyy/dsLCw\\nRCaToaOjy/z5zxd7XuyzQGG++66O4remZm6hc5qaOe+U/uHUqUiFIQfw4EFrTpzYSb9+X7Yx97ZI\\nJJJSW9wQ+LIQvgIBAYFvntWrlxET85CBA3ujrKyMuroG06dPLpIzLTz8ZrE52b4GSiI7XkBg4Hpi\\nY2OQSCRs2yaPtevXbxAeHq6IRCL+/fc/7t1LJiurPioqAezencfVq+vZuHELq1cvJzo6ioEDe9O2\\nbUe6d+9VyiMrfdq1K738Z18yUqkUDw9Xbt8Ox9i4Bq6u7ly/fo2VK32QSqVYWtZiwgQXVFRUuHjx\\nAitX+pCXl0dmpi6amr9gZSVTLBDk5GQzdeokWrRoSYcOnT9YH180Jl71281tNt7e8wgI8OPJk1SS\\nk2uTl9caJ6cM3N07fnH56D4V48ZZc/t2EP/99x2VKt1l9OiiapglwcREGxWVGPLy5EInWlo3sbJ6\\nPxXZklCQisPSslaJvumuXTvSsqUT58+HoqqqxsyZczAwMGTOnJk0buxA8+aOgDw9xpEjIUXuNXv2\\nDLKysgAYN24S1tY2XL58EV/f1VSoUI47d+4Wygso8O0iGHMCAgLfPMOHjyYy8h7+/lu4cuUSLi7j\\nC+VMu3btKrVqWbNkiRfz5xfNyfY1UBLZ8QL69/+V27dvkZycjI6OXMGvQoUKmJnV5MSJY+jolCc9\\nvRWammeIj19IdnY9unffjJqaGsOHjyIoaNNX6cYqUJjo6Pu4uLhhbW2Dp6cHQUGb2L17J0uXrsbQ\\nsBqzZ89g587tdOr0C3PnurN06WpmzTrP1av/kZOTzaFDXbGxWUxmZiZubi60bduBNm3afdA+Hj78\\nN1B4l/HF3zk5OYjFYry8lhATE0fr1gkkJcnj9qKiHmFhcYo+fUo/ju9rwNLSmAMHKnHvXhRVq1q9\\nc67ETp0aExa2l7/+0kBZWUq/fmLs7Vt94N4Wz4MH0UydOuON33T37r0QiUTo6OgQELCVgwf34eOz\\nkAULFhdj/BddDChXrhyLF69AVVWVBw+icXefjq9vIAB37txiyZJ9qKp+/eqpAiVDMOYEBAS+eV50\\nD5TJZEVypsXHx6GtrU1kpDwnG0B+fj7ly1f8JP0tLUoiO/6iS9n27bsV4haJiYloa+syZsx46tVr\\nSKdOEaSkVKNiRU/y8+tQv37DZ8qGX196AoHi0devhLW1DQBt2rRjwwZfqlY1wNCwGiBXI9yxYxt2\\ndvaK8n//vUVq6i/o6W0hOdmZ3FyYMmU8ffr0/+iuufv3X8bdPYn4eEOsrc/Qu7cGSUnPjQapVJ97\\n97I+ap++dDQ0NKhd+/3FStzcOjB9en6hGMePQUm/6QKPg1at2ij+u2zZohLfJy9PwuLF87l79w5i\\nsZiHDx8ozllZ1cbAwOCD5xEU+HIRv7mKgICAwLdF0XgxKSDPneXvvwV//y0EBGxl0aJlJWpv+PBB\\nwHPxj6+N8PBYmjaN4OBBNRYtukl+vozly8uhr7+MmjWb0bFjBitXziE6OqpE7W3btoWcnNKXoBco\\nXV6cZMtkMrS1dQqdL86wr1ixsHGkpAQ2NracOxdaOp18DQsWxBEZ+QtZWQ34558BnDiRjInJ8xyM\\n2tr/8f33gqrlp0IsFn90F9eSfNOv6lNBuZKSEvn58m8/Pz+/2Jycf/yxmfLlKxAQsBVf342KlB4A\\n6uoa7z0Oga8LwZgTEBD45ilJvJiRkTHJyU+5ceM6IA8+j4y8V6L2V63yA56Lf3xN5Ofnc+2ahMTE\\nVujohBIdbc/ChRdp1MgKNTUxQUH98fCYhqVlLaKj76OlpU1mZsZr2wwO3kp29rdrzK1fv4agoM9T\\nLTEk5CRRUZElqpuQEK/493LkyEEsLa2Ii4slJkaenuLQof3Uq1cfI6PqivKZM60wMVmEsrImjRtv\\nQE9PjcGDh6Gjo8vChfNLbVwvk5+fT0qKeqGynJwKLF9elbZtt+Lo+CceHpG0bm1XKvd3cnIotnzO\\nnJmcPHmsVO4p8GZK8k3Xrfv8mzh27LDivwU7epUrV+HWrZsAnD59ComksLgUQGZmhsLV/eDBfUIe\\nQ4HXIrhZCggIfPOUJF5MWVmZWbPm4+PjTXp6OlKphB49emNiUuON7RcEuL8s/mFv3wBPT3ckEgn5\\n+TLmzFmgcNf5/BEpkiXn5akWKs/KkkvpSyQS+vfvgUwGKSnJxMY+RCKRIBKJGDCgN61atSEs7DKP\\nHz8mP1+Ks/Ngnj5NIjHxMaNHD0NPryw+Pqs+zfA+IR9rt6Fgx/ltOHXqJI0bO2BsbPLaeiKRCCOj\\n6uzcuY158zwwNq5Bjx59qF27Dq6uk5FKpVhZ1aZz564oKyszdeoMRfmPP9Zm7NgJqKur062bPE5o\\n7NgJzJ3rzsqVS/nf/0a/03jfBrFYjL39E2JicgFVVFWjcXBQ5rvvLAkIsCz1+xcXRwV8dLdCgcKU\\n9JsuIC0tDWfnXqiqqjJz5hwAfvrpZ6ZMGc+AAb35/vtGaGhoKuoXvNuff+7GtGmTOHhwfzF1PtJg\\nBb4YRLLPJIBB8P39eqlYUUd4v18xwvt9M05OTTly5BRXrlwqJP6xZIkXtWrVoXXrH5FIJEil0o+e\\nU6xAoc3a2obr18OwtKxF27Yd8PdfS1paKtOmuWNgYIinpwexsbGoq6szadI0TE3NSElJZubMaVy7\\nFsGjRw5oal4gKWk2Xl4ZaGo+Yc6cmZiammFlVZv//W802to6tGrVBGVlFSpXrkKLFo74+a2lTx9n\\nQkNPo6ysjJfXEoYOHcD69RtLXY7+cyIgYD0HD+6jbNly6OtXwsLCCnv77/Dy8iQnJwcDA0NcXNyQ\\nSPKYMGEM69dv5M6d2wwa1Ic//9yLvn4levToTGDgVry956Glpc2pUyd48iQJAwNDzM0tsLCwIjQ0\\nBHPzmly7FsbPP3fC1LRWsQqtu3fvZM+eneTlSTA0NMTV1YPbt28xefI4tLS00dbWYvbsBRgYlL6K\\n4Idg/fo1aGpq0atX3xJfk5OTg4uLL8nJqjRvbkH//k2LKBF+CLZu3cT+/XsA6NChM92791L8zZDJ\\nZCxevICLFy+gr18JFRUV2rf/6Y33F/HSUSIAACAASURBVP4uf3jeNg9ct24/fZC/YzKZjKioKJSU\\nlDAyMgKE9/s1U7GizpsrvYTgZikgICBQAq5eDSco6ChxcY/euY2X185q167Dxo1+bN4cQHx83CdL\\nDh0T85CePfuyZcufREff59ixw6xa5cekSZMIDPTHz28tFhZWBAQE8dtvI5g9W67g6e+/Dlvbehw8\\nuId27bRRUYnF2zuZevUqcfz4EdTU1PH33wJAjx4DadKkI9nZ2WRlZbF48XJatnRCKpUSHX2f33+f\\nhJ2dPbt37/wkz+BTEh5+k+PHj7BhQxDe3j6Eh/8HwOzZMxkxYgwBAUGYmprh77+WsmXLkZubQ2Zm\\nBteuXcHSshZXr14hPj6OsmXLoaYmdw2MirpHmTJl8PXdSF5eHuHhNxX3k0gk+PoG0rdvX5Ys8WLO\\nnAWsX7+R9u07snbtSgCaN2/JunWBbNiwherVTdi79y/q1LGlSZOmjBw5Bn//LaVqyGVkZLB9+wmO\\nH7/wQURz3mU3S01Njdq1lXFy0qB//6bv3M7rCA+/yYEDe1m3LoA1azawZ89O7ty5pTh/6tQJHjyI\\nZvPm7Uyf7sH169eEnblPyNs9+/d/T/n5+Qwb9gdNmsho0iSbceO2CyJSAkUQ3CwFBAQE3sCKFcdY\\nuNCE9PQOGBkdZtWqJ3z33fu7Wjk5/Ujt2nUIDQ1hwoQxTJo0FTs7+w/Q47ejShUDatQwBcDEpAb2\\n9g0AqFmzJnFxsSQkxDFnjhcAdnb2pKSkkJmZQVjYFebO9UZFRYX588fQrt1uWrSow9Gjh7h1K5zs\\n7CwGDuzNgwexpKToc//+MczNbZBItElLS8PIqDoqKio0bdqcdetWUrZsObS1tT/6+D81165doWnT\\nFs+MeTUaN25KdnYW6elp2NrWA+DHH9vj6joFAGtrW65dCyMs7Cr9+g3k/PlQQKaoKxKJKF++AjY2\\ndTEzMyc5OZmOHZ/nZnN0bA3AvXv3XqnQGhFxl3XrVpGRkU5mZhbff99IcX1pTyafPHlKr17HuHKl\\nB0pKT+jZ808WLery1kZMcbudMTEPWbRoAcnJT1FXV2fy5GkYGRlz+vQpAgP9kEjy0NUtw4wZs8nO\\nzmb37h2IxUocOXKAMWMmAnD16hX++GMzSUlJ/O9/o99rl+7atavP3r3cCG/WrCVXr15RnL969QpO\\nTj8iEomoUKEC9et//L8PAnJeTFlREoKD/3rve27ZcpydO3sAuuTlQVCQAa1ancXZuc17ty3w9SAY\\ncwICAgKvQSaTERAgJT3dFoDo6PasWfPHOxlzmppahcQ/YmNjqFrVgK5de5KQkEBExN1PYsypqqoo\\nfovFYlRU5MfymDgpYrHKKyfwrypv27YD27f/gb//Fnr1mklcXFlACZlMCZEohadPn6CpqYWSkjKt\\nW7dFS0ubDRt8MTGpgaamJhkZGV+dm+WBA3v57ruGVKjwcqL5tzNS6tatR1jYFRIS4nFwaMamTRsQ\\niUT88MNz0YwX00C8/I4K1PBkMhkmJqasXu1X5B5z57ozb94iTE3NOHBgL1euXHre21LeGVq9OpQr\\nVwYAIqRSTbZt+54hQ25Tq5ZFidt4cbdTKpUwaFBfLCysWLBgLhMnujxLg3CDhQvn4+OzClvbeqxd\\nuwGAPXt2sXlzICNHjqVTpy5oamrSs6fcPXPv3l08eZLEqlV+REVFMmXKuPcy5op7li8WiUSlbzwL\\nfL48fZoHPM8nJ5VW4PHj9E/XIYHPEsHNUkBAQOA1yGQypFKlQmUvH7+JggmbmZk5SkpKDBjQm23b\\ntnD8+BH69evOwIG9iYyM4Mcf23+wfn9IbG3rcfjwAQAuX76Inl5ZNDW1sLW1U6RaOHv2DGlpqYhE\\nIurXb8CJE88V9+rXt0JD4zzVq3dEJJIgFlegfPkK3Lt3l5ycbAYO7M2GDeto3rwlIBcIGD9+FGPG\\nDP/4gy1F9u/fQ2Li4yLldevW49Spk+TkyN0nz5wJQV1dAx0dXcLCrgJyRbt69eoD8vdx6NB+DA2r\\nIRKJ0NXV5ezZM9jY1FW0Wb26MWfOhDyTNJcRGhqiOFdgHJiYmLxSoTUrK5Ny5cojkUg4dGi/4toC\\nQ7s0kUrFvGjg5uWpk52d++oLiuHF3U5NTS0aN25Kbm4ON26E4eo6mYEDe+PtPZekpCQAHj1K4Pff\\nR+Ds3JOgoI1ERT1Xqn3RlhKJRDg4yJOEGxub8OTJk3cfKGBrW/fZu5e7H586dUKxwyo/b8exY0fI\\nz88nMTGRy5cvvaY1ga+Nzp3rYWq6S3FsZRVMx47ffcIeCXyOCDtzAgICAq9BLBbTvn0Gvr7xSCSV\\nKV/+PN27F1W7fB2HD/8NyBUxX1Zn7Nt3wIfq6jvz8u7Ai8cikYiBA4fg6emBs3MvNDQ0mD59JgCD\\nBg1h5sxp9OvXHWtrWypXrgLIJ7lDhgxn0yZ/nJ17oaysjKNjM27erMHTpzNYsWIZhobVMDSshoaG\\nJv7+W7h1K4L9+w+Sl5dHly496NKlx0cb//vwsniFg0OzQiIJW7ZsJDs7ixo1TAkPv4mb2xQSEx9z\\n4MAJRYxkzZqWODo6MWBAL8qWLUetWrURiWDatJl4e3uSnZ2NgYEhU6fOAOQ7usnJyQoJdFvbes+S\\ntj93UTU0rEaTJk1xdu5JTk4OpqZmaGtrF1JDVFVVfaVC6+DBwxg6dAB6enrUrm2tSN3h6Nia+fPn\\nsH37H8yaNa9U4ub69KnD/v07iIj4BcjGyekwtrY937KVojteBXnBCuI4X2Tx4gX06tWPxo0duHLl\\nEn5+a1/ZcsHOdUGb70PNmpa0a9eBIUOcAejY8WfMzS0U76hZsxZcvvwPfft2o1KlytSpY/Ne9xP4\\nsqhWrTIBATkEBv6BWCxj6FB7ypUr+6m7JfCZIahZCpQ6gurS18238H5lMhk7dpzh/v0MmjY1xt6+\\n5O5exXHx4i2OH4/EwECd3r2bfbaCBh/r3S5ffpRFi4xITzfFwuIwvr61sLCoXur3fV/Cw2/i6enO\\n2rUbyM+XMXSoM25us5g1y01hzAUFbXoWOziEUaN+o2fPvqxZs7zEinjFcfnyRbZu3axQRX0VWVlZ\\naGhokJ2dzciRQ5k8eRrm5s+/3c/t3+6LaoH378cxb94KxOJcRKJkzM0tuHr1ElKpFBcXN6ysar+2\\nrdu3w5kzR/5u5G6W/ejU6RdOnTpO9+69adGiFTKZjIiIu5iZmTNoUB8mT3bFwsKSuXPdiYuLZdmy\\nNWzduomMjAx+/fU3QO5++sMPTRSulQWqk58bn9u7FfiwCO/36+Vd1CyFnTkBAQGBNyASiejSpckH\\naevIkSuMGaNMYmI3xOIkwsJ2smDBLx+k7S+JHTvOsmdPGioqWYSGqpOeLnchvHWrOytWbGXp0s/f\\nmHuTeEUBL66Zyt12pXh4uHL7djjGxjVwdXVny5aNhIaGkJOTg7W1DZMmTQPg4cMHeHl5kpKSjFgs\\nZtaseYXavnnzX7y85jJ79gL09MoyZ84xEhPVqF9fmbi4Y0RFRZKbm0vbth0KGXIlJS0tjcmTjxAZ\\nqUO1aul4ejanfPnS3xmoXr0KTZtakpWVyZUrl8jJycbffwthYVfw9PR4ozH8qt1ON7fZeHvPIyDA\\nD4lEQqtWrZ8Zc0NxdZ2Mjo4u9evbEx8fB0Djxk2ZPn0yZ86cUgigvLxzXVpER8exefNVVFRk/Pab\\nAzo6bz/JExAQ+PoRjDkBAQGBj8j27Y9JTOwCQH5+eQ4cqMDs2bmoqqq+4cqvh2PHrjJpUhVSU1sD\\nWYjFRwqdz839Mv7XJBKJOHBgL40bN8XS0gqZTEZGRjr5+c+Nt5yc7CKT/+jo+7i4uGFtbYOnpwc7\\ndmynS5ceDBw4BIBZs9w4cyaExo0dcHefTv/+A3FwaE5eXh75+VISEuIBuH49jCVLvJk3bxH6+pVw\\ndt7GgQPOgDJ79sQxdaqUGTPmvNcYJ08+wvbt/QAxly7JkEgC8fP7+IsPrVrJ1ftsbeuRkZFBRkY6\\nWlqvVz7t338Q/fsPKlK+cOHSImVNmjSjSZNmRcqrVTMiICAIkBvitWtbo6z8/PsscKH+0Dx8mEDv\\n3mHcvt0dkHLy5AaCgzuioaFR4jYOHNjL1q2bEYlEmJmZM3jwMObOdSclJQU9vbJMnepGpUqVmTNn\\nJmpq6ty5c4unT58wZYor+/fvITz8P2rVsla49zo5OfDTTz9z4cI5ypWrgLv7XPT09IrNSaimps6c\\nOTPR0tLm1q3/Cil/zp49g2bNWuDg0BwAd/fpODo6Ffv8BQQE3owggCIgICDwEVFWlhY6VlHJQ0np\\n7QRVvnRCQuJJTa3z7EiD/Pw4IAWA8uXP8csvFT9Z394GW9u6JCcnk5eXS1ZWFiEhJ2nY8AeSk5+Q\\nmppCbm4uoaGnFfU1NTXJzMxAX78S1tby2Kc2bdpx7dpVLl/+hyFDnHF27snlyxeJirpHZmYGSUmJ\\nikmvioqKYhfw/v1IvLzmsmDBYvT1K5Gfn8/Vq+UpWKOVSKpw4cL7R1FERenwfKogIjJS93XV3wsl\\nJaVChnBubs4r635s1+Rduy7g4HAAO7vTDBmy7ZmwzPsRHLyVvn27MWuWa5Fz27Zd4fbtbs+OlLhw\\noTuHD/9T4rbv3LlDYKAfy5atZsOGLYwePZ5FixbQrl1HAgKCaN36R5Ys8VbUT09PY80af0aPHseU\\nKePp3bs/GzduIyLiLnfv3gEgOzsbS8tabNy4jXr17PD3l8cVFpeTsIAC5c8FC5awevVyADp06MT+\\n/Xuf3TedGzeuF1JiFRAQeDu+jOVPAQEBga+E4cNrcvnyTiIi2qCldZsBA/Lfy5gbPnwQq1b5ER8f\\nx/XrYTg5/fhO7XTt2hE/v03o6pYhOHgrf/31JzY2dZg0ye2d+/YqDAyUkRtv8tQDeno1GTZsFzk5\\nWrRsacT339t98Hu+SFxcLOPHj8La2obr18OwtKxF27Yd8Pdfy9OnycyYMQsAH5+F5ObmoKamhovL\\nDIyMqpOTk83cue5ERNzFyMiYMmXKMHv2DNTU1LCxqceSJd6oqqrRpUsHzMxqYmxsorhvu3YdWbHC\\nh6SkRHJy5O3KZDJEIhGLFskTd1esqI+f39pnxkLxBktBHrm8vFxu3w6nUaMmiMViypXLIi6uoJYM\\nPb2s935WhoZpXLwoe9YXGUZGpRenU65ceYUhrK6uQWjoaUV+u+PHj2BnZ09Y2FW0tXXQ1NQqtX68\\nTEZGBh4eGTx8KBfl+euvHMzMdjF5crv3anfXru34+KyiQoWiixfq6gC5gHzHXix+Qpky6iVu+9y5\\nc7Rs6aRI76Grq8t//13H01NuwLVq1YZVq+Q7lCKRiMaN5caUiYkp5cqVL5R3Mj4+FjMzc8RisSJH\\nYevWbZk2Te52+qqchK9S/qxb146FC+eRnJzMyZNHadGiJWKxsLcgIPCuCMacgICAwEfE2tqUPXv0\\n+PvvY5ibV8XGxum92lu1Sp4jLDY2hiNHDr2zMffiTkfBJNPKqkapBNn/+mtLbt7cwYkT5VBTy2X4\\ncE2cnT+u615MzENmz16Ai4sbgwf359ixw6xa5cfp038TGOiPq6sHK1asQ0lJiX/+Oc/atSuYPXsB\\nO3duR0NDk02bgomIuMugQX1YuzaASpUqM336JHx8VqKmps6mTRuQSCQMGDBYcc9mzVpSs6Yl3bt3\\n4s6d21hb1+HIkYPY2Nhy48Y1dHXLkJmZyYkTR2nZ0glNTU0qVtQnJOQkDg7Nyc3NRSbLV6gyuri4\\nMnbsCNTVNahXrz7TplXD3T2IR4/0sbK6z/TpLd/7Oc2f3xKpNJCoKF2qVUtj/vzS20FRVlZmwIDB\\nDBniTMWK+lSvbqw4p6qqyqBBfRQCKMXxooDKh+TJkyQePTJ6oUSNR4/ebzfdy2susbExjB8/irZt\\nOxAWdoXY2FjU1dWZNGkagwY1Z/fuocTGaqOiEkOVKjLu3XPg9Ol9xMXFkpAQz6hRv3P9+jX++ecc\\nFSroM3/+IpSVlQkPv8nGjRtJT0/n5s3/mDZtBuXLVyAtLY1lyxZz48Y1HB0L/91RUVEhPT2dI0cO\\nFsk7KZVKeZmCRQh4fU7CVyl//vhjew4d2sexY0eYNm3mez1LAYFvHcGYExAQEPjIVKhQni5dmn+Q\\ntpycHDhyJITVq5cTHR3FwIG9adu2I/b2DfD0dEcikZCfL2PuXC8MDAw5dGg/27f/gUSSR61a1owf\\nP0WxKi6TyQpNMrt370b79l0+SD9fRCwWs2hRV6RSKWKx+JOoeVapYlBo98HevsGz36bEx8eSnp7G\\nrFluxMQ8QCQSKSa0YWFX6dZNLpNvamqGqak5AP/+e52oqHsMGyaP0crLk1Cnjg1btpzmjz8yUFKS\\n0b9/WRo2NMLIqDo7d25j3jwPjI1r8PPPXUlLS6N//x6UK1eeWrWsFf10dfXAy2suvr5rUFFRwcPD\\n81l6AShbthwLFixmwoTRTJ06g1atbGnZsg4ZGeno6DT4IM+pbFk91q//eIZ216496dr1eRoCiUTC\\n+fNncXRsw+jR4z9aP16kSpWq1Kmzm0uX5Hn81NUjaNjw/dxNJ06cyoUL51i2bA3r16/BwsIKT8+F\\nXL58kdmz3fD330KXLrU4evQIY8ZMpVGj+vj5rSUuLpalS1cTGXmP334bwNy53owcOZapUydy9uxp\\nGjVqwpIlXsybN49JkybTooUja9euZMSIMWhraxMVdQ9f30D2799TKJ8dQFpaKocOHUBFpfipYX5+\\nPidOHMXRsfWzRQj59S/nJNTXr/TG8bdr15HBg/tToULFQkb727Jt2xY6dfpF4X78vvUEBL5EBGNO\\nQEBA4ItGbggNHz6KoKBNCrn6JUu86NatN61b/4hEIkEqlRIVFcnx40dYvdoPJSUlvL3ncfjwAUWy\\ncpFIVGiSaWpqWKry129yL42Li2XChNHY2NTjxo0wKlbUx9NzIYmJj1m0aAHJyU9RV1dn8uRpGBhU\\no2fPXwgO/ou0tDTat3dk2bK12NrWZcSIIUydOqNQTrSXdx8KdhAKdiJ8fVdjb/8dnp7exMXFMnr0\\nsFf2s2DHwd7+e2bOfC44cvbsDfr31yQlxRaAW7fOsn17Fps3by/SxpAhwxkypGiSdEPDaoVyE2Zk\\nZHD9ejT9+snrVqpUmY0btxUai45O6cW1fUzCwu4wdmw4aWkShg07h5eXMg0bWr72mvz8fObPn1Po\\ne4mOjsLLy5OcnBwMDAxxcXFDR0eHkSOHYmFhSVjYVbKyMpk+3Z3AQH8iI+/h6OikeB/Hjh1GX387\\ntrZ+qKgY0a1bN7p1a/FBxiiTybh+PYw5c7wAsLOzJyUlhczMDJSUlGjbth0//GAPyP99Nmz4A0pK\\nStSoYYpMJlO4NJqamhEXF0d09H0iIyPw8PAgNzeXxYsXoKysjEwmw9DQiNTUVJyde1G2bFmFsElB\\n26tXLyMhQe6nu3KlD3p65ThzJoTLly9y9+4d1NU1+O+/f5k/fw4go2JFfXbv3qnISRgfH4e5eU3C\\nw2/y6FECKiqq+PquZuXKpYwePb7Qok3ZsuUwNq5B06bN3+v5BQdvpU2bdm800kpaT0DgS0Qw5gQE\\nBAQ+Ia9yDVu/fg22tvUUO0Zv4uWUobVr1yEw0I/HjxNo1qwlhobVuHTpArduhTN4cD8AcnJyKF/+\\n7RKgf2wePnyAu7snkydPw83Nhb//Ps6+fXuYONEFQ8Nq/PvvDRYunI+PzyqMjKoTGXmP2NiYZ5P0\\ny1hZ1eLRo0dvldy6QJWyIJapICk4QN269Thy5CB2dvbcu3eXiIg7iEQiateuw6JF84mJeYiBgSFZ\\nWVkcO3aVlJTnBtrjxw0JDd2OlVWNd3oW8fGP6ds3hGvXOqKu/pDfftvHtGnt36mtL4F5827x77+9\\ngd4ALFgQxI4drzfmHjyIZubMuYW+l82bAxk3bhK2tvVYv34N/v5rFcaF3OAIJDh4K1OmjMfffzM6\\nOrr06NGZHj368ORJEsePH8HPb6NiAaRKlcwPPtZXpfx92fhQVn6+6KCk9HwK93z3WIaJiSl//hlc\\nZCFm1KjfGDduMhYWhZ9hgVFnYWFFZOQ9AgP/4MKFc5w8eYx9+46Sn5/PlCnjkUqljBr1O87Ov6Kr\\nq0tOTjZDhjizfPk6OnfuioPDd/z22wi+/74RU6dOJCsrk4CArURG3mPOnBkcPvz3s53WMJSVRTx8\\nGI2TU5sSP6OsrCzc3Kbw+PFj8vOltGjRisTEx4wePQw9vbL4+KzC29uT8PCb5ORk07y5I7/++hvB\\nwVuL1Ltw4ZwiNtXAwJCpU2egoaHBqlXLOHMmBCUlJRo0aMiIEWNK3D8BgU+FYMwJCAgIfIYUJCl+\\nV5ycfqR27TqEhoYwYcIYJk2aCkDbth347bcRH6KLH4UqVQwwM5O7MlpYWBIXF8uNG2G4uk5W1MnL\\nkwBydcmwsMvExsbSt+9A9uzZSd26dlhZ1SrS7suunS8ei8VievXqz5w5MwgIWE+jRk0o2AHt3Lkr\\nc+e607dvN6pXN8bSUt62np4e06bNZObMqeTm5gHQqNGPaGndIiPD4lmdy9jbv5shB7B06XmuXesP\\niMjOLseGDUkMH55EuXKft0H+rqSmqhU6Tkl5867Ky99LTMxD0tPTFC6FP/7YHlfXKYr6TZo0BaBG\\nDVNq1DBVPMuqVQ1ISIjn2rUrpb4AYmNTj8OHDzBgwGAuX76Inl5ZNDW1XmngvQ4jI2OSk59y9epV\\nDAxMkUgkPHgQjYnJm7+7gvuFhJwkJORvrly5xMCBckM6KysbkJ8PDg4iJESekuHRowQePoymVi1r\\nVFRUCu0UqqqqKnYR4+LiyM7Opn//nVy4UJlKlbwwNbVBQ0OzxGM7fz6UChX08fLyASAjI539+/ew\\nbNkahdDL0KEj0NXVRSqVMnbs/7h37y7duvVk27YtinrJyckEBvoVim/944/N/PJLN0JCTrJly5+K\\n9gUEvgQEY05AQEDgE1Oca5i3tyeNGzvQvLljiVaLNTW1yMzMUBzHxsZQtaoBXbv2JCEhgYiIu3z3\\n3fdMmTKe7t17U7ZsWVJTU8jMzKJy5cofc7hvRWF3SCVSU5+gra2Dv/+WInVtbe3YuTOYpKREBg8e\\nRlDQRq5cuVQkNqhKlaoEBGxVHL/obvbiuaCgHYryApc7NTU13N3nFttXOzt71q0LfKn0GH/+eQOx\\nOJ/+/bWxtX335PN5eSq8qHCZk6NDTs6r5fs/Fi/HI02cOIaZM+e8Mg/c+vVr0NTUolevvq9tt2HD\\nbC5efIJMVg5IpUGDN0+uX/5e0tNf7yasoiJXiyzYpSvgxTjJ0lsAESESiRg0aCienh44O/dCQ0OD\\n6dNnKvrwcjjpi8dFFyTkIjKzZs3H29ubp09TkEol9OjRu0TGHIBMBqdOnSQ1NYW+fQfQqVPheMnL\\nly9y6dI/rFnjj5qaGqNG/aZI0/DyTuGLu4hy1+UTnDw5EFAhMvInoqOjCA29QuPGJVOvNTU1Z8UK\\nH1atWsYPPzhga1u3SJ3jxw+ze/cupFIpSUmJREZGUqOGWaE6r4pv1dLSRlVVDU9PD374wUGh8Ckg\\n8LkjGHMCAgLfNC4uE3j0KIHc3By6devFTz/9/NH7UJxrmHwiJyIlJfm1q8UFEzozM3OUlJQYMKA3\\n7dp1IDc3l0OH9qOsrEz58hXo338QOjo6DBkynHHjRpCfL0NZWZnx4ycXY8x9fEGSkqKlpUXVqgac\\nOHGUFi1aIZPJuHv3DubmNalVqzazZrliYFANVVVVzMzM+euvHYqV/NIiPT0dL6+TpKaq0LJlOTp2\\n/E5xbtgwR4a9OtzurejevTqHDx8nLq4lkEWrVleoXLnHh2n8HZFKpUXikd70vEsqeDN9egfKlTvK\\nrVv5mJqKGDXqp7fun5aWNrq6uoSFXcXWti4HD+6jXr36JbpWJBJRv36DUlsACQ5+no+tIGXAiwwa\\nNPS1xy8mLH/xnLa2NomJiZibW3L7djjnzp3FyelH7Ozs8faeS05ODtbWNkyaNA2AkSOHUrOmBVeu\\nXOLx40ckJSWirKzMpUv/YG1tw5w5M58pZapw/34UERF3UVNT4/79KP7990aJx5uVJQKeG9tSaVme\\nPr1Z4uurVTPCz28zZ8+eZt26ldSv/12h87GxMWzduhlf341oa2szd677K3MVvhzfWsC6dQFcvHiB\\nkyePsWPHtkLxqgICnyuCMScgIPBN4+LiVij+o3nzlgqXnY9Fca6EBWhr67x2tbhgQqesrFxk4tG3\\n74Ai93J0dCoiSw4QHLz7hd9/FTn/qSjOHdLNbRbe3vMICPBDIpHQqlVrzM1roqKiQqVKlaldW64G\\naWtbj2PHjmBqalZc0x8EmUzGoEH7nu04KLF793Vksgv89NO7q0kW5PmzsLBkyhQ3JkwYQ2pqMv36\\nDWLjRmP27QtGT0/EkCFdS10JtLjFDicnBzp16sLFixdo3rxlkXikF3MWHjiwl61bNyMSiTAzM2f6\\ndPdC7cfEPCwiZmNkZAzI3/WIEW+XuqO472Xq1Jl4e3uSnZ2tiI8q7rriHqWxsUkJF0A+L6Kiopg8\\n2ZXateswbNgIpkyZw++/D2HgwCEAzJrlxpkzITRu7IBIJEIikeDvvwV39+mcPXuG2rWtsbP7Dg+P\\n6cTExDBx4ljmzvUiLi6WihX16du3G9WqVcfauo7insXtFL54rls3G3bs2ElExM9APg0abKdVq5Ib\\n6ImJiejo6NC6dVu0tLTZu/cvNDW1yMjIQFe3DBkZGaira6ClpcWTJ0mcOxeqMNw1NTUV9WrVsi4S\\n35qY+JgKFSqSnZ1Fo0aNqVPHlh49Or37CxAQ+IiIZO/ilF0KlKZimsCnpWJFHeH9fsV86e93/fo1\\niviP+Pg4Fi5cpjAGPgYvC6AEBW0iKyuT+Pg4fvihCc2bO5KXl6dYLY6PjyuV1eLQ0KtERCTQunV9\\nKlWqAHz57/Zj8OjRIxo0eExmHMSaCAAAIABJREFUZkNFWc+e21m6tOTCDi/Tp09XRTLpGzeu4+u7\\niiVLVn6I7haiJO83NTX1JbGLtbT/P3vnGRDV0YXhZ5dd6tJEsGChWFBRhGCvUTEaNdEoAWzYWxJ7\\nrLFHsKAGjYoSaSrYjd1YY8GosYFG8TN2moKKtKXsst+PlZUqFlCT3OfX3r0zc2fuXS5zZs55T5cO\\nzJ3rzaefdgDA1fUL1q1br1kEyT1OTExk+vTvWbMmECMjY1JSUjA0NCQgYC36+vq4u/dlzJiRfP/9\\nNI2Yzdq1K4XdkHckLi6WMWNG0LOnG8ePqzh40BYTk18xNi6HtXUkKpWS5ORkevVyo08fT777bjhD\\nhozQuCN7ec3RvHsADh06yI0bf/Hdd+Pw8PgKf/8QjIzeTjH17t0YQkMj0dZWMWJEKwwNDV+77vnz\\nZ1m50hexWO3COXHiVK5di2D79i2Ym1vg67saL685XL0agYVFRQwNZbRo0ZrOnbuyffvmfOUuXbrA\\n6tXLNfGtw4aNws6uDlOmTHjhNqrCw6OfRun3Y0N4N/97MTd//b+JXISdOQEBgf8sRcV/ZGdnlfl1\\n3yS5sVwuL/PVYm/v/axe7UhGRjNsbffyyy/W1Kv39kIdH4qnT5+xadNZ9PUl9OnTNl/C4rJCJpNh\\nbHyNdI3AYQ6Ghq//G9q0aYNGLbNr1+48eHBPk+evY8fO7NnzK0lJzxg4sDc//rjojVQ5S4P8YheP\\nefjwIWKxWDPRLw6VSsWlS3/Srp2LxsgrOHGXy+VcvRpZpJjNx8DduzEsWuTHw4eXMDWV0a1bdxQK\\nBdraUnr1cmf58iXcvv03vr6ruXjxT/bt283MmfNwcWmFq6sHZ86cRkdHhwULlmBqWu61r6tQKJBI\\nJMUevw4ikYjQ0A1cvLgELa0cVCoxmZnHsbQcwPz57holx1x0dfUK1c+lbdt2BAau5ZNPnLGzq1Oi\\nIRcefoVbtx7RsaMjlStb5DtnbW3J9OmWbzSWXBo3bkrjxk3zfVe7th09e750NS5q1xWgZ0+3fOWK\\njm9Vu1kKCPzTEIw5AQGB/yzp6WkYGhq+VfxHaVKcq5xIJCI9PS3favE334wt1WtnZGSwcaOMjIxa\\nANy+3Z01azazfPk/y5h7/PgJbm6n+euvPkAGR46EEBzsVmIuu3dFX1+fCROk+PjsJSmpIp98coXJ\\nkzu/Vt2oqBscOLAXf/9gcnJUDBvmycyZ8zh37g+N8l7duvb58geWBampqRw+fJAePXpx6dIFNm3a\\nyKJFy4oRu8hEW1vntdw7RSLRKxUZVaocDA2LFrP50ERF3cPT8xQ5Obd58GAXn322kT17djJ58gw2\\nb95Ir17uREXdQKFQoFAoiIi4TMOGaiGPjIwM7O0bMGzYKCZOHIOnpwflyplha1sDLS2tfLteLi6t\\nOHxYncvtl1/8MDIy4v79e0yaNB1//9UYGRnx4MF9NmzYyurVK7hy5SJZWdl89ZUrX375FZcuXSAg\\nYC0mJqbcvXub2rXrMHToSGJiYtDS0qJSpRloaaXz9Olg9PQuo6WlR3p6OsePH6Fdu5curHmfU65L\\nYi7a2to0adIMH58FTJ0685X3bdGiA/z8syMZGc1ZtWo/a9c+p2HDmm/9HIpa+IqKusHBg/sYO3bi\\nW7dbsP3PPx/N0aNp6OtnMmlSYywtS058LiDwsSD+0B0QEBAQ+FA0adIcpVJJ376u+Pn9nC/+o6zJ\\nVbCcNGksZmblyczMJCYmmgsXzhMefoqYmGisrW0wMyuPlZU19vb10dHR5c6d26XaD6VSiVIpKfBd\\n2RpAZUFQ0LkXhpwI0OPQoS84derie7l2//6tOHPmE86dM2T7dtfXdkGLjLxC69afoqOji56eHm3a\\ntOPKlcv5yryPSIiUlGR27txa6Pu8ix337t0tdrGj4OQf1Iack1Mjjh8/QnLyc0DtspmLSqVWYK1c\\nuTLHjx958Z1azOZjIDT0Bs+eGZCa2hGVypTDh9tRr15Drl+/xs2bN0hPT0NbWxt7+/pERd0gMvKK\\nxk1RKpXSvHlL7ty5za1bN2nUqAlBQaGMGVOU8fHSKL516yZjx35PWNgOVCqV5jg0dDt79vyKTCbD\\n3z8Ef/9g9uz5VRNb+/ff/2Ps2Ils2LCV2NgYbt68gY2NDVKpNjLZc+TyBjx/7o5EUo+//vqFCRO+\\no27d/K7keY3z9u07Ehq6nkGD+hIbGwNAhw6dEIvFhXbG8pKVlcXGjTpkZNQEtLh3rxv+/qX7vgKw\\ns6tTKoZcLsnJcqZMqc6uXT0JC/Ng8ODTZGdnl1r7AgJljbAzJyAg8J9DpVLx+PFjxGIxPj7LP0gf\\nilKwzJsMOzh4B0OGTMLWdgAGBsloaalYsyaw1AUvDAwM6No1nvXrE8nJKU+lSsfp0+f9uvKVBoVv\\niwKp9P0ZpTKZDJmsaCn+4ijqWZaxnkmR+PmtICYmmoEDeyORSNDV1eOHHyZz587fpKena8QubGxs\\n8PX1ITMzg/Hjv2P69FmYmZWnatVqeHj0RCqV0Ly5WqBHLs8gLGw9CoWS7t07Y2ZWHkfHTzRucLnj\\nnDnzx0JiNrliQB8SsVj54pPamJZIMpBIxIjFIipVsmT//j3Ur++ArW0NLl36k5iYaKpXtwJeSvRf\\nuqRWg8zdHS7JyK9Tpx4VK1Yq8vjPP89y+/bf/P77UQDS0tKIjn6IRCKhTp16mgT3NWrU4vHjx0gk\\nEkxNTfH3D2b//ks8f76Hr76aRoUKhXPkrVixJt9x/foObNiwJd93kZFX6NLli1e+f1QqVaGFoZyc\\n0vtBx8REM2PGZDp06MSVK5dYtGgZ69at4dGjeOLiYnn0KJ6vv/agVy93AIKCfuHQoQOYmJhiYVGB\\n2rXr4OHRl6ioG3h7z0UkEtG4cRPS0pSkp9dBJMrEwmI2T55cZMCAMCZMmIKTkzP79+/h1KnfycjI\\nIDr6Ie7ufcjMzOLIkYNIpdosXuz71jGEAgKlgbAzJyAg8J9CpVIxZsw2mjRJoGnTGKZO/fW97H4U\\n5FXJsN3cerJ69QaePROzfbsbZ86k4+TUBJFIRFxcLP37F5ajX7duDRcunH+rvixa1ANf37NMm7aV\\n0NByNG9er1CZb78dRlSUWkb82LEj9O3rypgxI9/qemXBkCHNadgwCFAASXTrdoBmzRxLqPVhcXBo\\nyMmTv5OZmYFcLufkyeOFcuLlZf/+PSxbtqjU+zFy5GgsLasQGBjKqFFjXuwITWTjxm1UrFiJSZOm\\nM2/eAiQSKb6+qzl58jxdunRj7Vq1KMu1a1c5cuQUhw+fYtKkaWzduptff92Gs3Njtm7dxa5dvyGR\\nSBg/Xh0bN2jQMNzd+5KU9Iw7d+KYNm0WQUGhbNiwhQEDhpTauLZsCSUzM+Ot6o4a1YwqVeKRyQ4j\\nld7k668vEhl5GQcHJxwcGhIWtoGGDZ1wcHDk11+3U6tW7UJtqA2f/O8WLS0tcnLU3+Xk5KBQvNwB\\nKhi3VvB4/PhJBAaGEhgYypYtu2jUqAkqlSpffjwtLTE5OUrNsVgspnfv9owc2blIQ+5VbNoUztCh\\nh+natS/79+/B1dX9leV1dHT44ounaGk9AqBixd/p3fvt4uMK8uDBPWbMmMz06XOoU6duvnMPHz5g\\n2bKV+PsHExjoj1Kp5MaNvzhx4hjBwZvw8VlOVNQNzQKCt/ccxo+fTFCQ2r1XIlEBmZiYbATEZGSM\\nY9q0WcyfP1sTV3j37h28vHzw9w9h7dpVGBgYEBCwEXv7+hw8uK9Uxigg8LYIO3MCAgL/KTZv/p3N\\nm7u/SEQMwcE2tG17js8+a1qsDPu7iBkUx6uSYfv4HOT4cVfN+ZQUS27dintle4MHD3/rvohEItzc\\n2pRYJndVfu/eXUye/AP16zu89TVLG1NTE3bs6MT27buQybTp0cMNsfjjXq+sVcuOzz/vytChngB0\\n69ajUILjvJL5ZZWGIO9ihkqlKrTTEx8fh0wm4+7d24wdOwpQGyJmZuoytrY1mT17Oq1bt6VVq7aA\\nWnkwPPwkYWHrAcjOzubx43hN2oHw8OuMHfuY+/c/oXLlSLy99ejc+fWSR78OReW/exMsLMzYvbs/\\nXl4J3Lw5goQEPbp160HNmrV4/jyJ9esDNa7POjo6GiP8++/HaNpwcmrE+vWBNGyolsdPTn5OxYqV\\n2Lo1DCMjI9LT01EoXk/wpXHjZuzYsQ0QsXXrJr79diwWFkXHdRkbm7Bnzx4+/7yLRo7/Tdm58yxT\\npliTnl4b6IGzcxD6+gYl1ps//0ucnU/z8GEaHTrUKhUhpWfPnjF16kS8vHyoXt2KS5cuaM6JRCKa\\nN2+JRCLB2NgEU9NyPH36hKtXI2jVSi2CJJVKNSldUlJSSE1N1SQc/+yzLvzxRzjduq3n6tXf0NJq\\nxJgxWtSrV5+KFSvx8OEDRCIRjo7O6Onpoaenh0xmSIsWrQGwsanB7dsfh2uwwH+XMjPmTp48iZeX\\nFzk5OfTq1Ythw4aVXElAQECgjElIyNAYcgAKRUXi4s4CReecyytmsGrVcnbv3omn5+BS71feZNhV\\nq+qhpfUYLa2nZGXZIZEkU7lydU3Z3Hi7a9ciMDe3wNt7CT4+3rRo0Yq2bdvTq1c3XFw6cfZsOGKx\\nFpMmTcfPbwWxsTF4ePSje/eeJCYmMmvWVNLT01AqlUyYMBUHh4acP39Wo3RnY2PFhAnT0dNT7xCo\\nVCoCA/25ejUCb++5tGzZmlGjxhQ3pPeOTCbD07NjmbQtl8uZOXMKCQkJ5OQo8fQcgqVlFX7+eRly\\nuRxjYxOmT59FamoqP/44S6OKFxcXy5Qp4wkO3kRU1I1C5d3c+nDq1Alq1arNb7/tR6lUMHbs94wb\\n9w2ZmZmYmZUvMrlxURTlViaTydi9ewfZ2QqqVKnCjBlz0dHRZf782ZiYGBIZeY3ExATEYjE//jiL\\ny5cvanaOABITH7Nu3RqkUilaWhJWrVqHnp4eq1evIDz8FJ6eHjRq1ITmzVsSHn6KkJAA/PwCiY5+\\noDEIPT2H4Oe3AhMTUwCioq4zY8ZM7t//DTOzFeTkPGDBgssEB4vo06c/3bp159KlC6xbtwYDAwOi\\nox/i5OTMhAlTEIlEHD58kA0bglCpVDRr1pKRI78DKDH/3ZuiTjxdWB3R2bkxx4//oTnOjXFTqVT5\\nEqZbW9swfPi3hIWtZ8CA3tSqVZuRI7/jzJnT/PzzTzRp0gw9PX1N+YJ52fIed+vWnbi4WBYv9iIp\\n6RlLlizAy2txsfnxAL74ogcTJnynkeN/E06ffv7CkAMQERHhRGxsDNWqVX9lPZFIxFdftXplmTdF\\nJpNRoUIlIiIua1xZ8yKR5F0YE6NUKoGC4jsqTf/yolKpEIlErFvnxpQp5+jV6xOcnQvniMy/+CbW\\nHL+8noDAh6NMli2VSiXz5s3jl19+Yd++fezbt4/bt0s/CFZAQEDgTenatQFWVns1x7Vq/crnn6tX\\nzrduDWPAgN4MHz5II8OeK2YAULt2HeLjX71D9roUlwx7797dHDjgR/36PahYcTVVquykbt1kbG1f\\nxrE9fPiAnj2/Zv36Lchkhpw4cSzfzplIJKJChYoEBobSsKEjXl6z8fLyYc2aIAIC1gJw+PBBmjRp\\nRmBgKEFBYdSsWYukpCRCQgLw9V1FQMAG6tWrx+bNG/P1ceDAodjZ1WHWrPkflSFX1pw7d4by5S0I\\nCgolJGQzTZs2w9d3MfPnL2LduvUat8Pq1a1QKLI14hRHjx6iffuOKBQKfvqpcHlAk7T5l19CcHfv\\ny7Vr6Zw7N5JjxxYSFVWOdevUz+xV7sDFuZW1afMp/v4hBAWFUr26NXv37tJcMyUlhTVrAhk2bBTx\\n8XH07t2fadNmkZ6exq1b/yMpKYmoqBv06eNJUFAYAMuXLyU5+TknT/7OvHkLCAoKpXPnrjg5OTNy\\n5HekpqZy+vQJKlWyxNm5seZeFRSUyMl5Of3Q1r6FltYI1qwJIDDQn8TExBdjus64cZPYsGErMTHR\\nnDhxjMTEBPz8fmb5cj8CA0OJirrOqVO/A2oVyXr17AkKCmXAgCGUL2/OihVrSi1v3aZNG+jf343+\\n/d3YsiWM+Pg4PDy+4scfZ9G/vxvz5m2mbdsOjBmzieTkZIKCfmH9+kBkMkOsrKyxtrbF1LQc1apV\\nZ8CAwYwc+R1GRkasW7eGn3/+ifj4OB48uAeoXRafP3/OoEF9GDlyEA8fPmD48G+YMmUGDRt+gq/v\\nagwMZDg6fsLChS+VTseNm0Tnzl0BtRx/aOj2txq/mZkCeJm6wNz8AeXKvbtHwtsglUrx8lrMwYP7\\nOHz4YL5zRf9NiGjQwIHw8FNkZWWRnp7OmTOngdzYVkMiI68AcOjQAU0tR8dPOHLkNwAePLjPo0fx\\nVK9uVYIi60eRqlngP06Z7MxFRkZSrVo1qlRRTz66dOnC0aNHsbW1LYvLCQgICLw21taWBARkEBy8\\nBbFYxdCh9bGwMCtWhj1XzABALBaVyipspUqVCQ7epDn28Oir+bxkiVqQJS0tjYiIG9jYWFKxYocC\\n9QvH2xWkZUu126SNTQ3kcrnGRUgqlZKWlkrduvXw9p6LQqGgVau21KxZi8uXL3Lv3h1GjBgEqKXj\\n69QpOoH6f20SY2tbk5UrfVm9egXNm7fC0FDGnTtFux22a+fC0aOH6Nt3AMeOHWHevAU8eHCvWDdF\\nUCsIgnoHcM2aRKTS/VhaJvDsWTYnTmgxsQTxvqLcylQquH37b/z9V5OWlkp6upwmTZpp6nz66acA\\n2Ns3QE9Pj9mzp6Gjo4O+vj7x8bE8fvyI5ORkgoPXsXPnVmQyGeHhJ7l+/RqPHsWxePF8XF092LIl\\njPT0NFQqFa6u7tSrV5/U1BQiIq7w9ddfoqOjQ0pKcr7+lisHOjr3UalEyOVN6NEjC2NjE5ycnLlx\\n4xoymSF169ajUqXKAHTo8BmRkVeQSCQ4On6CsbEJAC4unbhy5TKtWrV9rfx3b0tRaSQcHZ1eiHLM\\nZe/eh/j4uGBtHcbmzT2Ji1uNgcFZgoM3kZ2dzaBBfbGzqwNQaOHFxMSUgIAN7Ny5jbCwDUye/ANW\\nVtasXOmPlpYWf/55jrVrV/LjjyXHS169eotz5/7ms88cqFq18luPd8KEDty6FcL581UwNExh3Dhj\\nZLI3T2ZcGohEInR1dVm06CfGjRuFp+eQfK7HRe1M2tnVpWXL1nh6umvSQuQKFE2bNuuFAAo0atRU\\n8yx69HDFx8cbT093tLS0mD59NhKJJN/zenHVfH0rK/dnAYHXpUyMuUePHlGp0ktFpgoVKhAZGVkW\\nlxIQEBB4Y+ztbVm8OP/i0uvKsL8Pbt68z7Bh17hxowVmZreYPv02ffu21JwvGG+nVGYWaiOvG1De\\n5Nm5bkEODo6sXOnPmTOn8fKajZtbHwwNjXB2bqJx6zM3NyQhIaXIPv7XJjBVq1YjIGAjf/xxGn//\\nVTg5OWNtbYufX0Chsu3auTBjxhTatGmHSCTC0rIKt2//XWx5eCl2kZ6ejpbWfhITx5GW9il6eucp\\nV+7Vub3UFJ3TzctrLgsWLMHWtgYHDuzl8uWX6RpyfxdisZiKFStpcnl5ec1BqVQiFmvRunXbIt08\\ns7OzuXDhPL//fhRtbW1Wr16X73xgYCh//HGa3bt38sknjfjtt/0a983MzCwsLU3p2zeKnTv/wsxM\\nwrRp37wciaiw01CuO1xhXn7/uvnv3oa8aSQA2rRpR0TEZSpUqETduvbMnx8LmOaOgHv3njJiROGY\\nraJo06YdoI6hPHHiGKCO7Zo3bxYxMQ81O7clsW3bWWbMMOLJE1d8fC4xe3Y47u4t3mq8Ojo6BAa6\\nIZfL0dHR+WDxp3kXvnJTMwC0bKmOWRs0KH8IT958dB4e/Rg0aBgZGRl8++0watdWG9O1a9tpxE8A\\nRo0aDajz6RWVdLxz566a3U6ArVt3FXtOQOBDUCbG3Nu8TM3NP8yKj8D7QXi+/27+Dc+3S5eO7N+/\\nC09PN6ytrXF0bIiJiT5isUgzPmNjfXR1pWU+3jFjorhxQ60c9+RJVfz8tjF2rAyRSERmpgESiZam\\nDzKZDmKxEl1dKUZGepibGyIWizAzk2FiYohMpoOenramfO659PQUatashp1dP3R0xDx4cJfhw4fj\\n67sYufwZ1apVIz09nbS0J1hZWSGVamFqqo+5uWG+z/8VHj9+jKWlGX36fE3lyuaEhYWRmppMTMxt\\nGjZsSHZ2Nvfv36dGjRqYm9dBR0fKpk3BfPllN8zNDTE2rkdKyvMiy0ulWpiYqJ9d+fIyjIySiI9X\\nKw+amQVgbq6Dubkhhoa6+Z5lXlq3bsasWbMYP3402dnZnDsXjpubGxkZ6dSqVR1DQ12OHz9ExYoV\\nMTc3RFdXbciZmxsW+k3p6koxNtbH2dm50O/h8ePHWFhYIJdn8cUXnfj00xZ06NBBU1cdf7me69eN\\nqFQJPDw8OHz4N6pXr0Zc3F1q1mzN+fOnkEq1GD78M7Ky/sfRo0cxMdElLS2NyMjL/PDDVO7cuUNU\\n1HUyM59TuXJlTp8+jru7Ow0bNmTFiqVIJAqMjIw4efIY/fr1w9zcEJEo/7vI0FCGjk7pvJ8MDXVR\\nKjM0benrayOT6WJoaIC5uSGWlgryKlcaGyvR13/5rPT0pMhkupp7n/dvtVIlU0xMDDEzkyEWq/u7\\nZMl82rZtRd++fYmJidGM0cREHx0dSZFjCgtL48kTdSLwp08/YdOmnXz33buO/Z/7Nz5hwmxu375N\\nZmYmPXr0oHlz53dqb9mygxw7loWRUSbz5jXHxqZ0lDrflv/S+1fg1ZSJMVehQgXi4l7GlcTHx1Oh\\nQtGqS7kUt/or8M/nVav7Av98/inP99Sp36latTpWVtaAWmr/22/HaVyfALy8luark5qayi+/rCcu\\n7hkSiQQnp+Y4OTUv8/EmJ+dfEEtNlfLo0XO0tLR4+jQNpTJH04fU1Ezk8kwyMrJJTpaTkJBCTo6K\\nJ09Syc7WIjVVfS63fE4OPHmSSnj4KcLC1iORSNDXN+CHH+agVEqZMmUmo0ePISsrG4lEzKBBIzAw\\nMCM7W8mzZ+kkJKTk+/xf4c8/I1i50hexWIREImXixKmIxWK8vReSmpqKUqnAza03xsbq/3WtW7dn\\n9erl9Os3VHOfZs/2LrJ8draSpCS5pty0aWNZsGAYIEWlSuX5cz06dHDByMgYU9Ny9OrlyrNnScya\\nNQ8AX98lZGVlkpSURMeOn2FhUQEdHT127PgVY2NTWrZsiYmJKZ9+2p70dPVzy8jIRiQSkZCQUug3\\nlftbKvh7ABg2bBR2diqmTJnwQrZdxbffjtPUnT9/L/7+lpibr+DmTTEXLybh57eYjIwM5s6dq4nz\\nUijU10tPz6J6dRs8PPqQlJRE//6DAF2SktKxs6vLjBmzXgigNKJhQ3XC6qFDR9GnT19UKhXNm7ei\\nfv1GL64vyveb7NLlSwYOHPRWAiAFsbWtw/z5c/jqKw9yclQcPPgbM2bMRaFQkpCQwpQpzbh/P4iH\\nD9Oxt9/G0KHNOHgwlK++6o1CoeDo0WN8+eVXmntf1N9qUlI62dnq9p48SUJXV/1uXb8+jJwcFQkJ\\nKSQlpZOZqSjyby8rK78LeGZmzn/qb7QgU6bMznf8Lvdi/fqTTJ1an6ysagBERQWzb9+XmhyC75t/\\nyv9dgTfnbYx0kaoMAh8UCgWdOnUiKCgICwsLXF1dWbp06Stj5oQf5b8X4aXz7+af8nznz5+tUXsE\\n+O674Xzzzdh8xlxetm49h5eXnISEqjRocJmAgDZUrKiOcVIoFEgkZZfZZcuWM0yZUoXU1HpAMh4e\\n2/H17VVm1yuOf8qz/bcSFxeLu3sPAgNDsba2YciQ/tSoUZOpU2dy+vQJ9u3b80KdUgctLS1Onz7J\\nwYN7+eGHufTr9zUKRTYbN25DIpHSu3dPVq9eh7m5hab9sni+ffoc4vDhnppjS8tfuXSpXbEeOwEB\\na9HT088XNwpw6dIFNm3ayKJFy4qs9yHYvHkj+/btBtRpJFq1asPkyePyxb+6un7BunXrMTIyJiBg\\nLYcPH6RcOTNMTU1p2rQ5Xbt2x8trDi1atKJNm3b5ykdF3WDVKl+WL/fj2rWrzJ8/Cz09PZo1a8mh\\nQwfZunUXly5dYPPmjflET3IJCTnJ3LnVSU62x9DwBtOm/c3gwW3f1+35VzNmzG+Ehb18B+vqXuDs\\nWRmVK3+Y3Tnh3fzv5W2MuTKZjUgkEmbMmMHgwYM1qQkE8RMBAYG35U3yv8XFxeLtPZfnz59jYmLK\\ntGkzefz4EeHhp7hy5TIhIQHMm7cQgOPHj7BkyQJSU1OYMmUmDg4NUSqVrF69grCw40gkMvT0+nDh\\nwgCmTfNCVzcSIyMj7t+/R1jYjjIZq1wu588/N+PoeJ/U1GwcHdvh5TWuTK71JmzfHs7//pdCkyYV\\nadeu4Yfuzr+e48cj8fW9ikplwq5dfzNunC3W1jYa2XRra1vi42NJTU1h3ryZxMQ8fPE3ksXgwWqx\\nDZnMSJMbzMrKmri42HzG3Juye/cFli17TGqqDi1aPGPJkh6FdiYqVEgHcsgVy65UKbXE0IuiTr9K\\ncv9V7N9/gXPnEqleXZuBAz8t1Rg6N7c+uLn1yfddXkMOYOvW3ZrPxcVs5Y3Lylvezq4Oy5f7AWBv\\nXz/fO2bo0JEAODk54+RUtLtg//6tqVHjKhcvbqNtW1vq12/7FqN8e1JTUzl8+CA9evQiMTGRn35a\\nzI8/LnyvfSgrKlVSABmAOmayYsUHlCtXuikYBATeljJbWm7Tpg1t2rw6Ca2AgIDA6/Am+d+WLVvM\\n5593o1OnLuzbt5uffvLB29uHli1ba1bDc8nJycHfP5g//ggnMHAtP/20ir17d6Gnp0dS0iQSE9tR\\ntaoHaWktyMjQ5uHDm6xfv4WKFSu9orfvRq4Efm6+qrS01NcSH1i3bg0ODo5F5kh6V7y99/Hzz63I\\nzrZEJrvOnDkn6devdakXG+jbAAAgAElEQVRfJy9xcbFMnjwun6ABlO04PxaePXvKxInPiIvriqXl\\nPpYsaUKVKuH5xGxyhWx++cUPZ+dGeHv7EB8fx3ffDWfjxm3s37+HmzdvaNoUi7XIycl56z4lJz9n\\n1qw0YmLcALh/PxUbm4OMHv1ZvnJz5rTnyZNgbtwwxcIijXnz6r6y3YICFrk4On6Co+Mnb9THkJCT\\nzJpVg7S0TxGJnvD337vw8ur+Rm2UJosWzefevTtkZWXRuXNXatasXXKlPMTHJzJ16iliYw2xtU1m\\n0aKOGkXG4mjevD7Nm9f/IDs3KSnJ7Ny5lR49elG+fPl/jSEHMH68C/fuhXL+fDmMjTOYNKkSurpv\\nnoxeQKAsKDs/IQEBAYFSYuvWME6dOgFQbP63CxfOAXD9+lW8vX0A+Oyzz1m9ermmnYJe5W3afPqi\\nvp0mf9yff57l9u2/qVhxF3p6qxGL05DJztCwoQ7R0fXK1JCDwhL4Dg4vd8Fy+1/UbsPgwcPLrE8H\\nDmiTna12J0pNrcvu3dfp16/MLvdKynKcHwvXr9/l4UMnJBK18ZWVVZWrV89ScO6oUqlIS0vVJOfO\\ndQEsjneJqoiPf0xsbI0838iIiSlcztDQkODg9+8SDHDwYAZpaWqDSaUy49ixDysQMWvWj+9Uf+LE\\nkxw61B8QcflyDlLpRnx9P5xxWhJ+fiuIiYlm4MDeVKlSjfv37xISspn9+/dw6tTvZGRkEB39EHf3\\nPmRmZnHkyEGkUm0WL/bFyMiImJholi5dRFLSM3R1dZk8eTrVqllx7NgRgoL8EYu1kMlk/Pzz2vc+\\nNm1tbfz8XF+hqiog8OEQjDkBAYGPmrfJ/1bcpLXgP2GpVPtFfa189cePn0SDBo4sXXqUxEQJLVoY\\nYmVlzqZNpZNixc/vZywsKvDVV66AerdJX98AlSqH48ePoKury8OHD/D3X0Xt2nacOXOaevXqc/Pm\\nDRYvXs66dX7cvHkDkUhEly5f8vXXHvliAi9cOM+qVb4olUrs7OoyceJUpFIpvXp1o3PnroSHn0Kp\\nVDBv3gKqVbMqsb9SqbLA8dvv8LwJOTk5LFw4n2vXIjA3t8Dbewk+Pt6acfbq1Q0Xl06cPRuOWKzF\\npEnT8fNbQWxsDB4e/ejevWfJF/kIqVPHiipVrhAf3wAAqTSGunUNuHMn/29YLBbj4dGf+fNnERy8\\njmbNWpKbA6uo/FfvMgmtVq0q9eod4do1OwB0dO7i7Pxxqenp6ORPTK6rm1VMyX8G9+8b8zKnmZi7\\ndw0+ZHdKZOTI0dy9e4fAwFDi4+OYNGms5lzu95mZmbi5fcmoUWMICNjIihVLOXhwH19/7cGiRfP5\\n/vtpVKlSlb/+usaSJQvx9V1NcPAvLF26kvLly5OWlvoBR/jfS8ki8M/gwyQOERAQEHhN3jT/m719\\nA44ePQTAoUMHcHBwBEBfX5+0tLQSr9e4cTN27NiGlpYWU6d+zpgxtfn8c8d3H0ge2rd34dixw5rj\\n48ePYmJiQnT0Q7y9l7J2bTAKhYKmTZtz585tYmKi+eorV9av30JS0jMSExMICdlMcPAmunTpBryc\\nvGdmZuLlNYe5cxcQHLwJpVLJzp3bNGVyExR3796LsLANr9XfoUONKFfuNJBK1aoHGDGiaqnej+J4\\n+PABPXt+zfr1W5DJDDlx4lihhMsVKlQkMDCUhg0d8fKajZeXD2vWBBEQ8P5X70uLcuXMWLjQkEaN\\nwjEzG8SYMadxd2/FtGmzNG7Cufm3cmOrAgI2MnToSE0OrM6duzJ27PeaNhctWkbDhk5v3SddXV1W\\nrbLniy/C6NBhOzNnRuLq2vzdBlrKjB5dC1vbnUAs5csfZdQokw/dpXeiWrXneY5yqF79wxoyJZF3\\nEa3ggpqjozN6enqYmJggkxnSooXaTdvGpgbx8bHI5XKuXo1kxozJDBzYGx8fL548eQJA/foOzJ8/\\niz17fs236CYgIKBG2JkTEBD4qGnSpDm//rqdvn1dqVq1Ovb29YH8K6R5P48dOwlv7zmEhq7H1NRU\\nIzbQvn1HFi6cz7Ztm5k3b0ERV1K30a1bd+LiYhk8WC19bmpaDi+vxW8tyFAUNWvWfmGUJfLs2VMM\\nDQ25c+c2f/55josX/yQhIQGVKoe7d28zduz3REdHU7euPQCWllWIjY3hp58W06xZSxo3bqppV6VS\\n8eDBfSpXtqRKFbXB1blzV3bs2MLXX3sARScoLgl39+Y0afKAq1dP0qSJHRUqmJfOjSiBSpUsqVGj\\nJqB2hY2Liy1UpmVLdWy2jU0N5HI5enp66OnpIZVKSUtLxcDg1TFGHysuLg1xcXk7oZkdO8K5eTMF\\nZ2cLXFze3oAriJ2dFb/8YlVq7ZU2jo41OXSoApGR/6NGjeolpkT62PHxacXUqeuJjTXExuY53t4d\\nNede5XL9MaKtLdV8FovFmuPc2E+VKgdDQ0MCA0ML1Z04cSrXr1/jjz/CGTy4n0b9U0BAQI1gzAkI\\nCHzUSKVSfHyWF/r+0KETms9t27bXpByoWLFikTml6td3YMOGLZrjFSvWaD6bmJhodjREIhHDh3/D\\n8OHf5Kv/NoIMr+LTTzvw++9HePLkCe3buxAfH0/fvgP48suv8pWLi4tFT+9lsJQ6JmkT586d4ddf\\nt3Ps2GGmTp2pOV9wclcwxiN3EqWlJX6jVW5r62pYW1d7ozG+K/kngFpkZ8uLLZNXHCT3+L+4ir9o\\n0QGWL29OVlYVDAyimDXrBAMG/HfEyAwNjWjRovT+TovKR1malORyrVJl4+bWlsGDhxMXF8vQof01\\nLtft2rmQkpLM6NETANi9eyf379/lu+/Gl0lfS0JfX5/09PQ3qpNrlOrrG1C5cmWOHz/Cp592QKVS\\ncfv239SoUZOYGPViVt269pw9G87jx48FY05AIA+CMScgICBQDCqVipMnL/D4cQqdOzcuUUnuTWjX\\nzoWFC3/k+fMkVq70JzR0FwEBIdSq1YA6dWqQkPAYiURaqN7z50lIJBLatGlH1arV+PHHlzLnIpGI\\natWqExcXS0xMNJaWVfjtt/1FutclJiZw48ZfpTaeVxEXF8uECd9hb9+Aq1cjsLOrS+fOXQkMXKtJ\\nfm1pWQVv77nExsaiq6vLwIFDAfXkNjY2moiIK+jrG2BjY0toaAgbNgSRmJjA9evXaNq0xTuJe3xM\\nBAX9wqFDBzAxMcXCogK1a9dBJpOxe/cOsrMVVKlS5UVuOV3mz5+Njo4ut27d5Nmzp0yZMoM9e3ZQ\\nqVIQGRkOPHrkze7df1G37lkCAtaSlZWFpWUVpk1T5y8TKJl32fl6nXyU7du74Ou7RGPMHT9+lD59\\n+nP1agT+/iHk5OQwZcoEIiIuY2FRgZiYaGbMmEvduvbI5XIGDPDgm2/GoqWlxYEDe/j+++lv3d93\\nxdjYhPr1Hejf343q1a3zuUPnv4/5vSpyz82c+SM+PgsIDg5AoVDQoUNHatSoyapVvkRHP0SlUuHs\\n3FizWy8gIKBGMOYEBAQEimHSpJ1s3NgWhcICB4ethIa2xtzcrFTatra2QS5Px8KiAkuXniYgwBWZ\\nzITBg8dhaSmhfHlTZsyYV2gilJCQgJfXHFQqtQjJiBHf5WtXW1ubadNmMWPGZJRKJXXq1KN791x1\\nwaInVO+DmJhofvxxEVOnzmTIkP4cPXqI1asDOH36BCEhgVSooDZcvL2XcOnSBZYuXajJYXb//n26\\nd+9JVlYWBw7spWvXL/H0HMxXX3XB13cJTZu2eOWE8Z/CjRt/ceLEMYKDN5Gdnc2gQep8cW3afEq3\\nbmoVQ3//1ezdu4uePd0QiUSkpqawZk0gp0+fYMqUCejoDOLmzW+oVq0n2tpRiMXJhIRsw9d3FTo6\\numzYEMTmzRsZMGDIBx7tu/M6iwRWVjYsW7aIu3fvoFQqGDRoGC1btnlthUWA337bz8KF81AqlUyd\\nOpM6deohl8uLbffEiWNkZGSQk5PD7NnzmTlzKunpaSiVSiZMmJpPofZVLtcDB/YGQC5X99HCogIV\\nKlTSuFzr6enh5NSI8PBTVK9uhUKhwMbmw+b0LUrBs3PnrnTu3FVznOsFUfBcpUqVWbKksBfG/PmL\\ny6CnAgL/HgRjTkBAQKAI7t69y6ZNDVAoqgMQEdGPVas2M2tWl1K7RnDwJtLT03F2voRCUZmkJE+S\\nkjxxctrCzz93zlculxo1ahIQUFi4JG8i4k8+aURAwMZCZfJOomxta1CxYqVCapEPHtxj8WJvMjMz\\nsbGxYvz4aSgU2UycOIZ169Zz69b/GDSoD9u378XCogJff/0l69dvQUdH55VjrVTJUjPRzJv82sam\\nBnFxsTx6FKeZtDk5OZOens6GDVvYtGkjLVu2pm/fAQDs2LGV338/yu+/H8XY2Jjnz5+TkZHxygnj\\nP4WrVyNo1aotUqkUqVRKixatUKng9u2/8fdfTVpaKunpcpo0aaap06KFOnGxtbUt5cqZ0bNnbWbO\\nPEVmphUVK+6hQwd99u69w4gRgwDIzlZQv36DDzK+sqCkRQIrK2ucnRszbdosUlJSGDbME2fnJsDr\\nKSyqVCoyMzMIDAwlIuIy3t5zCQnZTEhIQLHt3rr1P4KDN2FoaEhY2AaaNGlG//6DUKlUyOWFXYXf\\n1uUaoFu3LwkJCaB6dWu6dPmijO7yh+P8+WtERkbTunUdatWq/qG7IyDwUSIYcwICAgJFkJmpQKHI\\n64omQqksfQFg9Y6SqsB3pe8yGBh4gl27MpFKlQwdWpH69Svw8OEDZs/2YvLk6cycOZUTJ46xcWMI\\n48dPwsHBkbCwQAID1zJ69ASysjJJT08jMvIydnZ1uXLlMg0aOFCunFmJhhwUFkDIjW8TiUTk5CgR\\ni6XFukrq6OSdwKpYuzZYUz8jIwMvr8PExOhQt24O48Z1fK0k6x8noiLvgZfXXBYsWIKtbQ0OHNjL\\n5csXNefyJhHX1pbi6tqURo2iWbAggU6dHDA3Nyc+vgmzZ89/b6N4nxS3SGBtbUt8fCwJCY8JDz9J\\nWNh6ALKzs3n0KB6RSKRRWNTT0yuksHj79i1A/fvs0EGdGN3BwZG0tDRSU1M5f/5sse06OzfG0FCd\\ntqFu3Xp4e89FoVDQqlVbatasVWgMBV2ub9++hb+/Hx07dkZPT69Yl2t1+/Y8fvyY//3vJiEhm9/6\\nPrq4tOLw4VNvXb8s8PM7xqJFtqSm9qJChZMsXfoUF5fSVRYWEPg38E/9jycgICBQptSqZUvHjicB\\n9Uq6tfVu+vQpfREEPT09evVKQls7GlBRvfp+Bg2qUWK9N+Ho0cvMnVuDM2d6cuLE13z/fTZxcY8L\\nqUXGxESTmpqiSefQo0cPrly5DIC9vQORkRFERFyhX7+BRERcIjLyCg0avJ3iYkEcHBw5dOgAoM4t\\naGJi+kIIIr9x06hRU7ZufblTOXz4Ovz8XNmzpycLF7rg7b2/2GukpqZq0jRcunSBSZPGlUrfS4sG\\nDRwIDz9FVlYW6enpnDmjnlzL5WmUK2eGQqHgt9+KH18uVlZVqFatAqamJtSrV5+rVyOIiYl+0Zac\\nhw8flOk43ifFLRLkFcCZP38xgYGhBAaGsm3bHqpXtyqybkGFxeLI9eYtrt288YgODo6sXOmPubkF\\nXl6zOXhwX6H28rpclytnRqNGTXFx6cSIEQPx9HRn5swpyOXpL65d2H24XbsONGjQ8B1jel/fLVml\\nUr2XGNXQUAWpqfaAiEeP2hAY+KjMrykg8E9E2JkTEBAQKAKxWMy6dV8TFLSf5OQcevSoj7W1ZZlc\\na86cbjRrdpb79//gs88aYGVVuVTbv3DhEWlprTXHcXHNuXx5UyG1yNTUlHz18k7YGjZ0JCLiMo8e\\nxdOqVRs2bAhCJBLRvHmr1+rDqxJYi0QiBg4cirf3XFq1akS9evX54YfZmnN5q44dO5GlSxfi6emB\\nUqnk7l0LIHccJly6VPwuYUpKMjt3bqVHj17FlvmQ2NnVpWXL1nh6ulOunBm2tjWQyWQMGTKCYcMG\\nYGJiQr169vkUA4tL0ZGLiYkJ06fPZvbsaWRlqZNqDxs2iqpV368y6YeiceOmbNu2iXHjJgHwv/9F\\nUauW3SuNkYL50o4dO4yTkzMREVeQyQwxMJC9drvx8fGYm5vTrVt3srKyuHXrJp06FXbVzutKDeDq\\n6o6rq3uJ5QAiIyNwd+/zirvw+qSnpzN16kRSUpJRKhUMHTqSli3bEBcXy/jx32qUNBcvXs7Bg3sL\\nifV4ePQlJiaapUsXkZT0DF1dXSZPnk61alZv3JecnILKvP+8OFgBgfeBYMwJCAgIFINEImHIkI4l\\nFywFOnVqWnKht6RePVN0dO6RmWkFQPnyF7G3t+LEifzlDAxkGBkZERFxBQeHhuzatUuTjsHBwZE1\\na1bi6PgJIpEIIyMj/vgjvJAAS1HkJrjOJW9836lTv7N2bRA6Orp4e/vQsqUzfn4BmvODBg3TfM7M\\nzCQpKYkpU2Zqdj+6dduT71qmpoVjknLx81tBTEw0Awf2RiKRoKurxw8/TObu3dvUrl2HmTPnARAV\\ndYOff16GXC7H2NiE6dNnYWZWnm+/HUbt2nZERFxBLk/nhx/mEBISyN27d2jf3oWhQ0eWeC9KwsOj\\nH4MGDSMjI4Nvvx2GnV0datasnUfE5iV572Nx91ilUlGrlh1r1wb/Y3KSvQklLRIMGDAEX18fPD3d\\nycnJoXJlSxYuXPbaCosikQhtbW0GDeqjEUABGDBgCMuXLymx3cuXLxAWth6JRIK+vgE//DCn1Mb+\\n559XmDt3KnXq1MHJyblU2tTR0cHbezH6+gYkJSUxYsRATS7HvEqaxYn1ACxaNJ/vv59GlSpV+euv\\nayxZsrDIdDEl0asXLF16h4wMG8zMzuPhYVoqYxQQ+LchGHMCAgL/KPbv38PNmzc0K+ICJdO1axNu\\n3fqNPXsuIZUqGDLEhGrVqhc5EZ42bTY+Pt5kZGRgY2PFhAlqqfOKFSsBaNIcODg4kpiY+FquXa9K\\ncLx16yY+++zzAnFxakJDQzh+/AhZWdnUrt2AQ4cacOuWNVZWQ6hcOR09PW2++KILcvkGnj37AwOD\\ny6SnG7Jy5V2++WZMofZGjhytEb24fPkiU6dOYMOGrZiZlWfkyMFERl6hbl17fvppMQsXLsXY2ISj\\nRw+xdu0qpk6diUgkQirV5pdfQti6dRNTpkwgMHAjhoZGuLl1x82tj0YB8W1ZtGg+9+7dISsri86d\\nu1KzZu23buvy5Vt8//1fREdXwsYmmp9+akStWh92Ry4uLpbJk8dp4rtCQ9eTkSHH0NCIXbt2oKWl\\nhZWVNXPmeBWrGJnLqxYJ8p77/vtphfrxugqLefNR5kVHR+e12i14XFqsXn2MxYurkpp6iMePj3Dp\\n0v9wciocj/emqFQq/Px+JiLiCmKxiMTEBJ49ewqQT0mzKLEeULvxXr0ayYwZkzVtZmcr3qovY8e6\\nUK/eBaKiLtCihQ1OTo3fcXQCAv9OBGNOQEDgH8W/cXfhfTBu3GeMKxAilnci7OHRV/N5zZpAAMzN\\nDUlIeOl6uWPHy3iffv0G0q/fwGKvV9Atq06dety5c5vMzAzatm3P4MHD2bp1E4mJCYwePQITE1PN\\n6v3atas4cuQ35PJ0QkI2Y2xsQseO7iQnP8DS8hFKZSqpqd3ZunUUfn4raNbsL8LDT9OmzafMmvUj\\naWmpRfYp16h0cWnFwoXLqFOnHuXLmwNQo0Yt4uPjkMlk3L17m7FjRwGQk5ODmZm5po2WLXNFMmyx\\nsVErSAJUrmzJo0fx72zMFSXt/rbMmxdFZGQ/AJ4+hR9/3EhIyMflXpn797xxYzDbtu1BIpFonl9x\\nipG6uoUN/4+NR4+eEBR0DrEYhgxpjqmpSam1rVKpCAxUkJqqXli5e/cL/Pw2s3btuxtzhw4d4Pnz\\nJAICNqClpYWr6xdkZmYBFFDSLCjWo3rRtxwMDQ0JDAx9574AuLg44+JSKk0JCPxrEYw5AQGB98pv\\nv+1n27bNKBTZ1K1rz4QJU1i6dCFRUTfyTfRBnXdr+fIlyOUZaGtr89NPqwB1wusJE0YTExNN69Zt\\nGTVq9Icc0n8CuVzO6NH7uHrVlHLl5MyYUYNmzexeWSevW1ZycjJGRkYolUrGjh3FnTt/4+rqzpYt\\noaxYsQYjI2NNPXv7BmRlZbFnz68MGOBBuXJmZGREI5e7kJz8FVWq9CclZRMREc2RSrWJjY2latWq\\naGtrc+LEcc0uQfGoDQipVFvzjZbWS9ELa2vbfK6eecmtk7tLp2lRJCInJ6eE675fnj7Nnxj82bOP\\nN1G4rW1NZs+eTuvWbWnVqi1AkYqRjx/Hv1X81fvkyZOnuLuH89dfvQEVR44EsX1753cUKHlJTk4O\\n2dla+b4rePy2pKWlYWpaDi0tLS5dukB8fFyR5Ro0cGDRIi/69RuIQqHgzJnTfPnlV+jrG1C5cmWO\\nHz/Cp592QKVScfv230KibwGBMkQw5gQEBN4b9+7d5dixw/j5BaClpYWPzwIOHTrAsGHf5Jvo3779\\nN9WqVWfWrGnMnbsAO7s6pKeno6Ojg0ql4tat/xEUFIpEIqV37564urpjbm5R5DXj4mKZOHE0DRo4\\nFptPzdKyClOnztTIiQsUZv78I+za1ReQcucOTJ++kaNHa79ypzSvW9axY4fYvftXlEolT54kcvfu\\nXWxsilbtbN68JZcuXaBduw4ATJ78A+3atUEmO4aBwUlycgyRSJ7j57cCqVRKq1Zt6NPHkwsXzvP7\\n70fZsWNLkTE6+vr6+cRDVCoVK1f6cu7cGRITE8nJycHFpRPx8XEMHNgHS8sq3LnzN1WqVGXRop8A\\niIy8wty5P7yYUCuYNGkcixYte9vbWqY0bJjM9esZgC6QjKNj8fGE7wstLS1ycl7u6GRmZgDg4+PL\\n5csXCQ8/RUhIgGbXeP78xf84sZbNm8+9MOREgIjLl/uxY8du+vcvnfhbLS0tunRJZd26xyiVFpQr\\nd55evcq9U5u5f8cdO3Zi8uTxeHq6U7t2HapXty5UBooX6wGYOfNHfHwWEBwcgEKhoEOHjoIxJyBQ\\nhgjGnICAwHvj4sXz3LwZxZAhatevrKwszMzMCk307927A4CZWXlNUL2+vj6gnlB88klj9PUNALCy\\nsiYuLrZYYw4gOvohc+Z4F5tPbd26NZp8agJFEx+vw0vVSIiLM0cul2ueS1HkumXFxsawadNGfvll\\nPTKZDC+vOWRlZb7yek2aNGXJkoUaY1BXV4svv3Tj1q1satQwYPToTvzxRzgrV/pqlDibNWtB/foO\\nuLl9WWSbxsYm1K/vwJEjv7F69XJyclQoFNkEB29iwYJ5HD16mAEDhjB48HAWL/YmOzsbsVjM/fv3\\nuHo1gpycHNavD2Tt2iDi4mKZM+cHPmav38WLu2Fm9isPHkipXTuH8eNLP3brTSlXzoykpKckJz9H\\nV1ePM2dO06RJMx49isfJyZkGDRpy9Ogh5HJ5sYqRHzv6+hIgE7URDZCKTFZyLsY3Yd68L7C3P8WD\\nB+m0bl2Npk0bvVN7hw6p1ZCMjU2K3ZUuqKRZUKyndm31u7pSpcosWbL8nfojICDw+gjGnICAwHul\\nc+euDB/+jeY4NjaG8eO/LTDRz3rlJLmgpH5J7m0l5VPr1KkLM2ZMeYdR/fuxt1exZ89TVCr1DkDt\\n2rHo6zd7rbppaWno6uphYGDA06dPOHv2jEYlU19fn7S0tHxulqDOJ9egQUNOnfodT093RCIxOjrR\\n9O/vzMqVvvTtuw4DAwMaNnQiOzuLSZPGkZWVhTp2R0Ry8vNCbYI6Ju306ZP4+4ewfPkSatSohUgk\\nYurUmSgUM7lx4zoAtrY1CAzcCICPzwLi4mIZO3Yivr5LqFixEjk5Klxd+3LlynmgeKGMD4lUKmXG\\njMIy+B+ShITHaGlJGDrUE3NzC6ysrFEqlcydO4O0tFRUKhWuru7IZDIGDBjCmDEj6dfva0Adl3jp\\n0oWPLrl1Qfr0acuRI8EcOvQFoKBbtwN07+5WqtcQiUS4u7cuuWAZUlCsJzo6k8mT9yOXS2nfXsHE\\niZ0/aP8EBP4rCMacgIDAe+OTTxozZcoEvv66N6ampiQnP+fRo/giJ/rVqlnx5EkiUVHXsbOrS3p6\\nGjo6ukXmhyopgW1J+dQESmbMmI5kZh7g4kUp5cpl8MMPLUusk+uWVbNmLWrVqk3v3j2xsKhIgwYO\\nmjJffNGDCRO+w9zcAl/f1ZodV1C7W4JapfD58ySWLl3IypXhKJVKnJwaMXHiFAIC1qKvr4+/f7Cm\\nnqvrF6+V1FgkKijioI5Hio5+SEpKsua7l/F06vH4+PzG6tWVUalkVK8er4kHFHg9jI2NNWqWxZGa\\nmkpiYgKJiQmsW7ceY2O1gIiLy4c1YF4HqVRKcLAbp05dRCrVolkzN8Ri8YfuVqmTV6wnKekZ7dtH\\n8PCh2miNjIyhSpXTuLuX/J4QEBB4NwRjTkBA4L1hZWXN0KEjGT/+G3JyVEilUsaNm1TkRF8ikTB3\\nrjfLli0mMzMTXV1dli1bWUR+qDdXuCyYT+3gwX2anSKBohGJREye/Plrl3+VbHxeevZ0o2fPl7sW\\nue5eAG3btqdt2/aA2v1rzhzvQvWtrBrg77+cvXt3IxaL8PQcAsC2bZsJDz+FUqlg3rwFVKtmRXLy\\nc7y955KRIWf48IG0a+fC0aOHiYuL5d69u4SHn3whrR5BSkoKAwf2pl+/QZprVatWnejohxw+LCYl\\npTkVK27n2bNqLFt2klmzPrwL45vi6OjIoUMnC6ULKGtyd+L+978orKxsmDFjDlevRrJqlS9KpRID\\ngwqcO/c5aWl/U778Y4YPH0yFChb51E7PnDmNjo4OCxYswdT03eLFygItLS3atv3vSOlHRd3j4UNH\\nzXF2tiV//XXmA/ZIQOC/g2DMCQgIvFfat3ehffv8WtP16tkXWdbOrq5GJj+XgnmbXkd8oqR8apaW\\nVYo1NgQ+PlQqFWtzuAAAACAASURBVPv3n2H9+rNcvFgBHR0HDA0b4e9fHWvrCvj5rcDExJSAgA3s\\n3LmNsLANTJ78A+vWraF27TpcvHiB4cO/YcWKpTRq1IRff92OXJ7O1Kmz6NixE35+P3P06CGNvHpE\\nxCVAnVusX7+BLFz4M4aGG8jIqA+IyMiQvqK3AgV58OA+U6fOxN6+Ad7ecwkL28Du3TtZvtyPKlWq\\n0qrVEFJS0khKmoKx8SEMDNzw9VW7WmZkyLG3b8CwYaNYtWo5u3fvxNNz8AcekYCdnRVVq17h4cMq\\nAEilsdSta1BCLQEBgdJAMOYEBAT+MRw5cpktWx4jkSgZMaImDRqUrJBWcIfI3b0PV69GkZiYxsqV\\n/kgkwmvwn4RKpWLcuO1s2tSZnJwOSKVrKFfuFM+fm7B06U0CAkYC0KZNOwBq1bLjxIljgDrR8fz5\\nixkwQL179/z5cwYMGIKurh5isZiOHTsB6h24XBdPIF+C+s8++5xNm5ScODEIC4v5SCQGdOtWtczH\\nLZfLmTlzCgkJCeTkKPH0HMLq1ctxcenE2bPhiMVaTJo0HT+/FcTGxuDh0e//7J1nQBRXF4af3aU3\\nqQpWigZUBLEX7L3Ghl0Ru35qbFHRWFGJXWygKFgRxa7BCPYSY0Ox90pTUEDqwrL7/diwiqAxStFk\\nnl87s3PvnDtDmTPnnPfQqVNXUlNTcXefSFLSW7KyZAwZMiJH4+2ioHjxEtjbOwDK67lx43pKlixF\\n6dLK6yiV1kRb+zIJCa4AJCe/awGhrq6uuje2thW5fPlCIVsvkBeGhkYsWmTEihU7SE9X1sz16iXU\\nzAkIFAbCU4yAgMB3weXL9/jpJzViY7sBEBa2jwMHjCle3OSz51AoFPz88x62b69NZmZJGjUKYsuW\\nzt9FE+KCpEWLBv9IVOLq1Suoq6urHsgLkydPnrBrlxNyuTkAmZkjefbMCF1dPSIjV+Pvr/y3ll0n\\n+X7/OPh4faWm5rufgQ8juQqFgoMHz/HyZTJi8Qt0dI5TrdpmdHVL8tNPY6hXr1K+rjEvLlz4A1PT\\n4ixa5AVASkoyPj4rKVHCHH//AFauXMr8+bPw8fFHKpXSv38POnXqiqamJp6ei9DR0SUhIYHhw92K\\n3Jl7//oqFAr09PR5+zZRta9SpSSuXs3661gZ9eu/66Emkbx7bBGLRTnurUDR0rSpA02bFv7fBAGB\\n/zr/vopcAQGBfyUnTjwhNraeavvx41acOhX+j+a4dOkGAQHOZGZWBEpz6pQb69adzF9Dv0v+Wc1h\\nWNhlbty4XkC2fBqZLAu5/F1ao0QSi0Khjr6+Hj16dOH+/XsfHevg4ERIyGFAuQZDQyN0dHRzOXgf\\n9qP7+ec9DB1ajWnTuuLnV45fflnA778fZvfuDTRs6EhhYGNTgcuXL+DtvZLw8Gvo6ip7emU7ZtbW\\n5alcuQra2toYGhqirq6uUof08VmFq2svxo0bSVxcLPHxbwrF5o/x8mUMN2/eACA09Hfs7CoSHR1F\\nZGQEADY2b3F21qJfv12UKCHGxSX/61nv3r3D8uWL831eAQEBgcJGiMwJCAh8F5QurYVYHIdcbgqA\\nru4DfvihFHK5/LOV4uLjU5DJ3hdLUCet6PsoFzgBAZvR0NCgW7eerFixhEePHuLl5c2VK5c4dGg/\\nkLeoxPHjx1m5cjUyWSYGBsWYOXMu6enpHDiwB7FYQkhIMGPHTsLRsWqhraVChfK0bbuDAwfKAXqY\\nm3tTokQIRkZ6nD+vy4QJUz5oM/FOMGfgwKF4es7B1bUX2tra/PLLLOURIlGOVhhOTjXYunUjbm69\\n6dixC7t22SGXlwDgwYOubNgQyK+/Fm4j6zJlyuLnt43z58/i67uG6tWVfcWyI5BisRh19fdVW8XI\\nZDJOnTpMYmICfn5bkUgkuLh0RCrNKFTb30ckElG2bDn27t3Jr7/OwdLSmh49+lC5chWmT59MVlYW\\nFStWxsvLHTU1NXbvTsihdvp+VO+fCh+9j51dRVUPy89BJpN9Vkp2TEw0N26E06JF6y+27XOZN28W\\n9es3UIkECQgI/DcRnDkBAYFC5ciRYHbt2oFMlkmlSvbY2FQgJiaKkSN/AiA4+CD37t1h3LhJuY51\\ndX3D4cMm6Ou7Y2tbm2XLXtK4cVPu3buLp6fyLfulS3+yd+9u5s9flOvcjRo5UavWXi5edAPEWFvv\\nw8Ulb/GVfxOOjtUIDNxKt249uXv3DjKZDJlMxvXr16hatRpHjx7JU1SiRo0arFu3EYCDB/exbdtm\\nRo0ay48/dkVHR4eePfsW+lpEIhFr17rQqNFREhIy6Ny5D6VLj89xTFDQftVnO7uKrFjhA4CBgYHq\\n5+R9Bg4cmmPbwMAAX9/NgDKKBB9Gsgo/qSUuLg59fX1atmyDnp4+Bw/uy/H9x9JHU1JSMDIyRiKR\\nEBZ2mZiY6MIw96OYm1uwbduuXPurV6+Jn9+2XPs/pXZqa1uRdevWMH/+bG7cCMfOrhJt2rTH338d\\n8fEJzJzpAYCX1xIyMqRoamri7j6TsmXLERZ2mcDAbSxcuEylchoVFYWWlhaTJk3DxqY8GzasJSoq\\ngqioKMzNLXJI8X+MqKhIQkOP/CNn7nMdxQ/JS9lXQEDgv4fgzAkICBQaT58+4fjxUHx8/JBIJCxZ\\nsgBtbW1Onz6pcuaOHw/F1XVQrmMXL/6VJk008fCoS7NmmfTr144mTZoD0KdPNxITEyhWzJDffjtI\\n+/Y/5nl+LS0ttm9vw5o1O8nMFNOzpz3W1qULbf1Fha2tHffu3SE1NQUNDQ3s7Cpy9+4dwsOvMnbs\\nzx8VlYiOjsbDYx5v3rwmMzOTkiVLqeb8jDZuBYZEIqFfv/yNRhw+fIXff49DRyeDn392xtjYCFCK\\ndXTqdJbt28ujUBhjbb0fN7fPj+jkF48fP2T1ai/EYhFqauq5IpC5H+yV2y1btmby5PG4uvbE1rYi\\n5cpZ5RiT1+dvkVevXnPmzHUqVCiFg8MPqv2RkRHMnbsQd/cZDB7cn2PHQvD29uPs2VNs3uzP9Olz\\nWL3aF4lEwqVLF1i3bjVz5y7MMXe2yqmn5xLCwi4zd+4MlZLps2fPWLNmPSdOHGXIEFfVi6W2bTuy\\ncOE8fH03kZWVxdChrsye7YmPzyqeP3+Km1tv2rTpQLduPfD2Xsm1a1fIyMikSxcXfvyxC2Fhl1m/\\n3gcDAwOePXvKpEnT2LBhLYaGRjx58ghb24rMmKF0RjduXM+5c6eRSqXY2zswadI0le2f009RQEDg\\n343gzAkICBQaV65c5N69uwwe3A+AjIwMjIyMKFmyFLdu3aR06dI8e/aMKlUc2b17R45jpVIpJiYm\\naGhoIBaLc6QWtWrVliNHgmnTpgO3bt1UPQTlhb6+PpMntyvYhX5jqKmpYWFRiuDgg1Sp4oiNTXnC\\nwi4RGRmJpaXVR0Ul5s6dS7duvahfvwFXr17Bz29dUS2hQAkNvcZPPxmQkNAYUHDrlj979nRGTU0N\\nkUjEsmVdcXY+TWxsKu3bO1GmjHmh21irVh1q1aqTY9/7EcgPW3a8/52Pj1+OcfHxb0hKektYWBix\\nsUm5FF+/NcLDHzBs2AseP26Bnt49Jkw4xv/+p/z9t7AohbW1DQBWVtbUqFHrr882xMREkZychIfH\\nDCIjXyASiZDJZLnmz1Y5BahWrQaJiYmkpqYgEolwdm5IVFRkrpdQL148w9m5Ib6+3kil6bRq1RZr\\naxtGjBjN9u1bVS1T9u/fg56eHr6+m8nIyGDkyMGq+/jgwT22bNmJubkFYWGXefjwPlu3BmFiYsqI\\nEYO4fv0aDg5V6dKlu0qB1cNjBufOnaF+/QYFe9EFBAS+GwRnTkBAoFBp06Y9w4b9L8e+3347wPHj\\noZQrZ0mjRk0+eSyAhoZmjkhC27YdmTx5HBoaGjRt2vyza+j+Szg6VmX79q1MnToTa2sbVqxYSsWK\\nn1ZhTE5OxtTUDIDDhw8ByjTYS5cuUKNGLTZsWIuOji69en1+uuU/Vc4sDI4efUlCQre/tkRculSb\\n58+fqZwEkUhEt25FqwCZHygUCsaO3U1wsBUSSQZDhlxgwoQWfz+wiPH2fsDjx8pUy+RkJ/z9nzBi\\nhBx4VzMIOesGxWKliun69T7UqFETT8/FxMREM3r0sDzP8SmV0w9fQkmlUoyNjXFzG8KgQf3Q1NRU\\nta/4cJ5Ll/7k0aOHnDx5DFCmvUZEvEAikVCxYmXMzS1Ux1asWFn1+1a+/A/ExETj4FCVsLBLBARs\\nQSpN5+3bt1hb2wjOnICAgArhiUdAQKDQqF69FidOHCM+Ph6At28TiYmJoWHDJpw5c5KjR4/QvHnL\\nTx6bF6amppiamrJpkx/t2nUonMV8Zzg6OvHmzWvs7atgZGSMpqYmjo5OwMfT7UaNGsX06ZMZNKgf\\nhoaGqlS+lJQUfv89mP379xAdHaU6PizsMpMmjfsbS769dL5ixWTAu4iNoWEUhoaGRWdQAbF9+0kC\\nAzuRmNiYN29asny5I+fOXStqs/4WmUySYzszU4JcLv/bcQqFgpSUdy8kfvvtQJ7HfY7KqbIWLwB/\\n/wACAnbj5jaEhIQE0tPTSEtLRSqVftSO8eMnqcbu3LmfmjVrA6ClpZ3jOHX1d/30sltqSKVSli5d\\nyLx5C9m0KZAOHTqRkVF4AjbR0VH0798j1/4NG9Zy+fLFT47dsGEt27dvLSjTBAQE/kKIzAkICBQa\\nlpZWDBkygvHj/4dcrkBNTY0JEyZjbm6OpaU1z549wc6u0t8em1d9T4sWrUlMTKRsWctCXtWXER0d\\nxcSJY3BwcOLmzXDMzIrj6bmE58+fsmiRJ1KplFKlSuPuPgN9fX1GjRpK5cpVCAu7THJyElOmzPhH\\nKpLVq9fkxInzqu3t2/eoPoeEnOLw4UMEBm5DJBJhY1Oec+fOEBCwET09PfT19enVqx+6unrs37+H\\nqlWrMX78JPz81qGtrQMoa5e8vVfy/Pkz/ve/IUyePI2yZS2Jiopk9uxfSE9Po379hvl3AfORceOa\\ncvOmH+fPV0Ff/zWjRyswNv78/oXfCy9fSlEojFTbUmkZnj4Np379IjTqM+jWzZRz5y7w+nVtxOJY\\n2rRJVAmGfPi34P1tsVhMr179mTdvJps2baBuXWfef5mQfejfqZxWr16LKVMm0L17b4yMjHj7NpHU\\n1FSWLVvIkCEjiIqKxNt7BePGTUJHR5fU1BTVOWrVqsuePbtwcqqBmpoaz58/o3jxEp+99mzHzcCg\\nGKmpqZw4cZSmTYs+mjpoUN4Rzvf51uswBQT+LQjOnICAQKHSrFkLmjXL/TAikUjQ09OnX7/uuLj0\\nomPHzvz66xxcXHrxxx9n/6r7KklqagrFihmqFOBiY2MZMWIgdevWp0OHTkWwoi8nIuIFs2d7Mnny\\nNGbMcOfUqeNs27aZ8eMn4ejoxIYNa/H3X8eYMRMQiUTI5XJ8fTdx/vw5/P3XsXz5mnyx4/HjR2zc\\nuJ6SJUsRHx/P3bt3qFatBo0aNSI09ChPnz7BzW0AERGjSU9/g4nJUZWs+/PnT+nTpxuxsa9o2LAJ\\nRkbGuLoOYsmSBXh5eePltZguXVxo1aote/YE5Yu9+Y22tjbbtvUkLi4OHR0rdHV1i9qkAqFNGzu2\\nbAkhIkIZ/a5Y8Tdatcr/Hm75TevW1TE2vsPJk0GUKaNFz57K3/MPa/2mTp2p+vz+d++/uBgyZASg\\njPQXK6aMvn6OyumHL5YaNGiEuroGzZu3Qi6XM3z4QMLCLuPgUBWJRMKAAb1p27YDLi49iY6OYtCg\\nvigUCoyMjJk/f1Gudhgfbmejr69Phw6d6N+/B8bGJlSqlFN9tzAcJrlczoIF83K8dFq82FPVFuH8\\n+bOsWrUcLS1tqlRxICoqSlUz+PTpY0aPHsbLlzF0796Lbt16Fri9AgL/NUSKb0QKKTY2qahNECgg\\nzMz0hfv7Lya/7u/bt28xMDBAKk1nyBBXVq1aR7t2zVmwYBn16jmzZs0KdHV1cXUdxPz5s3F2bsSR\\nI4kEB99HW/s3TExM2Lt3xxdJfBcF0dFRjBs3isBA5YPmtm2byMjI4NCh/ezeraxPi4yMYPr0Kfj5\\nbWX06GEMG/Y/7O0dePPmNSNHDiYwcG++2LJrVyDXroWhr1+MyZOVSnk3b17H338tcXGvycjI4Pnz\\neKKiFiGRxGNk5IujY3Vq1dJnz54gVq/2ZcgQVzQ01FEoFJQsWYrMTBlbt+6kXbtmHDgQgkQiISUl\\nmU6d2hIaejpf7Bb451y6dJeAgKeIxXKmTauDsbHx3w/6l3H27Cm8vVfi7j4Te/sqOb5bsSKU/ftB\\nIpExaFAxevSoV0RWfh359Xc5OjqKnj07s2HDVsqXr8CMGe44Ozfk8uWL1K/fgDp16tOrVxfWrFmP\\nubkFs2ZNIy0tlQULlv2VinmBlSvXkZKSTO/eXVV/CwS+DuG56t+LmZn+Px7zfTz1CAgI/OsJCtrO\\nmTPKHlKvXr3ixYsXH5XM79ChE0uWLCMkZDUWFod4/nwHL16oc/LkVZo3r1lka/in5BRvkJCc/Ol/\\nztk1NWKxRKU4mR+IRCIMDY24cOE83t4rqVevAb6+a6hVqwZnz/5BSkoKkIaGxkOyspTph2lp6iQm\\nJmBgYICFhQX6+vr8/PNUDhzYq3orL/BpNm/2o3//gYV6zpo17ahZ0w747z4QOjs3wtk5t6BNcPBF\\nFi92Ij1d2b5h5sw/cHJ6Snj4n+zfvxtbWzumT/+4Um5BolAoOHHiEi9fvqVt25oUK1as0M5tYVGK\\n8uUrAMo2J9l1sgqFgufPn1KyZCmVkEvz5q04cED5kkkkElGvXgPU1NQoVswQIyNj4uPfqGoYBQQE\\n8gdBAEVAQKDICQu7zJUrl1i71p+NGwOoUOEHMjKkH5XMr1LFkbi4ONTVnwFZZGSU/6v+53URrSB/\\n0NXVw8DAgPBwpSjF77//hpNTwafBVatWkytXLuHl5Y2NTXl8fFby4sUzdu7cybx5C3Fyqo6amg4i\\nUToAYnEGFStqqsbr6OhSsmRJrl9X2q1QKHj48AGgvFfHjoUAEBLye4Gv5Xtiy5aNRW3CN8vHhDcK\\nklu33qgcOYA3b2pw5cpD9u3bxfLla4rMkQNwd99H374V+OmndnTqdJqoqFeFdu4PXzrlfJH0YZpn\\nzmQvNbWcaqMyWf69hBIQEFAiROYEBASKnNTUFPT19dHU1OTp0yfcunXzb8e0bt2aN29GExs7HgBL\\ny2Batfp8QZBvgbzEG6ZOncXixZ6kp6dTqlTpHHVAH4zONzusrKzp3NmFSZPGoqamjqGhISVKWHD3\\n7m0mTRpH1apOqKtnUKPGTeTyGJKSMmnQoCL3798jKektkZERzJgxl+HDB5KWlka/fj1o3rwl5ctX\\n4KefJjJ79i9s27YJZ+dG/1lRBHf3ibx69ZKMDCkuLr2IiookI0OKm1tvrK1titRR+KdER0cxYcJo\\n7O0duHEjHDu7Sn+pPa4jPj6BmTOVa/HyWkJGhhRNTU3c3WdStmw5goMPcvRoCG/exJGeLqVhw8aM\\nHDmGQ4f28/jxQ8aMmQDA0aNHeP06rlDXVb26OXp6t0lOVoowmZuf486dE0RFRTJhwmiaNWtJZGQE\\njx8/IitLxsCBQ3F2bsTPP//E8OGjsbEpj5tbbxo1asqAAYNZv96HEiXMv7qWNyoqku3bbZHJygJw\\n61YvvL134OGRf/0yv7RlSNmy5YiKiiQmJhpzcwv27dvN27dvAaGhuYBAYSE4cwICAkVO7dr12Ldv\\nN337ulCmTDlVHcvHJPMBevXqye7dATRvnoGa2g4GD65QJM2cv5QPxRve79W2dq1/ruNXrPBRXQND\\nQ8McTaHzg+zm4SKRshfWxInuXL58jgMHDnL37h2aNWuJubkFbm5DmD9fKcM+ZMgIHByqMmnSWDQ1\\ntWjatAVRUREsWPAuzdLComSOptXZAhT/NdzdZ+SqCd29eyf+/gFFbdoXERkZwdy5C3F3n8Hgwf05\\ndiwEb28/zp49xebN/kyfPofVq32RSCRcunSBdetWM3fuQgAePXpA+fIV8PRcQu/eXXFx6UmzZi3Z\\nssWf//1vLBKJhJMnj6Gjo8ucOdO5f/8ulpbWTJ8+mydPnrBq1TLS0tIoVsyQadNmYmJiSkTECxYt\\n8iQxMQGxWMzcuQswMjJmypQJJCW9JStLxpAhI3B2bkR0dBSTJ49j8+YdAAQEbCE9PY2BA4fStetc\\nLl78A5FIxA8/lGXOHG+6deuAlZUNu3fvRF1dnbFjJ+LoWI2hQ12pUaM2jo5OhIdfxdzcHDU1NW7c\\nuA7A9evX+PnnqV99rTMyMsnK0nxvj4isrPxOrPr4S5aPvYARiURoamoyYcIUJkwYjZaWNjo6Oqp0\\n8Y+JuggICOQvgjMnICBQ5Kirq7N48Ypc+0NCTqk+N27cjMaNm6m2r1+/RrNmLfjll86FYmNRsXHj\\nadavTyUjQ43WrVOZPbtDgUS3atWqQ61adXLsc3auSZ8+g3Idmx0tjIl5SVKSCC+vtZia5pTyj4h4\\nyZQpfxARoY+19VuWLGmKkdG/r3fb55JXTej3woeRuHLlLDE2NmbRonnExydQpkwZKleugrv7BJ49\\ne8arVzHcunWDPXuCePjwvirCNnBgXzp27ExWVha3bt1g2LABaGpqERMTTZUqxalWrSbnzp2hXDlL\\nZDIZMTHRzJw5F3t7Bzw957B7907OnDmJp+dSDA0NOXYshHXr1uDuPoPZs3+hf383GjRoTGZmJnJ5\\nFmpq6nh6LkJHR5eEhASGD3fLs1Yuu38iwIMH5zh27CBqamqkpCQDkJSURNWq1Xjx4jlSaTrTp0+h\\nbFlLMjMzefUqBkdHJ3btCsTCoiR16zpz+fJFpNJ0oqOjKFOm7Fdf/3LlytGmzQ727y8P6GFpeZC+\\nfW2/eL4Po8QdOyr/hq5cuZSLF//E2NiU2bPnY2hoSHJyEpqaWri69srVKqVECWWdnI1NeTIyMti0\\nKZA2bZoA4ObWmz59Bqj6hgIq51lAQCB/EZw5AQGB74qQkDC8vPxITX3A2LGTi9qcAuX+/SfMm2dC\\nYqIyncrX9yWVK5+hR4+i79e2f/9Fpk1T8OqVE2XKXGDpUiMaNXqnDDh58nlCQ/sDcPu2Ak3NLXh7\\n/7sd74/xfk2opqYmo0cPIyPj402mv0Xej8S5uvYkPT1dFYlbtmwRCoUCe3sHxoyZwJgxw5kxw53B\\ng4chl2cxatRYVq1azpo16zl69AgODo7IZFksXLiMSZPGqWqwOnT4kc2b/ShXzoqmTZuTnJyMvb0D\\nAK1atWXTJj8eP37EuHEjAaVkvomJGampqbx+HUeDBo0B5cshUEcmk+Hjs4rw8GuIxSLi4mKJj3+T\\n5/qyUwJtbCowa9Y0GjZsrJovI0NKUNB2VSqhiYkpHh6eqp6WMpmMu3fvULJkaWrWrE1iYgL79+/F\\n1rZivlx7kUiEj48L9euHkpAg48cfq2BlVeqL5/swSty4cVPS09Ows6vE6NHj2bhxPf7+6xg3bhJz\\n585k/PjJebZKyXaAjxwJJjb2FQMG9MbGpgImJuaEh9di/Hh1rKz2sHx57a+yV0BA4NMIAigCAgLf\\nDdevP2DcOHUuXdrErVt/MHu2Nk+fRhW1WQXG7dsvSEx811cqK6sET56kFqFF7/D2juPVq+aAKS9e\\ntGP16pyRpogIvfe2RB9sf3u0aNGgwOb+WE2ompoaMpmswM6bn1hYlMLa2gaRSETp0mVVzeKtrcuT\\nmprKs2dPadWqLQBaWlpkZEjR1zegShVHli1bREpKMklJbxGLcz92ZDtSlSrZ8+rVK0JDf6d+/Zz1\\nlQqFAl1dXaysbPD3D8DfP4BNmwJZunQlH4puZBMScpjExAT8/Lbi7x+AkZExUmkGEokEufzdGKk0\\nXfV50aLldOniwr17dxkypL/K0Zw2bTbdu/emRo1a7Np1kLJlLbl//y6gvI9mZsU5ceIo9vYOODg4\\nERi4lapVnb7iiudE2buuOWPHtv5qxygoaDsDBvRm2LCBqiixWCymWTNlFK1lyzZcv36NlJRkkpOT\\ncXRUrqN163Zcu3Y113w//tgVM7PibN26k44dO3P5cjwXL/YjJqYj58+7MmvW5a+yV0BA4NMIzpyA\\ngMB3w4kTj4iNfdf3KSKiOcePXy9CiwqW+vUrY2l5QrVtaHiVevW+jTfcUqlGju2MDPUc21ZWibx7\\nyM7C2jq5cAz7YgquuKd27XpkZWXRt68La9euVtWEduzYmQEDeuHhMb3Azp1f5FQ0FKscLZFI9Jcz\\nJlI5ZWKxGB0dHfz81nH8eCj16jVAoYARIwbx5s1rPrzW7zttTZs2x8GhKrq6urx8GcPNmzcACA39\\nncqV7UlIiFftk8lkPHnyGB0dXczMinPmzEkAMjIykErTSUlJwcjIGIlEQljYZWJiogEwNjYhIeEN\\nb98mkpGRwR9/nFWt4+XLGKpVq8GIEaNJTk4mLS0NTU1NDh7cw4ABg5HJZPTs2Zl+/bqzYcNald1V\\nq1bDyMgYDQ0NHB2rEhcXq3KCYmKiCQ39tJJrXi8TgoMPsmyZss4wOTmZvXt3fXKOz+FjysHwzqlW\\nKBR/m8otkUhQKOQAuaLMaWk5k75iY3W+2m4BAYGPI6RZCggIfDfY2BRDQyOCjIzSAOjo3KNixZJF\\nbFXBYWZmwurV5nh77yAzU0Lnzvo0bPhtNDFu1SqD+/ejyMwsibb2I9q2zdkIeMmSpmhobCEqSg8r\\nqyQ8PVsXmC1paWnMmDGF2NhY5PIsXF0H4+Ozkg0btmBgUIy7d2+zerUXK1euJTU1leXLF3Hv3h1A\\nxMCBQ2nUSFnns27dGv744yyampr8+usSjIzyp6H2x2pCnZyqM2LE6Hw5R2Gio6PD6NHjVNvFixen\\nevVahIQcZsCAwfz000RWrVqOn99WIiMjKFWqNJMmTeWXXyZjaWlFzZp1WLlyKUCunoTXr4fTs2cf\\nRCIRZcuW1bCnbgAAIABJREFUY+/enfz66xwsLa3p1q0ntWrVxctrMcnJyWRlyejRozdWVtZMnz6H\\nRYvms379WtTU1Jg7dwEtW7Zm8uTxuLr2xNa2IuXKKdsOqKmpMWDAYIYMccXMrDiWlsr9WVlZeHjM\\nICUlGYVCgYtLT/T09Ni//wgrVixh6FBX5HI55cpZ5hD5ARg8eDiDBw8HwNTUjNOnL6q+i4qKJDT0\\nCC1a5P4dkMlkqKmpkdfLhPcdqqSkt+zdG0Tnzt0++z5lO2fvz/OxKLFcLufkyWM0a9aS0NDfcXBw\\nQldXD319ZasUR8eqOVqlWFiU5O7d29jZVeLkyWOq+XV1dTE0TAYyAXUgHXv7b/1FjoDA943gzAkI\\nCHw3tG9fl+HDD7FvnxZisZw+fUTUrduiqM0qUN5v8vwtMWVKW6ysznD//jmcnIxp375pju9NTIxY\\nt65wauQuXPgDU9PiLFrkBUBKSjI+PivzPHbjxvXo6+urlESTkpTKe+npadjbOzB06EjWrFnBgQN7\\ncXXNLf7yNWzbdpZt21IA6NlTm/79i7728XPIysrKs43G+5/d3Ibg6TkHV9deaGtr88svswBlSl9Y\\n2GVEIjHW1jbUqVMfyE4b7E2bNu24eVOfK1dEpKWtompVW6pVqwHAtm25I1EVKvzAqlXrcu0vXboM\\nXl7eufa/r6T6Pt269aRbt5659q9Zsz7XvqNHQ7h58wYikRhbW1sGDx7OmDHDSUxMxNDQiKlTZxAb\\nm8qYMXNJTdVHR+cpRkZyRo8eS+PGzfDxWcXz509xc+tNmzbt0dc34OTJY6SnpyOXy5k3bxFSqRRX\\n115oaGggEomQyWTEx79RNev28VlJZGQEbm69qVmzDiNHjiEgYDMnThwlIyOThg0bM2jQMKKjoxg/\\nfhTVq1cjPPw6ixevoESJdyq/H1MO1tLS5vbtW2zatAEjIxPmzJkPwLRpebdK6dWrL9Onu3PgwF7q\\n1nUm2xl1cqqBmZk/1ao1R0enCY6Otkyd2j7PeyAgIJA/iBTfSCOQ2NikojZBoIAwM9MX7u+/mKK4\\nv3m9cRbIf76X390XL54zfvwomjZtQb16DXB0rIqLS8c8I3ODBvVjzhxPSpUqnWOOpk3rcfz4HwAc\\nOxbK5csXmDz5l3yz8fLlO/Tpk4lc/ozExN4YGFxn1qzLXL/+R67o1NcQHR3FxIljcHBw4ubNcMzM\\niuPpuYS4uFiWLl1IQkI8WlpaTJ48jerVq7B3729s3uyHTJaJgUExZs6ci5GRMRs2rCUqKoKoqCjM\\nzS2YOXNuvtn4PgsWBLNkSTtAWVNZqVJbDh1aj56efoGc75/w9u1bBg7cSlTUAbS1BzJzZmWqVi3D\\n3Lkzadq0Oa1bt+O33w5w9uxp7txpwKNHNxGL04mOXkaHDstJSfmNwMC9XL16he3bt6ruc3DwQdav\\n92HTpkD09fVZtmwh+/fv5eTJ81y6dIFVq5axaVMgu3btwNfXmyNHThITE82kSWNVipAXL/7JyZPH\\nmDRpGnK5nClTJtCnT3+KFy9Bjx6d2LFjBxYWVp9aXr6hUCh4/Pgx6urqlC379eqdAn/P9/K3WeCf\\nY2b2z//2CTVzAgIC3x3vK6kJCJQpUxY/v23Y2JTH13cN/v6+OUQupNKMHMfn9Q5TInmXqCIWi1TC\\nF/nFlStPSUoqh6HhdgDevnXg/v3Yr5rzYzZGRLyga9fubNmyEz09fU6dOs7ChfMZN+5nNmzYwsiR\\nP7FkyQIAHB2dWLduI35+22jWrCXbtm1WzfPs2TO8vLy/2pGLjX3N//63j+7dQ/DwOJTD7ocP1VA6\\ncgpAQUzMWESib+PRZM6cE1y/bkF8vAs3bgxi1qzHGBgYcPv2DVXKZKtWbblx4xoxMXqAiOTk5oCI\\nxMSyvHmjVM788OdNJBJRo0Yt9PWVD203boQjkSjTlJ2cqhMVFUm/fj3Yvn0z6elpxMe/yTXHxYt/\\ncunSBdzcejNoUF+eP39GRIRShKhECQscHBwK8Mq8IysriyFDdtCggQRn5zR+/nmP0CxcQKCQEdIs\\nBQQEBAS+a+Li4tDX16dlyzbo6upx6NB+VU1PnTr1OHXqXU1PzZq12bNnJ2PGTACUaZbZD9X5TWDg\\nVoKDDwJQtWpdSpYMRl39OWXLdiIrqwJ2dpU5fz6cX36ZzJMnj7C1rciMGR4A3L17J8/m2KNGDeWH\\nH2y5fj2c5s1bUry4ORs3+iIWS9DT02PatFlYWJRSpefZ2toRHR3FzZvhTJ/+rpVHZqZSRfPVq5fM\\nmDGFN29ek5mZScmSSoEdkUiEs3NDNDRyCt38HT4+qyhevARdurgAsGHDWnbvvkd0dCYSyVuePJES\\nF3cPL68JREdH8ezZEkqUOI+W1l0iI9dhZvYLMtnuXNevfftOdO/e65MNv4OCAtm/fw8SiQRLSytm\\nz57/Rfctm7g4bSCNbCGf2Fh9lfrohw6Lre0bwsIUKBTqQBqVKkk5f/7jTo22tnae+0NCDiOXK1iz\\nZj1nzpxk+fLFuV5GZNO37wB+/LELAIcPHyIwcBsBAVtISkokMjKSiRMn5UgFLVHCnHnzZqGpqcWD\\nB/eIj3/DlCnTCQ4+yN27t6lUyV6VRtmiRQM6d+7G+fPnMDExZfDgEfj4rOTVq5eMGTMBZ+eGSKVS\\nhg8fw82biZQsGUxs7BS2bWuKufkKXr+OQCqVEhkZQcOGjRk5csw/uPICAgL/BMGZExAQEBD4rnn8\\n+CGrV3shFotQU1Nn4kR30tPT+fXXOaxfr4eTU3VVJNfVdRBLly6gf/8eiMUSBg4cSsOGjXPVgH0t\\nd+/e4fDhQ/j6bkIuVzB0qCuurl0JCnqMsXFfevXSwc5Om82b77F1axAmJqaMGDGI69evUamSPcuX\\nL2LBgqUUK5azOXZ2PdX69Zv/Wk9Pli5djampKSkpybx9+/YD5UkJb9++QU9PH3//gFx2Llu2kF69\\n+lG/fgOuXr2Cn9+7ejRNTa1/vO5mzVrg5bVE5cydOHGUmJheREV1RaHQQyx+Q3h4e0DpTKenx1On\\njgkPHw6iXr2TZGYqa8byun5OTtVypV++H6Xftm0Tu3blbPj9NTg5wbFjtlhYbCE+fgCVKsWSmpqC\\nvb0Dx46F0KpVW0JCDuPo6MTkya3o1+8wCoU2Tk5vmD69DW3aLAJAR0eX1NQU1bwfOoIODk48fvwY\\ngHv37qCtrY2+vj7Pnj1RjdPR0SE19V1bktq16+Dr60PLlm2Ijo7C39+XxYtXoK6uzsSJY/Dw8KBt\\n2w6qVNDlyxfj6bkYgOTkJNau9efs2VNMmTIBHx8/rKysGTy4Pw8fPqB8+Qqkp6dTvXotRo78ialT\\nf2bDBh+8vLx58uQx8+bNxNm5IXv2BJGZCc+e/Ya6+mNKlx7E06cHSExM4+HD+2zcGICamjq9e3fF\\nxaUnZmbFv/qeCAgI5EZw5gQEBAQEvmtq1apDrVp1cu3fvn1Prn3a2tpMmzYr1/6QkFOqz40bN6Nx\\n42ZfZdP169do2LCJyiFq1KgpxYopsLTUY/PmVoBSJr5ixcqYmpoBUL78D8TERKOnp8eTJ48YOzZn\\nc+xssvuBAVSp4si8eTNp2rSFSpXzQ3R1dSlZshQnThylSZPmKBQKHj16iJlZNVJTU1TnP3z4kGrM\\nl6bKVahgS0JCPHFxccTHv0Ff34DixUVkZCxFW/syCoUYufytqnl3iRIW+PqOUo13cfFFoVDkef3C\\nw6/i7Nwo1zk/1fD7axgzpgUKRSinTtWgWLEOaGgYsGrVbcaOnYSn52wCArZgZGTE1KkzMTAwoHbt\\nctSvX5VGjZRiQNlOZvnyFVSCL23bKgVQ3n9hMHDgUPbuDcLVtRfq6uqYmprh6tpTpSYJUKyYIVWq\\nONK/fw/q1KnPyJFjePr0KcOHu5GYmIBIJP5LFVOp1nnt2jVmzfoVUKaCenuvUNlUv76yDYKVlQ3G\\nxiZYW9v8tW1NTEwU5ctXQF1dndq16/51XcujoaGBRCLB2tqG6Ghli4cbN8Lp3bszDx4c4MmTjmRm\\nlqRixXU4Olqjq5uFjo4uAJaWVkRHRwnOnIBAASE4cwICAgIC/1liY18zd+5Z3r7Vpm5dNYYObfr3\\ngz6DvKJ7eQX81NXfpTFKJGJVPZmVlc1HlRi1tN6l6E2c6M7t2zc5f/4cgwb1Y/78RXkqT86Y4cHi\\nxb+yaZMfMpmM5s1bUrduNQYOHMr06ZPR1zegevUaql5syojXP142AE2aNOfkyaO8fv2a5s1b8urV\\na3buvEZKygAqVUokOXmjKnVQWzvv6N+Ha8juffZ3Db+vXQvj3LkzbN7sx6ZNgapatC9BJBIxdmxL\\nxo5tCUzJ8V1eypnZKYrZZL8gUFNTy3V8mzbvFB4NDAw4derC39rzYe2ii0tPXFx6snv3Dl6/fq1K\\nkd20KZAOHVp81CFXV1dGbsVica7+gdk/f+/XkIpEyoj3h8cAmJubsmmTKVu37uTatVimT6/Kmzev\\nckWH5XL5365PQEDgy/g2qowFBAQEBAQKGYVCwZAhx9i+vQ+//daV2bOr4e9/6u8HfgaOjlU5ffok\\nUmk6aWlpnD59gipVquZIlfsYZcta5tkcOy8iIyOoVMmeQYOGYWhoiEgkVrVdAKWEvJvbECwsSrJk\\nyQo2bgxg69adDBgwGABn50bs3LlfJYyyYoUPoIwW9ezZ94vW3rRpC44eDeHkyWM0adIcU1M9fvzR\\nnosXW/DTT5a8evXyk+NFIlGu63fmzEkcHJwwMjL+7Ibf6elpX2R/UbBo0WFatTpKx46HCQm5+o/G\\nlitXnoCAfdSseZDOnfcRFnYLJycnjh0LAVClguY3jo5VCQk5jJ2dJUOHVkJLK4Pq1avl6UQKoigC\\nAgWHEJkTEBAQEPhPkpAQz61b5cnukZWZWYZLly7h5vb1c//wgx1t27ZnyBBXADp06IytrV2OVLm6\\ndevnGf1SU1PDw2NBns2xP2TNGi8iIl6gUCioUaOWSvjkSzh37jaHDr1ASyuT8eMbfbEwjJWVNWlp\\nqRQvXgJjY5OPNu+GvCKYyu28rl+FCj8AfHbDb11dvS+yv7AJCjrH8uX1yMxUtst4/vww1au/xsTE\\n5LPGr137ghcvJmFs7MeLF2ImT9YkOHg5EydOypEKms3n1Ifmju7m/q5zZxcWL/bE1bUnEomEadNm\\noaamlqfasKA+LCBQcAh95gQKHKEfyr8b4f7+e/m331uZTEaDBkd59Mglew/DhgXh4fHfaHL8/v09\\nf/4OgwdnEBvrDMipV8+PoKAuqpS8r6Vbtw74+W3FwKAYLVo0IDT0TL7M+29g1qzfWbPG5b09L9m1\\n6xYNG9b8rPEdO4by559dVNt2dnu5c6fzv/p397/Ov/1v83+ZL+kzJ0TmBAQEBAT+k6ipqTFzZgnm\\nzw8kPl4PJ6eXuLt/H45cbOxrAgMvoqkpxtW1MZqaml8136FDz4iNzXYoxJw/35CHDx9TsaLt1xuL\\nMjKTlpaOh8cM0tPT6d+/B66ugylWrBhr1niRlZWFnV0lJk50R11dnW7dOtCiRWv+/PMcYrGESZOm\\n4eOzkqioSHr16kenTl0BCAjYTGjoEaKiEjAwqMygQT1o3bp6vthcWDg4GKCp+RypVNlwu3TpK9jb\\nV/rs8XZ2Kfz5ZwagAcixs3tbMIZ+BgqFgt9/P8+rV0l06FALY2OjIrNFQOC/guDMCQgICAj8Z2nd\\n2olWraoik8nyLQpV0Lx69Zru3c9y+3YfIJPQUH8CAly+yn59/SxARvZjgZ7eS4yMSnzRXO7uE3n1\\n6iUZGVJcXHrRsWNnAK5cuYipaXG0tLTZvHkHycnJ9O/fgxUrfChdugxz585k795ddO/eC5FIRIkS\\n5vj7B7By5VLmz5+Fj48/UqmU/v170KlTVy5e/JMXL54TF9eJq1ddKVnyf0yYEIWamoTmzat+8bUo\\nbLp0qcfz5yGEhl5BSyuT//2vJMbGn5diCTB3blvU1IJ4+FCLUqVS8fBo+feDCoiJE/cQENCKrCwz\\n/P2D2LatJqVKfdnPkYCAwOchOHMCAgICAv9pRCLRd+PIAWzefPEvR04EaHDqVDeOHbtE69b1vnjO\\nMWOacOWKP+fO1UVXN44RI15jbv5lDpG7+wwMDAyQStMZMsSVxo2VCqFWVtb4+/uSmZlBePg1dHR0\\nKFmyFKVLlwGUCo979uyke/deAKo2BNbW5UlLS0NbWxttbW3U1dVJTk7m4sU/OX/+HDEx4ZQtewCx\\nOI3k5BaEhr6kefMvvhRFglI188vGamhoMH9+x/w16At4/vw5QUH2ZGVZAHD7di98fALx8GhXxJYJ\\nCPy7EZw5AQEBgSIkLS2NGTOmEBsbi1yehavrYEqVKs2qVctIS0ujWDFDpk2biYmJKZGRESxdupCE\\nhHi0tLSYPHkaZctaFvUSBAoZZTsxOZAtuy9FQ+PLJfhB2ZQ6MNCFp0+fYmBQFjOzL09VDArazpkz\\nSlXQV69e8eLFCwBKlSqNn982fvyxFb6+a6hePWdNWHb7gWyy5e3FYnEOZ1spjy8DoHv33nh4/MDr\\n19neWybFiu36YtsFvpysrCyysnK+FJHLBeETAYGCRmhNICAgIFCEXLjwB6amxdm4MYDNm3dQp05d\\nvLwWMW/eQjZs2EK7dh1Yt24NAAsXzmPcuJ9VMvJLliwoYusFioLBgxtQs+ZGIB14TYcOh2jc+PPE\\nMj6FRCLBxsYGMzOzvz/4I4SFXebKlUusXevPxo0BVKjwAxkZUgBev37zV/NpNXr16sfNmzeIiYkm\\nMjICgCNHgqlatVquOfPSaROJRNSuXYeTJ48xalQC5uaH0NcPpnHjVYwb93UN3wW+DEtLS9q3vwQo\\na/ZsbPbRv3/lojVKQOA/gBCZExAQEChCbGwqsHq1F97eK6lXrwH6+no8fvyIsWNHAiCXyzExMSMt\\nLY0bN64zffpk1djMTFlRmS1QhOjp6REU1J4DB35HT0+Ttm17IBZ//N1sXjVsLVo0oHfv3hw/fgIT\\nE1MGDx6Bj89KXr16yZgxE3B2bohUKmXJkl+5d+8OEomEUaPGUa1aDYKDD3L27GmkUimRkRE0bNiY\\nkSPHAHDq1HHu37/HqFFDKVHCnPDwdz3Tnj59zKxZU0lPT2PjxvVMnOhOcnIS06dPJisri4oVK9Op\\nU7e/js4pn59T2l75uWbNOjx9+pRDh/xxdJSjqanF7Nnz0dbWJjk5mdDQ3+ncWTlfWNhlAgO3sXDh\\nsvy5CQK5EIlEeHu70KDBceLjM+jUyYkyZcyL2iwBgX89gjMnICAgUISUKVMWP79tnD9/Fl/fNVSr\\nVgMrKxt8fPxyHJeSkoy+vj7+/gFFZKnAt4SOjg49e346AhUUFMj+/buxtrZhw4YtOWrY0tPTqVu3\\nLm5uI5g69Wc2bPDBy8ubJ08eM2/eTJydG7JnTxBisbIJ+fPnTxk3bhTbt+8B4OHD+2zcGICamjq9\\ne3fFxaUnIpGIc+fOYG9fhdjYV4SFXcbExPQva0RUr16DJk2a0bJlI3x9N6ns9PPbloft+1Wf27Rp\\nT5s27fP8zsWlJy4uPXONT0p6y969QSpn7mvJyspCIvm6VNb85syZk5QpU07Vay8/+Nq2EWKxmL59\\nhciogEBhIjhzAgICAkVIXFwc+vr6tGzZBl1dPfbt20VCQgI3b97A3r4KMpmMFy+eY2VlTcmSJTlx\\n4ihNmjRHoVDw6NHDr2oSLfDvZt++XXh5ebN//x4GDOgNvKthU1dXp0GDBsTGJmFjU/6v9EcJ1tY2\\nREdHA3DjRjjduvUAoGxZS8zNLXjx4jkikYjq1Wuho6MLgKWlFdHRUSQkJODkVJ1p02YBsGtXIC9e\\nPMfJqXoOBywk5NRXr00mk7F//1mkUhldujizb98ugoMPAtC+fSdu3bpBZGQEbm69qVmzNnXrOpOW\\nlsovv0zmyZNH2NpWZMYMDwDu3r2TZ43qqFFD+eEHW65fD6dFi1b06NHnq+3OT06fPkn9+g3+kTMn\\nk8lQU/vUo59Q4yYg8L0hOHMCAgICRcjjxw9ZvdoLsViEmpo6Eye6IxaL8fJaTHJyMllZMnr06I2V\\nlTUzZsxl8eJf2bTJD5lMRvPmLQVnTgCAwMCtOZyZ58+fEhUVyciRgwARW7bsRFNTk9Gjh5GRIUUi\\neffvXyRS/uxBtrhI1t+eL1ucRDlGQlZWFqIP/ACZLIuwsGdMmHCEOnUMcHGp+/ULRRklGzBgJyEh\\nvQF1tm1bhLHxH6xfvxm5XMHQoa7MmOHBkyePVJHssLDLPHhwj61bgzAxMWXEiEFcv36NSpXsWb58\\nEQsWLKVYMUOOHQth3bo1uLvPQCQSIZPJWL9+c77Y/XdER0cxceIYHBycuHkzHDOz4nh6LuHIkWAO\\nHtxLZqaM0qVLM336HO7fv8e5c2e4du0qmzf74eGxAE/POYwaNQ47u4q8efMGF5euBAUdIDj4IKdO\\nHSc9PR25XM7ChcuZMmUCSUlvycqSMWTICJVyqICAwPeH4MwJCAgIFCG1atWhVq06ufavWrUux3Zc\\nXBxZWVksXuz1Qf2QQF6MGjWU0aPHY2trR7duHfDz24qBQbGiNqtAuHv3DocPH8LXd1MOZ+bChfMM\\nHjyCY8dC0NTU5OnTJ9y6dfOz53V0rEpIyGGqVavB8+fPePkyhnLlLLl3706uY0UiERUrVmbFiqUk\\nJSWhra2Nn18QkZENiI3tRlDQY5KTT+Hm9vVOw8GD5wgJ6QnoA/DgQWmaNi2HpqYWAI0aNeXatau5\\nxlWsWBlTU6W4S/nyPxATE42enh5PnuSuUc2mWbPC7dkWEfGC2bM9mTx5GjNmuHPq1HEaN26q6tXn\\n6+vNoUP76dq1B87ODalfvwGNGilbP+SuLXzHgwf32bQpEH19fbKysvD0XISOji4JCQkMH+4mOHMC\\nAt8xgjMnICAg8I0zb95vbNxojlRqQNOmO/D17fpd9UUrCt5/qM0v51cul39SaKSouH79Gg0bNsnT\\nmalWrQYhIYfp29eFMmXKYW9fBch9Td7fzP6uc2cXFi/2xNW1JxKJhGnTZqGmpvZRp8HU1Ix+/dwY\\nMsQVAwMDEhNLIJcrHej0dGtOnLiKm9vXr1cmyyLn44sYuTyn4mVet1xdXUP1WSJ5F4HMq0Y1Gy0t\\n7a819x9hYVFKFW23tbUjOjqKR48e4uvrTUpKMqmpadSu/S7CmZfSZ17UrFkbfX191Rgfn1WEh19D\\nLBYRFxdLfPwbjIyM839BAgICBY7gzAkICAh8w9y8eZ+1ayuRnu4AQHBwJdatO8j//te6iC0rHAIC\\nNqOhoUG3bj1ZsWIJjx49xMvLmytXLvHbbwdo06YdGzasIyMjg1KlSjN16ky0tXM/gO/atQMDA4N/\\nPE+3bh1o1qwlly5doE+f/ujrG+Dn9/fnK0zycqyyd2loaLB48Ypc379ftzZw4NA8v9PQ0GDq1Jm5\\nxn4oSPK+QmSLFq3p2LEzmZmZNGkygPT0Kqrv9PTSP3NFn6Zjx/oEBGzj7Fk3QIK5eRCJiWlIpenI\\n5QpOnz7BtGmzCQzMLawCykjmtWtXsbOrxK1bN3ny5HGeNapFQe70VSnz58/h11+XYGNTnsOHD3H1\\n6hXVMe/fe4lEgkIhByAjIyPHvFpaWqrPISGHSUxMwM9vKxKJhLZtmzJixCAqV7YH4KefRpKUlEif\\nPgO4fPkCPXr0+Whd3tmzp3n69DF9+w746JqCgw9y794dxo2b9PkXQkBA4LP59l4xCggICAioiI5+\\nQ3p6qff2aJGYWDDnGjFiYMFM/BFiYqIJDf39k8c4OlYjPPwaoHwIT0tLQyaTER5+FRub8ixa9CsL\\nFizDz28rtrZ27NiR9wO8vb3DJ+fZtMmP5cvX5JpHJBJRrJghfn5bqV69Fps3++Hllfu4osTRsSqn\\nT59EKk0nLS2N06dP4OjoVGjnT0tLY8KEfXTpEkrfvj/j6tqTAQN6Ua1aSYyMUlBTu07VqluYNKlG\\nvpxPQ0ODgIDOzJ27n5kzd3HwoA9durgwZIgrw4YNoEOHztja2lGliiP9+/dgzZoVf0UTlePt7CpS\\ntary+qipqVGnTj18fFYyYEBv3Nx6c+vW9Y+eWyYr/HYgaWmpGBubIJPJOHIkWLVfR0eHlJQU1baF\\nRUnu3r0NwO+/f/z3KiUlBSMjYyQSCWFhl3n79i0zZ85l+nQP5HI5IpFSYbRZsxZMnvzLJwVWnJ0b\\nftKRg/yLjAsICOSNEJkTEBAQ+IapV68Kjo6HCA/vD4iwsDhOu3Y2+Tb/+/23vL3zTjUrCGQyGVFR\\nkYSGHqFFi49HGW1t7bh37w6pqSloaGhgZ1eRu3fvcP36NZydGxITE8Xo0UORSCRkZsqoUsUhz3nK\\nl6/wyXmePn2scmY/nKdZsxYA3Lp1g6dPHzN8eN7HFRU//GBH27btGTLEFYAOHTpToYJtoZ1/8uTD\\nBAb2QflI8SOdO29h7dquACQnJxMXF0upUu3yJTU4LS2NGTOmEBsbi1yehavrYKZN+5nRo8fTo0cf\\nWrRoQGzsS/r1646JiSmTJv2Cj89KTp48xpgxEwDlz3x0dDTjxk0iOPgghoaGzJ49n7NnT7N5sx97\\n9gRx9GgIc+Z4YmRkzIYNa4mKiiAqKgpzcwtmzpz71ev4GHk5PoMHD2Po0AEYGhpSubI9qampgLKe\\nb8GCeezatYO5cxfQq1dfpk9358CBvTRr1pRsZcr302IDA7dy8OA+YmKiOXHiGLq6SkVSD48ZtGvX\\nkYwMKXfv3mbgwD65RFX+/PMP1q1bg1wux9DQkOXL1+SIumVfP5ksEwODYsycOVdI3RQQKAQEZ05A\\nQEDgG0ZXV5etWxuyYkUgmZnquLiUw9GxYBQss3tMhYVdxs9vHSYmRty5c5cmTZpjZWXN7t07yMjI\\nYP78xZQqVZp582ahoaHBvXt3SUlJZvTo8dSr5/zJZtPZqnpZWVlkZmby7NkT3Nx606ZNBxo2bIyH\\nxwwN72x0AAAgAElEQVTS0tIAGD9+Evb2Dujp6ePm1gd1dXWePXtCWNhlUlJSePz4EQqFApFIhIFB\\nMby8vD+6NjU1NSwsShEcfJAqVRyxsSlPWNglIiMjsLAoRY0atZk1a16eY99Po/zUcUVJjx596NGj\\nDzKZjPj4eORyOUFBBwrl3A8e6PPucULCgwfvhGb09PTQ09NTbW/YsBYdHV1SU1NwdHSiRo1ahIdf\\nZdEiTzQ01PH29mP9eh/+/PMcdes6q5qRZ3Phwh+YmhZn0SIvQNl/cd++Xarv09PTqV69FiNH/vTR\\n/nkAWVky9u7dhaampmqso6MT7u7zWL78KlFR4cyZs4BlyxYA8OzZM9asWY+GhgYFhYVFSTZtClRt\\n9+rVV/X5XTP1d1Sp4sjWrTtz7Nu0aTsAZmb69OkzCHiXFpstlOPntzWHUM7UqT/j4+OHgUExKlWy\\nZ/v2rarU2WxHMD4+noUL57FmzXrMzS1ISkpSfZ+No6MT69ZtBODgwX1s27aZUaPGfnZdX0GQ/fP2\\n/rV8n4Lo1ScgUNgIzpyAgIBAEfO5kuQeHnPQ1NRi3rxZaGpq8eDBPeLj3zBlynSCgw9y9+5tKlWy\\nV9U5Xbz4Z571XX/++QcrVy5FU1MLB4eq71ny7sHs4cMHrF79OxkZYlxcOtKhQ6f/s3eWAVFlbxx+\\nhu6yQLBAKSXEVuze1V1XxVpFReVv69qFhaBgIq4oJuiCK3Z3d6CirphgEAoiHQIz/w8jIwgoKpj3\\n+TQz99xzzo2B+877nt+PVav8CQraxNat/8qyHM+fR7N6tT/Pnj1l5MjBbNq0/b1m07lV9a5du5rn\\nwTEjI53Fi/9GSUmJp0+fMGvWNFav9sfEpCoHDuxl1ix3bG3t6Ny5AzVqWOHsPIw9e3YxZcoMzMws\\nSEtLIzY2hgoVKhZ4nm1sbAkM3MiUKTMwNjZh6dJFWFhYUr26FYsWeRAR8QxDQ6NC+7G0rFGkdl+L\\n8+dDmTjxAc+eVcLE5Aze3jUxN69c4uMaGCQDEnLuH+n7gsl5+B8w4H+yzw4d2o+jY3/atGkPwO7d\\n29m//3iBWSoTk2r8/bcXPj7eNGzYGBsb2zzBgqKiokwgpDD/PJBmhrdvD6JHj7cP+U+ehDN4sCvp\\n6QqIRJmEhWlx4kQIIpEIe/sm+QK5qKhIJk78C3//f4t4pr4uuYVyrl69R1xcBUaODEQsTpO1KSjw\\nkkgk3L59E1tbO/T1DQBkYiq5efHiOdOnTyIu7iWZmZmUL2+Yr82X5kMlnp/i1Scg8K0hBHMCAgIC\\n3wAfI0kuEolITk5i5cp1nDlzkkmTxrJixVqqVDFm4EBH7t+/R5kyZWXru5SVVdi4cT3//vsPPXv2\\nwdPTDW/vlRgaGjF9+uQClf8sLCwpXbo0MTFJGBlVkD0gGxubEBx8BZA+KLVoIS1BNDKqQPnyhjx+\\nHP5es+natevmUdXLTWZmFosXe/DgwX3k5OR49uwpIH2AB2jUqDHKyiooKytTrlw5dHR00NHRwcNj\\nDtnZUuEHZ+eh7wnmarJhwzpq1LCS9WNjUxMdHR2mTp3JzJlTeP06s9B+dHV1i9TuazF37gNCQ3sC\\ncONGQ9zd/8Hfv3KJj+vu3pj09A2EhWlRsWIi7u55rTb8/NZw4MBedHX1KFu2HGZmFri7z6JhQ3uS\\nk5M4fvwoly5d5MKFc6SmppCWloaT05/07t0fO7vaLFw4l+fPowEYOXIsa9f+w9y5s5k2bQKKiopk\\nZmaSlJTEtGkTyMrKYtAgR0aOHItIJCI4+ApPnz4hKiqSlJRktmzZhLFxVR4/DiMpKYk1a1agra0D\\ngIfHXJ49G0BKSldUVS9RqpQ3x49HUqkSMqXQ75mcwCY5OZmRI8OJi6tOdrYeenqX2bnzEn36tP7g\\nvu9j8WJPevbsQ6NGjbl27Spr1/p+cJ+SoKD7bffuHezate2DXn1Xr17O5+n3I1x7gR8bIZgTEBAQ\\n+Ab4WEnyRo0aA1JZdT29Uhgbm7x5b0x0dCQvXjwvcH3XkyePKV/eEENDIwDatGnPrl3b880nt4y7\\nSCSSvReJRO81lf7QQ9/7lB///fcfSpUqjYuLK9nZ2bRo0RAAU1MzGjSwR0lJGYlEQrt2v2BubgmA\\nsrIyS5Ysz+ch5+29UvY6p9ywVq06HD9+XvZ5TrYQpBL+q1blN4d+t1SxsHbfAvHxKu+8/zIqm/r6\\npQkI+KPAbaGhdzh27DDr1weSnZ2Fk1NvzMwsAOm90qFDJ0JCbuTxS2vduonM7HvmzKl069YLa2tb\\noqOjGT16CH5+mzAxqcqjRw+oVKkKqakpBAT406/fAC5evICrqyfjxo2Q/dDw9OkTvL1X0qqVPUuX\\nLqJy5SpkZ4tRV9egQ4dObN8eRN++PYmOjkBV9RUpKaCjsw5l5dtcvfqE+/dVaNOmnex45s6djUgk\\nom7deiV9aosVGxtb3NxmYW1dlwcPLKlYcT3R0Z5IJOsICUkqdD+RSET16lYsXDiPqKhIDAzKk5iY\\ngJaWdp4fZFJTU2Q+fvv37ynx4ymIgu43c3MLmjZtTseOnYD3e/VpamoW+AOagMC3jBDMCQgI/NTk\\nrBOLjY1hyZIFzJnjUaT2xc3HSpLniEnIycm9s6/UP0tOTr7A9V337997Z+RPX88ikUg4fvwI7dt3\\nIDIygsjICCpVqpzHbNrHx5vbt28yc+YUTEyq8fTpY7ZtC6JzZwfU1TV49OgBgYEb6dmzN8HBl4mK\\niuLixQsYGBggFouJiorE1XU6AI6O3fPJ7Oco+mlpaRMcfI9Fi+6TlqZEixYwbFjhmYZPOdbNm0/w\\n8mU6v/1mh5FRuWLru7ioUyeR0NBUQA2RKI569TK+9pQICbn2prRPGVCmUaMmBbYrbF3VlSuXePw4\\nTPY+KSmJQYMciY+PR15env79B/H330v4779bLF7sSUZGOpMnjyE1NZWsrCxEIhENG9pz//5dsrPF\\nVKxYkV69+jJ/vhvq6hrs3r0DKytb3Nw8mTFjMqdOLUNHJxBIQE1NnZ07dzJq1BAuX75Iv34DmTt3\\nFmPGTMLGxpbly71K4IyVHDlCOUuWuGFikk5s7AAyMiwAMZUrS/+G5Fb9zI2Ojg4TJkxl6tTxiMUS\\n9PT0WLRoWR5xFScnZ1xcJqKpqUWtWrWJjo7K1eeXUbQs6H6TSCiyV1/+dvULGEVA4NtCCOYEBAR+\\ncqQPGaVLl/lgIJe7/ZfgXUnysmWLFkDk/JJe0PquSpUqExUVKfv88OGDefZ7+7rwvnO2iUQiypXT\\nZ9CgvqSkJDN+/GQUFRVlZtPdu/9BTMwLPD0XY2VlS8+enbG1rcmxY4fp3NkBE5OqxMe/Yu/eXURE\\nPKVUqTIkJ6cgEkFY2COZOEVsbAxWVjYsX74633x+++0Pxo4dgZ5eKa5d68L9+z0AuHTpMWXLnsPB\\noWGRztn7kEgkjBgRRFBQZyQSHTZs2Mn69emYmVX67L6LEw+P3yhXbg+PH8tjYSFi2LBfv/aU+Pzv\\niwRfX798Sphr1/qiqqqGubkF3t4r6dChVaHtFBQUuXnzBv37D+TEiaPY2trRunU7zp49hUgkws3N\\nEwBn52E8ffqUJUv+pl+/XmzbtheAyZOn4+IyieTkZJKTk7Gxka4zbdv2Vy5cOPeZx/dlyRHK2b8/\\nmCVLokhL206TJgMZOlTqG1izZi1q1qwla587w12/fkPq18/7fcrtOWhv3xR7+6b5xnzXl7BkKfh+\\nK6pXn7v7LObNW1RgOwGBbxXBZ05AQEAAqZiBo6O0nGbfvt1MmTKesWNH0qNHZ5Yvz2+6HB8fz+DB\\nTpw/f5bY2FiGDRtE//69cHTsLvMz+xjeJ0k+ZMiAfAv08wZeeff181uLoqICU6fOZMqU8bRo0ZDB\\ng51Ys2YFW7duZsKEqUyYMBonp97o6ZWSBWc5ZtF2drXx8HhrBO3tvRIzM3NA+rCXe1udOvVYvdqf\\n2NgYGjSwB96aTXfp0o0//3Skbt0GqKqq0qHD71Svbk18/CtiY2MJC3tEtWpmbNy4GRUVVe7cuY2c\\nnOhNwCgnMxnW1y+fJ5D7668JsofDLl26ExCwFWfnUdy/bydrk5FRiWvXEot49t9PZGQEu3bZIJHo\\nAiIePuyEn9/tYum7OFFQUGDChPZ4eDTE0DBBtmZswoS/vtqcbG1rvvHAyyA1NYWzZ99mtYuiclin\\nTn2Cgt4qPObPLBe1nSifUEpmZmaBfcnLy5OVlc0ff+yiUaMjTJ16ALFYnK/d11Rp/Fzat7fj4MFf\\nOXWqFXPm/F5o5qwo3pM3blyjd+9uODn9SVzcS/76awfduh1i2rRd+czLi4tr165y69ZbP8AdO7Zy\\n4MDeQu+3tLSUInn1FebpJyDwLSNk5gQEBAQK4MGDe6xfH4CCgiK9enXBwaEHZcqUBeDVqzgmThyD\\ns/NQateuS2DgRurVa4CjoxMSiUQmrV9UPlaSPEetsqB9c2+zs6vNvHkLmTjxL/z8AmWCBPXqNeCf\\nf97KuRfG+9bG5Sf/w+C7D4hSGwFo3rwVJ04c4eXLl7Rq1Ua2vXfvfvz+e+c8+0RFRaKqqiLbf/bs\\nPZw9q4aGRhrjxpnQsKF0/VWlSoYYGt4gIsL4zdhxVKnyab5mmzZtZN++3QB06NAJMzNzDAxmkJJi\\nj6rqNbKyyiEWN/ukvr8ESUmJbN8exB9/5L93vjSmpua0bNmafv16oqurh6Vlddm2wn6QyP169Ohx\\nLFrkQd++PcnOzsbW1o5x4ya9aUeR21lb2+Dp6S77fl65cgkDA0MePw5j5sypzJzpxoEDe6lZsxbq\\n6hrEx8sTHm5Genpt4uK8sLLSe2OzoElIyHWsraWlxJ9LUNAmdu7cipmZOS4urp/dX3FTFO/J3Gqk\\nAwduZdcuR0COEycyyMoKYt683/O0z8rKQkHh8x4/g4OvoKamTo0aUp/HTp26yLa9e7+JRDBw4OAP\\nevW5us4rtJ2AwLeMEMwJCAgIFECtWnVRU5Ma6lauXIXo6CjKlClLVlYmo0YNYezYSdjY1ATA0rI6\\nc+fOJisri8aNm1GtmmmRxti/fw+bNv2DSCSiatVqDBw4GHf3WSQkJKCjo8uUKdMpV04fN7eZqKtr\\ncPfuf7x8+ZKhQ0fSrFlLYmNjmTFjMqmpKWRnZzNu3GSsrW3p2rUja9duREtLm61bN/P06ROGDh2I\\nRCLHvXv3uXjxLhDH/ft3SU1NRUdHBy+v5VSsWJlhwwYRFvaQjIwMTE1NmTbNlalTJ/DyZSwVK1bC\\n2tqWo0cPsWTJcu7fv8exY4e5ezeU9PQ01qxZydmzp8nOzsLVdZ5McKF3776IxRJOnz6Bi4srCgoK\\neHjMISEhnr//XgVAvXr1WbVqBW3atEdVVZWYmBcoKOQNxnx9j7F8eVskklIAPH++hSNHKqOqqoq2\\ntg7u7posWbKJ1FRlmjRJZuDA39895R8kx4tr1So/mRdXzZquKClFExXVnhcvZmFq6oCVVcqHO/tK\\nrFjhTUTEM/r374WCggIqKqpMmzaRsLCHmJlZMH26NGg4f/487u5zyc7OxtzcknHjpGWyPj7enD17\\nGnl5eerWrc+wYaN49epVPlVJKyubIs3H0dEJR8fCMzy5f4CAtxliAG1tHWbNmptvHycn5zzvi9LO\\n3r4Jhw8fYMECd0xMqlK/fkPMzCxZsGAuffv2lNl3iMVikpJ6UqbMfOTk0nj9uiKKim1kc5UKoEiz\\ngZ+7FmzHji14efnIhEOgeIKd4uJd70kdHd0899Hu3TtkaqQXL54jNLQppUvPR139DADXr0tLNoOD\\nr7B69Qq0tLR4/DicCROmsmbNSjQ1NXn48EGhXpYFGZGnp6eza9c25OTkOXRoH6NHT+DKlYsyP7kG\\nDRpx5swpUlJSSE9P59dff0dTU5MjRw5RvboVwcFXSE5O4saN69jY2Mq8+pKSErGwsMbff9N7hZoE\\nBL41vo2/FgICAgLfGPkFSaRZKgUFBczNLblw4ZwsmLOxqcnff6/i3LkzuLvPpHv3P2nX7v3rlR49\\neoi//1pWrlyHlpY2iYmJzJkzg19+6Ui7dr+yd+8ulixZwNy5CwCIi3uJj89awsPDmDRpDM2ateTw\\n4QOyjKBYLCY9PR14m9kIDb3DuXOnMTQ0olmzznh5zebVK0ceP66AgcEc1qzxo3JlY7p27cjcua64\\nukqNvq2ta+LpuZhJk0YzZco4xo+fgpfXAoYOHcXEiX9hYFCe+fPdsbCwpH79hrIHUR0dXdau3cj2\\n7VsIDNzIxInT+OWXDgwa1BeAjh3/kAW6aWmplC1bDj09aWBWp059wsPDGTy4PyAtf3Jxcc0jnnD/\\nfrYskAMICzMnOjqKKlWk2bj27e1o3/6TLreM3F5cAE2btuDGjWsYGhoxcWIi0dFbyc6uR1ZW+ucN\\nVIIMGTKSsLBHrFsXwLVrV5k8eSwbNwZRqlRphgwZwM2bNzA1NWfy5MksXrwcI6MKzJkzg+3bt9Cu\\n3S+cPn2CgICtgNSUG8DLa0EeVclx40awcWPQ1zzMj+b33zsTGlqOiAh5wsN96d27P9WqmbJy5bp8\\nbY2NVTl9ehPSjHMKDg5SVVMzM3PWrw+QtXvX1PxjmD/fncjICMaOHcHz59E0atSEyMgI9PUNGDVq\\nHAsWuOcLntPS0li82JOwsEdv1BqdC1ynVnzk9p68l+c+Cgm5TseOnbh5860a6cWL80hJieLx413I\\ny79EQ6M9L19Kv//3799lw4bN6OsbEBx8hQcP7hMQsAVNTa1CvSwLMyL//fcuqKmpyXwCr169JMvU\\nzpkzgzFjJmJjU5M1a1aybp2vzKpCLBazapUf58+fZd06X5YsWQ7A/v3BTJ2awLNnplhYHOfvv82o\\nUcOkBM+rgEDxIQRzAgICAh+FiMmTpzNt2gT++cePP//sS3R0NGXKlKFjx068fv2a+/fvfjCYCw6+\\nTIsWrWWS+lpaWvz3301Z8Na27S/4+EjX6olEIho3lj6wVa5chbi4OOD9GUGJREJIyDXq1WvAkSOH\\n8PX1JjGxI1lZZRGJ5BCLJbi6zkBBQZ709HRiY2O4c+c2pUqVpk2bdigoKNCmTRvc3NxYuHAejx+H\\n4+npRlpaKq1bt2PVKh/KldPHxqYmVlbWLF7sKZP3NjU15+TJY8BbwYV3yV0amoODQw8cHHoU2tbM\\nTAE5uVjE4tIAGBvfwcCg2XvP88dSWKZFSUmRDh0aARAYuJG0tG+3/Cr3Wi6JRIKFRXVZwF21qilR\\nUZGoqKhiZGSEkVEFQCpSsW3bZrp06YaSkjJz586mYcPGMguMd1UlU1NTSU9PR0Xl+/Hg6t17DDEx\\nYkSi1yQmdiYo6CEuLhYFtvX2bszMmf8QG6uOlVUakya1JzLyBfv2BVOhgi5t236+LcH48VO4dOkC\\n3t4r2bLlX86dO8Py5atRUlLKZ8mQEzz7+6+ldu26TJkyg6SkJJyd+1K7dr0vch3evY+io6Oxts7b\\npk6ddLKz9VBU3ImxcTwmJnW4c+c/1NXVsbCoLjMdl/ZnKfsxpzAvy/cZkRe0ZDElJUekRvpDW7t2\\nv+LiMkm2vWnT5oA0KM9R2wRYvDiKZ8+kf3vu3DFnwYJA1q8XgjmB7wMhmBMQEPipKWitzvuktHO2\\nzZzpzsSJY1BTU0dFRYXAwA0oKCigpqbOtGmzijRuQQIKhYkq5Fbpy2nz4YygCIkENDQ0EIuVUFB4\\nTkaG2ZttCvj4rEZDQ5MJE/6iZ8/esixMzoOhWCxGUVGJdesCmDfPlapVqxEaegdra1uys7O4dSuE\\nkSPHyOaTk82Ul5f7yPV2+fH03M/u3QrIy2fj5KSOo2NjBg5szvPnezlzRhlNzdeMG1e12B9i3y0N\\nPXXqOC4uswv04vteyO0ZmHNtClrPKN0uz6pVfly5cokTJ46ybdtmvLx8KExV8nvi1au+PHnSSfY+\\nJGRroW3Lly+Lr+/bMt07d8JwcnrEw4ddUFSMwslpF66uvxXLvHLOvb19E5SUpNeqoOA5LS2NS5cu\\ncPbsKQIDNwCQmZnJixfRVKxYuVjm8j7y30dZ+dro6WkxfHhVfv21JQCurjdk95qKSt7SxaJ4WRa3\\nEXnOGLmrLQCSk/P+HUlJUUJA4HtBCOYEBAR+anLW5+QWEnlXStvTc3G+9oqKiixa5C37/GOlt+3s\\n6jBlyjh69PjzTZllAjVqWHP06CHatv2FQ4f2y35dLoy8GcGMPBlBkUiErW1Ndu7ciry8PG5u0xk+\\n3InsbG0yM01QUVHi8uWLNG/eColEQmRkBPXrNyQu7qVsDd6xY8coV64cx48feVPutJyOHTthamqG\\nvLw8GRkZqKmps337h8VUPoZdu87j7V2fjIyKALi6XqJ27QdYWlZl2rSSlTjP8eLKXRqqqamVL/j5\\nUr5Zn4KamtoHhRsqVqxERESEzKLi4MF91KxZi7S0NNLT02jQoBFWVjZ07y4NaHLUInv16gPA0KED\\nmT9/CRIJHD58QCa2Ehx8hU2b/snzncnBw2MO3bv/mU+Z9UtRunQKjx7lvJNQunTRs6urV4fy8GE3\\nADIzDdm8WZ/x4xPymdV/DjmlvTnzKyx4dnObT4UKFYtt3OIgJyC1tq7Jzp3baN++AwkJCdy4cY3h\\nw0cTFvboAz0UTGFG5O+qUErnAOrqGmhqasnWw+WI2nyIRo0SePAgAdBGWfkJzZsLYu8C3w9CMCcg\\nICDwidy9G8a1aw9p2NCSihXLf9S+VaoY4+joxPDhzsjJyWNqasbo0ROYO3cWAQEb0NXVzSMMUVAG\\n8dq1K+/NCJqamtOwoT1btvyLr+8iGjduxPXr52nXrgzJyc3Ys2cXfn5riYh4RqlSpfj1198wM7Ng\\nzRpfNm36h1atWvLXX5NYsGAe0dGRxMS8IDk5GTk5OUxNzXj27Bl9+/YoYM3O55kE37uXIAvkABIS\\nbLl+fQ+WllU/uc+PIXdpqEQi4fLlEAYNGicTpsitNvotoq2tg5WVDY6O3VFWVpaVsuVGSUkJd3d3\\nXFwmkp2djYVFdTp16kp8fDyTJ499IykvYcSIMUDBapHq6hpERUUWWTlz4sRpxX2oH8Xs2RZMmbKR\\nqChNTE1fMXNm0deavZswF4vlCrQrKC7eDZ7v379HtWqm1K1bny1bNslsO+7dC8XU1LzE5lEU78nc\\n7Zo2bc7t2yH069cTkUjE0KGj0NXVIzw8LM/+hZmTv7utMCPyRo2aMG3aRM6ePcWoUePzzG/q1Jks\\nWDCX9PR0mahNISPJXnl4dKJy5SM8fizBzk6DHj1aFX6wAgLfGCLJN2KUEhOT9LWnIFBClCmjKVzf\\nH5if9foGBp5l1ixt4uLsKF/+DAsXKtGype3XnlaxUti1zcrKIj09DQ0NTeDtr/LFla06c+YW/fur\\nkpAgPZ+GhofZtasSFSoYfGDP4kUikTB8eBBbtzZHLFahRYvd+Pt3lZXCfe+877sbEOCPkpISXbv2\\nYOnShTx8+AAvLx+uXr3Mnj07uXUrhNWr/Vm0yIMzZ05RsWIl6tSpR4MG9qxd64u2tk4+9czhw50Z\\nMWIMZmbmtG7dGAeHnpw7dwZlZWXmzVuIrq7eFznu7Oxs5OXlP2qf69fvM3BgFE+e/IqcXAy9e+9l\\nwYIuH97xAzg4/M7q1X5s3bo5j6BHQkI8ixZ5EB4ensdqISMjg6VLF3LrVghisZjy5Q3z+D7m8LP+\\nXf5ZEK7vj0uZMpofvY+QmRMQEBD4BNasSSIurh0AkZEt8fXd/MMFcwURGHiOhQuTSUrSoU6dhxgZ\\nyXP0aCkUFTMZNEiV/v0/X1nP3r4Gc+acZevWLSgoiHF2NvzigRzAoUMX2LLlVyQSfQCOHevH+vW7\\ncXZu+8Xn8qWxsbFj06aN3Lp1k5Mnr5CRocavv+6gQYNQbG3tuHUrBJFIlEc5E6Rllvfv382nnmll\\nZZMn2E9PT6dGDWucnYeyfPlSdu3aTt++A77IsX1sIAdga1uNzZtV2b9/M/r66nTu3PnDOxWBoKCd\\nQNGsFtLT03n27ClDhoyQ/ZAi8GHOn7/DiROPMTJSoXfvpt90ibSAwKcgBHMCAgICn0BWVt4HwszM\\nj39A/N5ITk7CwyOTyEhpRuLQoSbAv4BUVMLd/QL29mFUq/b5a6K6d29E9+6f3c1nkZCQhkSSe02U\\nEqmp30QxS4ljZmbO3bt3yMjQJTm5FKmpDXj2zIqkpH/o3bs7GzeuBwoW7MmvnhmVz5NOUVGRhg3t\\n34xlwZUrF0v2gIoBY2Mjhg0z+ipj3779iOHD/+POHRsMDa/g6qrFL798eC3Yz87evZcZM0aDV68c\\nEIlecvPmdjw9iycQFxD4VhBWeAoICAh8Ar/9JkZZ+TEAmpq36dz5+5Fo/1Ti4+OJjc39MKsIaMne\\nJSRYEhr67IvPq6To0KE+tWoFAtK1Uaamm+nWze7rTuo9REVF0qtXF9zdZ9GzZ2dmzZrGpUsXGDzY\\niR49OnPnzm3WrFlJYOBG2T59+nQjOlrqZbZ//x769u1Jv369mDfPFQMDQ5KS4hGLVVBTO0X58s5k\\nZcV8UHyjKKqH8vJvf0uWkxN9tvrpj87Chbe5fbsnYrElT5/+xqJF0V97St8FO3a84tWrugBIJKU4\\neFCXrKz896OAwPeMkJkTEBAQ+ATGjGlLtWoXCA29RJ06BjRr1uRrT6nEMTAoT82a27h40QYQoaJy\\nCzm5RHKEE6tUOUGDBtbv7eN7Qk1NjX//bcvKlUFkZYno3duO8uXLfu1pvZeIiGfMmePJ5MnTGTjQ\\nkaNHD7FixVrOnDmJv/+6PF6E8Had47sm9klJSQQFBXL9+jUkkrI8e/YPlSr9jpxcfJ4yxaIoZwp8\\nPikpynnevyulL1AwCgp5AzclpUzk5IQ8hsCPhRDMCQgICHwiHTvWp2PHrz2LL4e8vDxr17bAwyOQ\\nlBRlWrTQRF6+LDt2bEVRMYthw6pSunR+5cTvGS0tLcaP/+VrT6PIGBgYYmwsNTuuUsWY2rXrvvOR\\nnUEAACAASURBVHltQnR0ZL5gTookn4m9pqYmNjY1EYtXU69eJV6+PEFsbHY+Vcfcypn16zeiQYNG\\n71U9zKEgdVaBwmnWTI5z5568UXlNolGj+K89pe+CoUNNuX59Ow8ftkRT8x4DBigKwZzAD4cQzAkI\\nCAgIFJkyZUqxYEHeCLaYtCAEioEc43YAOTk5mU+ZnJycTMVRInkrqS+1ICjYxL5WrTq0b/8rDRvW\\np1mzlkAbWreWZqCDgnbJ2s2YMSfPfrl9vXIk9AG8vVfKXuf4NQI0a9byTf8ChTFkSCt0dc9w9eol\\nKlYUMXRopw/vJICVVVX27SvFmTNnqFatPObmzb/2lAQEih3h5wkBAQEBAYH3MH78KFJSkklOTs5j\\nkB4cfIUJE/76ijP7eAwMynP3bigAt2/fJioqEhBhZ1eH48ePkJiYAEBiYmKJjJ+QkMCkSbsYOvQQ\\nmzadLZExflR69LBn/vy2jBjR5pMUOX9WdHV16dixMebmJl97KgICJYKQmRMQEBAQEHgP8+d7AVKB\\nka1bNwHwxx9duX//Lnfu3P6icwkOvsKmTf/g6ZnfWwzylyy+W87YtGkLDhzYS58+3bCzq0mFCpWA\\ngk3sc8yWi6skUiKR4OR0gNOnnQA59u69h5zcObp1a/jJfQoICAj87Aim4QIljmBu+WMjXN8fl5/l\\n2n6MQfbp09LywK5du1O6dBnWr1+DnV3tfAbZuRGLxcW2TudDwdzH8KWvb2xsLHXrRpGc/DZ469Zt\\nK8uWtflic/hZ+Fm+uz8rwvX9cfkU03ChzFJAQEBA4KfGxsaOGzeuAxAaeoe0tDSysrIICbmOra3U\\niiDHIFtRUQmRSMTlyxfZsWMrKSnJvH6dAcDFi+cJCZH207VrR3x8vHFy6s3x40c4fPgAffv2wNGx\\nOz4+3rKxW7duLHt9/PgR3N1nAVJVSmfnfvTt2wNf3+WytWoAaWmpTJs2kT//7Mrs2S4lck7S09MZ\\nP34nf/xxmBEjthdL2aWmpia6us9zfZKNru7rz+5XQEBA4GdGCOYEBAQEBH5qcgyyU1NTUFJSokYN\\nK0JD73DjxjVsbGrK2kkkEvT09DA0NGLdugCsrW0Ri8VMmDCVjRuDkJeX4/Jlqfm1SCRCW1uHtWs3\\nYmNTkxUrlrF06QrWrQsgNPQ/Tp8+8abXgksYvbwW0L17L/z8NlG2bLk8871//y6jR49j48YgIiMj\\nZAFkcTJ16n78/Lpz9mxn/v23N3/9dfiz+1RWVmbSJE0qVdqKtvYJmjdfy8SJzT5/sgICAgI/McKa\\nOQEBAQGBnxoFBQUMDAzZt283VlY2mJhUJTj4MhEREVSuXKXQ/apUMUZLS5vSpcsAoKOjS1zcS9n2\\nli1bA3Dnzm3s7Gqjra0DQOvW7bh+/RqNGzcrtO/bt28yb96iN+3b8vffXrJtFhbVZWNWrWpKdHQU\\n1ta2n3bwhXDvngZSU3gAOe7f1y6Wfh0c6tGpUyYpKcloa9sJtgSfyKZNG9m3bzcAHTp0okmTZowZ\\nMxxzc0vu3QvFzMyUCRNcUFZWITT0DsuWLSYtLQ1tbR2mTp1BqVKlGT7cmerVrQgOvkJychKTJk3H\\nxqZ47yMBAYGSRwjmBAQEBAR+emxsbAkM3Mj//jecVat8SEtLw8LCkoCADSQlJbFz5zb2799DZGQE\\nKiqqgDRIS09PA8DNbSYvX8Zy6tQJLl++SFpaGqqqqojFYnbu3MbNmzeIjY1BQUGBcuX0ZX3kDmYy\\nMjKKNFdFRSXZa3l5qeVAcVO+fAogISdzaGiYXGx9KyoqoqOjW2z9/WyEht5h//49rFrlh1gswdm5\\nLzVr2vH06ROmTJlBjRrWLF48l23btuDg0IMlS+bj4bEIbW0djh49hK/vciZPno5IJEIsFrNqlR/n\\nz59l3TpflixZ/rUPT0BA4CMRyiwFBAQEvnOioiJxdOz+tafxXRMf/4rnz6M5evQg8vLyKCsrY2NT\\nUxZsbdmyCX//f7G3b0p6ehrLly8lPDxMtn9qaiqJiUnY2zfB03MJSUnSNWYnTx4jMzMTTU0tRo0a\\nx61bN7l584ZsLZ6enh6PH4cjFos5deq4rL/q1a04fvwoAEeOHPrg/IOCNtG7twOursWzhs7NrQlt\\n2/pTteoOmjXbwNy5dYulX4HPJyTkOk2aNEdZWQVVVVWaNm3B9evXKFu2HDVqWAPw22+/ERJynSdP\\nHhMW9pDRo4fSv38v/P3XEhMTI+uraVOp75qZmTnR0VFf5XgEBAQ+DyEzJyAgICDwQ9G6dWMOHz5N\\nbGwMS5YsYM4cD/bt283du3fymFjn5urVy+zYsZ/MzEwmTvyLwMBtAAQGbqRbt57cvn2LmTOn0rRp\\nc+Tl5Th//gzh4WHo6+sD0gybiooy1ta2VK5cBbFYaswdEnKDdu1+RVFRkRkzJpOdnY2hYQXs7aWC\\nJoMHD2fChNFoa+tgYWFJWpo00zdy5Fhmz3Zhw4Z11K1bHw0NDdlcC6pM3LFjC15ePrLyS4CsrCwU\\nFD7t33zp0nps2CC4wX+LFFSaKhLl/Vwikbx5L6FKFRNWrFhbYF85WV45OfkSyfAKCAiUPEIwJyAg\\nIPADIBaL8fBw49atG5QpU5a5cxdy8OA+du/eTmZmFkZGRri4zEZZWYVjx46wfv0q5OTk0dDQYNky\\n3689/WJG+lBbunQZ5szxkH7ynrVZ8+e7ExkZwdixI4iKikJZWVm2LSDAj9at2zN27ESGD3cmPPwR\\nERHPsLdvRlhYGLGxCYwfPxYVFQU0NDQJCblBQIA/IpEIZWUVRCKIi3tJcPBVFBQUUVRUwM6uFiAt\\nzVRSUkJbWwdra1uGDx8tG7dMmTL4+q4H4MiRgzx9+gQAO7va2NnVlrX7668Jeeb//Hk0jRo1ITIy\\nAn19A/73v2G4u88iISEBHR1dpkyZTrly+ri5zURHR5OQkFu8ehXHpEku7Nu3m9DQ/7C0rCHzmBMo\\neUJD73DgwF5Gjx5XpPY2Nra4uc2id+++iMUSTp06jovLbLy8FnLr1k1q1LBiz5492NjYUrFiZeLj\\nX8k+z8rK4unTJ1SpYlzCRyUgIPClEMosBQQEBH4Anj59Qpcu3diwYTMaGpqcPHmMZs1asGqVP+vX\\nB1CpUhX27NkJgJ/fahYt+pv16wPw8Fj0lWdecuQuP81tqXru3BkGD3YiISGeS5cu8PDhAyQSMDAw\\npHNnB9LSUklMTOD169ekpqYikUiIjY0hNjaGiROnoaWlzblzVojFirx40ZnTp38hOTmVly9jZddA\\nJBJx8uQxrKxsCAjwZ/Toccyfv4TMzCx27dohm0tsbAwrV67LE8gBhIaG0q9fL/r27cmOHVtl2x8/\\njmTDhoPcuHFP1nb8+CmULl0Gb++VdOvWi/DwMLy8fJgxYw6LFnnyyy8d8fMLpE2bdixZskC2X1JS\\nEitXrmPkyDFMmjSWXr0c2bBhMw8fPuD+/XsIfBnMzS2KHMgBmJqa88svHRg0qC//+18/Onb8A01N\\nLSpWrMT27Zvp3duBpKQkOnXqioKCAq6uHqxY4U2/fr3o378Xt2+HFNLzlxGjGT7cmdDQO19kLAGB\\nnwEhMycgICDwA2BgYEjVqtUA6fqXqKhIHj58wKpVPqSkJJOamka9eg0AsLKywc1tBi1atJatmflZ\\nOHnyOJs3B7BgwVKysrLw91+Ll9dyevfuRtWqVQkJuUGdOvUYNKgvZcqURVFRiezsbJYuXYicnBzz\\n57tjbl6Xdev6UrXqMkDEo0edqFTJD11dPdk1kJOTIyoqEgeHniQlJeHo2ANFRQUkEkhNTQGk2cLm\\nzVsVmDW0sbFl/fqAd+Z+k9GjU4iI6ISW1k0mTTrJwIFNZdtzAtbGjZuipCQtn/vvv5vMnSsN4Nq2\\n/QUfn6W5xm4GQJUqJujplcLY2OTNe2OioyOpVs20mM76j09UVCRjx46gRg1rbt68gbm5Je3bd2Dd\\nOl9evYpnxgypmbyX10Jev85AWVmZyZNnULFipTxG8GvWrOT582iioiJ5/jyabt160rVrj3zjde/+\\nJ927/5lnfHl5eVxcpOPkNpWuVs20wOy7t/dK2WsdHR2CgnYWy7nIuQ8Ly4aLRCJBxVRAoBgRgjkB\\nAQGBHwAlJUXZa+n6lwzc3Wczb95CTEyqsn//Hq5duwrAuHGT+e+/W5w/f5YBA/qwZs0GtLSKR3r+\\nW+bq1SuEht5h8eK/UVNT4+zZ04SHP2LwYCdiYl5w9OgR1NTUqF+/IXPnLgSgR48/6NXLEYlEzMSJ\\nf+Hv/y8nTlzG3z8GUCAmZhqQRv36v3Lt2jbZWM7Ow94oXUrQ0dFl166DJCTE4+zcL8/6JRUVlSLP\\nf/XqCCIiHABITKyJn99DBg7M305ZOW+fubOSuVFUlN4zcnJy79w/JaOQCTBkiBM+PgWv3/reiYh4\\nxpw5nkyePJ2BAx05evQQPj5rOXPmJP7+63Bxmc3ff69CXl6ey5cv4uv7N3PmeObr5+nTJ3h7ryQl\\nJZlevbrwxx8OyMvLf3D8DwVI69ev5tCh/WhpaRMZmU1mZkVMTKqgqHiVxMREVFRUmDhxKhUrVsbN\\nbSbq6hrcvfsfL1++ZOjQkTRr1hKAgAB/jh8/wuvXmTRp0owBA/5HVFQkY8YMp3p1K+7evcP8+UvZ\\nuHE9oaH/kZGRTrNmLRkw4H+fdmIFBATei1BmKSAgIPCDkpaWip5eKbKysjh4cJ/s84iIZ1ha1mDA\\ngP+ho6PDixcvvuIsvwwikQhDQ0PS0lJ58uSx7PPateuxbl0AZcqUZcWKNXTv3ou7d0MBuHs3lKio\\nyHx9NW1amz//PIpEIkJO7jbt22+ga9cGedqIxdlcunSfxYtPk5qaSteuHRk2zJm+fQfw8uXLfH0W\\nhezsvA/rWVkffsCvUcOao0elapiHDu3PY4L+NfhRAzmQZseNjU0QiURUqWJM7dpSBdAqVUyIjo4k\\nOTmJadMm4ujYnWXLFhMW9ihfHyKRiIYN7VFQUEBbWwddXT1evYorwtjl8fPbVOj2O3duc/LkMfz8\\nNiGRtCY6OplHj6w5ezaYlJSarFmzgaFDR7FwoYdsn7i4l/j4rMXTcwkrViwD4NKlCzx79pRVq/xZ\\nt+4f7t4N5caNa4D070rnzg5s2LAZfX19nJ2Hsnq1P+vXB3L9ejAPHz74qPMpICBQNITMnICAgMAP\\nQEG/yg8c+D+cnfuho6ND9eo1SE1NBWD5ci+ePXuKRCKhdu26stLAHxmJRIK+vgHDho1iypQJuLrO\\nw9KyBosWeRAR8QwQkZ6egYlJNQ4c2EufPt2wtKxBhQqVZH3knGORSMSCBV2YO/c/goOHY2Jig6Ji\\nM9l2iURCUFAIoaGWxMU5YGAAlSptJjs7nYCADbRq1UZ2zj+m3MzBQZsrV64SH18LZeUndOr0OtfW\\nt/3k7nL06AnMnTuLgIAN6Orq5hE2yT32u/MoqTK4HKXR4OArrF3ri46OLmFhDzEzs2D6dNcSGfNL\\n8W52M3fmMzs7m9WrV1C7dh3mzl1AdHQUI0YUnKlSUMjbT1bW52dJb968QePGzVBUVOThwzIkJ7dA\\nJMpAVfU6oaGP6d//GACZmVmA9Po3biwt4a1cuQpxcdKA8tKlC1y+fJH+/XsBkJaWzrNnTylbthzl\\nyhlgaVlDNuaxY4fYtWsH2dnZvHwZS3h4GCYmVT/7WAQEBPIiBHMCAgICH8GnlonlXhdTVNasWYma\\nmjo9e/Z+b7t3f5XP3b5Tp6752ru5zS/yHL5HCgpSctbpVKxYmRkzXHFxmYSn52KmTp3JzJlTUFNT\\nY8KE0Tg7D2XRomUF9vtu5mPyZJcCt0dHRxEcPIj09DoAREU5oKsrYsGCtoDU0y409C5jx07Ko5z5\\nIf74oz76+rc5fz6IatW06djxF9m2nPVOTk7OefbR19fHy8snX19TpsyQratKTX1N375/kZycjIaG\\nRgkrWb69Ng8e3GPjxiBKlSrNkCEDCAm5jrW1bQmO/fWQSCSkpCTLrCP27t1VaLuSQSTr28AgmchI\\nADFisSbGxgNYt65Tvj1ygtF359W7dz9+/z2vbUVUVCSqqm/LeyMjI9i06R9Wr96AhoYG7u6zeP06\\no3gPSUBAABCCOQEBAYGPojjLxD7kfVbc2ZGnT6M5ePA6lSrp0br1j2sCfejQSSBvkNu+fQfat+8A\\nQLVqZmzcuBmA8uUNWbXKv1jHV1FRQUXlGenpOZ9IUFTMBCAo6AKzZ2fz4kUVrKwO4utrh7GxUZH7\\nbtCgOg0aVC+2uS5bdoRFiwxJTrbB0vII69fXpHJlw2Lr/32Ymprj4TGHmJgYXryI5ujRQwQHX+Hs\\n2VNkZGRQo4Y1EyZMBaQKiGZm5ty4cZ20tFSmTZuFv/86wsIe0bJlawYNGgLAwYP72LLlX7KyMrG0\\nrMHYsZOQk/syK0rel92Uk5OjZ09H3Nxm4Oe3hgYN7Ckomyr90aH452ZtbYOnpzt9+vTHxcUGZ+fl\\nZGQ0QFVVmQ4dpJk/iUTCw4cP3pupr1evPqtWraBNm/aoqqoSE/MiTyYxh5SUFFRUVFFXVycu7iUX\\nLpyjZs1axX9gAgICQjAnICAg8DEUpUzszp3bLF26kLS0dBQVFfNlRnIybjo6OgD06dON+fOXoq+v\\nj5/fGg4c2Iuurh5ly5bDzMwCkK5HWbTIk/j4V3mECopKSMh9BgyI4PHjrigpPWXgwN3MnNmxeE7K\\nd0ZmZiZz5hzg4UNlKlRIZ/r01qiqqhZb/7q6ejg5vWT58uukpxthbb2PUaPskUgkLF4cx/PnUruE\\nkBAzFiwIYPnyogdzxUlGRgarVsmRnCwN7P/7rydeXptYvPjLBHMpKSkYGlZg/nwvFi/2pEoVY1q0\\naEO/flJVF1fX6Zw9e5pGjRojEolQVFRi9Wp/goI2MWnSWNat+wdNTS26d+9E9+5/Ehf3kmPHDrNi\\nxVrk5eVZsGAehw7tp127X0v8WN7NjufObubelmNGD8gC0Nzege9mVv39/y2W+ZmbW2Jv34S+fXug\\np1cKe3tb6tevSa1a/2PBgnns2bOVrKysQkuAc17XqVOf8PBwBg/uD4CamhouLq75FCqrVTPF1NSM\\nXr26ULasPtbWNsVyHAICAvkRgjkBAQGBjyJvmdjChctwc5vBpUsX6Nz5V6ysbLh27Sq6unpkZGQw\\natRYHj16yKJFnsTEPGfIECdMTc1RU1N/26NIxNWrl9m8+R+ys7MZPnw0fn5rOHnyGOHhj+jUqQue\\nnm6MHz8FI6MK3L59i4ULPQosnyuMNWse8PhxNwBev65IUJAuEyakoqamVnyn5jth6tS9rF/vAKgA\\nmSQl/cOyZV2KdYxJk9rz++/3iYi4RoMGbVBXVyc7O5uUlLxKk6mpRS+zLG4yMzNJT1fP89nr1/mz\\nLCWFmpo6V65cxMfHm9jYGMzNLQkOvkxAwAYyMtJJTEzE2NiERo0aA2Bv3wQAY2MTjI2ldgogza4+\\nfx5NSMg17t4NZeDAPoA0WC1VqtQXO55PRSKR4OGxj1OnlNDQyGDcuKrUrWte7OP07NkHJydn0tPT\\n32Q6LTAwKM/ChUvztX231DYn2w3g4NADB4f8dgnvliEXVq6b2xJBQEDg8xGCOQEBAYFPxMKiOnp6\\nekREPKNp0xY0atSYDRvWI5FI8PML5MyZk2zeHIiLy2xGjx7H5s2BODj0wMtrAb/++rtsHUpKSjI7\\ndmyhVau2JCYmEhi4EW/vlfj6+vDkSTgbN67n5s0QXFwmysbOESooKhKJ6J33ciW4PqdgPmW94enT\\nJ6hQoRKVK1cptnncvq2BNJADUOT27ZKxZbCwqIaFxduSNXl5eeztXxIUlAqooaZ2l9atiy8j+LFo\\naGjQokU4W7cmAxro6V2gU6cyJTpm7uyNmpoqa9f+w/nzZzh4cB8SiYRbt26yZs0GypQpy9q1vrx+\\n/VbkRVFRSdZHzuuc9zlWCu3bd+B//xtWosdQ3KxdewIvrxZkZ5cFIDIyiMOHKxVrthjA09ON8PBH\\nvH79mvbtO1Ctmlmx9l8QZ8/e5syZpxgbq9O1q73gLycgUAIIwZyAgMA3QXJyMocPH+CPP/ILduQQ\\nFRUp8/r6Fsh5oDQwMERXVxexWIyhoRHZ2dJAK7ck+dq1K3nw4D7Pn0cRHx+PRCIGpN5nr169YsEC\\nby5fvkhMzP1c3mcxyMmJ0NHRQVNTk3XrAgqdy4dwdKzC2bMHefq0LQoK0XTq9AJ1dfUP70jxnfdP\\nWW946tQJGjVqXKzBXNmyKe+8Ty22vj+El9cfVKu2j+fPRdSvr83vvzf+YmMXxLJlXbGxOUhMjJhW\\nrSrRoEHJWhfkZHjs7GpTsWJllJSUaNOmPRoamuzevQORCLS0tElNTeX48SO0aNG6SP2KRCJq1arL\\npElj6datF7q6uiQmJpCamoa+vn5JHtJnc+fOa1kgB/DggSVRUZEyE/fiYsaMOcXa34fYseMCEybo\\nER/vgIJCNDdv7mb27N++6BwEBH4GhGBOQEDgmyApKZHt24PeG8x9q+SWJNfU1CQ5OZnQ0P/Q1tYh\\nMzMTX9/lmJqao6GhxZgxExg4sA9374ZSv35DtLW1CQ9/RGRkJLa2Ndm8OQA7uzpMmjQNJ6c+dOrU\\nmR49ejNkiBPHjx+hefNWRRIqeJfatc3YtOkJhw5txshIk99++70kTsV7ad26MZ6eSwgM3ChT9Vy0\\nyAMLi+q0b98BHx9vzp49jby8PHXr1qdp0+acPXua69ev4ee3hjlzPDE0/Pz1ZTNn1iUhwY9Hj7Sp\\nWDGJ2bO/nPeagoICo0e3/WLjfQh5eXkGD27zxcZLSkoiIOAsCgpymJur4Ovrg5ycCAUFRcaNm8yp\\nU8dxdOyOnl6pPDL3uSlIJGTHjq0yIZQxY4YhFktQUFBg7NiJ30QwFxS0iZ07t2JmZo6LS14LhmrV\\n5BGJ4pBI9ACoUuUe+voNAdi8OYDff++czwj+e2DbtkTi46XBeFaWPvv3qzNrlkTIzgkIFDNCMCcg\\nIPBNsGKFNxERz+jfvxd2drV58OABSUmJZGdnMWjQEOztm+ZpHxHxDBeXiUyYMA1NTU2ZOIiioiJ1\\n6tRjwICCPZzeR1HsA/KKAuTfLicnR48ef7J48XySk5OJjo7EyKgihoZGREVFsnfvLlRUVElKSmTl\\nyr9RV9fA0NAIb+9FzJ27gNat2xEQ4P/G2Ls6mZmZPH36hOnT57BgwTz8/NbmEyooKtWqVaRatYof\\ntU8OYrEYDw83bt26QZkyZZk7dyEHD+5j9+7tZGZmYWRkhIvLbDIzs+jXrydbtuwGIC0tjT//7EpQ\\n0C4kEgnLly/lyZPHDBs2iIkTp8rOZ2JiAqdPnyAgYCsgLT1VV9fA3r4JjRo1pmnTFp8074KoVMmA\\nbds6I5EID5ZfksTERBwcDnLtWj8gi8aN1xEY6IeS0tuSSTMzc5kwSG5yr7OqWbNWHmXEd9dgtWxZ\\ntGzel2THji14efnIrAly4+zcksjI3Zw9q4aGRjpjx1aRrWUNCtpE27a/fJfBnKJi9nvfCwgIFA9C\\nMCcgIPBNMGTISMLCHrFuXQDZ2dlkZKSjpqZOfHw8gwf3zxPMPXkSzsyZU5k6dRYmJlUZNWqITBzk\\n5MnjuLpO/6RgrijkLhOzs6tNVFQkIpFIZi9w7dpVjIwqsHLlOqKiIpk0aQy9e/fDzW0GqqqqVK1q\\nikgkx6JFy9i/fw93795h9Ojx3L9/V+Z9VqdOPXx8lvLw4QMePnyAsXFVGjVqXKBQQVH53FLJ8PAw\\nZs50Z+LEqUyfPpmTJ4/RrFkLfvvtDwBWrfJhz56ddOnSnWrVTAkOvoKdXW3OnTtNvXoNkZeX5/Xr\\n1zg49ODIkUP07TuAhQs9ZOWT6uoaKCkpM3fubBo2bCwTvYCS894SArkvi7//2TeBnBygxOnTf7J7\\n93G6dGn2Uf2sX7+aQ4f2o6OjK1N8ffDgHklJWmRlKZOdfQ8vL28g7w80ly5dkK3DMzQ0YsoU6Xey\\na9eOtGrVlu3bg8jMzKRcOX0GDhyCoaERy5YtJi0tDW1tHaZOnUGpUqULVZZ1c5uJuroGd+/+x8uX\\nLxk6dCTNmrVk/nx3IiMjGDt2BG3atOf06ZO8fp2BsrIykyfPoGLFSkyf/is+Pt5cunSe1avlePGi\\nExKJhNjYGEaOHIyOju5HCR59CwweXIWQkD08ftwCLa3bODkpCd85AYESQAjmBAQEvglyP7BLJBJW\\nrFjGjRvXkZMTERsbw6tXcQC8evWKyZPH4e6+gEqVKpOamsqtW2/FQaKiosjISKd//17UqVMPiQQu\\nXjyHSCTC0XEALVu2lmWI3v08N3fu3Gb+fHfmzPGkfPnCpdo/R5K8IO+z8PDHZGTI4+3ti4qKCsHB\\n99iy5REHD+5hxIgGlCnzddT5RCKRLBNoZmZOVFQkDx8+YNUqH1JSkklNTaNevQYAtGjRmmPHDmNn\\nV5sjRw7RpUs3UlNTEYvFrF3rS1xcHDExz8nMzJKdW3l5eVat8uPKlUucOHGUbds2yx5ei/IA+LHB\\n6v79e6hTpz6lS5f+lNMh8AnIyYmA3IF5NvLy7/eACw29w4EDexk9ehxr1qwkOTmJ69eD8fPbRGZm\\nJk5OvdHXN+DQoaNERMwjObktVas24sqV29SuXZ1jxw7TqlVb4uPj8fdfi5fXcpSVVdi4cT3//vsP\\n/foNRCQSER//ihYt2mBqasa9e6HUr9+AceNGMm/eIrS1dTh69BC+vsuZPHn6e5Vl4+Je4uOzlvDw\\nMCZNGkOzZi0ZP34Kly5dwNt7JQoKCvTo0Rt5eXkuX76Ir+/fzJnjya5d23n+PJr16wORk5MjMTER\\nLS0t/v03AG/vlWhplYxIT0lSp445e/aU5syZI1hYVMDSstnXnpKAwA+JEMwJCAh8cxw6tJ+EhHjW\\nrt2IvLw8Dg6/kZEhVbXT0NCgXDkDbty4RqVKlZFIxGhovBUHiY6OYsKE0axbF8CJE0fZZKTsEgAA\\nIABJREFUuXMbfn6biI9/xcCBjtja1uTmzRs8eHAv3+c53Lx5gyVLFjBv3iLKli33xY570aJDeHsb\\nkZJiQs2ae5k6tRKjRmUSEeEASLhwYT07dvzyyXYCRS2VVFZWITIyguHDZ5CUlIyNjV2efuTk5MnO\\nzsDdfTbz5i3ExKQq+/fv4dq1qwA0atQEX9/lJCYmcu9eKLVq1SE1NQUQsXTpCoYNG4Svrx/p6ek4\\nOfXG2tqWtLQ00tPTaNCgEVZWNnTvLl3Tp6amRkpKyruH8tns27ebKlVMhGDuC9K3b2P27VvHpUuO\\nQCatWgXSoUP39+5jbm6BubnUa1EkEhEVFUnjxs1QVFREUVGRRo0aExf3iqwsBaSPNPIkJrbB13cf\\ntrZmnD9/lmHDRhMcfEUmLARSNVgrK2vZOL/88huuri68fp1BeHgYz59H8+jRQ0aPHgpIvzulSpUh\\nLS2tUGVZkUhE48bSCoLKlasQFxeX73iSkpJwdZ1BRMTTPCqcV69eolOnrjKDcy0trY8/wd8g5cqV\\npkuX5l97GgICPzRCMCcgIPBNoKamRmqqVFUwOTkZXV095OXlCQ6+QnR0lKydoqIi7u7zGTNmOKqq\\nqrRu3Y7y5cvLxEHEYrFMzjwk5DqtW7dDJBKhq6uHra0dd+78x82bNwr8XF1dnfDwR8yf787ixX9T\\nqtSXe9BPSIhn1SpNUlLqAXDtWl9cXecRETH5TQsR16//xrlz12jVqt4njfH06ZMil0p6eS2gV69e\\nNGzYgvXrVxfYX1paKnp6pcjKyuLgwX2ywFdNTQ1zc0u8vObLDJ/V1TWQkxNx+/ZNmjdvRZ8+3dHR\\n0cHMTCqPnpqawqRJY99cOwkjRowBoGXLNnh4uLFly7+4us57rwBKdnY2s2e7cO9eKJUrG+PiMouw\\nsLB8pXIhIdcJDb3D7NnTUFZWZvToCWze/A9ubvM5ffoEM2dO5eDBk2RnZ9OnTzc2b95ZaGndq1ev\\nWLhwLs+fRwMwcuRYrKxsWLNmJc+fRxMVFcnz59F069aTrl3ze3P9KERFRTJ27AjM/8/eWYdFlbZx\\n+B6GlBIUMQETkEZsbF111bWwXQEDYy1s7Mbe1V0DXUEUY0WxVtfuTsDCVhqR7piZ749ZRhAwcY3v\\n3Nfl5cw5b533zDDvc57n/T1mtQvM/+3bwaxZsxKJRIKZWW22bRvL/v1/c+3aEeLiXjBo0N/Ur9+A\\nESPGcPLkcTZt2oCSkhgtLS3++GN9oX2sr1694sGDvRw9eph+/QYAoKKijNzjJwMkKCkl8fDhafr2\\nPY+urq5C4t/BoT6zZy8ocvwmJiZ4e29l9+6/uHDhLKdPn6Rq1eqsW1dQgTUtLfWtyrIqKq/FkN4M\\nD5bJZPz55zocHOri6bmMqKhIRo8eVmx5AQEBgfdBMOYEBAS+CnR1S2NlZcOAAb0wM6tNaOgLnJ17\\nY2pqjrHxa1l6kUiEuro6S5b8hrv7CEqV0iwgDpKRkfGvF0hetrgF0pvH80L5ypY1ICcnm4cPQ2jY\\n0PEzXW1h5OMunX9EiMXqQCZ5OdHU1aMwNCxdVPX3okKFSu8dKnnnTjAbNngRH59OkybN2bixcKLf\\nwYOH4ubmQunSpbGwsFQY4yAXoZg500MhTpGUlEiZMmX5++/9xMW9QllZmQYNGuHiMlhRZ8MG30J9\\nWFnZ4Oe3872uLzT0BR4eM7G0tMbTcy67d+/k3LnTeHquoHTpgqFyAQH+jBzpjqmpGbm5uTx69BCA\\noKBAqlWrwf37d8nNzcXCwgqg2NC6lSuX0bNnX6ytbYmOjmbChFH4+fkDcuP599+9SEtLpW/f7nTt\\n2gOxWPxe1/ItEhYWytSpsxTzv327H/v372HVqnVUrlyF+fNncfjwQX766UcOHVpXQOwGwNf3T1as\\nWE3ZsmUVx/Ijk8lIS0tFV7c0S5euxM3NGRUVFVq1+gE1NSnKyuHo6m6ibNlUSpcuTc2apjx+/JCo\\nqEhq17ZkxYrFRESEU6lSZTIyMnj1KpYqVeSCQPHxcVSoUIkGDRpx8uQx7t+/S2JiInfu3MbS0orc\\n3FzCwkKpWrVagYdHH6osm5aWphBBOXTogOK4g0N99u0LwN7eAbFYrAizzPNMf4thlgICAv8NgjEn\\nICDw1fA+eZDy9qBpaWmxYcNmxfE8cZCkpEQGDfoZAGtrW/bt20P79h1JSkoiKOgWI0eORSKRsG9f\\nQKHjz549RUtLGw+PGYwd+wvq6hoFVPM+J4aG5Wne/DT//GMDqGFgcJ7Jkx3w9vbl5MmGqKkl4+IS\\nhpVVx4/uI38KhXeFSuanfPnyqKu/TmDcp09/xesuXQqmkli8eD69evWjefNWnD17FYBXr2IZNWoo\\n/fu70L17z7eOUSKRsHz5VqKjE3F2bo+NTU3Onz/L8+dP6d/fhY0bvShVSrPAGPIoV84QS0t56Fzb\\ntj/i6+vN06dPcHcvGCqXR55Br6ysTKVKlXnx4jkhIffo3bsfgYG3kEol2NjYvjW07vr1q7x48Uxx\\nPD09nYyMDEQiEY0aOaKsrIyubmn09PRJSIgvUs3we+HN+d+06U8qVqxE5cpVAPke0YCAnXTv3rNI\\nsRsrKxsWLJhFy5ZtaNascGieSCSideu2iEQiRo8eSm5uLoaGhmhqaqKursK0aZmcP3+WlJQ4UlIy\\nOXv2NOXLlyc8PIy6deszbdpsZs+eSnZ2DgBubiMUxtzz58+YNWsa2dnZxMW9Ytq0OSgpKbFy5TJS\\nU1ORSHLp1asvVatWe6uybEG12/x7PUWIRCL69h3AggWz8PXd+O/DInmZTp26EBYWirNzH5SVlfnp\\np65069aDn37qyvjxozAwKPfNCaAICAj8NwjGnICAwDfNzp0X2bUrBbFYypAhFWnZ0kbh4WvQoBE1\\natTAxaUPIpGIESPGoKenT7NmLbh7N7jQ8efPnyESgZ6ePkuW/MqECaOZOnUW5uYWn/06RCIRGzZ0\\nZ+3a/SQkQPv2xtSvb06zZtaEhoaioVEeQ0ObEu+3uFBJKysbDh48SMOGLTh69PB7tzd58vRCx3R0\\ndLG1Hcy5c2Kys8/Tp0/RHk+ZTMYvv+zi+HE91NTiOX48mXXr7uHo2BRHx6bA28VQ8p+TyWRoamoW\\nGSpXVHkbGzsuXTqPWKxMnTr1OHx4FlKpjF9+GYNUKnlLaJ2M9et9C4TX5aGsnN94ViI39/uWZn9z\\n/rW0tElOTipwDIoXu5kwwYN79+5w6dIFBg36mY0btxTZT58+PzNwoBtz5kzn3r27VK9eg3LlyuHs\\nPIBHj+7g5jaYunUbFKpnb+9Q4AEQyI3vKVPmU7myIb6+2wvV+eOP9YWOVahQsUhlWQ+PmQXmIE/5\\nFsDffx8AlpZWRYohicViRo1yZ9Qod0D+4EEmk9G9ey+6d3/7vkIBAYH/bwRjTkBA4Jvl/Pk7TJtW\\nnqQkeRLm+/dPsG9fZCEP34gRYwrVHTFiTKHj+fNXGRqWZ8uW9wvvKylUVVUZM6ZdgWNKSkqYmJiU\\nSPtFGULFhUqOGTOBhQtnsW6dF46OzYqsm5GRwcyZU4iNjUUqleDsPJg9e/wZNWocpqZmtGnThB49\\n+rBjxz4yM9N49uwku3encOGCB/Hxj4iNjUFNTZ3SpfWQSHJp2NCRmzcvYmgYjkwmJjv7Bl5e1iQm\\nPuHBg/uK9A/FERMTrQiLO3bsMBYWlhw4sLfIUDl5+NrrUD4bGzvmzZvJjz92onTp0iQlJZGYmEC1\\natUBig2tq1u3Af7+O+jbV+4NfvToITVr1vroe/Qt8+b8m5mZs29fgCK08ciRQ9jZ1SlW7EaeW9GS\\n2rUtuXz5Ai9fvizQvkwm4/z5M4SGvuDZsye8ePGcfv0GYGJSTVGmXr2GBATsws7OAWVlZUJDX1Cu\\nnCHq6oXztD19Gs6gQbe4e9cRff3HTJ36kAEDmhQq9y4OHbrB0qXRJCerU69ePKtWdSnSuC+O/AnF\\np0+fy+TJezh+XBdV1RyGDtXA1bXZuxt5C5+alkRAQODrRjDmBAQEvlkuXw4nKamH4n1kZFPOnz+A\\nsXHFj2rv2LFb7N37ElXVHMaOrYOxcYWSGuoX580UCm8Llcwrv2PHDmJjUwCKTOR85cpFypYtx9Kl\\nKwH53qe9e3cpzmdmZmJhYUliogGqquvR1d1JfPxw7t27jZ1dNcqUKYOpqTmuroM5fvwoXl6rSUmZ\\nQ05OJnp63iQkuFCuXCYi0buFIUQiEUZGxuzZs5NFi+ZiYlINJ6fe1KvXsMhQuR9/7MSyZZ6oq6uz\\nbp0PtWtbkJiYgI2NXNW0Ro2ainQYQLGhdWPHTmDFisU4O/dBIpFga2vPhAlT/h3TO4f9XfHm/Pfq\\n1Q8LCytmzJiMRCLB3NyCLl2cSExMxMOjsNjNmjUrCQ8PQyaT4eBQjxo1anLr1g3FPIpEIqpXr0l4\\neBjZ2TlMnOhBx45dFLkeQR6uGBUVyaBB/ZHJZOjp6bNw4dIix/vrr4HcvdsXgPh4I1av3kX//lKF\\nouT7kJGRwaxZibx4IRe3CQvLxMRkH5Mn//jebeRPKO7jcxJf307IZPoAeHpeoGXLMIyNq7x3ewIC\\nAv9fiGRfiXxS3oJB4PvDwEBbuL/fMV/y/v7992WGD69FVpYxALq6V9m7VwULixof3NbFi/cYNAji\\n4uRKkZaWWzlwoBWampolOuavHX//S5w7l4yeXg5Ll/5Eerq02LJhYaGMGzeSli3b0KhRE2xsbBk1\\naqhCWKRly0acPHmRJk38yM7+i/R0R2Ji5mNqaomzszNBQbcYOvQXLC2t2bzZmz//9EJLqxzx8dmI\\nRNmoqNTBz288d+/eICTkHu7uk/D2Xo+GRqki98wJfBgl+d39Fr0/Q4YcZd++7or3hoaHuHGjPqqq\\nqu/dRkREOA0aZJKV9Tq1Sb9+u/j117bvVX/p0oUcOnQAIyNj2rfvyM6dRwgLkyKVahATM5fs7LIM\\nGTIPS0tTxWf+5597snTpKmQyKRMmjMba2q5AuhE1NTViYl4wadIURCIR9erV5/Lli9/UvRF4O8K6\\n6vvFwED7g+u8/+MnAQEBga+Mjh0bMGbMVczMArCw8GfGjNiPMuQAjh8PVRhyAHfutOLWrfslNdRv\\ngh07LjBhggk7djixdm1Pevb0f2v5KlWM8PbeSvXqNdiwYQ0+PhsKnBeL5cEfw4YZIhanIxa/xN7e\\nF1XV1z89efvKlJREaGhocOjQAVxdu1O3ri2HDs3CyOjb8o7GxSUwbNgefvrpGO7ue8nIyPiodiZO\\nHENaWiqpqans2fPa23nz5nUmTXIvkbFevXqVO3eCS6QteL/k7p+TR49CWb36Hw4cuPBe5du310ZH\\nJ+/602jaNOqDDDmQh2PXrv16DlVVX+Dg8P55ICdOnErZsgb8/rsXUVGRWFmZkpg4l1ev3ClffjI1\\napylcuWCojn55zk8PIzu3XuyZctOtLS0OXPmJAAeHh6MGzeZTZuKTqEgICDw/SCEWQoICHzTTJjQ\\njgkTPr0dAwMlIB2QL8S0tZ9TuXK5T2/4G+L06TQyMvL2e4m5fNmYlJRktLWLTmD86tUrtLW1+eGH\\n9mhqavH33/sKnM/KygSgV68W+PrOw9FRyuzZ7XByWs39+3cBkEolpKXJE5Nv2LCOhIQEVFVFhIbe\\nJzU1DS0t7QJpJIoLJsmvcrlxoxc2NnY4ONT7xBn5cMaNO8k//zgDIi5fzkUk2s6KFV0+uJ280NWo\\nqEj27PGna1d5KGx8fByBgYUVRz+GK1euIJMpKxQoP4U3w3j/ay5fvsfw4clERPRERSWSa9f2M3fu\\nT2+t061bQ7S1b3H2rD+GhiKGD+/+1vJFoayszLp19Vm8eBupqeo0bSqmX7+WH9yOTCbj9u0gFixY\\nio3NC/bvT+H580iWLDHkzp2nxdYrKt1Iaqr8IYCNjS0Abdt24PLlix88JgEBgW8DwTMnICAgAAwZ\\n0pKuXbehp3eCChX2MW5cNCYmRl96WP8pOjpZyBMvy9HTS6BUqeLDTJ8+fYybmwuurn3ZtOlPnJ0H\\nFTifl85AWVmZFi1aExh4g0mTxlKvXgOioiK5f/8e8+bN5Pnz51SpYoyOjg7jxv3CwYP7iY+Px919\\nBCdOHEMkEim8EfLXhceS31sxaNDQL2LIATx9qkue3Dwo8/hx0fO3bdtmdu2SGz+rVi1nzBj5nsQb\\nN64xZ850evT4iaSkRNat+52IiHBcXfuyZs1KQIRUKmX69Mn06+fE3LkzFG1ev36VgQP74ezcG0/P\\nueTkyCX4nZw6KVQlQ0LuMWrUUKKjo/jrr7/YuXMbrq59CQoKLHKc/v476N+/B/PmzSjy/KFDB/j1\\n1yWA3KDevt3vQ6arEHv37ubw4YMfXG/z5lAiItoAkJNTkYAAXbKyst5Zr00bO+bNa8fIkW0/Ogdg\\n1aqVWLeuE35+bXBz+3BDLj8ymYxu3RqyadMP6OurY2lZDbFYjEz2OtxZvtdQTuF0I4UVU7+S3TQC\\nAgKfCcEzJyAgIIDc4PDy6kVKSjIqKqpFqt9970yZ4sjDh94EBlpQpkw0c+YYvnWBW69eA+rVKygB\\nn5ckHFAsQG/evE5ERDj16jXk2bMnlCtnqEgYff/+XVauXE5qaiqGhhVYtWodISH32LFjK0uW/Mqq\\nVctRU1NnzBi5+/XUqeMsXSqXhff13cjhwwfR09OnXDlDzMzMAViwYDaNGzehefNWODl1on37jly4\\ncA6JJJd58xZhZGRCQkICc+ZMIy7uFZaW1ly7dgVvb79PTs5cqVIKDx4oZoBKlQonvwawsbFnxw4/\\nnJx6ExJyn9zcXHJzcwkODsTW1p47d4IRiUQMHz6aZ8+eKtIiHD9+hKysLKRS+dxevnyR7dv9CAy8\\nwaNHD1m1ah1RURF4es5nz55d9OzZp8jwx/LlK9C7d29kMjG9exe//zC/OEdRFJ9X7ePo0uXDvWPy\\nvgu+V1KSffGwzw/F2tqOo0f/wcVlMDdvXqd0aT1KldKkQoWKXLhwDoAHD0KIiop8aztaWlpoa2sT\\nHByItbUtR4/+818MX0BA4AsheOYEBAQE8qGtrfNVG3Il4f0oDn19PQICnLhypSwXLjSmX7/Gn9ji\\n68X048cPGTt2An5+/kRGRnD7dhA5OTlMnTqJJ0/qcOHCTEJCunL16pN/a8iYPHkPGzaks3JlFr/8\\nshOpVKpYoIeE3OfkyWNs2rSdZctWEhJy73Wvb3jySpfWw9vbjy5dnBRz5+OzHgeHemzZspPmzVsR\\nExP9idcqZ/HiBrRqtQVz87106LAZT8+iPTWmpmY8eHCf9PQ0VFVVsbS0IiTkPkFBtxSKmlC0V0Um\\nk9G3789s3bqL0qX1ePDgPk+fyo3kypWrcPDgAbp06UZQ0M13jvdtTpulSxcSGRnB+PGj2LHDDw+P\\n8Tg792HoUFeePHn81nYfPXqAm5sLzs59mDp1IikpKSQkxDNo0OsUDk2a1OXlyxgAevXqQlZWZoHP\\n98iRbqxd+ztDhjjTp083hfcwMzOTGTOm0L9/T6ZOnYibmwutW0OVKv8AMtTUXtCzZ9oH73/7csg/\\nrwMHuvHgQQjOzn1Yv34N06fPBqBZs5akpCTz8889CQjYSZUqxq9rvmGw5r339PRkxYoluLr2LbKc\\ngIDA94PgmRMQEBD4hvjcizIlJSUMDQ1LvF1zcwuFd6dGjVpERUVSqpQmKSmqBAfLpfwfPmzIkiXb\\nmT+/PK9eJXL4cEt0dJKRyUqxa1d3Gjc+9W9rMoKDb9G0aQvU1NQANRo3blps382ayQ2qWrXMFAIR\\nt28H4em5HID69RsWuy/wQzE2rsD27e/eI6esrEyFCpU4dOgAVlY2VK9eg5s3rxEREYGJSdW31lVV\\nVVPsczMxMSE09AWNGzfh4sXzpKSkcPfuHTp06MTDhyGAPCG1VCq32rKysott900mTpzK1auX+f13\\nLzZu9MLU1BxPz+XcvHmd+fNn4uOzrZCxmffxnD9/FuPGTcbGxo6NG73w8VnP6NHjyc7OIj09jeDg\\nW5iZ1SYw8BbW1jbo6emjpqZeIIxWJJKHlG7Y4MulSxfw8VnPb7+tISDAH11dXfz8dvL06RNcXfsy\\nfrwJu3frcOzYLkxM9GnTpsN7X+eXJi+hOICn57JC59XU1Fix4o8i6xaXbsTCwqKA+MmIEaNLYqgC\\nAgJfIYJnTkBAQOArx9d3I336dGPEiMGEhr4AYNSooYSEyNU2ExMT6dFDLvYgkUhYvXolQ4YMwNm5\\nD/v2BXyxcedHReW1l0QsVkIikSASgURS8GcoIUG+zy4zM4fc3PKAGJACusTF5eTbL/SmUVu8iylv\\nX1Fev4oaX3gvkY2NLdu3+2Fra4+NjR179+6mVq2CCcdLlSqlSOSeR357XiaTGz29evXj5csYdu/e\\nQcuWrTl69DC2tvaAPKQyz3N55swJRV1NTU3S09PeOc48cY62beW50+ztHUhKSiq2bp4KZ56HsV27\\nDgQG3gLA0tKG4OAggoIC+flnV4KCbhIcHFjAG5mfZs1aAHJPZnR0FCA3xFu1+gGAatWqU726XADE\\nxKQiQ4a0o02bL7Nf8mtAJpNx9ux1AgJOv9eeQQEBgW8fwZgTEBAQ+Ip5Vzjhm/z99z60tLTYsGEz\\nGzb4cuDA3nfusflSGBmZoKKSjIbGWQBEojgcHOSJug0N9TA3DyAnpxJqavcwMTmAlVWpf69FhK2t\\nHWfPyhes6elpXLhw/oP6trKy4eTJYwBcvXqZlJTkEr2298HGxo74+DgsLa3+9UypFTJqdHVLY2Vl\\nw4ABvVizZhUgIisrizt3bgMQFvaCKlWMqFChIqam5mzatJGzZ08hFosVyeBdXd1YuXIZgwcPQCxW\\nVnxuWrRowdmzp3F17UtwcNECKPkpbPx+uJfY1taOoKBbxMRE06RJMx49evhWYy7vIcCb4h5f2hD/\\nGpHJZIwe7U/PntXp3t2WHj32kppa9J5NAQGB7wchzFJAQEDgK+ZDwgkBrl27zJMnjzl9Wu6BSUtL\\nIzw8jAoVKv4Hoy1IQXGMwueVlZVZufI3PDxmkJqag7q6MgsW+PLkySNUVVXw9bVh3bqr3LnzgFKl\\ngrl82U6xX6hWLTNatWqDi0sf9PT0qV3b4n1GpBiTq6sbs2dP48iRQ1hYWKOvX+atyp2fgzp16nLq\\n1CXF++3bX3tR/f33K17PmjVf8To6OgpjYxP27NnJokVzMTGphofHTAB69OjNrl1/sW6dd4F+5B7A\\nwh5aExMTfH23v9dYixbnKJhPTSaTIZOBpqYW2to6BAUFYmNjy+HDB7Gzq/PvWOzw8lqNnV0dRCIR\\nOjo6XLp0gWHDRuVr5+1jkRvix7G3d+DZs6c8ffr2/XtfA6mpqRw7dliRYuJzcPVqMP7+LZBK5Sq8\\nly+7sn79LsaN+/Gz9SkgIPDlEYw5AQEBga+aor0f8n1Qck9FdnbBcKpx4yZRt26Doqr9pxw9egaQ\\nh+XZ2zsojru7T1K8Nje3YO/egoaGnV0dxeJ/0aIuQNF70AYMGMiAAQMLHZ86dZbidX6jyMzMnFWr\\n1gFyxb8VK35HLBZz504wDx7cQ1n56/9JLF++Alu37ipwLCbmJbdvP+XKlct06vT2/Xo5OTns2XMO\\niUTK0KHvs8h/Lc7h6TkXZ+c+aGhoKMQ5iksbMW3abJYt8yQyMgI9PT3WrNmoGD+gCAG1sbHj1atX\\naGlpve6xWIefiI0bvVBVVSUxMYH+/XtibGxM1arVCtT/GKRSKUpK8mClkSPdGDnSXaGOWhKkpCQX\\nyBf4OUhPz0Qqzf9AQkx2tiB8IiDwvSOSfWSswuLFizl9+jQqKioYGRnh6emJtrY2AF5eXuzevRsl\\nJSWmT5+Oo6PjO9uLjU35mGEIfAMYGGgL9/c7Rri/n5eHD0NYsGAO69dvQiLJZeDAn+ncuRuhoc8x\\nNTWjSxcndu7chr//Dvz997N//x4uXbrAvHmLUFZWJjT0BeXKGX6UQuf3eG/T0tJYuvQUL1+mEB29\\nEx0dDVRUlBk/3qNEF+//FYcO3WDKlBxUVX9HRSWXuXMn0aZNnSLL5uTk0L+/P6dO9QfEtGixHV/f\\njp9VvdXbez0aGqUKiHN8anvq6ho4OfVCVVWViIhwxo79he3bdxdrjEdFRTJ+/CjMzGrz8GEIJibV\\nmDFjDv369aBVqx+4du0K/foNQFtbB2/v9Tx58hgLC0s8PZejoaHB2rW/c+HCOcRiMfXqNeCXX8aQ\\nkJDA8uWeChXU0aPHY2Vlw8aNXsTERBMVFUlMTDQ9e/bByak3s2Z5cP78WYyMjKlbt8FnESTJycmh\\nT59dnD3rCqhQs+Yutm+3xsioQon3JfBl+R7/NgvIMTDQ/uA6H/0Y0tHRkYkTJ6KkpMSyZcvw8vJi\\nwoQJPH78mEOHDnHw4EFiYmJwdXXlyJEjiideAgICAv/PtGnThGPHzr13+XPnzmBgYFAgnFAkkivX\\nzZjhwf79e2jY0JE8D16nTl2Iiopk0KD+yGQy9PT0Wbhw6We6mm8LmUzGwIEHOHVqICBGS6sRy5dH\\n0bXrl/difixr1sQQHd0LkCfMXrfuL9q0Kbrs7t1nOXXqZ0AeHnnqVH/8/PYyeHC7Eh3Tm/n/TE3N\\niYgIZ8WKJSQmJqCurs7kydPQ1y+Li0sfdu06AEBGRgb9+jnh77+f6OioQuWNjEzIzs4mKOgZ/v49\\nUFMTExv7En19fWbO9MDDYyba2tqMHOlGzZqmBAbeQCKRMGTIcMLCQhk/fgrKyspcuHCOXr26IpFI\\n0NUtzdq1fzJ79jSuXr2Cg0M9qlathrFxVf76ayvduvXg3LnTiryIaWnyPWgrVy6jZ8++WFvbEh0d\\nzYQJo/Dz8wcgLCyUqVNnMWXKOHx8NtC1a49C+QJLkvyeRD+/rmzYsAdlZXU6drShSpXyJdZPVFQk\\nkye7s3nzXyXWpoCAwKfz0cZc48av8w/Z2Nhw5MgRAE6cOEGHDh1QUVGhcuXKGBntZbufAAAgAElE\\nQVQZERwcjK2t7aePVkBAQOCb58PCnkQiEXXq1GPZslWFzuXf7zRkyHBAvtjs3Lkbbm4jhNxSbxAX\\nF8e1axbIFTIhNdWSkycf0LXrlx3Xp5CZqVLgfVaWSjElITdXSt61y1FCIilZIZH8gj1yT3J/TE3N\\nWbJkIRMnelC5chXu3r3D8uWLWblyLTVr1uLmzevY2ztw8eI56tdvhFgsZsmSBUycOLVA+XnzFrNz\\n5yPCwxuRmLgUC4vWLF/+G/b2DgXSH4hEIrKyMvHx2UZQ0C0WLZpHuXKGXL9+FQeHerRr14Ht27dw\\n9eplHB2bsmfPLjIzM9HQUCcsLJTQ0OckJiZQp05dNDW1UFVVw9NzLo0aNaFx4yYAXL9+lRcvnimu\\nOz09nYyMDEQiEY0aOaKsrIxYLEZPT5+EhPjPKtiSP9RVXV2dUaPaC54bAYH/I0pkg8Du3bvp0EGe\\n0+Xly5fY2NgozpUvX56YmJiS6EZAQEDgu2Lbts2cOnWc7OwcmjZtzqBBQ4GiPRvvw9q1J/njD1VS\\nU8vQqNEpvL27oqGh8Tkv4ZtCS0sLXd2XvBb4k6Kt/W3Jtw8fPpC1a18LnLRrJyEkJJzs7Mqoqz+j\\nfXvYuXMbnTt3Q02tYPikk1MT/P03c+mSKyCiQYOt9OtXjBvvIylKsCc7O4s7d4KYMWOyolxOTi4A\\nLVu24eTJY9jbO3D8+FG6d+9Jeno6t28HFyq/adNFIiPtADFKShLS05WJjMzE3l6e/mDGjCmK8q1b\\ntwXke/IyMtJRUhJz9eplLlw4S1ZWFomJiYB8L1tQUCB2dnXQ1S3N7NkLGDiwP5MnT8fU1AyADRt8\\nuX79KqdPnyAgYCcrV64FZKxf74uKSmHjWVlZfkwikRAX94qRI92oVKkKMpkMH58NXLx4jqysLCwt\\nrZk0aRoA/v472LcvALFYjIlJVebMWUhGRga//rqEZ8+eIpHkYmtbh9u3g0hLSyUyMgIDg3IkJydh\\nZGRCZmYmP//ck7lzF1G+fAVcXEYRF5eARJLLkCHDcXRspgg3tbS05vbtIMzMatO+fUd8fNaTkJDI\\nrFnzMDe3YONGLyIjw4mIiCAxMZF+/QYU2ospkUhYt+4PAgNvkJ2dQ7duPejcudunfXgEBAQ+irca\\nc66urrx69arQcXd3d1q2lCdhXbt2LSoqKnTq1KnYdoSnwwICAgIFuXr1MuHhYWzYsBmpVMqUKeMJ\\nCrqFmpp6Ic/G++zlio2N5bfftElIkP9tPnHCjpUrdzNlSuHkyYcOHeDBg/u4u0+id++utG37I66u\\nQ0r8Gr821NXVGT9enWXLDhAfX4E6dQKZPLlkQww/N/kNOYAJE9phYnKB+/cvYW1dms6d29Cjx0+0\\nbftjIWNOXV2dHTs6s3nzHqRSGePGdSMjo6Q9RoV/72UyGVpa2kWGGDZu3JT169eQnJzMw4ch1KlT\\nl/T0NLS1C5dfterwR48qNvYl6uoaLF68gq1bfTExqcru3TupUkWu/GhsXJV9+wKIiAgHICsrk7Cw\\nUMqWNSAzMwMTk6rcvRtMQkICAHXrNsDffwd9+/4MwKNHD6lZ83WOwJ07t/HixXMqVqzEb7+txcvr\\nD+7du42jYzNOnTrO5s1/MW/eTC5cOEfjxk3YutWXXbsOoKysrAjl3LzZGweHekydOou7d+8wZsxw\\n9u07zNatm9i+3Y9Bg4YSHBzIgQN72bVrB23b/kjVqtWQSCT88ccfZGTISExMZNgwVxwdmwEQHh7G\\n/PlL8PCYyeDBAzhx4ihr13pz/vwZNm/2USQtf/r0CV5em8jISMfVtR+NGhXUPsifAiU7O5sRIwZT\\nr16DL6KaKyDw/85bjTkfH5+3Vg4ICODMmTP4+voqjhkaGhIdHa14Hx0djaGh4TsH8jEb/gS+HYT7\\n+30j3N/3RySSz9edOze5ceMqQ4bIF4MZGRkkJr4kLS2N9u3bUblyWQDatGmNpqbaO+c4NjaSpKRK\\n+Y6okJ1dCgMD7QJKfQDa2upoaKhiYKBNxYoV6NChbbHtl9S9bdmyJQEBcs/DgQMH6Nu3LwBXrlzB\\nx8eHdevWlUg/RREeHs7w4cM5cOAA7u7tcHNLIzExkQoV7L+5/dx2dnbcunWLK1eu8Mcff6Cnp8ej\\nR4+wsLBg8OBlbN68mVevYnF3H4G+vj6+vr78/fffeHl5AdCsWTOmT5+gaO8TRSAL0aKFI1OmTMHd\\nfRQ5OTlcuXKBXr16YWRUhRs3LtCuXTtkMhkPHjzAzMwM0MbGxpp1636jdetWlCunA+gUWX7ChLbs\\n3z+OsLAGSKXKaGpKqV1bHwMDbXbsOE7jxg0xMNBGRUXMxYunadu2BdevX0dHRwcdHR3EYjHDhrnS\\noEEDhgxxZefObZQpo4WjY0MCA6+yZMlipk+fyKNHj1i8eB4eHh5UqVKO8eMnkJqaSmRkJPPmzcPA\\nQJt582Yzd+5cBg3qh0QioW7dujRqNBtNTTU0NdVJTVVDR0cHLS1NypTRpH//Pty+HcjkyWOJi4tj\\n4MC+JCUlYWVVGwMDbczNzfD0nEXr1q1p3bo1pUqV4ubNq1y5cgF//23Ex8eTnZ3F8OGuREdHI5VK\\n8fffRk5ODiKRiNDQZwwY0A83twHk5OQglUoRi8WIxWIiIsJZt+43rly5QpkyZfDwGEfXrl2Jjo4k\\nOTmRlJRYHBxs8PX9EwMDbbS01Gnb9gcqVSoDlKFRo4aEhz/BzMwMZWUxBgbaBAff4MGDB5w/fxrI\\nSxQfh4GBacl+oASKRfjdFcjjo8Msz549y8aNG9myZcu/4RRyWrZsyfjx43FxcSEmJoYXL15gbW39\\nzvaE2O7vFyF2//tGuL8fhkwm/3uXnp5N377OXL58kZcvYxCJlEhKSiczM4sHDx7TqVNnpFIpKSlJ\\nODn14cWLGBYunMPFi+eoUsWYgQPdiIgI5+7d29y5E0xychI1ayqTlNSC6OhfqVHDmsDAslhbz6Bc\\nOQPq12/ElSuX0NTUJDk5mZSUFEJDI3j69Bnbt/szZowRI0e6YWFhxc2b10lNTWHRIk+MjU3JzMxk\\nwYLZPHv2FCMjY169imXcuMkfpP4olcqIi0slLS2NLVv8aNNGHs2RmJhOVlbuZ/0MxcenkZsrKdCH\\nqqoOcXFpn63P9+FjJPDzPj+Jiencu3cPPz9/ypQpy/Dhgzh58jzt23fF29uH335bi46OLvfvP2XJ\\nkqV4e/uhpaXNuHEjCQg4QJMmzT/Ld9fAoArNmrWiQ4eO6OnpU6uWOWlpWUydOodlyxbx+++ryc3N\\npXXrHyhTRv7wwdGxBTNnevD7716K8RRV3sVlME5O1Xnw4A729gdxcFjG4sVLC6Q/iI1NISdHglQq\\nol279kREhDN3rie///4r9eo1RCqVcOdOMF26dKVmTVNycsS0bt2RI0fGMGzYMOrVa4Cqqjrjxk1W\\nhFmuWeNNVFQkEyeO4fz5y6xb54WBQTk8PZfz6lUsK1Ys4cqVa9jY2ODt7YeRkQmrVi1HJpPh7b2N\\nkJD7zJw5lZSUVCSSXCpVqoy39za8vdcTH59MbGwKCxYsJzDwJhcunGP16jX4+u4gN1fKnDmLqFLF\\niN27/+LVq1cMHfoL7u6/cP36VdzdJ1O+fEW6dm3Py5cvmTRpMr//7kVwcCC+vn/SrVtPevXqR7Nm\\n9RGJVFm4cDmTJ7uTkZGBikopGjduilgsZs0aLwYMGEhWVjaxsSmkpWUhk8kU9yIzM4eUlKwC36Os\\nrBzGjJlQKAWK8Fvw3yD87n6//KdqlvPnzycnJ4eBA+U5fmxtbZk9ezY1atSgffv2dOjQAbFYzKxZ\\ns4QwSwEBAYE3qF+/ARs2rGP+/CWUK1eO8PAwJk92x919ImvWrGTz5r8oW7YsLi79EIlg06Y/0dTU\\nonLlKvj6biclJYVHjx5w/fpV1NTUOHz4NO7uI8nOVkdHZxdXr2bRtKkjY8dO5MWL5wwY0IudO/dz\\n/PhhduzYStu27enUqSsuLn0K5AmTSqVs2ODLpUsXWL16NUuWrCIgwB9dXV38/Hby9OkTXF37vvXv\\nuofHBF6+jCE7O4sePfrw009yhRGZTMa6db8TERGOq2tf6tatT8OGjmRkpDN9+mSePXuCqak5M2fO\\nA+QiE2vWrEQikWBmVpsJEzxQUVHByakT3t5+6OjoEhJyj9WrV/L7714kJCQwZ8404uJeYWlpzbVr\\nV/D29gPkecQWL17AnTtBioV4/geRxfGmV7MkyS9c8TGYm1tQtqwBADVq1CIqKgorK5sCZe7fv4u9\\nvQO6uqUBaNOmHYGBt2jSpPlH9/suisv/t3x5YREfgObNW3H27NUCxypUqFhk+WHDRhZ47+Xlo0h/\\nkD/XXNu2HejRow+TJ7tTvXrNf0NsJ7/ZHABqamq4uAxmx46tLFhQvPJrWFgos2cvZPLkacyc6cGZ\\nMyc5ePAAEyd6IBaLGTNmuELYRSaDlJQUxo3bwPPnf1OjRlXs7R348891iMVi0tPTOXXqOC1btkEm\\nkxETE429vQPW1racOHGUjIwM6tVrwK5dO3B3n0SdOvUYN24UPXv2pXZtC27fDqJsWQNmz54KyENo\\nZTIZlStX4dKlC5ibmxMcHEjNmqZIJBKFcEsezZq15MGD+1SsWIkbN64VOCeTyTh//gw//+xKRkY6\\nt27dYPjwUWRnZyvK1KvXkICAXdjZOXxyChQBAYFP46ONuaNHjxZ7btiwYQwbNuxjmxYQEBD4bslb\\nvNet24Dnz58zaFA/0tLSUFJSQklJzJ07tzExqcbkye7o6eljaWkFwI0b1xg5ciz3798BQFtbm5iY\\nGCpUqEiZMmWZN28mxsbGqKqq4O7eFkfHady4cQ1X176kpqaioqJKZmYGd+7cViwgq1evgb5+mQLj\\na9asBQCmpmZEREQAcPt2ED179gGgWrXqVK9e863X6OExEx0dHbKyMhkyxJnmzVsqrv1NifabN6/z\\n6NGDAh6m27eDqFXLjIUL57Bq1ToqV67C/Pmz2LNnFz179inWAPLxWY+DQz3693fhypVL/P33PsW5\\nohbiP/zQvkjDs02bJnTu3J3r168ybtwk7t27w6FDcvn8jh270LNnn0Iy7du2bSEzM4OBA90KeTin\\nTJmJjY0tWVmZLFw4hydPHmNkZEJWVtYnqRyqqKgqXovFSkgkuYXKiESiN/r4fKqKJUVReeGmT59D\\n//49ijTiAR4/fsiwYQNJTExESang56NChYqMGTOBSZPcWbLkV27duvGv5wzi4lKwsBiMiUlasQ8V\\nQkLus3z5IpSUxKxZs4pp02ZhampGcHAQN29eY8CA3mhqliItLQ1VVfkDgkuXnpCbq8fx46Foa0ej\\npVWWrl2dCA19waFDBxg/fhS1a1sCcjGRefNmkpaWikwmo0eP3mhpaeHiMphVq5bj7NwbqVSKnp4e\\n48b9QkZGJllZWQwePABlZWWMjEzQ19fnwYP73Lx5nR9+aMfBg3uJiopCU1MLZWVlxf7JvO+Oqqpc\\npEVJSQmJRFLgnEgkonr1mowePYzExERcXQdTpkxZoqIiFWWEFCgCAl8PJaJmKSAgICDwfhw9ekbx\\nunr1GlSpYsyvv65GTU2NUaOGUrOmKaGhLxQLyTyOHZPn65RKXy/Gc3NzABnLlq3k1q0bbNniw4MH\\n9xk9ehwikRILFy6jShUjzp07zZkzpzA2Nvm3ZvEL+jwDQUlJTG7ua+PgQ4wOf//tnDsnv86XL18S\\nFhb21nYKe5giUVfXoGLFSlSuXAWA9u07EhCwU2FUFsXt20F4ei4HoH79hmhr6yjOVahQiRo15Eao\\nqakZUVGRQNGGZ2ZmJhYWlowcOZaQkPv888/fbNjgi1Qqw83NGTs7e7S0CobC5Peyvenh9PFZz2+/\\nrWHPnl1oaJTCz8+fJ08eM3Bgv88SuVKqlNyw0NHRxczMgt9+W0ZSUiJaWtocP34UJ6feJd5nSZOX\\nq83S0hpPz7kEBPgXO1cymYwnTx6zfv1rwY6yZcsW8CTlZ8cOP8aPn4KX10P++acTV67ooa+/i4oV\\n77FzZ4DioUJwcCC1a1vy229LmTjRg/nzZ9GhQyfWr1+DiUk1Tp06jo6ODgcPnmDNmpVcvnxRYdy/\\neKFDfPxQkpOd0NAIIienF2pq6nTr1pM7d4JZu3ZjgTGtWfNnoXGqqakxceLUQsejoiLp2bMzixat\\nwNLSikWL5lGxYiUiIyMwNCyPrm5prKys6NixC05Ovbl16waGhuXQ0dHF13cHPXr8BMDUqbMICbnH\\n5csXqVChIr6+OxR9VK9ek+nT5xToN38ZqVSKm9sIhg79pbhbKCAg8B/xbe38FhAQEPiOyFPtU1NT\\n4/nzZ9y+HcyxY1e5deuGwthITk4CoG7d+pw6dYLExHiSk5OIj4/jxYtnREdH8fjxQ2xs7JBKpchk\\ncjEVsViJXbvkCy9zc0uuXbtCcnISVlbWnDp1ApFIxNOnj4mPj3vnOK2sbDh58jgAz5495enTx8WW\\nvXnzOjduXMPLy4dNm7ZRs2YtsrPfLv9f2MMkKbRwl8lkimNisVhh1GZlZRcqVxR5ngiQG6p53gh/\\n/+24uPRl6NCBCsNTSUmJ5s1bARAcHPiv1L46GhoaNGvWkqCgW0UaFvn7zu/hjI6OAiAoKJAffmgP\\nyA35d3k4iyJ/v8XZgT/91JXx40cxZsxwypYty7BhIxk9ehiurn0xM6uNo2PTD+73v6ZcOUMsLeX7\\n7du2/ZHbtwOLLSsSiWjSpBmqqqro6pbG3t6Be/fuFFveysqGlSuXc+nSfcRiCSAmI8MEZeVKlC1r\\ngEgkokaNWkRHRxEa+pxnz54wd+4MwsPD2LzZm9jYWLKzs8jNzcXIyIRTp47Ttm0HZDIZjx8/AkBZ\\nWYJIJEMq1f733xMAjh7955Pm5dGjF+zZc5by5SuyZ89O+vfvQWpqKr169WPq1FnMmDEZZ+feiMVi\\nunRxypuhN2eswOuiPsvFfbYyMjJwdd2Jnd05mjc/xOHDtz7pegQEBD4dwTMnICAg8IWoX78Re/fu\\npl+/HiQkKJOcbIWPTzuqVoVJk9wRi8Xo6+uzYsUfODsPYsWKxYjFynTu3I5KlapQu7YFhoYVGD58\\nEFKplFKlStG/vzNaWlqoqqqSm5ubL0RLn6FDXdHU1EJDQ4MjR/4hNvalwiNWFHmLvG7dejB//iz6\\n9++JsbExVatWK7A/KT9vGqh37xZcVJcqVYr09PR3zo2RkTFRUZFERIRTqVJljhw5hK2tPQDly1cg\\nJOQeM2ZMLpD/Sm50HqNfP2euXr1MSkryW/vIb3jmeUazs7NQVVUr4GXLT55Rmd+gBLmUff6y+T2c\\neYZjSZDn2bW3d8De3kFx3N19kuJ19+696N69l+J969ZtFXnXvhXyz6V8zpXeasQXrl/8s+r+/V1o\\n1MiRfv18qFKlD+Hhcq+YsvLrOnkPFQCqVq3OrFnzmTJlnMIztWmT3LM2c+Y8li1bRGRkBNHRkZw/\\nf4YaNWpSt646p08/ISHhJWJxW2Syvbi6nqNu3QYf7Y3dv/8qHh6qxMa6UqaMBb17pzJjxmsPfp06\\ndfH23goUFMjw938dbpybm4uXl7fCa21mZs6qVQWVZAcOdCt2DIsWneDgQWdAmehomDPHn9atc1FW\\nFpaTAgJfCuHbJyAgIPCFUFFRYdmyVdy//5BWrTTIza0NwL179WnV6i9mzHidI05DQ4Np02a/d9tH\\nj5794PHk7T8CKF26NCdOnCA2NgWRSMT06XNQV1cnIiKcsWN/wdCwfJFt5Bmo/fv3oEoVY8Wevzxv\\ngDwEzIYBA3rRoEFjGjZsXKQXQFVVVeFpkEgkmJtbKDwNrq5uLFo0l8zMLMRiZcXi2NXVjdmzp3Hk\\nyCEsLKzR1y9DqVKapKWlFWmUvcvwBLCxsWXBgjn07++MVCrj3LnTzJgxDz09fYWXVF1dg4sXz9Ow\\nYeO3zq+trR3Hjh3G3t6Bp08f8+TJo7eW/1SysrLw8TlFVhb07l0XQ8My7670lRATE82dO7extLTi\\n2LHDWFvbkJ6eRkjIPRo0aMSZMycUZd9HsCM/ERHhVKtWg19+ac+qVS9RVb2CkdFzqlbVKVTWyMiE\\nxMQE4uLi/lWYzCUsLBQXl0GcOnWc2NiXLF++ijVrVnH58gVcXAYDsGLFPCIjo3jwIBh7+5/Q1R2g\\naHPEiNEfNScbN8YRG9sTgLi4Rnh776R79/evf/jwLebMiSE21pDatU+wYUNLDA3LftAYXr1SJf/S\\n8eXL8iQlJVGmzLfz2RIQ+N4QjDkBAQGBL0xurgSpNP+fYxFSacnupfL3v8SmTUlIJCK6d1dlyJAW\\n76wjkUgYPXoXp07poK29mrJlZejpaTJhwpRin8TnGaiF+3/tHZg1a36Bc3Z2dRSv83uYHjy4z48/\\ndsLJqTerVi1n/PhRrFy5ltzcHMzMavPq1StUVFRITk5m6FBX5s1bxIoVv5OcnMzMmVNITU1l+PCB\\njB49Hl/fHWzc6EVkZDiRkZGUL1+Bn37qxpIlC2nZshGqqmqYmFQFCnqFatUy48cfOzJkiDMAnTp1\\nVSSHdnEZTIcOrbGxsVPULRp5e126OLFwoVzIw9jYBDOz2m+p82nk5OTQv38AZ864ACoEBGxn586G\\nH7x4/1IYGRmzZ89OFi2ai4lJNbp27YG5uSWLFs3lzz+1sLOr80GCHfJy8v/9/bdz8+Z1RCIlWreu\\nRLdulcjN1WfPnieFxqGsrMy8eYtZuXIZqany1AK9evWlatVqTJ06C0/PuYhEFOlxe/z4FTduJJCV\\n9Yh27ep+8pxIJOIC73NzxcWULIxMJsPTM4InT+R7Ti9fbsKCBVtZtarzB42hbl019u4NJyenMiDD\\nwuIR+vo276wnICDw+RDJPkVKqwQR8mV8vwj5UL5vhPv76UilUgYP/ou//+4HlKJWLX/8/GwwMalY\\nIu3fu/eEbt0yiI9vCICm5gO8vV/SooXtW+v5+Z1j3LgmgFzso3Tps5w+bUjFiiUzrndx9+4dduzw\\nY968RYwYMZjc3FzWrPmTLVt80Ncvw7Jlnixe/CuNGjmyZs0qcnNzCQy8QWRkJGXKlGHSpOn8+WcQ\\nt2/vwMFhKDVrRnDt2hXWrPkTVVVVZs+eRrduPbC2tiU6OpoJE0bh5+f/QWNs06Ypx459uBf0c3P0\\n6CX697cH8ow3GRMn+jNxYntFma/1u/umUui3yNat55g1y4jkZEvU1Z8yfvwtxoz54ZPa9PE5w9y5\\nNUhLM6NUqYdMmXKPYcNaFVn2zXsrkUiwsztHdHQnxbH27Xfj6/vhY/LyOsHFixJ0dTOZNq3RN/OA\\n4Hvia/3uCnw6/2meOQEBAQGBkkFJSYkNG3qybdsxkpNz6NbNgQoVit/L9qFcu/aI+PjX8VhpaaYE\\nBd2mxTuccxERueQZcgCJiTV4/vzRf2bMmZqa8eDBfdLT01BVVcXMzJyQkPsEBd1i7NiJqKio0KiR\\n479lzbl+/Qre3lvp2LENqqqqTJgwjYQEPcRiMVu2dKZJk3F07NgUVVX5frbr16/y4sUzRX/p6elk\\nZmYWyJW1bdtmVFVVFd7BJ08es3LlWm7cuKZIfbB+/RouXjyPmpoaixYtR09Pn4SEBJYv9yQmJhoA\\nS8sfiI8vS2rqecqUUSEqKpKYmGh69uzzWdQl1dSUEYkyef24Voqy8lfx7Pa9+Jrz0z56FMbs2bd4\\n9UqT2rWTWLy4o+Izlcfu3RkkJ8tTD2RmVmPfvkDGjPm0fl1dm2FsHMitW7extS1Hq1ZFG3JFIRaL\\nsbd/yaFDuYAyKirhNG78cUvAoUNbMXToR1UVEBD4DAjGnICAgMBXgFgs5uef339x9iE0aGBK2bKX\\nePVKvqdLS+s+dnZF73nLT5s2lfnzz0CSkuQePHPzc1hb/3dKiMrKylSoUIlDhw5gZWVD9eo1uHnz\\nGhEREZiYVEUsfv0TpqQkyic0ImP9el86dDhDaGhXRZm4ODVFvq385VRUVCgOGxt7duzww8mpNyEh\\n98nNzSU3N5fg4EBsbe05fvwIlpbWuLmNYM2aVezfvwdn50GsXLmMnj37Ym1ty/z5f7F16188f36M\\ncuUeUr36GXbv/ou0tFT69u1O1649EIvfP2TufWja1IHOnf9i797OgBb16m1nyJAfS7SPz8WbMvlf\\nG+PHX+fyZfkeuFu3stDW3s3cuZ0KlJGrZL5GWblkRHBatrSlZcuPq7t2bScWLfInNlaVevXUcHH5\\nyIYEBAS+KgRjTkBAQOA7x9S0KgsWXMbHxx+JRISTkzrNmjV7Z722be1ZuvQ4Bw/uRlU1m7FjrYtV\\nsfxc2NjYsn27H1OnzqJateqsWrUCc/O37zWrW7cB/v47KF9e7lVUVQ0hO9sMLa3sIsv17fszAI8e\\nPaBmTdMCZT7GOwgFvX5PnqQgEskQidLJzdUlJcUMZWVldHVL/+vFi3+rqujHIBKJWLeuJ926XSY1\\nNYsOHTqhoaFRon38PyKVSnnxIr9QihrPnqkVKjd4sCEhIaeJiWmMnt4NXFw+PHSqpNHQ0GDOnI5f\\nehgCAgIljGDMCQgICPwf0LVrA7p2fXe5N+nSpT5dury73OfCxsaOLVt8sLS0Qk1NHTU1NWxs7IA3\\nc669fj127ARWrFhMbu4jatdeR1ZWDapUaUXDhpULKGfmlXN27oNEIsHW1p4JE6YU6P9TvYMqKip0\\n6XKIixd75WuTfHWUyM0tudQF+VFSUqJdu0afpe3/V5SUlDA2TiYqKu9IFlWrFs6j2LatPaam4Vy4\\ncAB7+2qYm79d6VRAQEDgYxGMOQEBAQGBr5Y6depy6tQlxfvt2wMUr/NyrgE0b95KkehbV7c0c+Z4\\nvrPt9y33Kd7Bvn1/ZtSoSoSFrScsrBM6Og9xdCwsgS/w7bB8uQOzZ28lNrYUlpYpTJ/eochyJiaV\\nMTGp/B+PTkBA4P8NwZgTEBAQEPi/4+nTcDZtCkYkkuHm5kClSobFlv0U7/xS3VsAACAASURBVGCe\\n169bt9o0b16LwMByQk6ub5yaNauwdWuVLz0MAQEBAUBITSDwHyBI6H7fCPf3++VrubcfKlX/4sVz\\nZs2aipKSEvPmLaJSpYLekcjIl/ToEcijR90BGbVrbycgwBF9fb3PMPqvl6/l/gqUPMK9/b4R7u/3\\ny8ekJlD6DOMQEBAQEBD4Ypw9e5oWLVrh7e1XyJAD2LPnxr+GHICIe/d6sn//1c8+rri4eHbtOsWt\\nW/ffu87IkW6EhLx/+fdh4sQxpKWlkpKSwp49uxTHb968zqRJ7iXal4CAgIDA50UIsxQQEBAQ+OqR\\nSCTMnTuDhw9DMDGpxowZc3j27Bl//PErGRkZ6OqWZtq0WTx8GMKuXdtRUhJz8+Z1Vq5cy44dfhw6\\ndACAjh27ULp0OZSVQ6hceQwZGbaoqwehojKAbds2c+rUcbKzc2jatDmDBpVcMq17954xePATHj9u\\nj4bGY8aMOcq4ce9O2CwSiT4o55pUKkVJ6e3PaZcuXQlAUlISe/b407Wr03u3/zYkEkmJp1h4kzZt\\nmnDs2LnP2oeAgIDAt4RgzAkICAgIfPWEhr7Aw2MmlpbWeHrOZffunZw7dxpPzxWULl2aEyeOsn79\\nGjw8ZtK5c3dKlSpF7979CQm5zz///M2GDb5IpTLc3JyZPn0ubdse5OHDUGJj3WjZ0pQaNcpy5kww\\nGzZsRiqVMmXKeIKCbin2xn0seSGiOjoDePy4N3p6GxGJMti58yCqqvcICrpFamoKU6bMxMbGlqys\\nTBYunMOTJ48xMjIhK+u1UuLVq5fx9l5PdnY2lSpVZurUWWhoaODk1IlWrX7g2rUr9OvnTKtWbRR1\\njhw5xK5df5Gbm0Pt2paMGzeZXr26sHHjFlavXkFERDiurn2pW7c+DRs6kpGRzvTpk3n27AmmpubM\\nnDkPgJCQ+4UM5zJlyjJypBu1apkSHBxEmzZt6dWr3yfN17v5epOJCwgICHwJBGNOQEBAQOCz4+TU\\nCW9vP3R0dD+qfrlyhlhaWgPQtu2P+Pp68/TpE9zdRwByj1SZMvJcbTKZjLzd4MHBgTRt2kKRLLxZ\\ns5bcvh3IwoUdGT58L1u21KBq1aqsXr2Sa9eu4OraF4CMjEzCw8Pe25g7f/4sz58/pX9/lyLPSyR5\\nP7eif8eohESSy4YNvsyfPwsfn/X89tsa9uzZhYZGKf7H3n0GNHW1ARz/BwhhJYg4UARFRBxMte5t\\naaWOalUcxYWr1FG34sCtddVVd0VxK65XrVqte9Sq4N4DZYsDgQgEEvJ+SEmhYB1FcZzfp+Tm3nvO\\nvWHkyTnnedauDeHu3Tv4+emCo2fPnrF6dRDz5i1CJjNh7dpVbNq0jm7deiKRSLC0LERQ0Nocbd6/\\nH86hQwdYsiQIQ0NDZs+ezv79e/WjfUOHDuXGjZusXLke0E2zvH37JmvXhmBtXQR//x5cunSBSpVc\\nmDt3JtOn/4SlZc7AWSKRoFar+eWX1a90nwACAoYSH/+Q9HQV7dp1pGXL1nh51aNdu46cOnUCmUzG\\njz/OxsqqMDEx0UyYMIa0tFTq1Hl3BesFQRA+FCKYEwRB+Mj988Nz8+ZfM23aRG7evI5EIqFZs5b4\\n+HRCqVRy4MA+WrduS1jYObZv38SkSTNfuZ29e3fz2Wc1KVKkSK7XXmeqYF6yH6/VajE3N8fBwZEl\\nS4L+dd9/tqvVavXBjEIhp2zZsvrXfH278fXX37xR/+rWrU/dui8ONtq3L8Hx4yfIyACJJJXChdNp\\n0kQ3zfLo0UNYW+vu2cWLF2jXrgMAjo7lcHR0AuDq1cvcv3+P777zAyAjQ42rq5v+/NlH47KEhp7h\\n5s0b9OypK4qenp6OldXfSV7yyn9WsWJlfQHzcuXKExcXi4WFBeHhdxk4MHfgrGv75dNFswsICESh\\nUKBSpdGrV1caNmxMWloaLi5u9O79PYsWzWfnzu107dqDefNm8c037fjyy6/Yti3ktdoRBEH4FIhg\\nThAE4SP3zw/Pzs4Vefz4kT47pFKpBCA5Oek/raHas2cXDg6OzJ79Y66RlyxeXvXo0aNPjjVsPj4d\\nCQ5ewbZtIdSuXY/Dh3/HxsaGpUtXIZPJuHPnNnFxsXTs+A116zZg9+4d+Pp2Y9euHVy5chkXF1fU\\najWRkRE4OJTN0Sd3dw+mTJmAr29XMjO1HD9+hLFjJ+UIZAIChhIefpdHj+JRqzNo06Y9GzasYceO\\nrSgUlpQr54SxsTGDBg3nxIljrF4dhFqdgUJhybhxk7GyKsyePbu4efM6gwYNZ8qU8ZibW3Dz5jXi\\n4+PJzMykYUM3Fi48w+TJSwENGRkZhIff4/jxo6hUKuLiYpk0aWye9zWrr9Wq1WD8+Cl57mNqaprn\\ndm/v5vTp0zfHtr17d7/wPZRKjfWPDQ0N9EXQXxQ4A5iY5N32i4SEbOD4cV2NwPj4eCIjI5FKpdSu\\nXRcAZ+eKnDv3JwBXrlxi6tRZAHz5pTeLFy94rbYEQRA+diKYEwRB+Mj988NzRkYGMTHRzJ07k1q1\\n6lK9ek0AlixZoF9DZWRkhFxukef6qVWrfuHkyWOoVCpcXNwYPnw0hw//zo0b15k4cQxSqZRly4IB\\nrX7kJUtmpjbXGjZPzyp88YU3QUHLaNPGB41GTWTkA44ePcQXX3izaNE8bGxKUKlSZfbs2Ulmppa2\\nbTtQvXot5s2bhVKpRKNR0759J30wlzUgV758Bb76qjm9enUFoEWL1jg5lSc2NkY/apcV7G7YsIaF\\nC+exbVsIMTHRLFu2CgcHR374wR8np/KArubcsmWrANi1awfr1q2mX7+BuUYAnz59wuLFQdy9e5vu\\n3b8lKSmRyMhryOUymjf/mrCwc9jZlaZFi1Zs27aZQoWsGDt2Eps2rePAgX1UqVKNe/fucPfubSQS\\nCZUru/LTT9OJjo7C1rYUqampPH78CDs7+xe+71WrVmfkyCH4+HTCysqKpKREUlJS9K+bm5vneP4i\\n9vZlePYs4aWB86sICztHaOhZli5diUwmo3//PqSnqzA0/PvjiIGBRB9ECoIgCP9OBHOCIAgfsbw+\\nPKvVGQQHb+TPP0+xY8dWDh06QEBAIP7+AwgPv8fKles5fz6UUaOGsmbN5hzrp9zcPPjmGx+6desJ\\nwKRJgZw8eZxGjT5n27YQypVzIiLiAX36dOfx43iSk5OJjIxEpVIxffoUMjM1mJtb0KePHzKZjOrV\\na3Hx4nmio6OwsJBTrpxuWmHJkrbExsYQFhZKXFwcZcs6kpDwjIkTf2TevFnIZDKcnMrz88/Lcl2z\\nn1/vHM/bt/82V2KOEiVKEhy8EcgKdo8AEoyNjfH2bk5ExAOcnJwBaNSoCZGREQDExz8kMHAkT58+\\nISMjg5IlbYGcUxYlEgn16jUAwNHRCSMjI3r16oqpqRlKpZLz50NJS0v7x2iaLhhs1aotU6dOwNe3\\nHaVLl6FChUoAFCpUiNGjxzN+/CjS0zMA6N37+38N5sqUcaBXL38GD+5LZqYWqVTKoEHD9W1ZWVnh\\n6upOly7tqVmzDrVq1SGv2bBGRkZMmjT9hYHz60hJeY5cLkcmk3H/fjhXr1751/1dXd05eHA/X3zh\\nzf79+167PUEQhI+dCOYEQRA+Ytk/PD94cJ+rV6/w7FkCGo2aBg0aY2dnz6RJgUDOgESr1eLm5pZr\\n/ZSbmwdhYWdZv34NKlUaSUlJlC3rSJ069QAwNpZx48Z1tm37lUGD+nLnzm1SU1NIT0/H1dWNo0cP\\nUaxYccaPn8KiRfO5fv0qJUuWRCKR5Ehrb2BgQEZGBosXz8fKyooVK9Zw8OB+tmzZmK/358cfV7Jr\\n137S0nrx+eepyOX7KF26DA8e3M92L/7ef86cGXTs2Jk6depx/nwoQUG5g0kAqVSqf2xoaMSmTTsA\\nePLkMadOnWDbts1/jXhWACSEhPwPAJlMxoQJU/M8Z5Uq1Vi+PHeikZCQnS+8viZNvHKtp8tqC2Dc\\nuMk5XvP0rKp/rAv8dF4UOC9YsPSFbeelRo3a7NixFV/fdtjZlcbFxRV48TrHH34YyoQJY1i3Lpi6\\ndRv857WXgiAIHxsRzAmCIHzE8vrw/OjRI/r3/w6tNhOA777rn+exxsa510+pVCp++mkGK1asoWjR\\nYvpU+VksLQuRlpaGRqP+a/80IiIekJ6um5JpZGREePg9VKo0HBwc2bVrBwMGDCEqKipH21otJCY+\\nIyLiPhkZGXTs2BpjYxkpKamYmprky72JjIxm7VoJRkZliYlpx7p1YTg6TqdFi9ZcuBBGcnIypqam\\nHD16SD9imJLyXB/g/tvas7zExcVRtGhRWrRoRXq6itu3b9K0aTOMjIxQq9UYGb36v+RLl25z8eI9\\n6tVzoUwZ29fqx3+lUqmYM+cgjx4ZUq9eIVq1qvHKx0qlUmbNmp9r+/79R/WPGzZsQsOGTQDdCGr2\\ntXq9evn/h54LgiB8fEQwJwiC8BF70YfnrIyJ2ZmZmb10DVVW4KZQWJKSksLhw7/TuLGX/vhy5ZyQ\\nyWR06NAaCws59vZluHXrBhqNBnv70kilxvo1bEqlEjs7O/16tH+OukgkEhwcHBkwYDDTp0/BwEBC\\n3br1uXHj2hvdi3+6dSuK+Pi22NpeonTpr8jIcMDS0p5ixYrRuXN3evXqikKhoHTpMpibWwC6KZxj\\nx45ALldQtWo14uJi9X190ehS1uPz58+xYcMajIyMMDMzZ8yYCQC0bNmabt064uxcgbFjJ7203ytX\\nHmXq1BIkJraiZMkjzJnzmEaN3PPlnryK77/fwa5dXQBjtm69SVraSTp0qJOvbSQlJbFlyx/I5ca0\\nadPgpYXQBUEQPlUimBMEQfhEbN9+mjVrEtFqoVMnOe3a1c7xuqVlIf0aKplMho1N8VznkMvltGjR\\nii5d2lO4sDWVKrnoX/vqqxbMnTsTqdQIQ0MjhgwZSdmyjvTo0Zm6devra8xlrWE7fPh3/vjjJAAW\\nFhZ06OCrP1e9eg2oU6c+vr7tSE1NIzh4A2q1mkWL5lOxYqV8uR+ffVYBR8fT3L27HACF4jIDBybi\\n4eGOs3NFWrZsjVqtZvToYdSv3xCAunUbULdug1zn8vZujrd3cwBGjRqX47WsUafs+2Tn798ff/+8\\nR0fzsmpVKomJuumQMTGf88svm99ZMKdSqTh92hbQjdqmpDhz8OAVOuT+buCNPXnylPbtj3Dpki+g\\nZN++TSxf3v6FAV3W9GAxBVMQhE+RCOYEQRDekL+/H4sXB/H48SPmzp3F5MnTC7pLL3Tp0m1GjbLk\\nyRPdKNrVq6E4OFynWrWKOfbLvoaqaFE5jx4lAznXT/Xq5Z/ndLcGDRrToEFjQkPPMnToAFxcXJHJ\\nTJDJZPri2/82evXPz+KGhoa0adONiRPHkZycgFarxd6+DPPnL37Du5CTQmHJ0qVlmD9/I+npUlq0\\nsKBxY12AGxS0jHPn/iQ9PZ3q1WtRr17DfGkzy5495zh58jG2tgZ8993nrzXypFYb5niu0by7USup\\nVIpcruTRo6wtWszN0/K1jRUr/uTSpS7oErVYsmvXl8yYMZNr18IAXTmL+vUbMmhQXypXduXmzevM\\nmjWf4sVt8rUfgiAIHwKJNq+qoQUg6wOD8PHJ/oFQ+PiI9/fDsHTpXsaO9cmxbdy4zfTt6/3CY17l\\nvX306AlLlpxGozHg228r4+T04uyKr0Or1TJgwBZCQpqQmSmnVq2trF/fAnNz83w5f0Fat+44Y8aU\\n5fnzCkASnTptZe7cV6/tN2PGXhYsqIFKVZpChcKYNu0RbdrUfvmB//Cmv7ubN//B1Kkq4uNL4+ER\\nxi+/1KNkyWKvfZ4XmT59L7NntyMr66ZMdpAaNaawbt0mfTmLwMBJ9OjRmSVLgnKMDgs64u/yx028\\nvx+vokXlr32MmIQuCILwhry8dBkcY2Nj6NKl/Ttt+3Xb9PCwQ6G4pH9uYXENd/eS/6kPSqWSTp2O\\nsGBBexYt8qFz59uEh0f/p3Nm2bBhL5s2NSQzszRQmD/+6M7y5cfy5dwF7bffUv8K5AAUHDtmRWZm\\n5isfP3y4N0uW3CIgYAvBwelvFMj9Fz4+tThxoip//CFh586v8zWQA+jWrTqVK68HtEAKVapspmlT\\nb2QyE0xNTWnQoDEXL56nePESIpATBOGTJ6ZZCoIgvLEPZ41OjRoujBlzlHXrbqHVSujQQUrduo3+\\n0zn37j3DxYvtyboP9+61Yvv2EAYP/m/ZFR8/fsKPP0YATbJtNSI9/cO53//G1DQjx3Mzs/TXTvDR\\nrFlNmjXLz169HgsLORYWr/8N8qsoXtyarVvrsWlTCBYWRhgbe6JUKnPtl19ZTQVBED5kYmROEATh\\nA6XRaJg4cSy+vu0YM2YEKlUaN25cp1+/3vTo0ZnBg/vz5MljAK5fv8rhw4spVSqY1q2vc+zYEkA3\\nwte3by/8/Hzx8/PlyhXd6F1Y2Dk6d+7MmDEj+PbbtkycODZX+0WLyjE0fJRtSypy+X//t3L06CXi\\n4noDuwA1AIULr6RDh3eXsfFtGjy4MpUqbQLuU6zYAfr3L1TQXXrvFC5shb+/N507e+HpWZVjx46g\\nUqWRmprKsWOH9WswBUEQPnViZE4QBOEDFRHxgICAQFxc3Jg2bSJbt27m+PEjTJv2E4UKFeLgwf0s\\nW7aIgIBApk6dwMiRgVSu7MKSJT/rk48ULlyYOXMWYmxsTGRkBBMmjOGXX3SFqa9fv86aNZuxti6C\\nv38PLl26gJubh779Bg2q4eu7nQ0bPFCrTfjiiyN07+6TV1dfi5OTLebm4Tx/3gH4FXhO376G2NuX\\n+M/nfh84O5dhz55i3LhxF3v7chQpUqSgu/ReK1++gr6cBUCLFq2RyxUie6UgCAIimBMEQfhgFStW\\nHBcXNwC+/PIrgoODuHfvLoMGfQ9AZmYm1tZFUSqVpKamUrmybn2Rl1dTTp06DkBGhpo5c6Zz585t\\nDAwMiIqK1J/fzc1NXyC7XLnyxMXF5gjmJBIJM2d+Q58+d1GpkqhYsUO+1ANzcyvPoEEHWLUqnIwM\\nKd7eGfTr1/o/n/d9YmZmRpUqrgXdjQ9GVjmL7IKDNxZQbwRBEN4fIpgTBEF4Qy9Ks18Q7Wu1WszN\\nzXFwcGTJkqAc+yUn58x6lj2J8aZN67C2LsLYsZPQaDT61PwAxsbG+seGhgZoNJo8+1GunON/uo68\\nDBjgRd++GjQaTY5+CJ+uGzfuM2PGFZRKYxo0kNC3r1dBd0kQBKHAiTVzgiAIb8jRURfElChRskBG\\nCR4+jOPKlcsAHDiwj8qVXXj2LEG/Ta1WEx5+D7lcjpmZGdeuXQHg4MH9+kAwJeU5hQtbA7Bv36+v\\nlVXxbTM0NBSBnABAeno6ffteZvfujhw50oZp0z5j3brjBd0tQRCEAieCOUEQhNdw4sRVOnTYQ6tW\\n+/Hw+PblB7wlEokEe/vSbN++GV/fdiiVStq27cCkSdNZsmQB3bp1onv3Tly9qktoMnLkWKZPn0L3\\n7p1IS0vDzExXr61163bs3fsr3bp1IiLiAaamZgV2Te+T9etXs2WLLkCfP382P/ygK5IeGnqWiRPH\\ncvbsab77zg8/P1/Gjh1JampqQXb3vfcqXxIolUq2b98C6BLwDB8+SP9adHQU16//PS01Pd2OsLCU\\n/O+oIAjCB0ZMsxQEQXhFCQlPGTToEQ8e6Oq7xce7U6KEBS1b1njnfbGxKcG6dVtybXdyKs/PPy/L\\ntd3BwZHg4A0ArFmziooVKwFQqpSdfjuAv39/AKpUqcaXXzbSF6YdNGh4vl/D+8zdvQobN66lbdsO\\n3LhxnQcP7nPw4H4ePLiPo2M5goODmDt3ESYmJqxdu4pNm9bRrVvPgu52gQkIGEp8/EPS01W0a9eR\\nli1b4+VVj6+/bsO5c2cYPHg4sbExbNmyCbU6g0qVXBgyZGSONZbJyUls3x5C69a5C6gXK1YcW9vT\\nPHiQFdA9p1Qpba79BEEQPjUimBMEQXhFFy/e4cGD6tm2GBAWlkDLlgXWpVd26tQJ1q5diUajwcam\\nJKNHj8tzv4iIOMaN+5OHD82pUkVFYKDXJznV0dm5AjdvXicl5TnGxsZYWVkRExPNpUsXqFu3Pvfv\\n38Pf3w/QJZFxdXUr4B4XrICAQBQKBSpVGr16daVhw8akpaVRubIL/foN5P79cNatC2bJkiAMDQ2Z\\nNetH9u/fS9OmfxfLW7JkAdHRUXTv3gkjIyNMTEwZM2YE4eF3cXauyMSJrfnpp40olSeQy68QFmbG\\njBmhDB8+GoB+/XpTubIrYWHnUCqTGTkyEHd3jxd1WRAE4aMggjlBeAdiY2MYMWIQq1dveqX9z58P\\nRSqV6jMVCu+HihXLUKzYZeLji/+1RUv58h/GtMQmTbxo0uTlCSMGDTrF8eO6FPDnzqWj1W5hypQW\\nb7t7BWLjxrXs2bMLgObNW1G/fkOGDOmPm5snV65cRKlMZufO7bi6unPhQhh3797h3r27pKSkUK1a\\nDcaPn8LZs6fZvn0rI0aMKeCrKVghIRs4fvwoAPHx8URGRmJgYEDDhrrC76GhZ7h58wY9e3YGQKVS\\nYW1tneMc/v4DCA+/x8qV6zl/PpSAgCGsXRuiL41ha2vAgQPNSEqqh0KhAGDSpEBOnjxOnTr1kEgk\\nZGZmsnx5MH/8cZKVK5cxd+6id3gXBEEQ3j0RzAnCeygs7BxmZuYimHvPFC9ejKlTH7Bw4SZUKmO0\\nWg0dO9Yr6G7lG61WS3i4ZbYtxty7Jyuw/rxNN25cZ+/e3SxfHkxmppbevbvi6VmFqKhIJkyYxogR\\no+nc2Yc1a1YyceKPREQ84OzZP/Hw8OTu3Ts8ehRPdHQUv/66iy++aEpkZAR2dvYFfVkFIizsHKGh\\nZ1m6dCUymYz+/fuQnq7C2FiWI+Oqt3dz+vTp+8LzZM+yqtVqqVixcp6lMcLCzrJ+/RpUqjSSkpIo\\nW9aROnV0v4cNGjQCdCOrcXGxb+NyBUEQ3isimBOEd0Sj0TBx4lhu3bpBmTJlGTNmAr6+7QgKWotC\\nYcmNG9dYuHAeo0ePZ+fObRgYGLJ//x4GDhwupgq9R1q2/Ew/rdLLa+JHVbhYl1QliaiorC1q7Ow+\\nzsQely5doH79RshkJgA0aNCYixfPU6KELeXKOQHg4uLGr7/uxMXFld9+24NUaoS7uyfOzhV59Cie\\nsWNHcu/eHcLD79K7d98PMpjbu3c3GzeuQyKR4OhYjsaNvQgOXoFanYFCYcm4cZOxsirMihVLefgw\\njtjYGB4+jMPHpyNt23YAdBlR5XI5MpmM+/fDuXr1Sq52qlatzsiRQ/Dx6YSVlRVJSYmkpKRiY2Pz\\nwr5JpblLY6hUKn76aQYrVqyhaNFiBAUtIz09PdcxBgaGLyylIQiC8DERwZwgvCMREQ8ICAjExcWN\\nadMmsm1bSJ6BgI1NCb7+ug1mZmZ06OBbAD0VXtXHFMhlmTmzGoGB63j40AxPz1QmTPiioLv0Vrzo\\nvTM2luof29uXoVu3nvqAb+DAYTRs2ITY2BiGDx9IixatefLksT5pzIfm9u3brF4dxNKlK1EoLElK\\nSkIikbBs2SoAdu3awbp1q+nXbyAAkZERLFiwlOfPlXTq1IbWrdthaGhIjRq12bFjK76+7bCzK42L\\niy5JSfZ7XKaMA716+TN4cF8yM7UYGRkxZMiIHMGcmZkZKSn/nqEyK3BTKCxJSUnh8OHfadxY1JsT\\nBOHTJYI5QXhHihUrrp82+eWXXxESsuFf99eKRG3vvf37jxZ0F/Kdk5MdGzbYAVC0qFyfzfJj4+7u\\nwZQpE/D17UpmppZjxw4zduxEdu7c/q/HXb16j/79r5GQYMW9e8sZMGDEO+px/jt9+jSNG3uhUOim\\n1ioUCu7evUNg4EiePn1CRkYGJUvaArrArHbtuhgZGWFpWQgrq8IkJDylSJGiSKVSZs2an+v8//z9\\neNm6TUvLQri6utOlS3tkMpm+/mF2crmcFi1a0aVLewoXtqZSJZd/ucK392XL666DfpEVK5bi7u5J\\ntWrVX76zIAhCHkQwJwjvSPZvqbVaLRKJAYaGhmRm6qI2lSr9RYcKBUypVDJ//lFSUw35+msHqlVz\\nfittHDiwL8+07EL+K1++Al991ZxevXTJXlq0aI1crsg1Ypf9uUQiYebMq1y50gm5XIGBwRpWrsyg\\nfft32vV8I5FIcqxTA5gzZwYdO3amTp16nD8fSlDQ32UujIz+HrU0MDBArX61aYz79oUxb14cqanG\\nNGyYyrhxzV84Mjpu3OQ8t2cvjdGrlz+9evnn2mfMmAlkBXCFChUiJOR/r9S/gtSjR5+C7oIgCB84\\nUTRcEN6Rhw/juHLlMgAHDuzDzc0dG5sS3LhxDYCjRw/q99VNN3peIP0UcsrIyMDXdzdz57Zj6dJ2\\n+Pklce7czXxvJ6vG1vvg34o3vw2xsTF06fLuI6L27b9l9epNrF69iXbtOmBjU4Lg4I361zt29KV7\\n914AjBo1jgYNGpOcrJtyaWoaSmJiO/3zD1HNmjU5fPh3kpISAf5ax/Zcn3Rk797d+n3/GfS9qmfP\\nEhg1SkloaHuuXWvNkiVerFp15D/3PTutVsvAgVuoUeMxNWvGM2zYtjfu7+vIzMxk+vQpdO7sw+DB\\n/VCpVOzcuZ1evbrQrVsnxowZjkqVhlKppG3bvzPCpqam8s03zVCr1UyZMp4jR3R/+9u2bcGKFUvx\\n8/Ola9cORETcByAhIYGBA7+nc2cfpk+fTNu2LfTvmSAIggjmBOEd0CWWKM327Zvx9W2HUqmkdet2\\ndO/em3nzZtGzZxcMDY3031bXqVOfY8eO0L17Jy5dulDAvf+0Xb16i1OnGgOGAMTFNWbnzvB8byd7\\nja1Fi3JPWXuX3qfA8m04dOh3fH3b8cMP/pw/H8qVK5de6bgDBy4QHn4de/vmmJkdx8pqGUWLrn3L\\nvX17ypUrR5cufvTr15tu3Trx889z8fPrzdixI+jRozOFChXS/02ShAqJvAAAIABJREFUSCS8yRLR\\nu3cjiYr6eypkZmZR7txR5dclALBt23E2bmxJSkotnj+vzdq1Tdm9+1S+tpGXyMgI2rTxYc2azVhY\\nyDl69BANGzZm+fLVrFq1ntKlHdi9+39YWFjg5FSesLBzAJw6dZwaNWpjZGT01339+x4XKmRFUNBa\\nWrVqy4YNup+tlSuXUa1addas2UzDhk14+DDurV+bIAgfDjHNUhDeARubEqxbtyXXdnd3DzZs2AZA\\nQsJT7tyJJDk5CTs7e4KD/31NnfBuWFnJMTN7QkqK419bNJiZ5X+WvOw1tgpaVmDZqlUrQJKreHNg\\n4CQAzp07w6JF89BoNFSoUImhQwOQSqW0bdsiV5bWBQuWkpCQwIQJo3ny5DEuLm6cPfsnQUG6D6xZ\\noxxXrlykaNFiTJs2G5ns7ZRF2L37f4wYMQZXV3dWrFj6SmVAEhOfMXJkIlFRo4H9lC49gXLlPmft\\n2pePWqrVaoyM3s9/t97ezfH2bp5jW926DXI812g0tGnjo19bB7zyWrHy5cvg6HiWu3dLAyCTReDu\\nLv+Pvc4pPv45mZmFs/W3GHFxb3etZ3z8QwwMDPSZT52dKxAbG8Pdu3dYvnwxz58rSUlJpUaNWoBu\\nWurGjeuoUqUav/++nzZtfPI8b4MGjQHdNOCjRw8BcPnyRaZNmw1AjRq1kMsVb/XaBEH4sLyf/10E\\n4ROzZ08oo0alEBPjgqPjWebNs6V69QoF3S0BKF3anj599rJ0aSapqdbUrXuE/v3zv4j2u5gW9qqy\\nAssdO3awf/+RXMWbL1++SPnyFZg6dQLz5y+hVCk7Jk8ex/btW/Dx6fjC9VBZIwy+vt34888/2L37\\n7zVNkZERjB8/lREjRhMYGMDRo4f44gvv/3wtAQFDiY9/SHq6inbtOvL06ZO/PhxPxNHRiUuXzuvL\\ngAwaNBw7u9LMnj1NP/oxYMAQXF3d+fnnBWRkKLGzW4eh4WOMjBJ5+vQwISE2XLx4npiYGExMTBg+\\nfDSOjuVYsWIpMTFRxMTEYGNT4oVrwd53e/eGMWlSHI8eFcPF5R7Ll39OkSKFX37gX+RyBXPnlmTe\\nvE2kpEhp0kSLj0/+Zkht0aIKwcH/4969VgCUK7edFi2q5msbefv751xXCkHF1KkT+fHH2Tg6lmPv\\n3t2cPx8KwOjRE+jatQNJSUncunWDqlU/y/OMWdlUs0oxZHmf/j4IgvB+EcGcILwH5s+PIyZGV7Pp\\n7l175s7dyPr1Iph7XwQEeNOlSzTPnj3D2bntezvKkl9eVrw5NjYGExNTSpa0pVQpXeZLb+/mbNu2\\nGR+fji8877+NMGSv75Y1ypEfAgICUSgUqFRp9OrVlZ9/XkZo6Fn69RvEiRNHSUl5zmef1aBDB1+W\\nLl3I3LmzsLcvjUqlQq3WEBg4ku3b95KRkYZcfpI7d86i1Rrj6OhJ1ap1iY2Nwdm5ItOmzSYs7ByT\\nJwfqR1cfPHjAokW/YGxs/JJevp+0Wi3TpsVy547ub9PJkw2ZMmU9c+a0fK3z1KhRkfXrK76NLgJQ\\nqlRxgoNTCQrajESSSY8ertjYFH1r7f1Nq68damhoSN269VEqk5g5cwrp6RnExcXqs1TOmTMDa2tr\\n5s2bSXJyEkFByzh58jixsdGUL69LqKTRZDJq1DASE59RqpQdV69eJikpEVdXdw4dOsC333blzJnT\\nJCcnvYNrEwThQyHWzAnCeyA11fhfnwsFz9bWlsqVK7y1QO5VamwVlLyKN/9z9E2XoVXy1z4vztL6\\nohGG7PXd8rPgc0jIBrp160SfPn7Ex8cTGRmpf61Zs5bcuXMbrVY3zfPQoQNERUVy6tRxDAwMMDQ0\\n4MmTJ0RHRyGVSilatBANG26nZs1tyGQaqlZ15PLli3z55VcAVKlSjcREXRIRiURC3br1P9hADnTJ\\nf54+zT4lUkJiommB9effODuXYfp0b378sRlOTu+meHtGRgbffNOOtWtDMDY25tq1q8jlCh49eoSh\\noSEVK1bi1q0bgG49nKurBwcO/Iapqal+bZy9fRlOnDgGgFKZhIdHFdas2UzVqtX1NfW6d+/NmTN/\\n0qVLew4fPkjhwtaYmZm/k2sUBOH9J4I5QXgPNGyYioHBYwBksvt4eX18xaiFf5e9xlZBJ0B5lcDS\\n3r40sbExREdHAfDbb3vw8KgC8MIsrVkjDMA7GWEICztHaOhZli5dyapV63FyKk96+t/JN2xsSiCT\\nyXj0KJ4zZ07j5OSMRqOmf//BrFq1gTVrNuPl1ZTw8HsAWFiYsmnTV+zc6YWx8d9B/YsC1Kxi4x8q\\nY2NjqlSJA3SBtVQaRa1aH/eo9KsqVqw4xYvb6Nda+vsPIDNTS2LiMxQKBWp1Bo8fP6JkyVL6Y1xc\\nXDl27AzGxjL92rgBA4boX7e1LUXz5l8D0LZte/0aRQsLC376aQGrV2+iWbMWWFtbf/SzAwRBeHXi\\nr4EgvAfGj29BmTJHuHtXhaengjZtPi/oLgkF4H1ZV5UVWLZo0QJDQ6M8izcbGxszatQ4xo4dgUaj\\noWLFyrRqpauR1717b378cSK//GKBp2dV/Yhd9+69GT9+NL/9tofKld30IwzPnz//1/pubyol5Tly\\nuRyZTMaDB/e5evVKrn1cXd25fPkiT548olmzlty7d4czZ07TooVu/VVychISiQQDA4Nc008B3Nw8\\n2b9/L9269SQs7ByFCllhZmb+0axxWry4GVOnbuLJExnVqxvj59eooLtUoPz9/Vi8OAjIXTvU3Nwc\\nBwdHliwJeul5XmVtXHq6mhYtDpCWpqFQodWUKKFAKpUyfPiY/LocQRA+AiKYE4T3gEQioXv3T/tD\\n0qdoxYojnDiRjkKRyujRdSlWLHfQVFDGjZtM0aJyHj3KmRUwe/HmqlU/IyhoXa5js2dpzS5rhMHQ\\n0JArVy5x8+Y1jIyMKFGiZK76bvmhRo3a7NixFV/fdtjZlcbFxTXXPr6+3fDz+5Y7d27xzTc+dO7c\\nncWLF9ClSwcyMnSjKwEBgYSGnuXp06dkZGSQlpZGeroKAwMJfn69mTZtIl27dsTU1JQxY8YDb57K\\n/31jbm7OlCn5n/DnQ5UVyMHftUNdXFw5cGAflSu7sGvXDv02tVpNZGQEDg5lX+nc2dfGHTy4n9TU\\nFO7ebU1mZiGgHa1aHcTf3+stXZkgCB8qEcwJgiAUgNWrjzJ+vBsqVWlAy/37QezY0S5fRqTeVw8f\\nxhEYOJLMTC1SqRHDh49h4cIDHDmixcwsnSFDKuDmVi7f2pNKpcyalXvK6oIFS/WPHRzK0qxZS+Ry\\nBe7unri7exIefpfTp09hbCwlICAQK6vCDBgwGCMjIzp3bk/JkiWpV68BJiamKBQKpk2blasNP7/e\\n+XYdwvvDy6seBw4cJyEhAWNjY4YNG0BaWhouLm4MGjSc6tVrMW/eLJRKJRqNmvbtO70kmJPkOXJd\\nokQpNBorMjOz1sZZEhWV/yVRBEH48Em078lckH9++yt8PPL6dl/4eIj3983067efzZvb6J/L5Sc5\\nc8YWa+v3Z3Qu672NjY1hxIhBr1xb7FVt3HiCoUMrk56uS1hRocImfvutEaam7y7JRmZmJj16+DJ5\\n8gxsbUvleO3WrQjmz79MWpoRDRtqqVu3Avb29hgY5L3cPCkpifHjD/PokSmurmqGDm36wn3fhdTU\\nVAIDR/Lo0SMyMzV07doTS0tLfW1ADw93+vUbilQqffnJBAC8vOpz4MAxNmxYS0ZGOl26+KHVaklN\\nTcXMzOyNzxsdHUto6C2qVStPyZIlOHv2TwYNmsytW0cAMDG5w/z592nVqsYrnU/8Xf64iff341W0\\n6OvX4RQjc4IgCAXA2jodUJP1Z7hIkVgUireXvv1VZBXQTkl5jru7J97eTXK8HhZ2jo0b1zFjxpx8\\nae/8+ef6QA7g5k0PIiOjKF/e6bXP9SaFucPD7zFixCAaNGicK5BTKpX07HmFGzc6AP9j5045RkYa\\nGjXaSFBQmzwLmn///W/s398NMOC33xLQavcxYsRXr30t+eXPP09RpEgxZs6cB+iuqUuX9vragLNm\\nTdbXBhReT6VKlZk2bSJqtZp69Rri5FT+jc/1v/+dYfRoCQkJpbG398fWVoKVlZyAgAFs3bqBtDRj\\nvLyMadWqYf5dgCAIHw2RzVIQBKEAjBzZBG/vYIoV+xVn5w0EBhYp8BGSrOlePXr00dfHypKZmcmG\\nDWs5fz6UwYP7oVKpuH37Jr17d6Nr146MGjWM5ORkEhKe0qNHZwBu375FvXqfER//EAAfn69RqVQk\\nJCQwZsxwrl9fjL19a0xMwoBMHB27olD8nXK9Q4fWJCQk6Pfv1asLvXp14fLli4Au+Jw0aSz+/j2Y\\nMmX8a1+vg0NZNm/+H337/pDrtbCw69y40QAIB2yBxqjVHhw40J2FCw/l2l+r1XLtWmH+/rdqxaVL\\nBft+Ojo6ce7cnyxevICLFy8QGxuTozZgq1atuHgxrED7+KFyd/dk4cLlFC1ajKlTx7Nv369vfK6l\\nS58QH9+YjAxX7t49iETSm+XLV9OsmRdBQc1Zv/4LundvmH+dFwThoyJG5gRBEAqAqakpwcE+pKen\\nI5VKC2ytXHDwCvbt+xUrq8IUK1YcZ+eKTJ06gdq169KuXStOnz7FnDkziI6OwsnJGU/PqpiYmHD0\\n6CHWrVvN4MHDcXf3ZMWKpaxcuYwBA4aQnq4iJeU5ly6dp0KFSly4cB43N3cKF7ZGJpMxbdpEfHw6\\nMXGiG4MGrebcuR8wN+9L5cpVCAsL5auvSnL16hVKlCiJlZUV48ePxsenE25uHsTFxTF0aH/Wrg0B\\n3l5hbgeHElha3vmrrlqJbK8Yk5yc+72SSCQUK/acqKisLVqKFHmer316XXZ29gQFreOPP06wfPki\\nqlb9rED78zGJi4ujaNGitGjRivT0dG7fvknTps3e6Fzp6TmDfpVKfDQTBOHVib8YgiAIBaggi0rf\\nuHGdQ4cOsGrVBjQaNX5+viQmPiM5OZk6deqhUqmYMWMKY8dOZPr0KX8VCwdn5wpER0ehVCbj7u4J\\nQNOmzRg7diQALi7uXLp0kYsXL9C5c3f+/PMUoNXve+7cGR48CNf3o3hxCevXN+HOHTtWrvyFr75q\\nwcGDv9GkiVee+6ekpJCamvpWC3Pb2ZVixIi7LFkSRWzsCTIyBgASihc/SrNmDnkeM2GCM2PHruPh\\nQ3MqVkxg3Lgmee73rjx+/Bi5XM4XX3hjbm7Btm0hxMXFEh0dha1tKf73v//h6Vm1QPv4ocn60uX8\\n+XNs2LAGIyMjzMzMGTNmwhuf09tbzc2bEahU9pia3qF5c8P86q4gCJ8AEcwJgiB8oi5dOk/9+o3+\\nWv8lo06d+ty7dxfQTRu8d+8eJUvaYmNTAmNjKV984c3OndsxMDBEqXzx4nsPD08uXjzPw4dx1KvX\\ngLVrVyGRSKhdu95fe2hZtiw417TS3bv/x/3793j27BnHjx+jW7deufbv1683I0cGYmpqyubN62nf\\nPn/KGOSlZ88G+PllEh//mMWLN5KWJqVVK3uqVXPOc/8aNZzZv9+ZzMzMAk18kuXevTssXDgPAwMJ\\nRkZShg4NQKlM1tcG9PT00NcGFF7N/v1HAfD2bo63d/N8OeeQIU1xcDjF9et/4ulpzVdfNc6X8wqC\\n8GkQwZwgCMInSzfKkDXVMjU1FSurwhgaGhIbG8Pq1SuIiIhk5sypfxU2/jv5sbm5BQqFgosXL+Du\\n7sG+fb/qR3nc3T1ZunShvmC4QqHgjz9O8t13/QH47LOahIRspFOnrLV1N3FycmbkyLEsWjSPBQtm\\n4+DggEKhyLW/RCIhIuIBzs4V9P3PT1lJYLJq3RkYGGBjU4wJE/L+4P748SPmzp3F5MnT9dveh0AO\\noHr1mlSvXjPX9qzagCIj3uvJzMwkMHAX586ZUahQGqNGVcLNzTFfzv3NN7Xz5TyCIHx63o//OIIg\\nCMI75+HhyYED+/j9999YtOgXZDIZ8fFxAGzZspmGDRuiVqu5fv0asbEx7N+/j9u3b7Jhwxq2b99C\\ntWo1WLRoHp9/Xo/9+/dy7tyfdOnSnoSEpwBUquTC1KkTuHfvDs+eJXDhQigA/fsPYvfuHTRuXJtG\\njWozZ85MAPr1642jY3n279+HSqWiZ88udO7sQ9GiRbl58xpdu3bk+vWrHD2aPQGJlhUrlrJ58wb9\\nlqVLFxISspE38TprF9VqNUWKFM0RyL2vjh27Qo8eO2jXbgJ//HEdgIcPHzJmzIgC7tn7Yf361WzZ\\novuZmT9/Nj/84A9AaOhZJk4cy6xZP9KyZWt++20dDx5Ec+hQJ4YMucKiRfPx9fWha9eOLFw4ryAv\\nQRCET5QYmRMEQfhElS9fATs7e65evcLo0cOpXNmVhw8fkpKi5PlzJbdv32batFnMmTOT58+VxMXF\\nkpGRzu7dvwPw/LkSc3ML+vfvg52dPcOHj+bixfNMmzaRbdt+ZenShVSrVp1Ro8aRnJxM795dqVat\\nBkePHsbR0Ym1a0MwMDAgKSkJ0AVSZco4cPz4WZKSklAoFGg0GgYO/J6BA4fh6FiO/v374OZWk6NH\\nz2JhYUGbNj6kpKQwatQwfHw6kpmZyaFDB1i+fPUr34eskUmFwhKNRkOTJl/QrVsnzMzMWLToF549\\ne0avXl0ICdnJnj27OHr0EGlpaWRmZjJ69HiGDfuBNWs2s2fPLk6cOIZKpSI6Oor69Rvy/fcDANi9\\newfr1q3GwkJOuXJOGBsbM2jQ8Px/U9GVIDhwYB+tW7clLOwcv/yynJMne/HoUSNsbTfTt+8ztmyJ\\nokaNih9EIPouuLtXYePGtbRt24EbN66jVqtRq9VcvHgeD48qNGzYhHv3qnDmTCtKleqGsfFN7t+3\\n5ujRlWzatAPQ/T4IgiC8ayKYEwRB+IR99llNKlSoRI8efQBYsGAOFhYWbN68nqtXrxIVFY2xsRQj\\nIyMqV3YlMfEZc+fOpFatujmm8H3++ZeAborl8+fPUSqVnDlzmpMnj7FhwxoAMjIyePgwjtDQM7Rq\\n1VY/HTFrOmV2hw7tZ+fOHWg0Gp48ecz9++GUKePAjRvx7NhRCpWqIpUqPUetVmNjUwJLS0tu377J\\nkydPKF++Qp7nzEv2JDDR0ZH4+XWmSZMv/no171G627dvERy8EblcTmxsTI7RvDt3brFq1XokEgM6\\nd/ahXbsOSCQSgoODCApah6mpKT/84J+jLtnLintXqFCJoUMDkEqltG3bAi+vppw+fRIDA0OGDx/N\\nkiULiImJpmPHzrRq1Ybk5CRWrlzOnj07SUxMJDk5jdjYRtjYDEIqjUCrXcSMGUWYP38CPXv2YvXq\\nTezZs4vjx4+QlpZGVFQkHTp8i0qVzu+/70MqNWbmzHkoFAqio6P46acZPHuWgImJCSNGjMbevgyH\\nDv3OqlXLMTAwxMLCgp9/XvZK9/994excgZs3r5OS8hxjY2MqVKjIjRvXuXTpAgMHDuPQof08eLCa\\n0qVXYmj4BGPju9jZPcHU1JRp0yZSu3Y96tSp9/KGBEEQ8pkI5gRBED5hHh6eTJkyAV/fbmg0ak6e\\nPM7XX3+DTGaCoWEJYmM7YGCwlwYNKvPDD0Po06cvf/55ih07tnLo0AECAgLzPG9WfDNlykzs7Oxz\\nva7VanNtyxITE83Gjev45Zc1WFhYMHXqBNLTVWzbdownT4oBxYESpKZaEBJygj59vqZ581b8+usu\\nEhKe0KxZy1e+/uxJYIKDV6DVZrJp01pSUlKwsyvNmDEjuHPnFgkJCfpjypd3JiBgCKmpqZiYmPy1\\nnhDWrFmFiYkJAwb44+X1JUWKFGXYsIGkpKSQkZFBeroKuVxOo0ZNiIyM0J/vZcW9J08epy/uLZFI\\nKF7chpUr17NgwU9MnTqeJUtWolKp6NKlPa1atWHKlPEkJj7D2toahcKShIRn2Nn5YGiYgFYrJT7+\\nJ9q3j6Bjx46YmJgCEBsbw5kzp9m9+3fOnTvD6NHDKFKkKIUKFcLZuQL79v2Kj09HZsyYwrBhoyhV\\nyo6rV68we/Z05s1bTHDwL/z000KKFCnyQY5QGRkZUaKELXv27MLV1R1Hx3KEhZ0lOjoKmUzGxo3r\\nWL9+DdOmHeaPP/ZSseIRpkzpQOXKqzl37gxHjhxk27bNzJu3uKAvRRCET4xYMycIgvAJK1++Ak2a\\neNGtW0eGDv2BSpUqI5FAmTJNiI6+zpMnvxAdLePkSTvi4mLRaNQ0aNCYXr2+4/btm4AuMDt06AAA\\nFy9ewMJCjrm5BdWr19SvQwK4desGANWq1eB//9umD4Kypllmef78OSYmppibm/P06RNOnz711/YM\\ncv7bkpCaqgagQYNG/PnnKW7cuE6NGrVe4w78Parm7z8ACwsL2rf3xc6uNBER9xk4cCjz5i1Go1Fz\\n6dIFNBoN165dZcqUGaxYsYZGjT7n6dMnujNJdOf75ZfVtGnTngcPwunZsw/9+w+kVCk7li1b9Nf9\\nytmDlxX39vZunqO4d926DQAoW7YclSu7YmpqSqFChZBKpSiVSuzs7PWjhcnJSaSlpeDhURy1uiMG\\nBml07LiNRo2q5lofaGEhx9TUlN27d2BpWYhly4JZtGgFTk7OxMXFkJqayuXLlxg7dgTdu3di1qyp\\nPHmiu3ZXV3emTBnHrl079O/rh8bd3YMNG9bi4VEFd3dPduzYSvnyzvqfR4VCwfDh9bC0vE+/fp44\\nO5dCqUymVq069O8/mDt3bhX0JQiC8AkSI3OCIAgfqX9mZnyRLl386NLFL8e2gwf3ExdXg8KFl2Fs\\nHEFi4k3u36/IsmWL0GozAfTZKSUSCcbGxvj5fYtGo9GP1nXr1pP582fTtWsHMjMzKVnSlunT59Ci\\nRSsiIyPo2rUjRkZGtGzZmm++aadv28mpPOXLO9OpUxuKFbPBzc0dgDZtarFixWwMDIYDUqTSR1hY\\nPGbRonl8//0PVK36GfHxD5k7dyaDBg3nt9/2sGXLJtTqDCpVcmHIkJEYGBjg5VWPdu06curUCbTa\\nTNRqDb6+3UhJeU5KSgoAhQsXJi0tlSJFirJ583qkUilxcbE8ffqYpKREBg78HgCVSoVardb33c5O\\nF4BFRNwnJSWFn3+eg1RqTGRkBAYGhqjVao4ePUS5ck7Zjvn34t5arTZH4GVsrCvpYGBgkKO8g4GB\\nARqNGq0WChWyYuXK9YSFnWPNmpXMmTOVqKhIevUypUmT0nn+HBgY6NpwdXXnzJnT7Nu3my+//Aoj\\nIyM0Gg1abSZyuZyVK9fnOnbo0ACuXbvCH3+cpEePzqxYsQaFwjLPdt5X7u6erFmzEhcXV2QyE2Qy\\nGe7unpQr55Tnz2NKynNGjhxCeno6oKV//8EFewGCIHySRDAnCILwkXqdzIxZTp++xoYNEdy5cxOl\\ncghK5VcAeHqupkaNWtSsmXcK9S+/bMaAAUNybJPJZAwbNirXvoaGhvTvP4j+/Qfl2L5gwVL941Gj\\nxuU67v79cD77zAkbm6aAIenpZ7C1tSU4eAXffdefq1cvY25uweeff8n9++EcOnSAJUuCMDQ0ZNas\\nH9m/fy9NmzYjLS0NFxc3evf+nkWL5nPr1g26deuIubkFMpkMiQQaN/ZizpyZ+Pl9S61adQHJXyNO\\nEhQKS31AExsbw8iRWR/idfXcQDf6ZmZmxsiRgXh4VGHnzu2sX7+G77/vSenSZTAzM9df18uKe//2\\n2x48PKrkuh95TVWVSCR4enpy4MBeUlNT9fslJCQgl8vRaNRoNGr9+5Ale0Dq69uNLVs2oVKp8Pfv\\nwddffwOAmZk5JUuW5PDh32nU6HO0Wi13796hXDknoqOjqFTJhUqVXDh9+iTx8fEfXDBXtepnHD78\\nh/75hg3b9I//+fOYnJxEYmKi/udLEAShoIhgTsg3/v5+LF4cBMDChfM4ffoktWrVZdy40QXcM0H4\\ndGRlZrSyKkyxYsVxdq7I7ds3mTlzGiqVClvbUgQEBCKXy3Mls2jbtiuDBklJSrLA2vo0Dg6NMTZW\\nULJkV8aOdX+j4DA/hYae4d69uyQk6AKp9PR0Hj1K59EjCY0bN6F+/Tpcv34ZV1d3tm7dxM2bN+jZ\\nU1fLTqVSYW1tDYBUKqV27boAODtXJDk5iblzF5GY+IwePTrToYMvYWHn8PCowowZcwD0RdK//bYr\\ne/fu5sqVy7i4uFK0aDHGj58KgLW1NR076tqzty+NpWUhjIykaLVaGjRojKurO3Z29owePYz69Rvq\\nr+tlxb0rVqycrbj33++BRCL5x3uie1yzZh2MjWV89113UlNTSE5OJjU1hZIlbbG2LsKyZYsID7+H\\njY0NMTExANy8eV1/fHR0FFKpMe3adSA8/B5Pnz7RtxMYOJlZs34kODgItVrN559/QblyTixaNI+o\\nqEi0Wi3VqlXPMfL4sQkKOsqcOYY8e2bDZ59tYdUq71dOuCMIgpDfRDAn5JusQA5g167t7N17uMA/\\n/AnCpyR7ZkaNRo2fny/OzhWZPHk8gwcPx93dkxUrlrJy5TIGDBiSK5nFmDETiYnZS+nSLYiKCkKj\\nseLHH0Pw82vxr+1mH1F727y9m9OnT18Adu48w4ABpTAyuoKx8W2OHn1M69b18tw3O0PDv//1GRhI\\n9Gu8LC0L4erqTpcu7ZHJZBQubK3fLyMjg9DQs3h7N+fbb7syfPgPFCtmg0ajpn37Tjg4lAX+Hg2V\\nSqVMmjSdsWMDiYpSkpmZhExmSIkShalRozb16jXUn/tlxb2zCwn5X47r8/ZunudrNWvW5u7d21ha\\nFsLBwZGSJW31bVWoUAlv7+Y8eHCTkSMD6NmzC56eVfWjcyEhGzA1NaF//+8oW9aRvn0HYmSku2cl\\nSpRk9uz5ufo1ZcrMXNs+RkqlkjlzJDx86A3AiRPuzJy5iUmT8i4qLwiC8LaJYE7IN15e9Thw4Dgj\\nRgwiNTUVP79v8fXtTocO3xR01wThk5A9MyPIqFOnPmlpqSjHQyKVAAAgAElEQVSVybi7ewLQtGkz\\nxo4dmSOZRRa1+jkSyVNSU6tgYzOStLSq2NuXLaCrya1q1eqMHDkEH59OWFlZcfx4FOnpFUlL88Le\\nfjHJyVaUK/dNnvsmJSWSkpKKjY3Nv7YxbtzkPLd37tydESN000JtbUvh4uKuH7XL8s+g1sbGhhs3\\nviMmJisYTqV589388EPTN7j61/Oi68he265atWo5phJmGThw2Cu3o9VqOX48lMTEVLy8PsPExOT1\\nO/sBUSqTSUwslm2LAUql9IX7C4IgvG0imBPyke4b6enT5+DlVT/PRfKCILxNrz4SnlcyC61Wy5Ah\\nW9m9uz4SyR3q1LnI0qVbqF7d9b1Y/1SmjAO9evkzeHBfMjO1PH2agpGRM2lpNUlPL4ep6WVq166V\\n575GRkYMGTICGxubHDMGXnX2wJIlC4iOjqJ7904YGRlhYmLKmDEjCA+/i7NzRQIDJwG60dGff57D\\n06fPSExU8/BhIFJpBCVKDCQiYhtxcRIiIyMYN24UQUFr8/8m/Qdbt/7B8eNJWFllMHx4E0xNTf91\\nf61Wy8CBW9m0qQmZmYWoXj2EDRu8kcvl76jH716xYsWpWfMYR45UAQxRKC7g5VWkoLslCMInTARz\\ngiAIH4kX1YyTyxVcvHgBd3cP9u37FU/Pqi9MZvHTT23p0+c6pUp9TZkyfWjVqvV7lcyiSRMvmjTx\\nAkCj0TB48HaOH3+IuXljBgxok2NqZPZ9s9u//6j+ccOGTWjYsMlL2/X3H0B4+D1WrlzP+fOhBAQM\\nYe3aEKyti+Dv34NLly5QqZILc+fOpFu3AfTtqyQhIYMiRX7m4cNNZGZaIJcfoGZNBXv27HqtWnjv\\nwqZNpxg+vAypqc6Amtu3V7J2bYd/PSYs7CqbN9clM1NXR/DMme4sWRLCsGFfvYMeFwwDAwOCgpox\\ne/ZmkpKkeHkVpWnTqgXdLUEQPmEimBMEQfhIZK8ZZ2VVWF8zbvTo8cyaNY20tDRsbUvpM/P9n737\\nDIji6ho4/l+WooA0ARHsiqCiYDeW2KKxPyZ2LIAtajRqSGLvsWM3KhZQsPfXxB5iN5ZY0BixYqHY\\nkN7Z3fcDYZWIHUTw/L64M3tn7p0dBA537jkvS2bh5+dDSMh9lEodXFyqf7TJLJRKJQsWdEStVqOj\\n8/qyqXfuhPPjj6e5d68QpUvHMHduPWxtrV97HGTOHKnRaKhQoRKWllYAlCtXngcPwjE2NiY4+BYT\\nJ45DoTCjcGEVaWmmgC/W1kWoVWsTX301h27dZrJihd87XXNOOXw4/t9ADkCXs2fLEBcXh7Gx8UuP\\nSUxMQqVKz8ppZuaHqelGzp414tgxQ4oXL0mpUqXfe1zh4WGMGDEcP79N732u7GJsbMyECbJGTgjx\\ncZBgTggh8pGsasYBeHv7vrDvdcksrKwK8fhxbPYPMpu9SSAHMGbMGY4cSc82GRwMY8b44+vb/p36\\n1NPT175WKnW0SVRKly5LyZIdmDevMxkFzgsUOM/8+ZWZNGkUJ08ew9GxwkeX/dDYOAnQkPGorqlp\\n5Gsfs6xTx4WGDbdw5Ig7pqYb0NPrzKxZn7Fu3Qrq1WuQLcGcEEKIV5NgTmSbd1mHIoT4OERGRjFs\\nWAA3b5pRtGgc06dXw8qqYm4PK1s9eGD0yu1XMTQ01BYUf5kSJUoRFRVJ796W/PmnD6dOfYmR0WV6\\n9ozC0bEttWt/hpfXDG1R9Y/JiBH1uH7dhwsXqmBpGYanp8lL66dt3LiWPXt+BeCrr1pjZNSH27fv\\nYme3kaNHYzhx4hgXL15gzZpVTJ06G41Gk6kExogRYyhRohRTp07EyMiYa9f+ISIigkGDvsvykVeV\\nSsXkyeO4fj2IUqXKMG7cJIKDg1m8eB6JiYmYmpoxZswEChe2JCTkPrNnTyc6OgodHR1+/nkm5uYW\\njBzpSWxsDCpVGv36DaR+/YaEh4fh6TkEJ6cqXL4cqM3y6eu7nMjIKCZMmEKFCpVITExk3rxZBAff\\n/jdLbH/q12+Yo/dDCCHelARzIlukpaXh7e3L06cRWFgUzrQmRQjx8Rs79hB797oBCm7cgJEj/Tl6\\nNH8Fc+XLR3P5sgpQAmmUL//ms46vKluQQVdXlylTZrJggRfm5jE0aLCOL79sTf/+3wDwxRctOHr0\\ncJZlCHKbpaUFO3Z05OHDB5ialsDQ0DDLdkFBV9m79zdWrFiDWq2hf383xo+fwujRP+LtvQoTE1NC\\nQu5Tr14DGjZsAsDQoQMzlcCYM2cmCxYsBeDp0wiWLvXhzp1gRo78Pstg7t69u4waNR4npypMnz6Z\\nbds2c+zYYaZPn4uZmRkBAQdYvnwJo0aNZ9KksfTq5UGDBo1ITU1FrVahq6vH9OmzMTQ0IioqigED\\nPLTBWGhoCD//PItRo8bTt28vAgIOsHSpD8ePH8HPz5fp073w8/OhRo1ajB49gdjYWPr3d6NGjdr5\\nPnOnECJvkGBOvLeYmBh69drLmTN1MDW9wdChlxgwoHFuD0sI8RbCw415PhtmWFj+y0jo5dWCggXX\\nc/++IaVLJzBpUsu3Ov5N0v3b25dn8eLlmd6PjY3hzJmrBAWdpnXrdh/tkws6OjoULWr7yjaXLl38\\nt/xFeiDTsGETLl688EK7jDWGCQkJ/P135hIYqanp9ewUCgUNGqQHVaVKlebp06dZ9mltXQQnpyoA\\nfPllK9as8eH27VsMHz4IALVaTeHCViQkJBAR8URbw09PTw/QIy0tjWXLFhMYeBEdHQVPnjwmMjK9\\nr6JF7ShTpiwApUuXoUaNWv++LsuDB+kF1c+cOcWJE0fZsMH/3/Gn8ujRA0qUKPXKz0oIIT4ECebE\\ne/PyOsrJk70BHSIinFi0aB+urtEfTfY7IcTr2dsncPx4CqAPaLC3j3rnc/3441AmTpyKkZGxtv7k\\nx5DIwsjIiLlz322N3Lu6du0uffv+Q2zsAQwM7tCzZ78P2n92yyoQzSo2zWin0agxNi700lI16QEX\\n/7bVZNnm+T41Gg1GRkaULl2WZct8MrVLSIjP8vgDB/YSHR2Fj89alEolnTq1Izk5BQB9/Wf96+jo\\naMejo/NsHSSkryMtXrxElucXQojc9GarxoV4hYQEfZ7/UoqNtSIuLi73BiSEeGtTprSkd+8t1Ku3\\nnY4d/Zk/v8k7n2v27AUYGWVkQfw4Z6E+lIULL3PtWmfCwlYSHPw7q1frolarc3tY78zZ2YWjRw+T\\nnJxEYmIiR48e0hakz2BoaEh8fHpgZWRkrC2BAenB2M2bN96qz4cPH/D335cBOHhwH5UqOREVFand\\nl5aWRnDwbQwNjbCysubYscMApKSkkJycRHx8PObmFiiVSs6f/4sHD8Lfqv9ateqwdetG7fb160Fv\\ndbwQQuQkCebEe/vySyvMzM7+u5VGnTrnsbEpmqtjEkK8HX19fWbMaMeOHc1YsuQrLCzMX9p2/Xo/\\n7S+3CxfOYejQgQCcO3eWSZPG0qlTO2Jioj/IuD92KSn6mbaTkwuQlpaWS6N5f+XLO9KqVRv69XPj\\nm2/cadv2K+ztHTK1adq0OevX+9O7dw/CwkIZP/5nfvttF+7urvTs2YXjx5+tqX5d4iyFQkGJEiXZ\\nsWMzPXp0Ii4ujo4duzJlykyWLVuEu7srHh6uXLlyCYBx4yazdesm3Ny6MXBgH54+fUrz5i0ICrqK\\nm1tX9u3bTcmSpV/aZ1bjcXfvS1paGm5uXenZszOrVnm/xycohBDZS6F52XMNH1heSH8tXi4g4CIH\\nDjzE2DgVT88mmRbP55X05uLdyP3Nv152b69c+ZuNG9cyZcoMBg1K/0V3yZKV+Pv7YmFRmLVrV7Nq\\nlT8mJqY0a/Y5Bw8e/Sges8wNv/56Bk9PM6KiqgPRuLpuZ/78jrk9LCD//9/9VL/mIP/f20+d3N/8\\ny8rq7dery5o5kS2aNnWh6YtJyIQQ+ZCDgyPXrl0lISEefX19HB0rEBR0lcDACwwb9iNr167O7SF+\\nNNq2rYWp6WWOHNlC0aK69O79dW4P6aPwf/93mv37oylYMJkff6yDjY1Vbg/pBcnJyXh6/sqlS+YU\\nLpzAuHGOVKtmT3h4GN9/PwQ9Pd1PMlAUQnxcJJgTQgjxVnR1dSla1I49e36lcmVnypYtx/nzZwkN\\nDZVC0Vn4/PPKfP555dwexkdj//7zeHpaExPzBaDhypXV7NrVDn19/dce+7bep0adpWVzNm/uh6Xl\\nPBITjzN0aBQTJw6jYkUnkpKSePw4mlGjPLl16yaNG39B6dJl2LZtEykpKUyb5oWdXTEiIyOZM2c6\\nDx8+AOC77zypXNk5269TCPHpkjVzQggh3pqzswsbNqzFxaUazs5V2blzG+XLl8/tYYk84NChx8TE\\nVPl3S8GFC3W5fftOjvR1795dvv66E2vXbsHIyIht2zazYMFsfv55FqtW+dO6dVuWL18CwKRJY+nY\\nsTOrV6/H29uXx48LY2x8CAODa9y9u4unT7/jl18WEBkZiUajIikpiXPnzhIZ+ZQtWzZw4cJfqFRq\\n7t27S/fuHRk8+Btmz55K48bNMDExIzk5hWHDvuXevfRrnTp1IvPnezFwYG86d/4fhw8H5MhnIITI\\n3ySYE0II8dacnavy9GkETk6VMTe3wMDA4IWshvD6BBfi02NpqQaStdsWFvewtn6xCHt2+G+NutOn\\nT2lr1Hl4uOLn58Pjx4+zrFFXoYIOBQueIja2DaCgTJkEqlatzq1bN3jy5AkAc+cupkmTZiiVuvz5\\n50mUSh0mTpxGuXLlUSjg1KmTzJgxhYcPw1EqdTA0NGT27Gna8WUUTZ81az7Lli3Okc9ACJG/yWOW\\nQggh3lr16jU5dOhP7faGDdu1r7ds2UViYiIpKSkcOJCeubBoUVvWrNn4wnnEp2Hw4P4MGfI9Dg6O\\n/PnnL7Ro8ZDTpytibBzDkCH6WFjkTDD3PjXqPD2bc/Hibp4+TcbWNpHx4+vg738RhUKBqakpiYlJ\\nODlVISUlhT/++J3IyEiioiKZOHE0aWlpFChQELVajUaTpq1fZ2lpSWRklHZsb1I0XQghXkVm5oQQ\\nQmQbjUaDp+dWatQ4T82ax5k790BuD0l8ABqN5qVFvyFzUKWjo8PixV9x7lwFTp1qiLv75zk2rvep\\nUZeWlso333SgYsUwVq5sg6lpQQIDL1CunD2g0BZL12g06OjoYGhYkIoVnZgzZxGffVafgweP0qBB\\nI/T0dPH1XY+v73pGj57A2rWbteN7k6LpQgjxKjIzJ4QQItusXXuYtWv/h0ZjAcCCBZdo0iQIFxfH\\nXB7Zp6lRozrY2tphZmaOtXURHBwq8PnnjZg7dxZxcdHo6urj7FyVo0cPkZKSQqlSpXn8+DHx8XEM\\nGvQdjRqlpylev96PQ4d+JyUllc8/b0SfPt/8m9VxMJUqVebatavMnr2QtWtXExT0D8nJSTRq1JQ+\\nfb7JclwajYZNm9ZTqJAJnTt3A8Db+xcsLArTqVPXbLn252vUzZgxmVKlytCxY1dq1fqMBQu8iIuL\\nQ6VKo0sXV0qXLsO4cZOZPXsaK1d6o6ury88/z6Rhw8ZcuXIJd/duKBQKBg0aiqmpGdHR6bNrf/99\\nmYMH96HRqNHR0eHhwwfcuRMMpBdH79SpG+fPn6VDhzYULGiIi0tV2rfv+G9AKIQQ70+COSGEENkm\\nPDxZG8gBJCaW49at3yWYywVXr15BpVKxZs1GUlNT6d27Bw4OFZg1axo//jiKqlUrcvjwnwwdOoCN\\nG3fg7f0LFy6cY/DgYZQqVYaRI7+nUaOmnDlzipCQ+6xY4YdarWbkSE8CAy9gbV2E0NAQxo2bTMWK\\nTgD07z8IExMTVCoVw4YN4tatm5QtW+6FsSkUClq3bsfo0T/SuXM31Go1f/xxkBUr/LLt+m1sirJu\\n3dYX9tvbl2fx4uUv7C9WrDgLFix9Yf+gQUMZNGiodvvBg3Ds7IoRFRWJp+dgNBoN1avXomdPd+bM\\nmcGSJQtRq1Vcvx5E374DWLnSHy+vGUREPOHixQtYWlppgzlZUyqEeF8SzAkhhMg2zZqVxc/vOI8e\\n1QfA3n4vjRrVyOVRfZouXw5EqdRFT08PPT09ChcuzObN63jy5AmDBvWlSBFrwsLCSEpKwtNzCEql\\nLtHRUfzyy0KMjY2IiEhP8nHmzCnOnj2Nh4crAImJSYSE3MfaughFihTVBnIAf/xxgF27dqJSqYiI\\neMKdO8FZBnOQHmyZmppy48Y1IiIiKF/eERMTk5z/YN6TjU1RNm3a+cL+tLQ0Vq70R6FQsHfveRYu\\nfICXlz6NGp3Dy2vBC8Ha6NETMm1nrC8VQoi3IcGcEEKIbFO1qj2LF19i8+Zt6OqmMXBgBQoXtnj9\\nge8oLi6Ogwf38dVXHTl//i82blzHrFnzcqy/vEUBpK/DOnPmFLGxsfzvfx3YsWMrDg6OfPvtQEqW\\ndKBTp3YsWuTN4sXzMTIyomPHLjRs2IRmzZ6tZevRw53//S9zwfPw8DAKFiyg3Q4LC2XjxnWsXOmP\\nsbEx06ZNIiUlmVdp06Y9u3f/SmRkBK1bt8u+S/+A0tLS+O67HZw4YYmRUSJ9+ypZuNCQsLAuAAQF\\nPaZkySN4eDQCID4+nsmTAwgPL0ClSmn8+GMLdHQkhYEQ4t3Idw8hhMiHwsPD6NWrS5bvDR7cn6Cg\\nqznWd6NGVViypDkLF7aiQoWcLSIeGxvDjh1bcrSPvKpKFWdUKhUpKSmcOHGMu3eD2blzK/HxcQQF\\nBXH37l00Gg2pqamZjvtvIo7ateuwe3d6hlKAx48fERkZ+UJ/8fHxFChQECMjI54+jeDUqZOvHWPD\\nho05ffokQUFXqV37s/e42tyzeHEAW7d2Izy8HTdvdmHmzEeEhT2brVSrrbh1K0m7PWTIXnx9u7Jv\\nXwfmzGnB9Ol7c2PYQoh8QmbmhBDiE6NQKPLN+pxlyxYRGhqCh4crurq6FChQkLFjRxAcfAsHhwqM\\nHz8FgKCgqyxePI/ExERMTc0YM2YChQtbMnhwfypVqsz5838RFxfLyJHjcXZ2yeWryh6OjhVRKnVx\\nc+tKUlISZcuW4+uvO1G9ei28vGbg5+fH8uUrSUp6Fmg8/7WR8W/NmnW4c+cOAwZ4AGBoaMi4cVNe\\n+Dqyty9P+fIOuLp2wNrahipVnF87Rl1dXapXr0mhQiZ59mvywQOAgtrtyMjaFCv2JyEhJQAwMLiL\\ns3Mh7ftXrpgDyn+3TAkMfJbRUggh3pYEc0II8RHau/c3Nm5ch0KhoFw5e/r2HcC0aZOIjo7GzMyc\\n0aPHU6SIDVOnTqRevQbarIPNmjXg4MFjmc6VnJzEtGmTuHXrJiVKlCI5OTnfpEEfOPA7goNv4+u7\\nngsXzjFqlCdr126hcGFLBg7sw6VLF6lY0Yn582czc+ZcTE3NCAg4wPLlSxg1ajwKhQK1Ws2KFWv4\\n888T+PouZ/78Jbl9WdlGV1eXDRu2c+LEUSZMGEPJkmUoWtSWkSPHYmNjjkqlR6dO6Y83jh49gfnz\\nZxMfn15z7fk1XJ06dc0yy+R/awf+dx1YhkWLvLWvt2zZpX2tVqu5cuUyP/88690vMpfVrWvOxo3X\\nSEhwAKBKlSAmTizFkiWbSEzUo0kT6NSpmba9lVU8wcEZWxoKF0748IMWQuQbEswJIcRH5vbtW/j5\\n+eDt7YuJiSkxMTH8/PMEWrVqS4sWrdm9exfz53sxfbpXFrMZL85u7NixlYIFDVm7dgu3bt2kd+/u\\nH80syKpV3hgaGtGtW49M+8PDwxgxYjh+fpteefzzQalGo6FChUpYWloBUK5ceR48CMfY2Jjg4FsM\\nGzYISA8gChe20h7XsGFjABwcHHnwIDxbrutjkZqagoeHKykpKVSvXgMvr2kAFCxoyPz5cylQwIzn\\nv2aaNm3OzJlT2bp1E1OmzMDOrliOjGv16iNs336P6Gg/Pvusdo718yG0a1eLuLjjHDz4N4aGyXh6\\nVqNMmWLUr18ly/YTJzoyZsxawsNNcHB4ysSJDT/wiIUQ+YkEc0II8ZE5f/4sTZo0w8TEFAATExP+\\n+ecy06d7AfDll61YunThG58vMPCidlalbNlylC378dS4yu6gUk9PX/taqdRBpVIBULp0WZYt83nl\\nMTo6Sm37DyWrmdTsdOTI6Ze+Z2VViEePYli6dJX2PlSu7JypqHVOOHDgPJMmlSM+vg0wiLi4vQwf\\n/pgiRaxee+zHytW1Pq6ub9a2Ro3y7N9fHpVKhVKpfP0BQgjxCpIARQghPjIKhSLLxyCz2qdUKlGr\\n0/er1WrS0lJfaJOd1q/3Y+vW9EfrFi6cw9ChAwE4d+4skyeP4+DBfbi5daVXry4sXbpIe1yzZg20\\nrw8d+p1p0ya9cO6goKu4uXXD3d31jZOaGBoakpDw6sfUSpQoRVRUJH//fRlIzz4YHHz7jc6f83Jv\\nhlSlUjFgwCZq1w6lVq2/mTVr3wfp9+zZR8THP6s7eP9+Xc6evf5B+v6YSCAnhMgOMjMnhBAfmWrV\\najJ69A907dr938cso3FyqkJAwAG+/LIVBw7sxdm5KpBe8+ratas0afIFx48fJS0t7YXzubhU5eDB\\nfVSrVoPbt29y69aNF9qEh4fh6TkEJ6cqXL4ciKNjRbp27cT8+QuIjIxiwoT0RCL79u3h0aOHBAQc\\nICUlFaVSybff9qNUqTIUL16CZcsWY2FRmJ9+GsPixfM4duwwDRo04vmg5b+zcRmb06dP4vvvR+Ls\\n7MKSJQve6LMyNTWjcmVnevXqgoGBARYWhV9oo6ury5QpM1mwwIu4uDhUqjS6dHGldOkyWZwxe4Or\\n9ev90NfXp2PHrixcOIdbt26yYMFSzp07y2+//R8Ay5cv4eTJ4xgYGDBjxhzMzS0IDw9j+vTJL6yR\\nzE6LF+9jxw5XwAiAX365SOvWN6hUKWdnbh0cCqGvH0pKih0AlpYXqVKlVI72KYQQ+ZXMzAkhxEem\\ndOky9OrVm8GD++Pu7srixfMZNuwn9uz5FTe3bhw4sJehQ38AoF27r7h48Tzu7q5cuXKZggUNtefJ\\nCJrat+9IQkICPXp0YtUqbxwdKwLQsWNbYmKite1DQ0Po2rUH69dv4969u+zZs4elS30YPHgofn6+\\nlCxZmhUr1mBiYoKrqxuPHj3AyakyVatWY9eu7RgbF8LBwRGNRoO9fXmaNWuBt/cSDh8OeO01x8XF\\nERcXp80k+eWXrd/485ow4Wf8/DaxYoUfM2c+qzE3fPhPtGzZBkjPtLh48XJWr16Pv/9m2rRpD6Qn\\n5nBwSJ8lMjMzY8uW/3vjft+Es3M1AgMvAukzj4mJiaSlpXHp0kVcXKqRlJSIk1MVVq9ej7NzVXbt\\n2gHAvHmzadWqLWvWbKB58xbMn+/10j727v2NJ0+evPXYHj9WkRHIASQmluT+/cdvfZ7nhYeH0b17\\nR2bOnErPnp35/vvBJCcnc+PGNfr3d8fNrRvnz+/km28O4Ojoh4NDYyZNSiE5OZ4GDWry6NFDADp3\\n/h/Jya+uUSeEEEJm5oQQ4qPUsmUbbSCSYcGCpS+0Mze3wNvbV7s9cOAQAIoWtdVmGjQwMGDSpGkv\\nHPvfxzmLFrWjTJmyQHpAWbdu3X9fl+XBgzDi4mKZN282kZFPmTNnOmq1mipVXLh9+xYajQZra2vu\\n37/PV191/PeMGhSKF0shvMkv6R8i2+bOnae4eDGKChWM6dKlfo704eDgyLVrV0lIiEdfXx9HxwoE\\nBV0lMPACw4b9iJ6eHnXr1v+3bQX++it9jdvbrJHcs+dXSpcui6Wl5VuN7X//c2DFiqM8epReHNzJ\\naS/16zd+l8vMJCTkPpMmTWfEiDGMHz+KI0f+YN06P77//iecnauyapU38fF3OXrUk549N9C6dWX2\\n7v0NR8eKXLx4gSpVnLGwKIyBgcFL++jYsS0+PmsxMTHN8XWHQgjxMZNgTggh8qm4uHh++mk/N2+a\\nYmMTganpIWJjo1GrVbi59QVg69ZNnDhxjKSkRCA9gIqJiebChb+4dOkC5uar8fDoh0ql4qefhmNr\\na0u3bj3ZtWsHcXFxFC1qx8KFc1EoFERHR3P3bjBXrlxm69aNREZGYmtrh0ajwcLCgrt371C8eAmO\\nHj2EkZExkB60aTRgbGyMsXEhLl26SJUqLhw4kLOFlH/55XdmzHAhObk0enphBAfvZuTIN58NfFO6\\nuroULWrHnj2/UrmyM2XLluP8+bOEhoZSqlRplMpnP4Z1dBSoVCrCw8OIiYlh1qxp/PPPZQoXtkSj\\ngRs3rjF79nSSk5OxsyvGqFHj+euv0wQFXWXy5LEUKFCApUt9XhkEPa9mTQe8vSPZsmUL+voqhgyp\\nibGx8Xtfc9GidpQrl/6opoODI6GhIcTFxWofDW7RojXjxo0EwMnJmUuXAgkMvEjPnh6cPn0S0FCl\\nyqtr/WV+VPfjyMwqhBC5QR6zFEKIfGrMmANs3dqDixfbc/x4Ma5fT2X16vX4+W2iTp3PADAzM8fH\\nZy3Nm7ckOjoKSC8XYGJixsiRI/nmm29ZvHgeGo2G1NQUjIyMcHauytOnESgUCkxNzTAwMECpVLJq\\nlTd2dsU4evQQSqWSZs1aEBoagkKhYMCAwfz00zAGDuyjLR0AGbN26a9Hj57A3Lmz8PBw1b6XU/bt\\nU5OcXBqA1FRbDh58swDoXTg7u7Bhw1pcXKrh7FyVnTu3Ub58+Vceo1arsbOzw99/M/Hx8RQtasvP\\nP0/k22+HsmbNBsqWLYev73IaN/4CR8cKTJgwFR+fdW8cyGWoV68S8+e3YNas1hQvnj1r8vT19QgP\\nD6NXry7o6CiJi4slPj4eH5/lbNmyEU/PIdy5E8zEiWNwcamKn58PV65cpkGDhty4cZ1582ZTsmT6\\nvRk16gf69OlJz56dtY+gvsyUKeM5duywdnvSpLEcP37k5QcIIUQ+IDNzQgiRT927ZwykZ8xLTnYg\\nLu4uS5cuom7dBly79g8ajYaGDZsAUKZMWW3ylMuXA/EeK58AACAASURBVLGzK4ZCoaBq1RrExsZi\\nYmJC5crOHDt2hFu3btKzpwfr1q0BYMOG7TRr9jnGxsaUKFGKnj09aNWqLQAREelrsBo1aqotbP68\\n3r37a187ODiyevV67fagQd9l/4fyLwODzIliChRIybG+nJ2r4u/vi5NTZQwMCmBgYKCdpXo+YH3+\\ntY2NLefOnSUg4CDJyUnUr/85hw4FZDm7BR/msdR3ZWRkTIECBjx48IBdu3bQunU7kpKS6N27H7Gx\\nscydO5NixYqjUCgwMTEhMTGBChXS13WOGjUeExMTkpOT6NfPjUaNmmJiYpJlP23btmfTpvU0aNCI\\nuLg4/v77MuPGTf6QlyqEEB+cBHNCCJFPlSwZz4kTKkBJamopihXrTdmyZqxYsYSbN29gZGSEvr4e\\nANbWRbSJUSA9kHJ2duTx41iUSiXe3r5s2bKR7t174eraC4CAgAPa9hqNBrVaTdGiRd86sEhMTGTj\\nxmPo6kKXLo3Q19d//UHvaciQ4gQH/8b9+zWxsQlk0CDrHOurevWaHDr0p3Z7w4bt2tcHDjybOcoI\\neENDQyhQwEC7RnLDhrU8efLolX18LEXgIatspQqaNv2SP/44SHx8PLt376J37/7o6CixsSkKgK1t\\netFwZ+eqnD//F4aG6YlZtmzZwLFj6Z/Ro0cPCQm5R8WKTln26+JSjTlzZhAVFcXhw7/TuHETdHTk\\nASQhRP4m3+WEEOIjFx4ehqtrB6ZNm0S3bl8zadJYzpw5xYABvena9WuuXr3CqlXebNiwVntMz56d\\nGTq0Ch06+FKhQjsqVaqHQrEVpVKXkiVLER8fR0TEE0aN+uGF/qpUqapds3b+/F+YmZljaGhE0aK2\\nXLsWBMC1a0GEhYUyePBeatWaRVJSIq1adcLZuRoBAQdRq9U8efKE8+fPvfLaEhIS6Nx5FyNGtMPT\\nsw3du28jJSXnZskyNGxYmd9/r8yWLVf4/fdytG5dI8f7fBP791+gc+fD3LyZTMeOm3n6NBJIn90y\\nMTHRZsbct283VatWB9Jr7cXHx+XamJ+XkXgno/5ht2498PDoR6FChWjbtj0HDhxh/Pgp3L17h379\\neqFSqXB17aWdievZ0wNr6yJA+tfeuXNn8fb2ZfXq9djbO7z2a6NFi9bs37+bPXt+o3Xr/+X49Qoh\\nRG6TYE4IIfKA/5YNCAg4wLJlz8oGZDUbUrBgQbp0saRVq4rMnDkRAwN9/PxWcf36NQoXLkzhwlba\\njInwLONk7979uXYtiHbt2rF8+RLGjp0IQMOGTYiNjaFnz85s374ZPT0LTp7swJ07U1CpjNi504iG\\nDRtTvHhxevToxNSpE6hcucorr8vf/yinT7sDeoABR470YNu2o9n62b2MubkFDRvWxNra6vWNPwCN\\nRsPPP4cSHNwWlcqYo0d7M3VqepZGhULB6NETWbJkAW5u3bh16yYeHv0AaNWqLV5e0+ndu/tHk87f\\nwqIwUVFPiYmJJiUlhZMnj6PRaHj48AHVqtVg4MAhxMXFERMTQ0hINIcOHUOlUnHtWhDh4WEAJCTE\\nU6hQIQwMDLh79w5Xrvz92n5btWrL5s0bUCgUlCxZKoevUgghcp88ZimEEHnAf8sG1KhR69/X6WUD\\n7O2zSqihoGxZe375ZQEmJiZ8//0IbR23Tp3asWqVPyYmpgA4OlZg4cJlAJiYmDB9uhdWVoV4/DhW\\nezYDAwPmzl2s3T5z5iBpaemFn2/dOo+Ozg4iIiLo0cOD4cN/eourk8yEACkpKTx9akpaWjHu3v0V\\ngKioAnTr1k7b5vkyFBkaNmyiXfv4sdDV1cXdvS/9+rlhZWVNqVKlUalUTJ48jvj4ODQaDV991ZE+\\nffZz4sRgbG2H88UXrWjWrC7Fi5cEoHbtuuzcuY0ePTpRvHhJnJwqZ9nX83/IMDe3oFSpMnz+eaMP\\ncZlCCJHrJJgTQog8IGNtG4COjg56enra1yqVCqVSiUaj1rbJeBytePES+Pis488/j7NixRJq1KiF\\nu3vfbBlTxYoJnDiRCBQEVKSmXqR2bXPUaj3atTvCvHkdXruWq0ePBuzatZqzZ90ANZ9/7k+HDh2y\\nZXx5jYGBAS4uYRw8mL7OUU8vlNq1X/wxvX79cQICEjAySmLEiNrY2RV56742b15Pnz5u2u0ffxzK\\nxIlTMTIy1tZtCw8PY8SI4fj5bXqn6+nYsSsdO3Z96fve3vs4frw9oEdoqB+hoVEMHXqc0aMnaNt4\\neWVdX2/Lll3a18+vO0xKSiIk5B7Nmn35TmMWQoi8RoI5IYTIB4oWteXEifRH8p5/VO3JkycUKlSI\\n5s1bYmRkzO7d6b8Ep6+zitfOzL2LiRNboau7k2vX9FGp/uHEiX6kpaUnstiwoQJ16x6hc+dGrzyH\\nkZERW7a0Yf36Hejq6tCt29cfJAHKx8rbuzVTp24iIsKA2rX16d07cxHvHTtOMWpUGRITHQANN26s\\nYdeudtrg/k1t2bIRV9fOZPwaMHv2AgDi4uK0WU3f1NSpE6lXr0GW2UpfJTVVQ+ZfQwxISnq7vjNc\\nuHCD5cv3c+PGDnr27K5NoCKEEPmdBHNCCJEHZLUm7vnXDRs2Yd++3fTs2ZmKFZ20j6rdvn2TX35Z\\ngI6OAl1dXX74YTQA7dp9hafnEKysrLVZE9+Wnp4ekya1AcDHR8ORI7ba9zQaCx4+THyj8xgaGtK3\\nb4t3GkN+Y2xszPTpbV/6/okTMf8GcgAKAgOrERoaQqlSpV96TGJiIuPHj+Tx48eo1SoaN/6CJ08e\\n06tXLwoVMmXBgqV07NgWH5+1xMfHv3Uwl14r8O0fj+3e/TN27vTj0qVegIrPPltL+/bt3/o8ly/f\\nonfvJ4SGjgJGsnGjL126JFGgQIG3PpcQQuQ1EswJIcRHLiNDYIbnH0N7/r3n17NlsLGxoVatOi/s\\n79ChCx06dMm2MbZuXZ1Vq3Zw40b6I5IlS/5GmzavTn4i3p6VVRqQDKQXB7e2vkfhwlVfeczp0yex\\ntLTWzr7Fx8exZ8+v+Pv7k5qaXocwIxhbtmwRGo0GDw9XKlSoRETEE3r16oJCoaBXrz40bdoMjUbD\\nvHmz+OuvM1hbF8k0K+jru4KTJ4+RnJyMk1MVfvppDKGhIYwbNxIfn/Rsq/fv32PChNH4+Kxl8+bG\\n+PtvQVcXPDzavVMAtmvXDUJDO/27peDcuTacOnWJRo1qvfW5hBAir5FslkII8QlITk5m7NhduLoe\\nYNSoXSQlJWXr+YsUsWT1akfc3TfRq9dmfHzsKF3aLlv7EDB8+Be0beuPtfUeypTZzJgxBhQqlHUR\\n7Qxly9rz11+nWbp0EYGBFzEyMn5p24EDv0OhUODr+6wUwJo1G5k/fwlLliwgIuIJR48e4v79e6xb\\nt5WxYydz+fIl7fEdOnRhxQo//Pw2kZyczIkTx7CzK4axsTE3blwHYM+eX2ndOj2pi4WFOUOHtuTb\\nb1tiaGj4Tp+JsTHAs5IFBQo8wMrK7J3OJYQQeY3MzAkhxCdg9Og9+Pt3BfSBVOLi1rNo0dfZ2oe9\\nfQlmzSqRrecUmenr67NqVReSk5PR19d/o8cb/5sEp3r1mi9t+3zB96CgfzA2NkahUGBuboGLSzWu\\nXv2HwMALNGvWAoVCgaWlJdWrP6vRd/78Wdav9yc5OYmYmBjKlClLvXoNaNOmPXv2/MqQIcP544+D\\nrFjh934fxHMGDGjMmTO+/PFHAwwMound+w6VKrXJtvMLIcTHTII5IYT4BFy5Ykx6IAegxz//vHo2\\nR3zcDAwM3rjtf5Pg/Pbb/2FoaERcXBwGBq9KgPPyQPH5oC9DcnIyc+fOYtUqf6ysrPHxWa6te9ew\\nYWN8fZdTvXoNHB0rYGKSfV9/BgYG+Pt35dat2xgZmWNr65Rt5xZCiI+dPGYphBCfgCJFEjJtW1sn\\nvKSlyG9u375J//7ueHi4snr1Stzd+9KuXXv69u3L0KEDM7V9/lHHChUqEBcXj1qtJjIyksDAC1Sq\\n5ISzczUCAg6iVqt58uQJ58+fA56VwzAxMSUhIYFDh37XzhwaGBhQu/ZneHnNoFWrdmQ3HR0d7O3L\\nYWsrj/YKIT4tMjMnhBCfgEmTahIb60dwsAklS8YyeXL13B6S+EBq1arzQhIcBwdHBgzoqy0Kv2XL\\nLu1s2xdffEmvXl2oU6cuX33VAXf3bigUCgYNGoq5uQUNGzbm/Pmz9OjRiSJFbKhcOT3RTaFChWjb\\ntj29enXBwqIwFStmniH74osWHD16OMuEPEIIId6NQpPVsxK5IOMHish/rKwKyf3Nx+T+5i0ajeaN\\n08jLvc3fnr+/06fvYft2fZRKNd276zBkyBfZ2ldsbAxr165GV1ePfv0Gvv4A8V7k/27+Jvc3/7Ky\\nKvTWx8jMnBBCfELepR6YyN927z7FkiWfkZycnrxmzpyr1Kx5mTp1KmfL+VevPsrSpb5oNDHY2LSj\\nU6dozMzevVi9EEKIZ2TNnBBCCPEJu3kzShvIASQkOPDPP6HZcu6EhATmz1cRHLyVO3cOcOrUIGbN\\nOpYt5xZCCCHBnBBCCPFJa9SoLFZWJ7XbdnZ/0KRJ9szKxcfHEx1t9dweHeLi9F7aXgghxNuRYE4I\\nIYT4hDk72zNvXiotW26hdevNLFxoRKlS2ZMV0tLSklq1/gZUAJiYXOKLL8yz5dxCCCFkzZwQQog8\\nKjw8jBEjhuPntym3h5LnNW9ejebNs/+8CoUCH5+2eHltJiZGj6ZNC9OqVa3s70gIIT5REswJIYQQ\\nIscYGRkxYUKb3B6GEELkS/KYpRBCiDxLrVYzc+ZUevbszPffDyY5OZnQ0BA8Pb+jT5+efPttP+7d\\nu5PpmIEDewPw4EE4Bw/uy4VRCyGEENlDgjkhhBDZIi4ujh07tn7QPu/fv0eHDp3x99+MsXEhjhz5\\ng1mzpjF8+I+sWuXPoEFDmTNnZqZjli71ASAsLJSDB/fn6PiaNWuQ5f6dO7exb9/ulx53/vxf/PTT\\n8JwalhBCiHxCgjkhhBDZIjY2hh07tnzQPosWtaNcOXsAHBwcCQ8P4++/Axk3bgQeHq54eU0jIiIi\\n0zEZAdayZYu5dOkCHh6ubN68IYdGmHVdv/btO9CiResc6vPVOnZsS0xMdK70LYQQInvJmjkhhBDZ\\nYtmyRYSGhuDh4UrNmrXRaOD06ZMoFAp69epD06bNsr1Pff1nae51dJTExDzF2LgQvr7rX3FUeoA1\\ncOAQNmxYy6xZ8965//Xr/dDX16djx64sXDiHW7dusmDBUs6dO8tvv/0fAMuXL+HkyeMYGBgwY8Yc\\nzM0tWLXKG0NDI7p160FIyH1mz55OdHQUOjo6TJkyA4VCQWJiAmPHjiA4+BYODhUYP37KO48z09VL\\n4XghhMg3ZGZOCCFEthg48Dvs7Irh67ueihWduHnzOmvWbGT+/CUsWbKAiIgnOT4GIyMjbG3tOHTo\\ndwA0Gg03b97Isq1Go3nv/pydqxEYeBGAoKCrJCYmkpaWxqVLF3FxqUZSUiJOTlVYvXo9zs5V2bVr\\nB5AeUGXEVJMmjaVjx86sXr0eb29fLC0t0Wg03LhxjWHDfmDt2i2EhYVy6dLFtx7fqFE/0KdPT3r2\\n7KztO0NCQgI//jgUd3dXevXqQkDAQQD++usMvXt3x82tK9OnTyY1NfU9PiEhhBA5SYI5IYQQ2eL5\\n4OjSpYs0a9YChUKBubkFLi7VuHr1n2zv87+zTAqFgvHjp/Dbb7twd3elZ88uHD9+JNv7zeDg4Mi1\\na1dJSIhHX18fJ6fKBAVdJTDwAs7OVdHT06Nu3fr/tq3AgwfhmY5PSEggIuIJDRo0AkBPTw8DgwKo\\n1WoqVKiEpaUVCoWCcuXKv3Dsmxg1ajyrVvmzcqUfW7du1D5eqdFoOHbsGJaW1qxevR4/v03UqfMZ\\nycnJTJs2icmTZ7BmzUZUKtUHXwcphBDizcljlkIIIbKdQqF4YeYrux/vK1rUljVrNmq3u3XroX09\\nZ87C1x5vaGhEQkL8G/cXHh7GDz98R5UqVfn770CsrKyZPn0OFhaF6dfPjbi4OB4/fgzA/fv3+eGH\\n71Aq03/MJiYmMnfuDOrWbUBoaAj79+8lJSWZw4f/IC0tDYCpUyeir6/PjRvXsbGxQU9PX9u3UqmD\\nSqV647Fm2LJlA8eOpQezjx494v79+0D6vXBwcGD69BksXbqIunUb4Ozswo0b17G1taNYseIAtGzZ\\nhu3bN9O5c7e37lsIIUTOk5k5IYQQ2cLQ0JCEhAQAqlRxISDgIGq1msjISAIDL1CxYqUc7T8tLY2d\\nO4+ybdthUlJSXtouI6gsV84epVKJu/ubJ0AJCbn/QvbMiIgI4uLiGD9+CkOGDGPHjm04Ojpib19e\\nG4CdPHkMe3sHFAoFs2ZNpU6dz+jc2ZUhQ74nOTmJY8cOA+kB18KFS2nfvuP7fRikZ8Q8d+4s3t6+\\nrF69Hnv78qSkJGvfL1WqFD4+6yhbthwrVixh9eqVLwTc2fEoqhBCiJzz3jNzPj4+zJo1i1OnTmFm\\nZgaAt7c327ZtQ0dHh7Fjx1K/fv33HqgQQoiPm6mpGZUrO9OrVxfq1KlLuXLlcHfvhkKhYNCgoZib\\nW+RY32lpabi5bebgwa6Akg0bNrBu3VcYGBi80PbAgfSZKl1dXRYsWPpW/WSVPfPJk0ekpqayaNFc\\n7Yyks3NVzM0t+PPPEwD8/vsBKld2JjQ0hMuXLxEcfBsdHR0OHNiDubkFW7du4vr1IIyNC/H06dNM\\na+reVUJCPIUKFcLAwIA7d4K5cuXvTO8/evQIfX19mjdviZGRMbt378LVtRfh4WGEhoZgZ1eM/fv3\\nULVq9fcbiBBCiBzzXsFceHg4J06cwNbWVrvv5s2b7Nmzh927d/Pw4UM8PDzYv38/OjoyCSiEEPnd\\nhAk/A+nFvNPS0hg0aOgH6Xf79qMcPNgdMAbg6FE3/P130bfvl9o2cXFxzJp1mOhoPRo3NqN9+9pv\\n3U9W2TNNTEz5v/97sfh4QkICVlbWxMTEcP16ENOmzSYhIZ5z585m2X7atEnUrVsfW1s7LCwKM27c\\ns+yVw4f/9NZjrV27Ljt3bqNHj04UL14SJ6fK/76THiVev36dadNmoKOjQFdXlx9+GI2+vj6jR09g\\n3LgRqFQqKlSolC2zhEIIIXLGewVz06dP58cff2TQoEHafQEBAbRu3Ro9PT2KFStGiRIluHTpEi4u\\nLu89WCGEEB+/detOsGhRLHFxRtSrF8Yvv3RAVzdnl2inpKgAvef26JKaqtZuaTQaevfezeHDHoCS\\nXbv+QaM5xVdf1Xmvfp/Pntm48Rfa7Jn29uUxNDTE0bEiCxbMpl69BigUCoyMjLG1tc3U/tatm9rZ\\nPgAvr/2sWVOA1FQDvvwyhHnzvn6nP4jq6enh5fXi2sEtW9JLJpQtW581a549XhoTE83hw2ewty+G\\nj8+6d/g0hBBCfGjvPF32+++/Y2Njg6OjY6b9jx49wsbGRrttY2PDw4cP332EQggh8oynTyOYPl2H\\n27c78ehRK3bs6M6iRb/neL8dOtSnVi1/QAWocXFZQ/fu9bTvN2vWgL/+qggoAYiPr8gff7x94ew3\\nyZ554sRR7ftNmzbj4MH9NG3aXLtv/PifX5pt886dMBYtcuDhwzY8fdqMDRu+Yu3anMvGmeHSpZu0\\nbHmazp0r07TpQ9auPZ7jfQohhHh/r/xTqYeHB0+evFgXaNiwYSxfvhwfHx/tvlctkn6TDGZWVoVe\\n20bkXXJ/8ze5v/nX297b8PD7PHpU+rk9BYmNNfgAXyOF+OOPbixbtgeVSsM333TA1NRE+66Ojg4W\\nFhHExWXsUVOkyMuvLzY2ll9//RVXV1dOnz6Nr68vy5YtY8+e3do23303kLFjx2JkpIufn2+W5+nU\\nqT2dOrXPtM/KyiHL9vPmebFxYwCJiWWe22tGbGzO/R/LOK+3921u3OgAwNOn1qxYsY3hw+X/dV4m\\n35fzN7m/IsMrgzlf36x/OF2/fp2QkBDatWsHwMOHD+nQoQObN2+mSJEiPHjwQNv2wYMHFClS5LUD\\nefw49m3GLfIQK6tCcn/zMbm/+de73FsLC2ucnfcRGJj+2KCR0T/UqPHhvkZ69WoEQEpK5p8rGg0M\\nH66Hl9dmlMoNGBtHEBRkxI4dBahfv+ELZQdMTEyJjo6iWbO2BAb+w+nTp2nTpi01atTm9OmT+Plt\\nYs+eX1GrFZiYWPP4cSw//TSMbt16UrVqdby8ZhAU9A/JyUk0atSUPn2+AeDPP4+zePF8ChQoiJNT\\nFS5c+JsaNbrx+ecl2bNnMzdv3qB8+SmEho4jPr4pRYoco169Yjny+T1/f2NiMv9BNi5OyaNHMdle\\nTkJ8GPJ9OX+T+5t/vUuQ/k6LGMqXL8/Jkye1202aNGH79u2YmZnRpEkTPD09cXd35+HDh9y9e5cq\\nVaq8SzdCCCHyGAMDA1aurMWcORtISNDnyy+NadWqbm4PC4Du3evRrl0sERGOlChRkpiYGAYM8KB+\\n/YZAetmBSZOmM2LEGL7+ujVPn0bg4eHKvXt3KVmyNLa2duzevQu1+tlavCNHDtGqVTvs7ctz48Z1\\npk2bhKGhIU2bNueHH0aiUqkYNmwQt27dpFix4syePZ0lS1ZiY1OUr792IzjYlH37OlGq1FB69SqF\\nj88ELlz4hx9+GE758g/p1q0ULi72L7ukbNOmjTEnT/5NbKwTCsVTmjaNlkBOCCHygGxZkf78N/xy\\n5crRsmVLWrdujVKpZMKECfIDQQghPiElSxZl4cI2uT2MLBUsWJDt2zcTGHgRHR0FT548JjLyKZC5\\n7EDz5i3ZvXsXixYtx9W1A2FhIcyaNY+oqCi++caDy5cDM533+vVrpKSkMGHCz7i4VGPTpnX07t0D\\nlUpFRMQT7ty5jVqtwtbWDhuboiQnJ3P7dh3gNgBq9X127LjAuXN/AGBurs+oUZUpUaLUB/lcunSp\\nR+HCFzh5cgvFiunj4fHVB+lXCCHE+8mWYC4gICDT9oABAxgwYEB2nFoIIYTINgcO7CU6Ogofn7Uo\\nlUo6dWpHcnJ6gfHMZQcUQMajhxoqVKiEpaUVUVFRGBjoEx4ejlKp1La3sytGUlIiW7ZsICwslB07\\ntrJypT/GxsZMmzbp3yLmz/6wqVAo0NFR89wkH6VLd2Plyh45ePWv9sUXVfnii1zrXgghxDuQ4m9C\\nCCE+GfHx8ZibW6BUKjl//i8ePAh/ZXtjY2MMDAqQlJQMQEDAAUCBSpWGjY0tiYkJaDQaEhLiUSp1\\nsbd3YN++3cTERGNkZMTTpxGcOpW+LKFEiZKEhYXy4EE4+vr6lCt3Dh2dBECNnp4N1tb/aPu9fj0o\\npz4CIYQQ+UjOFv4RQgghPgIZj/s3b96CESO+x82tKw4OFShZsvQLbQD09PRJTU0FwNW1J0uXLsbD\\nwxUXl+ro6aXP4Dk7u6Cvb8DYsSMoXboM9vblcXGpxuefN+Lbb/vj6toBa2sbqlRxBtLXE3p6jsTT\\ncwgFChSkVq2KWFndp0aN7TRqNJitW/1xc+uKWq3G1taOmTPnfaiPRwghRB6l0LyqpsAHJFl58i/J\\nupS/yf3Nvz71eztp0lhu3bqBnp4elpZWzJw5D3//1QQE7KdLl+60bNmGIUO+YfDg4SiVSqZNm4RG\\nk/7c5IABQ6hd+7MXzpmYmEjBggUBmDNnJsWLl+DzzxsTFHSPqlXLY2pq9sGu71O/v/mZ3Nv8Te5v\\n/vUu2SwlmBM5Tr7p5G9yf/MvubfpAgIOsnatLyqVChsbW8aMmZAp6Lp3L4Rr10KoUcMBc3PzV55r\\n8+b17N37G6mpaTg4OFCuXHOmTCnAkyeVKFPmTxYvLkaNGg45fUmA3N/8TO5t/ib3N/+SYE58lOSb\\nTv4m9zf/ys/3tmPHtvj4rMXExPS9zrNu3XEmTzYiMtKJMmWOvnUw1rjxPq5c6aTdbtlyE2vWtHqv\\nMb2p/Hx/P3Vyb/M3ub/517sEc5IARQghxCdHoVCQHX/L9PaOJTLyc8CC27fb88svN9/q+KQkvUzb\\nycmylF0IIcSbk58aQggh8rXExETGjx/J48ePUatVuLn1BWDr1k2cOHEMlSqNKVNmUKJEKRITE5k3\\nbxbBwbdRqdLo3bu/tqh4VpKTMwdjKSl6L2mZtaZN4wgOfoJabYmh4TVatDB4+wsUQgjxyZJgTggh\\nRL52+vRJLC2tmT17AQDx8XEsW7YIMzNzfHzWsmPHVjZsWMuIEWPx8/OhRo1ajB49gdjYWPr3d6NG\\njdoUKFAgy3M3axbPypWPUautKFToCm3bGr7V2KZMaYe9/RHu3EmiZk1LWrV6eeAohBBC/JcEc0II\\nIfK1smXt+eWXBSxduoi6dRvg7OwCQMOGTQAoX96RI0f+AODMmVOcOHGUDRv8AUhNTeXRoweUKFEq\\ny3NPmdIOR8ejBAcnUq+eDU2a1H+rsSkUCtzcGr3bhQkhhPjkSTAnhBAiXytevAQ+Puv488/jrFix\\nhOrVawKgr5/+SKRSqYNKpdK2nzp1NsWLl3ijcysUCnr0kNk0IYQQuUMSoAghhMjXnjx5gr6+Ps2b\\nt8TVtRfXr197adtateqwdetG7fb160EfYohCCCHEO5FgTgghRL52+/ZN+vd3x8PDFV/fFbi59QEU\\nz7VQoFCkb7u79yUtLQ03t6707NmZVau8c2XMQgghxJuQOnMix0k9lPxN7m/+Jfc2f5P7m3/Jvc3f\\n5P7mX1JnTgghhHgHarWaESO2U69eAM2a7WH37nO5PSQhhBDitSQBihBCiE+et3cAvr7/A8wAGDv2\\nN+rXj8LU1Cx3ByaEEEK8gszMCSGE+OTdu6cmI5ADCA2tQEhIeO4NSAghhHgDEswJIYT45FWrVggD\\ngzvabUfH85QuXTLXxiOEEEK8CXnMUgghxCevU6e6REQEEBBwDkPDFDw9HTA0NMztYQkhhBCvJMGc\\nEEIIAQwY0JQBA3J7FEIIIcSbk8cshRBCCCGE/MFDnAAADGJJREFUECIPkmBOCCGEEEIIIfIgCeaE\\nEEIIIYQQIg+SYE4IIYQQQggh8iAJ5oQQQgghhBAiD5JgTgghhBBCCCHyIAnmhBBCCCGEECIPkmBO\\nCCGEEEIIIfIgCeaEEEIIIYQQIg+SYE4IIYQQQggh8iAJ5oQQQgghhBAiD5JgTgghhBBCCCHyIAnm\\nhBBCCCGEECIPkmBOCCGEEEIIIfIgCeaEEEIIIYQQIg+SYE4IIYQQQggh8iAJ5oQQQgghhBAiD5Jg\\nTgghhBBCCCHyIAnmhBBCCCGEECIPkmBOCCGEEEIIIfIgCeaEEEIIIYQQIg+SYE4IIYQQQggh8iAJ\\n5oQQQgghhBAiD5JgTgghhBBCCCHyIAnmhBBCCCGEECIPkmBOCCGEEEIIIfIgCeaEEEIIIYQQIg+S\\nYE4IIYQQQggh8iAJ5oQQQgghhBAiD5JgTgghhBBCCCHyIAnmhBBCCCGEECIPkmBOCCGEEEIIIfIg\\nCeaEEEIIIYQQIg+SYE4IIYQQQggh8iAJ5oQQQgghhBAiD5JgTgghhBBCCCHyIAnmhBBCCCGEECIP\\nkmBOCCGEEEIIIfIgCeaEEEIIIYQQIg+SYE4IIYQQQggh8iAJ5oQQQgghhBAiD5JgTgghhBBCCCHy\\nIAnmhBBCCCGEECIPkmBOCCGEEEIIIfIgCeaEEEIIIYQQIg+SYE6I/2/v7kKzrB8/jn98uPkF1cl0\\nbJJYoJRFrA6DDkpbc2s6FM0jBTWwDkKWppAPGPYgaxAdFQpp5YFgaCFoBLpSpFYY0QSDEmQo6UzN\\npzrYXNf/IBr/KP3lw4953bxeZ7vu2/GVD0Pe933NGwAASkjMAQAAlJCYAwAAKCExBwAAUEJiDgAA\\noITEHAAAQAmJOQAAgBIScwAAACUk5gAAAEpIzAEAAJSQmAMAACghMQcAAFBCYg4AAKCExBwAAEAJ\\niTkAAIASEnMAAAAlJOYAAABKSMwBAACUkJgDAAAoITEHAABQQmIOAACghMQcAABACYk5AACAEhJz\\nAAAAJSTmAAAASkjMAQAAlJCYAwAAKCExBwAAUEJiDgAAoITEHAAAQAmJOQAAgBIScwAAACUk5gAA\\nAEpIzAEAAJSQmAMAACghMQcAAFBCYg4AAKCExBwAAEAJiTkAAIASEnMAAAAlJOYAAABKSMwBAACU\\nkJgDAAAoITEHAABQQmIOAACghMQcAABACYk5AACAEhJzAAAAJSTmAAAASkjMAQAAlJCYAwAAKCEx\\nBwAAUEJiDgAAoITEHAAAQAmJOQAAgBIScwAAACUk5gAAAEpIzAEAAJSQmAMAACghMQcAAFBCYg4A\\nAKCExBwAAEAJiTkAAIASEnMAAAAlJOYAAABKSMwBAACU0A3F3JYtW9LS0pLp06ens7Nz6PqGDRvS\\n1NSU5ubmHDhw4IYPCQAAwF+Nvt4/2N3dna6uruzcuTOVSiVnz55Nkhw5ciS7d+/Orl270tfXl4UL\\nF+bTTz/NyJHeBAQAALhZrruwtm7dmsWLF6dSqSRJampqkiR79+5Na2trKpVKxo8fnwkTJqSnp+fm\\nnBYAAIAkNxBzvb29OXjwYObOnZv58+fn0KFDSZJTp06lvr5+6Hn19fXp6+u78ZMCAAAw5Kq3WS5c\\nuDCnT5/+2/X29vYMDg7m/Pnz2bZtW3p6etLe3p69e/f+4/cZMWLEzTktAAAASf5LzG3evPmKj23d\\nujVNTU1JkoaGhowcOTJnz55NXV1dTp48OfS8kydPpq6u7r8epLb2zn97ZkrIvtXNvtXLttXNvtXL\\nttXNvvzpum+zbGxsTHd3d5Lk6NGjGRgYSE1NTaZOnZpdu3alv78/x44dS29vbxoaGm7agQEAALiB\\n/81y9uzZWblyZWbMmJFKpZKOjo4kyaRJk9LS0pLW1taMGjUqa9eudZslAADATTaiKIpiuA8BAADA\\ntfHhbwAAACUk5gAAAEpIzAEAAJTQsMZcT09P5syZk5kzZ2b27Nnp6ekZemzDhg1pampKc3NzDhw4\\nMIyn5Hpt2bIlLS0tmT59ejo7O4eu27Z6bNq0KZMnT865c+eGrtm3/Do6OtLS0pK2trY8//zzuXjx\\n4tBj9i2//fv3p7m5OU1NTdm4ceNwH4cbdOLEicyfPz+tra2ZPn16PvjggyTJuXPnsnDhwkybNi2L\\nFi3KhQsXhvmkXK/BwcHMnDkzzz33XBLbVpMLFy5kyZIlaWlpyVNPPZXvvvvu2vcthtG8efOK/fv3\\nF0VRFJ9//nkxb968oiiK4scffyza2tqK/v7+4tixY0VjY2MxODg4nEflGn355ZfFggULiv7+/qIo\\niuLMmTNFUdi2mvz000/FokWLiilTphS//PJLURT2rRYHDhwY2q2zs7Po7OwsisK+1eDy5ctFY2Nj\\ncezYsaK/v79oa2srjhw5MtzH4gacOnWqOHz4cFEURXHp0qWiqampOHLkSNHR0VFs3LixKIqi2LBh\\nw9DPMeWzadOmYunSpcWzzz5bFEVh2yqyYsWK4sMPPyyKoigGBgaKCxcuXPO+w/rOXG1t7dArvhcv\\nXhz6cPG9e/emtbU1lUol48ePz4QJE/7yrh23vq1bt2bx4sWpVCpJkpqamiS2rSbr16/P8uXL/3LN\\nvtXh0UcfzciRf/zz8NBDD+XkyZNJ7FsNenp6MmHChIwfPz6VSiWtra3Zu3fvcB+LG1BbW5v7778/\\nSXL77bdn4sSJ6evrS1dXV2bNmpUkmTVrVvbs2TOcx+Q6nTx5Mvv27cvTTz89dM221eHixYs5ePBg\\n5syZkyQZPXp07rzzzmved1hjbtmyZeno6Mjjjz+eN954I8uWLUuSnDp1KvX19UPPq6+vT19f33Ad\\nk+vQ29ubgwcPZu7cuZk/f34OHTqUxLbVYs+ePamvr8/kyZP/ct2+1Wf79u157LHHkti3GvT19WXc\\nuHFDX9fV1dmwihw/fjzff/99GhoacubMmYwdOzZJMnbs2Jw5c2aYT8f1eP3117NixYqhF9iS2LZK\\nHD9+PDU1NXnppZcya9asrF69Or/99ts173vdHxr+by1cuDCnT5/+2/X29vZs2bIlq1evzpNPPplP\\nPvkkK1euzObNm//x+/jg8VvP1bYdHBzM+fPns23btvT09KS9vf2Kr/7a9tZ0tX03btyYTZs2DV0r\\nrvJxlfa9NV1p3xdeeCFTp05NkrzzzjupVCqZMWPGFb+PfcvFXtXr119/zZIlS7Jq1arccccdf3ls\\nxIgRti+hzz77LGPGjMkDDzyQr7766h+fY9vyunz5cg4fPpw1a9akoaEhr7322t9+j/nf7Ps/j7kr\\nxVmSLF++PO+9916SpLm5OatXr07yxyuFf97Wk/zxFvOft2By67jatlu3bk1TU1OSpKGhISNHjszZ\\ns2dtWyJX2veHH37I8ePH09bWluSPV/pnz56dbdu22bdErvbzmyQ7duzIvn378v777w9ds2/51dXV\\n5cSJE0Nf27A6DAwMZMmSJWlra0tjY2OSZMyYMfn5559TW1ubU6dODf26A+Xx7bffpqurK/v27Ut/\\nf38uXbqU5cuX27ZK1NfXp66uLg0NDUmSadOmZePGjRk7duw17Tust1nefffd+frrr5Mk3d3dueee\\ne5IkU6dOza5du9Lf359jx46lt7d36C9KOTQ2Nqa7uztJcvTo0QwMDKSmpsa2VeDee+/NF198ka6u\\nrnR1daWuri47duzI2LFj7Vsl9u/fn3fffTdvv/12/vOf/wxdt2/5Pfjgg+nt7c3x48fT39+f3bt3\\n54knnhjuY3EDiqLIqlWrMnHixCxYsGDo+tSpU/PRRx8lST7++OOhyKM8li5dmn379qWrqytvvvlm\\nHnnkkXR2dtq2StTW1mbcuHE5evRokuTLL7/MpEmTMmXKlGva93/+ztzVrFu3LuvWrUt/f39uu+22\\nvPLKK0mSSZMmpaWlJa2trRk1alTWrl3rLeSSmT17dlauXJkZM2akUqmko6MjiW2r0f/fz77V4dVX\\nX83AwEAWLVqUJHn44Yfz8ssv27cKjB49OmvWrMkzzzyT33//PXPmzMnEiROH+1jcgG+++SY7d+7M\\nfffdl5kzZyb5IwIWL16c9vb2bN++PXfddVfeeuutYT4pN4ttq8eaNWvy4osvZmBgIBMmTMj69esz\\nODh4TfuOKK72yy4AAADckob1NksAAACuj5gDAAAoITEHAABQQmIOAACghMQcAABACYk5AACAEhJz\\nAAAAJSTmAAAASuj/AKSSWUR2kw4CAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x2ee65e10>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"def plot(embeddings, labels):\\n\",\n    \"  assert embeddings.shape[0] >= len(labels), 'More labels than embeddings'\\n\",\n    \"  pylab.figure(figsize=(15,15))  # in inches\\n\",\n    \"  for i, label in enumerate(labels):\\n\",\n    \"    x, y = embeddings[i,:]\\n\",\n    \"    pylab.scatter(x, y)\\n\",\n    \"    pylab.annotate(label, xy=(x, y), xytext=(5, 2), textcoords='offset points',\\n\",\n    \"                   ha='right', va='bottom')\\n\",\n    \"  pylab.show()\\n\",\n    \"\\n\",\n    \"words = [reverse_dictionary[i] for i in xrange(1, num_points+1)]\\n\",\n    \"plot(two_d_embeddings, words)\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"colabVersion\": \"0.3.2\",\n  \"colab_default_view\": {},\n  \"colab_views\": {},\n  \"kernelspec\": {\n   \"display_name\": \"Python 2\",\n   \"language\": \"python\",\n   \"name\": \"python2\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 2\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython2\",\n   \"version\": \"2.7.12\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "deep-learning/tensor-flow-exercises/6_lstm.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"D7tqLMoKF6uq\"\n   },\n   \"source\": [\n    \"Deep Learning with TensorFlow\\n\",\n    \"=============\\n\",\n    \"\\n\",\n    \"Credits: Forked from [TensorFlow](https://github.com/tensorflow/tensorflow) by Google\\n\",\n    \"\\n\",\n    \"Setup\\n\",\n    \"------------\\n\",\n    \"\\n\",\n    \"Refer to the [setup instructions](https://github.com/donnemartin/data-science-ipython-notebooks/tree/feature/deep-learning/deep-learning/tensor-flow-exercises/README.md).\\n\",\n    \"\\n\",\n    \"Exercise 6\\n\",\n    \"------------\\n\",\n    \"\\n\",\n    \"After training a skip-gram model in `5_word2vec.ipynb`, the goal of this exercise is to train a LSTM character model over [Text8](http://mattmahoney.net/dc/textdata) data.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"cellView\": \"both\",\n    \"colab\": {\n     \"autoexec\": {\n      \"startup\": false,\n      \"wait_interval\": 0\n     }\n    },\n    \"colab_type\": \"code\",\n    \"collapsed\": true,\n    \"id\": \"MvEblsgEXxrd\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# These are all the modules we'll be using later. Make sure you can import them\\n\",\n    \"# before proceeding further.\\n\",\n    \"import os\\n\",\n    \"import numpy as np\\n\",\n    \"import random\\n\",\n    \"import string\\n\",\n    \"import tensorflow as tf\\n\",\n    \"import urllib\\n\",\n    \"import zipfile\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"cellView\": \"both\",\n    \"colab\": {\n     \"autoexec\": {\n      \"startup\": false,\n      \"wait_interval\": 0\n     },\n     \"output_extras\": [\n      {\n       \"item_id\": 1\n      }\n     ]\n    },\n    \"colab_type\": \"code\",\n    \"collapsed\": false,\n    \"executionInfo\": {\n     \"elapsed\": 5993,\n     \"status\": \"ok\",\n     \"timestamp\": 1445965582896,\n     \"user\": {\n      \"color\": \"#1FA15D\",\n      \"displayName\": \"Vincent Vanhoucke\",\n      \"isAnonymous\": false,\n      \"isMe\": true,\n      \"permissionId\": \"05076109866853157986\",\n      \"photoUrl\": \"//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg\",\n      \"sessionId\": \"6f6f07b359200c46\",\n      \"userId\": \"102167687554210253930\"\n     },\n     \"user_tz\": 420\n    },\n    \"id\": \"RJ-o3UBUFtCw\",\n    \"outputId\": \"d530534e-0791-4a94-ca6d-1c8f1b908a9e\"\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Found and verified text8.zip\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"url = 'http://mattmahoney.net/dc/'\\n\",\n    \"\\n\",\n    \"def maybe_download(filename, expected_bytes):\\n\",\n    \"  \\\"\\\"\\\"Download a file if not present, and make sure it's the right size.\\\"\\\"\\\"\\n\",\n    \"  if not os.path.exists(filename):\\n\",\n    \"    filename, _ = urllib.urlretrieve(url + filename, filename)\\n\",\n    \"  statinfo = os.stat(filename)\\n\",\n    \"  if statinfo.st_size == expected_bytes:\\n\",\n    \"    print 'Found and verified', filename\\n\",\n    \"  else:\\n\",\n    \"    print statinfo.st_size\\n\",\n    \"    raise Exception(\\n\",\n    \"      'Failed to verify ' + filename + '. Can you get to it with a browser?')\\n\",\n    \"  return filename\\n\",\n    \"\\n\",\n    \"filename = maybe_download('text8.zip', 31344016)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"cellView\": \"both\",\n    \"colab\": {\n     \"autoexec\": {\n      \"startup\": false,\n      \"wait_interval\": 0\n     },\n     \"output_extras\": [\n      {\n       \"item_id\": 1\n      }\n     ]\n    },\n    \"colab_type\": \"code\",\n    \"collapsed\": false,\n    \"executionInfo\": {\n     \"elapsed\": 5982,\n     \"status\": \"ok\",\n     \"timestamp\": 1445965582916,\n     \"user\": {\n      \"color\": \"#1FA15D\",\n      \"displayName\": \"Vincent Vanhoucke\",\n      \"isAnonymous\": false,\n      \"isMe\": true,\n      \"permissionId\": \"05076109866853157986\",\n      \"photoUrl\": \"//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg\",\n      \"sessionId\": \"6f6f07b359200c46\",\n      \"userId\": \"102167687554210253930\"\n     },\n     \"user_tz\": 420\n    },\n    \"id\": \"Mvf09fjugFU_\",\n    \"outputId\": \"8f75db58-3862-404b-a0c3-799380597390\"\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Data size 100000000\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"def read_data(filename):\\n\",\n    \"  f = zipfile.ZipFile(filename)\\n\",\n    \"  for name in f.namelist():\\n\",\n    \"    return f.read(name)\\n\",\n    \"  f.close()\\n\",\n    \"  \\n\",\n    \"text = read_data(filename)\\n\",\n    \"print \\\"Data size\\\", len(text)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"ga2CYACE-ghb\"\n   },\n   \"source\": [\n    \"Create a small validation set.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"cellView\": \"both\",\n    \"colab\": {\n     \"autoexec\": {\n      \"startup\": false,\n      \"wait_interval\": 0\n     },\n     \"output_extras\": [\n      {\n       \"item_id\": 1\n      }\n     ]\n    },\n    \"colab_type\": \"code\",\n    \"collapsed\": false,\n    \"executionInfo\": {\n     \"elapsed\": 6184,\n     \"status\": \"ok\",\n     \"timestamp\": 1445965583138,\n     \"user\": {\n      \"color\": \"#1FA15D\",\n      \"displayName\": \"Vincent Vanhoucke\",\n      \"isAnonymous\": false,\n      \"isMe\": true,\n      \"permissionId\": \"05076109866853157986\",\n      \"photoUrl\": \"//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg\",\n      \"sessionId\": \"6f6f07b359200c46\",\n      \"userId\": \"102167687554210253930\"\n     },\n     \"user_tz\": 420\n    },\n    \"id\": \"w-oBpfFG-j43\",\n    \"outputId\": \"bdb96002-d021-4379-f6de-a977924f0d02\"\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"99999000 ons anarchists advocate social relations based upon voluntary as\\n\",\n      \"1000  anarchism originated as a term of abuse first used against earl\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"valid_size = 1000\\n\",\n    \"valid_text = text[:valid_size]\\n\",\n    \"train_text = text[valid_size:]\\n\",\n    \"train_size = len(train_text)\\n\",\n    \"print train_size, train_text[:64]\\n\",\n    \"print valid_size, valid_text[:64]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"Zdw6i4F8glpp\"\n   },\n   \"source\": [\n    \"Utility functions to map characters to vocabulary IDs and back.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"cellView\": \"both\",\n    \"colab\": {\n     \"autoexec\": {\n      \"startup\": false,\n      \"wait_interval\": 0\n     },\n     \"output_extras\": [\n      {\n       \"item_id\": 1\n      }\n     ]\n    },\n    \"colab_type\": \"code\",\n    \"collapsed\": false,\n    \"executionInfo\": {\n     \"elapsed\": 6276,\n     \"status\": \"ok\",\n     \"timestamp\": 1445965583249,\n     \"user\": {\n      \"color\": \"#1FA15D\",\n      \"displayName\": \"Vincent Vanhoucke\",\n      \"isAnonymous\": false,\n      \"isMe\": true,\n      \"permissionId\": \"05076109866853157986\",\n      \"photoUrl\": \"//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg\",\n      \"sessionId\": \"6f6f07b359200c46\",\n      \"userId\": \"102167687554210253930\"\n     },\n     \"user_tz\": 420\n    },\n    \"id\": \"gAL1EECXeZsD\",\n    \"outputId\": \"88fc9032-feb9-45ff-a9a0-a26759cc1f2e\"\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"1 26 0 Unexpected character: ï\\n\",\n      \"0\\n\",\n      \"a z  \\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"vocabulary_size = len(string.ascii_lowercase) + 1 # [a-z] + ' '\\n\",\n    \"first_letter = ord(string.ascii_lowercase[0])\\n\",\n    \"\\n\",\n    \"def char2id(char):\\n\",\n    \"  if char in string.ascii_lowercase:\\n\",\n    \"    return ord(char) - first_letter + 1\\n\",\n    \"  elif char == ' ':\\n\",\n    \"    return 0\\n\",\n    \"  else:\\n\",\n    \"    print 'Unexpected character:', char\\n\",\n    \"    return 0\\n\",\n    \"  \\n\",\n    \"def id2char(dictid):\\n\",\n    \"  if dictid > 0:\\n\",\n    \"    return chr(dictid + first_letter - 1)\\n\",\n    \"  else:\\n\",\n    \"    return ' '\\n\",\n    \"\\n\",\n    \"print char2id('a'), char2id('z'), char2id(' '), char2id('ï')\\n\",\n    \"print id2char(1), id2char(26), id2char(0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"lFwoyygOmWsL\"\n   },\n   \"source\": [\n    \"Function to generate a training batch for the LSTM model.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"cellView\": \"both\",\n    \"colab\": {\n     \"autoexec\": {\n      \"startup\": false,\n      \"wait_interval\": 0\n     },\n     \"output_extras\": [\n      {\n       \"item_id\": 1\n      }\n     ]\n    },\n    \"colab_type\": \"code\",\n    \"collapsed\": false,\n    \"executionInfo\": {\n     \"elapsed\": 6473,\n     \"status\": \"ok\",\n     \"timestamp\": 1445965583467,\n     \"user\": {\n      \"color\": \"#1FA15D\",\n      \"displayName\": \"Vincent Vanhoucke\",\n      \"isAnonymous\": false,\n      \"isMe\": true,\n      \"permissionId\": \"05076109866853157986\",\n      \"photoUrl\": \"//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg\",\n      \"sessionId\": \"6f6f07b359200c46\",\n      \"userId\": \"102167687554210253930\"\n     },\n     \"user_tz\": 420\n    },\n    \"id\": \"d9wMtjy5hCj9\",\n    \"outputId\": \"3dd79c80-454a-4be0-8b71-4a4a357b3367\"\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"['ons anarchi', 'when milita', 'lleria arch', ' abbeys and', 'married urr', 'hel and ric', 'y and litur', 'ay opened f', 'tion from t', 'migration t', 'new york ot', 'he boeing s', 'e listed wi', 'eber has pr', 'o be made t', 'yer who rec', 'ore signifi', 'a fierce cr', ' two six ei', 'aristotle s', 'ity can be ', ' and intrac', 'tion of the', 'dy to pass ', 'f certain d', 'at it will ', 'e convince ', 'ent told hi', 'ampaign and', 'rver side s', 'ious texts ', 'o capitaliz', 'a duplicate', 'gh ann es d', 'ine january', 'ross zero t', 'cal theorie', 'ast instanc', ' dimensiona', 'most holy m', 't s support', 'u is still ', 'e oscillati', 'o eight sub', 'of italy la', 's the tower', 'klahoma pre', 'erprise lin', 'ws becomes ', 'et in a naz', 'the fabian ', 'etchy to re', ' sharman ne', 'ised empero', 'ting in pol', 'd neo latin', 'th risky ri', 'encyclopedi', 'fense the a', 'duating fro', 'treet grid ', 'ations more', 'appeal of d', 'si have mad']\\n\",\n      \"['ists advoca', 'ary governm', 'hes nationa', 'd monasteri', 'raca prince', 'chard baer ', 'rgical lang', 'for passeng', 'the nationa', 'took place ', 'ther well k', 'seven six s', 'ith a gloss', 'robably bee', 'to recogniz', 'ceived the ', 'icant than ', 'ritic of th', 'ight in sig', 's uncaused ', ' lost as in', 'cellular ic', 'e size of t', ' him a stic', 'drugs confu', ' take to co', ' the priest', 'im to name ', 'd barred at', 'standard fo', ' such as es', 'ze on the g', 'e of the or', 'd hiver one', 'y eight mar', 'the lead ch', 'es classica', 'ce the non ', 'al analysis', 'mormons bel', 't or at lea', ' disagreed ', 'ing system ', 'btypes base', 'anguages th', 'r commissio', 'ess one nin', 'nux suse li', ' the first ', 'zi concentr', ' society ne', 'elatively s', 'etworks sha', 'or hirohito', 'litical ini', 'n most of t', 'iskerdoo ri', 'ic overview', 'air compone', 'om acnm acc', ' centerline', 'e than any ', 'devotional ', 'de such dev']\\n\",\n      \"[' a']\\n\",\n      \"['an']\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"batch_size=64\\n\",\n    \"num_unrollings=10\\n\",\n    \"\\n\",\n    \"class BatchGenerator(object):\\n\",\n    \"  def __init__(self, text, batch_size, num_unrollings):\\n\",\n    \"    self._text = text\\n\",\n    \"    self._text_size = len(text)\\n\",\n    \"    self._batch_size = batch_size\\n\",\n    \"    self._num_unrollings = num_unrollings\\n\",\n    \"    segment = self._text_size / batch_size\\n\",\n    \"    self._cursor = [ offset * segment for offset in xrange(batch_size)]\\n\",\n    \"    self._last_batch = self._next_batch()\\n\",\n    \"  \\n\",\n    \"  def _next_batch(self):\\n\",\n    \"    \\\"\\\"\\\"Generate a single batch from the current cursor position in the data.\\\"\\\"\\\"\\n\",\n    \"    batch = np.zeros(shape=(self._batch_size, vocabulary_size), dtype=np.float)\\n\",\n    \"    for b in xrange(self._batch_size):\\n\",\n    \"      batch[b, char2id(self._text[self._cursor[b]])] = 1.0\\n\",\n    \"      self._cursor[b] = (self._cursor[b] + 1) % self._text_size\\n\",\n    \"    return batch\\n\",\n    \"  \\n\",\n    \"  def next(self):\\n\",\n    \"    \\\"\\\"\\\"Generate the next array of batches from the data. The array consists of\\n\",\n    \"    the last batch of the previous array, followed by num_unrollings new ones.\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    batches = [self._last_batch]\\n\",\n    \"    for step in xrange(self._num_unrollings):\\n\",\n    \"      batches.append(self._next_batch())\\n\",\n    \"    self._last_batch = batches[-1]\\n\",\n    \"    return batches\\n\",\n    \"\\n\",\n    \"def characters(probabilities):\\n\",\n    \"  \\\"\\\"\\\"Turn a 1-hot encoding or a probability distribution over the possible\\n\",\n    \"  characters back into its (mostl likely) character representation.\\\"\\\"\\\"\\n\",\n    \"  return [id2char(c) for c in np.argmax(probabilities, 1)]\\n\",\n    \"\\n\",\n    \"def batches2string(batches):\\n\",\n    \"  \\\"\\\"\\\"Convert a sequence of batches back into their (most likely) string\\n\",\n    \"  representation.\\\"\\\"\\\"\\n\",\n    \"  s = [''] * batches[0].shape[0]\\n\",\n    \"  for b in batches:\\n\",\n    \"    s = [''.join(x) for x in zip(s, characters(b))]\\n\",\n    \"  return s\\n\",\n    \"\\n\",\n    \"train_batches = BatchGenerator(train_text, batch_size, num_unrollings)\\n\",\n    \"valid_batches = BatchGenerator(valid_text, 1, 1)\\n\",\n    \"\\n\",\n    \"print batches2string(train_batches.next())\\n\",\n    \"print batches2string(train_batches.next())\\n\",\n    \"print batches2string(valid_batches.next())\\n\",\n    \"print batches2string(valid_batches.next())\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"cellView\": \"both\",\n    \"colab\": {\n     \"autoexec\": {\n      \"startup\": false,\n      \"wait_interval\": 0\n     }\n    },\n    \"colab_type\": \"code\",\n    \"collapsed\": true,\n    \"id\": \"KyVd8FxT5QBc\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def logprob(predictions, labels):\\n\",\n    \"  \\\"\\\"\\\"Log-probability of the true labels in a predicted batch.\\\"\\\"\\\"\\n\",\n    \"  predictions[predictions < 1e-10] = 1e-10\\n\",\n    \"  return np.sum(np.multiply(labels, -np.log(predictions))) / labels.shape[0]\\n\",\n    \"\\n\",\n    \"def sample_distribution(distribution):\\n\",\n    \"  \\\"\\\"\\\"Sample one element from a distribution assumed to be an array of normalized\\n\",\n    \"  probabilities.\\n\",\n    \"  \\\"\\\"\\\"\\n\",\n    \"  r = random.uniform(0, 1)\\n\",\n    \"  s = 0\\n\",\n    \"  for i in xrange(len(distribution)):\\n\",\n    \"    s += distribution[i]\\n\",\n    \"    if s >= r:\\n\",\n    \"      return i\\n\",\n    \"  return len(distribution) - 1\\n\",\n    \"\\n\",\n    \"def sample(prediction):\\n\",\n    \"  \\\"\\\"\\\"Turn a (column) prediction into 1-hot encoded samples.\\\"\\\"\\\"\\n\",\n    \"  p = np.zeros(shape=[1, vocabulary_size], dtype=np.float)\\n\",\n    \"  p[0, sample_distribution(prediction[0])] = 1.0\\n\",\n    \"  return p\\n\",\n    \"\\n\",\n    \"def random_distribution():\\n\",\n    \"  \\\"\\\"\\\"Generate a random column of probabilities.\\\"\\\"\\\"\\n\",\n    \"  b = np.random.uniform(0.0, 1.0, size=[1, vocabulary_size])\\n\",\n    \"  return b/np.sum(b, 1)[:,None]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"K8f67YXaDr4C\"\n   },\n   \"source\": [\n    \"Simple LSTM Model.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"cellView\": \"both\",\n    \"colab\": {\n     \"autoexec\": {\n      \"startup\": false,\n      \"wait_interval\": 0\n     }\n    },\n    \"colab_type\": \"code\",\n    \"collapsed\": true,\n    \"id\": \"Q5rxZK6RDuGe\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"num_nodes = 64\\n\",\n    \"\\n\",\n    \"graph = tf.Graph()\\n\",\n    \"with graph.as_default():\\n\",\n    \"  \\n\",\n    \"  # Parameters:\\n\",\n    \"  # Input gate: input, previous output, and bias.\\n\",\n    \"  ix = tf.Variable(tf.truncated_normal([vocabulary_size, num_nodes], -0.1, 0.1))\\n\",\n    \"  im = tf.Variable(tf.truncated_normal([num_nodes, num_nodes], -0.1, 0.1))\\n\",\n    \"  ib = tf.Variable(tf.zeros([1, num_nodes]))\\n\",\n    \"  # Forget gate: input, previous output, and bias.\\n\",\n    \"  fx = tf.Variable(tf.truncated_normal([vocabulary_size, num_nodes], -0.1, 0.1))\\n\",\n    \"  fm = tf.Variable(tf.truncated_normal([num_nodes, num_nodes], -0.1, 0.1))\\n\",\n    \"  fb = tf.Variable(tf.zeros([1, num_nodes]))\\n\",\n    \"  # Memory cell: input, state and bias.                             \\n\",\n    \"  cx = tf.Variable(tf.truncated_normal([vocabulary_size, num_nodes], -0.1, 0.1))\\n\",\n    \"  cm = tf.Variable(tf.truncated_normal([num_nodes, num_nodes], -0.1, 0.1))\\n\",\n    \"  cb = tf.Variable(tf.zeros([1, num_nodes]))\\n\",\n    \"  # Output gate: input, previous output, and bias.\\n\",\n    \"  ox = tf.Variable(tf.truncated_normal([vocabulary_size, num_nodes], -0.1, 0.1))\\n\",\n    \"  om = tf.Variable(tf.truncated_normal([num_nodes, num_nodes], -0.1, 0.1))\\n\",\n    \"  ob = tf.Variable(tf.zeros([1, num_nodes]))\\n\",\n    \"  # Variables saving state across unrollings.\\n\",\n    \"  saved_output = tf.Variable(tf.zeros([batch_size, num_nodes]), trainable=False)\\n\",\n    \"  saved_state = tf.Variable(tf.zeros([batch_size, num_nodes]), trainable=False)\\n\",\n    \"  # Classifier weights and biases.\\n\",\n    \"  w = tf.Variable(tf.truncated_normal([num_nodes, vocabulary_size], -0.1, 0.1))\\n\",\n    \"  b = tf.Variable(tf.zeros([vocabulary_size]))\\n\",\n    \"  \\n\",\n    \"  # Definition of the cell computation.\\n\",\n    \"  def lstm_cell(i, o, state):\\n\",\n    \"    \\\"\\\"\\\"Create a LSTM cell. See e.g.: http://arxiv.org/pdf/1402.1128v1.pdf\\n\",\n    \"    Note that in this formulation, we omit the various connections between the\\n\",\n    \"    previous state and the gates.\\\"\\\"\\\"\\n\",\n    \"    input_gate = tf.sigmoid(tf.matmul(i, ix) + tf.matmul(o, im) + ib)\\n\",\n    \"    forget_gate = tf.sigmoid(tf.matmul(i, fx) + tf.matmul(o, fm) + fb)\\n\",\n    \"    update = tf.matmul(i, cx) + tf.matmul(o, cm) + cb\\n\",\n    \"    state = forget_gate * state + input_gate * tf.tanh(update)\\n\",\n    \"    output_gate = tf.sigmoid(tf.matmul(i, ox) + tf.matmul(o, om) + ob)\\n\",\n    \"    return output_gate * tf.tanh(state), state\\n\",\n    \"\\n\",\n    \"  # Input data.\\n\",\n    \"  train_data = list()\\n\",\n    \"  for _ in xrange(num_unrollings + 1):\\n\",\n    \"    train_data.append(\\n\",\n    \"      tf.placeholder(tf.float32, shape=[batch_size,vocabulary_size]))\\n\",\n    \"  train_inputs = train_data[:num_unrollings]\\n\",\n    \"  train_labels = train_data[1:]  # labels are inputs shifted by one time step.\\n\",\n    \"\\n\",\n    \"  # Unrolled LSTM loop.\\n\",\n    \"  outputs = list()\\n\",\n    \"  output = saved_output\\n\",\n    \"  state = saved_state\\n\",\n    \"  for i in train_inputs:\\n\",\n    \"    output, state = lstm_cell(i, output, state)\\n\",\n    \"    outputs.append(output)\\n\",\n    \"\\n\",\n    \"  # State saving across unrollings.\\n\",\n    \"  with tf.control_dependencies([saved_output.assign(output),\\n\",\n    \"                                saved_state.assign(state)]):\\n\",\n    \"    # Classifier.\\n\",\n    \"    logits = tf.nn.xw_plus_b(tf.concat(0, outputs), w, b)\\n\",\n    \"    loss = tf.reduce_mean(\\n\",\n    \"      tf.nn.softmax_cross_entropy_with_logits(\\n\",\n    \"        logits, tf.concat(0, train_labels)))\\n\",\n    \"\\n\",\n    \"  # Optimizer.\\n\",\n    \"  global_step = tf.Variable(0)\\n\",\n    \"  learning_rate = tf.train.exponential_decay(\\n\",\n    \"    10.0, global_step, 5000, 0.1, staircase=True)\\n\",\n    \"  optimizer = tf.train.GradientDescentOptimizer(learning_rate)\\n\",\n    \"  gradients, v = zip(*optimizer.compute_gradients(loss))\\n\",\n    \"  gradients, _ = tf.clip_by_global_norm(gradients, 1.25)\\n\",\n    \"  optimizer = optimizer.apply_gradients(\\n\",\n    \"    zip(gradients, v), global_step=global_step)\\n\",\n    \"\\n\",\n    \"  # Predictions.\\n\",\n    \"  train_prediction = tf.nn.softmax(logits)\\n\",\n    \"  \\n\",\n    \"  # Sampling and validation eval: batch 1, no unrolling.\\n\",\n    \"  sample_input = tf.placeholder(tf.float32, shape=[1, vocabulary_size])\\n\",\n    \"  saved_sample_output = tf.Variable(tf.zeros([1, num_nodes]))\\n\",\n    \"  saved_sample_state = tf.Variable(tf.zeros([1, num_nodes]))\\n\",\n    \"  reset_sample_state = tf.group(\\n\",\n    \"    saved_sample_output.assign(tf.zeros([1, num_nodes])),\\n\",\n    \"    saved_sample_state.assign(tf.zeros([1, num_nodes])))\\n\",\n    \"  sample_output, sample_state = lstm_cell(\\n\",\n    \"    sample_input, saved_sample_output, saved_sample_state)\\n\",\n    \"  with tf.control_dependencies([saved_sample_output.assign(sample_output),\\n\",\n    \"                                saved_sample_state.assign(sample_state)]):\\n\",\n    \"    sample_prediction = tf.nn.softmax(tf.nn.xw_plus_b(sample_output, w, b))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"cellView\": \"both\",\n    \"colab\": {\n     \"autoexec\": {\n      \"startup\": false,\n      \"wait_interval\": 0\n     },\n     \"output_extras\": [\n      {\n       \"item_id\": 41\n      },\n      {\n       \"item_id\": 80\n      },\n      {\n       \"item_id\": 126\n      },\n      {\n       \"item_id\": 144\n      }\n     ]\n    },\n    \"colab_type\": \"code\",\n    \"collapsed\": false,\n    \"executionInfo\": {\n     \"elapsed\": 199909,\n     \"status\": \"ok\",\n     \"timestamp\": 1445965877333,\n     \"user\": {\n      \"color\": \"#1FA15D\",\n      \"displayName\": \"Vincent Vanhoucke\",\n      \"isAnonymous\": false,\n      \"isMe\": true,\n      \"permissionId\": \"05076109866853157986\",\n      \"photoUrl\": \"//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg\",\n      \"sessionId\": \"6f6f07b359200c46\",\n      \"userId\": \"102167687554210253930\"\n     },\n     \"user_tz\": 420\n    },\n    \"id\": \"RD9zQCZTEaEm\",\n    \"outputId\": \"5e868466-2532-4545-ce35-b403cf5d9de6\"\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Initialized\\n\",\n      \"Average loss at step 0 : 3.29904174805 learning rate: 10.0\\n\",\n      \"Minibatch perplexity: 27.09\\n\",\n      \"================================================================================\\n\",\n      \"srk dwmrnuldtbbgg tapootidtu xsciu sgokeguw hi ieicjq lq piaxhazvc s fht wjcvdlh\\n\",\n      \"lhrvallvbeqqquc dxd y siqvnle bzlyw nr rwhkalezo siie o deb e lpdg  storq u nx o\\n\",\n      \"meieu nantiouie gdys qiuotblci loc hbiznauiccb cqzed acw l tsm adqxplku gn oaxet\\n\",\n      \"unvaouc oxchywdsjntdh zpklaejvxitsokeerloemee htphisb th eaeqseibumh aeeyj j orw\\n\",\n      \"ogmnictpycb whtup   otnilnesxaedtekiosqet  liwqarysmt  arj flioiibtqekycbrrgoysj\\n\",\n      \"================================================================================\\n\",\n      \"Validation set perplexity: 19.99\\n\",\n      \"Average loss at step 100 : 2.59553678274 learning rate: 10.0\\n\",\n      \"Minibatch perplexity: 9.57\\n\",\n      \"Validation set perplexity: 10.60\\n\",\n      \"Average loss at step 200 : 2.24747137785 learning rate: 10.0\\n\",\n      \"Minibatch perplexity: 7.68\\n\",\n      \"Validation set perplexity: 8.84\\n\",\n      \"Average loss at step 300 : 2.09438110709 learning rate: 10.0\\n\",\n      \"Minibatch perplexity: 7.41\\n\",\n      \"Validation set perplexity: 8.13\\n\",\n      \"Average loss at step 400 : 1.99440989017 learning rate: 10.0\\n\",\n      \"Minibatch perplexity: 6.46\\n\",\n      \"Validation set perplexity: 7.58\\n\",\n      \"Average loss at step 500 : 1.9320810616 learning rate: 10.0\\n\",\n      \"Minibatch perplexity: 6.30\\n\",\n      \"Validation set perplexity: 6.88\\n\",\n      \"Average loss at step 600 : 1.90935629249 learning rate: 10.0\\n\",\n      \"Minibatch perplexity: 7.21\\n\",\n      \"Validation set perplexity: 6.91\\n\",\n      \"Average loss at step 700 : 1.85583009005 learning rate: 10.0\\n\",\n      \"Minibatch perplexity: 6.13\\n\",\n      \"Validation set perplexity: 6.60\\n\",\n      \"Average loss at step 800 : 1.82152368546 learning rate: 10.0\\n\",\n      \"Minibatch perplexity: 6.01\\n\",\n      \"Validation set perplexity: 6.37\\n\",\n      \"Average loss at step 900 : 1.83169809818 learning rate: 10.0\\n\",\n      \"Minibatch perplexity: 7.20\\n\",\n      \"Validation set perplexity: 6.23\\n\",\n      \"Average loss at step 1000 : 1.82217029214 learning rate: 10.0\\n\",\n      \"Minibatch perplexity: 6.73\\n\",\n      \"================================================================================\\n\",\n      \"le action b of the tert sy ofter selvorang previgned stischdy yocal chary the co\\n\",\n      \"le relganis networks partucy cetinning wilnchan sics rumeding a fulch laks oftes\\n\",\n      \"hian andoris ret the ecause bistory l pidect one eight five lack du that the ses\\n\",\n      \"aiv dromery buskocy becomer worils resism disele retery exterrationn of hide in \\n\",\n      \"mer miter y sught esfectur of the upission vain is werms is vul ugher compted by\\n\",\n      \"================================================================================\\n\",\n      \"Validation set perplexity: 6.07\\n\",\n      \"Average loss at step 1100 : 1.77301145077 learning rate: 10.0\\n\",\n      \"Minibatch perplexity: 6.03\\n\",\n      \"Validation set perplexity: 5.89\\n\",\n      \"Average loss at step 1200 : 1.75306463003 learning rate: 10.0\\n\",\n      \"Minibatch perplexity: 6.50\\n\",\n      \"Validation set perplexity: 5.61\\n\",\n      \"Average loss at step 1300 : 1.72937195778 learning rate: 10.0\\n\",\n      \"Minibatch perplexity: 5.00\\n\",\n      \"Validation set perplexity: 5.60\\n\",\n      \"Average loss at step 1400 : 1.74773373723 learning rate: 10.0\\n\",\n      \"Minibatch perplexity: 6.48\\n\",\n      \"Validation set perplexity: 5.66\\n\",\n      \"Average loss at step 1500 : 1.7368799901 learning rate: 10.0\\n\",\n      \"Minibatch perplexity: 5.22\\n\",\n      \"Validation set perplexity: 5.44\\n\",\n      \"Average loss at step 1600 : 1.74528762937 learning rate: 10.0\\n\",\n      \"Minibatch perplexity: 5.85\\n\",\n      \"Validation set perplexity: 5.33\\n\",\n      \"Average loss at step 1700 : 1.70881183743 learning rate: 10.0\\n\",\n      \"Minibatch perplexity: 5.33\\n\",\n      \"Validation set perplexity: 5.56\\n\",\n      \"Average loss at step 1800 : 1.67776108027 learning rate: 10.0\\n\",\n      \"Minibatch perplexity: 5.33\\n\",\n      \"Validation set perplexity: 5.29\\n\",\n      \"Average loss at step 1900 : 1.64935536742 learning rate: 10.0\\n\",\n      \"Minibatch perplexity: 5.29\\n\",\n      \"Validation set perplexity: 5.15\\n\",\n      \"Average loss at step 2000 : 1.69528644681 learning rate: 10.0\\n\",\n      \"Minibatch perplexity: 5.13\\n\",\n      \"================================================================================\\n\",\n      \"vers soqually have one five landwing to docial page kagan lower with ther batern\\n\",\n      \"ctor son alfortmandd tethre k skin the known purated to prooust caraying the fit\\n\",\n      \"je in beverb is the sournction bainedy wesce tu sture artualle lines digra forme\\n\",\n      \"m rousively haldio ourso ond anvary was for the seven solies hild buil  s  to te\\n\",\n      \"zall for is it is one nine eight eight one neval to the kime typer oene where he\\n\",\n      \"================================================================================\\n\",\n      \"Validation set perplexity: 5.25\\n\",\n      \"Average loss at step 2100 : 1.68808053017 learning rate: 10.0\\n\",\n      \"Minibatch perplexity: 5.17\\n\",\n      \"Validation set perplexity: 5.01\\n\",\n      \"Average loss at step 2200 : 1.68322490931 learning rate: 10.0\\n\",\n      \"Minibatch perplexity: 5.09\\n\",\n      \"Validation set perplexity: 5.15\\n\",\n      \"Average loss at step 2300 : 1.64465074301 learning rate: 10.0\\n\",\n      \"Minibatch perplexity: 5.51\\n\",\n      \"Validation set perplexity: 5.00\\n\",\n      \"Average loss at step 2400 : 1.66408578038 learning rate: 10.0\\n\",\n      \"Minibatch perplexity: 5.86\\n\",\n      \"Validation set perplexity: 4.80\\n\",\n      \"Average loss at step 2500 : 1.68515402555 learning rate: 10.0\\n\",\n      \"Minibatch perplexity: 5.75\\n\",\n      \"Validation set perplexity: 4.82\\n\",\n      \"Average loss at step 2600 : 1.65405208349 learning rate: 10.0\\n\",\n      \"Minibatch perplexity: 5.38\\n\",\n      \"Validation set perplexity: 4.85\\n\",\n      \"Average loss at step 2700 : 1.65706222177 learning rate: 10.0\\n\",\n      \"Minibatch perplexity: 5.46\\n\",\n      \"Validation set perplexity: 4.78\\n\",\n      \"Average loss at step 2800 : 1.65204829812 learning rate: 10.0\\n\",\n      \"Minibatch perplexity: 5.06\\n\",\n      \"Validation set perplexity: 4.64\\n\",\n      \"Average loss at step 2900 : 1.65107253551 learning rate: 10.0\\n\",\n      \"Minibatch perplexity: 5.00\\n\",\n      \"Validation set perplexity: 4.61\\n\",\n      \"Average loss at step 3000 : 1.6495274055 learning rate: 10.0\\n\",\n      \"Minibatch perplexity: 4.53\\n\",\n      \"================================================================================\\n\",\n      \"ject covered in belo one six six to finsh that all di rozial sime it a the lapse\\n\",\n      \"ble which the pullic bocades record r to sile dric two one four nine seven six f\\n\",\n      \" originally ame the playa ishaps the stotchational in a p dstambly name which as\\n\",\n      \"ore volum to bay riwer foreal in nuily operety can and auscham frooripm however \\n\",\n      \"kan traogey was lacous revision the mott coupofiteditey the trando insended frop\\n\",\n      \"================================================================================\\n\",\n      \"Validation set perplexity: 4.76\\n\",\n      \"Average loss at step 3100 : 1.63705502152 learning rate: 10.0\\n\",\n      \"Minibatch perplexity: 5.50\\n\",\n      \"Validation set perplexity: 4.76\\n\",\n      \"Average loss at step 3200 : 1.64740695596 learning rate: 10.0\\n\",\n      \"Minibatch perplexity: 4.84\\n\",\n      \"Validation set perplexity: 4.67\\n\",\n      \"Average loss at step 3300 : 1.64711504817 learning rate: 10.0\\n\",\n      \"Minibatch perplexity: 5.39\\n\",\n      \"Validation set perplexity: 4.57\\n\",\n      \"Average loss at step 3400 : 1.67113256454 learning rate: 10.0\\n\",\n      \"Minibatch perplexity: 5.56\\n\",\n      \"Validation set perplexity: 4.71\\n\",\n      \"Average loss at step 3500 : 1.65637169957 learning rate: 10.0\\n\",\n      \"Minibatch perplexity: 5.03\\n\",\n      \"Validation set perplexity: 4.80\\n\",\n      \"Average loss at step 3600 : 1.66601825476 learning rate: 10.0\\n\",\n      \"Minibatch perplexity: 4.63\\n\",\n      \"Validation set perplexity: 4.52\\n\",\n      \"Average loss at step 3700 : 1.65021387935 learning rate: 10.0\\n\",\n      \"Minibatch perplexity: 5.50\\n\",\n      \"Validation set perplexity: 4.56\\n\",\n      \"Average loss at step 3800 : 1.64481814981 learning rate: 10.0\\n\",\n      \"Minibatch perplexity: 4.60\\n\",\n      \"Validation set perplexity: 4.54\\n\",\n      \"Average loss at step 3900 : 1.642069453 learning rate: 10.0\\n\",\n      \"Minibatch perplexity: 4.91\\n\",\n      \"Validation set perplexity: 4.54\\n\",\n      \"Average loss at step 4000 : 1.65179730773 learning rate: 10.0\\n\",\n      \"Minibatch perplexity: 4.77\\n\",\n      \"================================================================================\\n\",\n      \"k s rasbonish roctes the nignese at heacle was sito of beho anarchys and with ro\\n\",\n      \"jusar two sue wletaus of chistical in causations d ow trancic bruthing ha laters\\n\",\n      \"de and speacy pulted yoftret worksy zeatlating to eight d had to ie bue seven si\\n\",\n      \"s fiction of the feelly constive suq flanch earlied curauking bjoventation agent\\n\",\n      \"quen s playing it calana our seopity also atbellisionaly comexing the revideve i\\n\",\n      \"================================================================================\\n\",\n      \"Validation set perplexity: 4.58\\n\",\n      \"Average loss at step 4100 : 1.63794238806 learning rate: 10.0\\n\",\n      \"Minibatch perplexity: 5.47\\n\",\n      \"Validation set perplexity: 4.79\\n\",\n      \"Average loss at step 4200 : 1.63822438836 learning rate: 10.0\\n\",\n      \"Minibatch perplexity: 5.30\\n\",\n      \"Validation set perplexity: 4.54\\n\",\n      \"Average loss at step 4300 : 1.61844664574 learning rate: 10.0\\n\",\n      \"Minibatch perplexity: 4.69\\n\",\n      \"Validation set perplexity: 4.54\\n\",\n      \"Average loss at step 4400 : 1.61255454302 learning rate: 10.0\\n\",\n      \"Minibatch perplexity: 4.67\\n\",\n      \"Validation set perplexity: 4.54\\n\",\n      \"Average loss at step 4500 : 1.61543365479 learning rate: 10.0\\n\",\n      \"Minibatch perplexity: 4.83\\n\",\n      \"Validation set perplexity: 4.69\\n\",\n      \"Average loss at step 4600 : 1.61607327104 learning rate: 10.0\\n\",\n      \"Minibatch perplexity: 5.18\\n\",\n      \"Validation set perplexity: 4.64\\n\",\n      \"Average loss at step 4700 : 1.62757282495 learning rate: 10.0\\n\",\n      \"Minibatch perplexity: 4.24\\n\",\n      \"Validation set perplexity: 4.66\\n\",\n      \"Average loss at step 4800 : 1.63222063541 learning rate: 10.0\\n\",\n      \"Minibatch perplexity: 5.30\\n\",\n      \"Validation set perplexity: 4.53\\n\",\n      \"Average loss at step 4900 : 1.63678096652 learning rate: 10.0\\n\",\n      \"Minibatch perplexity: 5.43\\n\",\n      \"Validation set perplexity: 4.64\\n\",\n      \"Average loss at step 5000 : 1.610340662 learning rate: 1.0\\n\",\n      \"Minibatch perplexity: 5.10\\n\",\n      \"================================================================================\\n\",\n      \"in b one onarbs revieds the kimiluge that fondhtic fnoto cre one nine zero zero \\n\",\n      \" of is it of marking panzia t had wap ironicaghni relly deah the omber b h menba\\n\",\n      \"ong messified it his the likdings ara subpore the a fames distaled self this int\\n\",\n      \"y advante authors the end languarle meit common tacing bevolitione and eight one\\n\",\n      \"zes that materly difild inllaring the fusts not panition assertian causecist bas\\n\",\n      \"================================================================================\\n\",\n      \"Validation set perplexity: 4.69\\n\",\n      \"Average loss at step 5100 : 1.60593637228 learning rate: 1.0\\n\",\n      \"Minibatch perplexity: 4.69\\n\",\n      \"Validation set perplexity: 4.47\\n\",\n      \"Average loss at step 5200 : 1.58993269444 learning rate: 1.0\\n\",\n      \"Minibatch perplexity: 4.65\\n\",\n      \"Validation set perplexity: 4.39\\n\",\n      \"Average loss at step 5300 : 1.57930587292 learning rate: 1.0\\n\",\n      \"Minibatch perplexity: 5.11\\n\",\n      \"Validation set perplexity: 4.39\\n\",\n      \"Average loss at step 5400 : 1.58022856832 learning rate: 1.0\\n\",\n      \"Minibatch perplexity: 5.19\\n\",\n      \"Validation set perplexity: 4.37\\n\",\n      \"Average loss at step 5500 : 1.56654450059 learning rate: 1.0\\n\",\n      \"Minibatch perplexity: 4.69\\n\",\n      \"Validation set perplexity: 4.33\\n\",\n      \"Average loss at step 5600 : 1.58013380885 learning rate: 1.0\\n\",\n      \"Minibatch perplexity: 5.13\\n\",\n      \"Validation set perplexity: 4.35\\n\",\n      \"Average loss at step 5700 : 1.56974959254 learning rate: 1.0\\n\",\n      \"Minibatch perplexity: 5.00\\n\",\n      \"Validation set perplexity: 4.34\\n\",\n      \"Average loss at step 5800 : 1.5839582932 learning rate: 1.0\\n\",\n      \"Minibatch perplexity: 4.88\\n\",\n      \"Validation set perplexity: 4.31\\n\",\n      \"Average loss at step 5900 : 1.57129439116 learning rate: 1.0\\n\",\n      \"Minibatch perplexity: 4.66\\n\",\n      \"Validation set perplexity: 4.32\\n\",\n      \"Average loss at step 6000 : 1.55144061089 learning rate: 1.0\\n\",\n      \"Minibatch perplexity: 4.55\\n\",\n      \"================================================================================\\n\",\n      \"utic clositical poopy stribe addi nixe one nine one zero zero eight zero b ha ex\\n\",\n      \"zerns b one internequiption of the secordy way anti proble akoping have fictiona\\n\",\n      \"phare united from has poporarly cities book ins sweden emperor a sass in origina\\n\",\n      \"quulk destrebinist and zeilazar and on low and by in science over country weilti\\n\",\n      \"x are holivia work missincis ons in the gages to starsle histon one icelanctrotu\\n\",\n      \"================================================================================\\n\",\n      \"Validation set perplexity: 4.30\\n\",\n      \"Average loss at step 6100 : 1.56450940847 learning rate: 1.0\\n\",\n      \"Minibatch perplexity: 4.77\\n\",\n      \"Validation set perplexity: 4.27\\n\",\n      \"Average loss at step 6200 : 1.53433164835 learning rate: 1.0\\n\",\n      \"Minibatch perplexity: 4.77\\n\",\n      \"Validation set perplexity: 4.27\\n\",\n      \"Average loss at step 6300 : 1.54773445129 learning rate: 1.0\\n\",\n      \"Minibatch perplexity: 4.76\\n\",\n      \"Validation set perplexity: 4.25\\n\",\n      \"Average loss at step 6400 : 1.54021131516 learning rate: 1.0\\n\",\n      \"Minibatch perplexity: 4.56\\n\",\n      \"Validation set perplexity: 4.24\\n\",\n      \"Average loss at step 6500 : 1.56153374553 learning rate: 1.0\\n\",\n      \"Minibatch perplexity: 5.43\\n\",\n      \"Validation set perplexity: 4.27\\n\",\n      \"Average loss at step 6600 : 1.59556478739 learning rate: 1.0\\n\",\n      \"Minibatch perplexity: 4.92\\n\",\n      \"Validation set perplexity: 4.28\\n\",\n      \"Average loss at step 6700 : 1.58076951623 learning rate: 1.0\\n\",\n      \"Minibatch perplexity: 4.77\\n\",\n      \"Validation set perplexity: 4.30\\n\",\n      \"Average loss at step 6800 : 1.6070714438 learning rate: 1.0\\n\",\n      \"Minibatch perplexity: 4.98\\n\",\n      \"Validation set perplexity: 4.28\\n\",\n      \"Average loss at step 6900 : 1.58413293839 learning rate: 1.0\\n\",\n      \"Minibatch perplexity: 4.61\\n\",\n      \"Validation set perplexity: 4.29\\n\",\n      \"Average loss at step 7000 : 1.57905534983 learning rate: 1.0\\n\",\n      \"Minibatch perplexity: 5.08\\n\",\n      \"================================================================================\\n\",\n      \"jague are officiencinels ored by film voon higherise haik one nine on the iffirc\\n\",\n      \"oshe provision that manned treatists on smalle bodariturmeristing the girto in s\\n\",\n      \"kis would softwenn mustapultmine truativersakys bersyim by s of confound esc bub\\n\",\n      \"ry of the using one four six blain ira mannom marencies g with fextificallise re\\n\",\n      \" one son vit even an conderouss to person romer i a lebapter at obiding are iuse\\n\",\n      \"================================================================================\\n\",\n      \"Validation set perplexity: 4.25\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"num_steps = 7001\\n\",\n    \"summary_frequency = 100\\n\",\n    \"\\n\",\n    \"with tf.Session(graph=graph) as session:\\n\",\n    \"  tf.global_variables_initializer().run()\\n\",\n    \"  print 'Initialized'\\n\",\n    \"  mean_loss = 0\\n\",\n    \"  for step in xrange(num_steps):\\n\",\n    \"    batches = train_batches.next()\\n\",\n    \"    feed_dict = dict()\\n\",\n    \"    for i in xrange(num_unrollings + 1):\\n\",\n    \"      feed_dict[train_data[i]] = batches[i]\\n\",\n    \"    _, l, predictions, lr = session.run(\\n\",\n    \"      [optimizer, loss, train_prediction, learning_rate], feed_dict=feed_dict)\\n\",\n    \"    mean_loss += l\\n\",\n    \"    if step % summary_frequency == 0:\\n\",\n    \"      if step > 0:\\n\",\n    \"        mean_loss = mean_loss / summary_frequency\\n\",\n    \"      # The mean loss is an estimate of the loss over the last few batches.\\n\",\n    \"      print 'Average loss at step', step, ':', mean_loss, 'learning rate:', lr\\n\",\n    \"      mean_loss = 0\\n\",\n    \"      labels = np.concatenate(list(batches)[1:])\\n\",\n    \"      print 'Minibatch perplexity: %.2f' % float(\\n\",\n    \"        np.exp(logprob(predictions, labels)))\\n\",\n    \"      if step % (summary_frequency * 10) == 0:\\n\",\n    \"        # Generate some samples.\\n\",\n    \"        print '=' * 80\\n\",\n    \"        for _ in xrange(5):\\n\",\n    \"          feed = sample(random_distribution())\\n\",\n    \"          sentence = characters(feed)[0]\\n\",\n    \"          reset_sample_state.run()\\n\",\n    \"          for _ in xrange(79):\\n\",\n    \"            prediction = sample_prediction.eval({sample_input: feed})\\n\",\n    \"            feed = sample(prediction)\\n\",\n    \"            sentence += characters(feed)[0]\\n\",\n    \"          print sentence\\n\",\n    \"        print '=' * 80\\n\",\n    \"      # Measure validation set perplexity.\\n\",\n    \"      reset_sample_state.run()\\n\",\n    \"      valid_logprob = 0\\n\",\n    \"      for _ in xrange(valid_size):\\n\",\n    \"        b = valid_batches.next()\\n\",\n    \"        predictions = sample_prediction.eval({sample_input: b[0]})\\n\",\n    \"        valid_logprob = valid_logprob + logprob(predictions, b[1])\\n\",\n    \"      print 'Validation set perplexity: %.2f' % float(np.exp(\\n\",\n    \"        valid_logprob / valid_size))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"pl4vtmFfa5nn\"\n   },\n   \"source\": [\n    \"---\\n\",\n    \"Problem 1\\n\",\n    \"---------\\n\",\n    \"\\n\",\n    \"You might have noticed that the definition of the LSTM cell involves 4 matrix multiplications with the input, and 4 matrix multiplications with the output. Simplify the expression by using a single matrix multiply for each, and variables that are 4 times larger.\\n\",\n    \"\\n\",\n    \"---\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"4eErTCTybtph\"\n   },\n   \"source\": [\n    \"---\\n\",\n    \"Problem 2\\n\",\n    \"---------\\n\",\n    \"\\n\",\n    \"We want to train a LSTM over bigrams, that is pairs of consecutive characters like 'ab' instead of single characters like 'a'. Since the number of possible bigrams is large, feeding them directly to the LSTM using 1-hot encodings will lead to a very sparse representation that is very wasteful computationally.\\n\",\n    \"\\n\",\n    \"a- Introduce an embedding lookup on the inputs, and feed the embeddings to the LSTM cell instead of the inputs themselves.\\n\",\n    \"\\n\",\n    \"b- Write a bigram-based LSTM, modeled on the character LSTM above.\\n\",\n    \"\\n\",\n    \"c- Introduce Dropout. For best practices on how to use Dropout in LSTMs, refer to this [article](http://arxiv.org/abs/1409.2329).\\n\",\n    \"\\n\",\n    \"---\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"Y5tapX3kpcqZ\"\n   },\n   \"source\": [\n    \"---\\n\",\n    \"Problem 3\\n\",\n    \"---------\\n\",\n    \"\\n\",\n    \"(difficult!)\\n\",\n    \"\\n\",\n    \"Write a sequence-to-sequence LSTM which mirrors all the words in a sentence. For example, if your input is:\\n\",\n    \"\\n\",\n    \"    the quick brown fox\\n\",\n    \"    \\n\",\n    \"the model should attempt to output:\\n\",\n    \"\\n\",\n    \"    eht kciuq nworb xof\\n\",\n    \"    \\n\",\n    \"Reference: http://arxiv.org/abs/1409.3215\\n\",\n    \"\\n\",\n    \"---\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"colabVersion\": \"0.3.2\",\n  \"colab_default_view\": {},\n  \"colab_views\": {},\n  \"kernelspec\": {\n   \"display_name\": \"Python 2\",\n   \"language\": \"python\",\n   \"name\": \"python2\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 2\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython2\",\n   \"version\": \"2.7.12\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "deep-learning/tensor-flow-exercises/Dockerfile",
    "content": "FROM b.gcr.io/tensorflow/tensorflow:latest\nMAINTAINER Vincent Vanhoucke <vanhoucke@google.com>\nRUN pip install scikit-learn\nADD *.ipynb /notebooks/\nWORKDIR /notebooks\nCMD [\"/run_jupyter.sh\"]\n"
  },
  {
    "path": "deep-learning/tensor-flow-exercises/README.md",
    "content": "Exercises\n===========================================================\n\nBuilding the Docker container\n-----------------------------\n\n    docker build -t $USER/exercises .\n\nRunning the container\n---------------------\n\n    docker run -p 8888:8888 -it --rm $USER/exercises\n"
  },
  {
    "path": "deep-learning/theano-tutorial/intro_theano/Makefile",
    "content": "intro_theano.pdf: slides_source/intro_theano.tex\n\tcd slides_source; pdflatex --shell-escape intro_theano.tex\n\tmv slides_source/intro_theano.pdf .\n"
  },
  {
    "path": "deep-learning/theano-tutorial/intro_theano/intro_theano.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Introduction to Theano\\n\",\n    \"\\n\",\n    \"Credits: Forked from [summerschool2015](https://github.com/mila-udem/summerschool2015) by mila-udem\\n\",\n    \"\\n\",\n    \"## Slides\\n\",\n    \"\\n\",\n    \"Refer to the associated [Introduction to Theano slides](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/theano-tutorial/intro_theano/intro_theano.pdf) and use this notebook for hands-on practice of the concepts.\\n\",\n    \"\\n\",\n    \"## Basic usage\\n\",\n    \"\\n\",\n    \"### Defining an expression\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import theano\\n\",\n    \"from theano import tensor as T\\n\",\n    \"x = T.vector('x')\\n\",\n    \"W = T.matrix('W')\\n\",\n    \"b = T.vector('b')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"dot = T.dot(x, W)\\n\",\n    \"out = T.nnet.sigmoid(dot + b)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Graph visualization\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"dot [@A] ''   \\n\",\n      \" |x [@B]\\n\",\n      \" |W [@C]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from theano.printing import debugprint\\n\",\n    \"debugprint(dot)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"sigmoid [@A] ''   \\n\",\n      \" |Elemwise{add,no_inplace} [@B] ''   \\n\",\n      \"   |dot [@C] ''   \\n\",\n      \"   | |x [@D]\\n\",\n      \"   | |W [@E]\\n\",\n      \"   |b [@F]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"debugprint(out)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Compiling a Theano function\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"f = theano.function(inputs=[x, W], outputs=dot)\\n\",\n    \"g = theano.function([x, W, b], out)\\n\",\n    \"h = theano.function([x, W, b], [dot, out])\\n\",\n    \"i = theano.function([x, W, b], [dot + b, out])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Graph visualization\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"CGemv{inplace} [@A] ''   3\\n\",\n      \" |AllocEmpty{dtype='float64'} [@B] ''   2\\n\",\n      \" | |Shape_i{1} [@C] ''   1\\n\",\n      \" |   |W [@D]\\n\",\n      \" |TensorConstant{1.0} [@E]\\n\",\n      \" |InplaceDimShuffle{1,0} [@F] 'W.T'   0\\n\",\n      \" | |W [@D]\\n\",\n      \" |x [@G]\\n\",\n      \" |TensorConstant{0.0} [@H]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"debugprint(f)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Elemwise{ScalarSigmoid}[(0, 0)] [@A] ''   2\\n\",\n      \" |CGemv{no_inplace} [@B] ''   1\\n\",\n      \"   |b [@C]\\n\",\n      \"   |TensorConstant{1.0} [@D]\\n\",\n      \"   |InplaceDimShuffle{1,0} [@E] 'W.T'   0\\n\",\n      \"   | |W [@F]\\n\",\n      \"   |x [@G]\\n\",\n      \"   |TensorConstant{1.0} [@D]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"debugprint(g)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"The output file is available at pydotprint_f.png\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from theano.printing import pydotprint\\n\",\n    \"pydotprint(f, outfile='pydotprint_f.png')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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4iIiIiIiJoWBmFELZxKhaVL8emnGD8eH30EX1+pC7Ky1157zWg0Lliw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\\nceP40gEAcE0UYQAAwLVYrdbdu3cnJiZ+/PHHeXl5AQEB48ePj4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     \"text/plain\": [\n       \"<IPython.core.display.Image object>\"\n      ]\n     },\n     \"execution_count\": 9,\n     \"metadata\": {\n      \"image/png\": {\n       \"width\": 1000\n      }\n     },\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from IPython.display import Image\\n\",\n    \"Image('pydotprint_f.png', width=1000)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"The output file is available at pydotprint_g.png\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAABScAAAJ4CAIAAAAY0cErAAAABmJLR0QA/wD/AP+gvaeTAAAgAElE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GfvsdxLwqsnOd/E+RRK7NWYDYn0VWaH00lEDMNo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\\nCedktmgAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<IPython.core.display.Image object>\"\n      ]\n     },\n     \"execution_count\": 10,\n     \"metadata\": {\n      \"image/png\": {\n       \"width\": 1000\n      }\n     },\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"pydotprint(g, outfile='pydotprint_g.png')\\n\",\n    \"Image('pydotprint_g.png', width=1000)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"The output file is available at pydotprint_h.png\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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4iIiIiIiJoWBmFELZxKhaVL8emnGD8eH30EX1+pC7Ky1157zWg0Lliw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\\n0elwUZSWlm7evHn58uVLlizZu3dv8+bN77jjjiFDhvTt25cPHQ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fdp06YZnQVnZrVaJ0+e3L1797S0tLVr186YMSM4OPi81nDXXXe5ublt2LCh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\\nWwzLStd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     \"text/plain\": [\n       \"<IPython.core.display.Image object>\"\n      ]\n     },\n     \"execution_count\": 11,\n     \"metadata\": {\n      \"image/png\": {\n       \"width\": 1000\n      }\n     },\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"pydotprint(h, outfile='pydotprint_h.png')\\n\",\n    \"Image('pydotprint_h.png', width=1000)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Executing a Theano function\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([ 1.79048354,  0.03158954, -0.26423186])\"\n      ]\n     },\n     \"execution_count\": 12,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"np.random.seed(42)\\n\",\n    \"W_val = np.random.randn(4, 3)\\n\",\n    \"x_val = np.random.rand(4)\\n\",\n    \"b_val = np.ones(3)\\n\",\n    \"\\n\",\n    \"f(x_val, W_val)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([ 0.9421594 ,  0.73722395,  0.67606977])\"\n      ]\n     },\n     \"execution_count\": 13,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"g(x_val, W_val, b_val)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[array([ 1.79048354,  0.03158954, -0.26423186]),\\n\",\n       \" array([ 0.9421594 ,  0.73722395,  0.67606977])]\"\n      ]\n     },\n     \"execution_count\": 14,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"h(x_val, W_val, b_val)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[array([ 2.79048354,  1.03158954,  0.73576814]),\\n\",\n       \" array([ 0.9421594 ,  0.73722395,  0.67606977])]\"\n      ]\n     },\n     \"execution_count\": 15,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"i(x_val, W_val, b_val)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Graph definition and Syntax\\n\",\n    \"## Graph structure\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"The output file is available at pydotprint_f_notcompact.png\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAABXwAAALwCAIAAACrxiohAAAABmJLR0QA/wD/AP+gvaeTAAAgAElE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\\nOAWhAwAAAAAAcApCB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     \"text/plain\": [\n       \"<IPython.core.display.Image object>\"\n      ]\n     },\n     \"execution_count\": 16,\n     \"metadata\": {\n      \"image/png\": {\n       \"width\": 1000\n      }\n     },\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"pydotprint(f, compact=False, outfile='pydotprint_f_notcompact.png')\\n\",\n    \"Image('pydotprint_f_notcompact.png', width=1000)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Strong typing\\n\",\n    \"### Broadcasting tensors\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(True, False)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"r = T.row('r')\\n\",\n    \"print(r.broadcastable)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(False, True)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"c = T.col('c')\\n\",\n    \"print(c.broadcastable)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[ 1.1  2.1  3.1]\\n\",\n      \" [ 1.2  2.2  3.2]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"f = theano.function([r, c], r + c)\\n\",\n    \"print(f([[1, 2, 3]], [[.1], [.2]]))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Graph Transformations\\n\",\n    \"## Substitution and Cloning\\n\",\n    \"### The `givens` keyword\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([ 1.90651511,  0.60431744, -0.64253361])\"\n      ]\n     },\n     \"execution_count\": 20,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"x_ = T.vector('x_')\\n\",\n    \"x_n = (x_ - x_.mean()) / x_.std()\\n\",\n    \"f_n = theano.function([x_, W], dot, givens={x: x_n})\\n\",\n    \"f_n(x_val, W_val)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Cloning with replacement\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([ 1.90651511,  0.60431744, -0.64253361])\"\n      ]\n     },\n     \"execution_count\": 21,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"dot_n, out_n = theano.clone([dot, out], replace={x: (x - x.mean()) / x.std()})                        \\n\",\n    \"f_n = theano.function([x, W], dot_n)                                                                  \\n\",\n    \"f_n(x_val, W_val)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Gradient\\n\",\n    \"### Using `theano.grad`\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"y = T.vector('y')\\n\",\n    \"C = ((out - y) ** 2).sum()\\n\",\n    \"dC_dW = theano.grad(C, W)\\n\",\n    \"dC_db = theano.grad(C, b)\\n\",\n    \"# dC_dW, dC_db = theano.grad(C, [W, b])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Using the gradients\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[array(0.6137821438190066), array([[ 0.01095277,  0.07045955,  0.051161  ],\\n\",\n      \"       [ 0.01889131,  0.12152849,  0.0882424 ],\\n\",\n      \"       [ 0.01555008,  0.10003427,  0.07263534],\\n\",\n      \"       [ 0.01048429,  0.06744584,  0.04897273]]), array([ 0.03600015,  0.23159028,  0.16815877])]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"cost_and_grads = theano.function([x, W, b, y], [C, dC_dW, dC_db])\\n\",\n    \"y_val = np.random.uniform(size=3)\\n\",\n    \"print(cost_and_grads(x_val, W_val, b_val, y_val))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[array(0.6137821438190066), array([[ 0.49561888, -0.14531026,  0.64257244],\\n\",\n      \"       [ 1.52114073, -0.24630622, -0.2429612 ],\\n\",\n      \"       [ 1.57765781,  0.7574313 , -0.47673792],\\n\",\n      \"       [ 0.54151161, -0.47016228, -0.47062703]]), array([ 0.99639999,  0.97684097,  0.98318412])]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"upd_W = W - 0.1 * dC_dW\\n\",\n    \"upd_b = b - 0.1 * dC_db\\n\",\n    \"cost_and_upd = theano.function([x, W, b, y], [C, upd_W, upd_b])\\n\",\n    \"print(cost_and_upd(x_val, W_val, b_val, y_val))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"The output file is available at pydotprint_cost_and_upd.png\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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Vai2/m7u7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H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     \"text/plain\": [\n       \"<IPython.core.display.Image object>\"\n      ]\n     },\n     \"execution_count\": 25,\n     \"metadata\": {\n      \"image/png\": {\n       \"width\": 1000\n      }\n     },\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"pydotprint(cost_and_upd, outfile='pydotprint_cost_and_upd.png')\\n\",\n    \"Image('pydotprint_cost_and_upd.png', width=1000)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Shared variables\\n\",\n    \"### Update values\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"C_val, dC_dW_val, dC_db_val = cost_and_grads(x_val, W_val, b_val, y_val)\\n\",\n    \"W_val -= 0.1 * dC_dW_val\\n\",\n    \"b_val -= 0.1 * dC_db_val\\n\",\n    \"\\n\",\n    \"C_val, W_val, b_val = cost_and_upd(x_val, W_val, b_val, y_val)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Using shared variables\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[ 1.78587062  0.00189954 -0.28566499]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = T.vector('x')\\n\",\n    \"y = T.vector('y')\\n\",\n    \"W = theano.shared(W_val)\\n\",\n    \"b = theano.shared(b_val)\\n\",\n    \"dot = T.dot(x, W)\\n\",\n    \"out = T.nnet.sigmoid(dot + b)\\n\",\n    \"f = theano.function([x], dot)  # W is an implicit input\\n\",\n    \"g = theano.function([x], out)  # W and b are implicit inputs\\n\",\n    \"print(f(x_val))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[ 0.94151144  0.72221187  0.66391952]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(g(x_val))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Updating shared variables\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"C = ((out - y) ** 2).sum()\\n\",\n    \"dC_dW, dC_db = theano.grad(C, [W, b])\\n\",\n    \"upd_W = W - 0.1 * dC_dW\\n\",\n    \"upd_b = b - 0.1 * dC_db\\n\",\n    \"\\n\",\n    \"cost_and_perform_updates = theano.function(\\n\",\n    \"    inputs=[x, y],\\n\",\n    \"    outputs=C,\\n\",\n    \"    updates=[(W, upd_W),\\n\",\n    \"             (b, upd_b)])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"The output file is available at pydotprint_cost_and_perform_updates.png\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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wR66a3M7NBdFAKPAoV6a34EDgB/AEKa214AVgGv3223/foCgcBrpg6D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\\nA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REREQ1BPPyVE3t27dv0qRJjx49mjZt2tSpU+VyuaEjIiKimoM5eiIiIiIiIqrG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\\nD2WogUqO3Ut/AAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<IPython.core.display.Image object>\"\n      ]\n     },\n     \"execution_count\": 30,\n     \"metadata\": {\n      \"image/png\": {\n       \"width\": 1000\n      }\n     },\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"pydotprint(cost_and_perform_updates, outfile='pydotprint_cost_and_perform_updates.png')\\n\",\n    \"Image('pydotprint_cost_and_perform_updates.png', width=1000)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Advanced Topics\\n\",\n    \"## Extending Theano\\n\",\n    \"### The easy way: Python\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import theano\\n\",\n    \"import numpy\\n\",\n    \"from theano.compile.ops import as_op\\n\",\n    \"\\n\",\n    \"def infer_shape_numpy_dot(node, input_shapes):\\n\",\n    \"    ashp, bshp = input_shapes\\n\",\n    \"    return [ashp[:-1] + bshp[-1:]]\\n\",\n    \"\\n\",\n    \"@as_op(itypes=[theano.tensor.fmatrix, theano.tensor.fmatrix],\\n\",\n    \"       otypes=[theano.tensor.fmatrix], infer_shape=infer_shape_numpy_dot)\\n\",\n    \"def numpy_dot(a, b):\\n\",\n    \"   return numpy.dot(a, b)\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.4.3\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "deep-learning/theano-tutorial/intro_theano/logistic_regression.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Logistic Regression in Theano\\n\",\n    \"\\n\",\n    \"Credits: Forked from [summerschool2015](https://github.com/mila-udem/summerschool2015) by mila-udem\\n\",\n    \"\\n\",\n    \"This notebook is inspired from [the tutorial on logistic regression](http://deeplearning.net/tutorial/logreg.html) on [deeplearning.net](http://deeplearning.net).\\n\",\n    \"\\n\",\n    \"In this notebook, we show how Theano can be used to implement the most basic classifier: the **logistic regression**. We start off with a quick primer of the model, which serves both as a refresher but also to anchor the notation and show how mathematical expressions are mapped onto Theano graphs.\\n\",\n    \"\\n\",\n    \"In the deepest of machine learning traditions, this tutorial will tackle the exciting problem of MNIST digit classification.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Get the data\\n\",\n    \"\\n\",\n    \"In the mean time, let's just download a pre-packaged version of MNIST, and load each split of the dataset as NumPy ndarrays.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"import requests\\n\",\n    \"import gzip\\n\",\n    \"import six\\n\",\n    \"from six.moves import cPickle\\n\",\n    \"\\n\",\n    \"if not os.path.exists('mnist.pkl.gz'):\\n\",\n    \"    r = requests.get('http://www.iro.umontreal.ca/~lisa/deep/data/mnist/mnist.pkl.gz')\\n\",\n    \"    with open('mnist.pkl.gz', 'wb') as data_file:\\n\",\n    \"        data_file.write(r.content)\\n\",\n    \"\\n\",\n    \"with gzip.open('mnist.pkl.gz', 'rb') as data_file:\\n\",\n    \"    if six.PY3:\\n\",\n    \"        train_set, valid_set, test_set = cPickle.load(data_file, encoding='latin1')\\n\",\n    \"    else:\\n\",\n    \"        train_set, valid_set, test_set = cPickle.load(data_file)\\n\",\n    \"\\n\",\n    \"train_set_x, train_set_y = train_set\\n\",\n    \"valid_set_x, valid_set_y = valid_set\\n\",\n    \"test_set_x, test_set_y = test_set\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## The model\\n\",\n    \"Logistic regression is a probabilistic, linear classifier. It is parametrized\\n\",\n    \"by a weight matrix $W$ and a bias vector $b$. Classification is\\n\",\n    \"done by projecting an input vector onto a set of hyperplanes, each of which\\n\",\n    \"corresponds to a class. The distance from the input to a hyperplane reflects\\n\",\n    \"the probability that the input is a member of the corresponding class.\\n\",\n    \"\\n\",\n    \"Mathematically, the probability that an input vector $x$ is a member of a\\n\",\n    \"class $i$, a value of a stochastic variable $Y$, can be written as:\\n\",\n    \"\\n\",\n    \"$$P(Y=i|x, W,b) = softmax_i(W x + b) = \\\\frac {e^{W_i x + b_i}} {\\\\sum_j e^{W_j x + b_j}}$$\\n\",\n    \"\\n\",\n    \"The model's prediction $y_{pred}$ is the class whose probability is maximal, specifically:\\n\",\n    \"\\n\",\n    \"$$  y_{pred} = {\\\\rm argmax}_i P(Y=i|x,W,b)$$\\n\",\n    \"\\n\",\n    \"Now, let us define our input variables. First, we need to define the dimension of our tensors:\\n\",\n    \"- `n_in` is the length of each training vector,\\n\",\n    \"- `n_out` is the number of classes.\\n\",\n    \"\\n\",\n    \"Our variables will be:\\n\",\n    \"- `x` is a matrix, where each row contains a different example of the dataset. Its shape is `(batch_size, n_in)`, but `batch_size` does not have to be specified in advance, and can change during training.\\n\",\n    \"- `W` is a shared matrix, of shape `(n_in, n_out)`, initialized with zeros. Column `k` of `W` represents the separation hyperplane for class `k`.\\n\",\n    \"- `b` is a shared vector, of length `n_out`, initialized with zeros. Element `k` of `b` represents the free parameter of hyperplane `k`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy\\n\",\n    \"import theano\\n\",\n    \"from theano import tensor\\n\",\n    \"\\n\",\n    \"# Size of the data\\n\",\n    \"n_in = 28 * 28\\n\",\n    \"# Number of classes\\n\",\n    \"n_out = 10\\n\",\n    \"\\n\",\n    \"x = tensor.matrix('x')\\n\",\n    \"W = theano.shared(value=numpy.zeros((n_in, n_out), dtype=theano.config.floatX),\\n\",\n    \"                  name='W',\\n\",\n    \"                  borrow=True)\\n\",\n    \"b = theano.shared(value=numpy.zeros((n_out,), dtype=theano.config.floatX),\\n\",\n    \"                  name='b',\\n\",\n    \"                  borrow=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now, we can build a symbolic expression for the matrix of class-membership probability (`p_y_given_x`), and for the class whose probability is maximal (`y_pred`).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"p_y_given_x = tensor.nnet.softmax(tensor.dot(x, W) + b)\\n\",\n    \"y_pred = tensor.argmax(p_y_given_x, axis=1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Defining a loss function\\n\",\n    \"Learning optimal model parameters involves minimizing a loss function. In the\\n\",\n    \"case of multi-class logistic regression, it is very common to use the negative\\n\",\n    \"log-likelihood as the loss. This is equivalent to maximizing the likelihood of the\\n\",\n    \"data set $\\\\cal{D}$ under the model parameterized by $\\\\theta$. Let\\n\",\n    \"us first start by defining the likelihood $\\\\cal{L}$ and loss\\n\",\n    \"$\\\\ell$:\\n\",\n    \"\\n\",\n    \"$$\\\\mathcal{L} (\\\\theta=\\\\{W,b\\\\}, \\\\mathcal{D}) =\\n\",\n    \"     \\\\sum_{i=0}^{|\\\\mathcal{D}|} \\\\log(P(Y=y^{(i)}|x^{(i)}, W,b)) \\\\\\\\\\n\",\n    \"   \\\\ell (\\\\theta=\\\\{W,b\\\\}, \\\\mathcal{D}) = - \\\\mathcal{L} (\\\\theta=\\\\{W,b\\\\}, \\\\mathcal{D})\\n\",\n    \"$$\\n\",\n    \"\\n\",\n    \"Again, we will express those expressions using Theano. We have one additional input, the actual target class `y`:\\n\",\n    \"- `y` is an input vector of integers, of length `batch_size` (which will have to match the length of `x` at runtime). The length of `y` can be symbolically expressed by `y.shape[0]`.\\n\",\n    \"- `log_prob` is a `(batch_size, n_out)` matrix containing the log probabilities of class membership for each example.\\n\",\n    \"- `arange(y.shape[0])` is a symbolic vector which will contain `[0,1,2,... batch_size-1]`\\n\",\n    \"- `log_likelihood` is a vector containing the log probability of the target, for each example.\\n\",\n    \"- `loss` is the mean of the negative `log_likelihood` over the examples in the minibatch.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"y = tensor.lvector('y')\\n\",\n    \"log_prob = tensor.log(p_y_given_x)\\n\",\n    \"log_likelihood = log_prob[tensor.arange(y.shape[0]), y]\\n\",\n    \"loss = - log_likelihood.mean()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Training procedure\\n\",\n    \"This notebook will use the method of stochastic gradient descent with mini-batches (MSGD) to find values of `W` and `b` that minimize the loss.\\n\",\n    \"\\n\",\n    \"We can let Theano compute symbolic expressions for the gradient of the loss wrt `W` and `b`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"g_W, g_b = theano.grad(cost=loss, wrt=[W, b])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"`g_W` and `g_b` are symbolic variables, which can be used as part of a computation graph. In particular, let us define the expressions for one step of gradient descent for `W` and `b`, for a fixed learning rate.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"learning_rate = numpy.float32(0.13)\\n\",\n    \"new_W = W - learning_rate * g_W\\n\",\n    \"new_b = b - learning_rate * g_b\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We can then define **update expressions**, or pairs of (shared variable, expression for its update), that we will use when compiling the Theano function. The updates will be performed each time the function gets called.\\n\",\n    \"\\n\",\n    \"The following function, `train_model`, returns the loss on the current minibatch, then changes the values of the shared variables according to the update rules. It needs to be passed `x` and `y` as inputs, but not the shared variables, which are implicit inputs.\\n\",\n    \"\\n\",\n    \"The entire learning algorithm thus consists in looping over all examples in the dataset, considering all the examples in one minibatch at a time, and repeatedly calling the `train_model` function.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"train_model = theano.function(inputs=[x, y],\\n\",\n    \"                              outputs=loss,\\n\",\n    \"                              updates=[(W, new_W),\\n\",\n    \"                                       (b, new_b)])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Testing the model\\n\",\n    \"When testing the model, we are interested in the number of misclassified examples (and not only in the likelihood). Here, we build a symbolic expression for retrieving the number of misclassified examples in a minibatch.\\n\",\n    \"\\n\",\n    \"This will also be useful to apply on the validation and testing sets, in order to monitor the progress of the model during training, and to do early stopping.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"misclass_nb = tensor.neq(y_pred, y)\\n\",\n    \"misclass_rate = misclass_nb.mean()\\n\",\n    \"\\n\",\n    \"test_model = theano.function(inputs=[x, y],\\n\",\n    \"                             outputs=misclass_rate)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Training the model\\n\",\n    \"Here is the main training loop of the algorithm:\\n\",\n    \"- For each *epoch*, or pass through the training set\\n\",\n    \"  - split the training set in minibatches, and call `train_model` on each minibatch\\n\",\n    \"  - split the validation set in minibatches, and call `test_model` on each minibatch to measure the misclassification rate\\n\",\n    \"  - if the misclassification rate has not improved in a while, stop training\\n\",\n    \"- Measure performance on the test set\\n\",\n    \"\\n\",\n    \"The **early stopping procedure** is what decide whether the performance has improved enough. There are many variants, and we will not go into the details of this one here.\\n\",\n    \"\\n\",\n    \"We first need to define a few parameters for the training loop and the early stopping procedure.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"## Define a couple of helper variables and functions for the optimization\\n\",\n    \"batch_size = 500\\n\",\n    \"# compute number of minibatches for training, validation and testing\\n\",\n    \"n_train_batches = train_set_x.shape[0] // batch_size\\n\",\n    \"n_valid_batches = valid_set_x.shape[0] // batch_size\\n\",\n    \"n_test_batches = test_set_x.shape[0] // batch_size\\n\",\n    \"\\n\",\n    \"def get_minibatch(i, dataset_x, dataset_y):\\n\",\n    \"    start_idx = i * batch_size\\n\",\n    \"    end_idx = (i + 1) * batch_size\\n\",\n    \"    batch_x = dataset_x[start_idx:end_idx]\\n\",\n    \"    batch_y = dataset_y[start_idx:end_idx]\\n\",\n    \"    return (batch_x, batch_y)\\n\",\n    \"\\n\",\n    \"## early-stopping parameters\\n\",\n    \"# maximum number of epochs\\n\",\n    \"n_epochs = 1000\\n\",\n    \"# look as this many examples regardless\\n\",\n    \"patience = 5000\\n\",\n    \"# wait this much longer when a new best is found\\n\",\n    \"patience_increase = 2\\n\",\n    \"# a relative improvement of this much is considered significant\\n\",\n    \"improvement_threshold = 0.995\\n\",\n    \"\\n\",\n    \"# go through this many minibatches before checking the network on the validation set;\\n\",\n    \"# in this case we check every epoch\\n\",\n    \"validation_frequency = min(n_train_batches, patience / 2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"scrolled\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import timeit\\n\",\n    \"from six.moves import xrange\\n\",\n    \"\\n\",\n    \"best_validation_loss = numpy.inf\\n\",\n    \"test_score = 0.\\n\",\n    \"start_time = timeit.default_timer()\\n\",\n    \"\\n\",\n    \"done_looping = False\\n\",\n    \"epoch = 0\\n\",\n    \"while (epoch < n_epochs) and (not done_looping):\\n\",\n    \"    epoch = epoch + 1\\n\",\n    \"    for minibatch_index in xrange(n_train_batches):\\n\",\n    \"        minibatch_x, minibatch_y = get_minibatch(minibatch_index, train_set_x, train_set_y)\\n\",\n    \"        minibatch_avg_cost = train_model(minibatch_x, minibatch_y)\\n\",\n    \"\\n\",\n    \"        # iteration number\\n\",\n    \"        iter = (epoch - 1) * n_train_batches + minibatch_index\\n\",\n    \"        if (iter + 1) % validation_frequency == 0:\\n\",\n    \"            # compute zero-one loss on validation set\\n\",\n    \"            validation_losses = []\\n\",\n    \"            for i in xrange(n_valid_batches):\\n\",\n    \"                valid_xi, valid_yi = get_minibatch(i, valid_set_x, valid_set_y)\\n\",\n    \"                validation_losses.append(test_model(valid_xi, valid_yi))\\n\",\n    \"            this_validation_loss = numpy.mean(validation_losses)\\n\",\n    \"            print('epoch %i, minibatch %i/%i, validation error %f %%' %\\n\",\n    \"                  (epoch,\\n\",\n    \"                   minibatch_index + 1,\\n\",\n    \"                   n_train_batches,\\n\",\n    \"                   this_validation_loss * 100.))\\n\",\n    \"\\n\",\n    \"            # if we got the best validation score until now\\n\",\n    \"            if this_validation_loss < best_validation_loss:\\n\",\n    \"                # improve patience if loss improvement is good enough\\n\",\n    \"                if this_validation_loss < best_validation_loss * improvement_threshold:\\n\",\n    \"                    patience = max(patience, iter * patience_increase)\\n\",\n    \"\\n\",\n    \"                best_validation_loss = this_validation_loss\\n\",\n    \"\\n\",\n    \"                # test it on the test set\\n\",\n    \"                test_losses = []\\n\",\n    \"                for i in xrange(n_test_batches):\\n\",\n    \"                    test_xi, test_yi = get_minibatch(i, test_set_x, test_set_y)\\n\",\n    \"                    test_losses.append(test_model(test_xi, test_yi))\\n\",\n    \"\\n\",\n    \"                test_score = numpy.mean(test_losses)\\n\",\n    \"                print('     epoch %i, minibatch %i/%i, test error of  best model %f %%' %\\n\",\n    \"                      (epoch,\\n\",\n    \"                       minibatch_index + 1,\\n\",\n    \"                       n_train_batches,\\n\",\n    \"                       test_score * 100.))\\n\",\n    \"\\n\",\n    \"                # save the best parameters\\n\",\n    \"                numpy.savez('best_model.npz', W=W.get_value(), b=b.get_value())\\n\",\n    \"\\n\",\n    \"        if patience <= iter:\\n\",\n    \"            done_looping = True\\n\",\n    \"            break\\n\",\n    \"\\n\",\n    \"end_time = timeit.default_timer()\\n\",\n    \"print('Optimization complete with best validation score of %f %%, '\\n\",\n    \"      'with test performance %f %%' %\\n\",\n    \"      (best_validation_loss * 100., test_score * 100.))\\n\",\n    \"\\n\",\n    \"print('The code ran for %d epochs, with %f epochs/sec' %\\n\",\n    \"      (epoch, 1. * epoch / (end_time - start_time)))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Visualization\\n\",\n    \"You can visualize the columns of `W`, which correspond to the separation hyperplanes for each class.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%matplotlib inline\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from utils import tile_raster_images\\n\",\n    \"\\n\",\n    \"plt.clf()\\n\",\n    \"\\n\",\n    \"# Increase the size of the figure\\n\",\n    \"plt.gcf().set_size_inches(15, 10)\\n\",\n    \"\\n\",\n    \"plot_data = tile_raster_images(W.get_value(borrow=True).T,\\n\",\n    \"                               img_shape=(28, 28), tile_shape=(2, 5), tile_spacing=(1, 1))\\n\",\n    \"plt.imshow(plot_data, cmap='Greys', interpolation='none')\\n\",\n    \"plt.axis('off')\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.4.3\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "deep-learning/theano-tutorial/intro_theano/utils.py",
    "content": "\"\"\" This file contains different utility functions that are not connected\nin anyway to the networks presented in the tutorials, but rather help in\nprocessing the outputs into a more understandable way.\n\nFor example ``tile_raster_images`` helps in generating a easy to grasp\nimage from a set of samples or weights.\n\"\"\"\n\n\nimport numpy\nfrom six.moves import xrange\n\n\ndef scale_to_unit_interval(ndar, eps=1e-8):\n    \"\"\" Scales all values in the ndarray ndar to be between 0 and 1 \"\"\"\n    ndar = ndar.copy()\n    ndar -= ndar.min()\n    ndar *= 1.0 / (ndar.max() + eps)\n    return ndar\n\n\ndef tile_raster_images(X, img_shape, tile_shape, tile_spacing=(0, 0),\n                       scale_rows_to_unit_interval=True,\n                       output_pixel_vals=True):\n    \"\"\"\n    Transform an array with one flattened image per row, into an array in\n    which images are reshaped and layed out like tiles on a floor.\n\n    This function is useful for visualizing datasets whose rows are images,\n    and also columns of matrices for transforming those rows\n    (such as the first layer of a neural net).\n\n    :type X: a 2-D ndarray or a tuple of 4 channels, elements of which can\n    be 2-D ndarrays or None;\n    :param X: a 2-D array in which every row is a flattened image.\n\n    :type img_shape: tuple; (height, width)\n    :param img_shape: the original shape of each image\n\n    :type tile_shape: tuple; (rows, cols)\n    :param tile_shape: the number of images to tile (rows, cols)\n\n    :param output_pixel_vals: if output should be pixel values (i.e. int8\n    values) or floats\n\n    :param scale_rows_to_unit_interval: if the values need to be scaled before\n    being plotted to [0,1] or not\n\n\n    :returns: array suitable for viewing as an image.\n    (See:`Image.fromarray`.)\n    :rtype: a 2-d array with same dtype as X.\n\n    \"\"\"\n\n    assert len(img_shape) == 2\n    assert len(tile_shape) == 2\n    assert len(tile_spacing) == 2\n\n    # The expression below can be re-written in a more C style as\n    # follows :\n    #\n    # out_shape    = [0,0]\n    # out_shape[0] = (img_shape[0]+tile_spacing[0])*tile_shape[0] -\n    #                tile_spacing[0]\n    # out_shape[1] = (img_shape[1]+tile_spacing[1])*tile_shape[1] -\n    #                tile_spacing[1]\n    out_shape = [\n        (ishp + tsp) * tshp - tsp\n        for ishp, tshp, tsp in zip(img_shape, tile_shape, tile_spacing)\n    ]\n\n    if isinstance(X, tuple):\n        assert len(X) == 4\n        # Create an output numpy ndarray to store the image\n        if output_pixel_vals:\n            out_array = numpy.zeros((out_shape[0], out_shape[1], 4),\n                                    dtype='uint8')\n        else:\n            out_array = numpy.zeros((out_shape[0], out_shape[1], 4),\n                                    dtype=X.dtype)\n\n        #colors default to 0, alpha defaults to 1 (opaque)\n        if output_pixel_vals:\n            channel_defaults = [0, 0, 0, 255]\n        else:\n            channel_defaults = [0., 0., 0., 1.]\n\n        for i in xrange(4):\n            if X[i] is None:\n                # if channel is None, fill it with zeros of the correct\n                # dtype\n                dt = out_array.dtype\n                if output_pixel_vals:\n                    dt = 'uint8'\n                out_array[:, :, i] = numpy.zeros(\n                    out_shape,\n                    dtype=dt\n                ) + channel_defaults[i]\n            else:\n                # use a recurrent call to compute the channel and store it\n                # in the output\n                out_array[:, :, i] = tile_raster_images(\n                    X[i], img_shape, tile_shape, tile_spacing,\n                    scale_rows_to_unit_interval, output_pixel_vals)\n        return out_array\n\n    else:\n        # if we are dealing with only one channel\n        H, W = img_shape\n        Hs, Ws = tile_spacing\n\n        # generate a matrix to store the output\n        dt = X.dtype\n        if output_pixel_vals:\n            dt = 'uint8'\n        out_array = numpy.zeros(out_shape, dtype=dt)\n\n        for tile_row in xrange(tile_shape[0]):\n            for tile_col in xrange(tile_shape[1]):\n                if tile_row * tile_shape[1] + tile_col < X.shape[0]:\n                    this_x = X[tile_row * tile_shape[1] + tile_col]\n                    if scale_rows_to_unit_interval:\n                        # if we should scale values to be between 0 and 1\n                        # do this by calling the `scale_to_unit_interval`\n                        # function\n                        this_img = scale_to_unit_interval(\n                            this_x.reshape(img_shape))\n                    else:\n                        this_img = this_x.reshape(img_shape)\n                    # add the slice to the corresponding position in the\n                    # output array\n                    c = 1\n                    if output_pixel_vals:\n                        c = 255\n                    out_array[\n                        tile_row * (H + Hs): tile_row * (H + Hs) + H,\n                        tile_col * (W + Ws): tile_col * (W + Ws) + W\n                    ] = this_img * c\n        return out_array\n"
  },
  {
    "path": "deep-learning/theano-tutorial/rnn_tutorial/Makefile",
    "content": "all: instruction.pdf rnn_lstm.pdf\n\ninstruction.pdf: slides_source/instruction.tex\n\tcd slides_source; pdflatex --shell-escape instruction.tex\n\tcd slides_source; pdflatex --shell-escape instruction.tex\n\tcd slides_source; pdflatex --shell-escape instruction.tex\n\tmv slides_source/instruction.pdf .\n\nrnn_lstm.pdf: slides_source/rnn_lstm.tex\n\tcd slides_source; pdflatex --shell-escape rnn_lstm.tex\n\tcd slides_source; pdflatex --shell-escape rnn_lstm.tex\n\tcd slides_source; pdflatex --shell-escape rnn_lstm.tex\n\tmv slides_source/rnn_lstm.pdf .\n"
  },
  {
    "path": "deep-learning/theano-tutorial/rnn_tutorial/lstm_text.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Introduction\\n\",\n    \"In this demo, you'll see a more practical application of RNNs/LSTMs as character-level language models. The emphasis will be more on parallelization and using RNNs with data from Fuel.\\n\",\n    \"\\n\",\n    \"To get started, we first need to download the training text, validation text and a file that contains a dictionary for mapping characters to integers. We also need to import quite a list of modules.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"import requests\\n\",\n    \"import gzip\\n\",\n    \"\\n\",\n    \"from six.moves import cPickle as pkl\\n\",\n    \"import time\\n\",\n    \"\\n\",\n    \"import numpy\\n\",\n    \"import theano\\n\",\n    \"import theano.tensor as T\\n\",\n    \"\\n\",\n    \"from theano.tensor.nnet import categorical_crossentropy\\n\",\n    \"from theano import config\\n\",\n    \"from fuel.datasets import TextFile\\n\",\n    \"from fuel.streams import DataStream\\n\",\n    \"from fuel.schemes import ConstantScheme\\n\",\n    \"from fuel.transformers import Batch, Padding\\n\",\n    \"\\n\",\n    \"if not os.path.exists('traindata.txt'):\\n\",\n    \"    r = requests.get('http://www-etud.iro.umontreal.ca/~brakelp/traindata.txt.gz')\\n\",\n    \"    with open('traindata.txt.gz', 'wb') as data_file:\\n\",\n    \"        data_file.write(r.content)\\n\",\n    \"    with gzip.open('traindata.txt.gz', 'rb') as data_file:\\n\",\n    \"        with open('traindata.txt', 'w') as out_file:\\n\",\n    \"            out_file.write(data_file.read())\\n\",\n    \"        \\n\",\n    \"if not os.path.exists('valdata.txt'):\\n\",\n    \"    r = requests.get('http://www-etud.iro.umontreal.ca/~brakelp/valdata.txt.gz')\\n\",\n    \"    with open('valdata.txt.gz', 'wb') as data_file:\\n\",\n    \"        data_file.write(r.content)\\n\",\n    \"    with gzip.open('valdata.txt.gz', 'rb') as data_file:\\n\",\n    \"        with open('valdata.txt', 'w') as out_file:\\n\",\n    \"            out_file.write(data_file.read())\\n\",\n    \"\\n\",\n    \"if not os.path.exists('dictionary.pkl'):\\n\",\n    \"    r = requests.get('http://www-etud.iro.umontreal.ca/~brakelp/dictionary.pkl')\\n\",\n    \"    with open('dictionary.pkl', 'wb') as data_file:\\n\",\n    \"        data_file.write(r.content)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"##The Model\\n\",\n    \"The code below shows an implementation of an LSTM network. Note that there are various different variations of the LSTM in use and this one doesn't include the so-called 'peephole connections'. We used a separate method for the dynamic update to make it easier to generate from the network later. The `index_dot` function doesn't safe much verbosity, but it clarifies that certain dot products have been replaced with indexing operations because this network will be applied to discrete data. Last but not least, note the addition of the `mask` argument which is used to ignore certain parts of the input sequence.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def gauss_weight(rng, ndim_in, ndim_out=None, sd=.005):\\n\",\n    \"    if ndim_out is None:\\n\",\n    \"        ndim_out = ndim_in\\n\",\n    \"    W = rng.randn(ndim_in, ndim_out) * sd\\n\",\n    \"    return numpy.asarray(W, dtype=config.floatX)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def index_dot(indices, w):\\n\",\n    \"    return w[indices.flatten()]\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class LstmLayer:\\n\",\n    \"\\n\",\n    \"    def __init__(self, rng, input, mask, n_in, n_h):\\n\",\n    \"\\n\",\n    \"        # Init params\\n\",\n    \"        self.W_i = theano.shared(gauss_weight(rng, n_in, n_h), 'W_i', borrow=True)\\n\",\n    \"        self.W_f = theano.shared(gauss_weight(rng, n_in, n_h), 'W_f', borrow=True)\\n\",\n    \"        self.W_c = theano.shared(gauss_weight(rng, n_in, n_h), 'W_c', borrow=True)\\n\",\n    \"        self.W_o = theano.shared(gauss_weight(rng, n_in, n_h), 'W_o', borrow=True)\\n\",\n    \"\\n\",\n    \"        self.U_i = theano.shared(gauss_weight(rng, n_h), 'U_i', borrow=True)\\n\",\n    \"        self.U_f = theano.shared(gauss_weight(rng, n_h), 'U_f', borrow=True)\\n\",\n    \"        self.U_c = theano.shared(gauss_weight(rng, n_h), 'U_c', borrow=True)\\n\",\n    \"        self.U_o = theano.shared(gauss_weight(rng, n_h), 'U_o', borrow=True)\\n\",\n    \"\\n\",\n    \"        self.b_i = theano.shared(numpy.zeros((n_h,), dtype=config.floatX),\\n\",\n    \"                                 'b_i', borrow=True)\\n\",\n    \"        self.b_f = theano.shared(numpy.zeros((n_h,), dtype=config.floatX),\\n\",\n    \"                                 'b_f', borrow=True)\\n\",\n    \"        self.b_c = theano.shared(numpy.zeros((n_h,), dtype=config.floatX),\\n\",\n    \"                                 'b_c', borrow=True)\\n\",\n    \"        self.b_o = theano.shared(numpy.zeros((n_h,), dtype=config.floatX),\\n\",\n    \"                                 'b_o', borrow=True)\\n\",\n    \"\\n\",\n    \"        self.params = [self.W_i, self.W_f, self.W_c, self.W_o,\\n\",\n    \"                       self.U_i, self.U_f, self.U_c, self.U_o,\\n\",\n    \"                       self.b_i, self.b_f, self.b_c, self.b_o]\\n\",\n    \"\\n\",\n    \"        outputs_info = [T.zeros((input.shape[1], n_h)),\\n\",\n    \"                        T.zeros((input.shape[1], n_h))]\\n\",\n    \"\\n\",\n    \"        rval, updates = theano.scan(self._step,\\n\",\n    \"                                    sequences=[mask, input],\\n\",\n    \"                                    outputs_info=outputs_info)\\n\",\n    \"\\n\",\n    \"        # self.output is in the format (length, batchsize, n_h)\\n\",\n    \"        self.output = rval[0]\\n\",\n    \"\\n\",\n    \"    def _step(self, m_, x_, h_, c_):\\n\",\n    \"\\n\",\n    \"        i_preact = (index_dot(x_, self.W_i) +\\n\",\n    \"                    T.dot(h_, self.U_i) + self.b_i)\\n\",\n    \"        i = T.nnet.sigmoid(i_preact)\\n\",\n    \"\\n\",\n    \"        f_preact = (index_dot(x_, self.W_f) +\\n\",\n    \"                    T.dot(h_, self.U_f) + self.b_f)\\n\",\n    \"        f = T.nnet.sigmoid(f_preact)\\n\",\n    \"\\n\",\n    \"        o_preact = (index_dot(x_, self.W_o) +\\n\",\n    \"                    T.dot(h_, self.U_o) + self.b_o)\\n\",\n    \"        o = T.nnet.sigmoid(o_preact)\\n\",\n    \"\\n\",\n    \"        c_preact = (index_dot(x_, self.W_c) +\\n\",\n    \"                    T.dot(h_, self.U_c) + self.b_c)\\n\",\n    \"        c = T.tanh(c_preact)\\n\",\n    \"\\n\",\n    \"        c = f * c_ + i * c\\n\",\n    \"        c = m_[:, None] * c + (1. - m_)[:, None] * c_\\n\",\n    \"\\n\",\n    \"        h = o * T.tanh(c)\\n\",\n    \"        h = m_[:, None] * h + (1. - m_)[:, None] * h_\\n\",\n    \"\\n\",\n    \"        return h, c\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The next block contains some code that computes cross-entropy for masked sequences and a stripped down version of the logistic regression class from the deep learning tutorials which we will need later.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def sequence_categorical_crossentropy(prediction, targets, mask):\\n\",\n    \"    prediction_flat = prediction.reshape(((prediction.shape[0] *\\n\",\n    \"                                           prediction.shape[1]),\\n\",\n    \"                                          prediction.shape[2]), ndim=2)\\n\",\n    \"    targets_flat = targets.flatten()\\n\",\n    \"    mask_flat = mask.flatten()\\n\",\n    \"    ce = categorical_crossentropy(prediction_flat, targets_flat)\\n\",\n    \"    return T.sum(ce * mask_flat)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class LogisticRegression(object):\\n\",\n    \"   \\n\",\n    \"    def __init__(self, rng, input, n_in, n_out):\\n\",\n    \"        \\n\",\n    \"        W = gauss_weight(rng, n_in, n_out)\\n\",\n    \"        self.W = theano.shared(value=numpy.asarray(W, dtype=theano.config.floatX),\\n\",\n    \"                               name='W', borrow=True)\\n\",\n    \"        # initialize the biases b as a vector of n_out 0s\\n\",\n    \"        self.b = theano.shared(value=numpy.zeros((n_out,),\\n\",\n    \"                                                 dtype=theano.config.floatX),\\n\",\n    \"                               name='b', borrow=True)\\n\",\n    \"\\n\",\n    \"        # compute vector of class-membership probabilities in symbolic form\\n\",\n    \"        energy = T.dot(input, self.W) + self.b\\n\",\n    \"        energy_exp = T.exp(energy - T.max(energy, axis=2, keepdims=True))\\n\",\n    \"        pmf = energy_exp / energy_exp.sum(axis=2, keepdims=True)\\n\",\n    \"        self.p_y_given_x = pmf\\n\",\n    \"        self.params = [self.W, self.b]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#Processing the Data\\n\",\n    \"The data in `traindata.txt` and `valdata.txt` is simply English text but formatted in such a way that every sentence is conveniently separated by the newline symbol. We'll use some of the functionality of fuel to perform the following preprocessing steps:\\n\",\n    \"* Convert everything to lowercase\\n\",\n    \"* Map characters to indices\\n\",\n    \"* Group the sentences into batches\\n\",\n    \"* Convert each batch in a matrix/tensor as long as the longest sequence with zeros padded to all the shorter sequences\\n\",\n    \"* Add a mask matrix that encodes the length of each sequence (a timestep at which the mask is 0 indicates that there is no data available)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"batch_size = 100\\n\",\n    \"n_epochs = 40\\n\",\n    \"n_h = 50\\n\",\n    \"DICT_FILE = 'dictionary.pkl'\\n\",\n    \"TRAIN_FILE = 'traindata.txt'\\n\",\n    \"VAL_FILE = 'valdata.txt'\\n\",\n    \"\\n\",\n    \"# Load the datasets with Fuel\\n\",\n    \"dictionary = pkl.load(open(DICT_FILE, 'r'))\\n\",\n    \"# add a symbol for unknown characters\\n\",\n    \"dictionary['~'] = len(dictionary)\\n\",\n    \"reverse_mapping = dict((j, i) for i, j in dictionary.items())\\n\",\n    \"\\n\",\n    \"train = TextFile(files=[TRAIN_FILE],\\n\",\n    \"                 dictionary=dictionary,\\n\",\n    \"                 unk_token='~',\\n\",\n    \"                 level='character',\\n\",\n    \"                 preprocess=str.lower,\\n\",\n    \"                 bos_token=None,\\n\",\n    \"                 eos_token=None)\\n\",\n    \"\\n\",\n    \"train_stream = DataStream.default_stream(train)\\n\",\n    \"\\n\",\n    \"# organize data in batches and pad shorter sequences with zeros\\n\",\n    \"train_stream = Batch(train_stream,\\n\",\n    \"                     iteration_scheme=ConstantScheme(batch_size))\\n\",\n    \"train_stream = Padding(train_stream)\\n\",\n    \"\\n\",\n    \"# idem dito for the validation text\\n\",\n    \"val = TextFile(files=[VAL_FILE],\\n\",\n    \"                 dictionary=dictionary,\\n\",\n    \"                 unk_token='~',\\n\",\n    \"                 level='character',\\n\",\n    \"                 preprocess=str.lower,\\n\",\n    \"                 bos_token=None,\\n\",\n    \"                 eos_token=None)\\n\",\n    \"\\n\",\n    \"val_stream = DataStream.default_stream(val)\\n\",\n    \"\\n\",\n    \"# organize data in batches and pad shorter sequences with zeros\\n\",\n    \"val_stream = Batch(val_stream,\\n\",\n    \"                     iteration_scheme=ConstantScheme(batch_size))\\n\",\n    \"val_stream = Padding(val_stream)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"##The Theano Graph\\n\",\n    \"We'll now define the complete Theano graph for computing costs and gradients among other things. The cost will be the cross-entropy of the next character in the sequence and the network will try to predict it based on the previous characters.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Set the random number generator' seeds for consistency\\n\",\n    \"rng = numpy.random.RandomState(12345)\\n\",\n    \"\\n\",\n    \"x = T.lmatrix('x')\\n\",\n    \"mask = T.matrix('mask')\\n\",\n    \"\\n\",\n    \"# Construct an LSTM layer and a logistic regression layer\\n\",\n    \"recurrent_layer = LstmLayer(rng=rng, input=x, mask=mask, n_in=111, n_h=n_h)\\n\",\n    \"logreg_layer = LogisticRegression(rng=rng, input=recurrent_layer.output[:-1],\\n\",\n    \"                                  n_in=n_h, n_out=111)\\n\",\n    \"\\n\",\n    \"# define a cost variable to optimize\\n\",\n    \"cost = sequence_categorical_crossentropy(logreg_layer.p_y_given_x,\\n\",\n    \"                                         x[1:],\\n\",\n    \"                                         mask[1:]) / batch_size\\n\",\n    \"\\n\",\n    \"# create a list of all model parameters to be fit by gradient descent\\n\",\n    \"params = logreg_layer.params + recurrent_layer.params\\n\",\n    \"\\n\",\n    \"# create a list of gradients for all model parameters\\n\",\n    \"grads = T.grad(cost, params)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We can now compile the function that updates the gradients. We also added a function that computes the cost without updating for monitoring purposes.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/pbrakel/Repositories/Theano/theano/scan_module/scan_perform_ext.py:117: RuntimeWarning: numpy.ndarray size changed, may indicate binary incompatibility\\n\",\n      \"  from scan_perform.scan_perform import *\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"learning_rate = 0.1\\n\",\n    \"updates = [\\n\",\n    \"    (param_i, param_i - learning_rate * grad_i)\\n\",\n    \"    for param_i, grad_i in zip(params, grads)\\n\",\n    \"]\\n\",\n    \"\\n\",\n    \"update_model = theano.function([x, mask], cost, updates=updates)\\n\",\n    \"\\n\",\n    \"evaluate_model = theano.function([x, mask], cost)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"##Generating Sequences\\n\",\n    \"To see if the networks learn something useful (and to make results monitoring more entertaining), we'll also write some code to generate sequences. For this, we'll first compile a function that computes a single state update for the network to have more control over the values of each variable at each time step.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x_t = T.iscalar()\\n\",\n    \"h_p = T.vector()\\n\",\n    \"c_p = T.vector()\\n\",\n    \"h_t, c_t = recurrent_layer._step(T.ones(1), x_t, h_p, c_p)\\n\",\n    \"energy = T.dot(h_t, logreg_layer.W) + logreg_layer.b\\n\",\n    \"\\n\",\n    \"energy_exp = T.exp(energy - T.max(energy, axis=1, keepdims=True))\\n\",\n    \"\\n\",\n    \"output = energy_exp / energy_exp.sum(axis=1, keepdims=True)\\n\",\n    \"single_step = theano.function([x_t, h_p, c_p], [output, h_t, c_t])\\n\",\n    \"\\n\",\n    \"def speak(single_step, prefix='the meaning of life is ', n_steps=450):\\n\",\n    \"    try:\\n\",\n    \"        h_p = numpy.zeros((n_h,), dtype=config.floatX)\\n\",\n    \"        c_p = numpy.zeros((n_h,), dtype=config.floatX)\\n\",\n    \"        sentence = prefix\\n\",\n    \"        for char in prefix:\\n\",\n    \"            x_t = dictionary[char]\\n\",\n    \"            prediction, h_p, c_p = single_step(x_t, h_p.flatten(),\\n\",\n    \"                                               c_p.flatten())\\n\",\n    \"        # Renormalize probability in float64\\n\",\n    \"        flat_prediction = prediction.flatten()\\n\",\n    \"        flat_pred_sum = flat_prediction.sum(dtype='float64')\\n\",\n    \"        if flat_pred_sum > 1:\\n\",\n    \"            flat_prediction = flat_prediction.astype('float64') / flat_pred_sum\\n\",\n    \"        sample = numpy.random.multinomial(1, flat_prediction)\\n\",\n    \"\\n\",\n    \"        for i in range(n_steps):\\n\",\n    \"            x_t = numpy.argmax(sample)\\n\",\n    \"            prediction, h_p, c_p = single_step(x_t, h_p.flatten(),\\n\",\n    \"                                               c_p.flatten())\\n\",\n    \"            # Renormalize probability in float64\\n\",\n    \"            flat_prediction = prediction.flatten()\\n\",\n    \"            flat_pred_sum = flat_prediction.sum(dtype='float64')\\n\",\n    \"            if flat_pred_sum > 1:\\n\",\n    \"                flat_prediction = flat_prediction.astype('float64') / flat_pred_sum\\n\",\n    \"            sample = numpy.random.multinomial(1, flat_prediction)\\n\",\n    \"\\n\",\n    \"            sentence += reverse_mapping[x_t]\\n\",\n    \"\\n\",\n    \"        return sentence\\n\",\n    \"    except ValueError as e:\\n\",\n    \"        print 'Something went wrong during sentence generation: {}'.format(e)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"epoch: 0\\n\",\n      \"\\n\",\n      \"LSTM: \\\"the meaning of life is i�ateisn ^ltbagss7tuodkca r9 msd,forreypoctlluoiasrn?at�netteofkotenni�cf/vattosnlrxisiovu�al.hahau�ootwo tuost! ]cw� eweunhufaaecihtdtk tticiss cvt2f etoct bllstsluohh-,retti?eusrv eikly an�ade'i stiel�doelnamtuartoci�ht.�woi 2kfs$an tpeo�miiadain9.e eegtamiaesboeinne�unlocityqe dansapeaeiyo�ihaewmtrt�'aa svteatae ,otrr.gsac.-perioswetgoc�io froaoeismhsgtulherbttrh fl�i el  nnltnta�sat yhomsnttwlnwnenaee.mhits r�us-thist sn man4lamhpac.osdopl g�\\\"\\n\",\n      \"\\n\",\n      \"epoch: 0   minibatch: 40\\n\",\n      \"Average validation CE per sentence: 251.167072292\\n\"\n     ]\n    },\n    {\n     \"ename\": \"KeyboardInterrupt\",\n     \"evalue\": \"\",\n     \"output_type\": \"error\",\n     \"traceback\": [\n      \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n      \"\\u001b[1;31mKeyboardInterrupt\\u001b[0m                         Traceback (most recent call last)\",\n      \"\\u001b[1;32m<ipython-input-13-7c09df6ae427>\\u001b[0m in \\u001b[0;36m<module>\\u001b[1;34m()\\u001b[0m\\n\\u001b[0;32m      9\\u001b[0m         \\u001b[0miteration\\u001b[0m \\u001b[1;33m+=\\u001b[0m \\u001b[1;36m1\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m     10\\u001b[0m \\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m---> 11\\u001b[1;33m         \\u001b[0mcross_entropy\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0mupdate_model\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mx_\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mT\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mmask_\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mT\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[0;32m     12\\u001b[0m \\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m     13\\u001b[0m \\u001b[1;33m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[1;32m/home/pbrakel/Repositories/Theano/theano/compile/function_module.pyc\\u001b[0m in \\u001b[0;36m__call__\\u001b[1;34m(self, *args, **kwargs)\\u001b[0m\\n\\u001b[0;32m    577\\u001b[0m         \\u001b[0mt0_fn\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0mtime\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mtime\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m    578\\u001b[0m         \\u001b[1;32mtry\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m--> 579\\u001b[1;33m             \\u001b[0moutputs\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mfn\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[0;32m    580\\u001b[0m         \\u001b[1;32mexcept\\u001b[0m \\u001b[0mException\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m    581\\u001b[0m             \\u001b[1;32mif\\u001b[0m \\u001b[0mhasattr\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mfn\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[1;34m'position_of_error'\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[1;32m/home/pbrakel/Repositories/Theano/theano/scan_module/scan_op.pyc\\u001b[0m in \\u001b[0;36mrval\\u001b[1;34m(p, i, o, n)\\u001b[0m\\n\\u001b[0;32m    649\\u001b[0m         \\u001b[1;31m# default arguments are stored in the closure of `rval`\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m    650\\u001b[0m \\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m--> 651\\u001b[1;33m         \\u001b[1;32mdef\\u001b[0m \\u001b[0mrval\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mp\\u001b[0m\\u001b[1;33m=\\u001b[0m\\u001b[0mp\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mi\\u001b[0m\\u001b[1;33m=\\u001b[0m\\u001b[0mnode_input_storage\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mo\\u001b[0m\\u001b[1;33m=\\u001b[0m\\u001b[0mnode_output_storage\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mn\\u001b[0m\\u001b[1;33m=\\u001b[0m\\u001b[0mnode\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[0;32m    652\\u001b[0m             \\u001b[0mr\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0mp\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mn\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[1;33m[\\u001b[0m\\u001b[0mx\\u001b[0m\\u001b[1;33m[\\u001b[0m\\u001b[1;36m0\\u001b[0m\\u001b[1;33m]\\u001b[0m \\u001b[1;32mfor\\u001b[0m \\u001b[0mx\\u001b[0m \\u001b[1;32min\\u001b[0m \\u001b[0mi\\u001b[0m\\u001b[1;33m]\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mo\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m    653\\u001b[0m             \\u001b[1;32mfor\\u001b[0m \\u001b[0mo\\u001b[0m \\u001b[1;32min\\u001b[0m \\u001b[0mnode\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0moutputs\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[1;31mKeyboardInterrupt\\u001b[0m: \"\n     ]\n    }\n   ],\n   \"source\": [\n    \"start_time = time.clock()\\n\",\n    \"\\n\",\n    \"iteration = 0\\n\",\n    \"\\n\",\n    \"for epoch in range(n_epochs):\\n\",\n    \"    print 'epoch:', epoch\\n\",\n    \"\\n\",\n    \"    for x_, mask_ in train_stream.get_epoch_iterator():\\n\",\n    \"        iteration += 1\\n\",\n    \"\\n\",\n    \"        cross_entropy = update_model(x_.T, mask_.T)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"        # Generate some text after each 20 minibatches\\n\",\n    \"        if iteration % 40 == 0:\\n\",\n    \"            sentence = speak(single_step, prefix='the meaning of life is ', n_steps=450)\\n\",\n    \"            print\\n\",\n    \"            print 'LSTM: \\\"' + sentence + '\\\"'\\n\",\n    \"            print\\n\",\n    \"            print 'epoch:', epoch, '  minibatch:', iteration\\n\",\n    \"            val_scores = []\\n\",\n    \"            for x_val, mask_val in val_stream.get_epoch_iterator():\\n\",\n    \"                val_scores.append(evaluate_model(x_val.T, mask_val.T))\\n\",\n    \"            print 'Average validation CE per sentence:', numpy.mean(val_scores)\\n\",\n    \"\\n\",\n    \"end_time = time.clock()\\n\",\n    \"print('Optimization complete.')\\n\",\n    \"print('The code ran for %.2fm' % ((end_time - start_time) / 60.))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"It can take a while before the text starts to look more reasonable but here are some things to experiment with:\\n\",\n    \"* Smarter optimization algorithms (or at least momentum)\\n\",\n    \"* Initializing the recurrent weights orthogonally\\n\",\n    \"* The sizes of the initial weights and biases (think about what the gates do)\\n\",\n    \"* Different sentence prefixes\\n\",\n    \"* Changing the temperature of the character distribution during generation. What happens when you generate deterministically?\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 2\",\n   \"language\": \"python\",\n   \"name\": \"python2\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 2\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython2\",\n   \"version\": \"2.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "deep-learning/theano-tutorial/rnn_tutorial/rnn_precompile.py",
    "content": "\"\"\"This file is only here to speed up the execution of notebooks.\n\nIt contains a subset of the code defined in simple_rnn.ipynb and\nlstm_text.ipynb, in particular the code compiling Theano function.\nExecuting this script first will populate the cache of compiled C code,\nwhich will make subsequent compilations faster.\n\nThe use case is to run this script in the background when a demo VM\nsuch as the one for NVIDIA's qwikLABS, so that the compilation phase\nstarted from the notebooks is faster.\n\n\"\"\"\nimport numpy\n\nimport theano\nimport theano.tensor as T\n\nfrom theano import config\nfrom theano.tensor.nnet import categorical_crossentropy\n\n\nfloatX = theano.config.floatX\n\n\n# simple_rnn.ipynb\n\nclass SimpleRNN(object):\n    def __init__(self, input_dim, recurrent_dim):\n        w_xh = numpy.random.normal(0, .01, (input_dim, recurrent_dim))\n        w_hh = numpy.random.normal(0, .02, (recurrent_dim, recurrent_dim))\n        self.w_xh = theano.shared(numpy.asarray(w_xh, dtype=floatX), name='w_xh')\n        self.w_hh = theano.shared(numpy.asarray(w_hh, dtype=floatX), name='w_hh')\n        self.b_h = theano.shared(numpy.zeros((recurrent_dim,), dtype=floatX), name='b_h')\n        self.parameters = [self.w_xh, self.w_hh, self.b_h]\n\n    def _step(self, input_t, previous):\n        return T.tanh(T.dot(previous, self.w_hh) + input_t)\n\n    def __call__(self, x):\n        x_w_xh = T.dot(x, self.w_xh) + self.b_h\n        result, updates = theano.scan(self._step,\n                                      sequences=[x_w_xh],\n                                      outputs_info=[T.zeros_like(self.b_h)])\n        return result\n\n\nw_ho_np = numpy.random.normal(0, .01, (15, 1))\nw_ho = theano.shared(numpy.asarray(w_ho_np, dtype=floatX), name='w_ho')\nb_o = theano.shared(numpy.zeros((1,), dtype=floatX), name='b_o')\n\nx = T.matrix('x')\nmy_rnn = SimpleRNN(1, 15)\nhidden = my_rnn(x)\nprediction = T.dot(hidden, w_ho) + b_o\nparameters = my_rnn.parameters + [w_ho, b_o]\nl2 = sum((p**2).sum() for p in parameters)\nmse = T.mean((prediction[:-1] - x[1:])**2)\ncost = mse + .0001 * l2\ngradient = T.grad(cost, wrt=parameters)\n\nlr = .3\nupdates = [(par, par - lr * gra) for par, gra in zip(parameters, gradient)]\nupdate_model = theano.function([x], cost, updates=updates)\nget_cost = theano.function([x], mse)\npredict = theano.function([x], prediction)\nget_hidden = theano.function([x], hidden)\nget_gradient = theano.function([x], gradient)\n\npredict = theano.function([x], prediction)\n\n# Generating sequences\n\nx_t = T.vector()\nh_p = T.vector()\npreactivation = T.dot(x_t, my_rnn.w_xh) + my_rnn.b_h\nh_t = my_rnn._step(preactivation, h_p)\no_t = T.dot(h_t, w_ho) + b_o\n\nsingle_step = theano.function([x_t, h_p], [o_t, h_t])\n\n# lstm_text.ipynb\n\ndef gauss_weight(rng, ndim_in, ndim_out=None, sd=.005):\n    if ndim_out is None:\n        ndim_out = ndim_in\n    W = rng.randn(ndim_in, ndim_out) * sd\n    return numpy.asarray(W, dtype=config.floatX)\n\n\ndef index_dot(indices, w):\n    return w[indices.flatten()]\n\n\nclass LstmLayer:\n\n    def __init__(self, rng, input, mask, n_in, n_h):\n\n        # Init params\n        self.W_i = theano.shared(gauss_weight(rng, n_in, n_h), 'W_i', borrow=True)\n        self.W_f = theano.shared(gauss_weight(rng, n_in, n_h), 'W_f', borrow=True)\n        self.W_c = theano.shared(gauss_weight(rng, n_in, n_h), 'W_c', borrow=True)\n        self.W_o = theano.shared(gauss_weight(rng, n_in, n_h), 'W_o', borrow=True)\n\n        self.U_i = theano.shared(gauss_weight(rng, n_h), 'U_i', borrow=True)\n        self.U_f = theano.shared(gauss_weight(rng, n_h), 'U_f', borrow=True)\n        self.U_c = theano.shared(gauss_weight(rng, n_h), 'U_c', borrow=True)\n        self.U_o = theano.shared(gauss_weight(rng, n_h), 'U_o', borrow=True)\n\n        self.b_i = theano.shared(numpy.zeros((n_h,), dtype=config.floatX),\n                                 'b_i', borrow=True)\n        self.b_f = theano.shared(numpy.zeros((n_h,), dtype=config.floatX),\n                                 'b_f', borrow=True)\n        self.b_c = theano.shared(numpy.zeros((n_h,), dtype=config.floatX),\n                                 'b_c', borrow=True)\n        self.b_o = theano.shared(numpy.zeros((n_h,), dtype=config.floatX),\n                                 'b_o', borrow=True)\n\n        self.params = [self.W_i, self.W_f, self.W_c, self.W_o,\n                       self.U_i, self.U_f, self.U_c, self.U_o,\n                       self.b_i, self.b_f, self.b_c, self.b_o]\n\n        outputs_info = [T.zeros((input.shape[1], n_h)),\n                        T.zeros((input.shape[1], n_h))]\n\n        rval, updates = theano.scan(self._step,\n                                    sequences=[mask, input],\n                                    outputs_info=outputs_info)\n\n        # self.output is in the format (length, batchsize, n_h)\n        self.output = rval[0]\n\n    def _step(self, m_, x_, h_, c_):\n\n        i_preact = (index_dot(x_, self.W_i) +\n                    T.dot(h_, self.U_i) + self.b_i)\n        i = T.nnet.sigmoid(i_preact)\n\n        f_preact = (index_dot(x_, self.W_f) +\n                    T.dot(h_, self.U_f) + self.b_f)\n        f = T.nnet.sigmoid(f_preact)\n\n        o_preact = (index_dot(x_, self.W_o) +\n                    T.dot(h_, self.U_o) + self.b_o)\n        o = T.nnet.sigmoid(o_preact)\n\n        c_preact = (index_dot(x_, self.W_c) +\n                    T.dot(h_, self.U_c) + self.b_c)\n        c = T.tanh(c_preact)\n\n        c = f * c_ + i * c\n        c = m_[:, None] * c + (1. - m_)[:, None] * c_\n\n        h = o * T.tanh(c)\n        h = m_[:, None] * h + (1. - m_)[:, None] * h_\n\n        return h, c\n\n\ndef sequence_categorical_crossentropy(prediction, targets, mask):\n    prediction_flat = prediction.reshape(((prediction.shape[0] *\n                                           prediction.shape[1]),\n                                          prediction.shape[2]), ndim=2)\n    targets_flat = targets.flatten()\n    mask_flat = mask.flatten()\n    ce = categorical_crossentropy(prediction_flat, targets_flat)\n    return T.sum(ce * mask_flat)\n\n\nclass LogisticRegression(object):\n\n    def __init__(self, rng, input, n_in, n_out):\n\n        W = gauss_weight(rng, n_in, n_out)\n        self.W = theano.shared(value=numpy.asarray(W, dtype=theano.config.floatX),\n                               name='W', borrow=True)\n        # initialize the biases b as a vector of n_out 0s\n        self.b = theano.shared(value=numpy.zeros((n_out,),\n                                                 dtype=theano.config.floatX),\n                               name='b', borrow=True)\n\n        # compute vector of class-membership probabilities in symbolic form\n        energy = T.dot(input, self.W) + self.b\n        energy_exp = T.exp(energy - T.max(energy, axis=2, keepdims=True))\n        pmf = energy_exp / energy_exp.sum(axis=2, keepdims=True)\n        self.p_y_given_x = pmf\n        self.params = [self.W, self.b]\n\nbatch_size = 100\nn_h = 50\n\n# The Theano graph\n# Set the random number generator' seeds for consistency\nrng = numpy.random.RandomState(12345)\n\nx = T.lmatrix('x')\nmask = T.matrix('mask')\n\n# Construct an LSTM layer and a logistic regression layer\nrecurrent_layer = LstmLayer(rng=rng, input=x, mask=mask, n_in=111, n_h=n_h)\nlogreg_layer = LogisticRegression(rng=rng, input=recurrent_layer.output[:-1],\n                                  n_in=n_h, n_out=111)\n\n# define a cost variable to optimize\ncost = sequence_categorical_crossentropy(logreg_layer.p_y_given_x,\n                                         x[1:],\n                                         mask[1:]) / batch_size\n\n# create a list of all model parameters to be fit by gradient descent\nparams = logreg_layer.params + recurrent_layer.params\n\n# create a list of gradients for all model parameters\ngrads = T.grad(cost, params)\n\nlearning_rate = 0.1\nupdates = [\n    (param_i, param_i - learning_rate * grad_i)\n    for param_i, grad_i in zip(params, grads)\n]\n\nupdate_model = theano.function([x, mask], cost, updates=updates)\n\nevaluate_model = theano.function([x, mask], cost)\n\n# Generating Sequences\nx_t = T.iscalar()\nh_p = T.vector()\nc_p = T.vector()\nh_t, c_t = recurrent_layer._step(T.ones(1), x_t, h_p, c_p)\nenergy = T.dot(h_t, logreg_layer.W) + logreg_layer.b\n\nenergy_exp = T.exp(energy - T.max(energy, axis=1, keepdims=True))\n\noutput = energy_exp / energy_exp.sum(axis=1, keepdims=True)\nsingle_step = theano.function([x_t, h_p, c_p], [output, h_t, c_t])\n"
  },
  {
    "path": "deep-learning/theano-tutorial/rnn_tutorial/simple_rnn.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Recurrent Neural Networks in Theano\\n\",\n    \"\\n\",\n    \"Credits: Forked from [summerschool2015](https://github.com/mila-udem/summerschool2015) by mila-udem\\n\",\n    \"\\n\",\n    \"First, we import some dependencies:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%matplotlib inline\\n\",\n    \"from synthetic import mackey_glass\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import theano\\n\",\n    \"import theano.tensor as T\\n\",\n    \"import numpy\\n\",\n    \"\\n\",\n    \"floatX = theano.config.floatX\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We now define a class that uses `scan` to initialize an RNN and apply it to a sequence of data vectors. The constructor initializes the shared variables after which the instance can be called on a symbolic variable to construct an RNN graph. Note that this class only handles the computation of the hidden layer activations. We'll define a set of output weights later.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"class SimpleRNN(object):\\n\",\n    \"    def __init__(self, input_dim, recurrent_dim):\\n\",\n    \"        w_xh = numpy.random.normal(0, .01, (input_dim, recurrent_dim))\\n\",\n    \"        w_hh = numpy.random.normal(0, .02, (recurrent_dim, recurrent_dim))\\n\",\n    \"        self.w_xh = theano.shared(numpy.asarray(w_xh, dtype=floatX), name='w_xh')\\n\",\n    \"        self.w_hh = theano.shared(numpy.asarray(w_hh, dtype=floatX), name='w_hh')\\n\",\n    \"        self.b_h = theano.shared(numpy.zeros((recurrent_dim,), dtype=floatX), name='b_h')\\n\",\n    \"        self.parameters = [self.w_xh, self.w_hh, self.b_h]\\n\",\n    \"        \\n\",\n    \"    def _step(self, input_t, previous):\\n\",\n    \"            return T.tanh(T.dot(previous, self.w_hh) + input_t)\\n\",\n    \"        \\n\",\n    \"    def __call__(self, x):\\n\",\n    \"        x_w_xh = T.dot(x, self.w_xh) + self.b_h      \\n\",\n    \"        result, updates = theano.scan(self._step,\\n\",\n    \"                                      sequences=[x_w_xh],\\n\",\n    \"                                      outputs_info=[T.zeros_like(self.b_h)])\\n\",\n    \"        return result\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"For visualization purposes and to keep the optimization time managable, we will train the RNN on a short synthetic chaotic time series. Let's first have a look at the data:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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nbfpeIEcrBQM5cItuaUNpaewFQ2q42FzxDJBnc1gcHl808tQLo7085F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\\ngYGBgQGGMxgYGBgYAP5/+4PE7UVXfAIAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f0f3cac8750>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"data = numpy.asarray(mackey_glass(2000)[0], dtype=floatX)\\n\",\n    \"plt.plot(data)\\n\",\n    \"plt.show()\\n\",\n    \"data_train = data[:1500]\\n\",\n    \"data_val = data[1500:]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"To train an RNN model on this sequences, we need to generate a theano graph that computes the cost and its gradient. In this case, the task will be to predict the next time step and the error objective will be the mean squared error (MSE).  We also need to define shared variables for the output weights. Finally, we also add a regularization term to the cost.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"w_ho_np = numpy.random.normal(0, .01, (15, 1))\\n\",\n    \"w_ho = theano.shared(numpy.asarray(w_ho_np, dtype=floatX), name='w_ho')\\n\",\n    \"b_o = theano.shared(numpy.zeros((1,), dtype=floatX), name='b_o')\\n\",\n    \"\\n\",\n    \"x = T.matrix('x')\\n\",\n    \"my_rnn = SimpleRNN(1, 15)\\n\",\n    \"hidden = my_rnn(x)\\n\",\n    \"prediction = T.dot(hidden, w_ho) + b_o\\n\",\n    \"parameters = my_rnn.parameters + [w_ho, b_o]\\n\",\n    \"l2 = sum((p**2).sum() for p in parameters)\\n\",\n    \"mse = T.mean((prediction[:-1] - x[1:])**2)\\n\",\n    \"cost = mse + .0001 * l2\\n\",\n    \"gradient = T.grad(cost, wrt=parameters)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We now compile the function that will update the parameters of the model using gradient descent. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"lr = .3\\n\",\n    \"updates = [(par, par - lr * gra) for par, gra in zip(parameters, gradient)] \\n\",\n    \"update_model = theano.function([x], cost, updates=updates)\\n\",\n    \"get_cost = theano.function([x], mse)\\n\",\n    \"predict = theano.function([x], prediction)\\n\",\n    \"get_hidden = theano.function([x], hidden)\\n\",\n    \"get_gradient = theano.function([x], gradient)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We can now train the network by supplying this function with our data and calling it repeatedly.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch 0: train mse: 0.0515556260943    validation mse: 0.0469637289643\\n\",\n      \"Epoch 100: train mse: 0.0407442860305    validation mse: 0.0401079840958\\n\",\n      \"Epoch 200: train mse: 0.00225670542568    validation mse: 0.00203324528411\\n\",\n      \"Epoch 300: train mse: 0.00185390282422    validation mse: 0.00163305236492\\n\",\n      \"Epoch 400: train mse: 0.00161687470973    validation mse: 0.00139373319689\\n\",\n      \"Epoch 500: train mse: 0.00145859015174    validation mse: 0.00123134546448\\n\",\n      \"Epoch 600: train mse: 0.00134439510293    validation mse: 0.00111229927279\\n\",\n      \"Epoch 700: train mse: 0.00125755299814    validation mse: 0.00102029775735\\n\",\n      \"Epoch 800: train mse: 0.0011889107991    validation mse: 0.000946390733588\\n\",\n      \"Epoch 900: train mse: 0.00113300536759    validation mse: 0.000885214598384\\n\",\n      \"Epoch 1000: train mse: 0.00108635553624    validation mse: 0.000833337020595\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"for i in range(1001):\\n\",\n    \"    mse_train = update_model(data_train)\\n\",\n    \"    \\n\",\n    \"    if i % 100 == 0:\\n\",\n    \"        mse_val = get_cost(data_val)\\n\",\n    \"        print 'Epoch {}: train mse: {}    validation mse: {}'.format(i, mse_train, mse_val)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Since we're only looking at a very small toy problem here, the model probably already memorized the train data quite well. Let's find out by plotting the predictions of the network:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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xKRIKI9RPRY/aGTAMz7dPnePXikkLjrE5dhPGoR0V4oeOitxs9PlfR/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\\ngg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f0f35c39350>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"predict = theano.function([x], prediction)\\n\",\n    \"prediction_np = predict(data)\\n\",\n    \"plt.plot(data[1:], label='data')\\n\",\n    \"plt.plot(prediction_np, label='prediction')\\n\",\n    \"plt.legend()\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Small scale optimizations of this type often benefit from more advanced second order methods. The following block defines some functions that allow you to experiment with off-the-shelf optimization routines. In this case we used BFGS.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Warning: Desired error not necessarily achieved due to precision loss.\\n\",\n      \"         Current function value: 0.000218\\n\",\n      \"         Iterations: 5\\n\",\n      \"         Function evaluations: 31\\n\",\n      \"         Gradient evaluations: 19\\n\",\n      \"train mse: 0.000217512235395   validation mse: 0.00018158860621\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"def vector_to_params(v):\\n\",\n    \"    return_list = []\\n\",\n    \"    offset = 0\\n\",\n    \"    # note the global variable here\\n\",\n    \"    for par in parameters:\\n\",\n    \"        par_size = numpy.product(par.get_value().shape)\\n\",\n    \"        return_list.append(v[offset:offset+par_size].reshape(par.get_value().shape))\\n\",\n    \"        offset += par_size\\n\",\n    \"    return return_list\\n\",\n    \"       \\n\",\n    \"    \\n\",\n    \"def set_params(values):\\n\",\n    \"    for parameter, value in zip(parameters, values):\\n\",\n    \"        parameter.set_value(numpy.asarray(value, dtype=floatX))\\n\",\n    \"        \\n\",\n    \"        \\n\",\n    \"def f_obj(x):\\n\",\n    \"    values = vector_to_params(x)\\n\",\n    \"    set_params(values)\\n\",\n    \"    return get_cost(data_train)\\n\",\n    \"        \\n\",\n    \"    \\n\",\n    \"def f_prime(x):\\n\",\n    \"    values = vector_to_params(x)\\n\",\n    \"    set_params(values)\\n\",\n    \"    grad = get_gradient(data_train)\\n\",\n    \"    return numpy.asarray(numpy.concatenate([var.flatten() for var in grad]), dtype='float64')\\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"from scipy.optimize import fmin_bfgs\\n\",\n    \"x0 = numpy.asarray(numpy.concatenate([p.get_value().flatten() for p in parameters]), dtype='float64')\\n\",\n    \"result = fmin_bfgs(f_obj, x0, f_prime)\\n\",\n    \"\\n\",\n    \"print 'train mse: {}   validation mse: {}'.format(get_cost(data_train), get_cost(data_val))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Generating sequences\\n\",\n    \"Predicting a single step ahead is a relatively easy task. It would be more intresting to see if the network actually learned how to generate multiple time steps such that it can continue the sequence.\\n\",\n    \"Write code that generates the next 1000 examples after processing the train sequence.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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PFK3IVgCa4pYC9gHVV2UeUEVcapcp0qk5Nc/KvmQhQRAObj0kK33QpL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\\ngg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f0f3ca042d0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"x_t = T.vector()\\n\",\n    \"h_p = T.vector()\\n\",\n    \"preactivation = T.dot(x_t, my_rnn.w_xh) + my_rnn.b_h\\n\",\n    \"h_t = my_rnn._step(preactivation, h_p)\\n\",\n    \"o_t = T.dot(h_t, w_ho) + b_o\\n\",\n    \"\\n\",\n    \"single_step = theano.function([x_t, h_p], [o_t, h_t])\\n\",\n    \"\\n\",\n    \"def generate(single_step, x_t, h_p, n_steps):\\n\",\n    \"    output = numpy.zeros((n_steps, 1))\\n\",\n    \"    for output_t in output:\\n\",\n    \"        x_t, h_p = single_step(x_t, h_p)\\n\",\n    \"        output_t[:] = x_t\\n\",\n    \"    return output\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"output = predict(data_train)\\n\",\n    \"hidden = get_hidden(data_train)\\n\",\n    \"\\n\",\n    \"output = generate(single_step, output[-1], hidden[-1], n_steps=200)\\n\",\n    \"plt.plot(output)\\n\",\n    \"plt.plot(data_val[:200])\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"#Things to Try\\n\",\n    \"The quality of the generated sequence is probably not very good. Let's try to improve on it. Things to consider are:\\n\",\n    \"* The initial weight values\\n\",\n    \"* Using L2/L1 regularization\\n\",\n    \"* Using weight noise\\n\",\n    \"* The number of hidden units\\n\",\n    \"* The non-linearity\\n\",\n    \"* Adding direct connections between the input and the output\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.4.3\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "deep-learning/theano-tutorial/rnn_tutorial/synthetic.py",
    "content": "import collections\nimport numpy as np\n\n\ndef mackey_glass(sample_len=1000, tau=17, seed=None, n_samples = 1):\n    '''\n    mackey_glass(sample_len=1000, tau=17, seed = None, n_samples = 1) -> input\n    Generate the Mackey Glass time-series. Parameters are:\n        - sample_len: length of the time-series in timesteps. Default is 1000.\n        - tau: delay of the MG - system. Commonly used values are tau=17 (mild \n          chaos) and tau=30 (moderate chaos). Default is 17.\n        - seed: to seed the random generator, can be used to generate the same\n          timeseries at each invocation.\n        - n_samples : number of samples to generate\n    '''\n    delta_t = 10\n    history_len = tau * delta_t \n    # Initial conditions for the history of the system\n    timeseries = 1.2\n    \n    if seed is not None:\n        np.random.seed(seed)\n\n    samples = []\n\n    for _ in range(n_samples):\n        history = collections.deque(1.2 * np.ones(history_len) + 0.2 * \\\n                                    (np.random.rand(history_len) - 0.5))\n        # Preallocate the array for the time-series\n        inp = np.zeros((sample_len,1))\n        \n        for timestep in range(sample_len):\n            for _ in range(delta_t):\n                xtau = history.popleft()\n                history.append(timeseries)\n                timeseries = history[-1] + (0.2 * xtau / (1.0 + xtau ** 10) - \\\n                             0.1 * history[-1]) / delta_t\n            inp[timestep] = timeseries\n        \n        # Squash timeseries through tanh\n        inp = np.tanh(inp - 1)\n        samples.append(inp)\n    return samples\n\n\ndef mso(sample_len=1000, n_samples = 1):\n    '''\n    mso(sample_len=1000, n_samples = 1) -> input\n    Generate the Multiple Sinewave Oscillator time-series, a sum of two sines\n    with incommensurable periods. Parameters are:\n        - sample_len: length of the time-series in timesteps\n        - n_samples: number of samples to generate\n    '''\n    signals = []\n    for _ in range(n_samples):\n        phase = np.random.rand()\n        x = np.atleast_2d(np.arange(sample_len)).T\n        signals.append(np.sin(0.2 * x + phase) + np.sin(0.311 * x + phase))\n    return signals\n\n\ndef lorentz(sample_len=1000, sigma=10, rho=28, beta=8 / 3, step=0.01):\n    \"\"\"This function generates a Lorentz time series of length sample_len,\n    with standard parameters sigma, rho and beta. \n    \"\"\"\n\n    x = np.zeros([sample_len])\n    y = np.zeros([sample_len])\n    z = np.zeros([sample_len])\n\n    # Initial conditions taken from 'Chaos and Time Series Analysis', J. Sprott\n    x[0] = 0;\n    y[0] = -0.01;\n    z[0] = 9;\n\n    for t in range(sample_len - 1):\n        x[t + 1] = x[t] + sigma * (y[t] - x[t]) * step\n        y[t + 1] = y[t] + (x[t] * (rho - z[t]) - y[t]) * step\n        z[t + 1] = z[t] + (x[t] * y[t] - beta * z[t]) * step\n\n    x.shape += (1,)\n    y.shape += (1,)\n    z.shape += (1,)\n\n    return np.concatenate((x, y, z), axis=1)\n"
  },
  {
    "path": "deep-learning/theano-tutorial/scan_tutorial/scan_ex1_solution.py",
    "content": "import theano\nimport theano.tensor as T\nimport numpy as np\n\ncoefficients = T.vector(\"coefficients\")\nx = T.scalar(\"x\")\nmax_coefficients_supported = 10000\n\n\ndef step(coeff, power, prior_value, free_var):\n    return prior_value + (coeff * (free_var ** power))\n\n# Generate the components of the polynomial\nfull_range = T.arange(max_coefficients_supported)\noutputs_info = np.zeros((), dtype=theano.config.floatX)\n\ncomponents, updates = theano.scan(fn=step,\n                                  sequences=[coefficients, full_range],\n                                  outputs_info=outputs_info,\n                                  non_sequences=x)\n\npolynomial = components[-1]\ncalculate_polynomial = theano.function(inputs=[coefficients, x],\n                                       outputs=polynomial,\n                                       updates=updates)\n\ntest_coeff = np.asarray([1, 0, 2], dtype=theano.config.floatX)\nprint(calculate_polynomial(test_coeff, 3))\n"
  },
  {
    "path": "deep-learning/theano-tutorial/scan_tutorial/scan_ex2_solution.py",
    "content": "import theano\nimport theano.tensor as T\nimport numpy as np\n\nprobabilities = T.vector()\nnb_samples = T.iscalar()\n\nrng = T.shared_randomstreams.RandomStreams(1234)\n\n\ndef sample_from_pvect(pvect):\n    \"\"\" Provided utility function: given a symbolic vector of\n    probabilities (which MUST sum to 1), sample one element\n    and return its index.\n    \"\"\"\n    onehot_sample = rng.multinomial(n=1, pvals=pvect)\n    sample = onehot_sample.argmax()\n    return sample\n\n\ndef set_p_to_zero(pvect, i):\n    \"\"\" Provided utility function: given a symbolic vector of\n    probabilities and an index 'i', set the probability of the\n    i-th element to 0 and renormalize the probabilities so they\n    sum to 1.\n    \"\"\"\n    new_pvect = T.set_subtensor(pvect[i], 0.)\n    new_pvect = new_pvect / new_pvect.sum()\n    return new_pvect\n\n\ndef step(p):\n    sample = sample_from_pvect(p)\n    new_p = set_p_to_zero(p, sample)\n    return new_p, sample\n\noutput, updates = theano.scan(fn=step,\n                              outputs_info=[probabilities, None],\n                              n_steps=nb_samples)\n\nmodified_probabilities, samples = output\n\nf = theano.function(inputs=[probabilities, nb_samples],\n                    outputs=[samples],\n                    updates=updates)\n\n# Testing the function\ntest_probs = np.asarray([0.6, 0.3, 0.1], dtype=theano.config.floatX)\nfor i in range(10):\n    print(f(test_probs, 2))\n"
  },
  {
    "path": "deep-learning/theano-tutorial/scan_tutorial/scan_tutorial.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Introduction to Scan in Theano\\n\",\n    \"\\n\",\n    \"Credits: Forked from [summerschool2015](https://github.com/mila-udem/summerschool2015) by mila-udem\\n\",\n    \"\\n\",\n    \"## In short\\n\",\n    \"\\n\",\n    \"* Mechanism to perform loops in a Theano graph\\n\",\n    \"* Supports nested loops and reusing results from previous iterations \\n\",\n    \"* Highly generic\\n\",\n    \"\\n\",\n    \"## Implementation\\n\",\n    \"\\n\",\n    \"A Theano function graph is composed of two types of nodes; Variable nodes which represent data and Apply node which apply Ops (which represent some computation) to Variables to produce new Variables.\\n\",\n    \"\\n\",\n    \"From this point of view, a node that applies a Scan op is just like any other. Internally, however, it is very different from most Ops.\\n\",\n    \"\\n\",\n    \"Inside a Scan op is yet another Theano graph which represents the computation to be performed at every iteration of the loop. During compilation, that graph is compiled into a function and, during execution, the Scan op will call that function repeatedly on its inputs to produce its outputs.\\n\",\n    \"\\n\",\n    \"## Example 1 : As simple as it gets\\n\",\n    \"\\n\",\n    \"Scan's interface is complex and, thus, best introduced by examples. So, let's dive right in and start with a simple example; perform an element-wise multiplication between two vectors. \\n\",\n    \"\\n\",\n    \"This particular example is simple enough that Scan is not the best way to do things but we'll gradually work our way to more complex examples where Scan gets more interesting.\\n\",\n    \"\\n\",\n    \"Let's first setup our use case by defining Theano variables for the inputs :\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import theano\\n\",\n    \"import theano.tensor as T\\n\",\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"vector1 = T.vector('vector1')\\n\",\n    \"vector2 = T.vector('vector2')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Next, we call the `scan()` function. It has many parameters but, because our use case is simple, we only need two of them. We'll introduce other parameters in the next examples.\\n\",\n    \"\\n\",\n    \"The parameter `sequences` allows us to specify variables that Scan should iterate over as it loops. The first iteration will take as input the first element of every sequence, the second iteration will take as input the second element of every sequence, etc. These individual element have will have one less dimension than the original sequences. For example, for a matrix sequence, the individual elements will be vectors.\\n\",\n    \"\\n\",\n    \"The parameter `fn` receives a function or lambda expression that expresses the computation to do at every iteration. It operates on the symbolic inputs to produce symbolic outputs. It will **only ever be called once**, to assemble the Theano graph used by Scan at every the iterations.\\n\",\n    \"\\n\",\n    \"Since we wish to iterate over both `vector1` and `vector2` simultaneously, we provide them as sequences. This means that every iteration will operate on two inputs: an element from `vector1` and the corresponding element from `vector2`. \\n\",\n    \"\\n\",\n    \"Because what we want is the elementwise product between the vectors, we provide a lambda expression that, given an element `a` from `vector1` and an element `b` from `vector2` computes and return the product.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"output, updates = theano.scan(fn=lambda a, b : a * b,\\n\",\n    \"                              sequences=[vector1, vector2])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Calling `scan()`, we see that it returns two outputs.\\n\",\n    \"\\n\",\n    \"The first output contains the outputs of `fn` from every timestep concatenated into a tensor. In our case, the output of a single timestep is a scalar so output is a vector where `output[i]` is the output of the i-th iteration.\\n\",\n    \"\\n\",\n    \"The second output details if and how the execution of the Scan updates any shared variable in the graph. It should be provided as an argument when compiling the Theano function.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"scrolled\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"f = theano.function(inputs=[vector1, vector2],\\n\",\n    \"                    outputs=output,\\n\",\n    \"                    updates=updates)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"If `updates` is omitted, the state of any shared variables modified by Scan will not be updated properly. Random number sampling, for instance, relies on shared variables. If `updates` is not provided, the state of the random number generator won't be updated properly and the same numbers might be sampled repeatedly. **Always** provide `updates` when compiling your Theano function.\\n\",\n    \"\\n\",\n    \"Now that we've defined how to do elementwise multiplication with Scan, we can see that the result is as expected :\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"vector1_value = np.arange(0, 5).astype(theano.config.floatX) # [0,1,2,3,4]\\n\",\n    \"vector2_value = np.arange(1, 6).astype(theano.config.floatX) # [1,2,3,4,5]\\n\",\n    \"print(f(vector1_value, vector2_value))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"An interesting thing is that we never explicitly told Scan how many iteration it needed to run. It was automatically inferred; when given sequences, Scan will run as many iterations as the length of the shortest sequence : \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"print(f(vector1_value, vector2_value[:4]))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Example 2 : Non-sequences\\n\",\n    \"\\n\",\n    \"In this example, we introduce another of Scan's features; non-sequences. To demonstrate how to use them, we use Scan to compute the activations of  a linear MLP layer over a minibatch.\\n\",\n    \"\\n\",\n    \"It is not yet a use case where Scan is truly useful but it introduces a requirement that sequences cannot fulfill; if we want to use Scan to iterate over the minibatch elements and compute the activations for each of them, then we need some variables (the parameters of the layer), to be available 'as is' at every iteration of the loop. We do *not* want Scan to iterate over them and give only part of them at every iteration.\\n\",\n    \"\\n\",\n    \"Once again, we begin by setting up our Theano variables :\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"X = T.matrix('X') # Minibatch of data\\n\",\n    \"W = T.matrix('W') # Weights of the layer\\n\",\n    \"b = T.vector('b') # Biases of the layer\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"For the sake of variety, in this example we define the computation to be done at every iteration of the loop using a Python function, `step()`, instead of a lambda expression.\\n\",\n    \"\\n\",\n    \"To have the full weight matrix W and the full bias vector b available at every iteration, we use the argument non_sequences. Contrary to sequences, non-sequences are not iterated upon by Scan. Every non-sequence is passed as input to every iteration.\\n\",\n    \"\\n\",\n    \"This means that our `step()` function will need to operate on three symbolic inputs; one for our sequence X and one for each of our non-sequences W and b. \\n\",\n    \"\\n\",\n    \"The inputs that correspond to the non-sequences are **always** last and in the same order at the non-sequences are provided to Scan. This means that the correspondence between the inputs of the `step()` function and the arguments to `scan()` is the following : \\n\",\n    \"\\n\",\n    \"* `v` : individual element of the sequence `X` \\n\",\n    \"* `W` and `b` : non-sequences `W` and `b`, respectively\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def step(v, W, b):\\n\",\n    \"    return T.dot(v, W) + b\\n\",\n    \"\\n\",\n    \"output, updates = theano.scan(fn=step,\\n\",\n    \"                              sequences=[X],\\n\",\n    \"                              non_sequences=[W, b])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We can now compile our Theano function and see that it gives the expected results.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"f = theano.function(inputs=[X, W, b],\\n\",\n    \"                    outputs=output,\\n\",\n    \"                    updates=updates)\\n\",\n    \"\\n\",\n    \"X_value = np.arange(-3, 3).reshape(3, 2).astype(theano.config.floatX)\\n\",\n    \"W_value = np.eye(2).astype(theano.config.floatX)\\n\",\n    \"b_value = np.arange(2).astype(theano.config.floatX)\\n\",\n    \"print(f(X_value, W_value, b_value))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Example 3 : Reusing outputs from the previous iterations\\n\",\n    \"\\n\",\n    \"In this example, we will use Scan to compute a cumulative sum over the first dimension of a matrix $M$. This means that the output will be a matrix $S$ in which the first row will be equal to the first row of $M$, the second row will be equal to the sum of the two first rows of $M$, and so on.\\n\",\n    \"\\n\",\n    \"Another way to express this, which is the way we will implement here, is that $S[t] = S[t-1] + M[t]$. Implementing this with Scan would involve iterating over the rows of the matrix $M$ and, at every iteration, reuse the cumulative row that was output at the previous iteration and return the sum of it and the current row of $M$.\\n\",\n    \"\\n\",\n    \"If we assume for a moment that we can get Scan to provide the output value from the previous iteration as an input for every iteration, implementing a step function is simple :\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def step(m_row, cumulative_sum):\\n\",\n    \"    return m_row + cumulative_sum\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The trick part is informing Scan that our step function expects as input the output of a previous iteration. To achieve this, we need to use a new parameter of the `scan()` function: `outputs_info`. This parameter is used to tell Scan how we intend to use each of the outputs that are computed at each iteration.\\n\",\n    \"\\n\",\n    \"This parameter can be omitted (like we did so far) when the step function doesn't depend on any output of a previous iteration. However, now that we wish to have recurrent outputs, we need to start using it.\\n\",\n    \"\\n\",\n    \"`outputs_info` takes a sequence with one element for every output of the `step()` function :\\n\",\n    \"* For a **non-recurrent output** (like in every example before this one), the element should be `None`.\\n\",\n    \"* For a **simple recurrent output** (iteration $t$ depends on the value at iteration $t-1$), the element must be a tensor. Scan will interpret it as being an initial state for a recurrent output and give it as input to the first iteration, pretending it is the output value from a previous iteration. For subsequent iterations, Scan will automatically handle giving the previous output value as an input.\\n\",\n    \"\\n\",\n    \"The `step()` function needs to expect one additional input for each simple recurrent output. These inputs correspond to outputs from previous iteration and are **always** after the inputs that correspond to sequences but before those that correspond to non-sequences. The are received by the `step()` function in the order in which the recurrent outputs are declared in the outputs_info sequence.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"M = T.matrix('X')\\n\",\n    \"s = T.vector('s') # Initial value for the cumulative sum\\n\",\n    \"\\n\",\n    \"output, updates = theano.scan(fn=step,\\n\",\n    \"                              sequences=[M],\\n\",\n    \"                              outputs_info=[s])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We can now compile and test the Theano function :\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"f = theano.function(inputs=[M, s],\\n\",\n    \"                    outputs=output,\\n\",\n    \"                    updates=updates)\\n\",\n    \"\\n\",\n    \"M_value = np.arange(9).reshape(3, 3).astype(theano.config.floatX)\\n\",\n    \"s_value = np.zeros((3, ), dtype=theano.config.floatX)\\n\",\n    \"print(f(M_value, s_value))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"An important thing to notice here, is that the output computed by the Scan does **not** include the initial state that we provided. It only outputs the states that it has computed itself.\\n\",\n    \"\\n\",\n    \"If we want to have both the initial state and the computed states in the same Theano variable, we have to join them ourselves.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Example 4 : Reusing outputs from multiple past iterations\\n\",\n    \"\\n\",\n    \"The Fibonacci sequence is a sequence of numbers F where the two first numbers both 1 and every subsequence number is defined as such : $F_n = F_{n-1} + F_{n-2}$. Thus, the Fibonacci sequence goes : 1, 1, 2, 3, 5, 8, 13, ...\\n\",\n    \"\\n\",\n    \"In this example, we will cover how to compute part of the Fibonacci sequence using Scan. Most of the tools required to achieve this have been introduced in the previous examples. The only one missing is the ability to use, at iteration $i$, outputs from iterations older than $i-1$.\\n\",\n    \"\\n\",\n    \"Also, since every example so far had only one output at every iteration of the loop, we will also compute, at each timestep, the ratio between the new term of the Fibonacci sequence and the previous term.\\n\",\n    \"\\n\",\n    \"Writing an appropriate step function given two inputs, representing the two previous terms of the Fibonacci sequence, is easy:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def step(f_minus2, f_minus1):\\n\",\n    \"    new_f = f_minus2 + f_minus1\\n\",\n    \"    ratio = new_f / f_minus1\\n\",\n    \"    return new_f, ratio\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The next step is defining the value of `outputs_info`.\\n\",\n    \"\\n\",\n    \"Recall that, for **non-recurrent outputs**, the value is `None` and, for **simple recurrent outputs**, the value is a single initial state. For **general recurrent outputs**, where iteration $t$ may depend on multiple past values, the value is a dictionary. That dictionary has two values:\\n\",\n    \"* taps : list declaring which previous values of that output every iteration will need. `[-3, -2, -1]` would mean every iteration should take as input the last 3 values of that output. `[-2]` would mean every iteration should take as input the value of that output from two iterations ago.\\n\",\n    \"* initial : tensor of initial values. If every initial value has $n$ dimensions, `initial` will be a single tensor of $n+1$ dimensions with as many initial values as the oldest requested tap. In the case of the Fibonacci sequence, the individual initial values are scalars so the `initial` will be a vector. \\n\",\n    \"\\n\",\n    \"In our example, we have two outputs. The first output is the next computed term of the Fibonacci sequence so every iteration should take as input the two last values of that output. The second output is the ratio between successive terms and we don't reuse its value so this output is non-recurrent. We define the value of `outputs_info` as such :\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"f_init = T.fvector()\\n\",\n    \"outputs_info = [dict(initial=f_init, taps=[-2, -1]),\\n\",\n    \"                None]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now that we've defined the step function and the properties of our outputs, we can call the `scan()` function. Because the `step()` function has multiple outputs, the first output of `scan()` function will be a list of tensors: the first tensor containing all the states of the first output and the second tensor containing all the states of the second input.\\n\",\n    \"\\n\",\n    \"In every previous example, we used sequences and Scan automatically inferred the number of iterations it needed to run from the length of these\\n\",\n    \"sequences. Now that we have no sequence, we need to explicitly tell Scan how many iterations to run using the `n_step` parameter. The value can be real or symbolic.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"output, updates = theano.scan(fn=step,\\n\",\n    \"                              outputs_info=outputs_info,\\n\",\n    \"                              n_steps=10)\\n\",\n    \"\\n\",\n    \"next_fibonacci_terms = output[0]\\n\",\n    \"ratios_between_terms = output[1]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's compile our Theano function which will take a vector of consecutive values from the Fibonacci sequence and compute the next 10 values :\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"f = theano.function(inputs=[f_init],\\n\",\n    \"                    outputs=[next_fibonacci_terms, ratios_between_terms],\\n\",\n    \"                    updates=updates)\\n\",\n    \"\\n\",\n    \"out = f([1, 1])\\n\",\n    \"print(out[0])\\n\",\n    \"print(out[1])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Precisions about the order of the arguments to the step function\\n\",\n    \"\\n\",\n    \"When we start using many sequences, recurrent outputs and non-sequences, it's easy to get confused regarding the order in which the step function receives the corresponding inputs. Below is the full order:\\n\",\n    \"\\n\",\n    \"* Element from the first sequence\\n\",\n    \"* ...\\n\",\n    \"* Element from the last sequence\\n\",\n    \"* First requested tap from first recurrent output\\n\",\n    \"* ...\\n\",\n    \"* Last requested tap from first recurrent output\\n\",\n    \"* ...\\n\",\n    \"* First requested tap from last recurrent output\\n\",\n    \"* ...\\n\",\n    \"* Last requested tap from last recurrent output\\n\",\n    \"* First non-sequence\\n\",\n    \"* ...\\n\",\n    \"* Last non-sequence\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## When to use Scan and when not to\\n\",\n    \"\\n\",\n    \"Scan is not appropriate for every problem. Here's some information to help you figure out if Scan is the best solution for a given use case.\\n\",\n    \"\\n\",\n    \"### Execution speed\\n\",\n    \"\\n\",\n    \"Using Scan in a Theano function typically makes it slighly slower compared to the equivalent Theano graph in which the loop is unrolled. Both of these approaches tend to be much slower than a vectorized implementation in which large chunks of the computation can be done in parallel.\\n\",\n    \"\\n\",\n    \"### Compilation speed\\n\",\n    \"\\n\",\n    \"Scan also adds an overhead to the compilation, potentially making it slower, but using it can also dramatically reduce the size of your graph, making compilation much faster. In the end, the effect of Scan on compilation speed will heavily depend on the size of the graph with and without Scan.\\n\",\n    \"\\n\",\n    \"The compilation speed of a Theano function using Scan will usually be comparable to one in which the loop is unrolled if the number of iterations is small. It the number of iterations is large, however, the compilation will usually be much faster with Scan.\\n\",\n    \"\\n\",\n    \"### In summary\\n\",\n    \"\\n\",\n    \"If you have one of the following cases, Scan can help :\\n\",\n    \"* A vectorized implementation is not possible (due to the nature of the computation and/or memory usage)\\n\",\n    \"* You want to do a large or variable number of iterations\\n\",\n    \"\\n\",\n    \"If you have one of the following cases, you should consider other options :\\n\",\n    \"* A vectorized implementation could perform the same computation => Use the vectorized approach. It will often be faster during both compilation and execution.\\n\",\n    \"* You want to do a small, fixed, number of iterations (ex: 2 or 3) => It's probably better to simply unroll the computation\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Exercises\\n\",\n    \"\\n\",\n    \"### Exercise 1 - Computing a polynomial\\n\",\n    \"\\n\",\n    \"In this exercise, the initial version already works. It computes the value of a polynomial ($n_0 + n_1 x + n_2 x^2 + ... $) of at most 10000 degrees given the coefficients of the various terms and the value of x.\\n\",\n    \"\\n\",\n    \"You must modify it such that the reduction (the sum() call) is done by Scan.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"coefficients = theano.tensor.vector(\\\"coefficients\\\")\\n\",\n    \"x = T.scalar(\\\"x\\\")\\n\",\n    \"max_coefficients_supported = 10000\\n\",\n    \"\\n\",\n    \"def step(coeff, power, free_var):\\n\",\n    \"    return coeff * free_var ** power\\n\",\n    \"\\n\",\n    \"# Generate the components of the polynomial\\n\",\n    \"full_range=theano.tensor.arange(max_coefficients_supported)\\n\",\n    \"components, updates = theano.scan(fn=step,\\n\",\n    \"                                  outputs_info=None,\\n\",\n    \"                                  sequences=[coefficients, full_range],\\n\",\n    \"                                  non_sequences=x)\\n\",\n    \"\\n\",\n    \"polynomial = components.sum()\\n\",\n    \"calculate_polynomial = theano.function(inputs=[coefficients, x],\\n\",\n    \"                                       outputs=polynomial,\\n\",\n    \"                                       updates=updates)\\n\",\n    \"\\n\",\n    \"test_coeff = np.asarray([1, 0, 2], dtype=theano.config.floatX)\\n\",\n    \"print(calculate_polynomial(test_coeff, 3))\\n\",\n    \"# 19.0\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Solution** : run the cell below to display the solution to this exercise.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%load scan_ex1_solution.py\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Exercise 2 - Sampling without replacement\\n\",\n    \"\\n\",\n    \"In this exercise, the goal is to implement a Theano function that :\\n\",\n    \"* takes as input a vector of probabilities and a scalar\\n\",\n    \"* performs sampling without replacements from those probabilities as many times as the value of the scalar\\n\",\n    \"* returns a vector containing the indices of the sampled elements.\\n\",\n    \"\\n\",\n    \"Partial code is provided to help with the sampling of random numbers since this is not something that was covered in this tutorial.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"probabilities = T.vector()\\n\",\n    \"nb_samples = T.iscalar()\\n\",\n    \"\\n\",\n    \"rng = T.shared_randomstreams.RandomStreams(1234)\\n\",\n    \"\\n\",\n    \"def sample_from_pvect(pvect):\\n\",\n    \"    \\\"\\\"\\\" Provided utility function: given a symbolic vector of\\n\",\n    \"    probabilities (which MUST sum to 1), sample one element\\n\",\n    \"    and return its index.\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    onehot_sample = rng.multinomial(n=1, pvals=pvect)\\n\",\n    \"    sample = onehot_sample.argmax()\\n\",\n    \"    return sample\\n\",\n    \"\\n\",\n    \"def set_p_to_zero(pvect, i):\\n\",\n    \"    \\\"\\\"\\\" Provided utility function: given a symbolic vector of\\n\",\n    \"    probabilities and an index 'i', set the probability of the\\n\",\n    \"    i-th element to 0 and renormalize the probabilities so they\\n\",\n    \"    sum to 1.\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    new_pvect = T.set_subtensor(pvect[i], 0.)\\n\",\n    \"    new_pvect = new_pvect / new_pvect.sum()\\n\",\n    \"    return new_pvect\\n\",\n    \"    \\n\",\n    \"\\n\",\n    \"# TODO use Scan to sample from the vector of probabilities and\\n\",\n    \"# symbolically obtain 'samples' the vector of sampled indices.\\n\",\n    \"samples = None\\n\",\n    \"\\n\",\n    \"# Compiling the function\\n\",\n    \"f = theano.function(inputs=[probabilities, nb_samples],\\n\",\n    \"                    outputs=[samples])\\n\",\n    \"\\n\",\n    \"# Testing the function\\n\",\n    \"test_probs = np.asarray([0.6, 0.3, 0.1], dtype=theano.config.floatX)\\n\",\n    \"for i in range(10):\\n\",\n    \"    print(f(test_probs, 2))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Solution** : run the cell below to display the solution to this exercise.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%load scan_ex2_solution.py\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.4.3\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "deep-learning/theano-tutorial/theano_mlp/theano_mlp.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Multilayer Perceptron in Theano\\n\",\n    \"\\n\",\n    \"Credits: Forked from [summerschool2015](https://github.com/mila-udem/summerschool2015) by mila-udem\\n\",\n    \"\\n\",\n    \"This notebook describes how to implement the building blocks for a multilayer perceptron in Theano, in particular how to define and combine layers.\\n\",\n    \"\\n\",\n    \"We will continue using the MNIST digits classification dataset, still using Fuel.\\n\",\n    \"\\n\",\n    \"## The Model\\n\",\n    \"We will focus on fully-connected layers, with an elementwise non-linearity on each hidden layer, and a softmax layer (similar to the logistic regression model) for classification on the top layer.\\n\",\n    \"\\n\",\n    \"### A class for hidden layers\\n\",\n    \"This class does all its work in its constructor:\\n\",\n    \"- Create and initialize shared variables for its parameters (`W` and `b`), unless there are explicitly provided. Note that the initialization scheme for `W` is the one described in [Glorot & Bengio (2010)](http://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf).\\n\",\n    \"- Build the Theano expression for the value of the output units, given a variable for the input.\\n\",\n    \"- Store the input, output, and shared parameters as members.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy\\n\",\n    \"import theano\\n\",\n    \"from theano import tensor\\n\",\n    \"\\n\",\n    \"# Set lower precision float, otherwise the notebook will take too long to run\\n\",\n    \"theano.config.floatX = 'float32'\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class HiddenLayer(object):\\n\",\n    \"    def __init__(self, rng, input, n_in, n_out, W=None, b=None,\\n\",\n    \"                 activation=tensor.tanh):\\n\",\n    \"        \\\"\\\"\\\"\\n\",\n    \"        Typical hidden layer of a MLP: units are fully-connected and have\\n\",\n    \"        sigmoidal activation function. Weight matrix W is of shape (n_in,n_out)\\n\",\n    \"        and the bias vector b is of shape (n_out,).\\n\",\n    \"\\n\",\n    \"        NOTE : The nonlinearity used here is tanh\\n\",\n    \"\\n\",\n    \"        Hidden unit activation is given by: tanh(dot(input,W) + b)\\n\",\n    \"\\n\",\n    \"        :type rng: numpy.random.RandomState\\n\",\n    \"        :param rng: a random number generator used to initialize weights\\n\",\n    \"\\n\",\n    \"        :type input: theano.tensor.dmatrix\\n\",\n    \"        :param input: a symbolic tensor of shape (n_examples, n_in)\\n\",\n    \"\\n\",\n    \"        :type n_in: int\\n\",\n    \"        :param n_in: dimensionality of input\\n\",\n    \"\\n\",\n    \"        :type n_out: int\\n\",\n    \"        :param n_out: number of hidden units\\n\",\n    \"\\n\",\n    \"        :type activation: theano.Op or function\\n\",\n    \"        :param activation: Non linearity to be applied in the hidden layer\\n\",\n    \"        \\\"\\\"\\\"\\n\",\n    \"        self.input = input\\n\",\n    \"\\n\",\n    \"        # `W` is initialized with `W_values` which is uniformely sampled\\n\",\n    \"        # from sqrt(-6./(n_in+n_hidden)) and sqrt(6./(n_in+n_hidden))\\n\",\n    \"        # for tanh activation function\\n\",\n    \"        # the output of uniform if converted using asarray to dtype\\n\",\n    \"        # theano.config.floatX so that the code is runable on GPU\\n\",\n    \"        # Note : optimal initialization of weights is dependent on the\\n\",\n    \"        #        activation function used (among other things).\\n\",\n    \"        #        For example, results presented in Glorot & Bengio (2010)\\n\",\n    \"        #        suggest that you should use 4 times larger initial weights\\n\",\n    \"        #        for sigmoid compared to tanh\\n\",\n    \"        if W is None:\\n\",\n    \"            W_values = numpy.asarray(\\n\",\n    \"                rng.uniform(\\n\",\n    \"                    low=-numpy.sqrt(6. / (n_in + n_out)),\\n\",\n    \"                    high=numpy.sqrt(6. / (n_in + n_out)),\\n\",\n    \"                    size=(n_in, n_out)\\n\",\n    \"                ),\\n\",\n    \"                dtype=theano.config.floatX\\n\",\n    \"            )\\n\",\n    \"            if activation == tensor.nnet.sigmoid:\\n\",\n    \"                W_values *= 4\\n\",\n    \"\\n\",\n    \"            W = theano.shared(value=W_values, name='W', borrow=True)\\n\",\n    \"\\n\",\n    \"        if b is None:\\n\",\n    \"            b_values = numpy.zeros((n_out,), dtype=theano.config.floatX)\\n\",\n    \"            b = theano.shared(value=b_values, name='b', borrow=True)\\n\",\n    \"\\n\",\n    \"        self.W = W\\n\",\n    \"        self.b = b\\n\",\n    \"\\n\",\n    \"        lin_output = tensor.dot(input, self.W) + self.b\\n\",\n    \"        self.output = (\\n\",\n    \"            lin_output if activation is None\\n\",\n    \"            else activation(lin_output)\\n\",\n    \"        )\\n\",\n    \"        # parameters of the model\\n\",\n    \"        self.params = [self.W, self.b]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### A softmax class for the output\\n\",\n    \"This class performs computations similar to what was performed in the [logistic regression tutorial](../intro_theano/logistic_regression.ipynb).\\n\",\n    \"\\n\",\n    \"Here as well, the expression for the output is built in the class constructor, which takes the input as argument. We also add the target, `y`, and store it as an argument.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"class LogisticRegression(object):\\n\",\n    \"    \\\"\\\"\\\"Multi-class Logistic Regression Class\\n\",\n    \"\\n\",\n    \"    The logistic regression is fully described by a weight matrix :math:`W`\\n\",\n    \"    and bias vector :math:`b`. Classification is done by projecting data\\n\",\n    \"    points onto a set of hyperplanes, the distance to which is used to\\n\",\n    \"    determine a class membership probability.\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"    def __init__(self, input, target, n_in, n_out):\\n\",\n    \"        \\\"\\\"\\\" Initialize the parameters of the logistic regression\\n\",\n    \"\\n\",\n    \"        :type input: theano.tensor.TensorType\\n\",\n    \"        :param input: symbolic variable that describes the input of the\\n\",\n    \"                      architecture (one minibatch)\\n\",\n    \"        \\n\",\n    \"        :type target: theano.tensor.TensorType\\n\",\n    \"        :type target: column tensor that describes the target for training\\n\",\n    \"\\n\",\n    \"        :type n_in: int\\n\",\n    \"        :param n_in: number of input units, the dimension of the space in\\n\",\n    \"                     which the datapoints lie\\n\",\n    \"\\n\",\n    \"        :type n_out: int\\n\",\n    \"        :param n_out: number of output units, the dimension of the space in\\n\",\n    \"                      which the labels lie\\n\",\n    \"\\n\",\n    \"        \\\"\\\"\\\"\\n\",\n    \"        # keep track of model input and target.\\n\",\n    \"        # We store a flattened (vector) version of target as y, which is easier to handle\\n\",\n    \"        self.input = input\\n\",\n    \"        self.target = target\\n\",\n    \"        self.y = target.flatten()\\n\",\n    \"\\n\",\n    \"        self.W = theano.shared(value=numpy.zeros((n_in, n_out), dtype=theano.config.floatX),\\n\",\n    \"                               name='W',\\n\",\n    \"                               borrow=True)\\n\",\n    \"        self.b = theano.shared(value=numpy.zeros((n_out,), dtype=theano.config.floatX),\\n\",\n    \"                               name='b',\\n\",\n    \"                               borrow=True)\\n\",\n    \"    \\n\",\n    \"        # class-membership probabilities\\n\",\n    \"        self.p_y_given_x = tensor.nnet.softmax(tensor.dot(input, self.W) + self.b)\\n\",\n    \"\\n\",\n    \"        # class whose probability is maximal\\n\",\n    \"        self.y_pred = tensor.argmax(self.p_y_given_x, axis=1)\\n\",\n    \"\\n\",\n    \"        # parameters of the model\\n\",\n    \"        self.params = [self.W, self.b]\\n\",\n    \"        \\n\",\n    \"\\n\",\n    \"    def negative_log_likelihood(self):\\n\",\n    \"        \\\"\\\"\\\"Return the mean of the negative log-likelihood of the prediction\\n\",\n    \"        of this model under a given target distribution.\\n\",\n    \"\\n\",\n    \"        Note: we use the mean instead of the sum so that\\n\",\n    \"              the learning rate is less dependent on the batch size\\n\",\n    \"        \\\"\\\"\\\"\\n\",\n    \"        log_prob = tensor.log(self.p_y_given_x)\\n\",\n    \"        log_likelihood = log_prob[tensor.arange(self.y.shape[0]), self.y]\\n\",\n    \"        loss = - log_likelihood.mean()\\n\",\n    \"        return loss\\n\",\n    \"\\n\",\n    \"    def errors(self):\\n\",\n    \"        \\\"\\\"\\\"Return a float representing the number of errors in the minibatch\\n\",\n    \"        over the total number of examples of the minibatch\\n\",\n    \"        \\\"\\\"\\\"\\n\",\n    \"        misclass_nb = tensor.neq(self.y_pred, self.y)\\n\",\n    \"        misclass_rate = misclass_nb.mean()\\n\",\n    \"        return misclass_rate\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### The MLP class\\n\",\n    \"That class brings together the different parts of the model.\\n\",\n    \"\\n\",\n    \"It also adds additional controls on the training of the full network, for instance an expression for L1 or L2 regularization (weight decay).\\n\",\n    \"\\n\",\n    \"We can specify an arbitrary number of hidden layers, providing an empty one will reproduce the logistic regression model.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"class MLP(object):\\n\",\n    \"    \\\"\\\"\\\"Multi-Layer Perceptron Class\\n\",\n    \"\\n\",\n    \"    A multilayer perceptron is a feedforward artificial neural network model\\n\",\n    \"    that has one layer or more of hidden units and nonlinear activations.\\n\",\n    \"    Intermediate layers usually have as activation function tanh or the\\n\",\n    \"    sigmoid function (defined here by a ``HiddenLayer`` class)  while the\\n\",\n    \"    top layer is a softmax layer (defined here by a ``LogisticRegression``\\n\",\n    \"    class).\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"    def __init__(self, rng, input, target, n_in, n_hidden, n_out, activation=tensor.tanh):\\n\",\n    \"        \\\"\\\"\\\"Initialize the parameters for the multilayer perceptron\\n\",\n    \"\\n\",\n    \"        :type rng: numpy.random.RandomState\\n\",\n    \"        :param rng: a random number generator used to initialize weights\\n\",\n    \"\\n\",\n    \"        :type input: theano.tensor.TensorType\\n\",\n    \"        :param input: symbolic variable that describes the input of the\\n\",\n    \"        architecture (one minibatch)\\n\",\n    \"        \\n\",\n    \"        :type target: theano.tensor.TensorType\\n\",\n    \"        :type target: column tensor that describes the target for training\\n\",\n    \"\\n\",\n    \"        :type n_in: int\\n\",\n    \"        :param n_in: number of input units, the dimension of the space in\\n\",\n    \"        which the datapoints lie\\n\",\n    \"\\n\",\n    \"        :type n_hidden: list of int\\n\",\n    \"        :param n_hidden: number of hidden units in each hidden layer\\n\",\n    \"\\n\",\n    \"        :type n_out: int\\n\",\n    \"        :param n_out: number of output units, the dimension of the space in\\n\",\n    \"        which the labels lie\\n\",\n    \"        \\n\",\n    \"        :type activation: theano.Op or function\\n\",\n    \"        :param activation: Non linearity to be applied in all hidden layers\\n\",\n    \"        \\\"\\\"\\\"\\n\",\n    \"        # keep track of model input and target.\\n\",\n    \"        # We store a flattened (vector) version of target as y, which is easier to handle\\n\",\n    \"        self.input = input\\n\",\n    \"        self.target = target\\n\",\n    \"        self.y = target.flatten()\\n\",\n    \"\\n\",\n    \"        # Build all necessary hidden layers and chain them\\n\",\n    \"        self.hidden_layers = []\\n\",\n    \"        layer_input = input\\n\",\n    \"        layer_n_in = n_in\\n\",\n    \"\\n\",\n    \"        for nh in n_hidden:\\n\",\n    \"            hidden_layer = HiddenLayer(\\n\",\n    \"                rng=rng,\\n\",\n    \"                input=layer_input,\\n\",\n    \"                n_in=layer_n_in,\\n\",\n    \"                n_out=nh,\\n\",\n    \"                activation=activation)\\n\",\n    \"            self.hidden_layers.append(hidden_layer)\\n\",\n    \"\\n\",\n    \"            # prepare variables for next layer\\n\",\n    \"            layer_input = hidden_layer.output\\n\",\n    \"            layer_n_in = nh\\n\",\n    \"\\n\",\n    \"        # The logistic regression layer gets as input the hidden units of the hidden layer,\\n\",\n    \"        # and the target\\n\",\n    \"        self.log_reg_layer = LogisticRegression(\\n\",\n    \"            input=layer_input,\\n\",\n    \"            target=target,\\n\",\n    \"            n_in=layer_n_in,\\n\",\n    \"            n_out=n_out)\\n\",\n    \"        \\n\",\n    \"        # self.params has all the parameters of the model,\\n\",\n    \"        # self.weights contains only the `W` variables.\\n\",\n    \"        # We also give unique name to the parameters, this will be useful to save them.\\n\",\n    \"        self.params = []\\n\",\n    \"        self.weights = []\\n\",\n    \"        layer_idx = 0\\n\",\n    \"        for hl in self.hidden_layers:\\n\",\n    \"            self.params.extend(hl.params)\\n\",\n    \"            self.weights.append(hl.W)\\n\",\n    \"            for hlp in hl.params:\\n\",\n    \"                prev_name = hlp.name\\n\",\n    \"                hlp.name = 'layer' + str(layer_idx) + '.' + prev_name\\n\",\n    \"            layer_idx += 1\\n\",\n    \"        self.params.extend(self.log_reg_layer.params)\\n\",\n    \"        self.weights.append(self.log_reg_layer.W)\\n\",\n    \"        for lrp in self.log_reg_layer.params:\\n\",\n    \"            prev_name = lrp.name\\n\",\n    \"            lrp.name = 'layer' + str(layer_idx) + '.' + prev_name\\n\",\n    \"\\n\",\n    \"        # L1 norm ; one regularization option is to enforce L1 norm to be small\\n\",\n    \"        self.L1 = sum(abs(W).sum() for W in self.weights)\\n\",\n    \"\\n\",\n    \"        # square of L2 norm ; one regularization option is to enforce square of L2 norm to be small\\n\",\n    \"        self.L2_sqr = sum((W ** 2).sum() for W in self.weights)\\n\",\n    \"    \\n\",\n    \"    def negative_log_likelihood(self):\\n\",\n    \"        # negative log likelihood of the MLP is given by the negative\\n\",\n    \"        # log likelihood of the output of the model, computed in the\\n\",\n    \"        # logistic regression layer\\n\",\n    \"        return self.log_reg_layer.negative_log_likelihood()\\n\",\n    \"\\n\",\n    \"    def errors(self):\\n\",\n    \"        # same holds for the function computing the number of errors\\n\",\n    \"        return self.log_reg_layer.errors()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Training Procedure\\n\",\n    \"We will re-use the same training algorithm: stochastic gradient descent with mini-batches, and the same early-stopping criterion. Here, the number of parameters to train is variable, and we have to wait until the MLP model is actually instantiated to have an expression for the cost and the updates.\\n\",\n    \"\\n\",\n    \"### Gradient and Updates\\n\",\n    \"Let us define helper functions for getting expressions for the gradient of the cost wrt the parameters, and the parameter updates. The following ones are simple, but many variations can exist, for instance:\\n\",\n    \"- regularized costs, including L1 or L2 regularization\\n\",\n    \"- more complex learning rules, such as momentum, RMSProp, ADAM, ...\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def nll_grad(mlp_model):\\n\",\n    \"    loss = mlp_model.negative_log_likelihood()\\n\",\n    \"    params = mlp_model.params\\n\",\n    \"    grads = theano.grad(loss, wrt=params)\\n\",\n    \"    # Return (param, grad) pairs\\n\",\n    \"    return zip(params, grads)\\n\",\n    \"\\n\",\n    \"def sgd_updates(params_and_grads, learning_rate):\\n\",\n    \"    return [(param, param - learning_rate * grad)\\n\",\n    \"            for param, grad in params_and_grads]\\n\",\n    \"\\n\",\n    \"def get_simple_training_fn(mlp_model, learning_rate):\\n\",\n    \"    inputs = [mlp_model.input, mlp_model.target]\\n\",\n    \"    params_and_grads = nll_grad(mlp_model)\\n\",\n    \"    updates = sgd_updates(params_and_grads, learning_rate=lr)\\n\",\n    \"    \\n\",\n    \"    return theano.function(inputs=inputs, outputs=[], updates=updates)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def regularized_cost_grad(mlp_model, L1_reg, L2_reg):\\n\",\n    \"    loss = (mlp_model.negative_log_likelihood() +\\n\",\n    \"            L1_reg * mlp_model.L1 + \\n\",\n    \"            L2_reg * mlp_model.L2_sqr)\\n\",\n    \"    params = mlp_model.params\\n\",\n    \"    grads = theano.grad(loss, wrt=params)\\n\",\n    \"    # Return (param, grad) pairs\\n\",\n    \"    return zip(params, grads)\\n\",\n    \"\\n\",\n    \"def get_regularized_training_fn(mlp_model, L1_reg, L2_reg, learning_rate):\\n\",\n    \"    inputs = [mlp_model.input, mlp_model.target]\\n\",\n    \"    params_and_grads = regularized_cost_grad(mlp_model, L1_reg, L2_reg)\\n\",\n    \"    updates = sgd_updates(params_and_grads, learning_rate=lr)\\n\",\n    \"    return theano.function(inputs, updates=updates)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Testing function\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def get_test_fn(mlp_model):\\n\",\n    \"    return theano.function([mlp_model.input, mlp_model.target], mlp_model.errors())\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Training the Model\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Training procedure\\n\",\n    \"We first need to define a few parameters for the training loop and the early stopping procedure.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import timeit\\n\",\n    \"from fuel.streams import DataStream\\n\",\n    \"from fuel.schemes import SequentialScheme\\n\",\n    \"from fuel.transformers import Flatten\\n\",\n    \"\\n\",\n    \"## early-stopping parameters tuned for 1-2 min runtime\\n\",\n    \"def sgd_training(train_model, test_model, train_set, valid_set, test_set, model_name='mlp_model',\\n\",\n    \"                 # maximum number of epochs\\n\",\n    \"                 n_epochs=20,\\n\",\n    \"                 # look at this many examples regardless\\n\",\n    \"                 patience=5000,\\n\",\n    \"                 # wait this much longer when a new best is found\\n\",\n    \"                 patience_increase=2,\\n\",\n    \"                 # a relative improvement of this much is considered significant\\n\",\n    \"                 improvement_threshold=0.995,\\n\",\n    \"                 batch_size=20):\\n\",\n    \"\\n\",\n    \"    n_train_batches = train_set.num_examples // batch_size\\n\",\n    \"\\n\",\n    \"    # Create data streams to iterate through the data.\\n\",\n    \"    train_stream = Flatten(DataStream.default_stream(\\n\",\n    \"        train_set, iteration_scheme=SequentialScheme(train_set.num_examples, batch_size)))\\n\",\n    \"    valid_stream = Flatten(DataStream.default_stream(\\n\",\n    \"        valid_set, iteration_scheme=SequentialScheme(valid_set.num_examples, batch_size)))\\n\",\n    \"    test_stream = Flatten(DataStream.default_stream(\\n\",\n    \"        test_set, iteration_scheme=SequentialScheme(test_set.num_examples, batch_size)))\\n\",\n    \"\\n\",\n    \"    # go through this many minibatches before checking the network on the validation set;\\n\",\n    \"    # in this case we check every epoch\\n\",\n    \"    validation_frequency = min(n_train_batches, patience / 2)\\n\",\n    \"\\n\",\n    \"    best_validation_loss = numpy.inf\\n\",\n    \"    test_score = 0.\\n\",\n    \"    start_time = timeit.default_timer()\\n\",\n    \"\\n\",\n    \"    done_looping = False\\n\",\n    \"    epoch = 0\\n\",\n    \"    while (epoch < n_epochs) and (not done_looping):\\n\",\n    \"        epoch = epoch + 1\\n\",\n    \"        minibatch_index = 0\\n\",\n    \"        for minibatch_x, minibatch_y in train_stream.get_epoch_iterator():\\n\",\n    \"            train_model(minibatch_x, minibatch_y)\\n\",\n    \"\\n\",\n    \"            # iteration number\\n\",\n    \"            iter = (epoch - 1) * n_train_batches + minibatch_index\\n\",\n    \"            if (iter + 1) % validation_frequency == 0:\\n\",\n    \"                # compute zero-one loss on validation set\\n\",\n    \"                validation_losses = []\\n\",\n    \"                for valid_xi, valid_yi in valid_stream.get_epoch_iterator():\\n\",\n    \"                    validation_losses.append(test_model(valid_xi, valid_yi))\\n\",\n    \"                this_validation_loss = numpy.mean(validation_losses)\\n\",\n    \"                print('epoch %i, minibatch %i/%i, validation error %f %%' %\\n\",\n    \"                      (epoch,\\n\",\n    \"                       minibatch_index + 1,\\n\",\n    \"                       n_train_batches,\\n\",\n    \"                       this_validation_loss * 100.))\\n\",\n    \"\\n\",\n    \"                # if we got the best validation score until now\\n\",\n    \"                if this_validation_loss < best_validation_loss:\\n\",\n    \"                    # improve patience if loss improvement is good enough\\n\",\n    \"                    if this_validation_loss < best_validation_loss * improvement_threshold:\\n\",\n    \"                        patience = max(patience, iter * patience_increase)\\n\",\n    \"\\n\",\n    \"                    best_validation_loss = this_validation_loss\\n\",\n    \"\\n\",\n    \"                    # test it on the test set\\n\",\n    \"                    test_losses = []\\n\",\n    \"                    for test_xi, test_yi in test_stream.get_epoch_iterator():\\n\",\n    \"                        test_losses.append(test_model(test_xi, test_yi))\\n\",\n    \"\\n\",\n    \"                    test_score = numpy.mean(test_losses)\\n\",\n    \"                    print('     epoch %i, minibatch %i/%i, test error of best model %f %%' %\\n\",\n    \"                          (epoch,\\n\",\n    \"                           minibatch_index + 1,\\n\",\n    \"                           n_train_batches,\\n\",\n    \"                           test_score * 100.))\\n\",\n    \"\\n\",\n    \"                    # save the best parameters\\n\",\n    \"                    # build a name -> value dictionary\\n\",\n    \"                    best = {param.name: param.get_value() for param in mlp_model.params}\\n\",\n    \"                    numpy.savez('best_{}.npz'.format(model_name), **best)\\n\",\n    \"\\n\",\n    \"            minibatch_index += 1\\n\",\n    \"            if patience <= iter:\\n\",\n    \"                done_looping = True\\n\",\n    \"                break\\n\",\n    \"\\n\",\n    \"    end_time = timeit.default_timer()\\n\",\n    \"    print('Optimization complete with best validation score of %f %%, '\\n\",\n    \"          'with test performance %f %%' %\\n\",\n    \"          (best_validation_loss * 100., test_score * 100.))\\n\",\n    \"\\n\",\n    \"    print('The code ran for %d epochs, with %f epochs/sec (%.2fm total time)' %\\n\",\n    \"          (epoch, 1. * epoch / (end_time - start_time), (end_time - start_time) / 60.))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We then load our data set.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from fuel.datasets import MNIST\\n\",\n    \"\\n\",\n    \"# the full set is usually (0, 50000) for train, (50000, 60000) for valid and no slice for test.\\n\",\n    \"# We only selected a subset to go faster.\\n\",\n    \"train_set = MNIST(which_sets=('train',), sources=('features', 'targets'), subset=slice(0, 20000))\\n\",\n    \"valid_set = MNIST(which_sets=('train',), sources=('features', 'targets'), subset=slice(20000, 24000))\\n\",\n    \"test_set = MNIST(which_sets=('test',), sources=('features', 'targets'))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Build the Model\\n\",\n    \"Now is the time to specify and build a particular instance of the MLP. Let's start with one with a single hidden layer of 500 hidden units, and a tanh non-linearity.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"rng = numpy.random.RandomState(1234)\\n\",\n    \"x = tensor.matrix('x')\\n\",\n    \"# The labels coming from Fuel are in a \\\"column\\\" format\\n\",\n    \"y = tensor.icol('y')\\n\",\n    \"\\n\",\n    \"n_in = 28 * 28\\n\",\n    \"n_out = 10\\n\",\n    \"\\n\",\n    \"mlp_model = MLP(\\n\",\n    \"    rng=rng,\\n\",\n    \"    input=x,\\n\",\n    \"    target=y,\\n\",\n    \"    n_in=n_in,\\n\",\n    \"    n_hidden=[500],\\n\",\n    \"    n_out=n_out,\\n\",\n    \"    activation=tensor.tanh)\\n\",\n    \"\\n\",\n    \"lr = numpy.float32(0.1)\\n\",\n    \"L1_reg = numpy.float32(0)\\n\",\n    \"L2_reg = numpy.float32(0.0001)\\n\",\n    \"\\n\",\n    \"train_model = get_regularized_training_fn(mlp_model, L1_reg, L2_reg, lr)\\n\",\n    \"test_model = get_test_fn(mlp_model)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Launch the training phase\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"sgd_training(train_model, test_model, train_set, valid_set, test_set)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## How can we make it better?\\n\",\n    \"\\n\",\n    \"- Max-column normalization\\n\",\n    \"- Dropout\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### ReLU activation\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def relu(x):\\n\",\n    \"    return x * (x > 0)\\n\",\n    \"\\n\",\n    \"rng = numpy.random.RandomState(1234)\\n\",\n    \"\\n\",\n    \"mlp_relu = MLP(\\n\",\n    \"    rng=rng,\\n\",\n    \"    input=x,\\n\",\n    \"    target=y,\\n\",\n    \"    n_in=n_in,\\n\",\n    \"    n_hidden=[500],\\n\",\n    \"    n_out=n_out,\\n\",\n    \"    activation=relu)\\n\",\n    \"\\n\",\n    \"lr = numpy.float32(0.1)\\n\",\n    \"L1_reg = numpy.float32(0)\\n\",\n    \"L2_reg = numpy.float32(0.0001)\\n\",\n    \"\\n\",\n    \"train_relu = get_regularized_training_fn(mlp_relu, L1_reg, L2_reg, lr)\\n\",\n    \"test_relu = get_test_fn(mlp_relu)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"sgd_training(train_relu, test_relu, train_set, valid_set, test_set, model_name='mlp_relu')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Momentum training (Adadelta, RMSProp, ...)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# This implements simple momentum\\n\",\n    \"def get_momentum_updates(params_and_grads, lr, rho):\\n\",\n    \"    res = []\\n\",\n    \"\\n\",\n    \"    # numpy will promote (1 - rho) to float64 otherwise\\n\",\n    \"    one = numpy.float32(1.)\\n\",\n    \"    \\n\",\n    \"    for p, g in params_and_grads:\\n\",\n    \"        up = theano.shared(p.get_value() * 0)\\n\",\n    \"        res.append((p, p - lr * up))\\n\",\n    \"        res.append((up, rho * up + (one - rho) * g))\\n\",\n    \"\\n\",\n    \"    return res\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# This implements the parameter updates for Adadelta\\n\",\n    \"def get_adadelta_updates(params_and_grads, rho):\\n\",\n    \"    up2 = [theano.shared(p.get_value() * 0, name=\\\"up2 for \\\" + p.name) for p, g in params_and_grads]\\n\",\n    \"    grads2 = [theano.shared(p.get_value() * 0, name=\\\"grads2 for \\\" + p.name) for p, g in params_and_grads]\\n\",\n    \"\\n\",\n    \"    # This is dumb but numpy will promote (1 - rho) to float64 otherwise\\n\",\n    \"    one = numpy.float32(1.)\\n\",\n    \"    \\n\",\n    \"    rg2up = [(rg2, rho * rg2 + (one - rho) * (g ** 2))\\n\",\n    \"             for rg2, (p, g) in zip(grads2, params_and_grads)]\\n\",\n    \"\\n\",\n    \"    updir = [-(tensor.sqrt(ru2 + 1e-6) / tensor.sqrt(rg2 + 1e-6)) * g\\n\",\n    \"             for (p, g), ru2, rg2 in zip(params_and_grads, up2, grads2)]\\n\",\n    \"\\n\",\n    \"    ru2up = [(ru2, rho * ru2 + (one - rho) * (ud ** 2))\\n\",\n    \"             for ru2, ud in zip(up2, updir)]\\n\",\n    \"\\n\",\n    \"    param_up = [(p, p + ud) for (p, g), ud in zip(params_and_grads, updir)]\\n\",\n    \"    \\n\",\n    \"    return rg2up + ru2up + param_up\\n\",\n    \"\\n\",\n    \"# You can try to write an RMSProp function and train the model with it.\\n\",\n    \"\\n\",\n    \"def get_momentum_training_fn(mlp_model, L1_reg, L2_reg, lr, rho):\\n\",\n    \"    inputs = [mlp_model.input, mlp_model.target]\\n\",\n    \"    params_and_grads = regularized_cost_grad(mlp_model, L1_reg, L2_reg)\\n\",\n    \"    updates = get_momentum_updates(params_and_grads, lr=lr, rho=rho)\\n\",\n    \"    return theano.function(inputs, updates=updates)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"rng = numpy.random.RandomState(1234)\\n\",\n    \"x = tensor.matrix('x')\\n\",\n    \"# The labels coming from Fuel are in a \\\"column\\\" format\\n\",\n    \"y = tensor.icol('y')\\n\",\n    \"\\n\",\n    \"n_in = 28 * 28\\n\",\n    \"n_out = 10\\n\",\n    \"\\n\",\n    \"mlp_model = MLP(\\n\",\n    \"    rng=rng,\\n\",\n    \"    input=x,\\n\",\n    \"    target=y,\\n\",\n    \"    n_in=n_in,\\n\",\n    \"    n_hidden=[500],\\n\",\n    \"    n_out=n_out,\\n\",\n    \"    activation=tensor.tanh)\\n\",\n    \"\\n\",\n    \"lr = numpy.float32(0.1)\\n\",\n    \"L1_reg = numpy.float32(0)\\n\",\n    \"L2_reg = numpy.float32(0.0001)\\n\",\n    \"rho = numpy.float32(0.95)\\n\",\n    \"\\n\",\n    \"momentum_train = get_momentum_training_fn(mlp_model, L1_reg, L2_reg, lr=lr, rho=rho)\\n\",\n    \"test_fn = get_test_fn(mlp_model)\\n\",\n    \"\\n\",\n    \"sgd_training(momentum_train, test_fn, train_set, valid_set, test_set, n_epochs=20, model_name='mlp_momentum')\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.4.3\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "kaggle/__init__.py",
    "content": ""
  },
  {
    "path": "kaggle/titanic.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This notebook was prepared by [Donne Martin](http://donnemartin.com). Source and license info is on [GitHub](https://github.com/donnemartin/data-science-ipython-notebooks).\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Kaggle Machine Learning Competition: Predicting Titanic Survivors\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"* Competition Site\\n\",\n    \"* Description\\n\",\n    \"* Evaluation\\n\",\n    \"* Data Set\\n\",\n    \"* Setup Imports and Variables\\n\",\n    \"* Explore the Data\\n\",\n    \"* Feature: Passenger Classes\\n\",\n    \"* Feature: Sex\\n\",\n    \"* Feature: Embarked\\n\",\n    \"* Feature: Age\\n\",\n    \"* Feature: Family Size\\n\",\n    \"* Final Data Preparation for Machine Learning\\n\",\n    \"* Data Wrangling Summary\\n\",\n    \"* Random Forest: Training\\n\",\n    \"* Random Forest: Predicting\\n\",\n    \"* Random Forest: Prepare for Kaggle Submission\\n\",\n    \"* Support Vector Machine: Training\\n\",\n    \"* Support Vector Machine: Predicting\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Competition Site\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Description, Evaluation, and Data Set taken from the [competition site](https://www.kaggle.com/c/titanic-gettingStarted).\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Description\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"![alt text](http://upload.wikimedia.org/wikipedia/commons/6/6e/St%C3%B6wer_Titanic.jpg)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The sinking of the RMS Titanic is one of the most infamous shipwrecks in history.  On April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew. This sensational tragedy shocked the international community and led to better safety regulations for ships.\\n\",\n    \"\\n\",\n    \"One of the reasons that the shipwreck led to such loss of life was that there were not enough lifeboats for the passengers and crew. Although there was some element of luck involved in surviving the sinking, some groups of people were more likely to survive than others, such as women, children, and the upper-class.\\n\",\n    \"\\n\",\n    \"In this challenge, we ask you to complete the analysis of what sorts of people were likely to survive. In particular, we ask you to apply the tools of machine learning to predict which passengers survived the tragedy.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Evaluation\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The historical data has been split into two groups, a 'training set' and a 'test set'.  For the training set, we provide the outcome ( 'ground truth' ) for each passenger.  You will use this set to build your model to generate predictions for the test set.\\n\",\n    \"\\n\",\n    \"For each passenger in the test set, you must predict whether or not they survived the sinking ( 0 for deceased, 1 for survived ).  Your score is the percentage of passengers you correctly predict.\\n\",\n    \"\\n\",\n    \" The Kaggle leaderboard has a public and private component.  50% of your predictions for the test set have been randomly assigned to the public leaderboard ( the same 50% for all users ).  Your score on this public portion is what will appear on the leaderboard.  At the end of the contest, we will reveal your score on the private 50% of the data, which will determine the final winner.  This method prevents users from 'overfitting' to the leaderboard.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Data Set\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"| File Name        | Available Formats |\\n\",\n    \"|------------------|-------------------|\\n\",\n    \"| train            | .csv (59.76 kb)   |\\n\",\n    \"| gendermodel      | .csv (3.18 kb)    |\\n\",\n    \"| genderclassmodel | .csv (3.18 kb)    |\\n\",\n    \"| test             | .csv (27.96 kb)   |\\n\",\n    \"| gendermodel      | .py (3.58 kb)     |\\n\",\n    \"| genderclassmodel | .py (5.63 kb)     |\\n\",\n    \"| myfirstforest    | .py (3.99 kb)     |\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<pre>\\n\",\n    \"VARIABLE DESCRIPTIONS:\\n\",\n    \"survival        Survival\\n\",\n    \"                (0 = No; 1 = Yes)\\n\",\n    \"pclass          Passenger Class\\n\",\n    \"                (1 = 1st; 2 = 2nd; 3 = 3rd)\\n\",\n    \"name            Name\\n\",\n    \"sex             Sex\\n\",\n    \"age             Age\\n\",\n    \"sibsp           Number of Siblings/Spouses Aboard\\n\",\n    \"parch           Number of Parents/Children Aboard\\n\",\n    \"ticket          Ticket Number\\n\",\n    \"fare            Passenger Fare\\n\",\n    \"cabin           Cabin\\n\",\n    \"embarked        Port of Embarkation\\n\",\n    \"                (C = Cherbourg; Q = Queenstown; S = Southampton)\\n\",\n    \"\\n\",\n    \"SPECIAL NOTES:\\n\",\n    \"Pclass is a proxy for socio-economic status (SES)\\n\",\n    \" 1st ~ Upper; 2nd ~ Middle; 3rd ~ Lower\\n\",\n    \"\\n\",\n    \"Age is in Years; Fractional if Age less than One (1)\\n\",\n    \" If the Age is Estimated, it is in the form xx.5\\n\",\n    \"\\n\",\n    \"With respect to the family relation variables (i.e. sibsp and parch)\\n\",\n    \"some relations were ignored.  The following are the definitions used\\n\",\n    \"for sibsp and parch.\\n\",\n    \"\\n\",\n    \"Sibling:  Brother, Sister, Stepbrother, or Stepsister of Passenger Aboard Titanic\\n\",\n    \"Spouse:   Husband or Wife of Passenger Aboard Titanic (Mistresses and Fiances Ignored)\\n\",\n    \"Parent:   Mother or Father of Passenger Aboard Titanic\\n\",\n    \"Child:    Son, Daughter, Stepson, or Stepdaughter of Passenger Aboard Titanic\\n\",\n    \"\\n\",\n    \"Other family relatives excluded from this study include cousins,\\n\",\n    \"nephews/nieces, aunts/uncles, and in-laws.  Some children travelled\\n\",\n    \"only with a nanny, therefore parch=0 for them.  As well, some\\n\",\n    \"travelled with very close friends or neighbors in a village, however,\\n\",\n    \"the definitions do not support such relations.\\n\",\n    \"</pre>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Setup Imports and Variables\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import pandas as pd\\n\",\n    \"import numpy as np\\n\",\n    \"import pylab as plt\\n\",\n    \"\\n\",\n    \"# Set the global default size of matplotlib figures\\n\",\n    \"plt.rc('figure', figsize=(10, 5))\\n\",\n    \"\\n\",\n    \"# Size of matplotlib figures that contain subplots\\n\",\n    \"fizsize_with_subplots = (10, 10)\\n\",\n    \"\\n\",\n    \"# Size of matplotlib histogram bins\\n\",\n    \"bin_size = 10\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Explore the Data\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Read the data:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div style=\\\"max-height:1000px;max-width:1500px;overflow:auto;\\\">\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>PassengerId</th>\\n\",\n       \"      <th>Survived</th>\\n\",\n       \"      <th>Pclass</th>\\n\",\n       \"      <th>Name</th>\\n\",\n       \"      <th>Sex</th>\\n\",\n       \"      <th>Age</th>\\n\",\n       \"      <th>SibSp</th>\\n\",\n       \"      <th>Parch</th>\\n\",\n       \"      <th>Ticket</th>\\n\",\n       \"      <th>Fare</th>\\n\",\n       \"      <th>Cabin</th>\\n\",\n       \"      <th>Embarked</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 3</td>\\n\",\n       \"      <td>                           Braund, Mr. Owen Harris</td>\\n\",\n       \"      <td>   male</td>\\n\",\n       \"      <td> 22</td>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td>        A/5 21171</td>\\n\",\n       \"      <td>  7.2500</td>\\n\",\n       \"      <td>  NaN</td>\\n\",\n       \"      <td> S</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td> 2</td>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"      <td> Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\\n\",\n       \"      <td> female</td>\\n\",\n       \"      <td> 38</td>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td>         PC 17599</td>\\n\",\n       \"      <td> 71.2833</td>\\n\",\n       \"      <td>  C85</td>\\n\",\n       \"      <td> C</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td> 3</td>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"      <td> 3</td>\\n\",\n       \"      <td>                            Heikkinen, Miss. Laina</td>\\n\",\n       \"      <td> female</td>\\n\",\n       \"      <td> 26</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> STON/O2. 3101282</td>\\n\",\n       \"      <td>  7.9250</td>\\n\",\n       \"      <td>  NaN</td>\\n\",\n       \"      <td> S</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td> 4</td>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"      <td>      Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\\n\",\n       \"      <td> female</td>\\n\",\n       \"      <td> 35</td>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td>           113803</td>\\n\",\n       \"      <td> 53.1000</td>\\n\",\n       \"      <td> C123</td>\\n\",\n       \"      <td> S</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td> 5</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 3</td>\\n\",\n       \"      <td>                          Allen, Mr. William Henry</td>\\n\",\n       \"      <td>   male</td>\\n\",\n       \"      <td> 35</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td>           373450</td>\\n\",\n       \"      <td>  8.0500</td>\\n\",\n       \"      <td>  NaN</td>\\n\",\n       \"      <td> S</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   PassengerId  Survived  Pclass  \\\\\\n\",\n       \"0            1         0       3   \\n\",\n       \"1            2         1       1   \\n\",\n       \"2            3         1       3   \\n\",\n       \"3            4         1       1   \\n\",\n       \"4            5         0       3   \\n\",\n       \"\\n\",\n       \"                                                Name     Sex  Age  SibSp  \\\\\\n\",\n       \"0                            Braund, Mr. Owen Harris    male   22      1   \\n\",\n       \"1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female   38      1   \\n\",\n       \"2                             Heikkinen, Miss. Laina  female   26      0   \\n\",\n       \"3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female   35      1   \\n\",\n       \"4                           Allen, Mr. William Henry    male   35      0   \\n\",\n       \"\\n\",\n       \"   Parch            Ticket     Fare Cabin Embarked  \\n\",\n       \"0      0         A/5 21171   7.2500   NaN        S  \\n\",\n       \"1      0          PC 17599  71.2833   C85        C  \\n\",\n       \"2      0  STON/O2. 3101282   7.9250   NaN        S  \\n\",\n       \"3      0            113803  53.1000  C123        S  \\n\",\n       \"4      0            373450   8.0500   NaN        S  \"\n      ]\n     },\n     \"execution_count\": 2,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df_train = pd.read_csv('../data/titanic/train.csv')\\n\",\n    \"df_train.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div style=\\\"max-height:1000px;max-width:1500px;overflow:auto;\\\">\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>PassengerId</th>\\n\",\n       \"      <th>Survived</th>\\n\",\n       \"      <th>Pclass</th>\\n\",\n       \"      <th>Name</th>\\n\",\n       \"      <th>Sex</th>\\n\",\n       \"      <th>Age</th>\\n\",\n       \"      <th>SibSp</th>\\n\",\n       \"      <th>Parch</th>\\n\",\n       \"      <th>Ticket</th>\\n\",\n       \"      <th>Fare</th>\\n\",\n       \"      <th>Cabin</th>\\n\",\n       \"      <th>Embarked</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>886</th>\\n\",\n       \"      <td> 887</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 2</td>\\n\",\n       \"      <td>                    Montvila, Rev. Juozas</td>\\n\",\n       \"      <td>   male</td>\\n\",\n       \"      <td> 27</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td>     211536</td>\\n\",\n       \"      <td> 13.00</td>\\n\",\n       \"      <td>  NaN</td>\\n\",\n       \"      <td> S</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>887</th>\\n\",\n       \"      <td> 888</td>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"      <td>             Graham, Miss. Margaret Edith</td>\\n\",\n       \"      <td> female</td>\\n\",\n       \"      <td> 19</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td>     112053</td>\\n\",\n       \"      <td> 30.00</td>\\n\",\n       \"      <td>  B42</td>\\n\",\n       \"      <td> S</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>888</th>\\n\",\n       \"      <td> 889</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 3</td>\\n\",\n       \"      <td> Johnston, Miss. Catherine Helen \\\"Carrie\\\"</td>\\n\",\n       \"      <td> female</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"      <td> 2</td>\\n\",\n       \"      <td> W./C. 6607</td>\\n\",\n       \"      <td> 23.45</td>\\n\",\n       \"      <td>  NaN</td>\\n\",\n       \"      <td> S</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>889</th>\\n\",\n       \"      <td> 890</td>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"      <td>                    Behr, Mr. Karl Howell</td>\\n\",\n       \"      <td>   male</td>\\n\",\n       \"      <td> 26</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td>     111369</td>\\n\",\n       \"      <td> 30.00</td>\\n\",\n       \"      <td> C148</td>\\n\",\n       \"      <td> C</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>890</th>\\n\",\n       \"      <td> 891</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 3</td>\\n\",\n       \"      <td>                      Dooley, Mr. Patrick</td>\\n\",\n       \"      <td>   male</td>\\n\",\n       \"      <td> 32</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td>     370376</td>\\n\",\n       \"      <td>  7.75</td>\\n\",\n       \"      <td>  NaN</td>\\n\",\n       \"      <td> Q</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"     PassengerId  Survived  Pclass                                      Name  \\\\\\n\",\n       \"886          887         0       2                     Montvila, Rev. Juozas   \\n\",\n       \"887          888         1       1              Graham, Miss. Margaret Edith   \\n\",\n       \"888          889         0       3  Johnston, Miss. Catherine Helen \\\"Carrie\\\"   \\n\",\n       \"889          890         1       1                     Behr, Mr. Karl Howell   \\n\",\n       \"890          891         0       3                       Dooley, Mr. Patrick   \\n\",\n       \"\\n\",\n       \"        Sex  Age  SibSp  Parch      Ticket   Fare Cabin Embarked  \\n\",\n       \"886    male   27      0      0      211536  13.00   NaN        S  \\n\",\n       \"887  female   19      0      0      112053  30.00   B42        S  \\n\",\n       \"888  female  NaN      1      2  W./C. 6607  23.45   NaN        S  \\n\",\n       \"889    male   26      0      0      111369  30.00  C148        C  \\n\",\n       \"890    male   32      0      0      370376   7.75   NaN        Q  \"\n      ]\n     },\n     \"execution_count\": 3,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df_train.tail()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"View the data types of each column:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"PassengerId      int64\\n\",\n       \"Survived         int64\\n\",\n       \"Pclass           int64\\n\",\n       \"Name            object\\n\",\n       \"Sex             object\\n\",\n       \"Age            float64\\n\",\n       \"SibSp            int64\\n\",\n       \"Parch            int64\\n\",\n       \"Ticket          object\\n\",\n       \"Fare           float64\\n\",\n       \"Cabin           object\\n\",\n       \"Embarked        object\\n\",\n       \"dtype: object\"\n      ]\n     },\n     \"execution_count\": 4,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df_train.dtypes\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Type 'object' is a string for pandas, which poses problems with machine learning algorithms.  If we want to use these as features, we'll need to convert these to number representations.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Get some basic information on the DataFrame:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"<class 'pandas.core.frame.DataFrame'>\\n\",\n      \"Int64Index: 891 entries, 0 to 890\\n\",\n      \"Data columns (total 12 columns):\\n\",\n      \"PassengerId    891 non-null int64\\n\",\n      \"Survived       891 non-null int64\\n\",\n      \"Pclass         891 non-null int64\\n\",\n      \"Name           891 non-null object\\n\",\n      \"Sex            891 non-null object\\n\",\n      \"Age            714 non-null float64\\n\",\n      \"SibSp          891 non-null int64\\n\",\n      \"Parch          891 non-null int64\\n\",\n      \"Ticket         891 non-null object\\n\",\n      \"Fare           891 non-null float64\\n\",\n      \"Cabin          204 non-null object\\n\",\n      \"Embarked       889 non-null object\\n\",\n      \"dtypes: float64(2), int64(5), object(5)\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"df_train.info()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Age, Cabin, and Embarked are missing values.  Cabin has too many missing values, whereas we might be able to infer values for Age and Embarked.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Generate various descriptive statistics on the DataFrame:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div style=\\\"max-height:1000px;max-width:1500px;overflow:auto;\\\">\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>PassengerId</th>\\n\",\n       \"      <th>Survived</th>\\n\",\n       \"      <th>Pclass</th>\\n\",\n       \"      <th>Age</th>\\n\",\n       \"      <th>SibSp</th>\\n\",\n       \"      <th>Parch</th>\\n\",\n       \"      <th>Fare</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>count</th>\\n\",\n       \"      <td> 891.000000</td>\\n\",\n       \"      <td> 891.000000</td>\\n\",\n       \"      <td> 891.000000</td>\\n\",\n       \"      <td> 714.000000</td>\\n\",\n       \"      <td> 891.000000</td>\\n\",\n       \"      <td> 891.000000</td>\\n\",\n       \"      <td> 891.000000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>mean</th>\\n\",\n       \"      <td> 446.000000</td>\\n\",\n       \"      <td>   0.383838</td>\\n\",\n       \"      <td>   2.308642</td>\\n\",\n       \"      <td>  29.699118</td>\\n\",\n       \"      <td>   0.523008</td>\\n\",\n       \"      <td>   0.381594</td>\\n\",\n       \"      <td>  32.204208</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>std</th>\\n\",\n       \"      <td> 257.353842</td>\\n\",\n       \"      <td>   0.486592</td>\\n\",\n       \"      <td>   0.836071</td>\\n\",\n       \"      <td>  14.526497</td>\\n\",\n       \"      <td>   1.102743</td>\\n\",\n       \"      <td>   0.806057</td>\\n\",\n       \"      <td>  49.693429</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>min</th>\\n\",\n       \"      <td>   1.000000</td>\\n\",\n       \"      <td>   0.000000</td>\\n\",\n       \"      <td>   1.000000</td>\\n\",\n       \"      <td>   0.420000</td>\\n\",\n       \"      <td>   0.000000</td>\\n\",\n       \"      <td>   0.000000</td>\\n\",\n       \"      <td>   0.000000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>25%</th>\\n\",\n       \"      <td> 223.500000</td>\\n\",\n       \"      <td>   0.000000</td>\\n\",\n       \"      <td>   2.000000</td>\\n\",\n       \"      <td>  20.125000</td>\\n\",\n       \"      <td>   0.000000</td>\\n\",\n       \"      <td>   0.000000</td>\\n\",\n       \"      <td>   7.910400</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>50%</th>\\n\",\n       \"      <td> 446.000000</td>\\n\",\n       \"      <td>   0.000000</td>\\n\",\n       \"      <td>   3.000000</td>\\n\",\n       \"      <td>  28.000000</td>\\n\",\n       \"      <td>   0.000000</td>\\n\",\n       \"      <td>   0.000000</td>\\n\",\n       \"      <td>  14.454200</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>75%</th>\\n\",\n       \"      <td> 668.500000</td>\\n\",\n       \"      <td>   1.000000</td>\\n\",\n       \"      <td>   3.000000</td>\\n\",\n       \"      <td>  38.000000</td>\\n\",\n       \"      <td>   1.000000</td>\\n\",\n       \"      <td>   0.000000</td>\\n\",\n       \"      <td>  31.000000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>max</th>\\n\",\n       \"      <td> 891.000000</td>\\n\",\n       \"      <td>   1.000000</td>\\n\",\n       \"      <td>   3.000000</td>\\n\",\n       \"      <td>  80.000000</td>\\n\",\n       \"      <td>   8.000000</td>\\n\",\n       \"      <td>   6.000000</td>\\n\",\n       \"      <td> 512.329200</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"       PassengerId    Survived      Pclass         Age       SibSp  \\\\\\n\",\n       \"count   891.000000  891.000000  891.000000  714.000000  891.000000   \\n\",\n       \"mean    446.000000    0.383838    2.308642   29.699118    0.523008   \\n\",\n       \"std     257.353842    0.486592    0.836071   14.526497    1.102743   \\n\",\n       \"min       1.000000    0.000000    1.000000    0.420000    0.000000   \\n\",\n       \"25%     223.500000    0.000000    2.000000   20.125000    0.000000   \\n\",\n       \"50%     446.000000    0.000000    3.000000   28.000000    0.000000   \\n\",\n       \"75%     668.500000    1.000000    3.000000   38.000000    1.000000   \\n\",\n       \"max     891.000000    1.000000    3.000000   80.000000    8.000000   \\n\",\n       \"\\n\",\n       \"            Parch        Fare  \\n\",\n       \"count  891.000000  891.000000  \\n\",\n       \"mean     0.381594   32.204208  \\n\",\n       \"std      0.806057   49.693429  \\n\",\n       \"min      0.000000    0.000000  \\n\",\n       \"25%      0.000000    7.910400  \\n\",\n       \"50%      0.000000   14.454200  \\n\",\n       \"75%      0.000000   31.000000  \\n\",\n       \"max      6.000000  512.329200  \"\n      ]\n     },\n     \"execution_count\": 6,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df_train.describe()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now that we have a general idea of the data set contents, we can dive deeper into each column.  We'll be doing exploratory data analysis and cleaning data to setup 'features' we'll be using in our machine learning algorithms.\\n\",\n    \"\\n\",\n    \"Plot a few features to get a better idea of each:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.text.Text at 0x10af2a5d0>\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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91evuNApu5jAUCfSsvMGioiFvPB1J113pH/3iXp46RTdVvmW3+B6zCz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\\nbkiZmZmZdckNKTMzM7Mu/X+AP0780h2GHQAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10a9ede50>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Set up a grid of plots\\n\",\n    \"fig = plt.figure(figsize=fizsize_with_subplots) \\n\",\n    \"fig_dims = (3, 2)\\n\",\n    \"\\n\",\n    \"# Plot death and survival counts\\n\",\n    \"plt.subplot2grid(fig_dims, (0, 0))\\n\",\n    \"df_train['Survived'].value_counts().plot(kind='bar', \\n\",\n    \"                                         title='Death and Survival Counts')\\n\",\n    \"\\n\",\n    \"# Plot Pclass counts\\n\",\n    \"plt.subplot2grid(fig_dims, (0, 1))\\n\",\n    \"df_train['Pclass'].value_counts().plot(kind='bar', \\n\",\n    \"                                       title='Passenger Class Counts')\\n\",\n    \"\\n\",\n    \"# Plot Sex counts\\n\",\n    \"plt.subplot2grid(fig_dims, (1, 0))\\n\",\n    \"df_train['Sex'].value_counts().plot(kind='bar', \\n\",\n    \"                                    title='Gender Counts')\\n\",\n    \"plt.xticks(rotation=0)\\n\",\n    \"\\n\",\n    \"# Plot Embarked counts\\n\",\n    \"plt.subplot2grid(fig_dims, (1, 1))\\n\",\n    \"df_train['Embarked'].value_counts().plot(kind='bar', \\n\",\n    \"                                         title='Ports of Embarkation Counts')\\n\",\n    \"\\n\",\n    \"# Plot the Age histogram\\n\",\n    \"plt.subplot2grid(fig_dims, (2, 0))\\n\",\n    \"df_train['Age'].hist()\\n\",\n    \"plt.title('Age Histogram')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Next we'll explore various features to view their impact on survival rates.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Feature: Passenger Classes\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"From our exploratory data analysis in the previous section, we see there are three passenger classes: First, Second, and Third class.  We'll determine which proportion of passengers survived based on their passenger class.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Generate a cross tab of Pclass and Survived:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div style=\\\"max-height:1000px;max-width:1500px;overflow:auto;\\\">\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th>Survived</th>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <th>1</th>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>Pclass</th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>  80</td>\\n\",\n       \"      <td> 136</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>  97</td>\\n\",\n       \"      <td>  87</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td> 372</td>\\n\",\n       \"      <td> 119</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"Survived    0    1\\n\",\n       \"Pclass            \\n\",\n       \"1          80  136\\n\",\n       \"2          97   87\\n\",\n       \"3         372  119\"\n      ]\n     },\n     \"execution_count\": 8,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"pclass_xt = pd.crosstab(df_train['Pclass'], df_train['Survived'])\\n\",\n    \"pclass_xt\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Plot the cross tab:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.text.Text at 0x10a9eda10>\"\n      ]\n     },\n     \"execution_count\": 9,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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8wzp9w+InjlK1/JU57yFAAOP/xwPve5zwFw3nnncdhhhz3uPYsWLeLc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\\nnk7w9PBiAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10a9abf90>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Normalize the cross tab to sum to 1:\\n\",\n    \"pclass_xt_pct = pclass_xt.div(pclass_xt.sum(1).astype(float), axis=0)\\n\",\n    \"\\n\",\n    \"pclass_xt_pct.plot(kind='bar', \\n\",\n    \"                   stacked=True, \\n\",\n    \"                   title='Survival Rate by Passenger Classes')\\n\",\n    \"plt.xlabel('Passenger Class')\\n\",\n    \"plt.ylabel('Survival Rate')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We can see that passenger class seems to have a significant impact on whether a passenger survived.  Those in First Class the highest chance for survival.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Feature: Sex\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Gender might have also played a role in determining a passenger's survival rate.  We'll need to map Sex from a string to a number to prepare it for machine learning algorithms.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Generate a mapping of Sex from a string to a number representation:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"{'female': 0, 'male': 1}\"\n      ]\n     },\n     \"execution_count\": 10,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"sexes = sorted(df_train['Sex'].unique())\\n\",\n    \"genders_mapping = dict(zip(sexes, range(0, len(sexes) + 1)))\\n\",\n    \"genders_mapping\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Transform Sex from a string to a number representation:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div style=\\\"max-height:1000px;max-width:1500px;overflow:auto;\\\">\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>PassengerId</th>\\n\",\n       \"      <th>Survived</th>\\n\",\n       \"      <th>Pclass</th>\\n\",\n       \"      <th>Name</th>\\n\",\n       \"      <th>Sex</th>\\n\",\n       \"      <th>Age</th>\\n\",\n       \"      <th>SibSp</th>\\n\",\n       \"      <th>Parch</th>\\n\",\n       \"      <th>Ticket</th>\\n\",\n       \"      <th>Fare</th>\\n\",\n       \"      <th>Cabin</th>\\n\",\n       \"      <th>Embarked</th>\\n\",\n       \"      <th>Sex_Val</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 3</td>\\n\",\n       \"      <td>                           Braund, Mr. Owen Harris</td>\\n\",\n       \"      <td>   male</td>\\n\",\n       \"      <td> 22</td>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td>        A/5 21171</td>\\n\",\n       \"      <td>  7.2500</td>\\n\",\n       \"      <td>  NaN</td>\\n\",\n       \"      <td> S</td>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td> 2</td>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"      <td> Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\\n\",\n       \"      <td> female</td>\\n\",\n       \"      <td> 38</td>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td>         PC 17599</td>\\n\",\n       \"      <td> 71.2833</td>\\n\",\n       \"      <td>  C85</td>\\n\",\n       \"      <td> C</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td> 3</td>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"      <td> 3</td>\\n\",\n       \"      <td>                            Heikkinen, Miss. Laina</td>\\n\",\n       \"      <td> female</td>\\n\",\n       \"      <td> 26</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> STON/O2. 3101282</td>\\n\",\n       \"      <td>  7.9250</td>\\n\",\n       \"      <td>  NaN</td>\\n\",\n       \"      <td> S</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td> 4</td>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"      <td>      Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\\n\",\n       \"      <td> female</td>\\n\",\n       \"      <td> 35</td>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td>           113803</td>\\n\",\n       \"      <td> 53.1000</td>\\n\",\n       \"      <td> C123</td>\\n\",\n       \"      <td> S</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td> 5</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 3</td>\\n\",\n       \"      <td>                          Allen, Mr. William Henry</td>\\n\",\n       \"      <td>   male</td>\\n\",\n       \"      <td> 35</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td>           373450</td>\\n\",\n       \"      <td>  8.0500</td>\\n\",\n       \"      <td>  NaN</td>\\n\",\n       \"      <td> S</td>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   PassengerId  Survived  Pclass  \\\\\\n\",\n       \"0            1         0       3   \\n\",\n       \"1            2         1       1   \\n\",\n       \"2            3         1       3   \\n\",\n       \"3            4         1       1   \\n\",\n       \"4            5         0       3   \\n\",\n       \"\\n\",\n       \"                                                Name     Sex  Age  SibSp  \\\\\\n\",\n       \"0                            Braund, Mr. Owen Harris    male   22      1   \\n\",\n       \"1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female   38      1   \\n\",\n       \"2                             Heikkinen, Miss. Laina  female   26      0   \\n\",\n       \"3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female   35      1   \\n\",\n       \"4                           Allen, Mr. William Henry    male   35      0   \\n\",\n       \"\\n\",\n       \"   Parch            Ticket     Fare Cabin Embarked  Sex_Val  \\n\",\n       \"0      0         A/5 21171   7.2500   NaN        S        1  \\n\",\n       \"1      0          PC 17599  71.2833   C85        C        0  \\n\",\n       \"2      0  STON/O2. 3101282   7.9250   NaN        S        0  \\n\",\n       \"3      0            113803  53.1000  C123        S        0  \\n\",\n       \"4      0            373450   8.0500   NaN        S        1  \"\n      ]\n     },\n     \"execution_count\": 11,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df_train['Sex_Val'] = df_train['Sex'].map(genders_mapping).astype(int)\\n\",\n    \"df_train.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Plot a normalized cross tab for Sex_Val and Survived:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.axes._subplots.AxesSubplot at 0x10a9ddd50>\"\n      ]\n     },\n     \"execution_count\": 12,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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74ESVVwbZkR9tprLzZu3MgNN9ywedvVV1/NggULRnYMw5MkSWqNHXbY\\ngVe/+tW8+93v5v777+db3/oWl1xyCccdd9zIjmF4UvPYlyCpCq4tM8aHPvQhHnjgAZ7+9Kdz7LHH\\n8pGPfIR99tlnZPv3Pk+SJGlEhr8XU5V23nlnPve5z1W2f8OTmudG/A1R0ui5tlQqs8obZDaLl+0k\\nSZJKMDypefzNUFIVXFs0IoYnSZKkEgxPah7vxSKpCq4tGhHDkyRJUgmGJzWPfQmSquDaohExPEmS\\nJJVgeFLz2JcgqQquLRoRw5MkSdpqEVH5n2GceeaZHHDAAWy//fYcf/zxlTxX7zCu5rEvQVIVXFuq\\nN17/vufPn8+73vUuLrvsMh544IFKSjE8SZKk1jjyyCMBWLlyJevWravkGF62U/PYlyCpCq4tM0qV\\nn7U3bXiKiMURsSYiro+Itw14fJeIuDQiroqIH0bEskoqlSRJGtKwPVKPxZThKSJmAWcCi4F9gWMi\\nYp9J004GVmXmIqAD/J+I8HKgHjv7EiRVwbVlRqnyzNN0Iecg4IbMXAsQESuAI4DVfXNuBfYrvt4J\\n+HlmbhxxnZKkssbrLkCqT5VnnqYLT/OBm/rG64AXTJrzMeDrEXELsCNw9OjK04x0I/6GKI1Edb95\\nb5u69C6Q6JGqCxl12LRpEw8++CAbN25k06ZN/PKXv2T27NnMmjVrZMeYrudpmL957wCuysxdgUXA\\nWRGx41ZXJkmSVNKpp57KnDlzOP300/nkJz/Jk570JE477bSRHmO6M083A7v1jXejd/ap328BpwFk\\n5k8i4kZgb2Dl5J0tW7aMsbExAObNm8eiRYvodDoAdLtdgBk33mziXSC7O2b3htXTpHGhKa9fx80e\\nP2xi3HHseMC495oZ9vU1pfHpp1RtfHyc8fHxUt8z8Ry73S5r166ddn5M1VBVNH5fB7wUuAX4HnBM\\nZq7um/MPwN2ZeUpEPAO4EtgvM9dP2ldW2by1rYqIRrzYtA0Yr7YBUu3S6/fw9aJhRKm1JaLc/Kbb\\n0vMptg+8pjnlZbui8ftk4DLgWuCCzFwdESdGxInFtPcCB0TE1cBXgb+aHJykUrwXi6RKdOsuQC0x\\n7S0FMvPLwJcnbTu77+s7gCWjL02SJKl5vMO4msd32kmqRKfuAtQShidJkqQSDE9qHnueJFWiW3cB\\nagk/RkWSJJVS5d27twWGJzWPPU+SKtGpu4BWaNNtCh4rL9tJkiSVYHhS89jzJKkS3boLUEsYniRJ\\nkkowPKl57HmSVIlO3QWoJQxPkiRJJRie1Dz2PEmqRLfuAtQShidJkqQSDE9qHnueJFWiU3cBagnD\\nkyRJUgmGJzWPPU+SKtGtuwC1hOFJkiSpBMOTmseeJ0mV6NRdgFrC8CRJklSC4UnNY8+TpEp06y5A\\nLWF4kiRJKsHwpOax50lSJTp1F6CWMDxJkiSVYHhS89jzJKkS3boLUEsYniRJkkowPKl57HmSVIlO\\n3QWoJQxPkiRJJRie1Dz2PEmqRLfuAtQShidJkqQSDE9qHnueJFWiU3cBagnDkyRJUgmGJzWPPU+S\\nKtGtuwC1hOFJkiSpBMOTmseeJ0mV6NRdgFrC8CRJklSC4UnNY8+TpEp06y5ALWF4kiRJKsHwpOax\\n50lSJTp1F6CWMDxJkiSVYHhS89jzJKkS3boLUEsYniRJkkowPKl57HmSVIlO3QWoJQxPkiRJJRie\\n1Dz2PEmqRLfuAtQShidJkqQSDE9qHnueJFWiU3cBagnDkyRJUgmGJzWPPU+SKtGtuwC1xLThKSIW\\nR8SaiLg+It62hTmdiFgVET+MiO7Iq5QkSWqI2VM9GBGzgDOB3wZuBv4zIi7OzNV9c+YBZwG/k5nr\\nImKXKgvWDGDPk6RKdOouQC0x3Zmng4AbMnNtZj4IrACOmDTndcBFmbkOIDPvGH2ZkiRJzTBdeJoP\\n3NQ3Xlds67cn8JSI+PeIWBkRx42yQM1A9jxJqkS37gLUElNetgNyiH1sB/wm8FJgDvCdiPhuZl4/\\neeKyZcsYGxsDYN68eSxatIhOpwNAt9sFmHHjzSYCw+6OHU8xLjTl9eu42eOHTYw7M3zMNI/P1HHv\\nNVP367Xu8cTXa9euZTqRueV8FBEHA+OZubgYvx14KDNP75vzNuBJmTlejP8vcGlmfmbSvnKqY81U\\nEQHjdVehbcI4+HdIw4oIhvv9VwrXlgEigsyMQY9Nd9luJbBnRIxFxK8AS4GLJ835PPDCiJgVEXOA\\nFwDXbm3RkiRJTTRleMrMjcDJwGX0AtEFmbk6Ik6MiBOLOWuAS4FrgCuAj2Wm4UmPnT1PkirRrbsA\\ntcSUl+1GeiAv2w3kZbsBbsTbFQwy7mU7Dc/LdoN08XYFg3jZbpCtuWwnPf4MTpIq0am7ALWE4UmS\\nJKkEw5Oax54nSZXo1l2AWsLwJEmSVILhSc1jz5OkSnTqLkAtYXiSJEkqwfCk5rHnSVIlunUXoJYw\\nPEmSJJVgeFLz2PMkqRKdugtQSxieJEmSSjA8qXnseZJUiW7dBaglDE+SJEklGJ7UPPY8SapEp+4C\\n1BKGJ0mSpBIMT2oee54kVaJbdwFqCcOTJElSCYYnNY89T5Iq0am7ALWE4UmSJKkEw5Oax54nSZXo\\n1l2AWsLwJEmSVILhSc1jz5OkSnTqLkAtYXiSJEkqwfCk5rHnSVIlunUXoJYwPEmSJJVgeFLz2PMk\\nqRKdugtQSxieJEmSSjA8qXnseZJUiW7dBaglDE+SJEklGJ7UPPY8SapEp+4C1BKGJ0mSpBIMT2oe\\ne54kVaJbdwFqCcOTJElSCYYnNY89T5Iq0am7ALWE4UmSJKkEw5Oax54nSZXo1l2AWsLwJEmSVILh\\nSc1jz5OkSnTqLkAtYXiSJEkqwfCk5rHnSVIlunUXoJYwPEmSJJVgeFLz2PMkqRKdugtQSxieJEmS\\nSjA8qXnseZJUiW7dBaglDE+SJEklGJ7UPPY8SapEp+4C1BKGJ0mSpBIMT2oee54kVaJbdwFqiWnD\\nU0Qsjog1EXF9RLxtinkHRsTGiHj1aEuUJElqjinDU0TMAs4EFgP7AsdExD5bmHc6cCkQFdSpmcSe\\nJ0mV6NRdgFpiujNPBwE3ZObazHwQWAEcMWDem4HPAP8z4vokSZIaZbrwNB+4qW+8rti2WUTMpxeo\\nPlxsypFVp5nJnidJlejWXYBaYvY0jw8ThD4A/HVmZkQEU1y2W7ZsGWNjYwDMmzePRYsW0el0AOh2\\nuwAzbrzZRGDY3bHjKcaFprx+HTd7/LCJcWeGj5nm8Zk67r1m6n691j2e+Hrt2rVMJzK3nI8i4mBg\\nPDMXF+O3Aw9l5ul9c/6LhwPTLsD9wB9l5sWT9pVTHWumiggYr7sKbRPGwb9DGlbvd1lfLxpGuLYM\\nEBFk5sATQtOdeVoJ7BkRY8AtwFLgmP4JmflrfQf6BHDJ5OAkSZLUFlP2PGXmRuBk4DLgWuCCzFwd\\nESdGxImPR4Gagex5klSJbt0FqCWmO/NEZn4Z+PKkbWdvYe7xI6pLkiSpkbzDuJrH+zxJqkSn7gLU\\nEoYnSZKkEgxPah57niRVolt3AWoJw5MkSVIJhic1jz1PkirRqbsAtYThSZIkqQTDk5rHnidJlejW\\nXYBawvAkSZJUguFJzWPPk6RKdOouQC1heJIkSSrB8KTmsedJUiW6dRegljA8SZIklWB4UvPY8ySp\\nEp26C1BLGJ4kSZJKMDypeex5klSJbt0FqCUMT5IkSSUYntQ89jxJqkSn7gLUEoYnSZKkEgxPah57\\nniRVolt3AWoJw5MkSVIJhic1jz1PkirRqbsAtYThSZIkqQTDk5rHnidJlejWXYBawvAkSZJUguFJ\\nzWPPk6RKdOouQC1heJIkSSrB8KTmsedJUiW6dRegljA8SZIklWB4UvPY8ySpEp26C1BLGJ4kSZJK\\nMDypeex5klSJbt0FqCUMT5IkSSUYntQ89jxJqkSn7gLUEoYnSZKkEgxPah57niRVolt3AWqJ2XUX\\nIGC87gIkSdKwIjMfnwNF5ON1rG1JRAD+XDSMwL9DGpZri4bn2jJIRJCZMegxL9tJkiSVYHhSA3Xr\\nLkBSK3XrLkAtYXiSJEkqwZ6nmtmXoOHZl6DhubZoeK4tg9jzJEmSNCKGJzVQt+4CJLVSt+4C1BKG\\nJ0mSpBLseaqZfQkann0JGp5ri4bn2jKIPU+SJEkjMlR4iojFEbEmIq6PiLcNePz3I+LqiLgmIr4d\\nEfuNvlTNHN26C5DUSt26C1BLTBueImIWcCawGNgXOCYi9pk07b+AF2XmfsCpwEdHXagkSVITDHPm\\n6SDghsxcm5kPAiuAI/onZOZ3MvPuYngF8OzRlqmZpVN3AZJaqVN3AWqJYcLTfOCmvvG6YtuWvAH4\\n0tYUJUmS1FTDhKehW/Aj4sXAHwCP6ouShtetuwBJrdStuwC1xOwh5twM7NY33o3e2adHKJrEPwYs\\nzsw7B+1o2bJljI2NATBv3jwWLVpEp9MBoNvtAsy48cMmxh3HjqcYF6OGvH4dN3v8sIlxZ4aPmebx\\nmTruvWbqfr3WPZ74eu3atUxn2vs8RcRs4DrgpcAtwPeAYzJzdd+c5wBfB47NzO9uYT/e52kA78Wi\\n4XkvFg3PtUXDc20ZZKr7PE175ikzN0bEycBlwCzg45m5OiJOLB4/G3g3sDPw4d5fWB7MzING9QQk\\nSZKawjuM18zfDgfp0n86WRP87VDDc20ZpItryyCuLYN4h3FJkqQR8cxTzfztUMPzt0MNz7VFw3Nt\\nGcQzT5IkSSNieFIDdesuQFIrdesuQC1heJIkSSrBnqea2Zeg4dmXoOG5tmh4ri2D2PMkSZI0IoYn\\nNVC37gIktVK37gLUEoYnSZKkEux5qpl9CRqefQkanmuLhufaMog9T5IkSSNieFIDdesuQFIrdesu\\nQC1heJIkSSrBnqea2Zeg4dmXoOG5tmh4ri2D2PMkSZI0IoYnNVC37gIktVK37gLUEoYnSZKkEux5\\nqpl9CRqefQkanmuLhufaMog9T5IkSSNieFIDdesuQFIrdesuQC1heJIkSSrBnqea2Zeg4dmXoOG5\\ntmh4ri2D2PMkSZI0IoYnNVC37gIktVK37gLUEoYnSZKkEux5qpl9CRqefQkanmuLhufaMog9T5Ik\\nSSNieFIDdesuQFIrdesuQC1heJIkSSrBnqea2Zeg4dmXoOG5tmh4ri2D2PMkSZI0IoYnNVC37gIk\\ntVK37gLUEoYnSZKkEux5qpl9CRqefQkanmuLhufaMog9T5IkSSNieFIDdesuQFIrdesuQC1heJIk\\nSSrBnqea2Zeg4dmXoOG5tmh4ri2D2PMkSZI0IoYnNVC37gIktVK37gLUEoYnSZKkEux5qpl9CRqe\\nfQkanmuLhufaMog9T5IkSSNieFIDdesuQFIrdesuQC1heJIkSSrBnqea2Zeg4dmXoOG5tmh4ri2D\\n2PMkSZI0ItOGp4hYHBFrIuL6iHjbFub8U/H41RGx/+jL1MzSrbsASa3UrbsAtcSU4SkiZgFnAouB\\nfYFjImKfSXNeAeyRmXsCJwAfrqhWzRhX1V2ApFZybdFoTHfm6SDghsxcm5kPAiuAIybNORw4ByAz\\nrwDmRcQzRl6pZpC76i5AUiu5tmg0pgtP84Gb+sbrim3TzXn21pcmSZLUPNOFp2Hb7yd3o9u2r62w\\ntu4CJLXS2roLUEvMnubxm4Hd+sa70TuzNNWcZxfbHqX31lk9mj+XRzun7gIayb9DKsfXy6O5tgzi\\n2lLOdOFpJbBnRIwBtwBLgWMmzbkYOBlYEREHA3dl5s8m72hL90qQJEnalkwZnjJzY0ScDFwGzAI+\\nnpmrI+LE4vGzM/NLEfGKiLgB2AAcX3nVkiRJNXnc7jAuSZLUBtNdtpMqVdw37AgefhfnOuDizFxd\\nX1WSJG2ZH8+i2hR3rD+/GF5R/HkCcH5EvL22wiS1VkTYWqKt5mU71SYirgf2LW7A2r/9V4BrM3OP\\neiqT1FYRcVNm7jb9TGnLvGynOm2id7lu7aTtuxaPSVJpEfGDKR5++uNWiFrL8KQ6/Qnw1eKdmhN3\\nqd8N2JPe7S8k6bF4Or3PZL1zwGOXP861qIUMT6pNZl4aEXvT+wzF+fTuTH8zsDIzN9ZanKRt2ReB\\nuZm5avIDEfGNGupRy9jzJEmSVILvtpMkSSrB8CRJklSC4UmSJKkEw5MkSVIJhidJtYmIv4mIH0bE\\n1RGxKiLLRwAeAAACSElEQVQOGsE+PxERJ0za9rsR8aUpvmd5RPze1h5b0sxgeJJUi4g4BHglsH9m\\nLgReysP3+9oanwJeO2nba4vtW5LFH0maluFJUl2eCdwx8fE8mbk+M2+NiOdHRDciVkbEpRHxzIh4\\nckSsiYi9ACLi/Ih4wxb2+3XgNyLimcXcHegFs3+NiHdHxPci4gcRcfak74tqnqaktjE8SarLvwG7\\nRcR1EXFWRLwoIrYDzgB+LzMPAD4BnJaZd9O76/zyiHgt8OTM/PignWbmJuAi4Ohi0xLg3zPzPuCM\\nzDwoM58HPCkiXlXtU5TURoYnSbXIzA3A84ETgP8BLii+fi69j+1ZBfwNvbvPk5lfBX4InAn84TS7\\nP5+HL929thgDvCQivhsR1wAvAfYd2ROSNGP48SySapOZDwHfAL5RfJjrScCPMvO3Js+NiCcA+wAb\\ngKcAt0yx6+8Az4qIhcAhwNERsT1wFvD8zLw5It4DbD/SJyRpRvDMk6RaRMReEbFn36b9gdXALhFx\\ncDFnu4iYODv0p8CPgN8HPhERW/zlL3ufO3UBcA7wpcz8fzwclH4eEXOBo0b6hCTNGJ55klSXucAZ\\nETEP2AhcT++y3UeBf4qIJ9Nbo/4xIjYCbwAOzMwNEfFN4J3A+BT7Px/4S+CvADLzroj4GL1Lf7cB\\nV0ya77vtJA3FDwaWJEkqwct2kiRJJXjZTtI2KyK+Czxx0uZjM/NHddQjaWbwsp0kSVIJXraTJEkq\\nwfAkSZJUguFJkiSpBMOTJElSCYYnSZKkEv4/NLYINFwG/3cAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10adb6d90>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"sex_val_xt = pd.crosstab(df_train['Sex_Val'], df_train['Survived'])\\n\",\n    \"sex_val_xt_pct = sex_val_xt.div(sex_val_xt.sum(1).astype(float), axis=0)\\n\",\n    \"sex_val_xt_pct.plot(kind='bar', stacked=True, title='Survival Rate by Gender')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The majority of females survived, whereas the majority of males did not.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Next we'll determine whether we can gain any insights on survival rate by looking at both Sex and Pclass.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Count males and females in each Pclass:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"M:  1 122\\n\",\n      \"F:  1 94\\n\",\n      \"M:  2 108\\n\",\n      \"F:  2 76\\n\",\n      \"M:  3 347\\n\",\n      \"F:  3 144\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Get the unique values of Pclass:\\n\",\n    \"passenger_classes = sorted(df_train['Pclass'].unique())\\n\",\n    \"\\n\",\n    \"for p_class in passenger_classes:\\n\",\n    \"    print 'M: ', p_class, len(df_train[(df_train['Sex'] == 'male') & \\n\",\n    \"                             (df_train['Pclass'] == p_class)])\\n\",\n    \"    print 'F: ', p_class, len(df_train[(df_train['Sex'] == 'female') & \\n\",\n    \"                             (df_train['Pclass'] == p_class)])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Plot survival rate by Sex and Pclass:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.text.Text at 0x10b610e50>\"\n      ]\n     },\n     \"execution_count\": 14,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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53ECSecwK233spWW201ocdr4aXW+qAnQHrBzHM1QY/n+YYbbsiNN97I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\\nggAuJN4AAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10b0c3710>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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sDNN9/MLbfcwm677fbovptuuikLFiygqioAzj77bBYuXMisWbN4wxve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\\nAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10b5b5b50>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Plot survival rate by Sex\\n\",\n    \"females_df = df_train[df_train['Sex'] == 'female']\\n\",\n    \"females_xt = pd.crosstab(females_df['Pclass'], df_train['Survived'])\\n\",\n    \"females_xt_pct = females_xt.div(females_xt.sum(1).astype(float), axis=0)\\n\",\n    \"females_xt_pct.plot(kind='bar', \\n\",\n    \"                    stacked=True, \\n\",\n    \"                    title='Female Survival Rate by Passenger Class')\\n\",\n    \"plt.xlabel('Passenger Class')\\n\",\n    \"plt.ylabel('Survival Rate')\\n\",\n    \"\\n\",\n    \"# Plot survival rate by Pclass\\n\",\n    \"males_df = df_train[df_train['Sex'] == 'male']\\n\",\n    \"males_xt = pd.crosstab(males_df['Pclass'], df_train['Survived'])\\n\",\n    \"males_xt_pct = males_xt.div(males_xt.sum(1).astype(float), axis=0)\\n\",\n    \"males_xt_pct.plot(kind='bar', \\n\",\n    \"                  stacked=True, \\n\",\n    \"                  title='Male Survival Rate by Passenger Class')\\n\",\n    \"plt.xlabel('Passenger Class')\\n\",\n    \"plt.ylabel('Survival Rate')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The vast majority of females in First and Second class survived.  Males in First class had the highest chance for survival.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Feature: Embarked\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The Embarked column might be an important feature but it is missing a couple data points which might pose a problem for machine learning algorithms:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div style=\\\"max-height:1000px;max-width:1500px;overflow:auto;\\\">\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>PassengerId</th>\\n\",\n       \"      <th>Survived</th>\\n\",\n       \"      <th>Pclass</th>\\n\",\n       \"      <th>Name</th>\\n\",\n       \"      <th>Sex</th>\\n\",\n       \"      <th>Age</th>\\n\",\n       \"      <th>SibSp</th>\\n\",\n       \"      <th>Parch</th>\\n\",\n       \"      <th>Ticket</th>\\n\",\n       \"      <th>Fare</th>\\n\",\n       \"      <th>Cabin</th>\\n\",\n       \"      <th>Embarked</th>\\n\",\n       \"      <th>Sex_Val</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>61 </th>\\n\",\n       \"      <td>  62</td>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"      <td>                       Icard, Miss. Amelie</td>\\n\",\n       \"      <td> female</td>\\n\",\n       \"      <td> 38</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 113572</td>\\n\",\n       \"      <td> 80</td>\\n\",\n       \"      <td> B28</td>\\n\",\n       \"      <td> NaN</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>829</th>\\n\",\n       \"      <td> 830</td>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"      <td> Stone, Mrs. George Nelson (Martha Evelyn)</td>\\n\",\n       \"      <td> female</td>\\n\",\n       \"      <td> 62</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 113572</td>\\n\",\n       \"      <td> 80</td>\\n\",\n       \"      <td> B28</td>\\n\",\n       \"      <td> NaN</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"     PassengerId  Survived  Pclass                                       Name  \\\\\\n\",\n       \"61            62         1       1                        Icard, Miss. Amelie   \\n\",\n       \"829          830         1       1  Stone, Mrs. George Nelson (Martha Evelyn)   \\n\",\n       \"\\n\",\n       \"        Sex  Age  SibSp  Parch  Ticket  Fare Cabin Embarked  Sex_Val  \\n\",\n       \"61   female   38      0      0  113572    80   B28      NaN        0  \\n\",\n       \"829  female   62      0      0  113572    80   B28      NaN        0  \"\n      ]\n     },\n     \"execution_count\": 15,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df_train[df_train['Embarked'].isnull()]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Prepare to map Embarked from a string to a number representation:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"{nan: 0, 'C': 1, 'Q': 2, 'S': 3}\"\n      ]\n     },\n     \"execution_count\": 16,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# Get the unique values of Embarked\\n\",\n    \"embarked_locs = sorted(df_train['Embarked'].unique())\\n\",\n    \"\\n\",\n    \"embarked_locs_mapping = dict(zip(embarked_locs, \\n\",\n    \"                                 range(0, len(embarked_locs) + 1)))\\n\",\n    \"embarked_locs_mapping\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Transform Embarked from a string to a number representation to prepare it for machine learning algorithms:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div style=\\\"max-height:1000px;max-width:1500px;overflow:auto;\\\">\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>PassengerId</th>\\n\",\n       \"      <th>Survived</th>\\n\",\n       \"      <th>Pclass</th>\\n\",\n       \"      <th>Name</th>\\n\",\n       \"      <th>Sex</th>\\n\",\n       \"      <th>Age</th>\\n\",\n       \"      <th>SibSp</th>\\n\",\n       \"      <th>Parch</th>\\n\",\n       \"      <th>Ticket</th>\\n\",\n       \"      <th>Fare</th>\\n\",\n       \"      <th>Cabin</th>\\n\",\n       \"      <th>Embarked</th>\\n\",\n       \"      <th>Sex_Val</th>\\n\",\n       \"      <th>Embarked_Val</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 3</td>\\n\",\n       \"      <td>                           Braund, Mr. Owen Harris</td>\\n\",\n       \"      <td>   male</td>\\n\",\n       \"      <td> 22</td>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td>        A/5 21171</td>\\n\",\n       \"      <td>  7.2500</td>\\n\",\n       \"      <td>  NaN</td>\\n\",\n       \"      <td> S</td>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"      <td> 3</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td> 2</td>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"      <td> Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\\n\",\n       \"      <td> female</td>\\n\",\n       \"      <td> 38</td>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td>         PC 17599</td>\\n\",\n       \"      <td> 71.2833</td>\\n\",\n       \"      <td>  C85</td>\\n\",\n       \"      <td> C</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td> 3</td>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"      <td> 3</td>\\n\",\n       \"      <td>                            Heikkinen, Miss. Laina</td>\\n\",\n       \"      <td> female</td>\\n\",\n       \"      <td> 26</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> STON/O2. 3101282</td>\\n\",\n       \"      <td>  7.9250</td>\\n\",\n       \"      <td>  NaN</td>\\n\",\n       \"      <td> S</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 3</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td> 4</td>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"      <td>      Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\\n\",\n       \"      <td> female</td>\\n\",\n       \"      <td> 35</td>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td>           113803</td>\\n\",\n       \"      <td> 53.1000</td>\\n\",\n       \"      <td> C123</td>\\n\",\n       \"      <td> S</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 3</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td> 5</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 3</td>\\n\",\n       \"      <td>                          Allen, Mr. William Henry</td>\\n\",\n       \"      <td>   male</td>\\n\",\n       \"      <td> 35</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td>           373450</td>\\n\",\n       \"      <td>  8.0500</td>\\n\",\n       \"      <td>  NaN</td>\\n\",\n       \"      <td> S</td>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"      <td> 3</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   PassengerId  Survived  Pclass  \\\\\\n\",\n       \"0            1         0       3   \\n\",\n       \"1            2         1       1   \\n\",\n       \"2            3         1       3   \\n\",\n       \"3            4         1       1   \\n\",\n       \"4            5         0       3   \\n\",\n       \"\\n\",\n       \"                                                Name     Sex  Age  SibSp  \\\\\\n\",\n       \"0                            Braund, Mr. Owen Harris    male   22      1   \\n\",\n       \"1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female   38      1   \\n\",\n       \"2                             Heikkinen, Miss. Laina  female   26      0   \\n\",\n       \"3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female   35      1   \\n\",\n       \"4                           Allen, Mr. William Henry    male   35      0   \\n\",\n       \"\\n\",\n       \"   Parch            Ticket     Fare Cabin Embarked  Sex_Val  Embarked_Val  \\n\",\n       \"0      0         A/5 21171   7.2500   NaN        S        1             3  \\n\",\n       \"1      0          PC 17599  71.2833   C85        C        0             1  \\n\",\n       \"2      0  STON/O2. 3101282   7.9250   NaN        S        0             3  \\n\",\n       \"3      0            113803  53.1000  C123        S        0             3  \\n\",\n       \"4      0            373450   8.0500   NaN        S        1             3  \"\n      ]\n     },\n     \"execution_count\": 17,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df_train['Embarked_Val'] = df_train['Embarked'] \\\\\\n\",\n    \"                               .map(embarked_locs_mapping) \\\\\\n\",\n    \"                               .astype(int)\\n\",\n    \"df_train.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Plot the histogram for Embarked_Val:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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949c96HUd8BqCULNEmSpIGxB02SpBnZg6b27EGTJElaFyzQNJV9It0z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\\nQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10b0c3dd0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"df_train['Embarked_Val'].hist(bins=len(embarked_locs), range=(0, 3))\\n\",\n    \"plt.title('Port of Embarkation Histogram')\\n\",\n    \"plt.xlabel('Port of Embarkation')\\n\",\n    \"plt.ylabel('Count')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Since the vast majority of passengers embarked in 'S': 3, we assign the missing values in Embarked to 'S': \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"if len(df_train[df_train['Embarked'].isnull()] > 0):\\n\",\n    \"    df_train.replace({'Embarked_Val' : \\n\",\n    \"                   { embarked_locs_mapping[nan] : embarked_locs_mapping['S'] \\n\",\n    \"                   }\\n\",\n    \"               }, \\n\",\n    \"               inplace=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Verify we do not have any more NaNs for Embarked_Val:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([1, 2, 3])\"\n      ]\n     },\n     \"execution_count\": 20,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"embarked_locs = sorted(df_train['Embarked_Val'].unique())\\n\",\n    \"embarked_locs\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Plot a normalized cross tab for Embarked_Val and Survived:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.text.Text at 0x10b7e28d0>\"\n      ]\n     },\n     \"execution_count\": 21,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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s1gRcQa/V9vfetbOffcczn88MNXrRu73aJFi1b1hI0666yz+PznP8+C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\\newAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10ba3e790>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"embarked_val_xt = pd.crosstab(df_train['Embarked_Val'], df_train['Survived'])\\n\",\n    \"embarked_val_xt_pct = \\\\\\n\",\n    \"    embarked_val_xt.div(embarked_val_xt.sum(1).astype(float), axis=0)\\n\",\n    \"embarked_val_xt_pct.plot(kind='bar', stacked=True)\\n\",\n    \"plt.title('Survival Rate by Port of Embarkation')\\n\",\n    \"plt.xlabel('Port of Embarkation')\\n\",\n    \"plt.ylabel('Survival Rate')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"It appears those that embarked in location 'C': 1 had the highest rate of survival.  We'll dig in some more to see why this might be the case.  Below we plot a graphs to determine gender and passenger class makeup for each port:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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h/YW+P+hqKbsn7zKtdX3x7H1r2u/Q9inqW+/X6f9fV1AFZXV5nWmG3/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\\nRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10b30de50>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Set up a grid of plots\\n\",\n    \"fig = plt.figure(figsize=fizsize_with_subplots) \\n\",\n    \"\\n\",\n    \"rows = 2\\n\",\n    \"cols = 3\\n\",\n    \"col_names = ('Sex_Val', 'Pclass')\\n\",\n    \"\\n\",\n    \"for portIdx in embarked_locs:\\n\",\n    \"    for colIdx in range(0, len(col_names)):\\n\",\n    \"        plt.subplot2grid((rows, cols), (colIdx, portIdx - 1))\\n\",\n    \"        df_train[df_train['Embarked_Val'] == portIdx][col_names[colIdx]] \\\\\\n\",\n    \"            .value_counts().plot(kind='bar')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Leaving Embarked as integers implies ordering in the values, which does not exist.  Another way to represent Embarked without ordering is to create dummy variables:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"df_train = pd.concat([df_train, pd.get_dummies(df_train['Embarked_Val'], prefix='Embarked_Val')], axis=1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Feature: Age\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The Age column seems like an important feature--unfortunately it is missing many values.  We'll need to fill in the missing values like we did with Embarked.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Filter to view missing Age values:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div style=\\\"max-height:1000px;max-width:1500px;overflow:auto;\\\">\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Sex</th>\\n\",\n       \"      <th>Pclass</th>\\n\",\n       \"      <th>Age</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5 </th>\\n\",\n       \"      <td>   male</td>\\n\",\n       \"      <td> 3</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>17</th>\\n\",\n       \"      <td>   male</td>\\n\",\n       \"      <td> 2</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>19</th>\\n\",\n       \"      <td> female</td>\\n\",\n       \"      <td> 3</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>26</th>\\n\",\n       \"      <td>   male</td>\\n\",\n       \"      <td> 3</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>28</th>\\n\",\n       \"      <td> female</td>\\n\",\n       \"      <td> 3</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"       Sex  Pclass  Age\\n\",\n       \"5     male       3  NaN\\n\",\n       \"17    male       2  NaN\\n\",\n       \"19  female       3  NaN\\n\",\n       \"26    male       3  NaN\\n\",\n       \"28  female       3  NaN\"\n      ]\n     },\n     \"execution_count\": 24,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df_train[df_train['Age'].isnull()][['Sex', 'Pclass', 'Age']].head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Determine the Age typical for each passenger class by Sex_Val.  We'll use the median instead of the mean because the Age histogram seems to be right skewed.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# To keep Age in tact, make a copy of it called AgeFill \\n\",\n    \"# that we will use to fill in the missing ages:\\n\",\n    \"df_train['AgeFill'] = df_train['Age']\\n\",\n    \"\\n\",\n    \"# Populate AgeFill\\n\",\n    \"df_train['AgeFill'] = df_train['AgeFill'] \\\\\\n\",\n    \"                        .groupby([df_train['Sex_Val'], df_train['Pclass']]) \\\\\\n\",\n    \"                        .apply(lambda x: x.fillna(x.median()))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Ensure AgeFill does not contain any missing values:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0\"\n      ]\n     },\n     \"execution_count\": 26,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"len(df_train[df_train['AgeFill'].isnull()])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Plot a normalized cross tab for AgeFill and Survived:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.text.Text at 0x10c4125d0>\"\n      ]\n     },\n     \"execution_count\": 27,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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0uWLOFTn/oUQ4cOZfz48ey1115VvoU+54QASZJUmRkzZjB69Ojlyp54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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10c054390>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Set up a grid of plots\\n\",\n    \"fig, axes = plt.subplots(2, 1, figsize=fizsize_with_subplots)\\n\",\n    \"\\n\",\n    \"# Histogram of AgeFill segmented by Survived\\n\",\n    \"df1 = df_train[df_train['Survived'] == 0]['Age']\\n\",\n    \"df2 = df_train[df_train['Survived'] == 1]['Age']\\n\",\n    \"max_age = max(df_train['AgeFill'])\\n\",\n    \"axes[0].hist([df1, df2], \\n\",\n    \"             bins=max_age / bin_size, \\n\",\n    \"             range=(1, max_age), \\n\",\n    \"             stacked=True)\\n\",\n    \"axes[0].legend(('Died', 'Survived'), loc='best')\\n\",\n    \"axes[0].set_title('Survivors by Age Groups Histogram')\\n\",\n    \"axes[0].set_xlabel('Age')\\n\",\n    \"axes[0].set_ylabel('Count')\\n\",\n    \"\\n\",\n    \"# Scatter plot Survived and AgeFill\\n\",\n    \"axes[1].scatter(df_train['Survived'], df_train['AgeFill'])\\n\",\n    \"axes[1].set_title('Survivors by Age Plot')\\n\",\n    \"axes[1].set_xlabel('Survived')\\n\",\n    \"axes[1].set_ylabel('Age')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Unfortunately, the graphs above do not seem to clearly show any insights.  We'll keep digging further.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Plot AgeFill density by Pclass:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.legend.Legend at 0x10be093d0>\"\n      ]\n     },\n     \"execution_count\": 28,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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FwcrVq14o8//gBg1qxZtGjRgtjYWBITExkxYoRboQmIzh5VShVsxgwr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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10b6d4b50>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"for pclass in passenger_classes:\\n\",\n    \"    df_train.AgeFill[df_train.Pclass == pclass].plot(kind='kde')\\n\",\n    \"plt.title('Age Density Plot by Passenger Class')\\n\",\n    \"plt.xlabel('Age')\\n\",\n    \"plt.legend(('1st Class', '2nd Class', '3rd Class'), loc='best')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"When looking at AgeFill density by Pclass, we see the first class passengers were generally older then second class passengers, which in turn were older than third class passengers.  We've determined that first class passengers had a higher survival rate than second class passengers, which in turn had a higher survival rate than third class passengers.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.axes._subplots.AxesSubplot at 0x10d6c22d0>\"\n      ]\n     },\n     \"execution_count\": 29,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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6urAKysrLC2tsZgMAC2umbXo/VIAwzGLrNE681jk9czse6rvlrWPMr1\\ny7AeTF3f9/O3lvWmWuqpYT0YDKqqp6b1plrq6eP/v2kahsMhs5r7TUQj4lzgZ9rfFrwcuCszL4uI\\ng8CKA+3zcaC9xEymmck0B9olza/PNxHdfAV7J/CyiLgNeGm71mMw+S8GbWr6LqBSTd8FVKjpu4Aq\\n+dpSZi5l5tKNud7nCiAz/xT40/by3cD5835PSZKkReVnC1bObcESM5lmJtPcFpQ0Pz9bUJIkqWc2\\nV5Vwn3s7Td8FVKrpu4AKNX0XUCVfW8rMpcxcumFzJUmS1CFnrirnzFWJmUwzk2l+6HmJr7/Szswy\\nczX3bwtKUr1sJB7JhlPaDdU0V3fffTff+q1n8eCDfVfSj2984wFOPvlxfZdRoYZHvlu7RhrMZVKD\\nmZQ0mMu0pmkefmdubTGXblTTXGUmX/vaN/i3f/t836X05P8FXjxx7K/wbcMkSVos1cxc3XXXXTzt\\naWfxb/92167WU7fPAN+FWxuTnC+aZibTzGSa7/0l7ZTvcyVJktQzm6tqNH0XUKmm7wIq1fRdQIWa\\nvguoVNN3AVXy/ZzKzKUbNleSJEkdcuaqas5clTlLM81MppnJNGeupJ1y5kqSJKlnNlfVaPouoFJN\\n3wVUqum7gAo1fRdQqabvAqrkbFGZuXRjpuYqIs6IiBsi4uaI+GxEvKU9vjciDkfEbRFxXUSsdFuu\\nJElS3WaauYqI04HTM/NIRDwJ+DRwIfBG4MuZeXlEvA04LTMPTtzXmavHzJmrMmdpppnJNDOZ5syV\\ntFO7NnOVmUcz80h7+Z+BzwFPAw4Ah9qbHWLUcEmSJC2NuWeuImIVeAHwCWBfZm60V20A++b9/suj\\n6buASjV9F1Cppu8CKtT0XUClmr4LqJKzRWXm0o25Pluw3RL8EPDWzLw/YuusWWZmRBTPP6+vr7O6\\nugrAysoKa2trnH322e21TfvfwZKt2eb6zWN919fX+sg21zOx7qu+vtY8yrrv+lzXsz4ytn7kB/Nu\\n/kXq2vXm+siRI1XV08d68/JwOGRWM7/PVUQ8Dvg94A8z84r22K3AIDOPRsR+4IbMfPbE/Zy5esyc\\nuSpzlmaamUwzk2nOXEk7tWszVzE6RfXbwC2bjVXrI8DF7eWLgWtm+f6SJEmLaqbmCngx8AbgvIi4\\nsf26AHgn8LKIuA14abvWY9L0XUClmr4LqFTTdwEVavouoFJN3wVUydmiMnPpxkwzV5n552zfmJ0/\\nezmSpONpfDZWI26Vqmt+tmDVnLkqc5ZmmplMM5NpZjLNOTQdm58tKEmS1DObq2o0fRdQqabvAirV\\n9F1AhZq+C6hU03cBlWr6LqBKzlx1w+ZKkiSpQ85cVc2ZqzLnRqaZyTQzmWYm05y50rE5cyVJktQz\\nm6tqNH0XUKmm7wIq1fRdQIWavguoVNN3AZVq+i6gSs5cdcPmSpIkqUPOXFXNmasy50ammck0M5lm\\nJtOcudKxOXMlSZLUM5urajR9F1Cppu8CKtX0XUCFmr4LqFTTdwGVavouoErOXHXD5kqSJKlDzlxV\\nzZmrMudGppnJNDOZZibTnLnSsTlzJUmS1LPOm6uIuCAibo2Iz0fE27r+/ieupu8CKtX0XUClmr4L\\nqFDTdwGVavouoFLNw5ciwq+JL82n0+YqIk4GfgO4AHgu8PqIeE6XP+PEdaTvAiplLmXmMs1Mysyl\\nbDyX9Ovhr1+bLU49Qtdnrs4BvpCZw8x8APivwGs6/hknqHv7LqBS5lJmLtPMpMxcysylzFy60HVz\\n9TTg9rH1He0xSZKkpbCn4+83169cPPjgP/P4x/94V7UslK9//XpOOeVLjzj20ENf4utf76mgagz7\\nLqBSw74LqNCw7wIqNey7gEoN+y6gUkMA567m1OlbMUTEi4BfzMwL2vWlwEOZednYbfydV0mStDB2\\n+lYMXTdXe4C/AX4I+Cfgk8DrM/Nznf0QSZKkinW6LZiZD0bETwAfBU4GftvGSpIkLZNdf4d2SZKk\\nE9muvkO7bzA6EhFXRsRGRNw0dmxvRByOiNsi4rqIWOmzxt0WEWdExA0RcXNEfDYi3tIeX/ZcHh8R\\nn4iIIxFxS0S8oz2+1LlsioiTI+LGiLi2XS99LhExjIi/bnP5ZHtsqXOJiJWI+GBEfK59Hn2vmcR3\\ntI+Rza/7IuIty54LjObF27+LboqI90XEN+00l11rrnyD0Ud4N6Mcxh0EDmfmWcD17XqZPAD8VGZ+\\nJ/Ai4Mfbx8dS55KZXwPOy8w14HnAeRHxAyx5LmPeCtzC1m8qm8soi0FmviAzz2mPLXsuvw78QWY+\\nh9Hz6FaWPJPM/Jv2MfICRh9i+1Xgd1jyXCJiFXgz8MLMPJvRiNPr2GEuu3nmyjcYbWXmx4B7Jg4f\\nAA61lw8BF+5qUT3LzKOZeaS9/M/A5xi9R9pS5wKQmV9tL57C6Il+D+ZCRDwdeCXwLkafSAzmsmny\\nN5uWNpeIeArwksy8EkazwZl5H0ucScH5jP5+vh1z+Qqjf+yf2v6S3qmMfkFvR7nsZnPlG4we277M\\n3GgvbwD7+iymT+2/HF4AfAJzISJOiogjjP7/b8jMmzEXGH1Ox88CD40dM5fRmas/johPRcSb22PL\\nnMuZwJci4t0R8ZmI+K2IeCLLncmk1wFXtZeXOpfMvBv4VeAfGDVV92bmYXaYy242V07OP0Y5+i2D\\npcwrIp4EfAh4a2beP37dsuaSmQ+124JPB34wIs6buH7pcomIVwF3ZuaNTJ+lAZYzl9aL262eVzDa\\nXn/J+JVLmMse4IXAb2bmC4F/YWJLZwkzeVhEnAK8GvjA5HXLmEtEPAu4BFgFvgV4UkS8Yfw2jyWX\\n3Wyu/hE4Y2x9BqOzVxrZiIjTASJiP3Bnz/Xsuoh4HKPG6j2ZeU17eOlz2dRuZfw+o/mIZc/l+4ED\\nEfF3jP7F/dKIeA/mQmZ+sf3vlxjN0JzDcudyB3BHZv5lu/4go2br6BJnMu4VwKfbxwss92MF4LuB\\nj2fmXZn5IPBh4PvY4eNlN5urTwHfHhGrbaf8WuAju/jza/cR4OL28sXANce47QknIgL4beCWzLxi\\n7Kplz+Wpm7+VEhFPAF4G3MiS55KZP5+ZZ2TmmYy2NP4kM3+YJc8lIk6NiCe3l58IvBy4iSXOJTOP\\nArdHxFntofOBm4FrWdJMJryerS1BWOLHSutW4EUR8YT276XzGf3SzI4eL7v6PlcR8QrgCrbeYPQd\\nu/bDKxIRVwHnAk9ltHf7C8DvAlcDz2D04U4XZebSfDx5+xtwfwb8NVunWy9l9C7/y5zL2YyGJ09q\\nv96Tmb8SEXtZ4lzGRcS5wE9n5oFlzyUizmR0tgpG22Hvzcx3mEs8n9EvPpwC/C3wRkZ/Dy1tJvBw\\nA/73wJmbYxjL/lgBiIifY9RAPQR8BngT8GR2kItvIipJktShXX0TUUmSpBOdzZUkSVKHbK4kSZI6\\nZHMlSZLUIZsrSZKkDtlcSZIkdcjmSpIkqUM2V5IkSR2yuZIkSeqQzZUkSVKHbK4kSZI6ZHMlSZLU\\nIZsrSZKkDtlcSZIkdcjmSpIkqUMzN1cR8daIuCkiPhsRb22P7Y2IwxFxW0RcFxEr3ZUqSZJUv5ma\\nq4j4H4A3Ad8DPB94VUQ8CzgIHM7Ms4Dr27UkSdLSmPXM1bOBT2Tm1zLzG8CfAv8eOAAcam9zCLhw\\n/hIlSZIWx6zN1WeBl7TbgKcCrwSeDuzLzI32NhvAvg5qlCRJWhh7ZrlTZt4aEZcB1wH/AhwBvjFx\\nm4yInL9ESZKkxTFTcwWQmVcCVwJExP8J3AFsRMTpmXk0IvYDd07ez4ZLkiQtksyMndx+nt8W/O/b\\n/z4D+F+A9wEfAS5ub3IxcM02Rfo18fX2t7+99xpq/DIXczETczEXc+nzaxYzn7kCPhgR/x3wAPBj\\nmXlfRLwTuDoifhQYAhfN8f0lSZIWzjzbgj9YOHY3cP5cFS2p4XDYdwlVKuUSsaOzsyesX/qlX3rE\\netZ/YZ0ofA6VmUuZuZSZSzd8h/ZKrK2t9V1ClbbPJZf869cm1vI5VGYuZeZSZi7diN3+125E5LL/\\nC1vzGZ258jH0SLH0Z64k6XiICHK3BtolSZI0zeaqEk3T9F1ClcxlO03fBVTHx0qZuZSZS5m5dMPm\\nSpIkqUPOXGnhOHNV4syVJB0PzlxJkiT1zOaqEu5zl5nLdpq+C6iOj5UycykzlzJz6YbNlSRJUodm\\nnrmKiEuBNwAPATcBbwSeCLwfeCbtx99k5r0T93PmSnNx5qrEmStJOh52beYqIlaBNwMvzMyzgZOB\\n1wEHgcOZeRZwfbuWJElaGrNuC36F0Qc2nxoRe4BTgX8CDgCH2tscAi6cu8Il4T53mblsp+m7gOr4\\nWCkzlzJzKTOXbszUXLUf0PyrwD8waqruzczDwL7M3GhvtgHs66RKSZKkBTHTzFVEPAu4FngJcB/w\\nAeBDwH/KzNPGbnd3Zu6duK8zV5qLM1clzlxJ0vEwy8zVnhl/1ncDH8/Mu9of/GHg+4CjEXF6Zh6N\\niP3AnaU7r6+vs7q6CsDKygpra2sMBgNg65Ska9fHWm/ZXA+WfN2uKvnzce3atetFXW9eHg6HzGrW\\nM1fPB94LfA/wNeD/AT7J6LcE78rMyyLiILCSmQcn7uuZq4KmaR7+A9aWUi6euYJRUzUYW3vmyudQ\\nmbmUmUuZuUzbtTNXmflXEfFfgE8xeiuGzwD/N/Bk4OqI+FHat2KY5ftLkiQtKj9bUAvHM1clnrmS\\npOPBzxaUJEnqmc1VJcYH6bTFXLbT9F1AdXyslJlLmbmUmUs3bK4kSZI65MyVFo4zVyXOXEnS8eDM\\nlSRJUs9srirhPneZuWyn6buA6vhYKTOXMnMpM5du2FxJkiR1yJkrLRxnrkqcuZKk48GZK0mSpJ7Z\\nXFXCfe4yc9lO03cB1fGxUmYuZeZSZi7dmKm5iojviIgbx77ui4i3RMTeiDgcEbdFxHURsdJ1wZIk\\nSTWbe+YqIk4C/hE4B/hJ4MuZeXlEvA04LTMPTtzemSvNxZmrEmeuJOl46Gvm6nzgC5l5O3AAONQe\\nPwRc2MH3lyRJWhhdNFevA65qL+/LzI328gawr4PvvxTc5y4zl+00fRdQHR8rZeZSZi5l5tKNPfPc\\nOSJOAV4NvG3yuszMiCjuU6yvr7O6ugrAysoKa2trDAYDYOsPdtnWm2qpp5b1kSNHitdv2VwPlmxN\\ncd33n5fr+tZHjhypqh7Xda99vPDw5eFwyKzmmrmKiNcA/yEzL2jXtwKDzDwaEfuBGzLz2RP3ceZK\\nc3HmqsSZK0k6HvqYuXo9W1uCAB8BLm4vXwxcM+f3lyRJWigzN1cR8URGw+wfHjv8TuBlEXEb8NJ2\\nrcdg/HSktpjLdpq+C6iOj5UycykzlzJz6cbMM1eZ+S/AUyeO3c2o4ZIkSVpKfragFo4zVyXOXEnS\\n8eBnC0qSJPXM5qoS7nOXmct2mr4LqI6PlTJzKTOXMnPphs2VJElSh5y50sJx5qrEmStJOh6cuZIk\\nSeqZzVUl3OcuM5ftNH0XUB0fK2XmUmYuZebSDZsrSZKkDjlzpYXjzFWJM1eSdDzs6sxVRKxExAcj\\n4nMRcUtEfG9E7I2IwxFxW0RcFxErs35/SZKkRTTPtuCvA3+Qmc8BngfcChwEDmfmWcD17VqPgfvc\\nZeaynabvAqrjY6XMXMrMpcxcujFTcxURTwFekplXAmTmg5l5H3AAONTe7BBwYSdVSpIkLYiZZq4i\\nYg34z8AtwPOBTwOXAHdk5mntbQK4e3M9dl9nrjQXZ65KnLmSpONhlpmrPTP+rD3AC4GfyMy/jIgr\\nmNgCzMyMiOKr/fr6OqurqwCsrKywtrbGYDAAtk5JunZ9rPWWzfVgydftqpI/H9euXbte1PXm5eFw\\nyKxmPXN1OvAXmXlmu/4B4FLgW4HzMvNoROwHbsjMZ0/c1zNXBU3TPPwHrC2lXDxzBaOmajC29syV\\nz6EycykzlzJzmbZrvy2YmUeB2yPirPbQ+cDNwLXAxe2xi4FrZvn+kiRJi2rm97mKiOcD7wJOAf4W\\neCNwMnA18AxgCFyUmfdO3M8zV5qLZ65KPHMlScfDLGeufBNRLRybqxKbK0k6Hvzg5gU2PkinLeay\\nnabvAqrjY6XMXMrMpcxcumFzJUmS1CG3BbVw3BYscVtQko4HtwUlSZJ6ZnNVCfe5y8xlO03fBVTH\\nx0qZuZQ5S7sIAAAgAElEQVSZS5m5dGPWd2iXVJnRdqnGuVUqqQ/OXGnhOHNVYibTnEOTND9nriRJ\\nknpmc1UJ97nLzGU7Td8FVKjpu4Aq+RwqM5cyc+nGzDNXETEEvgJ8A3ggM8+JiL3A+4Fnss3H30iS\\nJJ3I5vlswb8Dvisz7x47djnw5cy8PCLeBpyWmQcn7ufMlebizFWJmUxz5krS/PqYuZr8YQeAQ+3l\\nQ8CFc35/SZKkhTJPc5XAH0fEpyLize2xfZm50V7eAPbNVd0ScZ+7zFy20/RdQIWavguoks+hMnMp\\nM5duzPM+Vy/OzC9GxL8DDkfEreNXZmZGhOfkJUnSUpm5ucrML7b//VJE/A5wDrAREadn5tGI2A/c\\nWbrv+vo6q6urAKysrLC2tsZgMAC2umbXrjc1TTN1/ZbN9WDJ1zzK9cuwHkxd3/fjt5b1plrqqWE9\\nGAyqqqem9aZa6unj/79pGobDIbOaaaA9Ik4FTs7M+yPiicB1wC8B5wN3ZeZlEXEQWHGgXV1zoL3E\\nTKY50C5pfrs50L4P+FhEHAE+AfxeZl4HvBN4WUTcBry0XesxmPwXg0bMZTtN3wVUqOm7gCr5HCoz\\nlzJz6cZM24KZ+XfAWuH43YzOXkmSJC0lP1tQC8dtwRIzmea2oKT5+dmCkiRJPbO5qoT73GXmsp2m\\n7wIq1PRdQJV8DpWZS5m5dMPmSpIkqUPOXGnhOHNVYibTnLmSND9nriRJknpmc1UJ97nLzGU7Td8F\\nVKjpu4Aq+RwqM5cyc+mGzZUkSVKHnLnSwnHmqsRMpjlzJWl+uz5zFREnR8SNEXFtu94bEYcj4raI\\nuC4iVub5/pIkSYtm3m3BtwK3sPVP5oPA4cw8C7i+XesxcJ+7zFy20/RdQIWavguoks+hMnMpM5du\\nzNxcRcTTgVcC72K0JwFwADjUXj4EXDhXdZIkSQtm5pmriPgA8MvANwM/k5mvjoh7MvO09voA7t5c\\nj93PmSvNxZmrEjOZ5syVpPnt2sxVRLwKuDMzb2TrrNUjtB2Ur2ySJGmp7Jnxft8PHIiIVwKPB745\\nIt4DbETE6Zl5NCL2A3eW7ry+vs7q6ioAKysrrK2tMRgMgK393mVbbx6rpZ5a1ldccUXx8bFlcz1Y\\nsvXmsfF1TfX1sd68vHV934/fGtZHjhzhkksuqaaeWtaTr71911PL2scLD18eDofMau63YoiIc9na\\nFrwcuCszL4uIg8BKZh6cuL3bggVN0zz8B6wtpVzcFoRRIzEYW5tJKRNfa3xt2Y65lJnLtFm2Bbtq\\nrn46Mw9ExF7gauAZwBC4KDPvnbi9zZXmYnNVYibTbK4kza+X5mqnbK40L5urEjOZZnMlaX5+cPMC\\nG9/r1RZz2U7TdwEVavouoEo+h8rMpcxcumFzJUmS1CG3BbVw3BYsMZNpbgtKmp/bgpIkST2zuaqE\\n+9xl5rKdpu8CKtT0XUCVfA6VmUuZuXTD5kqSJKlDzlxp4ThzVWIm05y5kjS/WWauZv34G+2SUSMh\\nSZIWhduClTj2Pncu8dcNhWNyvqik6buAKjlDU2YuZebSDZsrSZKkDs00cxURjwf+FPgm4BTgdzPz\\n0vazBd8PPBM/W7ATzheVmMk0M5nmzJWk+e3a+1xl5teA8zJzDXgecF5E/ABwEDicmWcB17drSZKk\\npTHztmBmfrW9eApwMnAPcAA41B4/BFw4V3VLxH3u7TR9F1Cppu8CKtT0XUCVfG0pM5cyc+nGzM1V\\nRJwUEUeADeCGzLwZ2JeZG+1NNoB9HdQoSZK0MGZ+K4bMfAhYi4inAB+NiPMmrs+IKA48rK+vs7q6\\nCsDKygpra2sMBgNgq2t2PVqPNMBg7DJLtN48Nnk9E+u+6qtlzaNcvwzrwdT1fT9/a1lvqqWeGtaD\\nwaCqempab6qlnj7+/5umYTgcMqtO3kQ0Iv4P4F+BNwGDzDwaEfsZndF69sRtHWjfAQfaS8xkmplM\\nc6Bd0vx2baA9Ip4aESvt5ScALwNuBD4CXNze7GLgmlm+/zKa/BeDNjV9F1Cppu8CKtT0XUCVfG0p\\nM5cyc+nGrNuC+4FDEXESowbtPZl5fUTcCFwdET9K+1YM3ZQpSZK0GPxswcq5LVhiJtPMZJrbgpLm\\nt2vbgpIkSSqzuaqE+9zbafouoFJN3wVUqOm7gCr52lJmLmXm0g2bK0mSpA45c1U5Z65KzGSamUxz\\n5krS/Jy5kiRJ6pnNVSXc595O03cBlWr6LqBCTd8FVMnXljJzKTOXbthcSZIkdciZq8o5c1ViJtPM\\nZJozV5Lm58yVJElSz2b9bMEzIuKGiLg5Ij4bEW9pj++NiMMRcVtEXLf5+YN6dO5zb6fpu4BKNX0X\\nUKGm7wKq5GtLmbmUmUs3Zj1z9QDwU5n5ncCLgB+PiOcAB4HDmXkWcH27liRJWhqdzFxFxDXAb7Rf\\n52bmRkScDjSZ+eyJ2zpztQPOXJWYyTQzmebMlaT5zTJztaeDH7oKvAD4BLAvMzfaqzaAffN+f0ma\\n1egfJxpnwykdf3M1VxHxJOBDwFsz8/7xF7LMzIgoPovX19dZXV0FYGVlhbW1NQaDAbC137ts681j\\nk9ePNMBg7DJLtL4CWCtcz8S6r/r6Wm8eG1/XVF8f683L49ffUFF9fa2PAJe066Bpmt5f72pYT772\\n9l1PLesjR45wySWXVFNPH+vNy8PhkFnNvC0YEY8Dfg/4w8y8oj12KzDIzKMRsR+4wW3Bx2b8BW+c\\n24INW39RbFr2TGA6FzMxk+00bOXiVumm7V5zl525TJtlW3Cm5ipGf+MfAu7KzJ8aO355e+yyiDgI\\nrGTmwYn72lztgM1ViZlMM5NpZjLN5kraqd1srn4A+DPgr9l69boU+CRwNfAMYAhclJn3TtzX5moH\\nbK5KzGSamUwzk2k2V9JO7dqbiGbmn2fmSZm5lpkvaL/+KDPvzszzM/OszHz5ZGOl7Y3v9Wpc03cB\\nlWr6LqBCTd8FVKrpu4Aq+ZpbZi7dmPu3Bbty//338yM/8h/4+tf7rqQfX/7yBk996rv6LkOSJM2p\\nms8WvOuuu9i//5k88MD/tav11O3vgF/ArY1JbvdMM5NpZjLNbUFpp3p5n6sunXTSNwFv6LuMinyG\\nUXMlSZIWxUwzVzoemr4LqFTTdwGVavouoEJN3wVUqum7gCo5W1RmLt2wuZIkSepQVTNXT3vaWfzb\\nv921q/XU7TPAd+HcyCRnaaaZyTQzmebMlbRTu/ZWDJIkSSqzuapG03cBlWr6LqBSTd8FVKjpu4BK\\nNX0XUCVni8rMpRtV/bagJOn4Gn3qg8a5VaquzfPBzVcC/xNwZ2ae3R7bC7wfeCY7/PgbZ65KnLkq\\nc5ZmmplMM5NpZjLNOTQd227PXL0buGDi2EHgcGaeBVzfriVJkpbGzM1VZn4MuGfi8AHgUHv5EHDh\\nrN9/+TR9F1Cppu8CKtX0XUCFmr4LqFTTdwGVavouoErOXHWj64H2fZm50V7eAPZ1/P0lSZKqNtf7\\nXEXEKnDt2MzVPZl52tj1d2fm3on7OHP1mDlzVebcyDQzmWYm08xkmjNXOrYaPltwIyJOz8yjEbEf\\nuLN0o/X1dVZXVwFYWVlhbW2Ns88+u722af87cP2wpqJ6alnzKNcv25pHud61a8aO1VJPLet21W6J\\nDQYD10u83rw8HA6ZVddnri4H7srMyyLiILCSmQcn7uOZq6KGR74AgmeuoJyL//qezsVMzGQ7DVu5\\nmMmWhlEunrka1zTNw82GRnb1twUj4irg48B3RMTtEfFG4J3AyyLiNuCl7VqSJGlp+NmCVfPMVZn/\\n+p5mJtPMZJqZTPPMlY7NzxaUJEnqmc1VNZq+C6hU03cBlWr6LqBCTd8FVKrpu4BKNX0XUCXf56ob\\nNleSJEkdcuaqas5clTk3Ms1MppnJNDOZ5syVjs2ZK0mSpJ7ZXFWj6buASjV9F1Cppu8CKtT0XUCl\\nmr4LqFTTdwFVcuaqGzZXkiRJHXLmqmrOXJU5NzLNTKaZyTQzmebMlY6ths8WlCRpoUTs6O/NpWDD\\nOZ/OtwUj4oKIuDUiPh8Rb+v6+5+4mr4LqFTTdwGVavouoEJN3wVUqum7gEo1Y5fTr4e/bpgpTT1S\\np81VRJwM/AZwAfBc4PUR8Zwuf8aJ60jfBVTKXMrMZZqZlJlLmbmUmUsXuj5zdQ7whcwcZuYDwH8F\\nXtPxzzhB3dt3AZUylzJzmWYmZeZSZi5l5tKFrpurpwG3j63vaI9JkiQtha4H2ueagHvwwft50pPe\\n0FUtC+Vf//XjPOEJX3jEsYceupuvfrWngqox7LuASg37LqBCw74LqNSw7wIqNey7gEoN+y7ghNDp\\nWzFExIuAX8zMC9r1pcBDmXnZ2G38FQRJkrQwdvpWDF03V3uAvwF+CPgn4JPA6zPzc539EEmSpIp1\\nui2YmQ9GxE8AHwVOBn7bxkqSJC2TXX+HdkmSpBPZrn62oG8wOhIRV0bERkTcNHZsb0QcjojbIuK6\\niFjps8bdFhFnRMQNEXFzRHw2It7SHl/2XB4fEZ+IiCMRcUtEvKM9vtS5bIqIkyPixoi4tl0vfS4R\\nMYyIv25z+WR7bKlziYiViPhgRHyufR59r5nEd7SPkc2v+yLiLcueC4zmxdu/i26KiPdFxDftNJdd\\na658g9FHeDejHMYdBA5n5lnA9e16mTwA/FRmfifwIuDH28fHUueSmV8DzsvMNeB5wHkR8QMseS5j\\n3grcwtZvKpvLKItBZr4gM89pjy17Lr8O/EFmPofR8+hWljyTzPyb9jHyAkYfYvtV4HdY8lwiYhV4\\nM/DCzDyb0YjT69hhLrt55so3GG1l5seAeyYOHwAOtZcPARfualE9y8yjmXmkvfzPwOcYvUfaUucC\\nkJmbb8hxCqMn+j2YCxHxdOCVwLsYfSIxmMumyd9sWtpcIuIpwEsy80oYzQZn5n0scSYF5zP6+/l2\\nzOUrjP6xf2r7S3qnMvoFvR3lspvNlW8wemz7MnOjvbwB7OuzmD61/3J4AfAJzIWIOCkijjD6/78h\\nM2/GXAB+DfhZ4KGxY+YyOnP1xxHxqYh4c3tsmXM5E/hSRLw7Ij4TEb8VEU9kuTOZ9DrgqvbyUueS\\nmXcDvwr8A6Om6t7MPMwOc9nN5srJ+ccoR79lsJR5RcSTgA8Bb83M+8evW9ZcMvOhdlvw6cAPRsR5\\nE9cvXS4R8Srgzsy8kemzNMBy5tJ6cbvV8wpG2+svGb9yCXPZA7wQ+M3MfCHwL0xs6SxhJg+LiFOA\\nVwMfmLxuGXOJiGcBlwCrwLcAT4qIR7y7+WPJZTebq38Ezhhbn8Ho7JVGNiLidICI2A/c2XM9uy4i\\nHseosXpPZl7THl76XDa1Wxm/z2g+Ytlz+X7gQET8HaN/cb80It6DuZCZX2z/+yVGMzTnsNy53AHc\\nkZl/2a4/yKjZOrrEmYx7BfDp9vECy/1YAfhu4OOZeVdmPgh8GPg+dvh42c3m6lPAt0fEatspvxb4\\nyC7+/Np9BLi4vXwxcM0xbnvCiYgAfhu4JTOvGLtq2XN56uZvpUTEE4CXATey5Llk5s9n5hmZeSaj\\nLY0/ycwfZslziYhTI+LJ7eUnAi8HbmKJc8nMo8DtEXFWe+h84GbgWpY0kwmvZ2tLEJb4sdK6FXhR\\nRDyh/XvpfEa/NLOjx8uuvs9VRLwCuIKtNxh9x6798IpExFXAucBTGe3d/gLwu8DVwDMYfbjTRZm5\\nNB9P3v4G3J8Bf83W6dZLGb3L/zLncjaj4cmT2q/3ZOavRMReljiXcRFxLvDTmXlg2XOJiDMZna2C\\n0XbYezPzHeYSz2f0iw+nAH8LvJHR30NLmwk83ID/PXDm5hjGsj9WACLi5xg1UA8BnwHeBDyZHeTi\\nm4hKkiR1aFffRFSSJOlEZ3MlSZLUIZsrSZKkDtlcSZIkdcjmSpIkqUM2V5IkSR2yuZIkSeqQzZUk\\nSVKHbK4kSZI6ZHMlSZLUIZsrSZKkDtlcSZIkdcjmSpIkqUM2V5IkSR2yuZIkSerQzM1VRKxExAcj\\n4nMRcUtEfG9E7I2IwxFxW0RcFxErXRYrSZJUu3nOXP068AeZ+RzgecCtwEHgcGaeBVzfriVJkpZG\\nZObO7xTxFODGzPzWieO3Audm5kZEnA40mfnsbkqVJEmq36xnrs4EvhQR746Iz0TEb0XEE4F9mbnR\\n3mYD2NdJlZIkSQti1uZqD/BC4Dcz84XAvzCxBZijU2I7Py0mSZK0wPbMeL87gDsy8y/b9QeBS4Gj\\nEXF6Zh6NiP3AnZN3jAgbLkmStDAyM3Zy+5nOXGXmUeD2iDirPXQ+cDNwLXBxe+xi4Jpt7u/XxNfb\\n3/723muo8ctczMVMzMVczKXPr1nMeuYK4CeB90bEKcDfAm8ETgaujogfBYbARXN8f0mSpIUzc3OV\\nmX8FfE/hqvNnL2d5DYfDvkuokrmUmcs0MykzlzJzKTOXbvgO7ZVYW1vru4QqmUuZuUwzkzJzKTOX\\nMnPpxkzvczXXD4zI3f6ZkiRJs4gIcjcG2iVJklRmc1WJpmn6LqFK5lJmLtPMpMxcysylzFy6YXMl\\nSZLUIWeuJEmStuHMlSRJUs9srirhPneZuZSZyzQzKTOXMnMpM5du2FxJkiR1yJkrSZKkbThzJUmS\\n1DObq0q4z11mLmXmMs1MysylzFzKzKUbNleSJEkdcuZKkiRpG85cSZIk9czmqhLuc5eZS5m5TDOT\\nMnMpM5cyc+mGzZUkSVKHnLmSJEnaxiwzV3vm+GFD4CvAN4AHMvOciNgLvB94JjAELsrMe2f9GZIk\\nSYtmnm3BBAaZ+YLMPKc9dhA4nJlnAde3az0G7nOXmUuZuUwzkzJzKTOXMnPpxsxnrlqTp8kOAOe2\\nlw8BDTZY0nEXsaMz1kvDEQRJfZh55ioi/j/gPkbbgv85M38rIu7JzNPa6wO4e3M9dj9nrqSOjZ5u\\nPq8eKWyuJM1tV2eugBdn5hcj4t8BhyPi1vErMzMjwlc2SZK0VGZurjLzi+1/vxQRvwOcA2xExOmZ\\neTQi9gN3lu67vr7O6uoqACsrK6ytrTEYDICt/d5lW28eq6WeWtZXXHGFj4/CevPY9HzE5nqwhOvN\\ny1vX1/Ln1ef6yJEjXHLJJdXUU8t68rnUdz21rH288PDl4XDIrGbaFoyIU4GTM/P+iHgicB3wS8D5\\nwF2ZeVlEHARWMvPgxH3dFixomubhP2BtMZeyyVzcFoRRczUYW7stCD6HtmMuZeYybZZtwVmbqzOB\\n32mXe4D3ZuY72rdiuBp4Btu8FYPNldQ9m6sSmytJ89u15moeNldS92yuSmyuJM3PD25eYON7vdpi\\nLmXmUtL0XUCVfKyUmUuZuXTD5kqSJKlDbgtKJwC3BUvcFpQ0P7cFJUmSemZzVQn3ucvMpcxcSpq+\\nC6iSj5Uycykzl27YXEmSJHXImSvpBODMVYkzV5Lm58yVJElSz2yuKuE+d5m5lJlLSdN3AVXysVJm\\nLmXm0g2bK0mSpA45cyWdAJy5KnHmStL8nLmSJEnqmc1VJdznLjOXMnMpafouoEo+VsrMpcxcumFz\\nJUmS1CFnrqQTgDNXJc5cSZqfM1eSJEk9s7mqhPvcZeZSZi4lTd8FVMnHSpm5lJlLN2yuJEmSOuTM\\nlXQCcOaqxJkrSfPb9ZmriDg5Im6MiGvb9d6IOBwRt0XEdRGxMs/3lyRJWjTzbgu+FbiFrX8yHwQO\\nZ+ZZwPXtWo+B+9xl5lJmLiVN3wVUycdKmbmUmUs3Zm6uIuLpwCuBdwGbp8sOAIfay4eAC+eqTpIk\\nacHMPHMVER8Afhn4ZuBnMvPVEXFPZp7WXh/A3Zvrsfs5cyV1zJmrEmeuJM1vlpmrPTP+oFcBd2bm\\njRExKN0mMzMiiq9s6+vrrK6uArCyssLa2hqDwejbbJ6SdO3a9WNfb9lcD1xTz5+Pa9euF2e9eXk4\\nHDKrmc5cRcQvAz8MPAg8ntHZqw8D3wMMMvNoROwHbsjMZ0/c1zNXBU3TPPwHrC3mUjaZi2euYNRY\\nDcbWnrkCn0PbMZcyc5m2a78tmJk/n5lnZOaZwOuAP8nMHwY+Alzc3uxi4JpZvr8kSdKimvt9riLi\\nXOCnM/NAROwFrgaeAQyBizLz3onbe+ZK6phnrko8cyVpfrOcufJNRKUTgM1Vic2VpPn5wc0LbHyQ\\nTlvMpcxcSpq+C6iSj5Uycykzl27YXEmSJHXIbUHpBOC2YInbgpLm57agJElSz2yuKuE+d5m5lJlL\\nSdN3AVXysVJmLmXm0g2bK0mSpA45cyWdAJy5KnHmStL8nLmSJEnqmc1VJdznLjOXMnMpafouoEo+\\nVsrMpcxcumFzJUmS1CFnrqQTgDNXJc5cSZqfM1eSJEk9s7mqhPvcZeZSZi4lTd8FVMnHSpm5lJlL\\nN2yuJEmSOuTMlXQCcOaqxJkrSfNz5kqSJKlnNleVcJ+7zFzKzKWk6buAKvlYKTOXMnPphs2VJElS\\nh2aauYqIxwN/CnwTcArwu5l5aUTsBd4PPBMYAhdl5r0T93XmSuqYM1clzlxJmt8sM1czD7RHxKmZ\\n+dWI2AP8OfAzwAHgy5l5eUS8DTgtMw9O3M/mSuqYzVWJzZWk+e3qQHtmfrW9eApwMnAPo+bqUHv8\\nEHDhrN9/2bjPXWYuZeZS0vRdQJV8rJSZS5m5dGPm5ioiToqII8AGcENm3gzsy8yN9iYbwL4OapQk\\nSVoYc7/PVUQ8BfgocCnw4cw8bey6uzNz78Tt3RaUOua2YInbgpLmN8u24J55f2hm3hcRvw98F7AR\\nEadn5tGI2A/cWbrP+vo6q6urAKysrLC2tsZgMAC2Tkm6du36sa+3bK4Hrqnnz8e1a9eLs968PBwO\\nmdWsvy34VODBzLw3Ip7A6MzVLwH/I3BXZl4WEQeBFQfaH5umaR7+A9YWcymbzMUzVzBqrAZja89c\\ngc+h7ZhLmblM280zV/uBQxFxEqO5rfdk5vURcSNwdUT8KO1bMcz4/aVtjRoJSZLq5GcLauF4lqbE\\nTKZ55krS/PxsQUmSpJ7ZXFVifJBOW8xlO03fBVSo6buAKvkcKjOXMnPphs2VJElSh5y50sJx5qrE\\nTKY5cyVpfs5cSZIk9czmqhLuc5eZy3aavguoUNN3AVXyOVRmLmXm0g2bK0mSpA45c6WF48xViZlM\\nc+ZK0vycuZIkSeqZzVUl3OcuM5ftNH0XUKGm7wKq5HOozFzKzKUbNleSJEkdcuZKC8eZqxIzmebM\\nlaT5OXMlSZLUM5urSrjPXWYu22n6LqBCTd8FVMnnUJm5lJlLN2yuJEmSOuTMlRaOM1clZjLNmStJ\\n83PmSpIkqWc2V5Vwn7vMXLbT9F1AhZq+C6iSz6Eycykzl27M1FxFxBkRcUNE3BwRn42It7TH90bE\\n4Yi4LSKui4iVbsuVJEmq20wzVxFxOnB6Zh6JiCcBnwYuBN4IfDkzL4+ItwGnZebBifs6c6W5OHNV\\nYibTnLmSNL9ZZq46GWiPiGuA32i/zs3MjbYBazLz2RO3tbnSXGyuSsxk2o5eC5eGr7/SzvQy0B4R\\nq8ALgE8A+zJzo71qA9g37/dfFu5zl5nLdpq+C6hQUziWfnHD2GVt8rWlzFy6MVdz1W4Jfgh4a2be\\nP35de3rKZ7MkSVoqe2a9Y0Q8jlFj9Z7MvKY9vBERp2fm0YjYD9xZuu/6+jqrq6sArKyssLa2xmAw\\nALa6ZteuNzVNM3X9ls31YMnXPMr1y7AeVFZPTestpefTMq4Hg0FV9dS03lRLPX38/zdNw3A4ZFaz\\nDrQHcAi4KzN/auz45e2xyyLiILDiQLu65sxViZlMM5NpDvlLO7WbM1cvBt4AnBcRN7ZfFwDvBF4W\\nEbcBL23Xegymz8gIzGV7Td8FVKjpu4BKNX0XUCVfW8rMpRszbQtm5p+zfWN2/uzlSJIkLTY/W1AL\\nx23BEjOZZibT3BaUdsrPFpQkSeqZzVUl3OcuM5ftNH0XUKGm7wIq1fRdQJV8bSkzl27YXEmSJHXI\\nmSstHGeuSsxkmplMc+ZK2ilnriRJknpmc1UJ97nLzGU7Td8FVKjpu4BKNX0XUCVfW8rMpRs2V5Ik\\nSR1y5koLx5mrEjOZZibTnLmSdsqZK0mSpJ7ZXFViu33uiPBr4kvgHE1J03cBlWoeser7+Vvjl7Y4\\nc9WNmT5bULttmU/jN8Bg4pgvhtLslvn1ZFzD6LXF1xN1z5mryo3+VWVej2Qm08xkmplMM5NpzqHp\\n2CKcuZIkSeqVzVUl3OfeTtN3AZVq+i6gQk3fBVSq6buASjV9F1Al/y7qhs2VJElSh5y5qpwzVyVm\\nMs1MppnJNDOZ5syVjs2ZK0mSpJ7ZXFXCfe7tNH0XUKmm7wIq1PRdQKWavguoVNN3AVXy76JuzNxc\\nRcSVEbERETeNHdsbEYcj4raIuC4iVropU5IkaTHMPHMVES8B/hn4L5l5dnvscuDLmXl5RLwNOC0z\\nD07cz5mrHXDmqsRMppnJNDOZZibTnLnSse3qzFVmfgy4Z+LwAeBQe/kQcOGs31+SJGkRdT1ztS8z\\nN9rLG8C+jr//Cct97u00fRdQqabvAirU9F1ApZq+C6hU03cBVfLvom4ct88WzMyMiOK51vX1dVZX\\nVwFYWVlhbW2NwWAAbP3BLtt60+T17VG2Pl9v8/bLsj6yzfVMrPuqr681j7Luuz7X9ayPjK03j9VU\\nXw3rdlXJ3wd9ro8cOVJVPX2sNy8Ph0NmNdf7XEXEKnDt2MzVrcAgM49GxH7ghsx89sR9nLnaAWeu\\nSsxkmplMM5NpZjLNmSsdWw3vc/UR4OL28sXANR1/f0mSpKrN3FxFxFXAx4HviIjbI/7/9u4uxq6q\\nDOP4/6G1SgtICAlqqWljhKBBbTWlfiAUB9MaKV4pTTSEROKFBjSNIl6Y3lVjjJAYL6pASKMQRcUS\\nmmiV7mhiwlc7pbYU/BptwRaiFLRToaWvF3u3czr7zNBpt2etmfX8kpPus890ztun+2S/56y119EN\\nwHPu4lEAAAdHSURBVDeAqyU9DVzV3LeT4HHuiVSpC8hUlbqADFWpC8hUlbqATFWpC8iSz0XdOOU5\\nVxGxeoKHhk71d5qZmZlNd/5uwcx5zlU/zqTNmbQ5kzZn0uY5Vza5HOZcmZmZmRXNzVUmPM49kSp1\\nAZmqUheQoSp1AZmqUheQqSp1AVnyuagbbq7MzMzMOuQ5V5nznKt+nEmbM2lzJm3OpM1zrmxynnNl\\nZmZmlpibq0x4nHsiVeoCMlWlLiBDVeoCMlWlLiBTVeoCsuRzUTfcXJmZmZl1yHOuMuc5V/04kzZn\\n0uZM2pxJm+dc2eQ858rMzMwsMTdXmfA490Sq1AVkqkpdQIaq1AVkqkpdQKaq1AVkyeeibri5MjMz\\nM+uQ51xlznOu+nEmbc6kzZm0OZM2z7myyXnOlZmZmVlibq4y4XHuiVSpC8hUlbqADFWpC8hUlbqA\\nTFWpC8iSz0XdcHNlZmZm1iHPucqc51z140zanEmbM2lzJm2ec2WTO5U5V7P/X8WYmZlNB/WbWOvl\\nhvP0dD4sKGmFpN2S/ijplq5//0zlce6JVKkLyFSVuoAMVakLyFSVuoBMVT3b4dvx25ZTStNO1Okn\\nV5JmAd8FhoBngEclbYyIJ1/r7x46dIj169d3Wc60UlUV27dvT11GhoaBK1MXkSHn0uZM+nMu/TmX\\n/oZTFzAjdD0suBT4U0SMAEi6F7gWeM3manR0lDVrbmHWrM91XNL0cOTIs2za9JcT9kU8l6ianBxI\\nXUCmnEubM+nPufTnXPqrc/FQ6enpurmaD+zpub8XuOyki5k9j5dfvr3jkqaLtbzyytpx+7YC9yao\\nxczMyuY5V2Om3mh23Vyd1v/G4cMvcc4513RVy7QyOrqNuXMfP2Hfq68e4ODBRAVlYyR1AZkaSV1A\\nhkZSF5CpkdQFZGokdQGZGkldwIzQ6VIMkpYBayNiRXP/VuBoRHyz52fcDpuZmdm0MdWlGLpurmYD\\nTwEfAZ4FHgFWn8yEdjMzM7OZoNNhwYg4IukLwC+BWcAdbqzMzMysJANfod3MzMxsJhvodwt6gdGa\\npDsl7Ze0o2ffeZI2S3pa0q8knZuyxkGTtEDSFkk7Jf1B0k3N/tJzeYOkhyUNS9olaV2zv+hcjpE0\\nS9I2SQ8094vPRdKIpCeaXB5p9hWdi6RzJd0n6cnmdXSZM9HFzTFy7PaipJtKzwXq+eLNuWiHpB9J\\nev1UcxlYc9WzwOgK4B3AakmXDOr5M3MXdQ69vgpsjoiLgN8090tyGPhSRLwTWAZ8vjk+is4lIv4L\\nLI+I9wDvApZL+hCF59LjZmAXY1cqO5c6iysjYnFELG32lZ7L7cCmiLiE+nW0m8IziYinmmNkMfBe\\nYBT4OYXnImkhcCOwJCIupZ7idB1TzGWQn1wdX2A0Ig5TL+B07QCfPxsR8TvghXG7VwF3N9t3A58Y\\naFGJRcS+iBhutv9DvfDsfArPBSAiRpvNOdQv9BdwLki6EPgY8APGFqIpPpfG+Cubis1F0huByyPi\\nTqjnBkfEixScSR9D1OfnPTiXl6jf7M9tLtKbS32B3pRyGWRz1W+B0fkDfP7cXRAR+5vt/cAFKYtJ\\nqXnnsBh4GOeCpDMkDVP/+7dExE6cC8B3gC8DR3v2OZf6k6tfS3pM0o3NvpJzWQQ8L+kuSVslfV/S\\nPMrOZLzrgHua7aJziYh/Ad8G/k7dVB2IiM1MMZdBNleeOX+Sor7KoMi8JJ0F/BS4OSL+3ftYqblE\\nxNFmWPBC4MOSlo97vLhcJH0ceC4itjHB8skl5tL4YDPUs5J6eP3y3gcLzGU2sAT4XkQsAQ4ybkin\\nwEyOkzQHuAb4yfjHSsxF0tuALwILgbcAZ0n6dO/PnEwug2yungEW9NxfQP3pldX2S3oTgKQ3A8V9\\nsaCk11E3Vhsi4v5md/G5HNMMZTxIPT+i9Fw+AKyS9Ffqd9xXSdqAcyEi/tH8+Tz1HJqllJ3LXmBv\\nRDza3L+PutnaV3AmvVYCjzfHC5R9rAC8D/h9RPwzIo4APwPezxSPl0E2V48Bb5e0sOmUPwVsHODz\\n524jcH2zfT1w/yQ/O+NIEnAHsCsibut5qPRczj92VYqkM4GrgW0UnktEfC0iFkTEIuohjYci4jMU\\nnoukuZLObrbnAR8FdlBwLhGxD9gj6aJm1xCwE3iAQjMZZzVjQ4JQ8LHS2A0sk3Rmc14aor5oZkrH\\ny0DXuZK0EriNsQVG1w3syTMi6R7gCuB86rHbrwO/AH4MvJX6y50+GRHFfG17cwXcb4EnGPu49Vbq\\nVf5LzuVS6smTZzS3DRHxLUnnUXAuvSRdAayJiFWl5yJpEfWnVVAPh/0wItY5F72b+sKHOcCfgRuo\\nz0PFZgLHG/C/AYuOTcMo/VgBkPQV6gbqKLAV+CxwNlPIxYuImpmZmXVooIuImpmZmc10bq7MzMzM\\nOuTmyszMzKxDbq7MzMzMOuTmyszMzKxDbq7MzMzMOuTmyszMzKxDbq7MzMzMOvQ/TlHIjGujWYkA\\nAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10d3a5290>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Set up a grid of plots\\n\",\n    \"fig = plt.figure(figsize=fizsize_with_subplots) \\n\",\n    \"fig_dims = (3, 1)\\n\",\n    \"\\n\",\n    \"# Plot the AgeFill histogram for Survivors\\n\",\n    \"plt.subplot2grid(fig_dims, (0, 0))\\n\",\n    \"survived_df = df_train[df_train['Survived'] == 1]\\n\",\n    \"survived_df['AgeFill'].hist(bins=max_age / bin_size, range=(1, max_age))\\n\",\n    \"\\n\",\n    \"# Plot the AgeFill histogram for Females\\n\",\n    \"plt.subplot2grid(fig_dims, (1, 0))\\n\",\n    \"females_df = df_train[(df_train['Sex_Val'] == 0) & (df_train['Survived'] == 1)]\\n\",\n    \"females_df['AgeFill'].hist(bins=max_age / bin_size, range=(1, max_age))\\n\",\n    \"\\n\",\n    \"# Plot the AgeFill histogram for first class passengers\\n\",\n    \"plt.subplot2grid(fig_dims, (2, 0))\\n\",\n    \"class1_df = df_train[(df_train['Pclass'] == 1) & (df_train['Survived'] == 1)]\\n\",\n    \"class1_df['AgeFill'].hist(bins=max_age / bin_size, range=(1, max_age))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"In the first graph, we see that most survivors come from the 20's to 30's age ranges and might be explained by the following two graphs.  The second graph shows most females are within their 20's.  The third graph shows most first class passengers are within their 30's.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Feature: Family Size\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Feature enginering involves creating new features or modifying existing features which might be advantageous to a machine learning algorithm.\\n\",\n    \"\\n\",\n    \"Define a new feature FamilySize that is the sum of Parch (number of parents or children on board) and SibSp (number of siblings or spouses):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div style=\\\"max-height:1000px;max-width:1500px;overflow:auto;\\\">\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>PassengerId</th>\\n\",\n       \"      <th>Survived</th>\\n\",\n       \"      <th>Pclass</th>\\n\",\n       \"      <th>Name</th>\\n\",\n       \"      <th>Sex</th>\\n\",\n       \"      <th>Age</th>\\n\",\n       \"      <th>SibSp</th>\\n\",\n       \"      <th>Parch</th>\\n\",\n       \"      <th>Ticket</th>\\n\",\n       \"      <th>Fare</th>\\n\",\n       \"      <th>Cabin</th>\\n\",\n       \"      <th>Embarked</th>\\n\",\n       \"      <th>Sex_Val</th>\\n\",\n       \"      <th>Embarked_Val</th>\\n\",\n       \"      <th>Embarked_Val_1</th>\\n\",\n       \"      <th>Embarked_Val_2</th>\\n\",\n       \"      <th>Embarked_Val_3</th>\\n\",\n       \"      <th>AgeFill</th>\\n\",\n       \"      <th>FamilySize</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 3</td>\\n\",\n       \"      <td>                           Braund, Mr. Owen Harris</td>\\n\",\n       \"      <td>   male</td>\\n\",\n       \"      <td> 22</td>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td>        A/5 21171</td>\\n\",\n       \"      <td>  7.2500</td>\\n\",\n       \"      <td>  NaN</td>\\n\",\n       \"      <td> S</td>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"      <td> 3</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"      <td> 22</td>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td> 2</td>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"      <td> Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\\n\",\n       \"      <td> female</td>\\n\",\n       \"      <td> 38</td>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td>         PC 17599</td>\\n\",\n       \"      <td> 71.2833</td>\\n\",\n       \"      <td>  C85</td>\\n\",\n       \"      <td> C</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 38</td>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td> 3</td>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"      <td> 3</td>\\n\",\n       \"      <td>                            Heikkinen, Miss. Laina</td>\\n\",\n       \"      <td> female</td>\\n\",\n       \"      <td> 26</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> STON/O2. 3101282</td>\\n\",\n       \"      <td>  7.9250</td>\\n\",\n       \"      <td>  NaN</td>\\n\",\n       \"      <td> S</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 3</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"      <td> 26</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td> 4</td>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"      <td>      Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\\n\",\n       \"      <td> female</td>\\n\",\n       \"      <td> 35</td>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td>           113803</td>\\n\",\n       \"      <td> 53.1000</td>\\n\",\n       \"      <td> C123</td>\\n\",\n       \"      <td> S</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 3</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"      <td> 35</td>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td> 5</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 3</td>\\n\",\n       \"      <td>                          Allen, Mr. William Henry</td>\\n\",\n       \"      <td>   male</td>\\n\",\n       \"      <td> 35</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td>           373450</td>\\n\",\n       \"      <td>  8.0500</td>\\n\",\n       \"      <td>  NaN</td>\\n\",\n       \"      <td> S</td>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"      <td> 3</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"      <td> 35</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   PassengerId  Survived  Pclass  \\\\\\n\",\n       \"0            1         0       3   \\n\",\n       \"1            2         1       1   \\n\",\n       \"2            3         1       3   \\n\",\n       \"3            4         1       1   \\n\",\n       \"4            5         0       3   \\n\",\n       \"\\n\",\n       \"                                                Name     Sex  Age  SibSp  \\\\\\n\",\n       \"0                            Braund, Mr. Owen Harris    male   22      1   \\n\",\n       \"1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female   38      1   \\n\",\n       \"2                             Heikkinen, Miss. Laina  female   26      0   \\n\",\n       \"3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female   35      1   \\n\",\n       \"4                           Allen, Mr. William Henry    male   35      0   \\n\",\n       \"\\n\",\n       \"   Parch            Ticket     Fare Cabin Embarked  Sex_Val  Embarked_Val  \\\\\\n\",\n       \"0      0         A/5 21171   7.2500   NaN        S        1             3   \\n\",\n       \"1      0          PC 17599  71.2833   C85        C        0             1   \\n\",\n       \"2      0  STON/O2. 3101282   7.9250   NaN        S        0             3   \\n\",\n       \"3      0            113803  53.1000  C123        S        0             3   \\n\",\n       \"4      0            373450   8.0500   NaN        S        1             3   \\n\",\n       \"\\n\",\n       \"   Embarked_Val_1  Embarked_Val_2  Embarked_Val_3  AgeFill  FamilySize  \\n\",\n       \"0               0               0               1       22           1  \\n\",\n       \"1               1               0               0       38           1  \\n\",\n       \"2               0               0               1       26           0  \\n\",\n       \"3               0               0               1       35           1  \\n\",\n       \"4               0               0               1       35           0  \"\n      ]\n     },\n     \"execution_count\": 30,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df_train['FamilySize'] = df_train['SibSp'] + df_train['Parch']\\n\",\n    \"df_train.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Plot a histogram of FamilySize:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.text.Text at 0x10db78590>\"\n      ]\n     },\n     \"execution_count\": 31,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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7e9HTir2z4TuKKq7quqJWAncEqfE9bwzA20y9q1zfq1zfqNx3pWld4I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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10be12450>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"df_train['FamilySize'].hist()\\n\",\n    \"plt.title('Family Size Histogram')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Plot a histogram of AgeFill segmented by Survived:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.text.Text at 0x10dd85bd0>\"\n      ]\n     },\n     \"execution_count\": 32,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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x9vfX7u3Lm8853vbD3epmPOmDGDefPmtba79dZbGT58OOPGjaOlpYWT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\\nQn26Z51/swAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10c473890>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Get the unique values of Embarked and its maximum\\n\",\n    \"family_sizes = sorted(df_train['FamilySize'].unique())\\n\",\n    \"family_size_max = max(family_sizes)\\n\",\n    \"\\n\",\n    \"df1 = df_train[df_train['Survived'] == 0]['FamilySize']\\n\",\n    \"df2 = df_train[df_train['Survived'] == 1]['FamilySize']\\n\",\n    \"plt.hist([df1, df2], \\n\",\n    \"         bins=family_size_max + 1, \\n\",\n    \"         range=(0, family_size_max), \\n\",\n    \"         stacked=True)\\n\",\n    \"plt.legend(('Died', 'Survived'), loc='best')\\n\",\n    \"plt.title('Survivors by Family Size')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Based on the histograms, it is not immediately obvious what impact FamilySize has on survival.  The machine learning algorithms might benefit from this feature.\\n\",\n    \"\\n\",\n    \"Additional features we might want to engineer might be related to the Name column, for example honorrary or pedestrian titles might give clues and better predictive power for a male's survival.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Final Data Preparation for Machine Learning\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Many machine learning algorithms do not work on strings and they usually require the data to be in an array, not a DataFrame.\\n\",\n    \"\\n\",\n    \"Show only the columns of type 'object' (strings):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Name        object\\n\",\n       \"Sex         object\\n\",\n       \"Ticket      object\\n\",\n       \"Cabin       object\\n\",\n       \"Embarked    object\\n\",\n       \"dtype: object\"\n      ]\n     },\n     \"execution_count\": 33,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df_train.dtypes[df_train.dtypes.map(lambda x: x == 'object')]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Drop the columns we won't use:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"df_train = df_train.drop(['Name', 'Sex', 'Ticket', 'Cabin', 'Embarked'], \\n\",\n    \"                         axis=1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Drop the following columns:\\n\",\n    \"* The Age column since we will be using the AgeFill column instead.\\n\",\n    \"* The SibSp and Parch columns since we will be using FamilySize instead.\\n\",\n    \"* The PassengerId column since it won't be used as a feature.\\n\",\n    \"* The Embarked_Val as we decided to use dummy variables instead.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 35,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Survived            int64\\n\",\n       \"Pclass              int64\\n\",\n       \"Fare              float64\\n\",\n       \"Sex_Val             int64\\n\",\n       \"Embarked_Val_1    float64\\n\",\n       \"Embarked_Val_2    float64\\n\",\n       \"Embarked_Val_3    float64\\n\",\n       \"AgeFill           float64\\n\",\n       \"FamilySize          int64\\n\",\n       \"dtype: object\"\n      ]\n     },\n     \"execution_count\": 35,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df_train = df_train.drop(['Age', 'SibSp', 'Parch', 'PassengerId', 'Embarked_Val'], axis=1)\\n\",\n    \"df_train.dtypes\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Convert the DataFrame to a numpy array:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[  0.    ,   3.    ,   7.25  , ...,   1.    ,  22.    ,   1.    ],\\n\",\n       \"       [  1.    ,   1.    ,  71.2833, ...,   0.    ,  38.    ,   1.    ],\\n\",\n       \"       [  1.    ,   3.    ,   7.925 , ...,   1.    ,  26.    ,   0.    ],\\n\",\n       \"       ..., \\n\",\n       \"       [  0.    ,   3.    ,  23.45  , ...,   1.    ,  21.5   ,   3.    ],\\n\",\n       \"       [  1.    ,   1.    ,  30.    , ...,   0.    ,  26.    ,   0.    ],\\n\",\n       \"       [  0.    ,   3.    ,   7.75  , ...,   0.    ,  32.    ,   0.    ]])\"\n      ]\n     },\n     \"execution_count\": 36,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"train_data = df_train.values\\n\",\n    \"train_data\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Data Wrangling Summary\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Below is a summary of the data wrangling we performed on our training data set.  We encapsulate this in a function since we'll need to do the same operations to our test set later.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 37,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def clean_data(df, drop_passenger_id):\\n\",\n    \"    \\n\",\n    \"    # Get the unique values of Sex\\n\",\n    \"    sexes = sorted(df['Sex'].unique())\\n\",\n    \"    \\n\",\n    \"    # Generate a mapping of Sex from a string to a number representation    \\n\",\n    \"    genders_mapping = dict(zip(sexes, range(0, len(sexes) + 1)))\\n\",\n    \"\\n\",\n    \"    # Transform Sex from a string to a number representation\\n\",\n    \"    df['Sex_Val'] = df['Sex'].map(genders_mapping).astype(int)\\n\",\n    \"    \\n\",\n    \"    # Get the unique values of Embarked\\n\",\n    \"    embarked_locs = sorted(df['Embarked'].unique())\\n\",\n    \"\\n\",\n    \"    # Generate a mapping of Embarked from a string to a number representation        \\n\",\n    \"    embarked_locs_mapping = dict(zip(embarked_locs, \\n\",\n    \"                                     range(0, len(embarked_locs) + 1)))\\n\",\n    \"    \\n\",\n    \"    # Transform Embarked from a string to dummy variables\\n\",\n    \"    df = pd.concat([df, pd.get_dummies(df['Embarked'], prefix='Embarked_Val')], axis=1)\\n\",\n    \"    \\n\",\n    \"    # Fill in missing values of Embarked\\n\",\n    \"    # Since the vast majority of passengers embarked in 'S': 3, \\n\",\n    \"    # we assign the missing values in Embarked to 'S':\\n\",\n    \"    if len(df[df['Embarked'].isnull()] > 0):\\n\",\n    \"        df.replace({'Embarked_Val' : \\n\",\n    \"                       { embarked_locs_mapping[nan] : embarked_locs_mapping['S'] \\n\",\n    \"                       }\\n\",\n    \"                   }, \\n\",\n    \"                   inplace=True)\\n\",\n    \"    \\n\",\n    \"    # Fill in missing values of Fare with the average Fare\\n\",\n    \"    if len(df[df['Fare'].isnull()] > 0):\\n\",\n    \"        avg_fare = df['Fare'].mean()\\n\",\n    \"        df.replace({ None: avg_fare }, inplace=True)\\n\",\n    \"    \\n\",\n    \"    # To keep Age in tact, make a copy of it called AgeFill \\n\",\n    \"    # that we will use to fill in the missing ages:\\n\",\n    \"    df['AgeFill'] = df['Age']\\n\",\n    \"\\n\",\n    \"    # Determine the Age typical for each passenger class by Sex_Val.  \\n\",\n    \"    # We'll use the median instead of the mean because the Age \\n\",\n    \"    # histogram seems to be right skewed.\\n\",\n    \"    df['AgeFill'] = df['AgeFill'] \\\\\\n\",\n    \"                        .groupby([df['Sex_Val'], df['Pclass']]) \\\\\\n\",\n    \"                        .apply(lambda x: x.fillna(x.median()))\\n\",\n    \"            \\n\",\n    \"    # Define a new feature FamilySize that is the sum of \\n\",\n    \"    # Parch (number of parents or children on board) and \\n\",\n    \"    # SibSp (number of siblings or spouses):\\n\",\n    \"    df['FamilySize'] = df['SibSp'] + df['Parch']\\n\",\n    \"    \\n\",\n    \"    # Drop the columns we won't use:\\n\",\n    \"    df = df.drop(['Name', 'Sex', 'Ticket', 'Cabin', 'Embarked'], axis=1)\\n\",\n    \"    \\n\",\n    \"    # Drop the Age column since we will be using the AgeFill column instead.\\n\",\n    \"    # Drop the SibSp and Parch columns since we will be using FamilySize.\\n\",\n    \"    # Drop the PassengerId column since it won't be used as a feature.\\n\",\n    \"    df = df.drop(['Age', 'SibSp', 'Parch'], axis=1)\\n\",\n    \"    \\n\",\n    \"    if drop_passenger_id:\\n\",\n    \"        df = df.drop(['PassengerId'], axis=1)\\n\",\n    \"    \\n\",\n    \"    return df\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Random Forest: Training\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Create the random forest object:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 38,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.ensemble import RandomForestClassifier\\n\",\n    \"\\n\",\n    \"clf = RandomForestClassifier(n_estimators=100)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Fit the training data and create the decision trees:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 39,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'Mean accuracy of Random Forest: 0.980920314254'\"\n      ]\n     },\n     \"execution_count\": 39,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# Training data features, skip the first column 'Survived'\\n\",\n    \"train_features = train_data[:, 1:]\\n\",\n    \"\\n\",\n    \"# 'Survived' column values\\n\",\n    \"train_target = train_data[:, 0]\\n\",\n    \"\\n\",\n    \"# Fit the model to our training data\\n\",\n    \"clf = clf.fit(train_features, train_target)\\n\",\n    \"score = clf.score(train_features, train_target)\\n\",\n    \"\\\"Mean accuracy of Random Forest: {0}\\\".format(score)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Random Forest: Predicting\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Read the test data:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 40,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div style=\\\"max-height:1000px;max-width:1500px;overflow:auto;\\\">\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>PassengerId</th>\\n\",\n       \"      <th>Pclass</th>\\n\",\n       \"      <th>Name</th>\\n\",\n       \"      <th>Sex</th>\\n\",\n       \"      <th>Age</th>\\n\",\n       \"      <th>SibSp</th>\\n\",\n       \"      <th>Parch</th>\\n\",\n       \"      <th>Ticket</th>\\n\",\n       \"      <th>Fare</th>\\n\",\n       \"      <th>Cabin</th>\\n\",\n       \"      <th>Embarked</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td> 892</td>\\n\",\n       \"      <td> 3</td>\\n\",\n       \"      <td>                             Kelly, Mr. James</td>\\n\",\n       \"      <td>   male</td>\\n\",\n       \"      <td> 34.5</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td>  330911</td>\\n\",\n       \"      <td>  7.8292</td>\\n\",\n       \"      <td> NaN</td>\\n\",\n       \"      <td> Q</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td> 893</td>\\n\",\n       \"      <td> 3</td>\\n\",\n       \"      <td>             Wilkes, Mrs. James (Ellen Needs)</td>\\n\",\n       \"      <td> female</td>\\n\",\n       \"      <td> 47.0</td>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td>  363272</td>\\n\",\n       \"      <td>  7.0000</td>\\n\",\n       \"      <td> NaN</td>\\n\",\n       \"      <td> S</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td> 894</td>\\n\",\n       \"      <td> 2</td>\\n\",\n       \"      <td>                    Myles, Mr. Thomas Francis</td>\\n\",\n       \"      <td>   male</td>\\n\",\n       \"      <td> 62.0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td>  240276</td>\\n\",\n       \"      <td>  9.6875</td>\\n\",\n       \"      <td> NaN</td>\\n\",\n       \"      <td> Q</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td> 895</td>\\n\",\n       \"      <td> 3</td>\\n\",\n       \"      <td>                             Wirz, Mr. Albert</td>\\n\",\n       \"      <td>   male</td>\\n\",\n       \"      <td> 27.0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td>  315154</td>\\n\",\n       \"      <td>  8.6625</td>\\n\",\n       \"      <td> NaN</td>\\n\",\n       \"      <td> S</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td> 896</td>\\n\",\n       \"      <td> 3</td>\\n\",\n       \"      <td> Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\\n\",\n       \"      <td> female</td>\\n\",\n       \"      <td> 22.0</td>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"      <td> 3101298</td>\\n\",\n       \"      <td> 12.2875</td>\\n\",\n       \"      <td> NaN</td>\\n\",\n       \"      <td> S</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   PassengerId  Pclass                                          Name     Sex  \\\\\\n\",\n       \"0          892       3                              Kelly, Mr. James    male   \\n\",\n       \"1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \\n\",\n       \"2          894       2                     Myles, Mr. Thomas Francis    male   \\n\",\n       \"3          895       3                              Wirz, Mr. Albert    male   \\n\",\n       \"4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \\n\",\n       \"\\n\",\n       \"    Age  SibSp  Parch   Ticket     Fare Cabin Embarked  \\n\",\n       \"0  34.5      0      0   330911   7.8292   NaN        Q  \\n\",\n       \"1  47.0      1      0   363272   7.0000   NaN        S  \\n\",\n       \"2  62.0      0      0   240276   9.6875   NaN        Q  \\n\",\n       \"3  27.0      0      0   315154   8.6625   NaN        S  \\n\",\n       \"4  22.0      1      1  3101298  12.2875   NaN        S  \"\n      ]\n     },\n     \"execution_count\": 40,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df_test = pd.read_csv('../data/titanic/test.csv')\\n\",\n    \"df_test.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Note the test data does not contain the column 'Survived', we'll use our trained model to predict these values.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 41,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Data wrangle the test set and convert it to a numpy array\\n\",\n    \"df_test = clean_data(df_test, drop_passenger_id=False)\\n\",\n    \"test_data = df_test.values\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Take the decision trees and run it on the test data:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 42,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Get the test data features, skipping the first column 'PassengerId'\\n\",\n    \"test_x = test_data[:, 1:]\\n\",\n    \"\\n\",\n    \"# Predict the Survival values for the test data\\n\",\n    \"test_y = clf.predict(test_x)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Random Forest: Prepare for Kaggle Submission\\n\",\n    \"\\n\",\n    \"Create a DataFrame by combining the index from the test data with the output of predictions, then write the results to the output:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 43,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"df_test['Survived'] = test_y\\n\",\n    \"df_test[['PassengerId', 'Survived']] \\\\\\n\",\n    \"    .to_csv('../data/titanic/results-rf.csv', index=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Evaluate Model Accuracy\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Submitting to Kaggle will give you an accuracy score.  It would be helpful to get an idea of accuracy without submitting to Kaggle.\\n\",\n    \"\\n\",\n    \"We'll split our training data, 80% will go to \\\"train\\\" and 20% will go to \\\"test\\\":\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 44,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"((891, 8), (891,))\\n\",\n      \"((712, 8), (712,))\\n\",\n      \"((179, 8), (179,))\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from sklearn import metrics\\n\",\n    \"from sklearn.cross_validation import train_test_split\\n\",\n    \"\\n\",\n    \"# Split 80-20 train vs test data\\n\",\n    \"train_x, test_x, train_y, test_y = train_test_split(train_features, \\n\",\n    \"                                                    train_target, \\n\",\n    \"                                                    test_size=0.20, \\n\",\n    \"                                                    random_state=0)\\n\",\n    \"print (train_features.shape, train_target.shape)\\n\",\n    \"print (train_x.shape, train_y.shape)\\n\",\n    \"print (test_x.shape, test_y.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Use the new training data to fit the model, predict, and get the accuracy score:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 45,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Accuracy = 0.83\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"clf = clf.fit(train_x, train_y)\\n\",\n    \"predict_y = clf.predict(test_x)\\n\",\n    \"\\n\",\n    \"from sklearn.metrics import accuracy_score\\n\",\n    \"print (\\\"Accuracy = %.2f\\\" % (accuracy_score(test_y, predict_y)))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"View the Confusion Matrix:\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"|      | condition True | condition false|\\n\",\n    \"|------|----------------|---------------|\\n\",\n    \"|prediction true|True Positive|False positive|\\n\",\n    \"|Prediction False|False Negative|True Negative|\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 46,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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5dKIi51IZmoRBhyebxza2T7xyJgEQh4BCzBCvgmsgW0CFgEjogACZaH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\\nYII=\\n\",\n      \"text/plain\": [\n       \"<IPython.core.display.Image at 0x10e2207d0>\"\n      ]\n     },\n     \"execution_count\": 46,\n     \"metadata\": {\n      \"image/png\": {\n       \"width\": 800\n      }\n     },\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from IPython.core.display import Image \\n\",\n    \"Image(filename='../data/confusion_matrix.png', width=800)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Get the model score and confusion matrix:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 47,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Model Score 0.83 \\n\",\n      \"\\n\",\n      \"('Confusion Matrix ', array([[98, 12],\\n\",\n      \"       [19, 50]]))\\n\",\n      \"          Predicted\\n\",\n      \"         |  0  |  1  |\\n\",\n      \"         |-----|-----|\\n\",\n      \"       0 |  98 |  12 |\\n\",\n      \"Actual   |-----|-----|\\n\",\n      \"       1 |  19 |  50 |\\n\",\n      \"         |-----|-----|\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"model_score = clf.score(test_x, test_y)\\n\",\n    \"print (\\\"Model Score %.2f \\\\n\\\" % (model_score))\\n\",\n    \"\\n\",\n    \"confusion_matrix = metrics.confusion_matrix(test_y, predict_y)\\n\",\n    \"print (\\\"Confusion Matrix \\\", confusion_matrix)\\n\",\n    \"\\n\",\n    \"print (\\\"          Predicted\\\")\\n\",\n    \"print (\\\"         |  0  |  1  |\\\")\\n\",\n    \"print (\\\"         |-----|-----|\\\")\\n\",\n    \"print (\\\"       0 | %3d | %3d |\\\" % (confusion_matrix[0, 0],\\n\",\n    \"                                   confusion_matrix[0, 1]))\\n\",\n    \"print (\\\"Actual   |-----|-----|\\\")\\n\",\n    \"print (\\\"       1 | %3d | %3d |\\\" % (confusion_matrix[1, 0],\\n\",\n    \"                                   confusion_matrix[1, 1]))\\n\",\n    \"print (\\\"         |-----|-----|\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Display the classification report:\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"$$Precision = \\\\frac{TP}{TP + FP}$$ \\n\",\n    \"\\n\",\n    \"$$Recall = \\\\frac{TP}{TP + FN}$$ \\n\",\n    \"\\n\",\n    \"$$F1 = \\\\frac{2TP}{2TP + FP + FN}$$ \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 48,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"              precision    recall  f1-score   support\\n\",\n      \"\\n\",\n      \"Not Survived       0.84      0.89      0.86       110\\n\",\n      \"    Survived       0.81      0.72      0.76        69\\n\",\n      \"\\n\",\n      \" avg / total       0.83      0.83      0.82       179\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from sklearn.metrics import classification_report\\n\",\n    \"print(classification_report(test_y, \\n\",\n    \"                            predict_y, \\n\",\n    \"                            target_names=['Not Survived', 'Survived']))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 48,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 2\",\n   \"language\": \"python\",\n   \"name\": \"python2\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 2\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython2\",\n   \"version\": \"2.7.10\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "mapreduce/__init__.py",
    "content": ""
  },
  {
    "path": "mapreduce/mapreduce-python.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This notebook was prepared by [Donne Martin](http://donnemartin.com). Source and license info is on [GitHub](https://github.com/donnemartin/data-science-ipython-notebooks).\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Python Hadoop MapReduce: Analyzing AWS S3 Bucket Logs with mrjob\\n\",\n    \"\\n\",\n    \"* [Introduction](#Introduction)\\n\",\n    \"* [Setup](#Setup)\\n\",\n    \"* [Processing S3 Logs](#Processing-S3-Logs)\\n\",\n    \"* [Running Amazon Elastic MapReduce Jobs](#Running-Amazon-Elastic-MapReduce-Jobs)\\n\",\n    \"* [Unit Testing S3 Logs](#Unit-Testing-S3-Logs)\\n\",\n    \"* [Running S3 Logs Unit Test](#Running-S3-Logs-Unit-Test)\\n\",\n    \"* [Sample Config File](#Sample-Config-File)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Introduction\\n\",\n    \"\\n\",\n    \"[mrjob](https://pythonhosted.org/mrjob/) lets you write MapReduce jobs in Python 2.5+ and run them on several platforms. You can:\\n\",\n    \"\\n\",\n    \"* Write multi-step MapReduce jobs in pure Python\\n\",\n    \"* Test on your local machine\\n\",\n    \"* Run on a Hadoop cluster\\n\",\n    \"* Run in the cloud using Amazon Elastic MapReduce (EMR)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Setup\\n\",\n    \"\\n\",\n    \"From PyPI:\\n\",\n    \"\\n\",\n    \"``pip install mrjob``\\n\",\n    \"\\n\",\n    \"From source:\\n\",\n    \"\\n\",\n    \"``python setup.py install``\\n\",\n    \"\\n\",\n    \"See [Sample Config File](#Sample-Config-File) section for additional config details.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Processing S3 Logs\\n\",\n    \"\\n\",\n    \"Sample mrjob code that processes log files on Amazon S3 based on the [S3 logging format](http://docs.aws.amazon.com/AmazonS3/latest/dev/LogFormat.html):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%%file mr_s3_log_parser.py\\n\",\n    \"\\n\",\n    \"import time\\n\",\n    \"from mrjob.job import MRJob\\n\",\n    \"from mrjob.protocol import RawValueProtocol, ReprProtocol\\n\",\n    \"import re\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class MrS3LogParser(MRJob):\\n\",\n    \"    \\\"\\\"\\\"Parses the logs from S3 based on the S3 logging format:\\n\",\n    \"    http://docs.aws.amazon.com/AmazonS3/latest/dev/LogFormat.html\\n\",\n    \"    \\n\",\n    \"    Aggregates a user's daily requests by user agent and operation\\n\",\n    \"    \\n\",\n    \"    Outputs date_time, requester, user_agent, operation, count\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"    LOGPATS  = r'(\\\\S+) (\\\\S+) \\\\[(.*?)\\\\] (\\\\S+) (\\\\S+) ' \\\\\\n\",\n    \"               r'(\\\\S+) (\\\\S+) (\\\\S+) (\\\"([^\\\"]+)\\\"|-) ' \\\\\\n\",\n    \"               r'(\\\\S+) (\\\\S+) (\\\\S+) (\\\\S+) (\\\\S+) (\\\\S+) ' \\\\\\n\",\n    \"               r'(\\\"([^\\\"]+)\\\"|-) (\\\"([^\\\"]+)\\\"|-)'\\n\",\n    \"    NUM_ENTRIES_PER_LINE = 17\\n\",\n    \"    logpat = re.compile(LOGPATS)\\n\",\n    \"\\n\",\n    \"    (S3_LOG_BUCKET_OWNER, \\n\",\n    \"     S3_LOG_BUCKET, \\n\",\n    \"     S3_LOG_DATE_TIME,\\n\",\n    \"     S3_LOG_IP, \\n\",\n    \"     S3_LOG_REQUESTER_ID, \\n\",\n    \"     S3_LOG_REQUEST_ID,\\n\",\n    \"     S3_LOG_OPERATION, \\n\",\n    \"     S3_LOG_KEY, \\n\",\n    \"     S3_LOG_HTTP_METHOD,\\n\",\n    \"     S3_LOG_HTTP_STATUS, \\n\",\n    \"     S3_LOG_S3_ERROR, \\n\",\n    \"     S3_LOG_BYTES_SENT,\\n\",\n    \"     S3_LOG_OBJECT_SIZE, \\n\",\n    \"     S3_LOG_TOTAL_TIME, \\n\",\n    \"     S3_LOG_TURN_AROUND_TIME,\\n\",\n    \"     S3_LOG_REFERER, \\n\",\n    \"     S3_LOG_USER_AGENT) = range(NUM_ENTRIES_PER_LINE)\\n\",\n    \"\\n\",\n    \"    DELIMITER = '\\\\t'\\n\",\n    \"\\n\",\n    \"    # We use RawValueProtocol for input to be format agnostic\\n\",\n    \"    # and avoid any type of parsing errors\\n\",\n    \"    INPUT_PROTOCOL = RawValueProtocol\\n\",\n    \"\\n\",\n    \"    # We use RawValueProtocol for output so we can output raw lines\\n\",\n    \"    # instead of (k, v) pairs\\n\",\n    \"    OUTPUT_PROTOCOL = RawValueProtocol\\n\",\n    \"\\n\",\n    \"    # Encode the intermediate records using repr() instead of JSON, so the\\n\",\n    \"    # record doesn't get Unicode-encoded\\n\",\n    \"    INTERNAL_PROTOCOL = ReprProtocol\\n\",\n    \"\\n\",\n    \"    def clean_date_time_zone(self, raw_date_time_zone):\\n\",\n    \"        \\\"\\\"\\\"Converts entry 22/Jul/2013:21:04:17 +0000 to the format\\n\",\n    \"        'YYYY-MM-DD HH:MM:SS' which is more suitable for loading into\\n\",\n    \"        a database such as Redshift or RDS\\n\",\n    \"\\n\",\n    \"        Note: requires the chars \\\"[ ]\\\" to be stripped prior to input\\n\",\n    \"        Returns the converted datetime annd timezone\\n\",\n    \"        or None for both values if failed\\n\",\n    \"\\n\",\n    \"        TODO: Needs to combine timezone with date as one field\\n\",\n    \"        \\\"\\\"\\\"\\n\",\n    \"        date_time = None\\n\",\n    \"        time_zone_parsed = None\\n\",\n    \"\\n\",\n    \"        # TODO: Probably cleaner to parse this with a regex\\n\",\n    \"        date_parsed = raw_date_time_zone[:raw_date_time_zone.find(\\\":\\\")]\\n\",\n    \"        time_parsed = raw_date_time_zone[raw_date_time_zone.find(\\\":\\\") + 1:\\n\",\n    \"                                         raw_date_time_zone.find(\\\"+\\\") - 1]\\n\",\n    \"        time_zone_parsed = raw_date_time_zone[raw_date_time_zone.find(\\\"+\\\"):]\\n\",\n    \"\\n\",\n    \"        try:\\n\",\n    \"            date_struct = time.strptime(date_parsed, \\\"%d/%b/%Y\\\")\\n\",\n    \"            converted_date = time.strftime(\\\"%Y-%m-%d\\\", date_struct)\\n\",\n    \"            date_time = converted_date + \\\" \\\" + time_parsed\\n\",\n    \"\\n\",\n    \"        # Throws a ValueError exception if the operation fails that is\\n\",\n    \"        # caught by the calling function and is handled appropriately\\n\",\n    \"        except ValueError as error:\\n\",\n    \"            raise ValueError(error)\\n\",\n    \"        else:\\n\",\n    \"            return converted_date, date_time, time_zone_parsed\\n\",\n    \"\\n\",\n    \"    def mapper(self, _, line):\\n\",\n    \"        line = line.strip()\\n\",\n    \"        match = self.logpat.search(line)\\n\",\n    \"\\n\",\n    \"        date_time = None\\n\",\n    \"        requester = None\\n\",\n    \"        user_agent = None\\n\",\n    \"        operation = None\\n\",\n    \"\\n\",\n    \"        try:\\n\",\n    \"            for n in range(self.NUM_ENTRIES_PER_LINE):\\n\",\n    \"                group = match.group(1 + n)\\n\",\n    \"\\n\",\n    \"                if n == self.S3_LOG_DATE_TIME:\\n\",\n    \"                    date, date_time, time_zone_parsed = \\\\\\n\",\n    \"                        self.clean_date_time_zone(group)\\n\",\n    \"                    # Leave the following line of code if \\n\",\n    \"                    # you want to aggregate by date\\n\",\n    \"                    date_time = date + \\\" 00:00:00\\\"\\n\",\n    \"                elif n == self.S3_LOG_REQUESTER_ID:\\n\",\n    \"                    requester = group\\n\",\n    \"                elif n == self.S3_LOG_USER_AGENT:\\n\",\n    \"                    user_agent = group\\n\",\n    \"                elif n == self.S3_LOG_OPERATION:\\n\",\n    \"                    operation = group\\n\",\n    \"                else:\\n\",\n    \"                    pass\\n\",\n    \"\\n\",\n    \"        except Exception:\\n\",\n    \"            yield ((\\\"Error while parsing line: %s\\\", line), 1)\\n\",\n    \"        else:\\n\",\n    \"            yield ((date_time, requester, user_agent, operation), 1)\\n\",\n    \"\\n\",\n    \"    def reducer(self, key, values):\\n\",\n    \"        output = list(key)\\n\",\n    \"        output = self.DELIMITER.join(output) + \\\\\\n\",\n    \"                 self.DELIMITER + \\\\\\n\",\n    \"                 str(sum(values))\\n\",\n    \"\\n\",\n    \"        yield None, output\\n\",\n    \"\\n\",\n    \"    def steps(self):\\n\",\n    \"        return [\\n\",\n    \"            self.mr(mapper=self.mapper,\\n\",\n    \"                    reducer=self.reducer)\\n\",\n    \"        ]\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"if __name__ == '__main__':\\n\",\n    \"    MrS3LogParser.run()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Running Amazon Elastic MapReduce Jobs\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Run an Amazon Elastic MapReduce (EMR) job on the given input (must be a flat file hierarchy), placing the results in the output (output directory must not exist):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!python mr_s3_log_parser.py -r emr s3://bucket-source/ --output-dir=s3://bucket-dest/\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Run a MapReduce job locally on the specified input file, sending the results to the specified output file:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!python mr_s3_log_parser.py input_data.txt > output_data.txt\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Unit Testing S3 Logs\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Accompanying unit test:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%%file test_mr_s3_log_parser.py\\n\",\n    \"\\n\",\n    \"from StringIO import StringIO\\n\",\n    \"import unittest2 as unittest\\n\",\n    \"from mr_s3_log_parser import MrS3LogParser\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class MrTestsUtil:\\n\",\n    \"\\n\",\n    \"    def run_mr_sandbox(self, mr_job, stdin):\\n\",\n    \"        # inline runs the job in the same process so small jobs tend to\\n\",\n    \"        # run faster and stack traces are simpler\\n\",\n    \"        # --no-conf prevents options from local mrjob.conf from polluting\\n\",\n    \"        # the testing environment\\n\",\n    \"        # \\\"-\\\" reads from standard in\\n\",\n    \"        mr_job.sandbox(stdin=stdin)\\n\",\n    \"\\n\",\n    \"        # make_runner ensures job cleanup is performed regardless of\\n\",\n    \"        # success or failure\\n\",\n    \"        with mr_job.make_runner() as runner:\\n\",\n    \"            runner.run()\\n\",\n    \"            for line in runner.stream_output():\\n\",\n    \"                key, value = mr_job.parse_output_line(line)\\n\",\n    \"                yield value\\n\",\n    \"\\n\",\n    \"                \\n\",\n    \"class TestMrS3LogParser(unittest.TestCase):\\n\",\n    \"\\n\",\n    \"    mr_job = None\\n\",\n    \"    mr_tests_util = None\\n\",\n    \"\\n\",\n    \"    RAW_LOG_LINE_INVALID = \\\\\\n\",\n    \"        '00000fe9688b6e57f75bd2b7f7c1610689e8f01000000' \\\\\\n\",\n    \"        '00000388225bcc00000 ' \\\\\\n\",\n    \"        's3-storage [22/Jul/2013:21:03:27 +0000] ' \\\\\\n\",\n    \"        '00.111.222.33 ' \\\\\\n\",\n    \"\\n\",\n    \"    RAW_LOG_LINE_VALID = \\\\\\n\",\n    \"        '00000fe9688b6e57f75bd2b7f7c1610689e8f01000000' \\\\\\n\",\n    \"        '00000388225bcc00000 ' \\\\\\n\",\n    \"        's3-storage [22/Jul/2013:21:03:27 +0000] ' \\\\\\n\",\n    \"        '00.111.222.33 ' \\\\\\n\",\n    \"        'arn:aws:sts::000005646931:federated-user/user 00000AB825500000 ' \\\\\\n\",\n    \"        'REST.HEAD.OBJECT user/file.pdf ' \\\\\\n\",\n    \"        '\\\"HEAD /user/file.pdf?versionId=00000XMHZJp6DjM9x500000' \\\\\\n\",\n    \"        '00000SDZk ' \\\\\\n\",\n    \"        'HTTP/1.1\\\" 200 - - 4000272 18 - \\\"-\\\" ' \\\\\\n\",\n    \"        '\\\"Boto/2.5.1 (darwin) USER-AGENT/1.0.14.0\\\" ' \\\\\\n\",\n    \"        '00000XMHZJp6DjM9x5JVEAMo8MG00000'\\n\",\n    \"\\n\",\n    \"    DATE_TIME_ZONE_INVALID = \\\"AB/Jul/2013:21:04:17 +0000\\\"\\n\",\n    \"    DATE_TIME_ZONE_VALID = \\\"22/Jul/2013:21:04:17 +0000\\\"\\n\",\n    \"    DATE_VALID = \\\"2013-07-22\\\"\\n\",\n    \"    DATE_TIME_VALID = \\\"2013-07-22 21:04:17\\\"\\n\",\n    \"    TIME_ZONE_VALID = \\\"+0000\\\"\\n\",\n    \"\\n\",\n    \"    def __init__(self, *args, **kwargs):\\n\",\n    \"        super(TestMrS3LogParser, self).__init__(*args, **kwargs)\\n\",\n    \"        self.mr_job = MrS3LogParser(['-r', 'inline', '--no-conf', '-'])\\n\",\n    \"        self.mr_tests_util = MrTestsUtil()\\n\",\n    \"\\n\",\n    \"    def test_invalid_log_lines(self):\\n\",\n    \"        stdin = StringIO(self.RAW_LOG_LINE_INVALID)\\n\",\n    \"\\n\",\n    \"        for result in self.mr_tests_util.run_mr_sandbox(self.mr_job, stdin):\\n\",\n    \"            self.assertEqual(result.find(\\\"Error\\\"), 0)\\n\",\n    \"\\n\",\n    \"    def test_valid_log_lines(self):\\n\",\n    \"        stdin = StringIO(self.RAW_LOG_LINE_VALID)\\n\",\n    \"\\n\",\n    \"        for result in self.mr_tests_util.run_mr_sandbox(self.mr_job, stdin):\\n\",\n    \"            self.assertEqual(result.find(\\\"Error\\\"), -1)\\n\",\n    \"\\n\",\n    \"    def test_clean_date_time_zone(self):\\n\",\n    \"        date, date_time, time_zone_parsed = \\\\\\n\",\n    \"            self.mr_job.clean_date_time_zone(self.DATE_TIME_ZONE_VALID)\\n\",\n    \"        self.assertEqual(date, self.DATE_VALID)\\n\",\n    \"        self.assertEqual(date_time, self.DATE_TIME_VALID)\\n\",\n    \"        self.assertEqual(time_zone_parsed, self.TIME_ZONE_VALID)\\n\",\n    \"\\n\",\n    \"        # Use a lambda to delay the calling of clean_date_time_zone so that\\n\",\n    \"        # assertRaises has enough time to handle it properly\\n\",\n    \"        self.assertRaises(ValueError,\\n\",\n    \"            lambda: self.mr_job.clean_date_time_zone(\\n\",\n    \"                self.DATE_TIME_ZONE_INVALID))\\n\",\n    \"\\n\",\n    \"if __name__ == '__main__':\\n\",\n    \"    unittest.main()\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Running S3 Logs Unit Test\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Run the mrjob test:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!python test_mr_s3_log_parser.py -v\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Sample Config File\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"runners:\\n\",\n    \"  emr:\\n\",\n    \"    aws_access_key_id: __ACCESS_KEY__\\n\",\n    \"    aws_secret_access_key: __SECRET_ACCESS_KEY__\\n\",\n    \"    aws_region: us-east-1\\n\",\n    \"    ec2_key_pair: EMR\\n\",\n    \"    ec2_key_pair_file: ~/.ssh/EMR.pem\\n\",\n    \"    ssh_tunnel_to_job_tracker: true\\n\",\n    \"    ec2_master_instance_type: m3.xlarge\\n\",\n    \"    ec2_instance_type: m3.xlarge\\n\",\n    \"    num_ec2_instances: 5\\n\",\n    \"    s3_scratch_uri: s3://bucket/tmp/\\n\",\n    \"    s3_log_uri: s3://bucket/tmp/logs/\\n\",\n    \"    enable_emr_debugging: True\\n\",\n    \"    bootstrap:\\n\",\n    \"    - sudo apt-get install -y python-pip\\n\",\n    \"    - sudo pip install --upgrade simplejson\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 2\",\n   \"language\": \"python\",\n   \"name\": \"python2\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 2\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython2\",\n   \"version\": \"2.7.10\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "mapreduce/mr_s3_log_parser.py",
    "content": "\nimport time\nfrom mrjob.job import MRJob\nfrom mrjob.protocol import RawValueProtocol, ReprProtocol\nimport re\n\n\nclass MrS3LogParser(MRJob):\n    \"\"\"Parses the logs from S3 based on the S3 logging format:\n    http://docs.aws.amazon.com/AmazonS3/latest/dev/LogFormat.html\n    \n    Aggregates a user's daily requests by user agent and operation\n    \n    Outputs date_time, requester, user_agent, operation, count\n    \"\"\"\n\n    LOGPATS  = r'(\\S+) (\\S+) \\[(.*?)\\] (\\S+) (\\S+) ' \\\n               r'(\\S+) (\\S+) (\\S+) (\"([^\"]+)\"|-) ' \\\n               r'(\\S+) (\\S+) (\\S+) (\\S+) (\\S+) (\\S+) ' \\\n               r'(\"([^\"]+)\"|-) (\"([^\"]+)\"|-)'\n    NUM_ENTRIES_PER_LINE = 17\n    logpat = re.compile(LOGPATS)\n\n    (S3_LOG_BUCKET_OWNER, \n     S3_LOG_BUCKET, \n     S3_LOG_DATE_TIME,\n     S3_LOG_IP, \n     S3_LOG_REQUESTER_ID, \n     S3_LOG_REQUEST_ID,\n     S3_LOG_OPERATION, \n     S3_LOG_KEY, \n     S3_LOG_HTTP_METHOD,\n     S3_LOG_HTTP_STATUS, \n     S3_LOG_S3_ERROR, \n     S3_LOG_BYTES_SENT,\n     S3_LOG_OBJECT_SIZE, \n     S3_LOG_TOTAL_TIME, \n     S3_LOG_TURN_AROUND_TIME,\n     S3_LOG_REFERER, \n     S3_LOG_USER_AGENT) = range(NUM_ENTRIES_PER_LINE)\n\n    DELIMITER = '\\t'\n\n    # We use RawValueProtocol for input to be format agnostic\n    # and avoid any type of parsing errors\n    INPUT_PROTOCOL = RawValueProtocol\n\n    # We use RawValueProtocol for output so we can output raw lines\n    # instead of (k, v) pairs\n    OUTPUT_PROTOCOL = RawValueProtocol\n\n    # Encode the intermediate records using repr() instead of JSON, so the\n    # record doesn't get Unicode-encoded\n    INTERNAL_PROTOCOL = ReprProtocol\n\n    def clean_date_time_zone(self, raw_date_time_zone):\n        \"\"\"Converts entry 22/Jul/2013:21:04:17 +0000 to the format\n        'YYYY-MM-DD HH:MM:SS' which is more suitable for loading into\n        a database such as Redshift or RDS\n\n        Note: requires the chars \"[ ]\" to be stripped prior to input\n        Returns the converted datetime annd timezone\n        or None for both values if failed\n\n        TODO: Needs to combine timezone with date as one field\n        \"\"\"\n        date_time = None\n        time_zone_parsed = None\n\n        # TODO: Probably cleaner to parse this with a regex\n        date_parsed = raw_date_time_zone[:raw_date_time_zone.find(\":\")]\n        time_parsed = raw_date_time_zone[raw_date_time_zone.find(\":\") + 1:\n                                         raw_date_time_zone.find(\"+\") - 1]\n        time_zone_parsed = raw_date_time_zone[raw_date_time_zone.find(\"+\"):]\n\n        try:\n            date_struct = time.strptime(date_parsed, \"%d/%b/%Y\")\n            converted_date = time.strftime(\"%Y-%m-%d\", date_struct)\n            date_time = converted_date + \" \" + time_parsed\n\n        # Throws a ValueError exception if the operation fails that is\n        # caught by the calling function and is handled appropriately\n        except ValueError as error:\n            raise ValueError(error)\n        else:\n            return converted_date, date_time, time_zone_parsed\n\n    def mapper(self, _, line):\n        line = line.strip()\n        match = self.logpat.search(line)\n\n        date_time = None\n        requester = None\n        user_agent = None\n        operation = None\n\n        try:\n            for n in range(self.NUM_ENTRIES_PER_LINE):\n                group = match.group(1 + n)\n\n                if n == self.S3_LOG_DATE_TIME:\n                    date, date_time, time_zone_parsed = \\\n                        self.clean_date_time_zone(group)\n                    # Leave the following line of code if \n                    # you want to aggregate by date\n                    date_time = date + \" 00:00:00\"\n                elif n == self.S3_LOG_REQUESTER_ID:\n                    requester = group\n                elif n == self.S3_LOG_USER_AGENT:\n                    user_agent = group\n                elif n == self.S3_LOG_OPERATION:\n                    operation = group\n                else:\n                    pass\n\n        except Exception:\n            yield ((\"Error while parsing line: %s\", line), 1)\n        else:\n            yield ((date_time, requester, user_agent, operation), 1)\n\n    def reducer(self, key, values):\n        output = list(key)\n        output = self.DELIMITER.join(output) + \\\n                 self.DELIMITER + \\\n                 str(sum(values))\n\n        yield None, output\n\n    def steps(self):\n        return [\n            self.mr(mapper=self.mapper,\n                    reducer=self.reducer)\n        ]\n\n\nif __name__ == '__main__':\n    MrS3LogParser.run()"
  },
  {
    "path": "mapreduce/test_mr_s3_log_parser.py",
    "content": "\nfrom StringIO import StringIO\nimport unittest2 as unittest\nfrom mr_s3_log_parser import MrS3LogParser\n\n\nclass MrTestsUtil:\n\n    def run_mr_sandbox(self, mr_job, stdin):\n        # inline runs the job in the same process so small jobs tend to\n        # run faster and stack traces are simpler\n        # --no-conf prevents options from local mrjob.conf from polluting\n        # the testing environment\n        # \"-\" reads from standard in\n        mr_job.sandbox(stdin=stdin)\n\n        # make_runner ensures job cleanup is performed regardless of\n        # success or failure\n        with mr_job.make_runner() as runner:\n            runner.run()\n            for line in runner.stream_output():\n                key, value = mr_job.parse_output_line(line)\n                yield value\n\n                \nclass TestMrS3LogParser(unittest.TestCase):\n\n    mr_job = None\n    mr_tests_util = None\n\n    RAW_LOG_LINE_INVALID = \\\n        '00000fe9688b6e57f75bd2b7f7c1610689e8f01000000' \\\n        '00000388225bcc00000 ' \\\n        's3-storage [22/Jul/2013:21:03:27 +0000] ' \\\n        '00.111.222.33 ' \\\n\n    RAW_LOG_LINE_VALID = \\\n        '00000fe9688b6e57f75bd2b7f7c1610689e8f01000000' \\\n        '00000388225bcc00000 ' \\\n        's3-storage [22/Jul/2013:21:03:27 +0000] ' \\\n        '00.111.222.33 ' \\\n        'arn:aws:sts::000005646931:federated-user/user 00000AB825500000 ' \\\n        'REST.HEAD.OBJECT user/file.pdf ' \\\n        '\"HEAD /user/file.pdf?versionId=00000XMHZJp6DjM9x500000' \\\n        '00000SDZk ' \\\n        'HTTP/1.1\" 200 - - 4000272 18 - \"-\" ' \\\n        '\"Boto/2.5.1 (darwin) USER-AGENT/1.0.14.0\" ' \\\n        '00000XMHZJp6DjM9x5JVEAMo8MG00000'\n\n    DATE_TIME_ZONE_INVALID = \"AB/Jul/2013:21:04:17 +0000\"\n    DATE_TIME_ZONE_VALID = \"22/Jul/2013:21:04:17 +0000\"\n    DATE_VALID = \"2013-07-22\"\n    DATE_TIME_VALID = \"2013-07-22 21:04:17\"\n    TIME_ZONE_VALID = \"+0000\"\n\n    def __init__(self, *args, **kwargs):\n        super(TestMrS3LogParser, self).__init__(*args, **kwargs)\n        self.mr_job = MrS3LogParser(['-r', 'inline', '--no-conf', '-'])\n        self.mr_tests_util = MrTestsUtil()\n\n    def test_invalid_log_lines(self):\n        stdin = StringIO(self.RAW_LOG_LINE_INVALID)\n\n        for result in self.mr_tests_util.run_mr_sandbox(self.mr_job, stdin):\n            self.assertEqual(result.find(\"Error\"), 0)\n\n    def test_valid_log_lines(self):\n        stdin = StringIO(self.RAW_LOG_LINE_VALID)\n\n        for result in self.mr_tests_util.run_mr_sandbox(self.mr_job, stdin):\n            self.assertEqual(result.find(\"Error\"), -1)\n\n    def test_clean_date_time_zone(self):\n        date, date_time, time_zone_parsed = \\\n            self.mr_job.clean_date_time_zone(self.DATE_TIME_ZONE_VALID)\n        self.assertEqual(date, self.DATE_VALID)\n        self.assertEqual(date_time, self.DATE_TIME_VALID)\n        self.assertEqual(time_zone_parsed, self.TIME_ZONE_VALID)\n\n        # Use a lambda to delay the calling of clean_date_time_zone so that\n        # assertRaises has enough time to handle it properly\n        self.assertRaises(ValueError,\n            lambda: self.mr_job.clean_date_time_zone(\n                self.DATE_TIME_ZONE_INVALID))\n\nif __name__ == '__main__':\n    unittest.main()\n"
  },
  {
    "path": "matplotlib/04.00-Introduction-To-Matplotlib.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<!--BOOK_INFORMATION-->\\n\",\n    \"<img align=\\\"left\\\" style=\\\"padding-right:10px;\\\" src=\\\"figures/PDSH-cover-small.png\\\">\\n\",\n    \"*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\\n\",\n    \"\\n\",\n    \"*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*\\n\",\n    \"\\n\",\n    \"*No changes were made to the contents of this notebook from the original.*\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<!--NAVIGATION-->\\n\",\n    \"< [Further Resources](03.13-Further-Resources.ipynb) | [Contents](Index.ipynb) | [Simple Line Plots](04.01-Simple-Line-Plots.ipynb) >\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Visualization with Matplotlib\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We'll now take an in-depth look at the Matplotlib package for visualization in Python.\\n\",\n    \"Matplotlib is a multi-platform data visualization library built on NumPy arrays, and designed to work with the broader SciPy stack.\\n\",\n    \"It was conceived by John Hunter in 2002, originally as a patch to IPython for enabling interactive MATLAB-style plotting via gnuplot from the IPython command line.\\n\",\n    \"IPython's creator, Fernando Perez, was at the time scrambling to finish his PhD, and let John know he wouldn’t have time to review the patch for several months.\\n\",\n    \"John took this as a cue to set out on his own, and the Matplotlib package was born, with version 0.1 released in 2003.\\n\",\n    \"It received an early boost when it was adopted as the plotting package of choice of the Space Telescope Science Institute (the folks behind the Hubble Telescope), which financially supported Matplotlib’s development and greatly expanded its capabilities.\\n\",\n    \"\\n\",\n    \"One of Matplotlib’s most important features is its ability to play well with many operating systems and graphics backends.\\n\",\n    \"Matplotlib supports dozens of backends and output types, which means you can count on it to work regardless of which operating system you are using or which output format you wish.\\n\",\n    \"This cross-platform, everything-to-everyone approach has been one of the great strengths of Matplotlib.\\n\",\n    \"It has led to a large user base, which in turn has led to an active developer base and Matplotlib’s powerful tools and ubiquity within the scientific Python world.\\n\",\n    \"\\n\",\n    \"In recent years, however, the interface and style of Matplotlib have begun to show their age.\\n\",\n    \"Newer tools like ggplot and ggvis in the R language, along with web visualization toolkits based on D3js and HTML5 canvas, often make Matplotlib feel clunky and old-fashioned.\\n\",\n    \"Still, I'm of the opinion that we cannot ignore Matplotlib's strength as a well-tested, cross-platform graphics engine.\\n\",\n    \"Recent Matplotlib versions make it relatively easy to set new global plotting styles (see [Customizing Matplotlib: Configurations and Style Sheets](04.11-Settings-and-Stylesheets.ipynb)), and people have been developing new packages that build on its powerful internals to drive Matplotlib via cleaner, more modern APIs—for example, Seaborn (discussed in [Visualization With Seaborn](04.14-Visualization-With-Seaborn.ipynb)), [ggpy](http://yhat.github.io/ggpy/), [HoloViews](http://holoviews.org/), [Altair](http://altair-viz.github.io/), and even Pandas itself can be used as wrappers around Matplotlib's API.\\n\",\n    \"Even with wrappers like these, it is still often useful to dive into Matplotlib's syntax to adjust the final plot output.\\n\",\n    \"For this reason, I believe that Matplotlib itself will remain a vital piece of the data visualization stack, even if new tools mean the community gradually moves away from using the Matplotlib API directly.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## General Matplotlib Tips\\n\",\n    \"\\n\",\n    \"Before we dive into the details of creating visualizations with Matplotlib, there are a few useful things you should know about using the package.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Importing Matplotlib\\n\",\n    \"\\n\",\n    \"Just as we use the ``np`` shorthand for NumPy and the ``pd`` shorthand for Pandas, we will use some standard shorthands for Matplotlib imports:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib as mpl\\n\",\n    \"import matplotlib.pyplot as plt\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The ``plt`` interface is what we will use most often, as we shall see throughout this chapter.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Setting Styles\\n\",\n    \"\\n\",\n    \"We will use the ``plt.style`` directive to choose appropriate aesthetic styles for our figures.\\n\",\n    \"Here we will set the ``classic`` style, which ensures that the plots we create use the classic Matplotlib style:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"plt.style.use('classic')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Throughout this section, we will adjust this style as needed.\\n\",\n    \"Note that the stylesheets used here are supported as of Matplotlib version 1.5; if you are using an earlier version of Matplotlib, only the default style is available.\\n\",\n    \"For more information on stylesheets, see [Customizing Matplotlib: Configurations and Style Sheets](04.11-Settings-and-Stylesheets.ipynb).\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### ``show()`` or No ``show()``? How to Display Your Plots\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"A visualization you can't see won't be of much use, but just how you view your Matplotlib plots depends on the context.\\n\",\n    \"The best use of Matplotlib differs depending on how you are using it; roughly, the three applicable contexts are using Matplotlib in a script, in an IPython terminal, or in an IPython notebook.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Plotting from a script\\n\",\n    \"\\n\",\n    \"If you are using Matplotlib from within a script, the function ``plt.show()`` is your friend.\\n\",\n    \"``plt.show()`` starts an event loop, looks for all currently active figure objects, and opens one or more interactive windows that display your figure or figures.\\n\",\n    \"\\n\",\n    \"So, for example, you may have a file called *myplot.py* containing the following:\\n\",\n    \"\\n\",\n    \"```python\\n\",\n    \"# ------- file: myplot.py ------\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"x = np.linspace(0, 10, 100)\\n\",\n    \"\\n\",\n    \"plt.plot(x, np.sin(x))\\n\",\n    \"plt.plot(x, np.cos(x))\\n\",\n    \"\\n\",\n    \"plt.show()\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"You can then run this script from the command-line prompt, which will result in a window opening with your figure displayed:\\n\",\n    \"\\n\",\n    \"```\\n\",\n    \"$ python myplot.py\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"The ``plt.show()`` command does a lot under the hood, as it must interact with your system's interactive graphical backend.\\n\",\n    \"The details of this operation can vary greatly from system to system and even installation to installation, but matplotlib does its best to hide all these details from you.\\n\",\n    \"\\n\",\n    \"One thing to be aware of: the ``plt.show()`` command should be used *only once* per Python session, and is most often seen at the very end of the script.\\n\",\n    \"Multiple ``show()`` commands can lead to unpredictable backend-dependent behavior, and should mostly be avoided.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Plotting from an IPython shell\\n\",\n    \"\\n\",\n    \"It can be very convenient to use Matplotlib interactively within an IPython shell (see [IPython: Beyond Normal Python](01.00-IPython-Beyond-Normal-Python.ipynb)).\\n\",\n    \"IPython is built to work well with Matplotlib if you specify Matplotlib mode.\\n\",\n    \"To enable this mode, you can use the ``%matplotlib`` magic command after starting ``ipython``:\\n\",\n    \"\\n\",\n    \"```ipython\\n\",\n    \"In [1]: %matplotlib\\n\",\n    \"Using matplotlib backend: TkAgg\\n\",\n    \"\\n\",\n    \"In [2]: import matplotlib.pyplot as plt\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"At this point, any ``plt`` plot command will cause a figure window to open, and further commands can be run to update the plot.\\n\",\n    \"Some changes (such as modifying properties of lines that are already drawn) will not draw automatically: to force an update, use ``plt.draw()``.\\n\",\n    \"Using ``plt.show()`` in Matplotlib mode is not required.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Plotting from an IPython notebook\\n\",\n    \"\\n\",\n    \"The IPython notebook is a browser-based interactive data analysis tool that can combine narrative, code, graphics, HTML elements, and much more into a single executable document (see [IPython: Beyond Normal Python](01.00-IPython-Beyond-Normal-Python.ipynb)).\\n\",\n    \"\\n\",\n    \"Plotting interactively within an IPython notebook can be done with the ``%matplotlib`` command, and works in a similar way to the IPython shell.\\n\",\n    \"In the IPython notebook, you also have the option of embedding graphics directly in the notebook, with two possible options:\\n\",\n    \"\\n\",\n    \"- ``%matplotlib notebook`` will lead to *interactive* plots embedded within the notebook\\n\",\n    \"- ``%matplotlib inline`` will lead to *static* images of your plot embedded in the notebook\\n\",\n    \"\\n\",\n    \"For this book, we will generally opt for ``%matplotlib inline``:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%matplotlib inline\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"After running this command (it needs to be done only once per kernel/session), any cell within the notebook that creates a plot will embed a PNG image of the resulting graphic:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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MQrLeJz59QevXd75/HFdQ0bBgsXenfHvIAo/l98obpDrqVwaGHW3b+O\\ngiEu2cMxFwYPhqVL4fhx3UkML5kxQ42DL1dOdxJ7lC6tNqeZPFl3Ev/wfPE/eFBtzh4VpTuJfUqU\\ngD59vHvRGnpMmADDh1//NXtO7WF/0n5b8tjhctePF2f8er74T5igWsJu2ZzdKpe7frx40Rr227dP\\nbdiS0+bsU7ZO4a1Vf97m0a1uv12NEty0SXcS63m6+GdmqrG62XX5eFmHDpCc7M2L1ukOnz3Mqz++\\nqjuGpSZOhCFDct6w5e4mdzN923QupV+yJ5ifBQWpux0vLvbm6eK/YoVap7tlS91J7BcUpH7pmQe/\\n9puydYqn1vHJzFR30Pfem/Nra5SsQaPyjZif4J0d0YcOVasDpKXpTmItTxf/y63+3A62mLJ1Cov3\\neGeM5LBhaoir1y5aJ5NSMil2kqdm9K5cCYULQ/PmuXv98CbDmfDzBP+GslHdulCzphpE4SWeLf4X\\nL8KcOXDXXbl/T1pGGh+s/8B/oWxWsybUqwfffac7SeCIPRbLuUvnaFutre4olvnyS9Xqz20jqn/D\\n/vx44EcSz3tng4m771bLWniJZ4v/ggW/r3SZW/0a9uPHAz9y/Lx3xkgOHWpG/dhpUuwkhjUe5plF\\n3C5cUPNkhg7N/XuKhRXj0z6femq5h0GDVE05d053Eut44wq9hsmT89bqB3XR9qnfh2lx0/wTSoNB\\ng9REFS9dtE6VKTOZFjeNYY2H6Y5imfnz1QTJvDSiAAaGD/TMekYAZcvCbbepX4ReYUnxF0J0F0Ls\\nFEIkCCGeucbXOwghkoQQm7L+vGjFebNz+jQsW5a/3bruaXwPE2O982i/TBl10c6ZozuJ9wWJINb/\\ndT0NyjTQHcUyU6fCnXfqTuEMw4Z5q+vH5+IvhAgCPgDuAMKBO4UQ17r6f5RSNs/649dxcLNmQZcu\\narJTXt1e83YOnz3sqXX+TdePfSoVq6Q7gmWSklQjqm9f3UmcISJCrQ78yy+6k1jDipZ/K2CXlPKA\\nlDINmAZEXuN1tnUATp6ctz7KKwUHBRP7t1hqlqppbSiNIiJg7Vo4dkx3EsNNZs2Czp2hZEndSZyh\\nUCH1i3DqVN1JrGFF8a8MHLri48NZn7taGyHEFiHEAiHEjRac95qOHIEtW6Bnz/wfw0t9laA2p+jT\\nB6ZP153EcBMrunyklKRmpFoTyAGGDoUpU3SnsEaITefZCFSTUl4QQvQA5gD1snvx6NGjf/t7x44d\\n6Xj14uHXMW2a+u0caMs55GToUBg1Ch59VHcSww2OHlVdHHPn+nact1e/zYkLJxjTdYw1wTTr0AEO\\nH4bdu6FOHX05YmJiiPFxswEhfVz8RQjRGhgtpeye9fGzgJRSZvuvLYTYB7SQUp66xtekL5latIA3\\n31S3q8bv0tOhcmVYswZq1dKdxlvOXTpHXGIcbaq20R3FMmPHquLv67IGscdi6TO1D/se2+eZ4a8P\\nPwyVKsELL+hO8jshBFLKPHWtW/GvsQGoI4SoLoQoAAwB/tBeEEKUv+LvrVC/dP5U+H2VkKAexuTh\\nRiFghIRAv37wzTe6k3hPdHw0r618TXcMS02ZYs0on0blGlG0QFHWHFrj+8EcYvBgNXPe7Xwu/lLK\\nDOARYDGwDZgmpdwhhBgphHgg62UDhBBxQojNwH+Bwb6e91q++UYN7wwOtuZ4249v50DSAWsO5gCD\\nBnnjonWa6dumMzjcL5e0Fvv2qW6NLl18P5YQgjtvupOpcR55Sgq0awcnTsDOnbqT+Mbnbh+r+dLt\\n07QpvP8+tG9vTZZ/Lf8X51PP884d71hzQM0yMlTXz8qVevsrveT0xdNU/291Dj9xmOJhxXXHscSY\\nMeoXwCefWHO8Paf20OazNvzy5C+EBNn1mNG/Hn8cSpVSz9GcQFe3jyMkJKihjG0tXFJlyE1DmL5t\\nOpky07qDahQcrO6MTNePdebsnEPnWp09U/hBXR+DBll3vNqla9O1dld+OeeRAfKo78/06e7eL8Mz\\nxf+bb2DAAOu6fABuLHsjpQuVZtXBVdYdVDPT9WMtL3b5HDxo3d3zZZP7TaZaiWrWHlSj1q3Vfhlx\\ncbqT5J+niv/AgdYfd+CNA5mxfYb1B9akXTs1jC8hQXcSb4isH0nvejlsb+UiM2aoodIh3uid8Zug\\nIPc3pDxR/BMSIDHR2i6fywbcOICZO2Z6qutnwADT9WOVB29+kKIFiuqOYRl/NaK8aPBgd3f9eKL4\\nWz3K50oNyzbk+due99QsRbe3WAz/OHBAdfuYodK507Klmj8TG6s7Sf54ovh//bW1D6iu9tDND1Ew\\nxDtThtu2VUPV4uN1JzGcZMYMiIoyXT65JYSaOzNzpu4k+eP64h8fD8eP+6fLx6uCgsyoH+PP7Ojy\\n+WjDR2w4ssG/J7FR//6m+Gszc6b6Bwhy/f+Jvfr189bGFHZz2vwYXx08qCZ2derk3/OcuniKr2K9\\nsyj+Lbeopa/dOOHL9SVz1qz8bdoS6G67DQ4dgv37dSdxn7OXzlL/g/qkZaTpjmKZmTMhMhJCQ/17\\nngE3DmDGjhmeGUARFOTerh9XF/8DB1SLpV07e84npfRMiy84WP2wz5qlO4n7LEhYQJ3SdQgN9nOl\\ntNGMGWoUmL81KNOA0oVKe2qtn/793flz5OriP3u22qjErgdUXSZ1IfaYSx/tX0O/fu68aHWbuWMm\\n/Rt653bz119h+3b7VsLt16Afs3d6p8+xXTt1F73PZZv/ubr4z5pl7xZzzSs099SEr9tvh23b1A+/\\nkTsX0i6wZO8SIhtca7M6d4qOVpsfFShgz/n6NuzLrB2zPHMXHRLizrto1xb/Y8fU+Fo71+3v17Af\\ns3a67F/4OsLC1A99dLTuJO6xaPciWlZqSZnCZXRHsczs2fY2opqUb8LSe5YihG07u/qdG0f9uLb4\\nR0dDjx727th1S5VbOH3xNAknvbM2glv7K3XZcXyHp9bySUpSG/x0727fOYUQ1CrlrR2Fbr9djfg5\\nckR3ktxzbfGfNUv1WdspSAQRWT+SOTvn2HtiP7rjDli3Dk5ZvrWON73Q/gUeaPFAzi90ifnz1fDO\\not5ZoUKLAgWgd293DZ92ZfFPSoLVq1XL3279GvYj/oR3psYWKaK6zubN053E0MHuLh8v69vXXV2o\\nrtzM5auv1GxEN32jneyrr9QSGb5u1m24y4ULULEi7N0LN9ygO437nT+vvp8HDqiNXuwUMJu56Ojy\\n8bJevSAmRl28RuBYvBhatNBX+KWUbD++Xc/J/aBIEdWFtnCh7iS547rif+ECLF2q+tcMa5QqBa1a\\nqWJgBI7Zs/U2olIzUmn7eVuOJh/VF8JiUVEwxyWPBF1X/Jcu1dta8aqoKNONdj1zds5h5wkXLuCS\\njbQ09bA3KkpfhrCQMHrU6UH0Tu9ceL17q0ZUSoruJDlzXfGPjlYTKgxrRUSoYpCerjuJMz33/XOc\\nvXRWdwzL/Pgj1KoFVarozdG3QV9PzfYtWxaaNoXvv9edJGeuKv4ZGWpUihOK/6/nfmXizxN1x7BM\\ntWpQvTqs8s52xZbZeWInZy+dpWWllrqjWMYpjajudbqz+tBqT/1i7dvXHV0/rir+a9aop+k1a+pO\\nAiFBITz67aOkpLvg/i6XIiPdcdHaLXpnNJH1IwkSrvpxyZaUzin+xcKK0bZaWxbtXqQ7imUiI9XI\\nuYwM3Umuz1VXs1MuWICyRcrSuHxjlu9brjuKZS73+zts9K92c+LnENVAY+e4xX7+Wa3qetNNupMo\\nD7Z8kCKhRXTHsEzNmqqRunat7iTX55riL6Vqlep8QHW1iPoRRMd752FVo0bq+7x1q+4kzvHruV+J\\nPxFPxxoddUexzOVGlFOW1omoH0Gver10x7CUG0b9uKb479gBly5Bs2a6k/wusn4k8xLmeWZjCiHc\\ncdHaqWiBoswaPIsCwTYteWkDJ91Be1VUlBpK6+S7aNcU/+hoNSLFKa0VgLo31KV4WHE2/rJRdxTL\\nREaaIZ9XKhZWzFOt/kOH7N0AKVA1aQKpqc7e3tE1xd9pXT6XfRbxGTVK1tAdwzLt2v2+Q5rhPXPn\\nqmW87doAKVAJoRqrTl4yxRXF/5dfICEBOnTQneTPbq16K2WLlNUdwzIhIao4zJ+vO4nhD6bLxz4R\\nEc6+i3ZF8Z8/X63g6e/NpQ3F6S0WI3/OnFEjUO64Q3eSa5u1YxYfbfhIdwzLdOigtsc8dkx3kmtz\\nRfGfO1cVJMMed9yhJnud9c68mzzLlJmkZaTpjmGpb7+F225z7tr9pQuV5rPNn+mOYZmwMPWztGCB\\n7iTX5vjif/68mopu505Dga5YMWjbNrAXeltzaA2dJnTSHcNSTm9EtavWjv1J+zl05pDuKJZx8l20\\n44v/0qVw881QsqTuJNfntZaiky9aO8yNn0unGt4p/mlpsGgR9OmjO0n2QoJC6Fm3J/MTvPPAqUcP\\nWL4cLl7UneTPHF/8nd5auWz4nOF8ve1r3TEs06ePWpc8UBd6i46PJrKBd56MrlwJtWtDpUq6k1xf\\nn3p9mJfgnW3lSpeG5s2dudCbo4t/ZqZ62Ovk1splHap38NRFW7WqWuxt9WrdSewXfyKec6nnaF6x\\nue4olpk3zx0/R3fUvoNVh1ZxMc2BTeV8cupdtKOL//r1aonUWrV0J8lZr7q9+G7Pd57r+gnEvX3n\\nJcyjT70+nlrIzS130CUKlmD/Y/spFFpIdxTLXP45ynTYQgCOvrrdcsECVCxWkbql67Li4ArdUSzj\\n1BaLv528cJJ+Db2zT+jOnWq2aZMmupPkTqlCNm+A62e1a6vunw0bdCf5I1P8LRRRP4J58d5pKjdr\\npkZbxcfrTmKv17u8Trfa3XTHsMzcuarLx0lLowSaPn2cdxft2OK/dy+cOKH2lnWLiPoRnLx4UncM\\nywihLlonz1I0cuaW/n4vc2LxF9Jhy84JIaSUkvfeU0sL/+9/uhMFtoUL4fXXYYV3erMCyvHjUKcO\\nJCaqSUeGHhkZUKEC/PST2jHPakIIpJR5urdzbMt/7ly1GbKh1+23q80/TnrnhiagLFwIXbq4r/Bn\\nykzWHnb4bih5EBzsvDWzHFn8z5xRI326dtWdxChYUP0CWLhQdxIjP9zc5RM1LYq9p/fqjmGZPn2c\\nNYDCkcX/u+/UGiRFvLOzm6s5sb/SH6ZsncL249t1x7DMpUtqhnwvF26SFSSC6FW3l6cGUHTrpubN\\nnDunO4niyOLv5taKF/Xqpdb5SU3VncR/pJS8tPwlT83TiImB8HA1V8aN+tT31mzf4sXh1luds2aW\\nI4v/t9+6u7//8NnDjN84XncMy1SoAPXrqwX2vGrHiR2kZaTRuHxj3VEsM2+eu3+Outbqyvoj6zmT\\nckZ3FMs46S7akcW/alX1x63CgsN4esnTXEq/pDuKZZx00frDvHg1q1d4ZDC8lO5ZGiU7RQoUoV21\\ndny35zvdUSxzec2sjAzdSRxa/N18wQKULVKW8HLhxOyP0R3FMpeLv8NGBltmbsJc+tR3+YV3hbg4\\nNU8jPFx3Et88fPPDlC5UWncMy1Svru6k163TncSi4i+E6C6E2CmESBBCPJPNa94XQuwSQmwRQjS9\\n3vHcXvzBe6sTNm6sVvjcsUN3EusdP3+cuMQ4T23Ufvm5mdtvZHrV60WXWl10x7CUU+6ifS7+Qogg\\n4APgDiAcuFMI0eCq1/QAaksp6wIjgU+ud8wWLXxNpV/ver2ZnzAfp02iy6/Ls32dcNFarUiBIiy4\\nawEFQwrqjmIZM2jCuZzyc2RFy78VsEtKeUBKmQZMA65eCD0SmAggpVwHlBBClM82lCM7o/ImvGw4\\nQgjiEuN0R7GMUy5aqxUOLUy7au10x7BMYqK6Q+vQQXcS41patVIzr/ft05vDijJbGbhy37XDWZ+7\\n3muOXOM1niKEYGr/qVQt4eIn11fp1EktuXHihO4kxvUsWKAmSBYooDuJcS1BQWr4tO6GVIje01/b\\n6NGjf/t7x44d6dixo7YsvmhdpbXuCJYKC4POndVohXvu0Z3GyM68eRAVpTuFcT19+sBHH8Gjj+bv\\n/TExMcTExPiUweeF3YQQrYHRUsruWR8/C0gp5ZgrXvMJsFxKOT3r451ABynlsWscT3qln9yLvvhC\\nFf9vvtGdxLiWS5egXDnYswfKlNGdxjpfbvmSTJnJiGYjdEexRHIyVKwIR46oyV++0rWw2wagjhCi\\nuhCiADAJnS8DAAAc+klEQVQEuHoFi7nAPVkhWwNJ1yr8hvP16gVLlnhjtq+UkgtpF3THsFRMDNx0\\nk7cKP0CpgqWYvHWy7hiWKVoU2rbVO9vX5+IvpcwAHgEWA9uAaVLKHUKIkUKIB7JesxDYJ4TYDYwD\\nHvL1vIYe5cpBw4bwww+6k/hu+/HttBjvgaFlV5g3z10bIOVWl1pd2HBkg5ntayFLxtVIKRdJKetL\\nKetKKd/I+tw4KeX4K17ziJSyjpSyiZRykxXndYu0jDTSM9N1x7CM7ovWKvMS5tG5ZmfdMSxzea9e\\nLw7xLFKgCLdVv41FuxfpjmKZ3r31zvb1wKBK5+s9tTdL9y7VHcMyXpnte3mjdq+IjYXQUHVn5kVe\\nmzhZvTpUqgRrNW1bYIq/DTrV6OSppWlvukkV/m3bdCfJv+Pnj7MtcZuZ1esiveupRlSmzNQdxTI6\\n76JN8bdBRP0I5iXMM7N9HWThroV0rtWZsBCXbXF1HV6f1VuleBUS/p5AkPBO2TLF3+MalmlIaHAo\\nscdidUexjNuL/8mLJxl04yDdMSxz9CgkJKhNkLyseJgF4yId5Oab1RapezVsWGaKvw2EEJ7rr+zQ\\nQXX7JCbqTpI/T7R5gsE3DdYdwzILFqidosysXnfROdvXFH+b9G3Ql6SUJN0xLBMWppYQMHv7OoPX\\nu3y8LCJCT/H3eYav1cwMX/eYMEENLZw5U3eSwJaSAuXLq66DG27QncbIq/Pn1WzfQ4egRIn8HUPX\\nDF8jQPXsqTYIT0nRnSSwLVsGTZoETuFPy0hj9aHVumNYpkgRaN8eFtk8hcEUfyPfypaFRo3UkgKG\\nPoHW5ZOWmUb3r7pz+uJp3VEso2MAhSn+hk8iIlTXj1vM3D6T9UfW645hGSm9u6RDdgqHFqZjjY4s\\n3OWdB069e8O336rd8uxiir/hE7fN9h2zagzJqcm6Y1hm82bVbVC/vu4k9ro8d8YrKleGmjVh1Sr7\\nzmmKv83iT8Tz5ZYvdcewTIMGULAgbNmiO0nOfj33K7tO7eK2at4ZDD93bmC1+i/rXa833+35jtQM\\nDywvm8Xurh9T/G0mhOCFZS94Zoq6m2b7zk+YT/c63QkNDtUdxTKBWvwrFK1A/Rvqs+LACt1RLGN3\\nF6op/jard0M9ihUoxsZfNuqOYhm39Pt7bSG3Q4fg4EFo00Z3Ej2ebvs0RQsU1R3DMk2bwsWLEB9v\\nz/lM8dcgsn4kc+NdUC1zqW1bNcb8yBHdSbJ3PvU8Mftj6FGnh+4olpk3T80ODXHkZqz+169hP26p\\ncovuGJa5fBdtV0PKFH8NIupHEB0frTuGZUJDoUcPmD9fd5LsFQguwKJhiyhVqJTuKJYJ1C4fL4uI\\ngGibSoMp/hq0rtKao8lH2Xd6n+4olrHzos2P0OBQbq16q+4Yljl7FlavVuv5GN7RqRPExdmzZpYp\\n/hoEBwUz/675lCtSTncUy3TvDitXqo2pDf9bvFh1txUrpjuJYaWwMPUL3Y67aFP8NWlVuRVFChTR\\nHcMyJUpA69bw3Xe6kwQG0+XjXZGR9vT7m+JvWCYqCubM0Z3C+9LT1WqqvXvrTuIMY9eNZdLPk3TH\\nsEyPHrB8OVy44N/zmOJvWCYiQhWltDTdSf7oTMoZ3REstWoV1KgBVavqTuIM5YqUY2rcVN0xLFO6\\nNLRooRZN9CdT/A3LVKmipqivXKk7ye/iT8TT5JMmntlCE9TdVWSk7hTO0aNuD1YeXMm5S+d0R7FM\\nZKT/B1CY4q/ZhbQLpGU4rKnsAzsu2ryIjo+mR50eCI/sai6lKv5RUbqTOEfxsOK0rdaW7/Z454FT\\nRIR66JuR4b9zmOKvWa8pvVi2b5nuGJa5XPyd0tCOjo8msoF3msmxsWrrv5tu0p3EWaLqRzFnp3ce\\nONWsqTboWbfOf+cwxV+znnV6euqibdRI/TfWAXvVJ55PZFviNjrV6KQ7imUut/o9ciNjmYj6EcTs\\nj/HMmlng/7toU/w1i2oQRXR8tGcuWiGc0/UzN34ud9S5g7CQMN1RLGO6fK6tYrGK7Hl0D0HCOyUt\\nMlL9e/vrLto73ymXqntDXUoVKuWpDUaiopxR/C+kXWBoo6G6Y1jmwAE4fBhu9c5EZUt56Zc8qBE/\\nFy/Cjh3+Ob4p/g7Qt0FfT3X9tGunCtXBg3pzPHrLo0TU985MqOhotfBXcLDuJIYdhFANqdmz/XN8\\nU/wdoH/D/qRn2rh/m5+FhKgiZSZ8Wct0+QSevn39V/yF08Y/CyGk0zIZeTd3Lrz7rtnc3SonT0Kt\\nWnD0KBQqpDuNYZf0dKhYETZuhGrVsn+dEAIpZZ6GAZiWv+EXXbuq/WWPH9edxBsWLIDbbzeFPyfp\\nmenMT3Dw2uJ5FBKilvHwx120Kf6GXxQqBHfc4Y4dvtxg1izo1093CucLEkE8MO8BEk4m6I5iGX91\\n/Zjib/iNP/srr+f9de/z89Gf7T+xnyQnq4W++nhnB0q/CRJBRDWIYtaOWbqjWKZrV9i0CU6csPa4\\npvgbftOrF/z4o9p4xC4ZmRn8Z8V/KBbmnYXuv/1W7dNbsqTuJO7Qv2F/Zu6YqTuGZQoVUr8A5s2z\\n9rim+DvIkbNHeGnZS7pjWKZ4cTXs89tv7Tvn6kOrqVC0ArVK1bLvpH42cyb07687hXu0r96efaf3\\ncfCM5rHGFvLHXbQp/g5SulBp3l//PonnbdjDzSb9+qn+arvM3jmbvg362ndCP0tJgUWLzCqeeREa\\nHEpE/QhPdf306qVGzlm5U54p/g5SKLQQPer08NSEr4gItbtXSor/zyWlZMb2GQy4cYD/T2aTJUug\\naVMo550dP23xSKtHaFahme4YlilZUm3buXChdcc0xd9hvNZfWa4cNGmiipi/bfhlA4VDCxNeNtz/\\nJ7OJGeWTP80rNqdDjQ66Y1hqwAD45hvrjmcmeTlMcmoyld+tzL7H9lG6UGndcSzx/vtqtMKXX/r3\\nPOmZ6Rw+e5gaJWv490Q2SUtTE3w2bza7dhlqtE/t2vDLL1Dkqu2/zSQvDyhaoCida3Zmbrx3Bsj3\\n769GKqSm+vc8IUEhnin8AD/8oH7YTeE3AMqUgVtuUc+ArGCKvwON7TGWITcN0R3DMpUrQ8OG/t+T\\n1GvMKB/jalZ2/ZhuH8MW770HW7bAF1/oTuIOGRnql+bKlVCnju407ial9Mw2nomJUK8e/PrrH5f6\\nMN0+hmP176+WevB3149X/PCDKv6m8PvmxwM/0u9r7zwxL1cOmjdXI+h8ZYq/YYsqVaBBA/j+e+uP\\nnXg+kaPJR60/sEZffw2DB+tO4X7NKzZn2b5lnLxwUncUywwcaE3Xjyn+hm0GDlRFzWpj143l7dVv\\nW39gTdLT1RDPgQN1J3G/ogWK0q12N2bv1LDIlJ/07atWefV17owp/g6WlJLE3tN7dcewzIAB1nf9\\nSCn5Zvs3nprYtXw51KgBNWvqTuINg8MHM33bdN0xLFOhgpo7s3ixb8cxxd/BFiQs4LFFj+mOYRl/\\ndP3EHoslJT2FWyrfYt1BNTNdPtbqWbcnG45s4Ph572wuMWiQ73fRPhV/IUQpIcRiIUS8EOI7IUSJ\\nbF63XwjxsxBisxDCOzuV+1lE/Qh+PPAjpy+e1h3FMlb1V142NW4qQ24a4pnRHGlpagGvAd65kdGu\\ncGhhBocPJvZYrO4olhkwAObPhwsX8n8MX1v+zwJLpZT1gWXAc9m8LhPoKKVsJqVs5eM5A0axsGJ0\\nrdXVU/2VAwaojcgvXfL9WFJKpsVN486b7vT9YA7x/fdQty5Ur647ibeM6zOOzrU6645hmfLloVUr\\n9Qsgv3wt/pHAhKy/TwCy215aWHCugDQ4fDDT4qbpjmGZKlWgUSNrZileSLvAvU3vpXH5xr4fzCFM\\nl4+RW0OGwDQfSoNPk7yEEKeklKWz+/iKz+8FkoAMYLyU8tPrHNNM8rrChbQLVHqnEgl/T6BcEW8s\\n7ThuHCxbBtO98wzOEqmp6mFebKz6JWkY15OUpO4QDx6EkiXzPskrJKcXCCGWAOWv/BQggRev8fLs\\nqnZbKeWvQoiywBIhxA4p5crszjl69Ojf/t6xY0c6duyYU0zPKhxamDe6vMGFNB869xxmwAB4+mk4\\ndw6KeWfDLZ8tWgTh4abwGzmLiYkhJiaGihVh+PD8HcPXlv8OVF/+MSFEBWC5lLJhDu8ZBZyTUr6b\\nzddNyz8A9OmjujeGDdOdxDkGD4bbb4eRI3UnMdxi+nS1ZMp339m/vMNc4N6svw8Hoq9+gRCisBCi\\naNbfiwDdgDgfz2u43F13wZQpulM4x5kzquVvJnb519rDa5ke553+xt69Ye3a/L3X1+I/BugqhIgH\\nOgNvAAghKgohLj+HLg+sFEJsBtYC86SUPk5PMNwuIgJWr4bj3hl67ZNZs6BTJyjtjS0cHCtTZjL6\\nh9F4pXehSBHo2TN/7/Wp+EspT0kpu0gp60spu0kpk7I+/6uUsnfW3/dJKZtmDfNsJKV8w5dzGt5w\\n+aLNz5j/+QnzeWThI9aH0mjyZNMFZoc2VdpwKf0Sm49u1h3FMnfmc6SzGX7pMl5psUD+u36+3PKl\\np4Z3Hjmidjrr3Vt3Eu8TQjC00VC+iv1KdxTL3HFH/t5nir+LTNgygWeXPqs7hmW6dYOdO2H//ty/\\n59TFUyzZu4RB4YP8lstuU6eqxboKFtSdJDAMbTyUqXFTSc9M1x3FEgUK5O99pvi7SJuqbZgYO9FT\\nF+3gwTBpUu7fMz1uOt3rdKdkwZL+C2Yz0+VjrwZlGlCleBWW7VumO4pWpvi7SL0b6lGtRDWW7vXO\\nfoj33qs2ds/MzN3rJ8ZOZHiTfA5sdqC4OLUxd4cOupMElhkDZ9CpRifdMbQyxd9l7m58N5Ni89BU\\ndriWLVV3x8psp/z9LiklifTMdLrV7ub/YDb56iv1wC7I/CTaqnrJ6oQGh+qOoZXZw9dlTlw4Qe33\\na3P4H4cpFuaN6bFvvw3bt8Pnn+tOYq/0dKhWTW1sf+ONutMYbmb28A0AZQqXoW+DvsQlemee3NCh\\nahnj5GTdSey1aJFam8UUfkMH0/I3HKF3bzW7Nb/rlLhR377Qqxfcf7/uJIbbmZa/4VqXH/wGimPH\\n1HaNg7wzYtWVzqScYc2hNbpjaGGKv+EIffrA1q2wb5/uJPaYNEm1/IsX150ksB05d4T+X/f3zPDp\\nvDDF33CEsDA16uVarf/pcdP5dte3tmfyFynhs89gxAjdSYwby95I9ZLVPXV95ZYp/oZj3H+/Korp\\nVzTCpJS8uuJVCoZ4Z/rr2rWQkQHt2ulOYgD8pdlf+HxLgA01wxR/V9t3eh9PLX5KdwzLNGmihj5e\\nuS/p2sNruZR+iY41OmrLZbXLrX6P7DnveoPDBxOzP4Zjycd0R7GVKf4uVrFYRSb8PIF9p73TUf7g\\ng/Dxx79/PH7TeB5o8QDCI5Xy7FmYOTOwRjU5XbGwYvRt0JcJP0/I+cUeYoZ6utzjix6naIGivHr7\\nq7qjWCIlBapWVV0jN1ROouZ7NUl4JIGyRcrqjmaJsWNhxQq1UbvhHDtP7CQ5NZmWlVrqjpIv+Rnq\\naYq/y8UlxtFtUjf2P76fAsH5XN7PYZ56Si13EH7XBBbtWcTU/lN1R7KElNCwIYwfD+3b605jeIkp\\n/gGq04ROjGwxkiE3DdEdxRK7dkHbtnDggCQj+DxFCxTVHckSS5fCE0/Azz+b/n7DWmaSV4B67JbH\\n+GZ7PrbEcqi6daFpU5g5U3im8AN88AE88ogp/IYzmJa/B2RkZiCRhASF6I5imdmz1YJvq1bpTmKN\\nAwegeXM4eFBtYWkYVjIt/wAVHBTsqcIPasbv4cOwYYPuJNb45BM1wscUfuc7kHSA5FTvrzJoir/h\\nSCEh8Pjj8M47upP4LiVFje1/6CHdSYzceGrJU0z8eaLuGH5nir/hKEkpSfz7h38DasbvkiV52+PX\\niSZPVpvW1KmjO4mRGw/f/DBj148lU+ZyezmXMsXfcJRPN35K/Ml4AIoVU78A/vtfzaF8kJEBb74J\\nTz+tO4mRWx2qd6BwaGHmxc/THcWvTPH3mLnxc/l6mztnEKVnpjN2/Vj+0fofv33u0Udh4kQ4fVpj\\nMB/MmQOlS5s9et1ECMFz7Z7j9ZWv4+XBJ6b4e0ypgqV47vvnXLlE7YztM6hRsgYtKrX47XOVK6uH\\nv+PGaQyWT1LC66/Ds8+a4Z1u07dBX06nnCZmf4zuKH5jir/H3Fb9NqoUr8LUre6aFZspM3n1x1d5\\nrt1zf/raU0+pZREuXdIQzAdLl8LFi+qXl+EuwUHBfB7xObVK1dIdxW9M8fegl9q/xGsrXyMjM0N3\\nlFxbdXAVRQsUpXud7n/6WqNG6s9Elw3AeOMNeOYZtVSF4T5tq7WlesnqumP4jZnk5UFSStp81oYn\\n2zzJwPCBuuPk2sW0ixQKLXTNr61ZA0OGQEKC2vjF6davV3sS794NoaG60xheZyZ5GYC6EF5s/yIL\\ndi3QHSVPsiv8AG3aqNb/p5/aGMgH//kPPPmkKfyGc5mWv0dd/h56ZR18gE2boHdv1ZouXFh3muyt\\nXv37XUpB72xAZjiYafkbvxFCeKrwg1ob59Zb4aOPdCfJnpSqn/+VV0zh95LYY7HEHovVHcNSpvgb\\nrvLyy/DWW3DunO4k1zZ/PiQlwd13605iWGnjLxt5eOHDnhr3b4q/oc0nP33C2HVj8/Se8HDo2tWZ\\ns34zMtSY/jfegOBg3WkMK93T5B6SUpKYnzA/5xe7hCn+ASI1I1V3hD84dfEUo2JG0aFG3qe+vvwy\\nvPeeWvXTSSZOhDJloGdP3UkMqwUHBfNG5zd49vtnXTmB8lpM8Q8AF9Mu0uCDBhw+65xqOTpmNAMa\\nDqBx+cZ5fm/t2mqj9yef9EOwfLpwAUaNgjFjzGxer+pZtydlC5dlwhZvbPRuin8AKBRaiMHhg3lx\\n2Yu6owCwLXEb0+Km8UqnV/J9jOeeU2Pply61MJgPRo+G226D1q11JzH8RQjB293e5q3VbzliAmVG\\nZgafb/4833ciZqhngDh76SwNP2zI1P5TaV9d3+7hUkq6fdWNiHoR/P2Wv/t0rLlz4Z//hNhYvRO/\\nNm2CHj1g61YoV05fDsMeyanJjthe9JOfPmHy1sn8eO+PBAUFmaGexrUVDyvOx70+ZkT0CM6nnteW\\nIy0zjVaVWvG3ln/z+VgREVCvHvzf/1kQLJ/S09Wy02+9ZQp/oHBC4T9x4QT/Wv4vPuz5Yb6HdJuW\\nf4C5Z/Y9lCpYivd6vKc7iiX27oVWrWDtWj2bpbz9NixeDN99Z/r6DXtIKRk2exhlCpX57ec4P5O8\\nTPEPMKcvnubQ2UP5etDqVGPHwoQJarN3O7t/Lv/iWb8eanl38UfDYSZsmcCYVWP46YGfKByqprqb\\nGb5GjkoVKuWpwg/wyCNQtap6CGyXlBQYPBheeMEU/kCWKTPZdXKXbeeTUrJozyKmD5j+W+HPL9Py\\nNzzh1Clo1gw+/FCt/+NPUqp+/nPnYPp0090TyLYe20qXSV1YPWI1tUvX1pbDtPwNx0k8n0jvKb39\\n/pC5dGmYMkUVZX9P/ho/Htatg88/N4U/0DUq34h/tf8XUdOjSE5N1h0nT0zxN9h9ardfjpucmkyv\\nKb1oWaklRQoU8cs5rtS2LTzxBPTqpe4E/GHNGnjpJZg9G4rqH/RhOMBDNz/EzZVu5r7o+1y19o8p\\n/gHu13O/0uazNqw5tMbS46ZlpDHwm4E0Ld+UUR1GWXrs6/nnP6FbN+jeHc6etfbYsbHQv79q8det\\na+2xDfcSQvBRr484eOYgzy591vJfAJky09LjXWaKf4CrWKwiX0Z+SdT0KMs2q07PTOf+efcTLIL5\\nuPfHti4tLQS8+SbcfLO6AzhvUW/TunW/Lyjn72cKhvsUDCnIgrsWcPLiSS6mX7TsuNPipjF01lDL\\njvcHUsp8/wEGAHFABtD8Oq/rDuwEEoBncjimNOz3/d7vZdk3y8qJWyb6fKxJP0+SXSd2lcmXki1I\\nlj8ZGVLee6+UHTpIeeyYb8datkzKsmWlnD/fkmiGkaPMzEw57qdxsvxb5eXWY1tzfH1W3cxb/c7r\\nG+QfC3V9oC6wLLvij7q72A1UB0KBLUCD6xzTl++ZpyxfvtzW821L3CZr/LeG/HD9hz4dJzMzU6Zn\\npFuUKv/fh/R0KZ97TsqKFaVctCjv709Lk/K996QsU0ZKm/8psmX3NeFkXv1enL54Wg78eqBs/HFj\\nueP4jly9Jz/F36duHyllvJRyF3C9+/pWwC4p5QEpZRowDYj05byBIiYmxtbz3Vj2Rtb8ZQ096vTw\\n6ThCCIKDrFvQPr/fh+BgeO01mDxZjQJ64oncbwKzYQPccot6sLtiBXTsmK8IlrP7mnAyt3wvklKS\\nuJR+KVevPXTmEM3GNaN8kfKsu38dDco08FsuO/r8KwOHrvj4cNbnDAeqULQCNUvV/NPn5e93ZoDq\\n1084mcD3e7+3M16+dOoEW7ZAYiJUqwYjRqjZwFc/lzt+HL76Su2/26cPPPYYLFsGDfz382cEgC+3\\nfEmjjxvx+orX+emXn667Imjl4pWZEDWBsT3HUjDEv/uAhuT0AiHEEqD8lZ8CJPCClHKev4IZzrL9\\n+Ha6TOpCvRvqceTsEQ6dPUT5IuW5v/n9dK7VWXe8HN1wgyrsR4+qTVdGjIBjx6BECShWTD0oPnhQ\\n/aLo0UPtE1y6tO7Uhhc83vpxmlZoypydc7hn9j0knk+kVqlafNDzA1pVbvWH1waJINtW3bVkhq8Q\\nYjnwpJRy0zW+1hoYLaXsnvXxs6j+qTHZHMs9A2UNwzAcQuZxhm+OLf88yO7EG4A6QojqwK/AEODO\\n7A6S1/8BwzAMI+986vMXQkQJIQ4BrYH5Qohvsz5fUQgxH0BKmQE8AiwGtgHTpJQ7fIttGIZh+MJx\\nC7sZhmEY/ueYGb5CiO5CiJ1CiAQhxDO68+gihKgihFgmhNgmhNgqhHhUdybdhBBBQohNQoi5urPo\\nJIQoIYT4RgixI+v6uEV3Jl2EEP8QQsQJIWKFEJOFEAV0Z7KLEOIzIcQxIUTsFZ8rJYRYLISIF0J8\\nJ4QokdNxHFH8hRBBwAfAHUA4cKcQIlAH2KUDT0gpw4E2wMMB/L247DFgu+4QDvAesFBK2RBoAgRk\\n96kQohLwd9TE0saoZ5dD9Kay1ReoWnmlZ4GlUsr6qEm3Oe5u4Yjij5kI9hsp5VEp5ZasvyejfsAD\\ndl6EEKIK0BP4n+4sOgkhigO3SSm/AJBSpkspLV66zlWCgSJCiBCgMPCL5jy2kVKuBE5f9elIYELW\\n3ycAUTkdxynF30wEuwYhRA2gKbBObxKt/g/4J2puSSCrCZwQQnyR1QU2XghRSHcoHaSUvwDvAAeB\\nI0CSlHKp3lTalZNSHgPVgATK5fQGpxR/4ypCiKLADOCxrDuAgCOE6AUcy7oTElx/GRGvCwGaAx9K\\nKZsDF1C3+gFHCFES1dKtDlQCigoh7tKbynFybCw5pfgfAapd8XGVrM8FpKxb2RnAJClltO48GrUF\\nIoQQe4GpQCchxETNmXQ5DBySUv6U9fEM1C+DQNQF2CulPJU1lHwWcKvmTLodE0KUBxBCVAASc3qD\\nU4r/bxPBsp7aDwECeWTH58B2KeV7uoPoJKV8XkpZTUpZC3VNLJNS3qM7lw5Zt/SHhBD1sj7VmcB9\\nCH4QaC2EKCjUZhGdCbyH31ffCc8F7s36+3Agx0ajlTN8801KmSGEuDwRLAj4LFAnggkh2gJDga1C\\niM2o27fnpZSL9CYzHOBRYLIQIhTYC9ynOY8WUsr1QogZwGYgLeu/4/Wmso8QYgrQEbhBCHEQGAW8\\nAXwjhBgBHAAG5XgcM8nLMAwj8Dil28cwDMOwkSn+hmEYAcgUf8MwjABkir9hGEYAMsXfMAwjAJni\\nbxiGEYBM8TcMwwhApvgbhmEEoP8Hf41iEpQega0AAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10c21e668>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"x = np.linspace(0, 10, 100)\\n\",\n    \"\\n\",\n    \"fig = plt.figure()\\n\",\n    \"plt.plot(x, np.sin(x), '-')\\n\",\n    \"plt.plot(x, np.cos(x), '--');\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Saving Figures to File\\n\",\n    \"\\n\",\n    \"One nice feature of Matplotlib is the ability to save figures in a wide variety of formats.\\n\",\n    \"Saving a figure can be done using the ``savefig()`` command.\\n\",\n    \"For example, to save the previous figure as a PNG file, you can run this:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"fig.savefig('my_figure.png')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We now have a file called ``my_figure.png`` in the current working directory:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"-rw-r--r--  1 jakevdp  staff    16K Aug 11 10:59 my_figure.png\\r\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!ls -lh my_figure.png\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"To confirm that it contains what we think it contains, let's use the IPython ``Image`` object to display the contents of this file:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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5FFXh7bS6uwf2i3Rt2QnZ+NI7eO8A0mo7Fj2f+jIDVZdaiAlUHh5I1C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\\nAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<IPython.core.display.Image object>\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from IPython.display import Image\\n\",\n    \"Image('my_figure.png')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"In ``savefig()``, the file format is inferred from the extension of the given filename.\\n\",\n    \"Depending on what backends you have installed, many different file formats are available.\\n\",\n    \"The list of supported file types can be found for your system by using the following method of the figure canvas object:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"{'eps': 'Encapsulated Postscript',\\n\",\n       \" 'jpeg': 'Joint Photographic Experts Group',\\n\",\n       \" 'jpg': 'Joint Photographic Experts Group',\\n\",\n       \" 'pdf': 'Portable Document Format',\\n\",\n       \" 'pgf': 'PGF code for LaTeX',\\n\",\n       \" 'png': 'Portable Network Graphics',\\n\",\n       \" 'ps': 'Postscript',\\n\",\n       \" 'raw': 'Raw RGBA bitmap',\\n\",\n       \" 'rgba': 'Raw RGBA bitmap',\\n\",\n       \" 'svg': 'Scalable Vector Graphics',\\n\",\n       \" 'svgz': 'Scalable Vector Graphics',\\n\",\n       \" 'tif': 'Tagged Image File Format',\\n\",\n       \" 'tiff': 'Tagged Image File Format'}\"\n      ]\n     },\n     \"execution_count\": 8,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"fig.canvas.get_supported_filetypes()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Note that when saving your figure, it's not necessary to use ``plt.show()`` or related commands discussed earlier.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Two Interfaces for the Price of One\\n\",\n    \"\\n\",\n    \"A potentially confusing feature of Matplotlib is its dual interfaces: a convenient MATLAB-style state-based interface, and a more powerful object-oriented interface. We'll quickly highlight the differences between the two here.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### MATLAB-style Interface\\n\",\n    \"\\n\",\n    \"Matplotlib was originally written as a Python alternative for MATLAB users, and much of its syntax reflects that fact.\\n\",\n    \"The MATLAB-style tools are contained in the pyplot (``plt``) interface.\\n\",\n    \"For example, the following code will probably look quite familiar to MATLAB users:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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A1TSJEs3/XAA13//D4jI4OMjIxgsbhtWra0ndN69oTOnUNH4/KTlWWb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\\nUTSOiswAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10d658438>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.figure()  # create a plot figure\\n\",\n    \"\\n\",\n    \"# create the first of two panels and set current axis\\n\",\n    \"plt.subplot(2, 1, 1) # (rows, columns, panel number)\\n\",\n    \"plt.plot(x, np.sin(x))\\n\",\n    \"\\n\",\n    \"# create the second panel and set current axis\\n\",\n    \"plt.subplot(2, 1, 2)\\n\",\n    \"plt.plot(x, np.cos(x));\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"It is important to note that this interface is *stateful*: it keeps track of the \\\"current\\\" figure and axes, which are where all ``plt`` commands are applied.\\n\",\n    \"You can get a reference to these using the ``plt.gcf()`` (get current figure) and ``plt.gca()`` (get current axes) routines.\\n\",\n    \"\\n\",\n    \"While this stateful interface is fast and convenient for simple plots, it is easy to run into problems.\\n\",\n    \"For example, once the second panel is created, how can we go back and add something to the first?\\n\",\n    \"This is possible within the MATLAB-style interface, but a bit clunky.\\n\",\n    \"Fortunately, there is a better way.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Object-oriented interface\\n\",\n    \"\\n\",\n    \"The object-oriented interface is available for these more complicated situations, and for when you want more control over your figure.\\n\",\n    \"Rather than depending on some notion of an \\\"active\\\" figure or axes, in the object-oriented interface the plotting functions are *methods* of explicit ``Figure`` and ``Axes`` objects.\\n\",\n    \"To re-create the previous plot using this style of plotting, you might do the following:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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A1TSJEs3/XAA13//D4jI4OMjIxgsbhtWra0ndN69oTOnUNH4/KTlWWb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\\nUTSOiswAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10d64f240>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# First create a grid of plots\\n\",\n    \"# ax will be an array of two Axes objects\\n\",\n    \"fig, ax = plt.subplots(2)\\n\",\n    \"\\n\",\n    \"# Call plot() method on the appropriate object\\n\",\n    \"ax[0].plot(x, np.sin(x))\\n\",\n    \"ax[1].plot(x, np.cos(x));\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"For more simple plots, the choice of which style to use is largely a matter of preference, but the object-oriented approach can become a necessity as plots become more complicated.\\n\",\n    \"Throughout this chapter, we will switch between the MATLAB-style and object-oriented interfaces, depending on what is most convenient.\\n\",\n    \"In most cases, the difference is as small as switching ``plt.plot()`` to ``ax.plot()``, but there are a few gotchas that we will highlight as they come up in the following sections.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<!--NAVIGATION-->\\n\",\n    \"< [Further Resources](03.13-Further-Resources.ipynb) | [Contents](Index.ipynb) | [Simple Line Plots](04.01-Simple-Line-Plots.ipynb) >\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"anaconda-cloud\": {},\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.4.3\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "matplotlib/04.01-Simple-Line-Plots.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<!--BOOK_INFORMATION-->\\n\",\n    \"<img align=\\\"left\\\" style=\\\"padding-right:10px;\\\" src=\\\"figures/PDSH-cover-small.png\\\">\\n\",\n    \"*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\\n\",\n    \"\\n\",\n    \"*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*\\n\",\n    \"\\n\",\n    \"*No changes were made to the contents of this notebook from the original.*\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<!--NAVIGATION-->\\n\",\n    \"< [Visualization with Matplotlib](04.00-Introduction-To-Matplotlib.ipynb) | [Contents](Index.ipynb) | [Simple Scatter Plots](04.02-Simple-Scatter-Plots.ipynb) >\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Simple Line Plots\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Perhaps the simplest of all plots is the visualization of a single function $y = f(x)$.\\n\",\n    \"Here we will take a first look at creating a simple plot of this type.\\n\",\n    \"As with all the following sections, we'll start by setting up the notebook for plotting and  importing the packages we will use:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%matplotlib inline\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"plt.style.use('seaborn-whitegrid')\\n\",\n    \"import numpy as np\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"For all Matplotlib plots, we start by creating a figure and an axes.\\n\",\n    \"In their simplest form, a figure and axes can be created as follows:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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rVKW7Zs0VtvvaX6+nr5/f5CjDlnAoGA5s+f/9j52XYzp2HnZqaMbLuQ\\nHn7HePjwYf3888/q6OgoxIhzItsezp49qzt37mjv3r365JNPFIvF9N133xVq1LzLtotly5apvLxc\\nlZWV8nq9qqure+xTrCXZdjE0NKTz58+rp6dHPT09unXrls6dO1eoUQtqtt3Madhramr0008/SVLW\\nm5mSyaT6+/v1yiuv5PLt/1Wy7UKSDhw4oPv37+v48ePpr2QsyraHcDisb775RidOnNA777yjxsZG\\nbdy4sVCj5l22XZSVlenevXvpHyLG43G9/PLLBZlzLmTbRXFxsRYtWiSfz5f+2+3o6GihRp1T//s3\\n19l20/XO06cRCATU19eX/r40EokoFoulb2bav3+/du/eLcdxFAwGtXz58ly+/b9Ktl2sWbNGp0+f\\nVm1trcLhsDwej5qamrRhw4YCT517br8n/kvcdtHe3q59+/ZJktauXav169cXcty8ctvF3/9izOfz\\nqby8XJs2bSrwxHPj758lPGs3uUEJAIzhBiUAMIawA4AxhB0AjCHsAGAMYQcAYwg7ABhD2AHAGMIO\\nAMb8H/Ams7WyTe/nAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x102e12390>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig = plt.figure()\\n\",\n    \"ax = plt.axes()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"In Matplotlib, the *figure* (an instance of the class ``plt.Figure``) can be thought of as a single container that contains all the objects representing axes, graphics, text, and labels.\\n\",\n    \"The *axes* (an instance of the class ``plt.Axes``) is what we see above: a bounding box with ticks and labels, which will eventually contain the plot elements that make up our visualization.\\n\",\n    \"Throughout this book, we'll commonly use the variable name ``fig`` to refer to a figure instance, and ``ax`` to refer to an axes instance or group of axes instances.\\n\",\n    \"\\n\",\n    \"Once we have created an axes, we can use the ``ax.plot`` function to plot some data. Let's start with a simple sinusoid:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAXoAAAD/CAYAAAD/qh1PAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xl8TWf+B/BPIiQk1FJbtEJTUR1LJcqoJiixJgiJJiHR\\n0o3pypQuM4zWOjO/1rRlqqjYKrXEFnsIbY0WUZTSUVVUaIsiScl6f398J9ZEcu899z5n+bxfL69p\\nJPeczxzH13Oe8yweNpvNBiIiMi1P1QGIiMi1WOiJiEyOhZ6IyORY6ImITI6FnojI5FjoiYhMzqlC\\nv3//fiQkJNz2+1u3bkV0dDRiY2OxdOlSZ05BRERO8nL0g7Nnz8aqVavg6+t70+8XFBRgypQpSElJ\\ngbe3N+Li4tClSxfUrFnT6bBERGQ/h1v0AQEBmD59+m2/f+zYMQQEBMDPzw8VK1ZESEgIdu/e7VRI\\nIiJynMOFPjw8HBUqVLjt97Ozs1G1atVrX/v6+iIrK8vR0xARkZM0fxnr5+eH7Ozsa1/n5OSgWrVq\\nWp+GiIjKyeE++mK3LpUTGBiIEydO4PLly/Dx8cHu3bsxbNiwEj+bkZHh7OmJiCwpJCSk3D/rdKH3\\n8PAAAKSmpuLKlSuIiYnB66+/jqFDh8JmsyEmJgZ16tTRJKxqRUXAv/4FTJwIDB8OvPwyUKtW6T9f\\nUAAsWwa88QbQti3wwQfA3XeX/LOZmZnw9/d3TXCD4bW4zqzX4quvgGHDgIYNgbfeAtq0ufPP798P\\nvPbaFRw7VhmzZgEdO7onp17Z3Ui2KbRnzx6Vp7fLxYs2W+/eNtsjj9hs//2vfZ/NybHZRo2y2Ro2\\ntNm++qrknzl9+rTzIU2C1+I6s12LoiKb7YMPbLY6dWy25GT5urxOnz5tW7XKZqtXz2abOtW+z5qN\\nvbWTE6bKITMTaN8eaNQI2LYNaNLEvs9XqQL885/AtGlA795AaqorUhLpm80mT8Effgj85z/A448D\\n/+sQKLc+fYBdu+RJedgwoLDQNVnNhoW+DD/9BHTqBCQmStdLxYqOHysqCli7FnjqKWDJEs0iEule\\nURHw3HNSpL/4AggMdPxY994LpKcDp04B8fHSRUp3xkJ/B+fOAY89BjzzDPDaa9ocs21bYNMm4IUX\\ngLQ0bY5JpHcvvwwcPiz3/l13OX88X19gzRrg0iVgxAh5WqDSsdCXIjcX6NcPiI4G/vxnbY/dsiWw\\ndKm0Rr7+WttjE+nNe+8BW7ZIYb5hio3TfHykCycjA3j7be2Oa0Ys9CWw2aR7xd8fmDDBNecICwOm\\nT5funPPnXXMOItXWrQOmTpUuSy1a8rfy85Njf/wxkJKi/fHNgoW+BLNmAQcOAPPmAZ4uvEIxMfJr\\n8GDpwyQyk1OngCeflPdRjRq57jz16skT8nPPAd9/77rzGBkL/S0OHgTefBP49FOgcmXXn2/SJCA7\\nG3j/fT/Xn4zITQoKgLg44JVXgA4dXH++hx8G/vY36Wq9etX15zMaFvob5OYCsbHAP/4BPPCAe85Z\\nsSKweDEwe7Yv9u1zzzmJXG3SJHlhOnq0+845fLiM5hk/3n3nNAoW+htMnAjcfz8wZIh7z3vPPcBf\\n/3oZQ4YAeXnuPTeR1g4dAt5/H5gzx7Vdn7fy8AD+/W9g7lyZeUvXsdD/zzffyE0yfbr9kzi0EBNz\\nBQEBrnv5S+QOhYUykWnCBGnAuFudOjLK54kn2IVzIxZ6yM351FPSom/QQE2G4tbIjBnA0aNqMhA5\\n6/33Zdjj00+ryzBwINC0KfDOO+oy6A0LPWSUjbe3FHuVGjQAxowBXnqJE0DIeM6ckZb8Rx+5t8um\\nJO++K4X+5Em1OfTC8oX+4kV5W//ee+pvTkCK/PHjwOrVqpMQ2efNN4GhQ4GgINVJgMaNZfa51pMd\\njUoHpU2tCROAyEjgoYdUJxGVKsmaOi+/zD5GMo6MDGD9ein2ejF6NLBnj8zKtTpLF/qjR4GkJP1N\\nn+7SBWjeXFb5I9K74lUp33rLNbNfHVW5sszKHTOGExItXejfeAMYNUpm1unNpEnA5MnA5cuqkxDd\\n2Zo10gU6dKjqJLcbMED+d/lytTlUs2yh37dPlkt96SXVSUrWogXQo4esY0+kV0VFwNix8lRcoYLq\\nNLfz9ASmTJEupfx81WnUsWyhHztWlh6uUkV1ktKNHy/j+n/+WXUSopKlpABeXkDfvqqTlK5rV9my\\ncO5c1UnUsWSh37VLlgd+9lnVSe6sUSNg0CC26kmfCguBceOkb17FJEN7TJokTx1WnXluyUI/dqw8\\nyvn4qE5SttGjZSr5uXOqkxDdbMkSoFo1oGdP1UnK1rYt8OCDwPz5qpOoYblCv2ePrMWhxxdHJbnn\\nHlnKeNo01UmIrisqkpa8EVrzxd58U/rrrbj1oOUK/dSpwMiRMl7dKMaMkeURLl5UnYRIrF4tm350\\n7ao6SfmFhQH161tzv2ZLFfqjR4Ft29Suw+GI++4DIiJkHREi1Wy26+PTjdKaL/bmm9Jfb7Vx9ZYq\\n9P/8p6xZ7WfAPT5ee01mzHK2LKm2Y4e8M4qKUp3Eft27y7s5qy0xYplCf+aMbDf2wguqkzimWTMg\\nJARYtEh1ErK6qVNlDRk9jpsvi4eHDHCw2sqWlin0770nQxVr11adxHEjR8qqfFzZklQ5dAjYvRtI\\nTFSdxHH9+wM//ijr81iFJQr9lSvA7Nn6nQVbXl26yEy/zZtVJyGrmjYNeP559+yn7CpeXvJk/69/\\nqU7iPl6qA7jD4sUyjvb++1UncY6Hh2y2/M47QLduqtOQ1Vy4ACxbBnz3neokznvqKdlf9swZGYlj\\ndqZv0dtsMlrFqH3zt4qPB/bvl0doIneaO1dGf9WpozqJ82rUAOLiZEc3KzB9od+xA8jJMU8L2Nsb\\neOYZ69ygpA9FRXLPPf+86iTaefFFYOZM6do1O9MX+vffB/70J33sHqWVp5+W7qjsbNVJyCo2bABq\\n1pQuULNo2lRGsi1bpjqJ65mo/N3u9Glg0ybZEd5M7rkH6NgR+OQT1UnIKj74QFrzRpsgVZZnn5VW\\nvdmZutB/9JH0w+lp1xutPPec7EDFoZbkat9/L2tEPf646iTai4iQPZoPHlSdxLVMW+gLCmTVx+ee\\nU53ENcLDgUuXZEwzkSt9+CHw5JPGWO3VXl5ewLBh5m/Vm7bQb9wINGgAtGypOolreHrKY+e//606\\nCZlZXh6wYIEMRzSrp56SbtDff1edxHVMW+hnzzb3zQlIK2vFCuC331QnIbNas0bWcW/SRHUS12nY\\nEHjkEeDTT1UncR1TFvqzZ2WVythY1Ulcq3ZtoFcvYOFC1UnIrGbPlq4NszP7S1lTFvr582VlvapV\\nVSdxvaFDrb0XJrnOyZOy7eaAAaqTuF7PnkBmJnDggOokrmG6Qm+zyUtYs3fbFHvsMeD8eZktS6Sl\\npCR5KjbyujblVaGCLNQ2b57qJK5hukL/xRfyh9a+veok7uHpKTcoW/WkpcJCazWYAPl7tGgRkJ+v\\nOon2TFfoi/sUzTax406eeEJGDVh1h3vS3pYtQK1aQOvWqpO4T1CQLHS2caPqJNozVaG/fBlYtQpI\\nSFCdxL0CA2VjkrVrVSchs0hKkvc/VjNkiPx/NxtTFfqUFFkawAyr69nrySfZfUPayMoC1q0z/6i1\\nkgwcCKSlyXsvMzFVoV+4EBg8WHUKNaKjgc8/l6GlRM5ISQHCwoC771adxP2qV5cROMnJqpNoyzSF\\n/qefgL17gchI1UnU8PMD+vXjQmfkPCs3mADpvjHb6BvTFPrFi2UvSDOux1Fegwax0JNzTp+WvVSt\\n2mACZB2p06eBb79VnUQ7pin0CxZY7yXsrTp3lkkfZtjqjdRYvFgmG1ph7HxpKlSQJ5r581Un0Y4p\\nCv2BA7KSY2io6iRqVaggL9DYqidHLVzIBhMgW3YmJ8vOWmbgUKG32WwYN24cYmNjkZiYiFOnTt30\\n/aSkJERERCAxMRGJiYn48ccftchaqgULpNvCTLtIOSo+XiZ9cJ16stc338hok7Aw1UnUa9kSqFIF\\n2LlTdRJteDnyobS0NOTl5SE5ORn79+/H5MmTMeOGTUwPHTqEv//973jwwQc1C1qawkJpwW7e7PJT\\nGUJIiLTsd+0C2rVTnYaMZOFCNpiKeXjIpkWLFwMdOqhO4zyH/kgzMjIQ+r9+klatWuHgLduzHDp0\\nCDNnzkR8fDw++ugj51PewbZtQN26spQqyQ0aH8/uG7JPYaE8CbLb5rq4OGDpUtnEyOgcKvTZ2dmo\\nesPSkF5eXii6oTOrd+/eGD9+PObPn4+MjAxs377d+aSlSE6WwkbXDRoka2ub4QYl9/jsMxk3/4c/\\nqE6iH/ffDwQEAFu3qk7iPIe6bvz8/JCTk3Pt66KiInje8Lw3ZMgQ+Pn5AQA6duyIb7/9Fh07dizx\\nWJmZmY5EACCLDy1fXhcbNpxDZmahw8fRg6ysLKeuxY2qVAH8/e/GkiVZ6NQpV5NjupOW18Lo3HUt\\n5s69C716FSIzM9vl53KUivuiVy9fzJlTEc2bX3TrebXmUKEPDg5Geno6evTogX379iEoKOja97Kz\\nsxEREYH169fDx8cHX375JaKjo0s9lr+/vyMRAADr1wNNmwJt29Z1+Bh6kZmZ6dS1uNWQIcDGjbUM\\n+bSj9bUwMndci4ICYMMG4MsvAX//ai49lzNU3BfPPAM0bw7UrFlFV3N0zpw5Y9fPO1Tow8PDsWPH\\nDsT+bzGMyZMnIzU1FVeuXEFMTAxGjhyJhIQEeHt7o3379ghz0Wv8JUvMuTO9FgYOBMaPB3JzAW9v\\n1WlIz7Ztky6K++5TnUR//P2BVq2kURkVpTqN4xwq9B4eHhg/fvxNv9e4ceNr/92nTx/06dPHuWRl\\nyM2VlSonTHDpaQyrfn1piWzaZO1ZjlS2JUukYUAli4uTwQ1GLvSGHUi1ebO8OGrQQHUS/Ro4UEYN\\nEJUmP182mGehL92AAdJgunxZdRLHGbbQf/opu23KMmAAkJoqTz9EJdm6VfYzCAhQnUS/atUCHn3U\\n2Ps9GLLQX70qBewO73gJN3ffEJWE3TblEx0NLFumOoXjDFnoN2wAHnoIqFdPdRL9Y/cNlSYvD1i5\\nEoiJUZ1E//r2le7ibP2OPr0jQxZ6dtuUH7tvqDRpacADDwD33qs6if7VrAm0by+jb4zIcIX+99/l\\nYg8YoDqJMRR333AtILoVhyfbx8jdN4Yr9OvWAQ8/DNSurTqJcQwcKH+piYrl5gKrV7PBZI9+/aTb\\n+PffVSexn+EKfUoKX8Lai903dKu0NA5Ptlft2kCbNsDGjaqT2M9QhT43V7pt+vZVncRY2H1Dt0pJ\\nYWveEUbtvjFUod+6VVohHG1jP3bfULGCAum2MfJMT1WiomQ8/dWrqpPYx1CFPiVFNgAn+/XvL903\\n+fmqk5BqX3wBNGzISVKOqFdP1r4x2tOxYQp9YaGsbcNWiGP8/YGgIFnAiqyNDSbnGLH7xjCFfscO\\neXF0w9ppZKeoKFnXhKzLZpN7gA0mx/XvD6xZIxPOjMIwhZ6tEOdFRclMSLPsbE/227MH8PUFmjVT\\nncS4GjSQiWZbtqhOUn6GKPQ2Gwu9FoKCgBo1ZONwsqaUFPkH38NDdRJji4qSrmSjMEShz8gAfHy4\\nAbgW2H1jXWwwaadfPyn0Rnk6NkShX7FCbk62QpxXXOhtNtVJyN0OH5ZZnW3aqE5ifE2ayPLFRnk6\\nNkShZytEO8HBMgb4229VJyF3Y7eNtvr1k3deRqD7Qn/4MJCVxVaIVjw8jHWDknaKn4xJG0b6e6T7\\nQl/cCvHUfVLjYD+99fz4I3DypOyURNoICZH16Y8cUZ2kbLovn2yFaC80FDhxQv7ikzWsWAH06QN4\\nealOYh5GejrWdaE/cUJ+hYaqTmIuXl5ARIQxblDSBhtMrsFCr4GVK6UgsRWiPXbfWMevvwL79wNd\\nuqhOYj4dOwL//S+Qmak6yZ3putCvWcMliV0lPBzYuxc4d051EnK1deuArl1lLgppq2JFoFcvWQ1U\\nz3Rb6C9elDGq4eGqk5hT5cpybdesUZ2EXG31aumfJ9cwQveNbgv9hg1AWJisy0Gu0a+f/lsi5Jyr\\nV2U3qV69VCcxr+7dgf/8B7h0SXWS0um20K9eDURGqk5hbj17ysJMV66oTkKukp4OtGzJPZZdqWpV\\naZSuX686Sel0Wejz86VFHxGhOom51aoFtG4tO3eRObHbxj303n2jy0L/xRdAYCA3LnaHPn3YT29W\\nNpv82bLQu15kpDROc3NVJymZLgs9WyHuU1zojbIKH5Xf118DVaoATZuqTmJ+desCzZtLV5ke6a7Q\\n22zsn3enJk2AatVkqCWZCxtM7hUZqd+nY90V+sOHpY++VSvVSawjMpKjb8yIhd69ip+O9bgEuO4K\\nffHNyaVU3adPHxZ6szl1StYyeuQR1Ums44EHgEqVgAMHVCe5nW4LPblP+/bA6dOyrhCZw5o1Mnae\\ny4e4j4eHfp+OdVXof/lFNsTo2FF1EmupUAHo3RtITVWdhLTCBpMaeu2n11WhX7tWpuV7e6tOYj3s\\nvjGPrCyZqdm9u+ok1hMaCnz/PXDmjOokN9NVoWcrRJ1u3YCdO4HLl1UnIWdt2iTdcVWrqk5iPRUr\\nyj+wa9eqTnIz3RT6q1dlOj7X5FDDzw/o0AHYuFF1EnIWG0xq6bGfXjeFfutW4KGHZFo+qcHuG+Mr\\nKJDWJOehqNOzJ7Btm77WkNJNoWcrRL3ISFm7vKBAdRJy1M6dwL33Ag0bqk5iXTVqyH6yW7aoTnKd\\nLgp9URHX5NCDe+4BGjWSF3lkTGww6YPeum90Uej37pU+4qAg1UlIbzco2YeFXh8iI2W4sl7WkNJF\\noWdrXj/69AFWrdLnNG66s+++A7KzgeBg1UlIb2tI6aLQsxWiH61by0uk775TnYTsVdxg4vIh+qCn\\nJcCVF/qTJ2VdjvbtVSchQIoER98YE1d91Rc9dYMqL/SpqVyTQ2/0dINS+Zw7B+zfDzz2mOokVKx9\\ne2nEnjqlOokOCj27bfSnc2fgm2+AX39VnYTKa906oEsXwMdHdRIq5uUljVg9rCGlvNDv2ME1OfTG\\nxwfo2lWKBxkDG0z6pJenY4cKvc1mw7hx4xAbG4vExEScuuXZZOvWrYiOjkZsbCyWLl16x2N16MA1\\nOfRILzcolS03F9i8WVYgJX3p3l0as9nZanM4VOjT0tKQl5eH5ORkjBo1CpMnT772vYKCAkyZMgVJ\\nSUlYsGABPv30U1y4cKHUY/HlkT717g2kpckaRKRv27bJfqW1a6tOQreqVg1o107+IVbJoUKfkZGB\\n0NBQAECrVq1w8ODBa987duwYAgIC4Ofnh4oVKyIkJAS7d+8u9Vgs9PpUuzbQooUUEdI3dtvomx6G\\nWTpU6LOzs1H1hv4WLy8vFP1vCtit3/P19UVWVlapx+KaHPqlhxuU7sxmkz8jNpj0q3iWbGGhugwO\\nDWr08/NDTk7Ota+Liorg6el57XvZN3RI5eTkoFq1aqUeKzMz05EIppOVlaW7a9GunRemTauFN974\\n2a2TcPR4LVQp61ocPOgFT8+auOuuX2D2S2bU+6JSJaBWrdpYu/Yi2rTJV5LBoUIfHByM9PR09OjR\\nA/v27UPQDYvUBAYG4sSJE7h8+TJ8fHywe/duDBs2rNRj+fv7OxLBdDIzM3V3LerXB3x9gV9+8Ufr\\n1u47rx6vhSplXYs5c4CoKKBBA/NfLyPfF1FRwJdf1tasi+2MnVtYOdR1Ex4ejkqVKiE2NhZTpkzB\\n66+/jtTUVCxduhReXl54/fXXMXToUMTFxSEmJgZ16tRx5DSkWPEsWXbf6Be7bYxB9Wxzh1r0Hh4e\\nGD9+/E2/17hx42v/3alTJ3Tq1MmpYKQPkZHAn/8MjB2rOgnd6swZ4OhR2aeU9K1tW5mA+MMPwH33\\nuf/8yidMkb516AAcPw6cPq06Cd0qNRXo0UP2KSV98/QEIiLUzZJloac7qlhRtkbTwzRuuhm7bYxF\\nZfcNCz2VibNk9ef332WOQ8+eqpNQeXXtCuzaBVy86P5zs9BTmXr0AD7/HLhhRC0ptmWLbDBSo4bq\\nJFRevr5AWBiwYYP7z81CT2W66y59TOOm67grmzGp6r5hoadyYfeNfhQVyTsT9s8bT0SEtOjz3Txv\\nioWeykUP07hJZGTIYllNmqhOQvby9wfuv1+6Qt2JhZ7KpXFjoF49eZlEarHbxthUdN+w0FO5sftG\\nHzis0tiKC73N5r5zstBTuamexk3X9yBt3151EnJUixbynuXbb913ThZ6KreHHwbOnweOHVOdxLrW\\nrJGx814OLV5CelC8hpQ7G00s9FRuxdO4uciZOuyfNwcWetI1dt+ok50t+4927646CTkrLAw4cgQ4\\ne9Y952OhJ7t07Qrs2QP89pvqJNazaZNMXLvDPj5kEJUqAd26AWvXuud8LPRklypVgI4d1UzjtjqO\\ntjEXdz4ds9CT3dh9436FhdL6Y6E3j549gfR0WaDO1VjoyW4REcDGje6fxm1lX30F1K0rE9fIHGrW\\nBEJCZIE6V2OhJ7vVr69mGreVsdvGnNy1VScLPTmE3TfuxWGV5lRc6IuKXHseFnpyiIpp3FZ17Jjs\\nN9q2reokpLXAQOnC2bPHtedhoSeHtGghLwjdOY3bqlauBPr2lQlrZD7ueDrmrUMOUTGN26pWrgT6\\n9VOdglyFhZ50zV0vkqzs3DlPfPMN8NhjqpOQq7RtC/z8M3D8uOvOwUJPDuvYUbpufv5ZdRLz2rzZ\\nB926AT4+qpOQq1SoAPTu7dpGEws9Oczd07itaMMGH3bbWICru29Y6Mkp7L5xnexs4MsvK6FXL9VJ\\nyNXCw2X3tkuXXHN8FnpySs+eMrPvyhXVScxn40YgODgP1aurTkKu5usrK1quW+ea47PQk1Nq1QKC\\ng4G0NNVJzGflSqB796uqY5CbREUBK1a45tgs9OS0/v1dd4NaVX6+vPtgobeOPn3kKc4VT8cs9OS0\\nfv3kRVJBgeok5vHZZ0CTJkD9+i6eG0+6Ubu2656OWejJaQ0byqqKn32mOol5cJKUNUVFASkp2h+X\\nhZ400b+/a25QK7LZWOitKipKRrFp/XTMQk+aKO6nd/UqfFawd6/s5PXAA6qTkLvdey9w333aPx2z\\n0JMmmjYFqleXscDknOLWvIeH6iSkgiu6b1joSTPsvtEGu22szRVPxyz0pJnilgjXqHfc0aPAuXNA\\nu3aqk5AqxU/Hu3drd0wWetJM69byEungQdVJjGv5cmDAAK49b3Vad9/wdiLNeHiw+8ZZy5YB0dGq\\nU5BqxX+PtHo6ZqEnTbHQO+74ceDUKSA0VHUSUq11a5kdrdXTMQs9aap9e1mf/vvvVScxnmXL5JG9\\nQgXVSUg1Dw9t175hoSdNVaggI0a49o392G1DN9Ly6ZiFnjQXFSUvFan8TpwAfvhBdu0iAoBHHgHO\\nnpWRWM5ioSfNde4sXTcnT6pOYhwpKUDfvkDFiqqTkF5UqCBPeEuXOn8sFnrSXKVK0n2jxQ1qFey2\\noZIMHAh8+qnzx2GhJ5d4/HFtblArOH0aOHIEeOwx1UlIbx59VCbQHTni3HFY6MklOncGfvxR+p3p\\nzlJSgMhIeRIiupGnpzzpLVni5HG0iUN0My8vGTXA7puysduG7uTxx1noScfYfVO2M2eAAweA8HDV\\nSUiv/vhH4NIl4NAhx4/h5ciHcnNz8eqrr+L8+fPw8/PDlClTUKNGjZt+ZuLEidi7dy98fX0BADNm\\nzICfn5/jSclwwsKkkB09Ktvi0e2WLJHRNt7eqpOQXnl6AjExcq+MH+/gMRz50OLFixEUFIRFixah\\nb9++mDFjxm0/c+jQIcyZMwfz58/H/PnzWeQtqHh4mLOPnWb2ySdAXJzqFKR3xd03jq5941Chz8jI\\nQFhYGAAgLCwMO3fuvOn7NpsNJ06cwNixYxEXF4flnD1jWVoNDzOjY8fkhXWXLqqTkN61bQtcuQJ8\\n841jny+z62bZsmWYN2/eTb939913X2uh+/r6Ijs7+6bv//7770hISMCTTz6JgoICJCYmokWLFggK\\nCnIsJRlWhw7AhQvA4cNAs2aq0+hLcrI88Xg51IFKVuLhIY2mJUuAli3t/3yZt1h0dDSibxkS8MIL\\nLyAnJwcAkJOTg6pVq970/cqVKyMhIQHe3t7w9vbGH//4Rxw5cqTEQp+ZmWl/ahPKysoy7bXo2bMa\\n5swpwsiR2WX/MMx9LYrZbMD8+bXx979fQmZmXqk/Z4VrUV5WvxadO1fEiBE1MHz4L3Z/1qG2RHBw\\nMLZv344WLVpg+/btaNOmzU3fP378OF555RWsWrUKBQUFyMjIQP/+/Us8lr+/vyMRTCczM9O012LY\\nMODJJ4F//KNaufZBNfO1KHbgAHD1KhAZefcdNxmxwrUoL6tfi/r15b3X2bP+AM7Y9VmH+ujj4uJw\\n9OhRxMfHY+nSpXj++ecBAElJSUhPT0dgYCD69euHmJgYJCYmIioqCoGBgY6cikygXTsgLw/4+mvV\\nSfRj8WIgNpY7SVH5eXgA8fHyAt/uz9ps6nb4zMjIQEhIiKrT64rZWyvjxgGXLwPvvlv2z5r9Wths\\nwH33yVLODz105581+7WwB6+FvOvq0gVYs8a+2sn2BLnF4MHSii0oUJ1EvS+/BHx8gFatVCcho2nW\\nDKhXz/7PsdCTWzRpAjRqBKSlqU6i3uLFMna+PO8riG41f779n2GhJ7cZPBhYuFB1CrXy8mRYZXy8\\n6iRkVM2b2/8ZFnpym8cfB1JTgezyjbI0pfXrgaZNgfvvV52ErISFntymdm0gNFS7fTCNKCkJeOIJ\\n1SnIaljoya0SEqzbffPrr0B6uixQReROLPTkVpGRwJ49gBUnOC5eDEREANWqqU5CVsNCT25VuTIw\\nYIBjIweMjt02pAoLPbndsGHAxx87vuSqER04IF03nTurTkJWxEJPbteuneyP+vnnqpO4z7x5QGKi\\nrFVC5G4s9OR2Hh7Sqp89W3US98jPBxYtkkJPpAILPSmRkACsXg1cvKg6ieulpsq4+aZNVSchq2Kh\\nJyXuvhty8WsbAAAJF0lEQVTo1k1Gopjdhx8Czz2nOgVZGQs9KTNsGDBnjuoUrvXDD8DevbKTFJEq\\nLPSkTNeuMhJl3z7VSVxn1izppvLxUZ2ErIyFnpSpUAF4+mlgxgzVSVwjLw+YOxd49lnVScjqWOhJ\\nqaefBpYuBX77TXUS7a1cKeuH8yUsqcZCT0rVrQv07i0tX7OZOZMvYUkfWOhJueefl+6boiLVSbRz\\n5Ahw8CAQFaU6CRELPelAu3ZA9erAxo2qk2jnvfekNV+pkuokRCz0pAMeHsCf/gR88IHqJNq4cEHm\\nBwwfrjoJkWChJ12IjQV27QK+/151EufNmgX06ePYJs5ErsBCT7pQubKMwJk2TXUS5+Tny5PJyy+r\\nTkJ0HQs96caLLwKffAKcP2/c2zIlBQgMBFq3Vp2E6Drj/o0i06lXT5YKSEryVR3FITYb8M47bM2T\\n/rDQk66MGgXMm1cFOTmqk9hvyxYgK0v654n0hIWedKVpU6Bt2zxDTqCaMAF4/XXAk3+rSGd4S5Lu\\njBiRjf/7P3mxaRQ7dgAnTwJxcaqTEN2OhZ50Jzg4H4GBsv2eUUycCIwZA3h5qU5CdDsWetKlt9+W\\nX7m5qpOUbe9eYP9+4IknVCchKhkLPelS+/bAH/5gjI1Jxo6V1ry3t+okRCVjoSfdeustYNIk4OpV\\n1UlK9/nnsngZ15wnPWOhJ91q0wYICZE9V/XIZgNee03+QWJrnvSMhZ50bcIEYPJkfW5MsmYNcPky\\nMGiQ6iREd8ZCT7rWooWs6f7WW6qT3Cw/X8bMT5okWyIS6RkLPeneW28BCxYA332nOsl1H3wA3HMP\\nEBGhOglR2VjoSffq1JG+8FGjVCcRZ8/KuPn33pO19In0joWeDOHFF2Wt+pQU1UmA0aOBYcO46TcZ\\nB+fxkSFUqiQbesTGAp07AzVqqMmRng5s3QocPqzm/ESOYIueDCM0FOjbF3j1VTXnz8oChg4FZs4E\\nqlZVk4HIESz0ZChTpgCbNskvdxs9Wp4mevd2/7mJnMGuGzKUatWAuXOBhATg66+BunXdc97UVGDt\\nWuDAAfecj0hLbNGT4XTpIi9DExKAoiLXn+/4cTnf4sVA9equPx+R1ljoyZDGjZM1cMaOde15rl4F\\nBg6U4Z0dOrj2XESuwkJPhuTlBSxbJpuJu2rd+qIieWoIDOQ+sGRs7KMnw6pTR/rNO3UC/P2B8HDt\\njm2zAa+8Avz6K7BhAydGkbGxRU+G1qwZsHy5LCy2YYM2xyxelTI9HVi5EvDx0ea4RKo4Veg3b96M\\nUaXMS1+yZAkGDBiA2NhYbNu2zZnTEN3Ro49KQU5MBJYude5Y+fnA8OEyKSo9nS9fyRwc7rqZOHEi\\nduzYgWbNmt32vXPnzmHBggVYsWIFrl69iri4OHTo0AEVK1Z0KixRaR55BNi4UVa63LtXFkKz93Y7\\ne1ZevPr5AVu2yFBOIjNwuEUfHByMv/3tbyV+78CBAwgJCYGXlxf8/PzQqFEjfKenpQfJlFq3Bnbv\\nlvH1Dz8MfPVV+T5XWCjLK7RsKUM3U1NZ5MlcymzRL1u2DPNuGdYwefJk9OzZE7t27SrxM9nZ2ah6\\nwxzxKlWqICsry8moRGWrXRtYvx5YuBAYMABo3ly2+evWDfD1vflnf/oJWLFCVqGsWxfYvBlo1UpN\\nbiJXKrPQR0dHIzo62q6D+vn5ITs7+9rXOTk5qMYmErmJh4cMixw4EFi0CJg+Xb4OCJDROYWFwIkT\\nwKVLQPfuwMcfSz8/R9aQWblkeGXLli0xbdo05OXlITc3Fz/88AOaNGlS4s9mZGS4IoIhnTlzRnUE\\n3dDqWrRqVb5W+t69mpzOJXhfXMdr4RhNC31SUhICAgLQuXNnJCQkID4+HjabDSNHjkSlSpVu+/mQ\\nkBAtT09ERCXwsNlsNtUhiIjIdThhiojI5JQUepvNhnHjxiE2NhaJiYk4deqUihi6UFBQgNGjR2PQ\\noEEYOHAgtm7dqjqSUufPn0enTp1w/Phx1VGU++ijjxAbG4sBAwZg+fLlquMoUVBQgFGjRiE2NhaD\\nBw+27H2xf/9+JCQkAABOnjyJ+Ph4DB48GOPHjy/X55UU+rS0NOTl5SE5ORmjRo3C5MmTVcTQhdWr\\nV6NGjRpYtGgRZs2ahbffflt1JGUKCgowbtw4+HDNAezatQtff/01kpOTsWDBAsu+hNy+fTuKioqQ\\nnJyMESNG4N1331Udye1mz56Nv/zlL8jPzwcgw9tHjhyJhQsXoqioCGlpaWUeQ0mhz8jIQGhoKACg\\nVatWOHjwoIoYutCzZ0+89NJLAICioiJ4eVl3nbmpU6ciLi4OderUUR1FuS+++AJBQUEYMWIEhg8f\\njs6dO6uOpESjRo1QWFgIm82GrKwsS86uDwgIwPTp0699fejQIbRp0wYAEBYWhp07d5Z5DCVV5dYJ\\nVV5eXigqKoKnp/VeGVSuXBmAXJOXXnoJr7zyiuJEaqSkpKBWrVro0KEDPvzwQ9VxlPvtt9+QmZmJ\\nmTNn4tSpUxg+fDg2aLVqm4H4+vrip59+Qo8ePXDx4kXMnDlTdSS3Cw8Px+nTp699feP4GV9f33JN\\nRlVSWf38/JCTk3Pta6sW+WJnzpzBkCFDEBUVhV69eqmOo0RKSgp27NiBhIQEHDlyBGPGjMH58+dV\\nx1KmevXqCA0NhZeXFxo3bgxvb29cuHBBdSy3S0pKQmhoKDZu3IjVq1djzJgxyMvLUx1LqRtrZXkn\\noyqprsHBwdi+fTsAYN++fQgKClIRQxfOnTuHYcOG4dVXX0VUVJTqOMosXLgQCxYswIIFC/DAAw9g\\n6tSpqFWrlupYyoSEhODzzz8HAPz888+4evUqatSooTiV+911113w8/MDAFStWhUFBQUocsf+kTr2\\n4IMPYvfu3QCAzz77rFzzkZR03YSHh2PHjh2IjY0FAEu/jJ05cyYuX76MGTNmYPr06fDw8MDs2bNL\\nnGBmFR5ciwCdOnXCnj17EB0dfW2UmhWvy5AhQ/DGG29g0KBB10bgWP1l/ZgxY/DXv/4V+fn5CAwM\\nRI8ePcr8DCdMERGZnHU7xomILIKFnojI5FjoiYhMjoWeiMjkWOiJiEyOhZ6IyORY6ImITI6FnojI\\n5P4f+jwGTxP/IVYAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x107527f98>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig = plt.figure()\\n\",\n    \"ax = plt.axes()\\n\",\n    \"\\n\",\n    \"x = np.linspace(0, 10, 1000)\\n\",\n    \"ax.plot(x, np.sin(x));\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Alternatively, we can use the pylab interface and let the figure and axes be created for us in the background\\n\",\n    \"(see [Two Interfaces for the Price of One](04.00-Introduction-To-Matplotlib.ipynb#Two-Interfaces-for-the-Price-of-One) for a discussion of these two interfaces):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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P2BVq2kURkVpTqN4xwq9B4eHhg/fvxNv9e4ceNr/92nTx/06dPHuWRl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\\n5P4f+jwGTxP/IVYAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x107066518>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.plot(x, np.sin(x));\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"If we want to create a single figure with multiple lines, we can simply call the ``plot`` function multiple times:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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4povPcesH49kM/G313MUVZehiXJSzChywTaUkRj507AMvNj7Hr4J20p\\nomFnR06yRUXRVqJ+mDb6zExg927gB99P8MfJP2pUY1vKNGsG9O0LrFtHWwnnTey4sgONTRrDqYkT\\nbSmi8eefwFfDhuL6k+s4c/cMbTmi8cEHZFbPOkwb/dKlJA7n3a4/BEF4LcNPzkyYQDpQMfJ3F1P8\\nlfQXJnaZSFuGaFy5QmpEhQTpYILzhCqLBsoJLy/So/nCBdpK1AuzRl9aSqo+TphA6na86/Qulp1a\\nRluWaLi5AU+ekDPNHOmQ8SgDJ26fwIh28j5S+SJ//w2MHUsySsd1HoeolCjkF7MRN9TRAcaPZ39W\\nz6zR79oFNG0KdOhAfg7rGIbYtFg8evaIrjCR0NIiy86//qKthPMiS5OXIqxjGAx1ZXzQ/AWKi4GI\\nCHIcEQCamDZBX6u+2HBhA11hIvLuuyQM+vTtbSxkDbNGv3z5/x5OADA3ModHGw+sPc9ODYGxY4Et\\nW4BHbPzdJXuKy4qx8sxKfODMxpFKgPSDbdsWsLH532vvOb1X4/7MUqZFC6BHD2DjRtpK1AeTRn/3\\nLqlSGRT08uvvOb2HZaeWMbMpa2EBDB4MrGHnQJGsiUuLQ1uLtrAzt6MtRTSWLyehjRcZ1GYQbufe\\nxvns83REqQHWN2WZNPrwcFJZz9T05df7t+qPvKI8JGUl0RGmBsaNq929MKXEqjOrMLbTWNoyROPm\\nTdJ209//5dd1tHQwrvM4pmb1np5AVhZw7hxtJeqBOaMXBLIJ+2LYpgIthRZzm7IDBgAPHpBsWQ49\\n7uTdwdFbR+Hv4F/1h2XC6tVkVfymujbjOo/D2vNrUVjKRt1sbW1SqO2fN/crkj3MGf2RI+R/Wvfu\\nb35/bKexiLwUycypAS0t8oDyWT1dIs5FYJj9MBjrGdOWIgplZZVPmACgZb2WcLZ0xuZLm9/8ARkS\\nFkbKgJeU0FYiPswZfUVMsbKqsE1Mm6BXi17YkrJFs8LUyJgx5NRAbe1wTxtBELD6zGqM7cxO2Gbv\\nXsDMDOjcufLPjO88vtKWnXLE1hawtiYn9liDKaPPzQViYoDQ0Ld/LrRDKMLPsdMO3tqaNCbZto22\\nktrJycyTKC0vRc/mPWlLEY3Vq8n+z9vwtvVGUlYSMnMzNaJJE4weTf7bWYMpo4+OJqUBqqqu523r\\njeSsZNzOva0ZYRpg7FgevqHFqjOrMKbTGNk2F3mVvDxg+/bXT629iqGuIfwd/Jk6sjx8OJCQQPa9\\nWIIpo1+zBhg1qurPGeoaIqBtANadZ6dYTEAAcPgwOVrK0RzPSp4h8lIkQjtUsYyUEdHRQJ8+gLl5\\n1Z8N7RiK8LPhzBxZrlePnMDZwE4+GACGjP72beDUKcDbu3qfD+sYhn/O/sPMA2piAgwdygudaZqt\\nqVvRxbILmtdtTluKaFR3wgQAvVr0QkFJAVOFzkaPZu/0DTNGv3496QVpYFC9z/ds3hPPSp7h9N3T\\n6hWmQUaO5EavadaeX8vUbD4zk/RSre6ESUuhhdAOoYg4F6FeYRrEzY18D5cu0VYiHswYfURE1Zuw\\nL6JQKMim7Fl2NmX79ydJH2lptJXUDnKe5uDIzSMYaj+UthTRWL+eJBvWpCdsaIdQrDu/DqXlpeoT\\npkG0tcmKJpwda2DD6M+dI5Uce/eu2XWhHUOx/sJ6lJSxcXBWW5tsoPFZvWaIvBgJTxtPmOiZ0JYi\\nGmvW1GzCBAA2ZjZoVb8V9lxlp2t9SAiJ05eX01YiDkoZvSAImDVrFoKCghAWFoZbt2699P7q1avh\\n5eWFsLAwhIWF4fr162JorZSICBK20Krhf02bBm3QpkEb7L66Wz3CKBASQpI+GNl6kDTrLqxDSPsQ\\n2jJE4/x5ctqkT5+aX8vakeUOHQAjI+DYMdpKxEEpo09ISEBxcTE2bNiAKVOmYO7cuS+9f/HiRfz0\\n008IDw9HeHg4WrZsKYbWN1JWRmaw1d08epWRjiOx/sJ6cUVRxNmZzOxPnqSthG1uPL6BlPspGNRm\\nEG0porFmjXITJgAY0W4Etl/ezkzGuUJBmhatZ8QalDL65ORk9P7/OEnHjh1x4ZX2LBcvXsSSJUsQ\\nEhKCpUuXqq7yLRw4ADRqREqpKkNA2wDEp8fjaQkbxagVCjKr5+Eb9bLhwgb4O/hDT1uPthRRKCsj\\nK8Gahm0qMDMyQ68WvRCXFieuMIoEBwORkaSJkdxRyujz8/Nh+kJpSB0dHZS/EMwaMmQIZs+ejfDw\\ncCQnJ+PgwYOqK62EDRuIsSlLQ+OGcGnqgu2Xt4snijIjR5La2iw8oFJl3YV1CHFkJ2xz6BA5N9+u\\nnfJjjGg3AhsusnMAvU0bwMoK2LePthLV0VHmIhMTExQUFDz/uby8HFovrPdGjx4NExOyQdW3b19c\\nunQJffv2feNYWVlZykgAQIoPbd7cCDt35iArq0zpcQY1HYRVSavQo14PpcdQlby8PJW+ixcxMgIs\\nLc2xaVMe+vUrEmVMTSLmd6EOUh+m4l7+PVjrWqtdp6a+i1Wr6mLw4DJkZSkfenGp64KPrn2ElIwU\\n1NWvK6I6Ao3nYvBgY6xYoYv27R9r9L5io5TROzk5Yf/+/fDw8MCZM2dga2v7/L38/Hx4eXlhx44d\\nMDAwwPHjxxEQEFDpWJaWlspIAADs2AHY2QEuLo2UHgMAxtYfizm/zYGJmQnq6NdRaSxlycrKUum7\\neJXRo4Fdu8xUWu3QQuzvQmwWpSzCqA6j0KxpM7XfSxPfRWkpsHMncPw4YGmp/PNvCUu4WrvixJMT\\nGNNpjHgC/x8az8X77wPt2wMNGhhVO0dHE9y5c6dGn1cqdOPm5gY9PT0EBQVh3rx5mDFjBuLj4xEZ\\nGQkTExNMnjwZoaGhGDVqFGxtbdFHmW38arBpEzBChB7MDQwboI9VH8Smxao+mEQYPpy0gSuS34Re\\n0giCwFzY5sABEqJo3Vr1sUa0G8FUP1lLS6BjRzKplDNKzegVCgVmz5790mutWrV6/u8+Pj7w8fFR\\nTVkVFBWRSpXffy/OeEHtgrDh4gaM6qDk8R2J0aQJmYns3l39LEdO1ZzIPAF9bX10atyJthTR2LSJ\\nTAzEwMvWC+/Hv4/7BfdhYWwhzqCUCQ4mhxv8/GgrUR7ZJkzt2UM2jpo2FWc8HzsfHLpxCA+fPRRn\\nQAkwfDg5NcARj6hLUQhsG8hMpcqSEtJgXiyjN9YzxmCbwYhOiRZnQAng708mTLm5tJUoj2yNfuNG\\nccI2FZjqm8KttRtTDUn8/YH4eB6+EQtBEIjRtwukLUU09u0j/QysrMQbk7XTN2ZmQK9e8u73IEuj\\nLywkBvaWPV6lCGofxNQD+mL4hqM6SVlJ0NPWg2NDR9pSREPMsE0FHm08cObuGWTlSffkVE0JCACi\\nomirUB5ZGv3OnUCnTkDjxuKOO8RmCBIzE3Gv4J64A1OEh2/EI/JSJFNhm+JiYOtWIFDkBYqBjgF8\\n7HwQeZGdB8/Xl4SL82Wa+CtLoxc7bFOBoa4hPNp4YGvqVvEHpwQP34hDRdgmoK3Iy0iKJCQA9vZA\\nczWU0h/edjiiUmQ8BX6FBg2A7t3le/pGdkb/9Cn5sv391TO+v4M/Nqew09m+Inyzh53CglQ4decU\\ntBRazJ22UceECQBcW7viwr0LuJvPTsszOYdvZGf027cDXbsCFmo6ueVp44njt4/j0bNH6rkBBYYP\\nJ7/UHOWpmM2zErYpKgJiY9U3YdLX0cdgm8FMHW4YOpSEjZ/KsCyW7Iw+Olr8TdgXMdEzwYBWA5hK\\nnuLhG9UQBAFRKeRYJSskJIh7PPlNsLY6trAAunQBdu2iraTmyMroi4pI2MbXV733Ye0B5eEb1Tib\\nfRal5aVwauJEW4poREerbzZfwSDrQUjMSsSDpw/UeyMNItfwjayMft8+MgsR+7TNq3jZeuHA9QPI\\nK8pT7400CA/fKA9rSVKlpSRso+5MT2M9Y7i2dmVqdeznR87TFxbSVlIzZGX00dGkAbi6qWdQDz1b\\n9MS2yzLOkHiFYcNI+KaEja6JGkMQBEReimTqtM2RI0CLFuImSVUGa6vjxo1J7Ru5rY5lY/RlZaS2\\njabqTbD2gFpaAra2pIAVp/pcuHcBhaWF6GrZlbYU0dDUhAkgq+NDNw4ht0jG9QNeQY7hG9kY/dGj\\nZOPohdppasXXzhe7r+5mpvMUQP6S3MLOIQiNEHUpCgEO7Jy2EQTyDGhqwlRHvw56W/VGfHq8Zm6o\\nAYYNI5Vhi4tpK6k+sjF6Tc5CAMDC2ALOTZyZahzu50cyIVnpbK8JYtNjMdR+KG0ZopGUBBgbAw4O\\nmrsna6vjpk1JotnevbSVVB9ZGL0gaN7oAfYeUFtboH593ji8utx8chO3c2+je/PutKWIRnQ0+Qtf\\nkwsUXztfJFxLYG51HBNDW0X1kYXRJycDBgbKNwBXFj8HP2xL34biMhmt0aqAh2+qT1xaHIbYDIGO\\nllJtGyQHrQmTmZEZulp2xc4rOzV7YzUydCgxermsjmVh9Fu2kIdT02FSS1NL2JvbY+81Ga3RqqDC\\n6AWBthLpE5MWAx879TbQ0SQpKSSrs0sXzd97mMMwplbHNjakfLFcVseyMHoas5AKhjkMw5ZUdqbA\\nTk7kDPClS7SVSJsnhU9w7PYxuFu705YiGjTCNhX42bO3Oh46lOx5yQHJG31KCpCXR2cWApD4Ylx6\\nHMoFmazRqkChkNcDSotdV3ehd4veMNEzoS1FNCpWxjRoYtoEDhYOOHD9AB0BakBOv0eSN/qKWYgW\\nJaU2ZjZoYNgAJzNlskarBjxOXzWxabFMhW2uXwdu3iSdkmjha+eLmFQZ7WBWgbMzqU+fmkpbSdVI\\n3uhpzkIq8LXzZapGfe/ewI0b5Bef8zolZSXYcWUHvGy9aEsRjS1bAB8fQIfivrKvnS9i02MhMLJB\\nJKfVsaSN/sYN8qd3b7o6fO18EZPGzkxERwfw8pLHA0qDo7eOolW9VmhWpxltKaIhhQmTvbk9DHUM\\ncerOKbpCRIQbvQhs3UoMieYsBAC6Nu2KJ4VPkP4gna4QEeHhm8phLWxz/z5w9iwwcCBdHQqFgrnV\\ncd++QHo6kCXx9riSNvq4OPWXJK4OWgot+Nj5MBVfdHMDTp0CcnJoK5EWgiAwZ/TbtwOuriQXhTa+\\n9mytjnV1gcGDSTVQKSNZo3/8mJxRdXOjrYTAWvjG0JB8t3FxtJVIi5ScFBSXFaNjo460pYhGbCyJ\\nz0uB7s26427+XWQ8yqAtRTTkEL6RrNHv3An06UPqckiBAa0G4MK9C7hXcI+2FNEYOlT6MxFNUzGb\\nZ6WIWWEh6SY1eDBtJQRtLW142XoxNWkaNAj491/gyRPaSipHskYfGwt4e9NW8T/0dfThZu3GVBU+\\nT09SmOnZM9pKpENsWix87SQQLxSJ/fuBDh3U12NZGVhbHZuakknpjh20lVSOJI2+pITM6L0kdrqN\\ntY0kMzOgc2fSuYsDZOdnIyUnBX1b9qUtRTSkFLapwM3aDclZyXj47CFtKaIh9fCNJI3+yBHA2lq9\\njYuVYbDNYBy4fgAFxQW0pYiGjw+P01cQnx4Pd2t36Gnr0ZYiCoJA/t9KzeiNdI0woNUAbEtnp4Ob\\ntzeZnBYV0VbyZiRp9FKchQBAA8MG6Nq0K/Zck1kfsbdQYfRyqcKnTmLTY+FjK8EHT0lOnwaMjAA7\\nO9pKXoe18E2jRkD79iRUJkUkZ/SCIL34/Iuw9oDa2AB16pCjlrWZpyVPsT9jPzxtPGlLEQ2pTpgA\\n0mJwz7U9KCyVWZftt+DtLd3VseSMPiWFxOg7SvR0m6+dL+LT41FWXkZbimh4e/PTN3uv7YWzpTMa\\nGDagLUU0pGz0FsYW6NCoA/ZlsLNBVLE6lmKFB8kZfcXDKdXTbVb1rNDUtCn+vfUvbSmi4ePDjT42\\nja2wza1bpJZRjx60lVQOa0XO7O0BPT3g3DnaSl5HskYvZVgL33TvDmRmkrpCtZFyoRxx6XFMZcPG\\nxZGz87TLh7yNiiJnLJUAl+rqWFJGf+8eaYjRV+Kn24baD8XW1K3MVOHT1gaGDAHi2UkRqBGJmYkw\\nMzKDdQNr2lJEQw4TJhszG9Q3qI/EzETaUkRDqnF6SRn9tm0kLV9fn7aSt9OpcScUlRUhNUcGhair\\nSW0O37AWtsnLI5magwbRVlI1vna+iE1j58Hr3Ru4cgW4c4e2kpeRlNHLYRYCkCp8PrY+TIVv3N2B\\nY8eA3FwykKlCAAAgAElEQVTaSjRPbDpbRcx27ybhOFNT2kqqxseOrd8jXV3yF+w2iaUISMboCwtJ\\nOr5UanJUha89WzMRExOgZ09g1y7aSjTLtUfXcK/gHlyautCWIhpymTABQLdm3ZDzNAdXH16lLUU0\\npBinl4zR79sHdOpE0vLlQF+rvrh0/xKy87NpSxGN2hi+iU2LhbetN7S1tGlLEYXSUjKblGoeyqto\\nKbTgZeuFuHQJBraVxNMTOHBAWjWkJGP0cpqFAKTImbu1O7ZdltgaTQW8vUnt8tJS2ko0B2u1548d\\nA5o3B1q0oK2k+vjY+TC1Oq5fn/ST3buXtpL/IQmjLy+XZk2OqmDtAW3WDGjZkmzk1QYePXuEpKwk\\nuLZ2pS1FNOQ2YQIA19auSMpKwqNnj2hLEQ2phW8kYfSnTpEYsa0tbSU1Y7DNYOzL2IenJU9pSxEN\\nqT2g6mTHlR3o17IfjHSNaEsRDTkavZGuEfq17IcdVyRc57eGeHuT48pSqSElCaOX42weIEXOnC2d\\nsfeahNZoKuLjA8TESDONW2xYC9ukpQH5+YCTE20lNYe11bHUakhJwujlOAupwMeWrQe0c2eyiZSW\\nRluJeikuK8auq7vgZSuxpgcqUDFhkmr5kLfhbeuNXVd3obismLYU0ZBSCXDqRn/zJqnL0b07bSXK\\n4WPng7j0OKbSuGvD6ZtDNw7BzswOjU0a05YiGlKu+loVjUwawd7cHgevH6QtRTSkFAalbvTx8dKv\\nyfE2rBtYw8zIjLk0bqk8oOqCtbBNTg5w9iwwYABtJcrD2uq4e3cyib11i7YSCRi9nMM2FbBW5Kx/\\nf+D8eeD+fdpK1IMgCMwZ/fbtwMCBgIEBbSXK42Png9j0WGZqSOnokEmsFGpIUTf6o0flUZPjbbC2\\nkWRgALi6EvNgkfP3zkNLoYV2Fu1oSxENFiZMbS3aQkdLB+eyJVjnV0mksjpWyugFQcCsWbMQFBSE\\nsLAw3HplbbJv3z4EBAQgKCgIkZGRbx2rZ0951OR4Gy5NXXgat4yomM0r5Lhr+QaKioA9e0gFUjlT\\nUUOKpUnToEFkMpufT1eHUkafkJCA4uJibNiwAVOmTMHcuXOfv1daWop58+Zh9erViIiIwMaNG/Hw\\nYeXd3uW6efQiLKZxDxkCJCSQGkSsEZsWC187X9oyROPAAdKv1MKCthLV8bVnKwxapw7QrRv5i5gm\\nShl9cnIyevfuDQDo2LEjLly48Py9q1evwsrKCiYmJtDV1YWzszMSEyvfqGTB6AH2qvBZWACOjsRE\\nWCIrLwtXHl5Brxa9aEsRDRbCNhX0bN4TGY8zcDv3Nm0poiGFY5ZKGX1+fj5MX4i36OjooPz/U8Be\\nfc/Y2Bh5eXmVjiWnmhxvw7W1K5KzkvHwWeWrF7khhQdUbOLS4uBp4wldbV3aUkRBEMj/I1YmTLra\\nuvBs44n4dAnsYIpERZZsGcU200odajQxMUFBQcHzn8vLy6GlpfX8vfwXAlIFBQWoU6dOpWNlZWUp\\nI0GSdG/SHesS12FYm2E1vjYvL09y30W3bjr49Vcz/Oc/2RpNwlHnd7Hp3CYE2ARI7ruujKq+iwsX\\ndKCl1QB1696DTP6TqqSXRS9EnouEj+XLyxQp/o5UBz09wMzMAtu2PUaXLiVUNChl9E5OTti/fz88\\nPDxw5swZ2L5QpMba2ho3btxAbm4uDAwMkJiYiPHjx1c6lqWlpTISJMnwDsOx+9pufNTnoxpfm5WV\\nJbnvokkTwNgYuHfPEp07a+6+6vou8ovzkZidiOiQaNQ1qCv6+Oqgqu9ixQrAzw9o2lRaz44qBDcI\\nxrQj01DHvA5M9Eyevy7F35Hq4ucHHD9uIVqI7U4NW1gpFbpxc3ODnp4egoKCMG/ePMyYMQPx8fGI\\njIyEjo4OZsyYgXHjxiE4OBiBgYFo2LChMreRHV62Xth9dTczadwVWbKshG/2XN2Dbs26ycbkqwNL\\nYZsK6hrURfdm3bH76m7aUkSDdra5UjN6hUKB2bNnv/Raq1atnv97v3790K9fP5WEyZFGJo3gYO6A\\nA9cPwN3anbYcUfD2BqZOBWbOpK1EdWLT2eoNe+cOcPky6VPKGhVJiMMcah4GlSIuLiQB8do1oHVr\\nzd+fesIUa7CWPNWzJ5CRAWRm0laiGmXlZYhPj4e3HTvT3/h4wMOD9CllDW87b2xL34bScja64Ghp\\nAV5e9LJkudGLTIXRs5LGratLWqNJIY1bFY7fPg5LU0u0rNeSthTRYDFsU0GLui3QvG5zHLt1jLYU\\n0aAZvuFGLzIO5g7Q09bD2eyztKWIBgtZsrFpbIVtnj4lOQ6enrSVqA/WsmRdXYGTJ4HHjzV/b270\\nIqNQKJgL33h4AIcPAy+cqJUdselsFTHbu5c0GKlfn7YS9VFR5IwVjI2BPn2AnTs1f29u9GqAtWqW\\ndetKI41bWdIfpONJ4RM4WzrTliIacu3KVhOcmjihoLgAaTnsdMGhFb7hRq8GerboieuPrzOVxi3n\\n8E1FETMtBRuPe3k52TNhNT5fQcXqmKVJk5cXmdGXaDhvio0nX2LoaOlgsM1gxKUxcgAd0kjjVhbW\\nas8nJ5NiWTY2tJWoH9bCoJaWQJs2JBSqSbjRqwkfW7bii61aAY0bk80kOZHzNAdns89iQCsZt156\\nhdoQtqmgf8v+OH/vPO4XsNMFh0b4hhu9mhjUZhCO3jyKvKLKC7rJDTmGb7Zf3o6BrQbCQEfGrZde\\ngeVjla+ir6MPt9Zu2HZ5G20polFh9Jo8gc2NXk3U0a+DHs17YNfVXbSliAbtNG5lYC1sU9GDtHt3\\n2ko0B2vhG0dHss9y6ZLm7smNXo2w9oB27Qo8eABclUkjrcLSQuy5tgdDbGTeeukF4uLI2XkdpYqX\\nyJPBNoOxN2MvCkvZ6IJTUUNKk5MmbvRqxNvWG9svb2cujVsuRc4OXD8Ax4aOsDBmoPXS/1Ob4vMV\\nmBuZo1PjTjiadZS2FNHgRs8Qzes2R4u6LfDvrX9pSxENOYVvYlJj4G3LTjA7P5/0Hx00iLYSzeNj\\n64NdN9gJg/bpA6SmAnfvauZ+3OjVjK+dL2JS2TkH7OoKJCUBjx7RVvJ2BEFAbHosfO3Z6Q27ezdJ\\nXHtLHx9m8bHzQcLNBJQL5bSliIKeHuDuDmzT0B4zN3o1U5HwwUqRMyMjoG9fOmncNSH5TjJM9Exg\\nb25PW4po1KbTNq9iY2YDUz1TJGcl05YiGppcHXOjVzOdGndCUVkRUnNSaUsRDTmEb2JSY+Brx85s\\nvqyMzP5qq9EDgHsLd6YON3h6Avv3kwJ16oYbvZpRKBTMVeHz8gJ27dJ8GndNiEljy+hPnAAaNSKJ\\na7UVdyt3ppIQGzQAnJ1JgTp1w41eA7BWha9JEzpp3NUl41EGsguy8U6zd2hLEY3aHLapwKmhE+7k\\n3cH1x9dpSxENTbXq5EavAfq17IeL9y4iOz+bthTRkHL4JiYtBl42XtDW0qYtRTRq47HKV9HW0oaX\\nrRdTq+MKoy9X8x4zN3oNoK+jD3drd57GrSFi0mKYOm1z9SrpN+riQlsJfVhLQrS2JiGcpCT13ocb\\nvYZg7QF1dCQbhJpM464OD589RHJWMlxbu9KWIhpbtwK+viRhrbbj1toNJzNP4nEhhTZNakITq2P+\\n6GiIwTaDsS9jH56VPKMtRRRopHFXh23p2zCg1QAY6RrRliIaW7cCQ4fSViENjPWM0ceqD3Zekfj5\\n3hrAjZ4hGhg2gFMTJyRcS6AtRTQ0tZFUE1g7bZOTo4Xz54EB7FRZVhnWVscuLkB2NpCRob57cKPX\\nIL52vkw9oH37ktBNtkT2mCuKmHnZetGWIhp79hjA3R0wYKfKssp423pj55WdKCmT8PneGqCtDQwZ\\not5JEzd6DeJj54O49Diexq0m9mXsQ4dGHZgqYrZzpwEP27xCE9MmsDGzwaEbh2hLEQ11h2+40WsQ\\n6wbWMDMyQ2JmIm0poiGl8E1sWixTYZv8fOD4cT0MHkxbifRgLQnRzY10b3vyRD3jc6PXMKw9oJ6e\\nJLPvGeU95nKhnDmj37ULcHIqRr16tJVIj4okRFZqSBkbk4qW27erZ3xu9BrG196Xqa72ZmaAkxOQ\\nQHmPOSkrCfUM6sHGjJ2O2Vu3AoMGsdFsQ2zaN2wPALhw7wJlJeLh5wds2aKesbnRaxiXpi7IeZqD\\nqw9l0qapGgwbpr4HtLrEpMYw1TKwpITsfXCjfzMs1pDy8SGrOHWsjrnRaxgthRa8bL0Qly6RwLYI\\nDB1KNpJKKTbSYu1Y5aFDgI0N0KQJGxv36sDX3pepGlIWFupbHXOjpwBr54BbtCBVFQ9ROgRx+cFl\\nPHj2AN2adaMjQA3wJKmq6d2iNy4/uIw7eXdoSxENPz8gOlr8cbnRU8C1tSuSspLw6JnE2zTVgGHD\\n1POAVofNKZvhZ+8HLQUbj7MgcKOvDrrauvBo48HU6tjPj5xiE3t1zMZvhsww0jVC/1b9sf2ymrbY\\nKVARp1d3Fb43EZ0SDX8Hf83fWE2cOkU6edmz0xxLbbC2Om7eHGjdWvzVMTd6SvjYslWj3s4OqFeP\\nnAXWJDef3MS1R9fQx6qPZm+sRipm8woFbSXSx6ONBw7dOISC4gLaUkRDHeEbbvSU8LL1wq4ru1Bc\\nVkxbimjQCN9sSdkCHzsf6GrravbGaoSHbapPPYN6cGnqgj3X9tCWIhrqWB1zo6dEI5NGcLBwwMHr\\nB2lLEY2KmYgmc1g2p2zGMIdhmruhmrl8GcjJAbqxs6+sdlirIVWxOk4UMYGeGz1FfGx9mEqe6tyZ\\nbCJd0FAOS3Z+Ns5ln2Oq9vzmzYC/P689XxO87bwRnx6PsvIy2lJEQ+zwDX+cKOJrT2YirKRxKxSa\\nDd9sTd0KTxtPGOiwU9oxKgoICKCtQl60rNcSTes0xZGbR2hLEY2K3yOxrIEbPUUczB1gpGuEk5ka\\n3sFUI5o0+uhUtk7bZGQAt24BvXvTViI/AtsGIvJSJG0ZotG5M8mOFmt1zI2eIgqFgrkHtHt3Up/+\\nyhX13ufRs0c4fvs4PNp4qPdGGiQqiizZtdnpaa4xAtsGYnPKZmbCNwqFuLVvuNFTJrBdIKIuRTET\\nvtHWJidG1F37Ji49DgNaDYCJnol6b6RBeNhGeWzMbNDIuBGO3jpKW4poiLk65kZPGceGjtDX0ceZ\\n+2doSxENPz+yqahONqdsxjB7dk7b3LgBXLtGunZxlCOwbSAiL7KzOu7RA7h7l5zEUhVu9JSpCN/E\\nZ8TTliIa/fuT0M3Nm+oZP68oD/sz9sPbzls9N6BAdDTg6wvospMOoHEC25HwDSsd3LS1yQovUoS/\\nu7jRS4DAtoGIvxbPTPhGT4+Eb8R4QN/E9svb0aN5D9QzYKcjBw/bqI6tmS0sjC1w9CY74Zvhw4GN\\nG1Ufhxu9BOjQqAN0tXSRlJVEW4pojBghzgP6JjZe3IgR7UaoZ3AKZGYCqanAgAG0lcgf1g439OpF\\nEuhSU1Ubhxu9BFAoFPBq7cXUA9q/P3D9Ook7i0luUS72ZuzFUHt2agRERwPe3mQlxFGNitM3rIRv\\ntLTISm/TJhXHEUcOR1UqjJ6V8I2ODjk1IHb4JjYtFn2s+qC+YX1xB6YID9uIh525HcwMzfDvrX9p\\nSxGNESO40TNDuwbtoKOlg+Q7ybSliIY6wjcbLmxAULsgcQelyJ07wLlzgJsbbSXswNrpm3feAZ48\\nAS5eVH4MpYy+qKgIn3zyCUaOHIkPPvgAjx693kDjhx9+gL+/P8LCwhAWFob8/HzlVdYCnidPMfSA\\n9ulDjEyM42EASZI6fPMwU71hN20ip2309WkrYYfAdoGISoliKnwTGKjarF4po1+/fj1sbW2xdu1a\\n+Pr6YvHixa995uLFi1ixYgXCw8MRHh4OExN2ElvURcVGEivhm4rjYaouOyvYkroFrq1dYapvKs6A\\nEmDdOiA4mLYKtrA3t0cDwwY4dusYbSmiURG+UdYalDL65ORk9OlDGj306dMHx469/IUKgoAbN25g\\n5syZCA4OxmZ1Z88wQqfGnaCrrctU7RuxjocB5LQNS2Gbq1fJhvXAgbSVsMfwtsOx4cIG2jJEw8UF\\nePYMOH9euet1qvpAVFQU/vnnn5deMzc3fz5DNzY2fi0s8/TpU4SGhmLs2LEoLS1FWFgYHB0dYWtr\\nq5zKWoJCocBIx5FYe34tM42ue/YEHj4EUlIABwflx7lfcB8nbp/AlhFqrq2gQTZsICsenSp/Czk1\\nJcQxBD1W9sAvg35hoimNQkEmTZs2AR061Pz6Kh+xgIAABLxyJODjjz9GQQFp3VVQUABT05eX0oaG\\nhggNDYW+vj709fXxzjvvIDU19Y1Gn5WVVXPVDJKXl4esrCwMbDgQQ+OGYqrjVOhoseEAnp51sGJF\\nOSZPrt4+TcV38SLhl8LRr1k/PL7/GI/xWB0yNYogAOHhFvjppyfIyqq8y9ibvovaSk2+C0MYoqlx\\nU2xK2oT+zfurWZlm6N9fF5Mm1cfEifdqfK1STuLk5ISDBw/C0dERBw8eRJcuXV56PyMjA59//jli\\nYmJQWlqK5ORkDBv25roklpaWykhgjqysLFhaWsLS0hKtj7ZGSmEKBrUZRFuWKIwfD4wdC/z8c51q\\n9UGt+C5eZNeeXfi026fMPC/nzgGFhYC3t/lbm4y86buordT0uxjrNBY7s3ZiZLeRalSlOZo0Ifte\\nd+9aArhTo2uVitEHBwfj8uXLCAkJQWRkJD766CMAwOrVq7F//35YW1tj6NChCAwMRFhYGPz8/GBt\\nba3MrWolIe1DsO7COtoyRKNbN6C4GDh9WrnrM3MzcfbuWaZKEq9fDwQF8U5S6mR4u+GIS4tjpnG4\\nQgGEhJAN/BojUCQpKYnm7SVFZmbm83+/k3dHqDevnlBQXEBRkbjMnCkIn31Wvc+++F0IgiD8dOQn\\nYXzMeDWookN5uSC0bCkIp09X/dlXv4vajDLfxaCIQcL68+vVoIYOly4JQpMmNfdOPp+QII1NGqOr\\nZVfEp7NT0XLUKDKLLS2t+bUR5yIQ2iFUfFGUOH4cMDAAOnakrYR9QhxDsPb8WtoyRMPBAWjcuObX\\ncaOXKKw9oDY2QMuWQEJCza47e/csnhQ9QW8rdvrrrV9Pzs5XZ7+Coxp+9n44dOMQcp7m0JYiGuHh\\nNb+GG71EGeYwDAeuH8DDZw9pSxGNUaOANWtqdk3EuQiMchwFLQUbj2pxMTlWGRJCW0ntwFTfFJ5t\\nPBF1KYq2FNFo377m17Dx28MgdfTrwN3anakHdMQIID4eqG41jLLyMqw7vw6hHdkJ2+zYAdjZAW3a\\n0FZSe2BtdawM3OglzCjHUYg4F0FbhmhYWAC9e1e/D+bejL1oWqcp7M3t1StMg6xeDYwZQ1tF7cKj\\njQdS7qcg41EGbSnU4EYvYQbbDEb6g3SkP0inLUU0QkOrH75hbRP2/n1g/35SoIqjOfS09RDcPhj/\\nnP2n6g8zCjd6CaOrrYtRjqOw+sxq2lJEw9sbSEoCqkpwzC/OR1xaHILas1PbZv16wMsLqFOHtpLa\\nx7jO47D6zGpmKlrWFG70Emds57EIPxuOsvIy2lJEwdAQ8Pev+uRAdEo0erboiYbGDTUjTAPwsA09\\nOjfpjHoG9bA/Yz9tKVTgRi9x2jdsjyamTbDn2h7aUkRj/Hhg5cq3l1xdcXoFxncerzlRaubcORK6\\n6c9G2RVZMq7zOKw6s4q2DCpwo5cB4zqx9YB260b6ox4+/Ob3rz6+irScNHjZemlWmBr55x8gLIzU\\nKuHQIcQxBPHp8XhcKP+ieDWFG70MCGofhF1XdjFzpl6hILP65cvf/P6GtA0I6xgGPW02umWXlABr\\n1xKj59DD3MgcbtZu2HhB5P6WMoAbvQyob1gfnjaeWHeenUJnoaFAbCzw+JXJVUlZCSIvRzIVtomP\\nJ+fm7exoK+GM6zQOK8+spC1D43CjlwnjOo3DytPsPKDm5oC7OzmJ8iLx6fFoXbc17MzZccW//wYm\\nTKCtggMA7tbuuJ17GxfvqdBpW4Zwo5cJA1oNwMNnD5GUlURbimiMHw+sWPHyaytOr0CwHTtNVK9d\\nA06dIp2kOPTR1tLGmI5jsOzUMtpSNAo3epmgraWNCV0m4K/Ev2hLEQ1XV3IS5cwZ8vPt3Ns4dvsY\\nvFqzswm7bBkJUxkY0FbCqeB95/cRcS6CmTr11YEbvYwY13kcolOj8ejZI9pSREFbG3jvPWDxYvLz\\nilMrMKLdCBjqGNIVJhLFxcCqVcAHH9BWwnkRq3pW6Nm8J1PNw6uCG72MaGjcEINtBjOVKfvee0Bk\\nJJCdU4wlyUvwYdcPaUsSja1bSf1wvgkrPSZ1nYRFiYsgvC2ZgyG40cuMSV0m4a+kv5hJ5W7UCBgy\\nBJi6YgvszO3QrmE72pJEY8kSvgkrVdyt3fGk6AlOZp6kLUUjcKOXGT2a94ChriH2ZeyjLUU0PvoI\\niLrxJyZ1+Yi2FNFITQUuXAD8/Ggr4bwJLYUWJnaZiMVJi2lL0Qjc6GWGQqEgD2giOw+ovtUZlNW5\\nDsMbvrSliMbvv5PZvB4bOV9MMrbTWMSmxTLVfaoyuNHLkJGOI3HwxkHceHyDthRRWJy4CF6NJ+Cv\\nRTq0pYjCw4ckP2DiRNpKOG/DzMgMvna+WHFqRdUfljnc6GWIqb4pxnYai99O/EZbiso8ePoAUSlR\\n+GXUuzh5ErhyhbYi1Vm2DPDxUa6JM0ezfPbOZ/jj5B8oLiumLUWtcKOXKZ92+xSrz6yWfYGmxYmL\\nMcx+GFqaN8J77wG//kpbkWqUlAB//gl89hltJZzq0KlxJ9ib2zNf/4YbvUxpXrc5PG08sSxZvhl+\\nz0qeYVHiIkztMRUA8MknwLp1wIMH8n0so6MBa2ugc2faSjjVZWqPqVhwbAHTRy3l+xvFwZTuU/D7\\nyd9lu+wMPxsOl6YucLBwAEBCHQEBwOrVxpSVKYcgAL/8wmfzcmOQ9SCUlZch4VoCbSlqgxu9jHFq\\n4oQ2Ddpg08VNtKXUmLLyMiw4tgDTekx76fUpU4B//jFCgQyz0/fuBfLySHyeIx8UCgWmdJ+CBccW\\n0JaiNrjRy5xpPaZh/tH5skugikmLgbmROXq16PXS63Z2gItLMVbJsM/K998DM2YAWvy3SnaEOIbg\\nfPZ5nL5zmrYUtcAfSZnj2cYT+tr62JKyhbaUaiMIAuYdmYdpPaZBoVC89v6kSflYuJBsbMqFo0eB\\nmzeBYHYKb9Yq9HX0Ma3HNHx36DvaUtQCN3qZo1AoMLPvTMw5NEc2s/rtl7ejsLQQQ+2HvvF9J6cS\\nWFuT9nty4YcfgOnTAR02UgFqJR90+QDHbh/D2btnaUsRHW70DOBt6w1thTZiUmNoS6kSQRAw68As\\nfNvvW2gpKn/8vvuO/Ckq0qA4JTl1Cjh7FhgzhrYSjioY6RphWo9pmHNoDm0posONngEUCgVm9Z2F\\nOYfmSP6IWFx6HErLSyudzVfQvTvQrt3rjUmkyMyZZDavr09bCUdVJnSZgH9v/Ytz2edoSxEVbvSM\\n4GNHjnpsTd1KWUnlCIKAbw98W+VsvoI5c4AffwQKCzUgTkkOHybFy3jNeTaomNXPPjibthRR4UbP\\nCAqFAj8O+BEz9s5ASZk0dzE3XtwILYUWfO2qV7ysSxfA2Zn0XJUiggB8+SX5C4nP5tlhQpcJOJl5\\nEsduHaMtRTS40TOERxsPNKvTDMtPLact5TUKSwsxY+8MLHRf+MaTNpXx/ffA3LnAIwk21YqLA3Jz\\ngZEjaSvhiImRrhG+6/8dpu6ZKslQ6Jpza2p8DTd6hlAoFFjgvgCzD85GblEubTkv8fuJ39GxUUf0\\nbdm3Rtc5OpKa7nMktj9WUkLOzP/4I2mJyGGL0A6hKCguQHRKNG0pL3Hj8Q18uvPTGl/HjZ4xOjXu\\nhEFtBmH+kfm0pTznfsF9/HT0J/zk9pNS18+ZA0REAGlpIgtTgT//BJo1A7zY6WPOeQFtLW0scF+A\\n6QnTJVViZHrCdHzs8nGNr+NGzyA/DPgBS5KXIC1HGs741b6vMNJxJGzNbJW6vmFDEgufMkVkYUpy\\n9y45N//770ANolAcmeHa2hUOFg5Y8K80SiMcunEI/976F1/0/KLG13KjZ5BmdZrh6z5fY9L2SdRj\\njEduHsH2y9sxp79qsZdPPiG16qMlsJL+4gtg/Hje9Ls28IfnH/jl2C+48pBuo4Si0iJ8EP8Bfhn0\\nC4x0jWp8PTd6RvnI5SM8evZIqY0bsSguK8aE+An476D/oq5BXZXG0tMjDT0+/pjuxuz+/cC+fcDX\\nX9PTwNEcLeu1xJe9vsSkbXQnTfOPzoetmS38HfyVup4bPaPoaOlgqfdSTNszDfcK7lHRMO/IPFjV\\ns0JA2wBRxuvdG/D1BaZNq/qz6iAvDxg3DliyBDA1paOBo3k+7fYp7hXcw9rza6ncPzUnFb+f+B1/\\nev5ZoxNrL8KNnmG6WHbBmE5j8G7suxqfjZzMPIlFiYuw1Gup0g/nm5g3D9i9m/zRNF98AfTvDwwZ\\novl7c+ihq62Llb4rMXnXZNx8clOj9y4uK8bI6JH4fsD3aF63udLjcKNnnDn95+B27m0sO6W5TlQF\\nxQUYFT0Kf3j+gaZ1moo6dp06wKpVpK5MdraoQ7+V+Hhg2zbSWIRT+3Bq4oQp3adgVPQolJWXaey+\\n3+z7Bs3qNMMHzqqlXnOjZxw9bT2sHbYWX+37Cuezz6v9foIg4MPtH+KdZu9geLvharnHwIFkMzQ0\\nFCjXQMHOjAxyv/XrgXr11H8/jjSZ2mMqtLW08f2h7zVyvz1X92DN+TVY7r1c5VUxN/pagIOFA37z\\n+A1DNw7Fg6cP1HqvP07+gdN3T+OvIX+p9T6zZpEaODNnqvU2KCwEhg8nxzt79lTvvTjSRltLG2uH\\nrcWyU8vUXin2ysMrGLVlFNYNWwcLYwuVx+NGX0sIcQyBv4M/hkcNV1stnL3X9uLHwz9i64itMNZT\\nb3Rjln0AAAgKSURBVN9XHR0gKoo0E1dX3frycrJqsLbmfWA5BEtTS0SPiMa7ce+qbYWcW5QLn/U+\\nmN1vdo0zySuDG30tYu7AuTDRM8GoLeLHGRMzExG8ORgbAzaiVf1Woo5dGQ0bkrj5F18Ae/aIO7Yg\\nAJ9/Dty/D6xezROjOP/DpakLfvP4DZ5rPUU/X/+05Cm813tjYKuBmNBlgmjjcqOvRWhraWNjwEY8\\nfPYQ42PHi2b2p+6cgvd6b6zwWSHaDKS6ODgAmzeTwmI7d4ozZkVVyv37ga1bAQMDccblsEOIYwhm\\n9p0J13BX3Hh8Q5Qxn5Y8xbCNw2BV1wq/ef4mypgVqGT0e/bswZRK8tI3bdoEf39/BAUF4cCBA6rc\\nhiMiBjoG2DpiK27l3kJAZACeljxVabx9GfvgscYDfw35C9523iKprBm9ehFDDgsDIiNVG6ukBJg4\\nkSRF7d/PN185lfO+8/uY2mMqeq3qpXJT8ZynORgYPhAWxhZY6buyWv0aaoLSo/3www/473//+8b3\\ncnJyEBERgY0bN2L58uVYuHAhSuTU6ZlxjPWMsWPkDpjqmaL3qt5If5Be4zHKhXL8fPRnBG8ORmRg\\nJPwc/NSgtPr06AHs2kWSqWbMUK6x+N275ETPzZvA3r2AmZn4Ojls8ZHLR/jF/Re4r3HHmnNrlMpX\\nOXH7BFyWuaCvVV+EDw2Hjpb4jYeVNnonJyd8++23b3zv3LlzcHZ2ho6ODkxMTNCyZUukSan0IAd6\\n2nr4Z+g/GN95PHqs6IGF/y5EYWn1Wjmdzz6PgeEDsSV1CxLfS9R4uKYyOncGEhOB06eBrl2BEyeq\\nd11ZGSmv0KEDMfr4eHJen8OpDoHtArF71G7MPTIXgZGByHiUUa3rcoty8WXCl/Be740F7gswz3We\\nqMmFL1Kl0UdFRcHb2/ulPxcuXICnp2el1+Tn58P0hRxxIyMj5OXliaOYIxoKhQKTuk7C0XFHcejm\\nIdj9aYe5h+e+MeZYWFqI3Vd3w3+TPwaGD0Rg20AcGnsILeq2oKC8ciwsgB07SKVLf3/AwwPYsgUo\\nKHj9s7dvA3/8Adjbk5M7e/aQY5tafOeKU0M6N+mMpPeS0L5he3RZ1gXvxr6LwzcOv7YPJggCLt2/\\nhBkJM2Dzhw2yC7JxZsIZDHMYplZ9Va4RAgICEBBQs1olJiYmyM/Pf/5zQUEB6vApkmSxM7dDTFAM\\nTmaexPJTy9FlWRfoaeuhdf3WMNQxRM7THKQ/SIdjI0eEdQjDat/VMNWXbrEXhYIcixw+HFi7Fli0\\niPxsZQVYWpIZ/I0bwJMnwKBBwMqVJM7PT9ZwVMFQ1xDf9vsWk7pOwqrTq/Dh9g9x7dE1tGvYDvUM\\n6qGguABpD9JgpGsEfwd/HBxzEPbm9hrRphBUKIJy8uRJbNy4EQsXLnzp9ZycHIwbNw5RUVEoKirC\\niBEjsHXrVujp6b30ueTkZGVvzeFwOLUaZ2fnan9W1Kj/6tWrYWVlhf79+yM0NBQhISEQBAGTJ09+\\nzeRrKpTD4XA4yqHSjJ7D4XA40odvO3E4HA7jUDF6QRAwa9YsBAUFISwsDLdu3aIhQxKUlpbiiy++\\nwMiRIzF8+HDs27ePtiSqPHjwAP369UNGRvWOqLHM0qVLERQUBH9/f2zevJm2HCqUlpZiypQpCAoK\\nwqhRo2rtc3H27FmEhoYCAG7evImQkBCMGjUKs2fPrtb1VIw+ISEBxcXF2LBhA6ZMmYK5c+fSkCEJ\\nYmNjUb9+faxduxbLli3Dd999R1sSNUpLSzFr1iwY8JoDOHnyJE6fPo0NGzYgIiICd+7coS2JCgcP\\nHkR5eTk2bNiASZMmVZqkyTLLly/H119//TzpdO7cuZg8eTLWrFmD8vJyJCQkVDkGFaNPTk5G7969\\nAQAdO3bEhQsXaMiQBJ6envj0008BAOXl5dDRET8rTi7Mnz8fwcHBaNiwIW0p1Dly5AhsbW0xadIk\\nTJw4Ef3796ctiQotW7ZEWVkZBEFAXl4edHV1aUvSOFZWVli0aNHzny9evIguXboAAPr06YNjx45V\\nOQYVV3k1oUpHRwfl5eXQqoWZKoaGhgDId/Lpp5/i888/p6yIDtHR0TAzM0PPnj3x999/05ZDnUeP\\nHiErKwtLlizBrVu3MHHiROwUq2qbjDA2Nsbt27fh4eGBx48fY8mSJbQlaRw3NzdkZmY+//nF8zPG\\nxsbVSkal4qwmJiYoeCFVsbaafAV37tzB6NGj4efnh8GDB9OWQ4Xo6GgcPXoUoaGhSE1NxfTp0/Hg\\ngXqbpEiZevXqoXfv3tDR0UGrVq2gr6+Phw8f0palcVavXo3evXtj165diI2NxfTp01FcXExbFlVe\\n9MrqJqNScVcnJyccPHgQAHDmzBnY2trSkCEJcnJyMH78eEybNg1+fnQLg9FkzZo1iIiIQEREBOzt\\n7TF//nyY1eKqYs7Ozjh8+DAAIDs7G4WFhahfvz5lVZqnbt26MDExAQCYmpqitLQU5ZroHylh2rZt\\ni8TERADAoUOHqpWPRCV04+bmhqNHjyIoKAgAavVm7JIlS5Cbm4vFixdj0aJFUCgUWL58+RsTzGoL\\n6irsJCf69euHpKQkBAQEPD+lVhu/l9GjR+M///kPRo4c+fwETm3frJ8+fTq++eYblJSUwNraGh4e\\nHlVewxOmOBwOh3Fqb2Ccw+Fwagnc6DkcDodxuNFzOBwO43Cj53A4HMbhRs/hcDiMw42ew+FwGIcb\\nPYfD4TAON3oOh8NhnP8DK1eUocOflXQAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1076d5cc0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.plot(x, np.sin(x))\\n\",\n    \"plt.plot(x, np.cos(x));\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"That's all there is to plotting simple functions in Matplotlib!\\n\",\n    \"We'll now dive into some more details about how to control the appearance of the axes and lines.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Adjusting the Plot: Line Colors and Styles\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The first adjustment you might wish to make to a plot is to control the line colors and styles.\\n\",\n    \"The ``plt.plot()`` function takes additional arguments that can be used to specify these.\\n\",\n    \"To adjust the color, you can use the ``color`` keyword, which accepts a string argument representing virtually any imaginable color.\\n\",\n    \"The color can be specified in a variety of ways:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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Yqy9xWV5kr+vO/XPNrqE555KhixL6ruZGRk0L5Fe95NepfEzESfyGCQ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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1077b3a90>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.plot(x, np.sin(x - 0), color='blue')        # specify color by name\\n\",\n    \"plt.plot(x, np.sin(x - 1), color='g')           # short color code (rgbcmyk)\\n\",\n    \"plt.plot(x, np.sin(x - 2), color='0.75')        # Grayscale between 0 and 1\\n\",\n    \"plt.plot(x, np.sin(x - 3), color='#FFDD44')     # Hex code (RRGGBB from 00 to FF)\\n\",\n    \"plt.plot(x, np.sin(x - 4), color=(1.0,0.2,0.3)) # RGB tuple, values 0 to 1\\n\",\n    \"plt.plot(x, np.sin(x - 5), color='chartreuse'); # all HTML color names supported\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"If no color is specified, Matplotlib will automatically cycle through a set of default colors for multiple lines.\\n\",\n    \"\\n\",\n    \"Similarly, the line style can be adjusted using the ``linestyle`` keyword:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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oUXT5xgXB2eJ9MPJRIBVO4iv3o1pKZWHA8apDZrEIGiKIwaNYrs7Owq\\n5319fYWJeEHBIdLSptV6zd09RIiIWwosGBcYSbojid1dd1N8spgbZt5Az+M9af3P1sJE/EhBAQcL\\nCsqPRzdtWqfXkTNyicTBfPih2qTBvmLx2Wfqf9PTxcei0Wh4+umn8fYWO9ssLc3k3LnFGI16SkrS\\naNZsAopiFZpxItpteTEsioKb7avXvvx8NBoNnWyNe+ICAkisw2tKIZdI6pmjR2HbNnjoIfX4b39T\\n+1qKJCUlhYSEBKKjo7nnnnuqXOvbt6/QWI4dexqjcT6NGg11Sm9LZ7kta2NHXh5vp6Sw3tbkdHSz\\nZvXyunJpRSKpB06frvjd21udgdsJDha3dAKwcOFCYmJiMBqNtG3bVtzAFyE09CF6906lY8eFwnpb\\nuoLbEqDYYmHSyZNYbTmkPfz8+LZjx3ofR87IJZKrxGRSO8rv3q0KeKtWMH688+K55557GDlypPDe\\nliUlaQQExNa4FhAQIyQGV3FbphYX08TdHS83Nzy1Wpp7eFBqteLl5oZOq8VfW/+xyBm5RFIHHn0U\\nDh9Wf/f3h337qs7CRVBQUEC3bt0oLa3aHszX11eIiFssRRiNi8p7W2Zn/+jwMWsjPzmfP//xJ3+0\\n+oOUt1IIvDWQXn/2ovPyzjS+u7Fwy/w//vyTZNsGpkaj4ZnwcLzcHPstRM7IJZLLwC7Uts5kPPVU\\nVZelM9IGfX19Wb58OR6Ca9VaLIWcOPE8588vx98/zmm9LV3FbflNRgYlVitPtmgBwGL7m0QgUsgl\\nkotQVlYxyz54EAICKoS8e3dRMZSxfv169Ho9jz32GIOqWT3btBFXKMqOVuuNn193Wrd+G0/PFsLG\\ndRW3paGkhL35+Qy2tVr6v+BgPJ3cQe2yhDwpKYlPPvmEefPmcfjwYd577z3c3Nzw8PDgo48+IiQk\\nxNFxSiRC2bQJ/vMf+O479XhcXVwa9cB7773Hxo0biY+Pp7e9cpYgzOY8FMWCu3twlfMajYYWLZ4U\\nEoOruC2LLZby5RGTxcJuk6lcyCO8vITFcVGUSzBz5kxl2LBhygMPPKAoiqKMHz9eOXLkiKIoirJ4\\n8WJl6tSptT5v9+7dl3rp64a0tDRnh+AyuOq9yMtTlH/8Q1GsVvW4tFRRioocO2b1e1FcXFzjMRaL\\nxbFBVMNqNStZWRuUgwfHKr/9FqgYDAuEjFv9XhSlFikpU1OUHTfuULa33a6ceueUUniyUEgs1Sky\\nm5WIbduUQrNZyHh10c5L7gJEREQwbVqFC+vzzz8v78FnNpudUodYIqkPjh2raMrg5wft2qnNGkBd\\nUhE50Tp58iS9evWqcV7rgAyH2igpSePPP19j+/YITp58g4CAXvTseYJmzcYKGR9cx20J8MHp0/xZ\\nVASAl5sbh+Pi8HbwhuXVcMnvJgMHDiQtLa38uLGtvuaePXtYuHAh8+fPd1x0EokDefNN9adzZ3Wz\\n8vHHxYyr1NLvPDIykq1bt4oJoBYslnzASnT0Bnx9xW3WlbstvzJwcsNJAnoHEPpwqHC35bnSUkqs\\nVlraPr27+vriU+lD1JVFHOq42blu3TpmzJjB119/TXBw8EUfl+4MD7ILYjKZ5L2w4cx7MXu2L56e\\nCuPHFwLw+efqeVHhpKamsmzZMpYtW8ZXX31FZGRkjXuRk5Pj0BgUpQyNprY8SX+8vV8gNxdycx1/\\nQ0pPlZK3PI+8ZXlofbV43uVJq19aoWumw4IFY7YRsi/9OvXFnOxsvLRaxgQGAtAdULKyEPLWsFdM\\nuwquWMhXrVrFt99+y7x58wgICPjLx4aFhdU5sGuJ9PR0eS9siLwXZ87AgQMwdKh6HB+v5nwHBwcJ\\nGb86n332GUVFRSxZsoTY2FgyMjKE3AtFUcjP34PBoOfcucV067YFH58oh49bHXOumXPfnsOgN1B0\\nrIimY5sSuTISv25+wu6FnV15eXxx9iwLbC7LSaL/PrOz1aLzer36Bp08ufxSRkbGFb/cFQm51Wrl\\ngw8+ICwsjKeffhqNRkNcXBzPPPPMFQ8skTiCggKw1R+ioACOH6+4Jqq7jsViwWAw0KJF1dS8Tz75\\nREwANkpK0jEa52MwJGC1Fl7XvS2LLRbmGgz8LSwMjUZDZ19fPnBC6mY5W7bAL7+oAn7nnVf9cpcl\\n5C1atGDx4sUA7Nix46oHlUgcQV4eREerm5ju7nDjjeqPaDZv3szcuXNJSEgQP3glzp9fTmHhMaKi\\nphMYeKtze1tOFN/bssBiwVOjQafV4qHVcqq4mBKbVd7bzY0IUeveJ09C9Q+Nu+5Sf+oJaQiSNGhe\\ne63CZRkQoFYeFGmVz8vLq7HE2L9/fwYMGCAuiIsQHv6s0PFKz5dybpG6dOJstyXAyORk3omMpGdA\\nAFqNhn85o4BYcTGMHKmWw3Rg6WAp5JIGRWqq2ozYvkzSt2/FUgqAKLf6unXr+Oabb/j55585duwY\\nTSs1BBDVmsze2zIn5zeio38SOuO24ypuS4Bvz50DYJTt32Jtly7oBKVvAqpoW63gU6lUgZcX7N3r\\n8KFl0SxJg2LFCnVyY2fwYLAZ7ISydetW7rjjDlJSUqqIuKMxm/PIyJjN3r23sWdPHGVlmbRp8y9A\\nnGgqikLezjyOPXOM7eHbOfufszS+tzG9U3tz47wbCbk9RIiI55nNJJpM5cc3+vjQpdKnujAR378f\\nnngCWrSA778XM2Y15Ixc4tL89hvMnAnz5qnHzz8vdnyj0ciFCxe4sdpi+/vvvy82EBvJySPQ6fwJ\\nD3+RRo2GXHe9LRVFKf/Gc7KoiHkGAz1sXTu6+PkJi6MKx4+rXxH37hW3o14NKeQSlyI/H+bOBXsi\\nVI8eYDMSO4Vt27Zx+vTpGkLuLKKjNwjtrmMptJD5XSYGvQHTbhNN7mvCDTNvIODmAGFLSHaKLBZi\\nEhNJ7NEDLzc3bvL35wuRrZeKimDXLrjttqrnR44UF8NFkEIucTrZ2epGpZubuh9kNKpWeZ1OXf/2\\nFbBXpigKR44cqSHYw4cPd/zglSgry8JoXAQotW5WihDx2npbOsNtCTAzPZ1hjRrR3NMTbzc3NnTt\\n6vDa3heloAC++gr69HFO3eK/QK6RS5zOiBFw6JD6u5sbTJkirjWa1WplypQpREVF8cADD1BWViZm\\n4CoxlHL+/EqSk4fzxx9tyMvbhq9vF+FxFJ4o5NRbp9jRdgfHnzmOT0cfYg/F0nVdV5qNbiZExEus\\nVrIr/RtoNRqKrdby43BRBXCOHoXCwqrnGjeGRYtcTsRBzsglTmDOHHUGbv9GunHjVTuU64xWq8Xb\\n25sFCxYQGxsrfLnAbM5nx452+PhEERoaT4cOenS6v3ZM1+v4tbgtOy3vhF83P+H3AuDjM2cI1Ol4\\nNjwcgEeaNxcbwNKl8NlnkJKiblyKKjx/lUghlziczEzVExEXpx7HxKjVBu2IEvEdO3bg5eVFdHR0\\nlfMvv/yymABqQafzIyZmL56e4gTLVdyWAPtMJhaeO8cLthzrSRERTvkAKcfDQ62kdscdQjtmV97E\\nrQtyaUXiECp9G+bkSVi7tuK4SxeIjBQf05kzZzAYDMLHtfe2zM9PqvW6KBF3hd6WpVYr32dllR9H\\nenszwlZRFcTl4HP2LKxcWfP8PffAkCFCRNxUYmLO3jn0m9uPr3Z/dVWvJWfkknonNxd69oTkZPXv\\nIS6uYjYugqysLJKSkmq4K++//35hMSiKQm7uFozGhPLelq1bvy1sfDuu4La0KgoaVJHWAAuMRm4P\\nDsZTqyVQp6NXYCDptmbFwigthRMnxI4JWKwWNqVsQp+kZ83RNfRt3Zfnej7H0PZDr+p15YxcUi98\\n/rm6hAIQGKjmfwv8ZgqodvmRI0fStm3b8tpAziAvbxc7drTj2LEn8PZuT2zsAaKjfyAwsGbjCEdg\\nLbFyfsV5DtxzgB3td2DaZaLNv9rQ+3Rv2kxtI9wyP+zAAXbk5QHgrtWysGNHPEWtpxUXw/LlFR1D\\n7LRpA05YUtuZtpNXfnqFmOYxHHv2GKtGr2LEjSPw1F1dgx45I5fUifx8dVJjb9caFAQlJRXXBZod\\ny/H392fEiBHMnj2boCDnlKoF8PZuR8eOS/D37yFsqUBxkd6WAOuzsnDXaLjd9uaYf+ONhIgsgGNn\\n8mQ1XbBbN7jlFggNFR9DNXqF92LP3/bU++vKGbmkTvzrX7BqVcXxQw+pDmVR6PV6kpOTq5zTaDSM\\nGzdOiIgrioULF37Caq2ZrujuHkxAQIwQES8+W8zpD0+zq9MuDo09hEdTD7rv7E63X7vR/KHmQkTc\\nbLWSYmuLBuDv5oZvpVxvp4g4QL9+qtvy55+FiXiZpYzVR1dz37f3kZKTUuO6o94TckYuuSy2b1c7\\nyn/0kXr87rvOTacNCgrC3QkCUVBwCINBj9E4H0/PMDp1WoaXV4TQGFzJbQnwR14eX2dkkGAzU90q\\n+tvQH3/An3/CuHFVz99+u5DhFUVhn2Ef+iQ9i5IX0T6kPfHR8TTyFlcESAq5pFaKimDDBrj3XvX4\\nhhtg/PiK6yL0QlEUtm3bhtFoZMSIEVWu3XPPPY4PoBKZmWs4ffpdSkrSadZsPNHRPzqnt6ULuC0L\\nLRbuTU7m+y5dcNdquTUoSLx4VyYwUDXrOImPt33MV7u/YmLXiWx7eBttQ8SXy70sIU9KSuKTTz5h\\n3rx5nDlzhtdeew2tVkv79u156623HB2jRBBlZeoGpUaj5navWgXDhqnnQkIq1sNF8OeffzJo0CB0\\nOh0vvPCCuIEvgk4XRGTkewQH3y601knhiUKM89RCVW5+bjSLb0bs+7F4Nr+6zbErZVVmJrcFBhLs\\n7o6PmxtTIiNxEz37P3YM1qyBF1+sOpNwVgcRG0/HPs3LN7+M1gllhO1ccuRZs2YxefLkcuvy1KlT\\nefHFF5k/fz5Wq5Wff/7Z4UFKxDBggJoyCODpqTowRWee2ImIiGDBggUcOnSIv/3tb0LGVBSF4uLU\\nWq8FBfUhJOROISJuzjWTPjOdPbfuYe/NezHnmum0vBMx+2No9XIrISKuKAollcwAB/LzOVfJOm9v\\n1iAERYGBA9ViVenp6i67QBRFYeuZrby7+d1ar/t6+DpVxOEyhDwiIoJp06aVHx88eJCYmBgAbrvt\\nNrZv3+646CQOZflytW2gnXXrVLOOaJ5//nlSUlKqnNPpdMTFxQlZ8y0pSefMmY/YtasLBw7chaIo\\nDh+zOlazlawfsjg05hDbI7Zz4YcLtHqlFb3TetP+i/b4d/cXuv79/unT/Ofs2fLjya1bc0Plhgki\\n0Wjgk09UE8+nn6qzDAGk5KQwZfMUov4bxaNrHsXDzQOL1SJk7CvlkvOtgQMHkpaWVn5c+U3u6+uL\\nqVJhd4lrU1wMaWlg73jVtKnaVd6OyIqglbn33nudki547twyMjJmYTLtoHHjkURFfUVg4C1CBdMV\\nelsCHCooYMOFC/y9ZUsAXmrZEi9nFMCZP1/9b+UNGVCbsQrkoVUPseboGkZ3Hs2CEQuIDRNfh+dK\\nuOIvztpK/7gFBQU1+hVKXJetW1VX8pdfqsd9+ogb+8KFCyxevBhFUXj66aerXOvfv7+4QCpRWHiE\\n0NCJdO68Ajc3cbNNu9sydVYqygXFaW7L/fn53GT79G7k7k5kpcqC3s4qFRsTI65f31/w2i2v8b+h\\n/7tqo86VkJ4OCxaoS5xXyhULeceOHdm1axexsbH89ttv9Op1cbdaenr6lUd0DWIymZxyL3JzNTzy\\nSAhLlmTh5laxJyQ6lK1bt/Loo4/Sr18/Ro0aJfxeKEoZGk3NGa6Hx8NYLGA05gA5Do3BWmKlYGMB\\neUvzKPqjCN/bffF9wZdGAxuhcdOQSy656bkOjaEyRVYrT6alsbBFi3KXZRyC/maLi/H66Sfck5Mx\\nvf46UOlvxD4xFBDHiZwTGAuN3BJ2S41r/viTdS6rlmfVL2p2mDdLl3qzd68HQ4YUiRHyV199lTff\\nfJOysjLatm3LoEGDLvrYsLCwK4/oGiQ9PV3YvVi4EO66S10mCQuD6dMhPDzMqTnfw4YN4/Tp0wQF\\nBQm7F2ZzHufPL8Vg0OPt3ZYOHeY4fMzq1Oa2DI8Pp8myJuj8dULfFwBjDx3i5ZYt6W6bhW+3lYoV\\nSmGhWjGtc2d48EH8mzcHjUbYvbhQdIHFyYvRJ+lJzU3l5ZtfFq5TigJbtkBCgrpPFRcHjz2mpvr6\\n+PiSmHhKG6uWAAAgAElEQVTlr3lZQt6iRYvy2hWtW7dmnr2BosTpWK2qNd5WBZRTpyArq2K9+6ab\\nRMZi5bbbbmP16tWEVMpV9Pb2xtvb8X0dFcVCdvbPGAwJZGV9T3Bw//LeliJxhd6WANtzc3HXaIix\\nzXLfad2a1qIaM1wMHx84eFB43ndRWRETvpvAzyd/ZlC7Qbzd920Gth2ITisuLevUKVW8ExLAywvi\\n4+HAgfpxREtDUAPnjTcgIgKefFI9njTJebFotVpmzpzptDonFksRp0+/T5Mm99Ou3b/x8BAnFq7i\\ntswzmwmw5YyeLyursmHZXnTWyauvQmws3Hdf1fNOMO94u3szqtMoZt09iyAvce/PvDy1V4VeD4cP\\nw+jRsGSJ2ou2Pt8WUsgbGHv2wK+/qp4IgHfeEZaNBVS4LRMSEujfvz+jR4+uct2ZTYp1Oj+6dftN\\n2Hg13Ja9nOe2BPg1O5t/p6XxXefOANztRLcjAE8/Dc2aCR0yw5SBRqMh1K9mbZVRnUYJicFiUbte\\n6fVqk6H+/dW/1yFDHLePK4tmuThms9q4205oKHTtWnEsUsQBvv76ax599FEiIyPpIzLthcq9LUdw\\n7ty3QseuzEV7W64X19sSoMBi4ZEjR7DaUoL7BAWxvJO4sgGA6racPBmee67mtVathLxBi83FLEle\\nwpAFQ+g4vSO/nRb3YV6ZQ4fULyGtWqnflHv1Ukuef/eduv7tyGQcOSN3cYqK1L+TdevUxsRhYeqP\\nCMxmM7pq1s6HH36Yxx9/XGx5VlMiRmMC584txsenA6Gh8YSE3ClkfDuu0ttyZ14enX198XFzw0er\\nZUijRlgVBa1GI94yn5ICffvC2LHwyCNix0Y17Ez9fSrLDi+je/PuxEfHs/T+pfh6iEvjzMpS+zHr\\n9Wqizfjx8OOPIPrzVAq5CzJ0KHz8MXTsqG5abtggPoacnBxiYmI4cuRIFTEXXXEwO/snjh17ktDQ\\niXTv/gfe3m2Eje0qvS0r93OcYzDwTIsWdPL1RaPRMLJJEzFBlJWpM4nKJqHWrVW3pZNyzjVoaB3U\\nmn1/20fLwJbCxi0tVSdWer3qjB46FN57Ty226Kz0eynkLsDGjWpjhh491ONp09SvZyKp3vw1KCiI\\nxMTEGjNy0QQH307PnieuS7clwKepqWih3HH5VVSU8BiACrWq3rNPgHKZSkz4edT85hMRFMHrfV53\\n+Pigpgzu2aOK9+LF0KGDmnWi11ekvjsTuUbuBBQFLlyoOM7PV9Nr7bRuLaazfFZWFtOmTSMuLo61\\nlbsj2wgMDHR4DIqikJPzO0ePPk5ZWXaN6xqNVoiIl54v5ex/zrK7x272D9qPRqchemM0PXb0oMXT\\nLYSJ+J9FRSwyGsuPJzZrxpOi/Ri11ZpZsUJo41WL1cJPf/7EhO8m0PLzlhzNOips7Mqkp6s1+Lt0\\ngfvvVyuA/vGH2srwkUdcQ8RBzsidwrp1agpSQoJ6LLi0djnTp0/n0KFDvPvuu9wuqAi/naKikxiN\\n8zAYEtBqvQgNjUcjuIKctcRK1vdZGPQGcjbn0PiuxrT5sA3BA4LRuIn7BnCutJSmtp0wLZBvqSjM\\n1ESUXb24GFavVqeYbdvCf/5T9bqfn5AwTlw4wTd7v2He/nk08WlCfHQ8n93xGU18BS0hoe5LrVyp\\n3oodO2DkSLVj3C23iJlg1QUp5ALIydHw+uswd66aOzp4sJqKJApFUcjMzKRJtfXUN998U1wQlTh9\\n+gPOnv2cpk3HXNe9LQHyzWb67N3LgdhYPLRaIr29eUyAeaoGe/bAjBnqekG1Jh4i2XJmC6WWUtaN\\nXUeXZuJKcdbmtpw4Uf0i4qyij1eCFHIHsWkT3Hyzmn0VGKhw//3qm8XetEEkhw8f5sknn2Tz5s1i\\nB74IzZs/TsuWL6PViiuO5CpuS4Bnjx/n2RYt8AP8dDoOx8WJq+0N6npB9eWam29WN2sEUX1Pxs6D\\nNz0oLAZwrNtSJFLI6xG7UIPq5mrZEtq1U88NGyYmhqKiIry8vKr8kXTs2JFNmzaJCcBGQcFhcnO3\\nEhb2aI1rohyXruK2PFJQgFajIco2tRvTtCnNPDwosF0XKuKKor4Z160T3lW+cm/LX1N+Zc/f9jil\\nIYMot6VIpJDXE++8A02awFNPqcfTp4sdf8eOHcyePZtly5bx22+/0dnm7rOjFfA1oKwsC6NxEUaj\\nnpKSdEJD4y8683IUruK2tOd2A2zPyyNApysX8pttm8gFF312fQVhVde+K68NaDSQmChUsTJMGSw8\\nsBB9kp68kjwmRk9k2ahlQkXcGW5LkUghryOHDsHevRWNu595Ru0B6yx++ukn2rRpw/79+wl3QlW7\\no0cf49y5pTRqNPS67m0J8FtODl+cPcsK24fpQ82biw0gJQVmzoR581S1qt7zVPC084nvn6CRdyO+\\nHPwlfSL6CBXwQ4dU8Z4/H5o3V5dO/v1vp/ZqdghSyK+As2fBrpHu7lXXuhs1EhODyWQiNTWVjh07\\nVjk/efJkMQFchNDQR2jb9lN0OnH5WK7itsw3m/kkNZW3WrdGo9HQMyAAfYcOwsavwfHj6kx87dqq\\n9RycxMoHVgr993AVt6VIpJBfJhcuqJ6IPXtUD0T79uqPaBITE/n+++/5+OOPhY9dUpJOaek5/P1r\\n1sYNDLx4g5H6xFXclqnFxYR6eOCu1eLj5kagTodZUXDXaPDUasubNTgUsxl27lQ3KiszcKD6I4iU\\nnBTmJamlrd/sWzMTSogPwAXdliJx0axI1yA+Xv2WCqoRYN8+sW+K6g2JAfr16ydUxC2WQozGRSQl\\nDWLXrk5kZ/8obOzK5Cfn8+c//uSPVn+Q8lYKgbcG0uvPXnRe3pnGdzcWKuIATx8/zmGbi0ur0fD3\\nli1xF52OZDbDlCnCu8qD6racs3cO/eb2I+brGAz5Boa0F1v3XVHU5f7nnlO/KX/2mbqPm5qqNli5\\n887rQ8RBzsirsHcv+PqC3QX91FNqg2I7or4dzpo1i5kzZ5KamsrBgwcJDg4WM3AlzOY8Tpx4kczM\\nFfj7xzm1t6VBb6DUWOqU3pZ2/peWhpdWy4O29e7VXcTlOANgNKr5cZU3Yry8YP16sXEAhWWFtPlP\\nG25ueTPP9XyOoe2HCu9tOX++mjJYWKjme//xB7QRV4bH5aiTkJvNZl599VXS0tLQ6XRMmTKFyMjI\\n+o5NCGYz2MuJ7Nmj5o/ahbxnT+fElJeXx9tvv83AgQOdVuvEzc0PP79oIiPfwdNTXFKtq7gt00tK\\nOFRQwO22TkcDQ0LwdYat79df4ZNP1M7ZixbBX7RWFIWPuw8nnzuJv6e/sDEbottSJHVSic2bN2O1\\nWlm8eDHbtm3j888/5z/VLb0NgPXr1U/1RYvUY9GVOI8ePUpeXh6xsbFVzr9o7xohALM5D6DGJqVG\\noyU8/FkhMbiK27LUasXDpgoXysrYbTKVC3lbZ7gtQd20HDVKrdQkyCYPkFWYxZKDS+gW2o3eLXvX\\nuC5CxBu621Ikdforad26NRaLxVYr2iS8tGldyc5W19GmTFGPBwygTh2r64uTJ0+SmZlZQ8gdjdrb\\nciMGg56srO/p0GE2TZqMFBoDuJbb0mQ203X3bo7FxeGu1dLZz4/OAoWT7GzVDlzdHi9wBl5mKWP9\\nifXok/RsPLmRwe0H0ytczCZ2Za4Vt6VI6iTkvr6+nD17lkGDBpGTk8OMGTPqO65649gxtQaQm5ta\\nqax5c9UnodWK665TVFTE5s2b6du3b5XzgwcPFhOAjeLiVNLS/ovROB8Pj+aEhsYL721pLbJiXGB0\\nutsS4J2UFB5t3pwWnp7463Qkx8aK37C0oyjq8omT6pzsTNvJXYvuon1Ie+Kj45l992yhvS1NJg2z\\nZ19bbkuhKHVg6tSpymeffaYoiqIYDAbljjvuUEpKSqo8Zvfu3XV56Xpn2DBFOXrUOWObzWbl8ccf\\nV4KCgpRBgwYpZrPZOYHYMJmSlBMnXlHy85OFjmu1WJXsX7OVww8dVjYHblaSBiUphkUGxVwo9n4Y\\nS0qUtOLi8uMV584phmrvW4djsSjKxo2KYjIpaWlpYsf+C/JL8pXjWceFjmk2K8qGDYoydqyiBARY\\nlHvvVZTvvlMU0f8krkZdtLNOM/LAwMDyTTh/f3/MZjNWq7XG49LT06/uU6YO/O9/vjRpYmXkyCJA\\nLeimxiI8FEBtRvzdd98RFRWFsVKdaUeiKGY0mtr+aRvj7f08ubmQm+v4G1J6qpS85XnkLctD66sl\\n4P4AGq9pTFDbICxYMGYboWYJcofx1YULNNHpuM9WRLonYMnMRNRbw2fuXPymTUMJDiZ72jRMoaFC\\n/0aKzEX8ePpH7oy4Ey+dV8348BESz7FjOpYu9WbFCh+aNrVw//1FPPvseVq1Uhe+MzMdHsI1h0ZR\\naqsi/9cUFhbyxhtvcP78ecxmM/Hx8QypVpc1MTGRHvaWNw4kJUU1stn9DydPQnCw+iOSdevWERIS\\nQq9eNdcU09PTCXNwcwBFUcjP34PBoOfcucX06JGIl5e49ld2anNbhk4MLXdbirgXdrbm5jLXYGDm\\nDTcIGe+SbNqkWoBtbktR74ttqdvQJ+lZdmgZMWExzLp7Fq0Cxbagqs1tOXFihdtS5PvC1amLdtZp\\nRu7j48MXX3xRl6fWC4WFFbvW2dlw9GiFkDsrl1Sj0Qhf4wXVbWk0zsdgSMBqLaRZM7W3pUgRdxW3\\nZaHFwuJz53jYlusd7evLJNE980B9Q27bBg89VPV8//5Cw1h6cClv/PIGOq2O+Oh49j+5n/AAcXV4\\n7G7LhATVbTlkyPXlthRJgzMEZWWp+d3Hjqkblt26qT+iOHLkCMnJydx3331VzoveuLRjNM6jsPA4\\nUVFfERh4i9AuO67Q27LAYsFbq0Wr0eCh0XCwoIAyqxV3rRY/nQ4/Z+The3urxXicTNuQtiwcsZCY\\nsBiBjTtq7205d67rtEW7FmkQqfT/+AecO6f+3qgRHDwo3gRw4cIFevbsyYABA0hOThY7+F/QqtWr\\ndOgwi6CgPkJE3FV6W9oZsn8/BwrUgrA6rZZP27UTl3liNMKXX6qussq0aqWuHQjAYrWQfK7292P3\\n5t2JbRErRMQbSm/LaxWXnJGfOaO6Le1LZn36VLgvQVzaYGWCg4OZOnUqt912m1C3ZVHRKYzGBEym\\n3XTuvNopyzfV3ZaNhjVyitsSYKHRiJ+bG3fb6pD+HB3tnJTBRx5RXSr33qt2KrAZh0RxJPMI+n16\\n5h+YT6vAVvz+0O/CmzRIt6Xr4JJCvnChapO3p9TefbfY8T/66CMGDhxIt0prNhqNhgGC3ENmcx7n\\nzy/FYNBTWHiYpk1HExHxlpCx7Sgu4rbMNZtJKS4m2mbO6eDjg08llXBa3vfTT6uFrUWahoBv9n7D\\njMQZpOamMq7LOKf3toyNVZdOpNvSubiEkP/0EyxbVpEq+Nprzo0nLi6OppWrZQlm//4heHg0ITz8\\nRRo1GnLd9bZUKnUVOlRQwHeZmeVC3t1fXH0PQC1kffIkPPFE1fPdu4uNw0axuZh3+r3D7W1uR6cV\\n9+cr3ZaujVOEPC9P3Qh5/HH1uGdP8fXvi4qKWL16NQUFBTz88MNVrvXr109sMNW46aZf0Qr8I3WV\\n3pYAebau8ok9eqDTaukdGEhvZ7ZeattWeDsZRVEoKCvAz6PmbP+p2KeExXEt9ra8VhGmFtnZEBSk\\nvgE8PdX8b7tVPiBA7GbIwYMH6dOnDzExMTz1lLg/DDv23pZarSdhYY/VuC5CxF2ltyWoJWJHNW1K\\niLs7ATodq7t0QSdyycRqVasM/vAD/OtfVVWqbVthYWSYMlhwYAEJSQn0COvBnHvmCBvbzrXe2/Ja\\nRZiQDx6sfi2LilKF/IMPRI1ckxtuuEF4b0urtZSsrHUYjQlkZ/9Co0ZDCAt74tJPrGeK/izCkGBw\\nam/LEquVYquVQNumsRXIt1gIsaXsRXjVdB06DKtV/Tro7q6uF5jNQlMHSy2lrDi8goSkBLaf3c7w\\nDsPLe1uK5HrpbXmt4lAh//57teUSqPWAnGECGDVqFB9++CFtKjmFdDqdUBG3WHLYvv0mfHw6EBoa\\nT4cOc9DpxC0X2N2WxgQjhUcLaTrGOb0t7bybkkJrLy8es6UlPeXMhVatFjZscNpir6IofHvwW8Z1\\nGcfS+5fi6yGuacb12NvyWsWhQl75DeEsJ9ekSZNo4eQdGTe3IGJi9opt0FCL27LlP1oKd1sC7MrL\\nY21WFu/Ymo9MiYxE64xF1v/8R92ps2/O2BH0/qi8iWvHU+fJigdWCBkfpNvyWsWhQt66tSNfvYIj\\nR46g1+uJjIzk8Wp/pNHR0UJisFiKyMxciZ/fTfj63ljjuigRdwW3ZYnVyuacHO6w5VZHenkxpFGj\\n8utOEXFQc74FZ72YSkwsO7SMhP0JTOw6kYe6PXTpJ9Uz0m157eMS6YdXw9q1a3n88ccZN24cffqI\\nXVdUFIXc3K0YjXrOn1+Ov38skZHvCY0BXKO3pVVR0KDm21sVhdkZGQwICkKn1dLYw4PGonbKjEZY\\nsEBd9J01q+o1QXVXLFYLm1I2oU/Ss+boGvq27suzcc8ytP1QIePbkb0trx8alJBbrVa01bIZ7rzz\\nTs6cOSO8t2Vu7jYOH56AVutFaGg8sbEHxC6dlFrJWusabkuA25OS+KJdO7r6+eHt5sYSZyy05uVB\\n587qxkx8vPjxbWw+vZlXf36V+Oh4Pr3jU5r6ivMkSLfl9UmDEXKz2UynTp3YuXMngZXyip3VZs7b\\nuz0dOy7B37+HwIJECqbdJgx657otAdZmZhKo09EnSO0i823HjuJm3aCuF1itVRd2AwIgLc3peXL9\\nW/cn8fFEYeNJt6WkwQi5Tqdjy5YtVUTc0ai9LTcRHDygRkEqD48meHg0ERKHK7gtzVYrhtJSwm2p\\ngd5ubnhWmuIJFXFQa53cfbe67l0ZAXHYe1vO2z+PLwd/SahfaJXroj7YpdtSYselhLy4uJhVq1ah\\n1+u5//77eahaPecmTcQIZ0HBIQwGfXlvyy5dVuPpKbbovSu5LQF+yclh6fnz5U0a/k90547qfPYZ\\nCP1QV9hn2Ic+Sc+i5EXlvS1rc186Eum2lNSGSwn57NmzWblyJfHx8QwfPlz4+OfPf8eZMx9QUpJO\\ns2bjiY7+EV9fcWu9ruS2zCkrY/zhw6zu0gWtRsMdISHlWShCsLst9Xr193nzql4PEtcYGGDKb1OY\\ns28OE7tOZOvDW2kX0k7Y2NJtKbkUdWr1BvD111/zyy+/UFZWxtixYxk5cmSV65dqV5Sbm1tjmaS2\\nPFuRZGdvRFHMBAffjkZTf8J5qTZWtbktm41rJtRtCbDy/HkGhoTga1t33pabS6+AgHpNF7zsll7H\\nj6uFrePjYexYaNas3mKoC/ml+fi4+9RrqdhL3Yva3JZjxlybbkvZ6q0CYa3edu7cyd69e1m8eDGF\\nhYV88803V/T8zMxMbrnlFg4fPlwlC0WEiCuKQmlpeq0ZJsHB/+fw8e24gttSURTKFAUP27/BbpOJ\\nLn5+tPVW195vFrV0kZ2tblRW3rhs3x727RMzPuq92H52O1vObOGVW16pcV3UEop0W0rqQp2EfMuW\\nLURFRfHUU09RUFDAK6/UfOPbsVgsALhV+iNt3LgxBw8erJFK6EgqelvqcXPzo3v3P4TP/l3JbQnw\\n5qlTNPPw4FlbuYL3nJVgPGKE6rrsIq6utp2UnBTmJc0jYX8Cbho3HrzpQeHfDKXbUnK11EnIs7Oz\\nSU9PZ8aMGaSmpvLkk0/yww8/1HjcG2+8wbx585g9ezZ33HFH1YEF5X2fO/ctGRnfYDLtoHHjkbbe\\nlrcK/UMtOVLCn5//6VS3JUBSfj7bcnN50pbW8HpERJUmDUIoLq55buNGpyQ5j1sxjg0nNvBApwdY\\nMGIBsWFi2qKBmjK4f787H34o3ZaSq6dOahoUFETbtm3R6XRERkbi6enJhQsXCKm2GZadnc3cuXO5\\n8cYbSU9Pr5eAr5TMzG14et5Fo0bT0Gq9KSyEwkKDw8c1Z5kxrTSRtzSPsnNlBN4fSPNFzfFsr657\\nny85Dw6+JVZF4XhpKTfYeuOVlpXhXVJS5d8i17EhAKC9cAHvpUvxWboUr549SX//fQGjXppHox7l\\nvdj38HRT709GRobDxzQYtKxY4cOyZd4UFAQyapSJVasKiYhQv7nm56s/1xsmk8lpGnEtUCch79Gj\\nB/PmzePBBx/EaDRSXFxMcC3paF999dVVB3i5WK3mWut4h4V9IS6GWtyWN3x6A0UdimjRUnxyb67Z\\nzD8PHODXm27CTaMhDIgVHgVw9iycPg3TplHcvr3QTa0jmUfILMzk1la31rgmKo7a3JYzZ0JkZDrh\\n4WGA4K5HLojc7KygLhOKOgl5v3792L17N/fddx+KovDWW285Jdukcm9LX99OREWJ++CwczluS5Ez\\njfuSk3kvMpIOvr4E6nT8XqnvqMNRFNi7t2YbtLg49QfUHTwHk1WYxZKDS9An6TmTe4Y3bn2jViF3\\nJJfjtpQTUEl9UeeF6pdffrk+47hsVLflRgwGPVlZ3xMc3L+8t6VIXMFtCbAlJwd/na68p+V7kZHl\\nWSfCURR49VV10bdStUNR5JXk8dCqh9h4ciOD2w/m7b5vM7DtQNnbUnLN41KGoMvBbM4jJeVtmjYd\\nQ7t2/8bDQ1xSrau4LfPNZvxsm8UZpaWUVLICdPAVVPEwP19Nt6i8L6LVqp20nYS/hz8jOoxg9t2z\\nCfISZxiSbkuJs2lwQu7uHkz37tuEjedKbkuA9VlZ6A0GFtsSi+9vKq6yHgBJSao9ftUq+PxzeEh8\\nfW1DvgGdVkdjn6of4hqNhnFdxwmJQbotJa6Eywl5RW9LPaGhj9C48TCnxOEKvS1Btcq/fuoU09u3\\nR6PRMDA4WKxVvjq5uXDTTfDRR0LdlsXmYlYdWYU+Sc/2s9uZc88c7u1w76WfWM/I3pYSV8QlhFxR\\nFPLz92Aw6Dl3bnF5b8ugoNuExuEKbkuAnXl53OTnh4dWS6BOR7+gIKyAG4jrLl9UpPayrF5d8Lbb\\n1B9BnMw+yYdbPmTZoWX0COtBfHS87G0pkVTDJYQ8K2s1J078nWbNJtK9+x94e4tzGLqa2xJgeloa\\nb0REEOXjg0aj4QHRyyegrnevWgXDhoHgph2VMVvNtAluw/4n9xMeIK5hdmkprF+vird0W0pcHZcQ\\n8pCQofTseVeNmt+OxBV6W9r54PRpQnQ6nrClNsy9sWbPT4eSlKSmVVReH/D0hDlzhIWQX5pfaz2T\\nqEZRvHbra0JikL0tJQ0VIUKu9rbcgtG4gLZtP0Gnq/oHW5uRxxG4Qm9LgGOFhRwoKGCkrb76w6Gh\\nBDlj1rt8Obz7LuTkqH0ubxWba22xWvjl1C/ok/SsPbaWfU/so3VQa6ExgOxtKWn4OFQ9iopOYTQm\\nYDAklPe2hDpVza0zrtLbMrO0tLyLjlVRyDOby6+FeordQC2naVP44gvo21dorZPjWcf5Zu83zD8w\\nnyY+TYiPjuezOz+TvS0lkjriUCHfsyeOpk1Hu05vy3k3ogsQP/PNsVgYsXcvB+PicNNo6ODrKy7f\\nGyAjA9auhcceq3q+Tx9xMVRi46mNmK1m1o1dR5dm4ioeyt6WkmsVh6pa795paLXikmpdxW0J8MTR\\no0yKiKCllxdBbm4ciour1wYNV4SXl+pacRGeiHlC6HjSbSm51nGokIsQcVdxWx4qKMBLq6WNzR4/\\nplkzAiutewsR8fx8dXo5fDj4VyrEFBwML73k+PGp2ttyR9oOtj28zSl1eOxuy4QENfdbui0l1zIu\\nkbVypbiK29KqKOUCvTknh3BPz3Ih7yu4pyRvv62ud/fpo+Z5+4utqJdhymDBgQXok/SYSkxMjJ7I\\nvOHzhIp4bW7Lv/9dui0l1z4NSshdxW0J8OOFC+gNBhZ07AhQ3qzBaQwbBk8+6bTelvEr4wkPCOe/\\ng/9Ln4g+9drb8lJIt6XkesflhdxV3Ja5ZjNfpaXxWkQEAH0CA+ntjOTiPXvU7vIvvlj1fEyM+Fgq\\nsWH8BqH/HtJtKZFU4JJCflG35aAQtB7iZnqpxcWEeXriptHg5+aGm0aDRVFw02jwdpa9LzQUunYV\\nPqy9t6W3uzcv31yzhLEIEZduS4mkdlxKyF3JbQnw8NGj/Ld9e27w8cFNo+EfrVqJGzwpSd2de+cd\\ncK/0/x8Wpv4IwFRiYtmhZeiT9CSfS2Z059E83O1hIWPbkW5LieTSOF3IXcVtCfDl2bM09fAor23y\\nY9euTsm4YOhQSE6GCRPUZsXu4j/IcopzaPPvNvSJ6MNzPZ9jaPuheOrE7UVIt6VEcvlclZBnZWUx\\ncuRI5syZQ2Rk5GU/z1XclmeLizlZXMxttgyTO0JCqljlnSLiANOmQatWTrUYBnkFcer5UwR6BQob\\nU7otJZK6UWchN5vNvPXWW3h5eV3W413FbVlmteJuUwVDaSk78/LKhfwG0fa+ZctUt8o//lH1fOvW\\nQoa397bsFd6L7s2717guQsSl21IiuXrqrKD/+te/GDNmDDNmzPjLx7mS2zKrrIyeiYkc69kTrUZD\\nTEAAMc5caO3du6IpsSDKLGWsP7EefZK+vLflLS1vERoDSLelRFKf1EnIV6xYQaNGjbjlllv43//+\\nd9HHJd2R5FS3JcA/T53iuRYtaOzhQSN3d/bGxIi1yufn4/3tt2p3+blzq9oKBavWljNbGPntSNqH\\ntCc+Ot4pvS0XLfJh9WrptpRI6hONoihXXI5w/Pjx5YJ85MgRIiMj+eqrr2hUqXN6YmIivjt98bvD\\nD623uAXOTFtVwca2te5VJhO3envTyBllYi0WmvbuTXH79pSOGUPxkCFOXezNK80jqyiLyMDL38+4\\nWiwW2LLFk6VLvdm40Yu4uALGjCljwIDi695taTKZ8BfswHVV5L2oICMjgx49elzRc+ok5JWZMGEC\\n70Fg/ZwAAAy3SURBVL77bo3NzsTExCsOpj54JyWFDj4+zumqoyg1p5aFhaTn5BAmKGWwqKyItcfW\\ncm+He3F3c07aJtTuthwzBkpL04XdC1cnPV3eCzvyXlRQF+286umh0zI7bGzKzua548fLj99q3do5\\nIv7OO2qKRXUE7NgpisLWM1t5fM3jtPisBTP3zOR84XmHj1udrCz473/VDcuBA9VzP/4Iu3fDs89K\\ny7xE4iiuer0hISGhPuK4bPLNZlZmZjI+NBSAbn5+5YWqnMozz0CguFQ9OwsPLOStX99Cp9URHx0v\\ne1tKJNchTjcEXQ4FFgs+Wi0ajQadRsMuk4mxzZqh1WgIcncnSJRhJilJVaxTp+C776peq7Q/IJK2\\nwW1ZMGIBsWGxAht31O62nDPHKZ9lEsl1T4OwWQzYt49jRUUAeLm58e/27cU3abhwAe67D3x94aOP\\nhA5tsVo4fP5wrdd6hvckrkWcEBFPT1f/17t0gfvvh5AQ1W3522/wyCNSxCUSZ+GSM3K9wUBzDw/u\\nCAkB4Pdu3fAQme1RXKxml1ROqwgJgWPHhObJHck8gn6fnvkH5tM+pD0bJ24Uvich3ZYSievjEn+K\\nuWYzBwsKyo87+PjQupJjVKiIg1rYY/PmmucFieisPbPoOasn/fX9MVvNrB+3nl/ifxG6dPL772qL\\nzxYt1PT3iRMhLQ1mzVJ7V0gRl0hcB5eYke82mdiUnc17topIPUW6Lc1mqJ5jvmCBUwpV2ckryePt\\nvm8zsO1AdFpx/0TSbSmRNEycIuSZpaUMPnCAHd27o9Vo+L/gYP4vOFhcAAUFap2ThARo2lTtUFAZ\\nASKuKAqFZYX4etSs8vhi7xdreYZjyMtTb4VeL92WEklDRdgX5OlpaZjsrksPD77t2NF5XeUzMlT1\\nevJJNdVC5NCmDD7Z9gld/9eVl34U0xC5OhaLmt89bpxaZHHNGrW3ZVoafPml2mxIirhE0nBw6Iw8\\n32zGz7ZsUWK1YrJY8LcdR4rK/T52DNq2rZrU3K6dql6CKLWUsiR5CfokPdvPbmd4h+HlvS1FIntb\\nSiTXJg4V8uWZmcTbjDt/b9nSkUNdnJdegk8/hago54wPlFnLmH9gPmO7jGXp/UtrXU5xFLK3pURy\\n7eNQIbeLuBDKysBkUtMEKyNw5n0xfN19WTNG4DcA6baUSK4rGn4SWUqK2lG+ZUu1s44TMJWYmLN3\\nDv3m9mNJ8hKnxKAokJgIzz0H4eHql5ChQ+H0aVi4EO68U4q4RHKt4hLph1dFdrbqtvztN6HLJxar\\nhU0pm9An6VlzdA19W/ct720pkvR0NVtSr5e9LSWS65WGI+QWC/zwg7pOUDmlols39Ucw60+s55+b\\n/kl8dDyf3vEpTX3FVVyUbkuJRFKZhiPkGo26a3frrS5R1GNo+6EMixombDzZ21IikVwM1xTy06dV\\nU07lQvNarZo3Jwh7b8v5++czY9gMgr2rGpZE2eWl21IikVwK1/oivnkzDBgA3burC72CURSFvRl7\\neeGHFwj/PJyPtn7EwDYD8XAT25MsLw+++Qb69lV7M2dmqm7L5GR45RUp4hKJpCquNSP38YGnnoJh\\nw9Tpp2Am/zKZBQcWMDF6Ilsf3kq7kHbCxrZYYONGdd37+++hf3/VbTlkCNd9b0uJRPLX1EnIzWYz\\nb7zxBmlpaZSVlfHEE08wYMCAy3+BvDy1I8Hjj1c9Hxur/jiJV255hSkDpqDViPuiIt2WEonkaqmT\\nkK9evZrg4GA++ugjcnNzuffee69MyD091fxvq1VomoWiKGxL3cbu9N083+v5GtcDvcRsokq3pUQi\\nqU/qJOSDBw9m0KBBAFitVnTVy8DaKSuDDRvUKkyVXZ6envDBB3UZuk6k5KQwL2keCfsT0Gl1PHzT\\nwyiKIrRJg3RbSiQSR1EnIfe2FbzKz8/n+eef5+9//3vtD2zZUnWmTJ9eVcgFMmrpKH459QsPdHrA\\nab0tp08PYM0a2dtSIpE4Bo2iKEpdnpiRkcEzzzzD+PHjGT58eI3riYmJhBcWYmnb9qqDvBoOZh2k\\nXVA7PN08hY1pMGj57jtvli71oahIw1135TBunIWICIuwGFwVk8mEv7+/s8NwCeS9qEDeiwoyMjLo\\n0aPHFT2nTjPyzMxMHnnkEf75z3/Sq1eviz6uWR8xZVqPZB4htziXnuE9a1wLq5yL7kBqc1vOnKm6\\nLQ2GUmFxuDrp6enyXtiQ96ICeS8qyMjIuOLn1GmnccaMGeTl5TF9+nQmTJjAxIkTKS0trctL1Zms\\nwiym75pe3ttyT8YeoeOD7G0pkUhcgzrNyCdNmsSkSZPqO5bL4kLRBR5b8xg/n/yZwe0Gy96WEonk\\nuse1DEGXQZBXEHdF3cXsu2cT5BUkbFzZ21IikbgqLivkGaYMvHReNWqcaDVaHrzpQSExSLelRCJp\\nCLjUCm5RWRFLkpcwZMEQOk7vyPaz250Sx6FD8OqramPiN96AXr3gxAn47ju4914p4hKJxLVwiRn5\\n8azjfLztY5YdWkZMWAzx0fGyt6VEIpFcJi4h5MXmYtoEt2H/k/sJDwgXNq50W0okkmsBoUJeWFaI\\nj3vNLghdmnWhS7MuQmKwuy31erVul3RbSiSSho7Dhbxyb8u1x9Zy6KlDNPdv7uhhayB7W0okkmsV\\nhwr56z+/zvwD82nq29TpvS137oQRI2RvS4lEcu3hUCE3W82sH7eezk07O3KYKsjelhKJ5HrDoUL+\\n8R0fO/LlqyDdlhKJ5HrFJbJW6op0W0okEkkDFHLptpRIJJKqNBghP3RIXTaZP1/tUSF7W0okEomK\\nSwt5bW7LDRuk21IikUgq43JCLt2WEolEcmW4hJBLt6VEIpHUnToJuaIovP322xw9ehQPDw/ef/99\\nWrZsecWvI92WEolEcvXUSch//vlnSktLWbx4MUlJSUydOpXp06df1nOl21IikUjqlzoJeWJiIn1s\\njZWjo6NJTk7+y8dLt6VEIpE4jjoJeX5+Pv7+/hUvotNhtVrRVptSS7elRCKROJ46Cbmfnx8FBQXl\\nx7WJOEBcnHRbSiQSiaOpk5B3796dTZs2MWjQIPbt20dUVFStj/vhh8Ty3/fsqVuA1woZGRnODsFl\\nkPeiAnkvKpD3ou5oFEVRrvRJlbNWAKZOnUpkZGS9ByeRSCSSS1MnIZdIJBKJ6yAT/iQSiaSBU+9C\\nrigKb731FqNHj2bixImkpqbW9xANBrPZzCuvvMK4ceMYNWoUv/zyi7NDcjpZWVn069ePU6dOOTsU\\np/L1118zevRoRo4cyfLly50djtMwm8289NJLjB49mvHjx1+374ukpCQmTJgAwJkzZxg7dizjx4/n\\nnXfeuazn17uQVzYLvfTSS0ydOrW+h2gwrF69muDgYBYsWMDMmTOZMuX/27t3kEaiMAzDbyCuSrwS\\nsFURRCyNnUS0CES7gIUiksIqaYKCDIhisVUqq4gDKYRJkSqFlYJN1CCoYCXYe0PwhkEQE8cthGC1\\ncSH4Ozv/001xDh9TfMW5zPyWjiSqXC6zsrJCQ0ODdBRRh4eHnJyckM1msSzL1Zt8+Xwe27bJZrPE\\n43FWV1elI327dDrN0tISpVIJ+NhznJ+fJ5PJYNs2Ozs7VeeoeZH/62Wh/9nY2BiJRAL4OKLp9f6I\\nT9uISSaTTE1N0dHxff9t/Yn29/fp7e0lHo8Ti8UYHR2VjiSmq6uLt7c33t/fKRaL1NXVSUf6dp2d\\nnaRSqcrz6ekpg4ODAAwPD3NwcFB1jpo3y1cvC7lBY2Mj8PFOEokEc3Nzwonk5HI5/H4/Q0NDrK+v\\nS8cR9fDwwNXVFaZpcn5+TiwWY2trSzqWCJ/Px8XFBeFwmMfHR0zTlI707UKhEJeXl5Xnz+dPfD4f\\nxWKx6hw1b9evXhZyi+vra6LRKJFIhPHxcek4YnK5HIVCgZmZGc7OzjAMg7u7O+lYItra2ggGg3i9\\nXrq7u6mvr+f+/l46loiNjQ2CwSDb29tsbm5iGAavr6/SsUR97svn52daWlqqj6l1iIGBAfL5PMBf\\nLwu5we3tLbOzsywsLBCJRKTjiMpkMliWhWVZ9PX1kUwm8fv90rFEBAIB9vb2ALi5ueHl5YX29nbh\\nVDJaW1tpamoCoLm5mXK5jG3bwqlk9ff3c3R0BMDu7i6BQKDqmJovrYRCIQqFApOTkwCu3uw0TZOn\\npyfW1tZIpVJ4PB7S6TS/XP5zUY/Lv9UwMjLC8fExExMTlVNebn0n0WiUxcVFpqenKydY3L4ZbhgG\\ny8vLlEolenp6CIfDVcfohSCllHI49y5eK6XUf0KLXCmlHE6LXCmlHE6LXCmlHE6LXCmlHE6LXCml\\nHE6LXCmlHE6LXCmlHO4PeozvnTG5Su8AAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1079d9160>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.plot(x, x + 0, linestyle='solid')\\n\",\n    \"plt.plot(x, x + 1, linestyle='dashed')\\n\",\n    \"plt.plot(x, x + 2, linestyle='dashdot')\\n\",\n    \"plt.plot(x, x + 3, linestyle='dotted');\\n\",\n    \"\\n\",\n    \"# For short, you can use the following codes:\\n\",\n    \"plt.plot(x, x + 4, linestyle='-')  # solid\\n\",\n    \"plt.plot(x, x + 5, linestyle='--') # dashed\\n\",\n    \"plt.plot(x, x + 6, linestyle='-.') # dashdot\\n\",\n    \"plt.plot(x, x + 7, linestyle=':');  # dotted\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"If you would like to be extremely terse, these ``linestyle`` and ``color`` codes can be combined into a single non-keyword argument to the ``plt.plot()`` function:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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jDz7rvrZRemJXBqhUimjRuBWbOMx127Ai4uwoZXFAUzZ868bT1wmVMG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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1077be9e8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.plot(x, x + 0, '-g')  # solid green\\n\",\n    \"plt.plot(x, x + 1, '--c') # dashed cyan\\n\",\n    \"plt.plot(x, x + 2, '-.k') # dashdot black\\n\",\n    \"plt.plot(x, x + 3, ':r');  # dotted red\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"These single-character color codes reflect the standard abbreviations in the RGB (Red/Green/Blue) and CMYK (Cyan/Magenta/Yellow/blacK) color systems, commonly used for digital color graphics.\\n\",\n    \"\\n\",\n    \"There are many other keyword arguments that can be used to fine-tune the appearance of the plot; for more details, I'd suggest viewing the docstring of the ``plt.plot()`` function using IPython's help tools (See [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb)).\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Adjusting the Plot: Axes Limits\\n\",\n    \"\\n\",\n    \"Matplotlib does a decent job of choosing default axes limits for your plot, but sometimes it's nice to have finer control.\\n\",\n    \"The most basic way to adjust axis limits is to use the ``plt.xlim()`` and ``plt.ylim()`` methods:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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r4bmDkTaNpUdhqy1bFjwJ49wNq1spM4T6dOwF13AR9/DERFyU4jDycf\\n1aKyUgwF09smGPZq0QJ48klgyRLZScge8+eL7jRvb9lJnGvcOHGF4spY1GuxbRtwyy3An/8sO4nz\\njR8vZs9WVMhOQrYoLRVT5/W+kqgtBgwAjh8XC9W5Khb1WqSnA6NHu05/5JU6dxZrbm/dKjsJ2SIn\\nR8ypaN1adhLn8/AQM2fT02UnkYdFvQZms9jZKCZGdhJ5Ro927V8MPatqkLiqkSPFln0Wi+wkcrCo\\n12DpUmDIEGNOq66rmBixKxJXb9SX//xHbMocHi47iTwtWwKhocDKlbKTyMGifo2KCjGcz5VbOoDY\\nqi86WvwDR/qRni5ukLq5yU4ilytfabKoX2PrVuC228Q6za5u9GixiBlvmOpDSYnodhg5UnYS+R5/\\nXGxqU1AgO4nzsahfY+FCttKrdOokxuh/8onsJFQXK1cCPXq41ryK2ri5iSsWV2yts6hf4cQJ4F//\\nEt0OJLjyZazeuPoN0mslJgJr1gAXLshO4lws6ldYskTcIPTxkZ1EO556Cvj8c/EPHmnXvn3AuXOi\\n24GEgACxQuWHH8pO4lws6r+zWkX/MVs6V2vUSOxradQNQowiPV2MzzbKptJqqbrSVBTZSZyHPwK/\\n++c/xWQNLa5OKNuoUeIqhjdMtamkRKzxkpgoO4n2hIWJhflcaQMNFvXfLVkibqzQ9Tp2FJey27bJ\\nTkI1Wb1a3CC9/XbZSbSnQQNgxAixOJ+rYFGHmKyxa5drr+x2M4mJHLOuVcuWicJFNUtIEEsnlJXJ\\nTuIcLOoAPvhALATkyjNIbyY6WuzVeuaM7CR0pW+/Bb77DujdW3YS7WrdGnjoIeCjj2QncQ6XL+qK\\nIlqg7I+8scaNxZK8K1bITkJXWrYMiI117U0h6mLkSNe50nT5or53L1BeLla1oxtLTBT3HlxpJIGW\\nVVSIjaXZ9XJz/fuL5XiPH5edxPFcvqgvWwYMH+6aS+zWV/fuYuW7/ftlJyFAdIe1agXce6/sJNrn\\n6SmG5mZmyk7ieC5d1MvKxMiBhATZSfShaiSBq1zGah1vkNZPYqL4f1ZZKTuJY7l0Uf/oI3EDpVUr\\n2Un0w9VGEmjV2bOipc4lLerugQfEvrvbt8tO4lguXdTZ0qk/VxtJoFUffgj06SNuYFPducLQXJct\\n6kVFYkOB/v1lJ9EfV/jF0Do2SGwzdKiYPX7unOwkjuOyRX35cnHp6uUlO4n+uNJIAi06eFDMF3j0\\nUdlJ9OfWW8WYfiPviuSSRb2yki0de7jSSAItqhqx5eq7G9nK6GPWXbKo5+UB/v5AUJDsJPrlKiMJ\\ntKa8XPSnDx8uO4l+PfqouNI5cEB2EsdwyaJe1Urn2HTbVY0k2LFDdhLXsnkz0KEDcNddspPol5ub\\n+EfRqIt8uVxRP38e2LgRGDZMdhL945h151u6lN2Gahg+XFzxXLokO4n6XK6or14N9OoFNG8uO4n+\\nVY0k+OUX2UlcQ3ExkJ8PREbKTqJ/bdqIJaU3bZKdRH0uV9R5g1Q9TZuK7dNycmQncQ3Z2cDgwdxu\\nUS1GXWfdpqKuKAqmTZuG6OhoxMfH48Q1G1ju2LEDkZGRiI6Oxpo1a1QJqoZjx9xx/DgQHi47iXFU\\n3TAlx1IUNkjUNniw2H/31CnZSdRlU1HPzc1FeXk5cnJykJSUhNTU1OrnrFYrZs6ciczMTGRnZ2PV\\nqlU4p5GR/qtXeyMuDnB3l53EOMLCALMZOHxYdhJj+/e/xUijbt1kJzGORo3ExjjZ2bKTqMumol5Q\\nUICQkBAAQKdOnXD4it/o77//HoGBgfD19YWHhweCg4Oxd+9eddLawWoF1q1rxJaOytzcgPh4ttYd\\njauJOkbVzX4jLSdtU1G3WCzwu2KbIHd3d1T+PmD52ud8fHxQUlJiZ0z7bdkCtGpVgfbtZScxnhEj\\nxO5Rly/LTmJMFy8Ca9aIfzxJXV27iobJ55/LTqIemzoifH19UVpaWv11ZWUlGjRoUP2cxWKpfq60\\ntBT+/v61vpfZbLYlQr1ZLJ4YM+YizGbjTsMrKSlx2v/PK/n4AHfe2RTZ2aUID//NYceRdX7OcKNz\\nW7fOGw884A2T6Rz0evpa/uwGD/ZFWpob2rQ5b/N7aOn8bCrqQUFB2LlzJ8LDw3HgwAG0a9eu+rm2\\nbduiqKgIFy5cgJeXF/bu3YuRI0fW+l4BAQG2RKi36GjAbL7ktOPJYDabpZ3f6NHAhg2eDt0WUOb5\\nOdqNzu3jj4ExY5z3u+IIWv7snntOTOhavNjH5pFFzj6/4uLiWp+zqaiHhYUhPz8f0b8v5pyamorN\\nmzejrKwMUVFRmDp1KhITE6EoCqKiotCiRQvbkpNuREUBSUnATz8Bt98uO41xHD8OHDoE9OsnO4lx\\n3XGH2M5y7VpjbJhjU1E3mUxISUm56rE2bdpU/71Hjx7o0aOHXcFIX/z8gAEDRN/6iy/KTmMcVauJ\\nenrKTmJsI0YA775rjKLucpOPyHGqxqwbaSSBTFxN1HkiIoBvvgG++052EvuxqJNq/vIXsYrgnj2y\\nkxjDZ5+JnY0efFB2EuNr2FCsB2WE5aRZ1Ek1JpOxV79zNq4m6lyJiaK7q6JCdhL7sKiTqhISxKJp\\nFy/KTqJv58+Lxaa4mqjzdOwI3HYbkJsrO4l9WNRJVa1aAV26cGNqe61aBTz2GNCsmewkrsUIaxmx\\nqJPqjLr6nTPxBqkcMTFi9rlGlquyCYs6qY4bU9vnm2+AoiLgiSdkJ3E9TZrof2NqFnVSnZeXGFu9\\nfLnsJPq0bJlY54Wricqh9ytNFnVyiMREMTyMG1PXj9UqloJl14s8vXoBp0+Lmbx6xKJODvHgg4C/\\nvxhrTXW3ZYvYau3uu2UncV1635iaRZ0cwmTS/2WsDEuXwqGLolHdDB8OrFghJtPpDYs6OcywYWKs\\n9XnbVzR1Kf/7H7BjBzBkiOwk1LatWLlx82bZSeqPRZ0cpnlz0T+5apXsJPqwYoVYjfEG2w+QE1Xt\\niqQ3LOrkUHr9xXA2RWHXi9ZERgL5+cANli7XJBZ1cqjwcODHH8XYa6rdV195wGIBQkNlJ6EqPj7A\\n4MH625iaRZ0cyt0diIvjDdObWblSbIregL+RmpKYqL+NqfkjRA43YoRo7XBj6pqVlgIbN3qz60WD\\nHn5Y/PfLL+XmqA8WdXK49u3F2OstW2Qn0aZVq4CHHipHy5ayk9C1qpaT1tN9IRZ1cgreMK3d4sXA\\nsGGlsmNQLeLjgXXrxBWVHrCok1M89RSwcyfw88+yk2jLoUPAyZNAz56XZEehWgQEiG6YdetkJ6kb\\nFnVyCn9/sXpjVpbsJNqyeLG4GcfFu7St6oapHrCok9OMHg2kp3ORryoXLwIffgiMHCk7Cd3Mk08C\\nR4/qY2guizo5zcMPA97eYio8AWvXAl27Aq1by05CN9OwofjHd+FC2UlujkWdnMZkAsaMAd5/X3YS\\nbVi0CHjmGdkpqK6eeQb44APt3zBlUSenio0VLfVTp2QnkauwEPjvf4G+fWUnoboKDAS6ddP+WkYs\\n6uRUfn5iJMySJbKTyLV4sRjm6eEhOwnVhx6uNFnUyenGjBFFzWqVnUQOi0XMsB01SnYSqq8nnhBL\\nJO/bJztJ7VjUyek6dQL+8AfgH/+QnUSOFSvEwl2BgbKTUH25uYlRXFpurbOokxTPPqvtXwxHURRg\\n/nzg+edlJyFbjRwJrF8P/Pqr7CQ1s2nKw6VLlzB58mScPXsWvr6+mDlzJpo0aXLV98yYMQP79++H\\nj48PAGDBggXw9fW1PzEZwpAhQFIS8P33YpcZV7Frl+h2evRR2UnIVi1aiCWls7KA8eNlp7meTS31\\nlStXol27dlixYgX69++PBQsWXPc9hYWFWLJkCbKyspCVlcWCTlfx8gISEvQx7ldNaWmilW4yyU5C\\n9qi60tTikrw2FfWCggKE/r6af2hoKL744ournlcUBUVFRXjttdcQExODdXpZNIGcauxYsc661sf9\\nquXUKSA3VywQRfoWGiqWdsjNlZ3kejftflm7di2WL19+1WPNmjWrbnn7+PjAYrFc9fzFixcRFxeH\\nESNGwGq1Ij4+Hh07dkS7du1UjE56d9ddQEiIuIwdM0Z2GsdLTxebcfv5yU5C9jKZgAkTgHffBcLC\\nZKe52k2LemRkJCIjI696bNy4cSj9vXlVWloKv2t+Sr29vREXFwdPT094enqia9euOHLkSI1F3Ww2\\n25O/XkpKSpx6PGfT4/nFxjZEcvItePLJ/9101x89nl+VS5eAhQtvw9q1Z2E2Xz+WU8/nVhdGPL+e\\nPYGXXroNu3efQYsW2jk/m26UBgUFIS8vDx07dkReXh46d+581fM//PADJk6ciA0bNsBqtaKgoACD\\nBg2q8b0CAgJsiWATs9ns1OM5mx7Pb9AgYMYM4KuvAtC7942/V4/nVyU7WwzlDA1tUePzej63ujDq\\n+Y0eDaxefRumTq1w6vkV32A3bJv61GNiYnDs2DEMHToUa9aswfO/j8/KzMzEzp070bZtWwwYMABR\\nUVGIj4/HwIED0daVhjhQnZlMwAsvAPPmyU7iOIoCvPMO8OKLspOQ2saOFfMOLlzQzp1vm1rqXl5e\\nePfdd697fPjw4dV/T0xMRCI3XaQ6eOopIDlZrIdy772y06gvN1csN/z447KTkNpatgR69wa2bvVC\\n+/ay0wicfETSeXqKG6U1tBMM4Z13xJh8DmM0powMoH//MtkxqrGokyY8+6xYX/z0adlJ1HXoEHD4\\nMBATIzsJOYq3t1hvXStY1EkTWrQQhc9ofet/+xswbpy4GiFyBhZ10ozJk8XGEefPy06ijlOngI0b\\nxQgJImdhUSfNuPNOsWlEDatO6NKcOWIphGuWRSJyKO5hTpqSnAz06iVm6zVqJDuN7X7+GcjMFP3p\\nRM7Eljppyr33is2Yly6VncQ+c+aIewQGnG9DGseiTpozdSrw9ttAebnsJLY5e1YMc0tOlp2EXBGL\\nOmnOn/8M3HOPfvcxnTcPGDwYaN1adhJyRexTJ016801gwABg+HAxDlgvfvlFrLO9Z4/sJOSq2FIn\\nTercGejSRX8jYWbOFIuU3XWX7CTkqthSJ82aPl2MhBk1Sh9rkJ88KfrSDx2SnYRcGVvqpFn33Qc8\\n9ph+ZplOnw48/bRY5IlIFrbUSdNSUsQQx1GjZCe5saNHgY8+Ar79VnYScnVsqZOm/fGPYlbmK6/I\\nTnJjyclivXTOHiXZWNRJ8159Fdi0CTh8WJsXllu3ipmjL7wgOwkRizrpQOPGohtm2rRboCiy01yt\\nvBwYP16sBc+VGEkLWNRJF55+Gjh/vgFWrZKd5Grz5gF/+pNYiIxIC1jUSRfc3ICZM3/FxInAuXOy\\n0wgnTwKzZ+tndA65BhZ10o3OnS8jMlKsuy6boogRORMmiJu5RFrBok668tZbwLZtwPbtcnMsXw4U\\nFwMvvSQ3B9G1WNRJV/z8gPR0IDFRrLMig9kMTJkCLFsGeHjIyUBUGxZ10p3evcViX6NGwemjYSoq\\ngNhY4PnngQcecO6xieqCRZ10adYsMYvT2ZtpvPGG+O/LLzv3uER1pc3ZHEQ34eUF5OQA3bsD998P\\nPPSQ44/56aei62f/fjEah0iL2FIn3erQAVi0SCx1W1zs2GN9/bXodlm1CrjjDscei8geLOqkawMH\\nAqNHA08+CZw/75hjnD4NREQA77wDhIY65hhEamFRJ917+WWxBV7fvkBpqbrv/fPPYk334cOB+Hh1\\n35vIEVjUSfdMJuC998R0/T591Bvq+NNPoqAPGiQWFSPSA7uK+rZt25CUlFTjc6tXr8bgwYMRHR2N\\nzz77zJ7DEN1UgwZi16GgIOCRR4Djx+17v0OHROv/qafEYmImkyoxiRzO5qI+Y8YMzJ07t8bnzpw5\\ng+zsbKxatQoZGRmYM2cOLl++bHNIorpwcwPmzgWefVYU5DVr6v8eiiJGuDz6qNhv9JVXWNBJX2wu\\n6kFBQXj99ddrfO7QoUMIDg6Gu7s7fH19ceedd+Lo0aO2HoqoXsaPF+uvv/wy0K8f8M03dXvd3r2i\\nu2XxYmD3biAmxrE5iRzhpuPU165di+XLl1/1WGpqKnr37o09e/bU+BqLxQK/K3YKbtSoEUpKSuyM\\nSlR3XbqILpT588WIlc6dgWHDgJAQoHVr0fpWFODYMWDXLiA7G/juO+C118QSBJz+T3p106IeGRmJ\\nyMjIer07UTruAAAGUklEQVSpr68vLBZL9delpaXw9/evfzoiO3h5iS3mnnsOWLtW7CGalCRupPr7\\nAxcuALffDnTrJlZbjIgAGjaUnZrIPg6ZUXr//fdj3rx5KC8vx6VLl/Df//4Xf/rTn2r8XrPZ7IgI\\nNSopKXHq8ZyN51e7Xr3EHwC4dAmwWBrAx6cSXl7//z1nzqgQ0kb87PRNS+enalHPzMxEYGAgevbs\\nibi4OAwdOhSKomDSpEloWEsTKCAgQM0IN2Q2m516PGfj+emXkc8N4PmprfgGU6jtKupdunRBly5d\\nqr8ePnx49d+joqIQFRVlz9sTEVE9cfIREZGBsKgTERkIizoRkYGwqBMRGQiLOhGRgbCoExEZCIs6\\nEZGBsKgTERkIizoRkYGwqBMRGQiLOhGRgbCoExEZCIs6EZGBsKgTERkIizoRkYGwqBMRGQiLOhGR\\ngbCoExEZCIs6EZGBsKgTERkIizoRkYGwqBMRGQiLOhGRgbCoExEZCIs6EZGBsKgTERkIizoRkYGw\\nqBMRGQiLOhGRgbjb8+Jt27Zhy5YtmDNnznXPzZgxA/v374ePjw8AYMGCBfD19bXncEREdBM2F/UZ\\nM2YgPz8f99xzT43PFxYWYsmSJWjcuLHN4YiIqH5s7n4JCgrC66+/XuNziqKgqKgIr732GmJiYrBu\\n3TpbD0NERPVw05b62rVrsXz58qseS01NRe/evbFnz54aX3Px4kXExcVhxIgRsFqtiI+PR8eOHdGu\\nXTt1UhMRUY1uWtQjIyMRGRlZrzf19vZGXFwcPD094enpia5du+LIkSMs6kREDmbXjdLa/PDDD5g4\\ncSI2bNgAq9WKgoICDBo0qMbvLSgocESEWhUXFzv1eM7G89MvI58bwPNzFlWLemZmJgIDA9GzZ08M\\nGDAAUVFR8PDwwMCBA9G2bdvrvj84OFjNwxMRuTyToiiK7BBERKQOTj4iIjIQlyjqiqJg2rRpiI6O\\nRnx8PE6cOCE7kmqsViumTJmCYcOGYciQIdixY4fsSA5x9uxZ9OjRAz/88IPsKKpbtGgRoqOjMXjw\\nYMMN/7VarUhKSkJ0dDRiY2MN8/kdPHgQcXFxAIAff/wRQ4cORWxsLFJSUiQnc5Ginpubi/LycuTk\\n5CApKQmpqamyI6lm48aNaNKkCVasWIHFixfjjTfekB1JdVarFdOmTYOXl5fsKKrbs2cP/vOf/yAn\\nJwfZ2dmaudmmlry8PFRWViInJwdjx47F3LlzZUeyW0ZGBl555RVcvnwZgBjiPWnSJHzwwQeorKxE\\nbm6u1HwuUdQLCgoQEhICAOjUqRMOHz4sOZF6evfujQkTJgAAKisr4e7ukAFNUs2aNQsxMTFo0aKF\\n7Ciq+9e//oV27dph7NixGDNmDHr27Ck7kqruvPNOVFRUQFEUlJSUwMPDQ3YkuwUGBiItLa3668LC\\nQnTu3BkAEBoaii+++EJWNAAOGtKoNRaLBX5+ftVfu7u7o7KyEg0a6P/fNG9vbwDiHCdMmICJEydK\\nTqSu9evXo2nTpnjkkUewcOFC2XFU98svv8BsNiM9PR0nTpzAmDFjsGXLFtmxVOPj44OTJ08iPDwc\\nv/76K9LT02VHsltYWBhOnTpV/fWVY018fHxQUlIiI1Y1/Ve1OvD19UVpaWn110Yp6FWKi4uRkJCA\\ngQMHok+fPrLjqGr9+vXIz89HXFwcjhw5guTkZJw9e1Z2LNU0btwYISEhcHd3R5s2beDp6Ylz587J\\njqWazMxMhISEYOvWrdi4cSOSk5NRXl4uO5aqrqwlpaWl8Pf3l5jGRYp6UFAQ8vLyAAAHDhww1MzW\\nM2fOYOTIkZg8eTIGDhwoO47qPvjgA2RnZyM7Oxvt27fHrFmz0LRpU9mxVBMcHIzdu3cDAE6fPo3f\\nfvsNTZo0kZxKPbfcckv16qx+fn6wWq2orKyUnEpdHTp0wN69ewEAu3btkj7/xiW6X8LCwpCfn4/o\\n6GgAMNSN0vT0dFy4cAELFixAWloaTCYTMjIy0LBhQ9nRVGcymWRHUF2PHj2wb98+REZGVo/SMtJ5\\nJiQk4K9//SuGDRtWPRLGaDe8k5OT8eqrr+Ly5cto27YtwsPDpebh5CMiIgNxie4XIiJXwaJORGQg\\nLOpERAbCok5EZCAs6kREBsKiTkRkICzqREQGwqJORGQg/wdNbw1oUJqhZwAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x107ab9f28>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.plot(x, np.sin(x))\\n\",\n    \"\\n\",\n    \"plt.xlim(-1, 11)\\n\",\n    \"plt.ylim(-1.5, 1.5);\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"If for some reason you'd like either axis to be displayed in reverse, you can simply reverse the order of the arguments:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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UtJkc3Q/fxUJzEfzvw2Pt0Vd96UxpGUxCYZlfr1A1av5m5nRqW74g7I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\\nAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1079fa160>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.plot(x, np.sin(x))\\n\",\n    \"\\n\",\n    \"plt.xlim(10, 0)\\n\",\n    \"plt.ylim(1.2, -1.2);\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"A useful related method is ``plt.axis()`` (note here the potential confusion between *axes* with an *e*, and *axis* with an *i*).\\n\",\n    \"The ``plt.axis()`` method allows you to set the ``x`` and ``y`` limits with a single call, by passing a list which specifies ``[xmin, xmax, ymin, ymax]``:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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r4bmDkTaNpUdhqy1bFjwJ49wNq1spM4T6dOwF13AR9/DERFyU4jDycf\\n1aKyUgwF09smGPZq0QJ48klgyRLZScge8+eL7jRvb9lJnGvcOHGF4spY1GuxbRtwyy3An/8sO4nz\\njR8vZs9WVMhOQrYoLRVT5/W+kqgtBgwAjh8XC9W5Khb1WqSnA6NHu05/5JU6dxZrbm/dKjsJ2SIn\\nR8ypaN1adhLn8/AQM2fT02UnkYdFvQZms9jZKCZGdhJ5Ro927V8MPatqkLiqkSPFln0Wi+wkcrCo\\n12DpUmDIEGNOq66rmBixKxJXb9SX//xHbMocHi47iTwtWwKhocDKlbKTyMGifo2KCjGcz5VbOoDY\\nqi86WvwDR/qRni5ukLq5yU4ilytfabKoX2PrVuC228Q6za5u9GixiBlvmOpDSYnodhg5UnYS+R5/\\nXGxqU1AgO4nzsahfY+FCttKrdOokxuh/8onsJFQXK1cCPXq41ryK2ri5iSsWV2yts6hf4cQJ4F//\\nEt0OJLjyZazeuPoN0mslJgJr1gAXLshO4lws6ldYskTcIPTxkZ1EO556Cvj8c/EPHmnXvn3AuXOi\\n24GEgACxQuWHH8pO4lws6r+zWkX/MVs6V2vUSOxradQNQowiPV2MzzbKptJqqbrSVBTZSZyHPwK/\\n++c/xWQNLa5OKNuoUeIqhjdMtamkRKzxkpgoO4n2hIWJhflcaQMNFvXfLVkibqzQ9Tp2FJey27bJ\\nTkI1Wb1a3CC9/XbZSbSnQQNgxAixOJ+rYFGHmKyxa5drr+x2M4mJHLOuVcuWicJFNUtIEEsnlJXJ\\nTuIcLOoAPvhALATkyjNIbyY6WuzVeuaM7CR0pW+/Bb77DujdW3YS7WrdGnjoIeCjj2QncQ6XL+qK\\nIlqg7I+8scaNxZK8K1bITkJXWrYMiI117U0h6mLkSNe50nT5or53L1BeLla1oxtLTBT3HlxpJIGW\\nVVSIjaXZ9XJz/fuL5XiPH5edxPFcvqgvWwYMH+6aS+zWV/fuYuW7/ftlJyFAdIe1agXce6/sJNrn\\n6SmG5mZmyk7ieC5d1MvKxMiBhATZSfShaiSBq1zGah1vkNZPYqL4f1ZZKTuJY7l0Uf/oI3EDpVUr\\n2Un0w9VGEmjV2bOipc4lLerugQfEvrvbt8tO4lguXdTZ0qk/VxtJoFUffgj06SNuYFPducLQXJct\\n6kVFYkOB/v1lJ9EfV/jF0Do2SGwzdKiYPX7unOwkjuOyRX35cnHp6uUlO4n+uNJIAi06eFDMF3j0\\nUdlJ9OfWW8WYfiPviuSSRb2yki0de7jSSAItqhqx5eq7G9nK6GPWXbKo5+UB/v5AUJDsJPrlKiMJ\\ntKa8XPSnDx8uO4l+PfqouNI5cEB2EsdwyaJe1Urn2HTbVY0k2LFDdhLXsnkz0KEDcNddspPol5ub\\n+EfRqIt8uVxRP38e2LgRGDZMdhL945h151u6lN2Gahg+XFzxXLokO4n6XK6or14N9OoFNG8uO4n+\\nVY0k+OUX2UlcQ3ExkJ8PREbKTqJ/bdqIJaU3bZKdRH0uV9R5g1Q9TZuK7dNycmQncQ3Z2cDgwdxu\\nUS1GXWfdpqKuKAqmTZuG6OhoxMfH48Q1G1ju2LEDkZGRiI6Oxpo1a1QJqoZjx9xx/DgQHi47iXFU\\n3TAlx1IUNkjUNniw2H/31CnZSdRlU1HPzc1FeXk5cnJykJSUhNTU1OrnrFYrZs6ciczMTGRnZ2PV\\nqlU4p5GR/qtXeyMuDnB3l53EOMLCALMZOHxYdhJj+/e/xUijbt1kJzGORo3ExjjZ2bKTqMumol5Q\\nUICQkBAAQKdOnXD4it/o77//HoGBgfD19YWHhweCg4Oxd+9eddLawWoF1q1rxJaOytzcgPh4ttYd\\njauJOkbVzX4jLSdtU1G3WCzwu2KbIHd3d1T+PmD52ud8fHxQUlJiZ0z7bdkCtGpVgfbtZScxnhEj\\nxO5Rly/LTmJMFy8Ca9aIfzxJXV27iobJ55/LTqIemzoifH19UVpaWv11ZWUlGjRoUP2cxWKpfq60\\ntBT+/v61vpfZbLYlQr1ZLJ4YM+YizGbjTsMrKSlx2v/PK/n4AHfe2RTZ2aUID//NYceRdX7OcKNz\\nW7fOGw884A2T6Rz0evpa/uwGD/ZFWpob2rQ5b/N7aOn8bCrqQUFB2LlzJ8LDw3HgwAG0a9eu+rm2\\nbduiqKgIFy5cgJeXF/bu3YuRI0fW+l4BAQG2RKi36GjAbL7ktOPJYDabpZ3f6NHAhg2eDt0WUOb5\\nOdqNzu3jj4ExY5z3u+IIWv7snntOTOhavNjH5pFFzj6/4uLiWp+zqaiHhYUhPz8f0b8v5pyamorN\\nmzejrKwMUVFRmDp1KhITE6EoCqKiotCiRQvbkpNuREUBSUnATz8Bt98uO41xHD8OHDoE9OsnO4lx\\n3XGH2M5y7VpjbJhjU1E3mUxISUm56rE2bdpU/71Hjx7o0aOHXcFIX/z8gAEDRN/6iy/KTmMcVauJ\\nenrKTmJsI0YA775rjKLucpOPyHGqxqwbaSSBTFxN1HkiIoBvvgG++052EvuxqJNq/vIXsYrgnj2y\\nkxjDZ5+JnY0efFB2EuNr2FCsB2WE5aRZ1Ek1JpOxV79zNq4m6lyJiaK7q6JCdhL7sKiTqhISxKJp\\nFy/KTqJv58+Lxaa4mqjzdOwI3HYbkJsrO4l9WNRJVa1aAV26cGNqe61aBTz2GNCsmewkrsUIaxmx\\nqJPqjLr6nTPxBqkcMTFi9rlGlquyCYs6qY4bU9vnm2+AoiLgiSdkJ3E9TZrof2NqFnVSnZeXGFu9\\nfLnsJPq0bJlY54Wricqh9ytNFnVyiMREMTyMG1PXj9UqloJl14s8vXoBp0+Lmbx6xKJODvHgg4C/\\nvxhrTXW3ZYvYau3uu2UncV1635iaRZ0cwmTS/2WsDEuXwqGLolHdDB8OrFghJtPpDYs6OcywYWKs\\n9XnbVzR1Kf/7H7BjBzBkiOwk1LatWLlx82bZSeqPRZ0cpnlz0T+5apXsJPqwYoVYjfEG2w+QE1Xt\\niqQ3LOrkUHr9xXA2RWHXi9ZERgL5+cANli7XJBZ1cqjwcODHH8XYa6rdV195wGIBQkNlJ6EqPj7A\\n4MH625iaRZ0cyt0diIvjDdObWblSbIregL+RmpKYqL+NqfkjRA43YoRo7XBj6pqVlgIbN3qz60WD\\nHn5Y/PfLL+XmqA8WdXK49u3F2OstW2Qn0aZVq4CHHipHy5ayk9C1qpaT1tN9IRZ1cgreMK3d4sXA\\nsGGlsmNQLeLjgXXrxBWVHrCok1M89RSwcyfw88+yk2jLoUPAyZNAz56XZEehWgQEiG6YdetkJ6kb\\nFnVyCn9/sXpjVpbsJNqyeLG4GcfFu7St6oapHrCok9OMHg2kp3ORryoXLwIffgiMHCk7Cd3Mk08C\\nR4/qY2guizo5zcMPA97eYio8AWvXAl27Aq1by05CN9OwofjHd+FC2UlujkWdnMZkAsaMAd5/X3YS\\nbVi0CHjmGdkpqK6eeQb44APt3zBlUSenio0VLfVTp2QnkauwEPjvf4G+fWUnoboKDAS6ddP+WkYs\\n6uRUfn5iJMySJbKTyLV4sRjm6eEhOwnVhx6uNFnUyenGjBFFzWqVnUQOi0XMsB01SnYSqq8nnhBL\\nJO/bJztJ7VjUyek6dQL+8AfgH/+QnUSOFSvEwl2BgbKTUH25uYlRXFpurbOokxTPPqvtXwxHURRg\\n/nzg+edlJyFbjRwJrF8P/Pqr7CQ1s2nKw6VLlzB58mScPXsWvr6+mDlzJpo0aXLV98yYMQP79++H\\nj48PAGDBggXw9fW1PzEZwpAhQFIS8P33YpcZV7Frl+h2evRR2UnIVi1aiCWls7KA8eNlp7meTS31\\nlStXol27dlixYgX69++PBQsWXPc9hYWFWLJkCbKyspCVlcWCTlfx8gISEvQx7ldNaWmilW4yyU5C\\n9qi60tTikrw2FfWCggKE/r6af2hoKL744ournlcUBUVFRXjttdcQExODdXpZNIGcauxYsc661sf9\\nquXUKSA3VywQRfoWGiqWdsjNlZ3kejftflm7di2WL19+1WPNmjWrbnn7+PjAYrFc9fzFixcRFxeH\\nESNGwGq1Ij4+Hh07dkS7du1UjE56d9ddQEiIuIwdM0Z2GsdLTxebcfv5yU5C9jKZgAkTgHffBcLC\\nZKe52k2LemRkJCIjI696bNy4cSj9vXlVWloKv2t+Sr29vREXFwdPT094enqia9euOHLkSI1F3Ww2\\n25O/XkpKSpx6PGfT4/nFxjZEcvItePLJ/9101x89nl+VS5eAhQtvw9q1Z2E2Xz+WU8/nVhdGPL+e\\nPYGXXroNu3efQYsW2jk/m26UBgUFIS8vDx07dkReXh46d+581fM//PADJk6ciA0bNsBqtaKgoACD\\nBg2q8b0CAgJsiWATs9ns1OM5mx7Pb9AgYMYM4KuvAtC7942/V4/nVyU7WwzlDA1tUePzej63ujDq\\n+Y0eDaxefRumTq1w6vkV32A3bJv61GNiYnDs2DEMHToUa9aswfO/j8/KzMzEzp070bZtWwwYMABR\\nUVGIj4/HwIED0daVhjhQnZlMwAsvAPPmyU7iOIoCvPMO8OKLspOQ2saOFfMOLlzQzp1vm1rqXl5e\\nePfdd697fPjw4dV/T0xMRCI3XaQ6eOopIDlZrIdy772y06gvN1csN/z447KTkNpatgR69wa2bvVC\\n+/ay0wicfETSeXqKG6U1tBMM4Z13xJh8DmM0powMoH//MtkxqrGokyY8+6xYX/z0adlJ1HXoEHD4\\nMBATIzsJOYq3t1hvXStY1EkTWrQQhc9ofet/+xswbpy4GiFyBhZ10ozJk8XGEefPy06ijlOngI0b\\nxQgJImdhUSfNuPNOsWlEDatO6NKcOWIphGuWRSJyKO5hTpqSnAz06iVm6zVqJDuN7X7+GcjMFP3p\\nRM7Eljppyr33is2Yly6VncQ+c+aIewQGnG9DGseiTpozdSrw9ttAebnsJLY5e1YMc0tOlp2EXBGL\\nOmnOn/8M3HOPfvcxnTcPGDwYaN1adhJyRexTJ016801gwABg+HAxDlgvfvlFrLO9Z4/sJOSq2FIn\\nTercGejSRX8jYWbOFIuU3XWX7CTkqthSJ82aPl2MhBk1Sh9rkJ88KfrSDx2SnYRcGVvqpFn33Qc8\\n9ph+ZplOnw48/bRY5IlIFrbUSdNSUsQQx1GjZCe5saNHgY8+Ar79VnYScnVsqZOm/fGPYlbmK6/I\\nTnJjyclivXTOHiXZWNRJ8159Fdi0CTh8WJsXllu3ipmjL7wgOwkRizrpQOPGohtm2rRboCiy01yt\\nvBwYP16sBc+VGEkLWNRJF55+Gjh/vgFWrZKd5Grz5gF/+pNYiIxIC1jUSRfc3ICZM3/FxInAuXOy\\n0wgnTwKzZ+tndA65BhZ10o3OnS8jMlKsuy6boogRORMmiJu5RFrBok668tZbwLZtwPbtcnMsXw4U\\nFwMvvSQ3B9G1WNRJV/z8gPR0IDFRrLMig9kMTJkCLFsGeHjIyUBUGxZ10p3evcViX6NGwemjYSoq\\ngNhY4PnngQcecO6xieqCRZ10adYsMYvT2ZtpvPGG+O/LLzv3uER1pc3ZHEQ34eUF5OQA3bsD998P\\nPPSQ44/56aei62f/fjEah0iL2FIn3erQAVi0SCx1W1zs2GN9/bXodlm1CrjjDscei8geLOqkawMH\\nAqNHA08+CZw/75hjnD4NREQA77wDhIY65hhEamFRJ917+WWxBV7fvkBpqbrv/fPPYk334cOB+Hh1\\n35vIEVjUSfdMJuC998R0/T591Bvq+NNPoqAPGiQWFSPSA7uK+rZt25CUlFTjc6tXr8bgwYMRHR2N\\nzz77zJ7DEN1UgwZi16GgIOCRR4Djx+17v0OHROv/qafEYmImkyoxiRzO5qI+Y8YMzJ07t8bnzpw5\\ng+zsbKxatQoZGRmYM2cOLl++bHNIorpwcwPmzgWefVYU5DVr6v8eiiJGuDz6qNhv9JVXWNBJX2wu\\n6kFBQXj99ddrfO7QoUMIDg6Gu7s7fH19ceedd+Lo0aO2HoqoXsaPF+uvv/wy0K8f8M03dXvd3r2i\\nu2XxYmD3biAmxrE5iRzhpuPU165di+XLl1/1WGpqKnr37o09e/bU+BqLxQK/K3YKbtSoEUpKSuyM\\nSlR3XbqILpT588WIlc6dgWHDgJAQoHVr0fpWFODYMWDXLiA7G/juO+C118QSBJz+T3p106IeGRmJ\\nyMjIer07UTruAAAGUklEQVSpr68vLBZL9delpaXw9/evfzoiO3h5iS3mnnsOWLtW7CGalCRupPr7\\nAxcuALffDnTrJlZbjIgAGjaUnZrIPg6ZUXr//fdj3rx5KC8vx6VLl/Df//4Xf/rTn2r8XrPZ7IgI\\nNSopKXHq8ZyN51e7Xr3EHwC4dAmwWBrAx6cSXl7//z1nzqgQ0kb87PRNS+enalHPzMxEYGAgevbs\\nibi4OAwdOhSKomDSpEloWEsTKCAgQM0IN2Q2m516PGfj+emXkc8N4PmprfgGU6jtKupdunRBly5d\\nqr8ePnx49d+joqIQFRVlz9sTEVE9cfIREZGBsKgTERkIizoRkYGwqBMRGQiLOhGRgbCoExEZCIs6\\nEZGBsKgTERkIizoRkYGwqBMRGQiLOhGRgbCoExEZCIs6EZGBsKgTERkIizoRkYGwqBMRGQiLOhGR\\ngbCoExEZCIs6EZGBsKgTERkIizoRkYGwqBMRGQiLOhGRgbCoExEZCIs6EZGBsKgTERkIizoRkYGw\\nqBMRGQiLOhGRgbjb8+Jt27Zhy5YtmDNnznXPzZgxA/v374ePjw8AYMGCBfD19bXncEREdBM2F/UZ\\nM2YgPz8f99xzT43PFxYWYsmSJWjcuLHN4YiIqH5s7n4JCgrC66+/XuNziqKgqKgIr732GmJiYrBu\\n3TpbD0NERPVw05b62rVrsXz58qseS01NRe/evbFnz54aX3Px4kXExcVhxIgRsFqtiI+PR8eOHdGu\\nXTt1UhMRUY1uWtQjIyMRGRlZrzf19vZGXFwcPD094enpia5du+LIkSMs6kREDmbXjdLa/PDDD5g4\\ncSI2bNgAq9WKgoICDBo0qMbvLSgocESEWhUXFzv1eM7G89MvI58bwPNzFlWLemZmJgIDA9GzZ08M\\nGDAAUVFR8PDwwMCBA9G2bdvrvj84OFjNwxMRuTyToiiK7BBERKQOTj4iIjIQlyjqiqJg2rRpiI6O\\nRnx8PE6cOCE7kmqsViumTJmCYcOGYciQIdixY4fsSA5x9uxZ9OjRAz/88IPsKKpbtGgRoqOjMXjw\\nYMMN/7VarUhKSkJ0dDRiY2MN8/kdPHgQcXFxAIAff/wRQ4cORWxsLFJSUiQnc5Ginpubi/LycuTk\\n5CApKQmpqamyI6lm48aNaNKkCVasWIHFixfjjTfekB1JdVarFdOmTYOXl5fsKKrbs2cP/vOf/yAn\\nJwfZ2dmaudmmlry8PFRWViInJwdjx47F3LlzZUeyW0ZGBl555RVcvnwZgBjiPWnSJHzwwQeorKxE\\nbm6u1HwuUdQLCgoQEhICAOjUqRMOHz4sOZF6evfujQkTJgAAKisr4e7ukAFNUs2aNQsxMTFo0aKF\\n7Ciq+9e//oV27dph7NixGDNmDHr27Ck7kqruvPNOVFRUQFEUlJSUwMPDQ3YkuwUGBiItLa3668LC\\nQnTu3BkAEBoaii+++EJWNAAOGtKoNRaLBX5+ftVfu7u7o7KyEg0a6P/fNG9vbwDiHCdMmICJEydK\\nTqSu9evXo2nTpnjkkUewcOFC2XFU98svv8BsNiM9PR0nTpzAmDFjsGXLFtmxVOPj44OTJ08iPDwc\\nv/76K9LT02VHsltYWBhOnTpV/fWVY018fHxQUlIiI1Y1/Ve1OvD19UVpaWn110Yp6FWKi4uRkJCA\\ngQMHok+fPrLjqGr9+vXIz89HXFwcjhw5guTkZJw9e1Z2LNU0btwYISEhcHd3R5s2beDp6Ylz587J\\njqWazMxMhISEYOvWrdi4cSOSk5NRXl4uO5aqrqwlpaWl8Pf3l5jGRYp6UFAQ8vLyAAAHDhww1MzW\\nM2fOYOTIkZg8eTIGDhwoO47qPvjgA2RnZyM7Oxvt27fHrFmz0LRpU9mxVBMcHIzdu3cDAE6fPo3f\\nfvsNTZo0kZxKPbfcckv16qx+fn6wWq2orKyUnEpdHTp0wN69ewEAu3btkj7/xiW6X8LCwpCfn4/o\\n6GgAMNSN0vT0dFy4cAELFixAWloaTCYTMjIy0LBhQ9nRVGcymWRHUF2PHj2wb98+REZGVo/SMtJ5\\nJiQk4K9//SuGDRtWPRLGaDe8k5OT8eqrr+Ly5cto27YtwsPDpebh5CMiIgNxie4XIiJXwaJORGQg\\nLOpERAbCok5EZCAs6kREBsKiTkRkICzqREQGwqJORGQg/wdNbw1oUJqhZwAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x107508b70>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.plot(x, np.sin(x))\\n\",\n    \"plt.axis([-1, 11, -1.5, 1.5]);\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The ``plt.axis()`` method goes even beyond this, allowing you to do things like automatically tighten the bounds around the current plot:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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u0JCADatJFGZXS06jT2s6vQe3h4YMKECTf8XrNmza7+d79+/dCvXz/H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\\nBk/pWBptAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x107d1c9b0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.plot(x, np.sin(x))\\n\",\n    \"plt.axis('tight');\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"It allows even higher-level specifications, such as ensuring an equal aspect ratio so that on your screen, one unit in ``x`` is equal to one unit in ``y``:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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duG/N/p0/LzeuQR+fkxxNW7/37pnD7xhPxXNUNBXlxcjMhfLR0zm81w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\\nYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1076ded30>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.plot(x, np.sin(x))\\n\",\n    \"plt.axis('equal');\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"For more information on axis limits and the other capabilities of the ``plt.axis`` method, refer to the ``plt.axis`` docstring.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Labeling Plots\\n\",\n    \"\\n\",\n    \"As the last piece of this section, we'll briefly look at the labeling of plots: titles, axis labels, and simple legends.\\n\",\n    \"\\n\",\n    \"Titles and axis labels are the simplest such labels—there are methods that can be used to quickly set them:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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Dw9YcQI67cipEBcZ+NGqFULmjXTncQx3N1V8/g//9GdRFhZejosXKiG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\\nIgAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x107d57438>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.plot(x, np.sin(x))\\n\",\n    \"plt.title(\\\"A Sine Curve\\\")\\n\",\n    \"plt.xlabel(\\\"x\\\")\\n\",\n    \"plt.ylabel(\\\"sin(x)\\\");\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The position, size, and style of these labels can be adjusted using optional arguments to the function.\\n\",\n    \"For more information, see the Matplotlib documentation and the docstrings of each of these functions.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"When multiple lines are being shown within a single axes, it can be useful to create a plot legend that labels each line type.\\n\",\n    \"Again, Matplotlib has a built-in way of quickly creating such a legend.\\n\",\n    \"It is done via the (you guessed it) ``plt.legend()`` method.\\n\",\n    \"Though there are several valid ways of using this, I find it easiest to specify the label of each line using the ``label`` keyword of the plot function:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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UvK2J1jFdfprkq3H7opJ+JPmO3YeU12szNPBrmiKMqDB8bHiYmK8vvv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\\nXAghNO7/ADRl1uEb30mBAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x107d7ca90>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.plot(x, np.sin(x), '-g', label='sin(x)')\\n\",\n    \"plt.plot(x, np.cos(x), ':b', label='cos(x)')\\n\",\n    \"plt.axis('equal')\\n\",\n    \"\\n\",\n    \"plt.legend();\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"As you can see, the ``plt.legend()`` function keeps track of the line style and color, and matches these with the correct label.\\n\",\n    \"More information on specifying and formatting plot legends can be found in the ``plt.legend`` docstring; additionally, we will cover some more advanced legend options in [Customizing Plot Legends](04.06-Customizing-Legends.ipynb).\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Aside: Matplotlib Gotchas\\n\",\n    \"\\n\",\n    \"While most ``plt`` functions translate directly to ``ax`` methods (such as ``plt.plot()`` → ``ax.plot()``, ``plt.legend()`` → ``ax.legend()``, etc.), this is not the case for all commands.\\n\",\n    \"In particular, functions to set limits, labels, and titles are slightly modified.\\n\",\n    \"For transitioning between MATLAB-style functions and object-oriented methods, make the following changes:\\n\",\n    \"\\n\",\n    \"- ``plt.xlabel()``  → ``ax.set_xlabel()``\\n\",\n    \"- ``plt.ylabel()`` → ``ax.set_ylabel()``\\n\",\n    \"- ``plt.xlim()``  → ``ax.set_xlim()``\\n\",\n    \"- ``plt.ylim()`` → ``ax.set_ylim()``\\n\",\n    \"- ``plt.title()`` → ``ax.set_title()``\\n\",\n    \"\\n\",\n    \"In the object-oriented interface to plotting, rather than calling these functions individually, it is often more convenient to use the ``ax.set()`` method to set all these properties at once:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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wtEGU2dCrz/PrB7N/DUU7LTlFS5srjJ98gjgL8/cOWK7ERE6vbvFwdX\\nCxaIDh6WZvBgMXapWzfRGcResUCUwcyZot/0d98Bjz0mO406vR748ENxyt6tmzijILIkBw8CAQFA\\nQgIQEiI7zb316iXuLQYHiwNCe8QCUUrz5on7Dl9/DVj6+D2dDnj3XaBRI6BHD15uIstx+LA4cPng\\nAzHg09J17Hh7JoMff5SdRnssEKWwdCmwfLkoDo88IjtN6VSqJC6FPfEE0Ls3b1yTfCdOiN5K774r\\nvnCthb8/kJgI9OwpCpw9YYH4B+vXA3PnihvAHh6y05RNpUpimg83N+DllzmlAMnz55/izGHmTOCl\\nl2SnKbvu3UXnlB49gN9/l51GOywQ97FnDzBsmJgLxstLdprycXAQI0RPnADeekt2GrJHubninsOA\\nAWJWYmsVEgK88YYodPbSAYQF4h5OnAD69AGSksTEe9bM1VUUubVrxVQgRFq5cUN8sT71FDBtmuw0\\nD27sWNH9NTDQPi7bskCouHJF3ECbMUMcLdiCmjWBf/9bzDRrz932SFsxMcDff4t7eDIHk1YUnQ5Y\\nvFjM1Dx6tOw05scCcYfCQnEq/PzzYmptW/LEE2Lkd0iImEOKyJxSUkS38M8+AxwdZaepOA4OwMcf\\ni4GyCQmy05gXC8QdZswQ60MvWiQ7iXm88IKYGiQwUMxCS2QO6eliANyGDXKnzzCXKlWAjRvFRH97\\n98pOYz4sEMXcWnFq7VrbXpIwOhqoV0+cIbFnE1W0ixfFAcjSpcDTT8tOYz716onvi6AgoIwreVoN\\nFoibMjPFEoTr1lnPWIfy0unEqfHhw6IbLFFFKSwUE0b27WvZo6Qryosviu+N0FBxQ97WsEBA9EYI\\nCQEmTxazONoDV1cxjcCkSfY3+IfM5513xCXat9+WnUQ7b70l7kvMnCk7ScVjgYDoaVGnjphV0p7U\\nry/+oPv1A4xG2WnI2v34o5iS5tNPbeum9D+5ddN62TKxRowt0bRAKIqCqVOnIiQkBBERETh79myJ\\n1xMTExEQEICIiAhERETg1KlTZs/0xRfinsOHH9pGN7yyCg8X60nYW3GkinX5srjM8sEHgKen7DTa\\ne/RRMR3HgAHA+fOy01QcvZY72759O/Lz85GSkoL09HTExsYiPj6+6PWMjAzMmzcPDRs21CTPuXNi\\nZOfatbbZ06K03nsPaNpUHAUNGCA7DVkbRREdHrp1E4NL7VWXLuLvZ+BAYMsWMdWNtdP0V0hLS0Pb\\ntm0BAE8//TSOHDlS4vWMjAwsW7YMYWFhWL58uVmzFBaK/5GRkcDNSHbLzU1MxxEVxfERVHaJiWL1\\ntYULZSeRb+ZM4NIlIC5OdpKKoWmBMBqNcHd3L3qs1+tRWGwR5R49emD69OlYtWoV0tLSsNOMQ37j\\n4sQcMRMnmm0XVuWZZ4AxY4BBg7iuNZXeqVPAuHFiAKaLi+w08jk6iraYPt02lizV9BKTwWBAbrHF\\nCQoLC1Gp2HnYwIEDYTAYAADt27fH0aNH0b59e9VtZWVllTtHZqYDpk6tgU2bLuDPP627b1pOTs4D\\ntUVxAwYAqak1MHPmNbzyivUtIlGRbWHttGiLwkIgLKw6Xn01DzVqGGGpTa/158LNDYiKckVoqCs+\\n//wC9Jp+y1YsTaM3adIE33zzDbp27YpDhw7Bx8en6DWj0YiAgABs3boVLi4u2Lt3L4KCgu65rTp1\\n6pQrg8kk+mjPmAG0aVO7XNuwJFlZWeVuCzUpKUCLFk4IDn4IGt0KqjAV3RbWTIu2WLJEdOyYMcMZ\\nDg5VzLqvByHjczFhgujRtGpVHYuaRTm7jCP6NC0Q/v7+2L17N0JujqCJjY3Fli1bcO3aNQQHByMq\\nKgrh4eFwdnZGy5Yt0a5duwrPMH++qPCRkRW+aZvg7Q3Mni16N/3wg22PKKfyO3ZMXG/fu1d086SS\\nKlW6vfRv9+7i39ZIpyjWN9lCWloa/Pz8yvxz6eliEr60NMtdU7qszHF0pChi/v5mzYCpUyt002bF\\nM4jbzNkWJhPQqhUweLBYL8XSyfxcfPwxEBsrvnMs4R5NWb87baAjVunk54teS/Pn205xMBedTkzP\\n/N57wB0dzYiwYAFQtSrw+uuyk1i+/v3FnE3WOsrabgrE/PliydCBA2UnsQ4eHmK6hCFDbHOOGSqf\\nEydEgbCV9R3MTacTPSZXrBBXMKyNXRSIY8fEIh/vv88PdVm8/LJYGGXxYtlJyBIUFoqJ6SZPtt4l\\neGV49FFxmWnoUHF5zprYfIEoLBSjPKdO5aWlstLpxJFPbCzw66+y05BsK1YAeXmclqU8hgwBHnrI\\n+taZsfkCsWKFqNrWcDPNEnl7i8GEL7/MAXT27Nw5MWtpQgJ7LZXHrYOtuXOt62DLpgvErQ/1ihX8\\nUD+IUaPE6nNcO8I+KQowfLjoGu7rKzuN9Xr8cTFztDUt1GWzBeLWh3r4cOCpp2SnsW4ODmLlrLfe\\nst2Vs+je1q0TN6c5Lc2DGzVKTK2/cqXsJKVjswUiNVV8qGNiZCexDb6+4jJTVJTsJKSlS5fEl9rK\\nlYCzs+w01k+vF20ZEwP873+y0/wzmywQly8Db7whrpfyQ11xJk8WI2f/8x/ZSUgrMTFifemWLWUn\\nsR2NGomb1mPHyk7yz2yyQEyeLEYCt2olO4ltcXUVfbojI8U9CbJte/cCmzeLqVeoYk2ZAnz/PbBj\\nh+wk92dzBSItTSwAFBsrO4lt6t5dTA3O9rVtJpM4EJg/X4yaporl5gYsXSp6V+blyU5zbzZVIAoL\\nxYc6NhaoVk12Gtu1ZAkQHy8GIJJtev990W8/LEx2EtvVs6dYF37+fNlJ7s2mCkRCgliwg9NpmJeH\\nh7iMN2yY9XTXo9LLzhbT4cfHc+YBc3v3XTFTQWam7CTqbKZAnD8vumHGx9vGWrCWbvhw4OpVMVsl\\n2ZboaDGlRoMGspPYPk9PsSLfiBGWebBVqq/SK1eu4JtvvsGGDRuwc+fOEqvCWYrx48WKaI0by05i\\nH/R64IMPxIf70iXZaaiifP01sGcPLGqRG1s3Zgxw9qwYb2Jp7rtg0KVLl7BgwQL89ttvqFu3LmrV\\nqoX09HTEx8fDx8cHo0aNQo0aNbTKek+7d4uul0ePyk5iX5o2BYKCxOpZy5fLTkMPKi9P3MN7913R\\nY4204egoDrZCQoAuXYAqFrQ4330LxHvvvYdXXnkFdevWveu1zMxMxMXFYarkFWVuzbO0cKFlNay9\\nmDVLXIrYuxdo0UJ2GnoQCxaIm6Y9e8pOYn/atAFeeEF0f7Wk2ZPvWyCmTJkCACgsLESlYhf2jUYj\\nvL29pRcHQHQVq10beOkl2Uns00MPiS+WYcOA/fth1Qu027OTJ8VMowcOyE5iv+bNE9MCDRokupJb\\nglLdg4iIiMCff/4JAEhPTy9aU1q2c+fEIJ733mNvC5lCQ0W34rg42UmoPBQFGDlS3JzmOg/y1Kgh\\nvs+GDbOcmZNLdbw3fPhwvPrqq2jatCmOHDmCJUuWmDtXqURHi2UP69WTncS+3Vo1q00bIDgY4LLQ\\n1mXjRtHNMjVVdhIaMkTM1fThh2LuM9lKdQbx5JNPonr16tizZw8aN26Mx8q58o6iKJg6dSpCQkIQ\\nERGBs2fPlnh9x44dCAoKQkhICNauXXvfbW3fDvz4I2eYtBT16wOvvcbJ/KxNbq6YjC8+HnBykp2G\\nKlUSgxQnTQIuXJCdppQFon///ggNDcUXX3yB2rVro1+/fuXa2fbt25Gfn4+UlBRER0cjtth8DSaT\\nCXPmzEFiYiKSk5OxZs0aXLpP/8nhw9nbwtJMmiSK9rZtspNQac2cKc78OnaUnYRueeYZcdl2wgTZ\\nSUp5iSkpKQmPPPIIAGDIkCFo1qxZuXaWlpaGtm3bAgCefvppHDlypOi1zMxMeHp6wmAwAAD8/Pyw\\nf/9+dOnSRXVb9eoBL75YrhhkJq6uotPA8OHA4cOAi4vsRHQ/v/wiZh/4+WfZSehOM2aI3oF79sid\\ndPS+ZxBTpkzBiRMniorDLb6+vvjll1+KejmVltFohLu7e9FjvV6Pwpt3Y+58zc3NDTk5OffcloXc\\nBqE7BASInhiWPL8MiRvTI0aIKVMefVR2GrpTlSqi6/6wYaIrvyz3PYOIiorC4sWLceTIEdStWxc1\\natTAlStXcOzYMTRu3BijR48u084MBkOJUdjFu88aDAYYjcai13Jzc1HlPgMbnJ2zkJVVpt3bpJyc\\nHGRZWENMnOiALl1qoHPnC/DyuqHZfi2xLWT5p7bYuNEF2dnuCAw8b/N/R9b6uWjbFoiLq47Zs6/j\\nlVckzV6hlEJOTo6ya9cuZfPmzcqePXuU3Nzc0vzYXb766itlwoQJiqIoysGDB5VXXnml6LWCggLl\\nhRdeUK5cuaLk5eUpgYGByh9//KG6nQMHDpRr/7bo3LlzsiOomjtXUbp2VZTCQu32aaltIcP92uLq\\nVUXx8FCU77/XMJBE1vy5OHZMUapXV5Tff6+Y7ZX1u7NUN6nd3Nzg7u6O2rVrQ6/XIyMjo1zFyN/f\\nH05OTggJCcGcOXMQExODLVu2YO3atdDr9YiJicGQIUMQGhqK4OBg1KpVq1z7IflGjwbOnGHXSUs0\\nfTrw/PPi5jRZtnr1RFf+6Gg5+y/VTeoRI0bg0qVLePTmxUqdToemTZuWeWc6nQ7Tp08v8VzxaTw6\\ndOiADh06lHm7ZHmcnER3vf79xRQCxW4vkURHjgBJSUA5j/FIgokTxX29bdsAf39t912qAnHx4kWk\\npKSYOwvZmHbtgE6dxBHrggWy05CiiB5m06YBPDm3HsV7B/78M+DsrN2+S3WJqW7duvjjjz/MnYVs\\n0Pz5wKpV7EppCVavBnJyxCULsi4BAUDDhtr3DizVGURaWho6duyIasXW8dy1a5fZQpHtqFVL9Oke\\nNgz47jsu5iTLlSti7Y7UVMDBQXYaKo8lSwA/P7EM7OOPa7PPUhWI//znP+bOQTbslVfE3DJJScDg\\nwbLT2KcpU4AePTgluzXz9ATGjgXeeAPYvFmbCUrvWyDi4+MRGRmJqKgo6O5Is3DhQrMGI9vh4CBu\\nWHfvLtYaqF5ddiL7kp4OfPopF9SyBVFR4kBr0yagVy/z7+++J/ydOnUCALRv3x5NmjRB06ZNcejQ\\nITRq1Mj8ycim+PmJNTtiYmQnsS+FhWKVuFmzxHTSZN2cnMTEim+8ISZaNLf7Foj69esDANauXQtv\\nb2/s2bMHUVFR+Prrr82fjGzOrFnAli3ADz/ITmI/kpKAggJg6FDZSaiidOwoRlnPmmX+fZXqluGt\\ncQ9Xr15Fjx49SqwuR1RaxVefkzm/jL24dEmcscXH88a0rVmwQEy0aO7LhqX6pjeZTJg/fz6ee+45\\n7N27FwUFBeZNRTYrNFTcg+Dqc+Y3bpxYwOm552QnoYr2yCPA1KlibISimG8/pSoQsbGx+Ne//oVX\\nX30Vly5dwty5c82XiGzardXnZs2CzU8SJ9PevU748kttLkOQHMOGie7Ln3xivn2Uqpurl5cXvG4u\\nVtu9e3fzpSG7UHz1OQ7Qr3h5ecD48Q9hyRJxWY9s063egYGBogtz1aoVvw/eTCApJk4E9u3j6nPm\\nMG8eULfuDfTpIzsJmVvz5qLr+OTJ5tk+CwRJ4eoqloyNjASuX5edxnacOCFG3M6efUWTgVQk39tv\\nA2vXAmlpFb9tFgiSJiAA8PUVR7z04BRFXJeeNAnw8NBuoSaSq1o1YM4c8f/+RgX/b2eBIKmWLBFn\\nEpmZspNYv+Rk4PJlYORI2UlIaxERYhDdihUVu10WCJLqscdEd8wRI8zbXc/WXbwo2nH5ckBfqq4n\\nZEsqVQI++EDcizh3rgK3W3GbIiqfW6vPrV8vO4n1Gj1ajDHx85OdhGTx9RUHWq+/XnEHWywQJJ2T\\nkzjyfeMNcSRMZbN5s5i+hGMeKCYGOH264sZGsECQRWjdGujXTxQJKr2//hI3JxMSADc32WlINicn\\n4KOPxBijiljjjQWCLMbs2WJsxOefy05iPaKigN69AS7lTrf4+QFDhojLTQ9K09tZeXl5ePPNN3Hx\\n4kUYDAbMmTMHDz/8cIn3zJ49Gz/99BPcbh4OxcfHw2AwaBmTJHF1FQsL9esnZqvkuhH3t3Ur8O23\\nXM6V7jZ1KvDMM8C6dUBQUPm3o+kZxKeffgofHx+sXr0avXr1Qnx8/F3vycjIwMqVK7Fq1SqsWrWK\\nxcHOtG0VYShWAAAPLUlEQVQr1o0YNUp2Est25YqYriQhAeCfCN3JxUUcbI0cCVy4UP7taFog0tLS\\n0K5dOwBAu3bt8MMdCwMoioLTp09jypQpCA0NxXp2a7FLb78N/PgjsHGj7CSWKzparNDXubPsJGSp\\nWrUSPdsiI8vfq8lsl5jWrVuHpKSkEs/VqFGj6IzAzc0NRqOxxOt///03wsPDMXjwYJhMJkRERKBR\\no0bw8fExV0yyQMUvNbVqBdSsKTuRZfn8c+Cbb4CDB2UnIUs3e7aY7v2TT4D+/cv+82YrEEFBQQi6\\n4+LXyJEjkXtznbzc3Fy4u7uXeL1y5coIDw+Hs7MznJ2d0aJFCxw7dky1QGRxrmgAQE5Ojk22hbc3\\n0KePO8LCHJGYeKlU8wrZalsU98cflfDqqzWxYsUlGI0FuOMYq4g9tEVp2XtbLFqkR1hYdfj4XEBZ\\n13rT9CZ1kyZNsHPnTjRq1Ag7d+7Ec3esZHLy5EmMGTMGGzduhMlkQlpaGvrcY0rKOnXqaBHZ4mVl\\nZdlsW7zzjjiD2LixDiIj//n9ttwWgLhMMHSoGAjVq9f9T6tsvS3Kwt7bok4dcUly/PjamD//9zL9\\nrKYFIjQ0FOPHj0dYWBicnJywcOFCAEBiYiI8PT3RsWNH9O7dG8HBwXB0dERgYCC8vb21jEgWxMkJ\\nWL0aaNMGaN8eeOop2Ynkeu89sYyouaZ2Jts1bhzw1Vdl/zmdoljfDDhpaWnw45wCAOzj6CghAVi6\\nVNy4dnG59/tsuS0yMsRYhz17gCef/Of323JblBXbQigsBA4eLNt3JwfKkcUbOlTck4iJkZ1Ejtxc\\nsbb03LmlKw5Easp6/wFggSAroNOJaYxTU+1vlPWtNR6aNQMGD5adhuwNJwYmq1C9OvDZZ8CLL4pZ\\nK594QnYibaxcCfz0k7i8xhXiSGs8gyCr0bw5MG0a0Lcv8PffstOY36FD4rLaunWciI/kYIEgqzJs\\nGNCokfi39XWvKL2LF8UcOkuWAPXry05D9ooFgqyKTgcsWyYuu7z3nuw05lFQIOajCgwEwsJkpyF7\\nxnsQZHXc3MQ8Ta1bi149XbvKTlSxoqLEGJA5c2QnIXvHMwiySo8/Lq7NR0QAR47ITlNxli8Htm0D\\nPv0UcHCQnYbsHQsEWa3WrYFFi0TPpopYPUu2L74ApkwBNm0CqlaVnYaIBYKsXP/+4iwiIADIybHe\\nfqB79wKDBolxHpy8mCwFCwRZvWnTxDKLgwdXw7VrstOU3bFjYtnQxESgRQvZaYhuY4Egq6fTAXFx\\nQO3aNxAcLHoBWYvMTKBLF3FDukcP2WmISmKBIJvg4AAsXnwZOp3oGpqfLzvRP8vMBDp1EoPhBg2S\\nnYbobiwQZDMcHYG1a4G8PDHa+vp12YnurXhxeP112WmI1LFAkE1xcQHWrxdjJXr0wD1XXJMpLQ1o\\n2xaYNInFgSwbCwTZHEdHsdCQt7f4Ij57Vnai2778Ugzsi48HXn1Vdhqi+2OBIJvk4CCm5AgLEz2D\\n9u2Tm0dRgHffvd2VtXdvuXmISoNTbZDN0umAN98E6tUTl5umTQMiI7WfNjs3V5wtHD0qVoR7/HFt\\n909UXjyDIJvXsyewezfw4YdAr17AhQva7XvPHuDZZ8XcSrt3sziQdWGBILvg4wP88IM4m/D1BZKS\\nzDtdeE6OOHvp2xeIjQU++ghwdTXf/ojMQUqB2LZtG6Kjo1Vf++yzz9C3b1+EhITg22+/1TYY2TQn\\nJ2D+fGDzZmDpUqBdO+D77yt2HyYTkJAgCtGffwKHD4siQWSNNL8HMXv2bOzevRsNGjS467ULFy4g\\nOTkZGzZswPXr1xEaGorWrVvD0dFR65hkw5o2FUt4JiUBAwcCdesC0dHACy8A+nL+RRiN4hLWokWA\\np6eYcO+55yo2N5HWND+DaNKkCaZNm6b62uHDh+Hn5we9Xg+DwQAvLy8cP35c24BkFxwcgCFDgOPH\\ngQEDgOnTAS8vcVlo+/bSDbI7f15MOd6vH+DhAXz3HZCSAnz7LYsD2QaznUGsW7cOSUlJJZ6LjY1F\\nt27dsO8efQ6NRiPc3d2LHru6uiInJ8dcEYng6AgMHiz+OXJEjMSeMkVcGnriCbHcZ82aQJUqYo4n\\noxE4fRo4cUIUiJYtxcpvcXFAjRqyfxuiimW2AhEUFISgoKAy/YzBYICx2NDX3NxcVKlSRfW9WVlZ\\nD5TPVuTk5LAtbnrQtqhWDXjtNfFPbq4Ov/6qR2amHn/9VQk5OTo4OQEeHoVo1uwGvLxuwNvbVLSo\\nT34+YEn/G/i5uI1tUX4WNQ6icePGWLx4MfLz85GXl4fffvsNTz75pOp769Spo3E6y5SVlcW2uKmi\\n2+IeHz2rwM/FbWyL27Kzs8v0fosoEImJifD09ETHjh0RHh6OsLAwKIqCqKgoODk5yY5HRGSXpBSI\\nZs2aoVmzZkWPBxWb6zg4OBjBwcESUhERUXEcKEdERKpYIIiISBULBBERqWKBICIiVSwQRESkigWC\\niIhUsUAQEZEqFggiIlLFAkFERKpYIIiISBULBBERqWKBICIiVSwQRESkigWCiIhUsUAQEZEqFggi\\nIlLFAkFERKpYIIiISBULBBERqWKBICIiVXoZO922bRu+/PJLLFy48K7XZs+ejZ9++glubm4AgPj4\\neBgMBq0jEhHZPc0LxOzZs7F79240aNBA9fWMjAysXLkSVatW1TgZEREVp/klpiZNmmDatGmqrymK\\ngtOnT2PKlCkIDQ3F+vXrtQ1HRERFzHYGsW7dOiQlJZV4LjY2Ft26dcO+fftUf+bvv/9GeHg4Bg8e\\nDJPJhIiICDRq1Ag+Pj7miklERPdgtgIRFBSEoKCgMv1M5cqVER4eDmdnZzg7O6NFixY4duyYaoHI\\nysqqqKhWLScnh21xE9viNrbFbWyL8pNyk/peTp48iTFjxmDjxo0wmUxIS0tDnz59VN9bp04djdNZ\\npqysLLbFTWyL29gWt7EtbsvOzi7T+y2iQCQmJsLT0xMdO3ZE7969ERwcDEdHRwQGBsLb21t2PCIi\\nuySlQDRr1gzNmjUrejxo0KCi/x4yZAiGDBkiIRURERXHgXJERKSKBYKIiFSxQBARkSoWCCIiUsUC\\nQUREqlggiIhIFQsEERGpYoEgIiJVLBBERKSKBYKIiFSxQBARkSoWCCIiUsUCQUREqlggiIhIFQsE\\nERGpYoEgIiJVLBBERKSKBYKIiFSxQBARkSoWCCIiUqXXcmdGoxFjx45Fbm4uCgoKMGHCBDzzzDMl\\n3vPZZ59hzZo1cHR0xOuvv44OHTpoGZGIiG7StEB89NFHaNWqFSIiInDy5ElER0cjNTW16PULFy4g\\nOTkZGzZswPXr1xEaGorWrVvD0dFRy5hERASNC8TgwYPh5OQEADCZTHB2di7x+uHDh+Hn5we9Xg+D\\nwQAvLy8cP34cvr6+WsYkIiKYsUCsW7cOSUlJJZ6LjY2Fr68vzp8/j3HjxmHSpEklXjcajXB3dy96\\n7OrqipycHHNFJCKi+zBbgQgKCkJQUNBdzx8/fhxjx47F+PHj8dxzz5V4zWAwwGg0Fj3Ozc1FlSpV\\nVLeflpZWsYGtWHZ2tuwIFoNtcRvb4ja2Rfloeonp119/xejRo7F48WLUq1fvrtcbN26MxYsXIz8/\\nH3l5efjtt9/w5JNP3vU+Pz8/LeISEdk1naIoilY7i4yMxPHjx+Hh4QFFUVClShXExcUhMTERnp6e\\n6NixI9auXYs1a9ZAURQMGzYMzz//vFbxiIioGE0LBBERWQ+rGiinKAqmTp2KkJAQRERE4OzZs7Ij\\nSWMymTBu3Dj0798fL730Enbs2CE7klQXL15Ehw4dcPLkSdlRpFu+fDlCQkLQt29frF+/XnYcKUwm\\nE6KjoxESEoIBAwbY7eciPT0d4eHhAIAzZ84gLCwMAwYMwPTp00v181ZVILZv3478/HykpKQgOjoa\\nsbGxsiNJs2nTJjz88MNYvXo1VqxYgZkzZ8qOJI3JZMLUqVPh4uIiO4p0+/btw8GDB5GSkoLk5GS7\\nvTm7c+dOFBYWIiUlBZGRkVi0aJHsSJpLSEjAW2+9hYKCAgCiF2lUVBQ+/vhjFBYWYvv27f+4Dasq\\nEGlpaWjbti0A4Omnn8aRI0ckJ5KnW7duGDVqFACgsLAQer2m/Q0syty5cxEaGopatWrJjiLdrl27\\n4OPjg8jISAwbNgwdO3aUHUkKLy8v3LhxA4qiICcnxy4H23p6eiIuLq7ocUZGRlHP0Xbt2uGHH374\\nx21Y1bfKneMk9Ho9CgsLUamSVdW5ClG5cmUAok1GjRqFMWPGSE4kR2pqKqpXr47WrVvjgw8+kB1H\\nur/++gtZWVlYtmwZzp49i2HDhuHLL7+UHUtzbm5u+P3339G1a1dcvnwZy5Ytkx1Jc/7+/jh37lzR\\n4+K3m93c3Eo1xsyqvlkNBgNyc3OLHttrcbglOzsbAwcORGBgILp37y47jhSpqanYvXs3wsPDcezY\\nMYwfPx4XL16UHUuaqlWrom3bttDr9ahbty6cnZ1x6dIl2bE0l5iYiLZt2+Krr77Cpk2bMH78eOTn\\n58uOJVXx78r7jTEr8TPmDFTRmjRpgp07dwIADh06BB8fH8mJ5Llw4QKGDh2KN998E4GBgbLjSPPx\\nxx8jOTkZycnJqF+/PubOnYvq1avLjiWNn58fvv/+ewDAH3/8gevXr+Phhx+WnEp7Dz30EAwGAwDA\\n3d0dJpMJhYWFklPJ1bBhQ+zfvx8A8N1335VqPJlVXWLy9/fH7t27ERISAgB2fZN62bJluHr1KuLj\\n4xEXFwedToeEhISiua7skU6nkx1Bug4dOuDAgQMICgoq6vVnj+0ycOBATJw4Ef379y/q0WTvnRjG\\njx+PyZMno6CgAN7e3ujates//gzHQRARkSqrusRERETaYYEgIiJVLBBERKSKBYKIiFSxQBARkSoW\\nCCIiUsUCQUREqlggiIhIFQsEUQVYvXo1oqOjAQATJkzAp59+KjkR0YPjSGqiCjJixAi4u7sjPz8f\\nCxculB2H6IGxQBBVkPT0dISEhCA1NRUNGjSQHYfogbFAEFWA/Px8hIeHIygoCOvWrcPq1avtehEn\\nsg28B0FUARYuXIhOnTohODgYbdu25SUmsgk8gyAiIlU8gyAiIlUsEEREpIoFgoiIVLFAEBGRKhYI\\nIiJSxQJBRESqWCCIiEgVCwQREan6f8M6gCXBCtQMAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x107f67cc0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"ax = plt.axes()\\n\",\n    \"ax.plot(x, np.sin(x))\\n\",\n    \"ax.set(xlim=(0, 10), ylim=(-2, 2),\\n\",\n    \"       xlabel='x', ylabel='sin(x)',\\n\",\n    \"       title='A Simple Plot');\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<!--NAVIGATION-->\\n\",\n    \"< [Visualization with Matplotlib](04.00-Introduction-To-Matplotlib.ipynb) | [Contents](Index.ipynb) | [Simple Scatter Plots](04.02-Simple-Scatter-Plots.ipynb) >\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"anaconda-cloud\": {},\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.4.3\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "matplotlib/04.02-Simple-Scatter-Plots.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<!--BOOK_INFORMATION-->\\n\",\n    \"<img align=\\\"left\\\" style=\\\"padding-right:10px;\\\" src=\\\"figures/PDSH-cover-small.png\\\">\\n\",\n    \"*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\\n\",\n    \"\\n\",\n    \"*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*\\n\",\n    \"\\n\",\n    \"*No changes were made to the contents of this notebook from the original.*\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<!--NAVIGATION-->\\n\",\n    \"< [Simple Line Plots](04.01-Simple-Line-Plots.ipynb) | [Contents](Index.ipynb) | [Visualizing Errors](04.03-Errorbars.ipynb) >\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Simple Scatter Plots\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Another commonly used plot type is the simple scatter plot, a close cousin of the line plot.\\n\",\n    \"Instead of points being joined by line segments, here the points are represented individually with a dot, circle, or other shape.\\n\",\n    \"We’ll start by setting up the notebook for plotting and importing the functions we will use:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%matplotlib inline\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"plt.style.use('seaborn-whitegrid')\\n\",\n    \"import numpy as np\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Scatter Plots with ``plt.plot``\\n\",\n    \"\\n\",\n    \"In the previous section we looked at ``plt.plot``/``ax.plot`` to produce line plots.\\n\",\n    \"It turns out that this same function can produce scatter plots as well:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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5iMRcEMlYdFIpHUviXRaJSJWGCCEfQoKKflYQCcY+gGAAxH0AOA4Qh6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\\nTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10bdaae48>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"x = np.linspace(0, 10, 30)\\n\",\n    \"y = np.sin(x)\\n\",\n    \"\\n\",\n    \"plt.plot(x, y, 'o', color='black');\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The third argument in the function call is a character that represents the type of symbol used for the plotting. Just as you can specify options such as ``'-'``, ``'--'`` to control the line style, the marker style has its own set of short string codes. The full list of available symbols can be seen in the documentation of ``plt.plot``, or in Matplotlib's online documentation. Most of the possibilities are fairly intuitive, and we'll show a number of the more common ones here:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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BV2QojG/vHLL6hpalLZVtPUhH/88otAiYRnbW0NjuNgbW3d5fe07Tuj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\\nYmKosBNCiIn5/5UUNVEacdwFAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10bf7e470>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"rng = np.random.RandomState(0)\\n\",\n    \"for marker in ['o', '.', ',', 'x', '+', 'v', '^', '<', '>', 's', 'd']:\\n\",\n    \"    plt.plot(rng.rand(5), rng.rand(5), marker,\\n\",\n    \"             label=\\\"marker='{0}'\\\".format(marker))\\n\",\n    \"plt.legend(numpoints=1)\\n\",\n    \"plt.xlim(0, 1.8);\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"For even more possibilities, these character codes can be used together with line and color codes to plot points along with a line connecting them:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAXoAAAD/CAYAAAD/qh1PAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\nTwghIkeFnhBCRI4KPSGEiNz/Aak6QmZ56l4lAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1023b1940>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.plot(x, y, '-ok');\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Additional keyword arguments to ``plt.plot`` specify a wide range of properties of the lines and markers:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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Flu62rq6+s5ak3gCDbQ82mz70CjySQSKDqdDnv27EFHR8eoC/GAofH6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\\nLjyprHyiCgAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10e7a6208>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.plot(x, y, '-p', color='gray',\\n\",\n    \"         markersize=15, linewidth=4,\\n\",\n    \"         markerfacecolor='white',\\n\",\n    \"         markeredgecolor='gray',\\n\",\n    \"         markeredgewidth=2)\\n\",\n    \"plt.ylim(-1.2, 1.2);\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This type of flexibility in the ``plt.plot`` function allows for a wide variety of possible visualization options.\\n\",\n    \"For a full description of the options available, refer to the ``plt.plot`` documentation.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Scatter Plots with ``plt.scatter``\\n\",\n    \"\\n\",\n    \"A second, more powerful method of creating scatter plots is the ``plt.scatter`` function, which can be used very similarly to the ``plt.plot`` function:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAXoAAAD/CAYAAAD/qh1PAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\nAJsj6AHA5gh6ALC5/wVDxW4vjHfp3gAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10e8a18d0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.scatter(x, y, marker='o');\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The primary difference of ``plt.scatter`` from ``plt.plot`` is that it can be used to create scatter plots where the properties of each individual point (size, face color, edge color, etc.) can be individually controlled or mapped to data.\\n\",\n    \"\\n\",\n    \"Let's show this by creating a random scatter plot with points of many colors and sizes.\\n\",\n    \"In order to better see the overlapping results, we'll also use the ``alpha`` keyword to adjust the transparency level:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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69Uq0iGzatnlYsl3J4ASquMIEqokkKxnMXmNK4bdysIAmaTlXxueYcr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\\nrkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10e8fce10>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"rng = np.random.RandomState(0)\\n\",\n    \"x = rng.randn(100)\\n\",\n    \"y = rng.randn(100)\\n\",\n    \"colors = rng.rand(100)\\n\",\n    \"sizes = 1000 * rng.rand(100)\\n\",\n    \"\\n\",\n    \"plt.scatter(x, y, c=colors, s=sizes, alpha=0.3,\\n\",\n    \"            cmap='viridis')\\n\",\n    \"plt.colorbar();  # show color scale\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Notice that the color argument is automatically mapped to a color scale (shown here by the ``colorbar()`` command), and that the size argument is given in pixels.\\n\",\n    \"In this way, the color and size of points can be used to convey information in the visualization, in order to visualize multidimensional data.\\n\",\n    \"\\n\",\n    \"For example, we might use the Iris data from Scikit-Learn, where each sample is one of three types of flowers that has had the size of its petals and sepals carefully measured:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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RSmaVKv15EkadU2qLA8NVTTNGw22y2rLdTd3U0kEqFcLmO1Wu/a1aDC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\\n4XkXT3EAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1107c79b0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.datasets import load_iris\\n\",\n    \"iris = load_iris()\\n\",\n    \"features = iris.data.T\\n\",\n    \"\\n\",\n    \"plt.scatter(features[0], features[1], alpha=0.2,\\n\",\n    \"            s=100*features[3], c=iris.target, cmap='viridis')\\n\",\n    \"plt.xlabel(iris.feature_names[0])\\n\",\n    \"plt.ylabel(iris.feature_names[1]);\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We can see that this scatter plot has given us the ability to simultaneously explore four different dimensions of the data:\\n\",\n    \"the (x, y) location of each point corresponds to the sepal length and width, the size of the point is related to the petal width, and the color is related to the particular species of flower.\\n\",\n    \"Multicolor and multifeature scatter plots like this can be useful for both exploration and presentation of data.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## ``plot`` Versus ``scatter``: A Note on Efficiency\\n\",\n    \"\\n\",\n    \"Aside from the different features available in ``plt.plot`` and ``plt.scatter``, why might you choose to use one over the other? While it doesn't matter as much for small amounts of data, as datasets get larger than a few thousand points, ``plt.plot`` can be noticeably more efficient than ``plt.scatter``.\\n\",\n    \"The reason is that ``plt.scatter`` has the capability to render a different size and/or color for each point, so the renderer must do the extra work of constructing each point individually.\\n\",\n    \"In ``plt.plot``, on the other hand, the points are always essentially clones of each other, so the work of determining the appearance of the points is done only once for the entire set of data.\\n\",\n    \"For large datasets, the difference between these two can lead to vastly different performance, and for this reason, ``plt.plot`` should be preferred over ``plt.scatter`` for large datasets.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<!--NAVIGATION-->\\n\",\n    \"< [Simple Line Plots](04.01-Simple-Line-Plots.ipynb) | [Contents](Index.ipynb) | [Visualizing Errors](04.03-Errorbars.ipynb) >\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.4.3\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "matplotlib/04.03-Errorbars.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<!--BOOK_INFORMATION-->\\n\",\n    \"<img align=\\\"left\\\" style=\\\"padding-right:10px;\\\" src=\\\"figures/PDSH-cover-small.png\\\">\\n\",\n    \"*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\\n\",\n    \"\\n\",\n    \"*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*\\n\",\n    \"\\n\",\n    \"*No changes were made to the contents of this notebook from the original.*\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<!--NAVIGATION-->\\n\",\n    \"< [Simple Scatter Plots](04.02-Simple-Scatter-Plots.ipynb) | [Contents](Index.ipynb) | [Density and Contour Plots](04.04-Density-and-Contour-Plots.ipynb) >\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Visualizing Errors\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"For any scientific measurement, accurate accounting for errors is nearly as important, if not more important, than accurate reporting of the number itself.\\n\",\n    \"For example, imagine that I am using some astrophysical observations to estimate the Hubble Constant, the local measurement of the expansion rate of the Universe.\\n\",\n    \"I know that the current literature suggests a value of around 71 (km/s)/Mpc, and I measure a value of 74 (km/s)/Mpc with my method. Are the values consistent? The only correct answer, given this information, is this: there is no way to know.\\n\",\n    \"\\n\",\n    \"Suppose I augment this information with reported uncertainties: the current literature suggests a value of around 71 $\\\\pm$ 2.5 (km/s)/Mpc, and my method has measured a value of 74 $\\\\pm$ 5 (km/s)/Mpc. Now are the values consistent? That is a question that can be quantitatively answered.\\n\",\n    \"\\n\",\n    \"In visualization of data and results, showing these errors effectively can make a plot convey much more complete information.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Basic Errorbars\\n\",\n    \"\\n\",\n    \"A basic errorbar can be created with a single Matplotlib function call:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%matplotlib inline\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"plt.style.use('seaborn-whitegrid')\\n\",\n    \"import numpy as np\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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9nVRZDhNLv8Tu58lO98do+5cV2Eaf6z25tm+SlM9Ri6eeRRQWvAe2Gq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\\nw/0Pa4w4rypgtw8AAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10bf54080>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"x = np.linspace(0, 10, 50)\\n\",\n    \"dy = 0.8\\n\",\n    \"y = np.sin(x) + dy * np.random.randn(50)\\n\",\n    \"\\n\",\n    \"plt.errorbar(x, y, yerr=dy, fmt='.k');\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Here the ``fmt`` is a format code controlling the appearance of lines and points, and has the same syntax as the shorthand used in ``plt.plot``, outlined in [Simple Line Plots](04.01-Simple-Line-Plots.ipynb) and [Simple Scatter Plots](04.02-Simple-Scatter-Plots.ipynb).\\n\",\n    \"\\n\",\n    \"In addition to these basic options, the ``errorbar`` function has many options to fine-tune the outputs.\\n\",\n    \"Using these additional options you can easily customize the aesthetics of your errorbar plot.\\n\",\n    \"I often find it helpful, especially in crowded plots, to make the errorbars lighter than the points themselves:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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8J5Peg9lWz19/cX/FlfMnKrhkWDc0/QptSFebaFlUxdW+Xl5a4dLyr1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\\nAjkAGO7/AZgUo2sIozQmAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10bec4d30>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.errorbar(x, y, yerr=dy, fmt='o', color='black',\\n\",\n    \"             ecolor='lightgray', elinewidth=3, capsize=0);\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"In addition to these options, you can also specify horizontal errorbars (``xerr``), one-sided errorbars, and many other variants.\\n\",\n    \"For more information on the options available, refer to the docstring of ``plt.errorbar``.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Continuous Errors\\n\",\n    \"\\n\",\n    \"In some situations it is desirable to show errorbars on continuous quantities.\\n\",\n    \"Though Matplotlib does not have a built-in convenience routine for this type of application, it's relatively easy to combine primitives like ``plt.plot`` and ``plt.fill_between`` for a useful result.\\n\",\n    \"\\n\",\n    \"Here we'll perform a simple *Gaussian process regression*, using the Scikit-Learn API (see [Introducing Scikit-Learn](05.02-Introducing-Scikit-Learn.ipynb) for details).\\n\",\n    \"This is a method of fitting a very flexible non-parametric function to data with a continuous measure of the uncertainty.\\n\",\n    \"We won't delve into the details of Gaussian process regression at this point, but will focus instead on how you might visualize such a continuous error measurement:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.gaussian_process import GaussianProcess\\n\",\n    \"\\n\",\n    \"# define the model and draw some data\\n\",\n    \"model = lambda x: x * np.sin(x)\\n\",\n    \"xdata = np.array([1, 3, 5, 6, 8])\\n\",\n    \"ydata = model(xdata)\\n\",\n    \"\\n\",\n    \"# Compute the Gaussian process fit\\n\",\n    \"gp = GaussianProcess(corr='cubic', theta0=1e-2, thetaL=1e-4, thetaU=1E-1,\\n\",\n    \"                     random_start=100)\\n\",\n    \"gp.fit(xdata[:, np.newaxis], ydata)\\n\",\n    \"\\n\",\n    \"xfit = np.linspace(0, 10, 1000)\\n\",\n    \"yfit, MSE = gp.predict(xfit[:, np.newaxis], eval_MSE=True)\\n\",\n    \"dyfit = 2 * np.sqrt(MSE)  # 2*sigma ~ 95% confidence region\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We now have ``xfit``, ``yfit``, and ``dyfit``, which sample the continuous fit to our data.\\n\",\n    \"We could pass these to the ``plt.errorbar`` function as above, but we don't really want to plot 1,000 points with 1,000 errorbars.\\n\",\n    \"Instead, we can use the ``plt.fill_between`` function with a light color to visualize this continuous error:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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UxJ2Wt9NFC4E0KyltfrBc/zCW8xIG301dzcjGuuuQaTJ09O69Z6dxTu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\\niMKdEEKyEIU7IYRkof8PKWVUtmMej+IAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10e55bba8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Visualize the result\\n\",\n    \"plt.plot(xdata, ydata, 'or')\\n\",\n    \"plt.plot(xfit, yfit, '-', color='gray')\\n\",\n    \"\\n\",\n    \"plt.fill_between(xfit, yfit - dyfit, yfit + dyfit,\\n\",\n    \"                 color='gray', alpha=0.2)\\n\",\n    \"plt.xlim(0, 10);\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Note what we've done here with the ``fill_between`` function: we pass an x value, then the lower y-bound, then the upper y-bound, and the result is that the area between these regions is filled.\\n\",\n    \"\\n\",\n    \"The resulting figure gives a very intuitive view into what the Gaussian process regression algorithm is doing: in regions near a measured data point, the model is strongly constrained and this is reflected in the small model errors.\\n\",\n    \"In regions far from a measured data point, the model is not strongly constrained, and the model errors increase.\\n\",\n    \"\\n\",\n    \"For more information on the options available in ``plt.fill_between()`` (and the closely related ``plt.fill()`` function), see the function docstring or the Matplotlib documentation.\\n\",\n    \"\\n\",\n    \"Finally, if this seems a bit too low level for your taste, refer to [Visualization With Seaborn](04.14-Visualization-With-Seaborn.ipynb), where we discuss the Seaborn package, which has a more streamlined API for visualizing this type of continuous errorbar.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<!--NAVIGATION-->\\n\",\n    \"< [Simple Scatter Plots](04.02-Simple-Scatter-Plots.ipynb) | [Contents](Index.ipynb) | [Density and Contour Plots](04.04-Density-and-Contour-Plots.ipynb) >\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"anaconda-cloud\": {},\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.4.3\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "matplotlib/04.04-Density-and-Contour-Plots.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<!--BOOK_INFORMATION-->\\n\",\n    \"<img align=\\\"left\\\" style=\\\"padding-right:10px;\\\" src=\\\"figures/PDSH-cover-small.png\\\">\\n\",\n    \"*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\\n\",\n    \"\\n\",\n    \"*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*\\n\",\n    \"\\n\",\n    \"*No changes were made to the contents of this notebook from the original.*\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<!--NAVIGATION-->\\n\",\n    \"< [Visualizing Errors](04.03-Errorbars.ipynb) | [Contents](Index.ipynb) | [Histograms, Binnings, and Density](04.05-Histograms-and-Binnings.ipynb) >\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Density and Contour Plots\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Sometimes it is useful to display three-dimensional data in two dimensions using contours or color-coded regions.\\n\",\n    \"There are three Matplotlib functions that can be helpful for this task: ``plt.contour`` for contour plots, ``plt.contourf`` for filled contour plots, and ``plt.imshow`` for showing images.\\n\",\n    \"This section looks at several examples of using these. We'll start by setting up the notebook for plotting and importing the functions we will use: \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%matplotlib inline\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"plt.style.use('seaborn-white')\\n\",\n    \"import numpy as np\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Visualizing a Three-Dimensional Function\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We'll start by demonstrating a contour plot using a function $z = f(x, y)$, using the following particular choice for $f$ (we've seen this before in [Computation on Arrays: Broadcasting](02.05-Computation-on-arrays-broadcasting.ipynb), when we used it as a motivating example for array broadcasting):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def f(x, y):\\n\",\n    \"    return np.sin(x) ** 10 + np.cos(10 + y * x) * np.cos(x)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"A contour plot can be created with the ``plt.contour`` function.\\n\",\n    \"It takes three arguments: a grid of *x* values, a grid of *y* values, and a grid of *z* values.\\n\",\n    \"The *x* and *y* values represent positions on the plot, and the *z* values will be represented by the contour levels.\\n\",\n    \"Perhaps the most straightforward way to prepare such data is to use the ``np.meshgrid`` function, which builds two-dimensional grids from one-dimensional arrays:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x = np.linspace(0, 5, 50)\\n\",\n    \"y = np.linspace(0, 5, 40)\\n\",\n    \"\\n\",\n    \"X, Y = np.meshgrid(x, y)\\n\",\n    \"Z = f(X, Y)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now let's look at this with a standard line-only contour plot:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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Xl5eeTl5UVHjx6lQYMG0aZNm4goX4m3aNEimjNnDrVr144iIyM1f3gC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\\nBAKB4MIQkSYQCAQX5n820q0uNZ2xtgAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10bed1320>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.contour(X, Y, Z, colors='black');\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Notice that by default when a single color is used, negative values are represented by dashed lines, and positive values by solid lines.\\n\",\n    \"Alternatively, the lines can be color-coded by specifying a colormap with the ``cmap`` argument.\\n\",\n    \"Here, we'll also specify that we want more lines to be drawn—20 equally spaced intervals within the data range:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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vHjx/PKK6+Qn5/PvHnzeO655/jggw+061cxefJkzjvvPJ577jkkSeLW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\\nXv33k7YAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10e2dd048>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.contour(X, Y, Z, 20, cmap='RdGy');\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Here we chose the ``RdGy`` (short for *Red-Gray*) colormap, which is a good choice for centered data.\\n\",\n    \"Matplotlib has a wide range of colormaps available, which you can easily browse in IPython by doing a tab completion on the ``plt.cm`` module:\\n\",\n    \"```\\n\",\n    \"plt.cm.<TAB>\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"Our plot is looking nicer, but the spaces between the lines may be a bit distracting.\\n\",\n    \"We can change this by switching to a filled contour plot using the ``plt.contourf()`` function (notice the ``f`` at the end), which uses largely the same syntax as ``plt.contour()``.\\n\",\n    \"\\n\",\n    \"Additionally, we'll add a ``plt.colorbar()`` command, which automatically creates an additional axis with labeled color information for the plot:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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wDVB7ipgkA7g5alRMtFYNV4IITAqhNuC2wC6/O7c51qLqxhSF1cKeLq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\\nuXA8TEFtIykAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10e63ffd0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.contourf(X, Y, Z, 20, cmap='RdGy')\\n\",\n    \"plt.colorbar();\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The colorbar makes it clear that the black regions are \\\"peaks,\\\" while the red regions are \\\"valleys.\\\"\\n\",\n    \"\\n\",\n    \"One potential issue with this plot is that it is a bit \\\"splotchy.\\\" That is, the color steps are discrete rather than continuous, which is not always what is desired.\\n\",\n    \"This could be remedied by setting the number of contours to a very high number, but this results in a rather inefficient plot: Matplotlib must render a new polygon for each step in the level.\\n\",\n    \"A better way to handle this is to use the ``plt.imshow()`` function, which interprets a two-dimensional grid of data as an image.\\n\",\n    \"\\n\",\n    \"The following code shows this:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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XdN02BJ+JAQ4zDNJWIt5vGAfbLDv72hvb5h6Dp2quwS7PpAtx3oQqSP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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10eec17f0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.imshow(Z, extent=[0, 5, 0, 5], origin='lower',\\n\",\n    \"           cmap='RdGy')\\n\",\n    \"plt.colorbar()\\n\",\n    \"plt.axis(aspect='image');\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"There are a few potential gotchas with ``imshow()``, however:\\n\",\n    \"\\n\",\n    \"- ``plt.imshow()`` doesn't accept an *x* and *y* grid, so you must manually specify the *extent* [*xmin*, *xmax*, *ymin*, *ymax*] of the image on the plot.\\n\",\n    \"- ``plt.imshow()`` by default follows the standard image array definition where the origin is in the upper left, not in the lower left as in most contour plots. This must be changed when showing gridded data.\\n\",\n    \"- ``plt.imshow()`` will automatically adjust the axis aspect ratio to match the input data; this can be changed by setting, for example, ``plt.axis(aspect='image')`` to make *x* and *y* units match.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Finally, it can sometimes be useful to combine contour plots and image plots.\\n\",\n    \"For example, here we'll use a partially transparent background image (with transparency set via the ``alpha`` parameter) and overplot contours with labels on the contours themselves (using the ``plt.clabel()`` function):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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W6+mbPObme5ki791KWXJmLKWVlZlJWVJQSv+AdKMk3Xfn/22Wfz9ddf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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10eeec5c0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"contours = plt.contour(X, Y, Z, 3, colors='black')\\n\",\n    \"plt.clabel(contours, inline=True, fontsize=8)\\n\",\n    \"\\n\",\n    \"plt.imshow(Z, extent=[0, 5, 0, 5], origin='lower',\\n\",\n    \"           cmap='RdGy', alpha=0.5)\\n\",\n    \"plt.colorbar();\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The combination of these three functions—``plt.contour``, ``plt.contourf``, and ``plt.imshow``—gives nearly limitless possibilities for displaying this sort of three-dimensional data within a two-dimensional plot.\\n\",\n    \"For more information on the options available in these functions, refer to their docstrings.\\n\",\n    \"If you are interested in three-dimensional visualizations of this type of data, see [Three-dimensional Plotting in Matplotlib](04.12-Three-Dimensional-Plotting.ipynb).\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<!--NAVIGATION-->\\n\",\n    \"< [Visualizing Errors](04.03-Errorbars.ipynb) | [Contents](Index.ipynb) | [Histograms, Binnings, and Density](04.05-Histograms-and-Binnings.ipynb) >\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"anaconda-cloud\": {},\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.4.3\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "matplotlib/04.05-Histograms-and-Binnings.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<!--BOOK_INFORMATION-->\\n\",\n    \"<img align=\\\"left\\\" style=\\\"padding-right:10px;\\\" src=\\\"figures/PDSH-cover-small.png\\\">\\n\",\n    \"*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\\n\",\n    \"\\n\",\n    \"*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*\\n\",\n    \"\\n\",\n    \"*No changes were made to the contents of this notebook from the original.*\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<!--NAVIGATION-->\\n\",\n    \"< [Density and Contour Plots](04.04-Density-and-Contour-Plots.ipynb) | [Contents](Index.ipynb) | [Customizing Plot Legends](04.06-Customizing-Legends.ipynb) >\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Histograms, Binnings, and Density\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"A simple histogram can be a great first step in understanding a dataset.\\n\",\n    \"Earlier, we saw a preview of Matplotlib's histogram function (see [Comparisons, Masks, and Boolean Logic](02.06-Boolean-Arrays-and-Masks.ipynb)), which creates a basic histogram in one line, once the normal boiler-plate imports are done:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%matplotlib inline\\n\",\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"plt.style.use('seaborn-white')\\n\",\n    \"\\n\",\n    \"data = np.random.randn(1000)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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L4eL49r0P/9LhdJqq6u1vnnnx+7vL29XY2NjcrIyFBCQkK8lgUAo3V1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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1023e5a58>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.hist(data);\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The ``hist()`` function has many options to tune both the calculation and the display; \\n\",\n    \"here's an example of a more customized histogram:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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d/vVyAQSI+Hw2Ft27ZNAwMD6ecSiYQkqa2tLQstAwDMmG7L9PT0qLa2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\\nhDsAWND/AL5epc4Uw0TUAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10bfc0588>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.hist(data, bins=30, normed=True, alpha=0.5,\\n\",\n    \"         histtype='stepfilled', color='steelblue',\\n\",\n    \"         edgecolor='none');\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The ``plt.hist`` docstring has more information on other customization options available.\\n\",\n    \"I find this combination of ``histtype='stepfilled'`` along with some transparency ``alpha`` to be very useful when comparing histograms of several distributions:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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Y2qIr1ioCeZoigQzSJK7yzVupSMZ7fn4dNPB9DZ2QWDYQSbNtXBZDJp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\\nOhGRTjDQiYh04v8DF2mFWN13xe4AAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10e666358>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"x1 = np.random.normal(0, 0.8, 1000)\\n\",\n    \"x2 = np.random.normal(-2, 1, 1000)\\n\",\n    \"x3 = np.random.normal(3, 2, 1000)\\n\",\n    \"\\n\",\n    \"kwargs = dict(histtype='stepfilled', alpha=0.3, normed=True, bins=40)\\n\",\n    \"\\n\",\n    \"plt.hist(x1, **kwargs)\\n\",\n    \"plt.hist(x2, **kwargs)\\n\",\n    \"plt.hist(x3, **kwargs);\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"If you would like to simply compute the histogram (that is, count the number of points in a given bin) and not display it, the ``np.histogram()`` function is available:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[ 12 190 468 301  29]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"counts, bin_edges = np.histogram(data, bins=5)\\n\",\n    \"print(counts)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Two-Dimensional Histograms and Binnings\\n\",\n    \"\\n\",\n    \"Just as we create histograms in one dimension by dividing the number-line into bins, we can also create histograms in two-dimensions by dividing points among two-dimensional bins.\\n\",\n    \"We'll take a brief look at several ways to do this here.\\n\",\n    \"We'll start by defining some data—an ``x`` and ``y`` array drawn from a multivariate Gaussian distribution:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"mean = [0, 0]\\n\",\n    \"cov = [[1, 1], [1, 2]]\\n\",\n    \"x, y = np.random.multivariate_normal(mean, cov, 10000).T\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### ``plt.hist2d``: Two-dimensional histogram\\n\",\n    \"\\n\",\n    \"One straightforward way to plot a two-dimensional histogram is to use Matplotlib's ``plt.hist2d`` function:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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BM0EZmG8tMf0Rhf+Pzzz5GSkoK4uDiPz8EETUSmoSoSa9A+mkFv3LgR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\\nAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1106647f0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.hist2d(x, y, bins=30, cmap='Blues')\\n\",\n    \"cb = plt.colorbar()\\n\",\n    \"cb.set_label('counts in bin')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Just as with ``plt.hist``, ``plt.hist2d`` has a number of extra options to fine-tune the plot and the binning, which are nicely outlined in the function docstring.\\n\",\n    \"Further, just as ``plt.hist`` has a counterpart in ``np.histogram``, ``plt.hist2d`` has a counterpart in ``np.histogram2d``, which can be used as follows:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"counts, xedges, yedges = np.histogram2d(x, y, bins=30)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"For the generalization of this histogram binning in dimensions higher than two, see the ``np.histogramdd`` function.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### ``plt.hexbin``: Hexagonal binnings\\n\",\n    \"\\n\",\n    \"The two-dimensional histogram creates a tesselation of squares across the axes.\\n\",\n    \"Another natural shape for such a tesselation is the regular hexagon.\\n\",\n    \"For this purpose, Matplotlib provides the ``plt.hexbin`` routine, which will represents a two-dimensional dataset binned within a grid of hexagons:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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jyO48nFGHHSOsYFWo5P4D1VtwNwIXXNcjQcH+MygfmmC0MjoblQ3LGC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\\nRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10e666550>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.hexbin(x, y, gridsize=30, cmap='Blues')\\n\",\n    \"cb = plt.colorbar(label='count in bin')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"``plt.hexbin`` has a number of interesting options, including the ability to specify weights for each point, and to change the output in each bin to any NumPy aggregate (mean of weights, standard deviation of weights, etc.).\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Kernel density estimation\\n\",\n    \"\\n\",\n    \"Another common method of evaluating densities in multiple dimensions is *kernel density estimation* (KDE).\\n\",\n    \"This will be discussed more fully in [In-Depth: Kernel Density Estimation](05.13-Kernel-Density-Estimation.ipynb), but for now we'll simply mention that KDE can be thought of as a way to \\\"smear out\\\" the points in space and add up the result to obtain a smooth function.\\n\",\n    \"One extremely quick and simple KDE implementation exists in the ``scipy.stats`` package.\\n\",\n    \"Here is a quick example of using the KDE on this data:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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VAIgUKtF0KgKOS6nAs09ZxuU+X0Pr2tWQgUBVAUQLMACgE0VbkmqUcn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\\nRK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10e36a198>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"from scipy.stats import gaussian_kde\\n\",\n    \"\\n\",\n    \"# fit an array of size [Ndim, Nsamples]\\n\",\n    \"data = np.vstack([x, y])\\n\",\n    \"kde = gaussian_kde(data)\\n\",\n    \"\\n\",\n    \"# evaluate on a regular grid\\n\",\n    \"xgrid = np.linspace(-3.5, 3.5, 40)\\n\",\n    \"ygrid = np.linspace(-6, 6, 40)\\n\",\n    \"Xgrid, Ygrid = np.meshgrid(xgrid, ygrid)\\n\",\n    \"Z = kde.evaluate(np.vstack([Xgrid.ravel(), Ygrid.ravel()]))\\n\",\n    \"\\n\",\n    \"# Plot the result as an image\\n\",\n    \"plt.imshow(Z.reshape(Xgrid.shape),\\n\",\n    \"           origin='lower', aspect='auto',\\n\",\n    \"           extent=[-3.5, 3.5, -6, 6],\\n\",\n    \"           cmap='Blues')\\n\",\n    \"cb = plt.colorbar()\\n\",\n    \"cb.set_label(\\\"density\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"KDE has a smoothing length that effectively slides the knob between detail and smoothness (one example of the ubiquitous bias–variance trade-off).\\n\",\n    \"The literature on choosing an appropriate smoothing length is vast: ``gaussian_kde`` uses a rule-of-thumb to attempt to find a nearly optimal smoothing length for the input data.\\n\",\n    \"\\n\",\n    \"Other KDE implementations are available within the SciPy ecosystem, each with its own strengths and weaknesses; see, for example, ``sklearn.neighbors.KernelDensity`` and ``statsmodels.nonparametric.kernel_density.KDEMultivariate``.\\n\",\n    \"For visualizations based on KDE, using Matplotlib tends to be overly verbose.\\n\",\n    \"The Seaborn library, discussed in [Visualization With Seaborn](04.14-Visualization-With-Seaborn.ipynb), provides a much more terse API for creating KDE-based visualizations.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<!--NAVIGATION-->\\n\",\n    \"< [Density and Contour Plots](04.04-Density-and-Contour-Plots.ipynb) | [Contents](Index.ipynb) | [Customizing Plot Legends](04.06-Customizing-Legends.ipynb) >\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"anaconda-cloud\": {},\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.4.3\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "matplotlib/04.06-Customizing-Legends.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<!--BOOK_INFORMATION-->\\n\",\n    \"<img align=\\\"left\\\" style=\\\"padding-right:10px;\\\" src=\\\"figures/PDSH-cover-small.png\\\">\\n\",\n    \"*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\\n\",\n    \"\\n\",\n    \"*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*\\n\",\n    \"\\n\",\n    \"*No changes were made to the contents of this notebook from the original.*\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<!--NAVIGATION-->\\n\",\n    \"< [Histograms, Binnings, and Density](04.05-Histograms-and-Binnings.ipynb) | [Contents](Index.ipynb) | [Customizing Colorbars](04.07-Customizing-Colorbars.ipynb) >\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Customizing Plot Legends\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Plot legends give meaning to a visualization, assigning meaning to the various plot elements.\\n\",\n    \"We previously saw how to create a simple legend; here we'll take a look at customizing the placement and aesthetics of the legend in Matplotlib.\\n\",\n    \"\\n\",\n    \"The simplest legend can be created with the ``plt.legend()`` command, which automatically creates a legend for any labeled plot elements:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"plt.style.use('classic')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%matplotlib inline\\n\",\n    \"import numpy as np\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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oPt3q16//2qZ5+t+uGHkblspHz2md2dbt3sBUVxZPVqG0Vs2lR1/ny/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\\nAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10c21e9e8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"x = np.linspace(0, 10, 1000)\\n\",\n    \"fig, ax = plt.subplots()\\n\",\n    \"ax.plot(x, np.sin(x), '-b', label='Sine')\\n\",\n    \"ax.plot(x, np.cos(x), '--r', label='Cosine')\\n\",\n    \"ax.axis('equal')\\n\",\n    \"leg = ax.legend();\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"But there are many ways we might want to customize such a legend.\\n\",\n    \"For example, we can specify the location and turn off the frame:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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vdXPXLEwRh9tGOHaufO1rBbutTpaAJTOBO7qurs2aqVKqn26aOLZh3R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\\nrkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10c21e9e8>\"\n      ]\n     },\n     \"execution_count\": 4,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"ax.legend(loc='upper left', frameon=False)\\n\",\n    \"fig\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We can use the ``ncol`` command to specify the number of columns in the legend:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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1t61xOZmTZfZNEijwIIv6VLbX+Ht96K/rURr+uxb9liC+IPHhyOhyFE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\\niBzDxE5E5Jj/B+vIEdfZxz8dAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10c21e9e8>\"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"ax.legend(frameon=False, loc='lower center', ncol=2)\\n\",\n    \"fig\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We can use a rounded box (``fancybox``) or add a shadow, change the transparency (alpha value) of the frame, or change the padding around the text:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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nY8VATt2KF64EChfiUnR3X8ePuk266d6qxZIYqtgLKz7U2lYUPVSy+1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\\nRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10c21e9e8>\"\n      ]\n     },\n     \"execution_count\": 6,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"ax.legend(fancybox=True, framealpha=1, shadow=True, borderpad=1)\\n\",\n    \"fig\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"For more information on available legend options, see the ``plt.legend`` docstring.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Choosing Elements for the Legend\\n\",\n    \"\\n\",\n    \"As we have already seen, the legend includes all labeled elements by default.\\n\",\n    \"If this is not what is desired, we can fine-tune which elements and labels appear in the legend by using the objects returned by plot commands.\\n\",\n    \"The ``plt.plot()`` command is able to create multiple lines at once, and returns a list of created line instances.\\n\",\n    \"Passing any of these to ``plt.legend()`` will tell it which to identify, along with the labels we'd like to specify:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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BMngKNHgd/8RgLB7CQ6mrmV33nHuuP/r6AAv+nRA8FubuIKxoMXw8OR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\\ncePGTS3ErfzduHHjphby/7xsYKsXV7EBAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10d779518>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"y = np.sin(x[:, np.newaxis] + np.pi * np.arange(0, 2, 0.5))\\n\",\n    \"lines = plt.plot(x, y)\\n\",\n    \"\\n\",\n    \"# lines is a list of plt.Line2D instances\\n\",\n    \"plt.legend(lines[:2], ['first', 'second']);\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"I generally find in practice that it is clearer to use the first method, applying labels to the plot elements you'd like to show on the legend:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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BMngKNHgd/8RgLB7CQ6mrmV33nHuuP/r6AAv+nRA8FubuIKxoMXw8OR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\\ncePGTS3ErfzduHHjphby/7xsYKsXV7EBAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x11025d908>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.plot(x, y[:, 0], label='first')\\n\",\n    \"plt.plot(x, y[:, 1], label='second')\\n\",\n    \"plt.plot(x, y[:, 2:])\\n\",\n    \"plt.legend(framealpha=1, frameon=True);\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Notice that by default, the legend ignores all elements without a ``label`` attribute set.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Legend for Size of Points\\n\",\n    \"\\n\",\n    \"Sometimes the legend defaults are not sufficient for the given visualization.\\n\",\n    \"For example, perhaps you're be using the size of points to mark certain features of the data, and want to create a legend reflecting this.\\n\",\n    \"Here is an example where we'll use the size of points to indicate populations of California cities.\\n\",\n    \"We'd like a legend that specifies the scale of the sizes of the points, and we'll accomplish this by plotting some labeled data with no entries:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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BFAc1x1oqKiqK2tbXXu7bffzo033shXv/pVOkohumHDBt566y2OHz/O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\\n+A6Hw7FAcAbf4XA4FgjO4DscDscC4f8Ba4Al2vEsdroAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x117d74cc0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import pandas as pd\\n\",\n    \"cities = pd.read_csv('data/california_cities.csv')\\n\",\n    \"\\n\",\n    \"# Extract the data we're interested in\\n\",\n    \"lat, lon = cities['latd'], cities['longd']\\n\",\n    \"population, area = cities['population_total'], cities['area_total_km2']\\n\",\n    \"\\n\",\n    \"# Scatter the points, using size and color but no label\\n\",\n    \"plt.scatter(lon, lat, label=None,\\n\",\n    \"            c=np.log10(population), cmap='viridis',\\n\",\n    \"            s=area, linewidth=0, alpha=0.5)\\n\",\n    \"plt.axis(aspect='equal')\\n\",\n    \"plt.xlabel('longitude')\\n\",\n    \"plt.ylabel('latitude')\\n\",\n    \"plt.colorbar(label='log$_{10}$(population)')\\n\",\n    \"plt.clim(3, 7)\\n\",\n    \"\\n\",\n    \"# Here we create a legend:\\n\",\n    \"# we'll plot empty lists with the desired size and label\\n\",\n    \"for area in [100, 300, 500]:\\n\",\n    \"    plt.scatter([], [], c='k', alpha=0.3, s=area,\\n\",\n    \"                label=str(area) + ' km$^2$')\\n\",\n    \"plt.legend(scatterpoints=1, frameon=False, labelspacing=1, title='City Area')\\n\",\n    \"\\n\",\n    \"plt.title('California Cities: Area and Population');\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The legend will always reference some object that is on the plot, so if we'd like to display a particular shape we need to plot it.\\n\",\n    \"In this case, the objects we want (gray circles) are not on the plot, so we fake them by plotting empty lists.\\n\",\n    \"Notice too that the legend only lists plot elements that have a label specified.\\n\",\n    \"\\n\",\n    \"By plotting empty lists, we create labeled plot objects which are picked up by the legend, and now our legend tells us some useful information.\\n\",\n    \"This strategy can be useful for creating more sophisticated visualizations.\\n\",\n    \"\\n\",\n    \"Finally, note that for geographic data like this, it would be clearer if we could show state boundaries or other map-specific elements.\\n\",\n    \"For this, an excellent choice of tool is Matplotlib's Basemap addon toolkit, which we'll explore in [Geographic Data with Basemap](04.13-Geographic-Data-With-Basemap.ipynb).\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Multiple Legends\\n\",\n    \"\\n\",\n    \"Sometimes when designing a plot you'd like to add multiple legends to the same axes.\\n\",\n    \"Unfortunately, Matplotlib does not make this easy: via the standard ``legend`` interface, it is only possible to create a single legend for the entire plot.\\n\",\n    \"If you try to create a second legend using ``plt.legend()`` or ``ax.legend()``, it will simply override the first one.\\n\",\n    \"We can work around this by creating a new legend artist from scratch, and then using the lower-level ``ax.add_artist()`` method to manually add the second artist to the plot:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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KpatSqtW7fOBFHqJyIigmbMmEHx8fFEZMWJPavY2Fjq3bt3kWsdaWlp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\\n2BljzMpwYmeMMSvz/8NbTOcDCpp6AAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x117f1e3c8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig, ax = plt.subplots()\\n\",\n    \"\\n\",\n    \"lines = []\\n\",\n    \"styles = ['-', '--', '-.', ':']\\n\",\n    \"x = np.linspace(0, 10, 1000)\\n\",\n    \"\\n\",\n    \"for i in range(4):\\n\",\n    \"    lines += ax.plot(x, np.sin(x - i * np.pi / 2),\\n\",\n    \"                     styles[i], color='black')\\n\",\n    \"ax.axis('equal')\\n\",\n    \"\\n\",\n    \"# specify the lines and labels of the first legend\\n\",\n    \"ax.legend(lines[:2], ['line A', 'line B'],\\n\",\n    \"          loc='upper right', frameon=False)\\n\",\n    \"\\n\",\n    \"# Create the second legend and add the artist manually.\\n\",\n    \"from matplotlib.legend import Legend\\n\",\n    \"leg = Legend(ax, lines[2:], ['line C', 'line D'],\\n\",\n    \"             loc='lower right', frameon=False)\\n\",\n    \"ax.add_artist(leg);\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This is a peek into the low-level artist objects that comprise any Matplotlib plot.\\n\",\n    \"If you examine the source code of ``ax.legend()`` (recall that you can do this with within the IPython notebook using ``ax.legend??``) you'll see that the function simply consists of some logic to create a suitable ``Legend`` artist, which is then saved in the ``legend_`` attribute and added to the figure when the plot is drawn.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<!--NAVIGATION-->\\n\",\n    \"< [Histograms, Binnings, and Density](04.05-Histograms-and-Binnings.ipynb) | [Contents](Index.ipynb) | [Customizing Colorbars](04.07-Customizing-Colorbars.ipynb) >\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"anaconda-cloud\": {},\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.4.3\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "matplotlib/04.07-Customizing-Colorbars.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<!--BOOK_INFORMATION-->\\n\",\n    \"<img align=\\\"left\\\" style=\\\"padding-right:10px;\\\" src=\\\"figures/PDSH-cover-small.png\\\">\\n\",\n    \"*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\\n\",\n    \"\\n\",\n    \"*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*\\n\",\n    \"\\n\",\n    \"*No changes were made to the contents of this notebook from the original.*\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<!--NAVIGATION-->\\n\",\n    \"< [Customizing Plot Legends](04.06-Customizing-Legends.ipynb) | [Contents](Index.ipynb) | [Multiple Subplots](04.08-Multiple-Subplots.ipynb) >\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Customizing Colorbars\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Plot legends identify discrete labels of discrete points.\\n\",\n    \"For continuous labels based on the color of points, lines, or regions, a labeled colorbar can be a great tool.\\n\",\n    \"In Matplotlib, a colorbar is a separate axes that can provide a key for the meaning of colors in a plot.\\n\",\n    \"Because the book is printed in black-and-white, this section has an accompanying online supplement where you can view the figures in full color (https://github.com/jakevdp/PythonDataScienceHandbook).\\n\",\n    \"We'll start by setting up the notebook for plotting and importing the functions we will use:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"plt.style.use('classic')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%matplotlib inline\\n\",\n    \"import numpy as np\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"As we have seen several times throughout this section, the simplest colorbar can be created with the ``plt.colorbar`` function:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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8a1ghCZEd54ML7IzLATNDS6nEdn3gSUdTRBZEQrUC1TGhA7iF3yJNYq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\\nNNNMMwHwf3ceQw5DTt3BAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10c21c630>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"x = np.linspace(0, 10, 1000)\\n\",\n    \"I = np.sin(x) * np.cos(x[:, np.newaxis])\\n\",\n    \"\\n\",\n    \"plt.imshow(I)\\n\",\n    \"plt.colorbar();\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We'll now discuss a few ideas for customizing these colorbars and using them effectively in various situations.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Customizing Colorbars\\n\",\n    \"\\n\",\n    \"The colormap can be specified using the ``cmap`` argument to the plotting function that is creating the visualization:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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06dHa9UC6sVvdYpjjk+9u5Vf0bPjXRYp4CyDs/+cUSO6/RnFUaGNyM+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\\nRfwfAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10d902588>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.imshow(I, cmap='gray');\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"All the available colormaps are in the ``plt.cm`` namespace; using IPython's tab-completion will give you a full list of built-in possibilities:\\n\",\n    \"```\\n\",\n    \"plt.cm.<TAB>\\n\",\n    \"```\\n\",\n    \"But being *able* to choose a colormap is just the first step: more important is how to *decide* among the possibilities!\\n\",\n    \"The choice turns out to be much more subtle than you might initially expect.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Choosing the Colormap\\n\",\n    \"\\n\",\n    \"A full treatment of color choice within visualization is beyond the scope of this book, but for entertaining reading on this subject and others, see the article [\\\"Ten Simple Rules for Better Figures\\\"](http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003833).\\n\",\n    \"Matplotlib's online documentation also has an [interesting discussion](http://Matplotlib.org/1.4.1/users/colormaps.html) of colormap choice.\\n\",\n    \"\\n\",\n    \"Broadly, you should be aware of three different categories of colormaps:\\n\",\n    \"\\n\",\n    \"- *Sequential colormaps*: These are made up of one continuous sequence of colors (e.g., ``binary`` or ``viridis``).\\n\",\n    \"- *Divergent colormaps*: These usually contain two distinct colors, which show positive and negative deviations from a mean (e.g., ``RdBu`` or ``PuOr``).\\n\",\n    \"- *Qualitative colormaps*: these mix colors with no particular sequence (e.g., ``rainbow`` or ``jet``).\\n\",\n    \"\\n\",\n    \"The ``jet`` colormap, which was the default in Matplotlib prior to version 2.0, is an example of a qualitative colormap.\\n\",\n    \"Its status as the default was quite unfortunate, because qualitative maps are often a poor choice for representing quantitative data.\\n\",\n    \"Among the problems is the fact that qualitative maps usually do not display any uniform progression in brightness as the scale increases.\\n\",\n    \"\\n\",\n    \"We can see this by converting the ``jet`` colorbar into black and white:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from matplotlib.colors import LinearSegmentedColormap\\n\",\n    \"\\n\",\n    \"def grayscale_cmap(cmap):\\n\",\n    \"    \\\"\\\"\\\"Return a grayscale version of the given colormap\\\"\\\"\\\"\\n\",\n    \"    cmap = plt.cm.get_cmap(cmap)\\n\",\n    \"    colors = cmap(np.arange(cmap.N))\\n\",\n    \"    \\n\",\n    \"    # convert RGBA to perceived grayscale luminance\\n\",\n    \"    # cf. http://alienryderflex.com/hsp.html\\n\",\n    \"    RGB_weight = [0.299, 0.587, 0.114]\\n\",\n    \"    luminance = np.sqrt(np.dot(colors[:, :3] ** 2, RGB_weight))\\n\",\n    \"    colors[:, :3] = luminance[:, np.newaxis]\\n\",\n    \"        \\n\",\n    \"    return LinearSegmentedColormap.from_list(cmap.name + \\\"_gray\\\", colors, cmap.N)\\n\",\n    \"    \\n\",\n    \"\\n\",\n    \"def view_colormap(cmap):\\n\",\n    \"    \\\"\\\"\\\"Plot a colormap with its grayscale equivalent\\\"\\\"\\\"\\n\",\n    \"    cmap = plt.cm.get_cmap(cmap)\\n\",\n    \"    colors = cmap(np.arange(cmap.N))\\n\",\n    \"    \\n\",\n    \"    cmap = grayscale_cmap(cmap)\\n\",\n    \"    grayscale = cmap(np.arange(cmap.N))\\n\",\n    \"    \\n\",\n    \"    fig, ax = plt.subplots(2, figsize=(6, 2),\\n\",\n    \"                           subplot_kw=dict(xticks=[], yticks=[]))\\n\",\n    \"    ax[0].imshow([colors], extent=[0, 10, 0, 1])\\n\",\n    \"    ax[1].imshow([grayscale], extent=[0, 10, 0, 1])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAV0AAABsCAYAAADJ2WELAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\nAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x11656ceb8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"view_colormap('jet')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Notice the bright stripes in the grayscale image.\\n\",\n    \"Even in full color, this uneven brightness means that the eye will be drawn to certain portions of the color range, which will potentially emphasize unimportant parts of the dataset.\\n\",\n    \"It's better to use a colormap such as ``viridis`` (the default as of Matplotlib 2.0), which is specifically constructed to have an even brightness variation across the range.\\n\",\n    \"Thus it not only plays well with our color perception, but also will translate well to grayscale printing:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAV0AAABsCAYAAADJ2WELAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\nQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1165767f0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"view_colormap('viridis')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"If you favor rainbow schemes, another good option for continuous data is the ``cubehelix`` colormap:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAV0AAABsCAYAAADJ2WELAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\noQtQSOgCFPoDAVPczXaO6jAAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1165caf60>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"view_colormap('cubehelix')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"For other situations, such as showing positive and negative deviations from some mean, dual-color colorbars such as ``RdBu`` (*Red-Blue*) can be useful. However, as you can see in the following figure, it's important to note that the positive-negative information will be lost upon translation to grayscale!\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAV0AAABsCAYAAADJ2WELAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\nAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10c20e908>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"view_colormap('RdBu')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We'll see examples of using some of these color maps as we continue.\\n\",\n    \"\\n\",\n    \"There are a large number of colormaps available in Matplotlib; to see a list of them, you can use IPython to explore the ``plt.cm`` submodule. For a more principled approach to colors in Python, you can refer to the tools and documentation within the Seaborn library (see [Visualization With Seaborn](04.14-Visualization-With-Seaborn.ipynb)).\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Color limits and extensions\\n\",\n    \"\\n\",\n    \"Matplotlib allows for a large range of colorbar customization.\\n\",\n    \"The colorbar itself is simply an instance of ``plt.Axes``, so all of the axes and tick formatting tricks we've learned are applicable.\\n\",\n    \"The colorbar has some interesting flexibility: for example, we can narrow the color limits and indicate the out-of-bounds values with a triangular arrow at the top and bottom by setting the ``extend`` property.\\n\",\n    \"This might come in handy, for example, if displaying an image that is subject to noise:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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LrjdK1Tr+s0eo4qXUvlgHMnkTVGVHr2L+hzmGsvCFNkL7zrExHbuOJ3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\\naNFiBvx/yiMZc7t4oy0AAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10d93be48>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# make noise in 1% of the image pixels\\n\",\n    \"speckles = (np.random.random(I.shape) < 0.01)\\n\",\n    \"I[speckles] = np.random.normal(0, 3, np.count_nonzero(speckles))\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(10, 3.5))\\n\",\n    \"\\n\",\n    \"plt.subplot(1, 2, 1)\\n\",\n    \"plt.imshow(I, cmap='RdBu')\\n\",\n    \"plt.colorbar()\\n\",\n    \"\\n\",\n    \"plt.subplot(1, 2, 2)\\n\",\n    \"plt.imshow(I, cmap='RdBu')\\n\",\n    \"plt.colorbar(extend='both')\\n\",\n    \"plt.clim(-1, 1);\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Notice that in the left panel, the default color limits respond to the noisy pixels, and the range of the noise completely washes-out the pattern we are interested in.\\n\",\n    \"In the right panel, we manually set the color limits, and add extensions to indicate values which are above or below those limits.\\n\",\n    \"The result is a much more useful visualization of our data.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Discrete Color Bars\\n\",\n    \"\\n\",\n    \"Colormaps are by default continuous, but sometimes you'd like to represent discrete values.\\n\",\n    \"The easiest way to do this is to use the ``plt.cm.get_cmap()`` function, and pass the name of a suitable colormap along with the number of desired bins:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAUMAAAEACAYAAAAp/xTFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x116d71940>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.imshow(I, cmap=plt.cm.get_cmap('Blues', 6))\\n\",\n    \"plt.colorbar()\\n\",\n    \"plt.clim(-1, 1);\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The discrete version of a colormap can be used just like any other colormap.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Example: Handwritten Digits\\n\",\n    \"\\n\",\n    \"For an example of where this might be useful, let's look at an interesting visualization of some hand written digits data.\\n\",\n    \"This data is included in Scikit-Learn, and consists of nearly 2,000 $8 \\\\times 8$ thumbnails showing various hand-written digits.\\n\",\n    \"\\n\",\n    \"For now, let's start by downloading the digits data and visualizing several of the example images with ``plt.imshow()``:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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JPJWMEDDwmtQHQdKz8O+7HmBvuATxlJGjAnh8tUQTf31sUglwtflxcH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\\nrkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10bb38d30>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# load images of the digits 0 through 5 and visualize several of them\\n\",\n    \"from sklearn.datasets import load_digits\\n\",\n    \"digits = load_digits(n_class=6)\\n\",\n    \"\\n\",\n    \"fig, ax = plt.subplots(8, 8, figsize=(6, 6))\\n\",\n    \"for i, axi in enumerate(ax.flat):\\n\",\n    \"    axi.imshow(digits.images[i], cmap='binary')\\n\",\n    \"    axi.set(xticks=[], yticks=[])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Because each digit is defined by the hue of its 64 pixels, we can consider each digit to be a point lying in 64-dimensional space: each dimension represents the brightness of one pixel.\\n\",\n    \"But visualizing relationships in such high-dimensional spaces can be extremely difficult.\\n\",\n    \"One way to approach this is to use a *dimensionality reduction* technique such as manifold learning to reduce the dimensionality of the data while maintaining the relationships of interest.\\n\",\n    \"Dimensionality reduction is an example of unsupervised machine learning, and we will discuss it in more detail in [What Is Machine Learning?](05.01-What-Is-Machine-Learning.ipynb).\\n\",\n    \"\\n\",\n    \"Deferring the discussion of these details, let's take a look at a two-dimensional manifold learning projection of this digits data (see [In-Depth: Manifold Learning](05.10-Manifold-Learning.ipynb) for details):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# project the digits into 2 dimensions using IsoMap\\n\",\n    \"from sklearn.manifold import Isomap\\n\",\n    \"iso = Isomap(n_components=2)\\n\",\n    \"projection = iso.fit_transform(digits.data)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We'll use our discrete colormap to view the results, setting the ``ticks`` and ``clim`` to improve the aesthetics of the resulting colorbar:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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LxY6ls7yWlxk5rqxKSHJJsdsznmRx4I+BGir6OrDHc2fr6STZ+vPGAf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\\nC18AwydDqAAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1139266d8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# plot the results\\n\",\n    \"plt.scatter(projection[:, 0], projection[:, 1], lw=0.1,\\n\",\n    \"            c=digits.target, cmap=plt.cm.get_cmap('cubehelix', 6))\\n\",\n    \"plt.colorbar(ticks=range(6), label='digit value')\\n\",\n    \"plt.clim(-0.5, 5.5)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The projection also gives us some interesting insights on the relationships within the dataset: for example, the ranges of 5 and 3 nearly overlap in this projection, indicating that some hand written fives and threes are difficult to distinguish, and therefore more likely to be confused by an automated classification algorithm.\\n\",\n    \"Other values, like 0 and 1, are more distantly separated, and therefore much less likely to be confused.\\n\",\n    \"This observation agrees with our intuition, because 5 and 3 look much more similar than do 0 and 1.\\n\",\n    \"\\n\",\n    \"We'll return to manifold learning and to digit classification in [Chapter 5](05.00-Machine-Learning.ipynb).\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<!--NAVIGATION-->\\n\",\n    \"< [Customizing Plot Legends](04.06-Customizing-Legends.ipynb) | [Contents](Index.ipynb) | [Multiple Subplots](04.08-Multiple-Subplots.ipynb) >\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"anaconda-cloud\": {},\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.4.3\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "matplotlib/04.08-Multiple-Subplots.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<!--BOOK_INFORMATION-->\\n\",\n    \"<img align=\\\"left\\\" style=\\\"padding-right:10px;\\\" src=\\\"figures/PDSH-cover-small.png\\\">\\n\",\n    \"*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\\n\",\n    \"\\n\",\n    \"*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*\\n\",\n    \"\\n\",\n    \"*No changes were made to the contents of this notebook from the original.*\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<!--NAVIGATION-->\\n\",\n    \"< [Customizing Colorbars](04.07-Customizing-Colorbars.ipynb) | [Contents](Index.ipynb) | [Text and Annotation](04.09-Text-and-Annotation.ipynb) >\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Multiple Subplots\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Sometimes it is helpful to compare different views of data side by side.\\n\",\n    \"To this end, Matplotlib has the concept of *subplots*: groups of smaller axes that can exist together within a single figure.\\n\",\n    \"These subplots might be insets, grids of plots, or other more complicated layouts.\\n\",\n    \"In this section we'll explore four routines for creating subplots in Matplotlib.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%matplotlib inline\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"plt.style.use('seaborn-white')\\n\",\n    \"import numpy as np\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## ``plt.axes``: Subplots by Hand\\n\",\n    \"\\n\",\n    \"The most basic method of creating an axes is to use the ``plt.axes`` function.\\n\",\n    \"As we've seen previously, by default this creates a standard axes object that fills the entire figure.\\n\",\n    \"``plt.axes`` also takes an optional argument that is a list of four numbers in the figure coordinate system.\\n\",\n    \"These numbers represent ``[left, bottom, width, height]`` in the figure coordinate system, which ranges from 0 at the bottom left of the figure to 1 at the top right of the figure.\\n\",\n    \"\\n\",\n    \"For example, we might create an inset axes at the top-right corner of another axes by setting the *x* and *y* position to 0.65 (that is, starting at 65% of the width and 65% of the height of the figure) and the *x* and *y* extents to 0.2 (that is, the size of the axes is 20% of the width and 20% of the height of the figure):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAXYAAAD/CAYAAADllv3BAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\nBwDLEOwAYBmCHQAsQ7ADgGUIdgCwzP8AxZhVoScunCkAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10bd88240>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"ax1 = plt.axes()  # standard axes\\n\",\n    \"ax2 = plt.axes([0.65, 0.65, 0.2, 0.2])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The equivalent of this command within the object-oriented interface is ``fig.add_axes()``. Let's use this to create two vertically stacked axes:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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9FRm2iYvvn5UdOVDz4QHQnTxsPlpytX8vJTuZg9G/jjD+rzLHdaJYW9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\\nmMb/A681T5mxFqFRAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10e296748>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig = plt.figure()\\n\",\n    \"ax1 = fig.add_axes([0.1, 0.5, 0.8, 0.4],\\n\",\n    \"                   xticklabels=[], ylim=(-1.2, 1.2))\\n\",\n    \"ax2 = fig.add_axes([0.1, 0.1, 0.8, 0.4],\\n\",\n    \"                   ylim=(-1.2, 1.2))\\n\",\n    \"\\n\",\n    \"x = np.linspace(0, 10)\\n\",\n    \"ax1.plot(np.sin(x))\\n\",\n    \"ax2.plot(np.cos(x));\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We now have two axes (the top with no tick labels) that are just touching: the bottom of the upper panel (at position 0.5) matches the top of the lower panel (at position 0.1 + 0.4).\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## ``plt.subplot``: Simple Grids of Subplots\\n\",\n    \"\\n\",\n    \"Aligned columns or rows of subplots are a common-enough need that Matplotlib has several convenience routines that make them easy to create.\\n\",\n    \"The lowest level of these is ``plt.subplot()``, which creates a single subplot within a grid.\\n\",\n    \"As you can see, this command takes three integer arguments—the number of rows, the number of columns, and the index of the plot to be created in this scheme, which runs from the upper left to the bottom right:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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o1QFAVpaWmYPHmyxxsn9zBXOTFXAjRY8/SHH37QvivyOOYqJ+ZKAC9Q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\\nYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10bec31d0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"for i in range(1, 7):\\n\",\n    \"    plt.subplot(2, 3, i)\\n\",\n    \"    plt.text(0.5, 0.5, str((2, 3, i)),\\n\",\n    \"             fontsize=18, ha='center')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The command ``plt.subplots_adjust`` can be used to adjust the spacing between these plots.\\n\",\n    \"The following code uses the equivalent object-oriented command, ``fig.add_subplot()``:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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EgyHOxERJLhYCcikgwHOxGRZDjYiYgkw8FORCQZDnYiIslwsBMRSYaD\\nnYhIMhzsRESScTjYhRDYvn079Ho9srKyYDKZbNZXVlYiNTUVer0eZWVlXmuUvI9ZBw9mLTePrqB0\\n48YN7Ny5E/v370dJSQneeecdNDc3e7Vh8h5mHTyYtdw8uoLSlStXEB0dDY1Gg1GjRiExMRHV1dXe\\n65a8ilkHD2YtN4+uoPTDdaNHj0Z7e7sX2qTbgVkHD2YtN4+uoKTRaNDR0WFd19nZibFjx9rcvr+/\\nHwDQ2NioSMNka/BxHXycPeFp1v/aB/P2DqXyZtb+z5OsHQ52rVaLqqoqPP7443ZXWpk6dSquXr0K\\ns9mMsLAwVFdXY/369Ta3b2pqAgBkZma63Bw5r6mpCdHR0R7V8DTrwT4A5u1tnubNrAOHO1mrhBBi\\nuA2EEMjLy4PRaAQwcKWVTz75BN3d3UhLS8OJEydQWFgIIQRSU1ORnp5uc/uenh7U1dVh/PjxGDly\\npIt3iRzp7+9HU1MTEhISEBYW5lEtT7MGmLe3KZU3s/Z/nmTtcLATEVFg4QFKRESSUXSwK3HQg6Ma\\n5eXlWL16NTIyMpCXl+d2L4Nyc3OxZ88et2pcunQJmZmZyMzMxLPPPguLxeJWnWPHjmHlypVIS0vD\\n4cOHb3mfAKC2thYGg8Fu+e0+oESpA1yUyFuJrJ2p40zeSmYN+Efe/pS1M3UGBfVzWyiooqJCbNmy\\nRQghRE1NjXjmmWes6/r6+oROpxPt7e3CYrGIVatWievXr7tUo6enR+h0OtHb2yuEEGLTpk2isrLS\\n5V4GHT58WKxZs0a8+uqrbtVYtmyZ+Oqrr4QQQpSVlYmGhga36jz00EPCbDYLi8UidDqdMJvNQ9Z5\\n8803RUpKilizZo3NcmcfWyUpkbWjOs7mrUTWztRxJm+lshbCf/L2p6wd1RkU7M9tRX9jV+Kgh+Fq\\nqNVqlJaWQq1WAxg4Qi40NNTlXgDg4sWLuHz5MvR6vVv3p6GhAePGjUNxcTEMBgPa2tpwzz33uNVL\\nfHw82tra0NvbC2DgCvJDiY6Oxt69e+2W++KAEqUOcFEibyWydlTH2byVyhrwn7z9KWtHdQA+twGF\\nX4pR4qCH4WqoVCpEREQAAEpKStDd3Y2kpCSXe2lqakJhYSFyc3MhhnnveLgaLS0tqKmpgcFgQHFx\\nMU6fPo2zZ8+6XAcAYmJisGrVKixZsgTz58+HRqMZso5Opxvy0we+OKBEqQNclMhbiawd1XE2b6Wy\\nBvwnb3/K2lEdPrcHKDrYlTjoYbgawMBrWrt27cKZM2dQWFjoVi/Hjx9Ha2srNmzYgH379qG8vBzv\\nvvuuSzXGjRuHqKgoTJkyBSEhIZg3b57dT2tn6hiNRpw4cQKVlZWorKzE9evX8f7779/yft2qvjOP\\nrZKUyNpRHcC5vJXI2lEdZ/P2dtaD+7ideftT1o7q8Lk9QNHBrtVqcfLkSQAY9qAHi8WC6upqzJgx\\nw6UaALBt2zb09fWhqKjI+mebq70YDAb85S9/wdtvv42nn34aKSkpWL58uUs1Jk+ejK6uLuubJefP\\nn8d9993nci9jxoxBeHg41Gq19bcWs9l8y/sFwO43EWcfWyUpkbWjOoBzeSuRtaM6zuatdNaA7/P2\\np6wd1eFze4DDI09dodPp8NFHH1lf2yooKEB5ebn1oIetW7ciOzsbQgikpaVhwoQJLtWYNm0ajhw5\\ngsTERBgMBqhUKmRlZSE5OdnlXpS4P/n5+di0aRMAYObMmXj00UfdqjP4SQC1Wo2oqCisWLFi2L4G\\nX6dz9bFVkhJZO6rjbN5KZO1MHWfyVjprwPd5+1PWzvSjxH0K9Oc2D1AiIpIMD1AiIpIMBzsRkWQ4\\n2ImIJMPBTkQkGQ52IiLJcLATEUmGg52ISDIc7EREkuFgJyKSDAc7EZFkONiJiCTDwU5EJBkOdiIi\\nyXCwExFJhoOdiEgyHOxERJLhYCcikgwHOxGRZDjYiYgkw8FORCQZpwZ7bW0tDAaD3fLKykqkpqZC\\nr9ejrKxM8ebIN5h38GDWcgpxtMFbb72Fv/71rxg9erTN8hs3bmDnzp04cuQIQkNDkZ6ejkWLFiEi\\nIsJrzZL3Me/gwazl5fA39ujoaOzdu9du+ZUrVxAdHQ2NRoNRo0YhMTER1dXVXmmSbh/mHTyYtbwc\\nDnadToeRI0faLe/o6MCYMWOsX48ePRrt7e3Kdke3HfMOHsxaXg5firkVjUaDjo4O69ednZ0YO3as\\n3XY9PT2oq6vD+PHjh/wmIs/09/ejqakJCQkJCAsL89p+mLd/uB15M2v/4EnWTg92IYTN11OnTsXV\\nq1dhNpsRFhaG6upqrF+/3u52dXV1yMzMdKkpct3Bgwcxa9Ysxeoxb/+mZN7M2r+5k7XTg12lUgEA\\nysvL0d3djbS0NGzduhXZ2dkQQiAtLQ0TJkywu9348eOtzU2cONGl5sixxsZGZGZmWh9npTBv/+SN\\nvJm1f/Ioa+FlJpNJxMbGCpPJ5O1dBSV/e3z9rR/Z+NPj60+9yMiTx5cHKBERSYaDnYhIMhzsRESS\\n4WAnIpIMBzsRkWQ42ImIJMPBTkQkGQ52IiLJcLATEUmGg52ISDIc7EREknE42IUQ2L59O/R6PbKy\\nsmAymWzWHzt2DCtXrkRaWhoOHz7stUbJ+5h18GDWcnN4dscPPvgAFosFpaWlqK2tRUFBAYqKiqzr\\nd+/ejffeew9hYWF44oknkJKSYnOSfgoczDp4MGu5ORzs58+fx7x58wAA06dPR11dnc36+Ph4tLW1\\nWU/9Ofh/CjzMOngwa7k5HOw/vExWSEgIbt68iREjBl7FiYmJwapVq3DHHXdAp9NBo9F4r1vyKmYd\\nPJi13By+xq7RaNDZ2Wn9+l/DNxqNOHHiBCorK1FZWYnr16/j/fff91635FXMOngwa7k5HOxarRYn\\nT54EANTU1CA2Nta6bsyYMQgPD4darYZKpUJERATMZrP3uiWvYtbBg1nLzeFLMTqdDh999BH0ej0A\\noKCgwOYSWqtXr0ZGRgbUajWioqKwYsUKrzdN3sGsgwezlpvDwa5SqbBjxw6bZVOmTLH+W6/XW785\\nKLAx6+DBrOXGA5SIiCTDwU5EJBkOdiIiyXCwExFJhoOdiEgyHOxERJLhYCcikgwHOxGRZDjYiYgk\\nw8FORCSZgBjs3333HebOnYtr165Zl506dQoZGRmYMWMGZs6ciaeeegq1tbVu7+PMmTNIT0+HVqvF\\nI488gldeeQVdXV1KtI/PPvsMCQkJKCwstFmek5ODnTt3KrIPmQRi3qmpqYiPj7f779lnn7Vuw7zt\\nBWLWzc3NyMnJwUMPPYTExEQYDAZcvHjRZhtfZ+3wXDFCCOTl5cFoNEKtViM/Px+TJ0+2rr906RJ2\\n7doFALjzzjvxm9/8Bmq1WtEm8/PzkZKSgrvvvhsA8PHHH+Ppp59GTEwMnn/+efT39+PQoUN48skn\\ncejQITzwwAMu1T9z5gzWr1+PBx54AP/xH/+BxsZG/PGPf8Qnn3yCgwcPetR7f38/tm7div7+frt1\\nGzduxOLFi7Fy5Uqbs+v5ij9kDQRm3leuXIFOp8PPfvYzm+WTJk2y/tuf8mbW7mXd2dmJzMxMfP/9\\n91i3bh3Gjh2LAwcOYN26dfjzn/+MmJgYAH6QtXCgoqJCbNmyRQghRE1NjXjmmWds1i9btkx89dVX\\nQgghysrKRENDg816k8kkYmNjhclkcrSrIX388cdi2rRporGx0WafCxYsEL29vdZl33//vZgzZ47I\\nzs52eR8rVqwQixYtsql38OBBER8fLz788EO3+h5UWFgoEhISRHx8vHj99dft1r/00kti7dq1btf3\\n9PH9V55mrUQ/gZi3yWQScXFx4ujRow639Ze8mbV7We/Zs0fcf//94ty5c9ZlTU1NYvr06eKFF16w\\n2daXWTt8KWa4S2g1NDRg3LhxKC4uhsFgQFtbG+655x5Ff/Ds378fs2bNwl133QUAMJvNqK+vx+LF\\ni21+g4iMjMTs2bNx4cIFl+pbLBZERkZi9erVNvXmzJkDIQSMRqPbvRuNRvz3f/83Nm7cCCHEkNuk\\npaXhH//4B+rr693ej1J8nTUQmHl/8cUXUKlUuPfeex1u6y95M2v3sn733Xcxf/58JCYmWpfdeeed\\n2Lx5M2bNmmWzrS+zdjjYb3UJLQBoaWlBTU0NDAYDiouLcfr0aZw9e1ax5hobG3HixAnodDrrMo1G\\ng+PHj2Pt2rV227e0tCAkxOGrSzbUajXefPNNPP300zbLP/30UwC2f0q7YvAlmIcffhhLliy55XbT\\np0/HxIkTceDAAbf2oyRfZg0Ebt6ff/45AGDq1KkAgO7u7ltu6y95M2vXs/7666/x3XffISkpybps\\n8LX69PR0pKWl2Wzvy6w9ujTeuHHjEBUVhSlTpiAkJATz5s2zuyiuJz788EPcvHkTjzzyyP83PGIE\\noqKiMH78eJttP/vsM1y4cAFardajfX7zzTc4cuQI8vPzERcXh+TkZLfq7Nu3DyaTye6c10OZPXs2\\nTp065dZ+lOTLrIHAzfvzzz/H6NGjUVBQAK1Wi5kzZ0Kn0+F///d/h9zeH/Jm1q5nffXqVesVpXbt\\n2oVZs2ZBq9XiZz/7Gaqqqoa8ja+y9ujSeJMnT0ZXVxdMJhOAgT/v7rvvPsWau3DhAsLDw23e1BlK\\nV1cXNm/eDJVKhQ0bNri9v7a2NixcuBAvvfQSLBYLcnJy3HrD6PPPP0dRURE2b96MCRMmONw+NjYW\\njY2NNp8M8AVfZg0Ebt5ffPEFOjs70d7ejt27d6OgoAAajQabNm3CsWPH7Lb3h7yZtetZm81mCCHw\\nX//1Xzh16hRycnKwe/duhIeHY+PGjThz5ozdbXyVtceXxsvPz8emTZsAADNnzsSjjz6qWHMmk8n6\\nbvmt9PT04Be/+AXq6+vx85//3O51LleoVCr87ne/Q19fH0pKSrBu3Tq89tprNn8uOnLz5k1s2bIF\\ns2fPRmpqqlO3Gfzm/vrrrx3eX2/yZdZAYOYNAGvWrEF/fz8yMjKsyxYvXoyUlBTs3r0bS5YsgUql\\nsq7zh7yZtetZWywWAEB7ezsqKiqg0WgAAAsWLEBycjL27NmDsrIym9v4LGu337J1kifv7C5evFjo\\n9fpbrjebzUKv14v4+HiRk5PjSZt2enp6RHJysliwYIFLt3vjjTfEAw88IC5fviyam5tFc3Oz+OST\\nT0RcXJz4zW9+I5qbm8XNmzdtbnPq1CkRFxcn3nvvPZf7VPJTMUoItryH8/rrr4v4+HhRX19vs1yW\\nvIMt64qKChEXFydyc3Pt1m3ZskXcf//9oqury2a5r7L26wOURowYcctPkzQ3N8NgMKCmpgZr1qzB\\nyy+/rOi+Q0NDMX/+fHz77bdobW11+nanTp1CX18fUlNT8dOf/hQ//elPsXLlSqhUKrz11ltISkrC\\nt99+a3ObwTetRo4cqeh9CDSBmPdwIiIiAMDuYBjmHZhZD356JzIy0m5dZGQkhBB+k7VfD/bIyEi0\\ntLTYLe/s7ER2djaMRiPWrVuHvLw8t/fx5ZdfYuHChTh8+LDduo6ODqhUKpdei9u6dSv+8Ic/oLi4\\n2Prfb3/7WwghsHz5chQXF+POO++0uU1raytUKtWQ3zDBJBDz/u6775CSkoKioqIh9wUAP/7xj22W\\nM+/AzDomJgZqtRpffPGF3TqTyYTQ0FDrD/NBvsrarwf7pEmT8M9//tPuJ/uOHTtgNBqxdu1abN68\\n2aN9REdHo6OjA6Wlpbhx44Z1+bVr11BRUYE5c+bgjjvucLreT37yE+tv6oP/zZw5E8DAE3zu3Ll2\\n30yNjY0A3P9opSwCMe+77roLZrMZZWVlNp8y+eabb3D06FHMnTvX7knNvAMz6/DwcCxcuBBVVVW4\\ncuWKdbnJZEJVVRUWLVpk814K4LusXftg6G02d+5cHD16FPX19YiLiwMwcOj2sWPH8KMf/QhxcXFD\\nfupg6dKlAAYe8IsXL0Kr1dr91jRo5MiRyMnJwebNm/Hkk09iyZIlaGlpwaFDhxASEoJt27ZZt3Wm\\nnjtqa2sRFRWFiRMnKlYzEAVq3rm5ufjlL38JvV6PtLQ0dHR04NChQxg1apRNvUHMO3Cz/vWvf43q\\n6moYDAZkZWUhJCQEJSUlCA8Px/PPP2+3va+y9uvB/vDDD0OlUuHcuXPW8Kurq6FSqWA2m/Hiiy8O\\nebvB8M8cN43cAAAJXUlEQVSdO4cXX3wRBQUFw4a1dOlS68EMu3btQnh4OJKSkvDcc88hOjraup2z\\n9YaiUqnsfpoDA+fsqKmpwRNPPOFSPRkFat7JycnYu3cv3njjDbz66qsICwvDgw8+iOeffx5Tpkyx\\n2ZZ5DwjUrO+++2688847+O1vf4s//OEPEEJg1qxZ+PWvf213O59m7fLbrS7y9F38jRs3ioyMDLf3\\n/8orr4i//e1vbt/e2/X+/ve/i/j4eGE0Gt26vT99SkII5u2ITHkz6+H5Mmu/fo0dALKzs3HhwgXr\\nwRKuuH79OqqqqpCQkKBIL0rXAwbOPZGUlOTzs/35C+YdPJi19/j9YNdqtViwYAH27dvn8m2bm5vx\\nwgsvICoqSpFelK5nMplQUVFhPRCEmHcwYdbe4/eDHRh4c6qiosLln+wxMTFun+vldtQrKipCeno6\\npk2bplhNGTDv4MGsvcOv3zwdNHHiRMXPLucPCgoKfN2CX2LewYNZe4fD39iFENi+fTv0ej2ysrJu\\n+ZM1NzcXe/bsUbxBun2YdfBg1nJzONg/+OADWCwWlJaW4le/+tWQP4lKS0t9fuEA8hyzDh7MWm4e\\nXUEJAC5evIjLly9bzxJHgYtZBw9mLTePrqDU1NSEwsJC5Obm3vKEPhQ4mHXwYNZyc/jm6XBXWjl+\\n/DhaW1uxYcMGNDU1obe3F/feey+WL1/uvY7Ja5h18GDWcnM42LVaLaqqqvD444/bXWnFYDDAYDAA\\nAI4ePYqGhgaGH8CYdfBg1nLz+ApKJA9mHTyYtdwcDnaVSmV3QeYfntgIAFasWKFcV+QTzDp4MGu5\\nBcSRp0RE5DwOdiIiyXCwExFJhoOdiEgyHOxERJLhYCcikgwHOxGRZDjYiYgkw8FORCQZDnYiIslw\\nsBMRScbhuWKEEMjLy4PRaIRarUZ+fj4mT55sXV9eXo63334bISEhiI2NRV5enjf7JS9i1sGDWcvN\\no0vj9fb24ve//z0OHDiAQ4cOob29HVVVVV5tmLyHWQcPZi03jy6Np1arUVpaCrVaDQC4ceMGQkND\\nvdQqeRuzDh7MWm4eXRpPpVIhIiICAFBSUoLu7m4kJSV5qVXyNmYdPJi13Dy6NB4w8Frd7t27cfXq\\nVRQWFnqnS7otmHXwYNZyc/gbu1arxcmTJwHA7hJaALBt2zb09fWhqKjI+qcbBSZmHTyYtdw8ujTe\\ntGnTcOTIESQmJsJgMEClUiErKwvJycleb5yUx6yDB7OWm8eXxvv000+V74p8glkHD2YtNx6gREQk\\nGQ52IiLJcLATEUmGg52ISDIc7EREkuFgJyKSDAc7EZFkONiJiCTDwU5EJBkOdiIiyTgc7EIIbN++\\nHXq9HllZWTCZTDbrKysrkZqaCr1ej7KyMq81St7HrIMHs5abR1dQunHjBnbu3In9+/ejpKQE77zz\\nDpqbm73aMHkPsw4ezFpuHl1B6cqVK4iOjoZGo8GoUaOQmJiI6upq73VLXsWsgwezlptHV1D64brR\\no0ejvb3dC23S7cCsgwezlptHV1DSaDTo6Oiwruvs7MTYsWNtbt/f3w8AaGxsVKRhsjX4uA4+zp7w\\nNOt/7YN5e4dSeTNr/+dJ1g4Hu1arRVVVFR5//HG7K61MnToVV69ehdlsRlhYGKqrq7F+/Xqb2zc1\\nNQEAMjMzXW6OnNfU1ITo6GiPania9WAfAPP2Nk/zZtaBw52sVUIIMdwGQgjk5eXBaDQCGLjSyief\\nfILu7m6kpaXhxIkTKCwshBACqampSE9Pt7l9T08P6urqMH78eIwcOdLFu0SO9Pf3o6mpCQkJCQgL\\nC/OolqdZA8zb25TKm1n7P0+ydjjYiYgosPAAJSIiySg62JU46MFRjfLycqxevRoZGRnIy8tzu5dB\\nubm52LNnj1s1Ll26hMzMTGRmZuLZZ5+FxWJxq86xY8ewcuVKpKWl4fDhw7e8TwBQW1sLg8Fgt/x2\\nH1Ci1AEuSuStRNbO1HEmbyWzBvwjb3/K2pk6g4L6uS0UVFFRIbZs2SKEEKKmpkY888wz1nV9fX1C\\np9OJ9vZ2YbFYxKpVq8T169ddqtHT0yN0Op3o7e0VQgixadMmUVlZ6XIvgw4fPizWrFkjXn31Vbdq\\nLFu2THz11VdCCCHKyspEQ0ODW3UeeughYTabhcViETqdTpjN5iHrvPnmmyIlJUWsWbPGZrmzj62S\\nlMjaUR1n81Yia2fqOJO3UlkL4T95+1PWjuoMCvbntqK/sStx0MNwNdRqNUpLS6FWqwEMHCEXGhrq\\nci8AcPHiRVy+fBl6vd6t+9PQ0IBx48ahuLgYBoMBbW1tuOeee9zqJT4+Hm1tbejt7QUwcAX5oURH\\nR2Pv3r12y31xQIlSB7gokbcSWTuq42zeSmUN+E/e/pS1ozoAn9uAwi/FKHHQw3A1VCoVIiIiAAAl\\nJSXo7u5GUlKSy700NTWhsLAQubm5EMO8dzxcjZaWFtTU1MBgMKC4uBinT5/G2bNnXa4DADExMVi1\\nahWWLFmC+fPnQ6PRDFlHp9MN+ekDXxxQotQBLkrkrUTWjuo4m7dSWQP+k7c/Ze2oDp/bAxQd7Eoc\\n9DBcDWDgNa1du3bhzJkzKCwsdKuX48ePo7W1FRs2bMC+fftQXl6Od99916Ua48aNQ1RUFKZMmYKQ\\nkBDMmzfP7qe1M3WMRiNOnDiByspKVFZW4vr163j//fdveb9uVd+Zx1ZJSmTtqA7gXN5KZO2ojrN5\\nezvrwX3czrz9KWtHdfjcHqDoYNdqtTh58iQADHvQg8ViQXV1NWbMmOFSDQDYtm0b+vr6UFRUZP2z\\nzdVeDAYD/vKXv+Dtt9/G008/jZSUFCxfvtylGpMnT0ZXV5f1zZLz58/jvvvuc7mXMWPGIDw8HGq1\\n2vpbi9lsvuX9AmD3m4izj62SlMjaUR3AubyVyNpRHWfzVjprwPd5+1PWjurwuT3A4ZGnrtDpdPjo\\no4+sr20VFBSgvLzcetDD1q1bkZ2dDSEE0tLSMGHCBJdqTJs2DUeOHEFiYiIMBgNUKhWysrKQnJzs\\nci9K3J/8/Hxs2rQJADBz5kw8+uijbtUZ/CSAWq1GVFQUVqxYMWxfg6/TufrYKkmJrB3VcTZvJbJ2\\npo4zeSudNeD7vP0pa2f6UeI+BfpzmwcoERFJhgcoERFJhoOdiEgyHOxERJLhYCcikgwHOxGRZDjY\\niYgkw8FORCQZDnYiIsn8H1BDee8pcOnKAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10be83ac8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig = plt.figure()\\n\",\n    \"fig.subplots_adjust(hspace=0.4, wspace=0.4)\\n\",\n    \"for i in range(1, 7):\\n\",\n    \"    ax = fig.add_subplot(2, 3, i)\\n\",\n    \"    ax.text(0.5, 0.5, str((2, 3, i)),\\n\",\n    \"           fontsize=18, ha='center')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We've used the ``hspace`` and ``wspace`` arguments of ``plt.subplots_adjust``, which specify the spacing along the height and width of the figure, in units of the subplot size (in this case, the space is 40% of the subplot width and height).\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## ``plt.subplots``: The Whole Grid in One Go\\n\",\n    \"\\n\",\n    \"The approach just described can become quite tedious when creating a large grid of subplots, especially if you'd like to hide the x- and y-axis labels on the inner plots.\\n\",\n    \"For this purpose, ``plt.subplots()`` is the easier tool to use (note the ``s`` at the end of ``subplots``). Rather than creating a single subplot, this function creates a full grid of subplots in a single line, returning them in a NumPy array.\\n\",\n    \"The arguments are the number of rows and number of columns, along with optional keywords ``sharex`` and ``sharey``, which allow you to specify the relationships between different axes.\\n\",\n    \"\\n\",\n    \"Here we'll create a $2 \\\\times 3$ grid of subplots, where all axes in the same row share their y-axis scale, and all axes in the same column share their x-axis scale:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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MYYNTU1KRqNyuv1qrm5WcXFxfHzvb29unjxonJycrRjxw41NTVlsl4A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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1023b3828>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig, ax = plt.subplots(2, 3, sharex='col', sharey='row')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Note that by specifying ``sharex`` and ``sharey``, we've automatically removed inner labels on the grid to make the plot cleaner.\\n\",\n    \"The resulting grid of axes instances is returned within a NumPy array, allowing for convenient specification of the desired axes using standard array indexing notation:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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sLCMHfuXNTU1PiuWiIikiW70MZIi1mHhIQ47Rs3bhw6OjocHn/jxg0A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\\nvvmiAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1023b3828>\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# axes are in a two-dimensional array, indexed by [row, col]\\n\",\n    \"for i in range(2):\\n\",\n    \"    for j in range(3):\\n\",\n    \"        ax[i, j].text(0.5, 0.5, str((i, j)),\\n\",\n    \"                      fontsize=18, ha='center')\\n\",\n    \"fig\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"In comparison to ``plt.subplot()``, ``plt.subplots()`` is more consistent with Python's conventional 0-based indexing.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## ``plt.GridSpec``: More Complicated Arrangements\\n\",\n    \"\\n\",\n    \"To go beyond a regular grid to subplots that span multiple rows and columns, ``plt.GridSpec()`` is the best tool.\\n\",\n    \"The ``plt.GridSpec()`` object does not create a plot by itself; it is simply a convenient interface that is recognized by the ``plt.subplot()`` command.\\n\",\n    \"For example, a gridspec for a grid of two rows and three columns with some specified width and height space looks like this:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"grid = plt.GridSpec(2, 3, wspace=0.4, hspace=0.3)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"From this we can specify subplot locations and extents using the familiary Python slicing syntax:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAXYAAAD/CAYAAADllv3BAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10f438128>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.subplot(grid[0, 0])\\n\",\n    \"plt.subplot(grid[0, 1:])\\n\",\n    \"plt.subplot(grid[1, :2])\\n\",\n    \"plt.subplot(grid[1, 2]);\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This type of flexible grid alignment has a wide range of uses.\\n\",\n    \"I most often use it when creating multi-axes histogram plots like the ones shown here:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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JSUbCv92/n0oGwUPT09XL9+nWQyyeDg4D2bVjQaZWhoCK1WSyaT2Vby\\nuNemsPX/P0zNtxRvieQ9xn4CsZtgHMZn5H7vufPrOp1OlAOOjo4SCATENROJBAaDgTNnzty3aWan\\nGALb3jORSKBSqSgoKODq1auiLb63t5f6+noymQyTk5NMTU2xvLxMZ2cnOp1uV8tZZW3BYBC9Xk9R\\nUdE2sd9ayqiwW8B2v7LKhx3yIMVbInkP8TADEHp6ekRX4m4lfru9fr/3VLooY7EYGxsblJSUcOfO\\nHTweD4FAgOnpaUpKSojH4zQ2Ngq72AfpTtwth6yYP1mtVtLpNNlslsXFRXw+H263m0984hNEo1Fa\\nW1v3PLxVNopoNIrVahUbjHJIurWUsaOjg3g8jtVqvW8UfdR14VK8D8Gv/Mqv8PWvf33X1/x+P+fO\\nnXuXVySR3OVBBWJn9+FWIQO2iRSwa5t4KBRifX2d0tJSYfKk0+n4v//7P1555RXUajW5XI7y8nLS\\n6TS5XE5Yo46Pj1NcXMzCwgK1tbWHNmpSPkdnZycrKyvMzs5iMBjIZDKk02mqq6spKytDpVIRDocp\\nKCgA7v69FhQUiPum0+m2tdUreWzlta2NQztLGffLWx9FXfhWpHgfgldffZXu7m7Kysp2fb2wsPBd\\nXpHkSWG/po6tlR0qleqeQ7addq7Xr18nHA6j1WpRq9XbrEoBIVIejweAoqIigsEgi4uLVFZWks1m\\n+ed//mempqYwGAy88MIL6HQ6+vr6GBkZwev14nA40Gq1ZLNZrFarODytrq7G7XZTVlZGNpulra3t\\nUGkEJRKORqPMz89TU1OD0WikvLwco9GIwWBgeHiYpaUltFotbW1tNDY2Mjo6ytzcHHNzc3R1dZHP\\n5/m7v/s7kskkdrt9m5Ws0Wi8p3FoZynjfr+rozSlAineh8blcgnvBInk3WC3VvSd5XRK+qOvr498\\nPr/tkG3nz7e0tDA0NIRGoyGTyXDq1Cmi0agQWmXeYygUwmazARAMBnnllVcoKirCZrNRUlLCxMQE\\nLpcLq9VKY2OjiK6LiopIpVKk02na2tro7e0FoLKyksXFRVwuF+FwmGQyicvluifo2W2j2s2kSoma\\nNRoNXq+XxsZG6uvrSaVSZLNZvvSlL7G5uUk8HufixYvcvn2beDzOxsYGPT09hEIhWltbuXbtGiMj\\nIzidTlFWudVK9kGi7b1SSUdlSgVSvCWSE8NWAQmHw7uWvMHd1IZKpcJms21LneyWUoG7B20qlYre\\n3l40Gg0jIyPcvHmTubk5ampq0Gq14vqLi4sUFBRgs9lEVO1yuQgGg5SXl4vRXhaLhYqKCp5++mm6\\nu7tRqVTY7XZcLpdIodTW1orGGYPBQC6XE591N/HLZrNcu3aNeDzO8vIyNTU1om47k8kwNjaGx+Nh\\nbGxM1Gbncjnm5+cJh8N4PB6SyST19fU4nU4SiYRIm2g0GlQqFfF4nPX1dRobG8WGBWzbyMxms4i2\\n95qx+W74nkjxlkhOCFtzpgC5XG5bU4oiDnvlVnd+vbCwkK6uLsLhMDabTaRMwuGwqIk2Go3kcjkR\\n2RYXF+Pz+VhYWMBisVBSUsKzzz5LKpXiueeeE00symGf0+kkk8mIdahUKvL5PEtLS6TTaTY2Nigv\\nL6eoqEikeHYOV4hGo0SjUW7cuME3v/lN8fPPPPMMBoOBeDxOMpkkk8nwoQ99SLgLLi4uEo/H0Wg0\\norTw9OnTlJWViVx7Op2ms7NTzLLs6OhAq9XS2toqNhNlcHE+nyeXy9HS0iK+vtfh8FHnt3dDivcB\\niUQivO9978Pr9bKxscHFixePe0mSJ4ytOVPlgHEvcWhtbQXYlo/drdSuo6NDfB8gjJyMRqMYnmAw\\nGISndT6f59lnn0WlUqHRaOjs7CSdTt/jm63RaLYd9inpDSUt4/F4WF5exuv1ks1mMZvNeL1ebDYb\\nV69eJZ1O4/V6aWhoEBFvLBZDr9czNzeHx+NhZmaG+vp6kXZZX18nk8lgtVppaWnh7NmzuN1uamtr\\nmZ6exmQyYTabKSkpYXJyEp/Px9WrV7FYLDz33HPbvFGU9nj4QRRtsVi4efMmmUwGp9MppgjtFV3v\\n9js4Sg4l3plMht/8zd9kZWWFdDrNT//0T/OBD3zgqNf2WBEMBpmdneXHfuzH0Gg0u07nOCqexPsr\\nORhbc6Z7ufcph5A2m020uu/8+d2ixlAoxLe//W0ymQz5fJ5f+7VfQ6vVotPpGB4eFpPP9Xo9arUa\\nlUrFwMAAKpVKNMkoqRij0bhtcALcTT0ozSzpdBq1Wo1er0elUjE/P8/bb7/Nm2++yfz8PCaTifb2\\ndgoLC1ldXWViYgKNRkNJSQmRSASDwSDa1e12O+FwmJqaGjo6Okin02g0GmKxGG63m+rqap566il6\\ne3uxWCx4vV6mp6dZXV3FYDCQSqV29UbZGUUrg4FdLte2z7lft6pSuXPUHEq8v/GNb+ByufijP/oj\\ngsEgP/IjP/JEiItGo3lXKkme1PsrOTh7VZ0oHX8ajYZcLnfPkIOdPtlbo8ZoNCpSFYlEgoGBAUwm\\nk4goo9EoZrOZnp4eotEofX19TE1NYbfbKSkp4dVXX+XOnTuo1WqMRiMNDQ2iisTpdNLf308ul+PM\\nmTPisHR9fZ3y8nLMZjMqlUpsDuvr6yLP7HK50Gg0VFVVcebMGYaGhpicnMRoNFJdXU17ezsajQar\\n1Uomk8Fms4ma9K2pJb1ej16vp6SkhHPnznHz5k1hVLXTG2Wrj8nWmm+bzSZqvncOmdirW/VR5Lvh\\nkOL9wz/8w3zkIx8B7ubd5KSYo0XeX8n9OEgjjpJb3vlzSlRuNBrJZDKEw2FMJhM6nY65uTmR625s\\nbESj0fD6668TCARoaWnhpZdeEhvB0NAQExMTrK6uEo/HqaqqYmNjQxz8KZUmb7zxBi6XC51OR1VV\\nFcXFxVRVVQnb1oqKCjo7O1laWmJ0dJRwOIzdbieVSnHu3DkymQzJZBKDwYDD4RCHovl8nrm5OZaW\\nlhgfHxeWsNFoVAixzWZDrVaLz6hExornS0dHh3hyGB8f31b+t5sPyc40kHLPH7RbVfldPGzJ4KFU\\nQfHKjUQi/OIv/iK/9Eu/dKg3l+yOvL+S+6FEycoA3a2RncVi2XYIuVVYlKhcrVYzOzvL5cuXsdvt\\nouQwl8vxmc98huXlZTo6OpicnCQQCFBQUCBy0EqFRSgUwuPxMDs7C0BzczOZTAaVSkU6nUar1Yo0\\nQzAYJBQKkc1mxYg0vV6P2+0mHo+LvLrP56O4uJhLly5htVpRq9XYbDZRkaJ8luHhYdRqNdlsVkS+\\nXq8Xp9PJyMgIQ0NDZDIZ1Go1jY2NoqxypxOiXq9Hq9U+ULPNQUr99qvnPqo2+UOHdGtra/z8z/88\\nn//85/noRz962Ms81uRyOS5evMji4iLZbFaI6rvBk3B/30scJJI6imgL7uaO5+fnhc/05cuXxWsa\\njWbX6HAr6XSaVCqFXq8XDn5KtBgOh7l16xbLy8uoVCpxILi+vs63v/1tNBqNmEYTCoWorq5GpVIx\\nNTWFRqMRpXhK/fT6+jpLS0skEgmWl5dZXl5Gq9WSy+WYm5sjGAxisVgoLi4W7eU2m008GShimU6n\\ngR+kJIqKilhcXMTv97O+vg7cfdpQPMHn5+eJxWJotVpaWlp2tXPdLUJW3udhuZ/IH1Va5VDi7fF4\\n+Mmf/El++7d/mwsXLhzmEieCXC5Hf38/v/ALvwDwSMp9duNJub8nhf1E9yCR1FGaEqXTaerr69Hp\\ndCSTSQKBgDiLUa6/c5KN8vWuri58Ph8rKyuMjY1hMpm4cOGCiBYXFxe5ffs2gUCA9fV1GhoaqKmp\\nEZGqkmrp7u4mmUyytrYmotyamhpmZmawWq1MTk5SXV1NY2Mj0WiUZDJJKBTC6XQyODhIXV0dVqsV\\nl8slcub5fB6r1SpKCouKiu6pZ1dmZCqt8PX19YyOjorWdYPBQDKZJJfLUVhYKMyqdvvb3a1656hM\\no+7HUZURHkq8v/zlLxMKhfjLv/xL/uIv/gKVSsXf/u3f3nfW3UnmUVaW7MaTdn8fZw4iugeJpI7y\\nEMtoNGI2m4nFYszOzop2brVavc17BO76jgwMDKBWqzEYDLS2thKNRsnlciwtLQlxfO6559BoNFRW\\nVqLT6RgbG0OlUrG8vMy5c+fw+XyEQiEMBgMajYahoSGRx25qauK73/0uk5OTZLNZwuEwBoNB5K7t\\ndjter5dIJEIqlRLpEWWIcC6X4+mnn6azs1N8xvHxccLhsFhraWmpsGndmpJQTKeUtX3hC1+gt7eX\\nGzdukEgkUKvV2zy293JcDIVCBAIBUUnyqA4Z4eF9vBUOJd5f/OIX+eIXv3ioN5Tsj7y/jw8HEd2D\\nRFJH2bSxdbjB2NgYy8vLwh+7tLRUNLWMjIxw48YNZmdnOXXqlGj5Vias37hxg/LycsbHxzl37hx2\\nux2NRsNHP/pRxsbG0Ov1eL1eIcbV1dVirNj8/DxdXV3MzMwwMzODz+ejvLycqqoq2tvbsVgshMNh\\nsUko+fmysjLC4TCVlZVsbGzQ0tKCTqejsbERi8XC9evX8fl82Gw2kskk4+Pj4gmgu7tbiN3WSfTF\\nxcUkk0lSqRT9/f1cunQJs9nM6Ogo+Xweh8MhhlLsthFns1mGhoYYGxvDYDDQ2dlJNpsVLoePgqNo\\nk5dlDBLJfTiI6B4kktpabnYUbB1uoBhQGQwGsU6AcDiMTqfD7/czMDCAzWajtbWVeDzOqVOneOut\\nt8hkMiwsLBAKhTAajQwODrKxsYFer6empoaamhrq6uqIRqPodDo2Nzfx+/34/X76+/upr6+nsLCQ\\nUChENBpFo9Hw7LPP0t/fz/DwMPPz8yQSCQoLC4UpFUAgEKCyshKHw8HU1BTf//73cTgcvPbaa8Dd\\nXofi4mJx6GkymWhsbNzWYBSNRhkeHmZmZoaFhQXa29uJRqMsLS3h9/txu92kUimRh1e6Nbce9BqN\\nRtxuN7du3RKe4LFYjMHBwUeePnlYnnjxzufzIte322tb/Q0kTx4HfcQ9aCQ1Pj7+QHnVnY/5ipcG\\nIHLYSmVJT08P4XBY+G3bbDbS6TSFhYU0NTWRz+eFCOdyOerq6qiqqmJpaUlMZc/n83g8HrLZLHfu\\n3KG5uZk//uM/JhaLsbq6SllZGcvLyzgcDlKplHiPzc1N2tvbMRgMBAIBBgcHWV5eFrXdfr8fjUaD\\nx+NBpVJhNpt53/veRyQS4Y033uDmzZui/txutwt/FrVaLYYTT0xM8Mwzz+ByuVCr1fj9fqampujt\\n7SWfzxMMBpmcnGR2dpZcLkc0GsXv97OysiLy5TMzM8J7+8KFC/T39+N2u7l+/TqlpaXkcjmampoo\\nLCx8pDXaR8ETL95wtyX4l3/5l3d9TeaZJUflBHeQFMxOy9atj/mdnZ309fUxMTGBWq2mq6uLp556\\ninQ6veuB2/nz52ltbWVgYEBUiSSTSZqbm1GpVHz84x/H7/ezvLyM2+1mfX2dmpoaQqGQaM7Z2NjA\\n7/fT2NgoXPhSqRSlpaU4nU5KSkpobm4mnU5TUVEBICo+3G43wWBQfJ/VamV2dhaLxcLa2hrV1dVo\\nNBr6+vrQ6XQEg0G0Wq2o9T5//jxut5tYLMbp06eFm6Fer6e+vh6Px8PCwgIqlYr29nZRfaNWq6mo\\nqKCyspLx8XEikQhms5lgMEhlZSV2u51MJkMgECAQCGC1WikuLqayshK9Xi8akgwGw56Tdh4HpHj/\\n/2ztQpNIHgX7pWB2Ho5u9c4Ih8NcvXqVkZERfD4fdXV1hMNhMWtS2Qx2bgwul4vnnntORLU3b95E\\np9ORyWTo7e0lGo2yurqKRqMhn8+L6qZIJMLw8DCjo6N4vV70ej02mw2Px4PdbsdoNFJTU4PJZGJl\\nZYVIJMLY2Bh2u52ysjJSqRRnzpxBpVKJUj673c7Zs2dZW1tjdHSUv/iLv6Czs5OGhgauXbtGJpOh\\ntLSUK1eukEqlqKmpoampCZ/Px/DwMOFwmM3NTWprawHw+Xy0t7dTWVnJuXPnGB8fZ3V1lVgsRktL\\nCx/4wAew2+2Mjo6SyWSEe6BSjXLnzh1mZ2fJ5/OcPn2ampoaHA7HfTfEx0nApXhLJO8S+6Vgdkbm\\ncLemW2mO0Wq1FBYWsr6+TjweF/XQsH1jUPy4lfmKWysz5ubmRIR66dIl4Umy1VlQGeyrROCdnZ18\\n8IMf5IPzl74JAAAgAElEQVQf/CC3b9/G6/Xi9/tpbm7mwoULIk89OztLOBzG7XZTXFyMRqPhzJkz\\nFBcX87GPfYzJyUnR2JPP54lEIoTDYT70oQ+Japbr16/zxhtvoNPp+MpXvsIHP/hBAEpLSykoKKCg\\noIDm5mbhQa60tFssFurq6sRaJicnefrpp7FarWLye0dHB0ajUQwUnpubExvkxYsXhbOgUha514b4\\nuCDFWyJ5F7lfCmYvAc7lcphMJrLZLE6nk+eff54LFy6I6hDluoq51MDAADdv3hQHl8lkEpVKRUdH\\nB2VlZaLpxev13iPegUCAZDJJYWGhsGjVaDTU1dVRVFSEw+FgeXmZkpKSbZ7WgUBA+KlEo1HKysoo\\nLS0VviaxWEz4lSifb2VlhTNnzlBZWYnX62VgYEA4AxYWFuL1eunr6xPvX1NTI4Y6/Nu//RtOp5N4\\nPE5lZSVvvfUW//qv/8ro6CgNDQ2YzWbW19dJp9MUFRUxMDBANpvFaDQKv5TXXntNDFxQPsvWjfXd\\nsHV9GN6z4v0f//Ef/ORP/uS+35fP52XKRPJYsLUiZWRkhHfeeYfZ2VnOnj1LKBRiYmKCeDyO0+kU\\nG0AoFAJ+4K8xNDTE8PCwGIagVqvZ3NwkEAgwMjJCPp8nlUqhVqsZGxsTgxDq6+tZWFigoqKCiYkJ\\nstksFy9eRKPRCDe+4eFhNBoNDQ0NGI13x4sNDQ1x/fp1IpEI6+vroktTce8bHx/H7/djsViIRCKi\\nq7O6ulq871e/+lUKCwtFtK5UiSgGV5FIhFgshlqt5rnnnhOe44pA37x5k+npafR6PVarFZ/Ph0ql\\noqysDLfbTSAQIJ/PU1BQQDQaRaVSia8pQ4qj0SiTk5P3pEiOcmzZUfOeFe/x8XHa2tp45pln9v3e\\nnXP+JJJ3i926NxOJBPF4XESJfr9fCExZWRnBYBCv18vc3BxDQ0PkcjlOnz5Nb28v6XQan8/H3Nwc\\niUSC+vp6AoGAEFW1Ws34+LiI4mtra4nFYtTW1uLz+fB6veRyOXQ6nRgYnMlkiEajOBwOrl+/TmNj\\noxhY8Prrr6PRaOjq6iKRSNDc3Mzo6ChTU1P4/X6efvppxsfHsVgsTExMiDI9ZUDDwsICc3NzWCwW\\nampqKCsr4/3vfz9VVVU4nU4xHae1tZWmpibW1tb4t3/7N+bm5tBoNLS0tFBeXs7q6ioATU1NVFdX\\n81M/9VPo9XpaW1tJpVJoNBrhiqgMKZ6amsJkMon0SywWQ6fTEY/HRYpk65PSUdkbHBXvWfGGu6L8\\nOOWoJE8uu81eBMTgXKUTUDmYe+211yguLsZisdDd3c3AwACbm5u43W5aWlrIZrN4PB4SiQSLi4tE\\nIhHgbn20Xq+nqakJtVpNKpUikUiIQ0G/38/S0pLIf8fjcU6fPk06nRYleS6XixdeeIFwOCxqtY1G\\nI2VlZaysrGC327FarcIiWensVJz+PB4PTU1NeDweXn31VVZXVwmFQmIEmkajYXl5GafTycbGBi6X\\ni4aGBpqbm1Gr1dy5c4dgMEgymeT06dOsrq5SUVHB5OQkX/va17h58yYlJSXU1tZSXl5OIpGgt7eX\\nz372s6I0EuDatWuk02mWlpaora0VVSl9fX3EYjGMRiOVlZXYbDZ0Oh3T09OijFBp6tn6+zsqe4Oj\\n4j0t3hLJcbJVsJWpN/Pz89TX12M2m0WrutLeHYlEyOfz6HQ6CgsLhaj29/czNTVFfX09RUVFFBQU\\nMDY2xmuvvcbCwoKIrJVa6NXVVdGVODg4iN/vJ5lMcvHiRUZHR5mfnxf5ciXKjMVidHR04PV6xWAC\\nxW61sbGRyspKKioqKCgoIBKJiAEMly5dorW1le9973ui3jsQCLC4uEg+nxf2rUpDUSqVIpPJsLm5\\nic1mE9c9deoUly9f5q233sLj8RCNRllbW+PChQssLS1RUFDA6OioKD/MZrOkUik+9rGPoVKpeOqp\\np0RZbzwe51vf+hZTU1M4nU5hKZvJZLh69SqvvPIKRqORqqoqOjs7WVxc5ObNm6RSKbq6usjlcvcY\\nWb1bHt0PghRvieQRsDVSy+fz5PN59Ho9wWCQcDgsOg3VajWhUEjUQM/OzqJSqdDr9eh0OrLZLCqV\\nio2NDQKBAE1NTbhcLmZmZgiFQpSVlVFbW4vZbOb69etsbm6KSTLhcBi/3088Hmdubo7p6Wnm5+fZ\\n2NjAarVSVlaGVqvF5/MRj8dZWloSDTVFRUUYDAbKy8tF9UVlZSWZTIZ4PE5rayt+v59oNIrP52Ns\\nbAyHwyGmt5eVlQnB1Gq1LC0tUVxczNramjgYnZqawufzodVqReXM1nmZuVyOkZER/H4/ZWVlJJNJ\\ntFqtsHEtLCzE4XCIyF+j0RAKhXj55Ze5evUqoVCIU6dO0draSiaTAe6ecRmNRjE9B+4afSkDHxKJ\\nBE6n857Dycfx8PJEiPft27d53/ve90B2jalUiueff/4Rrkoi2ZutkVooFBKTyYeGhpiZmcHhcHDx\\n4kXxeB6Px7lz5w4tLS1iurpWq6WkpIS3335bTC3XarUkEgkmJydJJpNEIhE6OjrEId/i4iJGo5Ha\\n2loxyWZ4eJjl5WUymQzLy8viUDCdTnP69Gnm5+eZnp4mn8+LYQeRSAS3201XV5fIHcdiMVKpFPPz\\n87z66qusrKwIy1eVSkVdXR11dXXCEVCJqufn5/n617+Oy+VieXmZbDYrfLxTqRTd3d3CIAsQlS3L\\ny8tMTEyQyWT4zne+g9PpxOFw8Mwzz9Dc3CzSRgUFBajVaq5duyY2EiWvnk6nefHFF7Fareh0Ovr7\\n+6moqGB2dhaTycTc3JxwJOzs7KSjo2PXmZOP4+HliRDvxcVFysvL+eQnP/lAP6eUSkkk7zZbIzVl\\ndJhSGudwOESNc0lJCVeuXCEajWK1WhkeHiabzeJyucjn8ySTSdrb20XqIZvN0tzczJ07d6iurmZ+\\nfl7kohX3y3Q6zdzcHMPDw1RUVIhNJBAIiNpvZTBCKBSiq6uLWCyG3+8nkUhgMploaWmhpqaGj3zk\\nI0xMTDA+Ps7U1BTJZBKbzSaGMczPz4vKj3g8TmFhIY2NjXg8HtF6n0qliMVijIyMEI/HxedSmmUG\\nBgZ4//vfL7ol5+bmMBgMIuWkOAsqjodGoxG1Wk1dXZ0w1Orr62NoaEiItNFopKCggPb2dkwmk0hx\\nnD9/nvr6egYHBykqKiKRSIhhD0o0vZdA71bmeZyHmCdCvIFtN1ciedzRaDRijqIyWb2yshKXy0Uk\\nEsFut2Oz2YTXiBLZpVIp4vE4MzMz1NXVodFouHLlClarFY/Hw/T0NLOzs/h8PhGJp1IpUTmhtIUr\\n7e6Kn7XL5UKv1wuR9Xg81NTUUFlZSWVlJVevXiWbzVJXV8cLL7wgxu+Nj4/z8ssvi0POuro6pqam\\nyGazrK2tiS7PYDDI6uoqc3NzpNNpiouLSaVSTE9Po1KpMJlMWK1WSktLRdommUwK725FyGdnZ5mf\\nnycajYqnhUgkQlVVFSsrK/T09FBRUSGajkZGRsTmY7PZCIfDfPCDHySXy6HX63G5XNsEVxlirHh9\\nK7MolXr1BzmUPO5DzBMj3hLJSSKbzd7TWq3X6/n85z9PIBDAZrMxPDxMLBZjbm6O6upqUSnidDqZ\\nnp4GEJNuWlpaePvtt5mdnRUdjK2trQwPD7O2tkZRUZFIQywvL6PT6UilUiLvrMyC7Orqoquri5WV\\nFTQaDdevX8ftdrO0tCSEURkH9tWvfpWbN28yOjpKOp0WpXRKDXZxcTEul0scetbV1eFyuUSEf/36\\ndXE/LBYLdrsds9nM5uYmlZWVIuVhtVpZW1ujubmZ8fFxotEobrdbmF5ls1kKCwspKCjgypUronrF\\nbDYTjUZ58803xbQrjUaD2WzGYrFsS4HsjJAfZHDwXtH1cR9iSvGWSB4Be/1hK9PLla/pdDpCoRCz\\ns7PE43EMBgM1NTUsLCyQTqdxOp3iUHFhYYHCwkJ8Ph8lJSXMzMzgdruJRCLiZ9fX19Hr9SJlkEql\\nhOFTaWkpm5ubvPPOOzQ0NIjKlVwuRzweZ2pqilAoREtLi/hZJcWjpCDX19cJh8PA3Si2oKAAr9eL\\nwWBgenpalN4tLCzg9/vJ5/P4fD7UajVms1lM5QkEApSVlQl7WmUST0FBAadOneLOnTviPpaXl+Px\\neER3aGNjI//4j/9IJpPh+vXr9Pb2UlRUJJ5wlGYcZdjCXhHyQXzZ7xddH/ch5okQ79LSUiYmJvj9\\n3/991Go1X/jCF6isrDzuZUkke7LzD3urOx0gSuay2SwGg4F4PI7L5aKkpIShoSExcebUqVOEQiHs\\ndrtIe5SVlfHpT3+aN998k0wmw8rKCrlcTkTYRqORZDJJW1sbwWCQzc1N0YGo5KWV8sFQKCSmqzud\\nTtRqNd/+9re5c+cOHo+HwsJCWlpaxACGeDwuKmCUAcHpdBq73U4ymcThcOD3+/F4PMTjcTGqzWaz\\nsbm5yenTp6mtrWVoaIiamhrUajXJZJKFhQX++I//WNyXpqYmccAYjUbFIGJlU6ysrBT+KAaDgXA4\\nLIyydoppIvGDgc3hcBiv10thYeGBDiXv529y3IeYJ0K8n3rqKVFe9elPf1pYO0okjxtbH7GVnLfN\\nZhMpFKX8b2RkhGQySVVVFS+++CL9/f1i/qJerxeDCFQqFWtra8RiMZqamvjYxz4m8rWbm5vMzMyw\\nvr7O5cuXKSoq4urVq/T394v0RmlpKVVVVaTTaWw2G5OTk/h8PpaWlojFYpSUlIjKEKWdvbi4WDTv\\n1NXV8XM/93O8+uqr6HQ6wuEwa2trFBQUYDQaKSoqEh2ZarWaN998k3g8jtlspqurS5T3mc1m0dbv\\ndDopLS3l7Nmz3Lx5Uwi0kuax2+0UFxezuroqRLq4uFg0NykRdSaToaioiJaWFvL5PJcuXUKv12+z\\n1A2FQqRSKWZnZ4lEImxsbIgNbOt4NIWdEfl+0fVR2QUfhhMh3vCD4b+PQ4mORAK7D0pQHrEVywWl\\nvTyZTFJUVEQgEBAVFwMDA0xMTPDOO+/wgQ98ALVazZUrV1hbW0OtVguBe/vtt2loaECtVmO32xke\\nHsbv95NKpWhubmZtbY0333yT8vJyMUDEYDDgdDpFqV88Hker1WIymcR/K3XRyqiwUCgkhhwYjUYx\\nMm1paUnUa7e1tdHd3U11dbXw6m5vb2d4eJg33niDqakpzGazmByvTNopKirCZrOhUqlEDn1ycpJI\\nJMI777xDKBQiHo+LrknlINNgMGCz2bh8+TImk4nJyUnUajUf+MAHOHPmDE6nUwx86O/vF4MUAK5f\\nvy6eYpRW+oWFBRYWFlheXqajo2NfX6Pjjq7vx4kRb4nkUbPXwdRuX98tF7rVHyMYDKJSqSgoKKC/\\nv59kMsni4iIdHR2YTCbu3LkjHPGU6NBgMKBSqcShplar5R//8R9Fe/oLL7wA/MCMKh6PE41Gqaqq\\nIpFIUFRURDKZFMOJFxYWMBqNeDweXC4XCwsLoookm81is9mwWq3E43Hcbjdut5vq6mri8Tgmk4nN\\nzU3R6VhXV4fNZiOXy5HL5aipqWFtbQ2HwyGGQ8TjcUKhkDhgLC0tRaPRUFVVRV1dHTMzM/zXf/0X\\ny8vLWK1WgsEg9fX1rK6u0tLSIiJum82Gz+ejt7eXoaEhYrEYfX19qNVqTCYT1dXVwhkQIJfLAfCN\\nb3xDVJJ84hOfwO12i8nxiUSCZDKJTqdDp9OJJqmDcJzR9f2Q4i2RsHfZ115f3+1AUqfTCb9sk8nE\\n6dOnReWF0nzT2dmJxWKhtbUVs9lMKpVibW2N6elpnE4nly9fFhUnf/7nf87Vq1cpLCyksrKS+vp6\\n4vE4//u//0s8HicQCNDc3Mzq6iobGxviUO+ZZ54hn8+LcWCpVIpcLofdbhct68qGpPxvVVWVaAqa\\nmJhgenqaUChESUkJGo1GHHymUikCgQADAwPU1NSQyWTQ6XQsLS0RCoXQaDRYrVZsNhtTU1NC9FKp\\nFLdu3WJ6eppIJEJNTc22LtNcLkc4HKa+vh6r1YrVasVut7OyskI+n2dpaYn29naRd1epVDz99NNY\\nLBbm5+dZWlpiamqKlpYWwuEwb7/9NisrKywsLFBXV8fZs2dpb2+nqKhI+Jco6ZDHLaI+KCdCvG/d\\nusWFCxfIZDLk83l+4id+4riXJHmPsVd1yF5f3y0XqpTL6fV60uk0HR0dhEIhXnnlFaamprDZbDz/\\n/PNoNBpcLhcvvfSSmAZjsVhER6AytX1ubo5cLsf8/Dzt7e1MT09z/fp1FhYWRIScSCRYWVnB4XAQ\\nDofp7u7GbDajVqtZXV3F6/UCd6NTxYDJ6/Vis9mw2WzikDMajfLss8/icrlYXFxkbm6OVCqF2Wym\\nrq6OS5cu0dfXJyayK23s4XCYM2fO4Ha7hemU0WjE6XSKZpmSkhJxzVgshlarFePGlI5OpUU+Foux\\nublJTU0Nd+7cwe/34/V60Wq1RKNRzp49K55cvvKVr9DZ2UlFRQUjIyN4vV6+9a1v8eyzz6LX6+no\\n6KCqqoqOjg6qq6vR6/VcunRpm9/M42Q09aCcCPFeWVmhqamJT3/604DMe0uOnr0Opvb6+s4mHEW0\\nLBaLMEIaHx8XbesdHR3E43HC4TB6vV4c0mUyGVQqFel0GrPZvO19zWazmA/5yU9+ktHRUcxmM3q9\\nHr/fTyAQ4Pbt27jdbuGTbTabqa2tJZVKick3arVaeFd3d3fj8/nweDwsLS2xsrIiqj7g7gSaUChE\\nIpEgk8mIiN3lclFbWyui/FwuJ7zBl5aWCAQCmEwmDAaDaHfX6/WkUimqqqpYWFgQUXhFRQU1NTWo\\nVCr8fj8Gg4Hi4mLRTFRRUcHm5iYrKyvCrrW6upqWlhZaWlp49dVX8fv94vAxmUyytLSExWIRnZgL\\nCwtEo1E2NzdFg5Mi0EajURhwKZ2n0Wj0xPn6nwjxBlCpVFK0JY+M+x1Mtba2AmzzvNitCWfrNZR5\\nkXa7XVRpuFwunE4niUSCUCjE9PQ0k5OT1NTU0NnZSWdnp7ie3W7nhRdeEFHy/Pw8CwsL9Pf3U1BQ\\nIErdNjc3MRgMokpDo9GwsbEB3H1iVQY4dHR0oNVqWV9fFxUnKpVKCFw8Hqe8vByv1yu6HZUSvlAo\\nhN/vx2w28/TTTwtbWMXDJBwO43A4qK6uxmKxUFlZSSAQYHNzk0wmQ19fH7Ozs6IGu62tDavVSiQS\\nobS0VJQCRiIR0uk0uVwOr9dLKpWiqKgIo9FId3c3BoOB1tZWZmdnsdlspFIpTCYT586dE+PPTCaT\\nGECs1+u5ffs2uVyOWCwm7m1/fz+xWIyZmRkxpd5qtYrqk0fZ8n6U1z4x4i2RPGqUg6lsNiva1rcK\\ndE9Pj/jvaDSKx+PBarUKYdhq3p9KpUT+u6mpiY9+9KOUlJQIm9Hp6WmGhoaIRqPU1taKAcNKu/ZT\\nTz3FxYsXiUaj3Lhxg9HRUQBOnz5Nd3c3Y2Nj4vCwoqKCK1eu0N7ejkqloqKigsXFRWpra7Hb7Vy/\\nfp319XWGhoZEhK34imezWWKxGPl8XkTViUQCuJuL1mq1xONxFhcXqaioEJ8tmUyiVqtxOp34/X5y\\nuRwbGxsiXRIKhUQXp1arFRuOx+PBbDbjdrupr68XNfDxeJxgMChsY+vr6ykpKQEQEbVarWZhYQGr\\n1Spy48qUnxdffJF33nkHk8kkDkpjsRher5exsTFsNhsXLlzA6/USi8Ww2+1UVVWRTCaF/8tWcX8U\\n6ZSjbqd/rMVbsdJUTpMlkkfNblaudrudQCDAd7/7XVQqFXa7nWw2y/e//33S6TRtbW1cvnx523XS\\n6fS2/LfiLQKIA8JsNssbb7zB5OQki4uLFBcXU1paSn19vegQVMTV4/EQDAbR6XRiiG84HGZiYgKT\\nycTo6CiXLl3CaDTi9/tFfnlqagqVSiXOi+DuZHi1Wk04HBYuhadOnSKbzRIKhchkMiK9oFKpxKGq\\nMluysrKS8+fP4/P5xBOA0+kkHA5jNBqZmZkhkUgIF9C5uTmam5upqKjAYDCQTqdFa/wP/dAPiUEQ\\nc3NzTE5Osrm5id1up729nY997GOcPXuWGzduMDU1xerqqoiWl5aWROPSpUuX6O3tBX4wEs7r9Ypy\\nw1QqxfXr18UZQkNDAw6HQ/w+tp5bPKqW96O+9mMt3l/84hf5gz/4A1QqFRcvXjzu5UieAHazclVE\\ncnp6WhzAKZaryrDeRCKxzbx/a/57ay5badBZXl4mnU5z7tw5mpubefXVV3G73ayurlJSUsKNGzeE\\n0Pj9ftbX12lqamJpaYnvfve7LCwsYLPZMJvNtLW1ifmNRUVFvPnmm8zPz6NWqzl37hyFhYWMjY1x\\n69Yt4X2dzWbR6XTi8LCqqorV1VXROelwOIR5lN/vFweGCwsLlJWVcfv2bYLBIGfOnKGqqorq6mp8\\nPh83btwQI86cTifRaJTCwkK6u7t56aWX+OY3v8m1a9dEffeNGzeorKzEZDJRWFiI2WymoKBA3MvR\\n0VFhbFVQUMDm5iZarZbCwkLh4ZJMJkWX51YKCwvFZ1AqWhwOB/X19bS1tYl68K1pjEfZ8n7U136s\\nxXt+fp5PfvKT9PT0HPdSJO9RduYgd7NyDQQChEIh4XIHYLVaWV9fF6mBkZGRe3KmPT09pNNp4Re9\\ntd64paWFlZUV0bii1WpFu7fSbalMXd/Y2GBqakocwilzIJWuxbW1NVQqFV/60pew2+3EYjEsFgtz\\nc3PCv+TMmTPU19cTiUS4desWU1NTIsKuq6tjbW2NjY0NEa2mUik2NjbEY73S/u7z+XC5XKLU7vbt\\n29hsNqqrq8W4NribcikvL2d6eppoNCry8B//+MfFYevy8jLLy8v4fD5KS0u5fPkyPp+P0dFR4vE4\\nm5ub9PT0CNfF2tpaqqqqxEGo0kWqVP/sZOsZxNZpRmazeVt7/E7XwUfVlHPU136sxVsieZTslYPc\\n+QemtHVXVlaSzWbp7e3l/PnznDlzhoGBAQoKCgiHw0SjUSwWy7Zr9vT0bMubd3Z2cufOHV577TW8\\nXi9nzpzhwx/+MM899xz/8A//gNfr5datW7S0tFBaWkpRURHBYBCz2UwgEGBtbU2UGlZVVdHV1cWd\\nO3dYWFhgbGwMq9UquhLVarXw8wiFQtTV1bG4uMjGxobw9S4uLhYVHUajkdLSUuLxOKurq8TjcXGv\\nFFc/tVqNwWAgFAqJipNgMEgmk8Hj8ZDL5SgpKSESiVBUVCSEenFxkT/7sz+jubkZu92OVqtlfn6e\\nTCYj5lheuXKFCxcu8Pbbb2O1Wrl16xalpaXYbLZtG6Hf7+fatWsiZ61Mud+NrQ02BxXOR9mUc5TX\\nluIteWLZKwe5c2L44OAg+Xyeuro6Lly4IDr7LBYLDoeDoaEhUYqnmCeZTCYCgQBut5tAIIDL5RJ1\\n0cXFxSLySyQS+P1+uru7KSoqEmPRHA4H2WyWU6dOCcFOJBJi+G9DQwNwd8juysqKaKDZ3NykvLyc\\nXC6H0+lkZWWF06dPi/I+r9crDhmViF+px/Z6vRiNRmpqaoSxFIBerxeWtsqYNoDLly+ztLTE4OAg\\nHo9HTK4PBoPYbDb6+/uFCZbL5cLj8aDT6SgtLRXGU+vr68ISd2hoiN7eXsrKyhgZGcFisaBSqejs\\n7ESv16PRaHjrrbf4j//4DzExqLe3F5PJxJUrV/b13n4cW9wfhsdCvFOp1K7tqkp0IJHsx24+I/v9\\nse6Vg9z6s4oj3fLyspg1eenSJRFNZzIZqqurKS4uFlUaOp2OgYEBMaE9m82iVqvp7OzE6XRSUlKC\\n0WgULelGoxGbzUZbWxvf//73Rd14U1MTsViMhoYGcWiZSCQoLi6msbGRmzdvMj8/j06nw2AwkM1m\\nxQGpz+djc3NT+GrbbDaampowGAzCMtXv94t8usPhoKCgAKfTSSaTweFwiM+nRPMej0e0+YdCIaqr\\nq2lrayOXyzE4OEggEMDhcBCLxSgvLyeZTFJXV0c6nRbt9MlkkuLiYjEnsqKigvLychwOB//zP//D\\n2NgYbW1tVFZWsrm5yezsLFarlStXrpBIJETTUSQSYX19ndLSUrq7u+97+HfcQxMeFccu3m+99RZX\\nrlwRJUxbUalUfO5znzuGVUlOEjv/OHemKvb6Y92ZIoG7viEjIyPCwa6np2dbNAwQCAREdK1Eq1sr\\nFurr6/H7/VitVoaGhujo6CASidDa2oper+f8+fOUlpbS19cnuiCHh4dpa2vjs5/9rMh1p9Np4QOi\\neHIojoMNDQ1cvXoVj8dDJpPh9OnTFBYWiohXrVYL46jNzU3KysooKiqirq6O06dP80u/9EssLy+L\\nVI/iSZJMJmloaBCVMnB3nKDb7WZqakp4sJhMJtbX1zEajWIYcDqdxuFwiNpxlUrF5OSkmCL0kY98\\nBK1Wi1qtZn5+XlSkKB2lSppHMcGKRCLbKkKUzs1MJoPFYqG4uJiqqio0Gs02y92dv+vjHprwqDh2\\n8V5bW6Ojo4NPfepTx70UyQll5x+nIq4H+WPdWtvd399PIBBgdnaWs2fPCnG5dOkS2WyWVColfEfy\\n+Tyvv/46BoOB3t5eOjs7MRqNDA4OEg6HWVxcpK6uDrVazdjYmDjU7OjoENNilPbvyclJuru7mZiY\\noKGhgXw+j9frpaKigmw2i8ViwefzCRtUp9PJwMAAa2trYtNRRoAtLy/j9/vR6XQiDx2JREgmk2Sz\\nWUwmE9/4xjeIxWJCKJVqjqqqKrxeLxMTE9jtdgoKCigvL2dubg63200wGBTpm9LSUgKBAPl8Xphc\\n5XI5MUxieHhYDPgtKioin89z8eJF7HY7Xq+XeDzO7OwsWq2Wqqoq4cMyPz9PW1sbHR0dIlViMpnE\\n59TpdDz//PNMTk7S0tIimmt2btbKvwvl6epBqjxOSorlUOKdz+f53d/9Xe7cuYNer+dLX/oS1dXV\\nR1lk7xIAACAASURBVL22JxZ5fx+MnX+cyiP5g5RkKRuAy+USbdvKdeBu555St5xKpbh58yZ37tzB\\nZrNx6tQpIWrKSLFAIEAsFqO2tpapqSl6enoYGRnB5/OxuroqStVMJhOrq6ssLi4yMDBAS0sLtbW1\\nNDc3MzMzw8zMDIODg+RyOUKhED6fj3/5l38RY8lyuRxWq1X4nSg5baU9XavVCge/wcFBdDodPp9P\\nDAVWrGdra2spKSmhpqaG2dlZUV+eSCRwOBxi01A2wQsXLlBaWsqpU6eYmpoSsyEV98JkMsna2hpa\\n7V2JUSpGNBoNNpuNO3fucPXqVZGv7ujoEL79yv1VPMGVc4hoNEoymcTpdGIymcjn8+j1etHerkT9\\n0WiU8fHxbWK+08pgL05SiuVQ4v29732PVCrF1772NYaGhviDP/gD/vIv//Ko1/bEIu/vg7FbhciD\\nlmQpG0AikaCzs1PMP4S7zR5Krtnv9/ONb3yDyclJMaoL7h7eDQ0N8b3vfY/bt2+L+Y4ajYbh4WHh\\n7dHc3Ew+nycUCuF2u8XgXLfbTTKZZH5+nuXlZfr6+tBoNCK6VipC4O5Go0ypUalUFBcXo1arhbjl\\n83mqq6uprKxkcXFRDHhQ2vKVVImSsgiHwxQUFHD27FkGBgYIBAIi7aPX62lqaiISiVBQUMDc3Bwb\\nGxt861vf4iMf+Qg/+7M/KwyuTCYTdXV1AJw9e5b+/n7Rwm42m8XTy9WrV/nmN79JIBCgqKiI8vJy\\nYYdrMpmYmJhAp9ORy+Xo6uoSvz/FtdHr9bK5uUl3dze3b98mHA4L73Cz2SzukfLktZuY7/Vv4iSl\\nWA4l3jdv3uTKlSsAdHV1MTIy8lCLyGazov1VcvT390lgZwnWg5Zk7Sb4ShQWiUSYnJyksbGRiYkJ\\nvF4vPp+PbDYrhgcoMxvLy8tZXV0V7eL5fJ6qqioKCgpYW1vj1q1bOBwOenp6MBgMzMzMiMg7Foux\\nuLhIQUEBLpcLo9GIWq0WzoGKA2A2mxWHkxqNBo/Hs62tXImalQk3ijuiItiKRawy3EGj0XD79m3y\\n+TwzMzOEw2GCwaCIaKenp7l8+TLNzc0sLy9TVFQk1jE3N4dKpaKlpYVMJsO5c+eYmZkhGAzS3t4u\\nBivMzc3xzjvviINPZaalcija1dUFQDQaZWxsjFQqJapaFJSu1cbG/4+9Mw+O+y7v/3vv+97VsVpp\\ndVnnSrIlxYrd2EmcoyHhKgRIm0CGQMpRoB1g0pYylP4YJjMMTZnpwEybcGSghQQDAdKGEHLVRI4i\\nW5d1S6vVfR9738fvD83nYSXrtmR5rc9rJgOWVruf/a70fJ/P83me97uEdjEAYDabaViJDd6k77wA\\nXBWQ2Y16/c39IId09ps9BW+/3w+NRvOnJxGL9xx8CwsLSeqST1Gusp/Xl7Nz1gf8cHjVXGFycpIm\\nDVmwXVxchNlshsFgwMrKCiYmJnDx4kUqRZhMJtTU1GB5eRlzc3MYGxuDUqlEQ0MDjdir1WqkUima\\naszPzydX9NnZWRgMBmrlM5vNZPKgVquh0+loUjIcDsPj8ZCrOuvcisVicLvdJIfKhoVUKhWKi4vp\\nJhONRuH1ejE+Po7Z2VnKgtkYezQaRXd3N0wmE5LJJN243nrrLfLgFAgECIfDeOGFF1BTU4NUKoWP\\nfvSj+O1vf4uRkREsLCygtrYWS0tL0Ol0yM3NxdTUFAwGA2ZnZ/HOO+9gfHwcWVlZ9HVmtcZgU6vM\\noq2urg6Dg4N0eJw+eLP+IDo9IDNvzI0y8RvZOWc9ewre7A7GuJbAUl9fj29+85v45S9/uaefvxnZ\\nz+vL2R3ph1VM24N1bQCr8sRsGvLRRx9FIpGAUqmEWq1GTk4OysvLyUR3dHSUDvSOHTuG2dlZ0vRQ\\nqVQoKyvD6OgoIpEI1YPtdjsp7kkkEuTm5gIAlpeXEY/HYbfbYbVaaWCHtfxFIhEaEwdA05rMqV0g\\nECCZTCIWiyGVSsHn86G6uprG4MfGxjAzMwOZTEaDSX6/H9FoFHK5HF6vF3q9Hg0NDejo6ACw2g0m\\nEAjw1ltvkUgV690OhUL4/e9/D5vNhpKSEnR0dOC5554DADgcDnz961/HxYsX4fF4SH9lbGwMbrcb\\nfr8fZ86coZ0CO09g3T/Nzc0QCARwOp1obGy8aoqVXcv0G3F6QN6uNHKjOuesZ0/Bu76+Hq+//jru\\nu+8+mgbj7B/8+m7PQXQEbHRYdfr0aQCrwZD1K7OyQyKRQG5uLjnNyGQyBINBGI1GRCIRjIyMQCKR\\nYH5+HoWFhbDb7aipqYFWq0U0GsX58+fR1taG5eVl1NbWQqfTQS6XY3h4mJT+ZmdnqS6cnZ1NGToL\\neKlUChKJBEqlEslkkjJmNnZvMplI44P1lbNpTTaEo1aroVKp4PP5yBQiJyeH6uzsdZj5g1qtRl5e\\nHgYGBnDhwgWEQiHI5XJIpVIIhUK0t7fTNamtrUV1dTV8Ph9cLhc0Gg36+/vxnve8BwUFBWhra6Ns\\nOh6P065mYGAAt956KyQSCVpaWuD1eqHVauFwOCAQCOhwkrV0rv/cAGwazDOpNLIVewre99xzD956\\n6y089NBDAIAnn3xyXxd11LkZr+9+BtuD6ghgGRmbjmQC/WxAhA3DXLx4EWKxGFNTU7BarWhsbKRa\\nbV9fH9RqNXk++v1+BINBGvC5cuUK4vE4QqEQ+vr6oNVqMTAwgPLychgMBnzwgx/E+fPnaTIxkUhQ\\nBjwyMkLaJZOTk+jv76fM2GQyQa/XQygU0gg7ABiNRjgcDvT09GB6ehp+vx8CgYAcddhNx2KxwO/3\\nY35+HolEAnl5eWRpxjpaIpEI/H4/7TDy8vLQ0dGBQCAAn8+HgoICfPCDH8R//ud/Yn5+HisrK9SX\\n7nQ64XK56CBSLpejqakJfX19JP1qtVoxNjaG4uJilJeX07RqZ2cnxGIx4vE4Kisrrwq86zPp7Q4o\\nM6k0shV7Ct4CgQD/8i//sq8LYa1PG8Fano4KB3F9D5P9DrYH0RHADgFFIhHa2tpo3L2pqYkew9xz\\n2Lg3k1VdWlqC2+1GT08PtFotaXWYTCZcvHgR8Xgcw8PDkMlkaGtrQzAYhEwmw/LyMsRiMQQCAZaW\\nlqBSqdDX14fGxkb4/X4Aqwd4P/3pT6HVajE9PQ2JRIKRkRHqGGEj7iqVCtnZ2SgsLCTtEYlEQg43\\nOp0OBoOBvCItFgtNMDMPSuacI5PJEA6HodFoIJVKoVAo4Pf7qVuF+WmyGnsikYBOp8Px48cxNjZG\\nwllarRYTExPo7e2Fz+ejx1ksFohEItrJKBQKKsWwCVSVSgWVSrWmfMgMWdYHXmYozM4L2O/IVr8f\\nmVIa2YobIiKWl5ejv78f/f39V30vHo/DZrPhr/7qrw5hZZz9YL+D7bVse7dzgo/H4ygoKIDZbKbR\\n+PQsrra2FhqNBlNTU0gkErhy5Qp6enrQ398Pp9OJrKwsuN1uZGVlIR6PQ6PRIJVKkXxpMBjE6Ogo\\n1Go1lEolNBoNLBYLenp64Ha7odVq8fDDD9NQTzQahclkog6X5eVlqFQqKpVEIhGIxWJYLBbk5ubC\\nYrFApVIhlUohHo9DKpVicHAQc3NzGB8fJzGpcDhMwZuZJLPpR5aZKxQKTExMQKFQQCKRkDDV/Pw8\\nxsfHEQgE0NTUhKGhIZSXl9PAzdmzZ2mgh5WNsrKyoFQqyRknkUigr6+PWhklEgm0Wi2EQiFKS0tx\\n+vRpCrB1dXXw+/1U3tlMe0YgEODEiROkDpnpZZHtuCGC9wMPPACPx7Ph93p6enDfffdd5xVx9pP9\\nrjHuddu7Eyd4lr2xcXfgT1mcz+dDS0sLBZiOjg709vait7cXSqUSEokEVqsVOp0OOp2OygZisRhi\\nsXjNQSPL7GUyGWlqs3o1q9H6/X44nU5EIhEEAgHU1tbC5XLBarXC6/Xi2LFjWFlZQTwex9LSElZW\\nVhAIBFBdXY2FhQXSQzGbzdDpdFAoFNQCyYZq4vE4FhYWoNFoIBaLYbfbUVJSAqFQiEuXLsHr9ZId\\nWUFBAR3cGgwGLCwsICsrC8lkEmVlZUgmkxgbG0M0GkVFRQUNPDGJ2pKSEjojYNOrJ06cwMzMDBQK\\nBUZGRmgEPr37o6mpadPPmn12Wq2WauBMSCvTyyLbcUMEb87NzUHUGPey7d1sB7Bew7u2thY+n2+N\\nsTDbvjNB//HxcYyNjSEUCmFlZQVarRY6nQ4NDQ343//9X7zyyiuQy+X40Ic+RAdwIyMjiMfjsFgs\\nSKVSkMvlJD7FphjLyspoHb/73e/Q2toKiURCwlHMTUen0yGVStE0ZU5ODtRqNZaWltDa2gq32w2p\\nVEo65OyAlZVZ0s2PWanCbDbjkUcewezsLB2kskNLtVqNe++9l9QHE4kE8vPzkUwmcezYMZrGnJqa\\nwtLSErlfMdEtmUwGq9WKkpISeu8SiQQdHR1IpVIoKSlBfn4+3STTDYG3+qw3Swy2+plMGX/fDh68\\nOdeFG6HGuNUfOhuf1mg06OrqWpOdbyTon76Ft9lsOHXqFAwGA5qamvD2228jkUhgdnYWzz33HHQ6\\nHZaXl2G328lZxmg0UoBjWix2ux0ulwvDw8NIJBLw+XxUuiktLcXf/M3f4KWXXsLAwAC8Xi8ikQgU\\nCgWVNdgIPZN+BVZla7OysihgM0Njr9cLu92OqakpmEwmCIVCnD59GmfOnEFLSwvGx8dhMBjoQLO0\\ntBRGoxFisRharRZerxezs7Po7e1FRUUFXC4XfD4fBgYGoFAoIJVKcerUKZruZD3xzBu0ra0N9fX1\\n8Hq9MBqN8Hq9GBsbI70ThUJB/pTrA+z64LvRcNVmwTmTxt+3IyOCNzOEPew/fk5ms9kOgNVNWfYo\\nEomgVCqpNY4dniUSCXKSZ61xLS0tNBLucDjgdDoBrB7As978cDhMmbpYLIbBYEBFRQVSqRTVs1lv\\nOOvCmJycJJcY1v+t0WggEokQCoXIyDcajaKkpARmsxl33nkn3njjDbS2tpJeN3NOV6lU9HixWIxg\\nMIhIJAKBQACHw4Hl5WU4nU48++yzZIfGRvDFYjHk8lVvypMnT6KtrQ0ej4e6QNrb27G8vAyhUIhg\\nMAiLxUKiVw0NDVT7jkQiaGtrw/z8PF566SUaVmLljvz8fKRSKYhEInR2dpLWTHqA3Sz4ptfAtwrO\\nmTT+vh03fPC2WCyQyWT4zne+g0cffRQ2m+2wl8TJYNbvABKJBPU/Ly4uYmlpCVNTU7DZbFAqldRt\\nslFQSHeTb29vRzQaRVdXF8xmM7XbTU1NIRQKoaioiA4v1Wo19Ho9lTMmJydhNBqh0+kQiUQwNjYG\\nr9cLhUJBJQepVIpf/vKXGBoaQiAQgEAgQE5ODml2Ly0tUV28v78fer0eOp0O0WgUeXl5EIvFcLvd\\nmJqaQiqVQk5ODnlHzszMUOfLzMwMJicnYbFYaJgoFArBarUiEolgYGAAyWQSJ06coD52v99PFm2h\\nUAg6nQ5lZWVko6ZWq1FZWYnW1lZMT09jZWUF+fn51PrX2dkJiUSCyclJ6kJhjvMs2LLPbLvgu933\\nb5YebyADgndWVhZcLhfOnTu3aSshh7MXEokEmpubcenSJerEYG1y09PTyMvLQ0tLC86cOUOTjOkB\\nRS6XQywWY3l5GWNjY3jzzTcxPj6OsrIyVFdXw2q14vTp02hubkYsFsPw8DB0Oh0F9HA4jNLSUqjV\\navLEzM7OJm0S1r3B6uEulwsul4tcc9gUZiQSQU1NDYLBIFZWVjA1NQW/349YLAaTyQStVosrV65A\\nrVYjNzcXExMTCAaD6Onpgd1upzbdgYEBKt8w5b6VlRUsLS2hs7MTarUa5eXlWFlZQTi8aozAphsF\\nAgGkUimysrIQDoepD551vbCAfPz4cXR2diI7Oxs6nQ4ikQgCgYBEvCoqKiCXy6nDZ32A3S74bvf9\\nm6XHG8iA4M3hHBRerxfNzc2Ym5uDVCqlFjzWv80GQwKBALq6utDX1wdgddSa6WP09PRgYGAAoVCI\\nJhuDwSBaW1thMBig1+uRk5NDo+t2ux1vvfUWgsEgRCIRxsfHqSyh1+tRUFCAQCCAkZERKBQKmiwE\\nQGuxWCwk7lRWVgaz2Qyv14vLly/jrbfeIsEmljnPz8+juLgYBoMBXV1dpKfNxKdYjRkAFhcXqWXw\\n7rvvxsjICDweD6qqqqiv2+fzoaenB7m5uZicnMTs7Czd3Hw+HwBgeHgYcrmcJFyZp2ZhYSGKioqo\\nns3OEtghbVZWFnWYpPd4M7YLvjsJzuka7psZOGQCPHhzjiSJRAJtbW20jTeZTDh37hzEYjFisRhe\\nffVVxONxjI+PU7Y4OzuLYDCI6upqhMOrfpSsv5hlmqyrw2g0Ym5uDk6nE6lUiswKWA06EAjQ5KLd\\nbqd+7cHBQVy8eBGBQAC5ubkoLi5GUVER3nrrLVqPz+eD2+2mUsaxY8fgdrvxxhtvYGlpifq73W43\\n6WDn5eUBWBU9k8tXLdjYgSmrtbNxeovFgqysLDgcDiqrGAwG9Pf3Y3FxkbJb1nUCgIwjdDodtFot\\nSktLUV5ejltuuQUmk4l6scViMfVwszKURCJBTU3NVQF0synJjUpfG43CbxWcb4aDy4wJ3h6Ph0R6\\nOJy9wv7QWdtcXV0dFhcXUVVVhbNnzyIWiyEQCFA2HIlE4Ha7EY1GkUgkoFarEY/HAax2cqS7thcU\\nFOD2228nhT9myBAOh0keVqvVoqamBmNjYygoKKA6s1AoxLFjxzA/Pw+FQkFKgYuLizQibzQaodVq\\noVAoqOvDaDQCAMbHxylrZn3PLHgxUwir1YqVlRVYrVYoFAoyBE4kEqitrYVaraYS0ujoKD796U/j\\nzjvvRDQaxdtvv414PI6xsTGyT5NIJLjrrrswODiIkZERGI1GqNVqGI1GlJaW4tSpUzCZTHQd0g8n\\nWalGIpEgFAqhs7MTAoGAAulODxY3C8JH4eAyI4L3xYsX0dbWxiVjObsmPSsDQCJHrL+7qKgIxcXF\\naGxsRHNzMwU+pVKJYDCI/v5++Hw+yOVy5OXlkQ2aXC5HYWEhzGYzKisrSWmQ9T+fOXMGzc3NGBkZ\\nwcjICKanpxGLxeD1etHU1AS73Y5z587RwArTQRkaGsLCwgJmZmag0+kgFAohEomwsLAAj8dD74fV\\nnKPRKABQv7RKpUJOTg4qKirQ398Pt9tNpsE+nw+VlZUwm814//vfj56eHvT19UEsFqOoqAg2mw3d\\n3d2kIf7666/jnnvuIfEnk8mE/v5+BINB1NXVUdtjaWkpzGYzrly5guzsbFRVVeHhhx+GVqul1srR\\n0VEUFxdTWyOzgfP5fFAoFKisrIRer1+jt72Tg8XNgvBROLjMiOC9vLwMh8MBi8Vy2EvhZBDrs6+y\\nsrI1IkcPPfQQ6VxfuHABL774ImQyGfLy8vDe974XXV1dcDqdmJqaQm5uLh544AEaP29vb8f8/Dzm\\n5uaorfDMmTMoLS2FzWaDVqtFJBIhPRK2Bq/XS1Zlg4ODJKEaDoeRSqUwMzMDlUpFgk7sYJJ1jIyM\\njNAh4KlTpzA+Po5XX30VExMTNEWpVqvhcrkglUppsIepBgaDQTQ0NODMmTMwGo1YXFxEMBik2jfL\\n+FOpFIaGhjA9PU2Hijk5OaRR8s4776C2thaTk5MkJWuxWFBRUYGKigokk8k1AZSJWbESytLSEoaH\\nh1FQUEDj+iyQr+/fXi/3ms5mQfgoHFxmRPDmcPZCOBxeszUPh8P0PSZyxMSPIpEIKdQNDg7inXfe\\nwdjYGD2P0+lEd3c3cnJy4HA44PP5MDExgUgkAq/XCwB4/vnnIRQKUVBQgNtuu43EqOLxOPVYK5VK\\nKBQKhEIh0keZnp6GwWCAx+PB1NQUFhYWkEqlyJmHtQFqtVrI5XLymVxeXkYymaQe7sXFRYTDq+YJ\\nQqEQjY2NcLvdAIBIJILS0lIUFBSgpKQEIpGITIdnZmZQUlKCgYEB5ObmIpFIwGq1YmpqClVVVWSO\\nEA6vGg/L5XIsLi5SoGVO72azGQAwMjICqVQKmUwGoVCIy5cvIxqNQqvVrnH1icfjiEQimJ+fh91u\\nBwDSJgFA061blT82C8K7ObjMVHjw5mQEexlpZp6HTNSoqanpKpEj9jiZTIbc3FwMDAwglUphcXER\\nMzMzpN0tEAgwPj6Oubk5lJWVob+/H2+++SZWVlYQDAYhEAjg9/uprU8ul1O7XjQaxcmTJ0mw6tix\\nY1haWkJPTw+AVY/M6elpLC8vQyaTkcxrJBKhf+fk5NDQDGtj1Ol0qKiowEsvvQSVSkW2Z2azmdaV\\nSqXwwAMPYGxsjBzfR0ZG4HQ60dPTA71ej56eHvT29sLj8ZAoV3FxMdRqNQ3zmM1mZGVloaioCIOD\\ngzCZTNS6aLPZUFlZiaqqKsq2FQoFotEoiouL0dbWBplMhu7ublRXV9O1USgUCAaDiMViNHHKtEkY\\nO6lNbxaEMz04b0fGBO9oNEotUOthAjicG4frod+93Wswz0Nmvuvz+a5yXmEj7wKBAHa7nWrJfX19\\nKCgowPHjx/H222+jvb0dHo8HVquVeqGZYQFzpwmFQvD7/fB6vSguLkZ2djZcLhdl35WVlbBarUgm\\nk8jKyqKJxLGxMeoKMRqNlHGyg1FmZyYSiZCXlwe1Wo26ujq88cYbpGeSn58PoVBI4k6shHHhwgW4\\nXC7MzMzAYrEgFotBoVDg7bffxuDgIGQyGXQ6HfLy8ihrn5ychFy+ajn2iU98Ak6nEyqVCmq1Gn/9\\n13+NV155herpOTk5KCwsxOnTp+HxePCHP/wBQ0NDkEqleM973kMlIJFIhHg8Thkx61RxOp1wu924\\nfPkybrnlll33bR9lMiJ4l5aWIhaLbWiVtry8jPvvvx8Oh+MQVsbZiOuh3y2Xy9HS0gKfzweNRoOm\\npqYN66Gs1sucZ9RqNU6cOIH29naqMzOxqXA4DKlUiuLiYgSDQVRUVMDn81EpJBwOk6+iXq+nsfiC\\nggIEg0GaWGRlGtah4nA4yLuRaWezMXWNRkOKhIFAAFlZWZBIJFheXiZvS7vdjoWFBbjdbkxPT6Oq\\nqooy1lAohJmZGWg0GuTn5+MLX/gCysrK8Nxzz1FZJi8vD/Pz8zAajZicnCSRLKZCqFarabLR4XBA\\nLBaTNdr3vvc9lJeXo7KyEmVlZVAoFHjggQegUqnQ09MDmUwGo9GIX/3qV5iamkJvby/uu+8+uN1u\\nlJSUQKvVoq6ujj4nNu5vMpkglUoRi8VQU1ND5aid9m3fLOJS10JGBO/y8nKMjIxs+L3HHnsMCwsL\\n13lFnK3Y7zasjbKvQCCAzs5OiEQiJJNJOBwOGmYB/vTHXV1djVdeeQWhUAiTk5Ow2+0kBKVQKLC0\\ntETqezKZDA6HAwsLC8jPz4dYLMbAwAB1f8TjcchkMrz99tsQi8UoLS1FWVkZampq8LOf/QwjIyPU\\nAsiyzHfeeQeBQIBEmZh/ZGNjIywWC+ley+Vy2O12nDhxgqYdTSYTua93dHQgFotBrVZDq9WioaEB\\noVAI/f39ZOgwOztLsrYA6LXa2towOjpKTvLj4+Po6emh8lF+fj7uu+8+mricm5ujmwfTUrl06RI8\\nHg8MBgNOnz5N05YymQyFhYV44YUXsLi4iNHRUbS2tqK2tpZUGTeSdBWJRGss5pRK5aa/Ixv1dWd6\\nj/Z+kBHBm5NZbLXV3UvGtNXhE/NXTH/+QCCA7u5uhMNhDAwMUDcFy2KZUW5bWxsSiQSNiCeTSbz9\\n9tvw+/00NMPcakpLS6FQKDA9PY1f/epXEIlEMJvNMJvN6OzshNPphFKphF6vRzweR09PD15//XXq\\nDIlEIpibm4PVaoXZbKaOk2g0CqPRCL/fj+PHj0On00GpVKK3txdOp5O6T4RCIWmkKBQK3H777bj7\\n7rvx1FNPwe/3Y3p6GjqdDjMzM6Qb4na7oVQqYbFYUFxcDLPZTHVxJktrNpshk8kwPDxMUq5ZWVnw\\neDxIJpPo7+/HxMQEfD4fent7YTabEQqFyLknEAhAJBKRx2dFRQXy8vJgt9vR1dV1lXBUOlKplCzm\\ndvP7cDP0aO8HPHhz9p2ttrr7lTExh5X07Th7/pWVFbhcLlRVVSEajUKtVkMsFqOsrAynT5+GVCqF\\nw+EgHRCWNbIOC2ZikEgkUFFRgdzcXGi1WrS0tGBmZoa6MSYmJtDU1IT29nZMTExgfn4eGo2GpFqZ\\nTncymYRer4fVal1jtiAWixEKhcgkuKqqCkVFRTSwMj09Te/35MmTKCkpwcmTJ6HVaiESiRCJRNDQ\\n0IC5uTmMjY3R1GNBQQEFcGYEkUgkyKLN7XZDIpGgrq4O+fn5KCsrQzgcxuzsLK5cuYLp6WlkZWUh\\nJycHpaWlkEql6OvrQygUglAopFLT0tIS1Go1srKy8P73vx8tLS003JSdnX2VqNRmvyu7Dby8Dr7K\\nTRG8/X7/jksnGo3myH7Y15ON/ij3mjGxoBwIBEh3WiqVXrUdZ89pNBpJX5rVsCUSCf0cABrS6erq\\nwsjICLxeL2WQfr8fUqkUGo0GZWVlaGhoQDQaxcDAAHV7rKyswO124+WXX0YgECD1P5vNht7eXnoe\\nJgHrcDhQVlZG5RuWYZ87dw79/f2Ym5ujMkV9fT3GxsbQ0dFB/dJdXV3Izc2FQCBAYWEhWltbAQBO\\npxMLCwuYnZ2FUChEVlYWysvLadyd2ZtZrVYUFhaiqqoKcrkcbrcbZWVlGB0dJTedUCiEVCpFDkKx\\nWIw+M5lMBrPZjMLCQnLTYdojIpEIZ86cQX19PdmbrReV2s8a9c3Qo70fZHzwPnXqFF5//XWMjo5u\\n+9hwOAyz2YwHH3zw4BfGuYrtMqbN/sDD4VVDgvHxcarnnj59mib/ANC4Nnv+kpISCAQCHDt2DGKx\\nGE1NTWuyXmD1LMVqtdJYudFoxJ133onBwUHMzs6SoYBUKqWas16vh1qtRjQaJQU9qVSKUCgE91eN\\nTAAAIABJREFUkUhEnpTM6SY7OxsWiwW33nordXvMzc3BZDIhmUxSt0pOTg7poszOzqKwsBBKpZKu\\nlcViQVlZGXw+Hzo6OqjPmmXSNpuN7NB+//vfQ6FQIC8vD4FAAG63GyqVCiMjIygvL4dIJKIhmQsX\\nLqCwsBCxWAxnz57F0NAQpqamIBAIYLVaoVarUVtbi0AggOPHj1Ofdl9fHywWy5qbMDtzWH9T3Wu3\\n0FbsZxtgph5+Znzwfvzxx/H444/v6LFvvvkmPvWpTx3wijibsVXGtFVJRS6XQygUwuv1QqfTkZQr\\ny1QjkQiEQiFZmLW0tJAOR0NDAwKBAFpaWpBMJiEUCtHU1ISuri4Eg0GYTCbU19dT2+Dc3BxUKhUK\\nCwspg2X1YzYg89hjj+Hb3/42XC4XgsEgSktLUVpaikgkguLiYqysrKCjowMejwdzc3MIhUJobW3F\\nAw88ALVajcXFRQQCAeoDZ++LOdInk0kUFhbigQcewNDQELxeL/R6PZaXlyESiRCNRiEQCDA3NweP\\nx4Pl5WW4XC4oFAqYTCbY7XaMjo4iGAwiGo0iHA4jmUxibm4O5eXliMViyMvLo8lGp9NJvexswlQm\\nk+HWW2/FlStXEIvFSKyKmVaMjo6iqKgISqVyw+nF7TS2Nxu+ud6BNJMPPzM+eHMyi80ypq1KKumd\\nCQBIqEmhUODixYsAgNzcXNjtdvh8PggEApjNZoyNjZGXYzwex+TkJLxeLw27yOVyFBcXIxwOo6Oj\\nA/39/YjFYnSwd+rUKcRiMfh8Pvj9fmi1Wmi1WsTjcdx+++04e/Ysenp6EIlEsLi4iMLCQuTl5eH+\\n++8nNcAXX3wRBQUFEAgE6O/vRyQSQSKRQE1NDS5fvoxIJAK/34/Z2VkYjUaYTCbYbDaq0zNTiPr6\\neszNzcFoNKK/vx85OTkwGo1obW1FKpWidsZgMIj5+XmIxWLU1NRgYGCAPCqVSiVkMhkWFxcxOTkJ\\nsVhMQz16vZ40XzQaDe126urq4HA4KONmE6uFhYWorq6GyWTaNthttOPaTUA/SDL58JMHb84NwXYl\\nFdaZEAgE0NbWhjfeeANjY2MQCAQoKSmhVjsmGhUOhynwyOVyXLhwAQsLC+TX2N3dDQCQyWTIzs7G\\n4OAgJiYm4PF4cOrUKdLSXlhYgNlshlAoJPPc3t5eUtabm5tDfn4+FhcXyXVdJBJR3VipVEIsFkMk\\nEqGoqAhyuRwLCwsYGBhAJBKhg8usrCxkZ2dDrVbToSCbUJybm6OpyZycHNjtdoRCIUxMTMBkMiGV\\nSmFiYgICgQA6nQ42mw0LCwukrV1XVwer1QqXy4Xvf//71PN955134p577qE6t9PpRDQaRSqVwvHj\\nx1FWVkb92MDVE6u33XbbjoLrRjuunQb0gw6kmXz4eeSCNztVZ8hkMhgMhkNc0c3N+m3wZtvinWpR\\nMBW+goICGnQxGAwoLy8nnWjWPcECTyKRgFQqJSswn8+HWCwGmUxGgTAcDiMnJwfAqomBzWZDbW0t\\nenp6YDAYUFZWRjVggUCAmpoavP766wiHw5icnMTKygq6u7thMBiQTCbR1taGmZkZ6g5xOBwkKsUm\\nM6uqqrC8vIxYLIZIJIJwOIzKykpotVo4nU4ShVpYWMBLL70EhUKB6upqVFZW4vHHH0c4HIbX60Vt\\nbS2dBXi9XrS2tiIajWJxcRHFxcUYGBiAxWJBTk4O9Ho9kskkaaekUinccccdSCaTuO222xAOr8rl\\ndnd3o6urCxqNhqZSE4kETawyWVcW2Lcrd6zfce00oB80mXz4eaSCd0lJCfR6PV577TX62tjYGD7/\\n+c9DrVYf4spuTtbXE9MnGzcTGdou05LL5dQqp1AokJ+fj8rKSpw5cwYikQgtLS3o7OwEsJpxssMz\\n1r7HfBUDgQAdcn7kIx9Bc3MzGRzccccdEAqFGBgYwMDAAGQyGWpqamiKt7u7G5cuXaIODabN4XA4\\nEI/HcenSJTpILCgoQFVVFYaGhjAyMkKHi8FgED6fD5FIBHa7HYlEgvwtmaKfWq1GJBJBdXX1mi4p\\n1nedTCYRjUah0+nw7ne/m+Rn0z0ns7OzMT09jXg8jsXFRVI1NJvNtCPo6upCbW0t3G439Ho9wuEw\\nuru7aaQ9FApRiYWdAaTXuvdaN94qoG+lJLjfZKoGypEK3jabDR0dHWu+lpubSxoSnP1l/TaYTTZe\\ny7aYTew5HA5ycUnPyL1eL7nhTExMoLKyEnK5HFeuXMHg4CB8Ph9UKhXEYjEcDgdKS0vh8Xhw7733\\nIpVKrTm8Gxoaoo6PvLw8tLW1rTlQBIChoSEUFhZidHQUly9fJpPhxcVFRKNRRCIRBINBem02NCQU\\nClFbW4uysjIYjUYEg0EEg0EMDQ3BZrPBYrGQO43D4cDExARNIrrdboRCISqj2Gw2cl0vKSnBzMwM\\njEYjNBoN7HY7ybYmEgm8733vg0qlQldXFwBgZmYGAoEAP/rRjxCNRqHRaPDe974XwJ8mNEOhELKy\\nsuB2u1FfX0996yyo7me5YydKgpxVjlTw5lxf1m+DWT16t9vijWyumFPM+sxeq9UiHA7jnXfegdls\\nhtvtRlFREXWMhMOrsqZWqxX5+fmYmJiAUCjExMQErFYrqqurUV5eDqFQiCtXrkAoFGJmZgbPP/88\\n5ubmUFNTg7y8PIyOjlLrIeuTFggEWFhYgNFohEKhQCKRwOTkJHp6eiCVSunGwdT6AMBisWBsbAyR\\nSARTU1OoqKigkgmbyKyoqMD73vc+aiV87rnncOLECczOzuLEiRNU23/qqacQCoVQWFiIz33uczCZ\\nTPjd736H2dlZyrYdDgfVxhUKBTweDx1QWiwWKuM4HA5cvnwZSqUSExMTGBoaglgsxvj4OIqLi6FS\\nqXDixAkS+drPckcmHyJeT3jw5hwYG9UTd1tf3GpLvv6PPBaLobGxEWq1mgwM5ufnqVOE1cBZPbuq\\nqgr9/f1QqVQ0kDI1NUVO7SKRCD6fD9nZ2VCpVPB4PFhaWoLH46GAbTQaaViFDe/cf//9EAqFUKlU\\nMJlM8Hg8qK2tRSKRgFAoxOjoKMRiMZRKJa5cuULBVKPRYGpqCvPz8xAKhTCbzZibm8Ovf/1r/OEP\\nf0BJSQmCwSBef/31NR03CoUCyWQSZrOZ1pNKpXDp0iU4nU4IhcI10ra33HILlWQEAgEaGxvx/PPP\\nY3l5GWq1GiaTCSqViqzL3n77bQSDQRiNRni9XiQSCfh8PmrXZDdO1nd/rVlyJh8iXk948AYwNzdH\\nmRDDZDJRxwBn72xU11yfRW112LVVFrb+j5zJu7LWvpWVFeh0OlgsFmi1WiSTSZSUlEAqldLh5vj4\\nOFZWVgCsfuaTk5Mk71pXV4dgMAixWAyn0wmbzQa73Q6xWAyXy4XW1lZYrVaMj4+TaBWrJZeUlJDB\\nb0lJCRwOB3Q6HYqLi/Haa6+RA8/AwAAEAgFeffVVKJVKiEQiqNVq+Hw+as2Ty+UIBoPUKhiLxVBV\\nVYXZ2VkcP34ciUQCvb29WF5extzcHJUdXC4XlpaWKEjX1tZCKpVuOJ36yCOPUM2bSbjq9XpqtdTp\\ndGQWEYvFoFKp1liXsa/t5DPdye9Mph4iXk+OfPB+97vfjebm5jVf83g81K/LOVg2yqyBP41eb5WF\\nrf8jZ4HeYDDg3LlzKC4uRl5eHh1QNjQ00M2DBYTGxkYEAgGSjlUoFEilUpBIJHQQeeLECTQ1NQFY\\nbZdraWmB1WqF1WolnQ+m761UKgEACoWCsufa2lrccsst6Ovrw69//WvE43FIJBKYTCbqs3Y6nSgu\\nLsbs7CwNy7BsX6vVIhqNkha3zWajzhE23SmRSPAXf/EXGB0dxeTkJC5fvozW1lZotVqUlJQgKysL\\nFouFdE7YeQFDKpUiKyvrqmvLRv+j0SjJEwCrzjzJZHJT8TFesz54jnzwfvrpp6/62k9/+lN8+9vf\\nPoTVHD3WZ9ZMETC9Y4S1qm2UhaVn8umBnrm5A6DnZM9RW1sLn8+35tAtlUpBIBCguLgYoVCIxuvL\\nysroNQKBANrb26kr5N5778WLL74IhUJBa62vr8eFCxdgNBrR09ODlZUVak1lh4rMhIHJyubm5tIh\\nZF5eHgwGAyYnJxGNRiGVSrG0tITc3FxYrVY0NDSQ9rVGo0FLSwsikQgGBwfJCJg5AOXk5JADjkaj\\ngUAgQGdnJwKBAKampmhCkt0wN5Jt1Wq1lKVHo1Fyjw+Hw6ipqaGR+fVyBtdSs+bBf2cc+eDNOVzW\\nZ9YAqGMEAI2Q7+SPf30mDgCXLl0i66+GhgZ4vV786Ec/QjgchkgkwrFjx5BKpeByuVBWVkYTi3q9\\nHolEgqzIZDIZ/H4/XC4XjdwXFRVBIpEgNzcXAFBfX4/f/OY31GZnNpsRCARgtVohFosRDAbh9Xrp\\n/VitVigUChw/fhznzp3D9PQ0Hez+4Ac/wODgIKxWKwwGA/Lz8zE9PY3x8XGEw2HcfvvtCIdXJW/H\\nxsYwOjqKUCiEU6dOob6+HgKBgCYh00s93d3diEQiiMfjKCkpQSQSQSAQIDGprYIlkwfw+XwoLS2F\\nRCJZoxWz2We6Wc16Ky0bfmC5PXsK3n6/H1/+8pep1vUP//APOH78+H6v7Uhy1K7tRgGXjaAz5xu5\\nXL7jGmp6Js7+8JlN3srKCpLJJEKhENxuN7q7u9Hd3Y1bbrkFLpcLV65cwfLyMuRyOSQSCZU+fD4f\\n5HI5Tp48CWDVc3JmZgaBQABSqRR5eXkAVl2duru7oVAo0NraiqKiItK+DodXXdldLheVctra2ug9\\nCgQCXLlyBalUCuXl5Xjsscdw4cIFDA8PUzsfABKDWlpaAgCEQiFy+1EoFIhEIrj11lvhcDiwvLwM\\no9EIrVaLCxcuULviW2+9RQNH7HeLBUvWB87G3tMVHUdHR2Gz2aDValFQULChrslGn+lGn9d2Wjb8\\nwHJ79hS8f/jDH+L06dP42Mc+BpfLhS996UsbWpRlMoFAAPPz82vqgNeDo3Bt17P+EJP1cQOgr+9m\\nG80CPWthC4fDqK2thcPhgEQiwejoKIaHh6FSqaDT6TA/Pw+z2UwtgCMjI9SpEo1GodfrYbPZ4PP5\\nUFdXR2PuCoUCOTk5CIfDUCqVeOWVVzA/P0/Th8ztPRQK0Uj82NgYlpeXcfz4ccqKmZ+jUCjE2NgY\\nGRzce++9OHv2LBKJBEKhEP71X/8VV65cgVqtxuDgIHW8MOLxONmwnT9/nrTOH3nkEdTX11Nv9+jo\\nKI4dO4bCwkLSLZHL5fB6vXC5XABA5RSWBTO3IVZKqqmp2VLXZLvBl+20bPiB5fbsKXh//OMfp7FY\\ndsp+M3H8+HGo1Wo888wz+MIXvnBdpy9v9mu7Gesz63RLM/bHrVAoyHeRfZ855wAbB/r1LWyJRAIf\\n+MAHYDKZMDs7S3VnsViMzs5OxGIxCIVCCIVCmEwmiMViLC4ukvogk1JwOp2kVMgmMLu6unDmzBnS\\n4e7q6sLMzAykUimEQiEZM8RiMczMzEAmk6GzsxP5+fnke8nG+pniHxta6ezshNVqpVIIC3bZ2dk4\\nefIknE4n7HY7otEotTNqNBp4vV643W6YTCbU1tYiFotBqVQimUyS9RgLliyb12q1a8Si5HI5fD4f\\nFhYW0NvbC41GQxOte2W77DpTpx6vJ9sG7/Pnz+PZZ59d87Unn3ySvP6eeOIJ/NM//dOBLfAwqKys\\nxKVLl5CdnX3Vqfx+88lPfhISiYT+fbNf241I30JLJBLKBtO30RKJBJcvXwawmhXW1dVRS1z64abD\\n4VhTAmCBiwVuNj4fi8VQVFSEkydPQiqVoru7GzabDcPDw5DL5RgfH4fNZsP09DTcbjf8fj/q6+tx\\n7tw5AKBaMQs6TDLWYDBArVaTIw6rCfv9fhpZNxgMdNMIh1d9Npn2+MDAAHWwtLW1IRwOQygU0s+x\\nEolKpSL9kmg0ioWFBRgMBkxPTyMajWJ+fh4ulwsajQYajQYi0aphArBaalEoFKTUyN6HyWRaoyGe\\n3pu/tLREXTBMg5wlGXuBZ9fXzrbB+8EHH9zQvGBgYABf/vKX8fd///d0Ws3ZPc888wxsNtuarx2V\\na8uybfa/CoUCbW1tWFlZgUqlIucbkUgEh8MBn88HrVaLjo4OBINByGQyOgAEVls8AWxYAqitraVJ\\nQ4FAgJmZGRKVKigowPDwMJklJJNJqFQqZGVlIRKJQC6XI5lMkg43uxEMDw9T0C4sLEQqlaLReFZj\\nj0ajmJ2dhdVqxbFjx1BaWoqsrCz8/ve/xyuvvAK32w2FQoGTJ08iNzcXWVlZaG5uht/vx//93/+h\\nsLAQTqcTZrOZbmhy+aq+eWVlJYlusevGvCn/7M/+DDKZDCKRiPrNmR4MazkEri5HbRRQRaJVt3c2\\n2KNQKPalDs2z62tjT2WT4eFh/N3f/R2+853voLy8fL/XdKQ5Ktd2fbYtkUjgdruRSCQwNzcHv9+P\\nRCKBW265BSqVCiqVioZGAMBgMMDv92N0dBTNzc1IJBKQSCS466671pQAVCoVFhYW8P3vfx+xWAyz\\ns7MkvsT8JkUiEV566SVIpVIEg0EUFhZCq9WSvvbAwAC8Xi8WFxfR1dWFiooKeL1eKBQK0iWJxWII\\nBoPIzc2l9rzKykpkZWWhv7+f1t7e3o6CggJYrVYIBAJMTEyQaQKwaj0mEAig0WgQj8cRDAYxOTkJ\\ni8WCYDCI6upqcqJn14XtCuLxOIRCIZXaWNmNBdpwOEzGCltpzWwUUHmmfOOxp+D91FNPIRqN4pvf\\n/CZSqRS0Wi2++93v7vfabgiY3Od2MCeUa+WoXNv1B1as60EkEqGnpwdqtRoDAwOIxWIwGAyUFbKh\\nEVZOsNvtcLlcNI0YDodpFF4mk6GtrQ0ejwfT09OoqqpCJBKBzWaDRqNBaWkpJiYmsLi4CLlcjsrK\\nShJ7UiqVUKvVeOihhzA3N4e+vj6YzWa0tbXh0qVLEAgEUCgUsFgs6OrqgsvlQl9fH1KpFBQKBUpK\\nSqBUKql32+v1Qi6Xo7S0FDk5OZDL5Xj55ZcxNTVF3SbpNeZQKETysJcvX8bU1BTEYjEqKyvh9XrX\\ndHowR3hmHpEerNMDrUQiIbEpVoePx+NXPd9m8Ez5xmJPwft73/vefq/jhuTDH/4wWlpatn1cJBLB\\n/Pw8Pv3pT1/zax6Va7v+wIqVBNhBWCgUwujoKIxGIwV65pPIhkYkEgneeOMNzMzM0AFc+hlFUVER\\nVlZWUFlZiaeffhqtra0Ih8MoKipCLBZDdXU1brvtNrzxxhtQqVRwOp3Iz88nDW6WBRcWFmJxcREe\\njweRSARKpRJCoRBnz56Fz+ejDLempgbV1dUYGhqinUA8HofZbKZeb4FAgOzsbFRWVpK7TzQahVAo\\npEB74sQJuN1uNDU1YWlpiWzd5ufnIRAIIBAI6CA2PZNe3w+/fly9vb2dtLxjsRh+/vOfU2viiRMn\\nKMhnqqfjUYMP6WzBv//7v+/ocePj42hoaDjg1dxcbLYNT3fM0el0CIfDG47Fs8BUX1+P//mf/yEr\\nMOBPJRm/308Hj01NTSgpKcHIyAgCgQD0ej2VMgQCAT7ykY/A5/Ph7rvvxujoKAXp9EO7QCAAhUKB\\n7u5uyrBHR0fh9/vJSYcF5GQyidHRUZSUlJBhQlNT05q1Z2VlUdbNtFai0Siam5up7l5WVkaCVIlE\\ngpx2WIa9vmMjvU0yvcuG3QB1Oh0WFxfh9/shEokgEAjWHEDy6cbMgQdvzqGx2TZ8/Vj2VhmgVCpF\\neXk5ksnkGl1vVitmRgy1tbUQCASor68nazRmqPCLX/wCWq0WOp0OH/jABzY9tNNqtTh9+jTq6uoA\\ngA4tCwsLEQqF8NBDDyEWi2FsbAxisRjJZBJ9fX3QarVwuVw4ffr0mhZIlmFrNBo6dG1ubkZnZyd0\\nOh3sdjsA4NixY7hy5QoUCgX6+/tx/PjxDVUagdUDyGAwCJfLhcLCQqhUKjQ2NlKgX1lZQSwWg0aj\\nwczMDFKp1BqjBz7dmDnw4M25ihtl27xdjZWVSGpqahAMBqHRaOjxrNtjeXkZAoEABoOBDj9FIhG8\\nXi86OzsRDAYRCoXQ0NBAkrBFRUWbvm56D3oikUBdXR0NwxgMBiQSCVRUVCASiaCgoAC9vb1Ua16/\\n9vb2dppcZD3aTM3P4/FQ8GcHke9///sRCATgcDjW3FTSJ0qDwSDi8TjVtZm9mkqlQm1tLX70ox8h\\nEolApVLhwQcfhFQqvaotk083ZgY8eO8TrJOB+SBmKtdr23ytN4j13SonT55cE9BOnz6NUCiEgYEB\\nGAyGNRocgUCAAr9CoYBUKsXg4CDcbjeEQiFuvfVW6oHeao3M1Sc9821vb4dQKKQJxcnJSWoHTA+E\\n4XCY1uHxeDA8PIxQKASZTIby8nLY7XbU1dWhq6sLRqORxvSNRiMF1/XrYgbBzDg5EomQvAAA+Hw+\\nRKNRmM1mLC8vI5lMrtkJsPfEu0oyAx689wGLxYI77rgDzzzzDJ544olrGl44bK7HtvlabxCJRAJL\\nS0tkFuDz+dDZ2UnGAI2NjZBKpbjrrruon5nplFy4cIG6M0pLSxGPx1FcXIzFxUV0dnZifn4ely9f\\nhsPhwODgIMmgsp7z9WykpcImFH0+H/Ly8lBWVkaTlelO7KOjo/B6vZiZmUFOTg4ZHzscDphMJnpc\\nR0cHxGIxpFIpamtrN/UBjcViZBDMVP/SR9j1ej00Gg2ZLuj1+g2vL+8qyQx48N4HFAoFXnjhBSiV\\nyqu2x5nG9dg2X8sNYr1QUnFxMYDVdjmdTrfm+ZjpwtLSEiQSCZqbm9Hd3U2thna7HSqVCk1NTWhu\\nbkZbWxtkMhkFP6/XS4a+ALYdCU+/dkxoanR0lPwq068lm/BkY/HxeBxyuRxKpXJNwHU4HPB6vdDp\\ndDQxuv7aMf0WiUQClUq15rCVXTP2uPWmC5zMhQdvzhqux7b5Wm4Q6V0TRUVFKC8vh1wuR19f31XP\\nxwI9qy3L5XKoVCrMzs5Sj3Y4HEYymaTRcebGrtfrMTw8jL6+Puj1eiSTyW1vMqzNb2lpCVeuXKHD\\nSqa/nX4tWaBmE5yszs68OZeWlqDX6+lrzDhbqVRCIpHQexUKhXjjjTcQiUSg1+vps+vu7kZ7eztJ\\nL7DOk8bGxusutsY5GHjw5lzFfm6bN6ptX8sNIl0oKZlMwul0Uk91ujEAC4DLy8vUSx2Px2Gz2SAQ\\nCMjkQKfT0RruuOOONZnssWPHIBQKaSBoo0w2fe3sZrGwsACXywWZTAaPx0OmEIlE4qpr4PV6cf78\\nebS1tUGpVOK9730vXnjhBXi9Xuj1ejz66KMkDWAwGBAOh+lGIJFIcOHCBbz44ouQy+XIz8+n78Vi\\nMajVaszPzyMSicBqta7pl7/Wz5Bz+PDgvc84nU7KdvLz84/0af1Wte293iBYdtvc3IxwOIzBwUHq\\nFAFA5YP0Tg72+jk5OdQrPTg4iKysLOh0ujXPne7Ko9FoUFJSglgsRj3arA+buaan18IDgQA6OzuR\\nTCbR0tKCmpoaaLVaiEQitLe3b3gN2Fi9Xq8nIavXX38dRUVFpMWSlZVFI/3pA03pZZNoNEqSAukS\\nr+zGNTMzQ2Je+/UZcg4XHrz3kY9//OPo7e0FsDq4MzExQSp0R5GDOvxkk49GoxHDw8OUQTOXmHA4\\nDLFYDKPRiIqKChQWFkIkEkEsFqOvrw+jo6OYnp7G4uIilU42Ms+tra1Fc3MzUqkU2tvb0djYiObm\\nZrS3t2N5eRkGgwGhUAh33XUXBXDmyhOPx6FUKlFUVIRwOExTkEzYimWx7BBxYWEBYrGYdgbBYBAS\\niYS6YjbaqTAVQ6vVipGRESofNTU1XaXv4na7ryrdHOZnyLl2ePDeR9I1SL71rW/ht7/97SGu5vDZ\\naW17t9tyJhHb2dlJXRiVlZXo6OjA+Pg4OZ5XVFRAo9Hg7NmzlCmzMohcLkckEqHnY7DMmsmy9vf3\\nQyKRYHx8HEVFRUilUlAqlRgZGUEsFiOlvjNnzkClUqG8vBw+nw8ymYy6XxQKBQKBAMRi8RovTdYV\\n88gjj2BpaQnDw8NIJBK4/fbbYTKZsLS0hKGhIUxNTaGxsZGCZvr1ampqQlFREdrb22E2m9eURpjE\\nKzsj2EvQ5X3fNy48eHMOjGu1w9rqeVkXBtM+YaPeHo8HOp0O+fn5qK6ups4NlhlvpWnNOlI6Ozuh\\n1+thMpnIBDiVSiEajSKZTKK4uBjBYBDAqiAZM05QqVTUkRKPxyEWi2nsnUnftre3X5XFSqVSkoMN\\nh8PUIdPe3k4WauyxG5VtsrKyYDKZNpQSqKysBICrTIL38zPkHA48eHMOlGuxw9oKlUpFB3isDswC\\nMcuON7Lp2krTOpVKIR6PQ6fTwePxwGazobGxkYyHf/GLX0AikaCqqgqPP/44Ll26RK/FAibTZlkf\\n7FQqFRKJxI7cYxKJBFwuF8bGxjA+Po7a2lpIJBJ4vV60traiq6sLy8vLMJlMJJu7kWPQ+pviXuF9\\n3zcmPHgfIEtLS+jv7wcAckph4kmcVfa6Ld8oI2SqhFtliVtpWjPXervdvkYsan5+Hr29vZiZmaHh\\nHoFAsOlrbaXZspMslumxVFVVIRgMorKyEu3t7XC73XA6nXQYmZeXR16WrE2QPSevVd/88OB9QNx9\\n99147bXXsLy8DABoa2tDKpVCRUXFIa9sf9iv9rFr2ZZvFCQ3C5zrTYnZzUKv19O/lUrlhhksADKM\\nYGUU9v3dBsSd/Awbc/f7/eSfGgwG6cbGWg9tNhsmJiZoB5IeoHmt+uaHB+8Dor6+Hr/73e/o3+9+\\n97vJjirT2axOvdeAvhMBqmu5UayXWV0foNffPFh9PH2aUyaTobGx8ZrMd9e/j83eV/qYO1MbZMGc\\n1envueceGsbZTDaX16pvbnjw5uyajbbkTGJ1t/3A2wXma+0zjkajePXVV9Hf3w+DwQDX/e9nAAAZ\\nNUlEQVS73X6VacFmN4/095lIJCAWi6FWq69ZSEsuX3W1Zxol602X2SQoG3MXiURrgrlUKqX/tpLN\\n5bXqmxsevDm7ZqMt+V5qrDsJzNeqg9Lc3IyBgQGsrKxAKBRS+95u3ufKygoEAgFyc3P3PKW4/n2w\\nWrtUKsU777yDlZUVmM1mugbrdbo30iwBeIA+yvDgfR2Zm5tDZWUlBALBYS/lmthoS76XGutWgXmz\\nGvVudVCSyST0ej0EAgHKysroEHI37zPdN3O9Yw1bz3blifXXR6/XQyKR4J133sHo6Cg0Gg31ZLMM\\nPD0o8xIIZz08eF8nHnnkETz++OOoqKhAbm7uYS/nmlkfXPZSY90o4CcSCQQCgTXDLOtr1BuxUfmF\\nlR9sNhuys7Nxxx13bKmkt5kOy3pXHwBrtMSBtcJPm2l/r78+DoeDnHT8fv+WuwKeYXPWw4P3deKh\\nhx7CN77xjYyXjN2K3QaYjcoDly5dwsrKClwuF2mWrK9Rr2ejenJ64G9uboZIJEJXV9emwXW7Es5G\\nut1qtRpLS0tIJpPUerhVSWX99UmfggSwq10Bh8ODN+dQWR8UmclvMpmE2+0m1b+tSC+/eL1eNDc3\\nUxbLylQajWbL4MqeQ6FQYGVlBYFA4CqXGQYbz19cXKQDxb2UdXhHCOda4MGbc8PA3GVYS9wtt9xC\\nqnxbkV5+EQgEZKobCATo+1sF10QigUQiAaFQiMuXLwMANBoNKQluBQvAG5V1dtLiyMshnL3Cg/d1\\nZmhoCEql8oadtLye2s3rXyvdXSYej0Mqle5oDekZbLocLBub3yq7TS+XxONx2O32qwSe1sNKOWaz\\nGYFAYMOyDpdS5Rw0N2YEuUn53Oc+B7/fjwsXLhz2UjaEBRz2H5suvF6vJZevusskk8mrDHu3g2Ww\\nUqkUjY2NaGhooIDJvrfZuDwruTDj4I2GXtLZSWdN+vMymVoOZz/hmfd15DOf+Qz0ej2+/e1vH/ZS\\nNuR66mFs9lr7UQNmrYs7eZ70QJw+Hi+RSLb8+e3U+vh4Oueg4cGbQ1zPgHOQr7WbksVmAleb/fxO\\n1fpEolXHH2b2y0smnP2GB28OcT27HzZ6rUQigZaWFni9Xuqt3ssadruDWH9ouNXP7/S5mXY3r3lz\\nDgoevA+BpaUlhEKhw17GhlzP7of1r8U8IMViMeLxOBwOx6bteltxrVn9Vj+/0+fmkqycg4YH7+vM\\n2bNnYTQa0dHRcdhLuaG5FgmBve4g0rtfNvv5nT43r3lzDhoevK8zeXl5+NCHPoTf/OY3h72UGw6V\\nSoW6ujr4/X6aOkwkEnu279pNprtRLXu7SUk2yr+Zoh8fwOEcJDx4c24YRCIRmpqaSNukvb39utWL\\nw+EwAoEApFIpgsHgtmWOnRyK8gEczkHC+7w5NxSs2yMWi23YI82y3f3uQWfTnW1tbXC5XCQ4tRm8\\nj5tz2FxT8HY6nWhsbEQ0Gt2v9RwJxsfHt33MUb62m9WLD3KIiE131tfXo7i4mBxsdrtGDud6seey\\nid/vx7e+9S3IZLL9XM+R4MKFC8jPzyd/y/Uc9Wu7Wb14vzs40g8o2XRnJBKBVColvZPd9IdzONeT\\nPWfeX/va1/DFL36RZxx7QC6XQ6fTbfr9g7y2B1V22G82GmnfTbabSCTg9Xrh9Xo3fK/rs3hg1fDg\\n+PHjAID29vZts/utxu45nINm28z7/PnzePbZZ9d8zWq14oEHHkB5eflNrU99PfjkJz+5pr56kNc2\\n08WSdprtsmGfzs5OAEBdXd1VAz+bZfHp9Xben825kdk2eD/44IN48MEH13ztz//8z3H+/Hn8/Oc/\\nx+LiIj7xiU/gxz/+8YEt8mbmmWeegc1mo38f5LW9GQZHdtLBEQ6H4fP5IBKJIBAI4PP5rnqvm2Xx\\nvJbNyRT2VPN++eWX6f+fO3cOP/jBD/ZtQUeBqakplJaWbvi9g7y2mRaYdiNPu75+rdFokEwmSdt7\\n/XvdLIvntWxOpnDNfd5M/J6zM7xeL2ZnZ2GxWLZ97H5f2/W614cZoLYLzLsp8USjUTQ3NyOVSkGp\\nVKKxsRFNTU1wOBwANlf+2yyL5/3ZnEzgmoP3q6++uh/rOFIolcotjXAZB3FtmVzqYda+dxKYdyMA\\n1dzcjM7OTuj1etjtdnrsXnRROJxMgQ/pHEEOe8BkJ6+/GwGoZDIJnU4Hj8dDP7sZmdJtw+FsBx+P\\nP4Icdu17J6+/GwEolUoFu90OgUCwpQP7bkox19MOjsPZCzx4HwKxWAzt7e2H9vqHfSi309ffSe15\\nN+9lN6WYTG6p5BwNeNnkOqNWq/H1r399TXvgYXDYAyb7+fo7fa69aHFz3RLOjQrPvK8zQqEQX/nK\\nV+B0OnH//fcf9nKOFFyLm3MzwYM350ix36UYDuew4MGbw9kA3uvNudHhNe9DgtdRORzOtcCD9yFx\\n8eLFw14Ch8PJYHjw5nA4nAyEB29ORsAnIzmctfADS84NDx+a4XCuhmfeh8Tk5ORhLyFj4EMzHM7V\\n8OB9SHR3dx/2EjKGTBya4WUezkHDyyaHhFDI75s7JdOGZniZh3M94BGEkxEcthbLbuBlHs71gAdv\\nDmef2WuZh5daOLuBl00OiWQyedhL4BwQeynz8FILZ7fwzPuQ+MxnPnPYS+AcILst8/BSC2e38OB9\\nSJSXlx/2Ejg3EJnYUcM5XHjZhMO5Aci0jhrO4cODN4dzg8BlaDm7gZdNOBwOJwPhwZvD4XAyEB68\\nORwOJwPhwZvD4XAyEB68ORwOJwPhwZvD4XAyEB68ORwOJwPhwZvD4XAyEB68ORwOJwPhwZvD4XAy\\nEB68ORwOJwPhwZvD4XAykD0JUyWTSTz55JPo6elBNBrF5z//edx+++37vbYjCb+2HA5nJ+wpeP/6\\n179GIpHAf//3f2Nubg4vv/zyfq/ryMKvLYfD2Ql7Ct5//OMfcezYMXzqU58CAHz1q1+96jHMh292\\ndvYalnfzwq7Ler9Cfm05nKPNZrFhPdsG7/Pnz+PZZ59d8zWj0QiZTIb/+I//QGtrK/7xH/8RP/nJ\\nT9Y8ZmFhAQDw8MMP72rhR43HHnsMSqWS/s2vLYfDAVb/zu12+6bfF6RSqdRun/SLX/wi3vWud+Ge\\ne+4BANx222344x//uOYx4XAY3d3dsFgs3BVkAxKJBBYWFuBwONZYXvFry+EcbTaLDevZU9mkoaEB\\nb775Ju655x709/fDarVe9RjmgM3ZnI3uqvzacjicrTJuxp4y72g0iq9//etwOp0AgK9//euorKzc\\n/Qo5V8GvLYfD2Ql7Ct4cDofDOVz4kE6G43Q60djYiGg0emhrCIVC+OxnP4tHHnkEjz32GObn5w9t\\nLQDg9/vx6U9/Gh/96Efx0EMPoaOj41DXAwCvvPIKvvSlLx3Ka6dSKfzzP/8zHnroIXzsYx/DxMTE\\noawjnc7OTnz0ox897GUgHo/jiSeewMMPP4wPf/jDeO211w5tLclkEl/5ylfwl3/5l3j44YcxPDy8\\n5eN58M5g/H4/vvWtb0Emkx3qOp5//nk4HA785Cc/wXve8x48/fTTh7qeH/7whzh9+jR+/OMf48kn\\nn8T/+3//71DX881vfhP/9m//dmiv/4c//AHRaBQ/+9nP8KUvfQlPPvnkoa0FAJ555hl89atfRSwW\\nO9R1AMBvfvMbGAwG/Nd//ReefvppfOMb3zi0tbz22msQCAT46U9/ir/927/FU089teXj93Rgybkx\\n+NrXvoYvfvGL+OxnP3uo63j00UfBqm/T09PQ6XSHup6Pf/zjkEqlAFYzq8O+udXX1+Oee+7Bc889\\ndyivf/nyZZw5cwYAUFdXh+7u7kNZB8Nut+O73/0unnjiiUNdBwC8613vwn333QdgNfMViw8vJN59\\n9904d+4cAGBqamrbvyMevDOAjXrtrVYrHvj/7d1vTFX1Hwfw971icFGJPyJMdNoDYTioha0UTRQB\\nQSEmLkS4xMzNyrYQ3EDD0R9DWmvIHLiJoFlRmcQyH5iTScVoyRKhyFCgWPJXEAFvwfBevr8HDub1\\nAD9QuN974P165P1w8L4n3jfnnnPu92zeDC8vL1jytMVIWTIzM+Hj44OEhATU1dXhxIkTVpGno6MD\\nKSkpSEtLk5olLCwMFRUVFskwEoPBgHnz5g0/trGxweDgILRaOW+8g4OD0dzcLOW5H6bT6QDc/zdK\\nTExEUlKS1DxarRb79u1DSUkJjhw5MvbGglQpJCRExMfHC71eL3x9fYVer5cdSQghRENDgwgKCpId\\nQ9TW1orw8HBRVlYmO4oQQojLly+L5ORkKc+dmZkpzp8/P/w4ICBASo4HNTU1iW3btsmOIYQQoqWl\\nRURFRYni4mLZUYZ1dnaK9evXi76+vlG34Z63Sj245klgYKBF93YflpeXBzc3N0RGRsLe3l76B4fq\\n6+uxZ88eZGdnw8vLS2oWa+Dn54fS0lKEhoaiqqoKnp6esiMBgEXfMY6ms7MTO3fuRHp6OlauXCk1\\ny9mzZ9He3o5du3bB1tYWWq12zHdHLO9pQKPRSH0hbN26FampqSgqKoIQQvoJsaysLAwMDCAjIwNC\\nCDg4OCA3N1dqJpmCg4NRXl6OmJgYAJD+8xmi0WhkR8CxY8fQ29uLo0ePIjc3FxqNBvn5+cPnTCwp\\nJCQE+/fvh16vh9FoRFpa2pg5eJ03EZEK8VJBIiIV4mETFeLCVETT15QuTEVy1dTUcDlYommusLBw\\nzAXoWN4q5OrqCuD+D9fd3X1C32s0GtHV1TXu7efMmYM5c+aMa9uKigocPXpUMa+srMTdu3cxe/Zs\\ns7m9vT0SEhKkXW9MNFF9fX3o7+83m3V3d6OsrEzxoaMrV67gzJkziv/39+7dg7e3NxYsWGA2d3R0\\nxIcffoju7m7ExcUNv85Hw/JWoaFDJe7u7li0aNGEv3/p0qWTnOi+RYsWISoqatzba7VatLW1Ka46\\ncHV1haOj42THI3psn3zyCXQ6nWKHIyIiYsS1a7744osJP0dTUxMA/N9DoixvkmbHjh3DS98O6enp\\nQVVV1Yh745s2beI65mQRNTU1uH79umLe09OD+vp66UtAACxvkqigoGDE+Uj37vv4449RWFiI+fPn\\nm821Wi0WL15sFdcM0/Rx/fp1REZG4vnnnzebu7q6WkVxAyxvskIjvV1cu3YtvvnmG/zxxx9m8xs3\\nbiA8PBzLli2zVDyaIV544QWEh4fLjjEqljepwqpVq0Zc3Ck0NBS3bt0yu4kzcP9WcS4uLpaKRyrV\\n09ODq1evKubt7e0S0kwMy5tUbePGjSgoKEBLS4vZvK6uDklJScOrxhGN5LfffsPAwAA2bNhgNt+w\\nYQPWrVsnJ9Q4sbxJ1ZKSkkZcxtPJyQmDg4MSEpHarFmzRvoNOx4FL7AlIlIh7nnTtDR79mycO3dO\\ncWcUDw8PrFq1SlIqksVkMqG6uhpGo9FsfvPmTaxYsUJSqsfD8qZp6YcffkBjY6PZrKmpCe+++y7L\\newZqamrCzz//jC1btpjNPTw8EBsbKynV42F507S0fPlyLF++3GzW1taG5ORkHDp0SLH9unXr4O/v\\nb6l4JMHSpUuRl5cnO8akYXnTjOHu7o6uri7Fh4BOnTqFkydPSkpFj8pgMODOnTuKeWNjo+LO9D09\\nPYo1RtSO5U0zykh3JpFx1xR6fOfOnYPJZDJbOE0IAZPJhOjoaMX2a9eutWS8KcfyJiJVMplM+PTT\\nT2fsOQxeKkhEpEIsbyIiFeJhE6JRXL16dcT1VGxtbREdHa1YT4XIkljeRKNobW3Fm2++iZdfftls\\nHhgYiH///ZflbSG//vorvv/+e8V81qxZiiWCZxKWN9EY3N3d4enpaTabbpecWbvu7m4cPHgQycnJ\\nZnONRjOjfxYsbyKyejY2Nryk8yEsbyIAzc3N+PHHH81mDy8zS2RNWN4040VEROCvv/6CEMJs7u/v\\nj5deeklSKqKxsbxpxnN1dcUHH3wgOwbRhPA6byIiFeKeNxFZBaPRiOrqasUdkFpbWyUlsm4sbyKy\\nCnV1daisrMTGjRvN5m5ubjz3MAKWN9EE2djYoLS0FHZ2dmZzFxcXrF69WlKq6cHX1xcFBQWyY6gC\\ny5togoqLi3Ht2jWz2d27d5GcnMzyJotheRNNkK+vL3x9fc1mt2/fVnwCkGgq8WoTIiIVYnkTEakQ\\nD5sQkcUZDAbFfSYNBoOkNOrE8iaSYGBgAM3NzYq5jY0NFi9eLCGR5fT39yMnJwfOzs6Kr7322msS\\nEqkTy5toEmi1WvT39+Pbb78d1/a1tbUYHByEn5+f2fz3339HbGwsPDw8piKmVTCZTLCzsxvxlxeN\\nH8ubaBI4OTnh4sWL6OjoGPf3hIWFmd35HABWrFgBk8k02fFoGmJ5E02SgIAA2RFoBuHVJkREKsQ9\\nbyKaMoODg4p10h9eeIoeDcubiKbM6dOnUVdXB41GYzZ/+umnJSWaPljeRDRlent78eeffypu4kyP\\nj8e8iYhUiHveRPTYqqurUVlZqZh3dXVBp9NJSDT9sbyJaNy6urpQXl6umP/zzz946623EB4ebjaf\\nO3futP/EqCwsbyIrYm9vjwsXLij2Vh0cHKzibjJ///03dDod3njjDbO5RqNBVFQUbG1tJSWbeVje\\nRFakqKgIDQ0Nivnq1autorwB4KmnnkJsbKzsGDMey5vIiri5ucHNzU12DFIBXm1CRKRCLG8iIhXi\\nYRMiFbC3tx/xrupeXl5Ys2aNhEQkG8ubSAVu3LiBrq4us1llZSUyMjJY3jMUy5tIBTw8PBQ3aOjp\\n6ZGUhqwBy5tohvvll19w+/ZtxXzhwoV49tlnJSSi8WB5E81w5eXlOHToEObOnTs8a21tRU5ODsvb\\nirG8iVRMCKFYL3vIw8uwjmX79u1wcXEZflxbW4ucnJzHzkdTh+VNpFJubm5oamrCe++9p/ja/Pnz\\n4e7ubjbTaDQICgrCk08+Oa6/32g04s6dO2az//77D46Ojo8emiYNy5tIpZYtW4a+vj7FvK+vD6Wl\\npYr5gQMH0NraOq7yXrBgAVxcXHDmzBnF17Zs2fJogWlSsbyJphmdTodNmzYp5tnZ2eP+O5ydnXHt\\n2rXJjEWTjJ+wJCJSIZY3EZEKsbyJiFSIx7yJZgitVovKyko0Njaazfv7+yd0WSFZB5Y30Qxx+PBh\\n/PTTT4p5amoqnJ2dJSSix8HyJpohvL294e3tLTsGTRIe8yYiUiGWNxGRCrG8iYhUiOVNRKRCLG8i\\nIhVieRMRqRDLm4hIhXidtwqZTCYAQFtbm+QkRDTZhl7XQ6/z0bC8VaijowMAEBcXJzkJEU2Vjo4O\\nLFmyZNSva8Ro91Aiq9Xf34+amhq4urpi1qxZsuMQ0SQymUzo6OiAj48P7OzsRt2O5U1EpEI8YUlE\\npEIsb5VraGjAc889h4GBAWkZ+vr6sHv3buj1erz66qu4deuWtCwAYDAY8PrrryM+Ph4xMTGoqqqS\\nmgcALl68iL1790p5biEE3nnnHcTExOCVV17BzZs3peR4UHV1NeLj42XHgNFoREpKCuLi4hAdHY1L\\nly5JyzI4OIi3334b27dvR1xcHOrr68fcnuWtYgaDAR999BFsbW2l5vj666/h4+ODzz//HBERETh+\\n/LjUPCdPnoS/vz8+++wzZGZm4v3335eaJyMjA4cPH5b2/CUlJRgYGMBXX32FvXv3IjMzU1oWAMjP\\nz8eBAwdw7949qTkA4LvvvoOTkxMKCwtx/PhxHDx4UFqWS5cuQaPR4Msvv0RiYiKysrLG3J5Xm6hY\\neno6kpOTsXv3bqk5EhISMHTqpKWlZVx3J59KO3bswBNPPAHg/p6V7F9ufn5+CA4OxunTp6U8/5Ur\\nV/Diiy8CAJ555hnU1NRIyTFkyZIlyM3NRUpKitQcABAWFobQ0FAA9/d8bWzkVWJQUBACAwMBAM3N\\nzf/3dcTyVoGioiKcOnXKbLZw4UJs3rwZXl5esOQ555GyZGZmwsfHBwkJCairq8OJEyesIk9HRwdS\\nUlKQlpYmNUtYWBgqKioskmEkBoMB8+bNG35sY2ODwcFBaLVy3ngHBwejublZynM/TKfTAbj/b5SY\\nmIikpCSpebRaLfbt24eSkhIcOXJk7I0FqVJISIiIj48Xer1e+Pr6Cr1eLzuSEEKIhoYGERQUJDuG\\nqK2tFeHh4aKsrEx2FCGEEJcvXxbJyclSnjszM1OcP39++HFAQICUHA9qamoS27Ztkx1DCCFES0uL\\niIqKEsXFxbKjDOvs7BTr168XfX19o27DPW+VunDhwvCfAwMDLbq3+7C8vDy4ubkhMjIS9vb20q89\\nr6+vx549e5CdnQ0vLy+pWayBn58fSktLERoaiqqqKnh6esqOBAAWfcc4ms7OTuzcuRPp6elYuXKl\\n1Cxnz55Fe3s7du3aBVtbW2i12jHfHbG8pwGNRiP1hbB161akpqaiqKgIQgjpJ8SysrIwMDCAjIwM\\nCCHg4OCA3NxcqZlkCg4ORnl5OWJiYgBA+s9niDXc9PjYsWPo7e3F0aNHkZubC41Gg/z8/OFzJpYU\\nEhKC/fv3Q6/Xw2g0Ii0tbcwc/JAOEZEK8VJBIiIVYnkTEakQy5uISIVY3kREKsTyJiJSIZY3EZEK\\nsbyJiFSI5U1EpEL/A5Irq324HXUvAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10ef598d0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Create some normally distributed data\\n\",\n    \"mean = [0, 0]\\n\",\n    \"cov = [[1, 1], [1, 2]]\\n\",\n    \"x, y = np.random.multivariate_normal(mean, cov, 3000).T\\n\",\n    \"\\n\",\n    \"# Set up the axes with gridspec\\n\",\n    \"fig = plt.figure(figsize=(6, 6))\\n\",\n    \"grid = plt.GridSpec(4, 4, hspace=0.2, wspace=0.2)\\n\",\n    \"main_ax = fig.add_subplot(grid[:-1, 1:])\\n\",\n    \"y_hist = fig.add_subplot(grid[:-1, 0], xticklabels=[], sharey=main_ax)\\n\",\n    \"x_hist = fig.add_subplot(grid[-1, 1:], yticklabels=[], sharex=main_ax)\\n\",\n    \"\\n\",\n    \"# scatter points on the main axes\\n\",\n    \"main_ax.plot(x, y, 'ok', markersize=3, alpha=0.2)\\n\",\n    \"\\n\",\n    \"# histogram on the attached axes\\n\",\n    \"x_hist.hist(x, 40, histtype='stepfilled',\\n\",\n    \"            orientation='vertical', color='gray')\\n\",\n    \"x_hist.invert_yaxis()\\n\",\n    \"\\n\",\n    \"y_hist.hist(y, 40, histtype='stepfilled',\\n\",\n    \"            orientation='horizontal', color='gray')\\n\",\n    \"y_hist.invert_xaxis()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This type of distribution plotted alongside its margins is common enough that it has its own plotting API in the Seaborn package; see [Visualization With Seaborn](04.14-Visualization-With-Seaborn.ipynb) for more details.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<!--NAVIGATION-->\\n\",\n    \"< [Customizing Colorbars](04.07-Customizing-Colorbars.ipynb) | [Contents](Index.ipynb) | [Text and Annotation](04.09-Text-and-Annotation.ipynb) >\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"anaconda-cloud\": {},\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.4.3\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "matplotlib/04.10-Customizing-Ticks.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<!--BOOK_INFORMATION-->\\n\",\n    \"<img align=\\\"left\\\" style=\\\"padding-right:10px;\\\" src=\\\"figures/PDSH-cover-small.png\\\">\\n\",\n    \"*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\\n\",\n    \"\\n\",\n    \"*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*\\n\",\n    \"\\n\",\n    \"*No changes were made to the contents of this notebook from the original.*\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<!--NAVIGATION-->\\n\",\n    \"< [Text and Annotation](04.09-Text-and-Annotation.ipynb) | [Contents](Index.ipynb) | [Customizing Matplotlib: Configurations and Stylesheets](04.11-Settings-and-Stylesheets.ipynb) >\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Customizing Ticks\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Matplotlib's default tick locators and formatters are designed to be generally sufficient in many common situations, but are in no way optimal for every plot. This section will give several examples of adjusting the tick locations and formatting for the particular plot type you're interested in.\\n\",\n    \"\\n\",\n    \"Before we go into examples, it will be best for us to understand further the object hierarchy of Matplotlib plots.\\n\",\n    \"Matplotlib aims to have a Python object representing everything that appears on the plot: for example, recall that the ``figure`` is the bounding box within which plot elements appear.\\n\",\n    \"Each Matplotlib object can also act as a container of sub-objects: for example, each ``figure`` can contain one or more ``axes`` objects, each of which in turn contain other objects representing plot contents.\\n\",\n    \"\\n\",\n    \"The tick marks are no exception. Each ``axes`` has attributes ``xaxis`` and ``yaxis``, which in turn have attributes that contain all the properties of the lines, ticks, and labels that make up the axes.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Major and Minor Ticks\\n\",\n    \"\\n\",\n    \"Within each axis, there is the concept of a *major* tick mark, and a *minor* tick mark. As the names would imply, major ticks are usually bigger or more pronounced, while minor ticks are usually smaller. By default, Matplotlib rarely makes use of minor ticks, but one place you can see them is within logarithmic plots:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"plt.style.use('classic')\\n\",\n    \"%matplotlib inline\\n\",\n    \"import numpy as np\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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cxXktSiWj2BiLgO+AnwYGZeMzi2CzgL3AC8BJwCbsrMMxFxAHgn8C/A\\nTzPz7yPiRGbetM3z2xOQpBFNrCeQmScjYvemw3uAZzPz+cFkTrC2/XNmsPd/LCJeB9wbEb8LPNZk\\nopKk9jVpDF8OvLhhfI61wrAuM38KfKzOky0tLTE/Pw/A3NwcCwsL69cBH+7r9XV8zz33FJVnlvJt\\n3FPuwnzMN9v5qqpidXUVYP3vZVO1TxEdrAQe2rAddCOwNzNvH4xvBfZk5p0jT6Lw7aCq8BtblJyv\\n5Gxgvr5rYzuoSRG4FjiSmfsG48NAZuZdI08iIpeXl1lcXCz6DZOkNlRVRVVVrKysTLQIzLNWBN4x\\nGF8CPMNaY/hl4HvAzZn59MiTKHwlIEnjMLELyEXEceAJ4MqIeCEibsvMV4GDwKPAU8CJnRSAoZIv\\nJV1qrqGS85WcDczXV1WLl5Kue3bQLdscfwR4pI2JtBVIkko33DpfWVlp/FyduXaQPQFJqmcqPYFx\\nsicgSaPzpjI9Ueq+5FDJ+UrOBuZTh4pAyY1hSWpTm41ht4MkqaeK2g5yJSBJ9bgS6JnSP7pecr6S\\ns4H5+q6olYAkafJcCUhSTxW1ErAnIEn12BPomdL3JUvOV3I2MF/fFbUSkCRNnisBSeopVwKSpEY6\\nUwRKbgyXmmuo5HwlZwPz9dXE7ycwCd5PQJLqKfJ+Al2YhyT1iT0BSVIjFoEJKHVfcqjkfCVnA/PJ\\nIiBJM60zPQHvMSxJ9XiPYUmSjeG+KH1fsuR8JWcD88kiIEkzze0gSeopt4MkSY2MtQhExHURcX9E\\nfCUiTo7ztbqs9H3JkvOVnA3MpzEXgcw8mZl3AA8DfzPO1+qy06dPT3sKY1VyvpKzgflUswhExNGI\\nOB8RT246vi8izkTE2Yg4dIGnuAU43mSiffbKK69MewpjVXK+krOB+VR/JfAAsHfjgYjYBdw3OH41\\ncHNEXDX43oGIuDsiLouINwOvZOb/tDjvX1F32Xehx231vc3HLjTe7r/bUOf5Rs221fFp5BvXe7fV\\n8ZLyjfr7Wlq+7bJO49/exR7X5b8ttYpAZp4Efrzp8B7g2cx8PjN/AZwA9g8efywzP5WZLwMfZa2I\\njFWX36jnnnuu1twupMtFoGm+LheBSb13F3vcuIpAn/PVKQJ9yjetIlD7FNGI2A08lJnXDMY3Ansz\\n8/bB+FZgT2beOfIkIjw/VJJ2oOkpop24qUzTEJKknWlydtAPgbdsGF8xOCZJ6olRikAMvoZOAW+L\\niN0RcSlwE/CtNicnSRqvuqeIHgeeAK6MiBci4rbMfBU4CDwKPAWcyMynxzdVSVLbOnHtIEnSdHT2\\n2kGlX3Ii1nwuIv4iIg5Mez5ti4jrI+LxwXv47mnPZxwi4vURcSoi3jftubQpIq4avG/fiIiPT3s+\\nbYuI/RHx5Yj4WkT8/rTn07aIeGtEfDUivlHn8Z0tAjNwyYn9rDXTfw6cm/JcxiGB/wZeS5n5AA4B\\nX5/2JNqWmWcG//b+CHjXtOfTtsz8h8Gp7XcAfzjt+bQtM3+QmR+r+/ixF4HSLznRIN/bge9m5qeB\\nT0xksjuw03yZ+Xhmvh84DHx2UvMd1U7zRcR7gH8D/oNfPmGiM5r824uID7L2P2DfmcRcd6KFvy2f\\nAb443lnuXAv56snMsX4B1wELwJMbju0Cvg/sBn4NOA1cNfjeAeBu4DLgzcBfjXuOU8p3APiDwbET\\n084xjvdvML4U+Ma0c7Sc78+Ao4Oc/wh8c9o5xvHeDY49PO0cY8j3JuDzwO9NO8M43z/g72q9zoTC\\n7N4U5FrgkQ3jw8ChLX7uCHDttN+MceQDXgd8Ffhz4I5pZxhDvg8BXwK+Brx72hnazrfhex8G3jft\\nDC2/d9cPfi+/VOjv5kHWTnH/S+D2aWcYQ77fAu4Hnt3u93bj17Q+MXw58OKG8TnWrkX0SzLzyKQm\\n1LKL5svMnwK19+06pk6+bwLfnOSkWlTr9xMgMx+cyIzaU+e9ewx4bJKTalGdfPcC905yUi2qk+9H\\nrPU7aulsY1iSNH7TKgKlX3LCfP1Wcr6Ss4H5RjapIlD6JSfMZ76uKjkbmK95vgk0No4DLwE/A14A\\nbhscfy/wDGvNi8PTbsCYz3yl5Ss5m/nay+dlIyRphtkYlqQZZhGQpBlmEZCkGWYRkKQZZhGQpBlm\\nEZCkGWYRkKQZZhGQpBlmEZCkGfZ/oG4thqPtPXkAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10346a438>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"ax = plt.axes(xscale='log', yscale='log')\\n\",\n    \"ax.grid();\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We see here that each major tick shows a large tickmark and a label, while each minor tick shows a smaller tickmark with no label.\\n\",\n    \"\\n\",\n    \"These tick properties—locations and labels—that is, can be customized by setting the ``formatter`` and ``locator`` objects of each axis. Let's examine these for the x axis of the just shown plot:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"<matplotlib.ticker.LogLocator object at 0x10dbaf630>\\n\",\n      \"<matplotlib.ticker.LogLocator object at 0x10dba6e80>\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(ax.xaxis.get_major_locator())\\n\",\n    \"print(ax.xaxis.get_minor_locator())\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"<matplotlib.ticker.LogFormatterMathtext object at 0x10db8dbe0>\\n\",\n      \"<matplotlib.ticker.NullFormatter object at 0x10db9af60>\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(ax.xaxis.get_major_formatter())\\n\",\n    \"print(ax.xaxis.get_minor_formatter())\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We see that both major and minor tick labels have their locations specified by a ``LogLocator`` (which makes sense for a logarithmic plot). Minor ticks, though, have their labels formatted by a ``NullFormatter``: this says that no labels will be shown.\\n\",\n    \"\\n\",\n    \"We'll now show a few examples of setting these locators and formatters for various plots.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Hiding Ticks or Labels\\n\",\n    \"\\n\",\n    \"Perhaps the most common tick/label formatting operation is the act of hiding ticks or labels.\\n\",\n    \"This can be done using ``plt.NullLocator()`` and ``plt.NullFormatter()``, as shown here:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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dixfDt4VDtdwF7bWFrRbWoCPvQh2hE9K7niBSA6YujpIVGrHp+X1ul2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\\nAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x110a2b7f0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"ax = plt.axes()\\n\",\n    \"ax.plot(np.random.rand(50))\\n\",\n    \"\\n\",\n    \"ax.yaxis.set_major_locator(plt.NullLocator())\\n\",\n    \"ax.xaxis.set_major_formatter(plt.NullFormatter())\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Notice that we've removed the labels (but kept the ticks/gridlines) from the x axis, and removed the ticks (and thus the labels as well) from the y axis.\\n\",\n    \"Having no ticks at all can be useful in many situations—for example, when you want to show a grid of images.\\n\",\n    \"For instance, consider the following figure, which includes images of different faces, an example often used in supervised machine learning problems (see, for example, [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb)):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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AyzgTCXFLWP7eeRLhCjT+qUpRIw1f+9c2w3O66eL9hcbujbHuccNjMY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\\ntV6C0st6WS/rz2q9BKWX9bJe1p/V+j8F2I3NFISt9gAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10dca2b70>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig, ax = plt.subplots(5, 5, figsize=(5, 5))\\n\",\n    \"fig.subplots_adjust(hspace=0, wspace=0)\\n\",\n    \"\\n\",\n    \"# Get some face data from scikit-learn\\n\",\n    \"from sklearn.datasets import fetch_olivetti_faces\\n\",\n    \"faces = fetch_olivetti_faces().images\\n\",\n    \"\\n\",\n    \"for i in range(5):\\n\",\n    \"    for j in range(5):\\n\",\n    \"        ax[i, j].xaxis.set_major_locator(plt.NullLocator())\\n\",\n    \"        ax[i, j].yaxis.set_major_locator(plt.NullLocator())\\n\",\n    \"        ax[i, j].imshow(faces[10 * i + j], cmap=\\\"bone\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Notice that each image has its own axes, and we've set the locators to null because the tick values (pixel number in this case) do not convey relevant information for this particular visualization.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Reducing or Increasing the Number of Ticks\\n\",\n    \"\\n\",\n    \"One common problem with the default settings is that smaller subplots can end up with crowded labels.\\n\",\n    \"We can see this in the plot grid shown here:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAXkAAAEACAYAAABWLgY0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\nAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10dc82e10>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig, ax = plt.subplots(4, 4, sharex=True, sharey=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Particularly for the x ticks, the numbers nearly overlap and make them quite difficult to decipher.\\n\",\n    \"We can fix this with the ``plt.MaxNLocator()``, which allows us to specify the maximum number of ticks that will be displayed.\\n\",\n    \"Given this maximum number, Matplotlib will use internal logic to choose the particular tick locations:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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3dEXIiIB8stUZI0ysQi3m/PdoTenuF3APsiYseIcU8Cr5depCRpdaXa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\\npIZZxCWpYRZxSWqYRVySGvb/Nl4/hirS/BsAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10dc82e10>\"\n      ]\n     },\n     \"execution_count\": 8,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# For every axis, set the x and y major locator\\n\",\n    \"for axi in ax.flat:\\n\",\n    \"    axi.xaxis.set_major_locator(plt.MaxNLocator(3))\\n\",\n    \"    axi.yaxis.set_major_locator(plt.MaxNLocator(3))\\n\",\n    \"fig\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This makes things much cleaner. If you want even more control over the locations of regularly-spaced ticks, you might also use ``plt.MultipleLocator``, which we'll discuss in the following section.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Fancy Tick Formats\\n\",\n    \"\\n\",\n    \"Matplotlib's default tick formatting can leave a lot to be desired: it works well as a broad default, but sometimes you'd like do do something more.\\n\",\n    \"Consider this plot of a sine and a cosine:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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Q1YsACYOlXW0Z7m6AgEBQEhIfKrVpMm8iApiHv3gL17gY0bgXXrgBs3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\\neTPGmBX6f/hhxZChns1rAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1152f8a58>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Plot a sine and cosine curve\\n\",\n    \"fig, ax = plt.subplots()\\n\",\n    \"x = np.linspace(0, 3 * np.pi, 1000)\\n\",\n    \"ax.plot(x, np.sin(x), lw=3, label='Sine')\\n\",\n    \"ax.plot(x, np.cos(x), lw=3, label='Cosine')\\n\",\n    \"\\n\",\n    \"# Set up grid, legend, and limits\\n\",\n    \"ax.grid(True)\\n\",\n    \"ax.legend(frameon=False)\\n\",\n    \"ax.axis('equal')\\n\",\n    \"ax.set_xlim(0, 3 * np.pi);\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"There are a couple changes we might like to make. First, it's more natural for this data to space the ticks and grid lines in multiples of $\\\\pi$. We can do this by setting a ``MultipleLocator``, which locates ticks at a multiple of the number you provide. For good measure, we'll add both major and minor ticks in multiples of $\\\\pi/4$:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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Dm2hgUHFjD/wHy2nN6SZrsOVTrwdou3aR3eOtsyx8fDjz/CqFHGD3gr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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1152f8a58>\"\n      ]\n     },\n     \"execution_count\": 10,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"ax.xaxis.set_major_locator(plt.MultipleLocator(np.pi / 2))\\n\",\n    \"ax.xaxis.set_minor_locator(plt.MultipleLocator(np.pi / 4))\\n\",\n    \"fig\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"But now these tick labels look a little bit silly: we can see that they are multiples of $\\\\pi$, but the decimal representation does not immediately convey this.\\n\",\n    \"To fix this, we can change the tick formatter. There's no built-in formatter for what we want to do, so we'll instead use ``plt.FuncFormatter``, which accepts a user-defined function giving fine-grained control over the tick outputs:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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1uw+cpmnLh3ItvtulTrgnFtxqF9QHur25yaCixcCEydKut2z3N1BUJC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\\nTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1152f8a58>\"\n      ]\n     },\n     \"execution_count\": 11,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"def format_func(value, tick_number):\\n\",\n    \"    # find number of multiples of pi/2\\n\",\n    \"    N = int(np.round(2 * value / np.pi))\\n\",\n    \"    if N == 0:\\n\",\n    \"        return \\\"0\\\"\\n\",\n    \"    elif N == 1:\\n\",\n    \"        return r\\\"$\\\\pi/2$\\\"\\n\",\n    \"    elif N == 2:\\n\",\n    \"        return r\\\"$\\\\pi$\\\"\\n\",\n    \"    elif N % 2 > 0:\\n\",\n    \"        return r\\\"${0}\\\\pi/2$\\\".format(N)\\n\",\n    \"    else:\\n\",\n    \"        return r\\\"${0}\\\\pi$\\\".format(N // 2)\\n\",\n    \"\\n\",\n    \"ax.xaxis.set_major_formatter(plt.FuncFormatter(format_func))\\n\",\n    \"fig\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This is much better! Notice that we've made use of Matplotlib's LaTeX support, specified by enclosing the string within dollar signs. This is very convenient for display of mathematical symbols and formulae: in this case, ``\\\"$\\\\pi$\\\"`` is rendered as the Greek character $\\\\pi$.\\n\",\n    \"\\n\",\n    \"The ``plt.FuncFormatter()`` offers extremely fine-grained control over the appearance of your plot ticks, and comes in very handy when preparing plots for presentation or publication.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Summary of Formatters and Locators\\n\",\n    \"\\n\",\n    \"We've mentioned a couple of the available formatters and locators.\\n\",\n    \"We'll conclude this section by briefly listing all the built-in locator and formatter options. For more information on any of these, refer to the docstrings or to the Matplotlib online documentaion.\\n\",\n    \"Each of the following is available in the ``plt`` namespace:\\n\",\n    \"\\n\",\n    \"Locator class        | Description\\n\",\n    \"---------------------|-------------\\n\",\n    \"``NullLocator``      | No ticks\\n\",\n    \"``FixedLocator``     | Tick locations are fixed\\n\",\n    \"``IndexLocator``     | Locator for index plots (e.g., where x = range(len(y)))\\n\",\n    \"``LinearLocator``    | Evenly spaced ticks from min to max\\n\",\n    \"``LogLocator``       | Logarithmically ticks from min to max\\n\",\n    \"``MultipleLocator``  | Ticks and range are a multiple of base\\n\",\n    \"``MaxNLocator``      | Finds up to a max number of ticks at nice locations\\n\",\n    \"``AutoLocator``      | (Default.) MaxNLocator with simple defaults.\\n\",\n    \"``AutoMinorLocator`` | Locator for minor ticks\\n\",\n    \"\\n\",\n    \"Formatter Class       | Description\\n\",\n    \"----------------------|---------------\\n\",\n    \"``NullFormatter``     | No labels on the ticks\\n\",\n    \"``IndexFormatter``    | Set the strings from a list of labels\\n\",\n    \"``FixedFormatter``    | Set the strings manually for the labels\\n\",\n    \"``FuncFormatter``     | User-defined function sets the labels\\n\",\n    \"``FormatStrFormatter``| Use a format string for each value\\n\",\n    \"``ScalarFormatter``   | (Default.) Formatter for scalar values\\n\",\n    \"``LogFormatter``      | Default formatter for log axes\\n\",\n    \"\\n\",\n    \"We'll see further examples of these through the remainder of the book.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<!--NAVIGATION-->\\n\",\n    \"< [Text and Annotation](04.09-Text-and-Annotation.ipynb) | [Contents](Index.ipynb) | [Customizing Matplotlib: Configurations and Stylesheets](04.11-Settings-and-Stylesheets.ipynb) >\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"anaconda-cloud\": {},\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.4.3\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "matplotlib/04.11-Settings-and-Stylesheets.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<!--BOOK_INFORMATION-->\\n\",\n    \"<img align=\\\"left\\\" style=\\\"padding-right:10px;\\\" src=\\\"figures/PDSH-cover-small.png\\\">\\n\",\n    \"*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\\n\",\n    \"\\n\",\n    \"*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*\\n\",\n    \"\\n\",\n    \"*No changes were made to the contents of this notebook from the original.*\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<!--NAVIGATION-->\\n\",\n    \"< [Customizing Ticks](04.10-Customizing-Ticks.ipynb) | [Contents](Index.ipynb) | [Three-Dimensional Plotting in Matplotlib](04.12-Three-Dimensional-Plotting.ipynb) >\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Customizing Matplotlib: Configurations and Stylesheets\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Matplotlib's default plot settings are often the subject of complaint among its users.\\n\",\n    \"While much is slated to change in the 2.0 Matplotlib release in late 2016, the ability to customize default settings helps bring the package inline with your own aesthetic preferences.\\n\",\n    \"\\n\",\n    \"Here we'll walk through some of Matplotlib's runtime configuration (rc) options, and take a look at the newer *stylesheets* feature, which contains some nice sets of default configurations.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Plot Customization by Hand\\n\",\n    \"\\n\",\n    \"Through this chapter, we've seen how it is possible to tweak individual plot settings to end up with something that looks a little bit nicer than the default.\\n\",\n    \"It's possible to do these customizations for each individual plot.\\n\",\n    \"For example, here is a fairly drab default histogram:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"plt.style.use('classic')\\n\",\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"%matplotlib inline\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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Xtsupo++a6qvgz8qO86ZqWqFqtqT7d8BNhPY+fXVNWzx2dvYLTP9JSj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\\nSUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1023b1a90>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"x = np.random.randn(1000)\\n\",\n    \"plt.hist(x);\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We can adjust this by hand to make it a much more visually pleasing plot:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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g6Iugvqsn3ABRGxFTgA7Ky5nm57Gfgd4B/rLqQbCrig34O0xjasjgJf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\\nSUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10c2bea20>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# use a gray background\\n\",\n    \"ax = plt.axes(axisbg='#E6E6E6')\\n\",\n    \"ax.set_axisbelow(True)\\n\",\n    \"\\n\",\n    \"# draw solid white grid lines\\n\",\n    \"plt.grid(color='w', linestyle='solid')\\n\",\n    \"\\n\",\n    \"# hide axis spines\\n\",\n    \"for spine in ax.spines.values():\\n\",\n    \"    spine.set_visible(False)\\n\",\n    \"    \\n\",\n    \"# hide top and right ticks\\n\",\n    \"ax.xaxis.tick_bottom()\\n\",\n    \"ax.yaxis.tick_left()\\n\",\n    \"\\n\",\n    \"# lighten ticks and labels\\n\",\n    \"ax.tick_params(colors='gray', direction='out')\\n\",\n    \"for tick in ax.get_xticklabels():\\n\",\n    \"    tick.set_color('gray')\\n\",\n    \"for tick in ax.get_yticklabels():\\n\",\n    \"    tick.set_color('gray')\\n\",\n    \"    \\n\",\n    \"# control face and edge color of histogram\\n\",\n    \"ax.hist(x, edgecolor='#E6E6E6', color='#EE6666');\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This looks better, and you may recognize the look as inspired by the look of the R language's ggplot visualization package.\\n\",\n    \"But this took a whole lot of effort!\\n\",\n    \"We definitely do not want to have to do all that tweaking each time we create a plot.\\n\",\n    \"Fortunately, there is a way to adjust these defaults once in a way that will work for all plots.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Changing the Defaults: ``rcParams``\\n\",\n    \"\\n\",\n    \"Each time Matplotlib loads, it defines a runtime configuration (rc) containing the default styles for every plot element you create.\\n\",\n    \"This configuration can be adjusted at any time using the ``plt.rc`` convenience routine.\\n\",\n    \"Let's see what it looks like to modify the rc parameters so that our default plot will look similar to what we did before.\\n\",\n    \"\\n\",\n    \"We'll start by saving a copy of the current ``rcParams`` dictionary, so we can easily reset these changes in the current session:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"IPython_default = plt.rcParams.copy()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now we can use the ``plt.rc`` function to change some of these settings:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from matplotlib import cycler\\n\",\n    \"colors = cycler('color',\\n\",\n    \"                ['#EE6666', '#3388BB', '#9988DD',\\n\",\n    \"                 '#EECC55', '#88BB44', '#FFBBBB'])\\n\",\n    \"plt.rc('axes', facecolor='#E6E6E6', edgecolor='none',\\n\",\n    \"       axisbelow=True, grid=True, prop_cycle=colors)\\n\",\n    \"plt.rc('grid', color='w', linestyle='solid')\\n\",\n    \"plt.rc('xtick', direction='out', color='gray')\\n\",\n    \"plt.rc('ytick', direction='out', color='gray')\\n\",\n    \"plt.rc('patch', edgecolor='#E6E6E6')\\n\",\n    \"plt.rc('lines', linewidth=2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"With these settings defined, we can now create a plot and see our settings in action:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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wI+AP4DuC4iflRvVd2Tmb8NTAKbgF+JiH+tt6LODfMF/TLzAeByYDoi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\\nwLCXpAIY9pJUAMNekgpg2EtSAf4fMlWmoloV10IAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10d680da0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.hist(x);\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's see what simple line plots look like with these rc parameters:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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7DP51twn+73+08CJ+NfP/HEE1TXl3PmZyNMjkrstiycLnN7rxwOB17v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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x11004e940>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"for i in range(4):\\n\",\n    \"    plt.plot(np.random.rand(10))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"I find this much more aesthetically pleasing than the default styling.\\n\",\n    \"If you disagree with my aesthetic sense, the good news is that you can adjust the rc parameters to suit your own tastes!\\n\",\n    \"These settings can be saved in a *.matplotlibrc* file, which you can read about in the [Matplotlib documentation](http://Matplotlib.org/users/customizing.html).\\n\",\n    \"That said, I prefer to customize Matplotlib using its stylesheets instead.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Stylesheets\\n\",\n    \"\\n\",\n    \"The version 1.4 release of Matplotlib in August 2014 added a very convenient ``style`` module, which includes a number of new default stylesheets, as well as the ability to create and package your own styles. These stylesheets are formatted similarly to the *.matplotlibrc* files mentioned earlier, but must be named with a *.mplstyle* extension.\\n\",\n    \"\\n\",\n    \"Even if you don't create your own style, the stylesheets included by default are extremely useful.\\n\",\n    \"The available styles are listed in ``plt.style.available``—here I'll list only the first five for brevity:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"['fivethirtyeight',\\n\",\n       \" 'seaborn-pastel',\\n\",\n       \" 'seaborn-whitegrid',\\n\",\n       \" 'ggplot',\\n\",\n       \" 'grayscale']\"\n      ]\n     },\n     \"execution_count\": 8,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"plt.style.available[:5]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The basic way to switch to a stylesheet is to call\\n\",\n    \"\\n\",\n    \"``` python\\n\",\n    \"plt.style.use('stylename')\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"But keep in mind that this will change the style for the rest of the session!\\n\",\n    \"Alternatively, you can use the style context manager, which sets a style temporarily:\\n\",\n    \"\\n\",\n    \"``` python\\n\",\n    \"with plt.style.context('stylename'):\\n\",\n    \"    make_a_plot()\\n\",\n    \"```\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's create a function that will make two basic types of plot:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def hist_and_lines():\\n\",\n    \"    np.random.seed(0)\\n\",\n    \"    fig, ax = plt.subplots(1, 2, figsize=(11, 4))\\n\",\n    \"    ax[0].hist(np.random.randn(1000))\\n\",\n    \"    for i in range(3):\\n\",\n    \"        ax[1].plot(np.random.rand(10))\\n\",\n    \"    ax[1].legend(['a', 'b', 'c'], loc='lower left')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We'll use this to explore how these plots look using the various built-in styles.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Default style\\n\",\n    \"\\n\",\n    \"The default style is what we've been seeing so far throughout the book; we'll start with that.\\n\",\n    \"First, let's reset our runtime configuration to the notebook default:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# reset rcParams\\n\",\n    \"plt.rcParams.update(IPython_default);\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now let's see how it looks:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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KGZtv7hB+/d9+RJePhhM0XtbpzgmKoGs/Xd8r15jziCZFe7Ys6WOW4l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\\nAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x11004ec18>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"hist_and_lines()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### FiveThiryEight style\\n\",\n    \"\\n\",\n    \"The ``fivethirtyeight`` style mimics the graphics found on the popular [FiveThirtyEight website](https://fivethirtyeight.com).\\n\",\n    \"As you can see here, it is typified by bold colors, thick lines, and transparent axes:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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T4GH38Ztq9+K6gAGwA0tyRHsfkEPmk04ZqPOvCHIwNeA+ylWXq8vCod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\\nhBBCCCHEx/4/P9Gwzw0UxnYAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1101a9390>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"with plt.style.context('fivethirtyeight'):\\n\",\n    \"    hist_and_lines()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### ggplot\\n\",\n    \"\\n\",\n    \"The ``ggplot`` package in the R language is a very popular visualization tool.\\n\",\n    \"Matplotlib's ``ggplot`` style mimics the default styles from that package:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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gr6L+E2jurMC6K6n6fHT2m2g8XFGexqGxo1KFZFJVBUAnVbEzLWmWLH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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x11006f160>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"with plt.style.context('ggplot'):\\n\",\n    \"    hist_and_lines()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### *Bayesian Methods for Hackers( style\\n\",\n    \"\\n\",\n    \"There is a very nice short online book called [*Probabilistic Programming and Bayesian Methods for Hackers*](http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/); it features figures created with Matplotlib, and uses a nice set of rc parameters to create a consistent and visually-appealing style throughout the book.\\n\",\n    \"This style is reproduced in the ``bmh`` stylesheet:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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Ww0us22yWjmyJ4SADbuyCQoeHK5wngojeME+LqOS/nnGkrj6Dy+7Ntc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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x11118d128>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"with plt.style.context('bmh'):\\n\",\n    \"    hist_and_lines()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Dark background\\n\",\n    \"\\n\",\n    \"For figures used within presentations, it is often useful to have a dark rather than light background.\\n\",\n    \"The ``dark_background`` style provides this:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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FWgdmx2B3A4aQgIiK9xxjE7LR1vX6bByNqSiVCbjMGDeyApKQOpqW/4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\\nAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1114bef28>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"with plt.style.context('dark_background'):\\n\",\n    \"    hist_and_lines()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Grayscale\\n\",\n    \"\\n\",\n    \"Sometimes you might find yourself preparing figures for a print publication that does not accept color figures.\\n\",\n    \"For this, the ``grayscale`` style, shown here, can be very useful:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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UAAb29vhIeHMxXukJCRkQEHBweFkzem6jnW1NQgMzMT48ePV7stShwJ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\\nhBJHQgghhBDSI5Q4EkIIIYSQHvl/RVB0HF7HtpYAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1114beef0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"with plt.style.context('grayscale'):\\n\",\n    \"    hist_and_lines()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Seaborn style\\n\",\n    \"\\n\",\n    \"Matplotlib also has stylesheets inspired by the Seaborn library (discussed more fully in [Visualization With Seaborn](04.14-Visualization-With-Seaborn.ipynb)).\\n\",\n    \"As we will see, these styles are loaded automatically when Seaborn is imported into a notebook.\\n\",\n    \"I've found these settings to be very nice, and tend to use them as defaults in my own data exploration.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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6+5CenxrJqfSUOrg3+8fwpZHj77I+0OJ5s/LkWlVHD/rRN9SuItdisy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\\nkUIIIYQQYtgkiRRCCCGEEMP2/wNVB0YyMqd1QQAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x111a0f278>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import seaborn\\n\",\n    \"hist_and_lines()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"With all of these built-in options for various plot styles, Matplotlib becomes much more useful for both interactive visualization and creation of figures for publication.\\n\",\n    \"Throughout this book, I will generally use one or more of these style conventions when creating plots.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<!--NAVIGATION-->\\n\",\n    \"< [Customizing Ticks](04.10-Customizing-Ticks.ipynb) | [Contents](Index.ipynb) | [Three-Dimensional Plotting in Matplotlib](04.12-Three-Dimensional-Plotting.ipynb) >\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"anaconda-cloud\": {},\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.4.3\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "matplotlib/04.12-Three-Dimensional-Plotting.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<!--BOOK_INFORMATION-->\\n\",\n    \"<img align=\\\"left\\\" style=\\\"padding-right:10px;\\\" src=\\\"figures/PDSH-cover-small.png\\\">\\n\",\n    \"*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\\n\",\n    \"\\n\",\n    \"*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*\\n\",\n    \"\\n\",\n    \"*No changes were made to the contents of this notebook from the original.*\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<!--NAVIGATION-->\\n\",\n    \"< [Customizing Matplotlib: Configurations and Stylesheets](04.11-Settings-and-Stylesheets.ipynb) | [Contents](Index.ipynb) | [Geographic Data with Basemap](04.13-Geographic-Data-With-Basemap.ipynb) >\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Three-Dimensional Plotting in Matplotlib\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Matplotlib was initially designed with only two-dimensional plotting in mind.\\n\",\n    \"Around the time of the 1.0 release, some three-dimensional plotting utilities were built on top of Matplotlib's two-dimensional display, and the result is a convenient (if somewhat limited) set of tools for three-dimensional data visualization.\\n\",\n    \"three-dimensional plots are enabled by importing the ``mplot3d`` toolkit, included with the main Matplotlib installation:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from mpl_toolkits import mplot3d\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Once this submodule is imported, a three-dimensional axes can be created by passing the keyword ``projection='3d'`` to any of the normal axes creation routines:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%matplotlib inline\\n\",\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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gUxsu3PrDrdWCxmWD7UjEiXxsNrrfJwIhTRqqUo2AgqlUqZ0p3VFbFC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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10bfc8780>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig = plt.figure()\\n\",\n    \"ax = plt.axes(projection='3d')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"With this three-dimensional axes enabled, we can now plot a variety of three-dimensional plot types. \\n\",\n    \"Three-dimensional plotting is one of the functionalities that benefits immensely from viewing figures interactively rather than statically in the notebook; recall that to use interactive figures, you can use ``%matplotlib notebook`` rather than ``%matplotlib inline`` when running this code.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Three-dimensional Points and Lines\\n\",\n    \"\\n\",\n    \"The most basic three-dimensional plot is a line or collection of scatter plot created from sets of (x, y, z) triples.\\n\",\n    \"In analogy with the more common two-dimensional plots discussed earlier, these can be created using the ``ax.plot3D`` and ``ax.scatter3D`` functions.\\n\",\n    \"The call signature for these is nearly identical to that of their two-dimensional counterparts, so you can refer to [Simple Line Plots](04.01-Simple-Line-Plots.ipynb) and [Simple Scatter Plots](04.02-Simple-Scatter-Plots.ipynb) for more information on controlling the output.\\n\",\n    \"Here we'll plot a trigonometric spiral, along with some points drawn randomly near the line:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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MbawqRgMJjyabE30XfPdUlClOqGQiFVTlGS2hXuPR4PsViMvLy8pAhX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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10e2fd898>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"ax = plt.axes(projection='3d')\\n\",\n    \"\\n\",\n    \"# Data for a three-dimensional line\\n\",\n    \"zline = np.linspace(0, 15, 1000)\\n\",\n    \"xline = np.sin(zline)\\n\",\n    \"yline = np.cos(zline)\\n\",\n    \"ax.plot3D(xline, yline, zline, 'gray')\\n\",\n    \"\\n\",\n    \"# Data for three-dimensional scattered points\\n\",\n    \"zdata = 15 * np.random.random(100)\\n\",\n    \"xdata = np.sin(zdata) + 0.1 * np.random.randn(100)\\n\",\n    \"ydata = np.cos(zdata) + 0.1 * np.random.randn(100)\\n\",\n    \"ax.scatter3D(xdata, ydata, zdata, c=zdata, cmap='Greens');\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Notice that by default, the scatter points have their transparency adjusted to give a sense of depth on the page.\\n\",\n    \"While the three-dimensional effect is sometimes difficult to see within a static image, an interactive view can lead to some nice intuition about the layout of the points.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Three-dimensional Contour Plots\\n\",\n    \"\\n\",\n    \"Analogous to the contour plots we explored in [Density and Contour Plots](04.04-Density-and-Contour-Plots.ipynb), ``mplot3d`` contains tools to create three-dimensional relief plots using the same inputs.\\n\",\n    \"Like two-dimensional ``ax.contour`` plots, ``ax.contour3D`` requires all the input data to be in the form of two-dimensional regular grids, with the Z data evaluated at each point.\\n\",\n    \"Here we'll show a three-dimensional contour diagram of a three-dimensional sinusoidal function:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def f(x, y):\\n\",\n    \"    return np.sin(np.sqrt(x ** 2 + y ** 2))\\n\",\n    \"\\n\",\n    \"x = np.linspace(-6, 6, 30)\\n\",\n    \"y = np.linspace(-6, 6, 30)\\n\",\n    \"\\n\",\n    \"X, Y = np.meshgrid(x, y)\\n\",\n    \"Z = f(X, Y)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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gS8pNsN6MpInPORndBRJCUlsWLFChobGykoKCAvL48NGzaICMeysjIs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\\nKbFT0DZMAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10e6a7fd0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig = plt.figure()\\n\",\n    \"ax = plt.axes(projection='3d')\\n\",\n    \"ax.contour3D(X, Y, Z, 50, cmap='binary')\\n\",\n    \"ax.set_xlabel('x')\\n\",\n    \"ax.set_ylabel('y')\\n\",\n    \"ax.set_zlabel('z');\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Sometimes the default viewing angle is not optimal, in which case we can use the ``view_init`` method to set the elevation and azimuthal angles. In the following example, we'll use an elevation of 60 degrees (that is, 60 degrees above the x-y plane) and an azimuth of 35 degrees (that is, rotated 35 degrees counter-clockwise about the z-axis):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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+fPMzAwwMDAAOPj41RWVlJXVyerXLfbLS/9fT4fU1NTUmoQqWIADodD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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10e6a7fd0>\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"ax.view_init(60, 35)\\n\",\n    \"fig\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Again, note that this type of rotation can be accomplished interactively by clicking and dragging when using one of Matplotlib's interactive backends.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Wireframes and Surface Plots\\n\",\n    \"\\n\",\n    \"Two other types of three-dimensional plots that work on gridded data are wireframes and surface plots.\\n\",\n    \"These take a grid of values and project it onto the specified three-dimensional surface, and can make the resulting three-dimensional forms quite easy to visualize.\\n\",\n    \"Here's an example of using a wireframe:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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vswie57gPhoKoqtKFDi7LggCFhbW6NWq1/Ib1sUcXFxXLx4ke7du/PF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\\n8+EtPkAn4IwZTVKoxPwfRbN8xzdvuqAAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10e6a7da0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig = plt.figure()\\n\",\n    \"ax = plt.axes(projection='3d')\\n\",\n    \"ax.plot_wireframe(X, Y, Z, color='black')\\n\",\n    \"ax.set_title('wireframe');\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"A surface plot is like a wireframe plot, but each face of the wireframe is a filled polygon.\\n\",\n    \"Adding a colormap to the filled polygons can aid perception of the topology of the surface being visualized:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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olEIh2RbTuWM9IdLT+FIlyqGHNsNhoSKUeY9eZazwPNrNpKvwgi2ayX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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10ecb7860>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"ax = plt.axes(projection='3d')\\n\",\n    \"ax.plot_surface(X, Y, Z, rstride=1, cstride=1,\\n\",\n    \"                cmap='viridis', edgecolor='none')\\n\",\n    \"ax.set_title('surface');\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Note that though the grid of values for a surface plot needs to be two-dimensional, it need not be rectilinear.\\n\",\n    \"Here is an example of creating a partial polar grid, which when used with the ``surface3D`` plot can give us a slice into the function we're visualizing:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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sQsYtPE03XKsktobBzRz7vXFhWpaCmbhhURaElJJrp8YAyOAM80KIsO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\\nde+rhRf0HQAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10eae2128>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"r = np.linspace(0, 6, 20)\\n\",\n    \"theta = np.linspace(-0.9 * np.pi, 0.8 * np.pi, 40)\\n\",\n    \"r, theta = np.meshgrid(r, theta)\\n\",\n    \"\\n\",\n    \"X = r * np.sin(theta)\\n\",\n    \"Y = r * np.cos(theta)\\n\",\n    \"Z = f(X, Y)\\n\",\n    \"\\n\",\n    \"ax = plt.axes(projection='3d')\\n\",\n    \"ax.plot_surface(X, Y, Z, rstride=1, cstride=1,\\n\",\n    \"                cmap='viridis', edgecolor='none');\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Surface Triangulations\\n\",\n    \"\\n\",\n    \"For some applications, the evenly sampled grids required by the above routines is overly restrictive and inconvenient.\\n\",\n    \"In these situations, the triangulation-based plots can be very useful.\\n\",\n    \"What if rather than an even draw from a Cartesian or a polar grid, we instead have a set of random draws?\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"theta = 2 * np.pi * np.random.random(1000)\\n\",\n    \"r = 6 * np.random.random(1000)\\n\",\n    \"x = np.ravel(r * np.sin(theta))\\n\",\n    \"y = np.ravel(r * np.cos(theta))\\n\",\n    \"z = f(x, y)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We could create a scatter plot of the points to get an idea of the surface we're sampling from:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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UFw6UgUqdJuNaLOhUQxULTD3qKnZ9odGXdVTB6I+yRysTAmzrG9vT2v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\\nONjCewrgEsX/B15cARoDcRRCAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10e7947b8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"ax = plt.axes(projection='3d')\\n\",\n    \"ax.scatter(x, y, z, c=z, cmap='viridis', linewidth=0.5);\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This leaves a lot to be desired.\\n\",\n    \"The function that will help us in this case is ``ax.plot_trisurf``, which creates a surface by first finding a set of triangles formed between adjacent points (remember that x, y, and z here are one-dimensional arrays):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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jqmiZCjEDK/QaSaKPkiKv8ptfafT85/pybfpRNWiXuRDI/rnJIkoV6v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\\nrkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10ef33e80>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"ax = plt.axes(projection='3d')\\n\",\n    \"ax.plot_trisurf(x, y, z,\\n\",\n    \"                cmap='viridis', edgecolor='none');\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The result is certainly not as clean as when it is plotted with a grid, but the flexibility of such a triangulation allows for some really interesting three-dimensional plots.\\n\",\n    \"For example, it is actually possible to plot a three-dimensional Möbius strip using this, as we'll see next.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Example: Visualizing a Möbius strip\\n\",\n    \"\\n\",\n    \"A Möbius strip is similar to a strip of paper glued into a loop with a half-twist.\\n\",\n    \"Topologically, it's quite interesting because despite appearances it has only a single side!\\n\",\n    \"Here we will visualize such an object using Matplotlib's three-dimensional tools.\\n\",\n    \"The key to creating the Möbius strip is to think about it's parametrization: it's a two-dimensional strip, so we need two intrinsic dimensions. Let's call them $\\\\theta$, which ranges from $0$ to $2\\\\pi$ around the loop, and $w$ which ranges from -1 to 1 across the width of the strip:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"theta = np.linspace(0, 2 * np.pi, 30)\\n\",\n    \"w = np.linspace(-0.25, 0.25, 8)\\n\",\n    \"w, theta = np.meshgrid(w, theta)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now from this parametrization, we must determine the *(x, y, z)* positions of the embedded strip.\\n\",\n    \"\\n\",\n    \"Thinking about it, we might realize that there are two rotations happening: one is the position of the loop about its center (what we've called $\\\\theta$), while the other is the twisting of the strip about its axis (we'll call this $\\\\phi$). For a Möbius strip, we must have the strip makes half a twist during a full loop, or $\\\\Delta\\\\phi = \\\\Delta\\\\theta/2$.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"phi = 0.5 * theta\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now we use our recollection of trigonometry to derive the three-dimensional embedding.\\n\",\n    \"We'll define $r$, the distance of each point from the center, and use this to find the embedded $(x, y, z)$ coordinates:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# radius in x-y plane\\n\",\n    \"r = 1 + w * np.cos(phi)\\n\",\n    \"\\n\",\n    \"x = np.ravel(r * np.cos(theta))\\n\",\n    \"y = np.ravel(r * np.sin(theta))\\n\",\n    \"z = np.ravel(w * np.sin(phi))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Finally, to plot the object, we must make sure the triangulation is correct. The best way to do this is to define the triangulation *within the underlying parametrization*, and then let Matplotlib project this triangulation into the three-dimensional space of the Möbius strip.\\n\",\n    \"This can be accomplished as follows:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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+/LpUsmU1N9lJs2Zc/j5IcVuUKw8q1B7ck0xx/xAR8ArS3mpvp55UwW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\\ngQIA/w9ULKic9q2iRAAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10e69c358>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# triangulate in the underlying parametrization\\n\",\n    \"from matplotlib.tri import Triangulation\\n\",\n    \"tri = Triangulation(np.ravel(w), np.ravel(theta))\\n\",\n    \"\\n\",\n    \"ax = plt.axes(projection='3d')\\n\",\n    \"ax.plot_trisurf(x, y, z, triangles=tri.triangles,\\n\",\n    \"                cmap='viridis', linewidths=0.2);\\n\",\n    \"\\n\",\n    \"ax.set_xlim(-1, 1); ax.set_ylim(-1, 1); ax.set_zlim(-1, 1);\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Combining all of these techniques, it is possible to create and display a wide variety of three-dimensional objects and patterns in Matplotlib.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<!--NAVIGATION-->\\n\",\n    \"< [Customizing Matplotlib: Configurations and Stylesheets](04.11-Settings-and-Stylesheets.ipynb) | [Contents](Index.ipynb) | [Geographic Data with Basemap](04.13-Geographic-Data-With-Basemap.ipynb) >\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"anaconda-cloud\": {},\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.4.3\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "matplotlib/04.13-Geographic-Data-With-Basemap.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<!--BOOK_INFORMATION-->\\n\",\n    \"<img align=\\\"left\\\" style=\\\"padding-right:10px;\\\" src=\\\"figures/PDSH-cover-small.png\\\">\\n\",\n    \"*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\\n\",\n    \"\\n\",\n    \"*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*\\n\",\n    \"\\n\",\n    \"*No changes were made to the contents of this notebook from the original.*\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<!--NAVIGATION-->\\n\",\n    \"< [Three-Dimensional Plotting in Matplotlib](04.12-Three-Dimensional-Plotting.ipynb) | [Contents](Index.ipynb) | [Visualization with Seaborn](04.14-Visualization-With-Seaborn.ipynb) >\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Geographic Data with Basemap\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"One common type of visualization in data science is that of geographic data.\\n\",\n    \"Matplotlib's main tool for this type of visualization is the Basemap toolkit, which is one of several Matplotlib toolkits which lives under the ``mpl_toolkits`` namespace.\\n\",\n    \"Admittedly, Basemap feels a bit clunky to use, and often even simple visualizations take much longer to render than you might hope.\\n\",\n    \"More modern solutions such as leaflet or the Google Maps API may be a better choice for more intensive map visualizations.\\n\",\n    \"Still, Basemap is a useful tool for Python users to have in their virtual toolbelts.\\n\",\n    \"In this section, we'll show several examples of the type of map visualization that is possible with this toolkit.\\n\",\n    \"\\n\",\n    \"Installation of Basemap is straightforward; if you're using conda you can type this and the package will be downloaded:\\n\",\n    \"\\n\",\n    \"```\\n\",\n    \"$ conda install basemap\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"We add just a single new import to our standard boilerplate:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%matplotlib inline\\n\",\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from mpl_toolkits.basemap import Basemap\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Once you have the Basemap toolkit installed and imported, geographic plots are just a few lines away (the graphics in the following also requires the ``PIL`` package in Python 2, or the ``pillow`` package in Python 3):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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waSVDOvSzpZRqIUPoRYmnUOlKD0DcF7OkmKCIJempInKVoKtJIYZcjS\\nFKNAKoXWivXVVV5/5w5vvLXLy29cI00TPvjkRZ594hzX7k1/6c6t26//5mde/Tv34TRe8g3IMmAu\\n+apwYr33ic214Tc9euHUf/4Hz79BCIFnHz/Phx4/x5mdDUbjCdP5nKrxtM5xXFe4xlE2Lcd1wXg+\\nZ1LVlE1NKjVSx0H/cVkgpMB7j8cig8QRA5BRMRBooSibKtrMhagArVtP8B65ENIIIaN6deHp6lxY\\nBERBXUdxixQCG1oEkCSKto0B0/uAdwHJorwZPBB7g4YY6ACEDPgQoopVSJASK2Og1F4SgkOpPwqy\\nzvpY3jQC6z0+vJtRBmQQeCPAxkAdRCCmth5JFPz4RfAUSiKCpyWgpUKoWGoNPkAAbRRGKZyH1lmy\\nNMV7R103GBMz0kRpQiA+3mhyi9FJnAsVkqpsyVND3VToJFkcS6BpG5QxVE2Nw5NnKcMsQwYZg6WO\\nwqVumqKkIDcpSoiFT25Ct9cjOIdKFEdITJnw2c89x6tv3mZjbcCZB8/z6CNnee6Lr/7E4eHxWzeu\\n3/6X9+H0XvINwjJgLvlz44lHzny3tfbkoxe2f+FLr15jMq146tI5vuXpC5zbWccB89mcum24czjm\\n7tEIFwLTpqZjUnKdclRM6acpu9NjqiZwdFhyOJ6ycbKLkC1JkqCkxCwyGuc9IYBWUaBjnSPLE6ZF\\nRdu0aGWieMd5fPAxoGqFs/FzISTOtrQ2jolIJfA+xBGORbBTMv5bSAgEcGGRXYLUCukcPggyaUAE\\nXAjY4AhEcY5zMUtTYpGxeYeXYeEPIJHvRVgILtZejTG0zmKShLKuEWGR1UpBsAF8LM1qKaP4J3hE\\njIcAOBneKxNnMvYsQwgILQmSPzJ4X4yUtMHTzRNMktI01cIcPj6vIgicDRijGB8X8dhVFBgpqSAE\\nrPW8u3BFKIFDIGQgVZpunpKJBC0kmUqRWpAoRaZ1VOISR2skkKcpeRYVyh7LVDtK06WXdajvlbz0\\n/Gu8+uYtdrbWSE8+wHH3GcrLv/NXZlVV1Afv/NpX/6xf8vXMMmAu+TPFaJVtbw4/9pGnHvjVy9fu\\n6beu7/HEw2f4yJMXuHB+i9xoimLOdFZQtZa98ZiDWcm1uwdUtAQcJ1ZW8I0nNwapBLcPjrh3VHJ8\\n2LBzoYs2kjxJOJ7F0qzzMeNyC3NyHzxSKlKVIIWkdTWttSAU3rYIudiitQiQgkCapggCrbeEGHEQ\\nIiCEIoQ46+g9GG0Y9NbwwTEaHdHpdEEEZvMpQngyNEJKhBAUroVFVhf7noHEaGTjqHxAKoXz7r2S\\nqhRyIaiJ2aoSIvYZAURUywohUEEgtaKuq5iNyhhYfRCYVMcAbD3BQRNiFm1SSah9LJEGh5CS4GOJ\\nNghPGxwahVIah6Vq3OJ5ciSpwdoo5pGLn7OtxeiYOXrhQUCSKrpZHmc+vedoVhBkwGhJL+/grUcJ\\nhZEKLRXeezKVokIAGV2FlFR0UgNCkEiFd55+3kHLQLffoRPrtbw6HfHKbJtOL+PTZ9eY3LrOZz7/\\nMleuH2DWH6Gz/QTJ6rn66MVf/FR9fPPzwdml09CSf2WWAXPJnwlbm4P3ZWnyqVObw59+8fWbPPzA\\nNh9++iE+9PgFjudzurlhOp1zNDpmVltG0xlF2/L69VvUwVHXLTrRCCFY6/ZY6/SobcuN/QOGgx43\\nrh+zuZWgtMR5T2sdVW0RSsSAp2IG5gW0TYsQmqax9LtpzCZdQBtB2/gotAmxrKiVxhOQIpClGWVV\\nxc+lIriAVmJRYQ0oqVHaEELAuhbvYypoTEJdN3gCrqlJ8wxrLUIpRPS9QypF8KAMGCUJLbjGI/1i\\n0aX36ETTuijC0TKOhlgPXsQAKog+sIiAEhojxeKxSqSUlGVJlmUgPa319DspSGiqlmYhxPEEXBsI\\nQqJkLPtKqUAIXHAEFZBa4myg8S5muT4gVXwTADEQCxl7pnXbxqAbPDKJKuLcJIxnBR5PlhqEFOTd\\nFNPK+KZDaRBQ1w2dPAMXrQONUkglkEJT2ZJhp8cw6+CswzYNq8MhOkCaZ+RZSr/X4yt3rvPa/FGO\\nix42adgOgr2bVxnffgNfz8lPvZ985ymmb/3WD9nZ3ivtbP/V+3aRLPkLzzJgLvlXopMl29/3nU/c\\n+dKrt7izf8w3Pf0gn/rWpzl7agMXFOODPWZVw2hyzNF0StU47oxG3BtPKJqWsm7wArSGTpoxyHuU\\n8ym1EzR1zcFhdOBZXddYFUh1vEl7G9BGxdlBoXCuwbqw6EcuPoJclAah00koqgbvLUJEheu7uygF\\nAuc8WhKVpPrdPqRABokQEKTC+xYRBN3OkKYpsLah3xtSlAUhONrWYozG+hBLpiIWJZV69/crpIhT\\nj8YkuDaOfsgQS7HNu57lIYqBkECQCzVsfCOAF+hURyMDIbBtuxACSRofFby9zFDVNu7ZzAwQR0a8\\nCFTOsZZnFFVDQ8B7R0cZvI3zpf1OyqSu0ZnBtdGTVhCwweORMTsNEH1mJa13QHwjQIhzq4nRGKOp\\nyoa0kyKCR0lFnmZUTf2eOlcKSVVbemmODw6lVLQGDII8zdA6lphBkKcJqTR4Z8lMgpaKQa+L9Z5e\\nljEr5sxNwh9cvkaZfQ+Z7FAXe0xuvEC99wbJ2gXys99EZ7DO7mf+/knXzPe+mtfJkq8PlgFzyZ8a\\nIYT42Icf/kWs/6EvvX6DzdU+n/74M1w6t0G/34E2cDCd0tQVRdMwLQtGRUVVNdzY3+O4spRVjTIa\\n8ATpMVIhraSTpXSHa9y+t8vN6yVtaXn/s6c4PN4jeIk0i1GLxhGkWJRfY1kxyldjeTZa3i3GOURA\\nS0PbtnE8YtGXFIKoaF2UQaOMVKGIv0cbg7MWIQTaJGRph/WN08yrQ4qioJt2aZqGaXFM3knJEoNt\\nDIGWo6MDQC56cgEjJVXVkucJ3sdg864zjxeWqJn1MQsVASENiZI0dYttA8oItAr0Ol3KqsFog3cN\\nzkNjHdJHiz2kiIuqW0eaLjyGgkdoiUwEWmkmTU1fpwQfqIRDBvA2EDTkUqOFxC7Ga4q2jSpdIal9\\n7KeqIGicj/3cECBRONeilUakCt/aqAT2DmE0qY5qX70o56ZKE2Qse2ulsXbR5/QWHzyJTqICV8eK\\nQ6INeBYleol1Dl+35FlG27Zsn9hgvdNhVlukiOXzf/7Km5Tlw8jz305oK8o7X2F24zmQig88cZ47\\nnfcz27318+PXfu3Hw/ImuORPyDJgLvkTI4QQH37q3OfHk+rZq7cP5fsvneHTH38/Z7fXaZuGqqxo\\nW0fSzbl24zY2WOq25c7+iEldczidUdWexlmECnTSDBlg0M2xjeN4XoBUoBU3bx5STRW9XodLj29x\\nPN2jaFq8c0gpo92b9VH9ulDJEkDLuH3DW4cLPqpBQyDYsCgp8kcZqIhZoJYi9hmjDBS0wvOu+UDC\\niY2TVLVldXWV/b1dvA+sra4QnOfkzkkm0xFCpFTVhLoKVK5kPhnTybtIJanLktm0xGiHR5Jrg1CS\\ntmlIk4S6dRA81kE3TwhCoZWkrS1N09DpdCnLkm6nw4WHLnL9ymWQgkF/g+BrxscV8/kszlr6loBA\\nyWi6YLRCCkHVtggJ3osoQvIOKQKp0DTBoXUsaSul4s86R9u2aGOwIc6nKiWROmacrgaFimImBULE\\nErdXQBnIcgOK2BN1DmWim5IWmiRVKKkoqpK28AxXepS+BiRGCsq6QWuJlnph/afIjUZIRSJEnPtU\\nCts0dLOM3BjyNMMYg3CQaIkNHlE7XpsccDivmY9Wqbe+i3J+i/b687Tja6xsaj7w6Jn2udHTv3X4\\nwj/+y/fz2lryF4NlwFzy/4kQIr30yMkru/emO4TAxz74CJ/85sfpd1KmsxIRArUNlFXFZDblcHKM\\nkbA/q5iVFXVwHE5LjucleZYinEcKjRCePNOIIKhqD8Zwar3P0x/4S3zhxc/xe7/3FbSWfPITH+GT\\nH/8U79y4wm/8y1+lrI8RIY5OCC2wLs5WSCR6sUOytQHnbRzMT1LqukJohVhkljF7iSpXpRXexpJp\\n6wNK60VpV/HgIxfBCozJ8L5hWoyRDoRWdPMezgvKYs7x9IimqVlbPUFRTGNPMyxUtDjmsymJACEN\\nCIkMDpFE1aoMhrzXY7w3YjZr6eSxlzfsD+h3+xwdjHC0gGR75xSPv/+D1MWI557/DHXl0UZjtGY+\\nqXG+wTuom4YQAnkqaWpLAFSqCEATorLXhgDO0TEGh6dxAaFigGptAyKWVx0ea13MwL3ggTMXeevK\\nG/FxmFjWlgKUgAaBby1KK5IkCqZi0Tva+FlnUVrHHq+AbjenqWukiEuyGx9IE0NjLWVR0e9kgEQh\\nsLWj20vRQWO0JNMG8CQqloC9teTakGYp62sbVLM5jW1pvWeYGSpvmMxnfPFWxfag5c6NXe7dmTPo\\n5Xzw6dMMOtnl/+WfvXhpmXEu+eNYBswlfyzdTnrpgTPrv3bjzuh8r5vynR9+lI9/8FGMkOyPxpR1\\ny6wo0ErTtC2j0QyTSaSSjIua2nvujg4pq4Zp3ZIbQ6KThWDGRiGMEyghOLG2xa3xiJ4OfP8P/Fts\\nndji8899li+98AIf/+jHkEEgpOTVt97g1cvPoZXGNRZktJ/zPtC2DcFblDTxxqyiQEhLFVWhIeCt\\nJ0skUiisj98Ty4MCozNSIynrOYPBkO5gg+FqnzZY5rNjqCWtjaMpnTTDSktZFITWItD0ugOqeh7d\\nbVqH0Jq6aal8TVMck3iB9aCMQUq/yHgFWZriLDSVpSxr1tZW6PY6dPKcopizuX6Cl197hVOnzrB1\\n4jS9YZ8s6/DW5de4d3CVteEGxaSkLIsoFrKWNM9ogqVuSoTUlEWB89HowHqP8oFOpki15uC4ICiJ\\nVhJPINXgvV4E0SYKoAKkSYdu1uVwdEjdWJQRSB0zcRN9/HBti5Rx0bZSAimh8f49l6DY45Qopagb\\nS1U3GKkRGtb6HRQCtMJaOD6akXY0wQqkMkyLKcO1Lm3R0skzlABrA4M0I9OGVGukjGImoxWpUAhl\\nKFyLBoadDINES4EXnknTMirnvPL2He5em6ETwSMPbbIx6H/xc89d+8HxpLx2ny/BJV9jLAPmkv8b\\n/X72yayjfraY2vMba10++oGLfPj9DyOdp6xqru/u4ZyN9mdSIqVCIJA6cDSZEVCMy4p5UzGaTmla\\nS0DQ73SZzOax9CcVeI8WGoJEqZRv/vi3ceX6FdbXt5kdj/jkJ76bK9evMJ9MWF9bJyj4Z7/2TxlP\\njgg2uuKEEMjyqFINwSEXvUGtFYk21HWLXKzIitmkhODiLKWMs4zOehBqMeAv6HRyivmcxHQQRtLJ\\nujjbxtETKZgVs8UWEYt3DqM1jas5tfMQ5XzC2tomR4cHnN46xd3xMcdH+4zHByBdNFEvLZ1Oztr6\\nBt1Ol6KsGPQGHO6N6Pe7mFSjVIJCIpUm7/QYDIaMR0dcuPAQ1669SXBwdLRLVZdMpyVCJ6ytrnA8\\n2qOqSpxzSKEp65KqrVFSUFdRCOQBLSDRmrltY09YBKRQCBmwziGFQimBXbj6BC9ItaFuGpyLQh9j\\nJD4ETKKoWhcFRHlK07q4dkzErDK4KGwKCxWUUCC1oNNJwfsoVrKQ5wmDboeyrFBaxuMvGmQi8T7E\\nsRsNbeso5zWdToZ3jixL0ULgGs+w00V4yLIUgSc1ktSkTOuajpEYmZCq2EtVUqCMYlIVTNqG19/e\\n5ehuTfCO0+eHbK0NP/vm5f3/5Nbd0R/e3ytyydcKy4C55D16g+SvDnr5Tx0dFqe2t4Y88/gZHr9w\\nKvYapWRe1dzavUMvz1CLIBmFLcQemRBUrWM8mRG04GA243g2x3pPXcXeYyAqQ7USpDKlm+U4H3j0\\n0jM89PBj/Ivf+RUyrfnQB7+Dg9E+AkG32+Ol11/i2vW3sK6mmBcoKWCxL1IrhRM2DuOL6EojiTZ1\\n3sbZTBVCnI9c/JxQAi0NWkmyvM/a+ibetRxPxnSyLrNiirMBLwKZyemvDsnTlONiSicbsLq6ztHB\\nLseH+2xubuGsY7i2zuzoiLIqsa5Fm9hXm0xGHB6NQAlWh8MYZJVCa0O/PyDVCT4EirLi5MkdJqN9\\nTJLwwIMPUzcld2/fYThcw9YVUgu8c4yOJyjh6Q1WwAZu3L7B7v6IThIzbUJcOH08m9LPMmrXRrvB\\nukFpDc7N5Z3DAAAgAElEQVTho8IIrRVeRMWskhoXAk3dotMUqaB1jovnHqUqC27cvRazTS1xPsRy\\nuBRxDhZQKgaiurV4H92O1EKh66XHOU+aJLHnLAJJYuLfVXEuUxvFoJ9R1jW2EVTzmk7XoBOD856q\\ntAQcaWqomhYlFFIEEhOdghKd4KxdbHsRGDTCw0qvw6QsUULSzzN6aYb1FogKZa3i/OqtyYi7+3Pu\\nXZ/Q1I7zF9Z4+qEzn3vxpZt/9/LVe79yHy/PJV8DLAPmEna2Vn4iCP6z/YPp5s72gMcf2eH8zgZr\\n/T6pMgQCZVNGM26tSY1B64RZOWdelAA0AUzW5fhojyA1k3LOzdGIxrb4JixKoC5mF0KipGRtsI5t\\nG554/COcOnMWiUYlEmsbvvji57HWce70eZ778nOMRgfYtiFNE+rKomRUmsZ1VgHnLBBnCLPUoFC0\\ndRODqfdAQAsVf0aAlAqp4nFAXKdVVxWdPGM2Lej3B6wMV8iznKaNYyAmNXQ6PcpyjpKGTt7l5NY2\\nR0djDvfvYl2D8oGj0SHD1U3OP3CBo9ERk9mEw8MD5mWB0YbUGIw2dLsZuUmZVRXT0QHz2ZzWSU5u\\nbdE2NSe3tlhbX+XocMz+vQOefPJxbNtQNgXCB965/Cppd8D4aI6Xmht3Ci49vMV8dkhVTFkZ9pmX\\nBVVZk5iExjY0zhG8xyCxIWDSBEfMDoUER3RK8taDSXjwgUuc3FxnWhzw+uXX0aliPi6i8AZBu3CB\\nt61fzGOCx5Ekhrax4OMWFUR0+kEHCJKmaVFqUZkQ0UnJeYd3kKSaNFcopWhbz2Q8p5Nk5EMTR4pc\\noNPJgLhT1C6M44UnznIujCPwgURrhBRkQpMlKanReOvo6OjCJKSkaCuM0uQ6pXGeJjjuzcaMx5aD\\nW1Pmk4qn3neGje21F15//fZ/c+3KvX98Hy/XJfeRZcD8Bub06dW/rrX+r+7cGa1ubHV48PwGj57d\\nobYtvTRl2ImlyHlVo4Rk0OsSvKfyAV/HYXhnHWVVcjgvKJylamJ2Y5Rmd3K8mGOMM4XWWoIQDLsr\\nPPbw40ghOHf+IV565cv84A/8KFVVcPvuLa5cu8bd/btsrm2AkHzppc9zPD6KPq1CooierSFIWldB\\niCITJ+I+R0nsY0kRt3IQRJzvk1HoY7KEJMmxTSy/iSAwSRqH912LkgrftnTzHnmvjxOCtq7p94e0\\ndc3mxgnOnHmAsqwpq4LVlRWkAGsda+sneOut19nc2ODwaISTnvX1UwwHQ778lS9wPB5TV3MIPu6G\\n9A0eTSfPyZIOk+kh26cv0NYVu3f34uhFklK3gQfObhGCZT6bsXf7GivrW1x+83VGxyW9bp+dnS12\\ndrZJsow7d24zn0zJOpq9vT2U1Hg8tnUkmUFKjVdxs8n21mkG/SHdbp/UJGgNWdZhXnm2t0/xldef\\n58svfYG6bKmritDGvZ9usW8z4GjdwlPBxfnYIFwUZLXRcy8s+rXeuWg+4TxSxYrDQryMAPJeQtvG\\n7S8heIxJSDPJ+KjEW49JNPkgIUmjt61to/8uIixMFgAX16KJEAMoxNVmqTIYpUikRAZBax15ntO6\\nBuvb95TCmUmpXcu0LXng7GNcfec6Vy/fZn5c8+yT59jZWnnzy6/c+jtvXrn3D+/TpbvkPrEMmN+A\\nnDq1+mOb672/f/nN3fzk1oCN7ZQ8Szm1tsZ4OuPBnS2yJMUkCePjObZpcN6T9lJSFHdmM/qLsYjD\\nouBwPqNsGoyS9NMOVdswq2qcD3j8ey4yUggkhvXBKr3eEKUNvW6fhx98nCzL+MKXPstkNmZz7SSd\\n7hBC4N7hXd565/WYuTgXzdK1pKNziqIE9e7Kq3jzD0Bb2yj+UIveZRsH7/M0j+MRQqGFJEkTkJIs\\n7TA62sOHQJZlGJ3QthWJyUhNiklTlIjG7WdOn4EgWFk7Qd7pEpyLhu4I0jThiSee4bXXvsxkOufh\\ni5d48KGH2d29wxtvvsarb7zFiZMnGI0Owbf4ckTeG3Bw7yZrG9tI79jfvcX2zoOsnthBKcnR/i7H\\nozGlDZw9vc1sOqaYHqNNzu/+/mdItef9zzzL5uZJvJBkaYpcmJ7XdYUPAhFqjidTuv0VhDIkWc6g\\nN0BrxerqCk1b8Jk//B0m8xEIwaVHnmY8npDmGW+/+Rr4wHg+wbYeLQ1+MS8pQjSFlwKs84vl2SxW\\npEVdrPUev8gy3zXJj7Z8/j0jerkwnBeLnrSQAWMkzgVs6+l0UubVHOk0arFIO0k0+cC863DIfBZd\\njrSWSCloXXQ2EiGeBxKBWrx5kkCaGPABFeLoi3ch9nF17LNKAUVbUbiG9eEaRgy4eu0qBzenNJXn\\nA0+e5bEHt0df+vK1f//5V27+o/t8SS/5KrEMmN9AbG2vfPfZs+u/8uortxiu5Kyfyrh48gSDTs6w\\n2+FgMqHXywkOnHMUlaPXSZnNC9b6PWobg97+8QQfHLvjMXWwtI3DLUpzUgqkjcPlUkMQxP/DY0TO\\n1tYOjW3JkpRTJ8/x1BMf4Dd++1c4sXkSQqCbdTl16hy1jXN9v/+532J39yZV0xL9eQJplqOCA2FR\\nQlHbliAEqZGLPqakqVqUjgbePgjwnjRJydMcISVSG9q6RNpAniVA3FAiTfx3WIw3SGXQUrG2foL1\\njZNsnTrH2uo677xzGYTm4sVHWFlZ4ZFLT/Hbv/s7DHs9RuMDvukjH2V0fMRLX3mRvNdhdW2T2bzE\\nu5aXX3qBj3/s29i9fY2manHB0TQ1o/275GnC3Xt7PP7EB+JezqzHl1/8HM89/yrPPPsMnUzhXcPt\\nmzd48+032dw6yyMXL5AlOaV1BFvh24pyPqc/WGNlbZ2j6RiB4LEnPsT+nRvsHR5QWcv0eJe6LlBa\\nMZ8f0zrHfD6jrTy1LdF5htCa4FvaosE2UbAT/MKij4WxvI2vL4vXJ5q8B6SO/UHvFwupF3vOBBB8\\n3Af6rj+ujw3P9557pRdGDiFmpmGx1gwR4qxtiH3MxCjqtsXod1sHDcIHtNEIvVhurRP0ImQG4ZFB\\nLlaPSYyXKCFBxr2eQkpssBAEaWKwwVG6uGll2D1BU5fc2ZtxeHWCCp7TZzb50e9+hv/pf/vcp19/\\n596vfzWv5yVffZYB8xsApcRKv5f/l977fzfNNZs7Oac2hnzksUukxnDzzj32igkr3S5l3TIvCjyC\\n4D39bpfWupgl4mnqlta2TJqKaVksdieGeLsMcWFyYgx126CIKkTXBDbW1vjwh76D6XzOX/3Xf5gv\\nPP88N+9cY9jfYGd7nf/5H/0DNtdX+eAHvoVub4Wr197mzauXefudNwgi4EMUDcVR9rib0QdP6yyu\\nFaR5dIRprUNIhbM23iylRghJt9NHBmjaaL5eLXxX41bJOBKhtWTQ7VM1JU0dR1TyTp+t7XOcPfsA\\nw9VVDvaPWF9boyxnJHmXBx+8uLDkU2ysbfL7f/gZPv5tn+Czn/09nnriSTbWNxgOBqysriOUpi4L\\n7h3uk5qENOvQzRJu3brGFz//WR577BI+KO7cusH4+AiEYG1tnddffoE7d28i9JCmLjk8nrK7d4Sf\\n75Il8C3f9j1xh6RwcSlz3qFuBc/fgA/ttAxW+thgWF0bcP2dywRl2Nu9RmISeitrtG1D3VTMJse4\\nULN/cJe6bsmyPiFIOlnG3bu7tMHGEuqix7nY7IUnLsRWUi8UtXFkyNq4oUVKhfcOFqM9cQA2gAox\\naC5eAqlYOC6Bc3GTTNu6xR5RoliLaK6vlIzuTZWn09O0IiqAnY0BOetrsLEcLJXG+xAdgxYrXAJx\\nXykyniMyEE3jrUfrGISDEkg0QgSC9LS+Zd46Tq48wFvjS7jJIeXV36bf1Xz0wxd5+dWbP3b99uiX\\ni7I5vj9X+pI/b5YB8+uYJNGDtZXu32ud+/GycTx8ccj57U0untrh4oXz7N69y6SquHt0RFmXlI3l\\nxugIjSQ3hkG3Q55mlG1Nax22dYyK6WJOzhECsdfkiQIKBd20Q1lXKB8t6BKp6eQDnn3/h3n51Zf5\\n4R/+CfK8Q6/b5fr1G7z6xku88NIXyLOMYB2PP/o087bkSy/8IZPZBCljaU/KuLA5hIAhDvZrFXcu\\nhmBobIvRsV9qZMxwQ/BkJmN1ZZXxaBTnRZuGNMnAB1xw5L0+eZbjhKLXXwOhCPWMB06f5YWXvkgz\\nn9IfrHL61IMIo7n0vg9w/vx5HnjwUZz39Lp9sjzn2pUrrK6tIYVnXlaMjke8+sorfPu3fyeDXo/b\\nd29SFjXrG2ucOX2OK1evsnPqNEYKDg4OOHv2HEJA29a88PwXuXDhIf7g93+ToijYOrmNB1556cug\\noZfn7Jw+x8/8D/89u/du8SM/8uPUVYnwcaPIrCjJ+mv80gsZwrd8z8NHbKzm7B8eItGMjvbYOXOa\\ne/v7CCXp5F20gOPphNY14DxKG5QSjEbHzMsZR0cHTKdz2rZFJRJ8FPH4xYLtIAQWEN4RvIhesUqi\\ntMIGH7O3RZZp28UO0RDXp5kkZpNexDVpYeFyKJVg4bGwUDbHr7+rgiVAkmiU0dimRRm9GBeBat6Q\\ndpLFGI0jTdPF+NO7/r5xdtd7957Lk1YK7xxaadRiGXcT4iaZIKL61+M4rmq2V3e4Wn2C+WiKnNyk\\nvPu7bK71+PgzZxhNwt/87c+/8XPzsp7cr2t/yZ8Py4D5dcpDD23+wmxe/5uH45bN88/w1Nkx2ytd\\nLpw6jdCSqqq5s3vA1JZYC3vHI47mZfT/FIJulpDqhMrWaKWYzEta7whKEKzHLvY0KhZK0xDXWCEF\\nZdPQFwlZlpDolI9/7Lt48aUXePKJZ1Am59qtt7h+4zp5NuRouovWhgyYVDWCwHg6IuazAY0GKVAh\\nCkycizdfRBR0OGtJlMY6j1nsU7TWghCsDNaxbU2iszibqTTWWVaGQ1rnWO2vcTQdk6QJIUga25D3\\n1nGu4vhwFykF1WzC5uoW3/zRT7Bz7hE2t7Y5ffo0N67fwNrA5toKq6ur5J2MW7duUZTRyOHSpUu8\\n/vqrCJlw+tQp+sM+WZLx3Mtf4CNPfzNFVWOMJtHxxry7d8S9e3c5vbNFkiQYbZjPC6QSzGcl2ki+\\n/MJzvPbai3z4mz7OrRtX+Qc//zMcHI/5j//mf0pdzZnN5rz1+lfIu11oHc8dDdncPMuLVzwPro55\\nsnebUs/pJCvgHNa2NM6RJQnWthyODlhZWWVt4xSEgKtqqmrOvcN9Dka3sY2lrCpMklEUBU1dIKRC\\nChjNKrq5wgtiBuksDkizBCHj+Mp02qCCJviAtc3CPF4gU2KvOTg8ixVmWuOdW6wuY1FCjWVWreR7\\ns6B+cd7pJPY88XHziwgK27Z0OjnzqmB9vUcIEi9j71UHSdPUSKnIsyj8UUYtEt/oIhVCIPi4xLvy\\nHqNir1oJQStr9o8djfx+fBsgFMjJ7zG/e8gDZ1b4se/7Vq7cmf+3/+Mv/ov/6D7eBpb8GaN+8id/\\n8n4fw5I/Q4QQ2f/6T36uePvK0bNh9Uk+9a0rnN9subi1yQOnT1O2DVJqyqJmMp+hlGFvMmFcFjgf\\ncM5hUkVZNczrChugKhtQEi8CrrUkSUZj2yjiWQg5lIyG3c47jFT4xvPIg4/xqb/8A+xsn2H/8A6r\\nqxtsbqxx4/pVRtMjjiZ74KEopoyPRxBa6qYkM3HdVCIUikAio39oXLslcAvBSNcYMq0JIZAIFTdb\\nBBj0Vzmxts36cJ0Tq5v0ukPqquT7v/+HkELz6e/6Po4nxxyNDkmNoZ6XtHVBW5RgCy6cu8i0KFhZ\\nWWdlOCDrZEgBk/GIthwTEJzc2mHn5CZZInn+uS8gleHq229z584tNtbWGA6G/wd7bx6t53mW9/6e\\n6R2+Yc9be2/NliXL8pw4ieOMTiCBkEASIARCAmHm0EPb09MeoKXAOUBZHSi0LE5XyaINJDQkoZA0\\now0ZnJEQW3bieJQsa7DmPe9veN/3mc4fzyvZSc9qOT2ZoLq9LC9rb0n7kz499/vc93X9Lg4cuIar\\n9u6h2+0k0o5zbN++OzUCIVJYtEhiplFVs21+FikleZ4Tgk+inWDJ8hznHM47Dh26DmsD733fhzhy\\n7H5+9Ed+mu989euZnV+imOixf+8+Fhd3ceimZ7NUDlGMePisZLnWPLY2zQ07ppjtFeBq5pf2sHvP\\n1Rx++AwPnutw7PQmL7n1GvKyx2Rvio3hgCzL6Xb7dIpp5mYX6BY9vHMUOsM6qOoxNnqMlJjcIPCk\\nPidT1qjW9CcmaSpHnpUpKkwqoooEl947JjOAp1NkBAc+tDA9KVE6AfYh5XrmyuAbi3WksaxOsWm5\\nzjBG4W1EKM/0bAcvUrRZDILGNRRlQWwnIR6PybPL/k9iEiWJSzv3VigUQhohayFStqpqPagjTV8L\\nRoMH8OpmRJRQXE02cyNu60t8+O6HmZ7Qt8eL9/3cTc//rl//Rp8LV+qrU1dumH+L6sDVc589eXb8\\nXIoFOjv3cPuuDeY7il3b5nFIJjodKhuwjWM43GI0HrMyHLA+GnB+tEXjE9asqR1Ga5yDTCQRhDQS\\nHxOhxzaJlIMgqRBbX4Brn+yNzimyjMFohb/3M7/CnXe9n24n59jxxxm7MU1TIVGMxyMwGXhLhiIQ\\nL4cRy0uKSZLSNVOakbMIkrJRCoWMaUwmSTuxIsuZnl5kcnKKmbk5JqdnmZyYYDQas3ZxmR2795Fl\\nBadOHeXkySc49cSRlliUY5sKbTIQsGvPPtbX1siyAmMUu3fuY+/V+1lYWOS6G25jam6GYfuwceTR\\nB7lw/jzTM3PcdvvtlGWXIi+pmhqtRBoBt+NAKVKz994TYgqThjSCXFlZ5bXf9x288x1/gpE5dVPz\\nyKOPcvDaa3nLW36DH3jdz7Bn916yUrOyskbT1Lzi21/CH77tnezdewAfLU3VsLq2jAuS97zz95nf\\ntZ0//nTgwmYS0njn0Zmkm0sOLURe+axpjh45yiceHXO63k3fnUDGAUsL21DDhziwd4n9B27g5KkT\\nHD/2CP3+FDJqhuMthsN1Hnz4EcpeCcJCJuh1JhlVG7imRslkAbLWE4VAaIWUBhlhsDnCR8gySeMs\\nroGiMLhY08lzvAu4mCDxQoiU23npmAqh/f60N47E1ueZRrhlWaQc0cYitUqpNjY1yYmJbgv+fxqE\\nvxUUyXYsK4XESIV1rdc3igRxUAolE6s4BMWJ6gCmu5f+6M9orGArvpLgEwhBKEVu/4r6zAmUErz2\\nJYeYnph/zz///Q+99ut+KFypr2pdaZh/C+qmZ+z83Nmz42dtbDp543V7yHoj5nslCxOTzM9N46yn\\n15mgaiyb4zFbgw3WBptEm7xwg6bi1OZq4qS2mY3epafuIJL60XtPlmlCSOxQicKYAltXQGpymclo\\nGstEf4qqGnFw743Mzc3z6GMPMHI1w2qAa1zriZQ475BR0QRLppN88pK1QJFCiaOQpIDI1lenk/Fc\\nI8iMJvr28EQw0Zsiy3LKss+4rjhwzfWcuXCCjfUVpNR4FzBZhiR5Qbv9STbWVpiemmV1bYWNtWVc\\n8Ni6xjYNtz7n+TzrOS/ghS/+dqRMHNZer0+WZeR5wXA4YDwet4epoOhOsLaywtT0JCFCnmUpE1NK\\nrHUo1QLf20gyYmzHx4rP3fMF9u6eY2gDO2ZmiSRxS1PVzC5MMtyy3PWRP+Ghx77A3/mxXyR4z8/9\\n01/g8Bce5GMf/DBVPUZrjdKacVWxsVVx6uRj/NzvPcDWyCVBVvt+ScKdQJZnvPgZV/Gz3/8CXv9z\\nb2PkHCFIJAHvxnD2Y1y9MKRe36Iaj1ncuY9ur0e/16GqRqwun2NUj+n2O0QVuLi2BrFOYP2oCNET\\nvUP4SNbNGdaWTGm2qgYhDTp6gvDEKBhsWHQukTriW08nUhCcT2SidsoQY5oupIxPyIzBATEmrrDw\\ngG5Vt6RcUqlAR81kt6TCUVU1Ok++VKN0GwEnkDp5VKIXKWM0xFYR3AIQhLhsS9FSM7IzXDB3YLKG\\nbHSMteEM0SczqFAGrTUTg/dw+sQm1129wI++5gWVt+Jzf+9fvOOOr/MRcaW+SnWlYf4NLmPUDjO7\\n/xPN6ol9U9uv5cYDI/pZATEw1e8xMzFBp+xiTI4PngceP8JU2WG9GVGPKhrnIUhGvqKKDT54qtoS\\nfBIxttrXNHaNnhhaQLdPgcWZ0XifUie0yLlq936GW2s0dUVZTOARPHnhSaKKSfARfLIMKJWanpTU\\nISABIyVZZmgaS+tQSeSZmFB6gkQIIkQ8gRyNEiBUGscaldPrTrNjxx7G1YiLy6eRJsfbhqLoMhgO\\n0VrRK7roImN+bgmlJY8feZQsy8m1YXVjjav2XMOrXvM61leWWVtb45pD17Fv30F27d3L1tYgAeND\\nYGX5IqtrK6yvrTEzM82uPfuQ0lDkBmNMoti0dom6adBag1BsbW0wNTkJMYmYvLc45yjKkpXVdX7s\\nl9/F2Uc+wpvf+MO8+bUvQpcFd3/qL/jYJz/EmRXJD7zmW7jn/lUeOHKMez/3l3z0/f+RpW3bqKuq\\nRctlfOzzR/mD993L8XOrbGy5y40ytCrT9DdeIgh4EfnHb3w+V++d4Sd+/YOoGFoAhMQHi/IWFda4\\nae8kz7hxH2effIT1c6dZmjCgI+eWz9LUNYOtdfbsuYatrQFnnnwCqQIxKkbNCOtcekhQSfaDSGPX\\nqnGXcYnWBryPKdbLRKwT+ABCSQhpZy5le1a1im0ZZZo4ZBKlNE1jWT1ek3UUEzsVSkEn7zIe13hv\\nyXWGpA29lrFV56aGSgStDWWRJw5vjOmG6jyCpKQNIQEb2h8OiLTScM+jznYwwWGG9hqG61sp4DsK\\nRKaZLR/Dnn6ECxcGvOqOG5ia6t71x++75/s3B+O1r+NxcaW+CnWlYf4Nrfk9Oz6ycXH9pUV/noWD\\nB5jSjzJhcroyT0kNWcFkv4cwCmKkGo7wwLmNNcajOok6BBAEUUYqXzNuGryLOB8ui3+kjAihCT55\\nHbVI5nQRQRuFloardh9g++Ie7rnnE+TGJPi2VBSdHqvry/hg22SOkDinAvxl5aSgo3JcaJAxGc09\\nDt0GEBNJnk6lEsyl/THEgNERCWQigwhGZnS6PSyepk7ghE7eZeeuvZw9fYrJyUmk8FRVyl4UCLZt\\n3801h27k6v0H2XfgEPPbFthc22A0GuCcZWpqio3NTQ4cuJat4ZDHHnmIJ08dpxrX+OC47vobueHG\\nmzAmuyw4yrLssiilaSxCCqy1FHmiCTkfWmsD1HWF0Zo8LxAiUodIrGpe/r/8DhOqIQubvOhZe/j8\\nZ97HF04O+NT7P8Ctr/1HiI0nePv//X9y6Nob0CaNrZ2LvPMj9/Ev3/qpVrkMsYU9INL413uPIKCU\\naR9DPNE3EAOLs10GlWNkM8AThET6kNJcRAKwJ2mrIBcjpHAQHVJ4OmGIlBC3TrAWFxChZkadpMsq\\no2aICxHr0sOP8mCjwyGpGkueydQErU9WFZFAB4GY8jQbi7uU750JomtH9oCLHnxKApda4CogevJe\\nRtaTGKXZ2qjSzVOkW3uWmZRcQ3ooy0uDE47oJARPWRStvSSxhwXi8oMeIaZ1hQ/gI1oZtBLYsJ0z\\n6jZKu4rVfYYrQ2Jwya7U6dIVR1gURzl5dA2jJT/1+hdzcWX8R7/1h3e+8et7clyp/z91pWH+DSvV\\n6b0wy6febqvN3VPXfRd75jcYV59DWkGpMqbyLt57+nlOFJDnhjwz1M5xfmWdsffEGMmlQQnN0I9o\\nYoN1DueSCV1AsnHEiGl5n84HnA9oFVv0nGGqO5nAAAjWt9bITdaOu3LKokPwgeFoSF7k1M2YEBPw\\nO7bKRiFAt+i8ENKuy0hDxCcLSXubTIh3iRKqFYAIjFBE7yhzg28hB0qqywKdGBWTk7NY3yBJoILJ\\nfp88Tzfwrc0trr3uZg7dfBs2WJ53+4vp9btJGBIiVVWztrJMf2Ky3Wdp1taWeezRh5md30bZ6WNt\\nzY6lRbYtLBJJh3gIAaUUg8EAgUAbzZOnTjA7O4cyGYPhkKqq8CEwNzOH0pqmakBC3i3JpQbn+NFf\\nfAvDjWVCfZFHHrqPxSlL5frs2DnNM5/9Mt7yjr/gObc+kx/8zudwaP8BJiY6HDlxnjf/yp8SSTev\\nSxLT2NJ1hNQQ0yHuXfKjRttA66983cuewT/88Vdw12ce4W3vu4djZ7ZIYhuVUkdEhBAgeOY7FYv5\\nOssDy/mwkwaJsi55MBkTZY8QIkvD96DsCpUz1GKOMm5g42aKGkuIWWQrKEu5pmlknRc69UHA2pgw\\nfEga14rKWqhBlIFo0+d5FdFRImMgaklQAYHEWYcyMgmRpCJKj8kkTe2ROjVB7zw6V0ll69JrNka2\\nWaMp8FqI9ACkpUkWkxiJzpPnBa6xdPOCcdzBMNaU0XN6ay9100HEiC5ypvun2aYeQ24J7vniKV5w\\n6z6u2bX0p3d+9pFfPXL87P3fsEPlSv2160rD/BtUSwee8bmLTx57Tj67n5lrXkjH34uKj4CDji7o\\nygwhFdY29LsdtEzCia3RmMpZXJAQHFEGMpVR+RpEpHYuqQEDGKUJ7fgsxIirA641kxspk4pTGRSy\\njYlSdIqc2ekFLq6tszZYpQmOfl5SVRVKKjye2tepASfcNkpIjJD4GACZRmAtczaIFDQsWuUirfgj\\nhAQGVyR/pzEtoACNVKQ8y8KkW0lII1whk/WlPzVNNRqTFwX79l/Pzc94NlOz81y192pm5xfI8yzt\\nKX3rL/U+BU3nBd4FxuMxp8+cZm1jPcEIJqcYjjY48eQpbnvmc8jzjBgj62vr9Ho9rLdsrK4xPTND\\nnuccP/EE09Oz+BCxTYOQKU5MyxSk7aLm/LmLjJuGY6eX+cBH7+VTn7mHmE0jpCRrTrN9VnBiWRCD\\nIGTTCNXhjS/fwY+84XX80m+/g0/fdwZdzoCUCaYOIHV6gACEUMToIXh8a+tIGaENkkhvssvhd/8T\\nGlpR3RAAACAASURBVC/ZHAz4jd/7AB/6zMn0eYI2nQYEkcLUvOLgBr52/MWRwJCdaYxvt7jGf5BG\\n9VkbR/JmBewG58uXIzq7EKPjTGx8Aq9qQjAI6fDSEX0keI3RBT4OyXJJHeq2ySe6D0HQeIe1oV0T\\nkN4bUrTqW4EWslW9tgQhTYImCHnZK2y0xseAkGkcK6XAOajHDb1ekaxL7Wt11qO1JDcm4RbNU15O\\no7IWzK8R0RODQgN5oSm0YTSqOb62wEa1K72PZcbc7BpXl09QDcYcPbKC95EfevVt5Lp436/9+/d/\\n1zfgWLlS/x/qSsP8G1Cmv/Ty3vT0nVsXzzB13auYn60pm08zsg2FNEzosjWNJ3KK1grrahypydgY\\niS5FJ0UZGDd1C7wW6VBxbT6kIC0vAdtEqqZJqRRKoKVCoelmJUob8qJgfWOdHMH89DwLSzs5v77G\\n46cepWqaJMNvm52N6VabSUWhDCJEpJS4kOKzREw3g+RMD2RG42JAIald02YqBqIQeB/JlcS0u6/o\\nEuhdS4XOM7Q26WvWqfHXtaVXZMzMLbK4uIPv/6GfRkjN3Nw2nnjica697nrKsovROvFORTLde+8v\\np14467HWUlUVTVMzOTWJlArrGqQWFCpPCmEhsE2DswnkLbUCoKoqrE3j3bqxeO9ZXb7AueUVvvjY\\nvfzu2z5BOXWQ4GBzc506dpDB4ZoNhFBIXYLQeFvh5RgVOghlkDoHt4VmRKMmkapAyAQbj26M1Aah\\nSxAyKThjaBtQgpODIASLG64Q8SzMzvKpP/6nRBRr66v80Xs/zZ98/CjLGw1SPO0NKVKmZhQC1T5M\\nPfVhz9zor9g9tc7J82tUZi8bYVcK657YgXA1WnTwjMjsEEb3gfTY1ZO42RchJg+iN+9HbXwc2aaV\\nGAkYUDHdEK0H6y0ueuwItpY93RmD6TgUac+e4AYqKXRFvNzwvY+Xd6lCCZRO8AuQLec24fyEJCXd\\nOEeeZWRSI7wAlVYRxIgyBtd4rIvkRiOJKAlESa4NQmhkLFh3lieXF2nCPAjolGe4ZuI8PaMZbTZ8\\n9vBxDu1f4Cdf+0L+7TvufsFDR898+mt/qlyp/5GS3+gv4Er9t0t3Z38luvGdPij2P+/bWCgeodn4\\nc+rgkUEgg2CIw6gkhEBFhnXF2HnGTcOosQzGIypXIfD0TMF8fwqDIhMKXKuCjR4fPM57xiPH2tqQ\\nTCZhjRISEdIhEIVncnKO5z3vDvKsw86du7mwfIGTF85wdvlJGmsT7FqCcMkCkFSGBikSgceFSBN8\\n2j9ZkF7RBE80gW6vpMyLJJZxnhAFwUciSSWbtTulEJIdQ7ZSf4DcFDjr0CbD2rRzVSLinGcwHNKb\\n3c7a+iZlWVJVIxYWliiKEtHCwr33rbqyPTRJXsnaOqxzDIabTM9ME0K6VUQimcrT7SUmD2uW50Qk\\nRaeb1KJIOp0OU1NTSKnoFAX9Xo+9V+3jlhtv5vyFAS+8/fmMrebCUFLFDr4ZENwG/W4OweGDQ2Q5\\nsjNBZuZQeY7udEEpounj80V0OYkyJULnCG1AdQhognMEZwl2gzheJobWU+safD0gNDVClfS7U/yr\\n/+P1aAIierQSvOT269i7e3t7Q00WHy7fNiMqxssPWfHyv5Llzu3cN3oxcuq5bHZvh94iqruEEBIt\\np+g19zK98W661WdQagJX3IjY9xOo6RuQqiBM3UosbsQ3AZvdSjXzQ0SfgwgoYcl0CrX2jaEaxiRC\\nUym+DdJURWaGIAJCpB289xGhJFJJvIt4G4g24uuIrcHaQAwxxb/Z1jYFRC+JLuJcUpT7KhCagEQR\\nGo8QkjzTbK6Psc7iPFjvqH2DItDIHkWxi972m9CT80SpGVW7ech+J4+eWUaUnjd8162sb1T8wu/e\\nyS037P7UwasXr9hPvknryg3zm7R0d+5FWWf6z5rBhZk91+/nwPYRq9UWy2ubaUclBJN5h6Gt0VIi\\nI5RZgQ+ecdXgRDrAgk3imvl+H6UktW8IrfjBuYB1rpXpCwhQVxatDL4WyCLtC3OZmh0xcv2hm7n2\\n2ltYXTnPQ4/cz/nVZYajIUpKpEjpJBqFFoIqeIJI8n+JQNo0Yo0yolFE5+nmJeNg8cGnA0+CCiKN\\nKlUCqUcfkELho8P6FBSdK41W6vKQUAqJMiah0bSk7PbodLsIqZnoTzM5Mc2O3fu49tpDHLjmOrRO\\n2Zh52aEoOk/5+lrLQlEUxBjTflRCU9dIEZFKtfaQS2NAycbmBt1+H+8CTV3hnEdnBVmWbh0pIDn9\\nHvoYcN5R1RalJOdWlvnFf/U2Dh+p0+g7jIlNjSjmCG6UDm6VEZoK8CANKsuTqIf2gI8WpTMi6ebj\\nbQMhjX29twg7BreGKBaRpotvBgiVIwhMTXR4/ctv5Ke+70WkzEqNaxzv/+Q9HNq3j1/8dx/i+OnN\\np5JE2v9+ZQkhvuz/Y4x82WdFSZA1E9UT9Dc+mt4XRlA3DY0N1DbDL7ySWO4hSAHjc8Qz78WLSdTu\\n7yZb/gJx9AmU9jgLjfVUtSTvSLZWa4pSUXQlQos2DSW970RscztFRISAJyL0JWAEeBdQURCUSBmh\\nRJCBTidDZ63QDJGAHEpjUClwO3ikloT2ZqqlZjgYMTHZSd7T1merYkHIFjnDHcSoGF48QSQgnANt\\nmBIf5/qlHtsnu5w6s8Xdf3WEuatu5PZrZw7fe/hLP3H0+NnDX5MD5kr9D9WVhvlNWFLnPyxN+dZy\\ndg+79lTkapPN8RhrPcao5OtzgRgT83Ox7LFWjal8kyaqIXExL+VSznS7DGNSxjZ1Cu51zl9ulL4O\\niacZIoXSVI0jakHwgdKUOJLQ4tDB6xlsDllaXKKyNQ88eD91PU57MakJIQGwRZS4ENJ4VRsyNHWw\\nrTVFIH36+voTXQbjMaXKsJXHBU+e69b0b1owQkqcUNJgjGSrGiMR6PYGqLWmyAt0luODoNftoQSM\\n6zFF2WXbwg6U1ExNTfPCF7+Mra0BN910E3nZo9fvoZTGBQg+oGUayWqtUFqmBhoCh++7h/37D15u\\nogDGGLzz3HP/PRy65iBSK/KsTDeZmMzrSqeGrkRioiqtaKzHx8D55XU+cPdn+Le/95/w2TU09QCd\\nKeb7JfPzfR58YrMddSqkUnjrEEoilEpil2CTp1MkcET0juBb+0bwoBMQIgiNUKLFo0uij3RyzaFd\\nc/z2L7+OXp5T1w0xBoZVw9ETJ4jdBf7Or70XFZMIh/Z56lI9/cx4eiP9f22a7QMIUiAJmFhzk/kk\\nZy6eIzeCqqkZ2xrnHfVYEna8AVvOQgCJpRksk3WnifUAceZdOL/JcDMyOVkShCXKFjygwTuBiJEo\\n0o5+baVmarpE5J4Ykkz2klq7cRIlIMo2NaVNSBExInTyd4o2waTMcsZVTQjJmzk10WU4ashLjXBQ\\ndDsQHFvjBpOpdiKTRFJaCargWDevodYzyGCo1i+AawiuIUZHVz3KwdkNdm+bJ4vw8b86xsX1MRMH\\nXsnZL37wUDNef+RreuBcqb92XWmY30QlhNC9q17YjE7fJyavfTFXTd/DoAo0dY02mvFoRLffpWNy\\nVrdSCHHWzekqw/pghDSKaBOlx1pPoTUmS0/clbMooVrKTOJzigAaReM8o6ZhttMBIRjamkwVzE3O\\nMjU5yytf9d089OAXeeDheynyHj/8hp/mz+9+P5/4zF0ooVAkeb8joki7To9FuIjG4IxDuSQ6sc6R\\nZQppZGJwhmQfcM6Bhaw0adzqLIF4udEYJcm0RgnB2DlEkEgJmVJ0yw5SG4w2TE5MobTmwoVzLG7b\\nicpzLiyf5+/+/V9gYftVTPR7QGR6agadmcts2sal5nBJ3DEej+n2OlSjdV760lv5i4/eR1H0kt3F\\nJ0GNEJLf/J1/wcnjj/Arv/ibdLodsqygqirKMscFgSJFnFk7xgsNPnLXp+7jH/7WnZRijUHdQWCJ\\naFRYQ5k+hIANGpRJrFapCDHt3nwQKHyyWTQNUrcPT3YDES1ZuQ0nBEppYnD4pgal6XY6HFia4pZr\\n5/nZN387AlhdX6OT5SyvrvOuDz/Auz9+BK+SKEuEeLkxJuGVSDYJpRHt91/+eNssL/XV+BUfTw31\\nUpC4ZQ9H6PIYw41NvJSsLK+QleAtNE7TLL2OmM0CChUBBriTb6fwI2p8EnzpwPJyQ97VZLlEa411\\nlhBIpB8nWT3bpJ/DWLZt74FyqEzgm+SnzIRC6FYNHgSjYPExoHV6WCNETGud6uSaGAXjpkaRJiVK\\nGyQwHFYIp5mZ6hJkYiAnvnKSSQUZicGxal5KpbdDzHF1hR8O8HaMCAFZTDIT/pgbdmxn/9ICZ5bh\\nPR+8m+7eb+P1t9X2d956Vx6vHNbf8LrSML9JSmXdF6nuzN1SKZ59g2TFDqmKH8IM/pAyl2xWFchI\\nXTn2Ts+xvLVF4xzdMme20+fcYIMmWFQrqhGt4lMGAVLgnU+UEilTpFWriq2J0Hhm+12GtsE5mJmc\\n59Xf+QPc+ZH/zPrKRSYmZqjGFS40/ORP/SPe+va3sLx2giLrg7M0eAiBIFLAcPQRk5ZbiKDSk7wA\\nFRUNgSZYAIJzdIserq6ZnJxkMNzCaMjyjLGtiS4RUySRGCRKagSCYVWRa42zDkGkzApmZ2eZm9uG\\nVIKNzU2q0QgfPIs7djE3v53v/b43o4seMzOzaC0pc0On02tvl54yz5JoxzoQDlrQwitfcwu/8et/\\nwG3PegHON0ih8UEQo+cDH3ovN9x4LXt2HMARic6hlEIqg8ASMKlhicB4aHnTP/l9Hjy5ivAV3e4M\\nWytPoswkURcIpfHVAO8lJlN4WyF0BiSAhJE6CVmMoR6tI5VBRkeMEu8rpD8P2TYwk9hqA9EkP+vi\\nwjRv+p5v5U3fdgtTc5OIkH6+8+dXuPvwo/z2uw6zNWjtPpcuiD487Z0Z8cGnWyyXGl/a7z19PPtl\\nStp2n3tJNAVf0VyV5/aFx6g3lmnGa6yNNtkarOKcRQRBHfrE7W+idpvkZ9+JtgJp0n5+3DhEjOR5\\nTt4DpGS46QghIEWKfGtsTQwZF54cgTIQAkUX5rbn6FxSj5qUj2kALVCZQQmwjcPojHFdEVt4R6fM\\nyE3ajTrvcbWjsYIdOyZZXx0lKpVSbG6OmJou0UGQd8vk8SSNaqVI6t4QI+SRKhxkXb4IRMRWmzSD\\nLYStCULTKy6yt3iQ5x7cSzNyvOND95H3pqj6t+FPffCatY3Rka/lOXSl/tt1pWF+E5Q05f+mpfvX\\nE9sK5pZyhhNvpGpgKbyTza0xeS4oOz1GzRhXexYnJtioxmRorEhWDURkeTwg+OSTFIFk6bgksSeS\\nFRnCe0IQNCGAjZR5BqS4JS0LvvWl386pk6c4fuJhlBZM9mdYXrtIjIGF+Z3c+IwX8MEPvw1CoJvn\\njK1DS4UlUHmHCJGu1EmpWydjt401zga8DEQb0Jmi7BR0VMbCth2cv3gG6xydsk/0aZTbKbrITLF6\\n7jwqKoSEUWPJy4Kt4YistRvEECiznKXFJZASKRTdbodOt89g3KCNYuXcaZ7/opfx6te9CaSkW3YR\\nIpKZHGOy1JTbBwwYISgY1SMuXrjA97zhe/nMxz5DbgwX19eZ6fdZW9vgV//ZL3HHHS/lNa/67tYM\\nn7W2l0hd1wzGNWMfYbTJqeUh//Jtn+HhY+fxboAyPdTwBFYvJvuH0kiVmqswyXgTXEUUKqHdZCAi\\n0w2uHbsSHEEkW8+Pfd8LefVLn8HsZJduJyfLFJAlb6cCLyIqGkbDASsbmwgib//wX/If3nck+Sp5\\nqqGF1t7zZY2Q5OWkbYZCSkA+NZoNHqHSqkAgLhOF/quGeokBS+DFuw5z9vQ5hlurNB6iAu8s48EI\\nlGTYeQ4UB1Fn3ooXNToYvLMUnbzdC4MyGqMTp7e2DY11NE2D0GCDR8Sc5QsDpFfMLuYEUaM6V5OL\\nFVReM96yEJNS1vuW4kNIAIQ6IKNARonKJJlRaJWmD3VdJx2AD8kepRVV3RBiZGqiJM803gtkm0dm\\ntKRpJz9Rkn4PQ58z/npcfjVERb15kVCPANATffZl7+PmnbuZLks+ePdDnDq7zq79z+XBh+5/thst\\n3/N1Opqu1FfUlYb5DSwhRNZbuma5Wj3W33NwEq8Mm+426kpz7Z7jeFtRVWcIUmBdIFeKjikgBAZN\\nw2S3l0Q9OnB6bQ3R7rOUT+LXENNfaN+KU7TRBO/wjrTn1AqJYH5miRBqdiztZW5mjge+9HnGjWdp\\naYnlCyl+y2SGCxsbVNWQbqYwSqfMSSmobA1SkRcFtqopjAaXdpLRpR2kbTyqMCgj6OQdnvnM57F7\\n9yG6/YKHHnoIYxR50WUwGFLmXXYtLfLgl+4j+oaNlYuMqzET/UnOnD9DYy0zs9tYX11lqtdnc3Od\\n/sQMRkme9azncfb8aWLrE/XjAbe/8GXsPXAtC9sWqcZjrHPsP3CQPM/w7b43ypzjjz/CtoUFfvLn\\n/x0bo1Mos4upxTn+6FffjA8wGAwxRmGtZ2KiTzVOQdOhtZToFu13+swpful3383N19/Apz9/nH63\\nYXVNcurUvejezYisoKcrVrdGxCCIKh3GwdZIk5HsHv7yTS76Ju2Jo0IZ2U4LQOUZHRwf/6N/wMLs\\nNv7wP/85f3r3w6yPA955fvb1L6TyDV/80lHGVcPhJ4asb9WExtFEcUn3+t+vmHivlxeaQnJ5qRkD\\nl5acISQiEy2JCZESSyCpjkOrmJbRcNPM5xmtPEllNxmNt1Cqg5AFPjTU9RrjITRzL0Gt3YUi0XXO\\nnBiwfWeXrWFNt2cwWlKUXTYHSWglgCZ4CA6tM8ajCp1lSAMhCGQMKGloJl5KJtYx9nO4UVKXJ88v\\ndHoF2kTqxtFUyTKTZZqy1DRjh7MAkcmJLpHAeNQgpcIHR9M4BIK8UJSdHEJqmiZLgjiRwvCwMSJC\\nsh5FEXh8TSNn3ogdbeC2VhFEZFmwN/8g+2fmue6qndx59/3c+9B55vfdyqbad2r9i2/f/VU7iK7U\\nX7uuNMxvUOUze/++aNb/ucnrbNtVBQP1CuphxA3WCFoQx1vs3GeJ7jFyZWi8wwhFR2fYEFFCUZiC\\nkd1iK9ZEB+ONhkzrFmQuUFEy9g5be4ySeJnCd7XW5EoRfKTfmWCqP0Ov20kCFZ2ztLiLx449BMFS\\nlh1On36Sxo4ZRw/tXSdrb0KNd9DmVGohUvZhC6vWGqwX2MrjQ6Cb5WzfvouFxX3s3rGXl7zk5dig\\naOqac2dPEoOk2+2yubGGUJHNzU2mpqYZDzYZjTaZmZ7h3e98K5P9acreBMsrK8z0J1BK0C26bG6t\\nM7+wRIyBA9dcz71/9UnKssOrv/dNdPuTOGdZWNiFKgqmuh2E1i1AAB47eoIf+7X3Mhw13Hp1j9/6\\n+Tfwg//gLbz93/wsc9NdZBQMBgOapib4BE8P3iGExGRZK+hxfO7wg7z1Pe/k4Ye+yHU3v4JqoCiL\\nMYePXMCbBWJwlDIw3becuwCWRKYJgJQZCIcSgtpHlDJAIIYk8BJEhE9GfYTAuxpkzo7FaWyIrGyM\\nE5yAAFERZUrwkDE13hAi4AH5Xwl0IBL8pRitluxDGqsSEn3nK+uSqEe1EWsxPO1zRMtnkvKyKOip\\nX0mgleVQ8Vlctc54vMm4rqhtgxRdNrdWMSanMtuwq8fI8jSK31wfsjDXwyHwIpAJCFEgjYao0Epy\\n9twqnU6GMZrBYISUBudrjM4JHvKuwk68BDNxE74+gbbr5INPUVWWalRTlJqp2S516owIlSwlWksy\\nnSWylU8wj16Zt2NqAUIyGI2JLrbYw0hZZgnAHwVKJ0BCJpJXdlQP6JSdtBoRgvVaMchvZeB3MXjy\\nGFmeozslS9kHuHZmDqMV4xr+/O5HqOUMu/fsOnLq9PIvjy8++o6v1pl0pf77daVhfgPK9OZ/NzSD\\nn1nclaGn5hmX34HwkuGFR4k+PZHn8zkT5tN0TQqwzVVOaHxSWdpANysxmeS83SC6SDWwSVGqEj5O\\nSUkIaWSnkLhoiUicDWS5JLjA1MQMtrEcOngDU5MzLJ87iQ+e1Y11iqLg4uoFapuweTGkQ73IdVKU\\nKp28k0S01sTo6RQqyfZjUsyOnUU3ASUyTKnZt/c6pvpzPPc5L2JxYTsXly+yML9Ep9enroY8+MA9\\nCKmZnV9idmYe19Qcvv9eut0uE/0JNjZXIQbyouD440dYX72I84FMSF7w0lcwPTnBVfuv4fEjj7C1\\nNeDk8UfZsX0vB667kenpWbK8ZDSumJyd4RMfuYtXvOq7+NhnH+Rdf/FFvnhsHesEUQhyJel1Sm7Z\\nbfmtX/qZNn+xoalrnG1QypCZDKEiCINragKRZlTxpaOP8b/+s/+A6uwgMkm3kyGQDOuQSDMI7GgN\\npEkHq0i3PBEjzlUJLCA1EJBIgqsQ0REB5VcJ+QLROoRJKpmIQJocoXQSmah2dNt+I6LER4/W+vIN\\n7xLc4HJ6SYyEmPbQl8bcQkmkVCSrdkzJMV8p8nna2XG5KQqIbRMQrfDlUl3eZ5LSbgq5xvT4L4nx\\nIuNqQIyOoujS789y6vQJtE52oq3NMVobqpGjU8g0MhYBlUmcI6WLtFD3GNP7cVTVeBeQSpG1GLvx\\nyJEXGikFcuZ5xKnbAIm2D8HZuxhWlumZHCXAh4gL6YFBa4UL/vLvWJHnVKOasszaEa1lbnaC9a0R\\n1rpEy3KRTCt0LvAetNJkWQoryGWOlgapYRxqdJRokUAc3uSsVRMsb91MXY0QRcnO7IPsmphkqtRo\\nnfGhTx3j4sWKzs7nYTeO/3C1/Ogffs0Oqyv1ZXWlYX4dSwihy8XrL9r1E1OL+2dxvdsYrEdCM0Yq\\nQ7ApKquY2c7k5BmK5ov0C8l8Z5a1rU3Wq7S78yEy153iya1zWOchShqbBBNaKnKdMajG5NowNznL\\nsB5SNTWl7rC2tYbOJL2ij1IaLTXf+uLv4Injj3D02EN0OiUbGwM63R7DeogIgZGtk8I2BpQEjSaI\\nSGiJ1kootFGUeU5d1dgAHockkCtJ2Zlkdn4vr/jW72F2os+ZJ49y/+HPMjU1x/T0PEtLS3ziIx9m\\nc2uNHTt385wXfDsmy+mUPWZnt2G95/jxY+zZs4fReMxgc4Xl82exzrO2fIH9B65lZnYbZadLv99n\\nMNjk6KMPEbyj258EJZmZ2cbMtj28432f5vTyiMOffT/7rn8xRy8EYrBJRKM1QWQJWBAc/9ePP4c7\\nnnsTkFijRmust2RZwfrWmM8/dJI7nrmPGDwmywjec2p5ldf8zL+m7F0FWl2KBmmjoVKDrNfPIvMO\\nQmYoLYgxIetE9AiZAo29d+CTFYgQkG4VIZqEtFNzuBiRKkOYDsrkrcVEEZODI4HVoyc4m2KvpEFd\\nSt+Qst03yvQgFNxTKlbiZfbs5WYnnlLMPqV4fWovGdMH2mYtwCdgALLlokSBEO3OU4TLuZOZrNgm\\nTtGNR9gcrTAarlM3sLC4nfXNC3hrKfISqTLGowG2dm2KDmgpifiEyxUCb8G5p+HutGBcuRaRl1Ju\\ngmtfhxR0uppY7MdNfBtRS8q1d2PECnVdk5mM6MEHT4wSodMvqnTapROhHls6eYkuFE3V0OuUCBlZ\\n3xyhtEz2lJD2o2WZ4yNoeUksBYUp6GYlVTMkqPRgY4zBe0/jamqrWQ3XMZTXIU3GnnAvHblOJpaZ\\nn+rz4BMN9x1+FDN/M53Fg4+u3ve2a79e59j/zHWlYX6dSggxnU3uOCOUKGb23ILVS9jxGDdYASmI\\nzqPzSXRe0O0dJoxPkueSTidH+mQD2Wpq+qYgFwqUYnlrjYggOEFsPLu2LXJ28yJTvSmGwzEHrz6Y\\nQNGZQgTDrt27+C8f+jNiaJiemEUowcL0AnlZcPTxRxi7CqMzYgg03qKFxGjDuKmwwaOkwChNmh/G\\nNp7LUeoch0/A7yhobMq77Pcz+hNLvPkNP8uwqjh17FEefOCzjEdbbG6sI1DYpgEhmZrsJ8M/gqnp\\nGXbtvYY7vuV7CDKwfXEnTV1z4eJ5zp87QzUeMjk1zczsLE88/ji33PJMbrrxRh577FG2NjdYW1um\\nKDtcddVBUILRuOYLX/giv/nWO9lgJ8JIpBf4aFG6ROiUNBLRCFp6TcJ28we/8nr2LHZQQiEC5J2C\\nUxdXefddX+LhE8vccmCOmVLxPS9/Fl5oXvXTv8Wo0VRBsjg/wfnl4VOWi5aQ0wzWkaaAdlwpST5N\\nISF6l/aVSGJ4qrm50QoI0O4Ejm2g+sisIISIMhn4hqCKNkw7STwDAhEDQucoo4lAsBa8T82shVGI\\nS3tHIdKfYVsxBrxLUIQ06o3tiLWduD6tQgsKCCGBJaKUlxtrogQ9BRUL0SGEwhCZzU8zYR+iGp5n\\n3IywIaB1BihCGCU7SxR4Z3FN2hU3LlB0DLS33uDT+LipBE0D/SlBEFBVDmeTQCjPdSIVINNtWimm\\nujmNzKm7ryKaSczah9Hhyfa1xyTmMmmiEtrXLoSg0y3wzqXYuShxdUPRKRAx0jQOk2liSKNc2ziy\\nQtPvdZKKN8TL1hshoSwKNraGDKuKOjgMiu3z09TBg49sjT2xeDYbG4+yMH0Vyp1nSq9zcPsCo7Hn\\nHR94gFDupFi86ejGg+8+8DU8wq4UVxrm16Xymb3/2Ffrv57PXYuZuxFcjRQCu7WCdzXC+8TWnJhk\\nrn8vdV2l8U30ZEWGFAnhlWtDqQ22cYxj8pzhIk3t6OqSsa+JItLJOvgYmOhNENvbipKGrCw4u9Kl\\nLDyHdkwwu22Bv/z83UkIBOnADAlWLYXAaANENusRWimMVJfVkpmQBCOprCWXEpf8CqgA3U4Xkyv2\\n7X0Gz771+XQ6He79wt2cPvEYW6sD6qZiNKopixxrmyRKEhIlBZ08h+jZsXM/L/qWV+GCZNu2hSRo\\nkgKtMrI8Z9vCAp1OhwsXzhNCZHpqGiJsDVY4fvw4vV6f/QeuQ2UaETWfOHwfX3qi4d0fvJfYoUk5\\nlQAAIABJREFUnvZSaKIU6XYpRDuqbKUtrcYlAgcWCp59025mJrocPXmOj37uCHVjQXW5/qpJ1gYN\\nM92MvOzy2Yef5LYD8/z49z6fLx05w1vee3/C7YmUioGKiADNYBkZxjgU+BoZLCKbIESB0IYQPTI6\\ngsjQWZdgK6QwROHIoqURKgWAFz1CMyT61qoTI0IapC6ASzi4jOgDwTeXjJKpmemMYOv0uqX6csGO\\nTLfP9OwgL2PxLtWX7yVjgiYEl6xLl27WTxvBXqpLN830M44QqqQbTjJdfw4XasZNReNcGmeqHG8t\\nMVhcSAk21gdCFBR5RnQO2YL6g5cokzIta9egDNR1xDvSfl2lyDDrUvZmCFAWkrxQZKpkmN9O7N1E\\nUd2JHBxJD4hO0qgZdJaDMMR6mSKvmJruMqrq9s81PSC6GrQWZFpijKKxMe3zvUDGSN7JMFJTB5cS\\n0mIiUo1sw9rqAOegKDRFYfDB0+93MW3gwVprg5kqDSNxPcRldnYrtk/1GVWOD9z9GOuDnO3Xv4SL\\npx/4kdHpL731a3GOXSnQ3+gv4G976c7Mb0RX/3z/6jsQ5XZEMyJKRbN5EW9HyPY20ZlYotv5KKOh\\nxWS6NfhnBOtxTUjaAJPk75qEcFNREiR45xnEMUWp0ShqXyGFZH1zheBS3JRShhV3HW7hOnbNnqZv\\ntqjGI3KT4YRsY5RqYoiUnR6Nbcg7OeO6QQpJIbJE6oEk9Mkycq2RjUVmGcpalABjFPOzi2xb2MHC\\n9u3kmWZ97RSnjj1IPWgYDNbJswyTgXXpsNaiJef4hkFVo1XKYNzc3CDXOfVgwMZgCylgcnqeuU6X\\n5dV19vW3cfW+KdLSyeG8Y3Nzg83NTS5cXOHAtbeQ5znadHjps5/JwuwJ3vdnxxjnexE6b29Uae/3\\ndJSNgMujVAkcO19x9K7HiNIjgwLRBWPBOe5/4CgL2+dZcXDhxAr/5n9/Fa+8/TqizhgMB9x+0w7+\\n7g/cwYNHnqTbLfnAxw5z+MgqHVkyWD+PdGPkeIVY9pCdnUhRoJQg2A1oHKVZ45aDhk/eX0OnixQ5\\ndUzCK18tQ90QRJFeix2iTAdClUaJUiUwRGzaJpoBMYl/oie6mmDHCFMmMcsl9B1fbi1Jitck4nk6\\n0SeGgBDgfZPsQMT0jw1IpWkHvIBIoqX25k7LoJ3tCn7wjinu/OQx1gcdfLVFYx1VZalVQ543aGEY\\njSqylg1rTEp7trFK9CAvQCoQjiYUKF0gwxjvJLkWOCESaq9NMRExoHTEyKSiDlawORqQNx8lNicY\\nzb4MpXcTL34CnY1xZg91/3kpABxg7T00zXnKQiOFxnlHpg0DPyQiqMeBslMwrkZUI8v0ZI+maqgq\\nS8gTpL12Hht8O6KO6bYtBcFB1TT0Jwo21gdMTXQpc0M/8wyCwwbNUvk4602PC1VBc3GVnbMzvPR5\\nV/Hpw8ucuv99dHa96D+WSzeMxme/9K6v4bH2P21daZhfoxJSFabo/vvo/Q/NPuP78U4SxgOaekgk\\nEO0wHVpIZJ5TTD1J2EqCARGS5L+pbBKEAMiWlO+hqusUaWUioYY8z9BGoklP25lReBtw1qOKvYxE\\nn6KYoZjYw0YoOPnk8P9h772DNTvvOs/PE054zxtuTp1zt6SWWlJLsizZkpyEBUZyINnF2sACu7C1\\ns1PAMMNgFpZhYIA1MDXgIRWMwQz2YIyNsbFBNsayZFnRrdTqVuccbnzzOecJ+8dz7u2WmZmdATNb\\na+up6ur3dnV1n/Q+v/P7fRP779xMp3+RDbNbOHHmON7mZFFC7hwzU3MIBBcXLrFt4w4uXLyAdYZI\\nCKTUIMBaQ6PRJE+GmHIQnGcQRFmNpe5liAoWl0+xdOEsJ44dYmlpiXozo5Zl1Ot1fK9POcyRUgTN\\nHJ7CGCIVY63k2EuH2TC7kXpzjH6/z9Ydu2i1Rrjh1jt55JHHOLnQ4ad/+wM8/5Vn2bMhY/umAW/7\\n1ncwOz3OSqfNnt3X8cgjn+Om/a/h3/zST/HwgaOkIxvJ081AHJipXuBxKJ9cIbFU4vuv5pAKfJBW\\nVJ2nFAorHCrWdPqWeTOgkTXYtmGOgbMsXbjA7LpJjh59hL0759g8k6J0wuv2b+eDH3+EoTV84M89\\nReccXhUYk4FMCPpGQV1ZOuYi3ULyyGNHEGPXIIOHDBIQTqKLBYxLcNqTZBPYCqt0/ZNgFvHJLCpt\\nYm1VrJzDlEOkNyAU1lXePKYIWZcqwVcykJDcoUKajXdVZ2gRoiIPOYfzFmFKrB0Cugp+VuG5dqvv\\nHGJt5Ot9iOUSQuAQXFxR5IMh99y5hz//6COoVCG9RMYR/UFB4Q0+ckRRcHXyleGGFw6cR2iBsyCN\\np0y24affRrt3EOnOENslhO0i9UrIz3QWJwVSaJQPOavWOYRytGoxcRzR7RzBnzmHW/+DyA3X0R+c\\nRZUvotwJjF2HRFLkHXJdMCw9sU7wFuJUMToygvOGbr9gcaWDVFCrxRSmpJZqSgu2tIhagnIe68KY\\n11dZsEmSIiJHlkVBI1xXrHSG9PI+60YmkSZncdCnKHPG6sEcf2kQE3c6TI9McNctnk8+HNM+8SC1\\nmZs/HI9u6dnOqQetdfk//m73jbNeGcn+Iywh5FhteseiGF6mue89mGGJNQW2fZlkYj1Fe5GyfS7g\\nJK6kueUaEvsRlPXk3RIVhbdztapdk4AKP8dK4UsLEIgmzuFN+AKWLmAzNaEoTISLZoiTdbhsJ63y\\nCeaj+zBRj6RY5K37hwwHimLY5eiJQ3T6Hbz31NKMIi/JGhlLi/M0m02Gecmm2fU0W5MkaYaSjsuX\\nL3Fh/ixx5UHrJehIMjbRYnFpASE012zfzhNPPUnRGaJSzb5b7uaZA19iZnIdzUaLIy8+S73epNvr\\nUlT5l0kUUxa26qAFu3dup9vpcsvt93DPvQ+w74abuLjQ5R0/+rtcvLCI651CmGW0XWR2zPHO7/ou\\n9u7eT9ZMOXzsJX79t/6A8/11+KRJ2pjGobDlCvgUbJ+0nmBFnbChizWei7zqaxFiiEOXvzrS9N4F\\nckuUIHEY71F4vFeYomDdlGRmYowDRxb56K+8i8nRBtJ7lto9/uPHvgg65kN/9SQqHq8kHBqcAXG1\\n52mwVQOBdBrvyxDTBeAHeGNxBDG9FxKtE2zeoyEv0zMtqLXWiD3e2dAl5u3QGaZhXC9cGca1QoSu\\nUOqAO/rgpYsUldzEXwmhXu1ShcMV4aXHe4H1BiWCJZ+vGKv40G2GcXLIKPVSIl0wPH/P68YYdM6y\\nsrTA2eNP0e2t4PKC0lhya7DWIKTA+RDX5Vxg9Iaot8oCEI+jSTn57bi4gfIxTlrwDl2cxyx8gcSf\\nJ0tTyrIIx1oGjLLwlnotQYsIYwpykdCXtxJN3hgyYVeeRfoeke1RZPuJ23+GKZZp1dMQFBAJnPSY\\nocUIUNojrURYD0pU4dMh+9V5gr1jpLm8sEKaaQoM3U4JztNoJjjjqMUxWirapk93pWCi2aCeJiRK\\nc6G7Qqo13kMTRZlsJ5KTbB5dQGH425cyTj37ENHIVl61f5JHPvdIZq0b/A/Y9r4h1isF82u8hBDb\\n4ubU0bge0bj2PbiyxHmHGw6wZZe4Nkr39HM4myMBUw6pze1hrHWYvH8I0y5RwfU5MBqFREcSqanI\\nCpI40iRakA8tUgZSgnCCZi2jxBELx6K8lXL0ZqAIuJJPkV4gGGKpM8UBot7TbJpdx6Dss7Q0j4pi\\nOitdoihCaUGa1tm8fid3vOYuPvvgpzlz5jj7bnwVW7fu5NDh55gYnWJhZYHG6AR5Z5kk0ezetYfL\\nlxfodOc5duwrDAeOxUuLzG3YwqX5s1hvGQ4KpAJnC7xXxFJTS2v0+j28A2MtEUEn+sZveguXL5xj\\nx45d7LvlLm6/4x7iJIj73/lDP8Xjx/OgFeycIXLnqOklhIjpDSOklli9FUZ2gkqQElzRQxZDfOKJ\\nohZORHgh14g3jlVyDqzieWXeR7hKiC+jSrNR4Zwu6CSlFBWRRiGECmkUSuNwJFJj8wV67QX++N/9\\nGG+8cx/HTl/k9T/wuziG4KO1WDF8IORIwni7ML4KhHYIa9b+nhBBaB+SayQCh7ee6zcpPIpnT/VR\\n3uFVkLU473AipNoo5bHGIbCYYS/oSnUcUj68x0uNkgKPBBdM+pWOAg6Lq8ayIT/VeYtCVF2sD6zc\\najQb2j+BIOg6pZAhck3HCOexAiIZ4csBU/FZdk/1uOv1r+fk4Zf4i09/EFW9tWitsaUhL0M0nFCh\\ngDtZ3TPnUS6m1BI38W5c3AAhkV7ipUM4jfI9bOcIcfkwPh+Ebt1BaT1p1giexd5TCuirm1Ct6yBq\\nAcGcI5hpBUP1tP0EDJ8gjkHJwJ4uigKtNLk2eAMUAumgWU+C2xZBt5tGEUmm6bbLkIIjDL1BifeW\\nRi1lbnyUdm8QiHY2EK96K4aRsZSxtIbUkgvLS4ymGUNjqY/cwanhBjbVDrJktiDLgoWuY+G5T+Jl\\nSrb/7XS++Fs1b8vh/5gd8Ot7vVIwv4YrHtnwk25w7ufqG26hse0tOFng+yFz0HmDF5L8wlFM0Q+b\\nKoDwZJtfRZysoJY/gi8cpTMIE/iKzpQoEYeNtxYjJEQJJEqx3M3RViBUwHG8gtI5mnGL6XqThezN\\n9GwDJ7469tQjfMHe1iOMxHVm56a5fOE8C/OXGBYDQHLNtTfQ7rYZaU2S1TI6nRWyeoN6azTYxM2v\\nEGcx0lpazTGmpqZZWFhE0uPCpTMcP/YCE2Nj9LqaWrNGJCKaoyN86aHPUBqD8RavYGJ0htnZdbxw\\n4GmkitBCEMcRcRyzsrzC6MgIm7fu5J7Xfwu33/FGJtdN8ru//X5+87d+nQ1793N0fhcmreP7PXAF\\n1gpEvoiu1TEyQQEiriF1EtJYuk8xSOdQ8RxChIzFtW+Av8JgRHhMUUKZY4s+1higIu9U7FPvilBQ\\nCCkZKkpxKkbpiCipVUSaKIw7hcQOu3ziV9/D/huv5cSZ87z2B3+HTRN1Ti30Ec7jbLnGRlUSvIrx\\n1ladmcAW+cvINq4c8n0P3ELuYxLf4bu//V7e8T//n3zkN36CRw+e5Sd/7ZMI08N4iTQW1WyC15VR\\nTyh+zlhc0Q3/ryuQOg3dptA4m1dkHxVi2UTQZobcyNA9WmfQXhDkw7KKPXNB0mItXkq0jiuZSzBd\\nkDoKl1sUQMwYl9kxchERj3LiyJeZ3ZBy6957+Ju//jOkMGgt6Q08g2GP3NhKlhLkOMYZvHJILasp\\nQAMnx2D8HnI1g1TBBSgWGUaBH5yHvI3rH0XVJlHjd1EyRHgZmN9WUQ6OE4kUp5sQ1QJTV6z6IkmE\\njxAiXBvh+ojyMl5OoEQNtfCH2NoSkRZIqxi0c+pZilYC4yxKhYzZJIkxzlEYg1fQ7g6ItWR2tIW0\\nktJb5js9TOmptWLaSwPGxzIynRBHkgvLHUaSmHPzbTZvfRPH21sgrjGnT7Js1tMZ9ll58TPY3jwj\\n1zzAVvfQe548cPQVveY/cL1SML9GKx7Z9D7bP/MjrU1TjMxMM9Bvpj9/HIyhNrMdJaB79jBm2Mab\\nAqXCm7EeWY+OU7KpjdhL7wcjUDbgR9bneCdpJhkOQy8vqGUxDa1p5zlCCWKtKQYFWdaiLHKstEzV\\nR5Bz38qpzmRIlvg7/mfBDmyHfJRvuvsWTp08xrDfZXp6luWVFY4deYGllUVuvPEObrrp1UzNTLGy\\ntESW1THGcv7iRdat30prpMXK0jz1NKbbXWFpaYUDT/8NFxeP0Ght5fY77iNNW1w6fwSlJCtL8ywu\\nLzI+OktaSynyAU89+iDO5UzObObUiSNoGfGG191Hr9/j7LEXqbfGGF+/lf233sHI2BTXXreX1771\\nHob6ZuLWHpx3kOesTkulkEEOURZQLuKjFroxiZIKbwxWRjSjLoMywUVJNXoNBc+JSnbhDKbXweTd\\noI+1Bm/KqsuoiC3ehlGmyQM+KARSJZXxQNWBVTrEdGwWFSV4qcBaxnuH6I7spkBVuKQABzbvI7QO\\n4J9SyFV2aiW892XB1ZoO50rG05QOktvXlTz0YptXb2+yZc9OPvTpA+hymdtvv52nnzvC2MgYZ5a6\\nSOJAXpIJwpfB8BxfxYM5VJRgepchbqCVxqJCp+dV1WkJZIVr4gSuHIBwSBmF8xAe4V3VCQc2Lt4j\\n4wQh9JWRt1CV0Z5BuRgfaxpmmQ2jF9i7OePiueOcOHOK7Vt3cvn8Wbr9y/QHOcYW4EOot1SrCL8I\\no+oIjJdEkaQc+jBmleGlQElBbKE99VZkbetV17F6clZjyITBiAjVPoIfHkdO3I0XNSDY3q1pT/2V\\nSLNVU3rnNZIS5ZaJ+h/AOYnILYPukNm5cbT05HkwkRASisIHs4ZaTGcwpHCWqdEmwoJ2ChVLOvmA\\nlc6QwluUF9RSxWS9RWFLFrp9hFPk/YKRuTtYMvsYeoPE0kwFw/6ApZc+y3D5PK0tr+M1m07/0F88\\n+JXf/MfcB7/el/qZn/mZ/6+P4f/3Kx3d/HtmMP/Ds7uuJ5vI6JSvZTh/EuEcIq4RpXV6Zw9hii7C\\nljjCeM/phChp4F3BYLhMbWwHkTxJJENcVixViG+ipPSOpErUiISikaTYfkksElqthDzvkruSWI+x\\n0nqARTMWNHl/xwHN4YXBEWNVA1+eJZOChc5FkjQDoDU2Qas5SqPR5PDh5xn2eoxNTXP0yDH27N5D\\no9nisace5vSZ5zly5AXaK0vML8xz6sxRrrnhFnJr2bNjP7MzsyhvSZMaFy9eYufOGzCFY881NzAx\\nPcPY5Ay33vp6RkfHOXboGWIhuOXWO0hrKQee/DKX5hfZve82br7lTtZv3MR1197Iz/7iT3DsdJuc\\nDK0n0Vis89jhAogYZ3s400MNDwWtYjIOgBARXipUcYI0rpHLOpWMPBRACcIayt4ytt/F2xxvLa4c\\n4osB3pWhaNoSbw2UJd4W4MowenQWZ/LqzxzYAtNvo5M0yEj6nXArXElZX4d1hJxLU6KkwlGZAbhq\\nsuvBVcxU7y3eFGFeDGtyFyEVrUzT7pecb4MthrTqEc898yyF0Fg9xcWjz7B56y62jVk6vQFDC2Xe\\nDf+IM7iywPsSay3e5Shv8DIKZg5eoOKKiFQ56chqPCxlpROtHH08LtQgqhgwTNAcVt2197KSel5J\\nOwlXX4IITlJGRCwPI7rtJfpign3X7iLOxnjP9/wwS4srCBlwVVX51AbcO+g/lQ4EJRwh6FuE10Lp\\nQnSc92C9Rw4OI3rHsfXdyMqAQ1UiWCEEwiuk95CMIWrr8CJD2F7I3UQhMCE1RuRYGSONrLpPEEGg\\njBUpVu/DMYX2J4hqVWGVkiRWlZkCDAZFcMqKFLFWDAYhTLvIDSIKxK6IiOmxBiv9nFY9IS8DOU5K\\nSWkscSyJZATmHHMjiosrDpWMMMyhlsZE0zdizJDeqUdYFLve8sxf/0777e/6vkf/kbbCr/v1SsH8\\nBy6dTX7UDhffue6aCUQtIlevww6XwZTYYkBtdJruxeP4coDwtnpDV8ikjqyNolSEjJtIZSHaAPXb\\nw8ZSnEVJwUQ9wzoCrlmRLcbrmvmlNi4SgexjDN3CIqa+i7x1F16lAb+5anqw5uKCJJhAe0ofoSiY\\nqveYnJhlenKW0pTc+01vZXJ8hj27r+O2O+5mZm4jmzds4s47XoO1hsOHn+fCxYP02otMTU1x/MRR\\nvLMst5eoN6a4995vJ89LlHKcOXOS6YkZ+r0l2ouXOHLoWZ547PP0uh367SUe/+JfIbxj965dJEmd\\n0UbK008+ztLSApt3XMfWXXspSsNgkPPnn/pPfOyTD1Ho7Ug9hxMREof0EmvaROTIooukxNemkOk6\\nZBQHIosKIzWpmpS2AJXiESHsOVwh8GD6PXyZY00R8ERb4k2Od3YtJcTbsjIXcFc53QTTb+8M3hQ4\\nk4PwoegWQ7wtA15ocvLuMr4YYMqSYvliMFO3gcjlvF3TKtrhAFf0MUVRsUslYIPhACCjCIcAEVFT\\nFkVJ2yT04gm8rOG8oFnXRHGdrzz5NN/8+v0cOtdFqgRsgc37+LIfxq9UpB8RjAmkrmFtkBSFFBhR\\nGR2s4qeh65U6DkVQxsH0QNhKnqIq8lAo7KHeyjX8d9VZaC3FpBKgWBJW8lHaZZP5Dhw9DWPpAje9\\n6m5mpjcyPrmR0dFJLp07iyktjTQFBHnuQji5cCgfzsFZi6rcj1adlqRy2KIH7ScRZgERT2BUDVWF\\nsYQlkV7hsPiLf4lc+Sy+dxRXtHF6BhWnCBGRFF9BdB/C1nYFG0Pvr+DfQuPVOGW6laY9BB60EGBh\\nMCxoZFmwLBSSIrekaYJQgk6nj5IRuc2xOJQIwHozTWlGCfVaTDvPGZgCrVSwZPQGKQC3wFh0jtJO\\ng4bSaWayIUlrDq0bzB/9Ahfdhm96/rO/N3zrd33fw/+oG+PX6XplJPv3XEIIodORB533r5/aPkK9\\nOc0wexPd88fxtsDJBIouCIHL29WGYRFSY4o+UWsW3ZzGmxylk8Ac1Ak6jhDJOJz9PWo1AXaAj9fj\\natsofQOFxfVepF4cxJYKX0IttixOfBsiWx8E+FwZG33VMa999tVISrqcHY1D3P+G23ns0YcYHR9j\\n77U30RwdwYqS5w+/xG17b2PQb2NNzsryMseOPs/BQwcQKmL9ui30+x3iWsqePbdiS8H4+BgvPPsU\\n09OTtDsrvPj8U1y6dL4q15JEWVqjc2zYtIWly+fB5gyHOXuuu5lG1mRh4TTPPPUYt9z1FkZGZ7j5\\n9jvx1vCz/+onOLkocI0bIZ5AKbCmi1YJ6DqxKGnUBJcXF8KoNcogGUeJGNxgNckKoXqoaAKPxlcc\\nWCkF/cXLuEEbZ/MQteXBe4MzRRgheltdvVWCTsWWXbvOV7nkrIpTZOg9gkSjKkhSgStRcYYzRQiJ\\n1gkirlXRWQJnS6q+jSjOEFGKVNEVuzmpgVXbNkEqYWgMQumAe3oqezsDNKknBVIJOoMyHDeAHWB7\\nlxF4XDSJlCGwW6oodLC+ROgUITXe2SqVRQY8c9X8YDXqCxfs4KqCGzDMMhjEszq+VIE0VclVZHUu\\nq3KeoItdtdNTSGVo6CHSLFJX8+zaOMLMzCRbNm5naanNuTMnadRqnDj+IivLl7k8fwFj8hCYXgxB\\nVA5E0oe0EhlwSmPDd6O0Nrw46RHIrkdnuyijMbT3WOmR3iIu/xFxuUiUKIqhxRJjR98E2VYcikh2\\ncGYRr2YRIl57DLywCB+himfJikextoPNYXJ6BIrgIGRsSVk4jDHkLshcrHS4EsrC0RhJ8MIzmTYA\\niGSlQXUlhXNY7+kPh9SzOsvtNonWTDYbTI5t59HLN+AGi8w2clbkFvJCkC18jqMHv8Ls5pu598b0\\nX37go5//hb/v/veNul4pmH+PJYRQMhnrCEGttWsfSTSPHLkPU3qKziJ22A0diTO4XgcoCexCidcZ\\nca2Gas6g03rAXqwhgJoeEccoHYOqhY1YujAyqt7One0TX/g4TeaDr6YTdJLt5OP3omSOUPWq43GV\\nsPzlyRRfnVLhsWw0X6RuF2iOjrJj57XESUKjNcrOHXsYG59k6+bN/PVnP8PS8kUunD/LoeefYnJ6\\nPQPbp9PtsGPHPlZWFqlnY1yz+wb+5EO/zsTUBuYvniDPh5UxmmTrli1cOn8OgWBkZIL+oMOw30cK\\nz6tvvwupNYeeewqlI7bu3EdjZIIoTpiY28qpCx2cVuzZsY1/+rN/gk40Mp3GmDZK1wCBKLtgOwi7\\nQjaakiYJ9915O3/6mWfpDQuidCNCF7T8i5jaDXSNDimTXuCEoOwuYzuL2KIf7p3Jw+bnDd46vF/1\\nJnWhYK0Wy3DBK4nm6txUrl7wK8WrisUKmGfIunSVXCMwcFWF7XnW4rF0HCDNqIbUEUInASP0DolD\\nNyaCsw5yDZMTvjIM8CCUqkx3PN6EtJlgyxbQU+k9dtDGuRK8BuWQ3uGTMags7GSFQzpnAmHHA5XM\\nRlUF2kmBkpq1fn21vfQOYwtsOUAIHdirIshglFJXTBDWxqJy9bLhvUUKTTo4yERLs2lDzNLFl7hh\\n7/Vk9XG2btrJjh17KIYDvvjoF7nh2hv46J/+ASeOvsDiyiK9Xo8kUUGCYhxaR4DFiSBLMS4wYG0Z\\nBgQqDkxW5HqUrmELgy/OgRoSxRG1NKHfLwL5d+JWTO0WXFQnthYnhkhRx8hwTVefAeGHxMsfQReX\\ncVaRZBJTGmanxymNwZlQMKWQrPSGyEjQz3OK3CE1ZM0E7SWjtTpaSpSSFIWpxvVBC9sryqD3jCJG\\naindfkE2dSNnLy0z3phmfdrnQO86ikKwQTzLU196mMn11/E77339hQd+4JfnvhZ74jfKeqVg/ncu\\nIUSCTDoybkTje19LMz5Hnt0NooazJfnSJWxvBVsOMUU7kG4QCAU6qhO3plDNaYSOEd4T2N7VSMpJ\\nZBoDEaoCskKpyXHC49svkHQPkOg+kZQU1tJSNc6LKdJokl7zBoRogLOBzCGrxIlqw/47kU7eE7kl\\nrs+eYmp6jkExYPfua0jq4zTSGutn5picnOTC/AWeefLLtMbGefrJh5lZv57xsWmUijly8ihZVmNy\\nahO93grHjjzHsZdeoLQ9xltzTE3PMD01w4Gnn2BYdFEiCtZ71SEkSrF9yzampmY4eOgFvI+48543\\nY2xJsznC6NgEtazO9h27iZMaf/6pP+P9H/w4tnEdr903wSNPHsFnOxAixbsBm0fbfOcD38LerZP4\\nKONTf/UpPvyZAzSUYxitw1lHLC8z0mjS9pswVN2UUJhem7y9gB22q2JpA17pXWCGEn73PvjfilXd\\npjeIitH6Mti4Khp+lSxSFbWrbRFW68pasQjO6aFIrnajBLs8pTReyFC8cQihiEeniZvja/eTyg82\\n4IwVVuhD8fGrbgKCqtsLz5gXGjfs4k0fRFX04pjpRp92L6U0ElcRmZwZoFSMFQKJQEbTA03mAAAg\\nAElEQVSCWgS9AVUHvfY9AYLNoveV3MSVOGuROug9vQxynrWUE0CsIstC4K3HKw8oJKFrHskfZcN0\\nxkLPsW6yBlbw7nd+FzYfcGl+gY0bNvLUk19meekCx44cJMsyjh47xOLySjXW9kQ1TWEcVgTPXe9E\\nMGYXAmHBCB+sH/HB3ME7ojhMB5JYUhiHcECcUQwMMprBJi0iMYTatehYkEebkD7Ef0k/IOn+BaJ3\\nAS+CD24aJ5SmRHhI45jSlvT6OVEcIyUsd3K8d6SZJoo0trSMNhpID5GKoDJfGJRDunlIFEqiAFHE\\nMiKpJaRKsWl0gvMdT8e0ODXcTWEixs1Rjjz9eZpTW/iDn33z/P3f/76pr8nm+A2wXimY/x1LCNFA\\nJitSJXLimpuZzF4KYy5xPUZcz6A3jx0MwA7xwx5qbB0+72NLh4oV8dgsMs6QSlcBwWEzcMUAqT0y\\nHsEOTxKrNjbPkc4he8+TyHYwtjYwktbweAalwDevoRNtx+rpsHlXOJtzYWOUOnQsV+/iVxdNh2e3\\nfpR1Eym33HYHY6MTJFlGvzfgpuv3EukI4eHZ5w+wuHiJE6dOcfvtd3L23BmOnXgOby1nzp3ixhvv\\nIo0bHDn6PF/60l+C1LzpTd/J2Og4s9NTfPiDv8Hy8gpOQlpLMcMcHTgnJFHEts0bOHniGM2JOV53\\n79uZ27iZNE2J44SyLEmTiKwxRr3Z4PjpM/xf7/sArYbin/7Au3j/hz7NTTu38PG/PcJSe5F/+5Pv\\n4u5XvRqtFcPhAGMt/8d7/w1Hz77EMG8wsC08AuUukzZnyaPNCCfxEvKVBcqV85gir/BLA96EQrhq\\naO7dqtkPAnAmp+xeRGpNOr6VMu+jVLCgu1INr6xV3eTaKHfVPGjVlq4aXVZVjbXsykoXuYpFC1fJ\\nYYREZyPItEGUNoPTToXbysr83AEYg8cG8ahULxuHegFm5SKZNgxIcSYn0RafbWDjhOfCsqemBYtL\\nHYRwyChDR4EgU/rwUmetRWldHd+VLnO10w3PpQ2et6vdpFSBSCRenpl55TwrxyHhwZqQ+JGlCFMi\\nBieIxHm2jEYcP+/YvVHzhte9ASlg/vI81+zZw45tuzj00ktcPP0in/zMJyjzIQJPu9+mlw+CTEaF\\nglkaW1noVjhtdQ7WhVg2rSVlGUhOOtJ46bA51BsR3W6Id1s95rgxjU33Ipv7K0KUx1FQaz9I2XuO\\nRCQhzN1ZRkYaCOcojcVYRxJHpGnMcrfHYFiA8GRxTLOR0R+GvFtBKJrWhtg3h2Ox38MZS6NeI9bB\\nTrCR1mjEmonWKMcvdBmtb+ZQbzuCEjm4wKnnH6Y+NsfHfuXbO69/58+2vmYb5dfxeqVg/jcuIcQU\\nqnZJCEVz216mW89jDJTR6ymZxqkEqSKK5cuYYRcVJ8SNglqSU8Z7cVajtMT7HKliHDHC95AS7OAQ\\niZC44hzx8CjWliEBHqg3MtJIM9+bokw2U0S7ECp4yGoPJh9gTYl3DhVpdFLDOoFSUQgafvk5XME1\\nhWdSX+CBO0fpLi4zv3iBzVt2ccONtzE7OY21hvmLF5mensELz7lzp0mShN/+zV+gVs/IGpOsW7+F\\njZt20MhG6HUu8bGPfwBrFG9883ewbmYdjz/+OQ4eeIxBUYYEEwVxHFMWJcpCPcso8wHCWhpjk+y9\\n6bVs3bGDJM7YtmsPCwsLlGXO3MwMkzPrOHniBN/7L36eO27ax/t//r2cPXOKH3nff+DofB0zGFBP\\nJX/9+z9Os1ajNxxy4LnnOXy2xyc+/QkW2hGDsomNPD5fQtgIpz02Xh+uS1kwXLyIKbq4fgfvV9mv\\nDmcDDucrqcTqWFNKhRcKqlQRX1qc6UJcR0cpTiiEd4FJW41jX15A/0sYs6gkGuHzWtEUcg1zXU3q\\nwNvQ+crgbSriGiptIXSMFDJ0c3GGTrOqk15l4ohw/KufReB4CptjjAFToISmkJZWTdMvRNAjesem\\nmYSPvO+HuLB4kV/6/Ud49OnDlFauddk6yQJmG04K6x3aOEZGCxa6dfBlMHzw/qvOm6rACDAFJh8G\\neZA1gaVcxY7JsRZpMkbSP4oaHEU3mqyYOWJzkZHGOLiTvPtt93PDtft58aVn0VFCr7PCqZOHWT+3\\nkae/8ggvHXmRwhSsdLsgJGUZOnIpfIjh8qwdX1GxgVdJPdY6almMKR2mcNSbKb1Bb+0eeysRwqHH\\n9+HH713lOeHxxOUTiKWHw4tKZbjeqNfw3mGMo5YkWGE5e26J5kiNkUadPB8y2myQG0ee56gkQjoX\\nTOWloLSWTj7ElJ440qRRjVRZhqWgVlN0u33AY90kE1mfSE5w0u6jOz9g8eTnqbWm+cgvvd196/f9\\nfFaU5hUrvf/KeqVg/jcsIcQ6RHJWqJj6xldxze6TzF+4jIwn6cf3IpTFiBGoZAfWDUjUEbQboPSQ\\nvPZ2jIeILsrNY8Q4svdZdHEET5Oh2kHc3IuwHdTwHEn/ACjFUM4RNTfQlftwMkIisD5HlAJXdHB+\\nNVtQVPZoKpBDdBScZ/6uAHNtzY1Z9mRHOHfqEK957T3UmyM4aZme2MD46BhSChr1JufPn+XchbMU\\n5ZCRRotT507S7xdsmJtlx85rOHvmLFJ6PvtXH2N8ao7p2Y1oLfnC5z4JxpDnJaVzqESAsWgZIXxw\\nfWk2gsn7urkNbNiyh1qtTpbVWL9pG+vWb8IJzdjYGAuXT9NoTvDd/+RH6eY53/LqfdTrTf70C+fx\\n9c3BNGDwEr/8k9/L+ukprPVs37yZLz/3BD/xvg8j9DSFmAwm9sKGDdmLQMKqtIFlv4/pLmB7S7hi\\nUKVvBE0m3gWMr1qr3aF3QQMiqpBlpYJrkCu6OGuQURwwu7gVnJ0qpqaQcs3ibo0lepWpeYVArjFS\\nA7O5wkeFqsb8rjJRXy3ioTuVsmKshlBIRJQSJRkyzhBxUuHjUSUXEWtFzbuA0UqpUMWAX/sX93Pq\\n0mU2zk7whx//HI985Rhe1EnThF/90W9lx8b1/O1TR3jxxAJ/+uDBYJ7hyiAlcTZMUrQOetTKxCAE\\nP4dRsHfmSsFcvbDOhy4UFzSueR9XDsM419s16aTwEpllxKPTSBGM5ePOIfrJelQU7ADj8kXeee+N\\njLZSHn/syySyJB92uO++d3Dq1FEe/JuPI9AIH9Mbdrm0cIFYR2gLKlYYG8auDqqgdMI4Ho9xbo11\\nW5aeJEkY9EuimguJKIVDKfAyIln/Hbh4I9IYkH1KndIYfgnROxAwZS/w1lFLw/g2TiIKUzAsLTiI\\nFDTTDAeoRJOXJcaWxLUUaR2pikLBLIb0+gFzn8qaJFlGPuwhqkg+bxy5MYzELXLboy+289Ipj9Qp\\nw4vP0GyN8We/9h6++T0/3ewP8u7XZOP8OlyvmK//vywh5Eahs1OIiObWe0jq55m/eAmhQDVvBDWL\\nMyWx72F5CZ3uwOZHUWobNfcXdPWdYB6jVpzGFT3s6LeBbyD1tiCwr78DqQzGK9BjRJc/yyCZJR/9\\nFoSMGTpBWfSRhC7HlXnQgcmrOhYJUicQxdXu4wD1VedxZTN23mMWBxw9+QSvuedNZI0xtm/fzdjI\\nKGfPnqHbW+HS/AKJinj4yw+SF33ufNXrSJIam9Zvp9VsUJSGT/75h1havAhRzA23vpbx8Rkef/yz\\nnDj0PFJGFKXBS4f0ngAXBUmN8YHEYrxgam4rO6+/mVhphsMeW3buZW5ulu1bd/KJj/8xd73pfhYX\\nVviRn/lpimiKljnMl57+Ap1yA4lPMJ2HMHKWxsgsX3nkIUbvvptS1Xjvr/w2n/7SKaLGdqyRwWxd\\nVLrBtTmoWuu2VBwKiRES64OWDynxyFCThKqK0xUxpBB+raD6gLLhhEbKGBU38XYA3mO6l1BxDetc\\n0F46Ac4io1pgn9pQqIjS0K1iK6nF6kSgMjqH4BoUZrFhTLy2fDBfMBYhTCX90HhbkBcDpO4hI4VQ\\nCagIGSVEWZMoyQh1N9gsegQmzjC9FX7wnd+KM5bb9u3hbf/rv+T8Zcu73nwNjcYs3/nPfpeVgQ4p\\nOniwJagI4QYY0w8G5zZGRUkgs4nQNVtTgDMoFaF0wI+vFH4qk4ji6gc3dHeOoBv1LmhU2zllb4Xa\\n5AaSRpO8cQNxcYmy7OJ0Had28Yd/C7b/EsrBWHSJ67dt5uy5SzitGG3N0Wxozpy5iEbjc42MBLfd\\n+moef+rLeCCpUoNWN0krwAYBSRjNqhqXL67QHIex6RhrLOXQEkcKZxxQIi5+mGT8Fqxeh1XXISno\\nJ3ehy0VUeYYsU5jSUFhThXlDUovp9juMNurISNLu9JkYaWKFpZHVGPSB0uGFZ2hNUH/K4KIkDCwN\\n2jRUwWDgkM03MNM4jMxXIBcsxpvQ0TgmN3g/T7F0gdrsrXTOP8aP/8IH+cwHfqrTyNJWtz/s/MN3\\nz6+/9UqH+V9ZQsgNIspOI1Li5iYaW68j85+mph1KRvj0Ogo/inALDBeeJBrZy6BMIHsV2eA/MjGq\\n6fZ7GOPo65sRtdtAOEzvOKgxlB4HGfxGFYbu4ufJRt+4xpIMgvUSV1ZuLFeNsVaX9x6dxJUkQVWY\\nmHr5mOtqso/3jNYKmuc+TFRLuPGWO9i183p27NjJhfNnOX3mKEjFwcMHOHPqRfZeu59i6Lnjjjcw\\nMzPLkWOHeO65J0DCbbe+idGxUZ557jGe/cqTnD99POCoQGkNk+NjeGNZ7vYZHxmh1+6iopgkTQDN\\n9NQs9dYYtbTOps0byfOSufUbEM7wwNvfjSmHPHbgSf75L36AfvcZrNfY2l0U7gJj4gzCeLp9R64S\\nxFDwxtfs4F1v/W5+9ff/hOeOniYevRFEAgRWZJiNVdfCr+KG4TMSyk6b4co8wvSCB7CzCFfpI10B\\nJsc5EwT73lUBwoSxaLWpu3KAqgVcysnQ6TtrqtHp6v3xeKGCwF/ocFzOISKFIAqFy7uXkXQqZtDq\\nDxU8ekXO4ledatb+DgiCBARZmap7CzoFH+z8FJKoNUM0NoNOamsEIC08g7OH+Z/eupvvf9e3YWXM\\nf/jo3zA9t4733HcbSgrOzl+i28mZGq+j1uwAI8xgyC/+3sf52F8fwSlNFTOCVJJyWODNAF9JaYSO\\nQ/qN0sFqcPW5NzZ0pWXAkNeyNm2wJ1wdjXvvkUoTjc6Sjk2DBSMMkY8wdrCWtuIRRDLFmgWc76PL\\nS+juQcaiHoaScliQRjHr5zbSrDeYmdvIo1/+AsUwp5f3SOOEleEA44P/Ld7jTZDgaC25dClncqZJ\\ncxT6nWDYYJ0LkxQtUJEka9Qoa29mqLeHeYQv8Of/iGbWRevwnXWFxZpgXK9ihTXQyCIsYI2hWUvp\\nlwZjLUVRktVrCASRFPRN8Izu9fogBNOjI5S553LygzhlGItOEtmLLA7WMywbzEQvcWq+wXDhGFJl\\nkDSxC8+xf0eDX/7n38m93/OvX+k0/zPrlQ7zv7CEEFMiqp8WMkXX55jY8VqK3vuJkhbD0iKdheJJ\\nUhnRKXKiZJS8nEHqPdT4EmbYYdhL6fWGuPH/Da9i8JYy72DzBmawQtRSxPVxcB2cHyMbfV2QMDi3\\nJpJ3plwbZ1XHtXaMFo+qkguwdm08eFVZAHhZkXXCcsPMCgdPg7CGrVt3M7duPZFSnD93mkG/y4Fn\\nnuT+B97Jpe3XIIRj+/b9LHcWeOHwixw58jRxUsMpwZFjL7Jz227+9q8+yVJ7kajyOrHWUs8yeu0B\\njXpGTSv6nS7GW4peh6mJSTZs3MrOHbvIS8eLLz7LmZMl+2+7m6zeYNeePSAUw+EK7/vVX6a/eBQd\\nRSBS8mIBVd9Ke2UUnYzQmDnDmB/yw9/+Zg6ePMw/+ZmfYND36JGb8FIjCMQI/1XsVKiEH6uF0wtk\\npInSLDRL2iGdRahAZHHFkLK3BGV/rfBKb1eHp+F+4RFRhC9CkLcrOzjdquK0BCiJxyJULTgCCYlW\\nmhLAdhEuxtl+FbMl1nhDq35yq5QYriqW/mVd5toJVSHglcOOsyDyQCYqA5FECoXTMcOVczgsrjFG\\nPDIe2JfWodft4g/+9DF+9If/F5596QQf/dTneejDv4xSijRN6Xd6HDx0mOzG6/n0Q58niyK+5zvu\\n53t/+oM88sISXmrieh3Q2LLAF0Mo+7hiEIpj5RTkpV7TcgrBVQYRrmIn27Xu01ci/lUascBjbYlf\\nOIPLc7LpjWgR4bxD6fQqLbLH+BxEilTjuGSGPNvPpfbD0L+M4ghdZlg4f5otzVH6gyFJnKK1pNVI\\nyMuSgbHYskRJj7USpT3Ohpeg0dGg8y3zhEDFKol0hEs1NF6FLE4howGZP4jxWzACav0PoUcgQiHw\\nFMOcSEXoOCVOFGVpWcnbJM06ucmRWnJ5eRkdBbevbsfgXJ9GluB1TKQknf6QJIspB47SONJUExeX\\n6JYJC/kcyDms64MtOLVURwhL1NxAsXicWGn0ult58vgT/Nyvf5TP/dFPd+JIp69gmi9fr3SY/5kl\\nhGiJqLkCEt3cikQysX4Dmfobhj4w6cLbuw/FQN/FwG9BWEssH6FcPshoc5Tl4RJ+7n9HlTFl0ad3\\n+rkgFVEJSXOCZHJjxRoU1cYXRnXg15iZYVoVMLMrQ1WBiiMg2G0JFEKpilRZyRDElfJwNdln/+Zl\\nav1jLC+c4dbb72Hj+q0cOPAks+tm6XQ7eCd5y33387GPfYj1Gzby6U/9CW+5/10MipyDB58iL4ZE\\nKubU8cMsLPWIIojiQJpQQhFHUcCfvCOWkihK8WlC0R8yPT1Dp9Phmu07mJmc5Oyp0zSadVoTMyz2\\ncjZv3s0N+27gy499gaQJTz72DE+8eIm+SugXkloyjcnPoJKtzLQ8U9OgL5xg73X7uPvN9/PP/u8/\\nYnm5jRzZj5SVR2tFk3mZosZXxeeqZ99LGYzI+x3yoo/GUxZ9dNJCKUlZ9CmWL+CKHEwfV4n3xGon\\nSJCPeG9DYfa++iyrCapAOIfFoORq1yuRhA7Ty6hKJQlm+8ElyOOFJ1YRxtugiUTgVrucv1MsV08m\\nGCpIqGQprsJsw33Bq9DFSlHZN2Ykk5uJG6OoJEMpiZUxylvap15gZtsmvvBHP0miJA8/8Qz93oCR\\nNOK9v/sgwyH84b96J+/+8d+gltY5teRQuk4+6OFNgayyKL21oUOv0kzCgykRetWg8KoQ74rM5K0N\\nNpLOXnmJhGoM7qv7V+HJUqGSOun4RuKsjvHhynvv8S5AGFdeMgDlKwmLALvA6OU/xivJysT9jC5+\\ngvWTc8yu28TjTz9MPavRbfeRUtEphgwKR6sVo7RCO0lvYMDHxBEYLSjUGFl2M/3atSiRU4qMWv8p\\nEvcQQ/E2bOQQ0Uaaw3+LRjHoG7RQxJEkiWOKYohAUOJwpWV2rM75hS4qURjj6Q2GIWTcamo1RZpq\\nBmVOvZax2Gljc08zblCvZ6yo17DSHw17glbouEZZ9sB4umefQ5Ue0hTnA1RTa00z3v4sOzZN8XM/\\n9t3ulgd+7OXYzjf4eqVgftUSQjSFSpeRidTNLUTNaZrjbV69JefguZMIbYnEJP3eAqVzlCPfTZkH\\ninzsHmemdozlTolBY0a/D+ebmOFFuhdeQpoSpCabWM+wvUAyMg06CbmDQoZR3er9cNWXvbJfE2sj\\nRA9SB/mArGj81ehJVD9/dYe5dm7eYoRjh/4i7/jmBzBFyezMFAcPPk1Wb+Kc59Lly5w7e4T5hXMM\\nBn12bNuNMZ4oTdi5Yy9/+cmP4swAgyPPDUppnLckcYz3ECuF8pYSiNIazeYowkKv02fnNXs5cewQ\\n+/ZcS61Wo9EapzkywlK7wx2vfSNz6zfzS//6Rzh5+ggDAfPLBX0xgddbIZpC+RzrX6QRGUayiNmR\\nacZdl+vvvI9//7HH6PUzouYGhNIVTcatnbtf++WrrblKg6leRLxzuGKIKwtsMajE/WDykqjRQuoI\\nlw8ou4vYQRs7WMbZYeh+qrHt2lh0dcx7NQu2un+rL0aBnepQMq7Yl6t3TCKjNGzyUYo1JVIKZFTH\\nF31KMyCKY7yLQNiKKVvhncJX3aSszo5A6Fk9rqsPjTCilio4+Yi4Rja1jXRsJrB7dZXPaQqMiJls\\nCb7/m3Zz065pWq1xZuemkVpx4fIy3/Fjv89KGSHtMJxj6TDDHrYcVF28Xyt4wZQ9+NAJUelSRUU/\\nWjMxqMbW1cuId2FkvXo/rxTP1YK5+t0QiKhGlI0ik4y4MXKlyK7qXKsCKqp8yuA/67EalC8ZaT9O\\nMXiCRCZMjU+zbmKMNMtYbK/w/KFDlIAxhsKUKC1JWxMIcROmtgWXjlWRYgYnPVHZw6kREMGxSfRf\\noGYeQqmIWDaYT97GlP1P9DvLxDIi5Gw7hPWkcUyjmbHU7aJlhBeCiwtLNLKYwaBktFVnpT0AKylN\\nQZxGyMgjowghJMVwyNaJKRbsNVy228hzh/IOldW5b/JLPHJ5N+fnC8zSKZyXKBmFoO+4jtSaqcEj\\n7Nk4zo//8Ds6d33ne1+RnFTrFS/Zq5YQoiZUbRmZqGhkN/W5a2jObmM2LukWh+kMYwbNd1PkowzV\\nZor4TsrcYooBaTND+SXaS0fRQjPM3oHtrNC9dBi7dB5vw5fUu5AQIZNW8KSMNMJf2cYgsPG46k9W\\nN0NvLUJrpFQIFQWMRimCRZle23SvdKKrfMuAawmhaHCRCXOKLVu3sW5uPYP+CgcPPstgOKBRb/Ds\\nM4+x3J6n1+2xZ+c+rtl7C53OEsdeOsix4y9SlAOkVpTWoLTCCUscaYphTmEs9STBCIkSEdbB5NQU\\nw7xPVkvJanVmJmdpNpr8P+y9d5Bl2X3f9/mdc254qXNPnp2d2dndWWzEBhBcCIlgACCQYALEZFI0\\nRYm0adK0nEqloqmSbBcly6Ql07JEumSWKYukyDIhZqEkQEQQuIu4OU/OPR1euukE/3Hue92zAbD/\\n5IJ3a2pnum/3e+/ec88vfYMyhuXVdU6fuYDoFE/JL/7Sz/PCuTPcmDo2i5SSdaR7N6pzBFFdmuIK\\nqrlGVyZoUnqr+/CdAX/y2ZcZ+0Mk+T6CiQPJLI0cQB92r2OYhwqZV5iziZ/3Dt9U8Y+zBFdFgfXQ\\ngK2pJjdIuiuoJEenOSrtISqNDiIt33BPTb97F2QWCGbflRgAld6lXszCmOxWTSHYKDae9mKrsilx\\nviDprhCsbfVZdx1BglIoSWL7uP20cw5u+9oSwLW/FyFyOW1JwJGlXYrpJlJX1NMxzc7V2NbN+yjl\\nmBaBleUuJ46scuzWQzzz4st8+vMv8vFPP8FXzg9RLgYtXzd4W+LqSBfy7bWdzR7DzACb2MauJpvo\\naowkvbk6VizQ/dxrNHJgo1hD8K5VZ9q9sTEo7n5O52oIHlcVKG3QSd6+op+3dyHSgiKqWKGcR1RO\\nQUlS7NBNA9//Az/OrSfvY9/qGp/53KdwzrKQ5tjGkSQJ3nlcVRLqs4TqaVS6hlUDlBiCBHT9JfCb\\nuHSNbPJlms692OQg4k8wNXfS9WcYp++ka18hSInWOgrFI1SNw4fIyyyqhu3RmIVuhjKxIg3Bs7jQ\\nIfiEbqeLtTXBamrnKSdD8qzDoDNApMMoHENn/cjPDYZVLjHyC2xNBeU8nrodk/v5PL5Wy1TbL/Hy\\nuavZonvlp04+8M3/4P/TJvomP/4iYLaHiGgx3RHKJPmBR+geOEnwDSvdT7Kt72as30Gd3odtAlWp\\n8GpAaGpcMcZ0+9jG0TUF2l9DVr6V8biDLbaj8HZw8yx6pqeZ9pbQeT7nWyK7kA3af6s9daIQ9UiV\\nThCTxCpKzyrL3XbWzQAfH9t+waK1pcOItx04zd33PMDDDz3K5sZFvviFz5N3Oiz0+2htuHrtMmUx\\nYWGwzC3HT/LMk4/zyksvE8TjAjSuxjWeVOXoROOwKAKpNjTO40XItIk+f8EjjaMYjlldP8jK2n4W\\nFhdwrqa3sExdV2xtb/L7n3iKTz72b7m6bRiGVWznbmThL6H6J1DpEloMnoDRCcpdYrDUp7LCxvYG\\nr1xqKJs+SmforI9INF/27maczDxYtlXWnBgvEEXFPbYq8LYGVyPexY1eInJTicFXY7yt46addjAL\\n66T9NUSlOFvv4Wn6+V2b/2/+172gnNnaa8FHSLtptcEigNIpBPB2RLZ4BK1UNN62Dkk6KJVGIQJA\\ntMRkawaengXq9iVFtWLzPs4QfYDekftJF/fjgpB0BtjGRrlaHRMzlMHoDl47zp3dIu9ofv2Pn+f/\\n/NiXOX1uxGee20CLARGayZCmHOKLEcE5lHhCU+NR4EN0ZWkKbLGJMR28i6bLKusjOFy5jWAIREoF\\n7bXYVSmageFioz2Wli7SbGY3WwRtMpRJ4zy2LmJLWFTUyA27XYeYy8wkDFVMENI1bHcfZeN5/PFP\\nc+L4Se666x5uOXoLvXyJv/zB7+bM2ZdwdYlGoUTT1DVaBDs8DaKwajkabusj9NznyKafRflr5M2T\\nWK/w2jKovsiEdTRLBA0dfRVvowhEZgzdbh59NBNNogx11YAI1lmcc3TSlDzL2R5vs7ywRjVtsL5G\\nicV7ode7FZUeY6dapDRrEeKlNFo3pBpW5DyXykVQKYpI/QlNOU9WgkqR/n42zj1FWftuZ/rcT9x2\\n//v+4f+/XfXNd/xFwAREREySfz6gjnRueTe9o/dGaLsBL3dQTKLlka8m+HKCrSaEckxx7eX4wKYZ\\niYGe/jhl+j42r1zDF21rz0eStgQBUegkR3cX0WmX2eQGmYFS5u+nJaq3m52K7hBKa9BtZdIqtewN\\nljcfoZVt89yxXuFVwmDyGJktuOXE7SwMBlRlzS233hr3GW3o9rp85cnH2BlvMSyn1OWUjY3reBfI\\ns5TaVWjRGFEYEwEnVeMINaA0qTazfhpN06BNggDLa+vcfue9TMcjtq5d4dChI4zHY1567ikUwpmt\\nVyj8YZw5gOrchukejIlB29YMTRPdMVSKtQ1lkVFUIxon6Pw4pn8clfbp9jrgA4H/3+IAACAASURB\\nVHbWjQx7zKHnN7vF0MzoELPqHQEbNU+DswRrY5U3+13exq/7SNmIgA+PSjrovAtEfmREZuoYmfe0\\nhNsb296ZPV2AmcJP+1llj8BBCBZE492ENF/GFiOaydWWwqEwWYYtt1HW4pQDlWG0mUveibOt+0kr\\nxg+IRFQqwWN6a+T9ZUTnkRIiJnInEVyItlezik8HwfrAU69scWVzSuOFG6Mx0tSMrp2j2b5OuXkO\\nN75OqKcEX+Bsy72sp0iwiKsICKa7jBdQqhMDTTMmeItOlxBKggu711jaMUU7xw/zINmKvc9va6RZ\\nxATBoNMOOu9F/V0imCiO+Nvr01aoYX4P9iSb0ke6J/DpEs8/+W+wDr79A9/OqVN3k3d69HuL3HXH\\n3VR1iWlqep0OVVXhfU3anCc0F0HfAllGbe4liJCGEUpGdMIGmQilCE7WUErous8QfIPWMYALxIRT\\naUbjMYpAaS1GK4J15EmC0QZjBGU006rk0IEjGJWS6BzEge+yYx5mqhdRqh/BZggqlKzq5zgyGLHe\\n3eEat3BsMGa76SMi2OlOBKSFwLT0LK4f56kvf5Zet9f/zV//lebDH/mRT33VzfRNfvxFwAT+3v/w\\nC59wXr5xcOJb6e47gR1fZ3r5adJ0gWq4hU5TfNNgiwnN9kWqG69QXHsSX++g0wVcqFhYX6JRD0J1\\njdC/Dz8ZRdUXP+PPtbOapEPSWZxbKSEyV+R5vbA346HFADoLli09odXgnG8k7S+ZNacCgqHi7v0T\\n3PUn2b+U8oM/8pM89MDDaJXy8ulneerpx/A+YF3NuQunOX3mRVKdctvR29jcukFRTvHaY5saGxy+\\nCYiOcmaxoFKkadyAvXf4ICRJCsqgjWYwWGJhsMjG5YtcvXiapeUVTN5le3uLYrRNMAkXxxVF0AS9\\ngslXCKpD8DW2HCLFdcRIzJAxEQjVXCIRQQ3eik6PgEQxhKZxM1oqtNd8frSV5Sy9iDEztlJtVRKK\\nMa4qcE3V2nT5ts3absZ7VX5aPqVvCmxdkHSXCK1vpdC2R3X0kVRqRq1Q8zcRi8ndRvH8Piu124lQ\\n7at7i5ge1k1RwSPZMjpdRCddEEElA9CxJU+5hSR9krQTEzVJY/Bu24/Svk4gzC23vHOY3jLpYAWd\\ndiNvUkVRet1dQJsE52cz1uiiEpoKVxX4akhTTrHVJiE06KRP0lslmD4m7UMr/B5UgsmipKNtGnxd\\nYILDlmN8U6DQOOcBS1BdlAJvS8Qk0dg6+HnyGILfI27eXsG9M+A2aVFJFqtKpWLCSvzZ2fBjrq0c\\nZi363W7AXKIvWcSa/Vw6/yQXL7zEQn8Rkxg+89hn8ZLz0z/x02xvj1lfXWFz8waDfjRx126KL57F\\nywF0OsCZdVx5AcSTErDuOoYRfb1D5p+Ntl8SUAFc4yjKBpMoVIDNzTGdfkaWpty4MaTX68zlGTWC\\n0QmNrdjZGfK+d7+fF158lvV9KxTJW9DuIsEca6+dJkQBXFLxHM6GXNk5T8e+wEp2BSddirCM4PFN\\nga8n4C07oylHTj3Kpz7xh3z3+x993z/9J79YftdHf/Tr1hrs6z5gKpP/TvB8YOnUhzD9Veqdy4xf\\n+iRKJZTDSzST64h1lFvnmV7+MnZ4AV/tRAFs30C+SJr0yFduwesV6tAgZUM92kB8vdsqattGpruM\\nznqRAqJkXimy56F/jUg6u8GSV4uo79079oBOvAgqwCD3PHBoyn33niLXKY7AeDLi8JHDvPTyi5hc\\nGG6NuXLpDM888yXEC1rgxnCTqqpB4sNpg0R5OxWJ/HmaoYDS1pFzJp7Q8j990CgtEDxZmiMipHmH\\nhZVVut0+3hvK6ZTJ5jWSXp+rO0PKpo/pnUBMNyoVteAX6xpwFegOwY4I5Samdzvd5VNYFuYZ+d62\\nZ6xybwbc7P32TZfLuwhQqSPlgbaSxHt2oTO7k+BZlehDaKu/EgmQ9Adga0gyTGeZpDNAmTy6i6gk\\ndhSUQXSGKINvZ85KorGxzH71HBzUVpvSWnT52NbHB1wzjOMAW4ONLiBKDMqk+GYH613UJ1a61S0W\\nTNppP3OcB4pXhNAQbIGvC3TSQyUpyuQordH5YBdApk1cf4To57lzLf5+EerJFibpYNJBTHBsTQgW\\n7xtcVSDOgqsJTQUI2pioi+xqCNERxnkfW5izqt0kMfEJDkk70bhbZE4x2UsPitcKZtW8xB46Os3i\\nNRc1TypnaeTMZcbPqkzxrX1ZnLUqiRq2WinQXRq1jyuXL/DK83/G6TOnue/eu7myMeHatTN8+vHH\\n+b6/8kM89NZHENEsDhbY2bmGeEdTno1emaYL2W1ADwtgCzrBtquqIYRAkuhY2RMwJs4y8yyNSQIe\\npYSytORZQqIUWZpR2QYRSExKUZcUkwlvuf0eXnzhRe4+XHDJvZNaRa5mEB8TExVIqXDll/B4xnXF\\n9ekCJg1kskmtjuBt086Ta0QbhpXi2Km38fu/+1v8tR/80Df/z3//vx9+9/d9fZpQf12jZEXMP0Op\\nHx/c/kFEd2iGlymvPEnwBZAgIbQ8sVYjS6KSzhyIoDT5vrtJ+kssHj2Fp4OafJ7CH6XauowrtnZ5\\nY6LRSUayfLSdb7RI1696qPmMMkBLGdmTAe+9d35PVFAGHRxOElJl+RvvzWiaMRcunOej3/djTKZD\\nDqwc4OrmVT7+8d/mhWefYjQaxupLBYIouv0+G1sbhODJdMKkKNCJJhpJCZlOqb2jdg6jNdZZQhAi\\nYEUhokmTnAcfeISmsSwMFhjtbHH98iXW1w6ws3UdX09x6+s89sQFpHMPevkugoqVWHAOpXVEr4aA\\nUgqFBjYgLOKNoIKZb56h/W82k4r0iZtDpNrz9xgrLaEuqCdDgq3wTR1bkMFFdxIJhLCXkqJmURNP\\nQCuDl4DJB7hqCjol7S2iRGGrMTqNLWKPBwHlA3UxRrzDNiUigtaGenyVYBtcOYyVrbR6sfOqiT3R\\nXmJ7ra1YRccq0nuHNikqX4zzTzzN6BIQ6Q86WwCTRvS1m8ZgC4jzBKNRyYC0v4ruLZEvrMbugcxm\\nrjHA2HJMMxlRb76CCpZycoNsYR/JwjGCKFxxI5o4ty1fafmg8yXaVql6ZkfmKupiFIN9krZVkLQ2\\nZl2MVJGf2F3GNxOaqoi8Y+eYCR7N29ni5wkHKMgXyPrLqCSPHRgJ80pUZs+WkjnCPEBcc7R0JFGI\\nNphOD2krazN5gv2dy/RSh2uE4ydP8uIzX4TsIHfdeYLnn/oyeSelGm9y9caQqbe45W9BD04RFKjQ\\n9jekwaExdogPCbr4EtGQ+ix5soXXNVmS0FQNyhjKVoSdFtA1SHO0xOffek+apGyMRhifcdcd91JN\\nJ7xy8RWOHF7ldPEdlMqgg2Ite5mjycsUtmA0vEGDxVmhkFvZVo8iStEZ/Xu2J0eoxlv4ekrAR2pV\\nfx8HVgJXn/jX/PLf/Qk+9ief+cl/9Yef+9+/xgb2pju+bitMUcn/iMjP9G97PyKG+sYZymvPoJRB\\nJEEkiZJzott/mwjOaKs9gkWZDJUukPZWUP39BA8ddwafLmAr8NNt4lyKGDDzHirNY3vMJF+VAhJn\\nMRHBF1rX+5v0RmfVpI+6mzM5sdk5XsVM/r5bAuuLS/z7J29QVQ0rS4q7Tz1Eb6nPhfMvkWc5B/Yf\\n5MTxOzl48DDjyZjt7W2K6TQ6Tfn2QW1bnUYpgg94URRVNX8/zlqyNJLGrQ0kYugkOYcPH+PwkWN4\\n3zDe2aHbX4DQsLN9jd7SCs9cuEjhDhK6h1DZIPJSmxK0AjRiC7BDlPhocKwWCa158mwKObte86Nt\\ns+1tv+69xt77yKO0NXUxwldTQlMRfAyYoQ04EaQ1qyhdnAu2KFmRdp7sA9XOJZLeIniLK7YQncYA\\noHQE5JgkJk5Kx7mu0iiTo7sLKGVwLooMuGrUtgT1zW/4VQtEtR0LUaatiBtwdQxOweKbEnENKu9h\\n0gxnXVT5iTc0zmbRgEPSFDxIsNESLOngyikSv4hzDldNqYbX8XUJTYn1DdngMDrroPNVgtIYHAGF\\nODcH6syoNbPARggonaBUghCwdUkrdbFnLBHP887G6lynODvCBU2qA3iFb+3Vbr5AM8hs+0/v29Zs\\nzkwOUUQQH1pN2FZFqaWuiI/0INeULZYoAo6ca6Jvp9K49CBFOMTWOGOzrLl07iVGE8XW6DwvX8zZ\\nmVq2xhM2/CoZG0h3ndB/B2HmSSuhNXiPtmZORZlCnxzFJqtU5jZcMyEzm3gfRUkSY9A6mkcrUfgQ\\nKJuGLElIEoXzES8gRmhUTbFVsdhfYTjeYjydcmrtEgvqDBO/yrK+DPVpru5sk+eaVHXIlGIn3EIZ\\nurjNTYrsNvZ3zzEuYssf28SkIgRKWYKFI/zh7/42/8WPffhDf/M//+nTP/zj/9lXXnf7epMeX5cV\\npog8gujH8iPvJMkGlNeeoplsxaAIcW7jdmdWuw/kLK1VcXPF0Vk6RbK8RvfIvdG+yt6gCn1GZ5/E\\nN61vYtsWkrRDtnQIbXJUYubIeLVbwszBPkFm8nYyn2POjtDOLOPmMms0zUuCqPDSzt8+8GCPJ841\\nXNt21GL4plMpf/unPkxdWwjCxvXL/PZv/RpNOebp55/AuyiUnaY5VT2l3x8wHG7jIAKXlIqi797j\\niRJ4idLRZcFaIiNGo0J0fXjokfdg8hRf1wzHW1zf3CTzNSbrcHVa8PK1BpfdTtD7MZ0evom0ARWu\\nEMwt2PI6WerophUlJ3GSxzAplr01Y9ybw00JiPCqKrw9vHfR5aUs8PWY0JRxVuZ9pCzI3rlY/DPn\\nUGpNki5SDs/hmyrKu+kMvMWkg3gXQoXOFpG0h0qzCBZyFklSqGtcM0ZMijEZZH1cNcL7QHXp2Tj3\\n3nOE2SLZEzRnFZL3tl2ZbWXtHegU0RnB12ilCUqTdhapxpt4arLB4Ujj8YZgt8HWeOdRSUIUNFCE\\npEN3cT91MQZx+KaMziao1jEkzkVn0nSIRPstW8e3Oke0hvk6RECphIDgXEWox1Gzt13/ojMkyWJb\\nH9+CjRQ672F0gi3HqCSnqYu2PWD3pEuz58RDUK0IvUFMAjoj6S6gdBSGgIC3NnYPRINr4uczWcsR\\njYmINgZRCaIVaIPJuq34wU13hyAJUKJ9BjIm2IpgL6M6x+OzJJ34+V5l5A7RXm9+a0NEyOKnLFW/\\niUhJ5g2Nd5gkmm1vTUao9h4EK6SJptNJGY6ndHo5jXf4Cu44ei87wx22RtdIc2Hf0gJKOzSBCxtb\\n5FlKWTXUwTFIe1wL38m00kyHl6i2NxjccjcMn2U6TfDlCGsL0u4KKh+g80Xs6DSD0Zf5pZ/7q/zQ\\nz/4vnbpuytc8ZG/S4+suYIrInYh+rnvoGwi6R3HhcZRK2k3SM4eFtLO72bF7ndq2nHfgS/TCUdJ8\\nhc6t96LzFVSzQ1VZyqsvEVBRxLvdcE1vBdNbiYAfrdpguicYzpwv2oqyfb+v+QwRKagxqZln766x\\ncxBDIG7uQUFKB6+iILcKQq1SFmWTf/Tffi/rSz0We30+/m//gN/72P/F4sICk/GEwcISL77wNGlu\\naDzUTYn4gPVRaq5xsSJQEoEHPiiC8uADaZphm0gDWBws8Q2Pfis3blxi8+pFuoNFiskEr+D01ats\\nF33K9AF09wgqifqcSkqaukSkQ6KmqLCJSS0lt6MkIyB4sRHtuXtB5pvOa67VHnk0UHjX4JsKOx1G\\nioit4ybXBqY5KnN3xbSdgJl/ZXuNnSU0JaIj1F8RrbdwjuBKbLmN7ixHVK23kBiC15iki63HBFvE\\nNnO2QNY/CFrhvae49tz8NfcOqOfvKYQ4yxOZa6XelNPJ7vmCgMpRKuCVwuguki2gQoWtt1DZOr6e\\nYKcbgCLpr6KzAVp3qOshoYqG0kY5qmKEKIXJFglpF+UdKu8gXuFs3c6uHTPNY4iuJQ6icXNrlRZb\\n0bPPE8cN3kcgmdadGOTmgTkaoActiEojvcg7cCXeO1w7U7e2iUmniiL4omIVPwveQRmSrBvb60k6\\nvyfextmw95EbqkwKolFpJ85zdRrpW8bESlXpOIoRib8LoBVij8uwTXa9QUlNaBNZrRLmgv+vc8wQ\\nzBClK7P6IqvhT5CgCE7jfYOYwLQsGE8dJlOs9HooL4yLKQfX19kYbeOCx4XAsX0nufPE3fTyHn/8\\niT9kYaDIO5o8Ubx46QqZTjiw3GdS1WCF0DvKMxfuIjQTmuF1QpZzcL0kFNvc2O5RFiWdpUM4NMqk\\n6LxPtfEER815/qe/9SN8z0/+/W5ZNcXrf7o31/F1FTBFZEVUeiNduRPJVqkufwFUp91rHEopnJs5\\n1d9MCfAubtLSGvkSGoIrUWkX0iX6h+8n3X8nB8xXuLRzkHLjIkmSY6sxhIDzDUlviaS3L7bodBsc\\n9Qy0EkUI2JOJvl67dubwEDcDwYlCB7dLTwAIHu/8brUaYnsH8UiI4A0RzXvfusRf/66HUEG4evUC\\nzzz5Za5fOcu165fZf/QYw50RC4MO/+Ezn2I6nWLSnJpJDIzaYLSOLaI6okN7acLmeEIQ6OU9Dh08\\nQlU3JMozHG4wDo66Bq80pc0okkfQvRMoE6iKDdLu/hh0qoK836Np2k0mqDiOnIN6XjWPfIM1vCuF\\nFvC+aYnm09ZrsYgBL7i52s+8zc0bPBMhVqe7bhqxvRcdSGLF451t239gmwpwsRMaZvO23cDnEIy1\\npKtH8aKQINSbp/HB7gJb2rZ4FDLQ85/dfYsxyRM1u9fM2hYg0gby3ffvtKLXPxA7BkmCHV5BRKNN\\nl6bYxDYFicmiIUDSj4AfFF4JSdbH5H08CpMkVMUUhUaCxfkqBmbbJp3tdTVJDqGhGm3EylMiF3JW\\nrcVuQWs0jWo5mbvXR5QmSXJ03qEabZIurBNsgZuM8LZuPzdxft6OPkQZTN6BANVkh1QLdVVCsEC0\\ntEJMO+ZoO0DtCCQqoRuSNIuGBiZDt0GTtpqeL8MQ5lXr7nx0977FcwQ9S2y+xuH2rLvF6b8jVy8h\\n2hAaS2ZyyrpgZzrFekenk9I3OesLq0zLMcNiShkc3npGo5oj+/bz3kc/yJPPfIHTF57n6KH9GOO5\\ntLFJ16TkKVhH5N0O3sLp+kEmZ5/Bu4CrpuQrB7nj4JD7l67xey/fxnRcofvrUQxCKXTWZ3zhs7zr\\ndvj5n/le3vv9fyev6uZNrzv7dTPDFBGVd7p/Kp19h1zoYDdfiIa3eLTWu8ToPYFyL7hm3ladQdFV\\n25b1HtEJpr9M2tvHeFxgTJ/QTKhaoenocB+RhyrJ4+yrhbyLUhE9aZIIdZ9tfALaRPCMtK4PohMc\\nlqCij14tkLmKIAZrGjQtrSG0zvHszjSZVRwyg8ck7Fcv8fYH72FntM31a5dx3rG4tEK3P6Ccjnjm\\nqS9RTgoWl5cR5ZlWQzQGhaZuDWzrGmxl6XRySlsRfECLxtqG7e1tBEddVWwXjo26hy0NlaxQqNsI\\nZimKmyMos4yogG9uIHQIKsUrNaeG7AW9qjnweDczv2kTm/8dZgHQWYvYilCXuKaKvMrg2+Rh7yZ3\\nc1U35zC2dAXaJKTNWOKsU/T89yidIVkvVisQZ4yvQnbO2uwGhxNFKK+Srd6OUgFnA7iyPX+G9t3T\\nHp5HRCEogeBa+bl2fQaJwFBJCEGhxCHSOqCodnbWlIh3BNegs0VsMYoJo05Ju2tI3kVnK4QQuaj9\\nw/fSWT1KvnyIZLCCUiXp5HFUth/V6WJCTXf1AI1zpGmK6XSxztJdWMN0lvEiiM6jtViwkZvZ3qeZ\\nTZ1WujUZ8IjJCYDRcQ0HV+G9wnSXEG+ZubA4V0abOGsxSYusFY1KewRbUIxuYEITq8lZhyA+FPH3\\nKx2lByMBMj6PIXZOfIjKFyKCaN2CoNScwzmTolSiWsUg3eId9gTgVlD+Zq/Tm4+961chLZgpUCa3\\n0HdfwXlLMFGKz6iEVCmMxMTeB8/GcIut7Smrq8tcvrJJ3kkZ9HJG9ZSm8CyvLHP50nnWlpdJjKGy\\nNUXpWMq7jIopq939vNy8i2ZaMb1xHl/F9rtzlkl+iv35C9x/pMs5/VZC0iHr9ubiHfn6nRQXHmc8\\nbTBGfe4//us/9eLrfsg30aG+9ilvjsMk2W9XVj/kXArjS22ma8mSPs65PZvmG7dCZ4i82C3ToNI4\\nS3ENNBXBlUw3blCMtsAZtGiQqLHqvcfWbWUyq2hE2qrVzF4AkSiMbXRCIM6NjFeAZ23yKd7S/D63\\nbf4mxzZ+jXuK/5ts+iw2TFisa0Ax85yE1wbNOUJQBKRi1CT8i1/7p2xvj3j28nXGdcm4GPLFJ7/I\\n6oGDfOg7vp+zF87zytkX2R5ucezYXRw7dgd5npAlrUm1NOjcMK1K6rKJCEjlSXJDJw+sDDJMotkK\\nt9Oou6i6J2k696J6t2PyJcRGg2BXX6GZXsfoVUKatrPAGBwF5oDX2TwsEFV69laeN1eaeypFDziL\\nLadRTMLWcf73upWpaltpCkS3KNkI+oiBM7ZdIwo0qgq5uoiBXUWwjxLARL6tmCwGs72vFXy0CUs6\\n7ZpKmJz/LJiUdP0Ypr9KMB3QbYtQYtUzWyOzxzbyNdMosD5TwBAQ4izeZD0Wb3s3vngJOxdhiGAW\\n24yw4+tU22dJsgV01kOyATZYdIg8U7N8mLVT34RZWMF0BqBid0OlfawsI6mQmIBaPEhIl0n665B2\\nMP19LBy9m3RpP9LJMXkfhUO0RmVLBB1BUzNEc2iN0LU2eO/n9l+odPb0xsTA16A1KuviJEHpDq4p\\nMUmk7oh31NWQZnSRcvsK2jc4Z2ls0/I641wyso4C3tvWfm0GVIq+o8F7xAdwUS7RVmWshL2NCF1l\\n5u3wNwqEu8/c195iX70KY+DUDMN7SFRgZXGdyajENg0L3T7HjhziyL590UHFg8oUNzaHnDp2iFwn\\nDIcV/VTzwrmnyZMu1gtVWYAD5yyFrblhl5FshVfKQ9TEfSlbPByt1sQTminFxac4M9lPxpjCB/LU\\n40WT9qOYu68Lprd+L7/zR5/hu77tbb//Td9470e+5of9c358XVSYotJf9kF+OF26MyqRKAGJPKgw\\n47fNzn2dh2AvMVpmOzatjJk0AKjuGslgP9XmOVb2L5Gv38bo+iUUFlSCFk3Qqp2TKFSatlJ3Zl7l\\nKpXsef3ofLFQneGW5uMcDl9hMZ2Am4B4gnE0KBbVee5eL/AyZtQsRnNipeYP6mu5nYL2Cu1LDtWf\\nY+3AYc6eeZ7VfoY4x3C0hdEpZ8++zIsvPUFVTtESTX5vv/NuLl84TfCexYUltobDlkoiKA1JlpAm\\nCb20i3MNmc5o6pokX+B6vUBQKegBOt+HMinKdAhKoVWgmI7pdON8NzTTlvh/831oqf+792R2xqsD\\nn5/NIfeIDbiGZrJN8M3cz3LWSttz92FvcjH7o+YROVaLSmJbTxmUSdBJD0k7sf0oqjVI1jTFmFCP\\nb1o3MtN3FWk9MNvEyCuqrTMQAr2Dp8gW1tHdJVTSjULoAlg71xBGtcnVq67RTGXIi8Z0VlD9ZXpH\\nvgE7vIodnY8zTGVaJG8euyzKowKYfJHOvqP09t9JunIY3QyZbpwhXTmyC6BSkd/r6htI4yFdxk4v\\n4aeX44zRlbFashU+6aNchWQ90sV92KZBSyvkMas0UfPKXbXXOgbPtAWYRcC0IGCiGAHViCCGNM+p\\nyxEqjYHTNTvotrLWJoGZc0ybzESp3lZ43Ue3mVn3enc+HSlAc0ZS+76Y9QjaClLeoM26t+2+9/tv\\nGFj3rGlpz5u39NUSNnRZaIaUpiI4S5JoyrpGEEbTAhFItEaLonIN29tjjh5YjRZlXcONjSH3veUB\\nXjz9AosLXZx1bBQlo+xbGSUPM03XUbWJoh3KoIGmHCMIQacU+ggH+hcpmyNMrSJVDV530FrjmgZU\\nxn/6bQf4B7/yMX7+Z3/wI3/7537u7Pf90F/78ut+2DfB8aYPmCJyPyL/R7J0Eju8uCuLFfO42Tmz\\nk+c/t7cdd/PCV9C2veKTFeHzOu/jKouIx+s+kuZ0Fpcx/TU63T5WIucuENBJGqklbVBTum27ygwx\\nG9hffYoD9WOsZ+dZXO6RdnK6/QWceEQCPgi2qegN+kwmDbnfZrNepkkXEWbC3LG1FPE4hqAdi3pI\\nXj5LwLJsbnD2lecwScZDDz/KzuYG25ubLKws43yJtZbVfev0eisc2n+Yl156jn2r64yLKTeGN+L1\\ncYF+3okeyCHgg6duKjKlOXb4FnpZh0vNMbbtMjpbRmdriDJtKzO+TZVkpPkitJW26M68Gp5f+ZtH\\nQ7v+oHvu9XxmSZz5OdvM/URDU+JaXd/dnwstSdvOq/zdQKnn7TTEoETjAJPEuZZIq+yTZOjOIFag\\nxrQglibq9yY5rhxHlad5YA4RadtdI+mvEWyJUjmio/h3qEfU4xuoLKpB6XyAzgYRwZsvIDrDN1O0\\njgGb+XVQEUimNEgSg3maE1AknT7ZyhE6S8cprj+Lb63plOmgu2vkq7eSrx0h6S+RdJbBaNYXFP/P\\nL/4Ef/nd9/G7//LX8IPDkZqiI5leJasRSR4qVKih2sH5BjEdgmtwtmwFPmwUVHCBLO+3zizR8WMG\\nspu3MdWsqp+1cRQ6SdvWZ3z2tBKsN1F9K1jSJI8VaQhRdzcuBAh2N9EhMBOqV8rMEyxl8si3hXhu\\nmKHH9nRjZiA8kXknHolc0bCnnX9zUv36ledXC5ryqi+IgMdj1Ro76jhahJVsROkqvBXKsiLPEyZ1\\nhSihk6ekBgaDLpeubaICUXdYF9x36iEW+iucvnialaUleolhPD3J1Ck0KQpIpENTXKPTXyC4ENeb\\nUpHqlR9gMR1xeTOltm5Oi4u4LcXzo/386KNdfuFX/zV/92c/8p0nH3jf33ndD/omON7UAVNE1pXJ\\nXtCDY8qNb7Sb3K7QdXvOa35ub7CcnXPTQyAQHyTfSt85XFWQmC4h7WKS3yGImQAAIABJREFUHtX2\\nFqGukaxP3ksInUVUkpPkAyAQdE4iFicJJont26Te4Ij7NOv+P7CQV5y6+z6OHL+Hu049yMLSOh/8\\n4EfpdJfJsh5lVXD06C1cuHCBup6SZV0GaoPCCpVabbNUjwoaKw0ZJe+50/HWQ0Pe8/Ap7j++yOaN\\nq6hQ0+8PuHrtEifvuIc067GxeQVEOHLkCCjPyeP38fyzT9PvLnDrbXdx8cpprHUEB6lOMCKkWULT\\n6l1205zjx05gRFPbigvVCSo1QKVLbTvLoHSCLzdROPCC91NSldHUF1G6HzclGvAGpVwkxO8Jortq\\nODeFzDZYeoJ10UfRxVldsHXkK7b5fJwVqYiA1Ok8SHqknVXp3eqn3cilddNQ0EodRvFtFxzaZOAD\\nSdaBtBdFDUKIUnuubjfXmfCEjopJBHTSbWfh7bo0GUplUfQi+IjSzLok3eVoiuxrTNbHltsx0LRK\\nQlEyMW0DfZzjJb1ldNaP2q6A7i8iGJJ0QLZyHGkqnMDg4AlU1kObWNUFb/FoPvyee3jno2/lIx9+\\nHxef/Cwv3mi5qSaNv7OZApZs4SiSLxJUF0mij6akOegeIlFHOeCwdkqS9SMv1FWtfrJvq+VdE+mI\\nLbDxmmqDajFOtqlBGfJuH29S3I0zeEnarGvPWEPNkuFZ+zrMg/KMihMf47g+dJJFEYs5gGqmNRta\\nUFKbKEuLAG5xCTNz8V23lPm+c9M+8qo96Q2/96rdC0EiYlynNOYoQ3WKfclltGrw3tPJM0yiKJsG\\n7wOdLIPgosF30yBa4XzglTMvkEjKHbfdzcWrZ9ECo/ErNOU+vK1AxmScpcmO0tgytqDbmW7wga0q\\nxyb7sCYjFEOCaLQ2iEmR4Gl0j/uOLbNgxvzWH36OQf3KD9379g/846/y4f7cHm/agCkiqUp710z/\\nUGqno/kmK7Lr6NCe93o/+xrk5c3nCVGLpTWllVhlStoh6a7GFl23g0nTuFlLyoGBpggZd9/WYbPM\\n6emKihyTZKhQsV5+kgPyRQYD+MhHf4p3vfMDrK4dYmV5DR8Cq2sH2d4ZobVhMp1im5oXXnyWxGjy\\nLKfT7bLYH7Ao11hsnkeHLaZ+Ca9SlBich7K2HFxWvPMb386hW09w4ZVnGY/HDIc3uP0t93H44FGu\\nXL3IyuoyVT2h01nk4L4TvPj8M3SyhEkxYmVxhetXz2KtxQWH8w6jFHhw3rG+so9Ma7I0YWN7i6LO\\nucQt6KTXZvMeh0d5i9EGW22SdVL86AKJv4RJD5N1xmSqoZP1sBYsccOa1wt7Epm9SFhaSkOwUSQ9\\nKsJ4rK0jhYDIURWlQCexejFpFB5XSURWtg4dSrUKP6FCVDYvA7RI614isb1uUozE6k61QCCtFFVZ\\nxMTAZK2YtSW2rtuZdTsPFDTBN3F7THKUyqJiT9qLG3QTwDeISaOgeGchCvjni0iS48tRu3mnKJPF\\nGZ/popOUbLAW/VZNTFCCq0mXD2OnWySdAWbpAGlnAZX3wDcoAlYZRBucF/74T/+Mb337W1hc6vP2\\nB9/KF7/yFOW1V6j0IirtoHvrqO4qmBxMD5P1EdMjNJu4psZ01hDvsdUGIh0kibQaleaR7uFqvGtQ\\n82A5qzRTtG5BQFkH56MZtkkyBI2lQgdBp31MdxFnHcG7uImr6AcZWllBrRQzIQghzvXDXGYyziKD\\nn4KS+DOeiJIXmJtTt+IdcZ0ITTWdt3BFmYgVCDVSvIDoxZZD/dq1+lX2qjf4Rvzj6hKlUxQJW+5W\\nfHmdblKTpIIRobaOqqpQiWpZbwIqxG5Hi6AfTUYcWD9EN19gMtpkbUmzM3yR8XaKs0J3rcta+Wms\\nvhXpr+ObKTO3GJV28GjSvB/RzCF2SRAVnwMseer5sfcd5FNfeIGXz1xZMTtPff89b//g//qGH/rP\\n6fGmDJgiImmn/8lgBie8FbDVq0+Ynfe6P/+1qDZ74eOh7dMILmZrKNLFNbxOyZYPo/MeQQwTScDW\\nPHKiw6BjuDBMue/olHWeYDD6d9x31+2gFU0DVV1QVSVPPvVZvvTlz/HCS0+zub3N4uICp8++xMMP\\nPsrJ2+9ClObhh9/BsWO3sbJymLe85a2srh9g0O1DcZnlLkyqQCUZWgmj2vDCNcXll5/mltWUoHs8\\n8/QXWVpZJ8kHLC71Sbsdzpx7ievXbrC8tEI5mTDavkxdlxw8cJTL1y4xHo9obEVmElYWV1EEGm/p\\ndnqcPH4Xk2KHhf6AG9s30MrQX9pP0RhcSMA3EdDTTLH1Fmk9wfkhiSroL1iQgoXMM1hcZ3sKaR6v\\nyeyKz0CiM3xsZFi088hW8ci7em6PFVr+n4jEYZVSbYVr0GkeQVJatZSeaHMk3rcztiTqjdrY9vLB\\n45oyVromjYo6QmxFOmmHbQbnGpK8i6un8c22k0alE6JyfYuMJtJlonlvRjR0TmKb2NUk/TVM3iHY\\nMgb8dgaukzxqviYZKIXJB/hmghApFSrJ0HmfoAw6zWK7X2tCG7DTpYPRYaXYhHpMufEK3//eE3z3\\ne+7k01+5yDF1jgsvPc1DDz7M+999HzsbV6msReoh3/fRDxGmO5TFiO0irv0QfMuDVFFXWK+gw7Qt\\n4xNUmqE7PRQJdrJBUEKysIo2Ga4uY1ASaWkzM6eXWG3iA1mnByFq7qI8RhmcDzz4toe4sVVg8hzT\\nWQBlsK5GiY6YgLQDOmvR57odeZh25bQVvlJgcnxdorxFJRnS+lm2TzshtNKMaSdW7VphqynKR+k/\\nZQzKaET1UdPPo8jwujMHpKlXAX9m3aq9+8yrhUl2v64hMdGZRaIgQ6VuITTn6aUVRicYLRSuJs9S\\nktByhtsOSKoj0ryYVFy/do3jJ27j9pP3c+b8WVKp8X6H2q1QjAy9nqdTf5Es63NkcJ3SZXjpE6oJ\\nprdCmCmd2RFJ2mvHSLHCfmC95P7DwqP3HOeff+wzVFW99qu/8r+d/sG/+jfeVEpAb0oepkn7/8wj\\nP67MIn6PCMXrzSRf73h1u+/Vi1nmSMWW9+hrQqiBEIW2u+vorEfSP4gPBdnK7eRL+/Bi0MGz1q94\\n9ynhO97/Tq6cP8efffGzPPLWd/GlJz/HCy8+y12338+tx4/z4stP82ef/xSjYoL1nswY1lcO8s63\\nv4eTJ+/g5Mn7OXbrCaqyICBY2zAajijKCdujEVvXL/PE00/RhB6/81iNlRwvNUfMyzjTZWerpN+c\\n5+Rhzb33vR2RgEoMTz//JQiaB+5/OxtXLqJVYGXlMCsry1y+eIavPPE4ly6d48EH3sbG1ctUdYET\\nQzMdcvDAAa5eu0Yx3qFJ+5Tcwr7kGtuduzk93o8yfXxowIEtr2G8Ym1xi+1ihUGnRmlHoqZs+dvp\\ndDKCM0yqGUNNEFcT/ISgBgQJqHZXCsHhmhpvmzYzjrfI2yj+He9dW6lqja/rNnBFPqx3dRRfb1ul\\n2JiZ40rwnqaaYkwbQF2N5N248amEtL9CU1Yw2zyJeryuLmMV1YqG4wVlTAy0OnIFnac1cq7wIdpp\\nSfCIyUj6yyAK7yy4Zj4zSnqL6KSDznK8LZhsXMAXIwgx4IoyqM4g/tvE4Gm6g7l/akAxufgU/+UP\\nv4dro5If+PZ3cfz4MZaXV/ien/llVpcW+OaHj/DE5z7BlXqRv/Uz/xG5WDa3dnjypTPcuH4dMzjA\\nP//Nz5B1NDc2R4S0R1GBwyFhhrQcEbzG+SEm30cIFlsMo7h70sWLQXzD9NqZKE3IzO6uHeIRg6g2\\nOQ6PVhItw7RCd1dI+sto7bFl6zCjkth6FwWuphleR7JBpIk0Fd7GipaZUXWIlSkIOuvibYErRiTd\\nlVbZyoOYlvusMHmPdGkfohJsMYyemGkHSXOSvDdHpfu2/xSwqJDGDtRrwFlvHDBv2oeI3Ok4k69i\\nsiQK53c4EP6Etb4wGo2oacizjIU0I8sNV7Z2GE9KEtEYYyidxdZR33ehu8KB5XW8d2wPd3j64gq1\\n7IOgyPtdVDdhf7rFUf0lzo33cWl6AucV2crRaP8WmFPlIKKNU+P4xx/YoZcZzm+M+Zlf+A3+5o9+\\ngM9++YUf+KM/ffJfftUN98/R8aarMEXkbhH5VWUG4m2z9+vzYf/roWBvOo/Xzi1fm1jElp1CIWKg\\ndbsPtiQ0Ba4aEupNcJqmqUAUttjCdLpMbM61jSG6uECqLPfd9wjPnD7DE59/nI/+lR/lhece5wtf\\neIzllTUGgyW2dnbod7t849veyx23neRjv/dbXLl0nve++1vQRrG9tUWSGXzwJInBGMP+9XW0Smmq\\nCWefe5y6vspW0UEpR+0XqJrIH6yt57YDikuXLhJE2Nq8znDzCq6q2L9+kCzLKRt4x6Pv5fHH/pQX\\nnn2Sajoky3KyvMeFcy8zLSt6nQ6pTinLKePhCCSwvfBedtRtZL2MS9N9ONVBS6CRgHY1yg/BxRmI\\nuJpydJoDy12mzoBawvkpvikIwdHJcsRXMeh5h1KegGkDWBMDnrWtR2KLbHW7Emsz5w8fQJuo9tJ2\\nUHHlCFdNUcrgyzG+HOKqIUqrFgDRxI28KdoWvEdcDKq+mdBMt0mSLrq3GoOziuAflXZaSTZp3Vei\\nU4lOcpQ22LpG6agiE5SK1A3XIMagsz6uruJMtqni/DWEtr1L3LCSNIJwfORLurpodWoTIOqWBh+5\\nprrbjzJ+CMFO+Ef/9Xfxtkfux9uaD33gm+l2O2ztFPzSL/8aa+ECjz27xc/9Vz/G4eWMP/ijj/O2\\nh97G7XfcytrigIP7lrjn5GF+5HvewVuOdrj7jjv40y+f4x0PHOKuWw/yyL23cPryNWrbRECQ6hOK\\n6wQbPVK9gKumGGK7L11YpymGiKuhncfOgmVshUagjpI4i3RiyPIOvikj0MxZXDWJ6k2NJdRTlEmR\\nLMeV09g+1AkyU+zRsb0tOmsxPablU0fktq2GIBkiIbbL22o+eIckOZKmmCz62eKr2XaA6LbNLpGa\\nJEGAVpWpNWH1Ksr3vcHeNduQ2goxcjnj1NyRNc+g/WWsWkFUn4JbGHCGLBFyHQVPNrZ3KKqaQS9D\\niaa2Fq2EXmJQWqhtoLYVWZrTyXOapkD5qwwn6wSlsFWDqx2F7OPIvoZlucRicolRmVM0mrSzQKKh\\nJzW1MqigEQWNV6yZIUdWFIvdhAPri/yT3/gkP/Dhd9z3jm/53jfNPPNNFTBFpKvS/rmAUYTX50m9\\n0czy9dokX+V12q5u2EXWzXhlKou2XzhCM8XagkRrGqfRSR6BGRoKl/HMRc8TL23wG584y9NnOzx8\\nSnPkwD6efPIp3vGObyF4uOXYCd7+8F/ikQffzcH1g/zBH/82p249zje96/1keR/nNf/ij7/AL/36\\nJ/nM558ns9vc2LzK6XOXuXzlAlU15rknvoCaXORQeJEDvMyyOc2+7hZm9DRHl6Mod11NeeThd6HR\\nPP/ck5w4cRdLy6ucP/cSBw8eZ2FhwIVzL2NSw4MPv5Ollf0YY7h66TzH77ib1bWDqBC4fv0KLjSM\\nV7+L63YRADu9wrH1AdftAqHYRvs41w3NdXSQaAVVb2DwLK8eYHUQ6GVDmsJReY0xmtJqjq4lbA8r\\nelxkNX0JV5WU0ifUVSsgHqeCEBMc55vYCjUJ3rdTZ5Ps4WFKC8iJ99RWOwQbW4Qo01YfIQpz7zaB\\ngYg+FqVRaZ+sswJYlGlFASTKxflqGuee2rTo1bhWtEkiDaXTi+spSYn6qSpSSHSCVklEuTZVnGHO\\nZntJFluxwaHTXpyd5n3SwTr4KBRuqyLWaioqU6ETkrQbnT6Abzy1zn/zk9/N2bMX6C6scOLYAZQo\\nzl7c4MrE8g//u/+Ez3/63/DR7/1OBgsDvu1b3s1v/Ma/4o7bT5BnCVUx4bkXnuXQoUOsrq5y8tgy\\nTz77Ml98/vL/y917R1t23XWenx1OuOeml0O9SqpSKZRKpZxlOQhsY7CBAYOxDR6CCUMYhjDugaFp\\nN9MD7rUamJ72kFav6aYNDGmatsGpjYxtbNmWhJBkhSpVzi+/d98NJ+2954997nuvSiXbTPfqlnqv\\nVateuO/ee+7Ze//27/f7BrTp8rPveoAjL57g3d/+Wn76e1/LV46dZWEtw2EIGxM4LLoYYF2GCCLs\\noEPQnEDHdcq8VwHWqqAiJFJpTJH73rN1XoRfBb5f6yDfWEUHAUVpkM7fc2tynIWoFmFMSZg0NjVz\\nVSUqIcPIH16CGB3VEVqjoxauXEc6gRO+nK/iRuUu5C3nVBh7kYMgwBrnXY2cqaoXHnzkceASWV7w\\nBxpVQ0jPNw3saQQCKyPfUxdiC1OBp7RY5xWivctKjsAwKb5AwjkaPMOgbFOqKbCWRFzyf2wMxilK\\nW5KnGVEUoHAU1jLISyKtGUlqjDbq9NINLlyax9oCLSVTzUWWOi3fr7cl5aBLX81RCw3Tdc01UwFn\\nNqYohUSEiun4Al3TphRy0+Tg6HrIa+d6KK04sHOSExdX+NJTx8frg6M/ePj+b/mNr7mxvgqG/toP\\nefWMWlL/00FaKiHrVw18Ug6d7V+eNgJ+87RV3+tqQXR7I99WjX9Mvs1VpFZlAhpKQ9Fbxg02kGYX\\nZX8NpR0mK7BFnwWTUh/bh925k+bYTfzy7z+Fc/dw9nPHeMcbrudbv+1dPltbmecv/uLfkQ82+N73\\n/Aq33vYgf/v5R1g+dYr/+OQya92YcxuCJ8+dRtgAIy2tcoEffngEHUTUa3VUWGd+8SKtRp3VtXki\\nKWmNjXDq6HFuOnSIwpQsrV1kZucedl1zLUqF7Nl3Iy+88ARLi8dpNMdxwhFGEdplnHj2MZrNhLm5\\n/cxfPMXxE08jrCaOIvLiDI1CMV0+Qxgqxtv7cMt9dHoeWW6QJteBGMXZHkW6QBQodOtaer0Vsn6J\\nTmKiKCMflBjToqFX6PZHEdZS9FdpTc6ytr6IMgobTACu6ifbytrLIYfMTQdBEPkemLVeVckU4Hx5\\nTkrlN+qy8K4sSoIZ2oMZ3LB057xnohQgnEAFEqSgKLsEQZuyn+KklzjzaFLfP3NCVFZu2mcoUkBZ\\nUGSDzQxTVuVeVxqMqbIW5x1DREUf8QhShbWOII6xZYbSjaHsMPH4LgZr87jOPCKs+55XEBMkLU8D\\nsA4nBaOtmDTNaTbq7JucQCHIsoyLZ09w8sVTxGHM73zwN0AIds7O4ITgvT/0fWRZRhwn7NlfZ/c1\\n+zezoKIo+M1f+B6kjHjkcx+nNBn/6w+/CScUC6vzJPWYX/u57+Dp58/yJx9/3IsNNMdoRzWEgqV+\\nD+0EImoRjUVknQV/fyogECpAxh6MYx2oMEInTUyZeR1ZHeCMRQchOA/ksUWGNAVZZtBSkKcpYVwn\\nG/QItEd4AsgowRal70uWJcIVyNokSsVYO8DmBSqIQQeeHxqFCBWA8wCiIGlT5l1f4s8GDDnWpXO+\\n5BvMovpPIF2fUrXRuo4s+szUv8RiejNpcB3gtawtyp/VcCjnEPlz1OQlanmHkg0Kpgl1B3LHbPQ5\\nLvXO0ov2E9FCu/OELiJUUFrtVbiygkBKcmfppTlSKrKix3ijRTuKUSOCjY2MOAZpSvaMneJM5wYs\\nGuEsSxeWWBu/ibgxwkz5PDuixzjVuZlAzzExnnL/zN/ymYu3UVOKk/0aeR7zieN13nKgSxQq/qd3\\nvoH3/sqH+NLfH9/1T/ZM33ns9Pzj/zn2+f+a47+ZDFOq4KeL0v2kkPWXZpabX18O6R4GRf+Ql///\\nqj1P4fuVUideXFt4CHpVfENUgu7CGaypQCOFI5BQ5DmYAilCgpEdiLiGSxf4/Gcfx5oahevjxCzf\\n/ZbD6DDmj//8L/kXv/W7nDh9lluv38f+g/dy9OwiH/vyOaZmZ/jo508SygFvvX8XuyebHJ/PUcYw\\nFq9x7YzgmgO3sGPfTczu3s2Zc8dYXLjI2MQ0WVmwtLSAkp5XOuinLC1eorQld9/zWi5eusDy0kWe\\nefIJ1lYW2LVzLxMzO1mZv8DpE8/SaCS87hvfzvTsDpbmT3PhwmmUDLDCIgYXaJgLSCE5sPd6Xlhp\\nspYGCALyaIcXEEg7SAriYEAyuoNa/jyDzgp9FxDEoENNqTZweU6tNsH6pacoO/PIOGZjw5D2linU\\nNE6EFZjHC0EMe5pCKoLIiyfkaRetJCZLocwxZeF9KfMe1pTYbOCRrA5wWwcrIXyQpQISeVk3L3bh\\nREhZ5r7064pK5UfiihQdN73Qtyl8kB26elQ9H6kTVBSTd1eweQ8V1BBh4vmDyvtWWusdZZBqk7uq\\nlJ9XaO1db3SAEApHiSkMLu9jihwZtRBSEjRG0EkTJwVyKPvWXeLjf/sc7/muNzA60kIpRZYVtJp1\\nHvubj/Kdb/92OhsbBEGAVN7BRGlFGIYYV2XqVfYqhfTArkadssgZHZlg795rcCjCmuLUyVMsLK5j\\ni5SdI6MsrKeEMmFm9yT/1z/6Hp589ihLaejRw9ohdETUaPsydJGCDryIgQpQQY2g1vLIWumFCWxZ\\noqNaZctmEFJi8oF3X5EaV5YeAWu8h61HSfs5IqVCSYWKI4QOkEGAFAFpdxElJCpqEsQJTkhUlHjR\\niCAmUApp+5W6kkTryHu3mmJTCUhJVVE6JTbYgRKWkaU/oFc/iFEBduVxXHqKwLyI0zdiZECSnyJM\\nP0soOmgzj+5/ikT1qMU5UmQ0ki41XaPAMChTWrpHxAssBt9Ml7upiRcJZElhLVOtCTYGHp2ttUQo\\ngXEljVqMcyXOSnqDlKnREaIoJAgDAp2zsNoGlbBZSel3KNOMgRgja92MVIpi0GPR7eVQ6zj3zaXc\\ntx+cyzndaXB8VfPWQynWgNaSQ/vn+Be//wl+4Ye+5Yf/u3f90B/+zM+9b+U/y4b/X2n8NwH6EUJc\\ni1AvopoINBVwtYJAS6TY4kld7Xq/Ghdz+99cnm0K3z+qhAsc+F6Jo+preAd1a9Kq6Y/XcgsiZDRG\\nNLITa3Pq9TaDQY5wBlcsY0uDk4p3fvd38PM/9U7e9J7fYOXFL2CTGb7tTdexZ6bOXz/teO6Ln6Ms\\n5inSSt6rXOfQA9/B/oPX8KUXSt/vkxu8YecqiRzQyy8yO3k9n3/00ziTevSd9K7zZWkwxmdFSRBw\\n9/0Pc899r+epp77I6toKO3bsRSvNySNfYXnpDIHWXH/dYerNCZJGnbQoeOqJz3P29BFMCWFN40pL\\nECfEKkAqOB3fx8W1EawpfEmsv0iRLWL1HHGUsjM5ycmlhFDFGKFRYYAMWkRBnVANaDQUGysXWV5e\\nYSSs0RFjWAJs0ES6EogREspsgKxg79ZanCnBlVWPSlPmHrxjsgxrcx88i7xC4FYC+1fQVWzeq0rv\\n/kDknH9OZOiPSJXEHSryPTrnQSe63mao2qTC2FNepEQ4hykzcI6gNUnZW8OWGVI4ZNjy97MCjtgi\\n9W4eFQp16OKmdISuN30JF4ktCmzaIVtfREV1dK2BihN0WPMYYodX0ckz3vO6caZ3TPCVEz1+55/9\\nGM5a8jynVqsB0O12qdfraL1VgHKAMQY7bENsXxdDhU3nKJwjL73Q/eKli0il+NCff5jbbznE/t2T\\nPPPsEfobG2gd8vRZ+LPPHue/f90+PvQ3R0lz6YXnlff5tEWB6S9h0gwX1AniGsgAU2aEUUTa7yFN\\nhtQhTiisLVF4vqaQAaLqH1pTei3dCjkqKqqJ8zwThNDoMPbAIQQbF59FB21EGBBGCegaUkp/8AlC\\nVFxHFYuE6x+j234rEKNUgCkKbJmDKQnrI5v32qOFBVPLv4eUjqXW/dSWP4+wBmtzdNJgo3aQRvoC\\nQTwgKwTG9gnCGsJpkkAQqYjUZAgNtoDSGEKtcUrQzUfp1N6OdI7J9A8RYgMtJPkgp1EP6ac5ST2k\\nLA3WSQIpmF/t0ogiBmXJeCPh+h07WOp1+bvzd9Hpdn01w1ScZuet0MLRaWpRg+7SaWRzkqnGOb7z\\nwDpzM5NM1pv81hOTHF0P+dU3DtiZ5AjhME7z8Uef408/9QQffP97+Jb3/rp0r+Kg86rPMIUQkQya\\nC7KxA1EahnY7AgMElwXLl/n7r/t3lwVQQUW6DhByywpIDJ0l/F8gZIxgKNptEaaEch2b9hBlxqC3\\n7IWb4zGKwZrf7JpzPHdykd/+vQ/RvXSMokiJxuYoUsPffPkMl86cgDBByToybqCjFqI2xsL8i8wv\\ndgnrUwgFgRMsry5TV2e4dH6e0pS0Rhr0uh3q9RrKGhSKft9D8aOwxu7dB4ACpSRPPv0oY+05BtkG\\nxsJDr3sj/W6fdNDnxItfoZ5EXLx4iW43ZffcDlrNJo6M0dExet0+RVaiq97upWwXAxP6vkyekhd9\\nVDSDVQ6cptuVqNHrkXYV1bgGka0wVs/JbARByNJKQd80oeyR1feh0pMIPeF7xs5QDDYwWQ9ncn+C\\nLjKsKXyG4QymLHymaB1l2qfM1lGbJx3/vz9oDfugAoSsOJKVXRWVq4ZwlaiBR06qMMYZgSszwuYk\\nzgy8YICqVGusxRgv1O9MTpn2ESokbIxUJHCFjhvoqLFpPO2pJcZnl0JWOrACpQDhCOqjOCmqvp7B\\npD3vGakVKm5Qa00ggqCahV4UQTrF3HSbfbNtakLz5gevJwgCRkfahGG0Ob/juOrRXXFgtNa8ZD1s\\n77854ZDCm4wHOuTkqVNcPHuSHdMT3HXHbdTiJmfPnqWzkXL23Hl2z7R518OHGJ+UzDbgseOdKgn3\\n4B7rQMYNr6tbepUm3+ow3sTcFgRh4u+Fqiy0rPGc00qYfksM3b9fqTRDAQlvIyMrwxHrlZKAKBnB\\nlhlRc9Lr3wYVv7USKkCAUw1MdCOBTJDa4Ygr1pL2vqDWK3o5IRBWeK/cwXNQDGDtOHkYEZYl0oYU\\nMkW5JYSOKdMMU1oCLQmsQjlBt8jJBpk/RDqF0w5jLMYZEpUwyJYxejdGjpAHo4xxljTboBYqamFA\\noEMWVjdoJt7z0wDtRoM8z4m1Ym1jQEFBu95g2R0iTXtg5ba+qj/sV/GKAAAgAElEQVQkuUGHPO1i\\n8wEuH2CS/XRWn2a2HSECzWt29fj8uQavv87QaEXYbhdnYc+OSU5eWObxp47x2//7j/7iNYdf/ysv\\nu+m+wserPmB+4AP//A9Kpw7ZQiIot0qptiLsvQzI52pfv9xjgM1Ttf8dSOEXqFARQuIXczGospTL\\n/nBr4kntgydgyx6m6ELex6RLlOsXsNkKtjSeaN85S5l2ESZFCofZWGJlbYUyL6pyclmd/sBSIJGo\\n0kA4Qtj0Nlk37W7yk+9+A3fefBeL6+cojWXPnmtYX18hG/ToZzmJrhEGknojYc+ufZw6dRZjLKcv\\nnGBxfonzp4/RqNU4deIZlhYuYh00Gk1qtYSyyAm1RIeKtdU1OhsbjI5Msrq6RFHk3rkilIRBxCDc\\nSacvsXgOowxboBXKOlyxgmjtoVw9C+0DSBVQqCZF2qPsLWL6F7D9DtJuoKwmUxLbX0GHNXJXw+Yp\\nyubYciiKPvzoLc4Z39MET80wuecy6hhTlT2HSj+bSkBSo4LIW1aBJ65Xz+X3Sy8UoIMIh0aIABUG\\nKB1jTYoTAmtBWV99sCb3RP0yq0rFAcKVlXF1BsKXPfPuKqbMEGXmXW0ApKq4pB4raZ1A10f9dQrf\\nZ7XZAFdmqCAAGRMkTdDeBcYi/Xu3npuX5RnHz56nrTqMTc4wOd5mfGKiKh9enTMIYGxld+wqyoer\\nbK3YyjKHYB0v+uCYmZ7ygJ2yJMtS2q02txw+zMGDN3L48K1Azmg74ezJ49xy03U8f+QUO6YTLq45\\nnPF6vAiJCgIvOJD28Fq+gdciVoG33ZKVQpD0JgUOCaYAFWEZtkr8GGoQCCFRQvrSLQ5rvMatkL7P\\nrOKG5+YiNsUuPI3Iq994AQSNlRZRSEy27k3VrTdgV1pXpXQP9pLOksa3kcWz6PQcbdsjF5JGorBl\\niZUOITK00ghpCZOQwhisNKQmJ4lq5NZQiIKaCjA4nIOsLHGFQYWjFGIHRiQ0yiMYVVAWviffqgVe\\nutLASE2TGcGgyCkq+lwjitnoFwSioMetpCpClEV1lhze34ora7f2t6zbJx29nx3qBUaaCTjFfXsk\\nT16QXFcfIJQiDhVZXnDowC7+7Ue+QCuU8of+h5/545/+mZ9fuurG+wofr+qAKWTwM6WxP4NsIly+\\nubhl3MKWxVXpI18PavbqL7b9cWLLWQFPa8DmCDHsiQ7/wbA4PHwtXxYKqx6nQqBxlNWGDK7s49I1\\nXNGFoo81mW/ChzVU0AIZbLugyp7KSTzh22DKAYOlsygEy2WL508s8uCtk5w4+gJhqBkMDHNz+9m1\\nazdae1uzNBtQq9dZXFyms77O8toSne4a3Y0epTEcOXIcnEMrweryJY6fPMb6xhpp7p0gFucv4JSg\\nsKX31y39c6ogYWJyJwcP3sSLa3XWiwTlhC9tOlPRHgqcUbiyj44ESmqKQhDJmDK/gHM1rNMQj1NG\\nM2QmJQSffakEoRuIoqh6a8ONcdh3rijqlZSXro8SNseROvKPV8qr5wiJk9qjGa3FYXx/u+j73pcU\\n1Sm7yuikwsnAS4MJj5500nPlhI9qSLz7hRB+87cmBwxCKC+UIHUFHAk3Ta1VWCOIGv4QVOYIW/jy\\n8nDuVlmtCio5OIvnnQpQUYKKG6i4htSVmHkVxlw1B02RYQyIvMeljuLvnn2Bjz96jB2T0+zfPbEZ\\nLK/s4ztc5RkqqvJiJRPHsBw7lJUQw2lZfe6S0ZExRkdHGWmPkCQJUkqiKCSpRezZtZN60kJIwSOf\\nf4zdcw3e86Y7yMp1jp7PKsqCB3J527QEU3huq3DC4wOqg8CmGLoMfJ+2zNHK22+VxYAgbnhXEuFL\\n0zIMCeLEZ21hjKo1/GuISoxAVRKDSm2u/+20D2/ZZr1esfOlWmsyfx+1p6uYtO/tv4IQgUG6Naxr\\nUTZvQnWPEciY0qUkcUyZ52QW4kSihWZQOgbudgTLSHszod4gUgKDIyDw25GQhNKrCklKBvIGhISN\\n4ACuSFDBJX94cJJG7N2FokAxKHKMg6KwWOErB7FS9AvLdHKGVX0XRXd1CyVeXbOoPgcvaQjSeQcg\\n4S4x3QpQQjDeiDg4G9Dr9qk3Y/JBhq5aVTcdmOPX/s0n+OUfectPvPu9P/WRn/wff/bi17f5vnLG\\nqzZgCiGmhBSfUM09QhSDzZtpnZdG2+6EfsXffdXvX264obakEDgUnl/lEM4DfnyJ9uWtfLYWGzis\\nR06KoWF0ACpCNyahtBWZ3p+cCRLimYOI0vqSj6B63Uq2a3MiO48mVAlSafLuMippMygDLhz5CBvd\\nVZaX5pmYnCDt51yz7wA75w5gjeL2ux7COcXS0nkMBTKQNBoJu3ftJAwDwsgx0kzI0gHdXp88t+SZ\\noShywFKrj6DDhFZ7nM7KJZCOejKKVo6Z2d2U9X184ajP0LzupuXAnOat9+9icXmRXp6jCCmcRbgY\\nMzhS8Se9vRbhlFeQ6V8EEeGMQ+o6xlmCxoQPbtb4z3JYYq22c6EkKoxR9RGCeguhY0RcJ6w10XHd\\nB0jhBRHKdAMpnO9LSk1ZFhXdwz/fZh9RarSueeJAEPreYyV1Z4vMP87hQSemxBaDStzC99NMmXm+\\nqJLIWhOlAlQQbRaDvQuOQgYxpS0qnSAveuCqJqbn+lmU1KhaUvEKvXqQqObWkBMIVJUJhwwCZidH\\n+eD7vo1v/YY72TUiOXzjHsbHxjZ7t1fyj0tj/H0YVkuGii9Dm7HNJbQVaIfi+AhHGIYEQejVcqpM\\nbeiDGYYRFy+dZ3q8xVizxbHjz5GE8MzpjKI0nv4jVCXaXoFvoKKfyOrg4DNoKf18UUFAUWz5XKqo\\nhlQRKow2UcP+8wowAv9Zb64jkCrchpKvgvawJL8pNVU53RgP6BIV3cd7euJ74c7hykp8XkiUq1Pr\\nfwxjBqS1SXLTwQZzlK5HjqY0iiK6iZKIIF+hSG4nTd5IXttJXx5GlhsIu0iJoTQlgVY45VDSkacD\\nbDCNE2M4pyh1k9nwacpCEGhJXpQEWhNUetoFjqI0FKVFa00oBbUwYLmzwWhzlL6coszTai9lW4Vt\\neO/9Z1WaAa1GF1F2mGg3CYKAOAwYGW1jS4PUMZoC6wQj9YRGI+KPPvY4P/vDb7vrjte89Xe/rs33\\nFTRetQHzAx/4558tjdrhCm/Hs+U7ZzcX8FcLji+Lfv0qQ0iPDgQf9HzFtypFXdEnvVwMQW5TD9o6\\nsQ0zIRE1qE3cgAwauNJz0xwOYXJGbvxGkAE26225L2zvuw3P9lKAriFUWPW8DFF7GrRFrz3K7NQO\\nHnroLaytrJMO1sl6BYdvvZubb7kTKmHpzvoyRTEg0po4Sti39zqwBluUDLKUovTi1GEQkcQRURQy\\nNTnD5NQ0CsHE+BiLF87QSwvmdu+m2+lw652v4cvHUs6vOazFowmlZmKkyR27JDdOGbLeOufSOkL4\\nUiUyQlHgigBvS+jFB3wQsLhyAG6AiCah4hsOy45C+c11aAWmwzpha3yTiwl4NGMVeFRUw5UZZXfF\\nl7g36SOlB3RVhU1R5a/OFshAe0UenC+VBgllMfCHGVNWDh3aG1Wb3AOPhiFGgAqTqp8JlBYnhht+\\nxWhzFh1FXqXKFJR57h1uhPDUCSG8V6jUBEndZzCiCh6SqmdQ6eZWLUZT5ugw5Pqxgt7SPB/+j48z\\nOjnJ97/rbWilqCW1l6wRz2U1+PPCVk/Tr7eK5iLwc1qIywLnVjVn2Oe8WnUHur11jhw5Sr1Z4/En\\nj3NqscMzR7s8eMsEP/Qt19OzMefmO949I/DIU6+rG/v1qOOqSlFgzZZDiclT/3ulGQo9KOXFHpT2\\nJXgrHNL5Pr5F0DanyHSrqgIpr8DE1gFi6yzmqweb4v6VobLUvlRcFimURfVYgysyf4+UIA+vJSkL\\nivA6pI4R4XXkwQiWCBFfC+UqWe1enGghxRqlniIwG6BjjNiDLh+nKEuccpXRdeUNgKJkAitHcUKS\\nmBOE9hwWb4RdDwLSwmGtI9aKWqgonKMRR4AjCkPqocY4aIkFbDRJygQm36jWgWMoZD/kIwsE0knW\\n1QEmo4tMtjxVpx7XEFox3kzIU5/MOKB0gj2z43zx6eOIMp/91V/71ad/8Ed+6oWvuum+wsarMmAK\\nGf5SWRbvRLUquTK77XQsK6GMyxc+/APKr7y0fLuFnBzW9LeKri99LYdzourlDCkKbvN7fw34slk8\\nTji2Fycsphz4Plq2AcWAxv67qU3tpXfuhWq7Fpf3Ure9Eam8HqnG4JTnikUTcxxoL3D79bsIo4Cn\\nn/pbZqd2s3fvTdx930Ps2DFHmqacPHGMtfVValHAwZvuZG73fsZHxzl17HmeP3qU0bFRbr7pdm67\\n/X4eeOjN7LnmWvYdOMh119/MWmcVkxfoqIZB0RoZYXZmJ+sbPe578I0oW/CFY5bVji+Ze9K4YK3b\\n4ztfd5gbbrqe5148y+nlEEGIK/o44/lwKm4j3QoymUYVGSTjqKiNSdf8KV41/GKUfgPUcR2pPJUD\\nAUGcICLPyfW+pz5zVCr0upiVvRTOUqRdqEyWBdZvvkNEqBwWHP3Gb4wntDscQnkFGKk0ZdbzEEZr\\nfAnWDbNTtrJToTzCVki00ljhwJR+83U+S8JZzyOsyvRBGFdBUFclSImulHtUGOBsFc6l9PJvRY+i\\nMMyOKDoDg3QOJ0si2+V3/+n3M3/mWX7h536AwwfmmJmept/vU6vVNqXdhsNa63t+yCooDiedZAiA\\n2mpBsDnXr1x/WwH1autJUE8SLl5aJFQFxy6sc/9No8SqJMsGvOXBg0yP1nnm+AL7d46xc7bJ0koV\\nDIPISxGGNWQY+h5i3vegHyCME4o8Q+lKwEH6EquoDibCQcQCrfQodb3KmPsyN8+VrCxcIA9mEDKg\\nyPpbFBF89i+q+TQ8pDnn59KwemLyvJrDlcWeKT3/N4oBSRGOodwi0uakYQOnp0CPY8MZhFujlAku\\n3IMTe3EuxokOgW1j7CoxL+BETIBBSIEpHW1RZyAyHDllcAiHJDFPoSVoMoIgJJACrQWhCmjEmlBA\\npDWDIXVJSawpmGq3WOt2aYhzzNYv0mjGLHdrFWUnAK0xg45XsBrex6zPaNKhHhQ0oogoCKjXG0TN\\nCWrKHxy09hKHwlluOzDNB37/Ef7xj73tu9/z3p/61I/+xE+f/Zob8itkvOoCphBiCvioiiaqyojd\\n8kYU4rJAd7Wg9/WMKwPslf8Pv756lnp1p5OtfhA+iQCEiglHdiBk6OkNaRdXbICzNKf2cOi2G7h4\\n/DRm0HtJBiuk2Cy5+efW6Jr3TkQHxO0R3vWmfXzj/ddy3bU3srgwT6fTYc/eG7jppluRWrO0dIkX\\njz7PRm+NfneNqYlZdu65DmdyPvpXf0I/zZhsjzI5PoEVivX1VR774iMcO/oVLp0/w6DfYW7uGtrj\\nO9BxjWZ7nCisMzYxRT0eQQWKF+cHfOk4GOnLcF4XNcOUJYvdgsPX7eK3P3KSrBz4Elp6iRrzCBUT\\nmHlKE+KcRNbHvYzdYNGXslWJcH7RBnEdMZSdCyOPNo0biLDmT/sIdBR7wXSg7C2RzZ/CFj1MUWB6\\ni57HV5Y4k+Gc9UCTYdYmuOzzl8J3BmEYdAU4A2XqS8JieKe3wCZCCK/MY3ypmcpPsxJR8xqn4Enx\\n+ANVmQ6IWuMUg55//1UPU+qQIGl6azQ0JtvALT3LN946zQMHJ/j+t91LS/V53/e9lumG5fCc4OnT\\nhn/6E2/idffczFve/A1YB/v37sHhqNdfyl12zlV94a35O8wsXtKXv6KMKyqA5WUBeFvVZ/t6klJS\\nloakVqPZaKKlo9fPefrEArX6GH/2yb9DYXn3wzvZt6vJfTfu5cffcQeNSNGsKU5fWvf9S+kFIGw+\\n8Oo8UYLEoqPE50TWI2aptIRBEJrzzHQfoS6X2Ne2jLQmOXfqOFPtjKzfIVNznr+Ydj11pbp+Z8zm\\nPZPVIUEKhYxi3x/PU7T2bivWOUSVgeow9kAgwMkaZTCFdA6LAlXDyQinJzGySZQ9Qcgj1MXzFNxC\\noSRORWTqDqzaSRoeROanwaT0bIIyhlK0KIKbEDgytYtczNCVh2hwDklBoASh8n1PiyUJNIGUFM5h\\nbIkUkloYMtPyQMAT5y+AWaCVSLrFONI6RKPO7glJLw98TxiHtIaenmFna5WpRpNOv0e7USOIWrRG\\nRhHZwK+FMKC0gpF6SDt0/N9/+Rjv/4m3/sCN93zz+3mVjFdVwBRCCB0kJ52M6s5pNvtK+FOtUl4M\\n2H9/+YK+8vT8NV7nqhnq9t9fOXzPY/h7Xwq7qtD7sIwrA4LGNFInYAuvZ5qvgS2IJm7ggTe+jq+c\\nh975o36RX/4G/CaufO8T6UnXqj5C1JoibIygohrPnVzlS8+ssG/3BN/w+tdx2233cOutd2GcobOx\\nxumTJ1hZW6LdGkUIR73RYn19mRMnX0QhaLfGUM6Qp316GxtsrK9w8Oa7ueX2e5id3Y0KFJOTu5ie\\n3U0tSSjylEGvy8lTZ7np8GGaSY3f/nSfwmiE80bO1pTeckkFXFop+A+feYGiKFEqRIoAJwRFvBtU\\nndJE3mA5P4FVk4iogVINiJvosEmRryPyDZzJMGUOJveL2Hr7LWc8AEMqjRmska2eZnD+WSwSFdYo\\nswFFbwnyLkXmaT4VYbHq3XgRdf+Zb7v31anH32PrS4Jlvu3+cxm60AcGRZl38RKKVel3c7Z62spm\\nqc8V2CxFhjFlnlbP67MAESdeuUcInAzI+2vcu0fyh//qf2Fuus0PfN/bmZ4YZdeOce6+6w5ec9+t\\n3Hv37fzGhz7LvfsD/vpzT3D9gWsYH2sTxxFKXS4h6f1EHaYqXw8rGFVnd9gavmwdXBUrIEWFoX3p\\nGhiux2HAzNIB6511nnjyCb7wxS/zmgceZNDb4Jabb2SsDm953Z10NhbYuHiMkVZEZ/kCIlvlxmtn\\nOL+wweLqFuBPRzXy3pq3XhOSIs99GTuIPEhu6HUqHFa1yeUIbXuSNMtYW131/E8jOHztJBcXLpBF\\nO3FZH5unnleJ8HBTDBLh0bXDa3WV1i/O08uEQIYJQgmKwQYChY5q1XnKi0gIFHJIb3GACFCAtheQ\\n2QmKdJ2QExh1CESIoMSKJogRnEwI5ClS+XoMGZFboJBTICtJShIcMdrNE9EhFL4HHkqNDDRaCJr1\\nGsY4uoOC3dOjKCxSBmilWOpmJHGdsr+E07sZlAopA+TobgaDgW9UOK9rVeYOdM7+iYCBcbSSGhNj\\nTY9IFwUiz9A4dFInzx0HZxM++fgJirzg0U/9+R1veOu7XhUC7V9/FHlFDPHzZZFNCusD4+U8MX9K\\n2n5JwxLRPyRYDsew53jZq79M3/OlziZuczN4SfCVAApUjAwbWFvgyhSTdyFLCZqzNOYO8PQ5xeDM\\nEYRwXBYuBTghkVGboDGFbk5RH9tJ2JoirLcxBvLOIuvHH2ft2JNcOP4Mv/MXx/nwoye5tFpw/Pgx\\nBDA2NsXU9Awb6ytorel0upx48XkWzp/lrtvv43u+98d5zQOvx1mPhFUCWq06zz79KI9+7pM026Pc\\nc9830GyPkGcZ9UaTkZEJJqZ38MY3vYWR0TF6jT2YzGFM11vzak0Y1UDFVJ01pFSEgcSZvu8bVnxW\\nZ0qMrZClUiBN5nt6QQAqQMsAly97eH+xgsi7ANj+AiZfxQoBolLXSVcpeuuIsMbIvgdwxm92Oqih\\nozaIGsJ4tKX3RCw2M/hhliiG7KBhiYCt6vim2Pu2+yTEMCeVlcCBRcp48+/8c/l+6ZAcDgLKApOl\\nPjhZg1YBMqwjVIAMYsK45d+MLZG24AcfnuMDv/RjPPXMszz88OsRCNojTW6/5WAVfOHRx57DCcvH\\nnxrwcz/5/Yy2mjTqdYwpLw+Wzve4jLEvnftVOXr7dX69ustXDim3evrOOZqtFrOzO9iz9zre9PDD\\nLMyf5cbr9pKEgn175nj0y49z8813sWv2Wr7w2JN87ukL3HrXnXTWU87Ob/DN98zirK8kIKSvNviT\\nKTrQvmJQ0UWQqnKPAVs6TDTG5NQcG1mKE5IoadLrlbzvFz/ALXsCtOmi62NeEWrQw2T9ij7ikcO+\\nuFBu9qmdcd4ZBXxpfdCpPmOJTbuky5coyszPNeePIdYNXU4cAi9c4vrnEFjCGJRYpVH8GXH5KJFZ\\nwdkBUGDVtZTswAVNytprEdLS7P97cKvVAUdgpSN31yKcoTsY0E0LumUJpSMzhizPiQPtea1liSNE\\na+OrC8LSiD0DYV/7GDoKcSZn0O0yOxUTKImTgb8W4zjZuZGzK+vUtObExUVyBzIIEHEdKxXOZARm\\nQNSoU4Z13vfd9/MHH32cG3ZNvPXaPTO3fs3J9AoYr5oMUwgxpZT6KxlNSSpQC9sD0WavZfPxVS/l\\nP+k1Nxf11YLuy6kGXe1EDVs9RyljgtYEACbfwKQbuKKDKA2iFjC29y66px8j31hBCr39ybEIwvYO\\norFZdH0EESUEjbb3PyxKTLZB0VvzL6QilChIs4wj5x2fefIspQzZNdciFoawVufShbN0OyskrVFG\\n2uOcOXWUL37ur3n0C5/myPPP0MszhFSkgz4CCJTi8G33cttdr6O30eXo83+HcIZGa4zzp45RS+rE\\nccJd97ye9/6TP6UQEoHCDYUDAGe9dZZQypdZHQgZYGzlcI/E5SmuXCGQ3sC5tAZVm0QA1uW4siSI\\nx8n7F4A6TkkvN1erPtdBD4o+2fwJbNj2Wa1VlDZFBzEurBPEEdZBUG97T0mhcXnHC6Vv3rxtYVAM\\nfyQYRlFfqnxpj3wzCAnlEbFS++xSKq95i+f3OUQlULBFsJc6RoYxMvZqPWG9Wan31AHruYBFhhOW\\nD/7j9zAzPcGhQwdxzqG13/yOvHCCftrjwsIqP/HL/5r+wDC/3mPPVIM7br4WIfCPddvmOa4CRnmX\\nDD932da73JyGL7neq62Drza2/36Int2zeyd79+xmdm4X1+2/ltF2i6XFFXbt2kG32+Ozjz3P4yd7\\nPHYq4o8+eYxHnrrArTft4Sfffjd//5WvsNwPfC84T0FWknrOVc4uUXWdQ3lDi9Bww56EXSOapBag\\nhaB0gr3XHOKt3/rtXHftDXz8Cy+QuRp20PMo76o36VHzlSh/9b+QEl1vI6X0gC88wMykPYSu6GC2\\nxAzWMUVGkfb94ajimvr3J3x/XTbR6bMUVvjArNeIxCVq7gi29xxO7cLJJoXaiyTGiTpZuJ+QF8j0\\nvSgbYaVBWcWU/CLOrSOFJi1LjGjjpGLZznBydR+xLmknkl5WcnFpGaVDklCxnpVYo0gHA0ZjR7sp\\nWOqPYKzhUOsYg84GfZrVYUogAkUzWOT6mRZRFNBM6jTakyglENKDjzwNSrIxsLRjSRBE/P5Hv8Rb\\nH7hh6o3f8f1/8lUnzStgvCoCphBC6DA5aqxqB8mER8BdtiDd5inOuSHs/j89WH6tn11ZknppRgqX\\n7zSAigmaM5R5H/INbJF71KctIG7iGiMoYrpLF7YI2ULihEPHo9QnryFsjXu7IuE8FypPMYMuxWCN\\norfi3VKqQK/CMaKRaVQYYqTi2JllTp45xTc9cJCPfeRPUdKxMH+BW297EIQg767TWVunLEpGmjWK\\n0viehzE0Wy1GJmZZWpwnimOmp+a4eP4khXU0G03OnT/G7M7rqcchv/vvP8ML5wqfUjuH0gHD+p4K\\nQqSOKoqG2tRZVUrjpEI4g8lWkGaVQOcYMQ66gVMhbrCBsDkmv4BzoyD9c6vaLEIqyrXzFN11XCVk\\nHTdGyHsXEDJGqsoyKs9A4E/2QiDjOqYcUK5frObOZmS8DFjlgck+m/R0nm3ZZzXhLj9cVa8nK9Sl\\nDvwG7qwPltXhSsqAIKp7abcgRictVFwnqDXQtboPsMIhhEJikELxj95xmKPPPcN1+3axa26GOA43\\n5+KJU6f5xCc+yW2338bffOaLvPkND/LYV07wSz/+NmZqOXv27Kyy463epHU+a9qcp5vNWMGV0/jl\\n1sfXGtvXy/a+p0CgtUZKSRiGNJtNksTbT11auABC8OXHnuCd7/hOdo6F/OC334lJUw7sHufX3/d2\\nRlt1mjXNp584S5n2sEWBUAonlZerFKKaY7K6d9JTUKRlrrbB3TfvI2i2uPve13Dq2BFuvf1uHnro\\nYcYnJ8hWnuGJY11wAltkVdD0gB9bIWU30cnG4gToIPRWc9V1e87m1vwQ1pduXdr33F9T+XQqjdTe\\nSdOqFkG2SuBKyiLDVvxrU0AY5yTuGUoXY+UunPJzA2KcmiMyJ8n0FE4INAW5S9DlEcrCkotRFtRb\\nmXfXsW72k+lJFu0+Vso50tUjlFlKp5cyNzVGmhU4EaBcydRIk0BbzvZ2gylYdNewf8axsOJBQ0L4\\nnm6r1me8Zhhp1Gk3EqSMCAOQFXvBlgWit4Gsj1AUhmunEj779BnmpkZvfOQv/+DAG7/9+/7ff/DE\\n+i84XhUB8/3vf/8PWGPfLeJRbNp9SX/Rn4K3ejEv2z/8OsaV5tHD8XKZ4/Ys9Eqw0Xagj0CBlITt\\n3YBEOC+XRpkibAY4konDjM7soHvhaKUKVL02Dh010a1xCEJfwiszBotnKDaWKbpLlP31bQLileKN\\nsNiih8tTrBQEOkZLzXw34pOPfIbdIz0OXH8r9zz4MO2GF5l+/rln6KwuYl3JfLfDzbfcx523P8Rr\\n3/w27rzzNbx45CnufvCbOHToECpuMjY+hVQBa2urzO3cy/rKecJGiw9+eJ5IhV5aTAp/zVJ57VNZ\\noUK2baBCBZubp8k2cE4ggiksdSjXEaqGjuq4sE4YNL26iezhkJRZFynrlNl69VwROtBYZyjzEoI6\\n6BiZ1MnWF3CVSoxSkiLrYzrLZOsX/Oe2Dd4zzKycG8ribQuWVX12k48ot13LMLhWsoCicrGxUGUg\\n4JExohKM90hvKQU6qhM0R71now63QDeAEyFCC2rZJW4/dJ/0aQoAACAASURBVID/4/0/wsRom06n\\n4wW0tcZYSz2pc++9dzN/8SLzC8t8x9se4n/715/j4ftv4nV3XgdIrHUsLCzSbDa8io+tgqPYhn4d\\nZpL4zOfKzPLlKylbz3HlWrjqWnrJ8/qv0zTl1KkzzEyOkxY5NV2iVcDszE4oB1ip+MZ7DlI6Q6IV\\nK+sDTiwMPIUo3/DVmWrO+YOM8gcVV3rrMwTLHcMLX3mKmEt8+TN/zfjYLCdOHiWutzl96gS/+Zu/\\nSh7spbR4pSgcwg0FN4ynOfmL92+8MB4g5CyuoiMJKTavabOiHyTEcUjZX8MIhbAlRb+DKy0qqvse\\na7yPgWoSFRdwwpugK+n5tUZYtJvHqNuGxzYADE0isUJpEhAh3gmlxoh8DudK0mA/PTmNJsQIhQSk\\nEFhh6Se3I0SBypep1zwNxlhFnmc0ooCxlqZej5jv1DBlSS/chwobZJ2L1X5VECUSlV9k3+4dxKEm\\nDiRLK6uMtptV+wGcdOgyp1A1dJly495pfu1Dn+a93/ng4fvf9F3/8hd+8ZdSXqHjFR8whRAjUqrP\\nOtmQ0qMPvkow2wL8vDTobUMrfPXX+5qZ5PafDzeJK/uV23/nv9eoZBQVt3BC+ayyHGAruTQdj6Bb\\nI3TXe9XhvgINRE1EcwYV1jD5AJP2KQYdTHe5AryUnuTvmU4evu6G6jReGk26ylxZBjgl0CKiryZY\\n7iq6Ky8wNTFKI0pY761jipw8S9kwDU7H30Rcn+TAtOIzj3yC55/+Mj01xh89GSJMj5nJhDhuMDYy\\nRhhFYEoWF1b4f/7mKbrZGKUcgjGUDzzbaDVDwYVKl3ozlRNCIoXGoVFS4HSANRlSNEALgiChKHOU\\nqnneaX4JIRJKJ/0GZgHTw6HQUR2dNNFJm6JzAXSE7a0Sjc0ShDH9tUsMVs7AYBGxjWd4RQVyM1hu\\nbew+6xdObAV/2LYpekWgTWEKWWmQInzJ0OLL0TJAVk4cOggRQQ0Z1ZFhsvXKQuA5kBIVKnaUL/Cp\\nf/er3HHL9QRa081L9u6eJgxjnjt2xoN5ogitFa1mk6nJMQYba/zeh5/ind90I4cOXEMQ6gogZwlC\\nz/llWJERV5aW2fzdJlr2ZUBwW/NfVjf18uB5tbV2WaZ5RVANwoCpqUnaIyNsrK+zd881fPrRJ1le\\nXqFZE4y1Q/bs3Uva7RPVQ+bG6vyHR54H5Sh7G14VS2191sPX8AIHYDFY2WbUnKO78iTKwdrqMj/6\\n47/Ar/2z9/Gpj32ERtMxKOsU4RQ2zxgedhmSMq2tmtnDw5PFFvlmZisqMBBiS4dWCCqnnAyLl7m0\\n1voMtRjgrEM02hXXdgYXzhGUzxAGwWYJ3QovipDrQ0hR2/ahOnI3h3A5VoRIYSkJqbnzWBswcNdS\\nhON+viMrL09wBIDC6J2UtbsYLHyWZjNiY5Bi8pwdoy0aSULsUi7Ia+h21pFlgdCCqDFKvrEKQtO1\\nM9w4u0w9CJgca1P0Bkw0myT1UaSymCKvbOxypA4oUNQjRRJo/uDjj/NTb3/otvvf8j0feslkeYWM\\nV3zA/NVf/92TZZG1pG54Pcxt42o9o+3fb/1suMD//2WcLxdAv97vw+YOapPXEbbmvItGtg5Fisk9\\nb0/VmojGXurNCYr+Kk44ouYMeX8dUfSw6Rp5fw1cibClNyYuh56J25U3tvVZq0UsMR7EYa0nzKNw\\nNkNrQcckrK0WTCVrTM7uoh7VuXDpPGfz3Ty1PIcI6lzqC548XqObNjhS7OVkuosD6ScoVp6k2Zhk\\nx85r6HbXCZSg319nZGKSD3++S0aJlokvQ3HFhujcFohm87ZspjNesUaCcQWyyJHRWMV16yHjOtIa\\n8mwJObiAzgaYoO77PPhyllMJMowRYeR5n8YQ1UdAaUQyTrl0gvUTn8OmfTQCJ3RVMtt6O5exQxCb\\nJcotGg+bGfLmZVS9T6Fj3z/TnhQuhPObt/SyeEqHDPuZqCGPsOZpB2GCGpoVV2o+Q3TRaCPkLW9+\\nAy8eO8odh28gzXMmx9qY0pJmKc8dvcS118xw/NgJlJLoICCKYoKoxv/5J3/Lj3/XA2Rpyscf+RKz\\nUyM0Ws3L5+w/pDJzlfPnlQdXqjty5dO9NMBWP3db7wPYLNFGUcTevXuJawl33X6YleUL9FPLyfMr\\nTE+Msmf3HElSZ3J0BCO7fPn5BS8XaUsPtlK1zT6pEx4050zBdK3D9ZMdTmZTjNsX0RTc88DDfO4z\\nn8RmGSUQ1lqYjZPk0bUYK73DSFXBGa4x5zwvd1iWR1BxcxUEMUEUkw06FcfWHyKss5U8osXZElkh\\nbx0WW6ZQGO94QkGYPoNKLyKjECtLiqxAoEEUqHyNIvRUEiFENU0tdtPGzLcONtx+UnU9VrYBz4f0\\n+ccQHuSQvYtsrCwTtEbo1+5AFPNk2QLaSfbNTVALIrQzuP7jLLfeTNY5Q1Ibx+qAfLDhM8i8wNQ0\\ne5s5k602/V5Gox5TpgOS1jRltuGnh5LItIeJY1y3y+TsGJ/4wgtcv29m/99//iOvv+8b3v5vvvYk\\n/C8/XtEBUwhxq8n7P4dsXnVxbiILhd5swG8txqEPuPDu5f/wWFmNoWbMFhx+OC7PIMVLfi5cgGhO\\nEo7sgqhJ2V/FZF0ocky6DGUOApLxm0im9tBbPO01VoOQorfq117lrSdFRTuoCPVbmzYMaSeXZ0YO\\nQUWyFlRAhRKX93HWEdTaSGmRoeC933YvjeYY5+YX+eAne5xfD1AqqJw9JaXM6YsawhkOlX/F7ok2\\nBw7fw9yua4jDCCUFnc46G50el7qCLx3pI7XXDWXbBripSuTY7LEOy7BDRw0P23dYKQmExDiBijya\\nWBQ9L2SeppXHaANnNjyHTTSQWlXehbH3wUwHXtw83WDt+JdI558lWz5GmReooFFZPl2+SW8Fyep9\\nSgFy2+ZeldXktqzI/9yXnK2MiJIGTmhMnvnMMAi3ZpOu+Sw6CD3kPqyhogQdJ8goQQW66nNSiXcL\\npAi4cYfj2ce+xJnVnN96/w9ireQPP/wIi5cWGGk1aLYT/tW//TBnTpzh/ntvod1qg7MULufdP/w/\\nc65X44//6u/4oz/9l3zhaMGPvePhKtuSlwetK+bx9ixw+xha521fc9vHMHZsyei9/AL0WfTWI4Zz\\nYvO5q+dXypcjwyACV7JjeoSbDx2sbMgE6511Jpo1/uijT6GimCL1NlX+HnoqhXUODTx0o+PwXMCo\\nXqTWf8bPFV1w8fwl3vGuH+GpZz6Ndo65HdcwPnfw/2PuveMtO646329V7XD2iTd17lZ3S90Ktqxo\\nWQ5yDrJsnEQYogEbPjNkjz2M3zDAwMyYMTaGIcybB3wAjzFmHjOWjbFxTsi2bKFgpVa3sjp33+6b\\nTtx7V3h/VO1zzr3dAt58Plgqfa5u6BP2qV1Va63f+q3f4tFTBiFioloNU/jyIwdTsonOK02FzyrC\\nJAgV46QkTjIckrjZIa43UWmd0dIJ3/sUB64c33cB2GKANpak3ga5E5WfJnZdKOpEjRkSWSAih9Q9\\nnNqOU22gqs0O87eOzxGUqqQcn5VWSF/La0vK4Rp5dwXTX4SojoxjBnorDO9hS7uBdI5Oq85sM2Wm\\n0WbG3ccZdR3D7hILbciNwBYFAoOQ88zIx5mfadCutxgOeiRxgs27WAfaVjXHCmHwUbY27Nk5z+/8\\nxZd52xuu23rJ89/4nqdcME/jeMYaTCGEUFF8yIksFSKegjPC8VodcMq3QxLOMJ1/cSJCxg1U0vT9\\nH6e83v+f18G4CSHrDxTBuV6yh18ULjLEsxcQZQso4TDDrpdTswYzOIPVIySQtLcj29vQukCUA6K0\\nTjnqBwMSuiQEiO+8B5wjPNZNDhkclbsQ4iKc0AgribMO8exm7/mrhNc/fycvuPJCPvPlu/jQV84w\\nLIKzMTYiEkKLNCEt0fAk//qdP4sQQRtUeSHo7vIyJ1bX+NMv9LDSEyzc1P1aF1GGUZUXjC2UhUqj\\nF23Gz7fW+shR1vH0gS5RlFHKFoYEWd8ESQ0pBTKqgXC4/ioUa+SLj9A9/TBKpb6vocrGc7kRfvQ3\\ncJoFW93z6m5Xn2f9+hAitG+TCicM2hgfYZrCQ7ZO+AbIaR0Z15BB01SlmRdMT2qhr+Ukh+jFvz0U\\n224mzMgBf/G7v8Cf3PJNHj26RNk/Staa5aaXX8vHP/s1HnpikW/c+Thvfu11XLTnAgBMaemv9Tjb\\nXePue48wGpxC2wYjN8OFFyxw8Z7N500lTH268WFbzc0Efg6rq4L2nqJya+KMTDtH5zHQG41ycEzG\\nzwjXIRHUG3W2bdvGzu07SBMvGaiNwWrDIM+55bN3YUWMTDL0sI/TpZckjHyjcYflxPKAHVmfZ1+y\\nn0/fcczrxxJh9Vkee+A2lvoFUd1yqn+WQ929FHkCKDCaSk2san5QEQ3HdbSEPSQ9C5RiiENMVImE\\nRKqEtDNL3lvyr1ntXecVpJxzuDInzhrYOEFn+yiiSzEuZRS/EFGexNo+FkfilsjjZ/FUWtbrIvpg\\n0L2AusHkA5aeuIdotMRo5Tim1CT1eYzzkpipXGEuGfKcC3einEUKL2h/cmmF2ajL8f4cI1tDZS3y\\n7goCx1BLWp2YjuozW0uxThBLh85zGonymtFCYB0oDDbOUM7QaGYcX1zlwGPHowN/9/GrXv6mH/l/\\nz7+qnr4R/eMPeXqGEOJHrJVtVC0sJelDfiEA48E3IRBxhh118a5ZSIhJRdRYAFXDjlYJJ/H/0aha\\naIVjdBI9Tnm+kwcLn+h3mrS2A50PUHGTfO0sKm35vMVo1bPXkiYmX4KkTtzZRH72GE5GOGe88ZfR\\nuAPD+aLXDXMVfrLn+vHC4oiQUQsrFbIxh4rr48jhq3ed4PETPQ4dKxgVbpybGb+v82UevglxzFL9\\nuZxeGqDLgk67zWjQ48Gjp7j1oRHHViRF6EbiUUwxjibXz+m5kbmxBRJJWRbEURzm2t9L6QROxL6N\\nmrZY20DKBkrAcNhjdOYJ4qyDcwJbrqGHa5iigKCMEyczE2hTWHCywgyojNQEemV87YzhxQ3GRFTZ\\nqvDgEIm6IHrg4V2JrM/5HosOzxJWsa+njGIQEpU2ULHEGr/GKsUqpSYdaayzxAJ+7d+8lWc9ey9v\\nuirjf37xftZWd7Np8xJ3P3icT956P6977h5+5z/8GNsXZgAvrH108RS3//2DbN++E5scJko7OKuZ\\nm22g+2eDIRLr7sn0rToncBTrIVu/B1x40iS/OZ0LdoEMM3kOY1iymjofyUsfoa0zoNOLZrJe0tir\\n7ggp0KVno1ptSNM0CEIYjNAoYrKZzRTDHs5ZilGfpD4DQlC4Jl99eMCBQ7dgkys5wSwXlB9nteiw\\nJmdIOMFy3uFs7VVYMmS8ih15opEDjNaoyrkRE3dcOgMmIFEyIvQzGjvszpSecCRBiJjm5gvon3g0\\nlFyJcbmKALCWwdmTNLZdBCiMauEaV+NsxCB7HWnxWdLyMNosI9M+VtapusecTw7U3xsv7G/zHvnq\\naWTeRxVdeqtrgMNFGd0jd5MtXIjNWlhGLK71WesWKBWRD1cpjGG2kfHo8Ye4aNMMT6y20EVJnGbo\\nYRfKnCcWM/a1FpEXbEcXmlGhSWoJq70B9ThGZDEyFpSFI5ZArUZN5Nz0wsv5D3/4KW68/tI38wwc\\nz8gIUwgxA/KbQmYIPNwqIl9jFaVtrPX5OxF5EWucD/E9HKGIWluJso5v3JuvgtXnGpKnGNMw1OR6\\n1hvIjYuxIrC4qlNE0iHZcill9wS2ewJdDIiyNuVgFfQI0Jiij4zrLFz9JtxwDTtcwhSjICCt1hnL\\n/9MCcX9xApXUkUkdmbZozG3x0V84kEZWcXq5xJj1MmjVa1YScN5oSnbUjrJvs8Qaw2CwxqnlNW69\\n5xSt/DFwJaeHHVSg0a8776bIHS7M5fjn6jPgBcWriMTZEqu17yQhFVXVg8Qh4gRbFuiiwOmCcu0U\\nZX8Jm/v+kkImXi5NRCGSnBiASgR//bQGdaYqIBpf13mQBTGJ4Ss4tvo5qs16+M0ZrB6iZOKNZRQj\\no5Qo5FajpBYMRiA/ieqgCz8HQ+uc5fL9m7h4W4N9e3byute+ildevZkzZ8/ykU8f5GXP3sS+OWi2\\nG1yydwuzMzOsra0yHAwxpqQz1+Jnf+MWchEjnWS2Xef3f/G7cMNT7Nt3ic+rVWvaCR8xblhK54Ns\\nn+r7xpSFhwanjOiG3OfGaH3jOFcUZGqNBlg0LwsQgtEop9COj3zqmxijfCmGsGEveT1ZZwwijlAO\\nctlCypjStRmpjHp0glPZWxjJi7FiM8ZElMkeHBpbmhAVabCOKKkFwp0ZX4cYa1pbnIq9IlMoyfL2\\nMtQhB/SoYo7LKEEPVqcmohJNsdhyiKq1II4mEaSwIGKM2kM8egLhMoycx8QNhFOTdblxXl212mC4\\n+BjFmcfIu0vogVcY891XhsgAM1skrZYlY4k4iXjs+Cmvh+0km1opzSxDDB5j01xCuzZkWc9jBisg\\noLAZF88vI4Rhc6vDUn9Au1FHighjPGKk0hSpJLrIfcQtNThJlKR89Mv3MDs4+O4rXvzMgmafkUo/\\ncdr6KiKGZNYfYlL4gldn0MVaOIy9vBwmHzMcPYKYINM2ILFFb2wsHf+40YFzDwd/0E7DRefCouC7\\nBqStrUQzuyFS5KcP4IZnsNYgsJjRKlAihPKqIK4kijN6Jw+i8z7lKMcvdIkYlyP8U808Y2bwuR9I\\nemWgKKE5txmqZlEiGEJr1zkAGw8nF6IAR8k1M4/zE298LmnapCwGrJ1d4cE7P8dF6RPU7Bn6Z48h\\nlfOH09R1jV9PCH9kVAYxdHr2v4Zcq5QIJ/1BZDSUffTwLK4c+MoNO8JRev1d5VsooWJf7B95Lc9J\\n2ykxNpaw3gGqvosxmWdyLRtncfreE4ylG//dw6aeCRth8y55PvDwbDnCOq/cIkMLL5XWpnRJqbw8\\nKihd4GUWlRM0xBCJZO3I4xiZhY4bkquvuIKMPt/3ojYvet5lfO/NL+LFV27nY3/zKf7u67eysrpC\\nt9/jy7fezp/95RfomgyHwmDYt3sTM7USkBitx2VZFdlTynDTJ59+3Rqr5mNj2QhTn6eC38dwrAgN\\nCKwvwti4TNflgtfNd+W0nWuYq0tTUpImqY/SheORxx6n08qQMgvt8Py9cVKG++YQxoaoz7LMHnLV\\nQTiHza4D4z/+KGoyinfhnEbnfp48aSsB4esJ3fjDivF1Vk6CNRpThLaDFcLlwJUlLh9R5gOs9U0C\\noqxD3Nk65dVV0bcDW1J0F5FWnrvHRUq39SqG2bWYeNs4mt04xqpKIhDwyhH56in0sIfNV3F25GuA\\nhfNpBOtJTLHQJEmGRTLKC5bWeggpmamnHF1c5eTSKmmSMW8eYU/6EBfNrRLV2zij0UXJqt3KYrfH\\n2nDIlpk2a/2eX4XOokuN7g0RMiKNEnAGITMWZlKes287g5HmwKOHs3/99u/6wHk+0tM2nnEGUwjx\\nwjLvXSFrs1D2QUiiKAuFxyp43Q6ibAJ2VIeYkL4zgTOY0QqmHE698vqPOi3NVf37ejhqOsJQ4ftT\\nG7Eoa0N9lqg2jx2cwgzO+ihH+Ea0ppzSKTU5QtaIW5tR9QXMqABXeDg27JmJXBrrorB/8ghImRMy\\n9IRM0U54HQEBuiyw2vezXP9518+RPzwjag6uu3iWNGsz7K8yKg1nTz1BIiRbdu/jdd/1Q1x95SX4\\njh/aG4owv9ZZbPVOlbOLAOvbillnfN7G+os2psCUI9/xQyhkXPcdQoxBqgYibmJGPXR/1Sup5MPQ\\nL5GxEZ4+WJzb6FB4p8THqiGimj6smCAH08/xwJqkyn0KobwqkIxBJb6hdJwSJynCOS9dmHiSj0OE\\nHOeUilAYlTPhxtdgca7gl//VK7EC3v3OH+b6y3exurLCYDjAOcPzr7mM73rlNXzx9oN87bYHSWop\\nr7jheUhb0m61OH78KMO84JrLtpHpM+jlkyQR/PTNV6L7Bc9+1uVEaUTVXFoKiZRiPFdGO6zxX8as\\nXyPjyHHDupk2kJUtIdzzyujJ8LurHod3EKZLwc4XVW4clXHQ1ng2bRxjtWXr1nls6fPuDjtGUqSs\\n+ABgTBkMi/T9Tx0IpzjZn/UUP+vLTYpoAeMkqmq7Zr26mJQxiKr6MZo4txVqgvNsdp1jtEXF9bDm\\n/f+dc6BLbD7AGs9WTTrz3tiEV53A5QKT54AZdz9Zt3bUZnR6MU4mKJuN36W6T9PDAcJq8v5Z7GDV\\nNxywFdnIYIzGWYcxQzzBMGJgEwqryYcj4kRx9NQqpZAMCs3qaMQo12yd6TDqDXjhrmNs2bLgO8c4\\ny30nmswkMywuL7O6uuZlF50/TUsLpixxhUYoSeQcKEmWxmxvp/zIG17IH93yTV77vP3vbDWy9nkX\\nwdMwnlEGUwgh0rT2V0I2sNqLPVurUWnd31jCHpMRKm0jowyVNhEqQSYNlFRYIsreIiZf83Ci8DVX\\nG0k/k/6ZFT3GVvt8+lFUhrLKY657RJVvUBnOgS5WcEUPEc2AkKikBZFCJm2QCQIV+ssJZJQiszk2\\nbduOw+CsL6x2eEHnKh/mhEL8A4b6vCOkIl2U+vrOuIF2oSWTNThrUFHkBaqf4rVtgJmscFiZ46zh\\n0UceQCmoNWcZdJd47PAhNu+6hDSOWRl0uetohLQRJsxNBStVh6WovGYH0lmE0OPQxvdujHxRc5wS\\n1VrItOmh5FobISRGr+H0AKxACN/7UqqIisxgpw7pcyGp6vdK8q0ioTA+pMa3NhCRxlC8p8mOlXn8\\na1SQWoSIYk9+SjKiWhuVNJFpw2uXRjVU1iBtz6FqjcobCvHkVFu6OCWJlC/fA0rteP8Hb0Oaknd9\\n4JMcO3UarTWLZ85w++2389zrrmftzDLXXZLRsxHLQ0Oz0cAhGA6H7Ni2gzfe9Epe/KLr+Njv/0vS\\nWovdc4prLt3L/udczs4LdiOdxFjL2aUlBsNuiJYqJ03igoKLDI7qxnG+/LqfOTcJ461jjKX7Z/nm\\nYK6a5w1L95+QfpjkWx39fp/+oM+Z5WWUilkZaHIjQ/SpxkasMphOeFjWWo3DYSsHXPjyECe8PKAn\\nmgnQ5UQAXwiwOjhMUQAHPMLgnHf4nK1E2DVm1AebezaoUv7pbrI3nLOYYuhLUAQks9tQSerLjcaQ\\nrW/Z5oSPjp3zDQysNePc9wRqtWPIdWMu2f9NoAdrFP1lnBl6xaKxB+trI8FiraYcLCPnt2DdJnAQ\\nJTGXX7CFvZvbCGtIhGRzK2WuVWOt1+O6S/eyudnkVXsfpz27AFZzeslwxmSMjODsWheF4eSqR/2c\\nKcmNoej1McahpEQ6Qy4SmllETQpecOWF/NnHv87rX3L5T/6ji+I7NJ5RBhN4e57rHY4Y4UocBukU\\nerAy9mgdoJI2sjaDTFrItIOqdbw6lUz9B3I6PNbDfMgo5JyqgmoZPGkPxbkAE63bq2MDWX3f4K1V\\nizSqk87sQkR1ZFkwWj7oSzcoPfNVtRBRAmboBQYCky5qb2P7RZfwqufuQuR9ZC3DSeXhJRl57cuo\\nhpCpj0w494B6yiF8VCqF8l1MmvPUO5uCnmbwiKUnnpw3siQYymDchI140Z7TvOam72HP3n1EkeTx\\no6fIGpu44WU3ccGei7n34BGOrdTAlaE43/eErODl6oWlM9QiCGJZ3lhW5SeRRwy0tMGT911NhFRE\\nWRsVN/2L2BIbNzCjASbve8ECVx0V64ksPkquDrb10Z0LcOg4HxYOsXPnZNJImKpFlJQI5ZVkEAKV\\n1FBpA5FmyHqLdGYL2exmanPbSFsLfv05i1TC5zNdjAzRCYCzlj/+le/hP//Ma5HCF9ef6ZVY4Vha\\n6fJXX3iQ//jfP83fffNuDh9+krvu+BZbduzjzMnTdFfO8o277uOrf38P2sGOHTuY37RAu9kkiSIO\\nHFnFKcujy4onT3cpS83h48c4eeo0D9x/iJ/91f/OrbfeGjqU+NbaUoKUDiEdniV9fi3lcwycq6r6\\n3IQlLSZqWM5VerXnjx6n790/WMIVfq3X6xRlycGHH+dLt9/BRz76RZb7I4zOwXrJuHVGXIb7WVaN\\nvadeX3hHUVYGEB/1OPB7VyqcE543IXxvV6HkeI35dwlYisBDk8OBv+9xIHzFkZfUE+Hxwbha6/Oa\\nUXPei8OHJtcgcE775gBhfp2zWF1idIEphuiyAO17qmLKsYMAnh7pr0sgKClWTuCMQyVtkIHh6yZG\\ntRI+0aNVlHCoWNKoZRjtaCUxnWaKxPL44iLzzRab23W2z7fRQeZvU8Pw5mv6CDvCmZKvPLJAs9Zi\\naTAkjlLKfEgpJTKI/Bs8TG2FQJQFMkqoz3SY7dR50ZX7+Ow3HuCmF1/xW816rXPOYnkaxjOGJSuE\\nSJI0/e1C1yYyY0is8+UQ48fJCFlreZk0Z70Ooy4x5QBrBTgz3uBCCKSqI2OBztdCE2GDBwVACIut\\nyBvjd6iA3sk43yEqhPA9GzftZXTqIMIUaDxRx/fpiVEqglhg8mUwFhuYmnHWpr51H7lrcssnv0bp\\nCqRogO3RW+0R1zsYa1EUCJn4VlPOrb+o888h4GM6EfotIhXpzGYfCU/BzNNkonVRwtQP3o4IlO5z\\n02tew87de/nMrXdyx7fu5MGVC/jRV27hxIljnDj8JF87lONE5jdjcFDclFycdNAY3c8VO86yOJjj\\nkL4MkF471o5Iix5CDJFxiyEzIMFYgxn1EJGCQFpxMsYM13BKhwiaUN8VqP0ifAgXcksVtjAF8fl6\\nuUnZy/gw3pCrHs/N2NhOSpoqODaKUohirEpJ6m2iWtPL8iHHTYaNKYN0mJeYixXMbU1ZWizQLgYs\\n0liG5YBXPG8vH3jnjbz3f3yVk8tDMhEzdIo///z9/PEvvZFLt7boLMyDsTzy5GG+dM8Z3vbGG2g0\\nYqyQPPLIwxjre1yWWpPZOv3VHEyBcI7Xv+ODzMRw9HfPFgAAIABJREFU8e5ZXnrtbm676wBvedlV\\nHDpwH6945asRQvkaU1diEZSlIYqUh9nDsVsZlHXrLjgd1ZCIsRHya+kpIP+p9bdxTW58j3W/h/8r\\npei02txw/TUYY7ju8ufwtbd/gG6pMLrwjpQKn0l4xMmY3HMbnEPEIkSMPv3ghNeDxVYngWc4m2EO\\nSngZOesNo68ZDsbPTQxa5WALZ7F5l7IfE2XtYOgDouAszgiiLPHmzPn39WVHNb92ZIgyraboL5G2\\n54NB9qiIcNprUYe0hzZemD+ud4iCmHvV8NCZEaI0lN3T3olQNYSq4fRo6l7YMaQunKHsLSIyydpw\\ngDAJyFl/vkrJpbt3oiIVHAqLFYpeP2dlMGBm1rC9rTg+sBw/scq3FjKu3pxyYmmNrbMtnjx6iv07\\nNxFbUFJ4IQgDWIMsSnJtuXC2xiNLju+98Xr+5KO38tLrLn4L8MFzFtF3eDxjDKaU6veKwrZkFGNF\\n5FlbU6bLBUaCVCkqaSDTJr67+Qjp+j4ajRPfFaN6mlDIJAs96xIc3pPzvAYPAVaemNO+awAy9e8t\\nDOuKp6eHE4i0RVqfZ3jivpDXisZntSAgW05h+ssBTq62uIaowdxMC6NGDApHrDKMHgDSd9DQOa3N\\nexjpAr180kfb0kOzTzUE3rB7WTafV1NR6jfqFER2Xvbc9HVX98Ph83vO4lSDP/jL21m2BxgMDKXY\\ngxKGv/nWGtZ2SeM1enYzwhiErCKwcMhVLAoxYl/zfvqLAxrRSXa6AYvuQoQp2N77FM7G1CKJibdz\\nkmdhyRCpom+bWIzv/qDAuAjZmCWWCYNyhC0Gfm6EZydCVaMbTr2gMTw9U0JWufCJEzHpObIhuhGC\\nquHzBJ6tPqOCOAsdReYQ4ZDyAZSdvE4VoDnLc5+1nZ///ldw2d4FDj5+hLf+6v/GWd+B5d//9t/y\\nX9/1ei7dO8+H3/N9vPlnPsgn/p9/xV998lY+9OlHecf7/ppfedsruOllmzl9dol6lrFl2xZa7QYj\\nrek0Ozzvuddz9MgT7LhgD6NRj8/deohHTvYZklA3mkgItm+ZZ75W8MBjJ/ix73kNV+zbSv3VLyGN\\nY3/4acO37rufex44zLcfeIiVXk4cl9xw7fN49Yuu5IJdW89tXbfB0I3Tl9XCckztZtY9djqqPN84\\nLys9wOnWgVDCtx2TiplmypX7WnzjUB7uhcUZi8F5TdcoRUURRbeHdC7kJT1aIPA5dY9V+Jy5QyCi\\nJDQFCJCnlGBCD1MVI8yEpT/x2IKh0rnXiMU3MUdJpPKKQU4q9LCPTGrYOJDxlETGKXqw5mtDdYlA\\nMTz9pO9eE6cgLFJJLBEIjS6GvnuT9axdrUtEex4VJwjhsE5ghkMGi48j45hydQmjh544F3RsKzOv\\nnEQ7AyKm6PUp2jVSAVpbYhlTTyPWRppGGnH/k8e4/pK91KKELKlx8swKJ5bWkFHElftjTnzb5yzv\\nPtzimu0xR08tsnmuTauZUWioKUEkJdJZDBZkjClLmp0ZlrWhO1rhZdddxMe/eCf/6Wfe8Gcvumb/\\nya/f9fBnzrtIvkPjGVFWIoRoOSc+StQK3s1ka017n1JGfsE3tyHjJOD/PnEtygFCpVg98oZORoi4\\njogy0AMvPxWiLg/f2RCNeiUdH33iiTfgoaUNhtKjLgpRayA7W4mSNmV/cZ3qixvXQlpPY7cmeLBe\\nuxPhSNrb0MkcJPMMjh/E5l2PFkuLIAHTJ2psJZvfRlmOcKMBSMH5Woz5S3XjCEsEiDCuz1Hfuh9Z\\nq3mJrimosnpO5flXDDrWHUxi3ELNEbM2HDEwznvUQftyrYzoa8FaXkdb78l7A0UgsEBVl5i6PnP5\\nA0gEuS6R5SIz9nHa+iDOOhQai0KVIzrlo2xJFtlVX6OozbJaNFC1BFc6nCiJnMJYi9EGbAE693Ji\\nU+pO3gdXwUsXU1/r19QkyzMhWUhZsWCld7pkHGTsYp+vjFI/p0mdpN4hac8FOb9pyNfn6pD+u1QR\\nV+2f4bGja9zyuTu448HDHD58koOHlz0aYC1//Gvfw6gcMt9sos2IBx87TZw6bn75lbz6+ov46Ofu\\n5u7HTvEDr7mSM2eWeOjoEn/81wdp1+CJE6tcvHuepaUltNZs374DQczeXQv8y9/4n4jBMORgJadX\\nujz46Fl+59/dzGwtQaUxDz30MLu2b2OkDcvdLidPr/Fzv/7nPHoaDq9Kjp2Br3z7JF//+1u57qpL\\nkUiUwB/+U+t/en6rgJ+p38d/W7//J/fjPGmH80WgMBG+mBheHw2+5qVX85cf/Vv6ZTyG+0XIUYsQ\\ncerRAGcKVIB3ndU4jFfxMaVHdazP51UCHTrvo5QKZRC+jRqCoMfqRQxEiDDHghxO+KbSgfwXqRQh\\nhV+7IsClxqcxKqKPwFH0VoiSNABLGucMurdK3Jr3ClAByq9myhRDhNE4YzxpzpgAA6foYZfB2aOI\\nokvZX0O70vdTdRaHrhKr4UuGrJWktf1ZpM15xPAeUpWQKUU7S3Glpt2Zp53FLHdHzLSapHFMkY8w\\npWVpMGDP5pTVss6ZNSh0xAsuGtGIG9SVYLaVkZeGWIIuS7J65omISAZ5QWmh1elwZrnPcy7bhzGG\\nj37+Dv7bf/ixH97x7Jf9+nkPwe/QeEZEmErFH/I6jYpxvqTaY+PNpnBWE2XzCCmwRZ+yewoV17Fl\\nH+IWtgzsLkBFHVTWwQx7mHLgJdKsHifJx7luM0Lr6n0qcYLzXKRzpLO7iGafRXnmfvIT30aLJPQ5\\nFBNSkK+N8JqxIWejVIoxOQKHjNsgMvrdJZRKsHkfGTcQrkA6jREGIeqMukeQWZO0Ocegu7gONJ5c\\nkgvC63JdBKSyDrVtFyFVHD6RPAfuMtp3P5Aiqo72dUMIQSklkdMoSmxUC30dfbcHgQslIAIhLcZo\\n0sKgoxpWTc5IhL+lyegQw3JE2mh4JnO/SxQpSm1JEonTAmdHOGcYWYEdFSRNeFbjSV6yeyuy5vj8\\nHU9ysnwWqJQIC7WMft9hROwj/BBZeigwmnJezjNvG5iYbvx9w9+s9aLxMkZGkZcykwoV13wrrtYM\\n1gnf828KlXQqYpT3SGVKqQ3b2hG//PabiCPLzp1befDQEX7g3R+CuOaJHSLibb/2UbZ0EmpZzLt/\\n8Abe8oor+MO/vpuLd27lsr27qDfaLJ9Z4srvfS+tdoe+MWybnyFrNfni1x/htTdcTHcwIssaHD58\\nmLn5BY6eOctoeZGk0UEicUYTxzVkVvLmd3yYtKZ49bUX8ItvfTnLq2t8+6HDLPc0P/V//Q6NhQsZ\\n9oZEZYkJTsIjJ+u8/uc+yHyyxk987/UsZCk3v/kNRGqSD1/HUBYhbVhNjggs0ql19g+NKkrcaDTX\\nEVqcjx5xEElJq97iSx9+D1d+z3twLhiXSCCs8+eDLsFZbDFCG+PRKgeYUGtZlERZDVNqkIJyNPRy\\ndpGvvYySWmBwCw/FCoGKa+giB1W1/Kryo4GOU/QpnEXFKUIpVBKj8xwZKW9UtcYag0wyRFQjbs1h\\n+svIOMMWBoHB6BG9I4dobL+QOI69olaUEAkB1lCsngSVQKmRcojL62htKPvLRFJQIlH1JrrnJeyM\\ndR59oxJnEd6AOoG0JVE2451EreiXQx4/vUIiJJfs2UZvVHDB5g5fvfcxds53OHL0FAudFvVYcuzs\\nCixs4qptx3h8cQ+lNnz80A6+e/9RDi+tMtNpoKzGyQRhLciIyJYQxbgIjDaoOCEVmsWVPjfecAWf\\n+NKdfP3OB/jV37vihz5z671/8Q8umn/G8bRHmEKITc65PxFRM0AE55eh8+2PJElrK1YX6MESNu95\\nPB6fj7DlAKvzUCiegSmwoxVc2Q+G1AtaBzxu7HlKWTE4BYhKsJjxqW+FAmfpbLuMtcNfxYzWQMZU\\nBJpwhVQFyUIQxJ9HPrIUUfhMJaqxmfaOy2ht24fROcXaIgKf63Ihh6KoIdMaSN9+yhYjT8AYzwvY\\nQCAK4aufKykRUUZzy0WIWh0h5Bg6mxhLf2hYa1EqHvdAPB+Nf1vDcU3r73lisC+kNoKKjQu1lkLh\\nRMQ2cS+uLBlke0BopNAezkWgnGf3dYo7sK6PcYLNW7ZTizMG/R4CiIJeq8Pfy0j6GkGlFI2ZWZJa\\njcVTxxmYEWeHmxBSYqTCjIboUR9RhrZm+ByRCPmq80UuTxnBSDUlmReiEuGLt0Wc+BrPuEZUaxDV\\nmqgkI6q3vHh61bcU8GUG8Avf/zx+9e2v5tl7Z3j7zS/kL/72Lv7iC/fxCz/4EiLpOLbU4+NfOYRw\\nDhVF7FpIWVqzDEaO97/zjfz1393P4WOnuePRAaO1E3z1rgM897I97LpwF489dpIbrtvH6cU+Ls/5\\ntz/+Il7z3Iv4+Je/zmiYc9EF2/j457+BBeabcwxHPQ482Q3aoQ5jNEJVBfUxT54e8PmvP8wjJ0/x\\nZx+7jf/1yXsQWQslFXGthnTWq9fgApnGMnINbr9/iYeOL7F4+H6uveoqT2iyT4XMjCc77LWJstHG\\nxz5VBAqsM5bT97b62TeQEcSR4urL9/KJL90LAi/MIQFnKIcD355LlzinvQEVlfiAV8PRRU4UxZ7B\\nGt5LJimmHCLwhkaKCn6dOACIqh4TqsbSBOg3ThKsLvxaCqgPxk5SJuE5MoqRQnmoVYhQniZC+qmg\\n6C4hojpWF6gk9e8ZxSghyFdO+PZmzuKkRKoIJROG3VOY7ilMMcDpEuG03zOBISwrkhOevw2Qze9D\\n6UNkHEMb33B8aa1HXmiaaUxRFJzu9imKksLAYJSjlEKpiIVtcyyfGbB9R5PHFmNGyQKXzqxg9Qqb\\nmh3qWUxpIIbQaACkEgxzS6lL8rxkfr5DXhq0g/mZJh+85Vbe846bX37RNTf+5jkL5zs0nnaD+Rv/\\n5b2fsKQXxo3N2HJ43oN7XEuVtJFRHT1awhX9oNNpcMXQFwE77WnfKkHJCFN2MWV/vEGnXjEYww0H\\np1SIKWNZwZbSWWStzaC3jCi9ERSi8nYntVH+eUHc2FmcjBCq5stFjME5Ta19AdH8LqRMwOUUZ48E\\nQ0SAqwTOlTgRkW26EPI+drSGC4beOhPUjcJ7jiNLD7dZW6LSOnFj7im9dxs86eq51eOmBRmcg2Hh\\nkMVxnnfthTx21GClbwjtyVQKJx312PB9N2yj/+gnmOt/k173DA3lKKIFzxmUJbP5fcj8QcqiRFvQ\\nZU6vNyCSkjTyDaaTOPJRmgVVbdkoZeuOC1jYvA2BYnHlDKeHMygR+ZpbLGbUDUIQljHxYurennN4\\nTx+4QgSjX81FpXJTOSESGddIkgaomDhrIqKMKGuhanVUFIdyGIEQCUbBVbvn+J133cgNV1xEpxVz\\n4dZNvPO/fIRTA8tMGvGZW+/l8187wIc/eTeFtojIM7c/9J9v5n9//h5KkfLJW+/mwSN9Hjk+xDnN\\noWMjfvotV5FkGUoZbrv/BP/uh6/jra+/hg997l7u+PZR3viK/ezZNs9su8WWLVu5cNdm9u7ZzWyn\\nxfWX7+IrX/82q4UKbEy/yqUUWOPQ1rI2LHnwkSX6Qy8tFwehc5zACp+qsNZr3MqQjyxKzdrQcuCJ\\nZT7/xS/zgqv306zXfbQ3NffTEXy1Uyo43FWLrdpAlRF6CqLQeK9tdHoCkuJwoa7TMlNz/PknbkWb\\naLxfbTHCjHohygy1wtaMjYU1ocRLKaw1fu8KsLrwbHd8NOcQmGI4pYdM6KZUQQxucjZUjrfWvozF\\nWr+fZVjnoduJxXk6YoiYnTE4XQRHTmJNjrOFZ8kOVnDGUPSXibOmN45BwzlfO4lzilqtjhMCXQ5R\\nMvVld6b0UqLWjBEYb+CNT19VfAuZUt99JSpKScqDtOoZ/eEQFSnqaYpEMtSaXHsVIymgX5QoCSeX\\nV7lgyyZqiWKl1+fJ7haKwYgn9SVcWHuU+XaLZpowKkuiOEYPPfGolsb084KiNIzyEa2FLRw/fJzO\\nbJvtm2f58jcfoJXF2W+9/zcf/aEf/6l7n3Jx/DOOp9VgCiF2O8fvIurCJQ2EMZ7ZyYYNIfCQWJxh\\nix626Hl2lxBQegkw4RxOe9gTmeLsCFMM/Eaqioyr6O8p9qFfOG68wZ2wRI2tAMSteUz3JBAhRGiE\\nWpEPgtfsjaB/IUeEjL3aiC9kNjhRUFvYS7ywl8hpRv01dH9lfD0CkKqGrHeI0hYqa5CfPYIwfpO4\\nkOdY762HHJ307EUZN0g684i4iWA9u9dDkWb8M0wRYoTwrFY3ObeE0zRWv8k9qxehlUbZxD9P4QkT\\nKEyxRjo8xOZZyWp3BVGs0tJHqHEa3F5yIdmUfxlV5iRZSiQEhSmopRnaCmZmZ7DFAON8flRIgZUC\\nFafEUY04qRHHEStnFylEQSNynB7UieMM4XwELp3xbEGcJ1gH6NjnT/0NcU6EwvUqXhA+54qiKn2p\\nBA79Z/d5YRmloFLiehuZtYjrzaAmVOV8QEjJRZtTfuy7ruYdP/IS5joZg9LQHfRIkog//OgdvPX1\\n13L3gcdZXCs5vjykLA1GgAo5td2bmvT6I04u93AiJsLfawcoYbn9wBE+9fXHuOPgaYQpeOh4n89+\\n/RDb59v8/i99L3MzM+zetYe5+QV63QHdXo9ICtrtGUQMv/3Hf0VJex2hSSqfkRnn4rA4qcAWSJmG\\nQ12hQv0iVvtDWfloPEpiikEfFzWwxvHFr9xKUa6ybctm0jiB6r3GeoNh3clz93e16Ny08XSMN+s0\\nhLuR/AOMFamEAFN68ktv0Oe+Bw7y+BlLJL2TbLXB5j1/r50NucOwt0SFnoRrDf/h8CVEtkCldUwx\\n8HW3gchVnRuE16m6gZwTfTrn+5AGOEJaERwuMMYjK248H14RzJa5d/gFOF34chjrPI+gLHD5KvnS\\nSWyRUw67HmUbrmKLPsaMGC2dxASnW0lB0TsDEqzNCZldH+0bi6u1fc2tLZEyJdu0B9XYTEJOpJeo\\n1WJ0bkkiSRr5frUr3QE9XZJKRWkMSihyUzI/M8PaaMhIW55Y20EcR5go5tKZRZr1hFbsG8sPc0Mt\\n9jW/KlZktZjVfkkcRaytrDI3P8eJ0ys0GzU69ZQ/u+Vr/NufuPHyy1/whj849wT/5x9Pq8F873t/\\n8zPaxrvibAH0cEqcYIP36JzfyKbElgNwOpSUVCoVZYgy/AIXSuLKPMimeYi1WoiC87crWvd+QiCc\\nIKp3aOx+HrZ3mnLlmD+AKyk1PDOv2mQWN67PEkL5foyVdQ76k84W6GIIox5xZxt6sIodLK+z4FGj\\nQ1xrQ9rA6ZJy5Vh19eNrH19jFQkhcMH4WVuisjZJ1g4cmPWF+s4an+RXDiVUKIFxk64oIcIqhSE2\\nI7aLh1liH0J4xh2yKuK3CCfYVXyG1aUnGHS7SGdwaKxxdNVOBskORL6MXXkAlVg2zW9hfmEryytn\\nKXWJcwUKRRRFDEcj31FCRd4Dd5Bkvlm0M5YRUI4K9u/axoPHSk+yEfiuJkYjxKSO0rMeA+RVzW3w\\n0r2/4clRHpWfUmkJc1DJoKm4jkozokabOOugavXwvODU+Zmllij+1/u/nysu2UaU+DzhT73nY1z3\\n7At49299mrnNTY4fPcOxs32EUKgoYfvWJvs31Tnd9cXjt997jOW+psz9gattWDNIXnF5jff/4g/z\\n4mt38ew9s9x2/zF0qbn5xqv40TddydbZBlEkKLSmu9bjkaOHOfDwcX7p/R/ipddfzr95z1/y6GKO\\ns3KsUezREz9fUki8aKHDaYMtSkzRxYwGvjTCWKSKfY2iCjl76xnISIUerJLT4EzR4OCDB7j9G1/l\\n5LFHOH7kSebnFihKLxKwvLxKvZ6tR5GmIvqNe19IUaHs4/leF5EyiV6rPeKcYzgccuzoEZxSHD5+\\nnG8/uuJJPNJ3NrFButCLKphJNOgCylQZTSHwkm0yGE4vLiKiGGdKH8nqcow0eZtpglqepGJKi+oM\\ng9DI3TscTnpSWZVDlJEvIfNdXf39MOXQpyysQxd9HxRY48vpzBCd9zCjNWy+hit6mMGK5zXYkdeo\\nLnvYoosreui8hxMlrhx66NzXzow/u2puQ1gPU6MLyv6ApLMTETfJ9AGwgigWRHFCp9FgOCqo12IQ\\nikRJ6rWYepJQTxIGRc6WhQU69YR7T3VwpkSmHXami9TJmW03SCQMRgVJmngCWRSRxjEDbRkMCySa\\nJKtTjIa0Oh0kBQ89cRolxPwHPvCbB9/69p954CkP8X+m8bQZTCHE5Q73Hieawkk3YbdyHhit2g5W\\nTz2/+u7GO0oIXyeF0WMDGccZ1ngYVco4dDOYeKrnZ+T5aKux+/kMjt6DGS1RqZ5MHsjYeI3FxPEG\\n1akIRC0YqBJMPs7jqFqH5ub9RGmDot+jHK76aw5iAk4oaMzTaLfpPnE36+snp3t+Vu8JiChomSqS\\nRgeRtonrDbz01+SavfSZYa4NxdBghfIzK6BSExICrIDI+qi1J3Zg4rpvfeQqQw3SWi4oPs2lezdx\\nanEJ7TQj4691FM2z1L4RIxVJcYTh4DSp0uSjHqNyiLWQ1WoYU5KlDaIoQpvc10c6h5U+6nEIjNZk\\nWYN6vYlygpNrpzjR3RIieF8Mbo0FXYZuF1ApPAlZ3RNf5uKjTDWGzqWolGCqg0uOfxcy8QcagqhW\\nR9Vb/u/jiMFNcH4Hf/m5O4nLEbt2bOGX/+BvueKyrXz4k3fwtjdew0/e/Dw+f8ejHF/1iMfFu2f4\\n019+Cx/6xG30cj/v1jne+NJ9HDy6xqW75/m+V1zKzq0NLt69wJ33HOCml13OVRfv5fKLdtDJIn7+\\nB1/MgQfuoru8ylI357Y7DvLhv/4Khx4/xq//10/ypdufZGAyvnbHIe5+7CxX7t3M6dVicvXTEHb4\\nLDbItZm8hzM5rhyhS4/eyCRZtz8qFjjGOyyq5uG8ganTTGG4cppYalyU8NkvfBmjFbfdeQ9pLWLz\\nwvx6w3ieiLH6+zoCUfU3JhHnOCIL/2atZTQacXaly8KmOY4fO8Vt9xzDGC+Xp4TwjO5KRCR8r6LM\\nKvobs1yFBGtD82lvGOO0TpEPiUK06Koo05op4YOqarU6wSbXa50hCk3ECW3cdDHyutMm986gUuE8\\niyhH3VBikmNLz/h3poByBDr3ZWem9OpF1qelnC6DM2Cwtgh1nzoY/TjUItt1M6kQGOe7BllnsPka\\nSWsrqrOdZHgP2hp8SbhlMBzSaDSoxRGrvSHz9RppFNNpNcjimCOLy2yZb1PmBU+u1shdEwfUaoYr\\nN1nSWuLnD0uuHVniVbuklNTSiNwIet2S+dka3UHB0vIqe3bvIhGGP7nla3z/a6+/8KWv+xd/xHd4\\nPG0G833ve9/fljrZXnnsUlRiAueLAIOhmM5ChjTkJH83URPxXrQCjM9tTsNnAjYayWpULFNETNze\\nxOjkAWy+NpVP8c/z+o9+E8jQG0+G7hJ+O0viJMWWI5+fwFT+LyKZIZnZzNYdCwwK4euwpELFDYg8\\njFuf207/7GHscGV8zdO50mko1hNWap6IknWQUULVIzaKa/56bSXB5hmLaQwjnQRuixzPfXhxhLMY\\nGaGcZBTVsUzuTZh9HIKaPkIqBjSadbTOQ/cGkG4Eokkh6yRLh1D2MBJHHMeMCkOr2fLNZp3F2BHN\\nrOFFqZ2H06RQlCFnFsUJSilMaSiHQ2w5Ii+7DNwMSmSoOMHpAqNH4bAIUb4MRKtA6BJCBZhWIETk\\ny45EFXWGCKeKQpUK9yT10FvqST7jNk5jKmz40Vq0UXzzwSN86sv38du/+BY+/Mnbedsbn88Lr92P\\n0CNe/fxLccTc8/Apdm/p8JZXXcnKoOCehxb9/YkS3vtzr+bBh4/zQ2+4liwWfP+NV/O6lz6H3/2j\\nz/LlOx7iJc+9kCdPrjA73+SK/TvZtX0rBx4/xa/93sf4yr0nOPTkMvc8eBxZ73DB7u286gUXc/01\\nFxOZgtsOHEEFRi5uyuhDcFQMxpSelFI5A/g9RNA1jpIszJX08GBAVSQCmWa+QQJwuicxcZv9O+eZ\\nb9Y4crbLh275HI16yl1338dqt0+7kdFsNtY5redzXs8h9UxBtpXzVu3pav8PRkO+de+DdJdWefLE\\nUQ4eXmOoPbRsrcHmQz8HzmCNT1OISiA9sM4FXubOow52cvYEoxzHMUaX473pAtdBSOkVsmDKuYLp\\nTyas8etPRURJgjMlQkLZO4srh153GoeKM4R1lEXXl9RJiR6tIfTIO+JBB9a6QF6qiIBW45UXqnPT\\nerSikj40FieML5eKMmQU4/TI37/IY9vCFP6+qxSZzGBaz8JFO1GyhrKLOCnJjcaEuvZ2rYaSglwb\\nlIBBkVPPUpq1GoPhkJODWZy1XLV1yK5Zi7aOuvLoWD8vaaQxzlmSJKaWxuSFZlAaGsoRpSkrvRG1\\nLGO2Xefw8bOoSG3797/0K3f89M+/82G+g+NpMZhCiEut4z852RRChNKE89iwdd4mlZGcLMGNnmn1\\n5ZTXSCVAZ4yN6rnvYbGBOQuoGqo+h3AaNzyDcyHanM6VOECYYGiqGr+qDqvKClisKf0mcSacrxYV\\n1ZC1GWpzuzFxCxdo3Sqp40XGU+oLOykHqxQnHw6pnekeBBV0NSHrOCE97GwtZX/RQ2VRSjK7JbRu\\n2uCPC8GwCFqZwWhspO17Q2KxYxJMlf8Ml2CMP1JsTlwcZThcI0ubzM5s5uzyElIJYr2KsqfJ7EnK\\noo6SI/Ki8MSGSNFsZJ48YqEoSy+CbQqM0yAUSgmU9NHjcDCgGPVRNUUnadBJNUbGdHWHSpjA6tAJ\\npuolSFW3KkDFYQ6kh9RUvA6KJRhTWUneCYFQMU4lpI0Zap2FAJO5cUTp8FCqcF5G0AExMVlc51sP\\nPsn1z9nLs/dvZb6V0ex0cM5w8a4Of/7JbxHHCd/90n3kI8ugKLjkwq08emyJm19yMft3zbK5nTAY\\nDLnkoguIFdzx0GEePy34whe+wEc+dT9/c+t2mGxjAAAgAElEQVQhfvxNL0CXAx45fIz7HnoSog5x\\nXENFjh943RX8t1/6Pr77xhdw3WXbectN1/Pdr7qaD37878d1k35UjPGQ17a+XMjh88lUPUSd80L9\\nwSA4Y3zdq7NEaRbmr6r1BSUj1oYR3dKyf5Ol5nI+fecaUjq++cBJHn30Ef7qY59CiYJ9F11EpM7t\\n+3q+M+B8/zbJ6VfPd9TiGpfs38sffvhjXHfVs7nzgeMs9ctQ3qJQUUzRP4OvfzXj2kn/Vv7cqNj0\\nzjmkUL4eM0rC7yCjGqbMUSr2cKsIBnZySEyy5ZUjHlahR3oNqIgoyXx3X1dSDrtgPQPe6QKdD5Fx\\nElSJPATuypGf+xBNuqBt6zWZ/T0TFZHJTeVTKyKi9fXnAocUVU1x4kvz0EjphWHQOc4ZTNFDOMdw\\nZBDNXeTZfiyWrY0Bg+GIUamJooiZLMNZSxxJYqlACobliE6zQT5c4eDqTpwe4VSD67YVSKVI4wgl\\nIFKS0kEsIIoj79g4i7aK1d6IhZkWthwxLA3tdot2I+VPP/pVfvTml+15+ev/xZ8+5QL5ZxhPi8F8\\n3/ve/xGt1T6IfTLdmXO8zHNIAecxkNNjkvyXRGnTq1c4rx6xHtYMw8FYhsRB3NpGc9OlFN3jvi0Y\\nKhjMSamIPzsMMMn3+UM0YlrcvTI6/npCGGItKm0RtRaob7qYSJaU+IbRUa2JUBFxvYVzEf3j901F\\nxpNJqNi009GlQCCtxYkI1dhMbXYr6cwWpEpYbwQnjoYUakz4mJ67seqRc+d0SBHBIXDCs2yVkpTx\\nFoYmRq8eYjgagowYDg1SWJJoRN31IRoy7PWJlU/qR5GkKEZESUqzPc8gt5RGk6YNlIrQ2iuWWCe8\\nsyEkRd5DRYqFziZm5rcQSUGrKTiyVsfKDCkjorSOsdbXdYmJCPjYOIaG3L67SLROzKGCs6sIcyx7\\nV2sSNb0ogec4hXpbJXHOOxXVYSiEwEnJ0BiW1ga89+dvZPvCLLFynF3q0qjVSIXj3odOYKTiqsu2\\ncfX+eV505V7e9JJL+L9vuYPFM0u89NoLmZvvcPllFyCF5wv/zVfuoqGGDIuC3Ts2cXqpz2zb8fwr\\nLuXyC/fQbNe5dO8Mr33hs7h8/yZ+8i03sHnbNqSzWBHhXMSBBw/xhW8eorA+92uDpJvPgxOiTg9h\\n+5Z0YZM4xnk4TzTJ0fnQRzEyCc2wRdBUrQgw3iE7u2p48uQq9x9e5cSaomn79OQc8+2MLdv2ovMB\\nw3JAvZZRz2peQKTKtW+ok12/dYNE3xSyFHYIXhzEN2i+7jn7+e53/Q9OrzmUinw7M3we1ulQVjI2\\nJhPQVIZIFBc+F76nprMWFRSBrC6IswbWjNDGYo1GRTHOaE/0C3lQa22IUqegZecd5ShKg7PqPNM/\\n93CrcMYzt/FRK9JHodVnt+UITOFz/RuJfUH8Pbj6k7NucopMXUcwppX4CwqV1MApjA5VB67AaIdU\\nNTAaXeaQXsRZcTnzs20GS4/TGwwx2pEmsTeCUjEY5RTa0s4izvZ7nOptwyKx0Ty7mo+zedMsZaFJ\\n0xhrDCWCeiyJ4ggpBEma0B+W5KWjlgAqohZLavWMdrPOwUeP0a7Hu372F971+Xe8691H+A6N77jB\\nFELsstb+npMNESXpWAB5HDFtMIY+qpyOktaXP1TPHf+shPfCrA6LvBwvmMoD9aw9ryMrcMSbLsHZ\\nksHJ+8AWjJu7iso4VaLKZoocUxlvQZRmgXgytYHHnp1P/oMhbm5HNmfpLGyi0NZ3tmg0PcFIRvRX\\njzI4ei8iOBDT8bVncbL+gCYwHVWEypokzXn+P97ePN7Sq6zz/a7hnfZ4zqlTp6ZUqipVqcwJSUgC\\nSUwI8xihBZEoaKPStN6mWz8Orfa1RS42ftTbjbNebzeIiiKCyCAgSAxzghDIHFKp1HRqOKfOtKd3\\nWmvdP9ba+5yqCoT2Gl6oVH322Wfvd7/7Xet5nt/ze36/dGoLUq/PBsI6JDv5syEwbpQ4m4hpyyAv\\n5yY1CFiDLRZh9QGsBRl1kUJQy5hmfZLpZszi6UVUltNKO571iiXWGUZUVKYm1gIn/HvWpUOqmE6r\\n4avevCBKp4i07/H4yRmBsRVaaqQDUwxRuoHQERmWQ4saq1M/e6cThA6EFGMC41YGNxONUD5REEoH\\neCskNTLIIwbT7o2wbNKcRmctxpWTn2lTXHj+DC9+1j5mpxooKdEahnkNwiIs1Nbx0Tv/me1tweqo\\n5MTiMo2sQSXgjldcx2tfeAMzrRgdtWm2Mv7iE19k/liPB4+skQ/XeOzQMcoczts2jVKasupx2d7z\\nefMdL+f251/De/7+Qd7wsuvYMdcFBbvP28QNl+3l0t1buHjPVrrTU2gEveGAJEmwWDbPTvOhj9/D\\n8qDysGRdh5EIsb6hhiF8X5kQRDE2rB3nqygpnO/VTcabDMZYryHsPEHIgzqW0wPH6b7wSF8U8Zpn\\nwsW7prj+6n1cesEW3v/hO/nT93+Q/Xu2M7dlK2NZPbHxfSd7gTvrMY8QiPDYWBLRIcA4/vDdf8Gj\\n84bBsPD3dGjXWOFwxRBbevs/MSYYjCHqYJAgpPSEpzHrOvzthBfHR2mqsiSJYx/kjK9CCQnzhDEb\\nZsjHfVF/aepw/3kUyJS5J9tIhclXQyIjg6B6jRUe4xAqxpkcjIdkx4F5nNGP9zgxDtqsC0VM2kLj\\nfVAIr+ZlKq8TLCJ0No3TCbZa80mRkMi4idSpf4+qphosM+yvMEgvxCbbmYuOsjIY0WlkJMpRWcta\\nMWJY1Mxu2UoSSfqrDzGQV1HEbfZEj5BEPig2Y40xjjhOKeqaRqLRSqKUQKcZzklGRU4cRdRlzqiy\\nNBopU+0G7/nQ5/ihV9x40fNv/8H/xXfp+K4r/aRp+t/yQkghErwQsRhrrQPnLhC/kYsAhaznaWeL\\nNE/+NqHZLnxj34tsrzPoPOJuQKSoqI2MMpSKqIZLoe+18Tgze/VVxSSEhMP64fmwWNc/g0THDYzx\\nCh5j6CtNZqEe0o4UUVyx1LNgS+pilXrxeKh8zjqLMzLsdXhVCA1CI9MO8dRW4s4snHX9xtfGhgpx\\nvHDr2kMpE+KScwjp/KydVUg0Jl/AMECXCzQ5xI7pmloNeGw1x7a2I5FgEpazm1gzy0R8DmEsveEa\\nOk6RwlKWI6JUEYcehXCOqqxRSUqWZbS7M9TVMUycsbi0SKfZREUN6noVIWJiFVHXlmGR+8rl1EG2\\nzO2gIOHiLX0e6m2mcGFMIopRcYZJSqTzbGChFH66LUBW0vtn+b7zuCKRk0prnKaoKMUlDf88uZ6w\\nGOe49ZrdvOlV15MozUq/xwOPn+TN/+2DCBdjhU+UVnqan/mjr7BlRnLzlRewPIDdW5tsm5lmbbDA\\nsDbMtRWjgeW333MvH3jnHfzsOz/NR754ElOssmlmmjS6CITkw5+8i9pFvPpFN4JUvPSaKT722ft5\\n5mU7EULSzroURZ84TulkCbFMWRvkPPTQAzTjBnPbt/EHf/b3HDs9Clrh3k7JhXnCseEzUuKMV6Op\\n7SgwSQ0b7z6HxVqfYFlTYnPjDRHiFKIoBFxPJrFB4NwZAzpibeT40D2n0W6BZnuRvs0YrQ156a3P\\n5A//+A/50R8xXHb5Zf4ekL6P7JUenzw5ZhIUfA68vlb9TOOOHedj8ocQLoaq8DOV1iClQLZnKNYW\\nkTrFCcs6FVd4aNWBNXVAHzzkL7XXdpWxTwxcXZJkLarREN/vrcPrB5u+YIZubOUTkzCj7Rw4ITBV\\n6UfPLCFJ9r1hJRW2Gvl+ptDIOEGpGOOMrzadD2LOCWQ1wrmgOiQEUiRYGwTVxbpbzzjpc24sAiMQ\\nGFxYO8JJjxYgvZrVWFMeQ5x1UJ05pFRUo76/f4RjdPIgbnobx9ULmU0+Rl6XWJoIY5lptFkoe/SW\\nTiNTwcWbNUfmDzItZlhcXqU0PbZNzzG1dxe1sJiioA6qWi5U/IkSxHGEUk1azYhTZc2gP0TriEv3\\n72TL5inKsrp5bqaz59TS2sFzNs6n4fiuBkwhxKwQ8g7VOA9X9DGVZzMCZ2zcY2hw4+KYPLb+Whtf\\nd/L3OjlGTsa/JqMCgVErVEQ6fQGi3aU4+U1Gxw9NINTwiuvkgrE7gKt9gGJjV3DMUiU8Z/2nTgpq\\nM3YWkF7LueqRrz4BeieWhKL3KMoaypXjXiR9fAqTNxBnnNfkPYUI9l0apyLS1jSq2Q2f9czDOetH\\nQDbOXOJVOybVsHH4UR3f33NiiB2eJB5+kd1dSafT5NTSMicXRrQbKREDRmWFSR1Sx9RyM6rfR7Zv\\nQVWfpLIGm48w0mGsoy4NkYqY3jRFkY8oa6htxdLyIsPhgN7qGoqKKMmQSUIsk0DZL8nLikhHxElM\\nZSzV2hLD/hJzm/ewby5CdwbcfXgaKYwXnI8zImupTOU3aqlCQhbGTJydaL76JEJPyEA+efPzmSpr\\ne9beGEnA70kKwfHjS6RpAni3jKv2buZ3fvp2/uCDdzOsFI8fP4l1ikRa/ssbn8dtN1zm0zRn+OXf\\n+yBfO3Ca/qDHdDtlOLKMyoI3/cpfcXzRO4QQZXzqK0cpy09z4thjPHxogeuuupQiL4jShHf8/L/l\\nste9k194/ZDcWKZmunzt/se4+rKLkUryxPxjfOmrj/GhT36Vrz66SKMRU5PgbIQ3fCr9UmA8TjL+\\njOvJp1BeqtIJ6825x/egY0NPHURtQBOg2hydZgGKrEPvr/LXUUXgHI3GNP/5jmfQmZ3l+utu5KMf\\n/mt+4fcfxEWbWHrn7/LaV7+A137fGzBjs2anJzf1k/Uzx+paYxzHhvWvteL7X/4C5hdW+a0/+wp2\\n/N1K3/cXDp+cOmCSBI9Vd5iQu5w1Hi2RKpg4OKgKbxjuAV6U8izrsvLuGxNrMKFw0vcynVs3Jhi7\\n4lhbelasVdTGV/VCqkk7wqNUNXY0woT71wd3vJxc2sEmGXXRC4p8OZYCK9WkWJhsCs6e0Wbx+YH0\\nARgD1lBXI6LWZoRUGGGxwie5DqiEJUs6yLoK33WferhC1VtA7rueKn05W+IvUSGIlUSYmu2bpqid\\n4gvz17CpvYxONnF5cid1pGhmKStrqxycP8me7Vs4tdhjcyvm1FLObAvaqoOWFe12ysqKIc9HNBsJ\\ndVly4sRJLty/hzu+9xb+4M/+np974yv+ErjhnJvjaTi+qwEzSZKfLiopTOntlbwwdci4NhB3zj6+\\nXeP/3OcGuBH8DQ+hH+CzrU53K0UyTfv8q1i8/++x+SkP2W3ohYyxfZ+RCRxmPVg+CfV9/Pj62Efk\\nhRaUwLkITOGHhasaO1rB1XPE3S0IJKPVeaJsK8YNzvwgXuOL8YdxG66NTxjdRIFEp5knJNiwuYn1\\nCO6s8Zvfhn7PxnN2wuC0RBiLNQZTHGeu+gxbGhErbsjSwojVeJFKOJpxTCfNmF9bJl75BOXm23Cx\\nAFMzO/ooQxxKR6RZwmpvSFFZ6tqRKE1lDKvLqzQabcpiiBCS4WBAI21RlSXNRkpv1McJR9yd5cJL\\nr+fEsYOsLB5HSkFeViRJwsz0NpYWTzAYLROvKNL6FPs23cKxnqQsC6ywyCRFjWJM6DFK6bN7XxXV\\nPkgI5TdNIQIM658jg8eljJue+TyB3x22GnDVnjl+/edfibD+mrZbGY8+eowrLtrOe9/+OoSKec6P\\n/DarRY2Tlre846N8z7X3YuseX7p3AZc10c5v0ItLOSZsoMcWLEY6pPN90q8/+AQPPPw4zXaLqLWX\\nux9x/Pqfvp/NM3t5/IkjxKbgxp94FzOtmO2zbe57+BjX7P8iB+f7HF0YEUUKrSJ0oxNaD5YiHxHH\\nBiMVu+eaPD6/hFJpgDXDOITyMKSQGictoILRin1S7oDFIq0AfH/TqgiVxJ6MWZdBkF9MWiRHVhW/\\n+4Fv8itv3s173vUuutu3Ukbg7CzHhqt86c7P8P3f90MIBMbUvhq0io357NlkQMY9d7GeHFsD80s9\\n7vrsl/EtDOXbAWL9XpBRhim9ko5TFkwowcbV2Li/GcyfPUrhR52UTvxznUGoCGNrdKSoinKC3Fhr\\nPPTvwDkZZlfH85mAtZhiiFVRUPYJ+4gQQTgjwMTWgqsmouvCOIwMfU6hiZKuP9cCtG7iK12LcxW2\\nGCGjxI+dmDIki4FXISKkThHCJ6dOlBRrJ0jacz7xkAphBMXaEaa2X0YUpVBrqrqkFho5PAkqoVpd\\nQE5t5mCxm6uSAxgjyLREC8FaOWTBbeJkL0WoJmZ6D6L/MCsDwd7ZTaz2BmAdVlpKYxmNcmba0whb\\nU1SSyuak3SZ20ENIgdSSuW4DKRzXXrqHSGsGw7XrpRTC2g0zSE/T8V3rYQoh2tbxUUQmhIA4m0XE\\nUVCyEE8ahMbHUwkNPNnz1oFL53ugIiKZ3ksyuwubTDN47FO+VzDeSMdM0Um/dNw2D1Xptzk//9j4\\nTcc9BIFDe3ab8xmcczWmGJFM70K3u2yehsHKGpWtkK4+E+kVZ/Zp2XCN/PXySiM66xJPb8MG8XEp\\n5cShISx94EynEyEEWDCuRAWpPwugICn7zNiHAoxkidKYvKpp6ZgsiqiEoBVVJAkM9MVom5It/DGV\\ndUy3uyiZsbQ6xArI85JMae9i78A6SX/Q9/0ka7AOjK1QSpLECVJIqrKiNxgihCJpNCnKGqE0s3Pb\\nuPLqm1nrD2l2uvRz3+MYDpc5L1tlqzzKsjgPU2WgHTpp+ETB2UDyiX0/U8cehlTer1RI39cUOkbq\\nGJE00EkDNdGWBWcddVmw9/xZ/vw3fpRIS7QaW3k52u0mjSSltgZjHEkMn79vPvRWJcdO9Tl6uvZj\\nQ8FL0RF4tq5AuAhLgXJ60qcWMsLKBu00I9KKQZ5z5Ik+dz10nIcPL4BOoa4ZFpaF06vUMmJ+qWZY\\nC6Ik8RWEjkFIpqYyXvOSK2m0Bnzwv/80b/o31/PGVz6bH3/1czhx4jiPHF0Ot324T4QMe+p4sN2M\\nFxjroxzrPbNxPx0n0JHGiTH0KdefK7zYgZARiz3DJz5/L4eXSr563wkWhhFKDMirBsunH2fX5hm2\\nbt2CjmLvuqHkGb32c48N5zSOeQ5aWcKzn3UNL7pxJw8dOMbCsp9xdEJBNaQY9Tz0HqDjQJiY7Bzj\\n1uA6w5TQ+yYE6cATEN57s6prLzLgLGPhAsL9I8ZGAD7CBxayW5/9JPRFnQUVYYIoi//RuMK3OFtR\\n2QLpggCBKRE6AlTYS8fSnCCNw9gCdIYUajLn7MCPkkmBUF7f1lenCkGNrQtcnSN15pN2U1INR4x6\\nJ6lGfZRMEKKiNIZqeJqqt0RVFoyi7UynAxqZpVcVTGcaV8PRwSbKKkNYOFrsZH9zge2JIM1S8qJm\\nqtthkOdMN1KaSngrNSlZXF6l2UhQkaKoHFI6IilYXBuBgzTL6DRT3vfxu/n5f/vSH7jxRa952tV/\\nvmsB8+1vf/t/sk6/wAusg61zTJWfASM+GSvOhqHhpzq+ldnsOGA4V2GGi9SLB6hWD2JrL6q8XpG6\\nyXPPfGEmC/VbL9jQE5M69DlDz1BocCXCCTwUZkE3kVFEc3orWWcrJi+pRiNw+QZIlzPPRYyvSbg+\\n43lCqdBRDE4ik4QJdT1samd0Wq1FqXVY1uJQ1Sp71d0ULqOki9SSdv8BUrdAWRmclAyqinYrRmjB\\nUjFCRYpGltKQhlb9AKp/N7H0DjNOSNaGfYqixBpfm/ixFReECHyGLaWkrkpUpCY928LUfnPEEEcQ\\nJzGjQUGr06XZ3sTMpi2cXukRpSmnTp/g2huew8OP3It2IehUJQv2PHKnQiYewPgoDsorDkJQVFHi\\nq0gd+b6NlEgdo6PUz8U5JuxDpyT18Cg63cTbf+wG9u7cQRTFQa8UjHWsrCyzaXYWrTS1MFy0c5oP\\n3PUAReUQRKEXp0BrtNJhMN0Tj6TVATLWWDG+1/wIixSGay/fxqXnz3L/E0tUUvHGF+7iV9/0At54\\n+3Xs3Nrl9udeTH95lSMnezihefZFm/kfP/difuK1N/HV+x5la1fy/l/7Qa7cMwPWUJmcrbMzpO0O\\nQtQ8/+YrGfaX+caji0glA6Li0M554QZbh7txI9Vyw90lwpwr4z4invkug7UWKsB6QSXHgbWOipTl\\nQcGpUQzlgHZ5iN3p/TA6zl1fPchfve9/8eA37uF5z38pY9LM2fvDJEEW66c0WZ4hOk21mmye3UQx\\nGPLFrz2GUwlKOPLVRb/qgxzjpMHHOvx8hsKQC6pIGxIpEdagJwN5pxRrDNZ4hruU2v/bWa80JJiQ\\nhsajKH40ssYJ5Q0WpNeoVWG0aqMik792hkZrG1FnK7o5g4pTqsEy2MILqJsS43KvfRtnXoqvypEi\\nmoix4+rwGSKf1Ckvh+esQ6VtbDlAqASVTvnzqQa4so+whnq4gi0XcfEUuh5gR2vYeoAohlSmpt+9\\nEVPCwdFuYvdN1vKCLG1wquyCqzB1yTFzCTtbR4i1ZTDI0VojEZ79nvipibK2lFbSTDUOR9psgqkR\\nSlJVFh1FGGu58IJtvO9jX+L6y3fP3vSi73/rk27O/4rHdyVgCiEShPg0IpPeXUSj4tSLG3/r3/m2\\nMC2cW3lO6NRi3PtzjHmefkBdYse9KikDOSjARRswn3Ef9anef/xcWCc5MIZv5Rge2kCacAYpdSAF\\nxEy1a/pFkzJfRdTgqP05j7NQhFc1ZkOwFiL05bxKz2iwjIqbRK2Z9aAeahhfBaxXneuB1OG9Imsu\\nTB4kY5lldsLq/TTdN1DW0Gq2qcsSIaE0Ff1R5aGgAEEtD0dUVUW33eLU6TXK0jAsijBb6lARKC2Q\\nSiAjQRSpcM0djSyjNjVS+8BvTO2tl4QXgpjQ/k3F2uoqa2unGQ0HDEcjP/PVnePkiXme8/xX4JCc\\nOPEYadYgbbWYH7bDxulZr+MqEhXIEzpGJamHzHUEURR6bDEq0h6KCgHWAeXSYeJsK697VsJLbruO\\nJEuR0kPMJszdFUWBtY6ystz38BE++A9fIlMRJweWyuSeWOEcAukdQ5xj57YutoTbbtxLGlkWVvJJ\\nZRklgt98ywu57YYLefG1e7nx2t28/1MP0Iglv/oTL+O8HdtIYsUVF+5g745NfPxzD3HoZI8r9nb5\\nk7e9gW4j5sCR4zzy2Cl+6UdfwLDOmeluYt+O7TxxcolTJxdYPj3Pr/z3P+fo/BFa2TSXXLKLg0dO\\nUciMsRi4jBIvxaZ8peo1ZPVkLZ0dwOS4u2ktSvmZVi8V52dWvV6rmtyLtfGQuMtzbNogqk9S5TnV\\naJlepVg5dYBPfez9vOxVr/cjJFKc855nrIszEBomMG5vrcfePdt47Quv5p++eC/LgxqT9/0GHM53\\nrAkrxrOnuMCe3ZBCO5BiTDISk9lIhEAJgalyUAkYPxPsf8eG5xFGb3x154LgiXfZcb6XKT3j2xl/\\nvTyEGk8E832wdajmZqzJvUYBAhU1UTrGClBRA5CoqONnKSVINNaVXhbQ4dWxxsbZQqIQflZZSKLG\\nlHdjstZrd9cjCIIUbiyYUNeYcoStekFAocTaGq0bRDO7OV0k9JZ7nEhvpKEcpZxiaZBhTYWsampp\\nOWlnuUAfpZ1kLPTWaGYNamNppXHQpLX0c0Mn9u2ytNVCRxE4Q5LE5HlFs+Wh2WaW8OE7v8ajd3/y\\nhhe+8gefVuuvpy7d/nWOH3BOahm30O1ZojgJgun2SZ98RoX4bYLVGT+bZJsboVMPMYyf6+2u1AbB\\ngfHIxZmX4exe31MdnmnmSTgeEgoVF3aS+o7FvZ0tqfM1qt5pBr2SmU0O3W7jVCB/C7me2p4dLMef\\nw8kJrNLsbCZqd/zCEF5lx7pqQ9Bd/0xj6MwJRy3BqoRTZYsT7lJsfpRn7VhgKmtQlBX9fEAjTZDC\\nUQd39NmZJnlRcnJhieFgxGCUM7/gxZx15DNEJR06FpTWUFuoaoszIix60EowGPV9f9dYqCyJ9PBy\\nGkfEkYSqoiwrBnlOXg6JlMDUQ4rBKU4cPYC0A/q9Nf75ni9y8sQ8cWMLC2sjLujm7O6OUEoG+zM8\\nbBanRGmG1g2UinAolNaeVav9H6EUtbVI52F55xy2yJFxg1vPP8b5W+ADH3gf/3zP3awuL3kvz6rk\\nwKHDLKyu8bvvej8f+Yc7qaqS73/ZjfzaT72Si3d0cYWZVChGWKSrffVaFbz+ZZfx9n//At71X99I\\nGgX4Xxb85L95Jrc96yJe8qz9XLB3MzPdFg1bM9XO6HamUUrSbDaJtEIJzZfuP4QxBa9/ydVIYUmy\\nlLvvO8TF+7ssnD7JoW9+k1ExYnW4yjX7d/H+Ox/lzW/9O0Q6y7Mu2ccrn/8M3vK6G/mr3/hhXnbd\\ndpyQVNYi0ia6M03cmEZ3usStKaJWF5W2UGnTJyHB1USE+004C6agHvaohj3KfOBNmaUOM43rM9dS\\nCFxVIRtNjJqmkLtpxSlCNWi4AlHXLJw6wTv/7//CyVPHKStDXdcT9vf6+nMTJAi/YibXXEiYmpmm\\n08qwwvLK5+/3oxpCgE4YO+Qx7lcixv8Pr3XGSg9oSU2dD6mNwZVDv66kDBXoxhEwPPM4QNR1USII\\niXjoGY9lGZ2IMOXA9zqdxdkcoRXO1SRpFka9FDKe8mxxY73Hr6mQeEN3ZOzREpUgkgQTKlipIi+H\\nFwQLQKDGYh62wtoKkXTAjWXzfFVs8mVclXvyZMCnpfAjclSr3n84SI1S51T9BfKleUZHvkZ+8gDF\\n6jKPFJdRFRWjlZPYUZ/aWSgqVgcdDg+3UFmHNZbFlWWWB0OEjjFxi8oKOrEiLyqK2lIPewipiJKM\\nOIlpt2JwNXVtueW6i3ni2CL7dm1+vvjfIbz8C47vSoX5a7/2jjuNS1PSGdxwGVuPEDL1nmwb4JXx\\n8VT9wrOPcaN84+/IpIuMm8HFIkKIQDQaZ4X4KmL8a09FNjoHBjrjcTm5oTz8u642QyAQQIBVdIqM\\nmhhb0elO8+xrd2Nlm6XF0xhnkHYcIKRuMIoAACAASURBVMdD9GeKDkySAiFxKKSKiVvToBverkko\\nhNCB5PMk86rCV5jKWoSTLIvdjGybbPAl3PAk0pVUVYWUGiUdRW1wCrSCvHaUpfFwJJ5ajhBoCSKw\\nEGtjfZC0PukoqxolBc3MS8vV1o+3tLIGyjlUJIkTTaeRESuNFJLSQVVVIMbzmDAsCoqiAFezsLhA\\nGmviSJIP1ijrCoFgNFpl9/SQVqvDiRV/7Y01uLrwsl9jGEwwYQd7BRsDWBRqnVBl/PccRTFbt87w\\n3OuvYPPcLNu3nccwH9Judxn0Vnni6Ane9nt/xw+87Ca+57orOX/HHNPtDitrKzx4cJ4fe/WtPHjg\\nOFIJrrpgjmOnejzjwq28+213kDYjpltNlpYXePdH70UKjRaaYVmyf8cMW2enEELwj196kIGzHD3R\\n446XXuyrf2MwxnDXVx7gw3cdQNqC//rvX0Kn06YYFbz99z7I62+/jfO2zZEmMc1mE600Ugo+cec9\\n/MQbnseVF+1kblMb6wRx5J1cXvQ9V/L4ww+y+bzz+E+vfRY3Xb2Hxw4do1d4rWMZae83q4J8nIyD\\nlF5AWQA/LrZetTnnENai0yZgw4yun7dFiSDoDkOa7NtiqPsLdDJBs9VktTfk+PHDfOKj76coLVde\\nec057ZGN62P9Pl/vawq8mszi4mkWThzn4Pwaq4Wk6p9GpS2/lgLCMR5p2MgEnoxnIfy8tfSVjtae\\nYa10hJ3UHtYnAkGmUch1WFUEJSnPWPVrZ/wzOZ7brHPW5yb98+uqxKtU+Ws1FkcXnvXniX7O4GyN\\nMSVKCFw19GIKSlOZ4NoE/nel9tWyin2W7QwySrwYfZR66T1TIXUjrIWxRnO4xk/WnhICrB/pK1dP\\nUA0XkaqNrSuWqxRhBl6kGok1JabMOa52sys5jLAVsVI4JVHGsjYsyJKYJEvIIoWT0ouzS4FQgkgH\\nIwDrZ9+lUgxGOQ89fly99OZLznvRq374787ZzP+Vjqc9YAohrrWWtyBbJJ3tmNEiQifIOMHV5dkp\\n3LlQy7/sXWnvvhFT9kkaHUy55nsr+AktodMJFOLG1Zxzk4H+pyIhnf2Y2DDnOf6Z33hDdbmhH+lc\\nhdQtpFCsLh2mVzbIRUqtEhh5Oapxz3LcJ/HHOBsd//FBX8cJKut4uytZIIjO6Qlv/PeYxIQQWGXA\\ngjQ50+XXKPIeWki0jsmLgqKqqX3yTl4YqroCJ4iUIoo9azhCM2m9SumZfc6F/p5FCYkxlqKo/JiN\\nA2ctWZx4EpExFGVNXlb0hiWDqsAGMQvrHLXzVYVUYcNRElMZbFVQlCNMWaCEYFQMsXWFq2tkuomj\\nw44PhsODIAxCNn32jZ4kNv7bCaLUY+UbJbxWsLWhZBcsrFl2zVact3UL23fsDH0szTve9Y+868MP\\nct3lu3juM/cwM7sZay2nThxlfnGZl99yNZft3cp1l27nI3fdx2//51dx7OQCTia88nlXsLmb0B/0\\neP0vv5/+sEZoxTt/9sV88NOPMD2luHL/DkxlOTi/xHQsuO/gCq+5bT8qShgMB1jn+I0//iCH5ntI\\nSt74qpvI0ozhaMCgGPE3n76Xlz/3Wrrttq+snaHX73NsYYWX3nw1phpQGYsW0MiaKCVYXV3jpusv\\n45r9m9i9rcvVF25n27Tiwt3bGBYl+86bZXamzfHFPippIrXCOj9aYesyqBNN7naYbP6gkhRn/OgF\\nAX3ROgpsTz93Oay63HT9+dzw7FtZWlzg1MIiqJROq83uCy5kz54LaDbbk0174709ud/dmff7OAFt\\nZCknTyzhRMH9h3J8685Xv+MN3zNhzeTsN64fJ/zSGdu+ieCYJJRXPJKYiSG7rSqUkB4CDZJ0QoSg\\n66xvQTjW0a1gXA+GsdWYr0IDDB7mI01VTOTwrK1CcDaYukIrMUFWrK1xzoQAC0L4zyXHkqFCouLU\\nj5HVte+lRok3ntYprhwwFlIQmAArn8uy37g/yShFmAKExhVefMGUA6TIqKshtn8aK7ybj6kKkqxL\\nIzLsbI1I44RenpOomKKuaTebaOeohSCRUDmLlKCkRCrhWdymDn1fyY4tM/z2uz/J/3HH86659Nkv\\nf+s5G/a/0vG0j5U0m81fHIwsyfR2quWjIARR1qUYLKxruPKdV5JPFbycAxE3sOUy9XCBouiHm1JP\\nqkshI5wJ4tEboNwzhvjPyV7PPZeNP3fuTCh5/KCQHo6U+L6pqQvqakCsJdSCIwfuY/bi59KZnmNN\\nZJSnHveByW5U8phgIjCpPP34JMJDM9YEhSJhzwjQZ5+vE15tRyDQJsHIGjt4BMWINE6xTjAKQ9qe\\n/WKp8ooklRghqUaWRpxQihopNFI6LGqSkcdRROUMxlZeGEA4nA2QrPUQeBwr8qpCJRmDkQnjA4AS\\nJCoikoKq8BV5XVukdNS2JtEJxvjNJ5KC4cDbTzUaDuUqdKQYrCzSyE5A3SXOH6KhjxGphNx0cFbS\\nt3tR6bT/XpyHyF1dYSnBxUSq4yt1u65FO6wtR4/12H9eTm91FR3HfOwzd/Phzz3BT9/xDL73lisQ\\nStFb6yGloNHI2N+dopE1sKZkqqn4+R+6mWG/x0++5gZ+68+/MCFZpEnGyZU+iAhhLddfcQE/84ac\\nvTs3M8pzXF3zghv3840H51n+y7vpVwKzusQvvfNDPDG/wIlTOUjBdFuRZSlKKRrNFm/+gZfy6f/w\\nO3z6Sw9yy9W7SeMY5yzdqSluv/VyTq8s0O22UFKTj0asrCyTNTKEc/TXlrlw1y6OHDuE7bbZuX0z\\nF5wvufaiLezYNs3M1BQ/8+vvI252+YcvP04kJLiMvL9MFEdn3XdBDN9Z6qokihJcMG4WSnktCQEE\\n5vBSFfH1g0Ma8SKnlryt0+zm7Tzruuu4/MrrGY28JBxIrFif2d6wGZyxhiXrgVNpxWWX7uPE6hKa\\nQ9RJi0g1qPuLIbQHVutGytxGrhMghPKJmDKAQiiJrYbUVU6UNMAZhI6xRT/sByJ44noSmR3PdQfh\\ndKES3z+0vkI31k6CIMGYwif6NWMymJLasyakwphgdYjBOomwFiEdSnmymXO132edwrkC8EpMpvbu\\nJkLFyLiBsyW2HqJUgpIa096Ky1eCxuyYBe1Je2fsceO9RQhk2vHetrbw6NLaUXR7J6U9jBUOm/dQ\\nCKRuIoSjzkc8MtzFVa15Op2UUVmzOspppxFrw5xGM8JGEb3ekKwdU1XK3191jYoyaq3B1iilmGpn\\nXHHRTj5770F+vNOYW14bnnrSjfv/5/G0VphCiKmqqt+NauPyHiJp4eocUw4YW2GtV17nEnjCa3xn\\nAWxyZwuQESYfYkfLeHNgFb5yh0w6OKFxppjg+eENJ4vtO+6bPsnPzq7uZBAE9ytP49WVDUI3EHEL\\nO1pFd7Zwzb4Wu7cbjq12PYNNehq+dA4XssxJVi1jTyUXUBcj6mqA0Ck6ak3g2o22XpOgHvwFfQB2\\nOGGRGMpaYMuKSA6wylCVJVoLnPKfJ0o1dV0ghEZrRWGrCdRk8Jt+PiqweMKPkz57TiNNbTzcFCnl\\n+yx4BnFpLEVeYqSfEoxizzB0xicxsVK+XyRABzqJtf5bro2htD6bL43FOUMsJWmUgBa00xhZL3Be\\n6zjbm4a5qYztsxmbpw3HFg4Ty5S6KMkyS6ZWMWaAs5qtTUVRSxza91gBmw8ROuXHX3crSZrxx3/9\\nGfojx3s+/jB5ZXjF9+xl66YuzUYafCUh1jEm9OdXBzlRFHPJhduZaqRUheFT987zipv3UxR+pOp/\\nfvg+wGFkRH91hTtuv57Z6Q6R1sjQm56davG3//g5PnfPN3nDy57Lcn+Fj3/hFLUIRClpueOlN5Am\\nCVpptNb87V3f4Kd+6DlkWYO6qjDGYE3NzMwUtnZURc72rVvpdjuUdUUap4AjSTLiVDE3u5XRqGB6\\nqkN/WKAlbJmd5f4HH+eCvdt5yw/cwjMu2sqHP/sgTkhMmRM1Op4XwJjt7f8jAiNVxYkPjc7L1J1B\\n7gu/tZxnfP2JAdvm5rjumiv48R9/M5dc8UyuvvIq5uePkVeGTrs1GZ86Z28IbzpOG0XYH5RUDEc5\\np1dWWF6tOXRiGZlkjFZPeTapc0HZSHg4WYR9AV8JOufJTBYJde7Ztc5RlDk6SnFCoqIYWfuZSVP1\\nUTICpXy/3j/dk2/sOIGFsf+llyQ0E0hYhmrUBpjY2nVXFS8p6nwAC7PFxhi0ijAb5EYJc6UOUAKf\\nJAZOxRgmnkh5mhKb9xE6Q6VdRJJhi0FQQPP39qSfCZNkQgiBTrrBg9YGndvaX8tqgEo7iLrA2cr7\\nFKtgjg3I9jZ2ZifpxoAU9EYjtnTbDEYlU80GKDCVIU0ilPbomdLBC1dGnv0vwThJEmv++uNf5v/5\\n1R/52f3PfPHTUmU+rRWmlOoXrNP+Q2bT1PkA8MapwlYhXH3rIHV2xfbtg+UGzcSyR133A5ThnwGe\\nJi+lwlVDxgon436WkOdmTt/ueDJi0jnVJQTGm8RJf4ZCxjgzxBQrqKAV2Tv0ZR7svhhHQtzyg8S5\\ns2gdY8sCUeUIIX2fQcfe4aDoI51Et1J0s4lqzgTwV5wRLMfn5RfFpBHhr5cDXISOp+iZvST1YWZc\\nRKsTUQdLMmNrRBJjjCc0VDhsbbEVKKkoipp8tIZxFmUVSIuwgjROQFgiKXEKTFXhRVu8bJiUzn9+\\n56FlpdUEFatr/52JsEEhhOdpSIFxjihVpDKirh15UVIODSaJSVKLVJr+YJnNSUppHHmZkMYRm7ub\\nWVo7zo2XzDK3pcWwN2BTO4VkEw8ePM7XHpe09Wli1Wa+mPN3krSIOEXUNb/4zk8yyB0qivjUA/eh\\nhcI6y7s+/Ag7t86ytJrTTDRppsmyDCUldW1oJdozs6uKvK7pdlNiBcZ4qDlOErLUMRwKnKj4qzsf\\n5sDRPr/0pls5f9smnBX0RkPSJOPAoSH9bQ1WiwG/896v42RNVYxI4y41fqbO1DVJkmGE4Pd+6Q5+\\n/X/+HZdcsIPvvfUqokiTFzlCwJa5Taz1Btz7wP3EkSLPC6687Eq01jRbLQyOPK84cnqRuU4XhSFr\\nJvTWVmm1JWnlOHriJPd87RF2bmlybLGgMbON2hi0M9SjgReEmCTEIExF2V9FJomHta0N/Lbxyh0n\\nmA4n26yplL0X7KDR7DJ/7ATNrMHKygqPHzmCUoLZmU1kWXrufjBGU2Cd6SoESJjdNM1Vl17EZfv2\\n84v/46/56iNLtDfvpFw6htFRQFWqEEhM0FR2GypAT+KSQbigqj0px1UjH7zijKJaJmtOY4p+EMXw\\nZgfW1kEJyCc5FhcC2ngGXOGk71EK4UKCOZ7FDL1P6xBaesUhW/uRFFehtfYawbb2Pb+oAVXud76Q\\nBIydV0SkvIWYM5jaV51axJ6QY0ts6eF2hyRqzlL1TyF1EgqdAKejQVjiOKYsKuqyj6zXiFs7KIpB\\nyFksxpaItAOrR6gGp70TivKBr1qx1HO7OJHPsrU6QSvLKPKShZUh0pYcS2J2b+6QNjKkNRTDIZWK\\nEEIRRaCiDKciHDVJrLj6sj38yV/fyTcPneL8bTOXHj6+9OB3vKF/h8fTVmEKIUSSJH9VuySLkilP\\n9nAGqLzyvbOTDAXWIU3/7/VA9JRjHQQmWJC1UvEUwuaMjYPXnwWoGCkUpu6zXgtuCH4b+phP8dnW\\niQAbzu9bQsZhmHlMq/fkgTxkeBpRVThpmZrbSr8UKCFRWoM1SFOAUkSNKWTaDt51NaYYQOTnCqPO\\nNKgYFWYcz7lGG89TrD8GYESNc6Cqik3qMJmqIdLUgMzabJ47j+OnjpPoBB1LiqL0/Y8xxOwcpanJ\\nQk+nqi0OTVUV1FWNVjoETa+r2VC+T2KMCVAOOFERq8hXl9ZXkNY5Yp1QYzDWu19YoLYWYz0sK4Uf\\nSUkbDaSwNLRCBe1Ya0pqE1G6CiUUs7NbmJ7bQdGvuOSii9k8M82DB4+yuGqIJExFQ27c14Y448hy\\n4tmNXvMNIQRlVREFNw2FCui45LJ9cyyuDGhmEft2zVEUuXddKQqEwJ+PtVR1zWjQR+qIu+97nJuv\\n2UOsI7TSfOaeh1hcrVBGIlCcWF7i3keO8IpbrkRrRax8hf+eD3yGW6+/kpuv3sHj84s89vgRdNTA\\nyQgnFc+6YpYLzt+Ftd7XstHIuO3Zl3Pw6ID9+88n0oo0zfz3rTUnFxYASbfdYeeO86ltTSwVxlkO\\nPnGY88/bQavZIopjmk0vebewuMCm6U10ux2WFpe5dN8u3nD7tezcvoV/uvthlBSoJMMM1xAbhMg3\\nLFivKlVXmLoISZuHbsdSdV5azrIykjz82DGWF59guttk+/Yt6CSmEWnmTxzl1NISiY5oNBrr5Joz\\nNwcgqDmFf+tIM9PtMD3V4fbbrqYoe9zz6ArWFNiqZmy1gAtm4W5jIrxRRtCLDkRJw/cHZYSzJcZa\\nsrRBMeohkP4zBTEIMWmv+CRQIrw3pgtiAoz3wEAccOsImHeRqf34CMYHTiE8BCt8e8G/dBQSUS/Y\\nMp4vH1uYAcF9J2hLCxOSBIeTXjnI2ir46qY4JG5sJ0YYHRIgRISKYqqqII4zrKvR6WZE0sCUA99S\\nCjm6NoGxXg9860FrXOUrzihpI9OM3dkJprpTKAurxZB2IyVOMk/uSxvIyM+LVjbyLS4pUEp6tNDW\\n4Zwko6Lmy984QLuR3PXGf/eWB55yM//fPJ62gPnWt771xXXtfkzoFtYUNLacT9VbxInQwJZ2ErIm\\nLboN1ebZ8Ow5gWgcVBE4lXmhgHgaU68gnJ8pmlSWAdZUUeptcly9IfPd2KT4zolGT0YM2gjJrr+2\\np5CLYBHkl4OXpxLWgo5AKKrhADm1kwv2dFgexijpsAiMccGlIw5zbTXlcJmkuxUlvUZn1OyEqvLb\\nn/+YuDD+Nwi0FYh6lf2t+7lk3y5IW5y350KWVxfYvOk8nIOlpROk7QylEqwT5EUBAqqqxtSGJEq8\\ndZLyPVQdJmNiFQGOwvg+ikRSGZ89+z1J4kSFcopYJsRao4RAxzE+6/azjdY5L3knFViIxgmKdVgn\\nqGrfv421YmrTFophH2sq1vICY6CZRAzLmsfnD3ulkONHOL1wkjofEFWnaLg+qjqNEoKHT7dYrTK2\\nTkteetMeHjy0gB3rzTovrefHTixJJPijX3o5L7rxKrZvnkJLR6R1cJKHOI6JIq8IZIwJUJLgyt0z\\nNFotryBUK557wyW892P3BpMAByhOr1b0105zy9X7+ZtP38dffuJeDi453vHzL6OB4sZrLuAf7zlI\\nb7h+X+3opuzbs52jJxZppBnOGfJBRXu6weZWk1a7idaaJE2pjOCxxx+h2WhQlpbKDBis9LBCMhyN\\nGI5yDs/Ps3/3TlqNBkmS4JylqipOnTjOli2bsaZmqdfnj/72Hn77PZ/G1QIXZz7o4tV7XVBYAhGg\\nYz9eJQXrY1PS2+NZ55BaAwYRAtagTvjGoSHD5aP8yZ++DztYIsk0n//sXfz9p+7k0QMHSCLFjh07\\n1qHZDW4OZ7dIxobhY/h3thPzqc89QGki6v4CKkrW4c7QClhnvRMCERMtWicE6AxXl14vV3pHG6o8\\nqAoF1S1bh/cMe4KzEyjUX4UwyuR8Uk14DGv8OQT/VesKIEYIP+9tbO11bV0QmFCxnzu2FU5I/wmc\\nDYiJF+swdR40gf07e8GM8dypClKSApV2sGaAqwucLUIP08slplmLssgBgYob3vkl6xIlHbC5N5wI\\n86e2LpjZdy3D04e9ApRKwuf1Fa6bvYwrOyf8/Hc7ZXGlR7fVZtQf0Wh3aWSK0kKCZW1YB2a2CQHT\\nI1ZKCJTSbN7U5Y/e+w+87T+++jXX3HL7O/7PX/6v5ltuiP+C42kLmL/xm7/1F1WtdiBjhIC6v4CO\\nu1hb4Ww+ySTXA8+5MOLG4+xepgv4vUzaTF38PEYLB8COkE6Fl1rPqAC/qcgIW4+QGzLFbxVgnuzx\\nbzebud4/efKqeKOH5US6rvZ9VBnFIHynric2I+0IJyMiIajyIVESgzET+EpHKTJKQTqiRgehk0nl\\nypOc3sZKfaLZ4rxvS12P2N89SCcZIeKYmdkttBpTJDrm+mc+l/N37+TQocc5eXKRfj9nVPrFRqDZ\\nx0nkxwOEIGlG6EihVViIzoKESHsRZ0PtN0nlNyMtvVSadY6qqohlRKQlZVV6CMs5SlMSRV4tqBUl\\nKAQ7t2xhOCzROngwBqr+9NQmBIJIaVb6fXrDAmsNw8GIoshZOjFPMegxf/QgiwsLrC6eZKqTcXL+\\nGLaq2LVrF4+uzYKM+JnX7OXPP/4woyJFYlHGj6QQYH0hJaZ2LC2vcOGuGZTGMxWlJMkSdBTxmbv+\\niW1bt2KMIcsyEFCaApEmHD18hNlNm1Cx4L0f+QL//MiJyUY5bhU8Pt/jGZefz6/9yce5//ASP3zb\\nVVD12Lt3M1SGn7zjNn7/vZ8NWqaGux96nPsfX+DRI/Pc/fWHefaV+1BK0M0EpfUZudCaSGkMls0z\\nW+h0W5xayvnDv/kSX3vkYW665lLf6xSCex/5JkefOIwQjtFoSL/fZ8uWLRw7epTe2hqbZzdBnbN7\\ne8axYyfIspj+sGJUDbwKVWOaWCucikCCihMvU6hjD5eG6lNGoT8f+tdeNKQGgqm3cxxeSeibLl8/\\nsMLCsUfodJosrqX01xaYP/QgF+y7hJnp6bPWImxcEn7UyruCjO264jgj1Tmfv/ckUWeaam0JmaRQ\\nVX5Tt4EhOm5h4NeurxoVCIXNeyEp8IpOlTEgFFGaUZQlSeSJUHXtxQP8/4IxvPDJ87qJu6/4xp6a\\nTgjSNKMqcqI4BSTOems2W+d4Q+wEYQxjFTXjxshG6QFUW4c60yKU17Ze7+86tEwgTvyISu0RJGcr\\ndGOT1xWuRr7qDO8thEJETVw1AGGxdQFCoHVCVfQQyidrQriJHJ9t78SOlsCUYS5UY4Uf+Upm91Hp\\niN3tNZ9sCsXWqSn6gxWEVLSzGC0FifawdWU1SnjrRpVEAY6uPaJhYf7UMovLPdZ6/U/9h5/6uUNP\\numH/C4+nJWAKIc6rquo3hWqHytHibAyu9D09QbDa8aIB4/t7o03XtxI53xgwpY7JzrsGGTUo105C\\nPToDhj3zpLyLtwwLceNxhj3YtyD+PJWQwlM9Pg5Yzo0FBHya6oJZctSapchHRM0plFYo49BZRtzs\\nYGWKc16Aeiy7FaUNpEqQiRdeX6/Sz37vJ/88AoGkZKb4ChknmJuboyj7JGmDdnuWC/deyhe+fCd3\\nf/7j7N23j5MLp4IiikBJiCIdYEnfS9o1tx2sJR/kJFGTLEmxxtDKMiSOONJkSYw1YcxGCoQWaK0o\\na0Ok/OyokgpjDUVekTaSQCyS2Np6ZR3h6A2HRFpjSjMRqBa1YLXv3eHXBqucON2nqko/+uUc7UYT\\nJSyuqnFWEGvJdHfKo1Gm5rrrb6EqSnZdsJ+bLptmuin4/IMjdmzSXL5rmoOnc0/GCvDZGC579EiP\\nu+49xEfufJi7vnyAd33ky9x/oM9V+zdx6UX7fa/S1FRVhQVWVnqMhn1mN82QpRkIx8c+f4CHj5z2\\nlP9x+YKjrC0f/NRXKUyENoLve9nFPOfaS4mjNlGkWFnp8dEvPMKw8ExiieTWy7fxqz95Oy+8/hlU\\nrqKuDbWTZLHDCoWyUNgS4RyjQZ+11VU2tRTnb93Buz/0Ba69Yi/zJ09RVjVH57/Jnl17GI2GFEWO\\ntZa6run1VpmemsEhSbI201Nz3HT9xTz/WRdy9QXTpEnCv3v1zbzopj08cewUy2sFtU6xlSVpNbF1\\ncPUI0czLyxEqsaC0Yww2KGN5EhsgNdY6jo62cH7zFDae5oI95/OKl76MLMvodLseqj/HL/fM+x68\\nxJyUCutq5todnB5wzwPzHkGwdoPxuwmm5GIS5Mcv6kk1NrQBgliBlNhyRJw1cDJGKUldDiEoHI2h\\n0vE6cCHZHE8M+KTJj4A4Y7ygRpkjpPLVOr4adE74+U9bo6T07i7YIPZusdZ4sqGt/PWQnvwohYd2\\nw1XAoT16EyUIlSKFA1FBWSB07I23bel7tG5dMc3VOXF3FlOWft5U4Bn41QgHRNk0mApn6pDoKyLd\\nxhZLSNUIwvQajCcerrYv5JruAYT0xvenTi+TSk8w7Ha6WFORNmIkgkHpkEpRVzlJFCGUNwgorUMp\\nyVQ74/99/538xn/83tddfvMr/68n3Zj/hcfTEjDf9ra3/74jvtJnPAIZd1GuACsQcexLdcbSbe6c\\nQPVkwckHhI0wLSTdHXQuvJ7+wa9gRksTok14xpkhU2ikqzinkh2/2OR9xtDm+H2+PUP32/VYzw6W\\n6+849l8MM1GuBBGjEBSLjyFURGvTHEorIgX5sMCq2osDBJPeqNlBJa1ws4hzrtt69X5mkhGoTjgh\\nqLSlEpsY2JTDq13U6DG2bNuJk4J//KdPc/joASIpOLlwksKNF5kjyWLGxgBplHD95TcgiBkOSn7w\\nB9/EzTd9D694yat43Wt+jOuuvw0lYmxVMxz0iRNNlkbUdYENtkZBpBCsRStNVXmYqi79WE5VG5RU\\nxFIRSUWklE+A8Nm0DdTy6U6bqW6LpbU1isLQamdgIIkjZqenMMZv+J5FKEizjKWlxf+Pt/eOlvM4\\nzzx/VfXFzjfiIkcSJJgFkRSTKIkUReVgpfVY0so+Xsue2bFXY89O2B0Pd3Ycxjpre7W25ZzkGUte\\n2WMlK9NiEINIEUwAkYEL4AK4uXN/oar2j/q6cQGBto9Fb51zBapv36+/7q6qt97nfd7nYcvWXVSr\\nk/hxTDRYZPuurTzxvaPML3T4sfvW8/633c4b927g8X1zdDN9YW4oxc+87yZef8uV3HPrdrZsmWKq\\nMcGjzx/k8996iWt2NShHHlJ5ZFlGFIQID8YqdSzCiTp4Pnfv3cXx2XOcWhxgbQYoxssB/UQjhQ/C\\n+QReu32KKzfXCSNJkmVgcrZuhQqDLQAAIABJREFUrPHIC6dxve0e+082SbKUm67ZwcJCh/lWj/Fa\\nCU0EWR9tDVYreknG9w7N8kt/+B3OLi/zl994jL03XMWv/87vsTrQnD3fxGjDpukpTs4eZWV5GWty\\nNmzYyOLSAvPnFxgbn6A/6BGHknLoUfYDtmwYY/v6Bjs2jFEL4IP3v5otk5K33HUNebLCT//wrWzZ\\nNMF3XzjlMoIiUMqCpToUbAeLtDhorwgmDir0yPodTnRrZJ0FKuWY1996E1EcI63l3MICY43Gxet2\\niKoUa0Mbw+pKCz/w8X1Xs732qm185/GDtLOQpLOI70dFwDEMZQyGCM6F0ouzvpLSQ/o+Q4UhJQV4\\nIcr3ydMEXwZomxYwZOG9WdQqLS54K88bFW+w2klD2hzfq2DMABVXHbPUJq4OrPwiG3ciCgDSiwsx\\nd8fGl6qoXRZ9nFI6WFYov9CS9VB+hFDRmn3Kdzq0RhdZrxMTQWeuFWa4k0iBzZw4hZLKqYsNLQTz\\nnstklYdQzilFhnUwKTrrEDc2kGctJ7wgNGlnBT+axkQNtpearN+wgcOnTlMOFVHhSVsul4gjDyXg\\n/OIK2rjPWUhFWCkjbU6WaaK4TKlU4sHHnmfLxinvX378Z7/7sX/x8cOX3aD/EeMVD5hCCBFG4e9q\\nE8bDiW6Vwg8ayLiCTpprnwtwkf/l5fRb3eNrAlYxCcrbbmMwaJMuHsOknTVQqxmFS4sqFmSh8nPp\\nuCiTtUU9vliyl5CPfsDPZfTfSin0cCMQCmwGOsGXEuOVyPsraG2J4jJaBii/gh838CJVNNXn5FlG\\nEPogvGLyX1y3uVxGbi66F4OwCmlDUtlAmT4VuUy70+LYkaPML8zioUlSd19CCkqliDR1/WMIQeD5\\nfPDdH2FmZhvP7Hucsel19PKU7zz1EFdfdT0nT56kNlZjdXWVI0f3EwYRaWKYmVxPo9Gg3V0hGziW\\noMkNgfQRSOIoIreWXDsZNE86l4/Q8yiHPvXaBK1Wk0Rr13ahJIGS1CtVlpor9AY5ypNoY/GFIteG\\nZruNzjVIhy/kOidJBkSxq+vFlTLVxjhhHDE5tY5GVXHFOs227btot1epxQGpELxwdBVwziuhzfit\\nn/8gV2ydYtfMerZsqPOa6zbytjv3cPerNrF53RT1eh2lvAL+dh6RQkGz1SQKIoQQ+NLnjlfvIgos\\nTz5/CuFJ7r1tCy8dW3YC7dZDCM2p+Q7vfe01eL6rWeo8Z91Ugxu2TvD1x48ypI7se+ks/+83nuFr\\nj77Alg1jPL3/OL/z2W/z8FOHeergOf6vT/0xUvr877/9MGNjPrs3j/PN787S8I9yesnjntddxef/\\n9mlUOM2te6+hHIecPXWEVqvN4tI8aW6JQrdhNxoNgjAg6WcEoUcYlijHAdpoJscnWFldIU/77N6x\\nnnV1wQ27dnDV1im2b67xt0+fKMhtw8yFosdyOGdxtT5s4adZPCoMUoU0kzLH5hOef/ZBbN4l8hVh\\nXGHQH1CtVtbMdVucuN3UXVhe5NFHH2ZhYYl166aQQvDM957lpj3TfPmpeeIoZtBdKWqMuVPAYXRb\\nl0WfnC51IfzveSPTeGENMgxJuk08YrJ0gO+H4Gh1CHkBVRHWIq2rWWI00nMHyiCquj1SO2k9qUKM\\nzVDSCZMjpeultOAHYVGmUE6qE4tFIVSIKNSFht64QhX7x1p0Srj3Ya1GBhWnQCTA+eUWri22qHfa\\nxLW8WdfGgvQRUmF0gjWaoDwJeYYq14lrG8jaJ7G5JvPK+BjHoaJQjMoSOpWr2BB12TVTZqnVJlAe\\nQRCQ5AalLLVKTKQ8mu0+uREooVzJZgj36wRZqpGlAwb9hCefP4ovxX//0Y/99CvGln3FA+YDDzyw\\nN8/NTwtVLk42xrGYsj65dieXS8fl2kf+TjhWBsTrriEo1UhOP+toz0P+uDuzAQIhI7yg4k5tOltz\\nznTjQt1g+JoUUMwFmHjt6/5jxsV/WxCQ3BzHld1dQB8KGCu/QhA3SHqr9PsJMvDZu01wx7U1rt3d\\n4MVTCUFcdb1gxjV/r2X2aimQxan9csFydF/IYjNSjjCkYoLlh0m7K+SpU80JAn84nQsRZ0mW585d\\n0WrGxqYI/RI7tm+n3hhHBSGPPfUQyyvneWrfozz1zGM8+uSDHDt8gA3rNxFFVSqVMu9/74fotPuc\\nnjuBsQKjNUp4+L5PuVx2QgZCoNGupmEMgfKolyM8FRCFAaudDiiwwhApn7FaGW1gpd11DN5CFtAW\\nnfECp9BitGHYa6CUBwYqtTGiuEyauJpPFJfoNVfYfdV16GTA1dfcQJZYdm9tMDURM3u2SScZYLQh\\n8i17tq8n0V0G3YRs0CeMfOYXz1IpVVCeR5qnrKw26fUH9JMBc+fmCMPIZZwCMp2hrObKbZO89tW7\\nOHvqLI/uO40unG+0pxHG5/odJd7yxuuggPPSNGXQT5g9e47vvHgGrMAi0UgGg4TVvubJZ4/zjSdP\\n8rMffj0/+u7buPWmnbzjntuoVkqsrs7x9ItNnj6yTC/JuWXHOs4sJzz+3Dn63hif/Nn387Gf/wte\\ntWcbVoVMjDc4dvIYPqB8B51NTEyRZSm9Xpcg9NA6Y2Z6PZNT09TqNaIwJPQlK8sL7Ni2kzCUpJnl\\nxKlFnnhhAV2QVYaHMAoIfYiMOF5MXoj6Z5h0AMY42TdfkeuMxc4YT+4/zxOPP8xDjz7GWCWiUo3x\\nChZyMkhQI5NwVy/8nT/6Y770tc/Tbrc4fOQYU1PTbJyaprN8kgPnMpLmIr4KnKTikA9RkGeGhgtC\\nyAtZpfIY+qraIbvVWjw/Jks6BH4FQxdPBgjPGWsPRUgcWdwW79NJ3BmduZ5KqwnKE2RpE4FjxSM9\\n0BoZxM6oWwUI7aBdU3huKmmxViI9J58pvRAlBXnunEoQCql8hCcd9Ctw2WixbxiT4yHJkiZe3HAS\\no1mfITI4VFAqvrBieyt8O93GhxdPIGvT+GGFpL2ITbpuLcd1B6/nSZG1C7ROIWywIDdzbe0U/Tyn\\nVqk6v18/oFaJKIc+KoqwVrOy0kcqifIV5VDhR5GzN5OKTMPkWMzvfuZv+eWPv+v9e257xwP/6A38\\nkvGKB8xf/MVf+pS2/m4pfURQxi/NgC7UZ2y6Bia5MC4XHLnk9+5LMkhZwm+sR3cXSZaPYDqLF7Hi\\ninMpyMBpISrPBWmdjV70cv2TQg4p3HZNPeEHzywveScMXVQcrDPcGDwnu2UyrHB6rFK65uhkYZbZ\\n802OrFZoLR8jE2XedWudM6sWrI/1FUhHMkCAMhLkhfBoYUQWAtcTCi5muMmqnQNC5zmU7BGYPpnW\\nRT3RSZ75niQ3roBfLlWJw4jJ8Un23ngHO7ftZu7cKT70Ix+jO2jz4MNfJkkyBr2UTjpgvFpn0Osi\\nUEhhaDZXqNbGeOMb38qj3/kWkOH5IUmaEHiuh9H3fXzlkeuMTBviIGJmcgIPQaVUZXllibzohxOA\\nJz3WT0xxdmkJYyye8qBgHJpiPlhA2uF3KxyBTwjWzWxket1Gup0OcRgSBiET4+Ns2riFKPZZWJyn\\nUmsQVzyyxDJdEWzZ2OCxF1YwwvLSiVWu3DbGhsYYwsvRaUo/Swg9V7/q9fqu79hTnJ9fYN36Dfh+\\nQKVcIyvmZG8wIMkNcRwzVStxx6uv4I+/tB+EQQhFRUrWTwUcOzfgR9+yFyGh0+3RbneoVsv86p8+\\nys6NDU4vuA1X2RStFNYI8txR8OcWz/PuN9xI6ClKUUCtHHLzdVfR7vd47ugcsnuON99/PY88fRBt\\n65B6PPnSEVZ6p6iPw+PPnuHQ8QHPz3XYvbGGxdJcWUFrzckTxwmCgDiuMDY2wdLyEouLCwx6vRHO\\nE0RllpcWmVm/kX5/AMCXHj+OJS8a9B38LoZHtGF5pSjbuHpYIQBiNTrL0IO++z4V5ChW7RjnO1UO\\nn55Fpot86SsP8vjTTzE+1mB29iT1egPf91lZWuQP/uILLJ0/y+FDh2k228wvneWxJ5/j4LkFFpsh\\ntr9a1DjNKJCJ0UwyuD7uYWllaEPoMjpB0coiFNpkIBQCx/ZGBuRZz2WKSCy5Izi5N4vNM7Qe4BGS\\nkxCV6iSdVQjKThhACbAUrHF3DecA5Q6GZlhPNE7RJ7cazw+L0hdF7TNwjHPh2jJckzMFC7/43C2Y\\ntIXvBWjjMnxbZI7Dw4wAF7zXjKEogijuK5rahRCQrx5HZz0EGhFPIIzGmqR42+4aNs/Q4RilYIVd\\nEzGdfopOBjQaY8S+olKKEZ6HNoY0B+U715U4kCgJg8QglCSKS8QBPHNgFmEs//bf/4dHPvYv/pfj\\nr8QO/ooGTCGEJ6X6PStiHyGJp64gWTkBeY70HONsWB+8hJIzYsddcr3iX3DwRoBfn8EMOmSDJUye\\nY/0y2GwUgId9mU5JJ0JJH5N2oXAtGV53jSZPcT/GZXvDasUrFChfTgzh4tOZKCYZmKwHeReTDlB+\\nACoi6y6Q5xkm3MqudYZaCOcXV9m5qcR8RyLzBM+2MDLEDG3LihOulcNDwbClxQ6XuVskbikj1RR9\\nbxqTrYLt4xWfuQHyTDA2VqLVT0EFRKUyN113B298/Zu45dY7iaIy3933GH/4Z79FkuVkaY6QMD3W\\noOSXCL2Ibq9Lq9PE9yOmJtexbet2Ou0WeW5YXJ3HYBmrVOn3+zQ7bZLUCarrXKMAaZ3ih+8pukni\\narmmQMuEE0HIjSbNc7yCeei8NgV5rkdQuxJDgQRDEIRs2bYLPwzp9XpIpYijMp6nGJuc5MSxI6Rp\\nQjpIWFlZoTvosrQ0z1U7tvC6W3Ywd65FL+3xzMF5PvutZ3n86aPs2Vai2WzRHyR4nu+YwgIi5bFu\\n/QzCwtLSPNWoRJ5k4EvanT6Hjx1jbqHJFx8/wFKzz2PPnkFahVCaT/zc/bzm2k08uf8szx86xmDQ\\nIyxVsbrPidlZrtuzkxt3b+Jdr9/FQnuZU8s5OPQZU8yruaWU+2/fSey58oSbfTmTjZgvfPMQRkU8\\n8vRJ3nTHlRw8+BJWJHhBh0/9h5/g1muup60zfvIDb2TjzCR/8M3vcP3mGbqtFVKrOXjoAI3xGkm/\\nz9JKi3qtQrvb4dz5OQ69tJ96fZJut02rucrU5CS+r6jVyzz1/BGWljPMpQdT66p5rMl4JBJh8kI6\\nTo32irS9RJ70sHkGxqKEYHFV8sT+NgdPN5k9cZBTx59ncXmJ48cO43uS/+e3f4NnnjvKitpBS2xi\\nOYs5dnyO5xc8lltVkl7b6cIOg7XVa1pLHLvcBUVvuIWM/hkJLwjHhjdJD6k8lAwweR8vKmOypLBO\\ncx64EldHVQhM1nGCBDLHL02TWUHgxZCuIESAEh7SZIWcnQAhXYapPNdSIhwpD+t6ud19OP9VkAjP\\nX0O2Gu4VBulFzprM81EqAOUjVYApSDzCc32XJutf2MMQWOEzMq0uHhsNKQkqkwRTO+mdfZHhrl+Z\\n3E3Smiv2H/c86ceu1hnXmVc7uK5yhoWFFWYm6hhjSDJo1Moo36c/SFCeGvVgVgK3znKr8GyG8j26\\n7S6eJ3nwyZfYMFl97Ic/+lNP/QO37L9zvKIB84EHHrjXWPlRqUoA6N5KgacX/WtrgtHaH7i47rZ2\\njOANFDJ0VGadOMk7GY3h+yEmGxRwgCOzCBWj4jHn45cP0FmHS6886ocaQSjuy5Qj8+F/2nEpVEtx\\nInP1gxx0OlL/l16JvL1E0pljKZni2KJH39RZbjYRNuZ/fvt2soVHqAarrHbLzqBZFLmkkLh6pWvD\\nUdY5t1hcm4QV7rO10kdYi7KLBJkk9LpkOqfds8web9LpJPgR7Nx2JW+69128eu9tTIxP8Tff+AJ/\\n8tnf4eHHv06320MJRZ7nlEoxr3nV63jzPW9h65YdnJ07yfzSeZK0x+LCOU6ePM6PffSnWDi3wIFD\\n+7Ha1fiKXNAxMq3LajfMzFCOYiSGII7JTcZN199Ep9MlSVOstY4Eo4taGK4uLqwTHZPKczC1FA5+\\nkhJPKQfZhRWU7xGErv/OGqfAE4YRSdKn02kjJYRRhVRb5o7uZ9uVe9hz9U4mmOMNe7fyvjffxTef\\nOMqLsx1+8j2vZrXVIs9ShOextLzsYDfp+hunxxs8eWAJTJe5xdOcP9/kxqv30Bif4sjcab72+Cwn\\nTyxxfjXDeDmVKODnfvh2tm6Y5K4bt2HTHp/8zGN84duHuXrHFMcPPs/2DdNcs3sHQSA4cXSWHRs2\\nc+9tW/nugdOFeTkom7JlymPnphmCIEQqxYMPP8r01CRf+NsXyLUGNNdds4FT8z1yMcHu7WV+/C33\\nUa9FLC51+eMvPcwXHnqO2bMdjs91ue+WK/GwxJUK4/UaZ86co9Va5cTJYxw5dpjnXzrC6blTNNur\\nRKUqlXKNTrtFtzPgmUNzPPLMcZo9XWSWbk3IoQCBGYqgCweFGrch6zRxLRymEAJRkrzXwiY9dL9D\\nPmiSD1pYDKY0SVdMMXs258jsAi8eO8/XvvJ59h+dp1t6NSacwMqIgS3TV+MYUaG7cAgyjV8dQw96\\nhd+jLrwzXXY51LIdcgEoDiUUJR5tMjypnEm69LDSZYZSeAiTYQoXElHUOA0G5UeYtEue9x37Veeo\\n0pgTAJAC36+RZ11M1kdb68QpdOZKO1I5opEfopSH1dppwnqqEDRwZRsHy7qe2CGj1zG/wcgQLygh\\n0IWqFggVYA14YQxe2floZr1ivyy6DYQgqs6g06Y7Xhf5wZCBLL2qQ6qCKqa7CiZH+iFG90fZrEAg\\n/TJCSScsQ4lTdoZd9SXKkU+W5cSlmEocEUYRQgmyJMEWkHk/TalEIdJTZP0OXhggrGLjuhqf+vO/\\n5ec++qa33fjad70isOwrGjB/4Rd/6fPW+lNC+mAKZ28A3Ddg19QmhmOYga01bF471gZSqUpF4M2Q\\n8RR+eQqdtp3EFinSbxBUZpx8XFjDmgzTXykyUHHR61lL4Qk3xN/t95FnXqlxaQ324tcQa34YFd3B\\nYnQXrINppZJkgy62s0y/eRbhlaBUwYqYxw4t0M2rvP3uHRw6/hKROcumeJWSfgHTOo5WEwgVYYV2\\nAVIY1/BrGTHVfL1Eo/d1SnoWK7q08hRtLUoE3PSq65hfOsd4Y4q77nwDt+y9na8/9FW+8fC3+eaj\\nXyP0BbW4QVyukfQ7hHGI73ts2bKDpNdleXGeQ0cO0O4N0LllkPRZbS+zuLDEzh1XcPzEEVrtFr7n\\nkxZkCWucdqYXBFhrmZyYwleKeq3G5OQMx44dJU0GZHk+gp1dX1sBlAlZcD2KgCEEXnEYcvZk4CkP\\nq3ziqMS5c+dASrqtVSanpl0WYRXrpjcQV8tIIWivLnPyxDEmJqeZnFqPkIJ+6zybN6/jyq3TPH7w\\nLHdeXef8wjl86WD2+XNnmZlYx+kTxzg0u8KLxxb4hd//FodPtLEq5q2vvZYcQRCE7Fo/gyfgzx88\\nxNVbxxgr+Xzo/uu5+brteJ6iWo0QQcRffO0wfuDxbz50N7uuvILl1SXWTa5DyoBvPbaP97zpRt76\\n2ut4zbVb+auHDiEwpMrjZ3/ktYzXqni+j1SKTRs28+uffpADp1tYa5D5IgeOniJJxgk8527y0fe+\\nDl8GXLF1PT//qS/TXNZI65GJAfffcRW37r2ZX/7Er7F191Ucmn2B2AuRvsf8/DKHT/f56nOw72iX\\npdUme6/fgUkSpmbWceLMPF96dLZgW19SkhE43WDt9EiNzrB5irEWJSVpr4k1pjAx7hUklALBMjlW\\nOzebwFPkSUIO9OQYHVulzzp0sIEUD5P2SVbPFzU9D91aJO318DzPZU7WuuxVDy3g1rDQi+Bgi4Po\\nhboeI0k7qfwLK9waMp1hZYDIUrzC2FxnqSPNGsh654t2O01QWY9NVt0+5UfkvSWQIV5YcQRGFIE/\\n9NF0gV3JqKiPGkc8MsZZYWnnWiKEcGIGnufIZ0I4SFX6KCUwgw5pZxFfWFdTRKLCEKNTB/N5oRNk\\nEDi/TkShhGTWMGgLRFC4rFHoFOuVkV5MnpzHahxLXQYI41A/K109VQZlrLWouMZA1pg3Y1w5YbFZ\\nFyEEcRQS+C5bHvR7BNIpg6UGaqHACEueGHxf4ClDFAQcnp2n0+vzh7/9SfPBj3zsoR90L3/FAqYQ\\noozlE8iyvID3WVQ85fojubgmeHFwXFtE/r4LuxuNGqjSOKY/D0jCsS2AIO+vYnQPrEVGdVQYutql\\nVNhBC502R9BjcZ+jyxqTjyb7WpLMKzEup4H7d7eoXACV3e+dQozJE2zmpKk8L3R2QTp3J0+vRBhW\\nENInFTHf279AtbqNqTHLB972Rg7OzlPyztJbmseLFP5gP0IvI7UFFL5ewM/mUYOj1HrfRph2Uf71\\naHYUtVKJd7z1A7zm5tdwdK5LUN/JwRMn+d7+M3znpXMcP79A2UtYPj/HuYVFtNVM1seoV8aJSjV2\\nbN3Nc899jyPH9tPqtEnTHGsMVggGyYDF8/NUK2WU8Dg3P+cgyKIVMY5iJ4dnDF4Ysn5mM1mW0um2\\naXc77LnqWjZu2ozJc7q9NlHgdFQNFk86eBYEGos2F/o1rVnDfAbSXOF5hmrkhOXb7RZRVKLT6VKr\\nVwniMktL83hezKaNm7FWcn7uNM1W0xn6ioCzi4skSZ8vf/Mw55YWOXl2kdnzbXrdLodONXnp1Ar/\\n/TuneHT/Ii8cPsn/+JZX8bEP3c3P/8Y3aQ9yTs036bRbfO/FY3zum/tYbuf80r+8j2cOzvLW115H\\nIDVh4PR8140H1BsxRuTsO/wCb3vd7cxMrANp0TbjzIrhlmu2EnqCmak6f/n1p+mnOUpLPnDvNUyM\\n14ijGCugVIrxPcmXHzqM0Qk6tRgR44VljMhYGfh86M3XI4Xg0acO8pXHjqONRBjB5HTExz/4ZpQX\\nMMhz/u2/+wUOnDmFSge8+qabieISGyfLzK8ucWIxY3auxb4Dc1y1fSOr7UWu37WVbz22n2bfjIKk\\nEIXCsyjWjwWrtWunyFPnMJRnGJ1e8DE1GllkfSP3IeuY5FmvSd5dQfda5J1VRNonzQxpkpLMHyTt\\ntrFpDzMYoLPcHa7xR6pSRqegh16qZkQEHPVtY4rD7eXWspOQQ/lgXXD3PHf480o18n4Hz4/QWdux\\n5vtLru5uQHihI/4IvxAnECPlHoFxRB+pnEQkEj+q4kdVctMDY9BpSqnWIC8OnUMlpeFnDEW9stj3\\nlJDkSZu8v4oMKsS1GQjKTlBFhZC46wqvTBDFpN1l57JSyOU5OHaYdRfvXyi8qI5JWkgvQlXGsfkA\\nYRKEihwcbByhSkgFSiG92EneFR6lrWaPfcsbuW1mEc9TREFAEPiU4sihZVIg/IB2b0A9gCTPSNop\\ncTkijBQmc+jmX3/rWW69bvu5N737w5/7R2/oxXjFAuYDDzzwXov3PqliZGFmjIoQZlDULr9/XPjy\\n1EVT7vsyTeER1NaRrJ4Ga5B+jIzHMEkHRepqAshCvd7iBU6qKk9WnRboJRPauaRcbM78Tw3D/t1Z\\n5toAa9b8jRNjFhisTtF5Hys9ZFjBDFrkgw7CCvwoxkiBtR0G2mOpW+bR7x2gXI65be9N7Nw+zS07\\nYmrJKmnnMNPyBEH2LLfsKtNffgyZniVLNd2uRWeCK3Zs4YptO8mt5gPvfD/ffOQx9i1toWMnaWeb\\nWGhHaK9ENXuBvLdMmmg8FbB543rK5Un+t3//n9mz+2bue/197LpyN+3egLlz8wzSQQGVWrxAIYXi\\n6IlDLCzMQwDk1hUrBdRrdXqDPgZLlqVMjE9QKddorTYZG68zd+4c5XKdjRs30VpdpRwHTIxNEQcB\\ng8GgoDhZPOUo+2ZEsHIbmfJ8PN8vIKKY+XNzpOmAUrnKlVdcTaXeYN30RvIsw1rD1PrNlMplMmNJ\\nBn2OHNjHmVPHiKoN/s8/eobvHFihqQckJuT4XM75Fc2jL6zwzMkeB070adQ93nOj4PU3zJBlTY7M\\ndtm6qUarY/j9v9rHV544xDMvnuKKTVXmlts88cxxDs31+drDL7DvyEluvnoLi82EoyeX+A+f+gYv\\nHTpFt9vkI+9+I1prpLXEccnR7BVUSxHNVp/WoM3RMwv83EfewPSY8yBVSuL5Tn1p5+Z1lEuSZ16a\\nx6rI1b6UQqIQ2vDtp57iG08d47c+9wTWeiMvxo0Thv/hHfeglMeN11/LZz/7WV6791Vs37yJaq3B\\nTa/aS9/m1JXHYheWWoLF5SZf/e5Jnj14kk//zfNkeU6ifRyJRjCE+KzWiMITksKpA2NAZ+g8xZVS\\nCrlJXDbqmvmLuueI1XoBPcJoTDbA9Fvk/ZYj82jj9npZKMga913LwnvSmgyT9sDkF661dk27izNs\\niRk5fwzXt3W20jrP3SFA66IeKxC+Tz5oE8YVhxgZixEehaGQ83r0QifYkBfwqx+is8w9ZgWmIFEK\\nCiF3o/F8Z8Q+6M7jKw+EQvllhPLQWl9S+nJa1nnaxWiDV6oRROMkxiLIsGmCzfsu0xd+IW6kMNmA\\nC8QsM7zUGkYGTobPdz6Y1hrIuvilGbygAioA3cNYVWSZAqFKhThFMIQVSfs9CANumcmIVYLODZVy\\nBbAMkgFxtUogDa1Wh0BIUp0TeIokzSlVYzxp2TgzwR987lHee+8N191y7/t+YFj2FQuYv/jLn/iq\\nNl7V9RUaiKcQuAn3csHIFsoRfhA4a6fRyQ0YkXEsUaWBFWXMYMF9GUEdFVaxaYc8HTjoV3oI4SED\\nV7zWSctJNw0X3kWveyEo/1MFyu+DlodM38vAzs4OCUbWCiNkH0C5Go8s4I+05Yy3VeQajHVO0lnF\\n83xUUMdSAuGT+zWSwSL7X2rTGkzSHuS8/c2vpz4+wdt+6Mc4feos1WqZ3btvplEfp91rUx8rEfqS\\nq666mX/2nh9h76vv4Oz8eV572518+7kT9NMS1nM14mrnUTBz1CsT3H/fO4jDCuNTG7nt5rvAq3Jw\\n9hzVUsAN176KqakpXjwWzZC8AAAgAElEQVT4AstLC4Bl88x6brnhVk6dPslYo16YSzu7H2EFwpN0\\nu71CPVMgFSzOLwCwa+cVDAYZ66Zn0Dm86b63cvqsa6nQWc701DTGGpJ0MILHPE+RG4PvuRYeYxgR\\nN4S1+EGIkhbl+fiex9bN2zl/9hzN9jIm0zz9xCMcPXyQiYkprM6pN8awxrC6tEi7s8xdd95Kt90j\\n6eV02in9DNqJIYwt26cjGnVJpDNmpqu8cCZH2JTt60tMB5rPPXKMTmrxbUDXwKnzCZ4MwJNkg5zN\\nU4Kf+fC97Nwwzp9/8VF+778+RLPTxijJX/3mx5koV+j3ewgs/V6P6XrM1HgDtCXNEj7+iW+AiPhP\\nH7mNqBJTikOiMGKQDPA8DyzsvXoLO9fHfP6bT7kqnc6cybuSrLYVp862UDYYlVgElve/7Xpuv+5q\\n+t02lVKZt7/tzQR+TtrrU6rWeNVNe5FGMDPe4J6bt3LoxBmW+hAEHr2+pJ8KEuNjC01UIaTjH1C4\\ndAxNl+UQ8rOj/j4XfAwCOSLjXNi87WWIdma0wQ+DJ8ZcIPJYzdB30jkKOe1XqRQm6Y4Cpi3W8Sgo\\nSqc1m2U5qpDbu7SX3LFrHQFtiGpYrcmTFvgxWXsJoXOsUmA1Sjr7MKcDazA6dULrBToi5VANKcMi\\nnQG3MVibF6IPgiTLUEGMtQprM6QKGDJ17bCVboi4IJFK4vtlhAzQwiJN31kGFW0z1mQIJGF1knTQ\\nAWsw/TYI5e5z5PSknWC7yR0ErIIRHC1EQFCewBgBeZNwbDO6fR7n0alQXoDWri1ESZ88T1Gek8l7\\nrj/NXTPLGASVShlpLdpq4koVYTSBH7GUJIyVI+j0SY2lVC0TBD7CaM6v9Dh+ap4//5PfHP/gR37q\\nKz/Ivv6K2HsJIcalUjNCjgEG64X4niHttv8OIs9w8mmypM/FsIYTYAZHj06yHN9LsFZihSUqN1DW\\nkFkcZR/pTjTK4AUVdD4A3XeL4P8HAs/LjUsFENxj3/+8CxPuwnNcaUQMSequSCeLrDNZRZsUL6zQ\\nyzMa666gu3iSyvqrkJHbYJSx5GxGhF3mVuHcssdzv/lVZupd/ujLS8hgM9HJjHUzPe6+/Q4ePjZN\\nPsixyvLX+3wOzH+RpbNnWcpvxKglpF9FComXa9bzNzQ2TvPhH/5J9l77amY2rONXPvmrXLXrasrl\\nKg/89mcIdR8/69GolojjEmPlBuVKmV6rwz2vews33HALwlq+t+9p0ixHIvFUoYMpnDmwMC4jlFLi\\neR5LKwvEZ2PSNCeKA6z1+bPP/Bnbt2/jnBS0Wx323vwajh09xP4Dz9Jpt9H4zoLJD9AmQxhLpVQh\\nS1LSQUKtMUa7vUS93qAUlGjUx2h1u2grOHT0FEqcJSrVmZyq89wzT5ClCdt2XsHU9DRSCsIwYvf2\\nSXZtqnN+qcm+Q0usn56iUfXYVAs4dHqFs60u3/rOCdZvmOGKLZa52Zcw+TTPPP8Y77h5C6eXfb5z\\noI1pghXOQ7S5nJKIAUGlzO6tUwxswEffdz9vu+8aPvSvf5N/9z+9l+1T61htLjkGde4k+LCWtJdh\\npSaOAt5/7xY++/BZ3vLx/8bXf/dHCaISvUGPbt+xgpXy6Q2alCKJNR4ohUJjTUZeaJZiUoxUCOua\\n8sMo5T2vuwMlfaq1GrnOKZXLhEGZ177uHqQXUK9WWTh/lj17rsEYy0+8cw9W1dmybSOdVPLP//N/\\noz0wmFShpAtpRa9PIdCu0HnmDjbSyVpekHRzCi/WDpGr4eY/zHFMEQ+KfcYO/6cgupmh3hWFi4cp\\nGJ9O3s4TzsbLGNfbOFyZrv/YFopboAsiknB2SaM1v1aabxhARyibMAX3PMCmHYxJUV6JLFvGEwHg\\nI0SGFAKpM/IsQ5gMGVXQeYoszBqwzrY9G/RcsiAFwovJdYI0BmEitNXIoIzO+kg/QnoRJm+PPjFR\\naN861xjj+ki1xelJDttmbCFJqEmSLn55gjTro6IKJmkivBI2G7jDyhDylS5Y6qSLNSkIDyu79Lsr\\nVKa30zs7T9I8h8HDC8cxSQ9LhtHGlZ9Ul6AgPNksw2SKJhPU7Cq9TofS1DihAc/z0RZ8k9GeO8em\\nWoD1FSbJXQdFFABw/517+PlP/jVvv/Oa+B++e19+vCIZ5gMPPPBDFv+HkJHLgvQAWZouDJwvpw1r\\nGSr3OFLjhShiiwk5LKIjYHLLq+gsHHV1OyzB+A50npP1ziM0jlItBFaE4Hnkg447FensMnd7YfxT\\nZpgvN4Zx8XLEp4vvx+lFDj+a4elWgFPosDk66SHsgKSzAFmPPE0xeUYYO9EIlXcwWYbxqxhqaL/K\\nspjBZwqjGgwULCcTPHOoiTYVcq+G9sv4Xpn5QZ22txGCEOl1GazsJwga5CokF+OUoohvP/k8Tx88\\nx//xa59mrD7OT/3oB/iFX/t9ji8KlrMZvrf/JAELNMp1NDnbtm7nzPxJtmzcybve+V4qcYlb9t7K\\nseNHabZX3aaI0+Y0uSHTObpgyuZGU6lUmZs7Q7vVZHllhXPn55BCMz05Tr+fMDGxjpXVZY4cfImZ\\n6RkaY+NYYWg2mxhl8Yp2hLwQdy+FEefm23T6ihMnFzh45Dz7D53g8See58lnXuDg0VmOHDtKt9Nk\\ndeEsE+MT7Ln6WrI045nvPUm90aA+vo6pmRmwFt8T7N66kRuunKYag5GwaabOoNdiKQmZLueMVSNa\\nC+dZXTzPytIsS2dPcv32Gf7Z225gsT2PssvcdfM0H37XXvZeNcn5+Rabx0Pe+7/+Fz72wfsw/ZyZ\\nTRt57bU7GQy6DuajsFrLXVYklDuISuWxd/dmpNLce9f1XLNzkiRJyaxFJwPAoDwJNuAXPvVFzqy6\\nnlgxNOYtGJ9KxeS585CVnk+WZWxY77F710b6vRTfV/zJp/+U62/ay6aNW9ixfRfGGpYWF9FGMz4+\\nwbZt25mZLiGtYuP0BDumYaIec+DwWccaLbJLnfUds1MKTJYxEri0BpsWRBMYMUyHmZIDZ+yFxTJk\\nwV9YZYBx8G2RibrfuWtY4diu1hqn8ypcZqR15hyOihAjizahIeRrodBbdQzftXvZ5covLnhapNHk\\n2SrCeGjbxyNG40Q1jAGRD0jSFE+A9AN0njgmbEFOtBjHbHdOqCjPJ8sGDl0WFp20Xe+kMXhRDYlB\\nI1CiIFMVtUY/iBj0Oni+06OW1gkyaJ2PMlFrc7AaYbVr5codAmHSPugBQiiEF7rMHQt+CSk8bN5H\\noF2/qBcQlhroguRlEte5YD0f8oRhLbNU20iaJSA9vLCGtE4DOYoEG8JV57iDpZeklKp1ZBBhkg65\\nlTQCWF3toJQkrsR4gY8XBDQqAZ/+4hO8557r9t523wd/IFj2FQmYv/grv/5FbURdSFcAF0I6ZpnJ\\nL5o0l5JfXK1ffv9jw2BZjN7qPBhnJeOXpgjGtqCzPiTLjiRiDUiFX5oEnFKGMHoUYC8tyl8Km/zT\\njJe77su/7sUCDgqUazW5GKqFUf+TFK7wbgzGpEhrsCrAovDDGC0VyMzVDEicbJe1CJGAGID13cZT\\n1GA8oShlX6WWP43Qi8jBLOHSlzCDZ1HRq8jjCsr65IQsJTW6psHpJUHmTzC70OWzf/UVTi316ds6\\n+OvQ+QJVtcCb3ngPZ86cpVqpc/jQQRq1Kre/5m7WTW3kL//6s0yMTXD42GHn8mDBl74zs7WgwkIw\\n2kLST7E426z16zczGPRI85SlpVWy3Omcbtq8hdOnjnHl7uuRSuFJj3qtTmu1RW5yrHEZTW4toecT\\nRpLATylXFOONkD1XbadcqbKw3CEMPO665Spu2rOTifEJglIFpMQPPPq9HtYYdu+5hlanixXWSe8J\\nZzmm0wytc9KBZnnxPLddvZVTi31u2L0ZfI9Ts0cIwzLrp6Zpd5u02n3uve1mPnj/3dywYyvTtTLb\\n1k3z9tffjPLg3XfehMJy4MRRHviNP+Uj77yH5uoqQkoCPxjBi6LYzKWU5DrHDyJu3LWBnRvHCb0y\\nQlla7Q6DZIBSIWEYceLMeX71vz6KMaIgluSuzw2LFD5CFcUB38fiYUyfbz/zEn/z+Lf4wH13Y5KM\\n19x6K3/y6U9z8Mgx1q9fR5IkGJ0xMTVNnuWUyyUqpTrVWp08Tdi2eSPX7NzKzXumef7ILK2us/dy\\na6MwS7a4tVzArTrtjdaBGMlcFjZZYs2OYS3C6lGN8UIgXVPfpAg+wiEyjmZdCJYLJ0JgTApZWjh0\\nuBYlg3CZJ5ZhGxjCohDOiWfYYjI6GBevu4ZsiDFkSRtP+Ajp/F/9IHSkJW1Rvgs2UrmM3uaJq+/h\\nPiOXhTvBeolFSJ886zqHn6wPXlg4IqnieRbr+8ghI1lKjHZ11VznKCVGe4ABMBmmqLcODxFDSTyr\\nU4S0CJ1gpatFuskRF8/LEaKQ7LOuVQkDJs8dkSgou/7gtOvE2rUuFMfcvqYlSD8mrk87MpDOQHic\\nySvcMbHE2FgDoQfkSKK4QhBGSOk+kzOzZ6lXY9IkI4oi4koJ5YeYLOXEmSWanYT/9F/+44af+Ni/\\n+uLLbM5/7/iBA6YQIrA6/SUrK9J5MrrJOyzWX3rKsrbokStOOGt/575+vWZ+CRQGKwpRAeFOL8Iv\\nQ95HJx3HovNCvPIUQilHSsCdoqweXPQ6wzHyoRN/fxvJ5diuw8df/m8LEsFaAsBlrnG5w8To78Ww\\nvcTd/1qhg+FbssiieVpjdYqxIK0m7bUYDFoEQRkV1B3gJGSh++jgFq3diU5KRywCH2uXqZkXuGb3\\n9ezZMc49d93JwVlDK7zNmSZ7FafXK9yGZYRAWuE2KxsCHUqlHlChIk6R9g4wNV7jnffcx+Ejz9Mf\\nDFBSsP/AC5yZO0mn22PfU4/Q7i2zvLqCkJI0cRZgUgiUJ7HGEPo+kQpI0tQ1KBuN0ZokHTAYJAwG\\nfcrlEtu37aBUqrB1yxWsNlfotJoszJ8jyzLG6w06/Q6J1kRhCFozGAqxW4knlWsvWeqyYdM2js/O\\nYbOM8wurnJlfptkZsLCwjOdbwrAM1tLvrrLabhGFEe1OmyzXtJurBL7LkKUQKAXVUozwDGlqaFR8\\ncq3R2hSlCE2pUqdSbVAuVUebWK1Wc3UrNA899A1uuO5G0jRl/cw63n/f3Tzz9BNs2bwN3/PJMgcf\\nBoHrsUuSBKWcDmmeaaq1Mqu9ASrISAYZtUqF6elJntp3hFLJ50c+/hsMdIzFuZgYW/QjW0uWdRAq\\nxJMhQnpOPUn12LNtit/61z+Bbyz1Wp08zbj+huv52Mf+OadOnuDqq6/CWkuapkw1GgyyBCE9PF/S\\nbK5SjitI0WfT+ilKnuShfbOj9SildAQVa1zLWNFzOSTPuL5i9++FIdasueGataNg5Q4UQ0GE0W/X\\nrnR3DQfhIIXA5umoXUIIBTZ3sKVyYubFiZ8Ry74Iwhef0YdymEUbSp66LDpPkeTkOsdTvnNnKey/\\nICfLEpTnucekcvcqlHMVyfvOtahQIMILitqr5xxATFbUEP0iS1T4foTuLyP9MqYwfRj2Jg8NHYo3\\n6oKbGPZIutKQyR1MPDwA6LSL75ddfVknqMAZaTtpPo0UARbHYh7yM4QKkX4JITx05mqhAgFKuTox\\nTkbBCypoC56wrrYrPbx4ir1bEqqBQVmNMYIgKuFJj36/B1bS7fSoVHxUbpCeJK7V8OMyeTogzzL+\\n5pEXufmqzYfe+cEf/0ezZX/ggPnAAw+8Qargw4hIjAKktTCEVS8KGkVjqx02mK8NEgVz9aLgIDFC\\n4kvPtVPIACE8wto0edJHpx388hRBZRohFEYPRidPnbYRDE+Zl8nm/gE9l0MoGS4IxA/v9fJtId9v\\nLbY2k137c+EzuewrO6PtNc+VQ83JoQ8fwtlVUShtCOn0evtNFBIlLHmqSZMuvu+hPOU89oRwUnj4\\nOBPvYmPKzuGTM1VPicIZEi04cvAgR5fWI6NJpCqNMt3R/Rd1J6MkQqSktkSaT6OJ6dtxPKmp6+Ms\\nrS5z/73vA5NRisscOnYYcs2dt91NrVrjmw9+FYBBkhMEToknVB5h6KPzDIMhNYZqXKFereMpwWDQ\\nRxZSYEophPCoVutEcYnZ2ZNs2LCRgwefJ4xCJscniANFnvVodp0GZb+fEoYhvlQOIvN8llYNc+fb\\nnJg9A8aVDXSe02z2WVhss21LA51rfM/j7NkzeH7A3KkTrN+0lVa7QxyF5GnC4UMvUavXmRgfJ8tS\\n4qhEq9dn3cwEvrCcOnEUYS3dbo8sTbl+721U6mNUSmUqlTJCSAJfcfbsHFjLrh270NoF4CNHjtFp\\ntdi0aQOTY5PDRMkZqBezJ8vcCXt2dpYoiujnKY1KxP6jS/zpF7/LI0+/xGtuuJLFZoff+sxDKCLO\\nrXTBKHTSRvhRgfxITN5BqpB6o8w122rE5YQfvv8Wfuwdd7NxvE5ULmOBbr9Ht9ViZXWZf/UzP8P0\\n5CRhGNJqNllqrdJPNGm3Q5b1qFRqaJ0y6Pc5vdDlk3/+bZZX3fIZEvKGcx6dg8ldVjeE/OyFn1H7\\nCUN4luKwXqzD0fXc8y/EseF/DymGa9ZtUSYypoAiKfYu4QKnKGTlLiA/Q5TWjhJYsSZpMKbwjDSO\\n7W6zxPU9WpDKd8bsOsPYFCkdtOpJz8kcqrBIQMCSIWyhu1rwP5xZdO6CjwrQOiugYbsmYFoQEhlV\\nMaaQ5ROFjJ70kV6AztKRfZ3JBwhxQSJPGIMUEp0NnHa1ECjhobOuO4RjnfasH6N15pivwivIQkMk\\n0elmKz/Cq4yTdpacqMiQp6EvOKF4cQPTX0WEVbxyFaE1shZy24aA0LaJw5BOf0AQhAwGTuAlTwe0\\nkoTpmst089xQnxwD38PqnPF6zO/8xSPcd+fumTe87cO/8jIb7987fuCA+YlPfOLfJJncu7YfyWU+\\nxaddDM8LnNEwF05l1po1QXVtEJEFf8sM4yvDYrkf11ClSWyWOcjCL6G80NGf8z4mH2CzFHRvVO24\\ndDhG3cUC65cbo5rhmmBprBOCtsXJ9ULmPLxYUTt5GSj60scuN5x0lbwowI4Cr/Rctj206pEK54Iu\\nkIXggU6b5MkKZD1snmPzzEGImTPulkYgvMxl5ygnCigcfN3Mr+Rc03Jqucr5wRgyHKPYAdz7L/wG\\nR+CWNZCm2Mw5cVCo6lipMbaKpyRzp59m57YrufP22wn8mObqItYKrrziam6++XYOHX6OleUlkjzH\\nk87qKQx8AuWjcLBqnucM0pRuv+eCVqjwvQAhHUlmfHySQb/PeKNKrVbl4KEX2bF1BysrC8RhTJJ0\\nabU6I9jXuC8EaQXaSpabcGa+jZJwzdUbuPKKXWgz4K7bb2TQ67Hn6g0MessIax3caC2t1UX6ScrE\\n+CRf+PJXuPmmve7aWY96vU67uUrST6nVK8RRiX6vQykICaOIuFRi/YZNTK3bSKM+weFjRwk95Ri7\\nSuL7PtoY2q0W1Uq9+NwVcRQipGLD1IxjlxuD7zvd2ixzjhJh6FxQKtUapVKJOC6TZjljjSpxUOb/\\n/ovHePDR5zBpQhx7fPWxw6CcdJuvAsdulAKTO0KKEgGNMnzmEz/OB+57DVfv2EhgDZm1tJpNlpdX\\nMBbKlRp33HUbtfoYnnDQealUwlMeL+x/kaWleZ576Rihp0nyFJt71CuCqdokD+476GQOhwng0Bjc\\nc6Q+azIYtpQUT7TWrP0Dl+3YIosUpkBhLr/C3I5jRz8XUJwiGBciCKJAooBCxF+N/v+wfDRc23aI\\nrlHYghkDuNrfhZWt0XmK8HxAFOIpBotGCB9jEjw/Hr03IT1XQzROv1ZS9BIXrTAU0KmQCukHCJ2T\\nFzrJQwRpSFuyQuAhnO+lspjioCiE07Advncph56ZxTq3jgRmsaO90A73N6uLQ9vQ8EKNWMYCZ9Y9\\n4mJYtx878lEXYaWz/0IjdO7emwVUgIqqBHGVuLKOLOnglarsX4B7dyWcOr/CINeUohDfDwl8H6yl\\nXKkTiYw0c4G6VKvh+YVjS6554fAZKnFYfvv7f+yBl918/57xAwVMIYTQxn7OylAJ/NGpS/qRK5Sv\\nmVBDppsYfigXrnFRcLHIYpMGbFoEgQtUaGs0qjIN2jH4rMQ1Mmdd0HnR3Nx3meZlYpKzzrnYpeTl\\nxtoMUwjhdEGNLibrxcHMXfnCKf/SoPhyhtiXvh4wYv5edA3haiTDzFz8f8y9eZCl13ne9zvnfNvd\\num9vMz37DACCAAYEdxIkwUWkGEkQRVGULFpKpaIqRVYldlxxHEexrJKjkm2WZUepKE4kquRIZUmO\\nrYQSxUXiLq4gCAIgsQ22AWYGmOnu6e3evuu3nCV/nPPd7gGxkFTRyiGBIbt7bn/3u9857/s+7/M8\\nbzBOlrNq3MyqeoG3DjPFEFuNMcWIyDnQFflwB+ss5WQQfk7ghMVFTRAdjBRYmVDLU7DOW3sJ/PT1\\n2rpMQDXeo+yvUfTXycc7lIMeYNDTMSpLQGSMckHev5/Xv/r1nDx5mvnOPCvLqxw7epRLly5w/Php\\nVpYO84UvfRapHEmUoKTz1mAyJi9LSmNRUhKnEcZoYqEwIUgarTHW0Wq0uPNH3sMjDz1ApSsajYxK\\na546/2SoXCRSOUajCVFwFIqjiCyOaTYS9oYDdGk4dqTJfKvN+fMXaTYjVlfmSFKFwlLmOY1WK8w5\\nnDKdTonjmLTR4M2338HeaECWxgz2BgS0i1a7jbWOVqvB1uZVDh8+TFlVWOvY2dpEJRmTvEDFMlSY\\nHQ/vlhVJmvmKUwp6kxHOGiZ5jnB+rJaQkul06tmCppxBmdPplDz3cq7CQqORoitNI0mYFjmYiv/8\\nva/jk19/ijfccpxuInhibQ8VZaASXx2hEEHYY4Xlv3zf7bzp1hMoJYik5OrWFoudBpeubJFmMa4y\\npGmEKS1VOSUJRhJSStrNJoVN+PBfPcld9z3MZ+/bYDroc+8D57j/8R0Or0Ts9DUIzXDkByFf085R\\nCcJZdDE9UDnuV491cPHb0O4HiHo/EdCQWfgU1P3L5zRrENSSFhdy++AvLcC3SuR+1eZ/CVCbrYdX\\nq4c3h+/P9rAzmLIkSlOsE/ueznX4tpU3OHfe39X3NP3n6mZ8EF8ZyrrXKwI8LSMP3RpDJKWHjp1A\\nqIA+OYdSMTbO/PkYp9iywinlA5UEjPUWgAEinp1TwpOw9HQXVDYrdGSowuvgjJCzsWKeBBQShgDj\\nEtpC2JIw9wWZ+J8TtcMXIIQkbsxhrKWa9IjiiCiZYyozfuwsrG9u0mw0WFlc8izZyDOqoyRir7dD\\nmiXYsiLNEpJGA3AYXTEY59zz0AU+9ck/estP/8wv/uG3HcDfwfrrykpeISBSqu0/gGJI3D5MNdoI\\nmdF+mW1MGfpngv0YcG0AcQ5k2kAqia10yE4OCPkBnPY0ZqmCFssTXkSApTyUtj/k9+Da31wvUVpe\\n+5cC/OH28fYwAPYgA3gW1HnxivKFzNgPLlnnvAcgbcJrzxyRVIzCZ3LOCBARfm+Hv+MSJBpXTtDV\\nkGp0lSjtELWOk092iLN5yukIEaYgRAqm4z7NuWPEzQZOBbmPKXGuniYBpsyRyntdumKCs4Y4nSPS\\nE3QxZO/8YzRaK1TjRRorR3EiYRLfwe/96ef41sMP87M/8QHe+IbbmYyHfOPer1OZilPXv4xX3vZq\\nvnbf15nrNkgiSW8wxJgS4yxaG7IkxmnBK255BWtr69643RrmuwuI/pBjx07y5a98hfe9970U04Jm\\nu8Hm1atsb66ztLTIzuYWo/GICocpCuJIkUjp/WW1ZKGdMN/OGE0KLl9+lmNLcySJYjyZ0O/3Obq6\\njDZNhBKYqgRrUCpmYekQq4ePsbi8zNfu+jLprWexsSKKY+I0Y239MtZYTpw6w/z8POvrl7l44WnW\\nL1/iZTfeTKvVJI0jstYS1TQnTVIaWZO89JDT3l6fRiNDFwWD6ZQ0ijl8eBWEYDqdYq0lz8f+MAya\\nY601w9GIL9x3L+955+soGx1wltIY/uNf3s0dZ49yemWJlVbM//IHX2ZPa5QFM90iaix7+zbpILAs\\nQXJiKUVE3i5OotkZFPzun32e0kj2+j1edrrN8eUF3v7am5nmA/q9AaBZWlhkMplw8tA8v/7f3sm9\\nD97IVx54hi8/doUrG0OGkw0aX7zAG88exuXaQ5dBT7iPPFlkkoWKzvf1Z7vYX17QZ8oAtdrZPpkl\\nqrZGgfx5Imf7sT5frt2XdWJvQ9Xo+6uB5BM4EHUJK2qpipQHvsfsPKhf3+oKFXnilJTeIMKZAqv9\\nOC8hLJGK0WEYM0744SRGg4pwQS7jC5Lc07AFHn2rJswC74xIWWvbQ8JvDdIarIhwZYlqzaOLoY9r\\nNpgshGRAJQ10MaYeQQYJKmmAKTyPxJlgs5f56r+e/yljhAzzX00Buh73FZA3DKYYeJkaPhlqNFvk\\n20WwPQVsga2KoDe1uOYCppwQJQnbE8nLTp9gOJ0ihUNFCU4q4qxBVRRMRUZbClrzHUxV+fclI4RQ\\nvOm26/id//hFbn/tmRte8hB+gfXXCphxHL+v0koIU4LNEUiMyT2kGGBPV0Ma1gW3lRer7Bwm38OK\\nPRz+gxL40VV+AgAIozHjHaLmCkSZd/wPjWUpJDqM3gkjXZ/nd4jZOLCZUcKLEHi+vdepvi3e+u8r\\nnrvqv1fP3XxpVu7Bajt4UgqufR8OXxHq4Eoiajp70HKKEG4FQAzOC6KxBj0doYtzqCih7Auc00iV\\ngRCeveYcu2sPo6IU0i5x1sHJhCiOMUIiKr+5natQaQeBw1hNOXqaariNdRYZJ+SDNdzwKmawTbx0\\nnGThBGtDwefuv8Sd797DGGg2u7zshrPMd7sYU/D2H3g33zz3OIcPH2PQ28RoS6OZMir9+6y09VM2\\nZIwQcOP1NzGaDriyvkEWJ1x65gI333SW68/czF13fYmlpRUazTGLi8s0mi2k2qbIK2KlqHRFIiOi\\nOEaXFYUt/BDfsoIeXIcAACAASURBVKTV9Js9SiTtuQ79wR5ZM2Ew2cNiMDpmvtOh211CRhFZq0OS\\nZTx75VlOHj9OM0pwaYtJnnPs6DE2N9bY2dlka2udM6dvRElFVUxIIom1hn6vx/bVNY4eP8VktMdu\\nv0e7PUeeF5w8fR15njMZj5ibm2MwHKKAJPIDyI0xbG9vo5Sg1Zqj0WqRZSlRFLGwsMTNk4hf+PW/\\n5O+8/2Zee/Y6Girhs/dt8t63v4YzJw7xM++/gx9/52v54v2P8+FPP8y0sQxJ4qF7Up/gOoFQgj/6\\n5CNcd3KJlbk2W3tTfu13PsuVXoUx4GTE/U8OQKyxNxyjneMV1x3iluuOMy1ytnd2aaURMk656boT\\nvPq2G2nGbQb5hA9+6DN8/v4LfP5bG2AtVlqiAzpsgiew1ydG1zzrNug2cUG76Tw64029DySXjhD0\\n9lsnXk95Ldfg2p1Y/xnm+9Qtp9A7dE4jXOgFhqRZ6Gof6p3Bu/gAao2foqTSUBn6/edCxSykD4bG\\ngVQNHyCiGGPwLGHnGczW4REW4d+hUgnWaX8fdBGM3iNvUKAiXzVGCo93B9KSkFgniPQEKWNcNUVI\\nvFuY9ffFSuWr0xomFyCiNk5PiZLMnwOm8P1eKVFK4KoJxhYkjS46H8zYr/4GC3AGW029x6502BhE\\nVUJ7FeJt0FMIvVUbNKdGFzTaDcqiQFrDpFKcWZ5nmBfsDUfMzSekSYwUsWewTyfEkfKDyQWYqkKl\\nKUmWMj/f5NTqAtNJceZFDuEXXX+tgJkkyS9oEuLOUfRgDZG0MNOxh07xot+6GkcwmzP5YssP8gkZ\\npIiwMkXY0LtAUGuMkrmjnt0pQGJwZoxWB9hezxMsXwwGfd5reakm53fxM9+ZfCXoU7l25LMjNOMt\\nvm8bGMO+Me//46cSEBIFWaNO+HDrK6l6eR/O0PvUHj7U+QAhLKBAStx4G61i4rljmADBeGKAQImY\\nfLDmqeEmx9sMRqi44QOwTHHKUhSbFJsTWkhYOMJefAN/8LG7OPfYY/zou+9kaXmJWEVEcZOXnb6R\\nG244w8aVC0gkWZZ53ZgUlP7hwVjN1sYa48GQQdZjd3eXG05fxzPPXGR3Z4tpnlNUObe/6XbyvMQa\\nzbve9cN86YufY5KPMdb6uZsqwTqHtobKOCKpqIRFRNJXIlIyzac4axkXFXOLbQo9JVYZSZTQXTxE\\nd2GF+YUuS4tLFGXJSneOrN3wkyKihLX1NYx1VNZSlTmj0YD+7harh44xHo9ZWD5Er7fLYhSxdOgw\\n890uo0GP7nyXS5cucfMttyKEIIpjJsWUCxcvsrR0iPm5BUYTL7HKsowoUjzx5OO8/OW3ICPFZDr1\\nQTVLeedrb+SGE4f5xFce58r6EOEE3UZKf1oAlldddwTlHG973Y389A+9nj/++N08eWmPB5/qIazG\\nSQ+FWwcPn9/iF371z1BBpL8zFsSBdCJMhXUKJwt++yNPoITg3W8qOHn8GK4cMs1HbOwYElHQSFOm\\nlebYkRNoXfBLP/cWfvwdp/h7//JT/Bc/9ko+8sXH2OwXXEPKEQKURGYtpDNU+YR9eLROeverRyuk\\n73OKg3t7H+2ZJchC+EAUeoz7iFCAd0OFKAME7ETQfqIQeEKQk1HgNOzPZ/UvVsOwFqMLcJYo61Lq\\niqzRxFnnTQmkwNkQqGaQrm9HeMKTCzmDRmgPq+pyGKR0UZC4eHjWhl6gpUTFmT9DdOlRMbzRg9HG\\na2+lwroSpWJMInHGKwtUlGJ1gXQRxN5i1Jpa65lihadSyjj1AdGUMzhaJClSC4yuEErtGzvALIFw\\ntsQKEC5HyjbWVaTROq4zz2S86+cXO+/qFMkO1sFgc4O5lsOwTD4qka5BGsU4gTftEBIpLGnWII4i\\njNUk1st7jK6QUUQUZaRJwmtuOcnTz+xy5Mj8nevre3/xHRzK16zvuYcphDiijfmfnWiKSEYYPUXg\\n/EgXER4257xesDY5e4mgIYTwmYfzjdooSsnmj6GnfeoH3glQSQfVPoyME2+pVBa+jLfaY/BWz/p5\\nL/R7no+1+v1a38nrP1e+su8vUvde6r5OcP3B7UPbs5/xpARkcD4SoZchBc5PlfSfi4iRSROhGj4Y\\nEyQnwjMjfdIrEDZHT3rYYoQth5jpLqYYo6c7UE0CXB0hZYJMOogoRagkTEaIUKKBijKssySNLirJ\\nuLixy/WHxrz5de9gNOpT6QolBYP+mG/c+1W2t7d9sHIOYwxKQlkZjDUcPXKc217xRowxXL5ygbnO\\nHLs7OxRlSRKnrK6u0m61idKMRKXMz81z/sknGYx22dq8SlH5/pByEicsxkKldejRqOBJCgZLIhSV\\nqeg0WvTHQ+JIEEUJ3e4SjaRBd3GRNM1IU18xtFsdFhYW+PKXvsjS0iJLSwv0e3t05jr0e7uU+YSF\\npWUG/T0azTZpljEZD5nrdIlURNZsIlVMFKdY6+h05rBW0+vvkmUZC90u7c48OMd4OiaJY4yz9Pf6\\nJHESPG8dURxTFDkb62usHD7Mwtw8t13f5eTxZRYXOnzk84+jRMlbXnkaaw0TXXof3m6Hd77uJn7i\\nh27jTz/1FYbT8CyFpFcCSZbQbZUMx1BYg5USGaZ4GGnBKQ+zCcFTa3t87POPMRn3qLTgt//8QZ66\\ntEfcnGP76hZfvf9JPvr5+/jwpx/hibU+URxzdDnhfe96JZ++6zwqML/DhgCc7/1VpQ8ighksWieX\\ndYB1QcM5O6RnaJM78HPX7s9r9qioKW0HEJ+DrSQ8lOvlHiJIJw4G57DPIARMXy3JOPVOPaEijKRA\\nl3kwRqjNWsIoPltR6XEoMiRID8kaXRCnTUwxCp9NIBvJOLBcQ4tKJr5CxDNx63splQx73r8/W46R\\nqhWGX3tdpAo+w6oe9iCEr1br0Ych0RAy6GXDXbHV0F+HSsBW3uoON7s3+wlJhBMSlbZRMkLGbcrx\\nxMPxAWwXURr6nRVUBSuLywwLy603phxtFszPz3N1a5t2s0mcpOCC/7BU6GpKImWYeRt7hUCUUBQ5\\nMlL8h098g3e/7cazP/mBX/zQt5/EL77+OhXmu5yLhBSKqtjzR7qtHTf8w2VFhJBNMIOXhD79cgjZ\\nRNgSKwS6HFH1LnjtHwRmVpg/JwwqavkMQrngd+FpypYDU9uFv55649Qb4W96PVej6S9zv9/hvxaw\\nkFBhebaaBukQNnwN5yHXGYYk9vsOwgbXJEUkBBbj4WsZgUxQSkGUeWNnp3HaIFzuM2cEkHjIN2ni\\njwzPaHN2fxith1dSH5ij2OMDgZAglQKVEKkEbXKkaROLBe49X/LL//KXue3s6/jAj72XoqyQacxk\\nWqIdqEigS41SimnhN2Sr1aLRmGfl6Alwhmcun2drZ4tG0kAKSZJG9PpDskaDBx94mNvf+EaWlpZQ\\nSiKF5M4feR9/+IcfIlIS7eoEznoTA6vBgZLefMA4S4FAISl0QRQpjPUa0UsXLzBZHmKt5vipM1ij\\n0dqzGCtdcPrUSdI4oSorimJKrzchSxuMowZZYw5dGKRURCrluutvpiwLqqqiqgo2t7aY7y6wcugQ\\njUZGr79Hu9miKHJfrWpLliYkaYO77rmbG69/mZ9jKXzgl0pQVQVal4DlmWcusbp6lHbapiImt1us\\n9QZk7RMYrZkWJcNBn257ga2rG4xGY06cOc7vf/AX+Bf/9uM0opjP3XsZY/zO2RvlDHLB3/9br6PM\\nBb/z4W9ghH9ehauDW4kTCdiIkcv5k7s3WUnWmU5TNvY09154BCV8X1zLFOkcj+/2Ec5y/tkh/OXj\\nRAqciD1pJFgj+p0bEbXmvE9PVfhdrUskXoJhnWd+gvQ9WOdwVoRY4XAuMEuFhzndbKftV0F1terh\\n0kDwc6HF42qynQwyM4/8CEn4vfW+9uxrJ1xwYgJnKqrpHkqlGD3FOH9KHYSLfVXsIWZnDVL5vq1F\\ngDG4qkA6r9d0tSGBC2YCTiDiNLDUgxeu88OinXWgfOCyukJKn0KoRoN8OiVtN3DTETUJx5h81vOs\\nzWjCf/3XnJkV7zJp4HSFEA5bhN8rhGfgysjfu+f6eVsvZTNVjmgsMp2O0MUwVPv+fuDwRCDnwFUM\\ndEo3nfCn97Z4x3sUVliOLC1429SiIE5jrKnQ1mHKCpElOG2x4TUEfjjB6tIcUgoOd+de9b2c2y+N\\nkb7Amp+f/xEVt8Mh7tmhUdKkuXiK+vRWWDAjahf/l9Y94nFxUdZDK/yDEp4jgfDknmpCOe5jnQs+\\niBAlc6FzCSg1izPhlf0mCM74/39Yz81qD0pXrl0hY66F3J6JQG2xRU1nd/tOJ75C9RWCFApBFFhs\\nocp0Qbhjg3dnkiLTNiprQdIGlaFkjItSRNICmXn5TtZGJnPIbB6ZdRBxCxE3kHGEkD6LdeFwC28K\\nB1STAXYyAKcRUcyT2y2++kiHzc1dBqMejUbGU0+fZ3FxkdFwwGJ3mUaj4StjIVhaXmH10BGyKObJ\\nJx5mNC45e/Or/VgkW4GQxHGD977nx8mnJceOHeXhh7/FeDzi4qVL3PGWd/LY4+dpz3exaKa6YFoU\\nCCdDQBUIaUgihZIKpRSNVgvQxEJw9qZXURUGXZTEsaTf22V97TLnn3iUQb9HVZYU+ZTBYEg+zTn3\\n+BNESYbRFY2sQdJocuL0dRxePUK700apiKKYsLm1QbvdJoljtjc3ObS8zGgwZK/Xp8gLut0F4ijx\\n1mu65OrVdS5fftZbfzVaRComjr20pNlqMhgM2Ov3qYwDFXH+/BOce/RRdosBsRzzKx/8KJUWfOQv\\n7yNOY9rtDkeOHKXf73F5a5f/+3OP8JX7zpGYgg/9kw/wv/2D9/K6s0epDQVec/NRfvcf/Sif+vJ5\\nPvSRbyCkRUpBEmQKnYZCiCw8nxZlFZFV7OUxparlHg4tJRqFNH7eiKwKSlNidEFRaqxIkcp40r1Q\\nYbCCl0cIlZLOLSGyDiJOPelD1J7LYkam8YHtmh0XqiP/v+t/ixA8DxaINetTSI901QiPNy4HayuM\\nMcEkvmb9W59oWu1Hc7kKazTWVnhjD4MzBboaesJYqLy81TpQ92Sd8YicFH4PWoewlZ8JGdAiUwyJ\\npCSKMmQUo6IUU01xppq9qgvJcUi7/XmBNymwDpw1uMrSmlsmcgWWMiB8Epm0vfes9EYJtSoAIYIc\\nBmY8EWcQURJgcx/ETTkK5jT+zPFgY32D/b3AeXavBJhOEVU+I4lKHwjCwAmLdYbR7ibLXctwr+Sb\\n2xJRWrJWg0pPqfICXVkipYjTlDQRSOECebRmUFviJGWu2+Xs9Ud46qmN7/CkvnZ9zwFzMBj9bXPA\\nnUEIiwGm4x3f33UOJ2Pq5v21jLEXXh5Oif0jKqJrDANEbZJc+ekMrsqpJruIpINMmv5KRAwyCc//\\n/lgxhz4QlF6aqfr9WgelKs/9+gst/xz7+1cbTAdkZNY38TCq3/j15gDlYY1w7wMRfP8M8Kk2OEma\\ndVBRhFQpIu5A4jdjDe04IbzIWcUoFXv4NUoQQlFPPBDhGnww9w+6MN7ObjrsUVUlWmik0wgu09t6\\nmtWVw2xv95DSsbl5hbe85W2MpwWtrE2r2UY6mI4m7PWGpFnKww/eR3/QI4kbnDp5A0napNPtMq0K\\ndnd2OXpkldMnTrGzvc1oPObM6euRMubEyVO02ivgYhpZCyek1wK6QIm3oKtweFlHMRkRZy2G1YRn\\nLl/wmbLAH5rWMRzuUZVTNjfXKcqcwXDC9u4Oo+GQ+XaH3vYmTz91HocgSlImRcFub4fRaESn0+bM\\nmRuDvV/O9vY2wlk2N9dAaAbDHp//q09z9913cd837+dLX/4K+XjEyvIK/X6PT3/6M9zy8ptRSjEc\\nDGbIRLvdJooVWSOlM7fA8uoiw2rEzsYaRaV4emtIJAyuMc8z67uURYFSEe1WQtzI+P1PPcb/9Ftf\\n5A8/dQ9/8bWHeOTZXe596FmMKLA4Hnj0Kv/+Mw+y0E2onPO+qt4CHCljXn79MX7ojht5wyuO0FSV\\nDzqqAlkzSgPqYxTSORwaaQyaCmV8rz6OPbS4nOXEMvI+487N2Kq1vjLpdEk7izgVQxT7ocjK/wM1\\nUU/65HnW7wxtB+/oPts53szj2rNJhCpSyH19pnPa9+ecCP27AmtLrNWz2a0h658FzoM1rAi2eS4E\\nbVGfBc6T+KwtZ8xWawwCP9bLi/qtdzJzzvcffTmCDa5+KmsGb90AaarU43LC+Zm61gQUUCOUh1Od\\nszjhyPMxcTLnYd/QSkMqnzAL4WHk2Y2RCBXXc2Fm55aTCSpuEqdNr/2uSh+w3bV8DH/PjbcdNP7e\\nGV1hrZmpDRwWTEWjPR9Y2o5qskPPnKIY7/D5R0FlnuTTbaR4KrFFOxt8on2QV4E1XZtbqCQjtSU3\\n3XCE9e0xd773lssveOi+wPqeephCiJNCyv8essBa9BelVEI8dxg77Xnhta6x+YM368VXnCb+Zs40\\nhp6lJmuhLiCEI+msUI2voveuIAElBbqaIoRAyQhn81CK169BsJHbh0H/Jtc+VPydrf2RQrCvxfy2\\nbkxgAIferJT7v8L5zeJ7BDZU4GETC+lNkaU3UHbCeg9R9nugQuyrxoRKfHLixEwbun8tdS81BPZA\\n85dCkDXnfPYqJE60ODS/w+tf9QbuuvsrbG2tsdRd5vbb34GtLHGiuLx2maL0QuQ0Tbj4zAWqImd3\\ne4M0TXjjG97B0SPHufLMBSZFzukT1yGVrzAePvcwzWaTw4dWmY6nPH7+SeIYrqz717SBLKKcQIlA\\nugjQN9aSNRXPru2h4ohiOg7uSV7y4302JUVZYKxmbmGFG17xGs49eo7DS4cZjwYsLS8hBDzw8KNc\\nd/okw0GPPJ9gTcnRY9ehIk9ueOLxc7gqZ6e3g5CC4d4e0+mIV7/29QgkWhcMBwO6i22aWZMTJ89w\\n9tazNJKMKJFoXdFsd2g2mzRbbZqNBlfXN+h051HRMk8+s8W4SPmV3/44l7YMQmk+9sGf5fSpYzy7\\nuUWnkbG5s8c/+90vsL41paoKHnhqwKe/+jT/4ZMPeT9jkyKtwMqKi1f6XLnapypzRJxRWYexgrk0\\nYns65Z///Nv4+ffdzp1vu4Fjyx3W1kfs9HNUpGawJ4ATjkkMiQGURKlG8KqVSCk4ttjkV37+zXzm\\nG5eo5RUHNcj1MxZnGSafoqLIw5T7OwZwwVPVu2MJDnIDYGZhOUv6w/8PaBZIJJKZ7h58MAxBIIkS\\nPyGmdhnyPxASx/CvWhd6oPUihAxVrU8asKWf8xn0pFL4XqHWVfB+dj5QIWa2fCIYsdfG8L4ST/aR\\nOFn73Ib3XDP1ayjXaISKUFGMitvocQ+RtYmlClW0T0xU5Kv4WutYH15CKt+qMYVn2qo4XHsDY6Y4\\nHLbyXATxbVK+4FIUN1Eq8UoHUwK6PjW87rOyHuoXvrJ3URM9GbGjO5xZcJxa7XqGrxCkjTbOSYTQ\\nYTas8oQ164jiCNVoIVXMZDyktJYPf/o+brv5xPAn/9Yv/OZLnb0H1/faw3w7KKLOKm684x8RoXy2\\nMNzEOUdVeShCXFPEPl8VJQ5811EVxYFAKeqWQ2DBidmPT7cvQtgA1WQLR6BYm9z3lFwY+4WHTPaD\\n5T408Nzf/59i7Ws3Z3voO/57uHo7frtkZtalrd2Twqbx+igT7p9DCA8zWUyQDSjAoOIl0laLaW/N\\n9w6kh7GFq4NfcF2Ssbe8EsFt6JrP1FFfYX2VdTdWOEG+t0mzOY+JUlDzPHzhAtu9baw2NJtd3viG\\nOxgMB/zUT/0Mf/npv+DeBx5EO4uQjt6gTxRFdDtdBnsDNrev8ujjj3D2plu8LtSOKXXFk+cfYzQe\\n0+k0+L3/63/n1lvOcuMNN3FsdYn/92OfpDIlRhucclgjMdIira8cgw+V32jacez4Ipev9ljuZBTa\\nGwkIASJSGKsxRnkThGLKk489yOqhBYQyDIZ9jD3B/HyXt95xB9tbG4wnBVkskdIzdKugETt+4hTD\\nQZ+5JKLR6LJ86BCTfMTTTz/N05ee4vjJ0xxeXWWhexhtodVusbOzgwCa7TlacwsMh0OqCvq9Pmma\\nMd/t0k5islbKv/mTc0xKi7UCKSq0tnzqvof4P3/p33LTySX+/b/4Oda2R9z96CaxtGgipkXOYrPN\\nlByDIpJTSmlRWiJQnDre5R/+7Ju55cwqhojLG7vceHqFajomz6eMJ1OOLC/ys3ce4s4338BXzj3F\\nP/6tu3DO0s4SFluCy5s7dG2DqYtISHDKYoUC7bXU6z1HJ6v4/V/+Yf6bf/VxxmUTIw3KBq0j3iZE\\nyJS4s4ibDrFUAQHwDNI6eMxil7M4GywmxUE2fS3zCMm9E6Bi/zNBDlIHTSG8C46zlqoaz570GRGP\\noAc/wKPwq4ZHw951FmNDkHQ2kNgtzuChyRnDyO1Xvw6vPw8VsnPOX58/IEMF7s8XCUFCUnNHnHcu\\nmg3I8MCtLnOirE3UWfLvM5KQT/YrX2GwUmGjBir1rl6V9X14KxwyzZDxHJEFLSyu8CbwEgc2DgW3\\nA1tCbQIR7trM8rB+76FFBN6gBp1jowjhYgSWvJgCoKcTHu0f4g1Wg4xxZe6ZspGD0mJwweHI+kTP\\n+kHhuizIkpjDS22SOKIj08Pf+Qns1/cUMOfn5+8cjCxmvAPOE21ElCGsRZcj9u/IC0cE/9DWVeM+\\nAUapDGsKANLmIsV4M/yc2X/4BEhbYp3ERjFSgNEFSdoFGYMdYk3kadnhtaVUvp8wW3/zVWZdjb3w\\nqkNPTXqS+5VdgL2/vcoMPU9bQlVbD1qkUEgZg1Sgi3CoWA8bxQ0QGqMnRNkC5WQLRPDQrBs9hFrf\\nGawUGBGRxE1voiwOql73K3qf6Uq8CLuimg5x5QQVZwhh0fIMrTTj7W97J3OdFh/5xJ8jhWNpaZky\\nL1hcXOTKlTFxIilK7c3HD60CcGhlmUcfu5/NjUsMRn2OHj3J1s4W0lnm57psbW8zmk64576v861H\\nHuIDP/63cVqhtcMIEE5irCFRAiVVGIUUqgRrcdphzYiji02csSwuLTKdlJSVN8MWQmK0YWdri73B\\nkLM338rcwmH6e9u87GUvx2jLdJqTZE2aWZMkzijzEcdPnEFJ74hlrCHOGixnKU8/9RRHj88zKaas\\nb65RFGP2tq+ydXWLt731B4ijjOXlZXRVMj/XodfvYa3l0Moh0jgmLyua2RxFUVKUhgcfvcjaRDLK\\n3f5hTIySkt/8w2/iRMoDT+3x+Xsv8G/++ItI5xCR4D1vuJFffP8bcRH8+H/377zRtciYUw6hFBNj\\nWF1p8bbX3EyUKozWzDf8/ms0UtIsoaq8Ty8qoj03x7te+yo++PdSpiW85uaTPPzYo6zvTvnt/+db\\nNBT8i3/0Hv7Hf/1xz3JUCusscWTY3ulx262n+KNf+0k+9OF7+cQ9zyKFH3Jcay8B4kYHqyKK3Q2v\\np1Wxr2ps4E5I6R26HEAUqsSaYbrfLnKzM0v4oct+4FbgGXrugB/aoAAdyEG1fM6fM7ZudThvU3hN\\nu2T2R+jjhfNpP+UMkG2oBlUNF9e7Samwt01oycj9/mIIgrUYx+jC+9BivfGBjHBS1D+JE/75N2W5\\nv0fjCJvnqDghEhFaj7FW+z6/jJDtQ+S7l4mSzL9XJkjXIml2KbBEU8l0vIMkwrocJ5MZW9mWGrFv\\nN+HfZzWFpBnkgXJ2JM7cllwBVlBFCqESTnUmXLxaIiY7XNrsoqucKG0gqXy7pE6PQtGAEMhIUWPX\\n1hRUWjOftrjpzGGubOx+1/Hve+phjsfT90uVodIWYDFWg2xgAr3aO2tcS+V+/uWe832L1sWMaVZO\\ndqmzshlWHsb4+ErKIUSMdJDNreKiDJU0PKQRN7xjSej811TzmqQUQNrv5e3/J1n73rxhI87gVnih\\nfmcNBYnZtrHekSew/JD++bABnvL6MS8StrrAWOXZdDXkepCY5AhMSN8LlVhMvo0nEQUkQMQIVOgL\\n1lCtr0RdsM6a7u0AXjokSBhPhjz0xGPs7Ax4w+vexKf/6gs0mx3Onr2VXn8PBMTKjyFTUtLfG3Lq\\nxPU00g5CRmxsbjCajNnc3ODZKxeZljmf/fxnePDh+9FaUxnDeNTnyvozfrKJLXDGekZwuEuFMRhn\\nUMrPy6xhXSVSErwJfCNRaFMQKYXDUlmNNoay9PdubW2N3u42zaRFPpmghENJRT4tQAhajYyFpUM0\\nmi2KMqeqKop86vtfRnDixEkWFhcoTMloknPs2CneescPcmzlEJsbzzCe7PGZT32cSxcusbW1Sb+3\\ny2iwy+72JpcvP8Pm+mXihh911Go2eeWrXsWffPTu2SEqpPd3FvW0GcBYyd/9jY/ywNoApGFpsc2/\\n+vs/yKljLf7dx75KDaFK4Bd/+nY+8Tv/FVpPOX9piIhAFyXFdOJHiWnPjnTB23QymeB0QaQikkTw\\nn73pFn7qB2/julNL/PAdryVNJEYpNAmf++L9ByBRgbMwyCOuXN5CSsvS0iJ/92ffwm/9gzfz+puW\\nUS60B4JBuHMOkTZoLhxGRKlHAlSESDJklPgkMZB4ZtZtwj+PUvhpGCKKvD2zkF4aFQdZgwiHeT3g\\nYJbgBrhTqNk1OGPxOvDQhwvbtOYtHOzRudkw7OfZxME3OxyDAQnyjFs/PaViRoqRUbhOAiwcPKdF\\nTQYkQKw+YFldeqYrgKsCCdjinEboyhdARiPiCBVlRFGCELG/fuGIO0sIFXvSn2xANkecraGsxYkK\\nYS0iyUAooqjp/cCdRSWtfUxPBCKSLTHFGOF8C6j21K2rW6MN1lmUUCx1NEeyHq0IbFnxxJUCSYoR\\nJlgCFlhnaTeb/h4FCd4sSXB+wpGvyB3HVxe4cGWX2167+uffyXlcr+86YgghTmljE2PAFiPA0+9N\\nOfSaGcQ1D8i+KfnzPRnPhfN8RbMPWxo8cBCyEqmoqSszmzszxcVzOJUi4wQZN0D6P1XcxEkVAm4g\\nADkVHka79C//zAAAIABJREFU/7W/sfX898a50EM4WH3jwLqgc3OzPsq1mzH8/Vpbaf0G9Y4mwmfw\\nVofXIDBnBcJarJkQywjnBFHSrJnd9Qv6awiZuHSA08Ttw6go9j2G8Hetq2YMOd9DratkEBjMdIDN\\nR+BKJpVjZ6/i+NET/LMP/QH33P91tooWx1eP0Znr0umusLq0zNEjx2i3WuGiLZevXObK1XUmIy/v\\nMNYynozY2r7Cgw/dQ6+/idaepIEUNBsdnnj6CUbFlE6zTRxJrHBYYciNJtdeQF5PTnBK4pRDO4OV\\nDuMsvX6P1ROrVM5D2bFQgaVtGI32mEyGbO5c5e777uOe++/hwUe+xebVK+zubFFpQ6PZottdpNIV\\n/cEAawz9vT32ej2cgDhrsDcY8c9//TfYWN+gmlY0O3OsHj7EztYGl556nEcfeQARRWhdsbO5yWg8\\n4itf/RJKKY6fPMm5h8+xsbVFsxWTqYJ3vv114XN77pN2AKZzEamV4BSLrQQjvRTjI58+j3G1EQCc\\nWu2yMt+g20zpDXN+9f/4DNO84N577+XKlSvEcYyutCesWEu/32Nvb8+POBOSNJKYqqIaTnnq/BP8\\n6FtvZqUV49D8xd0XD1RgwfzbwdfPPcvFZy4ghCKNFCcPLfFPf/EdvOPWBkmEnztaP6emQqZNssVD\\nyGYXmWR+5FSUBvebMLkjVGUy9DWlSlA1kU1FEGWorIOIEl9VBf0hIcDOdIg1qeiAjMvrHt3MTAAZ\\nEKFZXSeQRCFIy2tqywPbLKwQNGtP2UAywmqE3bcNdPWHWxP96v3mHJFqItME52qTd8BVQe9IkMn4\\n61VR4t3DjEVkLUwwnjfl1CcWMsJZQdJok7S6JMkCaXMJZTSCBRxe6ygb8550g8DayqOFs+r+IO/C\\nn/9WT3E6DxCzCM9BfTZ71nBk4NBKxsNXlylcjLMFlXVczh1KpsTNOShG4ByTydS7DuGotCFSfoSH\\nrkqsteiyQkrDq245yaUruywtd37oeQ/hF1jfCyT7bhmleFqx9JrAbBE57V3LNHsJVux+L/HACn2A\\n+q9J1QQl/LBobHDD2Icdamii2VlmOlgnaS55M+Eo8rRvIZBRiit16HgEtmct4n/R6vf7t17awAHq\\na7s2EPr34Ilsuv4JakJAPUEF9qc2WBOkI8pR6yutC1tYRKGp7sDAdLRB3D7ih9QSXG/EfiXmXECA\\ncEin0OMtjBMIFaOS1M/YtCYQgcQ+RAKz6xN6yvjqRVpHrkfEHX7rjz/J6UXDV761w6tuOEwjdRRa\\n02rNEUcJVSXR2jAaj2lmDbpzXRCKfr9Hzb8Twjt65DnoSmNiQewszgpMZXCZ4upOHyf96KvC+B6R\\nFAobTlzrfF9XRsE3FMjLirIoyeKMOInZuLpLryyZTxsoa/3ILQtIQX+U89SzFxDSksaCR889zhtf\\nexunTzdYWegihCBJEqSULC4sMdjrYa1jPJ1y4vQ8Qgh+64O/wfraVZy23HvP1+h0l4jiBoKYK2tX\\naGcZ68+eZ35hha3dHtOyoN1uM83HPHPpIgvzcxQW/vxLT7K+0eMTX77I/kzVb38GHQInDC48A089\\nu8vvfex+vvDlh9DKzrSVUsBvf/QbnDmxzKQwOAkfu+sxVuccb3z5PN35eQCubl5lcXHRB8aqYjZN\\nBMH29hZlWdJstVhePkR/d8L733GGP/jEY2iRgL1WFuWcYGdgeeLRR7nh9I0URcFoNGY8mvDet72c\\nQwuP87VzBYNCszPQOKFQQmCjlKihZuQtqSSuFD7QiAR0NdMQKiFCdRn7UVgAUhHHCeW4z+wgEvIa\\nLWeN3hxsmfiKMiRd9VSmGhoMz39NQIWwkZzk2hEt+71Sfx+CdGy/ZKh3okeRQtvSORNIfOFrwktL\\ndDnGaeFbALMz0yGEmU2/kQ6MLjFJ0+s4qxKrNUnSpKq2cUkbrMYpkNJrHdNmyXjgELnG4BhXc0iX\\nE2UNcmuQQuFnYdq6vMXo6ew82w+Z3nDeVDlKxbMT2TmLsHJ29hgEUeyQegs79X7Gpij48Dda/A8/\\nGKNFjjAFwhrGkykLc22UlORVQZomSOGwuqSovE4TJMdWFkiTCJ0bpZRMjbHF826U56zvmiX7m7/5\\nv/5KUambQSGEF9faA7PMahhn5tOKfckA4W/kc/6Hk1ilcDr3D0Hc8rqomWt/+D3WUI63sWZK1FhA\\nRpmf/h0ml1hdz2cjQBVy9vB+P8myLzR4+rnff7F74w5k3ddoNjmwieomvtvviDoIPZxQQdcGB0Kg\\nsjkfYKwJEK9n0nrdWYoUFmyJrUqU3G/Swz4b1tPVw2ETjJZlnILNETYKr1t/jswycikiZJT53kno\\n3+0Nxly+skGR9+i0SlaWlnjPD7wDrSuefuY8W1tr7PZ2MVqzvLhEq9FkrtNhNJ4wyScYa3yPJYI4\\n9h6r2ljfewnOJN3uItu7m2jjIVoI0x5dGFQtBBaLDHaD1jpKa4hlRGkcxmhM5eUk670Jw0LTTlOM\\nsVghKbXi8pVttB6TRJBElnYjYbDXxxjN8ROn2O3tEccxzUYDXWkGgz2iKGb1yFGarSZKKo6fPEqW\\npRw6dIi93hbDcY60BUkc8437z/Hq17yafm+H1SPHefL8eaypaDWbaG1IEi+larQW+dL9a/zFPZfQ\\nQofk8PlXfQjLcNAaC3c/+Cxre/lsH9eH9HZvxEe/8AhV5QKmBFujKR941ytI0pTLa1eIIu/Pu7a+\\nxpEjR7hw8QJSRTQaDZI0ZbffD3NtIUki3vH6V/Nnf/UAw8IHAGddgPIB6TjaNhzpWvJpTrs9x/XX\\nX89oMqLRyDj7sjP88Ntv5fRyxmPPXGVaBNfX8NjJ2LtOqThFKl/9eLmTh4xVlCHiDJm2iZpzJK05\\n4uY8UdrYN0N3tZcz1D02gQtzZQ8ww0UdEOteoreKdDVEOrvT9d2s0TQxg4a94fxBx6IDZ0T9z4Gj\\nwgN3MgTqOkEW+9pRAdb5GZzUSNIBpq6UodoO8LwUAhE3PBFHl6HqcyRp28PHKvIGJGWfSLSpsFTs\\nEUXzuKrAVGOstuh86K/HVr53aipcGHH2XKKiP0Z8ALum9xguUsqIKJvzsra5lzPe3cKVFisMUdpi\\n2y7w7lsEqYxxNkeJCBlFJKIijhSTfEIza86SEhHHlGXFNDc4HOef2cQaIdeu9n/jV3/1n5YvuFEO\\nrO8aknXO3hFnc/7glKnXPOoifEZi9iHLoPN5vpt07etdC8vWxsRKCBqto4FWjXfKDw12f6P3/xRY\\npGwhoibCaYQdICXegDckDvvXYa998r5P6zuxAXwhTeZzX+Pga9U9ktmjXxtPw8xMur5nNTFAhCzP\\nGeP9YAVEcdvPUxReRoIzYAp0PsTkY58YOuNDiwh2WqLe7G52OACgEkwxwYkYJ+211xqgKxF6RU4l\\nRGkTYSxVkaNG3+QNL1+kKVL+6uvb9HrrPPzk0/zpR/+c9WcvMx6NiWNP7Dpz+gxVMeH6627g2NHj\\nM/9Nh0PFEXle+s9ZeM/MSTGlqHzPRhsPF6owCUM4f8+ssBjr/GbXhtIYjHBMSsOkMjjriBC4KKE0\\neN/ZUrA1nDCuHLmGvcmUo8e6LHUbZJEjiyKcLSjyEdqUbO9sc+TIKlubG+zubqGkYHFxkYXFLtpa\\ntNFYB42sxa1nb6I1N8/8wjKduTammFAWE87e/HKeefYZdKUZjgacOnaMNI4YT8fs7G4zHA1JG23u\\nemiNLz10EW0F0jQ4iKJcg1bM9GmesT1Li5z1LlJuf76tf9IixoX2qIQAJwSvPLVEbi0PP/wgzWaD\\nsioRUtFqt9np7TLf7bKzu01elPT7PbIspdvtMjc3z/LSEsYUvO9Nx7ntZAupbJBCeG9VoTWvOF6i\\nqymLSwt0ux36/T7tVoeyKOm026w9dY7+xjl+4o0rrM55e0cbXK9qWZVQCpU2iNsLRK0ustWFtI1q\\nzxPPL5POLZF2FhBpCxGlyDhFpU2S1gIqayNCa0fGqe+BqgRkGv7cN1XwAbWGbiNE3CBuLfjBywf2\\ntPXR1z+nIeEUKvYeqqHKrdtNQrzQ6RkUAM74f3AIDMaWuDCWy+H5HbW9XfjUZ+eGMYX3uKV2PPLn\\naJzECGeImguo5or3u41TnKnI8wFZc4GiuOzfQQHWlDhXUk37mMmuTz6t9i0ZYXxgriFqnu9c9GV3\\nrRMNX5klFsYVrBzS4IYYazGBCV3mYzSSP75rTGO+DSLGlGOEhNG4xFjY2ctxwqECAc0aR5zETIoc\\na+Fl1x1md2f8HRV09fquKkwhxFJZ6l+zLvXNYTyuLgOGTn2gihq1J0Cgz7+eGyykDFmZACcNTkUz\\nkwIQOF1cE+tmh7ZQJMs3YvIe5eYjlKNNTD7CVqNZlfU3ob18IZ/aWeb5At/f/zl4LnQsD7DiwNt0\\nhW8AdpZwiFkGG/qX4BvekfLJh9VoU+53iF2dNe9DvzKQhHx2K2cPsQs9G+dAxbH3g5RJeB03SwYI\\nJIv6mvzUGR8c4qRF1Ori8vPkxRZbg2dB5uxs9lleWuDOd72byXTE408+yrQYg7V05+cYDccgUrTR\\nbG1v+APJGlTkoRucI4oisM4fVEIwHI8og2WXCjo0AGckkfQQWxwpsiT1om4hiaRvOljl4fsLm0P6\\nwxwnoN2JkEqQNROmgylL3ZRMOvKiwNqKSKWAI4ljxuMJu9ubdDrzLCwsM5lMaTVbVDpntzcgjmN6\\nvR2m05LLz16h1WyTtVtcXV+n1cjI2nNMR0OsKzh8aJl8MmY4GhAlGcPJmEMrq6weO87ifJdhofiD\\nTzzI3qhGL2rM4dpn8sWSNCs8UihmbFA3O7glta5X0G5EaCzj3R753iUuXngKgaA11+XJJx7n+PET\\nCGB3Z5fFpSVGgwGd+XkuXrhAd34BB4zGQw7NpRxaiqnGY266bolL60O0rFhuTFiJ+iRpg6IoWFhY\\noruwQJpmJEmC1hUbm5usrh4lVZr3//DruPu+bzEpfTvGhV69sRaPmktUlhClHZJGC5W2vPGGUoEY\\nst/Dlkr5yjTJ/D2IYn+shf1YB0sfuEL7QYhQsIXXEgKiJqq9jEoauGoyMxEROJxQoZfp9xZIz+QP\\nMrAZb0CKGeg2qzzrJPggfouY6R1F5ElyQijfgz1QDTtTso8N++sQ0jtc+Xm3HiGqkQkV+dPDWI1Q\\nCXGWYIsLCI77KtCVOK29lKYq0PWYMQdWV/7Mfh6uyMGzz9+/5zyjAo9k2AjROMlob4otcqQLI8CQ\\nZEtHuTIQvPU6QSsVUE4pDEzGljSDza0hh5bniZrtcN55yVF/WpKlKVU15XNfe5yzt6/847/zc//w\\n115wUxxY320P8zUibqMac7h8sN/c9h8FMmpgqzEiEGtAEqeL6Hw7/JSEA73L2YcYngjvch92rFOY\\n8U5wniAIVL/9ACA8GCJKsXubvj0gHNbms/7N80kvqK8HzbWgyfd/vdShtb9ckMPsk3/2ExNABKXp\\nDN5VdVk5g2hmGZwQICwuH+BUEjxAY3Bh0rr0GaaXcwnqQdFC7FvdHbxk5wRxElMVBU55f19qCn99\\nLbKepSdmqAFxglQxwgnfXzIxm9sThJtjXuQMbIet9Q3OnXuEt93xDr75ra+zu32FJM544vx5oigh\\n7W1Q5BXLy0uMhiOqScm0LJEonLVIFHMLXXZ3dz0xx2mE9JWUMQaFoNAVEQlKyHAvoCxLnLOUFm+z\\nJQWb/T2KKsYUFtWQLK3MsTqXMRnmVFXJykITKRyFgbS5gLMlg/GYdjPBSUlV5PR2Nzn3yDe55ewr\\nOX7iBrQ1lFXFddedJkkaPPTQA1Sp461vewsCGA5HCFNQ5AWxEpgy95NJ0iZJQ9Od77J+9SpHjx8l\\nyxpsblxl+aZX8LVHttke+t62T2K/PVi+1JIEnWOdPDnB87UPRlPNoxeHPLVe8Es/tshwdwOH4957\\n7kJKxWAwoCwKTp86xe7ONjjLsN+n02kznozIJwWjUZ9GM2VnfYt/8l//CImWrKgP8/TlPWIzoZEI\\nbnr5zXS7Xa5ubJA1mmRZE2M0k8mUU6fOkOc5RVlxz9e+xDtvafPZx3M2h51gBmD8/E7nIFIIa3FC\\n40L/Wljt36PdZ6BLIXwQcSBjiYxirC6plEJLhQpSEGs1VAXWKm9154zvXdpgRq68D7NKMlR7ERNF\\n5MNdhJ56Fq4MjmS1P7T1DFih/j/e3vPXsis98/utsNMJN1bdSqwim5nsbqoj1aNuSdaMpFHoSYAB\\nA4YNA/bAf8XYgAHBhgHPB/nj2F8GxsAWZEsjDTQjWepmB3Wg1GyGZiqycr7h3JPPTiv4w9r73FOR\\nRXmkRRD31jnn7rPDWutNz/s8UTBctI5n22d5VJpp8SN3BQ80NWlf41yCanQqnbMhgiUACb0pABXW\\nbrEgyjQojTEVUieYukTHnRC5Oo+LM1y5WNK1ByzJU9RuhJIyyJFJgTD1yvzwSAKGBNFWZe7e8+4q\\nM/kWX+JZlUn0WJwvmA1uEmdrgW4QhQeUzfF1RR3F/O6fz/gf/nFC6RzOg3WGIMgUhVSzt1gXlHcs\\nkEWaeV6w3uuQFxUdlX3iumjHpzKYQohXkQl+McF524T97Y2Qoc7oDFLG2HoW2BqKw+biNS0M/N40\\nkXcuwN5Vc7NEaxeWMQ1HFbqj4Ql7gupsYifXsPm0SU+q5jm4ZgN/kKENRwhJ40CwvPoRhwubx9/S\\neDh37ErkLWickpVaZvN3vnm/CQ2bz4u70s3tVbdpy/agploQpz0MDhmt46opqxfvm3pCOJZaElst\\nDy0C8szWRXhmQgWPOnRLh29ukbLIoGohJV7qhrZKI2KFVx5f3+b0mXPs+VfYv/p/0Dn2K/TWN3nl\\ns5+lqivSOGOjv8WdvTvoWGNry3Aw4KUXPkdZlVy8fB7jApDHlo40i/A4xuNxcAJUiBysNcun6bwn\\nTVK0aK81/HQiePmpFsSNluDZ7ePcHoxYO94hySJ2to+TOkdpFkGX0tWBP9dZisrSzTroSBPJiLoo\\nieMIKSW7t68zGh3wypfmnHniXEhhVgWm9pw79yST2QQBGGOIdEgvnzp5ilhHZN0eSZJy9cpVXvzc\\nF8nnc556+iXefvdtTp44Qzkc8r2f/JR/+3qBqZuatAtO0L2O2ZHxu9sBumd2hlmw8v7dZYGjjbuo\\nCv74zZQn7cdI7Xni3Es8+9xnGY8H3Lh5nZu3bqKlor8WBH13Tp3mO699m1OnToIzzBZzNtZSbt+6\\niYgU/bTgTLJAZhH5fM61KxfYy3p0eh0+vvAhv/hLf59ut4tSkrW1deoq59KlC9R1ibUzTkYTFggW\\nMoNYIQ2UfgZ1iUwkyqYctS+AsTlR1Lv/6oVgyeGsE9L1BJMusGWJr2uUN0GT0pjG6DVRoXdNT2tD\\n72ksKrLUFnTcxXmPTPsBjEgwKK4uggapL6EKJZYgKqHwrmr6g+3RXitap6Z9Js1uJSK8qAPaNerj\\nMeBrJFHQqpUhC6O8xZSmoa+sUT5pH2xYx0062EmFaoBbIsnwdU1VxejeNtXBAJ/5sJ5tCb7COhci\\ndG8DzUmTWm4zaktHfuXncqdaon4teL2yt1lcNaXSCbGyVEYs2bbq2Yhkc4ei1iilw3r34LXAWej3\\nk9BvaipMXRElXXyZo33ov46F4OTxdWaTEiFF5J2v+YTxqSxCv9//h940TaK6AXe0tHhpD9U/gYx6\\niKTXbOhxiCi9QydySeN01+QUIQUi7t3oZYTDLFO0D/aOA59llPaopzdWitqiIdSW7TxYwt2P6oYN\\nGbFKQ/pBRMtTcys1rr+LcXcbzkoE3mxcy383aYwjrti7Xw9o2VYR/m4XQTSpa4TEmTzQCMYbqCQL\\n4cgykmxzT+E7loa56ffyzeeCQ96SK4umDYbQxqPk8rsQKiiet+nZFjkpgkxwcupVfvVrv0o8/xN8\\n9gySAfPZiKefe5F33nuPoiq4cO0Ke+ND8qIgijQvPPcCOorI0h69Tp84ipHG0c/6nHviGdI4C/XX\\nRt7IORfq2W1PVgv/by5VSkFVG2rrqF0gZyjKBcZbIixP7qxzsq9Yl1Ds32RyuIeKQ68mQiw5TBUe\\nU5Z4Y7DWoKTEC41xEkdQYNndvcOlix9y6eIFbt64wVtv/RVVmQd1eDxRFFHXBolnf3CAiiJOnXoC\\npSI2NreYzedsbh3j+Rde4MXnXuLihY+4duc6792wjPNQkw1PTz4wi3GU1Xn4fAxz7MFzNDQ5uOZ3\\ngZOOK3sFW2eeZ33jDIvpjEsXP+Jn77yD8PDqV1+l2+2yvb2NTlL+6vUfceb0Wdb6Pbr9NTbWt4lc\\nzsH+DURdM9k/RGeacjFD65iDwR6371zj+tUr9Ho9rK0pigIlJGWec/XKVbI045mnPoPynq9+9gRf\\nf+oGr56d8fTaIUoakmiDrjLESDp6TD8ZEascrUoi1cUad/8a9A5nQl1NSYFDoeIuUX+DZGOLaP04\\nsruN7PRRSQ+Z9FFxJ9Tn4wyp4oajuKKaHIItQ79hElpdZNYj6W0S94+FemnSQaoOMumCzkJkSxNo\\nIELJpTUiEBDMrnVqgqMsdYzSXZTSCGyo59ogdo33uLqpWTbsQUqFtK41eXjflAEdITxOCJwp8L5a\\nijMgPEq6pt8dnDHU00MWkwHOBQdZyUCQErZtGQBDK1mvdlO6fz93KyT4K9k0H4wm5QRDtsRpSCQu\\nn4IQXBkZrCmZ5zXOO7SOMAi6sQxlEhP6T/M8b+oNNDVTydlTW/hSEifyiYeviKPxqWqYv/M7v/O/\\nGJdkQgiSzpPY6nBZy0pPvISOAgOEme8RJxm2GjepgwDrFUI9xPDdOzxy4yy+HCFEAjwYYRW2BYXu\\nHQtciPd5LiufvHcHaMSXnXV4oZt0r0XoFB1nASn2iPrrf6zxqE3tgZ9voNqiSbkt2wZE08QMoW77\\nMCejqfvKJjvg6kVT52gqxWLlc6IBF0Qxne42ti7vOu4SUNQYbB0lRGmGs6IBFcplWla2VGUisIzo\\nThcddUkGP+TDXUOZ/kN6aZ/J8C3+2a/9Esc2N3nj7Tf587/4M86cPkVdGop8watf+ho3bt6mt97n\\n9JlzbG2ewJic0XjE5z/3VUxZs7G+zuFkgGs2lHDFvqk4hWZmvEfLgDIMivYSqXVoh4giysqQqMCh\\nqYXHWaiMI4k0SRTSYUoKqsoGgoIi1De1VE0620DTC6sURFLT31pjsZiwP7iNljGXLnzI3u0bSBmh\\n44ztY9t4J4mjCLxjOB5wcLCP1jFZ1sE7Q1Eu+PKrP89kMufU6ZMMD/bwQvHRsMvhuA73us3hfdpx\\nV029zQWt0DD64KB+/rmT7A1zhAftIpK0Yqf8gKIuqU3FdDJBKkFlDLdu3QQJdVWRL2Yc29pmd+8G\\nH51/j6tXLvH1X/xlDkdDoijljZ+8zpe+/BUm0zF5ntPp9InTlPl8TifrEEUZzjsufPwRZ86cZbFY\\nkKQpx48f4+rVKzx57hxVWfP8iy+zoUueOxOzlsw41Sv5lS910YtrfPaZk3zzy2vM997hYHGaIhZE\\nbR8xHmkl1pumpBEyVELHTcallc2Tof0k6ZJ010EptNIIrXBNm9HRHTxKrzqhEMIjZETS30Rm/VD/\\njzNMQ0oQQFUS703Dw9wAgFb3gNao6Ci0HUm9BBAhgmPoXYVEBsPpmkxVe8wGU9A6xKpFyguJTjvN\\ncRRZv0udDxEiAyx1VVBPD8jWz2LrKaZYYPJxyMZIFRJLEIBHzhDI43NaMKhsek+PEMX3jqNSQLv3\\nBEdfhmqea/ETjZMuIpL+NkjJP3jJ0Y2jpiat6ShLFAUkeydLqRYL8ip0DpS1oSoramPZG824cG2P\\n+aL63X/xL/770SctkceOMIUQ24tFvhVvPgPeUBY3llGOEKD7J9FJh3qxBybHRX2Q6yA0XmZ4/XjG\\n0uNR3tPbPgc+QviSB6dTAS9wKqIY3mlSTHdHafd+31GhObCJOGcRogbRKgV4XF00mnGN0vrfwWjP\\n61EgoOX7DT/kcjvzLWWgABkDS5Gzh3xZAC1YBL4cBwkiYwGFVi3ogEaRocnbGZgeXFyyV4XzOUqT\\nt+dmnKWeN+oZgiXBezjZ0KtmbRAARimchM56xlSvUR/8AYvhH7OxtsaZ0+fI0hiA//l//JecOH6a\\nEydOsrl2jLfee4uD4W3e+dmb3L5zk+PHjzMeTYnihKeefIY4VjjjOHPiLFIIrA1IPdUAKzyBeBpv\\nMd5i8CilycsKU9f0ux1MVZIkmiwKaTmkQGiLlGBcSItrrUgijY403jdCtSpQkkWRphUMcM5RFjVC\\npRyOxgzHB3gvyfOa2nm6a9vk5YLtrTXm0xlvvPE60+mIKI7Z2tim3+thjaGqai5dusyJEyf52Ttv\\nUeULzn/wAS+9/DleeuVrXLq+OJoXj1Uff8DUaH4uMzD+yFi2jlG/n7J3MGheA0uFN5B015FkCG85\\nceoE5558mm63g6kr5tMpk8mUqiy5desqpirpph2++uWvcfnix2RJxN6dm9y5c5vX//p1qrqmrGsm\\n0yFSSY7tHGcym3Lx6iXeeuunvPzyy4zGA27dukmv2+PG9ZucPnOW7toGX//6N5gPR1y5eIFLFz6m\\nGF7kC89u8ZlTp/jPvvmr/Oe//Q1+9u4HfHB5wVb9A3rlgLia0K1ukM3ex8hoqVAiVBycwka/UdAm\\nzNyytuiFDLSgOkU2clstMQENAbpYomFbY9u4bypCph1k1qW3c47+iadRnW7IWOgMIaMVHABtfYWg\\nzxnALzJdD+egooZ0qwU5ymbNaQSGkIxt0qJi5Zm6OpB/2FBic9bSSMRgS4+Kj4UWkWKBqwp02sXa\\nKVWTEZFRipeNqpFWOCkCp6+QWFuD10uHQaj0rij5gcOvKps0e5n3S9rK5e4oBN4UgQRFwr9+3dHp\\nZGgBSip0HLHIC+bzBddv3iDcHENZFnjniLIMBJzcWWc4yvncN05eepw18mlSsl9CRFizwMkE5eRy\\n00Y0seqdAAAgAElEQVQnyDihrkqsyZHpBjpeo/fEK6RbT9HbeQ7h9QNv0/0RlsCICGEW6DihZQp6\\n0N8KAd6WxPoISXpvu8YS4XeXUaLxUhqkW0NTJZrSua0XYQL9DTeeTzMeZSBXfz4wEsUftZAA2EVD\\n/XTPMdvUBm39KZDiuyayFlLgvMU2G2TAVrXenSBWDp2uYW0L8lpWR5fpOQhACnSGEA7v255XwgJ3\\nDbsSoWYdxyne1uwvtlCHu4HcubLU0wXHjx/j3fMfBYPt4Dd+8x9RVTUvv/wKT559hqefeg5nKiIF\\n/V6Xl154hfXeFsVizj/+5n/K5176OXa2T4EHpRqybamxLqCIE6lQceAk9QBSEGcxOo1YVDkqjsB7\\nCuFYWMMsN5R1Q0kpoLIWKQRFVQYnwYWajfOeOIkxNrxWGUF3bZuNE0+RrB1Hqz5pus56b5u8qPn4\\n4ytsHX+Cy1du8OMf/zXf+fZ/QApJWZYsFgu0jinLmjSJsabg85//PFnWo5t1uHPrBpIgWfSTC4eU\\nSi/TVZ9m3N/adHcJ4N7PmdozWtTt9osUki+dS+mtbdBdy1hf36HXXePw8ID5fEZR5JhGumo4HDCf\\nTblz+xafee4FBsMBRZ5TV4bpZM7Jkycpy4LRaMj169fxAnbv7DI42Gc+mzIZDxiOBrz//nt477mz\\ne4uLFy9w9tyTrK9tcmrnNHdu3caYis0T6xwcHtJJMwaD27zxk3e4ceca3/72n/LWu2/T04ecXC94\\nQr1Pr/4BveK77PjX2SpfCw5W65hKibVmud68aynvmjKG9w1C24FSqDjI3ikdh0hNaVAKISOUUGgV\\nNesr1P2l0qgoAh0juhtkx55DdjaC7qRStG77cp35tmQi8EI3adi0WbcC4dWRVRFN5Ei7VzfR2jL1\\neZRHEC4AlkxZLGuYxppgqFSKEYKk08cLiStztGwQHtZi6xznDc7UaN1t1r1DiKihkgyZJmvaVseH\\nB05LwnrfUvs1LW8+EIuEeSkbZRpLMR0grOD9mw7noLaOKFIYJBsbfQ6HE6rKYG3Q33ReUteOqrLo\\nKCLNFEVRUy8eqw3z8Q2mlPIrCE0UpcFzp+F99AId9yhHu7jFIVG6RdR/Ci89XmXojSeZH15DL8VY\\nj8ZDjYDwVPkMJztNIu1htzik2Mr8iJVjNSW73AxWwDCt9yyWno64yyi3VFaf1PLxNxmf1gA/3vc3\\ntUMfmq2dvxskdMT+0x40pCmV0EhUQMI528gnCVAigJRFYwyFo6xb1XI4MpbgVzvMWmorAN96xU0U\\n6gmpb+/DotYa7xVWR4j1z1PUI4TXsPYFnDT8r//qd/nzb/0Zv/Ebv0mcpnzrtW/xm7/xT9jaOskX\\nX3kVQcyx7ZNcvnSJK1cu0+ttkGVdBsNd6rri61//BnEU4b3D+VDTq60JmTLhqbzD2oZFxBnyqsBL\\nT1mWOCEwxqAiTWUdlbEIqXENyYGxFuck03nBrAzqElgfjLFXWFvT4rkcgs2tE2yurZHFsLa+hpYZ\\ns+mc+WLCzs4mpprz2Zdf5vXXf8re3j69bpfrN65y6dIF3n7nLbq9NbrdHt1Ol3w+ZTKa8Mwzz6Oi\\nGKk121tb7A4rImebNfmQWdLoNbZ1/Lvmx8po6/1H/777/aKqsE4jncU76GSKr7y8w/raOsJLZlXO\\nPJ+RpSndbp87t28yONjj7TffoL/Wo64Knn76GWbTKdYaZrMZd+7c4cSJE5zYOcWLL74EQnLuqaeo\\n66Zm6qC/tkZdlYzHUz6+8DGz2ZT93ZucP/8ud+7cYnNjE7xje2uLL37hK/zC3/tVThw7zmh0yPXr\\n1+itRVy7cI2/fP0H5FWPL77yPGeffIbZ5CaunHH29Blq76mG76PrEieP7oeQMqjZ2FCP87glvWSY\\n+1HgqKWpI8ZpQMKqozSpUCpQ74lgRLWKcM4hVFuyCLU/mWg6x8+hs/VGYsuHNb40cg1frVQkaZ/a\\nVNS+xguNdx6pdVAHWQF3LcsoTcII70I909pmNQe5MG8rhDeN8HTgoQ7n50mUpi5LhI7x1uBMSVXm\\nTfTqwNcI0XC/drdDb77S4bxEFK4bi2palR7Zf+59IFzwTb2cwHXb9nOG0lHTEGcMdZkzn/cpVUSW\\nRIGjGUmkBL1eHx1l1NbgrCeOI2wjXZZXNVkcs7nRQfn4oeezOh7bYHY63X+C0Ki1HZyr6J15nhYc\\n4mTQq7P1HKQGaQNqSytIOlBPMQ01UjseesM8CJlivUQ3qt+PGsG4hd/g/shs9buOvlPAshG//ezR\\n5/+2jOX95/F441H9nAFlXDdal0c1j1XnxHuPlyEliWi13tvoWyBdszg8SDQQAQKpNcoJhFzhtfVH\\n90uICO6qS/vl/8KLpgG+qeFIsXxWXgisq5DWYKoZqnMSN/0AZxS4oIP34ouf5f/8vX/Dd7//fRCK\\n/tomL7z8OTaPHeO555/j1Mkn6HW7RJHm0pULjCZDrt24SZQk7O/vU1UFWkuElLi2HqWC1uKSXF0F\\nGrS1XkI3jdCRXuoAWgfSSxIVB8YgH4wlCMrSUNmAtvUKtA5N0U46FpWkKCxJ3CHWMJ0dUFYVVW0Y\\nHB6gogwZZ+zu7XPy5CnKImcw2OWpp5+gNp7Xf/xtzp9/l0U+YX+wy7XrV8jLkl5/nTSNiZOECx9f\\n4NjxY2xsHydJYoYz2zyPR8+r1Xn0SXPwQUA0IQTOS+zSTzLM85o/fWOf2wcBpt+NU+bzEJE554ii\\nCCUV21s7XL54kUVRk3XXOHXmNEJ6RsPD0BzfnNd4NCDNMuI4pdNJ2d7eDoAtAf/Ff/lfoSTcuX2L\\nN/76J1hbUxU53/vua1y/cYXLVy7ywYfvcfv2Dd5/503G0wl7gzF5kXPhwnv87IMfcX04pX/uK/z6\\nr32TyWiGko61DNb6G8RxB+9zuoM/QNWjMI+FYMkZS5udEmFfWjrngijrgg6k7TJqxNVV1BjCI85Z\\nr+NgTHUEzuJ8QyHZPBspNFLF1PMh0tU4awI1XQN+abM56cY5jFDEcQ+FwC8OsfUs8M2KRyU9mz3I\\n1YEn1tahnQ+B8IGZx1WL4CgIHxzASJPPhrhqEdKaiwmuylEi1BSdb3q2nQRXodNNVJw2RPYJIk6b\\n1haBda3U4CNnH37VSfAuCHb7AGCSDbtZoLas8XWF1Tm/8yclMlLMFnkgsUCQZQnOWbJul7IoqcuA\\nPq5Nhfegpeb4do/ZYUXWi179hBN7fINprXlBpluU0xF4x+zme7SICl+VwfNGh+ikGoItMPNDdL7f\\nPGR132J9oBEQgijboHPiM3gXFM0/aax6xfc2xK5u5ncbUcuq0fzbHqsI108zPokNiCZtIfENApXG\\n9q9ce0sHKOWKvuXReQUAgF8qm+goCwwhTZ3iqG2lPXab0pb3PNO2yODxBC5RL3RThwFESNMqoRDW\\nB1Lu9BhQojFovQVWc/XaNWazOS+++BInd3Z48tyTHOzf4cqVSwwO7/DRR+c5ffoJqtrw7ns/Q0nI\\nF3MGh7f58es/Ii8L4jgGH1JrUgqM9dTGUDcE4dY5yiroERoHZaOM4L1HiUCxp70iQuGMRSpF5Q0C\\nj04USkuiVm4JSSwUEsXhbEHlKow31LZkMh0yGO4yHA8AGwRs65xjx7aAANCJoy4bvT4nT51gPBoT\\nRwn9tQ3KsqKXpczmYw6H+yAV+7u3GI8GFEXF+kYviP+alcd+z5x72O+fNO8fVlNv0ebtvPJo3r1a\\n8Htvxvyr1/b57vtTrJ0wn8+4ffNq4GWtKsaTAXduXqfbiemt9fnoow852Ntja3ODXr9LVRUcDPaY\\nTmZkUUJtKg4OBlRlyc6JHSbjKd/73vf44s99ke3tLXQkSbMua+tr7O3d5uKFj3jn7Tco8wX/4d//\\nEd/78fe4fvsmztVMxwfs7t1mMJpQp7/Ai+f63Lh6kes3rlKVNZGOuHT5Q3xdkqkMLxasz3+EFmZJ\\npedbxqsjv3EZYrZGVSVBAEJEyVFKVcrmfw0qRmhNlPYRURx6Nk21LHKEgym8M4E5y4MzZZMGXmHC\\nEVDNByjhKQ4vUC8OGxq8GlPMG+7tu/dF19D9CB9q+AHsVuOMaZQ8DN6D9SHF6k2DHTEVwlpUZ4O4\\nuwZOYqpxMF4NHkJHMSAbsQUFtgpOuRANt3fagBWbXu1P3HL90U/fCmWEtLF3pqkfHwnauypHOsW1\\nA4k3ja6mjMjLmjTL2Fpfw1mP9YFrVojAOa2lxJSG/nrKZLwIqOFPGI9lMIUQa2VZrqe949jJ9XAj\\nbYHSHZYE286CLZBCYqoSYR2+npMPb6Cj9KER0n3fRQhc53c+wNfFXZv7fX8vInzcfZQ7tfJdD0o1\\n+abWtlrb/NszoI+KEh/1Nw+qJcEypm5eX/mb1ccq2r5NccT9KvTd0QYrtlBAXc6w5QKUxsrgKSNW\\nU9WrbEPtdx6pKrRHlFIhcY1Se9MbKxXeW6yt8V5jFtcRtsLLGi8tFSXTeckPXv8rhqMpv/SNXybr\\nZFy+dpmr1y5w+epVrIWTp06zdWybY8e2OH36NP1eD2Nqrl2/RhRpptMJutFBFPbImdIqAHGMCyAH\\n4xyTRRFQij6wBFXeYnHUwlJ5y8KXzOucWIfNoW24iSKNkpqirikE7A5ypoOKfKHZPVhgfERZ5QxH\\nB0zmU0rr8UJT5hVaRiwWCw4Hu8xmI6yrOX36Cc6c/Qx1XTEejzhz+jSDgz3m0wU3blzje9//AYO9\\nO1y5/BHeGT547z3Of/wevc79WZh7syePmtcPc2QflZptJ453rWNk2J3FfP98ze995wBjHGvr60SR\\nRgrBrZvX6fc7jMcjfvbOG1y/dplIa+7s7xHFMYfDYdAZVTFaR6RRTJbFKK24euUyx0+c5Omnn8EB\\nTz/9HDs7x9ndvU2SpiglefONn+BszaIIVJh5sWB7fZ1+ljEcDdkdDMkrgx+8Tlxe5zvf/RaT6QS8\\nIJ/n5EWF8zFZt8tmb5MXntzGqoZvuYkkpQpiEyG12c711fsY0KIyjpp0bJAVQ6qAtNUxKuoT9dfD\\navGBqpJmD23/c3WOr2fg64YH1R3d8Ga4YkQ9vok1FcLV4Vl4FzIprgIbBMrv2l98wDx417RwiNB/\\n7nE4s2iyTOE9Uy4Qrg5E53UZsiimRsRRQMHr7tE8kRrnTTieM9h6AUIjZYSIkqCU0rSXre4Zj9r7\\nhG+lFZrLdkfEEM7WS6ctYE5yTJVTWM9uHrPeTzEopFLYusQRpPv6vWxpr4x1GG+ZFgUbawmzaU3/\\nWPzfPnSRNONxiQuec14J7x2uUQ5ByCMGemER2IAsrqtmww6bg4o3qQ6vNoix+9OlqyN4bRqSDjLP\\nqU3VvHo3PVyoO3p0bwvqfEmB9rAhhGjy4Sv/JtT9hIiaYzv+BtS6n3o8yGN/VDvMvZ+752BL0nXf\\n1mkbL1i20HYhlzWJNiPQGrQ2gUoT/bVpp+AoZkgtg5qHDGK84aMiRJxBXHHFOxYrhw7HsaZCKIFW\\nMcimd9SHjUKqCFSNUxm+HiJtQuUn6GQDpW4xGI04vrXDm2+/RbfT4fLVj8kXOUnc5XMvvsx8sWC+\\nWLCxvsXu7h5xmobjK9g/OMCYsIC9A+fbWhEYGh1BCCxKAEriXKCyk1Ky2esSJzEHwyFOCEpbB+ow\\nb6ltYI7pphGlCeoHTisO5zOSNOKf/qNf4J33P8JUFYky5GWFQ2CNxduaMq/IkoQ4SpgvSiKlKPZv\\n8dSTT3Hz+lWiOMJYyeDggP7aBnk+YzwaoSPN008/xe7tmxwe7pEXBcePneTWrRssxhE0tX6/rMuv\\nzCvEXevjUShy4CiCXH7+6M+Xjpb3WA9Kykb9pnVtNR8dKn70/iHf+Pw2UsBbP32TLMvIy4pFUSCk\\nppt2mc/n9NY3KIuK6WRK1s3o9vpI4emZBRubm9RViVzfYDYb8+ZP32Rn5xiHhyOiSHI4OODdd98B\\n76lqi3GO7//g+2RpzFqny/bWcTq9DIvB7B6QFzkJd3jjvRpRe/JFTpZG1MbR6/coywXra9scDq4j\\n0i8gDnOiyQeU619o5n1zD6QIrROy7fVecUh8yL6oJMO5hkXMhfkuhUSnWdCLdHVYsy7U2WULxrEF\\nxeEtorRHVUxhlVZOrDwQOKK5a/ECvimBSIW3NUJ6BE2Gh/CesyaIy+MD6E9KXF0E+j5bBaJzFyOE\\nwZQFKsrAOqwpMKZEW4GSHoRtUslhPsmGNS0IbjtktonLB+DCvWqp+ladsUcHJ+F6fINp8Y2xd94h\\nrEXrBp/galyVY/IJWdLhX/7ZIf/Tb4ExJVIFA1ssapJeUzuVAlvXVGVJaTxKSfprCXle85nj/f8G\\n+OePOKnHNpjPIxSmmKGEwfkIloCP9kGBFylQHKl0petIP0OqkE++qyn/gTcrbKpKOMTpF6ivTEJt\\n7h4WHrwC4aintwC35Dx95BD3xKpCNB6ZJdTsWuP8t5+ifZCRbCde+F3c99l7f2/ebYgEGrIC2UZ/\\njecmW01MG4gYmgWslDpSlxetuWsWPuBQxHFKXUxY8okunYkmsly5VS1pVvjhG+cknI9rlC3aaDhs\\nuAonBJGR4GOE3iJngYrOwuT/ZSEitm3O62/9mF//T36NP/3W6+wO7vBbv/5PufDhh9zevcFocoj3\\nknPnnuHCxQ8YjQekaYyUiguXLnF7984yItZLpKMOokwSoubeNEDCQInmPNYZXG2QcUQA6ToSqcF7\\nEq1QSuOdoZdo1roJ00XJeF5wam0NbR17Ny/RS8AoSazFUhvQ1AZbF5i6xNmE8fiAJM7weCIhKQvD\\n8eNb9GWfwcE+xkHW6SOV5sq1j+n31omSlLzIERIOD3dZzCZsHdtBjA7w7lm80jgnA3nYynxq2Yza\\nIUQgmri3fPGgOeZXUmir8xZCk87xrQ67hzmRksQKFlWImP7yI8sXXxIcW9/kqc88xWB/lzTN+Ojj\\nD8jzkm5vjSRJKeqStbUNzp49y3Q+RQiJVIIk7TGdjnnqqc9w+/ZtTp46zbFjJ3j/vXcYjQ44OByw\\nvbHFtetXSJKURV7xF699l5MnT9LrRZw9ewZjLbdu3UYiybKEspwTxxnj3d1QKnCOKO5iTMViMUMo\\nQVnNefVL3+C1y4rk8K+I4j5101vpl5M+sEe5hglIKr28P8toUWqibA1v6wZDoFGNkLr0AickIkqh\\nCooiToBwDrsYUy0mQWTkKMa6y1gun45UTQN+kNSiiVCD8ocN2pbesySQaen32hUtGhRwXTXHd9i6\\nbDKHHtoaZkPMoZMU7S21D/uFaPZ5KQVO6JAR9A7va3RnjbpOkcUIL+NAfFLNmnT23VHlg4OGBo+B\\nC9u/EnjnSKOY0pSUeY6KNcJJMDUmn1Gmc27TQ3Rj7GQKmULgKMsSk2qMDXfTmNCbebA/oqprsjQj\\nTTXSf7IdeayQSkr5AiicM0Qbz+OFWVlsEm8qYmkQIkEIAhF31CHtrgeWBdl5rLSpIDRGl5M9xPwW\\ncf84Mg5p3+XNBaK1Y0Eh3Ukg5W9i5IJHrBqwjMPfo87wdzHuTnndn/a626Dem1ZrUsp4tI6CYfON\\ngoHwgbxYCGTT/xp0RF3g0GxMnNIhRSKkbFhwBFJF6Cihng9CSkWuGss2Ig0hxzJCbcYywdsougsh\\nUEo2lFlNNGptkArSUYCJK00tJNr10DJDZM9SOYgU7O4P+P0//H2u37hKXs6ZTmbc3r3N2voWu3u3\\nKcuKLOswHI3wHoyxTCdDZtMBW9ublFUVjJ4LfZKRVGghgwSU8IQ2N4k1LqRahSdKNIPZgt3hEGMC\\nFN1Yg8EzyUvmRUlu4WCaczgtKGpLHMUoa8EHXUwh2tqpIlKaSCqyLCXSiiyNyKIgU1eXOa4uKPIp\\nh8N9BuMJN2/dpqpynPMcDg+5vbfHbLZgNBxx8/o1bt28xvhwwHQ0wpmaw4N9djo5P7d1g2++XPHP\\nXrVY6uWDuRe4s3xWn1AiubcUcu/nHaF1CFuB9xhjSbM4rCsnKV3Ed968QxxlREkAfAwOD8g6a1R1\\naOOaTEdcvHiRv/7Jj4liyVq/T6/X5fixHc6ceQJjLHt7+3zuc5/n1MlTKCn56lde5Quf+wLDwzGj\\n0ZAkSZnPS65cucmT584ym055/oXnERAQxHu3WeSLAGyRKvAYx5KtzQ02ttZJYk2SxOR1xWg6JS9y\\nbh4cImwfPb3O4uAnWLvXOJse71VQHFER6Cjc25a+8siSIYQH3aQk44yo4VDWzVryQhMlGdY7bDHH\\n1hXWFKFmaQqkVE0E18J1WTGWRytPNK0tbcvWEkXbfMbaOqQwTQMgalDjopE7c3WBUFETtIqlsRUE\\n1K1zdRDYblv8pMD6YJa1Tli2uvhQb6Rxua2pUHEfKzRCZ6hkHZ1t4rF3YR8eNY7mY+OEe0O+mIR0\\ns60bkJINvaRVAcWcWgqcWkNHiqoOe6FzBh2pIMoQbiNJHDOcTKixHE7GdLoxZXE/Sfy947EMZpZl\\nvymiDO8sppqH/jjaUNyDq5lN9hDS49DIKCXO1pgdXkclm6jOdvNwPykl27wuFdVoD1ONoZGgaYfw\\nnmo+wNclSIEUn3yRDxuikeQJqVl48O14DEv/qb83XKe8h09Wyrsn0qMnVQNEgBA9NWlQHHgTFoZD\\nNg6BwqORNErzuEY4tkWLhBjQEXqvjDFkW6ewoumhWu4ER8uV5bevvm6bBRk2EWdN4MOEpRCucyak\\ns4QAEWGjhGjri9RrX0CmHei8BOWMw/mc9z48j9CGK9cukMVdvvXan7C21sFax3Qy5+atqyzyMUWe\\n412Q7pnPxxyOZty6dQfXRs9KtlwJ6AYdG0Rum+FguphjTM18ljcbUEDXOmdQQmKsxQIVQR+zwFMZ\\nTzftUdWWGr9sRXDOobUMNSoTGt6tqUgTTaJi4jhCKI1xnqy7gZCC9bUOo8FeoNWOEqpqxmi4y2w2\\nJC/mXL18gfloH1zNaDxm72AfKQVJknD27FnOdKcMLvyIU3g2uy0U/34HjFUD2jhpHu7/LHcb2fsM\\nLgKkZ/cwKN5YAYejWegGwCFFyfs3HIfjCVvbp9jc3KIsS370459wZ+8Aj+DmzZtYZzl14gyHhyOm\\nkynHj+8EkoPK8Iu/+MtEUYzWmtlsxnA44MOPPmBtvcsvvPpVhJJEUYKzgr39A7aP9fn6N77CYj7n\\n8HDARx9/iFaKsizJ8xwtFbPxiLqwFMUCZ2tqVxOnCfkiZ17VlF7x/uiLjJLTOOWIOhGxA7BIL1Ci\\nRtqgIRrWbBRiIb+SaWnvj7PLLMfyPjbLVDVYgKS/HiI/W4GHcjZqSgkG4UUgJG9DORqjdDdgIZgo\\nv1KvXAJlCDR4pmyOb5bObnBoZaDru+fJ13WO9BaTjwIvrhQgNNqDszWqIUB3PpB+4JroVargSLSZ\\nLaFJN88R6QS9+SSd7bPNPnt/pvFBKVrB6nWH6xIEEglnK5ytsLbEmxpXF5TzIdJovv3ujCTtoSJF\\nFmviJEGhGufYEKcJxjrOnjoBSoR1mGnm85r+ZvJbPGI8btHubFulMIuDe25vgEVH6RZRkgXPSHep\\nTRXkknREb3NnWez6RM9Ca6QHI2OkiHAueCytT+WReFvQFsi5ZyE//mgnvFx6ZuHlZkYvP/M3O347\\neVf5a+/19O+F7H/aIaBpVg8amVI0i4BQfBe2al47ip69sM31hr8TSi6ZP6QIqUnpLLO9m8QuX9bF\\nWi/WObsiOLMa+3PkZa54w66uA5XcUuQWZJOqFOY6qUlAKDo7X8MIg5YZLk4YTmo2epoP33+Xne0T\\nHN/aIUoi1jfX+Mu/fI3jJ7aYz6e89tpfMJmOUSqIw3oh6fW6lHXV1N4CpL/XTchiiVMe6zx5UWOM\\nD0xASnBio0+axE3qDZxxaAFJnAZhZBSJ0mRKI73A2pCqLKoy6HV6TxwpwFGbKkTWNuhrFlWF8IEQ\\nujaGvKrIOl2Sbpefnb+AVzFeJDhTMhoN8c5TLGYUizF1MWcxG+F8zWIxZTgaLufN+fMfMh0fMJ9O\\n2N45zouvfIH3z7/Bl07m2HaeiXYeBrBHeDSBfJylNuzRpvVJc7pN94eamGSz3+UXPt+nm0b0+x0k\\nDq0MSkQUVvJ//dlPee0Hb1PXoV70K3//l0jTjOlsyt7BPkWxIOuk3Lp1k8HhkD/8gz8M2YBI8/77\\nH3Dq5Gneffe9QGgwHHDjxnUuX75Ev9vhxWef5dTJk5w9t8PPvfIScRKzWMyJIk2nk2BMxYkTxzh9\\n5iwnTpzCGhOILJxjMpkGB2kxZ+9gn9JaOgJK8SyF7hO5mmee/zxf+8Lfw7s5EIjBKzRCWYQbELMA\\nXQeO5SaHImSbXgykDg8NElogkYyQWYYvFuA9UjVEKkIEmS4ZNc+xraG2Ds7RGmwXoW97F1vCc3yw\\nj6GK3u6YCKkwpg5Rs1BLo9weUpiaenHYOLWNwIA3uCbLFYB7IAnSaV6CExHEvaAhKkLJTEUJ9fQO\\nDofKuoj1M8ioF2xGC0Z85Jxzy73myNlriQwsmApvg6QeNseVCxaTXf71Dz1vHdS8dWeOkxlZmrVP\\n54iezwfBeOssxhj63Zg8rx4FhQEew2AKIURZlsdk9xTeO+RykTWTwEsQCd4aqobcF1/hkFjZQVrL\\nfDqB9sE8dATTq3SKUQplK/T6s8FbaWtlXiB0ErwqIUEm/1HiPykUztlgEFhl+DkiNQi36tODgpYe\\nlLvbM/z/Yyjv6+d0gbvRNfUSoRSgQoNxXQcEW7uwfJg4DsK99TScleF6EWHRp5s7GMLmwsq5C+HD\\nMvH2gWnk5gyXaZqqmFIuZngUzoMXEmM9ptzDXfkj6um/QfoaKUD7NQqzS6I2ObvZ4bd/7bfZ2t4i\\nihLKMscZw/kPznPmiRMcHOzjXGB7GU8OORweIoQiz3Pu7N7BGBs8dGvJkpg0DohLW9ZoJVGArUMd\\nu5NqNjsdFmUd2qO8o7Ym9JhZF6JN51FSYIwNkaSHytRMFnNMHdh45nlBnMSkaYKWCqViqoaKC7WF\\nAIkAACAASURBVETgGVWC2WKB856irHji9DYRgaw9zTJcVbAYT9BSY02NKUvmswmVd8RpB4TAOUuS\\npHR7PW7dvsntO9dJVMKJnSd45vnn0NPL2MWtZlMMjf/e1VhfB0I1D964gFRuAWHNXLzXsVslOmgN\\nQfu6cAUH8yHffTPny8922E7naAW9XhfrgmPy4mdO8OyT64znByEt6S3SV4zHQ+I4JU17fPTxVc6f\\n/xBjak6e3OHtd95CaY11gg8/+pAsS3n7p3/NlUsfk0SajY0N6iqgc01dobVmbW2NNOmwt7eH957t\\nYyd54fnPUleGxXxIpARZFgS1kySh2w0p8l7WZT5fkMSKJO0wS7+EExGWOR9cfp+rl89TlQsQIE2J\\ntGOkE/jZBfTw39O98b+hfE4kwVtPED948Lq9t1WnyYICEXWVU85G1HmBUjHIKAhKqySAZZq16fw9\\nDo4/wkAIsZIedQ24x4XWmCVDkRch80MT0YmAGme5p4TI0TiDTjbwEmxRgHPNvuKRUUycxKEVy5ZI\\nEYGwdHubRN01HBU67oF0eJkCEVnWQXlLHHdRyRoIfaTle8++du9eItprgiZ6buq9bY+qDaLSwtVI\\n66iV4H//0zH/3b+7Re4EOu7gnMU5R5qF3v4kTSmLmlQnRDKmu55SLuwnVuUexwKc0Fr7tVPPI23J\\nxulnlifvnCeKY3Sk8K4MBWxrsOUMaXLs/IDKlmglQTy61thGqs4LdLyBl5r+E+fCRFip1yWdHYQX\\nqLgDwj7mJXzyEELhqQlet2/mXVND8I8mT3is46+kXz9tL+bqeBBw6mj5OSBs+CgVFp0UIWUijoST\\nm1C/2chXj9dUMWQMddV8X2gYDrssQBCkdU0u6IFX4kPE6wk9nVpngQRBKgQBWam8odYlcQHz/f8H\\nIzQeQy/awPubfPnnv8zJnR3SJKGoSk6fPhMUCpxld2+Xvf19rLGYqqaXdelmXayp8dawvrZBlmYg\\nPEkSY0xFZQrSNKGbpqSx4tTpY2SJIulkCOUZVzXCC2IRkLJKa5yDvKyYlzleBgNZN+0oSRzhhMBK\\nEWTsnKObZWFRRhopwfuaTm8NS0ReG4qqpCwqIhnaepJIcHznOEQpt3YPuXj1NmVVMC/G5OUC431T\\nL/JQ10xmM+azGfjAqjQcj6mqisPDAe++/w6dToLznq+8+kv8yisS5+ZYU+PqMdYsUNYhTY4xk0D/\\naCpMXYQ0+T3ZjrbNSkpx10Z/tNl7rNLgEv7BV7b40jMxP//iFmkEk0mB9RAJwbNPnWR7vU+1qMmL\\nEikUzzzzHKdOneTFF16kNpbvff97jMZD9vZuI6VgcLDHD374l4xGh2idMBkfUteB63k0POT27i5F\\nVZAlMQjJfLFgc3MNKQXdbpdbt25z/fo1BsMRw8EAW0NV1NR1SRSFmr7WEfNZzmg8BqGoy4JCHkeK\\nBOUswmSg1jhz+im0KNCDP0SMvg0ywxfXieqrJPZWIDUvpiTOIewEXWucrO+qAbfp79WxRO43GVfh\\nKuziEOVKjpKksinTBFWghw+x/BHKIkcpTO8qrF00bSY1npY77SjLhDdB89K5RrjBk2TrOJMjHCBc\\nSIeWBuFjrLEhNRulwQC7mkgnFIsxNp+B7KCiBG9LtLB4YZgPblOUBTZKaJmRhFzK1z9iNOcomtps\\nG2E6CzSsRG2wYC02HyPyMQd2k8yu88GVW/goDQa6DjJsOgpEGKPJlMUiB+XoZTHFwtxXu793PI61\\neb4oTTy+9g5W9xje+jBstj6kpOo6x1UzvDHICLyUaJ3i6xnV9DKpTrAe4jhaekMPf+QCmWTo3hY2\\nHzK6+CbeFuE0ZQxRh3xyBRCYIhR/P4nd5NMMKRRLiC8eT9MkK1cn2OONe5GGzYv3v/Y3GPfVOVeP\\n29Q5xDIRIEM/mGg4ZtuosO0hWykRtP90tqaqJmTddeLu5vLIQjiklMRxhmyEbu+qia2MuwALKnjd\\nzgV4ubcWL7oINIU8JNn5r5uaz4j5+DsoJ/jhD3/AH/7x/82FKx8xmQ6p8opu1iFOgs6glIK4ATRN\\nx2PqusIYQ20Mw+GAfn8NIQR1XeGEx4lgeNb6GZ0soVhMWesmaOtQQpLXFVJJTMNbKYSntiYksqTA\\nSx+YgpTCOEssNaWxSCXROhhAJBSVZZZXLMog5Ly/P+JgOMG6wDVblhV5Pmc2PURLz2Swy9Vrd9jd\\nG9JNU4xzGGMoiiLUPxsC27wuKIo5OpJYWzEZ7WOrPNTmdMx8NmXvzk12jh2ncjk/d+4UFGNEdY0v\\nPKX45998gc3ugvDIYiTT5nkCLpDiCwwOF7RuXfDklyhqIVYqFQ3zSm2QxnL+4ojxaM71Gzm5izDC\\ngVDsbDjIB7z1kx8gFaRZynwxI4o0k+mYY8e2qMqSz3/uWbTWHA4POH/+AwC2t7cZjfe5cukiP33z\\nTWrrmC0W1B6u3bjOnf09rt+8GXRPhWI4POTWrZvcunWLxWKOs4aPP3qPWVEwmS3Y2Nxmc32T2lhq\\nUzPPFyAERWUaX1CjhMNJj/UVXjlk8iQbWxtE9XWclYhyF3X795GTHyLtHr7S+Mjhpt+n2v13SDsn\\n1zne65B548j5uBcZGt4kLFJnEFEa5NIceGeOMjoNO1dI3+r7M1KCJSjvqC4dkLPBmNim97JujGa1\\nci5NiQQPwmGqGbZeoOIOvjFqzlRgDKaeQxyBKoi0CTgJW6GiBIQMfLJ1ga3maFz4d1XhicCU2HwY\\nVHl6J8AVARnhBW0L4nLfeMi+KNp0tA9tab7RB3WNkxyMZoUv51STw5DWjjL+6GdzjJXEnR7WB7xH\\nWVXMFzm9bkZpK7y39PsZ1jhOPtf5kweeQDMep63keSETcBVRbxsznBE4A33zxA1epsi4jylnCJ1h\\n82mgUzOC2fAmOjtGPrrE4xgcXy4o6qshnTe81rA2hC4fKQXGS7z0RMkGppw+xul/8jhKcTYP0Nol\\nyhQ81lahx9Q9ugH8/jSrCA3Lpnysc3ic9OzDPnP366HuG/QqYUnWzArjDp6Wp9IDQmgcEuFtU9SX\\nzMcDdKfbTOlgbJ2vKPMKqWO8UggbUtltrfDofILxFkI0Ha6u2TjAVVPKRYX2GtX7dZya4bwkcxpb\\nTOl3O3z1K19juii4eOs8m/1t0iRDRynT8R7eeaqqptfpYqqa2oZzFjLUgMo6p6N7eCfQsUR4iSIU\\n/2fzkqqsWF/rkkYxmwjqqqK0jtksDwZQ6sD6YUJ6XkqIVRQEdwmprbmpkDKoOzgRWEQ8HhUrFnmJ\\nVppEa7rdmDQO9bTCNLJRMizc+WJGpEKLTZw4osRjbIl1AaAgpQiG1lpG2nDaa4qqRCQSrQKTio7U\\nEpD0x//29zl+YoezTzzB/mDAKztzZOXYcSMu//h9nvZTPnvuOVBdKjq8cWVMbvsIb9AywnmN0Lqp\\neQXfUbD6XAPS2rXv40F69mZTvvWmYjwHZxTaa6wwvLBjeOOvXmc2G3I6Tikri44kg8E+m2sbmNpy\\n7uwZxuMOg8GQqgqKMJGp2N/fZTqdMp3MgmKLFRw7cZrB4JA4Ttne3qSYz7h59Spx1uFg/wCco99J\\n2NjcJklj6rpkcLhPnKSMxgOMtSilg9KGdcyrAoRAK4UWAif7SCkxBoTS1NkZrlx+G1FeIxEx1lcI\\nN6OTamoLBTWpzIiqfXIvKNNtOosrEO9QxX2kD6ja1Vacu9Zsk1XDO7AVol5gXYUg9Bi2WTUhFM7X\\nd5V02mch2uyXD2oc4VE1AY1fSdM2qGZvKkQkCdyu4fk6PFL+f6y92bcd55ne9/umqtrDGQAQ4CyQ\\nFGmpJVHqSZ1Wd1sdOY7cTvsiTm6cfyMrl8n/kausXPdyYqft2F7pxEq32kPPbkkURYkUKZIgSAAH\\nZ9hDVX1jLt6v9jkADwBKK7UWSODss2vXruF73/d5n+d5HSl6bDOHYsmxx7l9QhRZSU4F12hImZws\\nRhWi91hnSDmhosD7SRtJLH11+ymRnBMazTh6utke427N0YLqXeC3PLqQyPIdlfQxVUmUyoJHa0pS\\nZOUhOhi25JBQao8f3p3x3/+zLf/zf7fE5yzX3nYoNDGL6M1Zx5g8TWsIfX7E58v2xAqz67ovUfOQ\\nplZdKV3MUKzAoyWggeX+NbSO5JLQZaRbXsUPd8QQ+InVdyGOpzQGKIrZciY6uVIgDaShBmIQJ4pf\\noP93WdB+EHIq1cpNegUKIczkx5zHy66xtAs0RpsaPR4Mpp8l8P7CWw1W0wNX673da7t/X0CKSu0L\\ni55Lgq3Whrxd1X9PEyWNDEaOHhV97Z1UeLZMVfgECdWdZ3n3BK8XDE7PiTkQ1BZdDDo5gn9X3Iiy\\n4i+/90P+7K//E194+Uu89vkv8vZPfsTJ6V2UAmNFrrLpNztdpdK1TVASKQeefvppVNEEnxmHyGo1\\ncHq25mx1RimJpmmIOaKMYn9vTmNrcpEzIQTGccQo0XA2xpIno+oi7OAUI2JcX9j6gdFHtuPI2dmG\\nUDKb3uOrU0ocPSGEeqwSCFGamAvrbU8za1nMGkqGEOQ6xRiJ3ovkeOl4ulmy8h5tLKUoQhanEh+C\\nVMEZtsOGj269z1/95X/kg/ff5v5773H68W3ef+9nrPsT7nx8izvv/EdO3/9z9sIH/OOvX+PXX2sp\\njIS8IeQ1xJXcImpSAcrx5JzJKROjRyuRI1njUFoTS8cH9zynfSBhQRWeXmgO1F1Oz46xTcdqvcZZ\\nQxgG7h8f12kg0LYtOcO1p65CicznLd57bt++xTgKoWq53Oe5558njIGvfPlrjEMghIixjqdv3CDH\\nHj8ObIYV870W1zjGYcPeYs7Ln3uJ/XnHYrGg73u8D4QQUEWs3FLOkCNto0h7v47oukWcq5vPcfXp\\nF+lcRwoj+66jUzBsAlplOtUQomfWRfZn0I236DZ/ixr/Gj3cY5JvwWMS3SLax5QiMcUdYiPEFkk1\\nbdvinNutn5MhOdRqciIQoaSqrMGSHVP6QiJLFrs/YBqSoFxLKhnTHGC6PXEzopCCF5a7KlA02cqk\\nD9c1eD/SOE0et1A1o6r6fmtliWFNLl50n0UqQGsiIcpZ0aYTWZkqDyzJj3WkokKy1CqzGjCUfN7L\\npAR0DvjVfcazI/zQc2fT8E//fCV9Ti12lGMYKKpqPVNm40fcTDNs4yM/Hz5DwHTO/TYYdNuyPbtT\\n4Sq5bEop0blFT449JWVW994njFuKHykoRr8VrYxKT4yXslu5Hcgjm+OjeuGV4PpKKkCRtTzYf/tM\\nW5mMxx/zvgdg0/Mq6WF6+MXfmYLEA0JwpPEe/JaL0Xa6uR/lsnLx936x7XyK5y5+1X+cw9fCUlNa\\njJ6pWW7OQdiUQFaiOzOmI6tmRx7OpZCQ9+6OWcG5fVf9uPNPQrRQtYpHUXLE9yPaLGmaVwFD0QO6\\n+RKue46jdebj+6e8+OxTfPGV17nx1FMcHBxWEgt1xqX0d6QPK2SEVNmFox85PTnlxvVnaZoZoAip\\nELPi2etPsb9YsN5siTlz//gYpy1D8HTNxBjOaCP9Uq3Ps14FWGWYN+KNqZSu5gYQQsSqBpUNIQSa\\nxqBL5REaTVGGVOFfXUyl4st0CXKgcVZ0oE4TYhbrLlXwVjx8yiiZeIPZDTimQEmJ+6fHnJ7dl4Wu\\nGMAxbBMUCQib1Qn3PrlNzpltv2WzOWZ9csztH/8VN8uHfOMlzzK+ie4/Jgd5lnOKpOhJyZOzl4Hq\\nyVPGNTmua4/2nPmc7QxlWopSdG3h1z53ht+c8ezzL7KYX8Faw6Zfoyj0m2O0MZydHnN072O6xhF8\\nIIwbZq1DUZjNloQQaLsZhwd7DMPAcrlksZjzta99ldV6zZ179/CpYFzDSzef59lnnmU+u8L777/H\\narUS/Z11bIYtn9y5Q4iZVIPImOL5dXWWoXuRbBcyxUYpdC5EHXjjje8zphHtVA0uDVZZdMjMyZiS\\nmWeHMS3N8BGbMjDbvoPxt8hK74ZzXbZNn2+amfQyjdkFhVKkn1iUkLPGsRdSYnlof9OzVyFKaYOk\\nB3+Hc6IQuqNYJ7IVIKNQpsO4Obpt69gspO1GEaNzbbFGUyKk7T1iCDjXEsNQzYWE5KlcJ/dqmuaH\\niuGHBLOR8d57mLAG06Hnh2g9l6O82Bu/0N65tCq/2AKqPU2BnyURKDGRwkDqTylhIPuevD3jn32/\\nxg5URW4gloBPEQxoNK6VBNvOrv32pReMzxAwU8rPK2Np9l6sriXUi6FrhaJQutk1nFVtTiudsPMb\\nWDTNtZtPDJbnB6TI2/vIzMZcg2TaBRkp5X++nuLuBO8uyuO/9vRZ54vluRPFxYuZL5BmPh2Ea8UK\\n57Z1l3zO4z7/F9kKZcdG3j0mNfCXIhDMriKkDowtkHMdMquyJAdFg+bC9PlzSzRyrGPE6mfmUuU/\\n5x6zavf96rXa6ceqPClGSvs8WWup1EpLVorQPo2aPw9+xWK24Gcfvo8xLSlnclKMQ8IaGWHUOIe1\\ninamiSmL32WR7390esRT154mjWKYPrGUhxBoXEtrW0rWbHzidLtBKc3hYknKMqNQ4Hezc3QxWmO0\\nxmkjZJgJ7q5V5nK/YwwjtjVYI1VWypmUEz56YoqoXMgxMYwyUUJpzThkGge5RFLMlfaeMNXzdtj0\\nxN6jlUDHMUfImd57fIz4nMFk8eKlEJJnGLe1CpF7PtVJNikJU9CPAz55EolP7t1CHb3Blw/W/Ooz\\nd3n1xoaFOSUN71PCGXk8E4lBOiONH9LpH2H9m6Sw3UHxqggOY23hy88ZfvelNcc/+yH9MLLZ9Hz8\\n8YfcunWLu/eOeOvttwgh8v7777Jabzg7O6YftrTOYqzj7r2POTk5YX9/jxdefJF+GDhdbdjb22Mc\\nR/70u3/Cx598zDgOLBdLSfRiovc9bWfRJLpW7B2PT09o50u6+YKUM/0gszdBTMabVjPrlrD4OurK\\nPyTqGYp0/tCogm9eIqaM03OsUfgQpH+bFckk4Z90knT27U2WeUXyA277A+kbFrerBh94Ti+sI8Y2\\nIkEp01PLrjZVpdRpIrIO6apxfHRCrSrn55LPBHAtulrd5Rwr1KsxdoYQA6cqjhp8NRRFSpEcByGh\\n5UAMXpjjrkPbRlyLSpZJRLGXBB1Ve+GVWZ9HhuNbqGaP+XOvoxuHwnIZDeWx0CxTlZmrtWChkKqT\\nkSR3hIESIyWOhPUp21HxH24lxhBJIaG1YYyJmAuxJFrXoJzGj4mDg/7VR3z4k3uYOad9Pb+Onu2J\\nOe8u6CiySijVYNqluEgoQyaAUuSsaWcHxOEYYi+V2pPCZs1qYtYo5eTmBZgGEVOX3zJdzMfjzZ/a\\nNJRs0erxZfcDb9Gf1gs92G+UgKAuyT2mIKvVOaz1iwbCz7pdPM/T39Xu+AWqKYpzXaSRoCa5cCGX\\naTK7SFPQeTd8Wx6CKVGRQKJBGG/1POzOzWTQXiEXWcDV7rWcFa67jomZ5BKOQrQK19xkOPkzbBx4\\n6cWX6AdPiIn1uqdrOobtgKH63arCYtGhbCHFWLPVgrYG4zQHV/b5yuu/zA/f/D4+DWy2PX0IrOyW\\nEDOL+QyU4v5qxZAKyUe0LcSkieNI1wk7sShFzJIMWqXIodQJFApTZIHajpH7xwMHh3t0riOmkbHm\\nFz54jLEinFagtKJpHJt+y8HhnGEI5JjoFgvGscdacVmxRozqvfcU45jPFyRdcM7gjGjKgg/kIEYU\\nJStSDNjqAVwotFYC8xAjzrYoJWSQ0+NP2Fvuo6wlRekPbc5u8dyzihefucI6Ft69fYvNZsvhfsP1\\nwyuE2PPMtQVnq543Tt5hzF+sC7MkhKrAK4v3uHvrXZwqnN5fM1sscFZz7+iIPhSuXFnyyZ0N7390\\nxlPXlsw6y1PXpG9qjaUftjjXcP/+XZzrCCHw47d/yutfasgls3+45NaHH2CMwvsB77esVscyoLhk\\nOudAFVbbNTkr3v7pW6w2G1JOhOLBFFLIzBb7zJtDNvtfJXS/RFYZQ5E+X71Pc7G0M8OwajF6oA8y\\nUupsM3Jw0DGWTDd3rOJzjOU+2exRZs+RS2Fe7hHShtAYVHGoqYd/yTqSUsbM5qT+hFznRu76m4DW\\nlqwbUSKUUvkCD1aZZep4TX3nhxL4XBsqhEECOVLBKlvdmbSRT9ROTAdKESZqijK/NkVKjnTLq0S/\\nohiPKa20cbQSj/gqQ5sM+VUou7WkUFAZ6euaFjdbYpfXiL20SC7ta/HwWjv9UAJl0dLLVElTjIjl\\nRO4USSlglCbnij75nvfuH/KFK56UM42zUgVrjS2G0UdsqxnOwmOj4mMDplJKaa3natliwhZKuPBi\\nxOiFuPErRUkRZQ06JaTFmYhxW+epzRHY7NEBruz+q2jmV0jDKTnlCiNOJ2zKfGxtC3624DPdqDkH\\nbDMnjQNKPT5XuHihpsryUubrDqq9/II/CXr9/3vTuwftoSpVTd6XQbK6qfpLqVrjTQbOEtzkBGsJ\\nihqpvJRkrrv0pf47p4ypEpacy+5hV9X0uZBRytYeoKJYjbNzojVYP1KaTjRZJaPKBpsLXlne++B9\\nnrt+g7PVMTeuX+f9986k15igNRaFot8OaCN2XcZprHOivRs2/Pidt7j5/Mu89uqX+OGb/4kcCqjE\\nmBVZibvPvG3oQ2I7SF9LU7BG0c5brCsMvhBTwlqLQyoToy0qZ/wQsNbIQ4ri6vUZ2+0aq2b4mEkE\\ndJaK3ljD0A9oKxV1TBG0VNpda8lWE5PIHjJgnEZbRQiJMSZ8TLTakZtCGDzz2Uzs1FKGMI1nk/tL\\nBn5DYzUhi2WfygVVq2ylNWenZ6QQMa7BGAmarm1wFo5v/YikDM87TdkrXNlfAlt8Gsn3C+HeETNj\\n8HYSKSiK1oQYeOt2Zi9l7n70Hu1sznq7YrtZkbJiu91gneJkveXpp6/Sb04I3gKZw8OrLBZzmsZx\\nfHqM2hici+ztLfjCa1+gm3Xcu/cx3nvxfu09xzEQYuLVV79MzpH3P3iPk9WpVKumYfArclD02+2O\\nhNZYjV9+jeHaL+NtR84LVKownRLWsyzeUq06UzApMmbIwdN0Ddf3Z1hr2bKkJ8HiFXK4ibUHlPY1\\nxuYqKkei2UcVodXAg0n3eTtDZEnt4TPkccvgb0uApAElPU2tFGQrSIFOQvQp533M6VnMNWgW9WA8\\nFaG+ALA5j6AcWrcU48TeDiTBzYFSKV26FEoaKa4VGFQVtGpJecAPa2x7SM79+XeqCJKqbl+USI5Z\\n7jlthJ2b64iysGVz+y1ikNmWKcddRX3Z9vDaq4oMtt/1NEuCrMBUuUlJwsTXEVSmxEIeVrx5cpXf\\n7HsOu5m0dIxDKcV2HDlebzFOkXw5H5N4yfakCvPAGFNss8/Z3Z9Qsj+vEafF1M6lH6aAFCnGgRqg\\nQOg9pfTgNzLk9HGfVOpNZGZkY7HdPml9h/wp9qc67538nMFHlUIc1peatT8cIC/+HC5Ui7Xi3FWL\\nqsLT05d41Nd7wvH+It/n0v08JAaeYOxztCdLk7wyQQWBKedZ4pSqlowioiZhs9JizKynqksGwQpR\\nqPpZTiN3tOFT56QIGFtUQWchV8XgCG0FcCdyQqfQ3QuU7Yfcuv0ef/+b3+Lt995hub8kpYRR5y47\\nVGgqIbM1hWaeyElcPGRItOb6tWeYz/fYbE9QWe7VEjPZKPox4GOmwWC1IyYZqaRsYT6f49MGp4SV\\nao0hRfHJjCnhrGjJjNYYU8gZnLOEMFKUwqCI1fkobsQ0XQOuEfcWp8UwA6NJRhKREMQwQcVCg8Ni\\nsK0mepHGpJhp2hbvAymIL2cOEWX1Lq8MKWKMIWVhUk4oh9G1p5oEOuvHARsjxjhyySwXc9bbwOk2\\nQOnx3jNfHnD77m2cM3TOEfxAM2/ZK5rj8QwzexajCqlkija8ebTP33HPsgk/ZgiSYK82Pet+YDFv\\nid6jCTgV6BYtwUdSymy3A23TEUJk7Ae22w2ta9DmGawpXLlywDvvvEUuiePTIwqGT+7c5Zvf/BbX\\nr13h3/37P2HY9qzHni1gm4Z+29N1c+m/W8vMPUW//GXU/qso2tojBKywfnWZUKsiTPy05XZ4illz\\ng1k5IWpDaxTaFoK5ymr2TbSxtGpL3r5DXHyBzfx1Cok1yH1+yTP6Kb5C1Vk2117Er++iEnUknbrw\\nTIn+efec7MCbh7kDavf6NHB6AnkhoXSLcfOKLjVkLZWZoohRR04YO5P7sojFZUlRiHV+wxg3tMtn\\nyHHYIWcAJY5obYhpmiFaE3DRLZHTiCqJpEFnj/Y9ZVxVzoCqBdDPwduoAVqYwglK1ZmXSmrKqVpw\\nShGQxp5bR4bNAFdaSDkzazp8TvRbL7Bs50Sv7B69Dj8pYD7XdrMc1keoHTPrAkSpEtrO5QbQUmXO\\nDl9ic+dNtHE1s5HFOUvT7LJW3u5yUhTazZhdfZnx9DZJ3Ufgid1ZQqmGoi2qjDus/bNupZyPvfr0\\na5dkf5e8lkuEUqW/uVZil7coH/x+T/iFywlF5ZGvXbaV6UG58J6pqQ7ndfpkxB5zklkiBdEyTUNZ\\n6zOpmXohirZ1+BSQQbrIjb6LwrqyPydj6frQllKJfjVDBSYbwjBsaK2Tz0sJmZdXCOUaZv+LmP4W\\nx8dn/O//8g956to1VmdnlYgjD0XOYKw48VhnsdYSciJF6S+qDLc/+oDnrj/LzRde4trhU0JeIZKj\\n4NKlQMoFoxRGKeZdw+lqpBgZ/TVJQJ46PJCF3HuyLoSSZcRTkarNVHG/VjCOkb1Zy+gzzipUyQwp\\nigBdCxRdTCYXDUrjnEC+uoi/qG0dwzDQdp2QglCEKF7BIYkmePQjJYo+lBRpG0fKiYwmp4SxZpfA\\nTO16rfUusZh8R30uaC0eqRrF0fER5vSY7XYr65HSzJdztBZY2W9XaG1YrXq6p+ek4rCliDtWDGJr\\niePNE8eNWGh1QBlN13UyuSYLrHf92pIcRgyOpu2Yz5e0zYz1eo1tGqwxbFfVszpnNts1n91D0gAA\\nIABJREFU7dkxpWQ2mxVt2/DR7XsY4/jun36XxsLB/hzrNIzgS2HsRQ+5GTfk2SHNwbfYzl4AVVDj\\nPWBE6QanFhTTUlSd0FHJcbpkAg3Wvkiyf8zV2ZwUE35I3C3PMs5+E9U+TVKnzI/+mKPlf1bRnVCl\\nHmnHGn3SGqBrn6k0HbOD5xnO7lUeQyDFnpymFoAWtcEuSF7gCMCOP8KFV0uRVsWO8FfEW1ZpK09j\\nDJTJFABVvaYFqRFlgxJ9ow+gIs38GXKJsl489Fk5jugSSQ+tPyV68W5WU/WX6bd3ZY4n5ecPltQ4\\nWHkUqijZL/Y8TpW0ay+VDCqOpHHN946f58Ub91G9prOOuWvwCmamYWN6UsiYvcUv3MN8dr0eGrMc\\nKX78VJ/ONtdoZnPCsJHFVrckpYQQUTTFDqgE2S5QKVLiw+yti2dAFtOMZNLatezW5Au/lFF0+9fw\\np7cfX7Fe9hE7EsqnN63FbHvq9z0g/3iAMKQQNfm5vmoyOn6QrHMO2T5qe3Rf9Px4f64baXp/qT2l\\ni9UlF29ifd7bnI6wyNmVfehdo176IppxHKvRtJwLXX9eanY47TuVmrEqs4ObpowiZQk0KXqM64iu\\nQ6WMJlJMQBeHwon9V3H4krlzfA/nIuMw8MrLn+Ojjz5Ca8UwjMSccI1AvboUmd+ZMwrN4cEVXvrc\\nS9z++BbLxVVufu4livLc/uQ28pAWjDY7ckRQmVW/RpnCfCZDiZezDoWiMYZ+7GmsI2aPT5FmZ8ZQ\\nqz7TopSl7SJFwXaI0Cn2FwvUOBBiYhgyOSRuXN9jGHvapqPvpS+Va4KilKpVaqCx0k9SWlMm4pHV\\nOOUotrAdRqkwi1QiPgah+udq72iceAVHgcKkGkdW6KJROdH7gDWFxjoxLFCNkJ5UJivFer1CFRms\\nnbNY7Y2xJ33yQ/av3WDkUJ7bTO0rdeA6Ci+iw3t1Ec/sz1s2Q0E7J+QmYxmGxP7VDq0t6+1WZCfR\\n03vP/v4+L73yGvfvfsKHH7zHyckRZ6sTTu4fsVjMCOOIaxu224HFtT3O1mfknGQQOJAj6GYGV78F\\ny79DJJDv/RtMfxsT17gZUCxNsawXL5MPfq8ynwVxSSWBMajQ85TpsO2Cj/yc7d7r5OYZKCPaZIq/\\nw2r/20L8yQWXLVkXIc1d8rw/bDpynhRXRm23RG9XFGPIo/TNi4rnbRZTatCs61PRsh6Vqped1iug\\nYIQ5nSfpiaaUUFtZFdZNEWUEObCmqShQqEbqU4WsQDfMDp7Db+5XXbk4EJXK6M1Z4M88ncMdVCtS\\nL1KdkzwtNnEUoqE679Vevsn3+zQHJtd1rp7X3SpXhAiUJzhYAZGSDHG74kdHV/gnHODdllQStrbm\\nMtA0QuS78eL+/wj8T5cdzRMrTLRF2zlp3DzwggJ0t4cfPTv78uwJ26NqlhMweklSA8afkqtv6ePO\\njSpSbWzvvsszX/7PuXPvx9S2Wl3cxfh3OLt7KbPqcZvcnHLkk0vRdANflA1cZMPuguZ07NQsTsUH\\nIrnaWenBxKq5KOF4/DE9eAyX/c5n/o5FWvvSL77w80/tUzO58JwThM5/9yKSIIbLZfeiobLwqGaG\\nWgKonDM5F6rKPABKqRBozihjJeMrkDLYXB/YXCCLJCFqhc4bcJmlhd/6ta8zbFd88eUv8pd/8Sdo\\npWTBVRpnNQkRRaMK1hpikAVvfXZGSQ3Odhxe3ePZG6/x1a+8zr//sz/lrbffIGUv0gg0MUW0thhr\\naIxh0Tnm3Uyy5iYTfaQUCCTaxqFRtK5hsx04Ph7ZP2iIWaEJ7C1mxDFI8MoN/djTOkPTOCGbzQxj\\n3IJW9N4LzGyQIFgKzkrmn0rGZ7E5jFWW5VyLdQadNWMYaJ3B+8zo6zR6LaYJbWOlgrb1/kKd9/Ep\\nlOqLa5Rm0uXFqLDW0ve9DOqlSBWdFVqLVMYYkSwYo2Wm5fr7bHkR1Tq02aMUz8vPNbz7s5ZT/3k6\\nfQuTC13TkIv4356tVjjTUJrKbPaBmM7wY2Qx71BaM1/MWS73MNay6dcMw0hIQSrvtmO92TCbN+RS\\n0Doz+IF+O2AbgcdDBg5uUq5/m6z2MCVjsKSr/4iitwyn3yOFFWZ2yNC+BjzFyEgzxZrCDs6M5YRu\\n4fi4b1nv/0MMEaUyuRgoDmdfpJSCSdJTz5VlPiUokkRPAeRigJS+szze03XSmHYmU3uGjUDzqF2g\\nUZUdTq3UUFPSax4sLi4GZerszapflL6f9J61dmhnUZk6mcfXHm7e9fFSDii7oFkciD2hm0P2mFLr\\nXEUdtSWwLtqQY9wdgi6yD6MQgwNMrQAn9AoexW1RSjHfv87m9OPLIbypDWT0Bb9ZSSymql1VmDar\\ngA4DHx61lMUZnGT6IbL1gZk13F2thDtgNZv1yaXHw25VfPT2rFKWdnlVLtKFrbt6E0OEMtSbLFHy\\niIlb0IaiNMlHinbEkkl+yyOry90Zki9pZ9f4+PvfIWe4OHxGN/ssltd3N9nPs50HSzCuuzRAPVY0\\nu4NdFWDP36/K7mGAcxr1OVzy+O1JRgZP0iU9tLe6rynD5NM32sRevfD7j9rPlEZIW/OCF+70f1VH\\njO2moUzyDSpWkChFoJuSCrokctVQujoTMm5XIm1RlRNdIAwfolJAozg5uU+Mka//+m9y9fAaWiuM\\ngUXXcLC/YL/rcEbjYyDlhDGG/eUe89keN2++xK/86q/yve/9LbZt+I3f+G02Z1sMwnCNRezq5rOW\\n+azFNYama4ilcLpeU4oih8zB/h7XDw6ZGcOisTx97YBZa7lydY9nnz9gseiwburLjqDh8GrHlBfH\\nVEg5YG2hc9UbFIVrDG2Vocy6FmUNIY6kmDFFE4OXvqdSNKYhlUTfjwxhIAapkDGFpBLKavreg57L\\n8GFt8SkyBE+i4FMkK+m9JyVTJmIBbQQpSSSCHzBWgHOjNQaF1WISoRTEJEYJFEMi0a/vMvp3MdsP\\nSP1fQPbcubNm3jm8PeA0vYgyYqkYojjVLOcL+iESqunFyf0jTs/OoCQxjPCecQw8++zz/OTHP2bb\\nj+QCH374AUdH90glsVgeiCH/MGKNoe/rcStFIOIW+zRX/gGZViqqnPA5VwG+Q+39Bv76txhmv0LW\\n+6C2NOmc7TutQ5QE+nl+Gr/N/ea/pORhl3AoZSnKi1G7rqjaYxLf8tBzuEteJvlP3bRWItPa9SpN\\nJeVZlHaVM6AE0VP1dWWotPELnyCBTwAhgVmFN23IRYuxu5a1IGsna8Y0EqwUyEHu5aKx7YyiNSX0\\nlHGosxhq5QqSoAC5chrkyMVEIedNNWdIlQOTpxMgyEo1a7hs/cs5szm9+4g1alqJJrnaucRPXVx7\\nJ61mStW6b81mVJKwDp5t9FijOZwvpQi0qsoFL98eGzAXi8XNog1+e/KpdmFMiaFfy5w1lUhxoCSP\\nX51Ajri9Z3GNFpy6IPTfJ8SPggJdiOMRztrKsqxGBWoywz7ezVz7eYLm5EEImeSHB977sO7xMvLP\\n+WtSWWmtdw31aQEUo2pToQ8ee3w/L2b/2X5f7bLOT8XJKQUtNY2e/n7xMzgPskqJTEDvkJVz76Dd\\nZxUwpsFoGRA8ZdNCqhJpwwTvQq7zNwsZRYyj/K41xHGL1gqHxhWF2/8thhzZaxeENHDzcy+zWq3Y\\n2z9ksdjHGEsza2itwbhqZN7OsEURUsTHgKwhmt/8td/ia6//Mv/qX/8L/uCf/gHf/N3fZbncg6So\\nxp0Mw8g4joTRk2NiO3ip5pRhf29OTIGiM3v7C/aXMyiFED3b7ZpcAqUkZrOWa9eusre3lOpMJbpW\\nkyopKSZJOJu22ZGRck4oDTEnVsPAZugpxlKMBLDDK3s4JcG9Tx7TCFHL6YZYq4JF19FYTUqRdmZo\\nbICiSCWRFHSzViQXWc670oacNSFlQpFAEqqpRM6ZYRh2d0PjDKkIo3cySkAZ0hRLkufLsx9wdfgT\\nrh//FYv4E876NZuhYIChfYVNP3A2bgk5ifl8GEGJB7T3I0YrVuszTk6PWa1PWZ2doSi88cb32G7X\\n9INnGL24RWnN3XsnfHz3E3E6SoXZrKuSmkxOBZMKQd+kNzOZgkNBmURTBlp/G0vGaEuDxiiBnpWa\\no2yLNhZjpfIqleuQSyI1izpI/eKzsxNqUGjOZ69eQI12Dlm5omN1ET1/ksWQZZd7A6REHFbyD2NB\\nG3aMfqVRpjkPkshaI/MqDZMr2WSJmWtQI9ekVRmM6YAEphVWe/IYa6AkUhjRuqBVYuzXaAraNuQw\\nQvJkMsYqckqEXBjHNXnsMQLVCDM4+rr+idYzRtFF5jxCqSS3kiqKVQk7l27TWUo8uvgosuQ9kHIg\\n3s9T4lMRLSnoxP/2//x+ZqecqW/00SNsdkX0v2DAbNv2ZaUcyjSfOtw8ntBY0FhSCJTomVwXVCnM\\nr36OMGzY0Yx3Ef8RlRS1YkkBvb2Pu/ICyi1Rdk7WCrQRSCtGgQt+zoB5EdhFSdb7qAD+KMbs+aYu\\naCrl9x52hHn0ex/8nM/y+kXrvsf+vpL/SGB7KAGoAbzWwTyq2ld1MZhCZKGgCwJt1NFek2VaURDD\\nmtGfolRlY2pLMSLENlaG/0rPV6yzzJTZTgOci1C++/u3iYyQ76C5g40b9KLj2uENvv33v42bNXzh\\ni69z/enn2b9ylRgSKcrDZIwhR5EzGa3px57VZs3f/uCvOT0748UXXmC9WVEU/OVf/Q3XDp/id77x\\nLXQ2GGWJsRBCEkKRgraxdRh6JmfPartl7lqsVoQoDMD5YiF2ZfWMGuO4c+8+wyiTKmbNHIoQdnIu\\ntM0MpSoLtkjCZbTGWakyffK0naNozehHdGvY+gFlNEYpnnv6acE2lGLsPX6IQt7JBavF0rBUJ6EY\\nI421GK2wquz00zHKcPDTs4HVuiemKJZ+IbL1kaQ0zgirMcXAZtvjY8J7cRkyThbkVDLKCb1iPpzx\\n8hXNUweJZ/X3UMNHaAxNNyPaA1bhJj4WQsrVpzVKUDK1QspyXyUyffBAZr3d8PZPP2K9HaYnlkxm\\nGHtOT1dstj1GR5pGc3RyStGKprUonYlKoW2HLYmoHbpk8v0/Zrj9vxA/+d8Id/5XSjqiKIu2Bmsa\\nipX1oOySYYW1jbRilBeJhZpg1AvPUxyrq82FEDhVmeMZm9MjQn+C356R18ds798mDGv82JP8iI6i\\nc6QiL3HY0t//BJUCOQZQtlaCVqzktAMzQzULtJ1J8NSWoqxUkboGUjUFyqq3RBJoo5w8h8ZA2NQZ\\nqVESWSX3YvSeMGyF9FNpejlHMgarLX6oATGMO3tDnQVVSTlhalGilGi7VQFtLJSMsZ2QkHKRwJnP\\n9ZePKk4kIl7e5VQgffMpiTn/6YXQIPcXJZNTJPvAz047+m1kPQwMfpT+u4aubTFWk+Kj1+Un6TCv\\nK23JwxkXF9gMMG5IvsfODoTlmINkOkWIH8P991BmTikBZY1UokAhVnjv09WNxAND1orZch+e/jzh\\n7C5h45ExMumBIFW0krX4M2wXjQZUlUGIXd+nx+ZcxpJ98GcPGxeoB34vl/zEAHdZr/RJx/HoLVNq\\ndql2x8RDZeZDWZqaYIxJK8mDCpkL76oYq0woqD6ocg1FYyXuQefyE61zlRk4Qp1XiNHnuaKxGCOm\\n4VqLXWEKA814l/HePyc3v4Revs77H/yI5WzO//Ev/pAQPb//7d/nbH3Cn373I4YhEJ0YRzeNzKA8\\n63saXShKceXgCiUH/vi73+HVV17luedfAA3f/OZ/wRs/+Cs+98IrPHvjx3xw+31s68gkcatDtHox\\nR7yK5JC4Mt/DTVKkAkqL4fTB3oJhlAn04+hljqD3zJsWXwLzRQsb8aNNIexo+Kqyag2KvfkMHwa0\\n1swaw2oTuX7tKmdhoBiH9wMlJ46OTikadJT+zMFeQyqFcYwYoxiHSIgJgma2bAR6LQlf3XhyycSY\\nmLUt87khl6YCxuCMLLKxyE9chWRTgjAGtLXkEBHKlyBF2ihShHePNrw+W7BlZLVRqP4epewxdtfA\\n360QsUJbyfht68hF4GRtRT4Xh0RSGR0KpUkc3Re9asIzBunjttZiteHK4YyQM6vVmuVMBgMPKRKL\\npHJFaXz/MerAMEtnlHv/HN3fRReFcoqSRszmR8TD3949K5O/sQZhkBoF2cmcUtVJIpoN6DDdArvl\\ncJLtyMURWDSnwNndWzg7YxhXqCgC+TCsxRxBW8Z+hZkfUApoZwjDVswB/BbpIRbpzRkrk2S0xSqx\\nbtS5BStBVSab5PM/WcZckcaq/5x8sSU4FjLKzNBorNYE5aAElLJkJWiNcTPQThx8kIBX/IqYM13X\\n4ceNJBZGWlMlAdpgXEseY4Vj67EpTa7HjzXgI5hGEjz8A/yNi3yOBwiaRT0cLi5sZXcf7/w7K9Fw\\nWqMmRyClLCEMHA8N/+5nIzaIycd642laRz8M2AZSevS6+9gKc7vtb8pomPMdCJVewARKIsdIChtZ\\nm2sVonIkmw7TWEhRSn6mG9pd+lm7L6wU3d7zlNSyvHYTrdvdLS1wo2Enf+AJ2s6L+99dGHGzQVWv\\nwwsN54v9wuk9D5sXwC4puhDQPg3HPM7M4OL+p33/PNuOgLZ735SAiFRBnTdcmaDTKcifI7Hnxz5l\\n1uffRF5X6vxGlQSD3X7FjKAGziJsUelzBfGOzCLQN8ZRqhRIa4GYQPodRlfHklKwtmPIB0QcjG9j\\nloeo5au88/77/OzW+6z7Lf/2u/8v/+j3/zF/93f+Hi+98hqbfhDLs35AZVnIQ8qElDg5vc/Hd2/x\\n9k9/xPsfvMs3v/Et3vzhG3TzhhQyr37+VV5+6fMY7YhB9KYoxeBHYki77zRrZhhtIcm/ta5auFJo\\ntaPTDqM1i9mcZ556itZZDg6XLLqOpw4OufHUAU9dXXL16h7OaA73Zly5MqexYkwQo5gfOKO4cnjI\\nwbIlh5HtaouKHqcUoWg+vnfGZoj1ic0oO/lianyIzGeWxbKl23ekLJIhq414fE7wr4KYIkYpuf8V\\nYjOYq6F8gfW2p/eh6p+F+JNykn1G2ccEz8s0PM3ffnCHZmjwKxnyHfo7cPzX7K+/zyK/RYpeUAaV\\nscbIWDij0E6+fymS9FgnkOfoI/vLjtY5qMntmR8lCXOKWdeQsqIfPW2jWc4aqVgUlO4l7MHv0JoN\\n8ewPiZuPSUqxOGjJZKJNlOUXpXDJFxbmIhKgnDbkqhrX3Kfb/BEog7aSLMo9X/uVu6gpM2NLrvd+\\nfwZ+IG6PKeNAGs+Im/uk/hR/dkwJgf7sCL++y7g5IW43xHFDDiMpBXLypDCIubjSKDOjKIXSdqc3\\nBiOJlzYo7aSKm57NFCR4PjRBqJS0q/giER8iul1IP5aJuClBtuS8s/TMyUOu/qsZgh+q2E/VoQQV\\nsdKuPt+KFIdzsy+k+iVJT3SXZHAeKC+uhxe3JxYeD/yjTkGpBKoHC4YCJaKS507f8qcfPssmZnQW\\ni9McFSFotFFkdQ3dHP4Pl33eYwNmLqWRZvXw0CsREGZSiSOUeE6qQQMR2zaksScnTx42aAW2u4Ex\\nlwfM6eSgCqG/y/qTH3H0078kUVDKSVaL3TWq5dN0/bzHb+LyM1lTgTSZtfQPUJ8KlE/a16P2v/sO\\n8lPOJSeP2/f02mcrlUvh/HsgZgOT8PkB7aUcDbtE7WFCwAPQxUPH98BDVuEozfl7dgtFzeRUkSwS\\naJRmDBGlK02oBkWlNaZd7o5RfE3lvtFaEcctJg/42EA+gZP/QAlHjCHyw5/8kBAC7/zsXd599wNe\\nfe2X+LVf/Qb/zX/9T1BoUkyEkGiNoXECO8USZQxn8bzz3lvcP74PKnPn40/4B7/3eyht+NrXfo3F\\nYiGLZZazMmsss3lH4xrpm2Vpd07IjlBhNFY5rDbM2gajDVZrVMpcvXqFMXicMTilWbYdC9dwdbFg\\nbzFjsZxxZb7gmStXWc46ErBaD4w+cPv2HYIfGXqPxZJCRjeO03FLMoqzbY/PkjieWxhKX8lYjbWS\\nyBijaRpTz28mpYRzDmstWhuZQFECKUbW/ZaQIn0IlY2L9JhTFnmXYncPp5QJaXJ0qVzoIpKYO5st\\nndMc6g9oSgI/YMaPOJwZGb1WirQLUkVhlPiyGqtpuwaxFE4oo5jNG7qu1Nur4JzDWUvRsB56fJJh\\n0mMUGdSy7VjOZmg1Q1/9JmZ+A7P5C9rtEcY6iobNZovGYd1rFLWAHEh6WqipWmGFsvuoYlEqQ4jM\\nws+w8QhKRaOqXETXYFTqOVAlUQrEsac/uyeVVRyIw5ocPH5YkWNP3N4jnn2MSj1he0IZt4T1fRh7\\nil9TfA8pUErcGeBrBOaXdkkk5UBKXozGk6cQJT5WdqvatcAuPMcgVaS2Vc5RodKHfseUOq7vwnqj\\nzQzddOgyUvwZRhVcO6vWmNLnLJkKsSpKltFeSjlKCLJf28i0KaNl2k0+d417nBvak9flqdV28Ue1\\noj7fSf2TyXGkbO4T7AKloNXCeFeAKRntFLk4lOku/bTHRpuSs8tFf2q0VZkEshopdXeQJCjdUNAM\\nxx+ibIN2LSkN4p8fN5QwfvoLUi5UPVlumO0ddLHsvfg1zOJGxeiprkKKaQL5Z4Mtpc8FoJXFNAuM\\nXcBDDedHZTOX/fxx/5aA9vge65RhSRJnKqT62TYJmpIsaGN308tlZ+eZ1XRIcs/VS61KLTofvhEv\\nwB7loRt1go5Bek5VezUtpvJu0bFGpelmC1QeKEZE+TlP+ttIjh6VRaSvVM1Qq93eEBL7z/63jGUP\\nnUd0uk+hsB4CP3r3XbbbLadnJ7zwwot8+9v/Fc888zwURdd1pIKMJssFa2p1qzRn61OySvzN3/4l\\nv/TFX+KHb73JT99/h9OzE3705ltcv3oDgzj4dE3DrG3F6UcrlIZ+9CLS1iLqTjGRvIivQ4rVmFuM\\nAqyzmALWaulv6sLGb7Gdo20d+8vJkktjO0dWmX4YUNphbAPaMJZCsobV6FlvPJvVVqz6WvGi3Y5C\\n/ddaxpxN0+NSSjgjgSlU3Z21llT7lt57+jGw3g74kLDWYZyl6RraWYs2hhAzRhuUUficSUmuuzHn\\nwSFXFEmTMUYSMqs0yhqiTxBXPNP+gGX+BOccR36ownhAK3wlj9y/fyyohFZYq2oCJXKadgYhFmIM\\nhBSEUKOLSFt0JVJVhuZqGDgaz4h6RO1/Fe0O6dIPSSf/iT5nIhmlI2b/dcyVr8PeL6OHN2hSX3PF\\nLLgwgBrFbEBFkoJiGsY4wul3WBz/ASne2611EwKjjFTwEUtBEfszoh+E05EixW/IoUclT45bkl8z\\nro/IfqCMPWk8IQ3H5GFFGlaULM9ITiMleawq0istmVICOQWIgRK35Lglhy2EnlJJl7maATy0Ykhy\\nVVGekgsqVylK9mglftGaIkQjJf3p6dkXg31fq0bHYrFPihGjhRSGsShtpe1WAjFs5LUU0MagjYMc\\n6zol5uylSBLwME/jwfXxs21T+SRJgayrYkKRyZVDMyV9JQfC5j6EwJvrlxhzRjVzTssrnJhvYVTl\\nSD1i6X58hZmTU0phzTl0KI4mF+A+4u7LCqxlKAp0SuhuH8HQBVFOYc3lAsqy+7Gqi3Uko9RIc3gd\\nNzuQxnaZYNgaNLUww550apVCpAsUipJsW7f7VMfT86N4wkW67IIqeNAialdtnkOWj5aMwDS0WoLm\\ndOkfA0HUhKFQQCuMnVfbrClYXpB/lLLLLJV2oBylyOKuH7j05yPBdjXqpaeisoILUBIpCesVqAYF\\n1IU3oMyiwrWqVidCalBay0M/0a5zooRITAOWQmoMpn2exi7pbEHlFTn33D3ecHJyxp/99Z9w7+gu\\n/9f//UfcvX+PL37xKxTEAch7L5rJBK1rBEZM4nX79I2n+fDDD/nqr3yV73z3O8znLTdfuslv/9bv\\n8uJzn8MqcQ3KMVfnH4FeGycLS4i+EniyVD2NwxRFoxWzphH9pE44Z9nvFsysw2lD5xqaSkqaz1oW\\nXQdKMYaRlDPOGLrWokxBW8XgExvvKa7QHczonGG/m2GdojECpSZiZXGq3f0lPeSCsw05F0KUarAo\\ng7MWa6UDaa1GKfmOMthXppfkmElZJA6T+XZCZm5qhQRHLRVsRkkPM8fqHSz3fFGQVGEcPFe7M1wL\\nY4kUJ8/o6Meq75T7xKfIMHhACcFKyfVyzpByxlhDO+uqPCZUNif4EFnMW4yBWeNwyhLCgrz4Eq3a\\nwvr/oXGKxopkx7orFN2CWqCHdzHbPyfphYxmKw3eirVayS1ZOVReIxYpPWhNtoWYNmg6JkKLPCsy\\naF1rK0b5wwl+dQ9NguxJMUCK5LgVJC6OJN9DWJHDluQH0nBGGlZEf0YKdb5j8hS/pYSx+gUnmRSS\\nfJVHbMjjGXE8o4Se7DeksJYAOg2rf6hdJET1qtcl1+JjQvU0KtfhCKaprkLstJs5bvEh0s6vEWJk\\njOKuFXIG4yjBS3WpdQ3YEwJSJx+hiXGkFH0+eeUSVO9xbmuP3upMz12lOQUSqH2U2mOuiX5KpGGN\\nPztmlb+Anrd8sH2F0/x10vIrFPs00Q9QOTef/rRHbEoplVKy6Eb0OI/8RZjgRwl8EUWmqA6rWynv\\nTYtMHi8PQYMXd3Kx+ZtRWQgU+A2+v8/OOaZWVlLyT7j9o6qzsvsjcUS0SOMYa0V2ngR81gs0/e4O\\nxlUXmHMX/r+DYh9GCx7OqFQhlVAXEnuubXzkVnYLJBRS6gVSxDxwjsCgTYfSc5QVNh1aY4yt9AZd\\nz3k9/xcQnMkF6LIidHc+q+Zq6gFNZhA5ZXl4KlIASQge2mKsrd6YwljTEzxer3caV6hosXs36YMm\\nhUzbNCxn14jtqxwdfYyzHffuHfOtb/09fvCj73Gy2tJ1M1Cwv1iyXCxoap9xtdnQzGbcv3/MV15/\\nnRs3nsFvA7NZx7/6o3/JtWvXeerqdV54/kWappGq0GiK0jhraayVYKF0/SM/s0bYYmVjAAAgAElE\\nQVS0cLNZx6zraBsrPUmtCVFg5pQyWlv2ujm2eriOIeCDSFdSSizmc9rO0XUylsyHyKxtmDnH9cMl\\nY5Ogobo8KJqDFtcKq1YgU4ExtZbjVWiGYaAxFmU0PkaUMSL8jxHrDI2zVRIFRhlyKuQi/SlttBgs\\n6JpAGUXWYp6f8/m4MKUURitKhb60FklV1gplC9sSOB5HTodAYyxWKZxzFCRQusbSdoZF19C2MjJQ\\naanWsspsBy+VSMpiR1gKZCXfRz6RrR+lLaYLVoN2Hc4uceN3GMfMpg/E7DGNgbIhr/8c1v+WvPob\\nUk7otEHnTJvepk0FRUSTmI1/Q+PfwmRLyIGCQevnCO7zKNVVhOa896aLIheDKpH+5A6kQA6RqcWS\\nkkCsOXshR6ZB5jXGLSpuKH5N8ivyuKIMJ5RxI89HHAU2LpkUt5TUy4xhvyGNZ+QwoEpCI9NnqGPf\\n1LS27NaaqW0iphMkX2H3wrk2E5I2FNvWo84ytSSlmnQbZstroDTWTTY1Gl0UzhjCsBKItRKPDEbk\\nkVlQg5x7GbowBbRpzblkXbzwg8+0Ju/eNUHkNUGfEvKSJy14JT2lSPJb/PoewWg+CdcI9mVU26BL\\npOx9gxwGLhuPBo+vMNvpxOeHfANhuigyfmiCH3OWbIQCWmeCX9cgaTCuAyYa/qf2dv71a+AtJeOM\\npT/6RN6na2O6ZGzTsqvD1GPrsfO/Tr0HNPtXXmQcTqv36RMu3KP2fCFoXlp5qnOT+It/Hv4MVbVc\\nqqQKXQoTbuq17vZ74W9FjD7Ryu16KNPepmrSmFYqcF2rUjIYQ9GObJv63atd1vm7P33eLp4b2C0W\\n0vdRD/2u+F0662T+o5Hvkeu+xXpX1TcXUprm/MlnZiXJmVt8BV9a9vcP+fznXkPZgeXec4yp582f\\n/IS33v4+//rf/CFtM+OF55/m5s1XKEURoielwPPPXKdxujrSbGi6GW+88T2cFX/Mv/uNb/HRrVt8\\n7wd/w9HxEfv7hxjlyDmiay8454xR0DYt1hicdbgqBWmMRZdCZy2NdVAS87bhcLFHV8cGSdAptI30\\nOjvbsmwWzGyLQqMVnK1W+JQ5Xm+k/+saruwvsFYx9iNOiZa1wbIwjqZ12MbU6SEa64zAwKY+Gwqs\\ns7LIpowxhmEY0MaQJueWKAE2VbLGhGpoJXDWzgULJBGrSVIsor2rNxyh9qlRipgzKPApEOtc1WQ1\\nppskRIWYKsRbK4FCISYohJ2bkUhfxAs3ZlnSp6N0RtFajbXSM9fO/H/MvdmPbNl15vfb0xkiIqdb\\nt4pVxUGkKLZk0bJsAxbcaHQ3LNhw+8EvhgH/gX7ymx9swE8No23YLTUgudnmaJlDVbGGO+TNISLO\\nsIflh7VPROS9WZekRIk+RLEqIzMizrjWXt/61vcpMzbDkBLZb8j2mt3dz5Do8HapigtziZjc6Fxw\\nzpDXpLt/RTHQpZ/Tb/8NV/wFV9O/4mL4awqWbDO03yQ1K7JcM1/8c8Q6ioSq+60EMOVWJOabz8j7\\nW0pU5bOcohoAWK8knKLzuvpAaAUa53vSvKPMezU6nnek/Qvy8AokKlKcJmQeKOOeMt2SplsosX73\\nUS3ImEcW8zU2LCpcIoo4WONrzCmHFsuSWENQRrSUqBKLRhfa2aBJWXQxmrPB5IESZ1zTanRIqc42\\nZ0w9Xn3UDc57YF64yG/EywdxZilEHomXb2yG2uu2h4XCIY8sxKJSIdmiLGLyTBruMMnw8+srHXXD\\nU4xlzkGvlX2ca/O2sZIz731Okv1iT/PIkWGMI4QVKe6gngwRAddBnnHNJWn/DMIaZDgEy3pYJ+HW\\nPIRrjZDiFnP7Oev3v03aP2f/7BNtgJtQA30l8lRrqMdxRIOupPLh5rm/+RsMTQ0iUgPDr19lwps3\\n5+nPS9VlXoOfTynUr7+uu66wiLUesR6RZUX9WsI0CmIhlSVnBIpUyxuPiFXJOm0qH5v4IphmhUw7\\nSimV2q3V5cKgfQAIv3Y6DkxaQXvJVXz+WGVmLI6SqUlT9EExBila7SibVmXscs5k0f6yQQOJSuVZ\\n1u/9E+5v/je6u1vysMP2I6X7E+72nzDHzPd+8Ff803/8n/P5Z59g0IHzcZ4pUnj+6hUxqS2ZDy0i\\n2v/6+JOfsOrP+OCDrzAOe376058w7gZ++elHbM565iJ4A96oZZcNnhAK06SLDh8sFEfbNthaGRdn\\naIzDOccwzlyebdjtB3ZpxhnBWSVXzKMSgQxC37Z0XaBUwfScMinPnK/Pcd6yHXfMWOI0k7P2eW0R\\nQt9i+54pJ0zRpGiKmoDP06zSaUYoAk2jIwLWqkxhnFXL2VT4Ogt1plSvay6Cb7xK9QV/4KD5ZdFV\\nCkUKWUw14K0G6xSywDTPuC4gSXkC3jnyPBMarXZzVkJPsDCPE1MqNG04jOhgVGu4CDgDMSWd87Pg\\nrKNpDF3o2O5mNj0Yb9R6rY5kG/cVVsO/YbaF2U2YrLO0q76lpExKILmAFQy3mOma5rbj1ebPWLkf\\nc1Fmtsazbf8pxgRC+hHcf4+UJlz7dbXFc6rFijEPhEvm3Ut2r55jsaR5qyCkgZxnLApdHwf0F3Hw\\nAiVhpZw830aJVlIQ6bU9le7J053GgsXM9sFmX4ujX7bVE1XdPFxY1/2vz68IkkamNOJ8qwtuY7BN\\ni7WQ7l/p2xdiUR5xbU8uhjLUeIIyeUuOS12rCS1OFK/+xocq82RbFh+PsWUfK0weFghLzVcFbuCw\\nqNMRN09K8aQxqbq3Jkfm2y/onny3ylMWSrGk/edISbi2efQsvq3CPAuhKcf278MVi76iRIh5ujtc\\nFB1YLRivD0O4+iqI4MMRNn18vVCOv62fn8vMfPdLhuc/IQ4z3ZNv6uvTbd1z0R6gXRQu3txOy3Nd\\nlTkkJW2Wl/DWfuGvs71+ERfHjtMV3+s3wKOJeYE76zCv8UGT5sJorTNYBxHmWsEcqtC6cBNZxI+V\\nRXkwrtWdRKY6ArQkuQc7cMqe/RIkQE7HTY5JWWevNBgUhJwVftNnRmdTc4pVQNyTS1FCgIo+IpJJ\\ncULiSJYJs/42e3vBFy8/JeaIzN/HXHyLWAx/8df/jucvXpBK4Qc//iE/+elPMN6SBK4un3B58YSY\\nheA846ijJ5v1mmfXz/ibn/2Ijz75mP/iz/8rPv30Y17dveSb3/om0zhTSuF8vaZtFDoOtibIUAev\\njWXVtXQ+sO5b1r3K8jlncUY433TM03QU8SYTvGdKkWIKMc+EzuGDI6fM0/NzztvA5apns1qDg89f\\nvFTiS+NYhQbxhuITNkAZR/IUmVKm7fwB4kwlYxsPVoURcknMMTJNs+p/FlHxAbRiG6dUiTR6DYuB\\n4D3BekLwpJy0qg6eLDDnKpmGthicd1hXdXxNrRqtIVVGZBEhpkisghBz1DrRh8US0BEahw+WcU6k\\nashtrWHVt+Qc8UsAFSUExZjJCJ9/sqVpGkoSrK8VKoVYRmT6FLJWyrZVof/dfg9WmdhFNQPJJagY\\nx/xDznb/Ejvd8+m44Tp8l9lA3v7vsPs/8flzQtxTwgZMi7AsQnTmWEQgJ6ZXzyHPxHnQRVTckeOk\\ndoJ5xvsGllSxBHPJB71WU2XdCmBkaTcF8nxPrNVmHSJ6EHOWp/bXiWBSx2GWBCnGHQhcGhuU+OO9\\nCtXkyoYueSZNY0WqjApB5Ih1FrEdJQ84Z7BGCVz4Foytn20wFJ3ntAFDVfrhGDOXY3lMju5tBcZx\\nbK+egYp2LKFJRFEIbQHVxbgkJEckT5BGxutPwbRYd44Yy/azn8I8ov6avzkkuwlNI1KOVcUyznA4\\nCGu1mlmEvA8apoYQemyzIu5vse0Zcbg/HMjxrxY8+xh/l5+Vqi2s12dYEVYXT3F9q2QHyVjbsQzl\\nnq7R3tw0IBy/wBC6MzAeiIfv+k1nIR/9pkdWSMv261Sv+jAapEyUOWNdh2pIHhPv4v4hpVLbWRZU\\nUuGhRYJLk5scT/EhB0qR5ewfqktdrbFk+jf26/WRFXuq3aiCpyquvrzdgveLWLTFezUqVjWS0+q8\\nHCAhQyHHRJpHjFjOnv43sPqPAWG8+yHt/nvw4X+Hde/w8efP+B//5/+B7f6GcdqrPmnM3N3c8tkX\\nX2CK6NiHb5n3A//Xv/0rLtYb3n/6Pn/1vb/E+xYXCh999AvO1pd894//Q4ZxYrvfkUqmaTyt86y6\\nDmcMXRNqEhSc1wWJC4Z11wCZttWgeH7es1mvOV/3XKw3FBGCUaJc6x3jflS1G+e52Q8UG/BNj/eO\\nXz5/zu2gQ96mCNZYbDCsLzvWT1Y03kMudKsVc4oKNZXFakyTZSkGxJKiQqAhtAzDrA+6UWi2GKkq\\nL6WOyuhYirNazTpbGZRiGOfIEFOV91NoVVDrJOdAbAYn9G1D4zxN06gNmym0qwoVF9Fq0oNrWkpt\\nheTqs1hMoQmBxnmEQtuqqbFxtkK6llTgfrvnK99o2e53DPNMGgdsyXjraZPCxsmD+Ia+7ZCc+eAr\\n7wFCaBz9qlcI3e6VvS0TXfmCID+g3/4fXFz/96x3/xMr+4owv9JEvflT6H8Paxuq4Ngh0It1xOme\\nNG9xpmBLVNnNUpC4V3ZsidrXr721SlJHxJ5ErJokl8BuBB+a+v5lRvjtceQYwx6m0IctItEpBuco\\nZSTYUBfhmgQporO2ACRyGmAedM+s1+Oqycm4HlBxQFn4E1ZZ2kvbxRhLilNNklndoB6gcA9jzOv7\\n/LZ4ejimel/rCxzil0Ewoau/E6BgSlVWqj3lON2Q9tfAniKG+eaXzMOg8ehLJPveWmG2TSv4HvMA\\nkj0Jngu54xiSNXgbQ5le0YQW5wPu4hvYsGIpg/SGeUx04PVXHNvbF8QEUSx5jlhaSlVdserR8yXv\\nXY6whcPNqYl1Hu6BCneWI+vtb5s0fxMo99f4tPp5A1YK1vdgGiyqHamJZSHr1O1Br/Ok6qyJEFn2\\n8bhCVdmvI2ln+fvjoZgHxyZL5pVl0VP344HzifoxqlJJZppU6kxbN1kTgTnKCi6r9dMkXdIWJ45M\\nYmq3pPYpc5qxOZCHn5N3z/jKhxPf/b2GZ89+yWcvPuH59XO6ptcHXgQpcLZekebEqun42tc+4NXd\\nK2X1etW0vb6+oWvOmKYt97sbnn3xGf1qrVJ03tD7hlXbQilMaQZbCI3FOiHliA++3j9JlXxyJmVV\\n2xnTrMP/5dgTBMAaxjnVxKaSaqXkangLX3vvPdbe0DvP1XpD6x3eWPY5I3MmG8E1hmG3w3untl1y\\nvG46SmIIQavD3W4mVbKPD46UlJEaYyLNqQZxheMUlo2sVr06wlS7sGK1KgWhcRClMIowijBnUcm0\\nEDRCWEMhE3NCpEYNYzHBEHPEWrTaloRv3TGZeu05KspQGMfIfh/JRkgolKs98KJ9TwMpRUo2eDzW\\nWNb9R2RTZQd9Zj/vCW2giApDxDyTJJJy0n50I7ShYWagJ4BTg++LxmkPKzYk3oP+H4N4iim1GrPV\\nnLxgTSYNd9gizKNanxnUhEJypOSRkscqOq5B+9Aaeq3NdXgKTa0yywx5OFSVvzJqHG6yk1ggwkHJ\\nrFTvSFu1aAnabkyRPM/kop6tzjly1J4qRXSF0G1gnshGVYqsXylEnUdMKaRYcGFV7+eISKz9xOrh\\nKYkS5xqL3oRjX//5dUTu4fE9fI/mSMdCdDy67hpWqwv86gxrm8rsVySLklTScN6zf/YxIhCHLSXu\\ndA4WoeT4KJP0bVej06CgD5b2PsrRgFkEawTFQB5+jJHM/voTdtc/o3/nG3SXH2KaMxWWrnR9qQcn\\nh8qPNyobQbBOKw/ftKzf/T2ap9+g5OkwmrAkiMfUBo0ILjSEVpO+VIx7GSdZEsfxtb/99ndJmqfC\\nB/qznu8cdyARE3q9UY0FE1i8OF+vBl9XzyiSD72R0/08hToWxvHDc2+OlefhI83J+yvDtj4AOSUd\\n8j9A0IBxdN2a9eYpWSLGtTr+wunfnex7hZm974jzjpIy5A1u8036d/9rSvB473D3n/Dz8c/ZlzXv\\nf3BF1/VcbS6IKbLZrPSIrCqVzJJJknn16obv/P4fMgyJYZj46odfw1oYpwFj4P/+d9/jxctnWBFy\\nKXgxbNpWFXEszCkR5xkphTjPpFyY54QPHUUsPijpIdbB/oUMM8d4kHN01jKlSNd3VVzBcNauCLb2\\nmw3kmPjq++9zdXFJ0/VcbNacdR3rpsG3jSIu7Yp123F1dsaqb1WlyXAggTinvWERuLxc4b2j71va\\ntqHvO5pGZ0KtcwqPW1srSkPw2rtVxrYjU3Rcxmj3KUomeJ0Btc5SrCXVnmgqwpTU4d5YJQHlLJo8\\nMQrtJhUsyGKYUyGRyUVnQqeYGadEzgrDnp/3eGfwQSsg6w3GW2JO5KpGU4yh2ELvLcFmSioKVmQl\\ns00pcne3o+97nLeIqLg2pkPkjGAc+9jz+W5DNH/EMH6DmykjuWWbE/H8PyO6vt6gBsTp9+LIxTHf\\nfMF895IUR0RGFeIvqn5GiVDmCgEmhQOLzhzLYeTiwRN3fDaANN1x9I/8taIIR86E1J9tla+rvjlW\\nYXsjUtsit0jc44yhCS1CIaVZ2aRVZUic0yrXgjMNpmoTx3lLKZbiAk3QtoCURfXHVih9h1/OX2Xw\\nH6rBkziwxKsvJQC95WdtB+m/D6L0NS4GB667xKzOKhpaz39JNWlG0nSL5IKZtpQ0QJmphd2jrJ+3\\nJUxrrdWBWFM91U6S26FJ/XplJwZMULpziuRpwrc9tllj2ncw7QUSzglnT5Xc8gCHPTkPAKbgu3cI\\n6yv86hy7uSQPt3gTkDxRks4tYR7vRAra74zzvq7ULN35O5yq4hz/1lB+C9Ds3+X9p0nzwFCe7inj\\nLcY6xNQehF36iHXFdmCEUeHYZa61aigitb94lCs4XblpgIcFsjamzpYu2owsgyY10drltpGTBKm9\\nqwJkHDa0pFK4v/8CHxoWOv5jPVzNxwY1y/V6088jVhJ2eoXjmvbpvyDnBn+2Ir7819xP7/D85ZY/\\n/P0/4d/7oz8hOE+cM048fduoj6coc3scR65vnvHy+nN++OPv8dHPf84nv/wpSOLJ1RUxR3LKjON4\\nqLjEWhKFeZ4JXrVvEQFn8Bb6tkWK4fp2WyEp7bnNcySlyKoPBO/qwkVZiglV3SmiM8G6QNAxnzY0\\neOfYDTte3Fzz7NU197d3GDH4BNJ5vLfMg454TdOA91YH+8sifK8EL2stobEVOk6He0J7bwq5aY6u\\nPUWj4ySxGGJS9Z1c5gMaELxThaC2ITRNrdA8bVCD4lKRAhVUUBTBOJgpFLdoDCt5SErWRbAR2uB1\\n4RIjWYSm7zAOut7jGw261miCLhWqNAdHIL1zUlHodrsfKFnF6HMRclFN0Lv9qNJ/zmFswXnwISOy\\nZZ8HBvNn5M2f0Yd71t0npAFSGmif/LdI9wFB6vEZrR7tovMaB4ZXL3T8w0Ql9eRYWwxVWDzr80eJ\\nqrpUjpqv5lfGiYfQ6q/ejgjS8lTpnGk+JBOxCwZYiVbFEsKlksXQ+9eZgrVORwEBW0xFHDKStS8r\\ncUezusB5reBzyeTpThEjanGVRgxeiZu2YSEWnQb5x7gdj/UtvxyiXVxJamvqUGkarMDtzT3/5D/o\\ncAcHnnKYRZWFtTxuKTkx319jsuas+mWPnvy3sWStBlP1LXTdGXn/qgbek4ArVqW2KllFvSaLmoUW\\ng6Qt805ozr7K5klDnLeQR8btHaZJmPEF5bUx+tObIEpi1Z7h+yuGT78PWLKkipVrIxl5TN2Cmkit\\n3sDV+X24fc7Si309cFv768EfX7Z9WZP613nfoz8vMKokyrTDdhulxkvUX8mxKtbGvdT31MQryjJc\\nlGr0nq1OI8JRHQg4OAMczsvxgRVDTcCizf6lIj3pq2gvGbxzLO4kQmZ99gFz3mvvgBMY/rCvHBJ/\\nKRFDizWGlCccZ6T5p+T9X1Km79C9/19i7Vfp/c+54F9zf1f4f376Y7711W9zvjnHsmF3f0/boPN3\\nphxMmj979ilt64lzIjeRn330N3hnuL2/U01NHJdXa6xkjIMhai9qs1pR9gON14osVTf3MU6kcUKM\\nDuTHWN0+qsDAnLR6akJAcqFIovWuSogtTigqDNDWz5UiXKw2rBu1GLubBhrjaARM39GIZbjesdvv\\n2ax6xmHEe0/JUj0ajTKDfdA+Y51iGEcVXOg6hdzE6HhVKQnvHVi42088/2JgvTacX3TErAlXXXgK\\nocqHqSWZXsVcMtaYOguqVfQcM9Y53a+YMSlytVEtVOsMd7tR2bLOE+eI814THVShBShG+5EYQ8x1\\nTpdqvuAtKVf7tmDpGg+uME8LxFwXj7aOn3jLMI31ubHkaMAmpHim+HXc1R+wSj8g7j+lzA2T7ynh\\nKbb/qi40rS4oTC0OBIPJE7ef/wK/KCKNu9obq6pQooLxVJRnWbjqEuUxnuivjglftj3e59O4oebg\\ngjG+2oIt89dZJzCtI5tKCqzGGTpf7bGhUxZ3iRgRrNVRH2MFE/qqCaBm44LeczkdjaTNMj0iGSke\\nIR7Kqy/rYy5x4XUY9rHqUyrZ8NS1ykgCW+9TY5Hhhh989jUSc22PUCt8XbRITloQlkQe71SZafHv\\n/RJbrV+ZMKXKmqX9HQtN+PSTxLiDfZCgEl1UlXrjYP/sh4TNh7RPvkWxDhPUCDeszyjBan8obVlA\\n1YMEWw3aZnhJmp6Q7z5nfvURtrtA0qg3QF2pLxXm6zfhsQlOFc6uGrKlvEFj/m2QfpbP+u2/zwKJ\\nPO9wzRqRtipRLDTtw4c8/EyRQ4/WLqy4opqZ2AWermdOHj5wh9OxjK3U/CaH/alxSdkktaGv51vw\\nukARyJIRcViOer7UMZcD41c/gsWrMyXBtY4iAyZ8HYa/4J0n10wv/xeyfwfXvM+WSEmFXdrxwfsf\\nsO4CH//ibxjjyDtXF6S6P9ZqtWMbz7iPWLNnv79V2NsUmsaTi3B+cY51E6tVrzqn9d4ap4HgDOuu\\nY551oTJJYT/sNPl5hV5j0QWIIaMm8JbZRrZxoPOBgFOxb6vwVcmCdyoROOR0IHAZEYJzXK7XrFct\\nMReSzaT7ROpUSME4T+cbUpPw4ohFVYMU2jXEGJUVKqWaPwvTpGpLhVJ1R2vvubJdp1hALLttZn2R\\nlNlo8kGHVvs6hVxEq9GYKGJomkbHX4rCfCKFVAp5nuiCzk9PMeF8YDcOZLHInJdxPlLUcRXM0vMU\\ncHVBgRDnQtd5DAZfhOyM+nSWrHOZFkLjKdp60tsoGcQWirU0QQUcSqL61VpihGK/Q37nn9Fgkemv\\nyXMh2wF/9i8o/Xtkqla2WSal6/1ZZvYvP8XFLTGOSJyQFDFprJDfqbqMkl2W/XrT4OC3sz2IHQuy\\nxNJDrBWtcVjfU/JYx49UeCCVEW97DQFi6zNcKEklCb0IlBljPI6khXOKWKcjQN4GprKvNnOmGkUn\\njPXkeajs2AqHlprkarun1MXNw9YMh9deH9c7/M0S1w/IYkbH6fQsW9sjTYDxlrsJxDYYc19Nuavd\\npIjC55Ip00Ac7ymSOAa+xxPmWyFZY4wO+koBeSheoEWB4JvupNKoZqaoULENPaTMdPMp8/4laf+K\\nNNyrnqxYsIHV1VdxfnXyyeVw4nRFK8TtNXfP/obuvT9mfvGzKnS89C6lPmxvQs6aKHTnrD2qQBxH\\nP47w4G+XuPPb26wsScgqZX28UzWl0AP+CMXWf45s1sXJvVLWpRxGVg7bAxj2tKpcPq2wzHty6PLq\\nim4RwTfmKE3oajWiv1MIv0iqZsnx8LkiNUie9Fvrf+i9lnbkLDjTKqPRCnmbePfigvn2C2KaCMYh\\nYuhaVd95eX2teq3rhvWq5263oxRIc9TEM8+6KHM6b5gkkWqiWvcrmrahac95td+xmyb2cc8Upyom\\ncEHjA03jGeLEy7sbhmliPw36uXXeUudLPb1vWTUtnXWcrXpMKTQ+HHqZ1hi6oEIIVTyRFHUYfU4R\\npYQUApaSsgoS5ILfz/Rtw5PLc4yDpmlIqS5WjVb/OavLRMxFXVAaj/OGplErJvUwFJwVrOOweNyc\\n9Thf8I0leO3/BBvogo6bmENCU2H2lNUNJc+xVq36bJWkcaL1ASlCFMd2ztzuR4Yp452nYJhiJqUC\\n1exXNUc1mBaj96u10LY6PpVyIldoO3jL5WZN2wRSEfaj6utiC8ZUMY5iVO4va79zEdnIpqGs/hPi\\ne/8c5y8o849I045JHEghkoj2rD4eJyIANeml4Z55e8087smzMmFt0STxMFlqpblM7/x9RZfXyTAL\\ntiNGn17EISahjisB4zzYBsmRXCacazA4XEWgrPPkPCJ5xNYiaEEwcpk0eZaokL1tmaY93jd440lp\\npMS9JuQ6b40xGJuw6Oz8YwRLecvxPKwuT2DqxQqlLPHxyPJfbdY8ebIml0i8ec7V+UrdjGoBcfx+\\nvW672+fIeMtitH3yZW9svyJh6sD/Ij33UC5Ibybn/Mkna5mr/aqkAsSlYMtI2T0n778gb5+Rxxsk\\nj4Bh2unYicEcNJCXKnMhsZTpFmccYgolDvWPFiNjORzeYyu3pS9ZFpbY/08T42ObIGRjac6e4nxb\\nk12hzFtlqIWgShzagT2853AmDkoeS/JbfvcmwUnvScuiCqKvyVH7EVh6BnKoFpfhba3kEFTMoAYM\\nW6HvNO1Z5mHl9DqcLuKW99UjCas1OI+n0PSO23jPx8+f03awCi3DdIv3hZwt//b7f82L688wJL71\\nwXt4A1aUINI2jSbyotXb+fk53jv1LHUeBMZp5vbVHU8u3kcmh5NCZz0r53FUuNiozNcqdFysN2y6\\nnovNhr7taLyj9RaLQpMLCaqxDQ2eLuhoilpxqbiCQxmnL29uGKaRxlfjX6cDO6kIsyls55FZEuSC\\nSYIRy3A/YI1jnpSyf2A3ZkhZ6ukUhnFmrrOR3itpynt7MJe2VnuYRTI2ZKJ5j1EAACAASURBVL72\\n++dcPO3JRdV5CoJYYcojuaiIOyiKtLCbjXPElMhZdWCN9ZSUibkwpUJKtYdqM+LURUafVTmYU+dU\\nmEZl9Fq1LaFYDfoqjlCzjhOMlSq358hVHnCcZ62nqrdqEl1kdL22inQSTX0abZ4Rc4aPCSMR2f8M\\nsqUkJSI23YcYmRCOldCyv3keifv7CjvW56nMhzbH6bO2oCj/4NHGaLVu/QrrW72G4rAmqyer75E4\\nHFokauQu2v+zpo6VWO1jHqQ2DUZGJO6RHLEmgFjSvMe5DimFUiZFvaqGTClTFWTPlJTVoeR1JG+p\\nLh+ge8eE+gbMvECwyz4trH/MoTgQDH/wwUvuX201l6aZuRjyyd8sC/MFmvVpIM87XfAsOU7k0dz4\\ntoRZu1Zy+DOFXR+uEKbdqwfhV7/UVt2+kVx2CttWFf883JCne6REmm6N6c+wNiButYBzmm+Vxa0n\\nNc+KMbMkB3NSyhuOCeG1A1gGgysEKRxXLb+L7Tf/bh0UnrevKrSqN7E1hjztYNpjfUCMqxVohVyX\\n6m1R7l8EjUAfJmerC/pr9+qhKq3fbsyB7LL8b3n9AXnnpDoVo7qxGCXcLI3+x8g+S2DRXmw5rBqN\\nD3UBJVAUrejW0DihxMiF/4hhGHCm42vvf4Xd/pYnZz3feO8d3j0/56zp+dp77+CNoekbsjE0Qe2v\\nXr66ZhwjjfeUnNnPA+JhTjPTkOnChnXT8+7qgnfPLwnWcLu949X9HUnUmaS1jlXb0ljPqmmxWKYY\\n8caqWLYpTDmSqwqOc/6AdjjvtIICgrE8vbjkrF/hnVpvOe+IWX0vU0xs+h5XwDcenLrekw3zmGrg\\nQEXQJTOOM6UoJFtEmxxFDCkK0zShrEkNgr4JOGuUeWwDOSrbuWk1kU+paPW2L6So1WkISn4yTqHc\\nVLL6h5aMsdrLzDV55FwObF1rHZtVx9mmp6C2VQtBbAEtrLMsbalSK3UcZKMJHatM3bZ1nK/OuDo/\\nZ7NqsUZIJRNrMtf9tITGYS00zlOAtmlogif5NXTvk10LZEwlNbkWrNmQuMCWgDH5kO0kF+Jwz7R9\\nRR5VGzbNSjoUUJ/O5RmRk+fvH3gzmINBhXMdrj3HoLB/ygmcq6Lve6AgJlTSjKgZNZbFxk+MPcZT\\nY8jzhPFtRRE1KXtbkDRSsnqaij20avX4S0QVhhwpHS0ilyT5EEo+LspfrzIXZr5xAewiNmNqWjoR\\nc5EMBX76MjDutpgSeXoW6eKtLnAOgXD5t37n7vrTk7gnr+/Qg+1tCTNL0bxcZKoryiMOvxy4VQuD\\n4wceDlbvfoNB0kAcrhXfzqni6A1xGrCuo5RI6FaHFY2YRf7teNJS3EPcs8yOmQX+Ocxzvnl8RnQO\\nyBi1tTlWrb/b7ddPnAK2xchMOaxkLaDBtZSZMt5pUqvah0YMFFub28vxFowoTCpWPTTjMhf1YJ9K\\n7UnKYRUHOq/JSbJeoFizrMBrVSgLcaj+vfehrkjfXGcf4ZYluNT3OWW6xXlWNDnNVddScEFYbxqm\\ndE0WhRVvb17iTOa98zXvXZ5TcqQLjg+fvsPX332KkULvLOdna9brljllnHNszs5pu4DzjnkesE74\\n+Uf/L5eX79DQ0HjPqu3om451v8aIwobrVa/zmaI9wWmacM6pW4UocWwaZ4ZxX5mRSmVXE+ZEyWqi\\nrG48EJzqMeecICeM6AxoaxXCTSnRNC3OOBpjcY3B9w1Tnhhj9TWsKi6hwq7VqEnnIkWwTuhWgXEa\\nmaYZMRBzJkuu/WlRtwoDzlvm0XH9LPH5x1u++GTPpz+7Y9xnsFQmKlgSKalerS6s7EGfeFHCUda3\\nVub7IRGsZd33qleKYJzBNY6mc1ivpCCFEgXnLaaOkpSqJtR4z1ffeReDKKt5nhFj8EHt7VIsWKv9\\nVt/UBYrzBONxaDVqVn+O8e9V/dAG232XWZRVLmf/jDmAWAt26YkJ8/6O6f7V0bMyDlBHSCTP2iJa\\nKhdEZRNfbzf8Q2yVDGBKIY47rNOZSzBY12LKSBqv1bjdWpzvVDcWlfKzRu9TFzp1N7LuIFiuM7a2\\n9uAF7wNxniiSCa7Ryq8YnKuqRifokZFcF0THc7KM8y3bQ4D24cIczCFZWl/nSA8xxVTuQJ0yiDuu\\nnw+I7DEiPL/ruLmdDwgXS8wR7S1jHQ6Fn42cFAzlAZx62N5G+iml1pi2FFTW4+TaLOW1UZKHIT/4\\nHeLU+1CzLAbBlT1FZiRZ2vUTUpoICMNeSAmwDdZ4SokULCFP5IVtlSaybXDrd0nbZ2AWiPFkfpHT\\nFJAR0xD6KzVxTfvDSuV3sfqDk/6ivPkwPQYVC4bQ9sRpPo6JoFCZNUszPZKHe3x3RrYthQGTNWme\\nltxFMhT1zrQVStNveM1+TMtQQFf0loUvprrBi/yh7j8HIlBFZLXKNUYfrBQPJB/BPli0PZASrL0N\\n1+iDGroO2/Q4Mnn+mFK0P+V8YUo7dneOb339O3z6859j2sSqaZmHmf04VPjRITnxwdUlF5s1292W\\nbR65x3Kdb9hPA6tVhzWGEFq8hcavGIaZjz/7hHfPWjarhkkizljO+zVPzs4Zx0nnFnvPbp7wdUES\\no0qXFSmIaI8y1AreGlVvsgIOh3foYqMoczUmVXJJFKIIndEA8HJ7x6praU2DZLXeKirSq1WraTAS\\n8fV6NsESpy3WwmrTgkEF0sViXMF4od/0pJyV7Qgq/+c81lucGIYZYoKb5xMSA8eLDDcvCq4VGi/I\\nZMneEhphnrUPvMx/GgMpZXx1bxGEOEemCeY40zQtwxDrPYJCl9ZhvGCLgKMSsfJhLCL0DRYh4GgM\\nhK6tEgB6HzbGMptCEaMjJKHCqKLjEMZ4klwRmz9Fuj8Ak3A4hAa5+APECjL9GO//mLZKtxW0pTQP\\nW+bdLVYKOQ1qAj0PmBxrMllgPE3qx7aHfXCv/9ZbQYdgZ5DD/9eXXMvm3X/EPL2sSE+GeavQO4Zi\\nPNZ4lTB1uigQYyAn/CExqjpXjLGizULwhrif8L5h3l/r7K9r1HYsDrpsLkulo3tlrFORA+sfxI7T\\nzXCSJqVobDfueN5cg9gW2/aUOGnOKVLNJNxhxhIsJU+QB0pd0MvwDIkzpuQKT1MRLc2JVgx5rip0\\nh76eUJujb2xvTZj6LwvtBtKwHNLxQOvB+26jX1oeJk3N+kvPzJKGm5pgYd7e4c+uKMMLEI91Kjk2\\nj3uc0eZ0NgaKCrZLTjSrFfniQ2S8VdscdEDaGo+UowWZgj0WMQWcR0qs5Kw6cH+obB5PVH/f25c1\\ntk8JSCIFTCCl6XDal7k4Y1VnF7EHNDRN97iwQuwZ2e7VOmupFFnOSVYyhJiD5NiyDwdIVfScHuDX\\nE+UMqXJRthom12+oM5v6kxiFV0scsTZUZppZTNAPVf5D8hUKK5eCtQK+UahZZkwcDgk5iTDNGYmG\\nPGb6ruNq3fH0fEPftDzfDXhree/inM45Gu+ZU2JkxjvDdr/DGDUfvrm5wTtLMIauXfHxZy958uSc\\nvusxvuFm3HO5WtMZw5QiGKGrJJv9NLIbBg0YdYXr9OxScqbvWlJWJmyO1TS7roL1uRD6JjDMM0LG\\nicUbR981mkSrJu/9bsu67QnO4xq1DptzJonqt/RtS86Jvm+Z04SwwoWAlEgsBe8DL5/vccFx+U7H\\ndqsziWBxLiAmV3gWSgp88dEN4JFkgYgO6iuWP+wyLz+daBrDsJt59xsrjLF421BEDaqd00WSs0pl\\nKoiOIRpd/cfZkOJcCUqaMIsYUtZAlgv4oveQiMEGjxh1jZFSuDi7wBnHnGaKre4oKbNue+asRtUp\\nJ7peTcCt80jMzP5PKK7BdN9WqFW012nMgBDImz/Fbf6EGZQ0hKXExHz3AkrCSCHHiTQPB8UcWRYw\\ncmRdQjnwLx571n9bm3KQdC4U8agC0dIeEfzFB+y2L5DxOda1ZGNBdKwDqz19SiLGe4xt8aap51wN\\nGUopWFfUF7Juzlm1MQst8/45NlxUyBVyGrFGURPVd4+HjqHkWJNfjSsnce50IuKIS8oRcDKLBnao\\nvW1dGIjR2G79ooB2emKyxn0RioMQYTSq8SuH67UEIpUzLCexbUnePG7c/FZItixEGRseV25fDtq2\\n5298lAClWvYcmrNWg611AYIwXP+U/ec/oJSB9WbDNMbKmos6OiIA3UmwFvr3voGENVC93EpRKxYT\\nDt8r1TMThBQLYf0E41qWkuvQenvLwf9DbaaSQeBh8lRMPqn6xIO/t4dEedDure/JcYfM1zS20cY8\\nS0O/fp45WcsdoNAl8dX1kand4FqFsFg+sfxpHURnoX6f7Fcl4Odxp0xoqg+doAPfuhcH6Gr5RyuD\\ngkgiJVUkkSJIfsmUbqCoeH+chU2/xgIxjbTO8sHVJU/O1nTes+n7CkNqI2WalaG76taMWSuSpmlo\\nfKjD/oY2tLx7dcXF5Yb1ek0aE8N+xLvA/d0dORVKLNzd3rPfj0zTxGcvX3Cz26rbR/XGzDUQlFK0\\nh5iLQl8ITdvg3Iml1oGpV2qlZau3paiJcprZBO2RlizElIhxqlV8oZiED5ZV39I2npRmFTpPat48\\nzcI4qGrO5Tsr1hcNd3cDMapknZCVXYvgnMrRvXyxw7lC31odf8mAHA3CwTGPcP/KkMYGyUEh1Ep0\\nAkuKavWXq4tNjFGPtWjVrwGw0HRgQiWnWGXWa0WjIgWmtg7EqD3aqmm4PDuDLNzcbDHOIVWFyKCk\\nn1IKOKHrAr4ykYPReqDxEbP6Lsacc1DJAopYbGmxqISjTlp5xMC8vaGkSEyZEkfKtMMkVe0ppfpd\\nlvggYaoYxJE09/exCTpX6UKPmsEHhZOr/KexZ/T9hibfa2zurpQJazw6tqVetGneIilpO8ckBfGy\\nGoL7ylswFdLWuWEPZSTPNzT9Fd4pCpDSXnv0vq+FC4c542Of8lg0LUnzsBnzRtw7VY4zJtRiR+Fv\\nkaRCCAeWfiUe1mMTmWtiLBgxjNwhueaJA59iKQaElAcO3Bk4RcF+44Q5p6TwRNq9/LL3A1Qz04eQ\\nr4GqI3j6PltnrjxmHnF2rRcyTdxe/4LgDKHp2Fy+T7j6JtKuECvYau5686N/SRpuWV++C/VhoCQ8\\nUvtm9nCoBsE354TQkuL0YCWyrHLk5Off9bYo95zOHClB71h1Lq9pwqulJUesX3uWhTjfg826OCHo\\ngqOqPh+JOg9VQTSxKfPVSKn99PoQHqTwBEjHvuVJlVjK0vfU3lSmegJWtRs5fTCWJC11FSoJRB/e\\n0HZ1ThRk+CnIFrzRxJ0y93cDwXneOb9g3aiN1HacMAZaQR8W69mPE2MacJKZZWZMkVzFyhciCpg6\\nGjFyebECUQWd29sbNu0K2zaMOWuiMYbdPJNz4WvvfcA7Z1eaVO93OOfoug5rLE/PL+naFSG0qn5j\\n1Qc0plyfByoclDBeCI1nlkg2mWgSqdLkjbOc9Sucs8y5YE1gjJEQAh4VGxAxeK/9+a4N+OrOoH1T\\nmFMmkcBmVuuWvu80KdESGr1OQuDls5mb5xlne/bbCSmLOEW9xawGsZzQFbwkPv3ZHfNc+5ql6uUa\\nU91oLHOccN7StJ71qiM0ltW6qf6iDdY47b1q0xLvLG1QtrEx4K1l5QPn654nmzW9c5Scac8Cd/uB\\nl6/uqzuGoZiqN5br/SqFYBxzmZnzml3zH0G4UBWpkxaOMYZkBiz2MEKBCE4S0/UvMXHETNqzlKLq\\nMCVFEPWLZBElqO9bnqG3xcq/06alpfZ410/x7QXtaq37YDJWIATH3bNfME8zGEfaqZOKIoxOF6KS\\nsNbjnJDTTElaRQfn6whY5UoYKjNaCNaQpjua9l2K36joRehwxlNK1dolY02pxc1pObKcj0rye3BI\\nb54rcxqbTEAoh9hmFvcREZUdhDrKo2pLRzWljEmZPFEXNfrPaW/ZCNBcKVx82I+loDCPqq+/DZLd\\nztPIQq/WA3lMocGQ9s/fyLynPaoiciB+GKCkid2LH7F+77vMvoM4EJo1OU5IysT9FhcCTXtJ9+7v\\nE8eRON4R08Du4+9jJGsvLuuYQ47jsc/G4hausFaa7nAlk0S7HnaBGOXLL9jvanudHHMk7ej2upRU\\n/S9ef5NIJk07jHH4ZkUhVKhDV/UqsXfydqmJufYbDSDWYg7oiBJeNEG6muRqcl2uq7VVd0AqsuBJ\\necZER2gaJbUYc2gNHPZ/WZUDuI7Qb+roikPGn0IOvHu+om8d13nPTZ6wJvHLLz6hrfu+WXfkOWkv\\nw1qG/UhJM9koLLrbj9giWFuUbINTLVUMt9s9u3mk63riuMO7wJMnT7kfd2xa9QTMKTOWyP125Kvv\\nPCFNkT40DHHm4uy89u+UDTjEmVe3O0pJbFYrGhvIonOYzuioixHR18QxS2bTtqSkezaRCEHnMX1o\\nyVhevLzGrK+wzuIIdH5ZVOk93nUNu2nkbr/FGMv5+QZXIjGXes2MavOiZBprdB5ye1d4+fkdJTkQ\\nx7SbAU+3yZw/7Xj+yxHJHszSp1M4H2MxJWNpIKjspKA9IuvUGs03VZ82OJxxOmoSM3f3E0+erjES\\ntUmwoFj13lr1PTHq4ql3gYAlpswwC85ZXux3gOH2LtOdCyIqFtEGT8x1fCEFBiLBf4Cc/SnBP9WK\\nVRJ1NJClNWNE79uDIYAt3H3+EcErBE6OyobNkZImJI/qQFKqlnVNYorQPN6j+21sy7y58Rc4X8e6\\nwpq4e44YXxGcgjQtTC/0Hi+CiCcbqyo4RirkmsEF7YVXAs7SLiilYLzBSiIng/MNEu8Zpz3t6imZ\\niJn26lZjW3IIyDxhsWQxVSrQYJaF30kBsLTKNEwd48ChgFlyS11VipEq5LMwdsvB+UQr+QqjLnPd\\nFfbVQFTPW9pCJd5pwj45qc7QNCtSHmulWw6tOqyNj12HtyXM+3meLcbrTXVS9ZxuyrJTesgDbHqp\\neBZ91MMba2O8GJDM+r0/Yn/9Y/Iw6QnOonTyNML8nNvdpxqw6WjX52Tb0vSXTNsXlP1zDogzSxVj\\n6NZPGLavKNMe23RI2MDwQk/sKV7+Gp6+XMDfxfZl8nhHTP1XJ/cj5r/MsWbSvMU2K7ztyEUr00Uc\\n+yh8UMFSU4ETazA4skEHkK0h5xnv+yqTZ06qVSUDiTEsvn62at5alNwyD7k6rnOoLpdjW75fKOBa\\ncB5nHC49Zzv9krM+cLXuSCnThAZXZlad52kf+MrT9xn3E+f9Ctc36ogxJ4WDrHB9f49zLReu4z4U\\n+jYQ44SpMHPTNqQ50TYtcZqrqDgMY+T+7iXrVUvbq9SWc462b4mpaoJi8U4Fy3c7hc2NVdWczaqn\\nbT3eKLyZjeCsI06zWlhZfdinHJlywePIxtQpMhVvb0Mdi/CGD99/yhRjTbpq2yVSyDnhG0VtWh94\\n9+JC1XGMOoZkKVg8jXGY4Ile5x1D67h9kbn+PCLSsqz8LQbXF77y7TWf/PBeCygToSjZa1nZWy+0\\nXcuzj2/48DtnEDI5F7xT8oV14L1FimW/H3Sw3Srsvtl0ypa3y7VXZm4xhgTc7wasgd46xhQRPFOe\\nmSTjrGU/R1rnyFGh3mUSDxGMr8+ynVmljm3/71O6P8SJMv1d8WjlVMNvebPakRhJ8x5bDJJjTZij\\nwoFphpJUvPuQLBco8di60AiXH/bX/g7bMlstOPqzC+akJts0PZJuSdMdNlxhz95hc/khr+6f6bFY\\njw2NFhQULJV7YBTBWEiDYld1YaziBnkeKdbTdj3zPCMSac/e15ZCHDBuhQeVzSsaL/I0qdScWa6r\\n3jPHmLWsvu0bVaa+5WSue4nJekfWaxUxoq05I+UggScl1TZP1mRqTV0ELX1KvUZLQl2IisYYSjgj\\nTnv9rkOSr4neP96GfGuFGeNsLSuEowLC8oUPtzcZYYcTVQeRl7B83Bx5uGfcXnP21f+U/bPvM7/6\\nef2kDmM82QrQg0SKScz7G8LqCZsPv4P5LDPsXyxqaiz9NSgqhmBcTbwRJ1t9oIzqKB4YWI8c9N8L\\no+3vsi2kndeS5eMJXhOftSckIhHKtENcVFk9HBSDmIKxC41ae1B6Divcauq8q61sWuvJRXsdzgVd\\nyZ/AW5Rychto9WOMogliXZ3Psgcxg+VGkoNMnsV1mpCzn0kv/1cClg+uzrjoOj6923E/DPTdGpMT\\n3/7gfVrjScFTSuLubmQgc9V0xJL49NUrPnl+Q+8avvaVK4btxKbriBmGQdnbc4xs+hV923CzH/Gt\\nw7vC9u6Oq4s1khI325nbu3u8dbx/ec7qiSflwov7Gz58+i6td5S2IaZMGwKNd5RcaNugDE1roNTK\\n0qqqT6bUfiIEa9neD3r6rbAKgbk6nIDBWY+URHBWP89X5RsB6wPW6ujJlKJWCpLZ3RemKXN23oMp\\nOKMG2IIQOkseGl69uFVGqSkUUelF0zo++EeBccycvd8x7SaGV41eZ1Po1oazy5bQae/x5kWi9R6x\\njt1uhOjIUZiGSXuMrSdPhhIjq7OW0BuyzZRsSbmKfPtaSRhDSqX2VQ0xJ5w17Cvsi4F5TjBD9Jnu\\n3OOdJcUZHzxSMt5YpilhO09uBO+/gi2OYjIu91XxRh5UGa/PFBtjsIumbRohjrWynECK2lcdFLMW\\n5xEeJAH9JPdIzPvNtsMM4uFTCsPt55hmg+vWBCaKXdOtL2iffIO8f8b9J99nGYcxLuj8JAJYrG0O\\nlXHJUQk5YY0O3aj+bc7aGxYccRpwzTm2aymzLhSc74hpJqWBUiKtW+uzba1C/NnUHrOpMOkSDwzg\\nDudJi/zTqtI8iMlaZDqVq8NQ8oxdZs3rwu2w8MmztjiMUXiW42iVoggVooVDvBMMq7P3mId7Fcgw\\nSlI7YmqPTpW8vcJMKVmMYtgLrvxm4K5IXn35AWzoA1IM1qg1kiyUTgQjwri7JjjP8PL79E9+D5OF\\n+f4jTBHEVlFgRoVxcdqknXakYojTTvssS1MIMOJVdLdqH4LOZeU04sLiIj7rKl4txH9rkOxvM9E+\\nSr1+rRJe/vs42GtOVmm1Lj392zyTxoxt1njXUsQe1HrqE19vs6Xo0ypqedhMtYASKVVL0tX9qmM6\\ny7W1aoG0fCbGYStLzrBozi7HUqBo1e+6DU2/QqzDlEycfkmwSmW/HQae3d0REzib+crlOTEKxSas\\nt/T9iifBqy6xGL64v+Wz6zuygO8aXt3tQDx907KdIrssnF+eU1Li6uoJcb4jl4JzLcE3dJcGUuLd\\np1f88vkzvv7eV/j082e8uttxdX7GzbBjF2fu9/f04ZI2eLrg6xgFhGqk7YwlxkTwep/6yi4OzhNm\\nS+stqQh2o42CJIVCxooKIczIgQgTrFbZiMF5r6NG6By0sZbtOEFRfdycE08uNhQzK3tRMmShSEJS\\nz2cf3dJ2jnDRsH05Yn3m8v2e9ROPaRK9tTRt5OKq475PzLNgnOP8qiEEqiB55r2vrcFmCp7trSFv\\nZ1yApg20G0cuhbvnhbgv7G5nrj5Y0V9YJGQojUrjiah1V72fbV1UxBgxTUOhaPDLkKIgWZhiVoH9\\n4HWEikUjWuj7RklkFrK9IrtSNXwTJ/Hw8Ewd/FhRzWNjDDlPGBySZq0sc1RYLx9JPgc259JPo7Y1\\nWBKCefhlv+H2ZlyqabMMlCkx58g8a7CPpqUER572SBkwpi5C43Ty7Yacj0ICiiRZrOsq3AvOVTWx\\n2q+17VolC40jyoBrWvJwq6zhLDjryUVHx7yzpLzECDTeRrXSMubh8Rzg15NjNTiEfJy/LwWM9igX\\njeJlbOcoomLrPsyVgOUP0Lg4r6IntWo9IAJ1s77T35eMc0G9Yg+/15bQY9vbEuY+52xNGzAlPgjM\\nb25H4PB4IsyhZC4s849LdZcpZLwRchpIz6+JL36KWz3FNOdY3yDzoCMJ5rhSEwRXZsZnP6KkiPEN\\nkpXhpmV0i7eWNNWbVgTJVaItTzi3oiQB5rcc9t8u+f1DVKaP9TNfF0E4upEcCTmH90qmTHfMVt0I\\nnGsrI7bC5NZWaF2hW/0e7feWkpUxJ8oyWwBElr6E0SpV5zqX761oQ1EXjcPc7mmvQvTzm7N3KFZX\\nkPPtX5LnPU8uey77np98/oocE53rWRvDh+cXGBGmORJMy7NX90xxwiIMKfH5q1v6EDjrrd7gRWXT\\n7vYjt68GSoFht8MaR9N4tvczwQesDVxs1vzisy9wOIq55d0nV1jjeHp1zuXZhnWv5Jn9sOPdy0uC\\nD3hvMaXgnCbNeZ5pm0DOmb7vmKaRpvFaIaGVondV29UZvDHkqMLP9U6uIxr1GK0KjOvImSWngrfq\\nxiKVoekMNMEDhtwbhQ2twVnIRUgkuqbj2cst7763pr+0bHcTRSybpz1hLTibydFii+DEA56rdz3/\\nH3Nv9iRJlp33/e7iW0TkWllLV1f37MAsACgKoIwyankgHySKxhc96J+S6W/Ri0yiJIoGEyUCMpEg\\ngCFgM8Ts3VNdXVVZmRmbu9/l6OFcj4jMyqruniGNumM5XRkZ4eF+3e8595zzne9LpnCyimCtZxgs\\nq8uBqhO2q4G4LYZTPOOQ6VfgVxFfK9csxpCS5fXzLYvBc/yowpCU37aUZ6yxNPVetmsXBZboI0XZ\\nbRisc2QDYwhUtSWGqQShz6KaXoeRijpZAhHt7YNdDQ1uqfUglmgs1TiCjKrGIQEzpWFTLI7kQH1k\\nalGYbN90XM0369o5iA+/aC2/a83vU5QgOGyOSFhOn8bIlrh6RVUtGIoT13VMCSgOnLvJIBZXWSzH\\nUNfqiArxeg5bxNXYao7BEIO2ZFhfk0JpsYmqYJIFnEXLNkJh+apJUgB2Evf92dPc3AlS9nYs7Tbd\\nE+5FKOWgOOzsjYLRdEOK81rZK0A+1WbW8s6O9azU3w9LW6DnOmyXWG/AzZGgUpEyZdnecU/e6TBF\\nRLz3EYeXsou77RRun8LtHkI1jCn0TNGJ9ZUKvEpGFb8jKWzAz8Co4YsTZAAAIABJREFUInbcvAbj\\niMEpAktKMzjl/AUyieHlT6nPvknEkNYvd5nEnLZ4f6LRUA6Ynbq5K2rgG3zdEEePMXs027tu4lcZ\\nd6XBftvj3Xecu2MfWd69jred9x5kUxznuEZsxrWLkjLVnbglIaYCh7a1iFdJn+KEFVFWdmtTZsGo\\nkVcQ0OE8qEO13hdNxLfnyAgYV2GajjgG6qrGbn6MeM/JouPF1RXroIxOp7M5nWSqpiEElWx7+eYN\\nfQja8ygwmoS1Fe2sYrvNHM9bbpZrxjGxDj2n8xPe3Gg6MufM5atLjHH4SmjbGX2/pXMVTVvxwcUC\\nUzW8ubqBouHY1hVHsxnm/Ih+TCw3PZWFpvLMm5oYImKEWCD7IYQCzc9FGsspothCjokhKHq2rhpC\\nTGXDWVh3KiGUTLcUwYGcMxbtWc5lozKMI13bUk+k4wb6fsC7CucMWCWtiJJ58nSBJCHZxMmDluMz\\nYYhBQXKDwTjBOCk+KxCzwXt9zkJvePH5hqEPSIZWamZHNaNNrK6FGANkNSlh1E1r3Ta0c8PydYBo\\nWb9OdHPP4kR1T2PKiAXnFSBkyzO1XUVCChw/qJFg6QvDEKXWa1GQXJ6MZcEv5JSYNa3y245/xjj7\\nHmTH1IN9+GhOQEFbbJkXYXP5CTZBTAFiIFFqltPPBC4pIJudDRFtx7LVTFm4xtIn/huOA+zLrdWk\\n36bo4mwMVXtM6JcQtoxhwNcdqZyW+p9dOF0cl0ZcKQmmVulG6xzkSBwD4PD1XMspYYtrF/hmThyu\\nNd/kKpwICU8crzF+pj3SYa0gMFspRV1MYLw6QvaqyfeVlna+w9XFxis4UZt494AzYyi6zBYh47Ck\\nNEIa2TFW7W5uAfnk+++BxB5rG5rF1wjDNdY5pd6ZCOPNV48wsc7FmLIvaJDd7dIHQXeHe3TTbQad\\nup0RtkvdFaRYGtoPCXAdhkSzeMT45lfabIqmRmwW4niNdS07BW3MbjFZMYzDJfXRM3K/RNIGjXKE\\n2K8w1pTm1tJ7WWpvxqDNxxz2D75vBv79jK8CMLqLTL4vkr0vXfueA+p7AZERkwZMfUTTtgzbLVkS\\nRkZMrDBOacl2vVRTncHqXE8ec+K4fXsydccWQsBXFYaiejGpApTdr29arKupaodPvyaMr3AmcbMZ\\nGFNkCEqTd3H2gCaPJCO8uH6D8xW18zQFZHS9WdP3I3VVs2g6juoa2zRsLy8xeI67Y8Zx5HjW0ThN\\n0d/c3OCdYXGy4PXlJQ+OWuZdw6OHZ5zNal5v1/T9ltPTU818JCGOgYQlxIBmW9V4Xd2sAKGuPFmE\\nNIw7UnrKQk9R2XAkTwJW2izujCW7PWikMYYhBEWlikbvkMkpa++naLoXEZxzbMdA05UoNIG3XknN\\nQ6RuXHGAmRA1kgghk/qI86XuEwWMRXLSSN9N4ImEpeXy8xVhY1hfp8KkIyzmDbNjsCctjz+y9OvM\\nr3+2JIaylsUybjPtwnH+UcPq1UAMQtfMmHeOX396wwcfzkmiaEhrLDEKYUh8+pMVIo7ZvGHMG3xV\\nKdk6BofZyaANCaIE/NSXjZLDe1eBeYzJLWI35FztXOOuvMTeYolRUNL2zSsMYUdpuDfa5XeZAFJT\\nLWx3BDVf1pPGVSlVWb6sebmv+4BdxW0/LCjJQFl7kkac9eSkkXxOAcTcWYqmXHs5nvW4ao41Qhq3\\nUBlC2uKac1xVo9SDQRGq4lQ8ISXwDTkNSEpKXcgCiT0lfaSRncnkMRSN5PLcy+FM37ro/TVPPsaI\\nKmRBQe+WIMsq9aSxTokRTBFL11XBxCg3XW0W5QlWRjjH3aBC12WAroNxDROJCtpFcHdzfzj/7xzW\\n2NFMRdRbd8DdPwEHY9jcKAdpufBcDMRkRK3Tfruhf6k9PKYY/mJ8DKig59R3Y3Sn4H1BZA49Teew\\ns4caQRqHrxdYDVkwtsZWLU13rNI/05iM//6F3UT++xhflG75qsd43/Hu/u1uuvZwWDImR9KwIm9e\\nkWJPNTumOnqEtGfkSknCLVl3ocbtovJDZiBjnUaktpAkyB3yBeOKMjuKYLO6eLGlP1SAqsXVjS6u\\nm/+9qFJof+VqPSAx48Tw0Ycf09Q1rfPM2obNes1202MwbMeeEBPeOGpnOZ21zOvMzevP6LqGROSj\\nj5/RNQ2zqqGpHF3XUNeOi4cPSHGgqysuHhzx5OlDVv2W5RBYh8DNuqfxetzlZksQqLxnNusU4Uli\\n0/fEBClmJKLiyNbiiiGIIWGNCiuDcq9qZFmXdLDDWUftPM6AEW2jsAYly7d7lqoYY5FrUy0h6yzO\\nKuG5McoJenrU0HrPJN3kvSu8z0qekHLh97TgrOpNau1I9zEZg2Tlun39oufmVWB1U9JWSQnO50cG\\n6wXsiDGZxanDz6QEVrrLN8ayepN58Ljhm7//kG/+/gPOLxrmref85IjKZ7wzpGB5/ouen/zwiuc/\\nXZFHi6TMpg+MUpEQqsZireqIeueUdMfuQTGCpglTzoRxxA7/Eyn/ECs1dvwcTMWO4P9grWTQVG8a\\ngIyRqjjJqFJ6ooorO73LiWz9sBRS/k+GFUYMvlmgiCa5E2fuM0B3M0Rf2h6IrivrHDEsyWlUpizJ\\nym17HwaiMH0haEvJcM24uSKL9rFW1aI8H5k4bHHGIqYi54jzE1dsKnKDibBdFt7YkTRqhkczUlF7\\nMfWCtNRzT4Bwl+1My3elXnynPcfYCS8BpKg2SUQ3NXHP8HZ7mgu5ysGc7+bYGDCqDiRb5TWv2nN9\\nr2Ssa7Cuufew740wZ7PuxdXaHB+SEhyWt3fJWcPut0PKOXVNugdw9SkprLHek1LCzU9JywGXDWkS\\nHt49MBmkUicpWXdMGDCZEJUz0GG5+fSHHD/7Q26Gl/ikhMxSDJQ1mp4Zhms4vIFGd8130x1vzfdX\\nSKm+L+q7b9xNAf82333fse8e5+757Wjv9ClF4shw9RxbvaZePKBuzrDulBQ2hGGNkUCKgyLhjMXa\\nquzwoKpbxiS6x7OV1o2n8y9O0poiF2QNKekCNL6AKozy5RprcKs/Zexf4Y0KNi+3AylAXdXMm4a/\\n/ot/xWLW8PB4waJuaM5rmq7j88s3ZBEWiwWr1YrHF48Yh5HaO7ra0YgjhsSrl68IIVJ7T1s71kOk\\nso7z4zn9+oZnH1zw4LRj20fGMfMqLrlcDTSzliFbfv36FbO2AWuY1zXWKzrzqOsYUyQOin4djSmA\\nn0BdxJfHcVTCazftxsFkq483ikO31hFDJMGOOcgZQyQRoyJJp0go5kzT1IxJ+yu9cyTJOBxN0+Cd\\nY2agSrrJDKGwbjkhpoSrvHJpGiUvV55R0K2Utmy0lbacbG42NF3DZrnFWEfdeeZHDa52qoWZEwbD\\nOA588OyEn1/d7NKkSObouOJ00bHcbvEtRCMcV0c8eeQZU+blr1d89ukNMRrITlHCLnPx4Yxu7lit\\ntxzNO8Q3kHVTEKPgcIxjxBXHU3lX6l6qM5rjhu76T8jza4w7I/OROg7jmGqPU71eRLh5/Uuc98Qo\\nCAN7sm7Z2SH9vRjjXfmppPHQdDpuAa6F0Gs10VZIHqbEL0JR1nhr8/5+e7CPxkAkIMUuihFUkTu9\\nZS9u2RKktH1MmYkOXx+RbckOyQjRatml2G8jQduTXE1II945bPJoojVjbA1eFDWfNG2dpUTyolJh\\nMgVA3LZ9e5t8UC4yu7+8fS2ltjjVhY0RlRW7x5LvX7lLnqNDaYsVMGRNJhTZyLIFJSx//t/fdy/e\\nH2Fau5KcS/3q9qnLgXCwRhq3b46UFJCb0JSi/WU5RgUi9Deas3Zvq4hoC83EoDEpPkhJc5R+QYlg\\nW3KM1LMLpG4KBFlnS8yedQaM6klOEyL2vjne7fhuXel7orSvMm5HXu92hP+ua5/3p2s1kp9IKawR\\nbUWJA8ObT9i++Dds3/yUPFxTeYcxVYlALBlDilPjtjCMPdaAd4dsGeU7ys5OAEmJOKg+qpI0Tzg3\\nsHWHSMZc/TOszdQOKiuq7GEcXdVw0jQ8fHCCiGW77fnGxx/z6OJMuXYRnG8IfeCo65h7eHRyzHHb\\ncX58zIcPHmCs5fr6mqauqKsKsRUmZx6eLug3W06Oz1h0NZ+/ekOKidXQ0zQzTHYcNy3Ehh98/2/T\\ndsdEEZz39KPyrYY+kGIi5kjMmWGI3NwsS60FYs5UTa1UYikW+ILfzZHS+upcjhKJSZ/3mFTVPsVM\\nImKyMq6owdRUVWUd3joqV+MKqYQxTgnQnaOraxpf463XiNcZ5rMOm6GrK6wI2URcZai6im6mEk6V\\ndcxmHhM9s0WjqT4sIpGnHy+4eNLirFUmmKzPhGTo6naXDaKs2YePj3BGWLRz5r6h9TWbYcvJacPf\\n/NVrPvnZNSlYus4zO4ocPTA8/fYRF0875nXDo7MTJBviGBmGkXGMhJDY9sp1mguDU1NV5BgJkjHe\\nkewxpv1DBveAka8hdgW30JhKfCBZea7jm88xSTAxQi4RG0xPqc47eZc5m55fwSrqUrSyZiyq+ZsH\\nxNZ08xPAk/FU9Qxra/Tbp3VyuN6/2EZQPm0KgtiIaDRc6pS3bM3BplnKGpYUlWiEQAzbKVkExpGx\\nKs+XInlck0Kvx0+ZupoV4gpPOz/bzYB1mtETHMZWON+Uio2mae8ztm/hLibHKdOs3vN+Y1V1pZrp\\nd0+anvfOz+3vOXybYUo0aBCmZb5CoAFMyk/3jfc6TOASmQgPygWKARyufcBhqJvLTnd3UsZpejUF\\nnK/J4xUm9sCImIo0rjGSqZujey/6sMF9h0Y7cJiqZZegMhw9+yNNwVpfNo7ls9ZhTK21o5Rx/gjV\\nBdUaxPuc1u12Df09H/ARHo77orp3jS9yiIfgqX/XY3ddu4dTDZvsvktTPQZD6q8YVy/YXn1C3Lwi\\nhRWSR7w1yuYiZUOC6iTmmPD2gLxiqvEYi2saEgnrLSkq4lAm5JvzZFvh2dJHT78UhtBws9GopXae\\ns27OUTdjs+752rOnWAub1RLvLF3dMKsrGis8Pj/h2aMHHLUNs6bm6GjO6dGCHBOpMLmkmDQdCTx+\\ncMJHHzyiazsq77haLjk9OWU1DHxwfkE3XzBftGTxLFdvaFzN3/nD/4LVMnBxeowlc9Nv+fTNG96s\\n13z28nOs8eo8RMXVmXiNDSWS05RqiBmcxTcF6FD4V0UyISeScURRoJQvLEGzrsEV7lhQOjlDxhlD\\nV7fU1iNxEunW6NUkSxgywxhwzmpJQ4R502GzxTvPaTfjpJ3T2Eo3NFEBMCZlPv9sydNnJ/R9BLHU\\nTUXdZaoOQKXSYhYF98xqPv/8CuOF+ZEpBBdCKLv52sK8bamdRjA//dHA69dr2iPPt35vxvf+8Ixv\\n/eABX//eGQ8ftszrCqxlSIHNOJKzoj9TNoQkjDFqLcp7sgUrFms6DI7tdk2e/V3W89/F+I9pbIvJ\\nXSkb6LMvxkLOxNVz1i9/QeUrogRSXCGx9P2ZWlPXpiBtXUszf6Sbiu4UmiNcfQKmZmrDkrCGHIrs\\nmrBdr9WJGE8IPeQe6zv2qeHbicN3VPxujUMQ07u6F2TKZOxsitZ5nau1eyHlUstTu+B8g687Qhh0\\nUyHaaiWuxlXFFo4DiIqJawnAqvqJLfR4u2hV0bjT2R468bvne0i4wr44cPBZCh9uQTUXdqgdFuJd\\nc3TwPcLtebblPHMyGFszVaVzjhjz7sTre1Oy4zj+/NapTxfpG1x7ROxfHHC672mPDh8EESGOveqZ\\niQAZWx+RU681zEFhy5j7Q2edYDSNYKpdlCki2BxY/+qv4YOelHpyGlG+kIJwEg9WQBzGjCTZUncn\\nhHED+f2tJXev+W70fF/a4F3OcnpQ7IFD+TLf+duMdznjW1HuBI6SQpVn9mi2qT1k6r0kqvZdCgPG\\nVfi6wxQjsbq+pJ4viNlhxewXgLGasu0H3Z0S8XWjaFEBEcPs5CG26YjxE5rKUhnY2oDJia6ucaai\\ndtUuXo1hw/yow/uK7WbDk4szFvOGVJRB6toRxxHnZiz7XlPC3uErQyYzn7U0vma9XnL+5Cmbfot3\\nRo3ZKHz26pI+R4yMxOtrDLaATYQXLz7lL//8z/hbP/g2H374jNPTc66vbvin//L/5dHFQ54+vqCm\\nwS0c/TbR91vqVmnv6qamNjV1JazXGxCrwISSgjJkrDM0vmIcEpux1/onYJ2hq2qqytP3PVVVaWSE\\nUNeKPjciOKNagYKAFXKORIm0dUdMgaqpGccBb52CfYKy80xlinEYcc4ybztaAyfzGd/5dsPryzWk\\nBmNUksw4z3K14fi4JWclnh9T5Ljt6O0Vf++//B5nRxV//E9/xM115Or1hu98/THOOwXROMPrMLJc\\nXfG133nA2cOWELbEMNI1Dct+yzYqqCeK6lgqaAwoGw6tCau6izfgs1WVF8lEPOb8HxOab9OEluhW\\n9F4djC9RYxaHyZl4/ZzVy1/hnCrr5KHHxlGb+GWEpITeE3uCwRLHG8Q3BClR6nCFcTUTDlXtH7va\\nHGjWAKkxRqXDjDFgtP4NESvmoE/9dgrznWv8TqCxd0baugcTel929tggRZJMMCYS+iXeNxjbkEMk\\nMygZRs4K3MEqW1fKeK/iGDmMagOsxfgOcEi/xhVCdklBW6Wm8zOFNKCk6W/ZSZnS2bcubDesdaQU\\nSSlgXE2Om100OgWkX6YtpwgV7uJ6yWCqCicBqRb4qiEWbI7tzt55vPdGmMvl8mfcZ3hthasqjDs6\\nCK0LwOcgWpn+YxxI3nv6CcDgfI33RuVnDo9/cLEHl/7WvyUmyJHNq1+QTYfN042ZUhFFM9MY5ZDN\\nI2N/g8kGDnZob12f2SuITL/fKtDfcp53neS7j/kfatzdzU3irZJFSVyM1gZ2iiOH7SHCjuqOnLFp\\nxIQNaXvF2H9GDC+ZtTOMaRGqnVPV9JSZ8GvEFBj7vmgmTqZFMPUch6GNS+a+pqkWzHzDs/MLTmct\\n3jnW/ZZJCOBmM3B2coKxwmLWImHk5GhBHEfapgGBrmkZ+kHTxM7gZw1z67WhvmuYLRZcPHrCarll\\n0R3jnaX1Kgd2PpszdzUhRUIa+Ef/8B+xWm5ZXo2sb7bU3nL56gWvXr/m6GTB00ePefrwKT/+6acM\\noiLXlas5OpopZViI+Krm9Zsb7WUEqqrCWl3IQxjBOmKGMKphCDnReE9ltS6JsYQxMoZITqqf6Ywt\\n6hqizqTcM+c8ki1kR0zCECL9oE42Ru2lFRGsEa1tjoFxCKSYNfXdLWi8p25a6qqmbT3eNUwEJouj\\nFnKmco4QMiFFjruWpvLUVcvD0yPOjj3L5QuefnSCbqhWmpnIRpUwUmLR1PzuD845P5/z4tMlKXv6\\nGLnp+xI9BtZDzzAO5BQwQIqJcQwYVG+0rjW5rX2mniSONPvbmPk/JFVP8dkyViNiGpwITibyQWVE\\n2l5+wvXLT/Dek8NA7G8UJTzVLHNE2WqMptBLCi9PrQ6jitoLokxA9wyRiOQRBKqmo5qdawEtrLF4\\njKgIQDa1Zm1uKcR8uXEYue3X+Z3NeZFM0w1VQLA4P6eenymblAQEzfoYY/C+ZtK/dUZXcupHxDl8\\nM0OwKmFX1F+MU8L+yncYMnHiCLZ+DwLTkz1Ive4dGpNNuGMnc44lete0uyl156/SaTC1r00bB0GU\\nUKGU6axRViMpldm0evHOY31RSva5lLD3EEBiiyH084dw4CD3oT/AXl8MoO7mGOsVjBPXZbeYGZaT\\nEsrtBt3DyRCRW/SDWhtWTTTJPRIjZ8++S7YT2BttZk0Kw9dcrsOYVh1oVuj1u5zb+8ZhnWB/PlM1\\n4i6ce/+ZL+sw9xHgPoWxTwX/5uM+UNL04E7uSxeZQqqnHXCZzd055al5OwVkCOTNltWbnzFe/wIT\\nrvHeI77afTT0a6pmTk6x9PMdLHDrcL7GMtJzghPDKlxzvV5xvd0QQ8JlQXKg7WraruLy6ppfPf9c\\nWxeyGr+b6yX4im2/pWta6kr181JWkubGWH736x9zvljQ91vW2xVv3lwhruWz12+UYKBtuLg4ol00\\nnJ+ecDJrsAb+z3/+f/E7v/MtvvWNjzh/cK51spQY+i2Xr1+DFX73ow/52rOv86//8meq6ei15luX\\nNpTlas3r6xWX10s+f3PDm+WaqnJKMZYErKGqvZIdZDDJ4PGa8gLCOBJl2iVrT6svJAnK7mQUgess\\niClAHCUZqKoGU6nIrjeOcYis+4FBEuJAjGWzCYxbbTMpsuTkLPz6s2tyDrx6sdrVesYx0rVKyVc5\\nR+Mdx12HFzhqPN//xlM+e/US0yw4e9hhm8gP/uDrGCvkPCqK2MJ6k/nTP/mUP//TX7K8HghKL8CQ\\nlMXGO4txUHmnbRNZkfR1Xe+uW0VIMz4OZHsMi3/MbPaMKq9wdFqzE3vQAqL/cjKyffUrxqsX1FWt\\nYEGZ4MFFjSSNSEqlBGXYbb5LSYMsiCTVlp2Ukt6xxqe1l+KW0F/tNvFZRn3+3ZxqdoTrTjHVOWZC\\ns37l9b230bd5cktmyZQWMFsrsMd5pPBEm6xybzGl0t+amNoh09gjOM22upZciE+sc9qok0dy0edl\\nfxoIolSCWN4yX9NcvaVo9fawTufX2qkeOmW+3o8vuY0+nvpmpzhbaf5yVhk+S5wcB37xwbvP5b1n\\nCr/+8IPzQa9vny/PeYCYdJc8MWe8M+oyaD/WdZkcyMMKVUCQkrHeO6+34MaTYZ+aMA+ca86iqMHQ\\ns/z0rzBo3RQpaDBryo0xiLWlv8bp64jWRu9M8LtuwFtR2r2eMX/lB/3u9+//fXvOv0qE+lWASoo0\\nS+QUSCkw8QbnKYVzSIg87SQn42AySMDlTOqvGa4/YXv5E3L/HJsNNqtsV9i8IUsqPcT7Zm+Mpn6w\\nHs/I8/UbYoTGVeSYOJstOJ23XDw4oaoMzqnDvbpZstkOrIdeH3bruFmulF5uDAiGECOr1YqTZs7Z\\nYsGiqli4movFgpNZxfHccr16zTBstdWk8UpYUFes+iXNrGVxtOC73/8+v/jkl+AMp2cPtBZVzRjH\\nLUftnH4ccFVG0oquPaaq920j1jqcUxL2edfwyZtX/OLFS4aQGYdAWzcczWZkSYQwIjlisXitnqkw\\ntXGYrPM0FMCQivnmYhSNos69wVglRjcGjFKw4J2ypgwxIFlomgZXWULWaFUjQ491Srm32m7JCRaz\\nTmnmsqaES44da1WMoasVsXq6WCAx89EHHzDGwPV6TUZYbzYkE/m9v/MBzx43uu5NzeUq8Pxqw49+\\n/JrVVca3ngdPazIjIQTIgjPac2fRGqtBU4veKgtRGEcymTQKJlUk+wBXfchs+Jfk4TVD9z2mPjoj\\nQsqQs5D6NePNa9786t8S11dYp2xMSGLStjSIApyyKpswRZvFWbJbW/qawuDu33rv121xqoK2qFgL\\npsYatPEeiP01EgZ8c6SG/A5BeTliqS3ethX77Bfs7Sh3nIqmG3MOiOimN4UNeRz1qGlLDIHaKKWh\\n9vyO5JRIccAZS0pBU9pFq9gIhDSQg+INjCTisNI2KDOhdg9Zv/bndLhhn2zWof06HMrjq10P07V/\\nlZa8KS196O7qxWOdB4mEYbMXCSGTh6v/4V3He28NE3je91s5jB4BJGyJ25eKXuUOJ+Adwz5dmDUG\\n52piTmW32CAxKgn4FBYeXORbLRGiTd4m210TqzUGkxziDYzXiPRMpLvqcaT08Bilfyv84pJCaab9\\nzdOkt6Lf6VzL4iFrv+Fvcsx7I9ev6Cy/+oi7TYuUxYJVxJspdHW6Q7UFwaw7brBgTbkXqE0JEQlr\\nNqu/wtUtrnuM6U5wOZBlmvFpp2ux3oL1jKs/J2ZHY9RYPz6as2hnGONIOeKtwWTLYt6RTWa1WnNy\\nNGcce0QM3/j4Iwgj4zBoPGEt2zGwHSJn53PGPPLk4pzNVt9/WresmoHtZmD55prj7iFZ4PpmSd01\\nPL+8YrMZaJuaMQRev3mFMxXOwafPP+Ns8RGvry9JKSIiPD09ZdW/YN40dF3HerPGOUOWRNs0LFLk\\n85Vlud1Sm2uOupaZV9akZLQNZda2KuxsLNswlIyFoa4cq3GDT6aIM6MEBgi2bCKUPDvhq8LMFM1O\\nRLytDKNVh+ktHHUtq35gHAO+MojL+MpDVDHvWV1DznQzzzAGhm1phgc2y5HL11tOHzRUtiKFiKs9\\nQ99jKouLmRAzMUba1jNva4yB9Tbwb374CVfXW4iOLNDM4fHXFpgqqsRYyQZJFkJMWO+0xzALYg1N\\nkQ6bwDUpJY7aGU8fnPG6X/Eq/wCq31FqOjG6KTdCGrbkYcm4vCTHUTcjxhJj0Cb4rP+VVH4vYvWl\\n0K7PqxzAEM1Evfn2mr13Te74mmHS7d33l2t0h8lIzAzr58px+vZREE0JlL72xK2s3C0HiqZfy+vW\\numL4DBgVEbeu0VYvyaQ0aASHYdhe4boZhjkYZaVSE9oC25JdrZC8IUvAMSPFNSZr6lzyUPwCeN/t\\n+WuZgoC3nea7gJB7+8fB36fM2/3B1d3P3vc3a2qMVZ1k4zwmDZr6zTs7/hfvupdf6DBXq5UTWdwx\\n2kLcXpYboXH73Qjs1igb1JyGnYN13mtvX2luvjumY9xKLUz9k+KL8zOKXnNzwGOiph0kx8JQrxZa\\nQQ3q0KyplOewEBTf953vGu9DteoNUl1AYzSFdJcu78uMr4K4fe9xvtJ7TflA6Z2VDEkVDUzhWHW+\\nIYn2XjlnCxKTW6nbPBEAScaYlpiEvPqMtHmBq4/x7TG56hBbq+i3r7G+wSC49SdUBRFaW6WSE1fx\\n6tUb5m3Dgwcn5BAxlWW9WdMtGmazToEKkqi9ZbONZBHmbUWII5V1rK6uyXlkPuvoGu053Gy25GQ4\\n6ea4VIAFYliuloQsvLlcsVqvqao5/+R/+1/4z/+zv8+f/MkfI3bk5Kjh4emHpJS5ul4qx643DBL5\\n+oMHJMnEmDTqc54YLZW3HM8XPNhuCX1hITGG2lrq+YK23xIrwbhdAAAgAElEQVSrhn4YSCmRJIDJ\\neDEEEXxdUaUCZMuQcqSuGkgGk7Xx3DpFv2qtF4X6i1NNQ2fobKX4NysMIrRtwzAGbOWJeQRJeF+R\\ngtDNGmLIBLR9JiRhR46dDZ99uuTiYkZTK+F9lS1DGtlsezKGmBJnsxkb03J1dcPDozNev7rk8tVG\\nMzwCzVz42u+ckxiJ2RTlFKMUh0WMPOeM84660Rp6zll1RJ3FeEsKmdoK1+MzLqvvI3KuZRoixmRE\\nHHF1zXj1OTlsy/OtBYicYyFVj1pmSEqBl/Nh1kntxy7Yeyuyeb+z1Ph/StWWNWYM1tY7pybZad+r\\nuLJ4JiCY4TYTrW4ADFpv1I6AvW28nYWSXeljCnZyLscVi3OVKg+lDGaLGEfTnZPilmp2pMhRM2Kr\\nGjEeZx0wKmxGRlWWCYWrOkXIIykkbNWQxox1SnaTUiz20LzltN4qEemL9wZd+2syB9e0P87t1Ovt\\nzNzu06LOFgx4TxyWWN9q+loSe5pDA7F/5z39Iof5ahxHL/aei5WsQsH3ZnVLzUCm3L9GfSn2ZZdj\\nSEl3+ka0WGzM/Q+ePcxxy54+b1fHtJ6qPccfPSWnFWnzRn+GJVZ2La4a8Qkl1aEO98siZb/M2M+P\\nf+cN/CrjN3WWuzTHV/rO/ULbP4wl/Z1HRanFAePqomgw7Rj3CxJKGsZo+kcQnChZWRLI44YUA9Xi\\ngvrkDMjgGq3Frf+YbCNeKirvwTher3peLD/DJKW/ulrecNzO6WzF+YfPOKprhs2WurI0VcPLzz/H\\nAPPZDEmRo3mHdY7tqte6W5VwVnvksoNcQD7H9QnL5Yqrfs2YI9frjdZxsNysb5j7jv/7X/wfzLoj\\n/uv/6r/lj//X/5EPLo6pu44c4fr6DSEmbq6v+MaTD4gx0dPrM23BuxpjYQyRp+fnjH2kahxd5Xn8\\n+CGualneXDKGESERtlpLMUXAm6zRZO2q0qaQ6KpaeVtsIRQXiqOBiSXFmKwMQkY3jtZY1asEsFqX\\nqgvqVowlR0Oyma5rESOknOnHQFs5To5rNsuRqT41bDMpK+PWEEd8pWLFCk4aaeua2WzOcdNwvmj5\\n0b/9nB//+LU+Iy5x8nDOxaMGMYEchByjgkOmZ9EoYtgaB1aYzRrGIRBjpq0qurZlO/R4B9F6VpvP\\nyYu/Wz6fMWKwqSGakfHqFXFY4crxRaTQ3QWMFKmu4ixlSs9OtUD9wO66766YYpaRu388eI8UJqK9\\nKpArlG9TSkaRvepLLSkHpNTVVDXF7JanKZkfSRSVjuEeoMyUhlVSCgw7Z6nOxai2pFrkMl+O0F/j\\n5uca+aaRIBnvzjGMJFNjU8CYBMkSw4CvHHEctL/e1ajMgWBdTQ7rYkqUlo4d9G8f7d39b/nju2fS\\n7G3q5BMOneV9kertv7G7p1NLjmY3M941hJK5wdW7O3vfeK/DFJHctu1mTDJ/62+72uPd14uhFaCQ\\nn+85Fc1+t5Un/r8pvfclDLyIpi6yaLRpNEWb4sBxZ5HZx0h8zPLTHyGx10gTrTQYV2lhWwIZC+LL\\njjFMZ/Ybj1s3a7r5E51mQeZpm8ZXP+ZvhK79TT6zG9NDV+6TlIUqIKnXRWwr3SXjEFSVQIwvlF16\\nnWYyNAjWQDYVxnutl+ZMe/KAbBw5fkr75l8RvdPvyZGb9YqUhRBHZm2DcTXJWq76DW3d8PH5BcPy\\nCu+cpu6qTNO2dHXFkAJJDF3lOXGWGBJDHLHGMsZI1VTM247r7YY+KYHCbD5nmwLbflveU+NixKeR\\n05MHhO2SeWX44V/+OScPnvHrV79i0W2Ydy1n58c45ziez8HmwqNa+tNypqosmUhtKgzCo9MjcJau\\nrlhv1gzjktX6RgESBpIUAsmsqdq449V0SBIlpzemMAE5Uhi1By6XKFAKzaSo7qS1FIBRJIgUtDg0\\nVU1oM/31QNOpwHPKufQhOmUBspaYIk+envH8k+flmVBjtR1D4ZS19MNIW9XElGgb5fEdxsjL5cDN\\nauCXz1/ps+zhyccndMcJ7xVlqcQmmmLzRUvU+5okibpS5qMcMykmKq8sRkMYccbia0ddQQ6XDNmB\\nDdjs1OETSP0VYVzjitPQ75lo1WIBBiZyKmokEgsJQMnE7vaR+5LLtD40EzPVMt+1DHW+zC7TtAfR\\nZQq9XgpYWymBi3E4U9pOKKnMW9k3FXw2BtV/fCuDtXeWer6mRNr7v+0RplNxxCsyVyw5rpGQsHVL\\nVZ2Twhasw7m2LGWHa2fkPNIPG5xx2LomR4c1CXKvQDVbMnoRjKlKYCJ3zvR2UGCt1Xryu4aUiPve\\nlO377d3+e0z5Lq/yX6WXPAYVkSYnjK1uRe53xxdFmNR1/Xrc5Dl32NttiWTe3hHs0x4iltv3tNyk\\nHSFvxrfHxP7m1oW9ewL0O41RJnwMZBkxm8959dNXLB5/j7MHP2clD3bGWiSreom1GNciAayJ6th9\\nDdkUibDfbuycpqmV/iwnUh6ZeqKExJ78+cs5tP+QrSiwsxNMwAUL2quWQ0kreqXE2kWW04NmDn5Q\\nvk9jwFQgkLY3xHaGb+b4EXrT06VTnBupUbDAydkJ2+2Aqxyzui11YTXOLz97QaflP3zd8sGzJyCG\\nV29ecvXZKx4/uFBHagw3Y89qs0asJ4aIXwnblLjZrDheHOGtoQ8jQcB7T13XjCEi2eJxHM3nuMZx\\nOu/4xU9+yHe+97f42U9vmD87p67UYaWAAnf6wARGU27XRDSRqvJKlG7geDErdUjh8vqKLE7bSkT5\\nXccYC+iCXVp/GEcy4K2jMaaoKlhyhLbptPctKAl7UtF5RaOahBODMSpxl0UIEvGgoB8R2qbBWkPX\\nNnrdQYhGGboaY+jHhPEZ66d+8YyzUFWZlKCrVJleDDhnaZxn0XZcrrZ8cr0kZUuuHd2Z8OThGc1c\\niQxEcmE4UnDU1GqWrNICOuMgQ+0rhnEk5sT5yVFpK7FU1tDVDZt+IGQPZouRhmwAcVgisV9jC9WZ\\n9mijab+ctGYVQ9kwJ3WGErkFBJT9IpBcAgG19PvA4NZ6mdoWyiZzx+Z0ECgUHlqTU+lHLqWjqeHf\\nGAy6QXjbIaL1WTN996FXh0Na0v1/LZQsw+Ga3GfuspaxJOETuOYEnKaJnZ8BSZPxEnEIw3ZNlaCu\\njoCoPZnWISmQY0JKC5rEUVl5SqbxFtbjjp0XkUKCMDnxVCLT6cLeDqi+TO343vc7zWBNXkqAPFyX\\nexMxpkby8JN3HefLOMyfyGb42PA2XdDb6QB9VSNAh7V76Z3bjkL5D02exI7fHvflo3cTnvPEVIXR\\nGAcnkELg5c/XGDki54idgEIKrSrIWSVQMKjhx1pIyv7zWw+jCyXng3PGYI2maSUXrtuy8O5PZ///\\nZ9y+tVkffEpPWnkti4o2a11ZAVm6EzbFkQJilFEEmAR487AmxoHl0WMW5hFdk+n7AeMN3//wI4wI\\nn6bENsJ2iMQkXJycMD9qIGut7vTsAd/9vf+Iv/7RX/DBoyf4pefiwQMqX5Mksx1GVv2Aq2pevLrk\\nZDFnPm+RkGhOz4kinBwtWG+3LFzFZ5ev8AbW40hXz3GSePniOT/45kc8vTjj5GTFL/7mr6h8w2q1\\nZV63u26CzWZD0yhJwdWqx1jDolaQSt8HxpRwVg2VNYZhDIQkiMk0XttgxjBqT2WhxgtZsDnjm5oh\\nZsYYiGiUqKlr5ayqawckUtbIRR2bEiPkqIw2FpRyzxjEWbypaYyh7jyr7YbtdqCtG3zjVGw6JiVs\\ntxa80HaOzSoCmZPzBV1XE0KgctpCM6QNXdNy3MzYDD0n85bvV55V33NdCWeLlmR6hiFiom5OkhhC\\nCkVaKWNRjtccElIciGSl1GzqCm8M9dGMq6sl3nn6kOgZIZyp0d9lNhJx+4bw5nL/DBuDkUyORYkk\\nDai+5YFsV3mfmpkDNLdo96amaLVE8N52Bj2QMmo5x06svqwZJCujjKR9ajYnxE6bT1euZXK0B99l\\n9v+YWr3e7zzclFs+sKUTNkSJSwwGd3SBGbKqE40jpq4RA3Hc6DPWLYjrlzSzJ6Q0krdXqpRihDiO\\nKGeuxQwrZdVyNUgmTWhjV2PS+NZ5Hl6DMVIE5ifdXXPwnun2vPtavzDSLNR6OK+bIwoB+zjs0uo5\\nRySs/vm7jvGFFrvv+x9zT4iqUf49yX0xYCvc/InyzVaLglqbPjeharUfK8mUkr19we/6t5T6nHV1\\n2SUUKjcMYXtJTk9xj76G3T2IJb8yXYMr0i1G2V12qNDfcohMjPsBkb5sJipFjxqNjqzzunhKpJwl\\nq97nwc7rt8D53HtOMNWSfwPn/NbJTLXmpB07uPIDELXIHwM5hGKEdOdegn1dFAXincYeyQbnLbL4\\nFt4FmrrBYmgxtM7QVg1jzKz6kZAy3gqkgJHIxePH/L2//w8YhjVtXbNdr6gwzJqOmBLX19dcvrlS\\n6rfZgoujObURurZm0dXUtjiRkPnw8QecHC847jrmba3k8gIn8wVdbXl4foQxwtniiG8+e8S3PnrI\\nBxcPMdbuQDaz2ZychbpuWK63fPryNTfbrcoCWgUzZRybPrDejqy3qj2YkxAlk1Kk8h7vPbPZjPls\\nBiJUVUXla7yxVL4ihMDUHSUmMcaemBNt4/He4L22nGiPp/LbppQZCyR/b4RQAo8kVF6lwLarHiuK\\n4nS+oq5r6sozbxv+zn/ybWxlqVrHww9m5ByofUWSTJ9GaudpvaaLndcNok8jTxYtHz2aU7uAt56u\\nbZgvOpJNhByx3k+4dlI5x8p7ckrEGGi6GueErqnYDiPrbQ+Cct1WhpQMef4RUvouMULOhuXVc7QZ\\nXzfGk6FNcYQ8Imkkp0kcOu0IA6Q4l8MfIwWRjNzrLHd9xZNDEls+WaK6gtS35Tj6mq4DKbKIJgck\\nbovayHSfDpz2W2OPPIWytsweDDM5Ik15H2R7yjou71LEcE7EzYo0viKuPyfnnjiuSOMW5xoq7xGx\\nuPYhRgJORnLuMTmpE/I1ThJp3CDW4OcnTFAKrZMarL3t9PYPoex25rtk927Dcni20+ffno8vcqC7\\nqN81WNdinQp7yCTdRoa41WfRte+Ybx1fGGGu1+sfKqT4zsnc814pIBuxFciI2Jbm5CnDqx9r7YTi\\nu8pDY0ym8cJ4P+H829+5m2ilX9oXz/W1HJaEMbGovkc/f0pa/hJTZHlMTpQcEJTHX+ttsaQKwzu/\\n98uO3cOL7iytb0jDsvxBwU8TcjZnBXBINiUlMkXU7AzbfYXtLx5T+mIfzStGK79L4u3dw9xdZJP2\\nTNmh2un1KVV++FCX5msx2v8qKPJVRJGJYQPzM+pqRh2viAm8FcYxMpt5+gjrYavMNynjrHB2doYJ\\nPb//B7/PzXrNH/+T/xnntLIUvGe7GTg+PaVpFIWIXdFZx6L2hNqw6kf9m408efKM+dEJIoZ+vUZS\\nZjHruHqzYrMd+M6HD+kqmLcNZycXgLDdrpnN4Lxd0Dbq0N5cvcH7ihcvXhLCyGzW8d1vfMiPf/Jz\\nXl/e8OHjC22LqRtkzNR1rfW6GlKKdG1LEK2nWWtx1hPHgHOOrm4QYxijRmEVlqquaKzFe48RYUgj\\nMWlrQFWp0kaWREaUJUgE0khdV1hRxKz2qQZtK8BRAd28Y9gqW01OSQnqY6bxDTGNzGbC9//gMRCZ\\nLTzYjDeekJR5B9FnI+WML4LkvqrxrkbGgSiasoXE2I8MSft9jQjeVko+H0d13iljrNAUDt2MksqH\\nkPAROtciGT6/7rSd5uh3NSUskIxFpCeurqjrjpzKEyqKhDUkpeokleex/GTZ6TZO/9unZPetc7ft\\n9eG6tAhxt8aTTGn1gDN6faooIkpJmXPZ9UwcwmrTBKNBB/cDKm/jGvIXpiZjfFsuq0w8GmE5TWOn\\nAcFRNcdkcVhrlBXKVaSxx88s1kZCvyaPA87Oga1OUViTwohvFqTSby0i2HpG6gNV5RjDyCGxizqx\\n4geYkLyKd5G7177DQ+zBO4dOX+d535Vw33wIFAEOJT2wouhw5ZEVfHvCePMKNzt951zClws7fvyd\\nb3+0ufXlArLLMWttZDdKFiGNS2WC8O3uhEWEuu5wdsr1OwL2lvLJl2ntMMbobmwX+eouLgddvOth\\nw8V3/gjbnk5ZamXmKDuK3Q6uhD6ZxH3V2K8yDmHdBovEDXlcI2WxT4/H4W7UGK9zZKfIk10DrUz1\\nki+Yl/vPe1rIk4MW5fG8+677HiyBnTrDvUfeO+Jpu1Iq1jtlGij9r8X5S4nwJ8BFjiMSetJ4g7Uj\\n6+tPQGCzDRx3HcbCq6GnctDnhDhDNPD88pKziwf8s3/xZ6yWS55cnOOsRVIghEg76+j7Df0wYKxw\\nPG85aiy+9vQxMT86ZrXZMD8+5fT8Cd/5/n/M04++xZurJT/95c9xreNquaJtPHMD5MTHz57ivRoP\\nZw2zpmWzXpMlMY5bmqbGu8z5xQmPLs6pa6Em8uGjhxwfLfC1o6o8fseMoyLaiaQcGi7TjwMpZ7bb\\nrSJiaxU+r4p8mLOWmFV9pLEesY6QMlGMOhssq74n5kjOka5r8d7q3IgaoZgS4mHIkc3YE3Jk3a9V\\nXs1XCqpxKr1XVRUYKbVSg/ctMQ6cHMNsrgCjFADJVMap1Fql6zyLlF7rhDMwpsAQA8t+y9VmzZAz\\nQ47gDFhDNImNDDtyfGstQwxaY0sKyjFWU/BdN+dmNbCYtdQ1dLMFw+K/IbsTXXMlWhovf01XVWSx\\nWAmIaItUzqOCe0rPqgLWJrL6iUf5wEGK7H6mdfv2epsi0Sl6zDsUqjICjcpGJiX9aQzIfuO8j/gL\\nWCxri8MXlWsOgTvTOExhqn3NlLt4q4aoL2jQYJ3HNzNsPcP6GRnNcqgoOkrF2J2Qxw3D8iXGHZfe\\nX0skaQ8pgp+d6zFzwkgoKWidmzGMJVC5k0nM03WWyH4KLA4uS4D8lumbrv0uCOh2rXSy84qsrcsk\\naf+uWKvlpKRCDNlqmjatf/XfvW/ev5TDfP78+VvW1tfd7tz9JOFVTj7HHlLEBK3FtO3x7nPjOO64\\nGp11mBTBt+wfvC83DCg928TfhMEbpXuqBk8r/w/16cfYZgL4au1g0rhTyqsy8cZ8la9+/3ntnKaC\\niUyeHh4lFMzlMS5x2u6ad/yt5QZL1siY3WN3/yzsdsO7cciRa3d9rFOD/ZcZk97fe67yrX8ZTEG5\\n7c8mS6nD7NJGFPj+qEZoFFK0zCqY+Yr5fEblHS+u3/DmZsXZySlOImOM1M7R+YrrqxWbZDQlXFUE\\nPAnH2YMLQhg5Oz1DJO3o8WpfcbXpWfWBjz58ytc//j5PPnjGh9/4Lkdnjzm9eEz3wUP+anzJ5fWl\\naixutwQi4hyv3lyzXK1wXkkIUiEi6Dc93lasVxteXq6QlLi+WbLs4Wo7UDcVs9YTxlHJ27Pg65qq\\nbfF1hfdK2qAOC6zJzLqKo1nH3Dm62uu6spacYN7N8FicdXTe44wFa7VHsoCqxpBo25bGW5ra4p2h\\nrj3WmtI7p+oe1jusd9StcmmGEMpmtt7d1yFF+hQ0rTsMiFjtlxTdGNVNhQC+rchk6qYiZAgibMdR\\nUa5Ng/MOYzxNNyMBm2FU+j+07SblTBIYBs3wjGFUdG5OiDXErACQlDIpBoL7On0qQKXZS879z0mm\\nUfFiY5AYGZbXRLdAK49K6qAygVHTsDIxcqkdMuZghR0+9nK4Tu9bE+bg5/D3UniyNVV1NgWp+g7r\\nyBJIaTyMWw/eMK2VL8fu9a736VK/7VTeIgiQSIojOY7k0FNVNSKZGHpcVWMEvK/Beq3/2gbxnnFU\\noQsVmbb42SPN8JGo/Jwcew0K07YQoFimVjNkHw0a6w/mdJ+a3QF+DNhqAeJ279tfQyG5kL39nI6z\\nn5MpL2wKgc2UYSwkLDRIGpBqThquwViMq1fvm+8v4zB/ud1u/V0miVgEN0GN8eEw5UEUAnlziXvw\\nzYOJKcTFqJpCDj3Oe95Fhv5OI3/4EBiDMQ7xDd4ZRnOFMQlbN4Aj50SSiJRU1f48LQpmejcP5Fcd\\n+3oku2uyvgZXk0t9CDmsm+4Xpe6wXfnxTNp96uzvmwcLqECxHk2h6dPCnXatitD9cqMA9XjbOJRm\\n6Pflzne7ylykgyx2936tBUmpbeacyXkk9z3RfZ3agLOWylmadsbjhxcc1YazeQcGqqrh1dUl1hs+\\nnFmWyyXBVHzru7/P+cVT+u1Wae2Wy2JwM9Z5shhW2y3/6R/9Ea9fveLR4wfkZDk5OYY48vr5L+ni\\nyLdPn0DTkCRzdHLC85srfvnic/71X/8Nl9dLyEmd48s3Gu3MZtR1i3Oen376ks9eX/HqZsvffHrD\\nauxJOeCs3g/vFTA3DgPr9YZh25PGgEmCFWjqFhGwtqLvR0KGMWS248B2GNiOw/9H23stS5JlZ3rf\\nVi5CHJmitEAXgEYDBDiwHkWQZjQahy/ACz4ZX4BvwJvhBY1DZaSRQ0INOEBPAyiZVZXiZB4RwsVW\\nvFjbI+KcPFmV1YNx6+rMPCLC3WP7Xmv961//z7pbM8SRpBIpK0IIjOOIHz2h2Fwpowk5MYSRqGSM\\nI6lI1DJaE1MmZYo1k1QeY/DySWsJhs45gVMzNNoydw3zpsVgmLkaZ6wkDinJ+8WEcU58PhV0Q48y\\nmn70rDZbtr0I8cdRgrJWRoQvkAAPSpZ3UYxKQdo6RmmMs/ic0NnKs2oU1A+46cVhpzIVJn+FogdE\\nms13N2JQTsZlkX1TMRQaQ5T2TCH9TQEx56I2dsB3kPGcePCs3v8ESVWzfzxTwV20bmiOHpJ0Jeu3\\nXqK1oHH6AMHZI4wil5d53dHodtW078sdChVMP3frexk4qLruDvJrLYzcFGX9DNsbNIM4t4yDzNmb\\nihxGVB7QHnL3AucaUGC1xc4eEEOHUkZ6/1qhdCuiAFgxz84JskdhyUY4DylNgjV7Buw0L7nbQ8mk\\nbNEq7fadXSAs+83B1iOcFOAwrKUJ7s0KvZvqEZayiERk8rhGJqR+eAbz9iu/4cg5p7puXk1i6ruK\\n6BYR6HYBqpQiK0gkgR3jdn9ToDh6Cxss5Uwcr7lNLNqnZLeEC26/y8HXFWryHgwBve0Zqn9Odfqh\\n0NPDAHGUajblW6+hNAeb+j/cMcGTmUweO7Rxct1Tn/dednDB45UVeraSedFJKCLnw0kwabaXxxPQ\\nZdOZHp79T07JpjL1vy/wvPvzFikL2Ddfy+IliQqIkjxfZRFRJ8UCcUXwHSl77PJPuNhmKqNQ2rJs\\nKpLfYq1i0ThAMY6e63Vm069ZLluWxyegFN3mhqNFg1KIsk4KWGcJMdHM5kSleXRyxrNvv+Hk9JRv\\nv/2aytX03Yrt9opnz75mtbrkw7MHzJTDVRXBB1bbwMOT92nnJ/zlr77gX/1ff87ff/Ocq6sNbdWy\\nWfdcX6+4vl7x/PoVf/H511xuej7/5iuGIRGiousHga5CJMVcepQKaw2VrZFbFFEJnLb44AkxsR1F\\nNacbB4bgCSpjjCIi1dt6u6UbxuLIYkSGrm1JUUhkPkUG7wkx4JNn0w/0cWQTBtbjIKIHOTN4L6Mm\\nZZRl1Xes+y1DkLk5Zw0+DIwp0vmepDJ15XBGKtyYc/l9zxglobLGFv9RSdOstczbhuVsjlGWMQap\\n6GKiMqb0VYVNHWOWaqQ8/uM4EnySe+ATXTdiw18yjBXfvexYrzb0Y8LEiqClD9mtXskaJBNLwpqK\\nnVVKk+theSqkt4S6laxnETbI4S2Qlgnc2iM6uiSWKUa67YpmMSPngPcrYvDTbA7GVHeSdFHbqZoZ\\n+WC0Zd++eXMQ3Z/L7Yr1FhFpamVNv6coAVoY7Cl4IJBDQsUepUVlKSdPHreo6hilB8BC6KlnC3y2\\nMo1ga5RVYCwJMPWcXJSunLHC69GFHJjjwXXn1/bB3bUqgBrYFhzw9lx6PkCudlV9mbuf7g8gEoC2\\nQhtL9MV0IwVRMcsJ3BG6mpFKPPqxwumtaCBt2/zFfj4p3/kziZr8nUNlcG5GGG7YPP0C7RZ7TDnm\\ngoYagZ2SbOSH1VnOP3bu8lTlLDCnfCBC/h6vf0X/zTcs62vM+YdliHskJY8urNgJWkop/XsGkTvX\\nPbHxYgRty4dgSeOmBI/DTOn+6zIg6hmmlsoZs6ecF8ZxzkEk1IpYuprmyHYxbSLhTLmZBKnDBfpT\\n5zxLqnTrdw/hYkG5cslqpy+mAhpPVXI5p3INPkb6+ohFfcLj2tIFz9BvOVrOmM3nVMZiVMYaxdNn\\nl1RuhvfQNDOyH1i9fM7zp08JITL6QD94Vjcb4hhYbzakBK11bPotRimur664vnrBenVDt9lycnTK\\ntt/gr9fMMGjBRxnHgYuL52xuVsQxsLpZo5Vi7D0XL6/IMfD90++52WzYDJ24kVxd8emjE65Xnn/3\\n+ROhyCOB0jqD1QZrrJg9G0tlHJV2RC/s0GEYGULPGIcy9xiLM4nBKkdla1JQDEFmi62zZMr8pQ/E\\nJJVjjEkqvoR4RDL5IiZGP9CNvQRFpRijzPvljHgdlg01qQxagvQYPR4JxJ0vM8s57wDImBKjl2pX\\na4g5khXU1jKzFdYYrlcrxiAC5045VCrSJ2UdmZJpxZjwPuJcQ4oZow0heJFZTBmtF0T7M7bVn9Cl\\nhLfvEzTorFGhQ5fscCLziAB4LsjGtDb3laT8KZUnOUorhGIltYM170e/JnRozzcQgltSGqUihC39\\n9fPiUdAzsViVKpXoQZGQ8fTbC3y3ZVILu0v6e9Of07m9Brmqg6T5ngC744CUvVAkPQdGP6B1LQmR\\n78HWZKWJ4w1xuESZY4bRU7VHsn8bQ4oGbSoZW8oRoxV+2OBjBF2hjSPtvIqn4J3uP7cMWVlp34Ru\\n9zOHRMjDrWu3j6lYXl/QuoQCLf17hchFZiUSiznKSI9WIlUY+0tBOPz6X97zQe+OtwqYq9XqL3Ke\\nYNc7v6LULUh2f/GZEAQS2Q3u7z7fadMMUCi+92dyd6XpjUoAACAASURBVG1q7rw1uZB5JAeBhHE1\\n0SfWN98TQuLk+Ag9fyg/Gz0pB4EvVcb74Xb/4j/AIc7eBm2a3QZxn3bu/pDZRqMMWblSaToJnqbG\\n2ApMg9I1hCLAMAXFPKl8yEM+fWenyVQ2j596TIFyB8e+tUbuRAcq66Jk9bI5G3EDGrZUyTK4JXqx\\n4J2zBbPZDJsTlVKcti2104Qc+fnvvE+MgeXpjBgDpycnIp8GeB8kiBiDc46mbRjHgY8/ep/lckFO\\nlm+++Zrj4xP+8q/+gpw8KXm23RqdArPlknY2o6kq1psNi7bhsw8e8fOPH/MHn7zLf/KHv8vPP32f\\ntrZc3ay4eHlBBkJKHC/mHM9mzNqGB+enfHtxycVqy3YQ0QJnLVqBtRplEmMaGGKZXVMKpwxOWxpb\\nM68bTBQiilaOWd1SGRHB9qMkRDFFfMyEnMm6PG9JQdIklYlZ9FjHFAkxl/GSVNjNQqKxVmTYtBKF\\nH1/0b2NO1HWNdQ4fAqP3kDNt5cg5EnJgiB6tFRbx1ZRxW4OPvoxJif2bVYrNuOXZ1SWb0ROzrNax\\n9yTEgSWmSE4Zk3UZEMvF0DsJfKl10ZT15KypdcLkv4b2nMRHUP+eQHbZ4Mdeao0Uy3iXVNwKhOw3\\nEUp2m+0U8A7YsjkyiZ/f+tF7j3SwypEZa2VEXs04lDKk4HfPnyCkxcg4jfvncnqfHDhsz9wd9r8/\\n6N3zPCuKOP/UnrkvOS4BqxQcWUk/3bgGbef4cS1+w9qijWa8/Bqt52gzJ+uI0uCqqoyPaYxz8vcw\\nkIInAdpKr1kbIwLv2qGVZbIvu4/9P42ACGgfCteEW4mDXPchgSjvPgWtHQVwR2kRSpFArsVpiIyy\\nlczH6jJeoiCnEUz7g582vMVYCcA4jn+DqsuJgmC/e5bstGD2F1OglTiAcWSVxWxVF1r1Dqsur5MD\\nuthI3b4pAl1O2fEbL8KIe0EqQu52doybLSCDm89ZfvTHXP3qfyqcH6HTYwwKTwphl2lm7ltYv/mh\\nynBsZqqaJ0+26UO+fc+A0oyGEDuscWTnSJLsCx6PQmclbddYSeaMSMrtANg86ffK6AoU2Ok3vLQp\\nHYH9gt6jIXegWWAnGj2hJhlQEzkpAlagmdwShhuGPnAx+89xr/4HPjtX1MbwYjOwtBJQcpzSgszV\\n9YbGarrBEzGcHJ/i48hqvaGeO46Pj3l1dYlzjodnp1y8eI61BqUS15c3nJx0/PKX/4Trq2u0sWw2\\nPednj7l48T2vNj2XL17SzhoqpTA28fjRIyyKuhZCxPmDEzof8MPAxfWKm6FnO4iLw+VmxXq9ppkd\\n47uOIUTGJDZcTSuC/KEEiLYS66wYYrE4k7unUczalpATIQ74nAmxOPNYTd8PVFYMo62dqsAorhA5\\no5NBFYGJGCMhCrs2K4jsKwpARimQXq+PgTB62qYhpogxWgyvtRLiRso0laPbjoWQk9ExcdTOUTky\\nmsTlugMFRimiUqxDz9OrK4aY5fOPE2hZOt1FvzOGKMlYcSURX89YpPkEwvOjxbmRmDzkHpsXuAq6\\nlFFZo5Swr1OKqFSYr4UBq3IiTXOL+wfu8OEre0Aoz+zbJoTTTPX0z0kz1kpVOwVrnZF9QMpLdSjz\\nttvz1P6ZKRXw3XbUfSIud7+XdpKjbz7rifdhjCOFQfbvSWRENeTcod0S42pIgXD9LapakEtLgTxi\\nzTkpyaysdg2hXxeGr8K4Gu8zmkgYOxRC8DG6kn5nWQNTMnR4/lN8ySkjgvRpD8Wqe/RnD68pa5Rr\\nUcPk31nt4orSVvZDbWTP1A60lrVSIHwhBv34J/42x6+Pj2bj4aZ++HHdzRCmhQAISxSRczKzx8Wu\\ncgoaFb6/xtrZa6+zfz2KUs6bTk3ml1LxNJPqxdJff88wOBr9JcaM0oRW0sskesmCkkDDv0HR9YPH\\nrX5BCgIUF7LBPmG4f0UXmpCo2Ywb4iCO7ilnUcooNG5VqjRlKpSpUdUcYxu0cgKLqKLAX5hstx7s\\nn3Tkskgm2OOArPADsDIAqVQJyhRGXFE+KtVmVqCbWYkVC3RV0ftA4xTOVaxGz7KpOJs15JQ5Pj0n\\nasfNdmDwgWHoeP7sCX0/FNUZy3q1JoTAarWiW2+kl5aEkX10dsrXX33F6fGC+awFbXn/o0+YzWfM\\nmxlt3fDg5IzNesOj4zOePHvJdedxbUNIQpBJCvq+IyvFahz47uqSys7QWdSm2rphu16DMbjKEUMg\\nhMC2EGBiiFTWYpCANOYIbidbtdswdYJF21DZkiVrhVFQVQ6joLYyvK+SVJBDkBm+EAJTP1mhsFqL\\nqICy0qtXUg0SFTmpHUSZU6JtpBecUhQ3i7LuRu/F2DmIxB7AzXrDxWrLd5dXJKVojGFeN2z6gXU3\\nsOk9q64nZiUVpBV2IgqpilMq/zTkJEQkHwI+BmLO9F6IamJ3pqgbjdGOGIAUGIcvCCzJ+ohJclIb\\nhz2QUVMpyBosffNS1sAUsndtg4xKhUF/TzL7xicjK3KWZ0vabhplhOgihvVWuBW6ks1ZyfvkHEov\\n/35jeLVn3r1Wad6719793YPfu32+8u+pABEvTrkPKXpy7shJ+u459MTNFX79XIRotEUV83Lfb4lp\\nTRg70DU5K4yuyNqhbEUuRLcYvKBrrsXY2S6pm9iq98+X77IP2OngytcVulBd9j9/eI1aa6kUASif\\ngXVln7TkHKfhoyKVWpO0gyDXncn/3Zs/bTneqsIE/l3fb1XOc7RWMstk9lJ5ez8SdjdgupAQfMGW\\nNVp5oq4gia6jtpropTKcqhIxLN5nFZKpTVDF6yBJpjDPtCFrLZtvGsnjhpsv/x84e0C0X0PVoMI1\\nQi8fIQuleWrs/0NWlnePSTd3Ov+JVn1IKri9wAskUyT1dBxAWWII2NoSU4QUStVoMMqUbN2grEIn\\nW5zOAzpn0JqsDaqQH35qhpCZ+jjy4GpV4NnD0nP62TvZn8lJDGhtWcDakIvqh1aZ3I/E0w6dZyT/\\nkrMHD7lcdzyen3DRdZw1De+eLvn84hoUPHrvQ1YX36JRjJstrtJolTk/OyalQYgsIXO8aKnrObau\\nWa8v6YeRdz/6kOPFDWO/4dsnz3nn/U+IfmC1WnN5s6JylqZynLZz/vk//ae8evGCf/1nf0b87FNc\\n3XJ1ecHLmxt8SMzamuvViqaes6xajpxltekYx0BdVfR+QJsFlXO0riYTRRUoi6vL4D0+RZSRUZE4\\nyoOutSYphdEQU2ZmDd4P8kwgGb5AXJ5xDKLlmzIxiCyecwY/jvLspD37s6osMcpIeIyRuqrko1My\\nRF85x+RtGlHic6n2FUBQgg5ZK8LsMUW0dfRjj2fGZvCs+yiuDzHhc2JEIGE1BXwrPrYpFhNsWTCy\\n4nPx1i3BMyVBjkAC+OgzTVVhNFitsepv6PJ/RnIzTKlgpYqIu7U5ITrCgo37KnN6zsrPpLyfz/4x\\nGObWJi/lo1ALsmzAWgujd1cY5EMEOLFzSmFqY90OgOVNikTcm+DUXU50z6ELcrdP2u++TkqTkEGp\\nbLXF1nOBowv/IqcBa2tUtUAbQwgdqmhvG7sQoQ0nY1JxXJONRatMCgniQAwBbZ0U78aR4ygBSxWx\\n/XI+ij3cPFXUQkSqSGlb2lcieAGGrPxrBVQubR5dzcTGTRmMqzFW9lBVKv+stYgjGAcK3OyYsHlJ\\nMjKlkYdX/+0Pfvi8fYX5EvATLOoO5iqh5GT3fXgKjKtFgitn/OrFjmCwHwLW+9moA7Bvnz2JKPUb\\ndmdpnCuFrhoZT0mBbAWOTWEktB+S9UfUR5+SqSAbctZF+UZPCdZvVnz9wPE6u20/G7QPjvfDKuWn\\nC7RSBLlyRhuBFbSaGGeZHLcEvyn3sJjKGo1tjqhmDwRKyWWUpQyLg/QrpjO4rzeyPxLs6NqTGsrU\\nMXj9mOD2219DZlJJBw8A4EdCt6J7/j3OX/Fso7nqPd+97Om2I21TMSo4WyxQKfHd909wVc3lZsWs\\nbdFGU1VznJ1zfv6Ipq6xRnN8dETbzPn4t35G3w87JwQVRjSBVy9fYbWisZnrF98x9FtuVltcXXF5\\necU/+aM/Io8dp4s5n370IX//5Vd88dU3KKV5fHYu1X/IvPvwAcva0I9bUspsIxydnvPxBx+UiiWX\\nik7IK3VdMzHxZA5NBtitkhniyhhqV9O4GmdrUfuxFadty1E7Z+acuNrnkUTEWI1SCadhVhuC8oxB\\nWKjalBlEJdLiq24rawiBdhMBTcRpSCkw+EG0amNi9B4fRKBckbAm02gtJt6y8IQsQWZeNwQv/dIx\\nj9IRNIBTZJ2RvUjGWWKM9H1HjIkcEylEwihVh9ESeISZmWmcMHF37R0yIQaUkl6wzTc8Sv8SwncF\\nPNFCsjNuV6HJ2k4FeZqqS7F0Erb89HcZMXjbnWBX9amMaZdkZdG2wcweoWdnYPT+ZfYxkJyyeHDu\\nCJTq1h6xH6dg9+/7qsspQNw5q1Lx7pOCN4V+rfV+L9IOXTUiGYgmjj2KLHu8tuRxwPttqaCzJARO\\ndGJz7Mm+h2jIlEInBlIccNaBacnaCmqmM9aKKEnKYIxj36+5XS1PhZKxs13rbkrY7wo67IKtsWI2\\nrhPaHcmM8uALebKI4Jsy96mArPDjALbB3zwB5XgtvtxzvFWFmXPO5+fnfz5cjX8CljBsdsSP2xv/\\nnWwpZ2Lw4glXcPNcxIuTLr6RKgn2XKCQ1xdCYZzdJxMFxc8uEoZrgSaDZOpTD6M6f4c6fMvLrRfc\\nOo7kNKJtSyyKIqI48Zsdb8TTd2d4eF9yqZjLXvqDb1p6WhpAzIHRqgTOSjLqLCLZGenDpomWTxL4\\nxBiMm4NtivBxEBgmR2mrlOCm1OE53z1M6esU+G5yod9f0p3T3leiU79zSnZ2LjXKMPVYtYrQbbn4\\n9gvO3vvHfPXqz/hk5rB6RKka4yxn1vFwOef7jefFs2c4t2DYrBgyPHr8CGMkq18eHeOMYRw8ymi+\\n+u5rbq6vWCwbjpZznj97wdnJEU09Y+h7vv/2C643N3z+9Xc4a/n2+UsePnjEb73/LviOQcHPP/sE\\nReL9x49xzrDarJk3lottx7/98hu6ceTB8oRXNz2/+9u/YNlannz1BUbBq4sr5nVN7zxN7aiMRUgt\\nQsYYY5R5Q23FjEApSFJFJpVEtCAErHU4BS4r6pll6wchdSjF1o9soycWVCbqtEsCY0E1jFIY53bu\\nF37sqdqKWdVireOSFYMPhDFgncC4Viuqqi5VQWbeNKy2PaREbStGl9l0HSenC242HU1jaaxlGz1O\\nGxrnSETGIQipJEZRE3IO7xNGTzBmGUGLxccyIxZmWvpcSSWUkb597Wo0GessV5utiC9c/jnh/H1Z\\nz1Utspz4HVolrF9hwKpSucmeKW2SlET557497P6nckJWFApHCAFDImOoT94Vfd/nV2SVbr2k2iXm\\nB3qy97zX/TDlm4+78/G3Ri/YB/fbM5hTTxAwlhg6clCQPM4axpDQoSflhGlmYObEcYXRDm00Pq+p\\n1ELWXNiiqYTNmhJog9FzQUOUxmqH9x0Kg1iSlxOdqsyDexDjZHINKY7oegG+O0g67kr87e+Vtgty\\nGsm6BiXjjKY9Kvum9PVVCqVgy6BrjFbSV9eGlAw5bf71j93vt1YXXa1W/+suM7rDknzjh6tAFu++\\n4a6iBztDmQaJ12VeUUnlM2UatyDKUpW8XrlIpq4zqBhQKKKpsdaAsRg359j/DefVr0njTcmspBmd\\nYwfZow07CPgNGMcbjzcHmcN7UpaFUkzA87Sd3T70nf92y0H+VxrSOQWiH2R+tcAXYnW2rzpBkZMn\\n+hvG/iVpvJHsSzmcW2DrJcq2AiORd6zhH9YU2l00OU9N+f3IyvT/ZS+RRGjHIoS9hu/BNSkFfiBk\\nj2sdN5uHPDz/Pc4fHFNrizGarcpk6/jZg1MaIk++/Zw//uU/kzkwH3n+/AUkz9PvvuLy1SXkzGp1\\nhR96ri4u+OSjD0slnrh4ccHTF5d8++QJl68uWK9XPHnylFerNS8vX/H82UvOl3O0H9H9yLuzikdN\\nyy9/8Qtmdc1XT77jV59/Q1PP+P6b7zA5YZXmvffe57133qOpLN9+/QXvPTjjeD4jRbhZbxl9IISI\\nDwFrREgj58i8bTBK46Mnq0l8gqIrm4sYgZB6NmGU0Q4fiBOjlcRq7PEqE5QkSz5EfCEWnSyOaF2F\\n01bmI1Mq2qKazRi46nqeXr4ipkDjHKfLJY11VFoxrypMqZJjTNxsNxKAM4xhpDYGpRSvVjdUlaWp\\nW1zlcEBTdG5jkPOlCEkoLc+rqzQhepnh04oYJHEgiWqYuPpksoaqqrDWQoZZY2hbR991nM2PqN2M\\navM5Zv1vZGVpg3Y1KYq61sQZoGjF5gkSLYzYNJGD0p5Z/qNVxi7Oaam2UhD4tao5ffcTVPd8P5pz\\nwPDPShXIWKqxNwXL18QH7vz9zcc+SX19FOX2a+6+nxLJ92jlQEVSEi5AffQuZn6Orc9AG9J4hU4l\\n4YgRo2boeonWwqamFNTK1lg3E6cZrUhR7qvoE6Qy6ymyiymOpY0nN3R/7ppJKyz2q7JVTBKPEyJ2\\n8HEoBcoQYy/sZCXvJa81KQSpXXWSS/9UW0Pobwh+JIU1vAXhB96+h4n3fhd9RY1f2Gs/hLNLCW13\\nmHpWjpRH6sUDkt+SsiF1A5pwoPdQfvMQe59StHuPRGJA6wbrGoxWjNsVWjvs/BS7/ZaKp8ybn/Mq\\n3KDzDNSk9gFxFFfziTnGPRXjm477Guuv34sS7PegJpOZ9JRdT1d5cNumuFcCuZeHLSNZuLVMWXHS\\nArcqlaUC1AZrG6IfhCObAhmxeFIpEk0FJGnkN5UsrhSJvkd6zQfl7+7h1QVGtKRCwji8TnXr2g4G\\nphUFwtUlI1coPSUMQI4kpdFa0SzeYTHfUm9/xWW0aG1YzFvQhk5ZlkeJ98+PeL7t2fYjo5kxbze0\\nbc2TJ9+ybGoZgahr5rMjZm2NcY71zTU5Zoa+4/Gjh4yjZxwHPv3ofZ48fcYQpJ981Mz54PExR7Wj\\nPTnGhSP6i+/oPFxdrfhuveHyZsW673j2/CX/6S//iBfX1/zp337DV198Tg4yqnG8nKNJhQWbmM9b\\njmYzXIFg0aoozuSCbGTRtFQZo+z08WOMQSfp642jsGVlA8oMMVApDTFTG8u23xLIGOuKs4RUZNtu\\ny5hKPyvm4okJIWW6scNkhTWGMSuMHsnrDcfzo8IsBGMdq/U18+VcRgyyQL1jhMaK9J4yFh8CL15e\\n0iwagsqMJfTHIryfQsRoUa0SgpEQvpR1hDHsNkKMofee2ol1mVZFUC1Jf6upG7bbDdpaQvBsrnuC\\nNaSL/5Ps3oH2HSpjkalRswuMgrgkiHEHWYrOqycnL1v2T+AwZBCT6ixJek4KHQM3F1/Qra9RWtxW\\n9AGrU6DhEZmbfh2Rui9Qvr6XvP61H0raoRBhMDuy4N3f04rS8gKtW3Y2ZlhGf4lTFZREXwh7CYj0\\nm1dUzZGwmPstbn5KGlZyP5WWz8xoQhwxKuFDh7UtIYmZe05eAmsuW3sJcNL31YK4HARUWSPC08gp\\nlX8XQqFtQAl6I+IogJkVFm4sATftg3GSBNpUS+LmqRRO9vzHP3h+QoUJ/Ol8Xg1yo/3ruPsbmpjC\\nBpVFo9OIVobQvSQpQw43KGO49zffegErmaqgYeiuiN01OQ24aka9fIRfPeHVN55qdgWqIulQFvxh\\nF+4QPvzxRfjm83wNn9wvTNsgQwNayDzKlHPQO5BmeoU8ZbHq8HVK6DVWfi8EUgjkPM1r2kLJr4gh\\nF3/AjFI1SlWAKeda+pzZk+NATNKcd80R1i1BNeRiCq1URk2C0VM+ONmj6WnOczrpfc/kdYRgyt4z\\nk31ETmJpJVl+YHXzjJsrxa9ejpwtT7GLGdo2VFaRlaJvjnj3eEZMI//H//avePDwHcYh0HU9lauE\\nBTcOvHzxnM12zc31NZv1hm3fY0pfaxgGZrMZP/v0Y5zVNI2YL8+UIkTPn/31r/jqq+/ohp71qwte\\nvbzier0h9gOuMhjb8Om77/Crb7/mi6fPuV4P2KR4fHrCO+en9JsNaMN3L1+ijCaRmVc1R4sl81nL\\nfNYwm81QzmEaRztvRZSBScheiDsheXwIZB8wWXM0W7CoZYZs8J5+7JmGuZwyVEZjFKAzMUR0zgx+\\nxMeILa2PmEQQoB+8rKWSBIUoM9J9CGxipBsHUJqYEv3YS+8xRKy1NM5xVNW8c3zKct5SWYvVGmMN\\ndSuzds7K4HgMnm6z3VVaSqvd6AxKYZ0TEpQSKciAsH1VUYshZJySyrgyDlvUK9umFeJYBjO3KNeC\\nDeS1CPj33QtIpsjhld5kzhAjKYfSW5Y+G8VU+jcj/Im8ms6RjIdxpHv6d5gyd2mqmowqTHxBlPLB\\nfOfdYzqFH+YTTD9z+HuvB9jDSjNNEw13Co4JnctZSDoaIUyl6InDNWq8xoUtflyJSYJRMjutEiSF\\ntYnQX0vfuJ7viEQpSV9UKt6ESr7IZMpok7FWWMvKkhEX+B1OVarIHRlod70lCaf4s+42GkNWlhQD\\nMRdPyxDItkJXFSlHjGul0hS5IUCjqxatNfXsGL95BkAanv3Wj37k/IQKE/g2xugh1bn0Bw7l7O5f\\ndApUADODsJULtS0peJaPP2bz7FfSf1P70d77eoI/fMjP2twR0KTibD+sn6PWC7b1CXXzilH1mPqU\\n1F+yb0PcuYby5+4DeisoZH+8qS+ZcyT4vmQ4SarFdBiw97nUmx6VqSrNOaGil5EMW2FsTdYiIpwK\\nm1IbRU5OrsEGVJYq/+5Dk7OQEJISzdOsHa6uxEQ1hx2RaOo7qPIn05+7gGh3mfsPN2dLFycO0ocF\\nrDX4fsPxyacwUyT/S168+lN+/2ef8eTpSx69c8Y49ow+Ulct7zUOTj8kjwPz5ZxhGOk2WxprOFq0\\nohJT17RtyxhSYVj29OOWjOHi8hWvrq7RzvLrL7/k5PiMj999h6tuzX/9X/1zqjGyur4mXF1jcHhX\\ncWN6FnWFOzO4yvDR+Xt8+c0zAhC8p9saFvM5q8tXdN01bdNgK8sffvq7nM4mv1DLcrEkxEhb19ys\\n16gk8nApCss5ZbUjvYQYZa4vAylRYZjZGpcNJBFqyCi64BljwFaOHIU8AwjzPIG1lKCV8WMUiNSI\\nYVZWIsQeY2bw4sd5td2ircVkSFqqhNF7LJqsYRM9eRRlGG0hTNCjUihlScETUsJaQ13VIlAQAsMw\\n3Hqecggopcs4TqauagIiduCMlfGVJPJ4s1kr4zWl2jRGiYZuPCWpFZ6fY+sleejory9wZiaVRhnM\\n33XMSkspRw9hRCpP+FEY9r7nsfRBwaBzJuUtDD1ZzwqPoogklJ6pVhSm+m2Rlh8SI3jz3jPtFm/4\\n7gF/QBWUYnou7+6vU3BKBIgFdRvWIiuYBbUQCyyDIZGSxqieGDTVbIk2LcFfYWxFxKGdQeVp9rvc\\nYWXBJBHJCD07Q9ckPU2jpWggvSlZ2JcTOUZR7IkJrauDvqYhB49yNdX8EXEiCikjhKAibmOtRUom\\nz9hvSv/Ssidi/fDx1hVmzjnPZrN/QyGA6JRvBZsf+E10VvuFmUa0VjLwWrUoLGgrAfOgWn27TGta\\nWAj1PvmCeQscGF58x/OngReXn3D9t99CXO0WyNRF2FdQHECQ+wc7pftnpe67zsNzmr62l6dLQpgp\\n/Y9b5pQl7UxTb1NJlXlYAefsC0SbSMWUNYWI7zfE7UtySOL3pqShICLIuvQnCp6v9jj91LNVyh4E\\n8kxMQnc39RLdnKLqJdo2aFsLaWrSW5yalcrInOcukJb7f6uhWe5Plvk7BeQoGXf0IyonRn9JTkds\\nm0+4GedE37M0iVYlnFagNElrHj06Yr294csvfs1nn/0OtVOcn55w/vAcV1W0dS0BabtF24rrmxXa\\ne95ZHvPe2Smns5bQbxmHkeVigSYw+J4P331E33csK8dxiiydZTMM/N1XX/PVy0uuVj3roefvX7zi\\n65fP8Vnz+7/3ByzmR4SoeHWz4exkyX/089/GaoV1lr/61a9ZdQNjSKAy1zdX5BwZg8enQCARtcCv\\nqqy/6D0pRqL3RbxACD0hJYYYRF5Pi1mzMVaM6XSZvyQLkSGncusnBxmZIY1ZTNu1VkySYRkIIe02\\ndW0NF5cvGeLIxeWK5+W/v//+Gd9fXnHZ9YwBbjpP7zNjCGzGLcZahtFL2yUqhlHQgxRFgMBai9VG\\nKumMkK+Lg0XWQgFrKsN81lA7SypVctPMMGictqQoykYmOvqYGGJmUP8l6vxnsPgd/PoanTweUClI\\nJTPZ+mWx+Aph2LscocqM8k87DqtECUC6KItU6JQQO/u9elDKvoyCHZKLDload3qVPzVRn47DRH/6\\nd/kLGukD34Vzc+n97ATp0ShlqObn2HYhfBVdg6khG+ra4IcRA4SuIwzXeC99cWVbjNbEMKCUSCYa\\n18geoR1hWMtnUiBWlTPaSFKVd+5RB/f5VrV8gFZFgWYxMrZCCqLmkzzV/CGBhIqx7FmaRBRsTzsy\\nuugfOSkKQicBnfTkbe7xT1otq9Xqf8mTe7mSeac3fbD7wFGYldPCjLHgzCPYVuCZ6qjIM02A9tsf\\nMp6Sygewo02A0vjYQ0x0V78mBkUe7lpc7c9xoqLfPV5bfG881C4YH35tR2RiP5KRS1C8u0CyqlC5\\n8K4nncuD15ogiynBVLoERut2X5dKtEgNlAA7SRNyR1VjIi7kLHAVZYyAYrartaFyM4F10WhtMKbC\\n6Fqa5EqLM4QutHL1ugpQkSgo8bJcU0qysSRP8BvwW8J2hU49OTti8xnXXc+Dxw9wWnG2mPHg5Ija\\nGWb1jBfPvuer779nu1mjMnTdGqUs3Qjvvv8xPibOzh7x8vIV3TCwWB5jteHTDz9iWdf8x599zGdn\\nR/zxhx/wi/ff42w+56RuqF3N1dBhVcVR03B+b/yd3gAAIABJREFUtODxcskvPnjMydECp2Dbb7le\\nbVhtOvp1ByjGMZBC4vR4gTPwu599CtHTOIsm46ylbirqusIYjXOOxWIBsJ/JNYbrsaOLwuSz1mIS\\nNHUj4gs5kLVI0zVNJcQ2lRlzEF1WpfBJfB5lZEXGiyZ6ptYGUxkqZ2icJaeEMRqrKUYJmc12pBs8\\nqQirt87SNg2ztqVuKpKBbT/yfHXNje8hSw+1aeekLAbPm82W4GX+MmZ5XWuMiO/niDYKV1fYqrDk\\nM6gsXqG6iDls/biDcvthy2wmP+t9JIZMz5qgpCel3b+lqj4lp0R3/TXWtKg4SHCNXpCVJNZeOXlU\\nHpnEzdNvBMXePQo7oSSdeXJjytOaL0zcMOwqL2B3fftH5e2D5Bv7mnIi+0ADTPpgWRVN53taaEJu\\nrVBaJOS01oR+RfIj2lXAiNWijNZ3HU3Tko2T4Bg91s1IRqOyxw9r6SVC8cQtFZ4XYo2pZoBoeyuk\\nr5slkovJtuLWOUqsAQlVpohCKHANOXiMrdBVS04K054SKcbXSsQKpjpXG/m9lKV/iTGMr75AesqW\\nnLN/m3v/UyBZIf5MxRiUG6M5BBfvZjhKIf6H08/kSI7g+1Vp9gK5qAGhYCII3blpd49bAewwO8kZ\\nVfQENT3Jb4EGg0AihxJ7uWRVh1j5ff2A+97zzczgN0DTU24yFZlI/3L/RYWrjwj9KyYT6cPwu6vA\\nDyo2BTJKo20xYSmdrXKeKYlkntxjtU9GlD6AINIO4JE+hFSQKfTCANS2vKciZ1GTURi0MiRksD0r\\njS5mx3u4qKyQPIHtAaOqHaVAxNjVTgRBG3lfQ+DX6Rc8XH2N8mvee3REXTm6TU/rNGEIPDqe8eDk\\njGdPv8M60YXUaPy45uLlBf3oufz+CcvFkkopHj08I4/H/O0XX9D7xPms5VHbcv3yig5pBzyczcnW\\ncHlxwc3NiorIg+Mj1jeXfPjuQ9ZRk7XnAYl173m12vD//fpv8D6wXMxBZba9p+16zo9mXN1sePjo\\nHKWMCPlkSerGYZSRiuBRUcyh/TDukqoYIj4pKm1orJWhemSgv9KWGHzZaBVjFp/NTBnXygqDwsdM\\nZSuyijijGVQgBmk3ZSWs8qauiTEw+iiVYVYlWYNsNLO6JZsglb1KaG3Zdj113VJXMjPprEVlxYv1\\nmnFa5yljnCJMay0ktBJrLmsdaMdms0VjiGNg3lTUrkIlmVUdvBfB7gTztqGpLCpr2qZm8JFuDIx9\\nBF9h62PS7B8RjSYOG0K3FlcgP5BiX0wXAuRQVGb2FZ5S/zAeRfKkilk3qZgbFGRlR+yaJPLecNyX\\nlL9Na+q1n9mJvuwD8H7U7PX3nKpLrTXGOLwfUVoTQ18cSFrpf3tPSleiDevmjElhtPS/3fKBVM5h\\ngOSFpezF0s1oS04jod9A1mjbEMO2JEm5BEKxNMwpotScnDYHXZ8iIVjaUWgjbSItsK/SYpSQlSNn\\nT3v2Mf36SnSftGEvxG5286AiyRnIUZxWyOXBeMvjJwVM4E8X87pfd6mRisyRCT+y8MpGbypIg1RG\\nGfJwhakXKCWMN6VAK0uKr0Og9y2ee4NZ0YGUuc1JtqoEj1JAamV2Gebd1/ihBbrLdu6c20/NCnfU\\nmNLzkXORexTHNTknjJE+opqy1AMElMOERJWNsiwiucdW4Ns8JTQlzmpTgucERov0FEq0LWU1FTba\\n1Hyn6GuWkJey9B9BYZQVF3MtzFyZuzIHecvUO5nCeN45wyhthTyAyIdlBWO/ZqY0vXJo5Rh5jyeX\\nf0s7a3nYLlAmMqscz1++5OSo4XRe8Tuf/Yzr6zXPXjzj5voFxijGYeC9x4/58snXnB4vqc7O+P7p\\nBe2spq4qbB7ZbHpuRo/Shi+fPef86JjgR9pseOfohKdX17zadFRW8duffogymkEZPv30Y4a/+xL7\\njmJ1dsKmH7nZ9MQUaeYzxr6naxUxjDRNy+dfPSH4kV989gkqwepmjULjqsL0K6IdxhiIiUXd4H2U\\nsRPAR8gmE3NkTLE87CKbh3yCVMYy5ExEEs2URULPKC2jJASUdUSbaazDK3F1IeUy/qCmEhPKZnt2\\nskTHRF035JwZwkDUimrWslpvyKqlKhWuH7yMGBXT56qtRJFKG/q+52i2wFpNyokhBPy2w2pL3w3U\\ntiqzl4ocE8ppZnXDuF4zqxpqY6isY911WGMYkmfdj4Rck8/+Bb37LdmIUxDugp50dAWhyUmqO532\\nFd8OTP3BXvvbH8IKj6JwQ9lkSv91Oo+7SNLbHBNrX9352r3zlEyXUyrIEojk7/rWz90uZuS5zjlL\\nb5EIiKclykAcSWNXktsW1AytjaAdaQTXSLlkIHghkuUgLFetIMeOMGxQ1qKy2wlGyF6UEeUvTQ6e\\nrB326AHjq9Xu/OVEp71OFJKyQsQWjEPockl0t+2cYXODczW+WIuhMsTAxL5NSsm6UA5iJwUBQLy6\\nrcTzA8dPDZgT8afJGETYu/S/yg/cDSC5ZNZoB6nfwbihv4KcScpgXEMOipQ6purEGLPrv9z+gLnz\\n+ne+ngOpVJwCTe5VNcTeSt96Tn6Mon1foP7pR6GVSxQqCzmznxMqD170qCwwhwQvvR+AVtPQxp3z\\nKP2qHCXQYgypULunpvoOLtZ7g1UZZ1C75xs1zblO9+5gY9nBIRJ4VZJgnQCN2VkqTYERVPHJjnIN\\nTO8hG8CELMg5eAwGxo7h+hn18gxtH2Ccx/qK71+84ni5YG41N+s1um5J3ZaPP3jEy2dfM5+fsKgc\\nyWjmy1NSigzdlvOjI0zKPHv6HWOK3HRrzmYLXm06bsYRQqR2hqubtSRqPEA7g/cR3Tji6NBnZ2AV\\n/XrLV9+/5Hf/8Pd4753HzGfXrLqe1brHKehDpBs6YuVYNHNiyvgQqZuWm82Wz7/5lvcfPRSIMnpi\\nKkxVJKj1Q0+M0u9TKqEtxBQIMUlVmTJDCNgkIga1tgzBUxtFbY04nwQZF9EONCJ11w8j2zRKwqU1\\n/VAUfDLi0ZkT1lmsVozRo9CcHS354NE5qQ/4mIhZHFdCH6myCAyshoHKG3IeaK1DK0XIEzgpfdQU\\nAm1d4awi5kQ/enwU0QGNyAHWlaGuKpk7tZq6wHxtJYpNjXNoY/CD+HvmbKXvufxnBPMZEDE5EWJE\\nZ6lqdo6xSpVZ4cKOfUtSx089BHtJJR6X5HMHsKg9NHv4TN0TQO+rLtUPfA8omrC773A4c3n3Z3e9\\n1oPvC6s1Qxn3gYi1CyGGha4gYBGt5yL7Z12pmAHXYl3DGHp06jF2Rhj6IjuaiEUC0NYLfOgxOYKK\\nQubSGmXmoBRxXBdjZ0XYvtxVlRJXoszrZ88UQI2ble9pZNyuxrlT1EwMun2QAk4rVXqj8vkIcxeB\\n5bVluPqWnEr/cufE9ePHT+ph5pxz0zT/t1bCTDLVQjQY71kAh4FlZ7GD3fW5FJCDx5J3FUrOSB+T\\nTPCTUO+t97/197tN8iljkp7FIQySYSfL9vbZ3l1Y+L4+w08jBe3l/1TORUhhsuCZglrpOxUavtAh\\n3vygTa+pUaQYilmuFihVVyjl2Bl8KyUBT0ptdrJ7B8FRKl65JmnE78UktLIYZVGmwbRn2OYI1N5+\\nN5f3mMgO+74K7C3N1C55FBjZFrq6JncbwtDjw4ZN55nVNZfbDV++eMWrl9eMY0/Xdfzs/cc8ePCA\\ns9Mzoh/ROXN6coL3A652dMMGXVtu1tcERpZtzaJpuSr+mJtuZLE8ZrFYcH58QtcPXN9sGLtAXTco\\nrUlG8evvv6cPidHUbAdP5SqWyznOVizmM06Pl7TGsGhr3n34gE0/YKLi26cXbNYbjmZtqXLgm+++\\npxuKChWi7qOyKkpNwoptqppZ02KNmIZrrSBmiInWGd5ZLjFOUVsZk7Faxj+yEhYsSjFEz5A9a9+x\\n8SPdEEhR0a16VtuOfvQEn9Ha4WyFKf3SytUs2ooPHp0xbHoW7QxnDP3Qi3OJgjEGgc5zJpaKMeYs\\nEnYx0NZ1gb0UtbMs5jN8imy2g/R5k8jujd6jcqQqurRjCGSlGIInkamMkb5vXUsCYaQv72OPro9Q\\n+g+QhB2Bjgp5LIeBTNiRfHIOpdKc5jFLWrfLEv/hDnlSCrkv7Z9x2e1uJ/5590zs+40/tIfcYhe/\\n8efK/qKE4Hd3tEvt3KBuo2u7nyuyKn7o5WW0EztBd4Kul2CbcnUBZYR7EmLAak0OkTCupGde9npt\\nLNrNhRmckySJTAIrrsznDqXVpCEFMdDIGnRb9kJXrksXv2MLuka5mcDcpiZpBVUlwjV6jqkXYOey\\ny4ZBxpSU3e1LylRlHE/QM5RLkMc33NTXjp9MEbu8vPzvyTK/FIZrJoP0+w5Vel8KVbBnC9mglFCx\\nsa00f0slokBgJyRwTNj79DoCL96fnd3qPZJRRFL2u817yr5+qEK8y1abXvu+P+V9bp/DFDzvPUem\\n+3QQ9JNoIIpn5h7OUcqgSAWaK/09EukgmN0O1PKw7O6xBmVkfrE+OqOan6FsIws2T9egD2aaCixX\\nFpGon5TXzQfXDiR0kTLrieNKGvpQHFIkk84HF6yUkoZ7UdJQpcqeqk2ZB9Sll7dFp4DNFb/u/5D/\\n/eknPE9n/OrJM757seLJsxV1teSD9x5TVTVKO05OTqnbluXJKadn5zhXYZ3DadA5MWsX2LrGqExV\\nVRwdLdE644eBtql59+yEeVMTjIKqJjpHu5xzfn7KyfEJWTu+fnbB568uuFivqduGBw/OUYg4+NHx\\nAp1h6RwqJvph4KrbkIylrWt+/2efcb5ccLo4YlbVVLbCFjKCUgrnHFppscSKxYlGIRZaZLwPNLZi\\nbg2LpmLeVGiTqJ1Fa/m5aXA3U2QnvaIb0m7ko+uEzKO1laInSW8vp0SKov6jcuLdk3M2fc9iNiPE\\ngHOG4+Ml1mrQMEy+t1mSYFMp0ImYE84aMQ2OufQrDSF6Bh+IheQmkJ30Wc9Oj3FG430glTGaPng2\\nXUfjKirneLVdkdA0VQspouMZg3+HztSgB3SCmEa5jpTQpiZHmevNwYuqWAmau2T1HzZO3nMk1M7f\\ndxIEqPixRP0uVPo6UnebsDP9J/3sXNouk870fReZQMUCt+7f8/b7SkWv3RxjW5licI181iXwkTPK\\nauK4xaoKpUCbGdadljy/zHEX8DL4HnIprnSFbmagMjn0ApuaBqXFDg2t0Laa7kjZ74vLk1uCm2Fs\\nUwwcZGTEzR5StQt0c4yKG7RxGOtIFEIiMn4nkqxRlH38Rl49e1A25JyHt/10fzqnGv5na2Ql7CSL\\nfrTAypAGlK5QBLKyRFUze/yZjC/Ux6QC92nMnQWjS1k9QQq3A+Rdks7he6opGNx6vbevMO9btLe+\\nd+e9D7PFKSjtvg+TbzYFoJHgsmP2HgZMjcKUl5/gCVHPSHdOfx/MpDluNJLljRt08vSrS0IUUoet\\nKsQuVpHKiIveiaHveyFyPRTW8V6UOkMJ3pmcRqZRFVUYaPtruL0xTYmO2vVPpyacEYp/CcraKLZX\\nF/TXXzAYw3D82zyt/gVr9ycknTmZHXN5c8V3T1+xWt+w2fSs12u0FtHn6EeaumE2n+OHgdmspq4b\\n+n4gxchysSSR+ODRY4ah52a1oa0dD86P6QdP38ssWuMcravo1xuGIXJ5s+bs6JSLp8/QKmOdZjFf\\nUFnHbDbjerWiC5F//Pu/R1M5rm7W5Oilx2gNy3bG0WxB48SfbxxHGbkg0409KSaqWjYKY0qXxBi0\\ntai6RllDVIZXY0RHgy+jEgJTykeksogfUO5z1/XEEKidY9Y2wnJNCWstrrKEGBjHQAwJowzLWcvR\\nfEEs85HZKDbDlrp2KC1qMMUNTBAjo2lrR1Si95qQMQIN6CwKLOMQ90PrKUIINEYzsxVWK0IuRtej\\nOK8ELwIHde1YLGYYY1ht13i/ZXC/RXf23+AX/wVGdahkJbcuBvLJiy2VRgQFyHvDemFh5v/wsfL2\\nit/9fbIzvLt/vM3o3OHPC+qgX68Os8Czd4XJp98rZ7Gv5vJ+vGznR6krrJuhbbWDanMcSWErfqIx\\niLB6iqRxW9R6enyCkEdU7MQtqm52lSaqVN26EoJOGsXyKxusm4OSXqRIc2rIYoCuckSZulSrEVXP\\nhbE/kwTWFM9RUy9I45asGoyrSaoGFCl24IeSUFA8PDPBj+K8sn3FpABFzv/jT/lkf2oPE+CvjVHb\\ncQxLYyoxmd0nLQcQwD0LpFQhrl6Q/Ibx1df4cUvlLLY+Kqoia4rxD4niGl76fmrXxE63gub0vrer\\nwgL9FsWavaXN6+d597ivX/pjrLW7ryf/nmDgQjpQGpULAalY/5CLaIOagtUUbDQ6FyunlDC6KnHt\\nMMgeHElef+w7rKmE/FFVJN9h4oaQMmMsFZ92pDS8voHsINS9VKBSpS+U5bOTGfW9/m9Ke8HkCfYp\\nQ3bS09yVmxIslZqUhYX0pEzxXUwJPW5wiyOqfEy63nI8v2S9ueGb+n3m8VMYvqBeLvh//+5znlxc\\n8cnDI47PZjT1jPXqhr4fub65QQEnx+cMo/QGQ/C0ixkvLi8YtgMhBFzbcr0dWF1c8eLmGp2g/yDx\\nj05/m9yvubm64uTomO+fvaALnkU7o523vHx1SVPXaANVbWlzQ58Cf/X5l5z8/GdcbDdc9QM//+g9\\njtuGftvRViL0bI14BAb//zP3Jr+ybml61281XxcRe+/T3j6byqwsF5UlFRgPSkbIhpKxB57wTzBl\\nxJQJ/wAwAYkpExAIbCOLMsJ0ZQQIqlRYLlels7uZN++593S7i/i61bwM3vVFxN5nn3PPbdJ4HW2d\\n3UR88TVrrbd73ueJbMctXdcVZ1AjA+eg7wemlIg5sl6vGPqpRG6ZGBOrqqaxnlEiV8Oo1egkauib\\nFkgMoxrSlARnhTFMpAzzHJnnjPcKvY9JJcfapubhySljGFk1LdthR9224C27aWA3z4xZEbfWaJ3I\\nW7t3+mpfY4zlantFXVWs2rUSC4REyiAkrFiaogNqRZCYiSFhxNBWNYKwWa8Yw0Q/jfTzhLMwZWEI\\nG/rTP0CoQBw2GbJJSk2XE0YikgpqUmLZayzGGiTb0u3JN56Gff0oziNwjMp/5VW39sjld6/bZ+4K\\nDt4GU7EHLCroQFtG8xE5i3VY3xJjLBFdwthO1T0yOKPEK85UiFF2JmMNKc1Kw1lVhDngmhVIxtqK\\nlHYYPM5rH+ZeSYmK+vQxob/AmFxUo3QOG18jccRUHTnOgNP2lXqD1kUtoP3J2EqdQ2vIOZKGgao5\\nIcYJkzLitXOgXm0I01iOl9Fiu2gW1ngkXf3tL7yBR+NLR5iljvmPrClaCGa/0994QDfrfiWyMGBM\\npV6KCGEetGn96gVp+5mG/e44NMmFgs3ozWNR3TSvfM6d54ogEvde1Nt4c7cn7PHEvMso3jbCS//T\\nAYGmwCjtSSr5dKSgREu8Jcfp1cVoaYoilTSYUGDYdwhPi0hpmUk4L2AF4zwpqleZsoJ89A6WdGCp\\nT+6fizWlvnPkqd6IEjNGtN6aRUoEXCxl6SXc36vSfyWFO9WU3i5TIk+tsSgMX/O8SRlJbMU87ZAu\\nEyvLMznjeqpZZ+HH/Xf5Py/+AJH3+I3vPCb7iU/OX/Cn//gX/LNfPKE7OWVzumG9PuH9D76Fr1ac\\n3HuEiOH+2T3qpqNutF/1/Pqaygt1Zbnc9vQz2KbFVYmrFy/wqwfEABtXUdU1TV3TNJ5nz1/yk598\\nypMnT0Eg5MTpWcu/8oPf4Lff/4DGNTx9ek7tPWfrFeuuw2Lodzty1CZ6bxzrtmO9WiMieOex3pEN\\nTCkUTVeDs46h75EYmfoelxIq7pawKJFBELMAZ8FYhnEmxEhda0N3NoZ+jgyTGi9NfWbmkAgplvnr\\nCCHiKPqTKbHqOj5/+jm7aWQ3zVxse7IIzoDkTOVVesskobEVa99y/vwlrW+wxrAdeqZ52pdZLIam\\nqql8Tds2GOfYjTMxU1JlmZOuK/25whwzuzlwNU30U2DkN0ko2EOXRyLHVGjuFsURwVYt2dRkpwZ8\\nmb8iR87or33cJlYva4TD3vG66PJNxvL2eJ2jf/z9vj1tyf7YQl8nsbBbHrFCLZy7Bd0uEhQ0mCbi\\nvNN7XaJAZUgzWjuez3FUWO8XMaXyeSrflSilGkmYeoNvVlouKo62QTR97jvd5dtTchgQybi6oa7v\\n6x5oGyxK5G9EcFVHCr0aewPWtXotOWOdauHaekWMWakxC7Anz5f6LPb1yy83vkqEyfn5+d/rVqd/\\nc4zGGOcKgwV7Y/FaTy4FnKuRNJaCfcDYGletSCKk1GOrU2R6yRKBSEYhxCnQdGumcVcm3AGufTtF\\nu0wcewtSfTy+Ctr1dRP6lci11OeW9gw12mqUrK+QdJjEb7Lhx0Kvgqa+F9i4oEiwo5PQ6DNDlkH5\\nHps1SE2Kc9m8jLaBaOytKfXjvtSjtNGh79MtF4axiSwO5x251FlJeX+uOlkLi1FJo8uh/fRw3vtw\\neukhhJwDFZmhf85qfh/qU5pqgzl17FzGBU+ohD+/fsjvrnf48Zr1vW8xmws++ewzHp6d8P1vfxtc\\nxfrsAe0w8Yuf/Zj7Z2e8fPmcSnLp9bOEGLkeJ+6dnvHhuw/orrY8u7zC2EdcXl4RDbz76BEpQu0c\\n796/x2XfM42Rpy9fUleOy6st55dbfvgvfY9vvfcOD88eMG17fue73+azywtW1ipqMGXunZ3qogYF\\nFOXM/ZNTjLNc7XqmfoevaypfqS5lmovsHHgsVaWo0WxgOwVtQ/EeZUpVoxtNLvJIjnme2Kyb8hSV\\ndSiGAGgL10LijlOx6pOqZpgm6rZimEfatGK17piMcHG9ZUr6rJxTQfglHY1kvDim6y3r1RpnDJKz\\nMgcZg7XQeIdJCgxyRll+QgjEDClGnBOatgUjylfbNlwPO3bDgEiNzyN5PbGP1HIqwLbinEomhUk5\\ndH2NF62pZlEacZOUQvDGOvqGkrNvylIdxmIw73ayj1/1dc/lFQN8hKLVCFMdXlvSuOq86lrMcdZ+\\nebMg5xM5zBjjcX6lWbGq2bc0ZWMxJkK04Cask72mpqDsVZp+TUhK+GoF1hPjSBh2WFPQzcZimo0C\\npFwHMoFkVqffYpxnshMcnRLlmApDxviWHCeyabRSPA84V5Ei+LYhb88J1QO980YAJfhPOZOna5w1\\nem529fzL3uevUsME+J+nsVc0aroFyb0j7bn/OUUOqEwDknG1Ippc1Siv6fBieXVJbCy1CGHsrzTq\\nzJkblHZ3fNY3Ob6McX31tUfnmAN5HsDkQ933NYc+TuroNyXVubTw3F4gIpRSDc44Ze2wnlQMpYhm\\nbWPSWqctUjg3ouW9J1zSvocyDPucsZTyROHp1PYY/VKPdYlUDQfN1GXTYKnccryJ1LXHe8807XAm\\nYuaAiSO7i6fYuCW/fEEK17TzU170Lf/Lj3+Tya95fv5zvIPa1azP7vOTj3/Jx7/4mKeff07ddjx8\\n/A5zDEzTrGjUlGnbmg/ffURM8Be/fMI0jJyuO9q65unLnt5CijPjbscYM23XgWSausJay6OHj/j0\\n82e07Yrf+u63WDUNwxT485/+jO0UOFmv+OD+PTbdirPVmtP1BiMagacY9xvbPE3stluyJOq6Uqez\\nRIBJ0LYL76m8Y1XVOMAKDPPMhDBlXRdLF8PioM1zYg5aQ3Te4RtT6PXg+vqanDPOGW3nSAHJM04E\\n31QkMs1qxW4eGUNg2+8IWUgplsqzxbqKaZyYppk5ZrbzQHDqvBlv8VVV0v4O7xwnzYqHJ/eoC+n2\\nbhyJotGFNXC6WYO11M6DCNf9FTk7muoMYwK5+0386q9pXVQo1I1lEhbVl9RfYo1+b1yFa9bY7hTq\\nDbZel3aqMs//uUSZN8dSo78TOGhuf/NNfN5Rear0oB4DBO86HxFlmFoceW3NczjfgVGnTdByjrZh\\nCIgFV5HTqBFgVSFGwVcx9OqcZKFqzrBVWxJnhsqWjghlp8VZj61XiAnkkPCbh0xhwNYVvjpFHNTN\\nGVXVYUppylYrqm6lz9M3ivGVjMSkxDUpgPPEMBHHa1JOyKyRq7btZbD1W7H7HI+vFGEC/7Rpqu0w\\npxPjGiTHsmm+mpM/HkbiAh05EjrOiATy0KNNsxMipT/RZLAtptDlLiAYBXSm27Kctwrhr07QLwTx\\nvGG8TZR6lwFf6qnqyJWqT5rJzmPwJW2ypEZL83N5n1CafBcEa1n2UiI5VVhfKPkO78tGvUuTEyYF\\nln5PQQnMsxiQRSmkRBtL7ZEjn3hpBTECos2/+izM3n4v7zfOKJ+jaDuEyuqU+ohRxKThoN7gnAMN\\nopjnuaCFa0wOXJ9/xvqD71PXKyVwqDLG1AxxZBZLa2r+9OI3+KH8Mc/mS7qu4ue/esKjh4+IYWIc\\nen7y059Qecs4T/zuD3/I5cUF63lmGnZ0bU0/PMFLwruGz19ckHLmp59/zst+y1/68H1O65kcLsBX\\nVHXFmCN148lz5ORkTZgHooFpp177ZrNmveqovbIo1VUFxdhoClyvO+dMCEXtx+rmEUsf2jhNhJwL\\naEb7Zp0vNeRklHN2tgxh5OXlDmdhHIOmyL32Lbu6JqXIbhhwTpGEMQrTqD25424Ek/Hecn+95v5m\\nw6rteHZxjvGFwWVOxMImlGOmbjytczjn2eWklHVxJsVM03iqxkHS3s4QAikJ3grW14U3urBNWYN1\\ntkTCwr2TNZKUqGEXIm23ImZHSFtMtqxXH3LV/S1SlfCppk9bKlMVZy4XwE8ipqR1ylLLTClgMfjm\\npAhrJ5jyXp3ksJd8M+OL8A3wRfvHq5mytz3uXcdbnKd97VIPtj+m3Tuyy754YBxb3uO9JSVKPG6J\\nMWNdIuUZaz3WattZyhN1dU9TokYJ/9M0a/nFQnXvQ/I8kyQqIMwIEQve43IiicE1Z4SknNJudY+U\\nA1WzIqSxlNMc09xDnqjqE2IA35SMWIxHihDXAAAgAElEQVTk1GPrFsMMk64FWwj8sxXSOGJcRZ4u\\nUCHxoOpO8/OP3vrmlvGVIsxSx/zfkAg54JwvFv7VnPyNvDpS9NJcEUQuTfpZit6jEhloTsAXGsyM\\nac72Bvlw7GUOmFcm2euM9u2eya+Slv0q44bBNgbEaLQdZ/WElnaRo9cu4WVp9d9HmwZN+ep1FlCR\\nWaS2SsghAnlS9g4p/JaUfj9JhdzgoCigfkjeLyqWflWzSACVzyycvZKUKabsgUuyQM/T2D11m16P\\nUa7Kgqh1rlKChfKcrVlo8Sx7qR+ZMXEizzO2anHdmsobVhV01QrE83n6CPfOt0g5kULkex99yM8/\\nt/T+lGGIrLp6D2z4+ONfkFLe9+aGOXDvZMM790+JOdDVFeu2Y7NakZPw7GrLz56/4Ofn51xOPVMa\\n2Ww6vLPUFk7Xa5qqoarqPXR96resG69EAI1KC0mJutu2VmCDCDHGfao9pUSIgRBmxnFCUqaSTFPq\\nQSHNZCMEyYySefLyJVMWduOMcY6qctS1GvR5mlSJIaZS6nOMU2LXD8Q8Y7yS3eeY9yxA/TyBWPpx\\nZLNZc7ntiXOiaRvW6xXTOEOJ7JT8ReF4xi7PTTfSECL9oFJeddOUhIRlHCd2Q09EmYumaSbGiDWu\\n9GsKp+sNXdORRRjnmTBH0tQQ7UO2zd8gVS0m1+ScqFKlvaulDIAxyl617CGSkDhic9C2pxxwVUPV\\nnkBzSi70i9/Uqn/7euOb33vjOHdEoV80XgkIjgFBexzJ4evmsZfedP1aIswYotL9SVZHXmZSmvCu\\nwnjtazQWnKvITkUbwqzIVMeAsQ2ue4AxHmpd74IoG5nVdhLrO3x9QnYOJ4KtzsgZfPeINPdF9MGr\\njFoK+PY+U/9Una4QMVF5gbVPV4ryUkldGO1LlzDo3hJnzKJfqvXLt76/x+OrpmQ5Pz//785O2wyW\\nGGMBhCzP6w3F61z6oqxnITxY2Dmcb/CuQYzF1ytVEsihtDLc1M28MQFuIWBvj+OJA69Oxl+H4Tyu\\nqZqySG/8Do0Q1VMOGjFLqTPuUbXFWO5zbhTATUm/clTL3auiiPa4Inv5QTWuCYMjp0iIA5hFCPoo\\nVbOkScUs2VeWdhC9hVbZgUQRy/vX71O7x9dcnJ39z5ZCFaQApaMWoVTQmqn0HpowM12/ZJ52xP4S\\nCSNDnNmNiXm3JTHgrFBlx2a1wnmPs44/+Ku/w4tPP2V9Kuy2W65ePoM5YFLk5bOnVM7i6waxjnv3\\n7vPw0QPefe8Rj87WrGuHCTMnbUucI8+utvzTT5/wTz75FZ9ebvn4yTM++ewZuylwdb1jiondOHBx\\neYWExHsPHtFvd4xhwhuLLeQDxhYggi1gi9JfO6dICAEn2qrjjC1tKBWNq8hJVVoqa2msSndhDwLQ\\n4zwzp0yIUZGMXtHkIcXSOK+p+apSujhnHHXT4CuHLeogYuDp9QUxJcIwMYeZmLVXTbIwzxFr0XQq\\nmtWpvG58yqeQkSh01QpnVfS5341A3oPaEjAlZerJoik9ax1NUZoYxhFvLW3bMkwjCKx8S+5+n1g/\\nxmXlj0pJo9SUorZC7Sco+5RjXij/kpKd2zSRs2Dcmro5xbf3wXZKkgEsvZlva5peKTG9tVG7aRyP\\n96FjB3/Zpw44gn8eo5R49j1vy56g6i4pjoUj2uJMo73h5bIlW4QaUiSHhSN5IOSIrTdUhXnM1Q2a\\nGfS4yuF9pa0/VklLCANYS86B+uQBOVxi6rVGsog6+CYTt89w/kzlB3MkLTVXDCYbTF46KhanPZOj\\ntlim6bLUbQ3af2l/9FXu1lc2mMD/mOIYNMrJ+KZ7q7SEyQnJKsUl1ir0t2rBVCXdKNRNq14xKg0W\\nd8/3G+xtoyfCLYIDbvx9/9ncnKzH5/TrHPu0yitR8PLZi+FQwgDRPEipBB5QrKBLe0EcLxWZJY1y\\nFJIC5qAcIoeFYEr91xuDyOKA3E7HLoxDvqQQVaU+p9Lis0S4R9doleBxX2teTsSWFLoBVQ+Q/YXs\\nt6ml3uWcwxVyA7FC2L3EE7UNZw7UORNMR05XpGkiTTUXuSPHyDRObLdbNm7iW+8rH+tpu4aYyVPC\\nRK0Zj+OIiKGuO4IIOWWcNTy4f0JbW9575wEnq5osM5t1h7Oep+db/uKnH/Ps8prdpAxBDx48IgPX\\nux3zNOEEuqbBe8+wG0kx09aOpvZ0TVuu2ajDIpR0emFDEnV2vLd4Z+inEUHw1qMiVCr+3PqKe92a\\nrnKaprYwhyLPJQlfeZyzNE1N3TiNCowaGbFoypaItZralZIPvxpHrvqes82Jtr94yxhmNUzWad9o\\nmBmmSdtJFsaYHMs8yoQ5QBRVXTIGiy8KJBU5CcMwEkKkqyucEawVfFUxp0BIEVd5fGWoXI2xll3z\\nO8j6+xhxmELBmYueq13ShyJqQKXML1HijpQHJI3klArDTyBOV8R5B6ai7k4x9UmJMBxL5f5t1vFd\\n/y9z+HXvOTaGtw3lmz6nvOktzuzN56AtXneDQXVfgoImZMEayH79F1Ula7UuKDMxT3u5DV93GGM1\\nYEkjqb/UPSepIEM2Ffgak2btJHeOnDxhuMLVq32JaokG65PHZBK+uw9VRSrEF9Z4pdaLI8bkJZ2F\\noIxVYIhxB96V+af7WNy9VJm3OCGhL/dXs3mk8d9765t7NL6ywRSRH1lrXxij9aU49RzQaK+8tvxf\\nfJeShjSAryuiXVGdvkeuGtX2S4LJWVllmq4ARQ4glOPJt9QGl3EceX7B+X/VS3/r8WZUraD+t6Yv\\nlcR51q+8cDsWDzQvPZu3FiHsJ4/+wu4NkRVYIKoa/JW+VFFvkByxViPGxYCzP6rZH1vyjAJ7QFM2\\nN6PJxdSqFxpKg3MhOyhZh4VUwRhfNiilLdMUOfvof9/+kxMmTcy7S1J22KohOkclgSxK2GzSFS/C\\nCd4pXd2nv/oll7sR72sq27E+dWzun+FXHbsID995h5OTE6ZhYLfb0jY1J5sTzs4ecHbvASfrDkg0\\nbcXDsw2WxOOH92icikPHnBEDYZ65On/J0Pds1ic0lbZKDMNAXTfMMdGu1tRtR7dSIwoQYySkRCik\\n54jgvcN6uwdmYAxV1ZCS5lw02yj0U2CKSq4e5pm69iVrHvfZh8o7mqbCWaOgnqai7hqs1bllEby1\\nOGdKv50aYm8du3HkfHdN47QVw3iH9zVCJuTEPGdihBAzqVC/GWOpfU0WGKYJYz1zH7HYAjyzxKCZ\\nk9o7TtqWVV1RW8PZek0oaf3aVYpavpoIIdHQMZ/8EKHGMpNB+zVZ5v4B7FM8S51fkhURGyI5jMjc\\nk8ctebiEaYtJM6Sgnd1NoVCzDW+zBb6y5pbfv8HUfpFB/KK2kC/ryN91XFko+eQQUNyF49ij5o3Z\\nS2mZYjitq0qt+LBnAxinGrl5OEdyJIcBUzUIDl+1VNairGGxaD54rKmQNCC+VVkwrxqbBkO1PlPh\\njSwKAAo7daDjjkQkxIRU3ZGEI/s5kdOIc4tjurCgJUiTfj9d769f97MakfB3v9QNLuPrRJiEEP6r\\n05NWtRStUt6J3P3gNTpSbTYjor13xpLmkWa9wTYnNPe+hatOlOXBWHyzoenewa/fL8byrkn4amE8\\nLxsQX37i/brHcVp2b3T2JAblHolq96mG34SSxmcWNYKlhYNyBGU2KcZVD1g+Q83ZwSAqc49IQGQi\\nTju0e+GQ/l0i+VxkwfYAH5PYi+5iCsXWIR2rnmZJBTtLjgeawJwDWWZSDsXwFkNZaqvLPdiXYEqE\\nPO/OsTYhaUS2L8h4qrreC1b/5OV90m7HPApX1xPXu55HD7/Nrn/B0yfPeHjvjPVmw/d+6wc0XUfX\\ndrz/3jucnWzYXV9jRWgr5VN9dP8eD++dggXvPB+c3efMGn7wwWMen2y4vNqymyaen19w0e/YdC1t\\nVVFVFSEnJRgPgctdz08//oRhSip5ZnV+ZmCOuvBr5+m8o3YGRGidoy51xVSUP3IWGuPJ80xMgTFF\\n5qRsO2OcESusu5amdqzqisY5PFAZQ+WrkprKykbUNLR1TeO1B9daVxwxYWEyjpIZ88w0z1SVJ0ja\\nq+QsWZJ5DqSUi2yT0A+DlhFzoqkqNl2Nc4a6Vjah9bqjW3VaW82qxvLowX3CHArHrvKX7vqeRCLl\\nyMT7eHsCZEV4q4etkUhSEeal/g4U2kVfzjMhYUeaLsnhipT6gswXENXhzWkmp6hk4tWqsFRpTHI7\\nRXp73d7OFB0D7d7ogL/hmMv/d+6ZX9Kpf/X1C4vXzb/fqGUKLNSKYFTrdt+XCTkERXAbdfyMrcgl\\nKzRd/ZKlz8Q0G+01NoBviYsoh4AQ8fWKGF7Q1i3eN7iqUVFtK6R5RLKek/cd8/UzZNyRo6bfZbzA\\n+EaxMnv1pMM1qc6w1eeKYOqG1L8kZ63BS54P1y8BjHv6pW7srTv6lccwDP/1PF4ng3oivl4tl3HD\\n+1pQnCKLcsVRnl5g2l1ADLh2g1+dInlGqhXG1czDC7JMSAGt3B53TSp7xEN7Z3/SV5yQv65xs+iv\\nyEZT0l0kBTWYkkowHFH+FUO4EDQsxMfLQpC8cOlqxL4IU2tqVmulkpOmMEwhV2BB51IMuKY8lltl\\n3PLzUodFIw6Ro/uaSzKxLDrRlLMpqGhAa1mmxrCIvKqB37sRxuHIjC9/gXct+BMMSm/lrSmRk+XJ\\n6nuQJ/COYdxRVY4QLI8fP+DRacsPvvcdVl3F6dkpbd1yfXXN5fkFw/WWuR+QWBqaswo911YZcO49\\nus87D85452zDSV3xr/72DzhpWnbzxNXYlzaJma5tePbigvfff59ZhJ88fcHm3gbvDM5Xmmp2jqqu\\n6FYr1uu1akCkQOc8lTf7jNk8q25szpmQIl2lNdq6bahrz5wTQwrqYpR7YMUog46xGDFK3F44ZQ1C\\nlMwwjoRZEaIpHZUyTEGslpLgPAu7YSJm4WLXU9U11lqsX0gqDNOkaFvrLOtNR1VZvHVIjpyedKxX\\nDdlk5qRqK2EOGIFVVYNTkXFXeaqq4mSzoa5rqkpBUt44QveIZGqkMHyxCDLnMvdhT5Kh59bQnT4C\\nVyG2pn74G3jXkMOotFSFYWYv7pxGmHtlfrEW63WfMYUi8o3GksN+8qYs1ito16Pfv+71b/O7W2fF\\nQhu6nOPx+zTdafbP7UZkXK7hQDayYBTcIU0rxfY5B67C1TX4Dlut8c2acPVE75tr8c4hYVYO1zgq\\nk1OK5PGKub+gcpZ5uKSuTunnRN2eYqoOjCDjObZakdNUCNlHSKNqsBKwRWnEmVyiXPY2RC+mOPKi\\nNVSKY5XGSySl0vN/HJgEwP6TL7i5rx1fy2AC/7tIGrGWFAbm8UqJcUs0c9tYGZPLpgySFOFkQNXl\\nmwbraiIV1rZlQnowCcKM7x7hxHCj0f7o2Lc/65gK73VG85uIPr+K0b0rLfJq/dUUZZAGYyo1Tqho\\ns35i1tQehw2QkuoUhJxjEUgV9SAXII6UOqiWzqAQYx9ODv2DUWDOTQmh5YXqsZtS+1zaX5aoUURb\\nfhYOSkOpPUlWAeQYSr1p3i9OiuHNRVlCChF5niemq6eksCPNExKVM3ba7RipoB+5jhONq3jxySfM\\nV1d8/7s/YLvrMSFC7hn7HReXl0xhZNtvsQY2qxWIaIr26oq67Yhiqb2CV3780x/z+IN3Wa1XnJ2d\\nYNLEu/fO6LzldLWi8h7rLDWGbr3md//l38dVEOeeFJbWEUtVVXhj8M5RNxVNU9NUltpZvAGbMrWv\\nSUBIicpqzFc7Tbtup5GLODDPM3NO9HNAsqWtGpxxrJqG+2cnBU9liQIpqUODWHLI5AxTyPRTwKDO\\nQYyBlDXj460n5aw9oAnOX14z7kacWCTkopsp+7p6CEFTuimDWCSrhFc/aX+mNbaAMIAkrJoW7wxz\\nCEjOeGPZtB0gjNO0NyitrXF2rQa/oLa1LeTG4tmn8JdJ7LtTuocfsXn/e5B7wniJQeeQZCWWP6Qe\\nyyFyJMcBclxKeCxgtbddu68DAb2KRL25er7o/Xd91qtHMq+87phre3ndslfs95jiYC+JIz1OLk63\\nEHORCSy16pi1kp6z1RrkvGUeX+Cr0wLczGQqbF0p0TqGlCN5ukRSoLIwDj3OWOY50GxO6YeniEDa\\nfo7YBkiqATzvSKHHtvcxdk0Mk+qMiiHsroowNa+UolKcSYU7FlMzX/5SOcmrFSlOh6yARJQO7/rf\\nfM2N/cLxtQymiMSmaf77das+jDKi6YIXeXXyqap7qT9IJM29PowpMA87kIxbPWD1zvewcVd6+Wr1\\nStcPkHoNckidvOacgJuT7dcVUX6d491psIux2ve0Fp9arCJLjfWlLaPFVR3Gd5oqOVroOmnyYg3L\\n0tIdYdkwco4lYkcn4B0GHCh8kWm/EJGExFA8/qgIuqRRkWCWmaxfojRaC6Lx+Jk5q6hQZ70u1NIP\\nKlkQCsJNlIrLeCUNd9ZgnaeqaiQlKic0c8/PzfdozAlTGJnDzCcf/5zzF5f82ZMdP9oZ/Bx5937D\\nk5/9jOfbZ3gq5jnQNg3jqECWqqpUjzInQk54a/lL3/ke8XLHvfUp7z58zEfvPObeuuX3fuM7/OYH\\n79HWNVXd4uoag5BiZNV2kKEq8lvq7GalnMuZynmMA58yK6324VAIv7eOddviDazqipNa22Ku55F+\\nnjkfdlz2PYjFG22t6LqWB2ennG021JVnGEe2w0DfD4SYSCErKF2sgrYEYtLadV3XWGexzuKcI6aI\\n8xqxXF71SDRFbB3ioIooS2vOAryJcyysPZFpDgyDEvK3TUNTebyBTdfgvaOfJtWDlaxMSbsd275n\\nFyZ204BkQx8DiUdkEwuZfJkbpZ62ANmOZupBQsu15GlH/8mfaSSCYHJUBGeaQUzRBFj2AiVoz1lb\\nu0QWkYH9QvpK6/n23nTje+6OBI+Pcdf3t193+3N1uP0pm2OHohC/Y522WR2VVTAFpYoB8Vhf4bwj\\nlXYsTIUxHisgaSCNV9TVCmcqUmmTor5XNBQGIGnhPShS2lhHnAawQhxHbFU6GlImPfuxBgNFuUdS\\nYtGtTP0FkpX03TlLDj3GHQMd0Tp2Kr3ey33ICkaTNJElksdzbCHEUJszg/Hbt36od4yvG2FyeXn5\\nX7S1iaDpwRTmUtqyRZz0kJLQTWQB5WiDd54DmUS9OkOwmBQU1u7XKs9TaqPp8lPa+x+wp2x7zfgi\\nJNq/KGnY22Px4Jc0a85JFUEI2IUAPUeECIUM3RiwvlL2i1KvsNbdPC6iPYH7usSxQ1Fqm3mpZejf\\nDqkafVbOlcWoBVP2pQ/RzdNCqbWGUvM8Pgeti2ZlbT948ftgVVQiTgRbVXTrjbafFI3MFCbyvCVN\\nAymNqqFnlTd1GC+5jvdovcNkwySGRx++y9iu+MMfPeA//IeBv/PzDf/NnxkeP3zEw/UjTu91NG1D\\njJHNZkM/zuwGhZ6fnWywxrLZbOg2G3ZhQrzF1hVNu+LDB/d4eO+MeycntE2LM5arYUtXez79i/+H\\nLif++l/5yzw8W5FSYpqV4q5qa7q6xmahdV75kb0Hq5uRc5acEpumY9W0OGOUBCBFxqzIVOs9IoaY\\nMtaCcwbnLXOOfP7yBeM047yn9ppCzVlRpU3rqbzF5MzZeoOzjhgTMWRSVOfVV77MuaiOjBgyiWme\\n9o5EnGasgLdOOawnrZtbY3Ben2ldVwpkQuvtjx7cY9O1VB4e3j+ldoY5ROZ5Vrq+LIxzYJwCxir5\\nvLWzEqezlHFuztmbhkQnUsZhCKThGitbddYwlBCXpX5p9pNYV8a+Z3nRnz02x29Itx7//0WZqteD\\n/l7/8+siz8Oetk/y3vj+QEBwPLTeb1yFcU51Lo3FknQepgRUutZSJIUJZNTnUXWAGkNnKlx9UhiA\\nPE17RnP2Ib5tSdMOkxfe4KwoW5R+zhpBwoBrGiXXSZHUP0NWZ8VYe1jKN5LJ4yXGK0MQIoS4K0GE\\n9otKmRNK26fO1CKPKLEnbT/T8pKvyDmU+5YPBlP4h699WG8xvrbBBP7w5flzq0g1sDLvH6p1XgEa\\n+8gmk8zxI9bFndLE3D8HiVTdGgmD8sdisb6FYhCGF0/2zcfwppTFYdP/IjTa1xnfRFr31RTPgfhc\\njGr7pTAiMagnHGZy3JHTgCEdyBiKAczpeFOg5J9sUUgR9cL2m5CmVPPCnCJSvO4l3XFAry7e855E\\nHUEZnNUL1d427bXMi0EsX/tvl9rqvhBRPqaggOcw0V9fYEwqi8/oAgw9c39F7HvG7QUSR5qieWci\\n/NJ8FwSudj19MITZcG4ekzjlv/0T+Pv/2HGxeohLM5VzWGuZJk3vuMoRUuT6esuzp88I84yzlvOX\\nL/FNx//9J39K3w/MYda55z3WejarlnXtaapK+Xkz7IbA9z/6gPWqUS1NMTjvERxpVhUFBHzb4ddr\\n5eO0bi8/JDFR+0oBT2RMOVcJwjhMimr1bk+HtxsGtv3ANM+IQZl+Cj1hXdX7OrggGGc5v76maT0W\\ninwTjONM3/d0q4a2bhmnEXGGKIlUNDFXq5a6rpVpyBQjU9KY3uvx6rqmriwnm477mw0PN2u8UXDY\\nqlsTk3A1TEQMYxGHT0nVSnQaZk43HRv7CS4vSOrSWnVLcOAoJkOdvgQmYzf3yWJfYQDbR1o5HjIq\\nJZuzx0XIl9sXbtc1j/9/03tuv868xV62v4obxz84Evo3Tcm+epwDneYi4CwkMoWQ3Hh8rSo3RjQ9\\nbWyrfK2SlQOg2ZAkkcNASgPYmlxVEDLh8jPyPGFEM0+CO8IiaEbCmYo490iaGM5/Rt09Rua+0Baq\\nJhVZez5TTsi40/LRfpMooELj1DhmLXeIrfR6yrOL87W2H4kQ563Sf+5r39pLirGff+GNfsP42gZT\\nRK7unZ39X9aV/HkRFQW0PUCU2kg9IIORqES8lBtqDd43SJiZ+i3imr3hMFVhSFkEpk3GpJvq5V/W\\nYP2LBvh5bQq51B4Xqmh74zKt0uvNA5ImjEhh0qGAXJdERUmVW1cckGVzYs9lvfziGKRzAAvoWOqK\\noCnG5Rzt4rxYi3EN3rUY7NJZevRVUmFHtzyLGmopqhnWK3jFe4816yK6CxiFClkSEgeauiJMW+ar\\npyCJcTrnWfyQWjJNDlxevcCn5/zmQ0ukwogwy5r/+H864T/7RwG8I4owI3z+7CkGIQSNclI2hGki\\nzdry0G93fPjBh2qsYmYIiavrLd57urYjhMBmvcYYS0SoKsv19grrPe264+nLF4xhpupqMGr8u7qm\\nW6+pqwacwdYtVVNpVNu2pJQU3BMi59fXhS4vUjcVXVfT1A5nHCkZhn5kj5oujzQW9GlMGj1ORXsT\\nDCkpAcD6tMWpljjOVapgUlh2QszMIVJZv2dKWohFctao1FmnotLGs+kslVtjpSPNM8P1zMvLS3bT\\nxHYasN7z5PPnPDu/4LofuNzt6KcZ5xzrVVfmrqFyjhCF/vrP8TmQyoQ/iJzfhPwJh4hLEEgWX59g\\n2tMb62tJjZuixqGvLwtFTClXHMBo5V2vLtQ7xqsYjTdHk19lz/niVpPFHS2OwytqKaCMahFrMjnP\\nGCps1QANxkGYrw9OrfNYVys1owHcCpvVoImvod7QrB4Qt58Tdr9CpiuyREKpXwpJ0fJS+mOtQ/KE\\nJRKmnqp5SBjOoSjjmAwOQ5578nQNYbe/pgXcuHcqXOkBjhFcpdcqGZzXevRyr2yDXWQTZUHhz2Br\\nJG3/nS/9EG7cyW9gvHz58j//zkcPlXLEqGTQ4UKl9BUeYNlh3u1vaA4K+fVOqPKW+PwngMNaj1hL\\nnIbCklLQcgcVZuDV1MjragPHHuHbGM3XpXO/aUP7plqr7hNlgXO4hsWgZRGt0cSBOPdKMF5SrwfP\\nOWvNMiqIxpjCHAXlNt6VDrorJXT7uu1inRFR6TBKI/4h/fsq8OvGSOngDBgK088+H3H02TrxnVF9\\nRVN3SBpxwxV2HLiYJi7sQ0WujsJ8dU0atFHZSMIz4Xzme99ymNwSp5mYMw/feUzKlH7URF3XbNYn\\nxBAYp4mmqjBRlS+mObPq1lR1S8iQfcO7H3ybrtvw8P4Dqm5Dt1lxdnZG07acnJxyenKiXro33Hv0\\ngPXJGoOicV2lqNmqq/FNg609Q470cSKFwGnTUa9anPN6fjmTooK4jNFGb1eyN8YZxnlmDIFYHJpp\\nmlWQGV/2Ee2/VF7Z6cB7S6JtG0Q0Ys3G4JxVZRenEWJOy5rOLA/MOUs2Aes66rWQ2OJ8TdsKMQu7\\nMTBOkZcXVwwhMoesdHzjyBQC8+WMqz2P753R1jXee0Kaqf0OH/9M06nGHKHd2Ue2WpJIxUCUJAqA\\nqzh593cQSa9kf8RQAHMlGyNlAeSlt/CAHXiTD/5VUrBves3t71+XEbt7DR2M5XGbGreM/z67k+IB\\nqBcjmMgiBN+uH6t6TY6kMJDDSE7afpY9VKcfsHr4Xbxv6F/+mDxckaYLdTpERcLjcI5M24JNUUcr\\nh54w7gjTQGU8cXpRsuUGQybGuQD6tvos3E168+PrMtnsFVWwVmueBgWILYpMRnstnV/6Msu9yDPk\\n/He+8OF8wfhGDCbw9z75xc8MRqOCmw/X7MNrEXsI1Zc/64UwXT5l3r7AdQ+R1Jeag9bP8hKpGlh0\\nGO+Cet82lm+qY2pO+/XG722L7t/EeJ1nqsZR/balF1P/BqokUGOcV+JzsrJh5MSig5dzVvFpg0bn\\nUmLIW5vC8X26jbRT+L65hZZl7xTpUGmphWh87+Peod25jziXTc4cOIasLWAZRkCBHnouutmFbFTx\\nY4r4s/eY8cwpMs0eaTdU1mLZEcaeq9ljc2Q2BpMNs6n51bBmHq7pVh11XRcigYita1zl2Q07duNA\\nEiHkRD8MVI3j488+YbVWZhJnDDkq2OXs0Tu0m1NW6w3jnDjZ3GOaAptuzbOnT6mcp/EVta/B1oUi\\nTB0D6yyV9zRtq+w2ux2hsOso6/fm5cwAACAASURBVFGhyrOe2lcYTOH21PtWWUfbNlTel30/U3ml\\nm7NWUbmpKNNopAje1cRJSEHYXm9xztFUHpOFfjcQUyE4MGCsxbkKycoFnLIgeK1VVpbObWjwDFcj\\nJMPGranchmH6EJFTjHjCnLjqB+YQgcQkj8nVX8HX7/MiXxO2M+M0sWpqQpgRa0gh48c/popPUe5i\\ng/EOp8qKpexzK2orOWoxUJ/9BmLifmkffEJ7NOmO2IL2c9jwVbfDL6pJ3mUI32R437gf3JlyXV6j\\nDtBhjR0HESU1X4IYJKgOZemXHIeLArLULxFVssmSWJ18xDycs3vxU8arX5CnS+1xtCrLZm0mDRdQ\\nHPcFZJinHXm8UjUTSZpyTQU0KIl52iJxROIIxuledvwM5DgdX5C+y76Qk+5jgLBQioK2wRWgWIl0\\nVS81g3Ef33nDv8T4qmolN4aI/OL+/ft/vpPTH8b+BdXJO4TrzzHGo20GS9wQEdtqGnGZqCKKYjOO\\nmCZWm3uE/gXGJpCIGAc2Qbb4dk3aXewNB9wRlb0merwT8n38PXdP1Lf52x3340u/53Wfc/N6FsNp\\nMNaRctINPC+1GMGIpq6VPcZgS68dJbo3xpV0Viq8tDdOHK3MH3rSDudTFmupOSmLSEkDCxgjhQWG\\nvTu0pJEPl2SOf9C6y/6FGv2KmIKWLSQLGJrKE+aJmGZMnRFr8X5Dc+8B/fUlIQV617BqO5q6ZYoT\\nnb/iRTrFhgpnLFUO/Okn7/Jvf3/SRumUAG3fSPvarGU3TsSc6JqWmCJ5jjx88AAQ5mFkHAZCSoz9\\nDl+3PHj0kKvrK3IWPv7VpxgDbdPSX1xxdk8jzDCXurC1WG8IRWXDORingXnW3kbJWXs2qwojUEvF\\nycpwvt1qy02puVkM2WRC1NRmiEqcEHIudSerdWl0s7DJMAwzTduQjbAgKodhYr1ulVLPNMxzr8TW\\npQE95VmJ0q1gTUSSElanBHUVqewJq5VlTL/NefMRxt5HTEuWnlV8Rrj+E5h/QfYg1Q/Jp38VW50R\\nw5ZO/pCcfoWrOrwzdG3DZT8yDoFTtpyt/phz+TfIpsW5tiBdlx7Im+tD06xgxZLqBt8+IE/DUbZi\\n6SNVlihZBO2tzmdlLvr6a/WutpCv42gfZ5qWY+0/c794lh8Luw2FwrL87bi97jZyVpH4AAnJSzBz\\nHKlqbX7aPoHxpdooMs7X2umbBwX2uFbrw67CuUrXeJrIoSdL1nueZpJBSTMwiKQCFoyaqbIL+Of4\\nspZrPMq4QUG+ln3ROuLuOdYqY5AYtMa6F7TIIBOYBsm7f/crP4wyvhGDCXB5efmffPDR/f/oyeBd\\n2D7XyGFR1T5KgTjnEVeR5y1LOkHSoEjP7Bmf/wiae+RhBDHkNOKbU0K6QKYBcRVEhY3nLK9EPm9j\\nAL8uUOf/j3HbcC5MPCmjQtKmCEPvdSx1Aew3Ez0KirI1YDSlUdACJV3uynYMC2XfAXG4nMjR/T1K\\nkeVslFDL5oPnt6zPW7Xm0jGiKLkiaG1EVBh4f50Jm0GsZS0vOM8NRiCNlzBvuR7Pac8+UORff8WP\\nnn6bd9+75OT6nLN3H/P771zz9392n1EC0RlEPFJN/OiXW07NuQIgnNN0kjGMO+0Vm+ZA23bEMJME\\nLs8vqOuGj6eZs7aj9o4UZmzTMu16LqxnDpkYR5yz7IaeTz//Fc46Qohs+57L7TNWq5azdYv3gFXB\\nginMDNNAP8zEbFRSyygYZppmQo4MkxKiZ1Qr03mHMZa26xhmrRNmjEaAGaq6Is5LhKW9ks47uk2N\\nsRmTFiYnQUS9Ha15qpCzdzBNgTkmrKmJWbRdzHh89z1mOWW2Jwyygqpj59ek6pFC/M2ClLzPdbvB\\n+w/B/A+QWuT+v6bOMoapXlOt/za7l/+AsP05D04qNqdrvHFc+Z7KtTD8CJFMtf4DolmRWSOy27e0\\n7J3wzJEh1Z7f7uFvsfv0TxDcoW0CizFOm+1LUCpZdENf5uVXwETctU4Pe9DXN5ZSarw3DG9Zs9ww\\nL8vf7f5Xd+2Fh1jB3Phea7i3I+yMzTOxf4bB4rzqTOYUyQRs9hhbk2Kv0a1rdB/KE7FgEzRNqmhc\\nayzZFmGNMJLTvG8rUWKWRRGpoOz3j0J3MIO6A0aklOyU0Wd/B2wFeS6BGlp3FVFiE9v8gy/7DO4a\\n31RKFhH5Lz9/8kvwm32jsEZEN9Ozad5hqkLaK0U+KgVyjBgScXtF051AtSnzwpKmHt9syiTJxRs5\\nnpw365OvK7Tf5aUdpy2+jDf4ptd+E+jZ489ZgB03PuPoC9C+NVFcqcHsBZ4NmpLV92iKzhpN+akn\\nWvpmc6kHuBrnV1i3Vifm2KPdI/OWryPIvwXxvvSLqveu92Ch5zKHKNguLfAoQjYvXu8BrSu5UJnl\\nyEUfEFdhXIV1jT7/EJBpoOlOmZ3jaor80fMf8kTe5eL8KQ/ql/yN7/+/ei7ZImamipYnwxq3uc9m\\n1VLZRJxVO9FZxzRPdG1NnCdSSiV9bUlZeHZ5xfluh4jypGLg5fPPuD5/zuX5C8Zh5NHjx5yePiAk\\nYYiBq+2Ol+eXfPLkM558/pTdOIKr6IeB6+01F9db+jEwp6xGUQIxTYxxYkqJMWXGpMomqSAW56DE\\nANOk9cqAYYyRpm6oS3QY5oRk8L7SuZMS1unzygUl7T1sTmoenJ1Q1w6sMOeZpqlx3qrKynQC5rsY\\nWTMPp1zP3yGe/mXy6e9hTr+PWX1ENI9IC6F1WffJiDKmmY58/98iPv6biDnDiNNtTwyx6age/TUG\\n96+zHTy7yx7fOM7ahkf3Wtb1GfftZ7j0Y7JNWF/Wpz3wGMGy6WesJJLVjV/as4MEmGjUtF+TWVg4\\nTiXHPUPV2yJd3zRul4iWtrqvsxfYfRRYLkcoiHR3iLKOzv11GbfD+xf3+fB7FVW484r2GVFjnN47\\nSjuRrXU1xxGLaNuKKRJrWdOu+3suovgcEXzVltSvYEuJQnKhuyt++c1nsJTynKZizdJqZLR0l4bi\\nqBW2It8A6gRLjojE5cL/6Evc9teOb9JgPttsNn9kbNhHKccP7/jBpd0zbRfB6cQWwaA9fNkIsb/E\\n+pUiJY0ucCX2BXDlRhwbptvmgxsG9K5FYMwBUHD8ui9rNI9ffzc45uuNN6Lsjn4s1OosotIUgEMm\\nk8rmoLI4mh7UDUVKbUz1SXOKpDQS46iRqDFo83J15NEePnz5h1k8V1NknkqLidF6nCnKGHvEol6Y\\n1kbNvvX4cFwpLEYlWo5SqRGF0sCulHNjr4Kw1hhSnLlONf/H1e9wER4yX/c8lIm//u7P+d3NPyGl\\nDkvif336Hqs0cXr/FGca1quG2hu6pqZtatpCB9c0HRI0FH6529HPoUh6TbSbDeMYwFiury65PH/B\\nNM8MY+Thw3f56KPvM0fY7gZOVis+ev9djLWEnHlxcUmIKvo8TIFhVsadiDCExCxGEbtIIcZXogdf\\notKmauiHsZC6azO+ZCVOqL3DmRJtivZyWmfJRvl9kwiucmxOOk5O1nTrGmMSm1VHV7fMceJiu6Vq\\naqqmoju95sOHF0hqaExPoicFhzJOVYhU2tQ+T6R5Isewf3aSPclkoMOJsMjJKeuTaGTrH2Hu/x5X\\n+T5eanbDSN2sqCtLyBNtndj4gOBJzpVMilVyFOsRo/2DYhRJHUMk9Rek7QuMK3PqaGppvTMqmCUX\\njlRuUt19XUf3eK/7qsc7NoD51b9qScb5/d9uBwJvwm182XMqV4LIkfExkCVoJskaLZsB5Ki84iXQ\\nwew3Bv3ZqhEjR5JzpEXPssj+3bV1Ziy2kLMo5a1GrTH25P6qZNYMYr0aXldh2vsq8ME+ukTy8B+8\\n9UW/YXxjBhPg4uLiP/3ue83o6g2C2z8gLXsdPVADKezwzeooBZcLZ6ow7p5rCqBZ6U10VtnwnfYL\\n3Tztsmnbm6mIuzzGtzWGxyCi173nNuL28N6v56W+7rPuQtMd/26hL1vkm8yiOVnqnTivXxZgadNZ\\n7qNyATvf4osGnbZ7BOXxlbnEgw2axbfFMB/fI3uAuwraXCK6eeMceO3X1HSJUzq1pF74QiqvXjk4\\nX2GtJS2m1FWYHLE5FXm4hDGJPF8zvvgUayx+tQFnSHj+5PoHbJPDCPzuu/CL3YZvNz8mYXBi+Iv+\\nHaYA9+6f4atKk1HOUTk1OFag71XtXbIwhZntNPDyesvL6y3Xu54UA2O/Y9peaV3DODanp6R54uWL\\nZ0zzQCLTzyN1rQbv8uqaOc4M48gcA96rUzOlyJwCU0qkbEo9E2KIxEIugIWmrbHO4KuaMcyEGLR3\\n02rvoRpZoXMV91YNTe1xnVeh6arjQbOmiRYfLHGeYRCmrWEcBzKZk9U9smSGNIAX4iR8/vQSJy8Y\\neETlN/i6RkzE5ozEgWn3jLi7JqVcskag3lHay7mZfd6jRHvZgwmInRCfOXvwAFfPuMoT6RkGIAsx\\nZFbyz2jzMzWQTteW8xW+afFdjVs3+K7DdSu8EdzqHtXZt3BuBdjjCoLu31n5k5V68SbN5jeBgn+d\\nLbrLUL3OsB3/b0g3/m4WI5UjS+x5OyD5pox/2Zlv1hRLGc1ZV7JYpUpTskKUMo0CdI5DGIsYiGlU\\nHcswHqL7Bb28/4xCqGIqJdffp9E0MjYYTJqKDJnDmqJgZGt9rmG7b1dDJpDw73+tG3E0vlGDCfzd\\nX3z8cytVDRwVm8VgTM2SOtWfLWm+wjdrsCWCSBNGMo1vWJ+eYmzhKM2ZPPcqjGyU6/PGZBCBYqCP\\nx3Fa4k2TZ5lwSz30dejb2+OL0HG/jvH6z9SUm8ii5rBMbi3caz+mxdlaJ1YBhWjtkv1rlYDZFw26\\nCocCPRRUoB7hDScEWFiHchZSmrUeKahXag24GuNrZRYBCkpo3wgvUkSvS1pWBYyV51TT5ipanAud\\nGaknTQGHMF09wZgaYxxOMtkYnuUTfhy+S06JZxe/5Lc2Oy6uPFEi32qfMV0+pUZoW0XLitWe0lXb\\nqfpITIwhMkWNijarDhFhCBMBOL/acd2PXF1vEWPp+5HKVhAjV1cvef7iWVHigDlEYsxsNh3rdcc8\\njUonFxNVVdG1FatVp+TpS/uIWPowM6f/j7g3+7Utu877frNZ3e5Oc+855zas5lYVxSpTEk1LomzZ\\nMhIEUBrrIQjgPAR+C5C3IMhL/gA/OQjykBcDiRLkIUAQJLCRBIiRWI4SK05kKxIlSqJIFqvIam5/\\n2t2tbnZ5mHPtvc9tireoKmaCl3XvOXuvPfdac84xxje+8Q2HsZ6mMzgfr9XWLat2jQd6Y2LeUyp8\\niGIMUgRu397nja/c5WT/gIO8ZFYWCNHjdY4Yv8WqeIe6+AZre0hvPcJqbmQjXh9lHEyijrOyGc44\\nOi9w8pjD8ZoT/QfI7gnB5vT1Jc3lY8z8HB88KssiEhRslKQb1tcgiHFtKKRXSJ8hQk6FQecVe9MR\\n46KiKCXOuCSpd8qb+R+igkdQRdUqGSMKZEbILZSBYtKyv+cZTw0inGNNF41XQkY22I8Y1vmw5p8f\\nn8d4Po8ysTlLXuU8ePlnDLna64Yw5uRilPbs2EXJXnT2bdNP2zPvJ8/xWRMhEsy/lROMSF0YvtAw\\nmTgPKTdehBSxSYAa9KuT4H2CSRisr0Ci8hKRFZHgw3CaBYTUsUE4Gpkc9JCesVA5XkiCrSGY+Cf2\\n4/m9n/AlX3l8YaQfgBBCvbe39z81dvm3w9CAFBjEvKPYeoJYBz3Kbkk+uUm3Ok81fA5j1rRNg3V+\\nS1gREu/ayPqUGlyfPlUQ5dqGRrIvXoAvS+g/69E9C6k85/H9hAX2ReYun/UaX+1z03x9TJ4LJTdl\\nJoSAJ+qCQiQ8SBLjbJM/gIEVMdSsBTRlXtH1K4alO7DvNplJb5KfGKMcpSXBDd0lBKDw0qVnJQnB\\nYl2XIt3I2hxAX5Gk0cLQfcUTvWrABxk3qkhtfYQjCEEuajICDUCQ/HB9i3fKc7L2KW/dPOXH6xmN\\nU5im5p+b1zi4avk51SJETl4K6vkq9qZ0ni5Y2taSZ1kkJhBF0ZVUZNUI19SoZPyfXi04PNhDacVi\\nMWfdrOlMj1CSo5MTLp6e0XUdJ7eOaU0LVCyXS1wQDM0IcgUUBb3zLOuWddNRty1N1+FTCYpNOTGl\\nBGVVUvcNNvgIuRLojEmlKDF/bJxl7yBn/nBJr27Tq3fp3DFyOsXLCTIE7HjBqr3isv4B4+6SkWrQ\\nQiGbGtw+Ro/IR29hb75Hs3qEd9/FB+jqx4i2xXUNwjmyUoBzeBlheKREer9xxIbIc7uvLIRYzhAI\\n5PvH1BcZt/Qp697jnGEyGeODoygmTPUVU/c9FuIeeEGwHgpJUB8zEadkPZjsGCOPefv4D/jB93/E\\nUmYM4eVmTSO2BLkkzflqe+rzjZ/mrHj+3Hq2tvL5M+pFKNfLzrkN8VKohOi8/Lv/JPKTEDGPHNEi\\ni98QBNmiVvGBD98kIgwyIzovQ/lLQh7ZOjWSjKBUdLKd3cxzczYkeFjJmGKQIsG5MgOVg2mSQ+AJ\\nvo1wrFv9k5d+mc85vlCDCbBYLH7rzp3pb541h5VZPt0+dN/Gc/pZCrcAszoDSgId3veoMMX2HXk1\\npltfAuCDi4ems1EP0Vu25SWCEPq4Gdg+8J/kIb6MUfuTfv6zYtn+9Ky9Yf5+o70co88khE6EbgkQ\\nhGNgzMaFnMSs0wKOMpuCzjS4oJDJWA3MvQi5J1jFOqSKyXnvooA2PmC7GpUXcbGjkCpHIKIIwbAN\\nlCYEiRLRkA+HXCD2xPOOqEIiBd4P1l2B9XSXHzKd/DxOSVyQyDCi0HPu3Jnx9MEZ3nl+872W//b7\\nCz7ubuHFjH/643PevLGiUg2+j3Bsa3vqusWa1MA5+NihwVgkUVR9fnHB7YMDVAYjqVis14xGFXVT\\n885rX+NsfolUoLLoiEymE548nrNaLxnYe1rnVHnJ5dUV470pwbY4H5tTRwWd2FZJaYWUgrbvCMYy\\nG48py4JVs6IzJm6rEDuuSCR5ltMbw8ViCW6F0KDH3+Aq/BI2m8XcYQgoDMiAkxMYj5DVLdpgqW2D\\n9wpd1KALtJoh/Bgf5vT6LVpxm6apkW6Nsd2m5Vy3umQkfdSAVVlUXhEChE7SfyFpQsPGAIhNYRn3\\n52/w+uiUTraI3mOVR6pYWnP24ApzbLkd/pxLcQxCEhxoU3MwmnP+CVy4WwQVKI6e8vjhlCeXS9B7\\nYNsIDQtBbATgU8S2Axu+JLXy/H568e9fBbl61XE9Ktw19oLdUpDPigyl3HXy01t3+AFShGv9RF88\\nh3hWvNgQ7yguhY4BmdolEm1eK+LPpQAhM4IIEV2UW8KSELGsZ3CshCoQ+YhIzHLb2rQQkKrE2TY2\\nbQgxYeNRqKwkliU2aATWDV2aDAT/777yA3iF8YUbTOB3zs7Ogs0gIBNOHf8+mt2jufqQ7YEOQ6Gq\\nzgXOEDVTKwGuI6/u0vBxhBMDMfSWChEcXuioXzh4KClEeVlE+FKviy2s8jKG7bO/20RXX6Lh/Kxo\\n+FU+V0AyiBDZkbsRc8C7Yf4+1mBJCcLj8ciwhWFCElcXRNjNORFLWqTYtk0KILVGyHggS3a90AiK\\neNshyNFZTm9iJ4JhXcS+mrHf6eAdbr5jgIDb5G1canY7CC573eBOH/K4OOK9uzP+8q1zHncHvLXf\\n8sbd28wyy9npBc3VA7w/IeDJuhUPGPPff8fwt95YMH/6KePZhK4zeBuPlyLLWa6XTGdTgov3SSqY\\njsbUfY9WsWlxS2C1WqG05unpE7qmpRxVnNw4Yrlccnk1R8ocLXOW8wWHt49ZLB/RmjUmwKPzS8ZF\\nTtv3ZHlO3zVYb5BKUlYZnbcokSWpMsdqPY/1bAis9RgbyUdSCZTMCSGwXDeMRwWov8Fc/gJBKRR2\\nQ7aKBeFxjfik/wsZQpUUDrzcj3nd4LFqDbXB2BoherLQ4l2szfTEKC24muXZInK0BGglCDKj3LuJ\\nLme4LEel2s9nl65A4MUxc/82cvWUXgQyBKu6RiI4v2goDjS3bnim9ZKlOUZkCm8OODvNEbpF5xK/\\n7lh9+BQrejI1Jdu7Q3PxSSxVCh7o8X5och8Sp+L6XtvdX7sG4rMM5nP77hUc9etjWPPDPweDlD53\\n2MgbiHZ3Xs/OKWz20K5DIkQWSUK2xnkbHafPzMa9vFQvzk8gxdaAR4LhdgyEnyFYii0dE/yaLi19\\n2GgiI0PUh1UVsphAluHrLhlTR0Ci8wJjG/A9QWSbzwkyksBCCEjvY9cU10LoECIjEP5C2rGffWe+\\ngBFCcFqr/1q6SNIJIrI2BQHTz5FaPf+cRcD2NTrfi//ulggR6KxF6ypGIZuu6AAismY3Hs91I/nM\\nfK4ZvpcxZl/0/hd5c6+a3/wix7M5id2fv/Q9acOp5KEJQEhJliK9zbVc2EmQQ5S6Sn9ny3YNQtJb\\nQzWZkArzEELHaF9mMdesNEFpvFS4EOcwlMMIANfSr84R3jAIuA8F12KTx7TPfedtsj9eaWh2HRAo\\nOcVoT3n1CQ+u4OHC8m++/ZR7NzwfPjnl6OiY0WREb3qO1AO8cbhuDk3NR6ea3/+4RwnJqCgIDjJd\\noKRK+qyBLjkHWVlRty3res1kOsK2Bh8EqshZrldIBKvFkthxwXL59BTX9ygZO4ZcXF0y2Z+wXq4o\\nyxEX8zkPnj7hdL7g09Nzns6XLLom6tTmGV46jOtouxbbG3Kl8CisBykkmUpEByEoi4Iqr1B+RaGh\\nGu3TZf8S58XXcDI5rUEwiE4M8YAEdPDoYFEYVAg43eGFJUiLUQ7pJbFkpMHZCZYMG1p8b+PhlXLa\\nMUce3V9nHXRzVo8+pL16AnbHILxgCODcv8MV7yKafWTvmFZjqlHFm+8d44Li/sf3mWgZ1wgSUWVQ\\n7OHGJ2Tjr1C9vsf0eEazWmPKnKOvf4PjX/oNDr/61yBFSwIfOyCFJBP5DNz58j39/Os+a7xa1Aob\\nYwmAiRDx5trRyEQwc1fdLD6P+LLtPEJIPQy8SPszkSBVgZRZbKi9SYVFQ/Y852OY78vzr2GAQIGB\\nDZ+O5BRVRvKfkCLB8yoiW8M5OrBnlY6EHpVHUqBQiCxHZmVMzZG0pgGkwNpVPCPIECGdSUh0PkbK\\nMkKyrt3O03cEkdvg+//lJzyEzzW+cIMJUNf1f15lpgtiF8fWoAtkdbIJx7cjkYC6BYjIYvPrU9z6\\nDH34WqpnkkkIQcSaPO8RIt8m819pvPrXfRksO0SXzwom/P81Pnvz+m2LmxSFm75n14EQUm7yyoPP\\nGn87qG+IKKAsFEp46uVl3G6bKFRsCMrR2RQkDObaHL2PZA2ZpST+4PhsaIwifWoqNXlhBB+Vi1Sq\\nL4wztSgxpm7OWHz6Z3x6X/P9Jz139JKv/9wtpFQ0bUvXtnzz8CmTwmBcrPdtguAPLt7idy/u8cNF\\nyWXdIb1FF4LeWixwuVywblouF3OavmdZt8zXS5SMEcpUa7RSLBcLur5jPJkg8DhjcKZnkhXMioqR\\n0qyvlhwe3EQrRVmWzPYPUKkemSCwzmGtpSqyqH6TZeRSMS5H1OuG+aqmM7HDRN8bfNLitdYhpGPV\\n36Upfo1l9XdYFj+HNnuosC3aEZu7vLN+oqgiUQ1YElxsQyZ7A3WDaVd4Y/FOE/wCCakjSrr918Kj\\n4cEGQtCoEOjmTzHrC2JrOtht17V96g7pHafyW5zpA/Jyn67v6doe0/WUbeDem3dj8/GwJoQeoR1e\\nO3IhCawRkxqx/phgJPu3vkVTHpG/ccLsF94iKyrGJ28CGisCAYcqDp6vbRZbp2wb2Q3uxfW79nnG\\nsw57SAYhOos2lj4AQkYxDQCk4fpT261l1ps7tzXgcedKJcnyMr42CLztsLYGXFI44gXfh2sGWCn5\\njIcjolAAIIRKez3xDDa3K0WRMWREbBrQ5xGmH+q9EfG1ugBdxY4iKkPkU1QRtZZd3wxXjhcPFinL\\nRCCSID1ZuRdJoan+E7OGJAHpQ3KKfPu3PteDeoXxZUCyhBD+dH9//wfY1S/KbIb3XerRB8X4Jq4+\\ni6Sdnd073HxBAKmxtiU3NWp8AEJsIBQpBc7EAnBEKjGREuEi9r4Ly76IURrCDqPrmd/tzP8zfw/R\\ncA5G88vObf608Ow2Gh7u7wBD7RijDbEgbCE7BELnCGLLqAEuRegEpabXhEEPaHj/8LfhOtsNLaUi\\nCB3hVbFJrMb3DDJ9AgIKgUv51M0XSZtUIIXGOxdz2iIdMkGi9AHedFye3ee3v/82xTTnG4/m9KsV\\n9955G2ctbV3z63d+xD/+0bv0/SPEfM1aet7vbzEaL/mlImBVQd7FJtKmN/TBUkwK9nRG03c4AadX\\nS45mU2xdc3MyQsqMgKNtG2TIKHNFu25QXoNwZEWOLEomRcn9h485OJygLhVlnvHR46fMJhVlppDe\\n04lA09RUWUZZTgDN1bqmKEfk+CjX5x1FmWObLipnAZ2/TVP9TVo1Q3qBtgqXLZEuiw6qGHCYuM9k\\nCNvmzKS94xzeBfqNWH8SCfEO4S3e9nEVuVg7F/ywhmTKKaV1FuJqCFKggqE+/QjvWqqjN3agwu3/\\ne+HxaDLn6IpvcSYeI8L/SbeeI4ymzASyd6At3pYIHXB7HlqB7S3ZgeBeaPj28pCTr5+gqhu0qwUX\\n7z9Ez2B89+uUN+6SzSbUDx9gbM348HX6uaFbrogEt9QYeycVsN0bNsHPcW0+73Zc35dbNuqztZHR\\nUMbbvYVchYw16dAj1JjgW6SXCKkRMvYYRgegBPqYsgiJk7DZIjGoCD7EhhU72rGDi/Ksw3QdORsc\\nHZE0odOpkIxxCNvrD0zbzRGC2twrgdpEl3H+iQlPYGjOIPMJQhexewoa5WIu1LU11rRRGzudWfFs\\ntzhig2kvJUJWGNdDsYfISkLf4EPAIwhmAd6AKCC0v/vSB/VTji8tTJrP53/vnTePmhAcspjibRf7\\nAus8CqyL50tygbgIuiVS1JZMJgAAIABJREFUaPrmCmda9HifwchZayOsEAYWXiqMH2AIfrLx260h\\n/LxjF6Z99hre+21/yp9ifJ73vSplPcJl8UDcSAQ8n0hKr3cEXOzsEGL9rFBbjUmx621LQZBsYMEX\\nolgp2pQ6iipnWZ4MYbqHRLjGCYUopgRRJOMZIy7pRYTgfFL/CQHnDQPbOkJY0QRY08U6zP6Kq/On\\n/KNvZ/wfP8hQU8nS5JRlwXzluNE/5u2DOU4WhCxH6H2C6FktLHo8IUMikRSZZq+qmI0nWGOwzlIW\\nBUWmKcoCEFGAABAyJOF0iRaKZdPTB3BS0vaG0XiK1AV3X79HNapYLleMRyVlrmnblrzIOJyOmI7G\\nSBRVMWacTaiKEd5Yqiwnl5JKZ1EsXQSCytifzBiPSrLqG1wU/yqd3k+RvwAcGIWzFmc7rOlwpieY\\nHqzFW0PfNbiuxtZrbL3GrNf4dkXo1gjXIp1BuA7hWtr1gmA6govi/sEl1aD0rIc41g+1jYIUZSgE\\nnnZxjvSGEOSmgcR2qQgyD50O+DDizB1zxSGtGzOeaLLJiHxWcrU+gCyylsVDhV5nCCdZ6xFCzjk4\\numI5h3UnQYwo9qfIJsNWN1jWBp3dIDt4nWzyFTolkcVtvCxAa2LZmwKRJcZlgSCP/njIEypmCGkf\\nDes9uRopen4+NeQ3XzYZy6FWEUEUBSkgSIKwqOKYLLWLK+58nez2L1IdniCKGZO7f518coiQIxB5\\nzPtd2+u7933oVqQiHB1SKR+fdS6KwWWOe1zsGuN4/d1XDN891leK6AiJwVjqzXns0x2L14lsVqEU\\n3nYYa5F5hcxLcB2hX0eR/fRdAqm2UxaRdU2GziboaobSBbqckY/2Yv9dZ6Iz5wHfQDD/YQih5Qse\\nX0qEmcY/vH//0//CmxKtxwhVIgh03Ro9voFvLwnu+vcRIrYjymZHmPqKfHqXvr4AUaSHJomU9FRb\\nKHRqtZkSM0IgvItqEzy/KMKGMvpiw/TqtUmw60E+61U++9nb6738s5+dw6uMV49o0wb14TnnOOy6\\nikPeMgiEN6BKpEjycTLWS8UoNOYtItttyJaGF361yFCMr3G2SSovsDGaQuGEQGlFUGO0GEWxeIiK\\nTi52sxmOJiEimzZeQqWNLFFa4r1EkaNRLOySf/74iG8/PeLe/vv8azdznNSsreOt8SmPRvss6wIv\\neoQt+aie8U+faL4x+Zj96Zi8iLViuYSzq6sYxTUNe5NJhJdFYDyb0hpLkaWDQil6axiVJcYYmqal\\nKEqc89i+4fzxQ9792tf4/vvfR+cF9eUVb925RV2vsVnOeFQk3VjAC/reMCoKrPeU5YhFU+O6Dplr\\nlGtQ+R6Ne51z8Q08RcpV+gi72+46CuIi+zhLDap9iMX7g+AFweOTTqhSEpdkK21zibcWiSLYVFcn\\n1UbcfRuIic2/09ONaE+EfMB2+L4mVBEuvLZGEvqReQkyEMKYpvo3yMSc87P/hjv7msdXPcgCsOAl\\nXrcgSkRVcvTOHif3G/TeU7794Aa2HcFkn2xyjBlfkT2+IkiBEWOycSAXBbabE0bHyHqBCA2aDOtM\\n6oxkkSIjKA9qjO+XCCeIjexjzj867ckZ3VnP232f0Bw/3AcX17EYyjGSEZIashHZ7CuMqhHN8hEH\\n3/zbjKd7BFNz+UHL/i/8DaoDxfmfPmFSHrC+/IDQZyAH4x2i88kWHg9kkRQTHOxExdsyjhfsVVJN\\no0yCD8Ftcp5SxHdunOshotw1liKVFYmB4yA3Ee6mu5EA19eI4BC6wncrvO0Ipo0O+LC305qQMp37\\nxQwtMor9Q7rlHLIRMhsh9BjniPKq3sT1ITQE/4Voxz47vjSDGULoqqr6+4TuP0KA0BnO9mSyRYxO\\nEFmFt831aDBtLiEEeI/rl5BPyGY3ce0Z3scuJxuWmAwILwliOKx9aljqntOIhOQpvWS5/KTo7kXM\\n2Bcl97cQTDpArnVWgVcxml/0iPWKMXfzvG6k2BjMQCBWAMgYYZoeT4zhQ8pBCVJjYXzs1Yjast14\\nmaPwTE3pRqAyfbzr8OsOiglOKkzfIiV4IWJj7N3C6O3Fr3nBwQWkNLEGsZ9T9Md0vYGs5f3zN5mU\\n9/nm0RmPnih6HvCrx2t+9+l7tCYqllgn+eDqJq0t+Lf2PkSECmtOWVtP2/UEAkorjPdoopG5Wiyo\\nlELpMWVVYr3DGsOoLGhbR1FWZEVFVlbItuH8/JR8VLFY1wTvOZjNuFxc8cadu2QispRHhaBumyjS\\n4AXHh4d0xrBcr+jaBiVjdxolJW3X0bkTKMbpEYaYO+3bqAQjBN6lWlbnkEJFghchlf24bSPvRLpy\\ntiM4Gcki3uA7k55tlPMTYVDwin88IeWOXILpEmksPeDoYnmC7akXV1TlAeDxQl+Ht+RgONN6Iacv\\nRpjxV3nUXZCN3yY0GhGSofAKGSSht7QPFlxMfo23f+2rXP6L9/n4yRJRCtpmjd47ZPLNEfbiDOop\\n6o0bLP7ke4TmBhjH+MZroAqcceSc45sCJ9d44xF2hcz2sfkIEXKk8Di7JDQrnAhoVcT7SNKmDWGz\\nu4ccfYRgk9auiCzdSL6KDml++A758VtMD49YPXyfyVu/TlaMCfSEvGT05q+gsjHCBcqbXyU4h1o/\\nxJmr6IgmlqqXHsg3Qi9ZuYe3Dd4s05k63O24izfn1IAcieFP+kkIbIiWw2YNW4g3QusyfadtzpIE\\nwcafRwMcjWV0uOVA5EHibRfJSEOMLmSaz5ZbIfUori9AZBltvUaoHJkVqHJCfnyH7OoT+r6AZg2u\\nRqgKbxd/zpcwvswIk7Zt/35VVf9B0y8LqcuILSuFa+dIXUYBgp0C2uHBmXaNxyFth3eBanKE3L+D\\nO/2YQV1xwMXDtYM0LdfB200bd+tpx+LZgI8R1O47nzGuz47dXMRPiuyGHF8YlCyS2onfwKMvjg5f\\nFWZ91ddu3rP5/5e8Z/OrSBKSQsaa1iR95YMky1UsYdAKica5Gmd7lMoZul68fE5h8FZ23JX07Hys\\np3JCoLzD9HUUc/YyyfttC9AZmmOnInTC0M8v5VlEHjt3tA1y8RHI12C6hxKabz+4w3wv5xdHP8A2\\nFxyWaw5GNU8WBV29oJoc4JzlcTvhHz26y8+PL9g7uIG/vKI4OuZ8fo7UCgms6i6q95QlushRUtIb\\ngwyBcZ6jlGY222O1qlmvV9y4cYNyNOPoZIL1PUpqFuslJ8cnNF3L147ewfcdD56eIgSM8grvXdLl\\njeUsIhOMqgwTPL1zdL1gnFnOszvx+Qbw1uJsv2ni7f1w/Eic7dGZwFm12SY+CZHjLJKoMlRojfGe\\n4FqadoncBi1x7QcfG/aKRPkPRNjMp24TgxM5PPqESkghCe0ylo1lOWJoD/eiIUAEhwoKP/vLrG2D\\nuDqi6y1axlx6EMkRMJbuQ8efF4JPvn6Pr7/2gIv1isVlgcpzMlPQXSn2ju/RrdboVeDkzj6Lj65Y\\nU3J4Z8bhoeOj7/U4RpT7ZzCvmPdzxP7bvPf1I/rVFRdnC1b9jPbqAVpMEKElHx/RO4sKPXb9FG+i\\nozKs7sh8TTcvEehSMTqyPKC685eoTt4lG49x809jzs+02BCfmRAeFDjfoikZH2pWZ5LgO7K9e/Td\\nOTIRIkVWIJOOM4BUBR5HsCptnwQjp/Z+8fRMkpmbHRS32lYsQAySBPHfYofks4HbxdZYpohy+F2s\\nAEsynUNAM7B3fUCGPtVwEw1wakfoBxazqhDexVIyVVFNb1I3sY+ryEYUo4Kp+TOWVDGn6/r4GW55\\n5yUH0V94fKlUzxDCx3me/zPMApVPkKqMorimBqL237ORQwieYJoEEfVIndOtH3H7tRuxtdcGPRwe\\n8TaZHgZ4JLwMdAggFUpVqePB9fEqBJr0vT7TuA6e9uZ9UiXBaLWd7aaG8cvPd24mJVP/yk2kvHO4\\n7ST3vYuC4H6AYISj77u4zr0FpcknR1FsgOdVQ7b5zuHqks1OY4ByogLRYAAFPrYJGjoRbGaXBLJh\\n82eYbmR2kqJnj8STaY3AYep1LF9BEBKM/yfLr/CxuEmhSprOcpCvqPL4XOr1JcF6ejTv12/zzy6/\\nwnRUkpUSnUm0jt3om77FB4exlqbtma8arpYNV4sVo9GIADRtR5YXTMYj7tw6YZoX3Lpxk6btWFwt\\nmJY5tmtp2o57d9+gaTrarifLMrrecbZYMV+t6V2HI9Abg5CCLMsYZVlURZOem9PbKBHbnglSQbr3\\nBGdiv8+U+5V4lJIEH9swOxflE7138Tlbh7Wxw0TbGQgS+hZpHd6YGJU6m3R8bcqLRpZu2Mjgxf6l\\nYiPLuF2fw76086d08yfxEN6GYZvXREgxOmpKWGbjj9DB4dUbhK6IoiZ5BaKI+ezU01SEgOgDq77B\\nvPYurryLLPdQVRUdrs6zuN+z/GjJkz/8Lg/fv6A2kiw/oDraJ5w4GrdG5hP6ZcaqljA55tZ7rzP6\\n1be4/cu/AnnJ6OCYanrI6J1voEROfnSP/Xs/T354l9HN1xjv30Zk0xgE+B5cYJunjE3eZTYiO3iD\\n8Vu/xvjOz1OMDpHNJ9RPHmCcxckcVVbgK2Iz7xWYHtdc4ewxo72K8dG7TN76BUaH9xA6CpMrmRNE\\nHltcqQqRKWQ2iizUYVdKCUJHgyYzZFakjkRbpd/tnk01uqlsZPePEHrzHjEwV5O0o0glaIPOqxAK\\nmcg/w+u2Y/fvMVjyIUo9oiqkUHghkTpDq5zeBYq9E2QxRY/2OSx/gHdzMJd400BoELIkhPDopefg\\nX3B86bUR8/n8P5lNMhv6Gql1LFCVGaY5R1UHbDyfNGJgk2TWvCW4Fa5d8ejBBcXkgCHfMTw9KVLX\\nBpGO2dTfcIAddmHUCMv3eHyKNK9v7M87XmbwYraNrXFPDDMpNFIVCJVHLzkZhthy6dWM54tqQ191\\naJVFXd7gkrrG7vzY5j+8R+zcG0GI0GgQBG9wrkMBWuZIVcR6KkRium6N4qb9EMOBuEUHdv+bZHVS\\njV2ayiavGiEtmdi1IrY9QWYl2WgPqcuUg7NY22H7GHXiLXZ1hW+bKGSOJAuGjy9nZOOKvu/J7JoQ\\nBMVohFQZpr9E9g1aNLw3O49qUzpnfjWP388HWhPhWec9TW94fHbJ44sFbe+wwcXoTEoa0zGezZhM\\nZkz29ui9QyjBvF7x9PQJX7lzG9u1mL7n8dNTzudXaBE4uXkjadl6jA3RmPYdCIkLUbB9Ns4pVcnZ\\n1ROOxnMGIpUUMqm4DGiIh2Bp6itcsyRYB64nmBWhXSFSeyuIqLdLDalDv6RfnILt4uEfXIoIXWqL\\n1cXrDJFlGLp+eAKOsJPfDGEHA5ICd/kAs7yItpwYvsanmnJkWEJYIcIZq/UdjL1NaNbYfkU1miLz\\nEV7a5LzFCM6LOLvufs8ffhc69tHTHJmNCNIig0AHQX/5I1RWIXwJ1tGtPuH0/opP/+9L9oo7+Czg\\ngifLAlVeMH+w4M//3zO++4Mek32FrmmZfOWXYwPlQqCFJpvso7zGrlbs3fsrqNEUfI9AI2WWIq2Y\\nWgj5PtM3v8X0nV9nenIXWZU08/c5ff87lNObZKN9imqCKnI8Hab3OJNjupa2Owe1Yqw+YnR8B28F\\nQk9ASLyt8bZOxbUSREy9SJUjVJHY6RG+lTKyb5HxDHoGD9juS7mt893s4U30GOFYIXfIljJ2kCHV\\nSEupAJ2YsunnOzWg7Hzu4DwJhrynRuUyoghSIVWGLmegC1Q1QY/3OTiUqO772L7Beot3HfiOINR/\\n95mH4F9wfKmQbBq/7b09w1zeophFdqX36KxCK4fPZnEDB/fcQSqCB9vj9YRudRVNpcrAdmk7ukSt\\nFhsDuo0yU4Pk3esBIVi8WaFUgY8aNK/8RV5dxUNcW4NCCHzYqjyJofN7Mq3BbftMhme8h89vFl8y\\nAri+AzEcZgF25a9SBCglsfCYbS3WoMYUNSFjN4DWtcl5SR5ulm0iHMQO1BZireWmVOyZ+zIor+A9\\nwnv8UMr1DII80NuHaDivJhjTYNpLtCoJRDm56NkCwdOvLxHFiFxlkJcoHErApMhpioI3qjU/dp7+\\nssP7gCZgmiXjMuO4OCPonJtTCEFysV7hfRSAF0rS1B1K5vTWQdOyNx5hO4fWGUIRmanWML5xE10U\\nhHVNZzyj8R7T0ZjF4oq267l5dMzVcsl0OuHk8BCRtqT3nkWzpiwE1SjDOE+R58ymY0bWk696Wluj\\nzUcEeQvpxLX9ExF2S1cvcF2N1BlmPU8dJQzIDJWNkTpDSoFJwu22aTD1JVrnUd4uzoaw8Z8GeH3L\\nkN3+ZXjG21SJCBvxQ5zMCf0a++gD3MEdRDWhrMYxEhmMpzV4K3FhSnA9wa4QqojIUFZiQjw7vGlj\\nNO0dOq03fRFwokCImIsnFAQMAeilprjxBnZ+ih5P6C7WaD2lffoxeu8eQRnM/BSZTZntXXL+6WNY\\nnSHOPiKcvE02OkBZz/mHv4PwGdpKlqc/olg9ZbX4MbJrOPvxH+PWT0FphM9QoxvkwmD0lMnhXeT+\\nLSbTjFuTH3J2/hGr5dssfvw9Jndeo10/omsdQhYIOwbbI0VO160pZ8e4WtE3SxZkyEzjXYeQHlUe\\nxTWTVWSjA3zf4voai0VIjcwn4D3erOM+lhpEbJUWmyQk0DU902vcgM1ZNBD+hggxalTHnycjKtX2\\n9wMbVojUbUTt5LXD8D+Gc3vjUgWRen0KhCE5HAI5uomeHlLu70NekrPkTv8/cy49ynRk+T7GrkBk\\nHrf8d15yAn4h40s3mCEEr7X+T2/cnP2983qlitldnG1BV7Tz+6CnwJCUfhaejQewVhq/voJijHR9\\noim7bUNT2Pin7B7MfuvdXo/IJMGmfmmf0yK9SEZrO1+2jMFnDnzw7Bz5DCW8PiRPDVLuaSDXpANk\\nJ2f6ufKWz+ZaBYBLTgMMHQJi/jFCqLHFWsydCL2TnE/7CSD4DBEsOE9QGiEdhKiMMxCypFKp/Od6\\nCc72YbzgvioFqJjV2uXsP/fa6AjVV0/QOkOnOkxEvKNSOLxP9aC2w3dLfDdBZZqA5quzC7SGoxt7\\nLJdz3taP+I66gzRznFnTzM9wbs3/5r/KL4//jPduK167MWFSFdy/Enz46CF70ykhRdTGGUTv0Fpg\\njKVtVlTjEWVRcnVxRtu2TGcH3Dy5E+HE4Hn69BHOz8nLknpdMxuPuXV4jDGGrmso8xzvDB88esxb\\nr98iDxphLHmWs24axtWYTBusVOTtFQgToUcRb67UGtO30DeEfo0IDmdSZ4lBFs07fDfHdXFVKKlw\\nKfepdQn41BEjbCQON88qJIWaEIXvY/PeeID64KLTxbZfowyAkCgPMgh8v6C/CsxGP4ft6rhzXZQ/\\njEYztZbzDqVLhIhkvr5egdAYYQieWINqPV4GvEpRHB7tBUHGHKLAoYLA+JYiz8nvvE1YXtL5NSFI\\nlBwh2jm+XqOrKXvjklI9JggZ6x9R+I+/S1dOMPUlUkq86AmiRnY9TufQz8n27yG6JfrwXdqLH6P3\\nbpLvv41dP2a2f8LsaJ8q/D5m9SHnq4Kr9RvUF2cUb/4K3dV9TLNAZXv4vkHpIu2BHts1tO0Vsp2j\\nq5t0vSYLP0SpryKnB0gnaZVG5gWub2PbxGyELCbRARIqdo9xPeg8RnzJ8dnlfwwqPIOcaUQLdtj/\\nJIdZiMhkTsZSpvzltTymHBTAJEJFuUZkQrWCBNzOgcImshzEUmRSsQpCoasZs9d/DlSBlisK94C9\\n7v8iCIML0Ix+jebyt8G3IPM/CP6nzHG94vhZRJg45/6rxfzi7zpTVy6foMY38c4SvEy6hiCzIio8\\nPGMUgndYE9mBKqswpk7tgwaPKEUvG8hv8GC2UEI0EkPvtYDOq6jyYdoXn8mvMF5svNJhNJjGwUak\\nvGHMI6YfpAhOboPjaEJlvpkvwUawasP43Sn4/ykg2esh2xAFD5+e2G1D7O66HRglLl6RcqA4jSD1\\n4wtZIuFEGxx5JFtjOdwLKa87Gs/duV0HRMSNeb1Bbrp+Ig1IGRmdUupopDfXF4CKpLHgaJdnZONj\\nQvCMs5zp5BKlpxGtEIo71RkPRhNO+0DvS8pJgewVj+ue3+eX+Pj+h/zV2w1j2VMFxf5sL8rVWUuR\\nxXlOxmOs85wvFuzNRskR8qAE62aNFwqhFcvVnPH4gHd//pf5xjcd3WrJjz7+MX/63e/igkBLzUWz\\nQjjYn064646QIoqq749GPJ5f0vpA4R2Zz1CZoqrGlOeBPtUmx5yyROAw7SpGaUFsdH+vL4P4fJRU\\n4G1ai3E/DemNMJRssYN2hISMhIDIqgiXyRxchw6Opp6jBUhhCGHLsgwiVllIOQY1oVsv0eUolqlY\\nExmUPuAwCJGjZJYK3UuQoLUg9HO0mGCSEL8QBQiHNA4RVmir6LOAEhW9XRBMbHTs5g8xxpLnMpYl\\nqBlSCEzosS6Q5SWv3Va8e/MDfv97JUVV0Cw/JIgSEXJMvyQfH8B4RllJRnqKKQOj8D6n07/J7BDq\\nxYo8H1Hd/ir55BYutPT1PiqXZP0/pO0Fk+pXOF/McOsGJXqaH/9erDPMKgiO0NeEfAQyi03UZUC2\\nLaDo2zOCcahwSCg8ajyia9YosY/KM3prUeOb6HIfiY/lG7oAXSKyUTqdBrLkdm/F/ybGK0Rnavj9\\nUI89HBWCTQ5zC8FmCT7TqfmCQgwt/YREOoOXOnVQihFkTLwnoQiXDKjUETJWGUJqVFYye+dbSNEy\\nC7+Hv/wTJlpQ6AwDWO+5vP8BmHmcrG/+6svPvi9m/EwMZgjhcjwe/9beNPv36+ZCyGyEUhlMbsZD\\nT2lc3xAt3RbOjAelJZiaUB3E3Fk+xjVz4iE+5NpShER6psGjZJY84xTZpRUiEQQX659EFtXvtzJM\\n1+b8uY3SYCBE6g85mG23OacSWyx5bBvFHOAaNLxBQmJXliGKZud1uyof289++b83Fw5iAw37jbfw\\n7HvjzwJEYgcGpfII1Q6eIDLR6F2MMGDjkYbApnPM7hhKc65/zbh9pZDPGNOtKxOdh6RQEzPQMcep\\nBIFU/5XeEbyLBBeG3wXM6pJsrKjXM/73T7/Jb9x9wp2DDtVoSttyM1twqg8oizG9rTHeUnWSeVmw\\ntr/M0w8+4F+++5CR9sxkTtAeay0mWFzwrJuGbjZhfXnBreMjmqZhsVgznoxwIbA8fYzQgslkwsHe\\njI9/9EOent1nfzJFCM/Xv/Yup6enOBn45PF9LusauYL9cYTTuq6NsLNwOOcYjSucczRmxZP+gDZI\\npI8dXfAehwXbYboahYWQ8I0NxL59vhF4SE9i498MtZlh+8rk/IXg48KRoMc3KCY3UWUV25WFWCOp\\nJwfY9YJu9SQaPKWiGAWkgzAHbbFdi7UtMiuRIdDLgPKxplYIjSgDKj9kos7I2z9lUhh6tyTL92jM\\nCQtxgJ46VNYxah8yGSlmt+9ydXXCd//4h2BBaQk+x/Y1igmmXSOKfaS19N6ixRjRLpHTkqtuwv/6\\nnRu4808iz0DfQEmPuvU2+/u3KcUle+G7OP9DkIGuB+Nbbh0s6NwRB+MzhP8UVR3Qir9GG75GPpoi\\nM0XfvcvFQvLofkvoz7H1eWpbF2t4h1I609aoUUfwPR5FITNMv0brAt/UCKmp+5xCfsi472nDa+iq\\nIpvsU+2fEGxH37Z40yHykowSIcA4A32daAXpQUuRSry26IQIAS/ivh4g0qE8JB4fsV/uQPaJ+dAI\\nycpk6ETqVSp1waYMxflYU+mGIELFObhYCoZK0a+uYkyhC6b3fgnla0aXv0VjHXtVSWsNKggWrsGp\\n9zDnnxLcEpWPH9pu/qVGl/AzMpgAdV3/x6X3/56xrgz1JdneCbQBKTOElgTbbQ7q69CsB9/jTRN1\\nLPMRTmdgWgbsdQO/owAXiSC2ZSdu2zmoI+y5KUcQIsk9bT/xLy6oHqsXh8g3SjxtwVjSf69BIsN8\\nwg4VyUcIVcrtY/LOJuvv2eVsvZqBj1cOYVDaeRYE385/kKaTqe+j9wa62ApMqxIpC/xGdSeVGIhU\\nxJCKunnOiF+H9hAD4csl/cfkuW7C8mfnPsDJAw83QnAy5YgR4HyPdQ3SS1Q5gRAw6wvq+YjixgLj\\nJP/k9Ca/UZzyzp03+eOPPuRKHDObldRMGC0ddbOkMRdJ4HzJcvI633nyKf/KbaCc4s8ETgbqtqMx\\nHU3TUhY5r908oumiQHtZVnRN1JZVQtCsllyenfM4e8y9e+/w2t1bfPDDD+lsy9V8yXRvxtXlirwY\\n0V+uuLqq0TJjKkrWpsVaR29S824p0NmY9Tjnh5/eS7XxUXhABEu9vkT3bWzmnFIBG0EN2LBqt3fV\\n795intd6HtbNwO72SOfoTUdZTdGjKcJ7grd4Y8BLymKGKgqaJx/G2kAXUQcpJM4ZtC5AOiQKbwxO\\nOGQYk5cFd26csye+jesalOpxwrE3G2G8x08s2l2Sl3MedJLsSnM0sqywlIucfnFF8D/mzTtHXJ6P\\nWNQ9vg94PUWInkKdIKXG5BLpWkK7IogxrBtWlx+iMpjc+SZqNkb4Ba2+QSUEN7JP+erkT1mYjrqp\\naEyPygUr05L7Rwjuc7OcgTrgyVXFfNWiik/orKO/eogSGXZ+jjNn2NaAzjakGZGEArzvCbbGmg6p\\nSwQd3sS97rtVXOCxWIS+P2JR3ERVNuYjZYaX0C0fImWJLKto/JxH2B6pcpxoIuy6Od9UZNUOe04Q\\nyVzoKIoeokCDzCcgJVJITLvaRIZCSoKMrFuRDGTsKVegdUGQ0SiKEBDObfLgQmqUzrB9mwxtbBQt\\nslFMA+iS0a23kMpxePFfYmxAolAIqmJEmWVcrmtW8xr6K0Dg+sW9Fx5lX/D4mRnMEMLD2Wz2P+wf\\nTv7Ok6dPhCjHoEoQHt9dooopoVsnWFVsDlYhtsozwXpCPorlCCIZmUGyaVgIQuG8I9NFzJVuiuTj\\nYoOA9xal8niASw0mCVblAAAgAElEQVSpTdgwXp3c81nfN+ncJoM5HPSw6xLEZPqWQDHg+m4XALs2\\nl8g4Iwlv+817EmUlRbevFhlvYJlNSJ8MWnCb3wa2gg3BCwge6xpyOUrfz6dcM1uBgQG/8RBUVCAR\\nbCPqa8ZvmIFI94K0sYTimnXdJl9JkiDDzcE6gxBZinsFPohImU/1mkJY7MUn6CLHjE/oV/A/vp/x\\nq7dq3sgsd6o1HR2PVoqrEBsxGwtFMFifIfoV56NvcT98h1us2ZuOWc2XVCpHzcA0nrbruFiuMC5w\\n69YtXNcgg49sXCWRwVMWmqIquf/Jh7RdgwiCvdGEShcEBPowRIg5WPSF4HSxRErJdFSSFyPGk30+\\nefqI86tLppMRj91XsK5CkRPMMkrfWYswdTx4QkjSZFsDub3/aY2/kFh2fe1vxb2H3wmy/deYnLyG\\nzPcIIqClxKMR2iF7gUPj+lU0kPlevGeLOXq0H4UiTBTIDqokq6aMC884f8CN4jGVO6ND0UuBbVtG\\nUrMIS4wxjMsDROnovOFWldHJFY1UKGepu0gGtN0pJ5NTDvZz9KxC7U3Iu5a6naFG3+eHT99BFjeh\\necza1Yi9d+h9wHWC0K+QkwPe2X9Ku/5DWt+Tqw7pWn507pmvOnwfONyfsVytCFkGhaGQr/Go/ia1\\nu4XK5pj2DN9f4o3Dhpb+/AG2r0FkZNU0OaQ+oTYi7iEhkCHqqGZZQb1eU5ST2NbRNWlvepR1ODmm\\nDA9wcoy1Ob4OjKYFN29lPHjoUVlMn0jlcUIgdBFLUIIA6QGdPjumWqKD7pHFjPHhXayPKRdvfXyv\\nt9h2jko6s1JIghzIQ+n6MkPmI2Q+iukb4nxd8AjTJShWorIKZ2pklhM8sXpCx56WQuUUk2Oy2SHj\\ns/+Mk+om590KrT0SQR4k1kSov1nMwP0I0D8Ooe9fftJ9ceNnZjABlsvl33XO/dshlIWrT0GV0XDl\\nB7jmdANTbhh5O3ChVNGb8aZF5RN8N485FiGiQR3e66Mot7NbgxXHNrKLmoORRSaEjLTrYK4dHC/T\\no/08MG1c4DFyFCmSiosm5TCvvZCtBdkY92G+bMpOhrSglGoTKYJHhDbCtEETsPAMUePZcf1ITHPa\\nROpi+6Jdm5WIAUIQjdTG0A7w3TbHGp9hSHWVMe+4vVx6nxQIH4ialzuM5pfmh2N+TapB8msH1k75\\nN4i9MkWCkLa5Y0H95FP0XQ35GG8y/p/HB5gbf4m/sv8BYTxj3TX8g+9pQpggjWG9OKW48RpSTmmR\\n/O7jn+fd8bf5xoFlfjXlKltg17FuzDpY1y15XvDg4QPuHB9hm56+d4g8wzuPznOC6ZlUGZNRwen5\\nOVJo2m7NeDpBihFdXfPGrWOKouDPfvQRl6voRH5w/yG9M5wcHtJkDtM2rNYTQhAY2UPXIYLF9Guw\\nHuv6BHNv11Gk7oed+/nsmht+FHbu7fDv5OIJhb5xh9nd9/AbBys6SVIqPKC1jwL5DqzImdy4S5Cg\\nehD5GN+vETpHFWNGhyeM1EdU7vc4LnKeNF/jgfwWQUh8/yFV80fUCPLCMC2PeXg5x4o1uYBM59zM\\nRgiXc+VvUuSnFCFjllucMEwLjWTFzAdMZhipNWubkXV/hPVrpPEUOHz9Af36TdorS/Hau7w++iNu\\nZBpzNILWYV1FXs64WK/QMqPpAr2LzdKFLTjv/nUaNUb5gPWPUVcGuhXO1Hi7ol+dQ/AIVSBVisJ2\\nARTvQQ21kB5nOrR3lKObBN9HkRfbQ4g6vqBQmUcWb9It13hrUKqjMSNWzT6ZbpKmb0yWCBVJOEP5\\nhxzSQkLEfCSBWMqRU+yfIMdHZGKowBTgLK6vcaZJUWRECtBZbA+Y8pgyH6FG+8g81gYHF7Wphe3j\\n2tMlEPA2ind4qVE6lqNInRMC6GpGefwmM/87WOe4qFec+5q9kOEyiXUWo2GxPKK9+oTgWwjhC+9K\\n8rLxMzWYIYQP9vb2/vEbxzd/8/5TI5SKXbSDBtsKEImsIZ49qwPGGqQMBOfJR3tRAo9ArEO4TnsX\\nm+ScgE1nDJ9QwmSAXBej00F54jMCyp8+4gwbuGOQzYr5oWgUnje+O4xSBAOhaRN9SrGpn5Q7zkQI\\nAhkqvHAM+pKRAJKMyfb+XyP2RNG7wdjszvr6ewYCVXRKhvs15DPF5h3BR0H2IRKMqcUAgypLkPiw\\n1TcdTo14BUUQkRUcn7/YzH4jUpHm7l2fGJW793G3hGjrLMS8aWpeJRyrx58wuX0PrytCgD++uoUu\\nM95zH1C4nrf3DvmTjwLOWWS/pFtcoE4KfCvxOuf79V8H/T1uHX5Ke9HRZ57VqiWInEwLWtszzgsW\\nyxXO9MggKXRGLjNMb+m9J1MCncFkXNG0HVoqggs07ZrJqML2jkmWkynNct0wKhTTUcW6E8z2ZsyX\\nl2hfYDjEIRFdi+17vF3h+1Vc7f66UPZAQLv2s2dX62aNp5g0hNRRIkYi1f4d1PSEYu8mQmxFFq+t\\n4wBB50jfU+2fUO2fIPICMz9D6wzbXhFkRjUaUx3kTNf/gBt7PftVxfftN1mNX8cEifI9mcrp/j/m\\n3uzXtixL7/qN2ay1dne620XcaLJ3ZTpdma4qSiALMPWAheqZB/ziByRUDwgkJCQERjQSiH8AgUDw\\nABKNxAMYW8aYQuCqTFFVWXZWZFb2mdFkRNz+nm53q5kND2Ouvfe5ccOmXFRELikjT8Q5e6+1155r\\njjG+8Y3v8/8kR03i5+8HuJgQI1R2yeT4GNOcsfKe7XAPkQ2L4e9xZN6niwMmzVjnc9gkNnM4q2eY\\nGp5ePcI1HXeaU+6fnfH7373i6eY2udtg73wB7Iz3ntzjR5eGN05e4fXpN+nylq7TZyWSOFnUdMMd\\nJN/lqhdSvEXVbdhcPyRuO/JwQdxekUKnc+dlPVNkHksXXoOIKNua3dOaiWEgp0yySRNjEsZ6hbvL\\nYTklSYuJHcnqsyV9hxfHkHOZt7TkWIoKsSCVfulmTDKNtlwyWhHahmynGOv2aJig3sPGIttrxG4R\\nP8GMLlFeNOD7Bjs7xU+OEGdV0clqcNQ+ZWHMxgExETs5pj55lWFzCX2HOIevpzR33mAmP2L96A+Z\\nOMd5bjECMSUkJKgc59slkT9P7n4fsc07aVj9gE/o+EQDJsD19fW/l3P+K7Gr6swC4wZcfYT4GcQW\\nodG5TJFdNSUiSovOCTHqWELoFVK1nhT60j8DJdYM7MgxN7b/F440oAICL9P9uXn8SSrLpFgkdscS\\nPXh9Lv3BnMoY0/7Mh7J5o0fiDuIcFXCMFIPV8nbl4ci5xzQnNP6IfntJSp2aqN4IdmMwNgfBTBmw\\n+248mn0eVh4ClOH4UTsy7ypCjY+p9IXJdgeXjqMkYy5jy2xWuXJVjhHtso2FijVuhxjsdvg8Xuv+\\nPu4YyFAq7QJxfQTuLd9JVjWh3F+zfPhTmqNbiF/QTqd8450F8e6r3Ms/5S++1vDw+RmPLsFPFsS4\\noX36mHp+QkqBvm744+XXqaeRs3rDJrf4ykOM2MqScsZ6z2Q6Z71esdm2+LZVnUtviNMZ3XWkqRwi\\n4L2naRq6rmMxmRHjACYzn01YzGZcdFsSmaP5FOeVVW6Moe3WRJkSGQjbFUgkxYQ1FUFajNWqfcd4\\n3d31m4Fyl0QdEIKUoCOIJGK2+NktprffxB3dvVkZjWvq4Bmz1ugorvdk5iCWHLeENGCNx/o5VXNM\\nfTojn/93+HlgMfkiby/vc+2/iKSeJntyboiv/hLZOZ49WJGrLWH5iH55QZ7MaDPUdooVjzUdSRZc\\npb9M236bWf2YDW8Shzdp0h9j4k/YbFqun61V1rGtuNgIP/7RhquNx00qqld+CT8/xQ1b+ukpi8rS\\n9iue+K9xVr+LdBc465gcV6yWme//IECzxVf36Yafkq4+JLZXEAeVdiv7lvpI5j2XsQg9JDJiBINW\\neEqw0fEe1QCOGqhyQgQOpyU0iLWkXGmbRMYkWQXpbdk6rHWEuNU6MwetNA/bIuM4iC2tKedxVaXB\\nrRD8xKrTkHOesJ6SYo9tFljriRkl+pAwtqFanOwk+YyxpKTelvgG5xtiCFjrqW6/QX10F7xjcnyL\\n2K4JYaCan7GI38Is/5DKGLwYNrTM8Pi6JubMkBPRf5mrB++TwxrE/Baf4PGJB8yc8x+dnJz8/uc/\\nd/effe9hizUOYyuFWXtIvVKEJSe1e6JnrLzIqlRCaMFWqntaBICNcQiJlPTfcx6KvuVBQDoIegKq\\nghF75MVd5E95KMlHlXzI2ksSU3qsOen5jBSFi0SR5L/5+p1YQIE7RUkuSg4KN4riUd1F2iu23UrP\\nlTJkiznoc473wIgnpOJiUIgX2v2TPd9j128dA2XJfwVs3RDbLZRQN1Z1qjUay/00e0eD8s8b/oCl\\n6icL1lUKOZUvZrwOUzLkmyxhyuv1GDPknEcYuqAJKWtCjy32Q4U5KyAxEK7PkaqFsABf887mLq8d\\nvc3CrDHuBJN6YAYuk0NHt7qgmR3pYLUXvrf9PL/snjCRLV2VGbaR6+WGoRvYdh3ttuV4NqNuPClE\\nrLf4poKm5nrV6dyhjYQY8M5DhqP5jHW/pe96BhJd2xNj5mK54ng6I2V4+8FDTo4WBBEygRyMytOF\\nuFsTgiHtNHgjEMgfs8BffCZ2KzBHVV5qFvjJCW52oj0rArue/I31On4fVkm0xmiSkgZNroxWLa6u\\nMfeOsVcdnz3JPFv9Mt96chvr7rBIP2Uh7/E0fw1XT+ne/g79asnqg6ekvCBJjzE1/TogmzXp+hnZ\\ngpufUk9rOpPJ019lazzYhM0RM/uAJ1cD50/X9FeG0DUYN2PoWvCW6uTzuNkZYoTu8c9YxcDk1pvU\\n7ilDcpzHKUfrlpQmXA09YbnkurOQb5PXWzZXb5HaJSm2JUharKlA1BUmG7Mj72n+HPYErCQkEcSM\\nAbU8IzESQ09jj4g5gwRtQYzFA5YwJGbVKZ1s1BzBgLgFJBVqMNYTU9D9J6VdlcrBcygjRGuKKlA1\\nwfgGsVb/f6cPmyF7pnfeJA53lKBTL5AyEZ+LEbdYr3hQLibwMZEnC/x0UX5fYa2UfmW5B7bCzDxV\\nTszyjwnn38BKhXeeuZ2wGjoq5+hjYFY1tEPL9VVN3D5G3OSDNKz+j5cu7D+j4xMPmABXV1f/rjHm\\n78aWxk1vkdOAn54xiJBCi/qZAblTqOEFHElSIIvgXBkLMYKQwFZYU4Zuo5AIZQGlj1aZgm40xn9E\\niP3w+McRO9dsT6tXHfo3+3PCzoZJoVaDpHSjqtuda5fNa2VXNK/Z81sPINaM9iNy0RKlDKnnmwLX\\nOWdi7jDitLIkk5La6CjVOyl7r/Qgx2ApRU0lZzWVxhpyPIS+ZSdlNCYGWmAq7DNuoPsaR5l9o7el\\nlD5YimkHEeciSKCwbNxDzCOsjuqijjZCI8y0n0fMuwJ5HKLIOUPsiTljMAwZXNPz7LnlneZ13v7x\\nGZdPzolhALeGXBH6gEuJTTI0JzVDvyZPKt4zX+PrR39AvAw89xnbB7I1LFdb3M6BPjHzHhLMEdJ6\\ni0lCH1qMy9R1w9AH6qbh+eUFy67laLZguV5xdnbMxXbNdDrjYrvm9OSYDy6f43tHtkI0iRwUNg1R\\nJen2iYTacOsXcnPt7oCEl/ysqU8iicWevs709BUGLOIcwqBr+GOIZeN6NiYj2WmF020IfYstKjCu\\nnuMuPqBdf5c//tCz7jqqyZo88bj5OXn1M549fwdJx8TVQHYTMFNN9KiJyambjnHqY0lF6AZStlRu\\nQStXVP4uYiyn/d/h6smSh+8viZtJsSaLSGyxzRH+6HX8/C5ihX79HCExP57gq2eIbDhqnlNtnvKd\\ndwPbbcXx/V/i3p3nLH/akYwUNaGxUqxR5EYZo9mWQbek+5FKfcZdcNFFqWtVdqNRZe/KkRw7yODq\\nKaFbI7Ev884GTMI2M0Lflz1ChQtAk+dsPCIKi+pgdETM+LyPe8ronuKVDGQrHQXxDaauoMyD79Jb\\nA2LmSDPFR4imjJxkirOQClSMovGCIXuDdye6p0gmYUshlA/Wnoqw//obj/jRd36bwTgasazaDqYN\\nLltCyupPW09YpjnrZ9dqFC3u3/rIIvwzPj6VgJlz/t2Tk5Mffe6z97/+80fnOOOw0zPyVjDOq+2X\\nUfcE80L5l7OKdLvmjKFbYownpQLPomwtUDKPCZ1WBLFnX6XIwT9VfeL/5wJzfxSLJcxBRn5gs6PQ\\natnksm6u7ITK8+41OwILkFGroH0CcODektUBYlQR0l983KdLGt/8HCuWGIdiUTSSdKRIlpVwk3N5\\nqEpXUSxiUvkM+75sZgycgsk6BpFTUke+nZqTVpYpxHLdw242VFnPlPccNxIDuUIOerpSgr1zlhgO\\n4eGR4WtGpOtmYDBqqEtOhKHFpkDbXbMNPf/Xg4Ckh4oOWENO9S74pxiYLCakYUvuNgT3Co/9KT/p\\nfoXbk79PcJesrhM1mWld4bxn3bXkHLHO4ZylT2CzCjKKFWrvscbgJg0hZYJitCy3G/oYSSlwNJ2y\\n3G5p8xapdN71YrXk5PiEhCXFlhQGREb4Ve9xKio9e6j6IPkb78cBFKtLrqAZrmJ273NUZ2+QxTCx\\nKqitaI9uiuNxA/ne5U4C2YG1ZfNXJEhEGC7f5/z8+9hY46afxfmK0J0zrJ/QPbiCeKowI5nknFq2\\nuQW4Csr9q8/ua4+v32ogMJa6mhCMwRrLcPmQtPoJP3zvZ7o25ASMYKenNEd3GXC4aoZt5tiqBmu5\\ndXKPhf0dbNcS3IJNf8bTp57l8j4Wj3DN+fNrmP0yXfwhObb0UZ9D4yclmdWkIBm9B5L2yl0pJ+31\\nEYqSzsgoF9Tvd2SF75POHKOKOcTIEAecnSAEEhXNrKbvBpyptJ3l0FlH6w/2hiJ9lxVNk3SgsFOe\\nEbEO42qyq3DNAlPpiMd4DcmkPVJV9qrBJmyCKBmLIWMxKZMl7RAicsRkAWJp4YARzfiVCBjIVGAC\\nk6v/jR+/9T5DCOX1hkWt4jJeHLOqxvVCmwaen58S1u8grnmahvV//5KN7c/0+FQCJsDV1dW/aYz5\\nW7GT2k5UVL2aHNHHDlAPtz05RI9RdcTYhhjW6MawVWasrSD15FwVtQmv2H/odeNP6jw/VmUjAWiU\\ni/u4kPmPp6hz8PqcAU+W0S9vr3GrfgDjkP2oxFIW1Q2Gb/n8u/nLG829g2N0jTi8b3Lw74dVKQrb\\npp4oTrM+YwpHymBdTYgBsbbM7QmjJq7eKrUHypIUVpby3mYkAo1wctpVq1roJEYN253gQM4HP6ey\\nbxSLMRmtjkpNWYrZlMZe5aHKz9i33ldAu29PS/uyR43VbtSkLAtJHGK9vmYn+puxBmICiQPb8weY\\nusHNTlAjY8+7w336asMt/w9IzYAzlkoMw9ABcHZ8wsXlOfbkBDtp8AK5V3nHploQQkQqQxsGujCw\\n7TsaW2HFMq2nPLEr1ps1JydTLPDq6S0uuw0mZMRrglAGscpnKVX8jVEQdvdutyp2ydjB2jAOM73F\\n9Pbr1Itjkq31+yPvlpz2lw+g+4PErmQyOv9nDFacjs8YR8aQ2kdsnj3RazWB9vq5rqvU6gwpCv+J\\nnRFyh8Vhm9s4PyXkHoOjmp/Q90tMzhg/xTVz7GROzBnpVsSnP+Dy/e8UVRkLZoqrp2RxmOltTH1C\\nXU2w1UQJalWNd4Zu/SHDleHstdf58N2eob0mZQe+IoQ15A2Sa/rr50hxSbGlD5gLeYYys2x26z4h\\nY/skl0pv30hRZMmMz0kE/C7ZpMzVOjI5tFTG6oiQOOrFLZBzSBPM7JR60pC6K9qQqFytHqdxDD7l\\nWbIV2D0CMRL2xFWYqsZVE/ziNsb58n2rIAkHAimK+LSYeInbvsNgvkhujiFnkokQSkLrteK2Wcdm\\ndPmMqFAmCcz6bxD4DHl4wLD5KRjDECKVdUwaTxcD2xxwzlBhuXvnhHc2G64vBmJ7DuL+fT6F41ML\\nmDnn3z4+Pv723Vsn/9Sz68fYeoH4BmMM0U7JccPIIttj7iVIlMVIUcGRrNZLYhwSB8Q2ZDMgODXf\\nDh1RCksr7WcuRzbqblzjT/4ZgH9IUBVQRaKMZLs7L2J3G5EGiBF2HHM4hRBVRurFoDgCjC/+dz3X\\nR4OovqsUhY64s2JCN9XQkSUonCui+IoYQlSngJQSORusLUmGKXJ5u6ZMKoLegMk3NlLG72ynqP5i\\nMOcj98+U81vXlI1m1LREN8HiBmKt22XPoGzYl71XzlFZ06NOppQqvWSypZZBPRZNuefluvJoIQUj\\n6mwB+gFxSrIxNvOo/zIzHvL67WveffhQN+iSnAwpMJnWdEQunz/jzdv3EF8rgSglnFeVlSyZ+bQh\\npcx7j59wtFhwMp8RH/fUzYRaPBPXsJjPcJfC4I9gqCBf4qIwZEUMUhk2B4uRrFKCHxEiKBCajHZM\\naj8ns1ucvPYlkq2KMhC7akVnWu0uuRzt9CQbXA5YtyHkipw21OmndHyRaBfgE2RP2jxj8/Q9bX/k\\nMgo2Jl7ZYMQTjb6f5E57YG6OqSZKdKOhaY6VVCUT7GSCcQK2YvPkJ4TrB6TNOXHYqI6s6IiVjrp4\\nFfCu5gTxNNMTNaN3NblfsXx2hZ/MaLt7XP5gSxzWJGokraC3hS3sqKoZbdsjvmbYtnjRkQrtZ+yl\\nJcc9Ssk/I2oztiN2y6nArxmbyz5Q7nkmQ4zE2JEy9JtLrK0Ra7DVMfXJfcKwxs9BfI2wxk6OcdIr\\n5Fva/uOeaaxVODoXRa1yzSIGcR5XzfBzHQfJKMciGV0kKbfU/ffJ3WM8hhivuOWv2GwuqGYVF90X\\nyaHFSiZ238XFhEy/DpM7u1ibd98z2Pghs+1bbNbfo5m9S9quVDIxZZqqohZPxhBjQpxwZGpuHR9x\\nsV3y7uNX6S9/hLjJRRpW//lHFvUncHxqARPg+vr6Xw8hfiMPTRXbFVVzhLgZbgq5r4jdxQHVXQ8t\\nwkIJOiOkoZi5Qh4drjojyozUXWmW6WokdKWK8gcefnuEYnyrP8kxBvP8QmB/8cg5aCW0+wD62vSC\\nwpDSerTtEKU0zktllHeQLLrBjpVjgTAP3+WFs5dryLtAsZPV24kmxGIYLSNSow+aqRBxBwbcha2X\\nAKMSewkpzX7DOGead8LuZg8PjzETUdiKPQ3lxf5yzonUb0gpq90U5dJSxJLJ2RGjKo2knLDCjft/\\naByu97rcgxQx+IOkeQzgpU8uGUNW4QOrgXtUqBnvYQgBYUseGqrJHIOjM5ln7Qn3wjNqZ9m0LbNJ\\nA1lYrVZUtccboWo829ixWm5YLBbUZR206y0pBdqQGBA2secYHY+aNROu1hds+8wte0zbq2eowSIm\\ngJ2T5ClSQLPxaTG2eJ/mWIgnB9UFRmcIc9bvzs+Y3f8S1XRR7khhecLuW0okdumbRMiGKl7huz/g\\nrHmfoduwXCb60BKNcDTvec7XYbD4quLy/H1oN2TvkFwcKRR41/6WdTrbV+B64yfUR7d1AKOsv35o\\n9QPUc4wJbJ68Q7r+kL69LnJ9HmgKqpPJ1iL+BDu/hXU1rp4i0xO67XNctkCLVJbm5DYmD2zIODvR\\n9dRvdJQCQAzSnFAtbrG5egZho6MOYhhJcer9qM9bilH1UVOpzFPU9gwHLi7lBo+tBXLW/WtUUsqJ\\nHAIGi4mR3Cyo5sfYumLIPbbxuFyRJJGiwUqN9ejrg5qZD0NGbEmOY9Dv2rriIKIIjqun2GaOOF+e\\nn0TIA3b9Pj4+IPU/o332mGSgNfpsDBaSm3E2eReznVLF32XRWF47Nqy6gSebcy75DarpqSbiKBIy\\n3fxN4uodtjkRQ2YaN1zEjhQy3npu1Q1dFxlyZNpUbLcdVWHsPug7Ns/W5P4KTPVv8ykdn2rAzDl/\\n6/j4+Lcb0m/264fk41dx0xP6i0sl4xyML4ybtooTJFw9KcPzoFUi5UEW+uUTqrPP4+0tutUTrViM\\nZi4p9ZjiC5nTUBaJ05y5ZIL/MBj2BtPzIPjtmG8fCZoJkj5QuTTUZPe6l1epyjSLILboOpZzjNDY\\n7g/lADI50KLdX+2N68ij7YgciC+PkEkugU0og9SRGFvUocARY9KH2loqX5EQhqjbq6Sg+QsjqSCD\\nOJw1Oodli+jyblzB7CHo0Qoqm7EFtrsXzpaRhRGmMnb3HTH+bSGoABg50EflIMM+YG7mnVj//p5K\\nuabxuoQyDpAjOfWanBXoUqyeYzqbkpwnM1BFT9ucItGzOFnw6PklJgyYpBB8EnDeEELgcr1mudkw\\nXczJRtfN0XxOFwIhCw8ePaTtelabDZOqJqfMMARSY1l1ayaVR2rDpn2Elw5TC7mbInQQdZ1R7mvM\\nWYNlNDsm9nhPklikuEYYV+HrZo/kFMu8BCADkipMyhgClQGX3mFifsLQvkPlHKttR5s2JDshY+ni\\nlr6/Jqc1UlX0q0fE9QOc8cRstAlSNGmNMUjV6GB9Budsgf4MKbT0XYurJuRssdMZUyecP/wu2/Uj\\n0vZaYWSrkoopC0hLrk6Y3XqTs1fewDYPID6nDS02ndHnK7z9DMlkYmoQWkLfwfoxEzulixsNOuPc\\nNpBsTdUsGNaXOCJDLKSVxK5NJLIXGxRKYpnVpHs0UdhXn7L3ex3bFymQ6VXgQYScBlI9RezA5Owu\\n2Z/gplNs6fvlbIg5ks7/V5rpL9NP3sRKRUaIxmJ9TWy3Rey+RuqE8ROsrxFjsFZnQrGVXnNOZBNI\\n20cs5HfYnj+g6wdCL0RJSDLaPsqKlsV8jeMBt+1z5tXA116fMZ9NeXa9pXl6wXb1Fj1/gWr6KuQL\\n7vR/g2fnF2Qz4KxjXlnWQ09KZeSr2DumFJmIQ3LkdHJMUyUeLp/z5PFnCdffQ9x8m4ar/+Ilm90n\\ncnyqARPg+vr636jr+p9PUXzYXFMtzhC7IA+PydYg8WWwZ2LoN4hEMgYrFTEPKJNUafT95fskP8UV\\nXdMsttC4hKdF3NYAACAASURBVJHZJqYmx0Hh3JGQImPw4GNLzpdVki8G0cPDAJL3nctcNmozZpek\\nHTSr9ZZoLyqNbNeiuVE0HJXQsQ8IvOSc5ao4CB97WDSV6z3s85VKVoNmETQoTgXkoBsryujtt0sy\\nCeMmiK1IxmgfsVy/JsuJIbTkGLUvNQZTGQPjqCOi834jangYCsefhRFJMMg4twaMs6mSRih7dHYx\\n5Tq0Co1hQJzfXZsYwRYLMmNGhZUb3xbD0JfERTe58etOIWCMsL14wPzuQJBjMo42vc6j+ofE63OC\\n7eiicFJNiAJiLVagDRFXNTRNQ86JTejZbNfcXpwQY2C53jA3nioZni1XNHVDiInKOaw1DERCFwgp\\nEc2ADN/Cu79M9i0kDy4wxF7Ne/MY8nTNaOKxN/EVKRXdaP+E7FdL6aWLCWS8jhLxhCP5v7kll2w3\\nmau4JeaW9QCSAyZNCWnDIAmfj5hUv8L58gpnZywvfoxQk8r6Ubtn/X5zFp319BOyrfFVQxg6FRFf\\nXVNPzqiOzzhaOB7+6A+4WF8S20H9GN2cLImcHdZ6hEh1/CVufe7P4ezfZdj8PlXXYLLj/tmbfPAk\\nY8Mt1iYyMR64JiQHoSWaCTn0GIS+2zLKKiagmiyKcXberwsgx64kc4acdIxKUtAkMQWtJ1MsKFDe\\nQdi6zvMeFYuDJnQpIqknJotZHFOfvcZyueL49hsl0U4kCQy5oYoXVPESW7/LJk6w6QtEE7DZYJ2Q\\n8NTWk6YTTEY9RfOYpBuyGQVdAlHA5UTe/hHV8vdZrjaaMERtFVj2PWuL0KaERMvT59f8M1/5LLPG\\nYcRhU+Le8RGNsTzdfsjj4TNI/xh3+ds8Mj1GLGIzQ+i1t79ek5Ku79P5EXHQoHl3MWe57li3lzze\\nDKQqsrq4JIcNSPWvfsxm94kc8qcXGv/TH0dHR//NcjX8NTu5y+z1XyeFDetH39PNcbje/d0NyE0A\\nU0Pq95mzaYipxRgdvjV+tiOhKMElEGOL5LTfTMr7pjhAThizr+Q+rtJ8scp8mYzezdfmAlWOm7NC\\nT/vNad/f2L/O7DbrPPplFvudlCI7jdfR5uylFSYcBs2braxSixUG7jjDKKWPKaUHtH+57CqxUZ10\\nFGE2kgjiOHQy0SBXxkXQ/mFGh7ZzTupWU2BgTQYOr7jAgCntrlHQmbaxCtZRnMLmzUKWRCqblDVV\\ngchsCRaxJAFlNrQkJ+PIi2DU9FekBBVLCoFUrlXvfUE3YlAt1LAB4zn93K+Smxk+TRB3yXzzP/CG\\nrdlK0M00wXEzxVlhtd3iXcVsOgUSm3aDM5bbixMm1YSL5SWTasLji3Mu2g3bPpAksgktnUSmE0vl\\nHSFGXD2h38JV/AsE/6sqvzYMOigetW8WQ08O/a6PpvdEnwfjKv3as2Cqmtn9L2NdGWwv6yFKACoq\\n+4ST5f+kCkuuYt2tSAlCLGszC1Y6hmDoTMY3/yLbq7+Pmf1zyPaC5Tt/SEjbkiCOoVLUDN56nJti\\nJydQz4ntJSlFUrfE+Bm+mSHtJZurt8ldh5gJeEogUpHvCNjJKc3JfXJzzGwecP4VTtw3aGJNP7nD\\n+w+PdPOvakhqPJ+zIfcbDWYxQByI7RX96qmu0ZyIdkZ9+qqS3fo1KbTKg0iRFNrCDSjwvYDEQYVU\\nUlRrtdSxD72mPAtK89NOsEWs1yfGTjCTM+avfYXZ2T1FbFyNmKiIU3a45Ijbt6D5LEZWmPb32Da/\\niQsRKUxhnwJpTECTQ0yHZK/nIpJMxOQy4oElS6RJf4em/QnnTzbkZBVWzok09rFF9ypN/PUTGWO4\\nf7em7QfuHs352t3bzGdTrrotP3pwxU9Wr2KG7yFV1jUrFWITsc1gDSZB4/T1Qua0mXPsGyqXebrc\\nUonQNoG3H32J9QffQcyU2D39ePjvEzg+9QoTYLlc/jtVVf1LQ7+sUg4YN+folV9i+ewDUjcyI/cB\\nSjtgFhkZjECmzC65qtC2M3nowdVlXkuzaGOcBscSLMdK0pTXxRQxORxAJh89bsCnH1Np3jzGgHhI\\nftlXU6Oha/mAe/ivVGzkoh4E7NQ5xr4HghzAjC87967iAnb0SGTfs2TPKs3jNeSEyM6LZ3dNYwWX\\nSzUOyjA15TpLQa/bQjajel1JE1RRJIagZJpd1Vg+thSmJ+PfC2JlZw01MkAVelNbpPGEgtWBajLW\\nGGLOpByLCLTHWLebY40h4Kz+PaaQOnbjFUYtzXZEMFPk9SDGgB0rzhhJ3Zr+8gH1K18mmw2WNd5t\\n2LqK1EEYelKKnDRTztdr+hyYGWFYX+G9ZxsjJmbWT54wrRru372NNY5ZP6MdBkwjPLpaY5qGvN6w\\nlVgIHJbNak3lMxM/YY3DV1P6tMG4QEgRIxYjhmStVvno5zfeaXtARE2YncLrKQasK/0mMskMClbn\\niG9/jvXCNiR1q8jq96m3PoIkWqmwsuGk/irXskAm/wKpfY/te28R0lqTEKGMGgBGSWjWT8lVTcoD\\nNrR4OyHGDcafkoZr2su3sUkQM8PPbxH6FamP4GbMj2+zXV0zO7mLHL9OdfIKEjoG4/B2wpP+1xmC\\nwa7W2NQR8KR2QKyogk2KRMmEocekwLBdEdr1br0H0zC79RrZTrCpp9tq4pFTJg1tIaiNkSlrsIyD\\niqukcS5W13O2DWI9OanJORhy1PnUbGpojpgd3cXObmPriiRCdjU+63UjFXb4HnLxO6TTvwpYpu33\\nCUPitfQ/k901dnuXB9s36CfHIGdI/1Oo38RmQ2xOyCbh48iEV8cdIVKtvkN//n3WIe4QOAoiYRFS\\nLP/OCEFbEL0PT1ZbjDjesKov+/ziimgq3rvaYvL3MVUhM/ZCG1umiwliMs4ZwpDYdh3GWCosLkHl\\nNIH96udv89Y77xL7gfbiXGfL0/oT04z9uOMXImDmnD+czWb/6a99/Sv/2re+8x0/e/3X6ELGEegL\\n8+ywuoxYhfiGAePUuHYMRiZDMk6p7bnHxITzjija4zI4MKP27IECilDk+ASyRWK/RzP/1Echv4ys\\n3MP3lXEpmt1M4widHPzJDjbdk5O0Ss3sf77Zs9wfO1VZKfq76JvJWGGO59n1YfSsh3N7Ny9q/7q8\\n+1WphotJ7PgqdZSJu35tigmMxXuvWWzSQWYjysy0zuw+xi6PkPGz59KTlB1ykDhIUrJ+qhATYqUE\\n0vKQZ7DOE3PG+XKdjPfT7M5l5UBUMUMOpU9uxs+tIylRDOJq+tUSP7QYX3MUvosfJqy2K+raMndT\\n/KRiO3RMak+FJfQDTa3+jwaoK0c1rei3LRera4aYMMZhxXDn+IQQI6s+kIwnDIH1usc5wRmLz1NE\\nfkifv0yuJtAPID3OeRVeGBmyBZmwTtVnxHhlxboy4D70pG4JTUXEYkvPKosh2i1pMDr+gtB3Pc66\\n0hvT5AIylYWUK4I9Ku2SnrReE9IGy4wkB+YGxpCNx9Rzsp9TLd7EsSWYjKuO6B/8Ie3qsSa8uSZ7\\n1NmIQpSz4L1hc/2I+vh17N0v4GbHVN4Ta4fH0cYBYxbMFp7LJ0umk2OqIdAPW8ROMAa61RU4VxI9\\np4SaTYeQMM4jbqomDnmrguHOkmNFSBljHdZYJSWNM5YplFnmuM8zpcFUR4ibYipbPEsHlf+MkWQb\\n6jufIQ09XchMxWKbY/BTpnnJTN7jIn4Jt/lfSKufIUw5Ov9v2UShyy1ZhAcSeKWZ8+fuXTNN3+Vi\\nveQyGiT32Pb3OJnPCau7PDV/nlDdJTtDlQfS9jHT/HsMFx/S9wMgLC8H5sduR7hPKe0e93GkbLT0\\nQ8BjMAbuVlPefnpOR+Ld6zUue/DqHJUT6gSDQISmqbQdgt62ma848jUZikFj4sMnzziZNvzRB/eJ\\nq5+AyH+W4+Zvv3SD+wSPX4iACbDZbP6jb3/7H/xWGBrfXn1AtXiVrZ1gzEUhtB6wUHPGz44Z1hdl\\nCFZ/r5tcQEQ1S3MadGi+24BY/cJEVOwgj4w0JZyMT7MZzXNNA1Gb9obDgd+XH/9oRaBx1y8qGC8E\\nzV0FmG921Mq7H/xc4MQDi6s8Qqp8HDS7D3B7ibr9Z1bI82OuegyihUhS7jJj/3GsnsfAs7sgEZCx\\n/B8rYVOmaVIZb1GyAaXZP46xjJWkuAbjKuLQMqr3jOdMaRzItgeEsIOqfKzmd7q0iRRUfalo2ajA\\nukk6opJ0bcVB+2Nii4ej86QYkKiVQsoKpI29TzGW7uoh/uiKbugY+pYjNyOFnt50NFKDNRzP51xv\\n1gw50G56DWC10G43RNPjrGHVbtn2PdZ6mqnO0907OeU0BJZdx3roeb66JKaEbyxREhNzSZd+QPRf\\nx9c1IXXEHDESiabEzCRk6xBfY40tiQMqS2a00h5iwhdj6IxgsiGZSE6GEBxd0IqishUxaxIqIsQC\\nmye23Du9TZh4Lj/cEofI5vEPIXmyHMw6754zHR2yk2OaqbBdZybTUzYf/BHp8jkiFbZZaEDqlvjZ\\nlNy3iJsgVQ22plossMf30DTYYOupSiKmiHMVLgfi5pzaN2TjCGyx9QSMJQa9z5ISfRjIuaO9foKk\\ngDWGnJ3qn7ZXmHpRGPiAGIxThao4tkVKhZ5LVSnGYyu1L7R+ivFzyC3d6iESAskIdnIHf1ZzdPYl\\nggnEvkdshZud4CYzXOqZuI5pFraX/xXL7bU6QkrLpsvEFFTwxSq34Lpf8f2HPQ/XW3BqVWYMGGno\\n+yd8/V5gEh6wHiZMdBfgcvuM68sNOcquVTFbFLZvZvdMjLPhpojJjHuId4YQMnMn/PjiiiFF2pSY\\nuYo2DnT9QF17+l5nQpupznjGENTlJGecccyqim4YOPI1bR945e4pbz95ykW7pb2qdd2k4a9/zBb1\\niR6/MAEz53xR1/V//KXP3/0Pfvbe21V19DqTszdYr58gEsgY5MDSaVg/1/5XHFR3dPc+WmUa6xjn\\nn1IMqmEoo3+m1Yooa19tt+jH+muMA9ZjnddZxNjv+g56lAqrBPBxkf2jjZx15mqv+HNwwkMSzkde\\npQFqVM3QOCQUVQH938e1MV84dg8Be0j05Wcccz7Q0ZZCqjH25vWPmSeFSYvdJQF6vhJcsxJ8di8T\\no8SuAldbZ8khF+kstQYaBtUSdlahdJ0TK/qWoiLV1tqP3vPx/EaNvJUAJFgi2UywdcQaR1hf7ec5\\njUWcKfOVFrGu2MXJAUxdepzREIeeyWJKrhfMbOCYd9m0NctwwWl1hqss626LNYbz62uMd1R1hWSh\\nj1F9W6uKlBPLviUmpddPnNOvM48fwfDqnVucX11zuljw/pMHDAG8CdRMOJI/xsXElf8qYRDcxtG5\\nFokWxCrU6modUjeGkCOumZJxZWxHdoGEscNWSC8uGCw/JCZVqNEevzDkoBMgRJy1yn5dJzZcIPIF\\npvU1m9wjMgXZcrjVZPG4o1epju5RNQtybOH6Qy7f+yYRh6mbMs5jsVmIUkha1Zzj+28i1ZE+j77B\\n1jNiuyKkRO46nPell9hy9ewBjdXAHNq1jk6lzNBdEdaXmBjAOtLQQ2qR1O3uu/gG08y1RyvCyHgd\\nHT6SFMJO6Mq6BDE11ewuVEfYZo6xjpgCcfMhadVR1WfI8QK7uM3RnTcZ+g8Q5tiUcIspzmq7qVuv\\n6SRya7Hl2cUf4lNgWnkkZzbDgJBoak+SpAlntqwGYbXtAYMLFmwgZoOkLRnLD55elfnL52ytfu9X\\nV1tIRhNESiJfUC4VBjEl15byDJV2TNnjpnUF9BxXnj4kzvsObzwyDKxCwDgh9uMIX+l/FnjXO0Ma\\nhOO6JobE3NV45zC15edPz+niwOPzW4TlzxFx/2XO6fL/2+72Z3v8QpB+xkNE6tls9s56a191t77C\\n9OwNlu9+U3sCgJ3eIm3Pd3+/s+e6MRAvYCqsm2hfszgHgGBspQGSqrA/I5RB6jFqlHoJygBv0e3Q\\nhZMTKfZwEDT3vysVZho3/I8/VD6usBWBEf/QgLP7FLu4feMwbl8Z7/qI5TVZc/2b36mSjTS5twf/\\n9ZBBeeNLKP8n5frGz1+G/kslKWiwO7zv2i9Uko0pwWrMJJTdKuis5v6Mu0QAq/3KoqKSpTxkGolV\\nqQSK0LxegxGDqWvN7mO6GTTFlk1MDXLFOcKQdB7P14SwJYYeCb3Ck8aQolKTVLVdh/mNsaQwkONA\\njAO+qohRZxW7zSXYgdmrv8bR6S1ebX6HuPyQgR43VBwdzdhs1lTWHbiyZAwqcEaKGFPEJMSQY2Ta\\nNNTWMfEVIUNXrJFqXzPzFavrJes48GB5ThSovMN7Qaxhlb/OsvsKYXlBkExqtzr/JxZbT1SRxhqG\\nPtDMj8mlanD1VJ0xJJc1L7gotD5Qh45p/z9CuCKFTE46ZpBRIsc48pOicJwrHvF56slvcP7eNwkX\\n7yKmKmuiQNziaGYnVLc+S7+9gu0Duqsn6lEgYCVDjDr+Y2qkmlMvXmN694sksbjpUUGRUBm80COY\\nYhov9Ns11jXEqFyEbrPEWkBqJCfisEbiwNCrpKB3mX6zKrPd48PnsPURtlnokg0DObS69sRAisRh\\nq+zWlLGTOX56qiRD1+CsJ7IlDQM5JnxVYQi40zeQDF3XqQ6ttZjpHFucPMz6EbY5ZjCexl9hnv0N\\nGjPgp/p9xBjpB52HxVnWQ0voAjEKgaz9YZM1ucwJEyGIgRQwYvDOEnMqMn1CHDQ4GmPIKWGt2QXL\\nnIqwhCk7Yiyje1ntyJzJHE1q7s4qnmxarocBaw1TowIUAcgxMZ/VrJYdKWeaiSaCOQDZUFvLaTMl\\nxcRRVRMNxCGSveFBv+S9H07J/TVpWB3nHPfsz0/x+IWpMAFyzp2I/Muz2fxvri9+4uLinvrAJWVR\\nubqm2wjjIPpe1uumekymqGyIHAQP9VFUJZsAWCgDvJJSGYwv4xplWxvDlUpEZbI4bOUU5g0aOKXM\\nNI6VpsbeA7n3l1Wcu0ryUJlHg8YYKMbLvlnPwk4T99BCiz0U/GL+s6uYXwjhN973gLhw+F4fPbsU\\nofs9GLszgRbVLzW22vcEyxD6OKphzJid7oP6bvpSMikdpAlFa3N/fVlDTRGGVxH5SOpbUj/g6nr/\\n2oPbnOMAOTIMHc7X5JToukvOjk65vFiCcarfEIrSiuj6UogtEY32WsR4DIGh2ypRQ1ArrW1LfP6Q\\npQcTfpUzf8Usd/QEltsNi8mMYdOSrLBsW0iJSV2jc5JgovZ3jxczrpZrluuW1DT0IWFFWHYbFvMF\\ni8mE2nq6SYdZB86qBVEGau9Z9z2btsfY7zCp73Odj/GbLb3THrEYu7N2sq7C1XPEVGSJWF9j/YRU\\n3EmyqABFb8FHkP5bhOGCyjQE0zHkQIyZyqp9XSojWs3Z6/RXj3H+66RmTe0t0UzZGwBEpD6imZ8S\\nhp58+Tbd+WNMGPR3MgZji9gp1eIu1ckb+NltbDNFqgormpQZW6t1VBzAWExS9ao4gK2muv76lji0\\neKekr2FYYTGEdoWxFcY4jB3o11e6TkcURFcqOfSEfq0VeL9V5m6KpYUzYMRi52e46S3s9ARbTQrK\\nY0hpwDPBNFti9Mxmjs3yOUOn3pAiluwc1ewMcaLs/e055ug+yXmO8hWz7v9ka7fcauaknJjVNf2Q\\nuNpsuXN3zuPlFd0mERAcRpP5rGMfo2BHMqqMJWJVEB+oxbEOfRF6yEiSveFBzrtevubL2vpQJbV9\\nO8RYwQo0laU1wtWgSS4xEXIgIAWJgBgyIWbmR56cDCkkZtYzpMTcK9pSWYfxlrbvuWzX1Kbm6eYv\\nEjd/D3FH//UvSrCEX7AKczyOj4//9/U2/ZVc3aOaHtFd/PyAkFJUffh4+FMdwFW/cpyn0l+UICuC\\nGKfKIFLUcgTVX8zhwGtxXCT7jTgZo91CZYToDGdOWj3kAmilDJLA1iWj6ktM0vfKZXyBA9eHfPBP\\nDQiFxCP5RgX8cYdAwfD0DXIJNllGJp/s7tvuJOj5X2QE3xRRsLu0YZzd3PUipVSRheQgpaIchfCV\\nSVuqUEHHCuTgc45XksudFlsg332/ZFdxj67iu+9iDLiUQKrwo9p8lesql5hRCNlYg6Ei0WJF6LsN\\nxk4VaQDKJLpuwtYq0UUyzlcIlpg7Qq/f5YgopH5D7Da4+W3q17/MreklJ/47VHEFg2UIgbr2WCtc\\nb1rEgrOO1XVPZYXKWZw1dH2gi9pzb6qGxnvqxtOFgdpYvvjGmzx+/pwsBufAZk9MA/dvn/LWz37O\\nddhiKmHjvsqWv8Tm+gk2DsTBk632S8V4MA7ra7JEoMFUHswAye5GEVzK2Lyi2vwtTH5OnyK57zCV\\nJeREjOCsIg4moRq8Z78J7be5uvoV0nrD+tFPEYnENJAz1M0Z1leksKVbn5OGDZIHEoZqcoqdnOpo\\nh1jqW5/BTk/1Oo3D+Hqn+AQoIiDQbVdIGEqLQjA5MeSMzR3rywvm0wXbtsU5R+pX9JvnJbBYdTzp\\nlqRhw74tUZ4TKVJyxSdW88GifuRq3OQEP78NboavauziWBEPAjZfse0aTGqp7ZqhHQhxoe0Ho+sr\\nu4rm+FRRlfCU3L+DdZ8Be8WxeYRNP8GGnplr8BhSHzFieHa5ZVo7QpP48NkFdV0zDD1DVA59Htms\\novZ+gnpi6mOT8MYRQ1Td3YPlrp66VgOnkf0olYEUiybTuDWVflBVO+7MKtZD4HKrBU1jLEMG67T9\\nYq1QV4ocrZYdtffMTIURXfdWDH03cHsxJ5jM0AVaCVyGKe/94AoRN4Tt4+rj9rxP4/iFqjDH4/r6\\n+rem0+n3N9vzSTbFKSGXam5Xxb38UNGBDCkeBKkxZSr9Q0TlqkR2BCFA/ReTColLKlViUYBRKLCM\\nH8hYLXmFX0LApEgm6HCuc2o7ljPGViQp3pNQAv/Yz3ux8iwzGVKy3AOt1JeUmgcfWpmw+3BYYLKS\\nNWsP7mPOpTdtX2WO9/AgkO9+KmxT7FgZJzWBHv8mpV2g2gXhUUw9S4HIbwZkxpmTjI4o7KTGHIgS\\ngFSVKd0olPUeqiBFHgN1ucdjsjB+NKQwOhPktMHYmphaRPw+WI5gfI7kmEkp4qwj54HQZ1WHwtBM\\njtXAPCVizviJxxph2Jyzfvvb+HtfZXX0GyzqB8zsj6hkibWGttXXLBZzQt8zbSzOWQzQtr0Gjqai\\n8lrxrtZrNr3nzt27PH38iIfn52zbgRgz9+4ds111HC1mPL9acr5eMniYi2POj7B+Rjz5GjEkbNgA\\nKr4tu5lTg8EiNpEYsMEieYXjOZ7HdNKRmZOGK3wToM/Uk4o2BmLO1LVW+TFEaqlJ2dFsf5f29CuE\\n84q0/gBhIItDzITKeYb1E4KgyEwaMCmCP2Z66/NUR6d0/QrfHDE9eYNtN4BJiG+ommkx/1bkZlSc\\nGtM/W6m4g4kD7foaayLr5SX15IhsHNYZQujI3TVhGBTyzR1DH9RUeUcSk9161LWl1VkiFQnBBj+7\\ng12cqei7qxhsoFrcLkpTCrjfcvCQmtwFevsq8TQhUZCuJw69Jg+LYxyJZvkNYvsWW0lY+V3OplPq\\n2rLNA9PphLv1jKEfEN/w+PyC41lNNat4sHzOa3dOaEPCy5RnyyvaISq5y2gVJ4CIJQ65jNEIfQjk\\nDNboSJGS3srzWgg+Ke6DpWqnm932MFauJiqbvEPY9D1EWDSONih7OQTdQyeVx3mh7wYq5zFBmDSG\\nbARnLderlpPphNm05vxqS7QZifD8+rOk4ZuQ+sVLdrtP9fiFrDABJpPJf7g4OvnrT59Hi4mF6KEy\\nSsY2qrIh4+YOu209Cztt2aJkUuiT7MYMoARLC6baCX7DXpljz6DRaimNSjwfIeyUvxJHjFtdhDtw\\nJxbfxRITSiDaB+KDIFWuaf8fSs2ZxmsZA/8LJ877x/0Qnk3jLcmyg64/VojhxXMzBnatMD9yStnP\\nduptlRvvv6tKtXkEmJ2jSCrn2o205PHc+nPeqQ0ZjHWqalSCpinf866aHd+jJAVS/AJzef3Nz6uV\\ntBUdls85FHF1HXcwRsgxloqiGHIXkphu2Pqge++VEGG9eooCKSasgbB5Qr95zuln/2la61nMTzj1\\nbzOXt4ippVsqhFrVDjEGh2Xo1ZtTmblSxnJ049puBx1vMMKk8dRo5bOYV5zMz7AGhn7grbffI1eB\\nunY4C9ZWnG++QJrcIwxC9veInBW4X2+biQZoETJV/DEyfBO2K2zyiDsm2Yi1W1KOtMMApvRgReE/\\nk4UYE1VSVaj+7lfpL16l3c4Iqyd0yyeAVRQmLGEohu5pIInD+Qnu+LNUx/cxtWDMEZmeOFxTVXP8\\n6f3d85YLVDzqKg/dgDWJoR+op3PC5pL1k/epfFU0mAsjviBMKSxpzx+W5yke+N8m1Q0+WPPjmtLn\\nSRMz0xzTHN8F32BwuPkxfjIvAgojaCJ8fvGYr1Vv8Y3lr/BsaFiEd1jwLp2JBPknOO/PCAgznjNZ\\n/m1S3dH2HWfTKafTuRp7OEsfB2wWfvmV13h6vWR93TKdzFh1G3726DGVm3K8sJyvNzjreba6pu8z\\nsdujM3GsFlPEWMF5owmU1U+bQtrny6kgTQWSzUlfawrZ53AGPme11HNWx8FCFmoRrSytx5hM16nL\\nyNG8wnlD6gUi3KomZJNZtQNDiISQ+fzdM7rQkqPh4XDFqq958MMlplr8P2Hz+C+9dMP6FI9fyAoT\\noG3b/8S51b/i/eTVGCoQnRNy1pJSqwPXcSjBwWBKgBMCmCk5dcqqzaiI8g2YccTroxqz4kpFIyUY\\njvDfQX9xFAt4SbAEikuEV8ZmyTZTKH52pWJ8Uf3n5eXizYpYSjZYpo1v/ukLuc7LAuL/y927xOqW\\nbfddvzHnXI/vsfc+jzp1qnzvzb342pFjbIJjnIdQwCIK6QShIIKEaJBOWohGJKCDkBKgQQvRoEcL\\n0QOEkiAeCgmEOER52MZObN9b5Xur7qOe57XP3vvb37fWmo9BY8y1vm+fOuXYaZCqO6Wqc87+vr3W\\nXGvNhlDdsQAAIABJREFUNccY//Ef/7EYsd+RuctnoszPi2j1zg9Pc8Sn86/fzdlUg9zxxXP1XMd6\\nU9vcxNVNrqR6Z0x9yaBvrbZ1jgKxbgyzd0w1aPVfp7J/x2EkroyjDR3jkHDONEhNFSce/aoqHbjU\\nfQJCoagjTyM5Z8K6JVcBawGc94TNQ8jK7of/L/3DHycF5dP8TYYucW/4VXRUkhPyWHA+s3JKIy1d\\nUHIodG1LyYVhsmbBFxcr9oeR9XZNKqmSj4WYE9e7a7rQMBxGVk3D1f5A0cJ63dIW4VH/LpF3uNVb\\nLl/AavMHmfp/CRTc8BEMf5uGTwlhzQqlQUmrNb4VmjJQRBiKEIu3DTFnI2Q5Nck0hawQi3Xb2IbA\\ns803mK6/Tdl/jPNr5p6OpTQoA2hEZEW3fUTz8Ot0Z48pFEo+kGWg6+/RXLxF6ebyI11YznODcbRQ\\n4i05jvjQMr38lGn3gtB0FN8sxs85EzfBBcpwhcZD1dm1NVoxJfzshJ2kJpaUjHP47pxu+9hQDNcQ\\nthd0my3/7Jsf8zMP3+eXP/hx3rl+i0LPIC26bvjDq3f4++9+h5KveeHEYOvtmq79eZon/xtvbCJT\\nC51b8WMXG87PVwzTiJOGhFJiomt7nl7fEKNSpDCUAw5ld610zcjZ2Yasnhcvr2sayNzUnIrVnJsn\\nZ45OVoqXJZdZiuUhrXzU3qlcSr3HMzHQ9ro7Da+hMvwLRYQYzSEZAHFKSZGuM3Jf01glglMI4olS\\nKK5wux/p25YSE2ebFerhoe/4tWfP8MDLTwviQ/kiGkv4AhtMVR1E5M+3bfyr6t5wFNvQcjaaf84T\\nS02hDyaRB1hhecL5LeQbg/lm4slSVrKcBCUfa6xcWAxLwS+wrrpZgg77/iuW6lSuT9VyaU4cNB5N\\nA1ImW9Qy5/UUPYncTiO8O9Hyogxnxf+vs6//GDP4u/rSEb7U5QdLhvBVQ/qZ3zp6n1IjzrtfOcLg\\nx5ZOJ3B0hUSNuOcWhHY2fM63sESCupyrlEKYc9HlyDC2ciKqCMSrU3dIGThcXtGt76FksmDEGCz6\\nUFjISXeMPxhzVguiESlG3vA+gJgubZAWv33EtH/KzfPfwl/fo7v4fbxYbZj6f47V2ae48BSmK9bh\\nnHGacO1ImhrOGkg50YRA71pSsnIN7xsKkGJmlECKA+v1A4b9gWYTaLuGtx9c8PLDa4bJcurh3IQ7\\nWnF4v6XbJobpO6y44nZ8H5FM26wIvkN6MzwuBXzOtqmWxlpnjTti1a6dUgRvzkNJxpR1JZAplADs\\nH8G04/7D38fT2+cE7UnxYKUa8SWSEs39b7D52s+iqwvy7UsIgZwHNpt7JOkI23OyFppYKjI/IxYV\\nEqyOsddM8g2aKwu2XS35e+dMWKJooGkDMQ4EaRhLXgzw7HrNcot1Rd5Zz0Im+5727E2ra23PWN17\\ng6Zv+fmvXPEvvr2jbR7wP3/nnKYI2d9we9uyfbjil7/9DsP4kihK2/TQjeSbd3ij/T5xnfAuEIIS\\nmTjfXKAp0fiOlDMuwVoaVi5wvR+4vd0TWs+4jzzqt3z1jS0fXl7y3ie3aJFaTwxeHTOtwAukDBoC\\njZfqARRCqNdqJQIn2R5l23eIKLtDtPVPIaeED9UprQ5vaDz9yrHfp+WdoiIwoRFa70nB2LGSFSbL\\n4HiFrl0xReV6PyLZjHSZIh+Pxim4OgzcvjgQ+sf/4DUbzhdifGENJoCq/i8XFxf/11cfnf0r739/\\nkiV6ABZoTgTNI6cEH9ER394nH3YVMkygnmMz09eMkmoJ4aynKUubqiOMy4l4cl5+9Zjzk6MIuZq4\\nujZr0A4tkxXAL2evzLLflck7Qpa/G6Opr/zw82UJTn5HZ4N+PIp83gk5taN68vcaSaI1ZVpFv5ey\\nn6OLcAr1WH1twWvVepXjOawerz7nmmexPn5Smc1lmYeIr7Vy/s512BwC0CDB4cVbZORlmSEuWEum\\nev/M/M+GvjpUOdZHW+tRcyLmiOIIIdia8B399quk2xU5XnN4+uu0/Vtcb9/gZvOTrNd/jHWvlO57\\nxOaaPn2IlIHLSwE/0bUNbRuqFimEJtCFgCsdMRXWTUc6jKyalo+fPuH8bMv1y2s2qw1Xwy0xKpc3\\nO/pVS+/MmI96QFvHqO/h6Gil4DQiJcAoTDEyqdU6d8DkEpOqFdiHQJ4mfKgkrDwjLZ5uBdOQidHD\\ncEXqvkF69i5N2jKFSI4HdBoJZ1/hwTd+hkkD3nXIMNCsH+DbM9bBURAaX9AyEYqSHXCS2LBUSSGV\\njCeToknO5TxVOT9j9opAnEaKFvrtA1IcGW4v8WkHkq0BQ33ep2Q3WwXz2eqKdS3t+gLRgl9tWT98\\nCw2BwIGvhV/no48Tv/zsF7gezxER/tDj9/iKe58fPrmh7zr8IeDawOEwsGpavFPGPHLWbhhlJLvM\\nOrSMqRC14EvEZ8e97YbNdsWqCXz/kyd4H/j05pZN51lv71OeRSYvxEERZxB5h4MCWRNJLIly1gpT\\nKFbgJYv7S84zcnLcHZwIm3XL7eHW7J8JyBIaMw8WoBRCIzSNkBKkJISmoGpEveCtQUAbPE3XItly\\nt048274lx8w0jRawqLLarHh0b8uzp5dcaQIpXD7r8avuvbj7wR/9x2xX/9TGF9pgAlxfX//5GONv\\n4jarU0t3J5ARqZtY3YS1PdZfLptvtiLdaszmoXOyAizfkQvOB7OtUltynR6n/pJBgCdF+KqG+csx\\neqr7uJECfI93VUghmcaoNbo9sQ6fIQHVCQoIvm7clT2rsxGdI6k5Mv4nH6Wc1DLKq8ZuuaJ6P+ab\\nUvOZdUrFedBokBDOeh+KGoW9ig3Mz/EOy7nirEZ2qj9zAHluXcosDC51fqa0c2RMI86IW3qMli0y\\nt++2wXM73uJDZ9HHNNbSiCqGV9TKY2vOtMyoRc2H4Xu0DHY8VyXVcFW0vHraYoILrNdwm/DSEMcX\\nxOE54eY++fwFu9Uj2ubHGdLIWxc/x+bsr1BuMvv9xP4QcTgrPXET27WQYkEK9CFQVIklMx4i4hxd\\n03LWr9jvdugAhxRp11ZX6hW8y6bkEx2+OFIZGH1AxeNLQRKVXSlAZPSemCLeB6Y4UWJhtenYdqaG\\nlFImBCO7vbx+TpnOcd01GjaEccfV9Z6wXrEehaG7x/rNtyglkHzP5uItpmLKSmH8R4bsTA5tOor+\\nBCAkZwLhrzprxQkhm9EMTUM83JiYgrO+njmN9ZkXvG+ZDtcQD4T9M9Lu2jSll5IoX1GeBctgdqyo\\nohnQ4ds16j2lachug/cDv/jg1yEF/tePf4H91JHzwL/+U+/w4fd+jXej0ktgHVp+/5tv8fT2mtz1\\nPL255NHFBVOK3OaB8+2KUiIhezZtx/V+z4PzM9bNikOeuLx6yaXveHqz58VhzyFFmtLzrQ8+4ZPd\\nDV6NjUqy+T48O6PvWq52NzSN8PVH5xyS8P7T5xQRpjIheHK2hughOIoegbWSC8k5hqw1+rRX1FVR\\nkZytPZ13gk6ZSSs9AdNuRuBwG0mto289LmZC6ECVQMGJIzrY7/f0TWDddjTe89HlNeqEtbR8cPmS\\ndOjI49M//nvbtf7/HV9Y0s/p6Pv+Pz47u/hLzy6zn6ONz4wFTqyGQ4JJXKWx5icc3teGyDrDCXri\\nWZo3hA8goa4mrQIDx7HktqBCgfaSnnYZYZadU62liHoyP4MOjVyUj1qNxxO85trsfwKVkDKLKrAY\\nn/kgd8mwZtjvlq4s337l9uliZGYjdISJj574Eu0u+d5Tb0JwTV/zIREhYkKg9QLFoUEQ9ce7/pnr\\nPbnfctTdPfX/TdrpKE13elXO+5O8qpDSRNOtQTpSGvCuEMLKmK5qij45jsy1l8DSuBoJ5JLxrkay\\nzlPiAdetEb+qhtsb5vQaf0fTSBxu8Agp7iANKA1hfU5Y38d1PW7zmNAUHnQfstZvoXHP7ubAMCTW\\nTY+0ymq9oRXrNtqEDcN4YyUKxdGK8ODefV5cv+STy1tiKUZG8QUXHKu+IWsh5sx+F1n3nTGBRWlC\\noBEP+UjiSjmjAlNKSAiM44FVG1itmkVP1zlHipmb3Q50hXQt681/wMcf/i2a9WPcZk0THBGhabZ4\\nXyA+w0//D40MjPsDJR4o4cCKDeSBHT9Pv36IuhE9+0OgK6NjLjezUIaJTCYPt2ic8G2F66cD6bCr\\nrbEKGWjacw5P38GV0cTNFcSVihBVWcm5oUA9fjUTqDT0j79OIy0qnu7hYzb9LT/j/yaTJspwQJo3\\n+UHJ/OT5mk+vvsPtIXPfr/jqGw+ZxoH19oxffuddNuctrgsQE0MsBk0Gy+vflw3Bt4xx4tEbF9zs\\ndrjGszsMfLK75fpwyyFby66Nb5hyZIwZXzwalbFkXC5stx3b0NI3ge1mRQ7Cbjgg3nN5ecvNOC23\\n0YlUgZDaUCApOSf6VU9MiRKrDJ63OToga6FvHA7l9qD4TshFsayTxzlHHJTNWcO6aXAiNAgb3+C8\\nZxxHkqqxz8VxvTuw7Rr6ENiXQtHMt799jXZv/p/55oM/wRd4fCkMpoiEs7OzX//mN3/qD/zD33xf\\nXld/+bo2WxLWkPZV5svXOrSFs2rfm88BzCUkuMZyHTnNrtZiExbYstY5mSGs/wks0KM7ESWYiSs6\\n+7E1otECzB1BaoNn5nrHYy9G6tzM0CaUbFT3egW64MV3hd0XZZ/ZYNbo+vMAWjm5j3fzqkcYfDGa\\ni8E8fQgzVC12v+vvHAULbA7HMpAGpLwiO+FOD8eCRJ/MYXZKXts1pjL7EDvvFCeatsf7FU2/ZTzc\\noPHALClYUsYJJC041xCCGUp1HuJgJS0L9GvlLb49ByevqBkd19RxTg1xeA4p45wnjteUNOKkOlnN\\nmrZ/hN/cozhl1YyszwpBntDyDEk3bLsOTYHDMDDEhBDxoWfMwjqIEShyxovw+P59nl1d83K/Zx8n\\n1IOSOLs442a3YzoYCrLZtGzWDeIc67YnTZEYI/thIhVFxaT7imZyUdrG0TqH60yiTou1wRuiMrr7\\nuNWfY3QT7WHC++cQHFoGRCby7nu08gGdTuzHidAEDvsDOSmugc43uOJosiOuPTIk8qM/Sex/1jSc\\n65rTksljRALkcU+OiktGdMpxpIzXpJRq/Cik4ZISR0CsCfvC6p4Z47Pk3XFZFQoStqzuvY3rL9C2\\no117WrfmQfkfYHjC7SFxb9PxlYf3KWz4cP8R+91Ingr3VyvatmM8HNjtJ5pVw+MHa27LxDhE9kMm\\nCKzaAJPNNMdIi6dZN+ynkYLw5OU1CSsHURU0laVUZFZqCuIpY6HrW7rW4cXhnLI9W0PjOGs8OcJv\\n/fCT2jGo1s1Wso+rQFtK2RSuRGoEjp1TC86QXkJwrBrHfrQ5LdwplJKUUqBtHBfbniYEJCsr1xha\\nV1tKRQqNeq4PE0kLD7cbU7hyyrfff86UH/7gcPnR1z9na/rCjC88JAugqklE/q1vf/sf/UrRVefm\\nAvffwWha78lSjQSYQTItSINN+YzxONZoRiuU96ESQnRpAP2ZckaxBTj3pzxCqwtFhbmH5IxkGpw7\\nR2Yz+BQsJ8GpYZgjWI7hy1wgVY99/GgWST/5dTi5H8uEubO5z/NVvWMkX72vdkh3EvG9ZshspM0g\\nmL23l76oee++2WAd6COimeJ8/WyJXU9GeWWabpmfERBaxsnYg+gxKl5y0OoITQtaSGmAEXCR4hKB\\nxp4ZVn/bOI+4dqn5CxKIaarlRMWiMnX4dlOj2qXqe8lfl3n9MAv410bHpZDKSNPfBwppuEHjnpIS\\nw8v30KeR0K6JZ2+T8kOk+ymKRh5djAzNt+n7a/qugwRNCVA6vNxwuIX9fiSPkbP1BieF1gt9E2zl\\neKXpOzrvuLh/n9165HY/cXG2ofOFISVijLSNo++2NE1Ec2ZKiV2MTKVALjTBVwTCkQsojmlKaIGU\\nWzr3f7PlJVMSQv5tyq2auHZynLU9+zyyn5T7mw03h1sa36DFGO7Zm3QfDbgkhJKJZYXT0Tg6FfrO\\nU6SUiC+BkhTyYGzQ9X1k95xyW4y2K5kYB3tvvImk2/PxRyhfjDEqi9NnjrJvetp7byOrNav1Chef\\nIsN7yOEd9uxomp7b/cijh+ecNZ5fefELCO/Td7/BEPfspgk5HNjfFPxa2a5XjBrRlCljIYjSqXV+\\nofXEeMD1njRG/FDYx4nbIXEYraG9+GKMbTXfnZJpVyA1B69i6Z2pWNPoe2crutDwxvmGNjh+9b0P\\n+drbD3n67IpxTjcUe9eXlhXiDOlCaxAgOG95TR88bQtBhNuDogE2m4bhkIhjRIvQdZZvX/UN3jla\\n7+346og5cW+9YkqRe13Lx9c7vPP0wXS+O+f47vMX3O4cOT3/d163pXzRxpfCYAKo6m/2ff+f3ztz\\nf/HlDd7N0OhnjKbtqQ6tZSem3AnO8ppF8L5dopBXI2xrXeOsrZO3iFSLVrDG6kBxs8GtvyTmBy9Q\\n7QxvumM0JlrrnKrav9SJznkX22flji2bReERK1SxHOmsdDTXUc1Gvx4XOc2scmqG5DhZXjVNp4by\\nlCEqr7lHFvXN+c3Pmrnj79bvaj5+Q6MZlvqhcOLk6N0jzVJcx9/Vem4j3UxDBHSxlfOmON9TzQVX\\nn6FoIU87JGwIbWedbFKsqiRV53SyOloADS0xDTipstQV3qfZQIkLgUTqnXdOsPahpW70BvtZFker\\nt27X7fst2jRoLmhrDkSe9sj197h69i0ILe3FV/josif0P0u3PiN0OzZ9olm9g89XhNTQtNV/Ci2a\\nBR+E1arn2fUVvg34EOi6jvX5Y64OF6i7oumesM8T+8OEU0+/mjiMVsTeOMemb+m1Y3j+giEKbduT\\nK8PUZSElu/fjlBh3wvmDjwnTE1JRtk5R3zJpJCBkV7g9DDg19ZfbaU9xxnT33ohbAmhwTEVBJ4J6\\n3PQhafXNmg+uzgnWq5aYLdrMhbZrIR043L5A88Hep5xxoUekJw63yPJKHZWqEGckL2f9UCV0+LYn\\n9PfpLh6gviHEv0X69O/xxvYh+2GkP19xvRvom5ZtE/juVc8L9zYhv0E/Cednv0o5CLurjPbKvXsd\\nSkZTQLPQ9IHeexgyiWyNwGNk2I8EbSgmEc0UE3lSQiuUaFrZuTKAxXsjPKmVexSneHEUKTy6d8Ha\\ne77y5kMa4PtPnvH22Tn9puOjZy9xXshTTQehuKDmyOayvPuLw++Mv+DazKrpudxNIELfC9OYGQer\\nPHDO4RrHpvN0bTDHKme2vmOKppA0jdY958lhIk2J0AYIgTFFXqaJTz840L3583/v9oN/8Lf5Eowv\\njcEEGMfxv2jb9t+WUn4av+J0e10MpwuV8aoGO2RFQlvZltbyK9cWPOJO8nunRKC59CRh/fLc7DVZ\\nfVPJRwN6OmZSCoh1M9djRMT8ss7RxxJLylILddRgrb+y/MWS7yaUHCpkoqD5CJ8wl2/AUkt6gjjx\\nmuMuEeVnDOJdFaLjdVXweS6ur3+6WUTglWdxTCUeo2Qt2aQFVM2A6Anc/dqZHmdbwMo6Kuzq5CQC\\nP/mdpQhbqmNSqvhAFa73BGKt99Qyw97z9ZixzWmo0GlFBdQ2A0cmTxHXWbePuTZUXLCm2IBoJsdo\\nz0OtcBxCDfPtXM63RvrC0IXiTF9W4oimgXj9hEkLyHe5bc5YPfw6h8099ts/juOSTfsp/cW77G8P\\nJDUI9Ne+l8mxEFOmdx4Xzhnkj/LpzT9D9gW8w7sJKS9x6/fx42+wv7mG6BA/sm4bghfilOh8R4qj\\nOSVhFu+wPoihadAA4Uzp28ZyoFqIBTRHJBfa0JKzo/HmKDS9pzhI42SRoPMGlLi6prxaVxJWcPbT\\n9hxqGsKc0IJowpfMOOxw4hiub9A8Qc4416IlmozdnDKg6gMvED7VWHrEBVzolq4kbn2P9aZn7b5F\\n3D1H03cQwRR0moyTlhjh4YMVhzHyafoJNIyUnBnjljGPMHqyU9bn1j7tPJhTNKZEyhmy0DZGKLw9\\njOz3mTO/4mYY2V3dGvMbCMFy58IMgSfEKTnPESCIV9M09sp63dIHz/3zNTlPfPfpC0oqXHQrvvux\\nQbLkqpJUI0iTxZsd4xmpqu+MKG3vedCveXp9iyo0nUW242BCIs458Fbj6YKn8d6cGBdIuRBzoQ2e\\ns82Ky5sdhxxZ9ytiHli5wEvNfPDRjtXDn+Tmg3/whWXFvjq+VAazQrN/tu/7Xx1T6nAtxxxi/U6O\\nzAw4wQr/S4oLPj+3tiplwNEuUm6fHRVCTQLBFgvCotH42eGWuagadq8pWYRa208t8ZLtADUa9niP\\nKdrMYyYj6JFMdCTcuJqPUFQ9orn24bP60Dn6gpNA8mh571il449ff0WfN2SBtV4xrPPJ7kS+xy4n\\niDs6JuIQKRVePs0D3pnZ6VmZo2pdnIujIot9Xo2gzrV1nqKpGi5FPJVZmXESKJIoZEjRjIGbSxRm\\nmb2ahxY1YYpuzVQSHjV9YAe+EijUNQs0vtx2EZBQCV4VUcDKdUopthlWdq9IZSgGj3SbKvY9IEnR\\neGD30a/jmpab5ox2c58XocWHP0jTwKrJrLY3SH5OyjtSfMwQ/nlSfEA5CKE74OlRcSTZQNfieYvi\\n/wBd/yu0+dtcXx7YDRMpC514xDseXmwZYyIWqwFOWTnsD5CUNy7O2E97pFT0pKgRhcTifMmwbjtu\\nDwfU18+8RSXidHltfWP/LsURIkzhghgeE0o+liYVW+Pjbme1za5BymSchJyhVNIWAmRKOuBEj0a3\\nIi8qYrXWvvIZfIM0Pc36gu7em3TTL/GmvstTvSSvFJWWfdzxxvmW4WBoRtsVdnlg/+Lv0K7eI4c/\\nQt/8fdrb+zy5ecLDt8/wvqBaSEUZYy1XK7WtWi6QTB/24WqN8w3PdnvUOesTKULO1UvwRloTxJQ8\\nEWt+IBiU6ljKfaI34YvDlHlydcVZt+aHh0t2Q0Kz9TWVmopyIksaBDj2k3Ue7wrrVtg2gf04GffR\\nsgrEXaoOpu1NTj0lwvq8MbaECJ0P7KaIF+H+2RYtmZiUxnmDZlcbDtPI08sDw95BuvpTv6fN55/y\\n+FIZTABV/a2+7/+zn/zm4//k3e8+644bpy4QbVm2UoPFFuHuU2OhsuRRFmP4GQJUQTUhVaNRamLc\\noruTYy2wZYUZ67Esoq0En9loVojXupwUtGrKGjHmKMlnxJuTCPXkXPNGboIGVf6vbiqyGM7XxGu/\\nN7t4ctNZbt7rlYPmvOHxe6c3e3ES6i26S+DBoofFsn+e0dQqeF6ftlqrpQVqW4hUytLRRa1eTysM\\nV/JEs9qQcsGLdT4psZYwqNR811yrl3E116xzxOIawtwqTRSkkFPNs+VEyREfjDDkXEBT7Y6jAhqX\\npyIyG4maP2A+JhV2t4jVywOkmdCuxZcH5OkWN+6Z9i/JOIIk9k3PdXNu16wdRI/4Bum/R9M+sbZm\\n/QXN2Vs0684ig7JGKBR3wcH9SSb9MVbnfxf2V9xMA64N9LolxmQlLd5ZfrRrebDZQMk11+spsRBH\\nE7wPlZG97lfkcUKlsOlXpBwZXSFWZS4XAC9ortAgEAp4Jm79fdqSSUJ9zoUSTcaw8R7vBFJ1n+KO\\nNBlsnvG1D7zV09r7NwvwS21CEBaRfqnP3/sG367YpF+hl79P1AbpBY+n34AvHpwSnbUym5KyHwbW\\nXSKX79OV71CuIhMrzs87gktMpeCyEDUjKjTeMR5GNn5FmITzTUdcwcvdjsvhmlSEkgo5C01r/ViL\\nK4RgbdOKSpV+rmkZtXffO7cEBcMw8fzmlg9fXDKlwlUZGKdkKj9ZESnL2lv4E6p1//GE1rPqHZu2\\nwyXY58j1UHP84kjj3CPTZCT7VcdhmLh/3rJuAzkp69aTpkxKmfPtmlXXcnNzw6PzMz65usZ7IefI\\ny2HixZMDSvhL6eaTv/a73oe+AONLwZJ9dcys2XsP3v7pDz96jmvPLD/F0VAB4DY023vE64+qJ39K\\nqqEunAoJiul7HsHSOUqrG5pvrObrBMJVWPo3L/Z4ju5OiDRGfrVNe+49t5RJzDlMVY7U9s+98qqT\\naXm7WX3IuoXoooWqVeByNpp3bZvMAS7LzD/PkN6ZSr0PC7dmZgGfKqXcCWeXezXr2mqFcuYmtKc1\\nq8djALzaELrUqVQS0InBPcJvfKbryqvzULUONd1qxWHY0TQr4nCNF6VkwQW/GM5Cws1dcRB87W5j\\n0mKCc4Hi5miybszB18+ERYe2ZGYBP8Aix2wtrWaDPM9RlYU45Hxt2YTUQnLFFWOteuettZWA5gmd\\nRqyfY1kckqLFWKLqcf2WsLqgvf91urOHtOtztGlw2aGhINoQeMq5/G2aw0dcD4kSE3myiL7rGi4u\\ntlxf3xBEWK17Bk3EfSaVCDg0qjlumGpNaArBNWyajjEOZogbgxyHPNF0HcGZDsI4TLQaSKkwhW/i\\nH/xrFAnVCS6UcSCn0VjPwwEHHJ5+D0ohhNZSLtWJ0jxRpv0isUgl/Djf1HIpew/FB1yzRjYPuffg\\nHhfyP5LjFZoyrhUaCeRUiCkh3vP8kx3ew/Z8jQtSCTmZfANrv+Xp9Qve/so5XmCfEq0KTfEMJJrS\\n0eFoA2zONqQpcbsf2ZP44bNLNBZzsFxNU9TelwI4FYqrEChUVKLMyDLiFecDiCn8DIdU15UdA7XI\\n1VVW+VyWNTdNF7Hc98W2pQ/C1TVIb3qwKRf6vjEYFiu3KqXQ9y3iFAr82Btb4mTEsMY7fIIhFt6+\\nf8EqeA7jyNPrA/tp5Kv37vHD55d878Mbkr/3Yv/8ycPXvrBf4PGlizDhCM2mlH61sOk0Tfj2nDxd\\n3zUO5ZYyBfMuNYPrrFN7/Y5UmA+xIttSIiFsrWD9lC+jBTTWSNNXSGQ2qzojkJWzczQSr2zrloUr\\np3mDOd82b7zzBvo7yw8sBSVzeYYKpIJ42xwoueZhZTGQ9qKckHRO8qmnHdA+O4551c+LUF81lp/N\\nMjQAAAAgAElEQVSnWzvnPaEsjgPV02eeY4VDVWcRgvnsM5Rb/67zb8hyz486mK/cr+UYBZFMSSO+\\nOCSPhjKEphIdLB8rOEzvwJwJwdrFiXPkPBlVHoGFvGTXYWSfVxCBZQ5UtaeqajSTT2aCkhqPWqqx\\nNUjWhK5naFLaTe2fqXiBoh4f1tBV2LgUHFL/BHGZePUJ7J8w7Z+Sbp8SN/dx6zfZ3P8qiZH12VfI\\nPqDhjBf6p2n8P+L+xW8xHC4pZwWNSslweX1tjkDXsos7vO9oLjKSVsYT0Mg0Tjg5g7BHnMep4qVw\\n1nUIyo2m2gbPIUWRxvSXtDFRhBIdnXuOZojtSCitISbFyrs0RXyeGPc3tWuQJ5WMTgdrY+Vm+LYu\\naGeSbVYm1JrTMcf5LtBuNvj7D8gpcjt8RJGAAzbaoV7IWP1qmTIpKk3rGePIyrf2vA7QSsfV/ooH\\nj89MMSknfIam2Zh+ahSThvNKVuHZ1Q1OHNu+4wdPLu39EjOU7qTOVQEpWpXDKnJWsjUvry24xPna\\nfqsYWlKghtl4L3gfyLks4uol31WuMulJaFtHTiNPBs80JXoXKLnQtVbCcjqcc/R9YNU25BQZpkyv\\ngYBDp8xuzKb2U9NKl4eIeI/i+XR/4NObPcMhkxj+zGtf1i/4+FJGmPPo+/4/+sY3vvEX3/3+7cpK\\nTSYziMBdaK/mj6SDsuduX0gAtQ4KYUWOe3AtSKoGxy8GQSosd+y1WA3XsoFTN9gjbHsXuDxGW3Mp\\nwvzTGdJ1YLV/vBptzsbEV4ixGkM3l3HUTivZcmRavfO5r6Z5qLWWk3KM8siVpHuSC5aTU54YzHne\\nx6jOIsxTg/k6Y3l6za+uN1k6xZzep/lYFR5deoO+cjvmby/PYQ5/T+/4yb/FIC4fHCXFSswqlQ1t\\n4vveBXIpaElHMtOCYMVKFAs4adAwz3uuvQ2L8XYuWF5UFLKRjrS2kzIdC2MaLjlnNejYndyLZU3U\\nojlr7lxq0bnDe7VclM45JV26VPgQSNOBkjOaB3TcQZ5sTr7BNSu6B9+k2TxGO+i3j2nbNdl5Go20\\n+R0kvQ9lJDUrirT07gbGF0y7GwrKqtuSx5HV1jFRjO3pEre7LX2f8d2AjoWVBFoXGIGr/Q3ammvo\\nxCTXFOp8M8U/Ip79GdSvERXGm0vyeItz3pjErkHITLtLdLJuK6XeP5GM5uooOg8E0xCppSOzjiwu\\nEDb3WT/4Km4d0JjZlv8GGSfO11uGcSDVXqwxWZS2v4ngFReE7bojx0w5mKzgvYdriiRiUtoQ6PGU\\naG5t3/VM00jwnozS+BafMrsy8tHVNdPB5uu84BtzovKYyak62mIC6kEgVvRsNqJafa5Q9yhrgwfN\\nyvKgMVqdJFSi4gn6Nucz2+DZdo7nQ8JpRYGCo2k80xRrbfj8+jjOzhrWfcsnz3c8vrflzLdcXd1w\\nb7slkpgmpW9bgihfe/CA3/7kCdu+ZxwGPrk58MH3d5Sif65k/W8/s1F8CcaX2mCKiDs/P/8bN3v9\\nRWmsiasWQfPuBN7DDKCedL4wqwa8AuGKQ53DO9MBVfE415rXfwqsOUGkqdGb3N3IjydlMYR6Gn+d\\nxI/L4p2R3JNylbm7K+Uk6hTEGEgmCO+Oc5qJqEpB5p52HKFj1cmOJ5ghqtJ6lkub5faoMPJRP1dq\\nfm2Gsmy+J97BEuEJIq86IifXuzgJx4hvcRpeo6Z0hIzn8Qr2/Zq86md5yye/LZjzIw2C1WX2qw3j\\nfo8LgeMdm/OxZuxEmjtQ73KtzqM+cOogOAk1h26fW56vGPsWaucZ09Kcc61LjltrKUr1BO7eo7pO\\ni5FC8KaLa8V5FrFSCilO0Hra7j7j9Qd2rU4sp1cKqiMlTqZ+pdb9x7Ur2u2b0D/Eb+6zffAYv74g\\nS8arx6mgGXJWdCUEFUL6GKcZa433HnL4AXG8gvANpvbrOH9B+/L/oG+uWa0EcRv6IkgZ2afI6OAw\\nGrs1BOsLmrUwThOl+WNMF38UKUK+3TFePUEkM43JBO9Rg11rWza03sM5v+08+MYcWzGH125pPj5h\\n37O+/xbh4SNcEhr9Nm83f51Vf494iHx6dUlSMaZn5/HqiLtMmhLdqmO9DtzcJGIeOH+wIVKsFjb0\\nSFYcQp4S+EAXTDox5kzOykoavIMf3DznMAllKqScCUHwrSOnbAF1ceCKMcHVVUasvQeW554BClPQ\\ncaU6W43iAqSoJs6ej228Xh1OYN0qhzIbXEM5mlaIk/Er3JJ2gG7VcL5e8fRqR1Dl8YMz0ij4kvCt\\np3MmTNB4T9943thsuB4jL25uWbWef/itp7Rv/JReff83Pv9F/YKPLyUkOw9VLSLyZ9fr9Tv7FB94\\n3yL9BRyiwTgzEaRE8B2UCZWAlngaN50csFS5uxHfv0GZbi2q0J5T7VTbfSNoMDLQ6y0mVOFj5UR2\\njoV7yZzjtLTcyTFmQyIzAzFXY1UsIuQoLTdzjVTqFFVM6HEWCKdaYzFVnVKOUaeldS3ClaVvaOFY\\n2DhHuTJPh6PpP7l/Mk/kdzdOI01FTRzixGgeEeLPwYr/CXw8RzVC1XBSMtPtJWH9kGkccSQQh3ct\\nKY741vKRVGKYDx4lLLnG+To+M7XKhj1eay1nmE3yUlpSKjPaHA43k8mY4bey5K2WUaFAq+mNiNTn\\nWKxG0bUtrV9xe/kevukBj2pecqqF3mqQy8qMTI4w7ZlevEdx30P8hvTJPfzqgub8q/jtA/x6C+MN\\nTehgOENDZvJvVx9KQR8hqz+C9LH2rFzj5Cmsf4xQdrjcsGpGGtfT5hbnPCkeaJySsqIF4phQb4ox\\nhJl9OXG4eYqLI0kzThVHJuZsvAFxpsyzKGkI+N76lLY9vumMfOVbBEjTS8owmR5qCLhuTSFwX/4G\\nq/IeTdsQ48Tz6x3RqYl8qdDjOT9fs8sDEc+hZK5vInFQ7r2xJeaJjCOhaFLGYaIPLWPC1JS8ZWHM\\n9y1MTGSFcVDiIVn06J2xrYuascxi7T0EqLlUyEtjhzITCmfyYTYFHnFGGEvRotacC2VpWHAX3RGn\\nrHrHkEwIxQIBR/COFI0a61xd4/bykFLm2cudEbA6t6gErbqW4Gxnayopbts2XO72HHKibwLvvv8c\\nEbj+wW+2v/e394szvtQGE0BVn4nIv3F25v/3m+h6P16bh4mAxiWq8o7aHi6yffzj3Dx5n1kD9lUR\\n8KwJF29sAYUza1OE1Dq+U2ORoPgTebTXjblgukKLr0Sbds6ajbtjNJfp1MitVMjx2APSNlcWw6hl\\nhp7Fvjdr2KpW9qzgXe3kocVQqqqXqlo7qdSG0+KOkePCALbEyskE717za4Uk7lzLXUho/kgrnDwr\\nCQkYIWi+0tnZ0M+e83c7lMo+VWvq3awvSLfXlPGWpl2bfyXGsG5WPXEa8KGrDkdmOhxwocWFDtd0\\nVUlKP7MRHec3X+v8pztCr0sOae4GMcPtFVKVWn4xk6ZqRxYtWiMO099cRB5V8T7gXcvh5gM6vyGW\\njMiJ+tQ8typQL14ozZrSbZE84eNALjvGm1vYfYB7/ttIt7WuIt0FyTX0mzNSUZpujfg13XqDbzsK\\nnhitV23weyIPSetfZMp/jEf6LXJ+B2Ukdg3f++CSOETeeLOnXfekOHEYBkoJiBeyv4ckmK5+CHmw\\nZsgKUjKlSHUvLA/p5lRLjSh9u8aFFu8DrjgIK0x9MSLhDYo/WAP6ZovbnNHkj9m479A56yuaysiY\\nEtkpw9XEqu0Z/EQ5FCYK45DoJTBlE6inNmcoudBkIZdIESVLYnPWUTRSsFpMxGTsDmPi+vaWlEx4\\nIDTeoOkaKVrLrox3vjKaTfTCewfuaCyL2m6gVQdYvJBK7fmazOClVJYI8XSNtq0nx4khuWXbCo0n\\nFzO2p0iH+NoSUTwxWsu5zbqnkYTgrH1Y1Vkexog6a/G1ajpShiFHXlzt2V0nCu2/rJruJkW/ZONL\\nDcmejtVq9Z9+85s/8R9+673r3oxBi+aBpd9i/Z4l/QOh25CHl8snx818LiOw5sJSPaelz6LMRfpl\\ntmYAzA2o73hxr5BQVBW3yNrdHQvV27A7qMUxR4M3iyV4irPi4rnprVRIyrYS6pylBsM1gnuFgWvT\\nmHOlft6u7YMaMZZqPJdIR4/GAOZrP27GciI/xt1vLnbk8whBx6fDAt/eFUCYGc53f//u8U4ishqt\\nFanPQeY5KgWHuoZVgP3uirC+MEdhfp5aoe3a+cSgvrqJqC7kCdd2Vm9bEQFX14bWiG6WMZyZ0KUo\\nXgwxKCXXNZWXz1Fd6tyOPTmPd1fUEACdmdAnlyriSLdPQHpjcS4Yymn/V1n+ODKUtbZEE6QIJUdK\\nOVj0WetTrQ9mg+tWpqLjGkJ3huvPcV1PygWRhvWDx+YENK0xUCmoKFt9n5X+TdzuwNOXE0+f31KS\\ncH6/5eHjnlQmUnSkEggP/l1uR0VvPqXQm6pSSZCiCVAUU4rSYu+18y3iO8S3+FVHu3mI88HytPIc\\nl1dMZUQnR0Zoux7fBpxe0x3+Cuf9Lefr+wzjwO04ogluDnv2L5VmhTXk7jxTjJRBuNeecfnimiIt\\nfg1hCyVGnDr8BMOY2G57ms4TnK/BWSGLMh4yL3YHpsnqwp0T+tAwTomk1knE1tOxJK2oEry3/q9N\\nQ67NGrSY8+XN651fBpS8GNHTvOWCWgBdH4h6DBZCsDKbaUycvAJYtsTWYc7F5PIcdF7wTsjZcdZ2\\nXPQdSQtjzjTes+0bWgLPdzummPnWu5eUXP5Czvpf/Q4v/5di/MgYTBHx5+fnf0fd5hduhyCEHhGP\\nxl3VkOW4MYkgrjMFjZKOpBJYNhGr8fN1o6zcOgm2gObcZSW8mKERKoZRo8i68k5Eni0PlV9rNE7z\\nlKpikkKnL0Nd/MZ+dFXRpBpEe4MsoBTq9RkDU1XsOtHZZtj57jx3BTWIy50Y/Vnldia1lJm0Ir7u\\n0u7k++VoUOW4Md+JvJcJOO6QjJbxeoM5T/HkIK+PZJcSF6D2d1QJuNAgYkzAmbCkYE6Ed4hY/z51\\nueYbHT60nKoIiW9wviHF0R7rfHfEL7W53hmyMTcldj5UVuxslDzUyFDV6trmhuTOVYSgRvOntaXL\\nHBSOUK7d7yIO0UwarmvpgDvJJb9yf+cIvRpMRY9F6wKqDrzHlRnCLcYiLpGSDtbEO0fLiUp17lzA\\nN2tcu0FDR7N9RLd9RH/+JtJ2FV0BX25p9B2a6du8+OATrm/3OO84u2jptw05RkQdB3efbvOn2cWC\\nz9HuUY6QE5pTbY2XEKDtLtDNOUELeE+zGShXv4TPB9QXGA6UkvAO0uohfv3Hce1josD68JdpeU7r\\nA955Q06KMhwSOStXlwlIbM9bmtYjObP2aw7jhMexXj3g5f4FzRrQzKrpGK5MWHx70S81qVkKBWUY\\nEy+v95Z2dkBWGmcNuGOytmVG1nIz7GL7UjG1n1loYGa1qwLJSEgSqNFkbXKA5ZzHIeGDI4Rj55G+\\n90zZ0jGuwsDOWc7zuNDkZOXM+4vSNw0iyqOLDS+v9zS+pQ+B8z6wHxMZZdUE1iEQs7IbB377u1eE\\n1t1cPjuc8yMwfmQMJoCIvL1er7/18O2fvPjwg2f49X20JMq4Y44OdFYzxpQtNMdaEP+ayE+qKMCd\\nCGUuMjdDaO2zvHnileo+b6CitUvCDIPCUcz9lVOdlpMs+T2ths+5BXoDkFlbUqxuT+sbdtwmDaJB\\nxFhuOtdn5mVDNTKQkYxE5xkZwUjxeHHkfFjYtYJDna81hLNmrt0iJ97qBaVGVjkfjdIMCcFy3oUJ\\nWkp1RObXst4VN5OLXrlL1ZqaQyHIQk6qzOH5LleHRRFTdJGTKGrOD9fcziJKMM9dsZZe3i3ftc2l\\nrfJ3e6vPnNfEIkM418ia46RgZTGVUGXefHXMijFdrXxgLrKnzmnu7PJZuJsSq4EzmLa4gIu35GGP\\nhCr1CCfR513nolRD7CrMnmt7M/vULetdEGZVR9E5Uq35Mi02jzxZVF4KKd6iZTTpSB/wq3t0b36T\\n9v7btKv7ON/hNTG6hk38FL//ywy3L2nWs+RjZDoEVDLBNUz+a8j6X2UYRyRnO1dUtNxQYiaXiWbz\\nBs3mAa6xyFcO3yK8/Ju0XYDunBh3pGnHqrZe208DFEG7C7r2q5TyIZQdoo7glVDLxW5vDiAwjYpv\\nHetNgxOY9olV1zEOkQfbMz59ek13EWga6EPDG+cXPH36EhecGaKspIoqjDGzHyZyqSo7U8Z7T6Is\\nGZ6lM1F9sUS8saFryKd1DTTirE1WAVRx3Yl2tNr7JmqatKjYOq5LdNV6xlTISU1DWaqIipMK487L\\n2S3zWfq8oqyaFtXM2w/OudlFomYerlZ457gZRsjK/bOV5TZV+eDjHU+fH6ZxSGeqeuwx9iUeP1IG\\nE0BE/tRms/2r+3zegset7iFppMRb+4LOcUPG+RVFjSBkRuiu0bT92coIqmWo/fRe2czn2r0aNQke\\nnSNDnRVH5JifqzDrq8SjY0RT51GpcKeQZr3KZRHPxfyVWjRvkdWw112PAiWTq/yecybCsJxdZ2NV\\nVYKqoc95QKoqCswlLcyhDmgtU6k9OpmNlDh0zpWd1CWWGkHVikfgJE93ogsr1aExh2QRwGM2XsEH\\n1HXkNOEWJZ8ZR1ocdOYjCt6agjtv0ngL+i0nm5E9U6tdM9TB1UL3WXDh+Ixq/etsfKHCt4J4+2x2\\ndmZ3wchWpvB01+HJx83p1VY4p35+KaB30z8l7knTYA2sj1Sy5epL4W4udHnOdcyIyAx115rYpcCd\\nY43rvNxnNrEhGAZdo86chTxR8kAZb8hF6dcPWL39+wn33kKa3qKcfGAb/wYb/wnDkNkdbmhkAzIy\\nxshqtWbMA8JjyurniPkrpDSS8i0aPeQR6c/p1xuQjtAdCIe/i+6/g+87dPNnGPwDQnyJL57x5pdo\\n9bfR0lB8odwO+HaFDytSuiZNyZCBorSdsyL8xjxBaRzBC1OcSKOjQTiv+evnw4GziwbNmVVoaaWx\\niLJkFCGmyH4YOcS8dEwSZzlCjQUJfonyj6xpW1M5Z5wzxR9F8SEASo6FddOxPww0vZXMlNmgFkO5\\nxIuRNQj1vbRNp288U8pVdm8mmXHnvCWriUsoiyGVuuadCKsm8HC75npvXXwuVmvutYHL2wOIY9u1\\n+IqZXN0MfOvdF6w24S/cXE1feih2Hj9yBhNgs9n8l1/92tf+vd/+3k3rxCHtWe1OMUDdrLVqjM7G\\nQMmWC/wMTEjVCXU1igi1POAuhLFsoGqtvJhzWNghF0m70z1xYbK+Yg9ZTA9wLMs4nq5GWTUyllq+\\nshic+n+rmfSLIS8lAphQ9RIhs5CGBMutzRHPHJGyHLWW15zkesVVCT8EU1LSKp1Wr2NBpn0trq4v\\ntlQZuOoNiCkFUCqMLYtjc6wrPZJnwIWWHCPB++pUVEHEGRHQWqfmvc25JOaQ+KhSNP/IVQfBviMn\\nHjYYJAayRKmLZGG9fzNzdum3KnO+sUqyqZWXLNezrLPjM7MIcjZM9bNaWjS3ndNqVB3KNN1CHk0h\\npjpKdyPTu4Z+vtYyE7i8W9ASZNYZ/Z3Ia8vqO/4pIC6griE4IzBprYdO+xfkYWelFQ++wvrhN/Bn\\nj2jY04//HW2OJM5JuaFMH+NlTRsct0NkihOaWrRXmu4RJfwE0T1GwgOaNqC6IpZbmvwDysu/Tu8j\\nTRfQ/hcZ2p8DIil+ROjehsP34eX/BNkxaarPS9lu18SxsL8Z0ALrTUsToBSHSsIHjzRiyyZnRB0r\\n17DtNjy5fomsTBwgqIMIXdsQgmOMmTEm9odIzAWcErytp5RLlf0yFCP4wLHs6/jscjZJPQEIlgcv\\nhfomm7PinRGF5jUzw+u2VCzHqCjBC13jmBLW6/SV/X7+pzVMtzRByRkVj/e2VgrW9HrbWKu1hoYs\\nysO+R7Wwz4UVQtsF07VNhV/7jacU9L+exvzv/2MW1Jdq/EgaTBFpzs7Ofsk32z98vXciYY1vOvJ0\\na/mQaiCdOEoeocKqWjd7eTXSlCqrVQ3QLEVVqnzVkqtk3mRnPVJAPG6OeU7gOxtHoXT0bhXhqVKQ\\nbf6y/OBO3WEpLMzLpT5ST74nUCHXORJ0Lpgwm/fVuz1K9dmx8sm1wBKBAMIxR6Zg8OOr+cZ6T2w6\\nujgmRxdAFpha1QhJxjo9HsM6sJxEgMXmPhdcz11OFNukTcS+5psXMlZBi9I2HfGkQqaUiGoiuK4a\\ncJMXOzo1DpiJNVo9cWp5htTz2XXcMUpyNJiu5p+lwrJacr3ebCo3UqOz5XrnHHVVcZnXTzlZIwjo\\nSJwGQu0pyRKdlJpjtrUsJ9H7yUlsk/XeyCWzI/c5BvP06S8/mB0Zwc7l21oP7JbnWu8M5Il0uCLF\\ngWb7Bv0bv4/N9k08f41QJjr3jGv/b6Lxb+Gnj/CHwJD3ED3qElYHWEje05z9C6TWIbcH2rOHpJsP\\naMbvc3GvoevPeBl+jkF+tqI4E4rloN0UmS7/e/z0KSVP1YEyFilaaJsWTYpzhcY3xJLJomw2HeM4\\nEQflbN1w0a3I6hjGwqB7+pUJj+excLbe4BsYU2R3O3IYEzEqiOKd5QlVjP2KCn5OTTjLNZb6bOZ7\\nbL13FedtTyllfnMyguXFramEkguEZmZS11pMMbKSpzBOGfUQYzWsd0iJR8jee4fzM/GnRpfOyn6a\\nJrBtAtumZ4jGB+l8YNN49jEiAn3wBOeYUuFb7z4n+/7l9YuX9/kRGz+SBhNARB5uNpvf2F689dbT\\nFwOu2yII+v+x926huiRZft9vRURmfpe997nUqctU91R1z2g042E00kjyGNkgPwiBQGAw9pOxwA/G\\nRs8GgYXBj3ozejMIYzDYLzYSFja2kS3GCOsyjDW6WOpp9b2ru7pup87Z1+/LS0QsP6yIzPz2OVXd\\nPZoXdZ2gq8/e384vL5GZ8V/rv9b6rzRgqfUNSYR8vCzeR/EQKItaya4FyNj2FjdzhXYrlFTp5CA1\\n9lMBoljv9vCV8gGllDhW0FnFLO8DZs3KrN1RjF9bLVZlcc9aPMPKR66Brno/L7nHIlQRap0ThgrI\\nac2eXaT0BJkl2lw1Kcp1mxrXyz0T4TQmV0E8FfCqnznncaGzWKgCFKUlTRbvm4UcoLjstuw5Z962\\nuGVB0OolGj0mfkPYWN/JabjDoTSbM2ICzWMBgRKzzQMOh2/OqYRUqsZDpdcLHVmNJKnxWikSe+JK\\nn0Ix40qtN2tBfqgeppzel1yymbXQ9UktTltBUTSTclGyUsg6IQqusAupgLJzfo4Tn2RJlvs+N+Ke\\nEbGUEs0GQyWS6z1k9nTnDjriTHTetdT+sJWnEMoxcKQ8oeM1cYr4JuAu3mZ7fkFo3sS3P2LSr9I2\\nR7zuyPIh2n+PMP4jJA/kIZPV4VTJ3uFCJtDROkU3mW1o2G/PeTb+KS7bP0zQgZx3aLqBYeLu8nuE\\n7gxcROJ7uPh1nN5BVjKBxmdyhKDCZtfineP67oBvA/vzhsPtiCicNR3eefoswMT5LpCmxKGP7DYt\\n3nmiJo79yKGfrAymGFpGsZYEL6c4jGr1vrA0almx9my7xSBZvaeaTYykBlx8MG+w3HUq85OmRBoz\\nXRM4f9RyV7Jec1rYkvUzsawP5i2LgynaM2otwMwYfON8g8cxjplGPW7jOStShMehpw2Bznu893zt\\nW59wfTP2h0M8V9V/pUtIXjZ+ZgETQER+bbfb/YNjOts71yI+0Jy9yXjzFGWi27/N2F+S+iucWqxB\\nfMAqMRJycr+dCbBLmKkoKd5A1XWU2bOslNq95A0X5gWnVpKdDF18uSyesDkj9tdQqeOaDHQCmBkT\\nX8+FLi3HrwlCnwWYyxwxk8KGOCyNobVgU8mSXR931QSaVd1W2em8qM+e6Ux912tY6l/LMoAUz10A\\nTXHeowL+XjnO2ixYe7i2SFWS0uKoznliGkACTrLtWyG057SbDSpNYRFAspLGG/rbpyCJPEWa7gzX\\n7lBxJWbkSwT29HwqkCA1dluZB0onGQPAlCOac+mTybJIai6xymRT78xg0pLJLWQ0myed0lCePWNF\\ntCQxOe9Wa6MUYkMKg+LuqSppmW8p3lwtg5LZe6930LJ6iyB41TB2AWoj7cqe1O4sLCAtZMiRFAcg\\n4M4e0oQOjQc07Aihpd2/jX/Q4XNHvPyb+HQL8iGtNGw7IaZAdhuCTuBurFlxgIu8o9k0fJB/lav8\\nR5G7j3j23tdpEfIIGjqkVZrzL7G5GEmX/5yg3yCTaaUhR4cXCBtBMxyPke7cs2lbDjcj29CwazsG\\nzYjLtI1jGCcu2g4vgbuxJ2G9Km/ujqUUS4tIuhBjJNXmOc6EFizJLpUm44Wsz8yepJZ3aH7fYzWE\\nanw5Fw9w8SxRh2ToGs9Eqnl3RbhgMSbvg2UurQibxhO859gPaKF0UXi46+gc7HYdx0MiZ8fDzpPF\\n0XhHihNt29IPIx8+veMHP7pjHNJjVX3Oz+D4V1644POGqv4zEfkP9nv5n+6ibx3CdPMp7cPXGS8/\\nZbz7iN3P/wmOP/xH5Hg0qTFL9SuLbgAt7Z8oi5mzLM9Z+EfcYoHXAnSpLcZOvT1yLAuLZSZqSY+v\\n8TDKO2L7GxmPl6UbioCbYbgscIoUEYETm7TE74TFQ/480JxjdcavLpZrzaidPb4q9sd8TfO5U86x\\n2gZ6utieUoPmqS6ZwOYtik5WuM0islD3+VLvdQ6OVqCs8Wjzemvnl5wSToQQtqYljIKMiEbidDTV\\nJ7HmyN5tkGYLfkM4ewsnDd4p0/Ga4XBH17YE58BZjaqupQDrNRZPUFUX6tYoAlK0GLI4W/BQmbNo\\na9mAyb9h+1ctlCzWdLlpCcERx8HmR+w5rQxGnfRCfqwoXzd7J+sxU+u63MPqtyyPVLVgdClbmRdu\\nmQ86s+Dz96vxUADUNTSNNXfP/RUpPCR6CIdrJm/Nt5u4J3QPcbzOFF7D+a8wOpg2f4wxZJBmuK8A\\nACAASURBVELaEuVTtrf/C3eHX2QTvsvzdMUmvcvN9ITb938bcS3B7RjGa5zzTMMndHpG5GPi9g8h\\nZ2dw5UECyU0ggZjBJau5deJonIcps2sdj873XF8eaDZ7pO3Z+sD5ZoNXIaFs2h03dweuDgfqu++c\\nI6VkPSTHbAaGq8ZhMUycKfFIAdhYYvuLoWMevXPOGnhnqK2RangASjhAIWkmeBhzWSNE5s46FG93\\nBtf6HirFaF8SgIJ3JBG2XrgdokmltFtin4jRIT4S1SFEstqzMIwTV7cj3//BLb7hL/+sgiX8jHuY\\ndex2u//yyZMn//kPP8mdx0HTcfYLf5K77/xD0Mz2y3+Cw/v/kBwnNB5xYWNUSCp8vk5lT2ryWwXw\\npCjBmLpcyUib6zTLN3Iq9MziTZmX4vDOW01dreEE6iNcjoZKpHSwtphrUX2BQq2UhJ2sppRDyX6r\\nvSOXVZTPBM35YGCAqSZgYFZtsOst4JmpQFVjupVXZfYsdT7kSkWpUtirWOx8DVRvreRmKpb8UzZc\\n9Ut58byLXJiWa5AZHNLqOwYErt0Tmg2gpKEvVrclOaWUaRpLhgqhscSLeq7VYPBm7KhAmqYCyuWs\\nnMcE3LGYnhYQ12p8WDxVq2pPBUgWh1CcMg09WkQNKjG6GDUeJ5CyxY0WIYnVTVzr/M7nvjAIc4LS\\n/NW6cBbj5F7cs5YeLftcZQev7lHNeM4spUuzt4TMwvKqivMNod2ScyJ0O/rjHZvzB2S/xblIg3CM\\nica3pLtr5PwBm/NHNufD1/AXf4ykgXD3D4k3N9wcW7i9Ad+urTrzwONAjIngG5pHb/Pg9R3T8/8V\\nnY40TkjZISq0OwjSwgjdmePm9o6z9oxd57m+S2wfOLzP7LTh0JuijYRMP8D1obdpSLrI1mXLUq4i\\nF4ha/61cKVFIKRNCKEzB8j7U++OdWGazCFqbSosl6Fg8tAoUZLrWM07W9FxV2W63DMNQdKKrcbQI\\nIii1K0qe78tu2+AlkNLIYbDSqou2pfNm3JHh4cWWmAWnkU3XQVau7u74J//sGSL8lWFIf/nFl/Rn\\nZ3whAFNE5Pz8/H/+9V//o3/u7/2/322d9xC2tE++yvTRN1Cgff1XmJ5+A02ZnG4Qv8EWk4SkBJQs\\nSxwStoWS8ghhTg+n0BunFKEWLyIXUNUTdRhLsHErD80t8ScKhJSavwpGsyW/AswKTDV51nEKkOs6\\nz5ePZaHVOV5YP7OaVau3vAeY61iYfX3Zoy4e9wKsS+3jnJ0rdZ0/fRbnxZYXS3DWIgfrhbvSh7aA\\nr+hpEZRgXUTyaPTW3AsVKxeq8ycgc09OW/jkheuyhWzutlKSu8SbVGJWgWwWeoojNR4sQhFTKvdj\\npmOVlEuJk/GohpG5JnMpte/p3C/0/pQstAdSypGW+s8KhhUw6/O5AsmVobfsc/2Jm8MK1cNZiGlb\\nkE2lpuoS+6IwVQDTe1QsVqhFeSPj2D54CNLgnCPGCec3pugz9aS7T8nOgzT44Nk+/BI69gyHD0m3\\nl2jyxHzAiTXtZu31l6coBOF4fcnm7V+l7X5AuP27IN4EDUaBlHj8ZM/hemA4Jl5/44xRM1ttubka\\n6B61hCahCToJjGlCRQjiOKaJfpyIo6JiyUSWR6bl/tnLmLRQsMloUIttrsQGpFCuTohTKkIWprG8\\njkMbDVuapyczDX1QYlwMVR/Mq7+/tjvn2HYbbg93iLjCrNizEZrAGw/OGOLE9W3PNFmPy3cfXjBO\\nkUPMPD7bmPKQQBM8nW+4vr3lH//ep+Qs/93xOP5H9x+fn7XxhQBMABHZnZ2d/eO7Xn5JmgdI9xgf\\nAioN+e4jaLZsHv48/dPvkOOddYYIO+aFKqVCcyrWbLoF73BicZyl7rIkaswJFCXLMlsikeY406pz\\n8kUFKRagrUkTs3yqFV1RrcRyVbb61oUV5l56rGIVilKbEM9AKy8sjSy+zkLTlsIJsiq+aYnJsvtq\\nHee8H72/nxUEz4C5Oubqe7nMsW3n5nOQ+4t1oZbqPteAqaknDSZALt7AcKnrhFqJb03CXTkeLABi\\ni524YHPvTsGlQP/JIjTPr5TvlW0ILeIcaRqtTVgBfZUSLZ5LkmoiTUneKC2lFgpdlyxo1OKZUu+f\\nvnROq1CGPRvz3We+Arn/nSXR5P4joSdbmoG4ANLLvf4aD6+SkvaJMzFnBB82+OAZYyZs9rS7BybH\\np0rbbk1oJJmSUe4P3D39Hk23I3RnROfYnb/GsT/CeAfDAdVoSTbOspwljaSUCCEwThEnHmkcub9l\\n/85v0DU/pOt/i5QSaUro1HD+YItI4vnHPecXLecXLWMOpMvIbd/z+Oc2RokrRkc7IebEOCWub454\\nqd611WBPw1SMLEW8K15hMZGiZSprTIizzFRN2WwRJ8SYEIo3nhXX1PujeO+s5CObB9u2jhA8wxhJ\\n1XN1iycPNYYpMyDP90xN9ESw8qt3X7/gOExcHyN9b4zaxbbj8bZliErbBrbBo06JUzRnISa+8b1r\\njslxuBq2mmPPz/j4mY5hroeqHkTkz2y32/+vj8cHos9oH7/NOEb85pzU3xKHa8LjrxIv30PjEU0D\\nPrRknIUGs4BOlgyUBVw3L4LA7HUVrKEW4Fsj4iIH5ywumnNEsnmXzlspQDnPQmXVTMri8pxoh1Yw\\nqd4eRkXOxfHl5dQEotawd70ayv393PvD7HZRoR0hEmNv8V3Xzhm1C+VaT475uzL/bsu2rLfRZTl2\\n6NJCa84YPPVQ63m9cBll/5mA3zQsmZ4lU7m6jN4WHqNaS2JNzbCt+xGFHC32qDX+BrNykwj3DY1q\\nNFSKtC5UTiroLzFusUDR6h4v98F7V6aklJVUoYSiJ2qKT1jcW8ozdeLZ13tWMp+RksRrz878vDBP\\n7zKH6xDC7Nevbpb4BYRfApT3jYhK+9rv5nnlpHjf4ULLNNyxe/CE6Fvy1CPOaPApRfLU2xylCaIx\\nAVnF3pdwTsYRfCBLQxJrC+a9R5OpFg3DNS5sSdnRBFvexv5As7mw47dfYrhK5unlhq5z+JC4fT7h\\nnOfxg3OaoFw/O3IcBh68tgOshjFlAzoTM8iM0YBZYybl0hmkzGcIpftINm885cpUGAUU2oaYihRj\\n6ZOQolG0UgBSS12zeDMkU7SsYRQ2XSBrZhgzMZYykDWTw/3327RkrauRbRuCsN90bDcBL86y1NOE\\n857GOy66ln23JenINniSCJ0Tdrstz28GPv7wwO3dxNCnR6r6Mw+W8AUCTABV/YGI/Pn93v2tY2p3\\nx+cfQrOlOX8LcR3p5hPax1uaL/86x/d+16zd6Yg6bxas96UqYMLpBGqdaqTQL+I9jqbEAKd5TZFC\\nQaJLdwADCastXBJOazr+fMbMCgA1PljS/l/GDJwKdsvsEVkGbfUs3LJUy+dRtPOkLYuoCpoSKfel\\nlMCXshYpzFxZVF0FeCn1lfP8l0uZEXs+zMsAvc5B9chPUXl9/SaOfzqWUhjbtYGoAVmt9tZlLlhb\\n30pSk36zjhFV7ejU56rbzzKDFM9PZE6wqJ6/UKi09XWLn2NMKWe8LF67YKU3KlOxw1yZWnu+TmKL\\nq0Vx8V2tSF6klows53oy1fcMgBNSVo3+daW35CLmUO6Lvuz7RjXbM78kmZi9MjEdLukuHnN7uDWP\\npTtDxJfmOaVed+pJZFSsZljFEmUmhabdkqaBlHqyjvgcTd7Se6b+jiZsUbchhECMg92TNOK3F9Rm\\n656G5BI+ORrnGG8nhhvYPFD6uyNHgXHIbM439NPA7dGyTp0TutbTbTqmQ4+JBViLKyN/xO6jX+LE\\n3jlqXLmGUrz3pJiK9FIt3/A4yfb8OGh9U3poZjwFcBW8CN4LU5GfWzMDIvfvxz0Dz0mpARZ2247z\\njadrPRvf8MGzW47DxFSex7cuznnQtXx4fcPFbkvbNniXGaJyuOt59mzgg08OTFP+BVW95Asy7hP+\\nP/NDVf/u8Xj8Cxt3NWaNkAZSfwXS4M/fYbr6EC+e/Vd+E79/g+xl6ZaAIK5BXINKsJpOrYosmCJI\\naHFnrxPO3kIpbcZKIkql9Ww/NYbm1idncc57nUXq32otnqyk6ERZ8jy4R+exeoFO1rVKHr7oMRnF\\nm+9vSlXIcc7jKALdJaFpaSMWzVBIk1FYKSEpzp0lyvzPMnF131po4s8MDyiFulyu+3Rq8ku+u7i4\\n9btm9AT05LF/kdYEezEsTFe80UKNSk7lHliZR80V9WJ5Ww4pSj5ld868MxWPemsPhg/oXI5RRBdc\\nByHM21r7puJNuoZqc9n1Vi/azxRyvTfVg1mLRiye5SoW9hJv2S5fTn623U0WXz0JG9TsytN9zndB\\nnF2z5VniXIfzLW73kKnvaQVys4cZjBNxGhn6G6bxiulwST7egbO2U6REt9mCb0ipZ7r5CB17onoS\\nkWm4wvsGaHBOGaceEUdMPQLmYbYt8eZ3yGr1obsNnO035EEInb3bjx5e0A+ZzXbHFHv6IYIGgg+Q\\nYRwitzcD0xSJ0VpcMWeT23uXVVfPZGnV5im1lxgFqzq33nKYJ+43gm+h8R7vHE0QujbgEbogeLH4\\n5hhTLdm099+LacOW57gKEZzc16IxGxpP2whtgOAdwxj54OkN3pl+NFn5ymsPebjbcjVMnG06uuAI\\novRjQlPm49uJ9354jXZv/heq+t0XHqKf4fGFiWHeH23b/sXdbvdXr8ez1oU9zgfC+VtoGon9DRc/\\n98sk1zF88j2my++brJwmpDE5KKclvlhpNxcQ34JrCedvsnn4Oul4w/Dsh6WOriRspFQol+JdVmej\\nLHDzmCm2Fxe1ZZP6t0UlZ5ZRU4B8mgNR3K3TbRcP0j5cFcoXLdXPO4eFgl15iwvKlq9bEf9MM5bM\\n2PUCWy/fUb+69nTuHVIXT7zWtN5XG1qX0lRDQ/2W4ANpHEBKU+5VTO7l9HQxBmRZGGcKs8afV3Nk\\nCS8B8W6WoXNFFrB6nlWZSXJVnVrFm4vXm2O0mlFdU56F0n3xDE/Ou2DmiVe5npeTrWWVyVyP/9L7\\nXe6duTFzl5qX0frifCm5KfffNTjXgisGV2fUrHUD8kYzp5E4HEAT09316r6YAel8A+0OPV4iROLx\\niLQdrn2Aa4KBUOxNuSvsgQhxJE0H/OYxZ298FXf+Or4R8o/+Bnn4PhfblvOzjk8/7hmnyPYscH7W\\n8fxqRGWi2zvSlAFPjJkUrVQEKSo8WbFay6UOVjXjg1+0g51YvW0BO+8DgpJyxBUBk6yJtgs4MZGB\\ncczzc6DYXKecyaPFNENjyj3WIECL5GRhNkopion7V6PM7pUPwtm2Y5pKc4GsbHxDjpnLcWTbtLQO\\nLrYNrW9BoC2ZvV6EcZr44PrAt791Rdi/81/1l9/7z17yoPxMjy8UJbse4zj+17vd7u0n59NfenY7\\ntJCZbj5k/8a/Ro49N+9/jea1r9A+/hI538HtczSP5Dga16/JqCqyFXRnMUqFidxfo8czhqsPyaFB\\nEpBKZw0vWGjRvUA9ngSXZi9xDVinC9mSgl6qlEuSUU34edEU0vq/maqrmbamf7s6B2H2nKUskjOI\\n131VL+6e0VUrRgHrEFGoIs0V2IpXWK5B5vgYi9rQ7E2tacAK9i96k8LysbhTsDTUyTgPMfWQDkiz\\nOzmuyGr7ea5zafdVvPk5plyynOc4bNU+KkleeULwxYMwVyCXMhXVbFmiKmh5BgQBXdqnlRMq01oS\\naIqXvY43sjrTk/mv116UWl7Y4P73V8bF/Z+XsoQCjFoSsyqrcgK4lk1dE4ssa1vwvsG1W1zTWkjY\\nN6XMRMjDDXG4I0dL1iGN+CI8oWLyk5Zd6/GpZ3IOZAdNIpy9xebx28S752g80o/XuMbKvnTq0WmE\\n5ozNG18lemHfBMbb3yPdfQcFpjFxdd2TSIhPNKHj0eMv8/T517l43AFCcJmU1IQAnOA9WP2sydpJ\\nvQ7BknhkocqdM+As8q4E58s7m006sRianfdoVoZUPq/hl1zuRSgefGsZsFkrxZ3LPbL7VUGzUrxr\\nlik0nq4xrduUEtsQOOs6Huw2fPeTZ/isnLeB1onVbkpi07XWfzMnBoSPbo5859tXNK3/b49fQLCE\\nL7CHCSAicnZ29t88evzaX3j/KY33LTRbuvPXGA835OnAxVd/E8nK7UffJ968B7V5r5qHIhIKdCi4\\nrujMOmT/Or57CIenhN0Z/dUHRYIvGYjkWFLuK8Aw07svrm9li2K1n4qH1wX2Zffxvi9SwK96havP\\nVRNI9S5hXfIwx2DnOGr9V+fF8XRVruCDfUekiEEsBfQ1YcgQGWYwdAutZPvQkwV8Od4Sc5TV9mXD\\nOVlmoVgttjZOA07ANeeEpiugXUFxBZhaEq8UrKazltGU9mgnx1xihVqBQ1yprZWZtquSet7BOB5n\\n2mwN7s6VWlsgxWmhOothc1p8fhqPXPaz3Efn/cncnHjdn0HLnm67VjVekpvqPdMixl/1la3xuszX\\nL3h8t0N8W/RRPd4HlEzuj/S3n1rC02SSlSlN+EIzZ03kaaDpNkxThBwJzpMUukdvEs6eEI834D3p\\n5lPy1Fttp/dMww05K2dv/hpu07LZv466O+IP/wd0eobgaDeBlsA4Ku0u8/rrP88P3vsRF48t5jsO\\nZojGuGShmmSzI8VEKhrMlXqtgFVnZjYU56e+vluWVbBpTXgiJpmTg2LMs8Gb0zqhTgvJ4a3HqtZM\\nZFnttz7+5S0VQcWalnebljRFtm0DKGd+w5P9lqtjz6hAVp6cb7k59GSBfRvIeWle/fxw5Pd+7zmI\\n/I2xj//eZz40P+PjC+thAqiqish/IiJv/dI7X/6z3/z+oXHi6W8u8e0OyZGb7/8jHv7hf5vu8Zs0\\nu4b+kx8iXsjHK9Bo2rCFnnV5Mj/De7R/jvjA/u2f5+6TH9pLgFhMp6qz5FjwovahxDI384ugKSho\\nsvo+0VIDSlEXglOva71ALmovVC+obDMvfrMXWxMUFCvDKFRmAY5FFSSXQ6xKDO4tvHURsZ9l3kTn\\nBWX27ezKZdmHeVzlLFcUsv3NwErXbiinXtdcYrMCFtvW0zab8n0pTpKCy3YP15nIJzWrwgs3pHZH\\nYUkGqsk/8wJXwKbSzaLmNcZkXub9sSQLVUr1RdpzXXYk8rLYLau5W5cScLLt54Hl/eQe+8+t9l3Z\\nBTc/Nlrun4glYEnTmSdZEuYQCK4pWa9KGgemw6f2fCVriq0540Kz8t4VfFPmOYML5HbP5uIJvttC\\n7JFpQKO39mGq6OEpY94gOdE9/irN48f4sCfyHPnob6H5EpzQOKFxjkcP3uTp9cf4tuPjTz7k7GHJ\\nJlXhcBgtA5d6S8UauRfVLwM5bzQti6EpIiVRy+5f8K546kah5gyNMw81FTELtFhSGaNw1Zn4eQmx\\nhMY8xho3rbTvmjFajClrx9VtWzPKxTrT7DtjxBrpeP1iTxwnGmdhhtf2G6YUSVHZbgMpWaeTnDM3\\n48Q3vnFF04S/fXc7fGHBEr7gHmYdIrI5Pz//OzH5f73XBwTfQvcA13ZI6skpcfaLf4rh0hKC4s0H\\nTIdnpOlIjkPpELB4Wq52cGg2PHr7DzEMEA/PGY/PkJzmxVhztgSR0mnkpPauDGUNDBXMtGBcwHyn\\nvPytUoTFA4CVp3APU+dYo6w+nA9cxQuKZb2S2lu2dvPLiKvHXu+jer+uZNU61NUm0muvl9W5ycm/\\n9zNrRVwRT4CqLjMDxPrcX0JIs/rUhw1J11Z4zVj9nOSjcpQqVG4e9toAqQaR1erN3rWKlaqUvqnG\\nUsSXeocnv+fTOV/6Jpb5kLJgru9TeYakTOx8fgWE7oPm6fzqIuJPtV9WdHgtsylUq2BC9DWDHOfn\\nZ19dwIWWtm05Hg502z2IdW7JcUTSgbvLT3ChsflKI2h+QeavnrdzQsITdhckFbZnZxyvnuE0oy7j\\naYnHp3jZMI7XhPPH7N/+NZrthvDs/ybefY3+eFVCJ8r5fkcchRA2DPkWJw3bPSAGSqjFLWPOkK29\\n1hxPViXHTKz6vSvxAWsB56xmMpthWV+/UvKK95TEoBKmQAnBlX6xtRxoeaObxhNTnN8nk8PTwoIU\\n71azdUGRKn6leB/oWk8/TDy+2DLFTJyUJ7str+32fHh9DcDFpmXKkMbIZtMQc8Q7K9mZcuZrv/cp\\nbvvkeP3xB3v9ggPGFy5L9mVDVfubm5s/61381sbdYuWWV0gei1RZ5vZb/w/nj9/FnT8ii+J3byEu\\nELoHONfMfSRrrCnlDHHg+fvfYBouifGICx3qLGsQsCL6mcari5YttAt+2NtWf88ltmPnHcl5oMq7\\n1fhIbU1VPSYTba5tl8p/88uf5wzQatXP3qErC6Iv2Zh1wbejUzuaKC/3dGwx8azbP9Ws0rLBPUr3\\nRXp3KYvw1Ebcp5C6/LKIPLzMcypCENicmyRhse7zKnP3pxj3/Noyn0XcYvaw7T+pNLcU8+ZzqNB6\\n3ZWehQKGBai0PB8W42L+e/2ezMlMi4eqn3F9a0+zLvr1v+X8a3cdo1lnoYZQDQdwPpBTUbDJJn+X\\nFYa+p91sIXQlbmne+xQj3eaMru0sg0WTtUDL9bggZDMHUwIJ+G6PDw1dtyHe3ZGHAxqvIXWk4Tka\\nJ8bY4ySwf/guTQjIp7/FcPs7aH5OCJ7g4eJsR4rKpj3j9nhDypHtmRmFfT9yezNwe3sk5UhYQoGr\\n+wwmQVfnr86ZPe8pWdwTpCS9CzhLAPJBwFkcst4b79bPvM5zoKqE4IkxLcZn2VILRRzE4Zy1DAOs\\nzEuE/X5HExy7TaBrG24PE3e3I63YW3AYBrrQcLHpiKompuFgKhKf0UES5Rvfeg7t4/H64w/Ov+hg\\nCa8Acx6qenV7e/unHf1HPj5FY2S6eUZ39tiy13A8/c5v41LCP3yHdveQcPYlfLu1dk6uRdM4e48W\\n/LcFfrx9SvvgS7jzt/F+w6LSwuyJzLG9ZbWa2S9lKXqvNZ+nJ59XCzVW36UFzDTO9X8vLtF6+uMK\\nPFVTKXExT1Jca95DqB1bGqpk32d5cwuILNdr8Znl8897B5dyhRf+wjquihg1unjMDq3lGTjyvcf8\\nxZrPpSTgX3ZNsC4VJe1pVe6iZaGdY4c/Zj+1/KZSqvfp04zVbv5+Tvez6Fit5wkzMK/n1T5zs/EE\\n1rUCEXJK1rQ4ZxMdmHqmu+eI8/jtAyS0aO1YkgZIEzlPHI+3Zc5ACoWLDzVAQYojKg63u8A3LdPh\\niun2KXm4xpFJuSX4I/F4h/iG4ITu9V8kbB8apZmf4p0jTsEoXyeQEmfbR+Qm4Rq797c3A9dXE9OY\\nCaGlacwAiFMyA8BJoVMtzqiiNI23Y2CdZ8TZDDonhGDXlLOJUjgnNI2BaYzJDNasOC+EpsatzQBy\\nzs0yefO9L+7mdrOZDZt915JTLG3dTtmH43HgbNcyjJHGeTQq++0WVIlYjW8THP04cpwi0zix2XQ0\\nzpOCoJL51rcv6eM+33760YWqvkgxfQHHK8BcDVX94O7u7k8FGT7J46doHuiffddiMjki0xWX3/9n\\nXDx8nfDwTTKJZv+6xU9cILTnhLAhjwcymRTvICckw/j0mzzYe8LFE3CtgQ33F+7VL7Mb5WbQhCqJ\\n9pkXwAxeFTgpHqpGk1WTEoOpy6PU+Omy8taGx1kjOY/E2JPjgRxHrA1Xi3cB7xpE2rlkoi7eVt95\\n79Tm86qu4GJh/6QgtbB0zrzf2dCoda5F9q7Qo9YC6RScXjZ3lY78cWP23j5nxJINm6qRUg64aK+u\\nmITV7Tq90Oqhrj6qm5bvCkt88r53vAbE+3WX90tvTq6v0s2YILmrz+FcZFrqh50jF//PYdv7ZgPt\\nHhWPazroduxf+zJNt0OnnjzcoWkkx4F4PJJjRHPEV/vQedNnbVoTCHGWTJei6bZuu448WneZOI3E\\ncSDlCeeF/jjguy0qnvD4XdrHj0gO+ue/RZPeQzD1muAdu3bDbvc6r731BofjFSGY9zb0E6mATy7i\\n9qpCSvZcW92llZa40v8T0QXcqgSm2ufDMDJNEzknfLB57Y/THC2o1HfTemJcmpA7b8lOKZfnKCUT\\nXlebp5RSyZL1xJzITkx1rL57YpJ3F2dbLm8HhkEhKcGZWOTFZsdGPIpwO/S40PCo7die7UEyk8tE\\nMt/65nNu+y399bMHqjp87kP/BRqvYpgvGSLyi7vd7rcPQ/Oaa89RheA9GrbgPLtH7+LOnyDiOHz4\\nbSATj5dIPBYWqwMvSJxIToowdId4T7M7I6kjHaxx9Sx0nk0/EyqQJGpIclGL0RdW/JfSbGvS8vPW\\n98oalp8r3Vo9pNrxw45RAytL1utyiJo1Wj5AsZjsKkNUFnk+ZTnuklX4ItF6f0FfavvcvH/m77MC\\nH50XsOo1utmOcKuG19UwqQlRnz9qz8f7Gbn1vGtcsxpDUgFdxDrTqFjPT1XQuLrme7WwJa51Iod4\\nMhH2j5dMjPGewtOL8/Y5V3Sy55lFVlnR6CDBI2INgzVOJRZtYufOlT6mLiBNi/NNEUGv+5Y5tjcd\\nrtHpbja8SNH+7gM5Hckp0e4emnpOTrgciVOPhj27izdIOqIpMt4+t2xF3+BCYDze0DYtsTnn4ud+\\nERc8rtuSPv7rdNN36IcEKfPo7IIUHWcPH3N1fJ+cHf3BdGyrcRmCQ9XqGGvnkSUhLC9arUCtYxYB\\nzW5OulHVQskqbRfM+Myn8eKcy980omot+XK2TNiS51O6GeXF0BElRftj01ppUk5aQjLMRqKdk+BD\\n4MsPHxJy5LzbcjkeyEpJOjKVo03T8MndLQ92G9TBpInvfOuSy8uesc9PVPXTn/Bh+kKMV4D5GUNE\\nfmm73f52P3WPpDlHNSPN3nwZaXj81V9hdA8RVfpPvm2WsA+kqx8hklAai1mWhdgUOQJh+xr7t97l\\n+v1vosMtWgTdNcUiyl08wAqYsPI2DIheNl4ovygL1axqc6I9KvcRpuzkNH64pMOvT1FKqAAAIABJ\\nREFUNy3AtxKbn7/jZK6zLG8w86IrdduyK7cqB1kj6By/OjkV+57zLJnGK2PhBYqzFH0vnPZsN+QZ\\ncO/P4I8ftaTk3ocnfwcD9kVYYdW9Rl25BgPDpThnBda2ErOUdCwfOwkGPmnE9IiX0pA5Q7LOxdo4\\nsTNZ4LFYN2t7yb5SPEld6EBKX0vxNX5s91VcAG+NB8R5XGjwTWfbAqk06fbeEccjTCM5jaQ4IpTe\\njDkt0onTnXlLfkttXqCxqAv5LT4EskYa33I8XNMGIWFhD9XMZneGv/gK/sE5LQl38zfRux/Qj5Gt\\nb3iw2SOy5fntNWPuCRsTHoiTmSpptGbx9misRDFqnLGUAMWYTjKPRYRpmmZR9hzNyKxZptv9lmEc\\nSZN9PlPsDmR+lwuNX7xOo2Xt2M45o21DYDgO4ISua/ACt3cjm03LFGPRiRUab8/XrmtpNfP6+QP2\\nXcf1NDLFicZ5rvoDZ6Fjt2npczZj3VtW7rcWsHxbVT944SX4go9XgPk5Q0R+ebvd/oM+dg9d85Cc\\nI6F7iKYjKGwevgX7x4T9G0zP37cFob8mHT8FdeAafGgxoCtWKRnnGlSj6YKmscQfrEDYLN00W6/k\\n9f35bMBcD9Uqme5WC2cBU1708pbY3Ux61qXx5DvL4upKWaKr6GeLNhSqlBkMqwkwU0azB1p+W8Vn\\nmIFloaQztf9F2fcKMFf36SWetp78WPuDVi3enx4w3SmlqVWooMzMOnlmDaxr4MSo5N8PYM6TVGPY\\n1Q05QbxVnaus7Kz5nGtWb3Xv5y+uPi+Zwy6UOsqXxDvFFVUrXwBc8SHgQjDP0NfM14nY31kMvXho\\nOaflGcimnOPEkYZrfLczqjdNCCVeD+Cach61p+gEvjOhgKRMHNm//RvsH12Qr/4Wcvh2oW8t8/Si\\nC1xsH3LZJw79DUkiofPkmM3JLfPp/NJ2bH3Jc3ZsfS9KfBlk5RGaJF4wZQO6JjBOk11P1pPX1gdn\\nBrGUEjJZMmTB4pqmUWF9bl9/9JC+nximga7ztI1H8dxcHwgBNtsNxyHOYO4dvH12wdhPbHctOVgt\\nqctCHzPOwT54CB6nSnYgTvjav/iEu+uRsc/vqOoPeDVeGK8A88cMEfmV7Xb7945T+yi0j8ia8C5Y\\ndmtO+P0b+G7H5slXmA7XpMNz0jiiuWezPWc83sDckNmhLhTLelHRMa3VvCozSaVPXTTJrZN6x5/w\\nfq2A7MduNl9sVQ66H1d0BbRW+5qL8oU1YEqlal0FDyn4WEBcjEqS4uFq5XJFlgUSS4+f46yz57yU\\n79TSjheu5yd4nqUAzU/z7FuW7noVzYUirvt7ETAXT694bpj3Yh4hyxz8GMCc2YPiPeY0MRe018Sc\\nl8VWyz5skS9lMKWDx2yo1POblWcoaj1Go+vctqyenuC9xY+zeHwIpRTEW0cfPMF7jjdPSf2ttb6q\\nyUB62k1Hatsy8UWr2QwkLX1Ea8BPvMlRCokcE6FtiYhV1aSe9tHbPPjyL5Ev/zbh8E/xXcs0RdKk\\nOBV+6a03+N73nxIuzhGXubq9BoE0WdN1LWBp72KhRet5qhZvL1tWMlI8wUxoBO8CMZnEYUpL3eRM\\nx9e5W4ULQlsyXxF7jrKVmXjvsI43YvdeYLttGMfEfrth23kcmdB4q/9Mlm08TMqxH/EK6gM/f3HG\\nNCaiRmJjz1znApfHnvN2g3OWu7BpGjLK1dDzwXt39LeJ42H6sqq+/xO+Fl+48YUWLvhJhqp+XUT+\\njd1O/v5xvHyNsDchanGINMS7j0Ff4/jxt+iefBWy4rcWA8rxiIsTmhI59ggZTUNZUIMtJIWiUozm\\nVARybQnlLSmnvoR/0BcnZa81k1MXX7LKwdnnucQL+QniY4XILYxshQkontWJxyI4VzxTYSVqsHxL\\nRXFzTPUUFNYF/fMlVdD6DDD8vKSX9fdOE4GKvz1zmKcuSBbhBbgqZSAzNb0Ciuooft5UrpOo5jnz\\nnhwjEjort8hxBuD7cdi5HlMNwAmlwXKBP0VLWVOh750p8NSLXN8L700gfZoG20YKrU1pCZUV1QmX\\nrKD+9vmHuHSLS4K6Gh6sVG6l4u37viS2WINlKThh852pOs1aWmipxTtdV57bEWk6tq99hei2NAHa\\nrkG8EnzL7dTTesfz6zsIG4bxyJB7QEiT1TU6b/NQ9WCtO40UR960mHOxbsRh73fj53lO2eKw9V6p\\nFqk8nW0kRIvGqy9dV6Yl1mlCBQ6nVWrPDFbnPD4I/THiAgRfsnEbK+3JKYMTxkm5O45s20CMyoO2\\ngZTAC7kNMJlh/unxyM4FRJRIZuPMuxyc8sH37zjeTPR9ekNVP/nsp/LVeOVh/oSjJAL9/cj+9Sm1\\nJQPTo9KYmkbY4HeP2T15lzQe0emA2zxEp5F4fE4eD9bdBFjk7yxxxdJpKmVoq6nmRE5xBi7VjKws\\nzx87fhIPU+55WlpJuXJuLIpDmmviQaXz3BwrrcdRCqtUvUC3FhaoniGsdYxm4bDVeTpqskzZ14pa\\nXCDs/qWcfvLjylVe8inLgV7213rdFXCqlywzg3y6jTuZq8UDrZ0t7F4uXuZyD+5nyNbryZpL57R4\\nsl8ni0bpvBCX/YoKrtlYdxSpxkgB8Llswepta3xNlVlkHZhl25wLZVsTJYCMpggaSSkWDWAhTiNe\\nWIQgcpprSueyGmfgrCmiGsnTYDS1ZnuvtCKOs6J8b51PXGiRsMOHwHD7lO1r77B9413c8Ztsx/+d\\nzplxeewz+Zh48uCcZ5/03E4j2sR66fbOZcCb51jnmMJ+rMuZzLDIpt9aykqqMMGarq030LKkrSTI\\n7nX5ucSaczLatRpFOdXuPTJnU4fgyUnZbBvaALd3E7tdR9d42mAZy30/EUrm8vX1gbbpeG23Y8qZ\\n3CYaGu4OA8c0sXcdQSB72LQBIZNwfPtbzzjcTHno00NVvfnMF+DVAF4B5k81ROSr+/3+72fZvdmn\\nLY5s8ZX2HLKljIftOe3DL9vLEBPSbRFNTLfPydOd9ddURWr3EopFfRKHW7Ikc5ogp/KSr2KNP46a\\nLYBZPaKXwub9WJ6uAFR1dX51QVhtXzt3UGlGtwLMesTqiaxBagGV++C3mg2Dr1KjugD5+mTl9Ms/\\nxXg5YN73Ee9nzdYYpjtVV13va/bKyrmu4pcUBgHA9BRByJgWsZoAeAFLw7o6+8UdrYZF+V3KKeu8\\nfT0Ho0WrF+hcgw9dAZ/VRrI6L6oTXO6Aq3HiwgzUZ02s60rt4yl5LF6uiaQvnqMYrU9RvtFK9UN9\\nttU1EAckTxa/y4kcJzuWeDLgfVOeKwXf0p09QpoNrulINx8zZeHsnV+mnS7Z9/8jLkX8pmEaI3HM\\nbLTh0dkZ333vU9y+oR/vqjVg+3SudIQpgJmrWk+lVY0qdaU8qWsDw1Da/GGec0rW/9LikGs94lrG\\nUzz2SquXJuOu9MysyT5WKmVGsXN2zSIQgmMcE14C3SbQNoIPjqGfONt3SBbu+omuC8QpETpP1zl0\\ngtvDxJQiGwK7zRZlwjdCK54R5dvfeE5/jHE4pkeqevuSl+LVuDdeUbI/xVDV74rIb5ydyd/ZuPyH\\nJv8GabrBDbdIu8W7ROqvOHxyYPv4HcLmAdPxku7h2+Aa0vGaeHhGHu/m5c8AKi9IM8OGLXriBZgQ\\nzeZhUNfPe6UIfBYQlIxD4zzv4YvOIFkOykyISW3BpasFYIGR+/vRZUW8t/+y7K+OUYHh/ljgxhZ7\\nyboWOIH19c6Ttz6bHw+in+VxzybBTMWu6krrsaTSx9WfXNPLq3jj8qXPHbUmlmJAoQUcirKTKwtw\\nFqNF0+yxxVKv5yrSgTNQcj7gfYPiSvZqiS9CqbldJ4LVOdQllAnM8n6Ux6O266pZwFi2Z5zybMRZ\\nf1fuPWM1i7fMS7WoRBDNxJQWG6LeW7sJFgv3Db7ZQtPR7M4hmD7tdLxmPB44e/NdcC17eZ+9wK0I\\nDxtPL46r4c6UfG56tvszrsfrWYlIHKQp47KQvdrnzs1CAosxJ/P0ehHGMc4Gq5bnQ3M2oYWanFfs\\nWeftvU7xlJKfy0SQWSpPcTiHCUKI4J3R1OMEU6n73G4DU4zcTtA2Voby7PIAKpyfbxFgf9YhKFOf\\n6fvIYRp52O7xTjiOA5uN0LjA3TDw3W/dkGIehmN6qKr9j31YXw3gFWD+1ENVPxCRP3l+Lv/nO2/z\\nx/7F912jeUBHrLWQTsh4R//R17l450/icIzPP4R2Szh/zazrHNEpk9NY0vWt2Fhm8XMAS0jwriGp\\nQBpwxWMUSWYV3wOdk5ieVO+jehYFBJj/zJLpeW9xF7H2Y7M3Uyk+Ma/6/pzMvt89AH75BL5IfBZB\\n+dmjqrzZyod9mZesM8zpfMyF7lx7fp9zPiwwrKt91MVyvY2wnEn1luo3cl39qiD5jzsY5r2ILg22\\nBYtJqjMZQA0WAGy8J+fJkm1wSLdHcYSVUH/WWJ6PUubhvQk+OeYuJ656fnP5QrkqL6iEQpuWWOJs\\njEBRV6e2TauJalZq4WbvMsvsA8/zaH0zLSZpU2RNt2OcFkpbs513KIlfTYu4hmZzAc0G322o2alp\\nPODGEXFCu3/Mk/x3CYff5TJPnDUt3jd4rL756uqSIC1ffucX+effvrTH0+mcwpZzJvjSrxIBVzRe\\nWd4WV7xE1UyatW5kzitwXmYgrh7jTG3XV0Wr4o9HxO5Xt2kZhsFitN6OlmNit+849iMx2SzuNy2u\\ncRyHEaeO4ASnwmFIKHC27UAzXjz93UiaMrHPDF5Jh4h/HQ59z/68wwn0U+TbX78G9b97PE6/+UrB\\n56cbrwDz9zFU9UpE/vSP3v/hX//51x//2fefNo3GI9n7Agi22Fx9/3fx528Quh0cBiJKu9mh6QEI\\n5Anrr1laMGXNyDphUhKi0eJGYQPiyOlIbbDntGii3j8/Kr5Vq1cwTzOhWrNbK8VpMRznwgKuha47\\nUeF5oUPJZ6PCSwngXCztl/xJseSUZbHVpRtJSbWXlQuU0cWzlPLdBe3Kta/8nHWI6d6ZzvA4C2gv\\n3uqSAFR38tmjxu+W/a6u757zXel3L57hcE1oTBRAXCCEPRlP0qKE45uiy2S6pGHT2VRSE7GqaERj\\n0m9l/+bJKJpyEb4odYtuiWtX1SgjOEoLKywxZeiPNG1XbnfxrrMSQiCnWMohzNjzJcM05TTPv5DJ\\nMTI3FdDSbk2VlEpCWSEgXOlIAqULjwv47oKw2Vp9oGLyyzlBgtg/JVy8TgqOu7s9Yx/ZBuHx+Tm7\\nzY7D1VPECefnO549u+O9998j5gnBvLdcpO6ct9iktYKT2ci0e2bXqprw3jONpRm7ykzZOifl3TkV\\nLdBi/Nbf7dEvSW9ipSK1dVfbeI7HCQFCG4hJickyykNT5OuGCUkmKqAS6PseEUcXPG0QfILDFLGa\\nbs/UmEHy5huP6KeB3S7gUcYh8c2vX4K43xr74c+80ob96ccrwPx9DlXtReTfUdW/9trZ7j+8vDtv\\nUxrmbguh25HGI/n2Y9T/HClF/M0I+yeE7YV1QBgPuJLgILJ0sZflICUWlouaizWtFt+Q8mjUnFha\\nvJWflI4kUrsYFPoMKUBZmlizUsmRQtcVL8R7PzuKM7Wo814W2u6EQl480M/3rsqCUdVdtYLSPYAp\\n/1r4Umfvom63pvzqxpaEYwBrYGtp+zN4rzzf00zY9b/3QbOOGo+qR9cVfi5xqs8bWq+/el2o9VNs\\nt7OHnjKksWez2yPZoRLsEmuyjFPScLCrDY3d43JZzjlUlBgHXByW0yler3PB7mlK81zWRJ6sWgT2\\nPTWJq2nbEsJsDKidCQRIWuJsZCWLxSm9qpVLZTN2UkrVaZvvUX32wAwU5xuadmveZs6I9yVByRHa\\njVHDKqh35HHCIeQ4Mo0H9mlrdmN7xpPNhj7e0Y8DV8dbpgRNasA7tg8e8fTyGSQQLyXmGBBn85mz\\nlnKopSdlNThUIyEExiFZa7ZcGrrVJKti3KYihxijbY8UxkgE8Y5QHz01QYJN42k7yAfheJxm6iLG\\n2sjAaNeUM5e3A092WyKJq7En5YR3jk1o2O86Gh/49PoW7xyTKt4nYo48fnDOyEi7sabWwxD55teu\\naBr/1w+H8d//3If11fjM8Qow/yWGqiYR+Y9TSj/aNsNfOrS/1OrxGZpH0ugJ23PS4Zrx+Q/wZ0/Y\\nPn6Tw+XHhP0T/M7Ug9LhOZpGA0eh1KzN0aXZk1gksmrCSA2U+JJZaIvVuim1zGBXT3gp11g8MYpn\\n6aApx67fcbUjQ20N5pZ1uPav1Dm9h3VocqmZXEDtJTPIZ+e9nsxzdXLKt8o3TvZbpPgEW2S5B7jF\\n65/npRxzAVTb4UK1yj3gXCkH1e9K8dBX4HlyovMo1HcxKGpyiOBpmoCSSMMd4gNCYjxYJxVCY2UH\\nzugIO8VMTBGvG5xvy9w4YiriADnNNbJaJkFyImuiJgLV+CQzQ+HQVL4rzqhH56y7WFGRac/OGRO4\\ndkOKkd3Gc3v5nODbOd6nzlksNRsYidSyFpgLjBwEkSKOb0ILvtsbqIeAzhnXSk6T1XRefoL3QsxK\\nHgc0RjQc2Ejiy2eXNOFL6PAp13JO37/PcDWxaT3HMXJ5uCkgZPfKOU/WuIjkq5aelrl4jBTxBVPX\\niVFPqNW1IHqlX1MytS4fXEnmUQRv5SIOmsYVIQJlu2tRzVxeDaQpGbugmZRh03ga5ziMI+dta567\\nwiFF7tKIbzyi0PqAd8LdcSTlHnGOcYpEMt4J5xtTGNs2Ho9wPCS+8XuXeO/+2uEw/qef+aK9Gj92\\nvMqS/QMabdv+xaZp/iqP/njbX74POeLCFo09uACa2Tz+CjEng6d2j2wvyHfPyf0VabilLqyFhWSl\\nkwOFTrMfxTo44Oe0/Lk919zWq7zlegpF972rueQBNxfizxSpSKHzal5oFQxgrp08GSvXcAbM+0iK\\nga2cfC4nO5vlBKsIwspDse+/OP9L3FGK/N09UJyVd1bXXks8Xvj8dM+nF/fi35a5rMXq98GWGayq\\nRJ7dGAsypmQi4ut+l+bTMycRVZr5pdrBljFibbBKzFmotX9LzaDF8NysxIOY95GzlXJYn0W7MSZs\\n3phKTR5xQfD+CU0Q0v4CHzvIR7LvGYdI4z3jNMyiB6K5XE82YXXEKMOcIEeqgIVz1mzaDAm1zkBx\\nRF1j8cG7K/M0ve0zjgPx7iM2b7zL9q1fxRNQd0dz+C3COCHTt3htc06g5fUvf5Xf+ae/Q39MiCjt\\nxltnj8KmzGVS2ajaaUooQtM4nChJmYXPYYnfWplLyYotD2PTlhrHGOd6VhVBo+KDzB1L2o3n6qpH\\ns+AdNL7hOAycbzrOtlsz2qIpfY3OcXm4Q1fe7cV2Q9IE3pFiJMb5VtKIcLHdcn04cnbe0qrj6dOe\\n939wS9eEv3J3GP/ySx7iV+OnGK8A8w9wiMi/u9/v//shP9yp79A04nyHxdVtsdw/eofj4RJ8h9+c\\n0e4fE8cj8eYT8ngLml7wuWpcxI7h5kQhkdLN3rg2Uh5LCYrOC6eu2n59pg9XwG2dQTkn+5R9GQ4I\\nJyUk9SSFQpsuZz0vRhVLdAFr+7zGsGZzYHVCBezvAebpOS9zA8yL/SxcUATh5+PPkngVqZk99lpW\\ncAKN9+jaz5i45af5uBUYT/++Bkyp2qGsDJuZ8l6xAnoPMO8JE5ToZamZXJoYz8eDmaJeYrxmlFlM\\nbckGtTRNyD5C9EhWurMzHkzf5jq/xv6tX8DFnqN/i8xgoYTSDc7phMZoJSbBeqa6UlyfUgSd0Gm0\\n/zRb95NZr7XI6xWpwJQTm7NHMN1x8+mPCN0FTkp/2ay4fGS4vebBV/84/uHP0XohOuXB3W+jw+8w\\n9CNtEqapYwqJPCbGOIFkfFPj8mYUZlUc5rXlVObFKd45xmnCO2fX56RoulqzdnGZNBlT4KsUZGnq\\nvK65rIyEPXsWA50iaFaCdzSNJ0+Ztg1cbDd2P9VilVfHHuc9x34q7zOcdS2xlKQ4ZxKwkUwXApNG\\nLjYbhpjxJFwQrj+a+PjDIzHm31TV3/mcB/nV+AnHK8D8Ax4i8m9ut9v/rc/7B657HZ1uQDxOQnmx\\nOrOoNaFhQ9i9Rrt/xHB3TTp8Amkgx2EGkFys8ap9gmqR0QqljswKyUHMu8yT0W/KyussPxMXynR1\\nzmsPq8APpsLiC2WX6gbLdVaPdHau1nss3lyNB4F1C1l5T8gKQF8A80orL0kqn/WczoXwxr9SvbsT\\nwNTirc0Avni0ttCV762uce3Fzj++JLa5/Om+R7mUYcz1sOU8rdGSsgZAO6ibM1FtrpbG4TOW2kW/\\n5JgrT3m9y7r9isdey+hVIQOdi+yTxfkYObtwiLY82F9zcL+Atlv88E+g+w2O4U06/Tbaj+T2HaZo\\nsm2qE2ka0PFoous5GhinvNR3uuXkapavc8I0mqi8qNHEOMFniA5EAjr1jDcfWtzyl/8tZLOl9Ymv\\n6P/Bjz5+DySxkU0xHrdEUZ4+/QRcot00jFMkxYwThyvPrjho28BwHFGgbVuGYSxhEQ9imbTiZW7T\\nFWNCcuF/5nIZludQdAZMESUEk81LKc/G0r5raLzHBcfZdsM0TrRtIObEh89vzCPPmaYJxBTZBE8X\\nWtquZYwjWQ0s4xTNexUhiBBcIKry8Xu39HeRYUi/rKrf4NX4AxmvYph/wENV/56I/PrZmf+//ugf\\nefLuP/inudV4Z/RY2JLziMtCyhHJZjn3cSBsH6LdnjyAFPUUi8n52VsR60tViq1LXqGWZJ/qQfkG\\nSYJKPikNsRZFrsSp6gJqAabqvawTXpRs1M8c81toWWbwZgaFGiuk7MsQceWtFcDQ9eJdxsrpK5M4\\na4pZdu19TYF71aAv91LvjROwt/833dLapPleXaueqiRV4PnMOs5abvGSbWZvc+W1as4LOFLBb2EC\\n6rxVynTx8E+v5eQ49z/I85mvTrTG2GyVzzUWXWteNeE0EbNjTG+w899mfPZ94vYR7L5C7v4cmhJO\\nR4b8ZSa9hJtnpOGI5hFS0YwtjcgpzINzDqLOnnV9kgxUnCWCSfWow3wxyXmcKMPhOUxX6HTD9q1f\\nQZ3nvMn8EfdbfPTJM97szrmbBlQyh95xyNdsdzvw3hJixgxRCOJNQEJAoynqTGPCOU9Mcb6X5oVn\\nfFjQPZc4JLC0/mJ1v1bPAUDbepyDccqW+VrqbLvG03hHCIIXIQ4TvnHcDUcyFvNMGcQ72uD40sX/\\n3967xlqSXfd9v7V3VZ3XfXX3dM+LM0NSfIgSRYqUZFmyLUqyI9uB7CROJEOBEzgGbBh+ILFjyw9F\\n+qDAjgEDhhEggBMZyhc7sQ1FEIIkMiQ4gJ1IVmRJFCVSEsnhDGd6Zrqnn/d1HvXYe+XDflSd07eH\\nQ4oczgxrATO37zl1qnbtOnf/91rrv/7riEuHh9w9O+f+8izEJbxHheipFmgX1oPNas3Nl2rUF79S\\n1+77VPX+hV+W0b4kGz3Mr5CJyHx/f/+fHR1d+r7b51cmzeYOEISkMTaSM0qK2QHWVHgxFOUMrx5X\\nn+Hrk7DwaNiV5sYgKlFqDBALabecO3nEPJgfhPuyxwLEUBQxxxQcU5+Dc3HwgzvRh/wbyKLfJtaj\\nb3s8CSSUSFxJZ9n9zsUQ2W6uMoHEAxZJMwNHkZSPTB4mDJSHNNKVoocWQKevKM2ewc69MXh/8MuD\\n4xmEofOCmVR0MlFLstKPkQLnXI44X2Rflr/LgVd/0ZthLqTf0ACqFtEWfBfLnGoUC5MZ870ncVWN\\nyAR/co+uWeKlCr55qt1MG6j8nQsbugQqW5sjTZ58v2kI/7KoWKwRuvUJGus8tdvQtmcsnvo2nnrn\\n03znpf+P0zu3mc6m3F6ec/v4FCfC2WlLY2umVUXXOdq6o+tSvatFy7C9KycGbZXN2uFVKUuhKGwQ\\nKIjdQiSCqwgDOTvFd4ONjo+s9HRcLMn1HsRILKMJUzwpDdZYFpMCKxYFnHqKSRHZtoblasP+bEZl\\nhf3JhMuLfV64c5/jzRL1MXpuorcb26iVhcFvlBeeO8Ma+9N13f6ApjzOaF82Gz3Mr5Cp6kpE/oOu\\n637Umrs/Yqp3VNrVqNsgWiLlHtPpgvXqBOZHGFXcZoOUC4r5ZZwt6VZ3Ee9RWsSXoA61BRRFzJco\\n3rehL2HyRlNMTqSPK27l7gSDQyjjOD3YoPcZvM8MO4G0QVpaB6vc9o2S9GC3GajD96MnsTW2IUAz\\nQDIGC/kwwze0wYGZ0fuaTyN7n+lSiu81ayNgb499mC+kB73BOF/LskZuump6FMTyDKMPx7LhyAcH\\nXejdxmc0HNdQvCKM+aIL9f74UKUpyPVFbVkRlAq8YLxjeXYdSxVSAsYgMh2mvEOkQ0O7KlJdIgzm\\nIXpmD+yMhvcawvC+29C1q/BZG2QmfbtCXIdWBc9cPufqwYLHr13m+OQYsYbVumXdbHjkcMGN+y2T\\nmVIYS7tu2Z+VLFfKpm3RxlLthTZYbZy8JG8nIkynFT4qEalqLvdSTxybzwxZH9tz9Y8mNHUO4Wff\\n11tay7SsaHwTHXqh9R0dihGlbTxNE2QC92czruzPMKVBa8+nXnoFp2AxWGuJzWJwCOumwYhwdq/l\\n9isrqsr+D6tV85e+0PdqtC/NRsD8ClosDP5xEfnEfH79nx5cfc/i9r11YM42p6y7GmMtbnWP4ugZ\\n6ru/hZ1dA1UmB1extqJd3kXbZWQbFkgCoAhsqSZMvA+lHpnp2O+GUwIshXDTco4KGBvzOYoaj1cX\\nPVtCLiYvcIHwMIQcIl0/BDMtqRwg5XMemI8IXLsR2BD2hYuQ6DVxkMFnBos+gxG+Fib1JSfpFK8V\\nco33LjIg0GTIyePIIe2EYpmd27vDoaby4XnZfMwDnvgXQNiYxMzh+qFjPDxk+K80dE+eCY1uUqCr\\nlEE0gDIuFr4vLhL/wPNRQm1j3p4McrEhvdd7nnksA8k98aHEBghlNgrqW9SSFUkNAAAgAElEQVSv\\n6eozTCUszBV+4/YBH3xqwl5xnycXe7j6OZ68tM/t+8Jp3VCJsFk3oIb9qsK5lrbxoSNIGUpeNiuP\\nqwk1luLxXuh8x2RSBq+xC9Ed7/oaaR+dNhPF6hNoQh9hCGpe4btURHavqmfZbbB4FrM5dduxaVuq\\nogjA6D11q1Q2EIQaVdxpzcn5GiOGKvbZVAKgrtqas/Uai3Byq2F50jnn9KOrVfsbr/0lGe13Y2NI\\n9g0yEfngYrH4+Wp+9dHz7kBcfQKqmGKKqeb4tqZY7OPWZ3jnKWZHFHtXwCvt8g6+OY81bbK1m00/\\nglyb7T1CCRJaqi4DXWaySvAzQzsx6H26oTSfhrZkPuiF4pPeTGLsDj3OKIRgUs/IVHM4DMMCJhJO\\nhm4J8KDQ+Vas9WLbIrIMr5XCwNtLud0F0zx1senyVtT1ta7d18Kma/f/7Ek+4fwWMaHQPWFHqPlz\\nBLbqRR5jAufgsX0xf569GpLk+2RwlRwpSB71zrvbJ0s5XEsOEeR7jN+a2M9S1OeP6/CqcU8WSGvp\\nAM2phnwdW6CuxbUbrElkqUC4wXtcu0FcS7u6jUyPmD/zYS5fez9PXNnwX3z3ZYw7pluv+fzzz/H8\\niy9z/e49OvVgPNOiRDctdes5XdXcun9KZ4RyGtMczoGWSOkpCkNZGbo2/D10zuG7JFnpKYoYSk+9\\nTVN+GQYbpQCU1ghdbJAt9CxbI8qkKDhdNiDKbBIEKNou5nVji7F3PnqFzaqlpcM6gydI6105mHNc\\nr7m/XKEOXr1+jnqzWS2bp3VszfUVt9HDfINMVT8pIh80xvyf735y/pHP3ZhX2tX4boPiQwf79Yrp\\n4TU2qyW+WdGedtjJIcXiEZypcPVx3KEzWBdtXHCa6GWZqOyjMcxog/qKOpDQeLZfUBMz9KJwqyC2\\nIGxsQ85T0+Kgia3rohcUc6A+FoWhUfQgLroiiKQGwsF7keR1xGttXzstyGTP+AEb5N16hqzJQBjl\\nacNcSE9ZGl6iz5k98Kwe8Hh3wWd4bBjDEET7kDIiuYl2AM2h73vRnfWEnOSlZmbz7kZp6/q9vmkf\\nVO+vmUUsUtPmwQl70hTxORlUu/C+dhE8wybI0wE+A4XRfr5ChNzHTVWcExVUXBpsvv8c/VCPa1fx\\nq2tRbHw2Dpyjc21o0tytUfXY5gS7UVRPeeaS4fqxsFc9xvnZS5w75ehoj65ruX7/Lu1auHRUYfcq\\nytUGVKjrltOmxgLVrKRpoK0ds8LQeaXrhOV5TTURyrKiS4LsGhLOwzZdiYSeHn16Tt4rrvNRkTJw\\nBpx4pAMxwrpOjeGhabswb41nOplQGFgsJsxkQkPLXjGns562a3j80h6nmzXHqyXdWrl5fYkxxf+z\\nXtd/UFXbC75Mo32ZbfQw32ATkXKxWPw9kL907V3fWl1/8Sb4GihCq7Byii0MUuzhm7NA8CkXmMke\\ntGu6zTGaai0hK49okN4m1RvuLseKIOpCU96kprILOvSL5wXjzu+FcKHP4TaUSKjQXikotuYSExIu\\nXgmlNL7DSpmFDxJoSfRitkLIYsitmeWCsQ3BdAiYuy3F4nBSnhUdQHQMPT4AXfncqTwgDnK4X8m5\\n4eThpvlMhB8TRQUGAEbQEhVJ6j87lkAthS0fAM1tsBz+LtHzTzKBO9ufIKt2AegOLr7108dnme5B\\n8oYk7dbiRiyFmL2PkY4I3smTTZuoHBQIWq6oo1mfUxSRCCcSZPZEcF2DiMVYoWnWGG1x6zNQZfbU\\nhygP34WdQbH/GBYQrdiv7vId125SbW5w5/arnC5bWl9jSstq1SLeIVJw5+SU++sGLYRiamgbB95S\\nTjwqhmbdAcpkWtJ1DlvY3g8XxXc+fkUNiVttY4g91J4Sai2NYVFVnNQbjAnEN99FCT4VqtIwKQqu\\nHRxQWLh1tkINXN6bU0noTlJNK06WSy5f2uf4/Jx103B2v+HOqxstC/MT63U7Kve8gTYC5lfJROQP\\nz+fzf17Orx4su32hWyMEyruKoVpcRib7tOd3Ud9gpMTOjkLNpVvRro7BpUbCxAUthENTz0Yg58HC\\nWm9iK6I25rmEodQXkBfo1zH+UDUwBE5hUE6QrhlAJcODpjCYDsCeHuTy78mJTFRSvXiNp3+b4YI+\\nDNMOTijR44Sgpep9CBFaW6CqOB/kxUIRe8hXeq/0IdQ+DA7Sbwywg/fCT5MpkzJoMh06gqikrhkB\\njcMmJG0KfPbc8+3lzQQZgEI60MWoQQ/kr00USt5fklvcmvXsJqmmMqOUX0x6uuF8WSlnGGrNiNgL\\nKARhiMGYJJZSNJtAPhMT4hPSq/xgC0TD9zRtlLSr8e0K7dbY2RGLpz7M5NH3Uhbz7N0isGDF+6/8\\nJpe7muXJK3StwWvBvbMTyrIE36A4nrt5jFNAfAidGjBlCVaoTzpsBc47qqoM3UQkdCVRl/7sFFtZ\\nvHpKbzAClSqFKbFVxXqz4bRpKUty1EU6mE0t73zkUQ7nhqktOF/XnG9a7i2XnLWe/VmFNYbOO6Zl\\nQd15vEDnO7rOcf/Vmnrj3GbTfUhVf+shfw2jfYVsBMyvoonI4wcHBz/91FNPf/Onr7up4GL7yriY\\nTWYYO8XuXaY7vYmKYMsZdnIUOkasb+O6qENL8DbDOlXEUGAMmeWoq4uLpQ1hSu1bEX0RY86eTWCY\\n5r03+Ej/T9J82WuMon9pfR0u/lvJQ3pQHOQkB3zOCwbUvyVJICCFnFO+VOOCH3Os2Rs0Nre1Cg3A\\ng4fmvKcoS1LvRlTxpFyWCSIUIdkYF2oJxCmU0IfS4F0QkBATusqkvLJIEWpwRTF+WPnZAxUpL3jB\\nnINkse8H5iSDVgIn8xDA1Hy+rSiEKllp6oGNk+RnkkF54PWHt6Keb35ug69HMudCOD6BnGp+7ImQ\\n1j/UQU2nb1Hf4dolqKM8eheLxz9MdXQJU84RazAopffUpuRA7vBdz7yIWR/z2U8/y2T/EovDy9y/\\nd4xrj/FNS9M57p2uOKs9VJa1W1OaMj5rQmcUp9hSsMbSti3GCfOyQrXlYP+A2WTGtf05h/OKwlps\\nFUKwzz53m2XrOFluWHc109JyMK9499Ur7O9P2NQdx2drjvYX3D075zOv3mFWFBhbMq1KBKX2Hict\\nXoXCF7zw3H2Mqf6P9Xr9g6q6ZrQ33EbA/CqbiNjZbPZjRVH88IqrU5EKo7FWEkI/Q7tgdvlJNvev\\nRz1ZwU6PMMWMrj7D1/dR15FCgiEsZlCxsTSA3olIbZ+I3s/rsAf0Z7NnNCi1yO/0XkbQuE0ScMMT\\npnBi79HG9ReR4ckCQPVhvvjhrQlMJxwSf6J6TV6JIwnJ2hgm7VP3ImDE4I1FfQeui+Hq2Hsyk6wC\\nYMSAca/7GgHOmDKPOZ2/H6lHcIEB6lI+OQ9+a257wOzD3/1z6LJ33IPSDiLF7hvWWrquy9038rny\\n+XqPL/yaVKF6AYdtSE4h56GU3yCIm3KT6UHGd0OxP/2z1pjPBlCX7yAMK+VXB4xudeEzGhjcrltD\\nd45SML32Qfaf+AB2doBUE4wpaW1D6UoMDZf9DX7ou/b57Cd+hRdu32GvEs6XNa0RcDWCcl4rd4+X\\nnNZLqmqCsYbGeUQCYcfVgPUUhSCd8K5HH2VhlIODPQorTErLpDRMCkOLcv1WQ9c1zOZTDueeroP1\\nquWpxy7jmoZpWXHndElhhXW74bxWfvPFm5S2Qgplfz5FjLJuWzoXmsa7tXD9xRNQ+Sudc/+Q0b5q\\nNgLmm8RE5GPz+fynN2522cweRZzrI4mmADFU+9cwsyPqO8+FDxlLMTlAAVefoM26j8sNQoUBPAcX\\ni7mleN3wM5eNQO/NfKFBpzDkzncoheXo83GJDALR63gAECLTMqmtRDA2RRFYpq/DlJCXFZUofGRw\\nXYuxFlPOgzcjCfjSZiHFSnuylKiiRujXfhPbsBEIS8YgzoX2XKlbpS0jIzKEMdU3+K4lhHZ3p0jy\\n/OjgFaKyUvK8tgk9w42EbJ0pTmT4bM5/hnfFmAw+iRm7K0nYjyJtB7YmNV8zjXr4zNUnclmIKohG\\nAYl8fBxb/N0nhuxWqsDFdquCJqEAF+8nfaHV47XD12uMb/HaIdUei2vvZ3LpaYqDy0i1wCJ4AYPH\\nITw5vcf3vOMGz3/us6w3Nd47ytKiNNFTLxEVXrl3SlWU3DldsqobWmqKsmLTOg4mU5585HHWp7d5\\n5NI+pYGjvRnee/ZnBXWnPHbtKv/4p34J5+HJdxzy7e99lIP5lCcfv8ZiMefZz1/nfLNiIiXeC9OZ\\n4cbxCb/y6RusW8d0MaEyxL6s4KKXe+OlJa7zuly271XVz134xR/tDbMRMN9EJiJXDw4O/sVjjz32\\n7Z+/N59psyEX1Kd+hKbEVgukmOI2xwAYO0VMEdiGXQPaxfKTHgwxxWB1HVyTRBDqF8ItoYKUG4xe\\nUV+6oBhTxI4WaZHuAbf/7FDE3Q0W82EwcvBv70OOT5XCFqFUJflTF4D4FqjkEKzEwvG+MXYiA/Xk\\nn111nz586dEk0xLD3EE3N4EhNjRRDuHEoNsb2kOl8cdmwzmMGaVfcm5XokeXmg6HGXWuC884D6sP\\nxabQue5sNLbuIRNt0iPa/tvO86yD53tRmFt3fhkcnzYyiaSFamiHZdKuICkubYNvzsEOmporPudt\\ncz40023Dtb0J15HYjNq7Gu1aoAnOrp1RXH4n00feQ7l/hIihqGaYskS9gDiemd7nvYvfpvIb7ty+\\nz3qzZjafhBCqEayEb+6t+2c0rWMxXVC3NffOVqFlF8L+tGJvVnK0N6fAU5QGU5S0bRs0YWOKYlrA\\nZL5gMZ2xXm3QzvPIk1d4bnmLg82UVd1y+84tJlXB8cmGX3z2Fa5cXlBGYtWmaVCBPZnw28/ewdrq\\nn63X6z+tqvWDD2q0N9pGwHyTmYiYyWTy14wxP3718fdMXrnryOophuA9iQn5uPIA3CZoeNoKkQLv\\nG3BtIPbkHFXqmFBkebYti+cM/06xUQgLdmgYnHNekkKs6cwgUvRMyJ1FMh80BM54nb6eM7lyg4U1\\nzMUg5LgLcBHcYx5WTQVobtybtHUlNU/WkL81JoRlw7jjcNLxaGiHpXF84QpkaUKxGFvEhs4OctNu\\njecf/i0lL2qoB6y4VNSfW7AFPzNkSENHjgRSSs+SNdHzGM7v64oC5OkfjC0D4IPe5FbQfYd4FA7Z\\nDgOrutccR/Yok3+6lY8lyxxmbeJ8fUn+MBJzy15daLzuNuCDJJyRDsRi959kcvQM5eIQqgOKqsBU\\nsWdoUfCuw9u8a/Iil1hx8+YtNnUdvrO+4dLlfVSVpq5pWkNVCufrjqZtEGOw4njqyWsY75iUls7D\\ntJqyv5hx/3zFoiw4XS6xYtnf36Ou1xRlxeXLlzFiaNY1ZWF58ZWXuX7rFouqZOMsv/DZF9iflzxx\\n5RFun5yxaVsW1YT7d9e8envpvfd/vev8P3hdD3m0N8RGwHyTmoh84/7+/r944oknv+65G36iSfYu\\naXaiYKaU0wPEWLrVHdRUIaTjO/AO7xrSKpTBR2yfVEqmkFp8DV7AiEVMhXN1hsfgJZgAnN6D9DV3\\nPeNy23aBMxw1IKgMcoFe+xrI5KeEMyZRhCGQ9Z6xN2Xo4BKp/lnmLzKGJTE+ZSA8MCzRSK23ck4v\\nlaaE152GDjEZLKNY/TAUyoCoo0mrN96vT3qodEjMj6pvQ2hYguRZ/GA+V/YB82Zlu1D+CwHmg5q9\\nKQb7EO9SdWvMF1veTfX3PXw3AqKPINdHJR4YXfi8G3w+52+H19H4HYv/+agJq6FBgSAhPEuJVHtM\\n9q9h9x6lmB1S7B1hZ4cUxqNSMSk3fPiRE/bWvwHLc5abhqIsqcSztz+l3rSsNi1lZVk3DdNJydH+\\nfnh2IhyfLFns7eG6lvnegvV6w/68Yr1ccri/H3Ki65pv/PBHsdWcennOR3/Px3jxs5/i5ec+yW89\\n+1kW+wvO1/Bzv/4p9hcV1hSowrQoMF74nc/dBSl+dbVa/9FRiODNZyNgvolNRIrJZPI3RORHaz2Y\\nmOkVUilIqKUsMIsjwGJsSbu8jZgCY0pcuwpECdeisT2XyeE06eFmSAoSu7O4xWbRulupGGAshCnD\\n74HNGEtFtpiT27b1fRsutmmhjmFFL7p1Rcmhvni9dAFN4VgbmyBHEfoIDBr7LKbm2yqpl2hckCMI\\nJzJVRugMmMFD9EloIeU4s7B9ZHK6rufP5Hvpw6dBkxS8Cz0hjdjQYJjkzCdxgEQmyne5RdrZqrvc\\n2aDsepLpXykKKvQh0X4TMwiIa2qKtrsmPAyYJW4e0iceXEuSOlTvtaeazLTB6J/z7lq0m7vVOO8m\\nHes93neh/ISwecMsKPeuMNl/nGL/ClpMqSZ72OkiTJgxXCnv85Gr9znUl7h37wzvPb7tWMyFSVGw\\naRxiPOva0WnJ3mTK/qXLHB7MKKs51k4ojKc+vYOjo1l31Otzjq5c4Xx5xjPvej/f+G3fw2NPvpNn\\nf+OXee6Tv8jnnn+e+dzSdZZ/8q9/maOjvVCypMLRbMrNm+fcuHWGoj/Wtv6/fciEj/ZVthEw3wKW\\nvM2inL7/rD20YksSexQxeK/Y2T7YKQaPb9aoKtaA69oYvnJxhQ2eZl6EU4hNBDWhmXKoSUyezDaT\\ndku8IHmq0dsRBtq14aD0qcGvunO+6Ink9wbHRo9Ito4N3T4SwzezKqPnHMZkcncSrwOvGNgutYih\\naIHAzo0gqX15SgJWzSCZPMpheNmjrt1ho/bt0noRAsW7NnbD7K8XUN/sePmv3x4gBWVLykzaP5K8\\nkeifzfbGRLd+y/fTX2wA5g+C64NebZwzvwPtw82S9JuKdD/D8/W55bBtSmDrU3N016Ea+7ZquKNy\\negW7fxUzO6BcXKWcHoI1TBb7OFvybY/e4g++V7lz7zZN07E6O8YidF3LcllTVXB49DiL+Zwr1x7h\\nbGM4X9Yc15d4fH9J15xzsi5595MVJ7ducfuV5zGFZTateOZ9H2VxcMBLz34KS8etO3epZpbbr97j\\n//3Mdey0Ymot55uGaVHymc/epiiKW6dnmw+OXuWb20bAfItY8jaNMT/6/m/4SPXJz94XiZ3rJXp7\\n+AYp5tjpHr7ZhNCVEBRYfAvooIBe0t6fVCIQ+DICMSeZ801pqdta2BJQDb3U5MVyIXBmj4edZXYA\\nKA/m13rEDLnAAjExV5vPlvAtHWty+LU/RuL4dzYCaRMhPniVkrx3E7xTCcIF4VS+B4qUa4xt1Pxw\\n7HlTkUhQ4TKurTEpjBlDlepD+NIWBSJlmFUZeNCDe+zntQfJLXBJR+vwlTgG716HkP3F1oddw408\\ncN20hoRwRC+hGPO2IdQ7ON9wXA9Zf3bBU0MCOsgvpgkN2nNB+SpeJ5QyBWEM1FAurlDMD5HpHnbx\\nGOXeZSaLA95zueFb3umYaMNUagoct09O6FrLyekKnVxj7S3PnwjX71kKKfBlFRocdOBxPLow/IXf\\ne8YrN27iXMvlvTmreoVq0IutN2ucd0z3F9w73fBzv/pJVpua+WxC5zyr05Zbt9ed9/5vt537+1/i\\n4xntDbQRMN9ilrzNyXTx/vv1njV2El5HwFYZdGy1F3NhAx1P38bFJBXH2fRWXpNzyysTyDJbtrPo\\nJUanJIFuoseavbOHL4hb3lX6OfBeh02it2TexPQ1kDEfue0xxtKS1FA7AmVSDArnSgs/OVMaNgaE\\nsgYMJl+wz9P2Y0yLcxynDwFb0T4vK6Q8XtI4UrRbx3xYYNoaKYKn1LVBdUhdIBWl0HkYcA6fp7ka\\nBGXDa0GVP18neYGx1Uec3pRT/eIsfCQJpQ++KPn93jMMQv2D9yNgDj3cPgvdS+c99Nr5ucS6Yr/B\\ne8L3Uuj7oKeBpg1R3qC14GLYvqww5RS7eCyoaBUlrRGm1T6FLKlXgUjenJ5S+FM6qVCvmHIKOLxz\\nAXiLCpksKPcOeWw65S/+vvscn9zn7P4x7fqEcrHHVGA2n3D/9AxbVDz70k1+++Vb7B0tODk7p960\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S2TULrJHliimQz/PoZ53aRGlIFYU+0lGKuxi4YfgKXGBTw4yM61kcTT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\\njTbaaK/D/n95DVTF7823aAAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10c5b12e8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.figure(figsize=(8, 8))\\n\",\n    \"m = Basemap(projection='ortho', resolution=None, lat_0=50, lon_0=-100)\\n\",\n    \"m.bluemarble(scale=0.5);\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The meaning of the arguments to ``Basemap`` will be discussed momentarily.\\n\",\n    \"\\n\",\n    \"The useful thing is that the globe shown here is not a mere image; it is a fully-functioning Matplotlib axes that understands spherical coordinates and which allows us to easily overplot data on the map!\\n\",\n    \"For example, we can use a different map projection, zoom-in to North America and plot the location of Seattle.\\n\",\n    \"We'll use an etopo image (which shows topographical features both on land and under the ocean) as the map background:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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5//xf/BmL9AmkBYd2jbcDvscc2Kj1/+EiyQcwQKbbNi1b1k1/2E0/AN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\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0YpKb4lQ0looIyFpSy8+7hhOzRQFUUJs7gqITE5o2g3HVZr9NBj1ExN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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x111f8e0b8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig = plt.figure(figsize=(8, 8))\\n\",\n    \"m = Basemap(projection='lcc', resolution=None,\\n\",\n    \"            width=8E6, height=8E6, \\n\",\n    \"            lat_0=45, lon_0=-100,)\\n\",\n    \"m.etopo(scale=0.5, alpha=0.5)\\n\",\n    \"\\n\",\n    \"# Map (long, lat) to (x, y) for plotting\\n\",\n    \"x, y = m(-122.3, 47.6)\\n\",\n    \"plt.plot(x, y, 'ok', markersize=5)\\n\",\n    \"plt.text(x, y, ' Seattle', fontsize=12);\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This gives you a brief glimpse into the sort of geographic visualizations that are possible with just a few lines of Python.\\n\",\n    \"We'll now discuss the features of Basemap in more depth, and provide several examples of visualizing map data.\\n\",\n    \"Using these brief examples as building blocks, you should be able to create nearly any map visualization that you desire.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Map Projections\\n\",\n    \"\\n\",\n    \"The first thing to decide when using maps is what projection to use.\\n\",\n    \"You're probably familiar with the fact that it is impossible to project a spherical map, such as that of the Earth, onto a flat surface without somehow distorting it or breaking its continuity.\\n\",\n    \"These projections have been developed over the course of human history, and there are a lot of choices!\\n\",\n    \"Depending on the intended use of the map projection, there are certain map features (e.g., direction, area, distance, shape, or other considerations) that are useful to maintain.\\n\",\n    \"\\n\",\n    \"The Basemap package implements several dozen such projections, all referenced by a short format code.\\n\",\n    \"Here we'll briefly demonstrate some of the more common ones.\\n\",\n    \"\\n\",\n    \"We'll start by defining a convenience routine to draw our world map along with the longitude and latitude lines:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from itertools import chain\\n\",\n    \"\\n\",\n    \"def draw_map(m, scale=0.2):\\n\",\n    \"    # draw a shaded-relief image\\n\",\n    \"    m.shadedrelief(scale=scale)\\n\",\n    \"    \\n\",\n    \"    # lats and longs are returned as a dictionary\\n\",\n    \"    lats = m.drawparallels(np.linspace(-90, 90, 13))\\n\",\n    \"    lons = m.drawmeridians(np.linspace(-180, 180, 13))\\n\",\n    \"\\n\",\n    \"    # keys contain the plt.Line2D instances\\n\",\n    \"    lat_lines = chain(*(tup[1][0] for tup in lats.items()))\\n\",\n    \"    lon_lines = chain(*(tup[1][0] for tup in lons.items()))\\n\",\n    \"    all_lines = chain(lat_lines, lon_lines)\\n\",\n    \"    \\n\",\n    \"    # cycle through these lines and set the desired style\\n\",\n    \"    for line in all_lines:\\n\",\n    \"        line.set(linestyle='-', alpha=0.3, color='w')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Cylindrical projections\\n\",\n    \"\\n\",\n    \"The simplest of map projections are cylindrical projections, in which lines of constant latitude and longitude are mapped to horizontal and vertical lines, respectively.\\n\",\n    \"This type of mapping represents equatorial regions quite well, but results in extreme distortions near the poles.\\n\",\n    \"The spacing of latitude lines varies between different cylindrical projections, leading to different conservation properties, and different distortion near the poles.\\n\",\n    \"In the following figure we show an example of the *equidistant cylindrical projection*, which chooses a latitude scaling that preserves distances along meridians.\\n\",\n    \"Other cylindrical projections are the Mercator (``projection='merc'``) and the cylindrical equal area (``projection='cea'``) projections.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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SS4RcjA2hK+89CSN9iSAFp4LVpcceFLKUBGYSnD4su0CAJKyShgJEr6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\\nXo6X4wXGS4X5crwcL8fL8XK8HC8w/h/sYPSV9cwiHgAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x111f7a7b8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig = plt.figure(figsize=(8, 6), edgecolor='w')\\n\",\n    \"m = Basemap(projection='cyl', resolution=None,\\n\",\n    \"            llcrnrlat=-90, urcrnrlat=90,\\n\",\n    \"            llcrnrlon=-180, urcrnrlon=180, )\\n\",\n    \"draw_map(m)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The additional arguments to Basemap for this view specify the latitude (``lat``) and longitude (``lon``) of the lower-left corner (``llcrnr``) and upper-right corner (``urcrnr``) for the desired map, in units of degrees.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Pseudo-cylindrical projections\\n\",\n    \"\\n\",\n    \"Pseudo-cylindrical projections relax the requirement that meridians (lines of constant longitude) remain vertical; this can give better properties near the poles of the projection.\\n\",\n    \"The Mollweide projection (``projection='moll'``) is one common example of this, in which all meridians are elliptical arcs.\\n\",\n    \"It is constructed so as to preserve area across the map: though there are distortions near the poles, the area of small patches reflects the true area.\\n\",\n    \"Other pseudo-cylindrical projections are the sinusoidal (``projection='sinu'``) and Robinson (``projection='robin'``) projections.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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/wL8YUvfGHjwoULf00HwW8l3cFvFZMRUdalu7LB+tYFzl+5zubWZZz3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\\nJ8wePXr06NHjfeD/AXY3HYb3yOBYAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x111f7af98>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig = plt.figure(figsize=(8, 6), edgecolor='w')\\n\",\n    \"m = Basemap(projection='moll', resolution=None,\\n\",\n    \"            lat_0=0, lon_0=0)\\n\",\n    \"draw_map(m)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The extra arguments to Basemap here refer to the central latitude (``lat_0``) and longitude (``lon_0``) for the desired map.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Perspective projections\\n\",\n    \"\\n\",\n    \"Perspective projections are constructed using a particular choice of perspective point, similar to if you photographed the Earth from a particular point in space (a point which, for some projections, technically lies within the Earth!).\\n\",\n    \"One common example is the orthographic projection (``projection='ortho'``), which shows one side of the globe as seen from a viewer at a very long distance. As such, it can show only half the globe at a time.\\n\",\n    \"Other perspective-based projections include the gnomonic projection (``projection='gnom'``) and stereographic projection (``projection='stere'``).\\n\",\n    \"These are often the most useful for showing small portions of the map.\\n\",\n    \"\\n\",\n    \"Here is an example of the orthographic projection:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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KMX5cRZAVxOjDsxIYQd9UklJVIJPFWqMn1PEnou9cApxSaeg+cI+r1V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\\n8Ax9n6joywAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10194d518>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig = plt.figure(figsize=(8, 8))\\n\",\n    \"m = Basemap(projection='ortho', resolution=None,\\n\",\n    \"            lat_0=50, lon_0=0)\\n\",\n    \"draw_map(m);\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Conic projections\\n\",\n    \"\\n\",\n    \"A Conic projection projects the map onto a single cone, which is then unrolled.\\n\",\n    \"This can lead to very good local properties, but regions far from the focus point of the cone may become very distorted.\\n\",\n    \"One example of this is the Lambert Conformal Conic projection (``projection='lcc'``), which we saw earlier in the map of North America.\\n\",\n    \"It projects the map onto a cone arranged in such a way that two standard parallels (specified in Basemap by ``lat_1`` and ``lat_2``) have well-represented distances, with scale decreasing between them and increasing outside of them.\\n\",\n    \"Other useful conic projections are the equidistant conic projection (``projection='eqdc'``) and the Albers equal-area projection (``projection='aea'``).\\n\",\n    \"Conic projections, like perspective projections, tend to be good choices for representing small to medium patches of the globe.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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u85XBzwycUlB5OKEDrmtmS73aJNgfeOtonU8wW7tmGzaSm1YrvbMa0m\\ntF1LfTBHVlOW56dEn0h0hBCx5YQYYNO0JCE4rEvW6xVKKqp6wmz2CENiu1vRIllJzXmqOGt2mBip\\n6hopBY3zVGUFKVEqQxSJ2lhSglAEksgGfXF5hRAKawvmkzlhWNwF2TFSKYKWA4UmUcoipUermHOn\\nUeJCwAfJ4bTg42ernHeKI0WUKdIkxAuGdjvX8mUgcTi1vP/59W+i3vlot6PL4ehqMK44AmLiRXC6\\nVRuQxC3jGEUO+SUxLEo5lCIJ8CkiosT5iFEqz10OU26u6pbDMFw8w9o20MY343kRvm/ZqRCZodFq\\nnwIpjcaagXROYgDwRIw3S9YNAI1HjbgInYeQYgbJmPaeOsAlieO6oHM5jxkGhyglOXxPkwaIN8PS\\nzC0GKiVBFLlGwIWAkhJ368ryXLy8EkGKEakkve958uwjVs2SQihsijRBkXSmMPvdlqooOd/tODq5\\nh5KKy+WSeZEB6vnlinp1DdNDNImyUHy6arBFyartcVITYksaHIZc16AIMSKFylFzihSqYNs0HEwq\\ntm1HWVY0naMLASUk56sdtlYU0ylCK0oiXQy4FJlMJ+AczbZBKgUholSFNYaYttybVmx2LSIlRPBY\\nU3E88UwmE6RraLoeozUFkotdy9RoXJRDflyiZdYjpSRdCFkPyU4cSbzQPpFiQklF8C7rwaCLWmdO\\nJoaICz7XicRAjJHL6yvW2zUnh4c8fviI51fX/OTDv+Xxw9cpTPHCfRMkzpen/O//4X/ln/zwX3D/\\n+JX9J18l4pbjdFtC9JmtFIrWddRFiZYCaw3vf/Jjvnj+GX/y+/+c6WSGlBYf+v25BKNOfbX8RoD5\\nVYeSA8UTRisegVJkD3IEyTFHuS8GkLl4xmiFkpJiBEgtKazGSBCpJbjEh+/9n3RBYkQiJUVqG5zR\\nnO8Cu7bFaoMuSlyA95+cMatqnp4+5wf/6A9JUhK2G3749ptcXV1RTyrOnn/B4fEJtjYsJsfM5of0\\nMTE5WPDZs1NeV5Y+eKRKWGUpJoc82Vzy6OGcNx6/xcP7ryKE4uNP38eKwAeffsoRgfPrK8J2xxu/\\n9QbTukKVhovlGmM1QSiEUmx3HZ4NWmtiCkyMQcZAOatpmobi4SPa6yWV9xiXFbHUBTsfuLy84uj4\\nAIqSq5hoVcWVT/R9TwpQaY2cGbrek3YN9fwABmqIokDg+WLVIicLnq3X/O6DOaefnxO2a6bzBUIK\\ngshecVkUaKlAQAyRJBNaW7TsESkXYTy4d8Lp9ZKf/vId6rLm/skr3Dt+gAwZPIwCkSTohBQaHSJW\\nC3ofcV5nsPQKFyIHkwKXEpWW+CjwIQ5FAgNAJohCjNiTdfJLIs6j2tL1gfZb0rECMVCvN07hCBZ5\\nEcmRsEQOjE7MkaYQLzuzvLSG39jOyxGQvEUHpYTzkaDj4GzcfHUf9e4v+O/a4fidl98fj2OkxJoc\\n5UkpKLXKkZeUgMjFVykRwuBlp3QTWg4OSQwMhVoxF0mMoD38d/uyN23k4aIixYhHEJMgKUHy4xj9\\nAHgKScpMAzKH8AikGOl6QQhQ6FugmNKwILIH/tXmmtPz5zw/P+U//8M/5sc/+0uqScXUFJAiLkQk\\ngk3TclJXRKc57zo2SSN6Qbe5oLAGbEFRGGbTBVMjaLueXy9bZrOSbei53jW0bZfz90ruHRc5VDAK\\nKQgpwPCeS4lZWdL0Pcf1NEfLWqE6R1FY+nbHlamptSBIiXeBVhUclZp2ecXElnQIbFnm/L9vWS47\\nGp/zvF1IFLpgVmlwWwyBq4tzppOKqihRfcvFZsvRdI5zLYXRNCmihcejcUqwc6CEAalouxalDc67\\nbA8iO7MhBqxShBCG6HJgAQQYZUALNtstWmuCu7G53ge2jaPUjsWspnWOP/vLf81/8Yf/lOlkdktR\\nBcvtFVJoZvXilgssvsbOXzQ0IaBpdvzbn/0Z33v1+/z8s3eRQvCv/rP/hpgif/3Lv2S5uUSFxL/7\\n+f/B1eoSlxL/9Pf/BY/uPSZ+u6XiN4wwx9EPRi1vjdnoHLKLEShFGiJKeQssbyLLXGmaaZoxiiyU\\nxBpNaSTt6jm7i/dYusTVrqGKnu8/fsivnpyybbb4piF2Oe85t5lGfHT/kPsnxyTvuLi8or53gru8\\npgieXdOyvLhADVz2gTVMtQbfcmgSE5uo5wecXd3nO4dz3GbNd958E9d3/PTsinsHx1SP3kBIxWdP\\nP2FWT/mrn/0lu6szfnde8J35hG0X8W2LS4nl9RUHx8dsdw2WCELS+R6VCpI1eCkpbfbkYohs+gBK\\n0EeQyysOtSaEkHOHzRZbTZCTjqNZTTWbc7bt0HXNc7/lt+2EJx9/wGuzks+fnWLKCb7ZMq0nKGPY\\ndh3FpGbVeWaVhVpz3XhEWfLOF2est1uEkJgQmBtLEIK6rlBRUBYFm7bFGkNC0Lucu0GAcx4tPW88\\neI2D+QmVnSCkynmoMSJMueiDCFHkBVsniVWRYBIhqFzUIUErQV1YtAoDoKYMqDHmytNhIR/p2jGa\\nfdmYjqYF19uebyP7grQ8hJzLGIxxzNsNCY59Pi3LDXKlOAD4Pnoccp9CvHCeF4sqbt7zKSFTpA9x\\nyCuOy8UtLE43bwjEt6qRGJkcq3PltFWSyqgcVQ5FIC4Eep/BcgTO/RWmXJgU0kAfx4GovQl92U/e\\ncD4xcNVtH7BG5upECcEDKjHCuxBhiGJHNipH2kmNQJyjSB8TShog7M8hR/ZKCt597x3Ors5JKXJy\\ncMDPPnhnn8+9WK7QWnG1vOb+vXtIAVedR6LAlqybFYujE5a7LS5I1m1i1zmk1sxlxbL3eFvwbLOj\\n63oi0PoeqSRaaUIMQ+oh4VxAGw1EYohoo3L0JyUxRk43ax4tDkmtoC41u94znxR41/HkcsOsrpgY\\nQ60CyScqbTi/PEdPZ/gQMeWEe8WML54/Z1HVnG16RBIYGREpIIVCCcliIodAI+cs55OCSanYCYOS\\nmhR6yqLE9R12WnO1WuGwbNuOiTG4GJmVVb7nKQyMSszOlsipFcFNbn9kk8rCEkKAlOi7HqEVdTnh\\nv/6jf4k1hg+/+IAnZ+cs6jm//PQ9/uCtP0Rrs1duqwvKuuInH/2YP/m9f3JLD0fd+hL93n+Q+Pz8\\nCX/+kz/j1fuPOVudcrm5AhLvfvRjfvjbv4NSgR+89jpKa04vLpgXJVIKnj7/Na+cPEbLYX1hfPxy\\n+UbA3C8o4/8i28c4WDGMWSuZF0qZF54RJOXQHqJlpgG0lFgt0CrTrqXJYGlNzqVYo/jovb9AxzVN\\n03MyK5kfTLm8uODXn31Ou96igKJQFMZQWMt0WqFTZBIdl598zP2Hjzg5mLFdXrPuPLPZlJQC00mN\\nVpK+dwgpePbZr6nrGRvfc3z4iObyjO/PFT/96JTvvvoId33GvfmMP/3O67z72VM+fbpFCYWNnuNZ\\nzWFhOZqWKAHbvudkWtMmQXt9AVLy0fMzhJacLKY0Adqo2PWeoijwSdK0OwqVq+K8ANP1VAhakQt8\\nSJFEpJ7PcUahZlOcSDRecbbzJLfmcFKijeEyKJpVy6ZJLIwmRMsiaY6k4gqL7wSdEzSd5LIL7HqP\\nFXA8nZGE3LfurF1P53J1bWEsu92OaVnQhbj3quuq2ifpf/u136Eq61xhlsYq0awTiux1i5SjhgSE\\nIJHkQpAYU85nxcTE5AKrWVVQ+EDnAq33OC/ofcD5SEwi58oSEBNaDHnE2/m9lJhXhqfL5lvp9ajb\\n4hble/NRRqgXjXXQc3JFHSnHQindRJFCjBhyizYWfxc0c6tKZmvjkMuURt361S26eSy6GT5LL2Pm\\nSzauhEQpBsZGUxhFaXLrhxA5eu98Bkvn41DYNlCyQ4AZh6h+hLkxx7U/3TAYmYaRpux1BGA7AOa2\\n8yRkHm+MIBQyjHUOYZgXdXNUkRBK3nIU8hyMc5orsPPacr284uLqYrgrsNqs0Vozm1UkErO6pulb\\nymrC2cUlRVFyHjzGSKZVSZcSz1ZLdFWz225pEzghSCFweXqGGwAgkehdLqQrCosUcn/tIUW0yPl8\\nQSKkhNYaNdZxkEHTapPpYB+YTgtS9KRuy9F0SqmmVFqiiRRkR1SSKGYzhC1oA6S+Zx019w8PuVqv\\nMUS0sWijid4hihIhE5W2KCkgRIgtpqroXE/sOxbzgoKCLgQqrenbHbOiQChFIQPeB9oA1kp2TQ8q\\nt8noBIWUtLCPMHN9ikSJ3F5iZaanBYJZWXG1XSPJbT0IwW+/8ibTcsau2XE4P+CL889449GbWd+I\\nfPL815RSc7U+Z9OsqMvZl6PkqCaDTsQUWTbXvPvJO0QSh/UhP/30p3v7/vGv/4Y3H71CYed8cn7B\\ntCyolURZy9F0wmfLZ/xPf/Hf89bj7/P4+DGz+oBbbRJ/R745wtzTXzeeuNgD5fCeyNHB2Hd2Q8Fm\\noNznKBVDVKkotaKwuQ/NKnDNBTvf8/TsI4rUo6Xg+KBAG8Oz0yuabYOWUBeGeT2jsJqyKChspmml\\n96y2DVpJQtcgtWViDMV8Tt91KCHp+w700N8oJa+89hpBCZQL7IJnvd2wa3ZYXfDB++/z9huPufpi\\nyezomLcn0BQ1ru+4XDtqt+O4kHy0TvgYqYzhyfkFJycnVPM55WzK60aTtMbFwAzBPCWS9xA8qqzY\\ntbl6dbdcYwSoohw8q4RQBqUVNrvSYC2zsuaq9Vw1PcLaTGMaidYGPZ+hpWQ6Fzy7OONeXdOaincu\\nN8QYCG5DOZlB57hfGtZuR1FNWYaQixVcnyOSIY9GyosuhWIXAilBkSJWKaLI9FxhC/7m53/D8eKY\\nt37rezl/w5BvHHTGtQ3r9Zqj43vEmFBiyH0JQRBD9CIipdXEmChMzmNrJTFO0KmIlpJeRbyPA0WU\\nBmo2t3jkcvCEGCjbeWX5xdNv306SGPJi8sZAM1n5IqgmssOQ24oSMokMmuyZyb095Je3qv1GuxG3\\nAIcM9pDnQabsFLy4TtwUHd0eyf6VGGoHBgo1t4rcAsvBES2MYvB3aH2g94HeDfMZhl7PKPaACbeL\\niG6NOb04jDHaT2IoFBnqGJresZjYnI+MCSHzOhRCxCEYakn3dQ5S5HYVlRIpRpKQe1AOKaGG+y1G\\n5ySR7d9Y2r4d5iEyn9UYrdg0DVjoQ8QYg1KazW5LjIHOJfxQvLJtd0CmUi/WK7TWOO/ou8xQOOdy\\nr6lSaKXz84FFEYTsTAzRsJI5jynIBY3Oe5QyKCG5Xq149eCQpUhcb1u0MpTVgi4GJoWlkIlSKJJ3\\naGMgJULvWHUeHwOTqiZEj9eG+WTCrr3GoyFG6kLh+56i0Oy6jpTIxyxLfMyFhMfzGZDQxlAYgVCC\\n4DLzZqSgrExuEyHT90sUWgs2u0ghEsoUbNKOJLIDACCkRCbQAowxrNuWxWzGZruhtCWdazm/POXh\\nySMEkgeHD+EwW8SsPmAsHrpcXtD0OyK5J/RH7/xb/vE/+FNmk4MvAc2EVJqYPH/7wV/z/pP36H1e\\nuwpT8uT8CdEHtFbEFHAhcr66YLW9ZDqtMAFSWXBvpjkqSz67vmYmJR/8+m/52Qd/zT/6nX/MW49/\\n5+UlYi/fCJiH0+Im13LLarKN3hiv1ZKY4q0G5bFpWQz5yZyrtEpRGDkYsaA00G2vuPrsHaxRPKgM\\nPhqS9zx/do4ymr53FFpSFZbZZMJsVjMrDSkEuu0WKQS7ZkepDa1LrLdbCtMBgvbiDCEkEYkQioCg\\nmNS4dpc3MVCGQsFsNiMR6CMUcsWb33ubJ599xmI2Y7fd0OxaDuYzejyLRcVy29A0nh88ukfTtPRd\\ny71HD4kI5nVFStmL7roOW5aslitk1zOZTemall3nqJBsrlfMixJhLW0MmEmF7wJBJqpcOowQgj7B\\nctuwciKTU0JQy8jBbI73ngmJMjlmsxm7fs6q6ziqQGjNQVGz3e1YTEpk9DzfRnQx5bp1Q7Qt0ZA3\\nOIiRECIxhIFezCSMMQoVQCsFQiK1ZGJLXn37O0ip8N5T2OxNDnaAIPHxJx+SZOT0/BmL2SGvvfYY\\nH8VQcZfwQwN1VSi6PmCVRIuUm+GVwISI0QrnPJ2O9D4XyPgROIcKN5HyAlvoTPk3LiL+jrF9iYwO\\noXwRHEdEEGOEKfL1MPRrJSGIYozAxN7j3ZOOOeS6nWZ54fjjxwxAE2MiIvDyJYDidnvNTWvFfpxp\\niGLETR9hmZQ+AAAgAElEQVSzliLbWZEdU60y9Hc+4nykcxkwXcytEGnwqANxuJ4vyxd92bzl7KMY\\nOOg0UHSkRNMH7i/UQOEONFcEIUfnIPdXqpirsEMUyJhyNb0YHI2hKT/GoUUj3eRKBdC2DZ3rAFBa\\nURYF1houV0vqqqIuC0SKtM7TB4+1lhAdzvdst7ubPDJgjSGEQN/3mVoEEAJjTHZCjB4KfLKOS7JD\\nmYE+065i6NNWSAJDkZZIGCmRRUkCjNG51SQ4rkNgoiXaSJRWaAXrtiVICD7iomBeFbTOI1IgBk/n\\nJcdVRWnW7GKmYyOayipScAiRaw5CiEitcH2PJ2EHGrZ3jqKskCmhtWFaJHrvCM6hhUBLRfKJqYoo\\naylExMlEJRONFTQ+3/cw2IcSkEIApZgZxfnyirKeImTuz/2Ld/6cNx9/j9976/f3bUwghh7lrL91\\nOcUaS9snYuzZ9Bt+9JM/55/9w/+KqpjsWQ4BbLuGv/rF/82zy6e44DIjkbliWt9SMwHA9y73Nks4\\nXT7HConftrQpMiHRK8V6u+O7j14h9j2BXD1+dfkr/mr19MtWCuBbAGZpbrWADE3Ho1eohECq/Hg8\\nLTiYGPpH7PMscmwpGRqWtc6FBlbJoX0kYWRive147WBKVRYkEhfLNdumpygnpBiZFBqrDYt5jRKe\\nMnrW5ytKWxLaHQ6JNpZEoqwrYtfTdx2mrJG2RimBdx5tNSKFfJONIbqObrfDVBO883jXE1xP6Ht0\\nNUHMjnCKnJd0ju3FOTZmb/dwNqU4nLPerLNxTg/pNmuiKbF68EaVYnN1zTQJ1qsNE6O5urxkNl+w\\nvF4y0ZpSafoUSX3D4uQeq94RpaCQnoigiblwpkexjZE+ZMNZlJJSF7QuEWPAkNBSsOscpbWcb7dc\\nb7a0zqGAEAN9iPiQKZDocuFK73JOo4uRsN4wKUvqsmC53lACurA0XT8AJ9iUmYNoFD56VttrHp28\\n9kI+IqXAcnXF+dU5SWaapus7rtdXnL/znHsn93nl0WtcX17Q9T33HzykMorOBYxRhJiQIeYdhqTE\\nqEivJcZ5OhXpVaT3Yk8lhpiZj5gSi1LTurDPoe6p0q/KS4gbY9w7hIJsiIzPxX6RHhI5uWI2jaRb\\nBrt9C0a6aYjen+blAiHGHOnN92KK+DC2bIg9GCbEwOwMUzyOd2Aj5L4tK1OuVivKYWMPAfQ+0QeP\\nc5HWe2LIUdvIZnALhL9t285IL49l+UPqciSxh0KbwckY5mYsKJIxOxteJNSQt1VS5t7dlPa1EeNI\\nQrqpoB1mDoAvTp/e0MII6knJrtmRfGYBQggUA8OjkyTERIiCsihRVQ63fQz0zmUWSufl0Fo7UI65\\n53ScE600SmXnENjTsKN6xZCwWpNCrh42Sg/R8qBdQrLtw00luUisO5fH0SWc8FxdrbCTCV4ZdPJs\\nNj3rpuH+/Qe40NN2jtYatLaUSeBCQGIGhsTS9j1lWeBiQKUMxCTYtR2lzf2cbdcznZQ5Ei4ss7Jg\\n0vU0TUvwHi8FZVkSBUglqG3eQMW3itZHmijxKeHTuIGEwEg4qisOdeTT5Y6jyZRO9pDg/Y/epa7q\\n3FKSRjdwYHYV1HLCH//un/DjX/17SmNYNVuUgr967//ih2/9Q+4t7u3Zk198+gHB77i/ONi3J+bK\\ncokivz6uJ9TVhBAC06riqKy5nExzy5AUbNqWg9mUdrlCSMvhtEQFz+luR58SlVJfqfffCJjPls2t\\nTQggN/WnARDZD9iHQNMXfHS2wapM7RklKLTE6NxsXySBMBGXPFKVOXmuJZuLZ7x+UNIDq3VL8InS\\nlqgiT0RhDfWkpJR5K6oQE7Z2GGso3NFAESekUjjnaJstpS1yCXYMINRAOYI1iuDz7jbGWLrlCmNL\\nlNIczOb0feCLAOttR6lytLxrGoqiJDiHiw5hDderNUdSYkyB7x3Hh0c83e3wCLaXV5SlxUrJarUh\\ndg4ZAs4HbGFZrjfUEmToKao5prQIrQjBDYCTcFHgpUAET2lKgs+rSBQCF2N2XpTGRxDRMSkMmy4Q\\noycKMLagCZ5JNQEh6Puc/+lD4MFiwbPVMtOuxuB9Btmm7+lDQMa8ocKqd8iu595iwbbLO7e4FLBJ\\nYFKiUJIYW5zv94AphKDZbbi4eEpE5J2GQsQWFiUVsixZrq45v7jIlLOxyJS9cMhUrBSZclVJomRE\\nh2FHGSlR0qOkHwrKAs4LRMjAKaJgWmjaPuaKVjH0VQ0R2jdBwQiQ+0hyXKTTrbxNvkjGvXDSQEHL\\nBGFYOUcq9jZ5+kJbyAh540I6RngpIWLae+Jp//vhaCnuQX0s6tMi9+RpKTFaUphcaZ6BMg7/wuBc\\npH3vZxraWm6D5YuA/jXz9AJYjhEh+6sei5JcyG0LPtzMJ7AvrhhPOUYa6eWBDPMeQ8IaSZ/yXGdn\\nBb7z+E0OF4dM65oPf/0LQgx0zoMkU5HB07UdfiiBtFIgrCEEhw8+F9UphTVmcDYy1TqC4pjTHal0\\nNfRIj9cQyZRtYcQeYIfuYlyM+3kJwZNipI0RLySlgvVux8SWKGOoS8NRCbUoUc7x0eklBydHTK3h\\n6uqSo/mMbdOgkEyKXCRXV5a46whSkEKPlwV4jzGaptkxracDYSzoh2sx1hDIKZE+RMzIqgiBthab\\nsrOw2W1hoNHLaoI0hsIUuK6hRDEJmdnxqLyJhZJUhWFWavRiwXzRsewjtqwI3hF8YLV8hlVvYrRh\\n31/N2MYClTngn/3wn/Nv/vpfczCZopWiLAt+9vHf8t3H3+fVk9e42lzy2flntK4npjD0leZNIxgw\\nKjvQkarMTs+imFAcP+DJ2VMKrVk1O5ZNwx9973ucXZ9C21I9fAXX7ZhKSUlgtfriK3X/GwEzo/ZN\\nfiSzVyOy530TBZm/FzIX/+TdQLjZi3Kft8kentImN8QnxZOP3+Pss/dY9CeEasbTp8+YTCp8l70T\\nYxSbvkXPZ3z45AsevvYGvt1RVXn7pazcgXJSIaVCSk3vWnbLawprmJQFylgIAWUMwWeD6LoOKSRF\\nVeNCRCmFVAUHi0Pu33uI0QWzucVow8HhfazRVFXBetvQ7nbIlGhTBoIUItttz8HRfXofkPWMdrch\\nJsHj14/pmwax22JNSecdU6uZFJY+JIq6AK1ww2KAEvjQUJd5uzTvIylGCq2YlwaXBF0IXG566onC\\nFBYfIoJIXRguXaBpO7z3GJF4dVaxjRn0d85jC8vZZjNsMagRQnK1XnOoNUpKdl3H2AvYuj5HwH2P\\nlQpN3uGjGMDRSomTOnvkt2Q6OyCdPmO7W2WaN4TcI6c1KSSSkCiTKze10iQiZmimL5QgaUnXNIS+\\np5rkKkEdI9rHnN90A3C6G6bDRUEIkbo0dD4MKpdQiLwzz7gavxS93AY19l8ZF79hIR+BcljAx2gx\\niZvoAsF+y7xxW8/xwxFkXzz32CwuQGYAE4hbkR+3R5YXl1svBewrzrWSWJMdVCGgczl3czsCz4zo\\nTUvIlxz+W4FlnoavcT1uga4LAaPypgN7VyG96LyMMfroqAgGCmsfTad91eKeMSDPfVlWPKpeRQq4\\nd3yfp2fPkEoSUmCzaygLi7EW5/qcZ1MSEQPS5u0ifYx451BG59Y4bfIa5fN+qaNTFIZe0BEsxxSV\\n1ZqmD7nwB4XznjgUPyYSbe9ygZIYiqycz/smS0M9meRINQq8D5yuPBWOWVlxXG4ogkP0kYN6AqbA\\nuUBZVUysZrnecnJ4QBIbTBRMqglN2yO1oLQ671PdNkQkZT2nIOFiYlIWBO+pUhrysrloZ2QAi+AQ\\nSKr5wT53G0VCmxItFbvtGpsS0WiaIS3iU6TSmma7xHWWRMwFoH2LizkPvO08l+s1/8tf/Bt+7+0/\\n4OTgHkrIPfUfQsyBSbflk+dPmUyqbL9rwbSs+NG7P2JRHxGIPLs6G0wp3bLXhDV237utlGLbdJRl\\nMeyOZnigNc+7htJYut2W56sV07LmpJ5y3vVU5YTZpGKREv3l5Veq97eokr0FlmLczm7ovdR63+Ca\\n98EctrST+XXekDnv1GPV0Eqi86NV+btpd86krFi3DbNqxtFsyoP792l2O5QUXJ+fgpJsneC33/ou\\npRZcuy2CSFEUGGuRCpqmRZDoXcfDh494fvqci+tr1NEh/XpFUU1yf5cQdK2na1s6IbHVhLZ3iEeP\\nuTr9HC80wbWcnS6BRFUUKJUXc2sNTdsxryusNSyv1yinmB8c0TjPs2fPqCcTlhcXLBYHVFaxXq9J\\nPiCVpvOOg9kUEDQ+UE1q6vmcnQDhAqUSFFKjzJTkex4ezulTwvUdR7ZgGxJFFdg4T/KOg8Jgi4rJ\\nZMrp8hqjNbMiA0RdlljX8WhWcuU1612zL/4w5YS2bahM3puzPDzi9PpyyL3d9MymvC0InfeUKivi\\nrM79VFobegTzxQO0sjeLIVlXjg6PabvNfjcQlKTtHZOyzO0MzqGVHBaNNOwhLDE6r5XXmxXPT8/4\\n3d/7AU6BG6hK5dnve5qLRQKdEAiftwkrrWK17VESInIozrlZmuMAYjd1mPEW1yf3RijHSHOImF6K\\niYbFPhcZvZBXHMLU/Xdv51FfeDrWBYh9Id24CPiYKcV4+9u3QEoN0Y4Z6gJyQUyi7UeAzDUGYfDA\\nx0jpZXkx6v37yzhHkVwh7ULejGG/9+HL8zA4GOPasmexxM3B9k9fGmYaQm053OPVZg1KDOCWewa7\\nLld8W63xzmF0MbLtuWAlJrTRaG3YOcfU5rqIIHJldkj5DuiBoht3GZLGUGhNiMP1CUFpLYWxudhk\\n0Hk5bGigpUSmxKKqiG6KsSZvzuE9WgoWhcX4htlkgg+Bw8V9umaL9y7PpdHUtSQM1bmvPnzE1eUl\\nSmlKKWiajnqSc5QxOPqQ00pCKa6WOwQK7x0brXO1eoporbOzRhj+qEJ2FozKG7dHUv5jDykymUwp\\niwk2OZarLV2MCK0orM1bZSrLRAaapmVeVzw7PeX45IjlasXWJ66bnk1M9P6S/+0v/mf+6Pf/lLcf\\nv41A0PYt/+OP/geMsfR9h0iCuphgTA5qWtdji4Lr3SVt32eHatxMf9CJsQJ5TE/kP6qR89EHh8c0\\nfU+oJlRCstqu+MHrb/Dz83O+/8ormBigabnoHedty6SsmJflV+r4N1fJ3lqcBAzRYwa9SubJdkKj\\nBu/WDMleo27ylVaq3FKiJSq5YdNmzdmv3+Xq9AsenSwgepaXp1itOf3iU1TwdH1C1QdsmyUlLR99\\n+DmPX3nIrtlRW533ObUW7z3zuebZs3Pm0xnr1TVGWx49esT56RnzxYJd13G92vLqq68Q4g6lDbvt\\ndr8ZuCbnKtV0zsFizvX1ElLKfxHFDruMpoDRQIrsNhukzJsSu9OnHC4OSF1D0/ccHCxoVldEmQsq\\nrFZIpRAhcPF8w+F8iq1KvNtyverZJMG2cSxKw0Xn6bu8rd9ENPQ+0CRF1zu0tVy3iYaEbx1mXpKk\\npHQKpOKi7RBkT9gaiyLlAhtgOplgZN5N6aCu2BiDUZqJEvRSsZhNUUrRtLnisLQlXd+hhKA0Ftf1\\nLCZVzguVFVEIFocPmVQLYGxBuIkcHt67z6N7D+i6jvc+fG+gESOSOPwVGIFzjlcevkpVVXuKP6+4\\nkaosgcRP3/0Jb7/1NkVZkcudBOCHXJnar79CZEeu1IrrIW8+0qNxiGCIY298/nWOXgaXLw1/HHag\\n1cbNNceFXOzPI/ZRzu2e0Pzlm79QMuZQx7bNeAtkR8Pa5yTTbdo45YhayaHvkX0uEyFylbnK15eA\\n3vmhsnWocI1pH6V+nfynBsvb1zbUtxID+5qH8fMRsPa/uTW/tyPLF8f6Qk3WHkhz9AbO+7zFJiCl\\nQhmJwA73IEeCJIMxBqP1Lccut0KUhaXrXW6tGvK+Ica9Lsd403aTXw89osNf+dAm5xClksN4456i\\n7ZwjyNyIuqhqfAhslltiTBijWVgNKiC6FZtuy+XVJcV0Ro2gXS9Z7jYcHR4TgCYFrDHslGRiJzjf\\n42Ki61o2zZqD6QwRA23fURWGGD30LUFm0N7u1hwtDun6JhcpJTL4aEnbd0ihBjo964axFtc2hBjy\\nzktaM69Llutd3kc6RiZasjCK7Tb3m0vX8p3jQy6uL+gdaDuhqiZsmoaUPFJILq/PkG98L28cHwJt\\nu8vFW4K8O5GWFCk7Jtuuoxt6v43OhVkx5fuaBrsobUEfPH3f552L3E0P9vl6xYP5Ick7LndbvnN8\\nzKfXS0wI/PL8lDcOjuhWK5IxeK05225o9YuM2W35RsCUQu6VdaQXxsegLJG886Qewnqjb9pIcsN0\\nbifRWqGlwJgSqyQpeq6f/op+t+bB4eu0wRFS5PriCg20u4auDxzEDhUTbpv3Z1wOFXAiOHqXK9p2\\nnSdJwaSe0nYdUkmMtcThmM1mw8NXX2WzXnJxcc50NkcZgTaOtm0QUtE0ueUkNlvuHR8BkabpcH2L\\nNvk6pLJcXS85P31GVRq0VFRFweXFBV3nOJlN6ZqG69NnFMYgTYXQ4PqORTXh9PycWV1n4Iie+uCY\\nTfLIIHCxZ+MinYfKKKbWct16UvRYK+hsxfl6g5eGLkZcjCxXK6qywnnPqm1Yth3WGP4f2t6zSbLk\\nOtN8XF0ZIkWJrKrWDQJkgwRnOGNrO/vD1/bD2tiMjXGX4BjlLtmQXS1Kpc5QV7naD+4Rmd1Aoznk\\nbMDakJURGRmRcd2Pn/e8Yt11hLBFe4tR8GY75M8vMZkvbwK6rBJ8IRTrsafQGikk/TSilWJWNRij\\ncdaxLGuiUtxcXVMplX4ncCSLg+vOXoC+d3iCyDAOFEbyv/zs3/Pm7avEaovkgp5M0V48e0SMSVy/\\nqHWKGAqS4mSBDGdsth03F285eXTC8dEJk49MTmaIK+k1x6zbnFxgVhnEekjymJDi3IRIh6Lcah5m\\nZqmwxcNOLh70nXBPKNjP6h9s4Yeu87t1SR7KQp5niXs/zL1UJRXq9HIeTFkPTybyprx/fkE8mJcL\\nIpN7QNaJ9/FoB8nKHyiGP/S4f2kh/f2PEYditj+BzAqNs/HehD3H8GmVnL1Ks5e+JOa8zvfJPeNU\\nCJSCttRoGbI5/YP3EAKSSGVAyTp9BjL9TfILTbMtH7HO0Q99Gr9IhQ8OEHR2YhgnIPvWOgcxzfsj\\n956p3nt0Lrjee0Q+9DkiTVFmzSEHlEIIKIxOnAlj6O3AutvRNjXbzYZoR3ax4rbredyWFHagKSpe\\nffkVn3z2GQsF/ThR6ESEeXJ6yjhl9v80oGJkHEai1CyqkugHttZz1FRsu46qrGibhqubG4qi4Mnx\\nEefXt8xnLT4vXK00LiZr0+g9PkKpDUPfo7MXbsxMaqkk0hQsWrjZbjF1lbyCo4Bdh5QapQuiglg2\\nKC2xQdB3Gwpl6HwgxMDJ8hEhJNThZn2d/N8DRJmu6XndooJFWI8zITsMCUZrU6E8WLCmsIB+nGia\\niqos6bqOuigZ7ERZFBilEjO4rGiKntVmh25qns5m/OryHddVzQftjN9cXMJyxtwU9N5973X/LyiY\\n8dBhIu7NCEQ+wQti1luKbJW0J/wkRxGjEgmhyBBtISWSyLvP/5JHy5bz3W1ynWhKjpZLFvMFMUZu\\n3r1l3rSMLtL1HboscnJGctvHWsLkcX7Eh8iTZ894+eWXNMYwTWO+6OGD9z9ku7rGTSPtvEUMA/24\\nQymNlZJ6scAoQ9FU6Dso25a//au/4s9+9jOKoma9uj0YTceYJA5Pnz2m0AVfvnzJjZ04WSxYnhyx\\nWm0YXaCQCjdO3O46tDHMmpphGHjy+AlRK6pSJ5bgNBJlxDpBW2p6rxACJmcZtGbyAe+hFZ6tt+yC\\nIHqbjA9C5KK3vJ/ZxpXRhG5gGMfcaQSsVLzb9vl0pg+xVUGblGYiBZOz2TItnY6bMkUC9dPArFzg\\nZTp9FnXDehjwdc3ddsOnzz9F6SoTKnIRCIFv3n7Fk5PH2Mnx65e/ZjGfsd12fPjeB9xtd6w2K6qy\\nynO3gnozUJqCulTsxsA0Trx69Q3vvfeCk0dPUcWKr79+xfj2nKJsKKoqE4TS/iylOJjzhxBZNCan\\nYkiEIyU+hHBIQNlr5g5dxnc96/bztMO8/v76f8h03XtuHuDOmCHg/Bwxw8Byv3M+gH5FTEzrVD73\\n6EXSpd7PSQ9fYpTEhchgv38hPyTgfMsP9wFM/l3ThIe3h4/7H7l929jkQbEUUCjJSVvyMId3f+BO\\nObfk4hOTJX8IOA1qb7SeTVCSkkOw6lxOMwIhJETP3/y//51xGrLOM2JMzr/M14jRmhBcsqqzFq0l\\nu3VPZQzOO4Zh5PT4+FDod8NIlVErAagcubZHRcJ3DhwRqE3B5Cwq+MP8X5IkGpCIiiJGPJKqSijN\\naVOgo8PiaVRAjB11VSL9yPuPTvmnn/+cP/v3PwMiQwgQLL13FE1LGAaUc+ADlQhM0eGcpChUCljw\\nkfl8Ad7hneXx8RFOKPrdlqbS7PoebYqE1Yg075+mNOPspxEpS2TWWysp8LmATNYiEGitqYSgu7lD\\naMOiqKmrimm3Y9dHwiDRdY0OUGCRjeGfzu9wpAPlP//mH5N1n1L81T/8t3S9k9j0RmuMFFx14yF3\\ncz6bHeQ+IQSigoBPfswxIkhFkZg8bwc7oVWSI669p3hesl53xCjQZcXldoVvZtRFxW7oqR4/5U+i\\n5zdDT3QO/W8rmPeG6VLkvEqVaez5D7o//ap9qGg2Ti9Vyn0rlaIoBMEO9NffsL74kqlbsXz0hKPj\\nU1RZEAL0fc9iseTmdsvgFcoG3pxfMW8rlIxs1nc8OjmlbirOz3dsbrc8P3tKyUAY04yubhvaquTt\\n+TlN03L55iWL2QIVLdEnucJ8ecJmt6WdL1jd3SH8lvLmls3NHW7s+clnf8K279j2PYt5mzfQFE1l\\nCk233VLONS/OntFtNyzqmrvrW4xW9H2CK04ePyYQubm+QYQ0bWkqg1eKzlvauiEqQUAyToloMwWP\\nURGnWi63O4QuGa3nuhsJZZIuhBgJfUdZlqh2xpshsBw2FNnBZbIJJtrPIa0PadacN0wlZHLHIQmj\\ny7IixpCDgNN7nJV16m6GiUVV000Tg03doZASfOBo/ghBOo13w5Z5u2CzXbNaXaMlCGEwpWawIyj4\\n+t03vPf0PYaxx9mJ4CVlUVEURZ5ZRd6dv6WuKz748CO01rw7P+ft+TlC6Zy151EGbIhMPuI8uMwC\\nTdKJQG+T/rXQycrL+YgTGUa7B08J+3iObzFp9kUsHjrKvaZ4P5skd88y7rupe09YsSenxEz8iA9L\\nieTQfvzOQE6AiBxECumfhJicZsYAP9Q9/lAu4He//m7x/B8plL/3sfv5Xv5aCOis53w1cLkZskZU\\n5gN1HtVoRRnT2kAk+8iYzwwS2FvM7vkd991+kmSEmDJsdV2z2m6Sl6tPmbB7+NR5nw5LIcGkPgTK\\noki+p0QWiwX9NBJCpK0rCp30ljJj/OnQInNs1+++d5X3vRDkAbkgJsa0UmSI3REjOOexwdNoQSUk\\nV9drHh0dUw472rLCb9YM3lMawx//6FPWNysenz3nH3/xOX/xpz/lepow3oExbHdbnj854/rinLap\\n6foBlzMxK1Ow6zuWTUPf7Vgsj3l3fs5iMUf7gFGB3TAhlKYuDeM4orRinCa0SioCQSSGNL+dMlIj\\nhUxdaAgs5nOOFgvGacRNPVprHh0tuLzb4LMWf1Y1vFpZXjw+Y7kZuep2SCRdv+W//s1/JpKdm7jn\\nn/XjgNaG2PfYoiRKQRSe0Tv28pG4N34V9w29cw5jDNZavHMYqUDC5B2XmzvqwtCNPauppzQFl6s7\\nZkWS4Zz3O4xUPF2esF7dUc3mfN/thwumTF3P3pBgHxarM/ym8wLYx3FVRSqQxsgkmtYKieXul39J\\nv03dRaPgg48+YL3bUvqJse/QVc00TthpRCv4+vVrlDLM5jWPFjVNXaPzQtytdygheO/Fe8zaijff\\n3CB0maKKiophGlnM5sxnLa5SRKlY7TpKXaALgx0HTFliraOqa67Pzzk6OeHtF59j2oZvvnzJz/78\\nL7j89S+Zz2uIgmmyuVhuePH0KSrANxfvEE3NGBPEaJRChIjQkm63ZRgtxpRIrXny+BTrJ1RhYHAg\\nFGOM+Og4bQwjmnGzo9LJX7eUFVpEWCy52gwYLdgF6McRATxtGlaTpVAGypYvvvwCpMzm2Imgs/e5\\nnJwjTgnX10JSVsmMoszFSgqJDIGqKHLig8KGwNY6SjtR1w1vr29o2zqZGoSAEPD6/Bu+evOSpqn5\\n0Qc/TjMJOyY7vXaJHUfa+RzvE4T1+u0rXjx/n9dv33JyfMqLZy/SRRbTBX99c8Ozp084P39DjILn\\nz1/w+PFjdrsdznk22w3NbIYMyc0GFRHojHwElBeUKhF/ahuYJCns2CeRfAgBkYkwcl8wfics9p6E\\nsu8q99Ds4fYgIf6A/OUzcow52f2eBvTgqe8L8R6aJe+xYu9alB8z7qmt+0LJwxnqv3z++H1d5R/+\\n+d/z2v/Ac4j01jIpJh/WeJB3K/Zs3oREmexv+xCO3e8h+4D2fWjDYQyUO86Hn003bHEh6acTquIx\\nUmUbvogQMsGohc5mBGlmlmZnLjn2hIDSGg3crFacLI8Q2YlKZdQkkqBhJzI8K/IBRyiCj0zWHUiQ\\nMUZ88GilMhnFU0nJaB2FkixlxO02lEXk/aM5l13P8vFTzNix7Tpss0RKQRsd2/WauX7CZ59+ypu7\\nG5QpGJWg6wfaxRFXV5c0RyeoGFjtdpT5KtmMIypAVVWMO8PtzQ1Pzs64vr6iqGpKldCYwXqcE0ht\\nUMnPFEFi9M7bBhEDuEjMRXwMHPxynbVEmUwbEpNAYb3j5PiY69sbtIjcbdbMy4b//otf4VBokQ0d\\n4HDgjA+upZg/568uLmjbBXfdGpv3s32hjiSTCRcTwSvN7312cptQWlOYguPlgvXdCmkUXd+z7UFX\\nJU13VX4AACAASURBVMFZ1sOOYAOjVgxDgrFrbbjYrPjx2VPc7vttNf8FBXNPX5cHk4KkiUvQq86s\\n131uZVNoSqMptCL01/Rff070E/Naczo7xkTBq3fvuBp3vL5cURdQH81xcUTpkpvbFRHFBy/OODk5\\nQivJsFlTmuTuv+vHBLkQuTx/y3R0xOnJMdM0Er1nPq8ZB0kfNmmMopOhQW00gcAoJWVTIXzEjhPH\\nsxmniwV+nDBKstntePL4Cavrd5RFjUIzjik4mWng8elpShC/uEQZw7Iouby8RKuCZtliZUBaj9KS\\nWVNxtFhgY8BFl+jnMsG+2+2GWBhaqZBlyegT7COkSjopa2krDSpyWkUqDV92aTYj7cgjM/J0MeNi\\nFLi86K21yXUj3mvjEmSaJmUycgiMNep+Y1GZMKEzlFYag43QheSL2U8jPoRk7hDB2gkfAxc355Rl\\nonMPQ0cMCucCPnjWmztSyGKyLHPOsVges9ms+elPfprmPQ9u3nuctfS7DqRgt93xT//8T3z22U+J\\nRJbHS2aLObvdhlffvObDTz5FiwypCpkZsyId2oyiLALSgRQhTxUDHoXH5+5tD1GKgyzkfvp4P4s9\\nzDEPYOP9bHHfURyirfLq3xtT7+HZh6ClePCv+1J2/wKkEIzWf+f+31e6vn176IX7sKD+zyb2PPx9\\n96/rgYlg/lvtDUtSo5YzbnOHWRiV94jMllepK0xoVXpulcMbRC5WUpJh7P3nFNFa4azLEghBWVUH\\nm8AQAsYkEsteQiIkh9HDfobuYsBNNoXCtzOiD9k0PX/uUhA87OUthSnSuhIiufbEmODb/PcPNlAX\\nZVqDMaClSqlHIo2jgoQXj0+Y1jeMbuKo0uy6gdmiYbhbc1Rq+tUdSko2qmC23dHbibMnL7BhoNts\\n0FIhvaNqZ+AsV7sdT46PuVhtCVLirWO+aLm4u0VLSTVfcH5xSVGVRAQ2QF1VKDky+IiMHheyCxER\\nJTXDMDAOPe0ssfq7oQeVmM9KCGROmBqHHUIZ9rmYMkZKqdhttpgASM1pIXnbu6TVDh4f769vuV8n\\nYs9GF6y2WyCFUjhnD9f0/rqb7JQPlveoi/P+wJAtCk1TVDx/pPin12+QR6fcrG7RZcGu67HOgoDd\\nLtA0NW9WNzTGsFlt+Kas+PGjk++97n+wYJrDhbwvkvfp7FrdzyiTubNKnr/Tjrsv/4FpfcmnHz7H\\n2sjpfM7V3R2/eXXOetcTgqMpTe40d5RoQhAgS+ZtTVsnY2ERk0j5t198wYvnz0FKlFAYozFqSnMA\\nL5FSoY3k5vKKiMBPlsuLb5jNZ8xnDT4GmuPHdH7CTSObuy1GKtbDLYUSNPPHKSTVOk4eP+au23G6\\nbLm6vuZ0MUNIUMrgx4nLVTJn8EpRLWbMpEAGWK1vOa5b5m3LdrPB2oB1EypDC9JUTCEk/9qqZtP1\\naGmphCCIpOd03mN0gQ2KdxsLwjFOFqUEY87zOyoEBIfvtzxdPOdyTJh7iPcncOfS3CZmckuSIehs\\nDHDvGVoqRSMlQikgxTGN00SIkVbrxJAVgrNH73G8fMx/+ev/zCcffEJVVpmYEihMyWhHjheP8fk1\\n+BCoqgoRU6dwenSC0QVN3R5kOjEmqOybN1/zsz/6mOdnzzg/f8vJySOiEHz84UcoKWnqGTGmkUC3\\n7Xn27Ay8ZexHmvkc4e+LnYgk+YwO32JjpsUWESL5fbIn1aS1ej//u9/3D4UgLe59j7fv9PYN4EPH\\n2TSL3PeOBxg3PnzO++d70KtlNFgw2nutX/7idyDc7yuC4gfu//23+/f1u8/0u8/33Znlnj2/7573\\nxXKPXIBIpL99sdSKIh+uy0wK1FkaY/K8cC+ZGYeel1+/5L3n73HcnGbyYSpiEBOkXxaMdkzrY5wo\\nyxLvfTrgxEiI4d6gIcOyMSYz9aooEQHGKREFtRAplScm3bAgzR2DCCgRmRVpVlmYEk3Eh1TEo5DY\\nHBqtCpNmmEKgEEzTBFIxK4sUIm0KhhBpZnOqcQStuOsHdoNAtjOuB8t7R0dcXl0xCUNRVQybO/r1\\nLRdv3/LRT/6Ivu+pqpow9GymkVlhuNt2zOdt4kWIyN1miyASlCYOI0VVJlgaR1SKMkPkMy2zPjTB\\n0ME5RmsxTc3J8oj17S110yJixE4TRpsEm8pkC2iUSYksPhVBKSKnJ8csg+dqtUVowdG8BXZcdSNB\\nakDcWwfyYK09SDHfbpJM6J5i8HBsIA6jkIfXZCrAUBUlxjuWheHZckkRJT4GdrsNpTRMIrkZh5jm\\nsjFGYlHx6dlz+q7nn9/+G6zxkoXdPo4r6b50/lopSaFy0oiKaLdl9/U/UTLwqJGo2WNWV+dcXd7y\\nj92Eriq883iX8vDOzp6w6yeG0RMZWM5mNIVmcBM+RG7fvOHk2RkIyXvvvcjBpxJTzihLSbdeUTc1\\nAcHYexASU6YN+Xp3ztGiIQhFWbcUNGhjmLodoetRzhOixShNN1i0KVBFhe03bFc3OAejnai1wo4j\\n42QRQqLLApSg73pkoXl3ccW03TFrZ6ioMGXFZuhzIkKNLg2qNqA0UarkgeoCvQ8EXdDjGPqecl4k\\nZmeEwToWpUSbGaveokxFH5NxuxCS6+3AyaxBKUcYRgTpZFiVRbL5muw9dEdy7xAkpvK+81FCUghQ\\nPkFoLgaENjgSuamWyQZscha04Xj5iMIU/K//7n/jaHbENA50Q0dd6sPJvzD7RSkZpo62rnEhJM3m\\nNGJ0wenJ6YOFEen7jl030vUdUmnKZsa262mqirpt06k10/VffvkVm82Wtq15dnbGu7dv+Kj5FKUS\\nepC6xr2kSYBW90zWGJlyB73f7vee5Q9W7WEBKnFv0HGoDpFEYBH3dSzuW83D4t273fBgrrnvVtO0\\nJjFf73tIKVOXO3lPiOFbr2P/Gf7/d/vDsOuDbx6msIeOUuwPIQLBQzZ9GuEYLbMETediqQ/M2CI7\\ngOksP1MyoQNGCNabW16//obBDixnLV+/+i3b7RWnp8/RSvGL3/6KT97/Ea/efkXIEqSY/+e9pyrz\\ntWstHoF3yWi90AnV0EpRKc1uTMSSk+URzrrMwOWAGNhsS1nk97yPrevtRK01MXgKVJJtVTXEgI9p\\nnamQjBuqsmKYJqzzVFXFzd0OFyOVcRgp2FrL3GhsTBrIxwo2q1tOF3O213dsN1vKGHDbLZWW6SAe\\nAtF72rZhIaAfLaWIbFYrjpZzhmGiNoZhGvcXfyLBhCl17ESmcTgw3I1WxCmgiCzmLXerVUZOAou2\\nzejahO17ilahY8RPE0GnA0IMyUu4UAXeOYZhSCMLO6XZp4dPnxyxWG359dUGWVTEmIk12WqS+B3D\\nivz/OhscxAfdZHpQdvdJxwIgHVJmdYt0jvcfzXhzd0u5WHDczrna3DF6y6wo6e1IXZb0/YDzguWs\\nRYfAJAJnJ0e8vb393tXygwUz+b4KyuxPafKFnujeaXgvw8Rw8QVIQR03CNtz827NKCPtbIE1DaIQ\\nh9mOMZr333vGetdTlwWFKXF24vrinPLZM0KArh8YpaBQmqOTY969fpOE+bMWa3f0O4e3I956ZrM2\\nMQmnkWB73r295snZM8btLX3f41zAGIHzju1mi5omvPU0TctkLUprdtstUiSW1TQ6truOpjSJ/eU8\\nAcF2dcuTR4+QAoq2JlqLqkpGmbxqY3SIaDk9WjL5iaquiUqCTJ6SWmvGIJhCIApFwLMN6T6xHVlW\\nis4L3DhRGomIjkZHpNaEKdBnD8uNrrncdBTtHLvrOJ4foUQ2AYi/pyPIM02RrrgEXcaAjGlepIqC\\nabI0SlMKSTdNKXWEFCCrhUfKvUC75D///P/EGEPIEUOjcwgkV7eXSKWY3JRg3clSVxWrzQ5lTD4l\\n3ntzAtR1Q4yRcRwhOLq+573nLzg5Pk4LZN+vxciTx4/Ybrc467EufW6b9Zqj00dYt5dYhAMiEmJK\\n0YnZmD0CwkdQMdnvhX0k2bdni1KIDMnl74hEGto/Ism69/PHhyzU/Ii4f+UgpHpw/7545riq3I2F\\nmEhM/gEB6VuzyrjvWP/nl87v7Va/00nuu+rD9/ZwdZbfSHFPyFEyhS5UhcL5QJ33jcKk7rI8mJrk\\nuWWWoWkhGMeOr7/5Ch88R/NZOvygicHxyy9+wU8+/mOOFsfM2zlFmbTCPiQP4hiS/VsI5I5P4b1D\\niLTn7KUg87LGWZsM1X1gsBN1njf244A2JnnCOpvY/lrQTZYyw64FIEKgUYoc5YrP2tlCJF1oghgj\\nPjgKnVbTME5MQaBFwKqCEB1GStw4ctdbgi5pqwbTOKKPiHGA2YzFbMFmHKnrBoaOAoGcL+h2HZVW\\ntHXJOPQcNzV2sgiSlrfWJqUChYAUgabQTNajVYGPSZ+6X0dVYVBK4n06gEgJo7NUIu33eI/ygWno\\nqYoKCwzDQNPWYF0iDLrkiOSio25autHSRcnxrKIVglerO561Nbc+rUmUImbLxIeG/2nKkrvLB+sk\\nPrifjDwesCWRTAscno9OT/ny+opeCIKdKJwlKMGRmRNC4Ml8wfXdGlOajMZ5irLierfDRmgW/wbS\\nT4JbJXWhU8EUAek3qOYULQRlqXn7zz/n+VFJ0TZM/YZtN4Ip8SFydbulG3pkBAeUZcls1tD3I8Hf\\n0+CM0XgZeXfxjsXRMbpsMdbz65dfMl8umC+POZq1dN0WBQx2op0dpX5Jl0zjDqRmvdlwenrCuNug\\npKauDcPQYcoF0zgwqwr6cWQaB5rZEpmhSa0k3TAgvcMCx02JLCoQMHYd5XJBFecMMTB0fTLoDYGq\\nrun7DqUERydPQEQ8YExFyLollxXc3ifpvvWeVNkUWkWETtFW1nlc1AipWduAGyZa5ZmXGlmli//S\\nbVFlwSvvKTrHkUwzyj0zNpLmjvuB+B5+lTKZTc9MiigK2RxbIhnGJBruh4GiKA8C8NSkKUbvGKcR\\now1aGbTWaJ2SKKyzGG24Wd2w3XaYIs14tDYpX2+yHC3mHB89ZtYu7hfFg435k48/hjhweXNDYQyL\\n+RwhBJcXVzjvefL0ESCom4bPfvoZk0tz2idnzzBVfW85Rko/STZ7aR4WUyrn/aZPQISAI3VHEnGA\\nVr9VLPaIKA89TtN39uqP9Nj0zA87wYdwrMg6zJg7x/tQ9fTYEBPT1+XP66H841tSkR9aqP/C2x8q\\nkN8BYrOLVzxsWhy6SXEooAfXpSw3O4xupGBWaAYXMCpSmBRgbR4UyP3BJlmzpa50shNaCU6OjpAE\\nKgXSqLTupoE3F2/48cc/4eU3L7m4Ok/Q9h5Szy1uNwzpwy4NdWmYLBmezeTF3M05n9aOtZ7KmENc\\nlYg51FsIJjehjKFWe5NFxUCkkJrJO4RSFEqzsxapijSuCgntOozp0+wBoxR936OaCmEqdqt0wFRS\\nYYVgIQPDboMJHqUkj2ZpJCTaGi0jZ8/f5/LiLRQlbuip6oYAOO8oqprNrqOqa8bMyq2qkmkcGYeB\\n+fFJ2qeSlodCK4ZpSix5nzxoD85CQqBjSoMJRYUxZTY5SBe+F8m0XcXE/FVSJrerTISTMmnPvXOU\\n7QwTPN4o/uTTT3l78ZYSxcWkGdnrXCMuB64eiuOhWOYLLR+dk7PWPTOA/PmnM3WyNRydY42gKSuu\\n1rfMqxaR/06zouKoqpDbHbf5F/TjiPOeShd0fU/1bzFfrwpFUyiaUhO372hP3gNZZ8NnTXf9Ndqu\\nKYpnWBcYYolZnNKcPKdePmLqN3z5D/+V4BxG67TIlKLrB6ZxIoZIVRVAZLI+aW5cpChLhFCcPnlK\\nt1mxs45SwjRNaCk5PT7i4voWZWqCuF+sRE3X9/gQaaoKEQO77Za6bQjWUimNN5opGy0XWrPZbtKi\\nKSumMVIWhqqd4YVg6HaYokA5R1tVuGmi0prgI03dILWinM2QRpF83dJGAInE40NECE2E1KkKCSIJ\\n71PgtqKfLEZKJiKOAAQKbbDespsCrbMoEXjS1Nx1isnbnPieRL1SyAN7FbG3mNOHQqm1TqQelZmY\\nPiBiQIjkFYm4T6LRMtHKpZBMeS5TmILt9oa2nqGVpixKfPAH+GocLTvX40OgKWqIuSMT6fmEkLx9\\n+xqlL/nko0/RSud9JEPGpmBWFhh9jvWR29tb3r47ByFpm4auG6jq+hBQneLaImXTkBjm8SADCKSE\\nnTRjiUiRw4qFOjjD6HDPmr0PS/7OhZ9d8kQUmWQGMQ9F914He5bsIfouL9x9Col4UGzvSTAZLkbg\\nQgrtnfy9q9B3b78jJ/l9I8d/we0PPbf41vf2B4t7ItI96Sl33uxJUXtNpTyQcnR2k9JaJIPwPhFy\\n7hOKUt6pFOCtxRRV9gPOOboxIpSiKjJBxFlMWWC7HVIqLm8vATg5OuHj+hN+8fIXWZaVpQ+5EEul\\naMuSVmuclBAC/WRBS7SAbrK46Kh0gclyiUR2Cyixv8bgqFTs+p6irJgmh82HheA9GgkBBtKcHiGY\\nvKdUisE6lErxasKHg6bRKMXdrmOwlqOipTWJUKOIlFVJGCeEFFjv8FLy7PSIy7s1jZas17c0Vc12\\nu6VZLhHB4X2yqZuCo25qNv1AWdX4cWSakgGDKQpCDBgl2UcDEBOZKnh3gM2jSx2vdR5dGLQYCN4R\\nTYEyBVrpbLm4Z0Lv10l670abBPWGSFkZyrJkshNNu+BuuwUdmDUtcbLcWAgx6SwdPnXaIRnQxDyN\\n3A8n4u9cn99BqiKIfXKJTGjA6Bx3NzdpTwwkzoVzlAJuths+PnvO+vXXqFlLjEmv27sJGSUX2833\\nrqMfLJhNoWlKCZu3xLuXlM8+IYbkjbq9+or1l3/Lclay6x1PPvkpL05/isym3kJAd3uR7OWqMsGb\\ndsIoxcnz5wzjyHazYRhHTo6PODYS70a0CiyXi8SQ8g5VljBNiOgQMb1oHyKz2YxhHFjddaiiZtHW\\nlLWizIVqHAa888zmc6x1xBhSvyFIXdVmDW1DlSGIqippjeDy5o62rtmsVzTzJbe3yX0oDfJhmixF\\nUVC3FcIY2ioRur13SfCbPkGs8wiZXFqs96kUBo+WEp/dMxL7WNNNlqrQEBVTiHjriAimKNl0I8t5\\ng7UTQoIfPVGmYmid42Bgnd+blHkBRLIzSERIRYVA6pxFhz5EMCXafkxM2/weAKJSTMEjENzsbjg5\\nPqMwJfNmwdXqCoHMrMS0wWidNlEfPG70CCGx0QJpA3j+7L1Dsfzu9q2VZtG2nN/ccnF1zcnJMc+f\\nPTto4e5jgXKBzxuXVEBUXF9fMpsvIGYkxCSLQBkkKgi0jDiV5AwuRLwPOe4pRZ75mLIh0/q7L3R7\\nKPQwmYwPmLK/b0F/61+5cxP7zkseIqF8fg3W/36f1/3tdyQk3/3D/WsL6L5LTC/yMA3aP5fgXk6T\\nCiT5YHX/ftQDychDjaXRaV45r9I1ticJ3neUkugdOodbQ8BOlqGfmM/mHHdzRutoRWTtHELJ5CiW\\nX/DlzTl1VfHBsw+4ubvm1cWrJKXSmqauGGKkrEvmZYGKAZ11sbosWA8D0hQUSuJCYnpOztHogs1u\\nlw/qAnwyPxBas6yLhGJIiYFEbIwBpbK5SNbLSpFsGKXUGOGAmOaYIjHZfYxYESmKgtFZ1jIhNaUp\\nECaw3uwwpmQmPKMPbCJU7YLZkKzu+n6grRuqqmbqttR1nfaQqkQ5jw+etjBsdluatk2ZnVJhh5Fu\\nt2XRzBmGHdKoJJnJYfGJXZoOwCldxBGpDppMFyJGClRZEHOQQlk32GkkSawyOhGTQcjkHN45JNBq\\nw+XNFcvlEdOuw2a0cT71dKpksTzhtDb4ceCLdxdUbUsoa1b9SF1VXN9cI9r2wRrYj09Evm7z9ZtO\\ntDTaMHqbMuHy/FZIwbrvOJ7NqE3BanvBbRP46PFTfnl1QV3XNEhCEBRFkYxxvucmv/eefGtKTRzW\\nXP/m5/Rdx+UXf4frVvQ3X3H5y59TKEDXLF58RlHPEOrej5AIm9tzgvdUZZmqeN/TdT0xTCgRWS4X\\nzGcNwXaEYeB4cYQCNhdvOa4rKgEvHj8hCtBFhVSw7bZM1mHtSNs0nJ6ecrycURTq4BdZGcOsbbEu\\n2V5N05iLSKQqDacnC7wb6XfbpD0Ugu22Y9uNtO2cm9tb2tmCoe+QeUF0ux14z/HxkrIqcqqBxgsY\\nncfFHL+Vv6+1RpIWqwspUscFGGw4eI76mHwqtZQYEWhMZFGXCVaNEHTB653l9e2Ol5ueYbTEjOf7\\nkOYvSulkA6WT9isZFSSGc5vNzodpZG8r5bJB9/4iTEUzh+DGQGnSRnDgs5C6SJsZZU9Onia6fXBp\\nQTqfoGBlCD4kaNkH+n4gBGjrOT/6+Mcopbm6uWS7+84JLmYo1chDcVIHz8/cBeW5xdXFRdLRquR6\\nhJvQwnN7ccm42yJIsgUlJVomxymj03+VUdRaUxtFleVPRus8V1NJ4iC4t2TLkKPMc81cSQ4LdF9w\\nDoVn/0LzfyIzEQt1zwzVMv2g84HRhYOF2x6K+lfdvr/eHgr2w6/3xXL/fh52lPfFkEMYtVIyG4nL\\nQ6jCnstQakVdKNpSMasMi8owrwoWtaHQktqYfOhWFMLjdrd8+ZvPEYTkbCNgvVohgM12xf/zz/+I\\nj+nv47Vhmiz7IOmkxUxGAa/Ov+Hq7prnT18k0gppTt9kbbHJQefBB4xM8pVHjeG0EOyydClGGPds\\ncmDeNvR9j3WeICUuBvw0MtmUulKKiFFJh2hMifUuJ9akotENAy4EttOUSIRCYmRCslAaFwNapEOu\\nzrPt803HKhrKoqStS5yb6ISkbhu6Xc9WaJZPnyB1gRtGNusVMe7TORKMPw0DIs0fkDGwqEts3yVS\\nlQQtIPZD8qmtGtxkkVIjgkcET3A2xZxlQ3hjdEKQlMY0DUKp5CUrkpF+kul4hBSJXS+TzAQpEteB\\nvd+uoChLFlUi+cyPjymqmmVZ8XxW82xW8GEjaFXgrCn4d2eP+LDVfHLU8ONZSdt3PCqSS5IQycZP\\nIBDx2zrpqigRCMrCUJuCbhxASaIIRAJCRCabjPgH73m0PEaXhtJoni2WFEbz/OSIx1VFISXvzxd8\\n3+0HC2apBNPVFzRNzd3NFRdf/D3f/P3/gd58xYsXZ4xOUJ5+iq6XB0p5Wpxghx122NK2bdoUvaMs\\nSkIMhw24beu8SBVt2zDt1hiludvs+OqL31IqgQguOUQME8vlknaxJBJo5wu6vmOzWaO1oqoqnPME\\nJGiDi5Hl0ZJpGgg2XcQhJveP5dGSojSs1mtA4OzI8ckx51cXuKnn0ckJZVXinSXGRGIQRmOqEoxG\\nGI1QChc8SiXxfASsdVgf2XW7FB4b92QUgc+p9PsFihCH01tpJDEK7GRRfmReFwn+kYJqseROFNiY\\nkt+JqTMtcsJAmc2ftVLZVCJt1OQLrTIGrTSd9QfLsH0MEkLgyDqmDM0GImP0jNlLc7SOx8dPKcsK\\na0dOjx+xaJYpm9SHfLqMB8PxEALjOECATz/8ESHAL3/7S15+/ZK3795irWW9Wd8P+kXSap49fsys\\nnbGYtYg8ixUxZCs5ye31DSfHJ7RNjZIJLr+6vOTLl19y+uiU2Xx2OIiUKovmv+Uwc68bLrKXaWWS\\n2UZR6BSRZVT2Or2frd3DQPuuKx468/v5Xfo9ez3o3v3q3k9ZZkg2dZejcwcikdx3d/A73ebDOebv\\n7UT31fo7d/2ge8/+lP7g50SOK9t3lPt5q5KCQqlvFclSSWojqEtFXRia0tCUmrrUVKWmLVPuYWkE\\npVF0mw27zQpNwE0ju+0GozXb7Ya3F+f84te/5Or6mrquU4i794zWMjnHME7suj6tMZ8Ykd45vn71\\nMjGZpaIoS4zRhy5ZS5kIfTkRxyEZA3gSDJgKWchTtLwWvUdphVSSIxWYy8hRXSBlsvycXGBwHo+k\\n9w6hNHZfGJSi1BqFwMh9xBe4GAgheW3nHuLQlU9T8jvtJ8vlEFECjpuCFk+363hSF3z5+pxpTBZ3\\nJ6ePOH70FO8dwTo2mx1lURB9kskUhUFpg/ORo/mC66triqJKh0KpqaTi9uaGuq6ZpgmDxE8jMv89\\n094gCCEReoJS3G13VM0M5zNMq3Vm0CaEy1uLtxNumti7oTXtjL7bEpxlt1kTgseNE12/ZXl8jJOK\\nqDVPj44Qk2VhDLOqpC4UpdSw2/In7z/nP77/hB8vanyOI1yUFY0y6SAbU0D3XFe0RUltCuZFyXoY\\nQMqk1Qx7W8u03w52YhxHghJcr+4YA6iqZlG3XIwjs9kM5UKW2P3+2w8WzP72Nau3v8EYkzIhq5K2\\nqrm8POd61XP66X/AeQ8Z174/u0auX/8Ku71FCijLZEdV1RXOe6YpXQRuGjg5WhCdg2lEBs+4WfF0\\nVjGfz/HeMa7XzMuCm5sVUTWURc3y6ISiMJweH3N8NGcadkxT8pFI0o0xYfZRMmsbJue5u7mmrhvq\\nZsY4jpw9ecSzs0fYsT+09h9//BGDj3z9+i1916VMv7pEVxVHJ0eYOuVXiqIgZL/FA+EmRpCpy4wy\\npaHbnHIg9prIXBz2xWycHDImaFRrxYTiajtyt8tzjeA4Nop5UWCyvKfKWrO91pIIc1OkGYnI8TfZ\\n3CGxmVXyzg2BfhxoypLBWiISF+7JJj5GpsihsCqRsgyd88zrJVJI/vaf/4a//sf/m4+ef4TIA629\\nRtGHABGMMSiZLMY+//XnXN1eHiJ4QoTVes0XX31B33fpSongvKQsC46WS56dPePs6RmbzZZXr17z\\n9u0bIPL4yWPKOuk/nbVorbm5vTvAp03TJNabj0g/UWlBoe+NN/YhweaBy0xldHabue8C1aFYPoQe\\nORTD+0J8L2F5mE25L5T7uV2SYyVdXggprzITmg91a88C+oOzxj9QAIUUD4r69z8H97/q8P17IHr/\\ngh7cJxKsJ1U61BZKUGlJXWrqoqAtDbNK05aaOnftlU4d57fYUjEVyvWuT/P8KbnzGGMgs461EA7r\\nZwAAIABJREFUTq4s622HUpLtMNDMZhRasxuntH9UVSa0wbbf8vr8FYt2gbcuH8oTlOpCkuh4IXEh\\nvbvdMCKUoVKSbhpTGHUmCw3jcGCCayFpjKHRyUe1UnDbWyYEUiX4UgnB4H0KQJepm5XJiihlkUaw\\nZOmSVmhtkuWcSHNMFSKamEcq6ZBZFwatzaGbb5oGsb5hVqQZ6+e//jVf70bq0yfU7ZypGxj6keAs\\nwTmmLNdTQjDaicfHR0ze4yZLVdfYrJ/cblYYpZORipA4lyazdppQQmK7LpH9hp7j2YzJOew0MvkJ\\nVRRUdZ0Y+UJiMppDCFg7EYMnxkBTt0QEu2Ginc3RQhFHyzh0SWrW1BRVSVlXbIeBYn7E4BzL4wWn\\nxwv6scNryenZYx7XJSIEni+XLIxGxGQ8USCRhaazA8ezOZObsN5SGp3MHfKcU0iJD55+GLASNtZi\\npeRqGrDeYpTherPi3bCjrCsuh+5719kPFsx2tsC6wPX1NWdPHzOfzzGFojl+wdkf/yfq2QmLJx8n\\nWyMJ3k+8/tVf8+u//t/ZXrzk6HjBYj6nbmrm8zmzpqUsS6yzOO/p+h1h6FjUBbvdlnEcKbQhxsBs\\nNsMiMU2LHTqM8NxdvmO961lteoQs0UUFsgAk3XbNbrdlsViglUHpBNH2w4j1gbZtubm4pDAls/lx\\ngkKVTK9jtwMkL19+SV0K2lnNzd0tpdGcHp+kiB4pUaYEkfwMZSaExAjamAMpIyUyqFQk86Jx3mUY\\nM2bSXHb1CWmuKVUmhCiJMDWTD+g0xWboR+ZKMNcGIxNsWVZVugj6PlmCaUUpFVpAJSVl9o/ddy5V\\nYdJpVGvW/ZDDciFlQKZimWy/UmcWYgRheO/ph3z26c/45Vef85d//1/40x//Gdt+x2hH/vijz5BR\\nZfKSPCSD7LtOUxqElkyTzSSshhdnz9nsdsxnc3R2CEGADR4t4OrmOs0RQuDV62+oqoIXL16whwsl\\n0Hc9w5BIDWVV0rQ1z54/I4SAlml+LbPO66EtWypcMhfGVCALI3OxTNaORuYMQ5UKp1YKIxNhKsG1\\noMW3LSGNVuln8+demPvUjSIXT0mKWUupKj4rx/a18v6g+QfDmX/o9j319CElf0/FT8rJ/Q/F/Ujo\\n/mkeYLZ7GUyaicmDm1Jbaea1YV4XNCZF98kwJoi2UPhhx827V6xuLpjP5zx++pyibimbWS6UUFc1\\nZ4+eIkQ82DqG6Nl0HVoXjOOYPGInSwwxsbXzWgsx8u7mHevtmhBC7ig1kpQioqVimEacSCQXpMIQ\\nEMEfOA0HdESKpKssSwSBzqXcyFVv2XnN5FP2qSRSCYHwjrnWTNOY3InyrF+QDqjJxi3NxkfvQSXS\\nmQg+8SikTNKJbCAehGRwiam+3XWYssQ5z2wxTyEH3vPZT36CuPiGX7y74ToIHr3/AXYcic4jQrLA\\ndEJQ1A1d36G0Zuw6lsdpr5u3M3a3NwTrGfodSmuUNqiYMneNKRL/IcttyAfstCdIpDFYl2e7h2sq\\ndXCClEsJKQFpu14TpObF8/fo+x6tQeLZrdYcNxV613H+1ZfcXF+x6ztuui3LxTHnr17jR4uwjqEf\\n6CbL0+UC4SM3u2TYcNK2GJ8OWLuhZ5wcWzsxxRQqPi9rlrMZRptDdwlp/HW7XnG72zDakX4YuOl2\\nvLq7RElFqUvebdaMfyDk4AdJP5e//L/4+MMPubq6xE0T6zFy9ORDHn38Z0hd7IdL+GDpbq+4/Pzv\\nGLdrlITFvKUsDOM4pGUZHMujJULAarUmRlivNnghePLoBKkLZk2TXDuGnldv3nD2+BlRBnRRELue\\nrusw9YwYJ1brDcNuzcnxUYJl6gJpLcMwYceJ9XrDfDZnsUhxXeM40TYF4+qOoAztfME4jIeQ20DJ\\nBx9+wqwxrNZbmjJl6uE9p8slm2nMEpEkiLfRYnSDQuSTbEhUdZGy3BIkEyEEYkxkE0hkAZdjiZTW\\n2OhhtJgiMc72szMfPEIk+HWyE0ZKGqkQCqaYxMieZA1WSJlmnpA2QJ+S0CfnkCq5eVSmxHuX2Ls+\\nDeiN1jnGKEFG0ntUhHGylAX86uvP0VKnkzWCL179lp98+BNevvotf/7Hf0E/dGluLUAphXOpO7XW\\noklZqD/69I9YLBaHC/f4+PSwSWcgEhfSz3/y0SdM08TrN694/uw5u92OfujR2lDkTXaxmPH27Tvu\\nViuen52xWB6lWUV+wtEFnj55xOvrXe4UU/ekdBKZx0gm/KS8UEnyxpUiWZ6pGPE+HaR8iAShkvxI\\nxNQLJd7RQce53zge1B6AQ4xV6r5ToRysS8xesZ/K7ZWm/8r55XduMh/aDvmb++6ULOD6Tge573O/\\nXbhzgUnVBBHToSppU+M9VCvSAcFIePXlVyyy7/L1+Tt++qMP+OWvf8MHz5/y8vUrCim4Xa34+NM/\\nYrlY8M3rVzwJT5BCcnr6CKkkr968RuewZ6U1/dgzb0oKY2jqkn4YUErmAieJIhvUi5jSLGJg3W2p\\njCaKiJGKru8SOqYkKgRMUVETuHH3TE+ByCbjjlJoCiEgBmyURALdMDJvKobJ4bJdpApJftaWFas+\\npRI5Z1OMYf5b7nWfaR6sDhZ6xMAU8ijFWmIMSQpRljxpWkxVEr2D0iBWnn+46/jovR8x2J5mecLz\\nWcHfffGS93/0Ex4tl9hpop9sgqulYpSRum7YdB1tUXK7ukvzfFPQ1hV+GimNxk2pa0/61ynJVawl\\neCh0ktbE4A+a1WE7UJYlY7dDFgY7jiBEIh/lNaW1ATwnpyd8/uU3HD86oZ01VGXDdnVH087QpuD6\\n+ppnT5+y63YgJG++ec2PfvxH6Lea9WrN/PiI4D3B7hDTSCU1wziiq5IfH815+6bj9eDTQQdY77Zp\\nhq0lN9ttbshs0mPnw0tbVGm+meewxhi0TOs8ELm8vaWZNd/K0/zu7QcLZm3g5LimKJ7Rh4Kf/Mf/\\ndKDV3777CtVdEGaPufvy7/nsT/88aa6KRHiBQFUVKJVObyl6asNut6OqqnT/vKFQJKp111ELqOdz\\nur6jqmqs63lzseLFs6eMQ0+3G6hPnxO6O1woWJ48RpUVu6t3tM2MGARX5+cczWcoIsPmluMnZxA8\\nk5AM48Rmu+X40VMCUNdzhDB4b6nnS377q9c8f3rCNOzQJumYyqphdbuiXi7p7JgNylMBGbodysPs\\n5IROSpqyYPQxOeRk/FwolWYlMTliTD7FGk9BZigmzy897Aab5h4xbbY2pNQASDZfCChE1ikZw9ZZ\\ngrdUhaHrE7QcYlrUNgQcEeE9RiWdlZIyE03SXNB6f9jhJeIAHZvCpBNonov6LH24Wt2w6zd88uLH\\nbHbbfJUk2DZm8VT0kb/42X9g1syz8899YQzxvuv91vyMdKAojUSIZKH3+vVrnj1/xrt373j/vfe+\\ndV2enT3j7OyMcRr51S8/5/mzZ8yXC8L/x9p7NcuSXfedv23Slj/2+nYwDYAASEokNRETE/M5532+\\ngGIe5mEeJoKhkEbkDGUINIBGN/r29ceXTbOdHlZWndMYQJBIZUTH7Tq3TlXeqsy99vqvv0niQbm+\\nu8Ykzatv3nJ2csLFlQSJh5A4f/SYniBQa0ySrhAEcrQ64GIiKDFA8CHi911Aup8zpgQYscLbd8ny\\n1/J3e3h2//w2JJreEYYZq/lORsP/mGK5/0i1QuDfhz978D73CPA9i/CeNZvuSU1qKKpK3Hv0sHkz\\n+8K6D8w2lqPjI3D7BXXD3WYLWc67myVZUfD1u/e8ePIMUORFyWeffu9wbgo4WhxzdHTMP37xCzab\\nLVmZEUMiMxm9D5JCoTSohNVWNpJKQdLy/8PcyWiDV6BiovM9SQsKtOo78kEytW794SPPrJVc0iR2\\niTZ6MqNR3sNw/xk7kNlcj7IZeW5ZOmkSeoTlnhsz4K/D3DIElDaD5lYY0j4GlMlwvcC/SUPSstmu\\njOZ605BNSsYhEJxjNh5xvN7wzcU7vux7kjaYfMoyGf7iJz/lqy9/xeSjF5B6RlXN9WqFqmQD3/vA\\nJC/p2gabF/SrJcE66tGU9eqOOKQaVWVJN0C1u6ahLiuCcxKCUY8IAyHI+5bMCLRprNjb5VbSQdqu\\no8wyGMiO3W5LTIlpXdP0DXVR0bQ7gtYUZUXyjrOTY968ecv52Snv3r/Hp8TdzTXj81O2LsLsiFHw\\nxN2Or2/XPJ2MuAuRcWhRMWOeG+oi56s+EnyPRhN8YOXW5DYTZMnKvDOmiMHQ9B0M1zoDL0K0t5Fc\\nW8azMZebmwPP4w8dfxKSnU8nfP3VS/rihKNP//pA103A+YvPeHf5npsv/w6tIkWRMxmPGFU1miBQ\\nlEpUZcl4PKEsa1LSVFVJNuwC9bDtPV0seLKYstluWN7dEI2lyhWx3/HTz3/Iarchy0rOH53y8ov/\\nQOy3FHHDzbtv+Ye/+3+4uL6lD55d21KMZkzOnnF+dspoyIWLMaGyClWMOTp9JIzd7Y7VakUIcdAW\\nJs7PzqjrijfvrsiHWZ8Pjqy0tK5FkRhVI0KSG0EZPSQmiKFv7/yBg6EV+BjEoiuK7rGL4gSyjwRy\\nXpz/i8yy7iNdUsPONtEPxSam+/lZipEUI5lSjArxlu2cI7f5wIw10mCGIFApe0ate9ABDeLgoSMN\\ncW/mtn+KdCkpJopCfHAjEH3Ae8fz80+koL3/lkTCO08IQWZLMfD5Z59Tl6PfK5YQQ+L/+4d/z7v3\\nbw8epErt47OEKWtU4jdffonSiizLePv+HT4EtrvdAQaS14vc3d3yyy++oPeSOiPQkPz+3WpNbLcU\\nOpDRUxU5za5hsZgTgufy7Rt8u6Ma0jLy/X+ZyFLyTB6LDlnmc9VADMoHZq0wbDVFbg/zz2z4nTK3\\nZFYWTB8T284d3IgE5hQ0XP3JO/C/70gpHeBeM5CTxLph4MHui+D+k9zDsGo/A71/jlL6wBAWIpCW\\nnx3MBmQu7H1gMl+Q1xOWux06M2S5ZJ4+OjmCvuPx2Rknx8eHjdPhfR+cd9/3jOuajz96IWELrqXr\\nW95eXgkL3TvapqFt2wHJUHR9K0xaJPHmbr2ibQS2DUl0yiEltNJ0PrDsetq95Au5FyOJaVlSaZnH\\nuZjw2sq4BCHFKW2YFDmVVqzbHtfImKfxvSAParARTWkIMRjcpkIYNiGapusPKSnEgIlQWU3qO9Zt\\nS5nlvLzb8tLnxPERN6sNJ0+e8NcfP+XHBZQXrwlty7bt+fXtHW5+SpdX2KrkdrlkNJ2iUqIySoKr\\njcYnQGvKekxVFAAYayU/MkTWd3coLUYnIvAZwrG1kaSXLMeYDI3MZJW1mLwYpGcJjFwbwYsdZ4iR\\nrCgH79mW66tbuSZDQGm43azxWU41m/GTn/2cvK753o9+xM9+9udcfvMtabvFXV7w//7d3/PVzZJN\\nXvH5k0e4bsdYByZVTX9zQZ5ZLntPoTV1LhGF4yxnqjVTIB+4FWogih2u3yTX2mg0YjqqMSFSZOL/\\n2/geHdV3HLd+//iTt+umDZz96H9l9uwngrkPV7lSiX/zr/83tssrOu8G/Y6jqjKMgaoqhI4eA12z\\nEVhWQV4I8ccYKDQ479iulzTLW9q2YTQbM5rNGI9qjhYnGFPQ9g0nixM2Xcvtas3548cszs65Xa54\\n/OgRf/GzH3E8q1ApURvFm9ev8WRgSwg9y5srri4uuHz7DXF7g2+2LNdbos4Yz44xeUlShno0IiKC\\n9s8+e4HNLMeLGeP5Edrk5HlBs92xvb6mrkqckxQCm+fkNjvAXykFIRSgSUoT1T5XTw1FQXacZWHx\\nKJa94mLV0YU4sF21GDkPC5IxCkUkM3s6NQNs6Mm1wTlPnueEKLqvPXvTavOgd5HZik8yt43DoqWV\\nmCjHJPMWub802hga19F23cDmlE7q2dlTnj56xqge0Tnxtp1P54N5gsC9o3r8nWtoP6v7+//wd0wm\\nU549ecbVzSW/+s2v+PbVNweiig9gVaLrWy6vrtj1rXRnbUOW5Q8WdjnH5XIpPrxZRlXds62vLy9o\\ndy2VUZzM5nQOXFI8f/GCqh5YmDFweXFB6HaD/MFQ5fes1jKzYo2YWWF6WiHvFEacr/JMD3+ae3KP\\nHUhEuT2wYlOKbBtP14dDiol4ku9lPdKpfVfZ+U8/HhpUkwZ92r5Ysp9hHm7iw9yNBz9/GGs2jO6H\\n4AU9wIt783y53vbQXDmeYLOCJ2fnvPnwgfOTMzAFP/n5v+Dx2ZN7cwglAPFDs+2YInme8+L5C5ab\\nW7bDjG0xmZDnGUfTMUbDbD4nKcgKSSGyWizxqlq+f+cCretxw5wyH+RJu7alj4E2SCalGrTK1hjM\\n4HrDMOu0yEbfamH2Z0ZL0LoVG8tSQbDCqG7WGwqTDeL4XOBYxKiBFDFatMpCOjMQZBRjrcVqSF4Y\\ntIXWXCzvmNcVu+2Gr27WvEkl3+4i3zjDuh7z/Ief8i9eLNC3H2QDnlk2u5ZGZzx//JjlegPa0Dgh\\n/2zahtnx4mBEEPOcIs/Q2lDnGa7Z0e5aurYlhkjftaQQyPMM5+4TWIxShCFJaLVaY/OC4JywZNse\\nExO+F8JP1zQoBVUtfJWqLGQD7x14T6k1frvh6u6GVd/iSTilcFbx47/4OdoaPvrB9/n5sydcffVb\\nmq7nd1uHrUZ8/uQp2rV8/OJjglLkgy42bncsyorPjmb8q/NzFtFzbC2jmBgVFcpo8jwTJUVRDBFy\\nChPhz548pd/sGGuDUVCXIybV6I/eX3+yYPp8RjE5ks4GYeNprfni3/zrIaYnoyoKqqokxUjbdRgD\\neWYZ5TmuaWgHk2NIQjvPDMb3FGWJX28okbTv0WxKlhfE0NO1Lb5ryYoMt91RWMujR094/PQjqlEF\\nMfDi449Ydx1Xt7f4oEi+ReeaZrvk+uqCZrvEmIyqrhhVBefHC4rMsnWR02cf8+b1K7Z95JuXr/j6\\n66/Y7jrmdYG2AkOZPOP91Q3NdkO72eD6Hp1ZvFb03g/kEJlnrjcrirwYiqKkTvTO44O0/hAJSUgD\\nVSbzzl3Xiag7K4jaDJCgDKl9jIN8QSA8H6ELibbvsXCIMdLasOubQ8FOKaGNERJP8IPUQV5FmG3Q\\nDnKRFO8nZ1rL7jClKHPaQboiUKRYiGmjubq9hgAqKWajKSlFZtMFP/nBT/E+8PTsqSwMD459EXau\\n5/r6il988Qtev307BPdGetehFPQ+UhYZeZ4zmYw5WRzxg+99n5//9OeMRyO+/OpL7m6vAfjyyy85\\nPT3hf/pXf8OPf/wjnHN88cUvWd5e8vGnn/K7l284XswwNmfT95wen+Bcz/s3b3j79h2aweVpueLu\\n4h2+78SQ3ijRaT7oPPdxdaUVX9F9+PFel2iHx9XglZoNMpMYE9vWs3P+UI8E2uRAlNg3e/vC9j/q\\nkG/sfoMlrkdD98hDwtH+z3SY40qh3MdqCQKwZw7bgTGr9t9sArKc1omu9OjsnHo6oyxGHJ+eMZvP\\nBOEYpqr7Wfle5Btj5PJKSBckJLMxKyiLHEUUwg+RgIxBnHdkmQUl3aOw0qUrqqoKrRXeO66XKyaj\\nEdVQjOrMCntcyfe+19U2w0a+jZJp27iOqBMuRDZegTbMC0NBZNfLjNIlxawq6TpHVdekJEVw1zRg\\nZU3YM4StFvivzHN631Fn4qG6dx5SRossLUVG1hJdz9P5iIURjbHrduiwEwP3kNAm58efPOXcbXDO\\nc7drudj03GBZVCUfLq/xUbrt0loiYlOnjaXf7fjm7TuOT87wMVHWI2aTCcRAs12LRMR7Uoy4tiFE\\n8bT1wRNDkE2gtbRtgy1LIVhpBYOpvdYGlQLtbsflzTXFeCxGBCGQa0v0wqBVKlFnlq7ZEGMA37Na\\nLVm3LflkShcUelLz5KMXaKtp2xZQvLy6ZZpn3Nzd0vZiMJ8FR15V/ORojl+vcK7neWY5T5E8wdTm\\nPMpyJigmKkd5YWaP8pK+63h9ccmjk1Ni72TGm1ua39eJPzj+ZME8//5fofS9O4tSiq/+/f9BZSPH\\niyPGo5qYAjEkXHBYk1EWOV3Tsl0thWUYE0WWc3d5xW6zQWto+o7d8o6qKsW4nIQxGV3XEwPURUnw\\nDpsUrXfsdhusTsRuy+npGc5HsqKirkvGowlnJ6ccnT1CG8NkPKLb3NGslygSZVFSlSXRO9ZdgGLE\\nuy//EwB9s+VoVjMpNbP5lD56upAweY6xlvMn53Suw9YSBO27nno0ErNwhNmH0Shrhs5H5pZVUQy+\\nsZJu3vtE03WUJpFnmczaBhKQHsxJNYMZ+L4YJklLKKwswCFClpesgxixdzGRWUllKLL8wFYVzs/g\\nLJLSgeiRtMENa1znHNuupel7WciSwLKd8/ggu3QfArJfliLee8df/dnfkEh0rkMpxU8//3Oenj8V\\nCMoYThanHPIhh0MBf/vv/habZWRFzrbdEqJHa+hdy9u3b+j7nrv1CpTmzz7/nNPjU2KMlEWJUopf\\nfvELnJeuwWaWqqp4/+ECO8yXXn7zO/AdtVLcfHjHycmxOBllisl4QpZbPgx2eyE42s2G0ii22y1X\\nF+9RJLrtkna7xhDvC6KVDjLLDDa7l55kg3OPSEuUmB4M8pK9f++2c2w6d/gs9gQTrcQUQO8LlrrP\\nAfmnHL8vIzm8mmKQLw1koEPzObilHKBXeXAP1+7PUZytjBJd477L1ENBZQ+Rp/u57jgXp6vJ8RFN\\n53CBwYZwmAPHeNAjyvxUc3pyCoi0YttusdaS5zlllrNpGrRW1GUxbOo8Pnh2Own5jYM0xXkxMkkp\\nYjOLcx3rtiEk2HYdPZDZjNpk6JiocpEqKBSbphPZB4rCZATvCUlRZZqJkc8gtwJqdz5Q5IYwjEnK\\nosDEiAVMghSixHhpSR4yCGM9xiCG7UXB1CSKwVBALCbFEGFUiK6584lZXfPpUc1pkTCuIdeRSV3x\\n9nZJyDMePXvK3K2p7JD32WxZ+cjpYsGkrnE+YouS6+WacV5wt1kznkx5cnrM8u5Wcm7XK3btjno8\\nZbFYUJSlXDspUZQlRggBpBiw1hBcTwqyPggLI5FZK8YHBzRLYaxlMp2wXm9Qg9VgH5wQ8kOAJDrK\\nXN0zigul8X1DDI5ud0fsPWa34eLbb2Vmri0FkrJSGs14MuIk9Rib8fFigQ4954sF7u6GN6sVKx/4\\nuMx5kiIL31MpRaUT56OaEYYCqI2hrira62uqzAxJNwVPi/qP3mt/smDGIUNOKYUxQgK5u7lmvdmK\\n9dFoxHg8EWea3kmqdtugnMMozW69wShFsx6IO6slt+/fSWHLc/LMMp1NQRuc69FG4fuW3WaD6x23\\n6xWjukIbzepuycWHC/qmZ1RX7LZb2qbh17/9Svw+U6QoSp69eEauPWQ1XluatiVqS+cTjQfb3VHk\\nmrowFGnHYjbi9PSEFD0RTV3VmLJm2zSU9Qg7GuGVEregsqAPfoAwEyEIKSei6AYtlywMnqLIScrS\\nJU3jFT7Cpotcbzq2LuKDWA9mWpHnFp8SAYnVMVoE3zEyQDpQ50NHozVFIaQpFyNtJzMcM7iTtGHv\\nSMtQiKWbsYO+yypFVRTUo5o+BNbbjVDSgyclCcSNg0+tGsgsKUZU5JAjmWUZn330PRbThZhOVCP+\\n53/5v1CV1SChkRlv23f827//t5DEK9P3PdF7nj56wtFsQfCRk9NTcQSZTul95P3b17x88+ogPYgx\\nkuUFKQYxd4+R5y+e8/3vfY+UErfLO5arJYU2dK7l+5+84LOnj7hebylL2UiUZc10NkWnyGI2xVgh\\nr4xyy+npI7758jdsbm/Ea/LqCqvTYY5pjSbTQzasuTcnOMz31H2xScMivusc29Yf5iH7rk0PIcb7\\neeE9FPrPh2Mfvor6zs0tBhjisTqYMei9e9G99d1+Q7yfT+7P2ZphbjlII/ZOSIc59PDG+041hMSs\\nyKgLgx4exwebqP2Gas9Y3m/YZLMXuLm7pneO6WTMerelGkwJCF6MPbTGWFngMmNFgzn8O2NMg9RD\\n07gemxeU1nJalRybBH1HbhQmeAol8Wvee9q+F4Rnb0qhFZPMYFRi03uaYEBbjFGse88uRoo8w/Qi\\nZ7BaJEQqRHQSx51MKwgS4xW8J2nDrhfLSzl/P3g4D8YQmSAcpMSq6dm5QFAZR9MZwTl831CZROMi\\nd03LJ8+eELdrUlJ0WsZK3juuV2vIcrGA9IGkNF4rVJETkti/5XlBNRpDgtvrC0GRBn9lF0S8H9LA\\neg3SaeZWPL9j8PfBzkqR2fzARHXeoW2GS4lyNBLEzMumwCgtUYPD6Md7T6YNKgR812KcxyRFDoys\\n4dnJMeeTkjo3LJ1nXBS0TYtLYLoeb3JS3zELLW+XG0ZVyfnJGX7bkpUFZ9MJk6pE9Z6zEDjKM0Lv\\nqJVwMp4eH7PpOv76hz+kXK7xXY9VivqfA8neb0sjX/79/8l//L/+d+azCUfz2X4MQl3JMDkmTx8C\\nXd8TQi878SKnKHJMjNxeXbBabjBVBZ0I863OqCYLsrxAKci0+NBOp3OOzh7x/KNPZGhdVoxGFUVu\\n2d5ecndzzdXVivnilJ/+2Y95+epbQpCLP3SddGt9gx1IJb7dQQqcL2pm8zE6yxiNa2xZoG02eFVq\\nyqIcPFsVahgGmywjsxlFUaKUAWTG6ELEGCO7r731XfBoLXKFphtsvQ4RNoqoDG2AECBXgRAiJnns\\nQKzZLyreizG3aLnEdLztegyBwmhScORKoMCkFZ1zFGVJQh2YmCDFJiTR/fV+CE5V+45SaPnKSACs\\nPXgAq3vCTtyTQ4SE89Wbr6RDSdC7novrC5SWz2Pf6WT2nnytgOdPng8JKZ7RZMJ0OsNay/Nnz/jp\\nT37KdDwdSEaR1WZLVWV413Nzd4tzwtJ78ugx49GYxWwmr5vg+vqaRGQxn3I6nzKdT7i4uOabb9/w\\n9cUtOyfi767ZslndkWLkm2+/5d3rVzB4Yj46Oyc3BmMMKSSshumoZnl9DSHch6Ybcyjm2dvrAAAg\\nAElEQVQSeqgu903bvQl7iJGm92xaN5iq39t4iV3fYA6+xz6HD+k7c8R/Yu1USg2pEvuXePj6eyLQ\\nULiHx1qlB3DsvTesHmaVB0MGvY/jUg8cjRj0w/LeRabpfGLVRm42Pa0T4t+sziSLckBVxKs33RfL\\n4b8YI95HTuZztEqMipx+MDH3PrCYzWT+HqVLMVYCCFBK0nPSfYxXCEFs8aymNopN09KERF7kZECm\\nxFf4dFwzLwuyBBF5fhoE7z4ldi7ilaUdghO0tuJQZiwqRvLJmMpq+kFulmXmYHKhSKAVxmS0Xoh/\\nXUx0SUP0TMoSnQIhJByJ3ose0wWH0SId80nhlaLtHT5ZdErc3NwKicxmfO/pKbq543K5ZmtK5pMp\\n7XZL70SVoIyh0Zq6qlhvVpBl2LIgxkDwgd55ptO52OvtQXOt0Ano3b3mUgmsnDqHNZqUIkkrbF4Q\\ng8dmGRpNQtN5jy0yyroiNxLrl9tMOtM4BBkMdqQqid6zyMTDNQ6OHiazpCKnGo8JTnI91WhEVZZS\\nXK0ltA2zUY1GUVQFoRP7u/F0ShkCyjlGZUXSirzIMUXBbDrm8XzCbrNh6QOZMfz2dsnHP/oxk74l\\nbLYc5/+MtBKtwRjF6uINt+9foo2mLue0bUddFeRWdoVaJ6L3WGtFtG9y1tudQKEpUpQFaMjKit5H\\nYlDcLNdoI5Z1mY743otNU4jc3t1S1E4Mh+sc52RHM64qgWAi+LDj5uqazCSmoxFt0xwgn0wL1NXt\\n1pRVNVicBazSFPWElES3WFYlKSW6tme32WDzDKXENivLC9q+w4QwBLDKDNDHIF3PEKOVVSV916ON\\nEfgJMxgaJKwKVLmh95E0QLQBSX83A325DwJV7G26JFEeuiBf6MEQICmC8+hh1tmFKAQFrWhdxygv\\naLtOZCPD4udjOkRxyHw1DvIIhUVR5Dld78QXcyAG7Z8rLJWhA0ERgKvbC2L4HGP1oQO8urnk7Pj8\\nAQyrDrWgKkuePn0qbEbnOF4cM51MqOsR282G7e4K7wPaGM5Pz2l9pMwsL549Zzado5VitVryu29f\\nMq4rVps189mcm9sbut2W8ZNTUJazJ09ISTNanLLZbHjz5i0hePA7pqOCm+WKj148R2nNu1evUCg+\\nXF7I7KuuyFXi7GTBu/cfODo7x2jNZr1hMpsJqzEFokbMKvaf7UDV2ZNZYkx0PrLpnMybFCIdSHv/\\nSw5VVq5Tmb+p9F+vkQ8TS/7Y3x0eH34O92zUfWzSPWlqH4X18HXui6VsHq2WUGcplOYQ3ZUZNbja\\n3EPMkKhyw6bxBzjYBelWjA7i/lNKao8LEe/l3A5+wUORefb4BV+9+jUn0xl97yjLkq7ryTKNc4Gi\\nyNl2Iu3yQQw7+ujRRkTzIYoOeC8B6npHZTU6RHIDLsIk1+jg0CQ+7DqSKqgzK6OTGLFWYZNj3Yku\\nt2s70JrCSOauIG6i460Q68ioDcmATveuWSjZSOvM4Jzc3W6I/ip0Bl4QLWug7R15ZjExMSpzSgv5\\nAGGutg3zxTF9AqMtRVEyLiw3NzfUo5rHpzPe3LZ4Y4lZyZOjGe+WW4wdM6oK2s6x2+yYTEcUSYuN\\nnTUcP3rE5cUH3OCSpLQm+ojWe+23gyFTVOQlGVWZ0/fi+NM7T2YNKkoX6UKPUrI5mZU5AgrIuq2N\\nsMpjiAOykSAO12JSNNsNWmu63ZY8z+iicClyoCay8Y512zKfH7F9/5ZyMsXGSOwd5WLKedOzfv+e\\nNkTMZExR19yuVjw/L6n6lmwLu64l2YxQVjyazVg2DZu+RRUZ/+n1a549eYrvWtw/J97LKM3y4hWv\\nvvh3lGXJbDbFWoPvPU3XEYLoDWeTMX3forFsGzE6N1VN2/f49Za6qijHFSOb45d31FXB3V0L/Y7g\\nZ2RW6O8oRZnlJJv4cH3NYlpLsGmKtK207DGrqay45HjfQTbG04IS2zirDdF3aFsSe2F+KWPo2p7U\\nt+S+Gm7+nMxm3N3coPMJqEBWVBjXiilzJhqrEKWQk8Bay2a7E6agNSKmj/GwCw4xCrQ6kKQkuV3I\\nNNooMqWIXuGRdPZMRfIip/UJHbXEe0XZAWcmo/N7uGlvvwdx6LoUMmdKIdC1HVVZ0dzd3nc9g1xk\\nT/AR03Y5t8xYiEkYfjlDHuNgmjx8D4kofrE+kTQQ5N9jrRWILUaadsfvXn3Nb1/+hjIv+fmP//JQ\\ndCEJzK4U3//0B8NMTQrsF7/5gjyzFEWBDxE7zBNm0znTasLYB377u1d0fSddZyEz5d1myWJc8eLx\\nCS6esGl72qZH64yvv/6SPMt58eI588URfd/z4f07fvLoDMyON69eMZvNmM3nUrBaS+s9H15+S1UW\\nlFXFo/Mz8QN2DqU0m+WS8XwuHRhKOrLDtOYeZoyJAR73eC+2hBJX9bCYqfsioTSBcA9p7otb4r7q\\n/d7x+36y300xue8m78/wPl9w//4yZ9/ncaqHv3afPKL2Lkb3elJjBjh5gHGt0fh2hy1rEpBnQnTZ\\nb8YenlpM0LhI68U3WWQ7Qcg8g1MUw3VqjCHThqSg8Y69cXZS4k3bdT1hCGgOMRwSgrquI8ssboi0\\nMkbuzYxIbgxHlcU1W/LMoFNg07V0eUleVNRWjMhHOLCWzEDnNbsQMUpLFz2I3RMwq0fE4Hk8KrnZ\\nbsGKc1lljdi+aU0Kkail+81sxrZtiUCGFFiGkIgsSdh1RHxuMyPktxBhmyRDczoecbcR1nBlFefT\\nkRSTqmDT9YyrEc/QvL7b8uHO8MhERjoRux27WOC9p6gKrM0G+U5klJc0TUOW59RVRdcgBih5JjPF\\nLEN1LUYb+lai+4KSdU61HRgx9ZBrJYOk0EkGQXsvVqMNUaWDpzZKPKz3kWIpBXzfk+cFKorz26iq\\n2Gw3jEYTCFFQteCxUXGxaRjPxrRtD2WPrWq26w07Zci7lnazkc3F03PaGHg8nXJ1c4fNIOUZk7Jk\\n03c0fc9sVHP1/gO5taiY8Fpz0TSUAxv/jx3/DR2m5ubdV/huS5FnED2LyZzOOwqrDmyvEAMxwuJo\\nwebmPZpE8pGT4yN26zVKS4pJigHXy4fz7OkLdrsNfddS2PqQ5xi1Jc8sH78Yy27GWHbrNX2zw+ma\\nupIg47Jy+JDx9uKO89NTXr97xQ8+/wmZ77i7WDNfnNBtBl58DBR5jguezeqO3js22x6bZyyXa8pq\\nydHZMRjLzdV78rKEQeahB2cfBqgyz/cdb6J30nUmbYlIYseuc4MN2l4TFw5kHJAcxJCAEHE+UaWG\\n3iUm9ZhNL3BFRAy6lVbiuJHUQU+njYig916JKNh0DdPpDKWGyK0HK69OCWUNbiA57MXoISXarkEP\\n9ngxxgNjb08C6nsPWlIfYop0fct//vV/ZLVe8uzxC65uLwUOU4nTk3ORddxeoRW8fPUSbSStJARP\\nWY3YbNYorSnzgt45tJVimeLe/AC2Tc/q9opN04hUqCx48ficcVVxc7fk/cUlKM3i6JhffvFrjo8W\\nPD5/RJ5LgO9qtSQpze31NX3XcX23IteKwlqsSownMnN//+49o/EE73o26x1v374j+MBssSBTBud7\\nupQYp3iAIPfwpcBWGkM8ZAQ6v0+BkUIjKgV1YMHKxlq6yjSQcdTQrRyGxb93/P86SPWHq6n6Yw/U\\n/cP79xPWq/7O66VhfqmxWhCavf+uSI/uzeaVko2VLSqMknHDJDfsOse+cX1IJOLwDkIA2vYBjZhU\\nlJl0sy4kfBASzLZrOZpN2HVilp3nGbumpci0sFq1+A6DdDuZtfR9L56oCnxIoGFejyiSF8F61MyM\\nIQTHbedJJkMhRJAcT9KBYDJAsWodbZQPzg7FsvGJSCSFSKHkPtEK2ihzUGVzCmPpe4cyGegBiUKR\\n25zeC7pFCrgkXsh1lksz0HUoJXpdN8izVEpioJACO22ZjUtW2waMZHbu5+VN7wk6MK0rpk1L2zv0\\nOCeLkc2uY35Usl1vqfMZd6sV47omy3JZV8JQIJMkQWWIRZ7NLH3XkmcWlURSYpUiBUEPVIzgvMhx\\ndjvx+81zkvekZGj6nil7oxW5roP3cj0Y0TEH72S90eCdw3sJ4I4hUJUiESrrmrys0F1Hu2tZ+x6S\\n8Cr65ZpYiB7U7RrmZUmYTmVei6JPUM4XVD5xd3fJYjbh9c0dIcsxCrqu5+dPH/PN7SWFBqOixDYq\\nzXrghPyh40/OMN999Q90qyseP35MXoi2st2tGenEyFoKayiNYlzXtMtbdEqcHp1Q5zm4lst37+ma\\nViCMvmezXDKfTFDK8O71S4oso2maYfem6TpHt9vQbtdE54iuF7bZoKFZr1Y0LuBNhVc55WghZB1l\\nQFmulg3YUhIFGgd5jU4SYROSOOmjoKwqTk7nTKc1n3zylLPzU5rdhqKsKOsKk1tJQzfmAFnsIUgz\\nuOjEYTYZosg9VEp0g2YxJmFWlpmVAf8wSwTZRUcFfYJOKVbe0CvL9a4RWUeIQwei6KOYvqHkInBR\\nnGckHkgRELZa03WUeS4LkTUyL917cyqRqezX0jLLyYyhsPLvCSEdPBdDEOh4X9z3pBAzJMMXecn3\\nP/kBfXDsmp24/4dIbnJOFye8v3zLr7/+gt+9+R3jUcl0NMZHT+c8mck4np9QFzWfffo9YQSHMHSt\\nBoUUkcYFikwzqwseHc/55MkjZrMFKa9wSfPm3QdSCFxfXjAa8ky//fYluc0JztG2HevVms12y2y+\\nYLPrMDEwKQx1ZtGuRwVPWRYsNxuq0YSsrvjm7QfeXlzz6tVbmq7DRbEf9MPGZS/u388jjdVDQsw9\\nccVohMBhRF6yhzBzo4ef388I9x3dd0ML/vuPvYfxvo98yJJ9mB60f64eoFVzMIcfPG/13ktXD3+3\\nZ/0+eLOUHr44gSRjGcAPRUYs9B52rw/IQfuXQTqpTRvYdnJfjErNfFQzG41o2o7e3Wezeu9Z7zqK\\nsqIoJc4pDgbrEjOncN6jlTg9lXmOb3ds+55cJY6sInQtWmlqa9DBc1JZgT7zjICl7Xp2zmOHPbY2\\nmlEuto/WKkqtaNsWFz1JwZ2TZKSYEhboQ0BbKyMXoNCGQmt8CqgkcLExFucDRmu2fce66ygHDalN\\nQo6xWkLfRVKj6L3oiMdVSa8yti6x9nC1C2ibsWp6PqwdLonsBmtR0RNdT4oi6LfGErdb0W8ykDm1\\nweQFbdeQjAEtYxYtmL0815hhzj3M+G12KJihE9Zs53qi0WibMZ1OWF5cEZOEYGR5ie97XN/T9T1a\\nSxceByOXlKQDT1HgWxcCaHEgC0DQEttVakVpFHkMWG2ZHR+xMYrSGHIVafoOWxR0mWVpcnYh0qZE\\nVRZ0fcd223I2mTLxDtv1+L7j/d0NySVmdcXxZMwi19jQU9t/BiR7/foXVGWB7zuePXrEZrNEG8Pd\\nzQ3zyYi8KMEH3GpJbQxZVnBx9YGsKJhOxvRdN+iCAkVmUURuri45mh+hZ1Nc3zGpC9abDQqoyoJd\\n31NPFmxX10OIaU+V5dxuGoJ3dG3L5eUlJ5OCL7/6mtH0hLKqOTk54fbqLTou0NWUaHNMcmTjBabf\\nsb66YjweUcyO2W7vqMp60ERBSAFNxGSy6BIDWVnhU6QsBSaoypIsy2idOPW3vYiZhSxgcUMSRUqJ\\nOrds+57KWpSxrGIS2qoebLyiORieKyLWWFJMuKTQmcxCtZbkBBfvbeeCkkUrhYg2ZugwDS6J40+Z\\ny4zHKA1moLOzn2fGg3O/UbLgK2VIxIGMoThIDpCMQQZyxtAc0buev/27/xujM06OTsms5fruhn/5\\ns7/i4uqCq5trccBRmuA9Zkiz7/ueR2ePxHpviN05Oz7FBwna3cN5WsGoyFBmxNxH3t6uybOOYjxH\\nkTg6WnBzc0O7WnJ2do7NMjbrDU3XiZwmBaz3LCY1s7rg+vqOfFSzVYkyU+y2iBOV0SyePaF3nqu7\\nFZOZ4fhEzDBWyyXdzS0n0zEpK2i3DeO5MASVHpxzkkS2AaggBcFqQ7KgQxRQI94XKzXM+2KK4MEz\\nBCnHhFL3gbV/KMZr300cesF0D7r+3h8POrr731Dcd5YPz8Wqfag1B9KPUnsN5p4Zqw+/ux+H6wjJ\\n3r/+KDNsO3/olB9CwIdzU398SxATBx1nbhRHozHaiNvVzXpDjLKh6/sOZdS90cbA5DZGDXkn8p4p\\nJWZVzbzK2G4bTmdj1G6NCpHGtxirGdcFd7uGHsuisMxqkZp0PrJsRRYyzg2rnXR1s1xMEeqqoDKa\\nXdvShkhdlhhEZ9p66WxTGsT+MZIVBbu2FwOTFNn0jiLLiN4f9MpN74kJCT4nsmx68TlVCqMiKXbE\\nVNA5QapS9CQlQvvoZaOSA6PCorrA9uYKa3J06qiMxWQ5d8sVpbI06zX18Qlut5ExQvDkeYHREPsg\\nbl5RzE+qaizQchSpzj5MXvI4Je1k7wjUe0+lDVk54tPnz7j4cMmjR6f0oaOcLWi3W9RAMCxMTlAQ\\nvB/8miVFxhiN33exqOH7tlgU+MB8lHH75i1t29CWGWdHp7jrK7rlkmw0ps804+Mj1usd0Rg+NA2P\\n5sdURcGb7Y4Tm3N0dEy6u2M8m7DILG/efcBtNmxj5GxcE7ueQ+zgHzj+ZME8Ojpis96w2i3Jk8OF\\nSD2qGC0W4D279QbfNRRljXOO3vUEpTEpEjGSTBADbdvhgjDBktK8fvf2EFU1nswoM4PzHp8Sq+Ut\\n69WGo0kFVkgvPnhmiwVrJ/ZpMu7RnJ2e0gSFKseMbEbSGbeXb/n0+5+zubtiGxV3qx0nY8O4zNlu\\ntsxPRnjnadNOoDVjhLDjOlJS2Ko6kHYUsiPP81yo584Ja3Zwwwjek2cGnxKNE6jNKJkZDlGqxCj/\\nl2catGG36zkej3mz3gzuOgrvxdtUGJdSTIlBWJWoQ4cji9YQDzYsi3vfynXTUFcjVm1HSoGHPj8J\\ngdf39H5tDbuuASUkKW3EVi5FCKSDuN4Fj1FG2L9KUxY1f/Ozf4VKYG3G6dEZHwc/FMAzbm4vD0kR\\nxWBA3/nAX/zZXwJqYN3Kov/s6XOGtZTcSuyRuMdEdl5RlQUqbUQXN7B4M5uJAcLNDSbLqSZj8txS\\n1wvapmFcVlgifdOgUqSqS15fXDIe1Xz69JT3F1fEACenx2w2a0b1mFwbovGk4JlPaibTKUYbmiEw\\nPLcWhu7XoFE6osT5e/hcFVYDmcZETdBhIFcNs0EtxSKmhA+KqIPoHI18PyEqUPHwff6h42EdPLBh\\nh8fp0L7tO7nvcGQPbN4DsUfd6ywzK8Seh93uni2771AV99rOA8M1ylx7lNshkFwge2P3mu3Du8tn\\ndzgX9Z1z20fL7f8xMWmUyfn24j15ZjmZTUmIU8/lrXQz3svvKKOHKDmN0YGUDDGKFefj6ZiJdqQu\\nstk2KC9B0jpEgZgRa8rxKOfy7o7JbMFyvSQoyyi3A4HLU2cyykBnXK7XVHVN6wPO5vS+Ya7VIE0Z\\n1gokxo1hZFJkGT44QvBk1tK5ntzIdawUWGSjmFtN78TQXStZb/q2wRYFk6og+J5SBW6aHq01o1yY\\n7RFDmSmOqox+13CeK3QvqS6jsoSU8F2D22x4+vEn3N1csbq9papGBN9SZyWua3BNJ5v+0KMGo3ZB\\nKwROtcbIKKpvhYjVNvTeE7QlrwqcUsL/UIrZ8TE+JpbLDYvFlBQ9RVHy6vUbTs9PqMtK/HtRGGTj\\nvrq9pWmE5NW3Dcbmw2eW0EWFLjKmMdAaw3g64SrCSIOOEVWXpIFs1CRDXlcUNmPVtPzy5pajoxOO\\n+vesXceq2TKuSy5vrtGLY6qjBad5wd1mhY2B9a5B59kfuQv/GwpmnWecvXjGarnEupZZofBdTwqJ\\nXddQF5I8bqJHG4WOEZNntG0Hgyh+NB6RlxkxJSazMSRFCAtc33F7e8uHly958uhMmKcKjo6PUSmx\\nWm+p7AibEjol7lY7bDWnyODm+gOpn9AH2O62xOCZTOdMZ3Nc37LuIo6Caa1IzRrvZUYSU2S9uhEa\\ntBkIB4MTjrDEJGUjDrtuBrp6kedkWU7bO9a7hqREtOyCkHpckEH9nkno0rAAROhTOECtWoscxTdr\\nPjs+5rdXNwcnnTQETIfBUitEkZQcFqsBYjXDLt4qhQMcUqRXTcOkrobXg6Q1auhWwkCE2G02gKI3\\n7uAApNRgUKwMLomn5B5i3EtEtLUUtuTRyTlff/slN+sbPn78KY9PH7PerPjFb/6RLMvJTU5CoCtr\\nMx4fPeaTj07+4LVlVBK7P2QG2MQ0dNJwe7vkZJLz6GjK7aah2W0Zj6corXj+7Dm3dc3F9RXzJOkk\\nu82K5ycnuKblannL0WLO24trnn30ERHFerPl5dtLPn3+iN98/Yqvf/ea87NTtrsrprM5sQsoJGLp\\n5uoNRVEymc8IA8yXRCEgBXIwTpfYo4S1GpTFxCgGHtpg432s0N4vWYhggRCG4pSE+LBPGPmvUWUP\\nJUXtU0eE6q+S2p/NoKV8AMGqe9P1w+xS37Nl90YEezMGDUNneY8opCjd20GniRR+BklVkWmWOyHF\\naW2kEBH3dZK9mft3jwN+fOio96xsq2E8mZPef+B6ueLdZc90VDEdj3hydISLkkjS9D0xxoHApAeS\\nlgKjWEwmFEZTGkssckxKdD5x2QVmVY43VkYAzrFqHb2y4HvGhUVrzaYPdDEyH9USn4Vi3XjGkxHv\\nLq55+vic1OwoioykFE0c0lNCIFOSA6uVoo9CrrlY3sq9bwSu7/ue6WxK572YawwbqpAkT1YbRdPs\\nSMMccLUTYmVuc+ajRNM5MmtouoA1illhqLXoM4+P5mxuHavdjno2J7YbvPNC3HG9hMtnGc3mDltW\\nggJpg3NbYfoPuiSRjWianaS97FUBrpfQ+aqu8UsJATdK43qHy3Nh/aKoqpL19SVdVaCrmiLPOZ7P\\n+c1vfssPf/AZmc3k/Z2j6z1VPSZzjna3RltpQGKEJgSaJAESI2uJRUbjA5vNhhdPn6HqmovNmn5e\\nMDqac3u9Ap1jgLqsWW43XPuWF2XBdrNlXBW4zY4nkymEnvfbLePFEbM8Zxcjnz17we9++9UfvQ//\\nZMHcLG/JQke323F5u2Q2KjgZj/EKZuMJyfdgMtZNi80sp1ZzdHTCbn0rN1y6z0r0nUORyIsCUmRy\\ndMRkPBoW5hxlDCkJBLDbtry/fMlRsmjfYuoZo6qk3y0Z2YzZ6YRXF7eYvOQHHz+jbxturj7wIUSa\\n3Qb/5h1PT+a8v9oynkxkuKxl7mFVoihKSeDQCu88Ns/IbCEFs6xotkv0kMruvJedsxIvzaosCQma\\nzmGspJb4+MB6TEsn1UctySVBusgsQW4iZVXxfrcjvH6DygtZBJUwWA9gmxrCD5SiD7LDU8jz9ulQ\\nfRK7vaS0GBC0O54cHaOUiPKlownCTBuIKVobnHNorWQWkwQaVEmz69ohbFqLDEKDUhGlDEorqtJw\\ns7ykcz2ZMXz7/hu++vYrqqKkHyJxYop88uxjFLLwHi9+r1imSNus2W2XNF1P4wRuTjEwGo05Pzkn\\nLwtOzs74+te/4Ieffcxqs6PZbiTPMq85OjrCGsnZjDFyfnLEhXfs2g5bZFzcLoXaHzy+bcmLio+O\\nz7i6vMBrw5PH57y7XvLm4kJCspPi6uqKo+NjxpMxL/7yz9lsN7heXIXq8Vi6KiXdRIx7+I9DAdLK\\nEJMmmIQJAnHzoLMLMeKCdPhexwGOjUN7+IBZOmxw/hA0C2owyJbDoIj7wnSYg6ZDZTt41SKbQpmb\\n6sG5h8HubjDCMGbwLJZrLLNyL4IkX6SU9lar7K2jxrlh3Qo3IClxbxFSxkNk4wEU+6C7fFhCD89X\\ngsL4UPD4/Amr9YqLmwsg4/WHSzSJyXjErB4xH41Y7rYsNxvc4EMrcJ54xGpk850HT17mKA/UJZvN\\nlul8SoiReSm/86H1bHtLlpe07VZkYV6yMHe9zPpKa8hToC5zxkaxVpZaaXSMJO+weS4wvA9kWSaR\\nWEpQEz/4svZdR1UUWG253e6w1oqHVgK0IdcWqzgUyqjFbSolT+8cDiEqFXlGCJE6N1gcR1nG+1ev\\nqY4WvP/wHkJgNJ1ijEj0Aon50RHaWJHFaE1tM9abDeV0jusdZTWm71q87wnOiXlLVhD7TnSMNjuY\\nPDBsUmIK6BRJQWK2us4xqWua1ZKqHrNwPTfXt8SjhK5hOpvy4x/8iC9+9StOTufMplNym5PnOV2z\\nQxtFPZnTtzuWyzVaCfRdVxVFXnJ3e8NkMuPuzRuU0jSbFe1yydHjR/z24pLanOKUJjfQti1FXmKV\\n4nrTc1EVPDk6YrW8pR6PyBDj/U8mczbtDhMSVZ7x7uaSk6fnf7Qe/mlIdlzR73aUSvP4oyds1mva\\n3lGUlsJqtn0Q30cFfe/wfUdEoEejpQjs2a9ZVrBrtmSFYb1Z8u7yinE15vTkiK7ZEvqe29tbyb8b\\nj/npD79HMZnz8qvfotoV2nrmswkkWO0c5eSIdn3D65e/ox5PCdsVpVUs5iXGiumvihZNIMsseVER\\nQqLZbSjLGpOJM0ay0sNprelCd8j5jAONWhsjob8poo1oiVISPWNpDFEZur4bBv0JtIVBd5lIZFbh\\nohS+IvQ8Ho+5bjWqKAR66XspZvuOF0hhn2go3rAy/pRFGy0kolwpfJKdqU771HhNaS2d93SDdlUP\\nhKV2b5Jd5LJEp4TRsntsdzvSAAXtpXEo2Rzs2bg+BCStQopqluW0bSdpFeMxaMlInI5njEeTA+lT\\nwUB2SXzz+iXbpqfrPb3zEoGlxE5rs2tIF+94/vwjjNYU9ZS+67n48I6Pzx8zNuDwxGgpqhrnWuZ1\\nzermmvOnj/nil79itljw6OlTri8kEuxuveGzH0twwGQ2IfjAIs+ZnpyhsuIAPX4cPpUzTbJpmU5n\\nxBjZrDfEIOL5fce17+ysUUMeZgIjBS6EiDd70pR0VykmQtSAJyR9IN34w+jyuxUBwPsAACAASURB\\nVDAl3GsupRDK6+ihWdP3TxpYqulBsZRubfASOswr93MhpWQDK0bqe2MCDkk31qiDtChFT1mUxG4H\\neXXYKJBgVAixrHHSIR1IYnFIINoXwpQG2Fg2G/ui+XAmuz9yoxDnNMXR/JiTo1M+evYR//jr/8z5\\nyQkX11ds25Z1syPLLeV/Ie3NviRJrjO/n5mbr7FlZGZlbV3VQKObIEEABKWhOEONdPSvS0cHEkeH\\nIMENS6O3qq4l91h9s00P1yKyGhyoHxRP3VVZmR4Z7nbv/e635IaPLi4YRstmv6OLskOscoFUrzd7\\nIjlq19GNQg4atcFnBdZZBgVDZzFVQ1NoeufoLLhM4bRhqjMmJrC2jqBz3m53KGO47qwkXQRJBDIJ\\nkp7khp0bRb6vIMtzRiskkn4c2QWPjYFxHCiNPHcuRupc8njzTGG9x6GY1SW7rsd5iSSbVCWjjyky\\nLjAtDcb2qHGk3zh0iLTbPU+nDa9ev+b5R8/p2pYhMxRNTXCeoW2l0I0Wn+LIgnfkRjMOHTHEo4HJ\\nOPY09YxhaMnyDIwhjpairLi9vuFkMSeiKHPDMFooS6y15EXFwIp+6CnKktMYGLuBHiUSPG/56U8+\\nw48DIUa6XlYnZZ4Rg6Lf3VPVEz7+6DnbfcsyO2Vwsqt2bsRrzfRkQa4UT6czXt+v+PbmGlOUDE6u\\nfbPv0KXhWVnx/v6OqOD8bMnQ7cjRhN2enTHMJg3OjRiveDybcXt3y7QoGHb7P1kPv7dg3t/eM61r\\nhr6nWMyZVLUYZg8d22EvCd2moK4b1n6L7VqKyQw37LFWzLWn0ynb/Z7RjcxmE7JMszw75VwrIGN1\\nd00WI5O64cmTx8Ioi/Dm3TVsNT/8819we3MNZUM1m+KsZbj/A6aaUFWG97c7VGb46NkF9aTBuxEX\\ngpj2xogyGpXnSXwfaepaZCFG3GmG7faY8pGVOc30NAmgM7wTdqtSGkJgHGXPGqLE+RzQtKA0KgZG\\nG4Q6HsT6TqUp+6wp2IeS19fXbLqBqCRtYRzHByguJhmKOuyS5I8PQbQu7S11EI/Xzvl0yJMIQIFN\\nt2c2mbG5ucI7J9luWtMNwxHW9Uodp/4kI01SlA8OOpWiwUIk6njcm3lncSFSlIYYA0VhqMsyWeqJ\\nbnO33zGdzIQhagQG/Nff/obT00e8enfDZDLBh0BmxD2nazuePH6K95672xseP3pCXdc8e/acd29e\\n8fTinOuba05PT7i7u2J2/oT3796QoyibGafzKXUz5T//l78DYBgGFidLlqenQJrUAqhkqZaZGad1\\nzu1uJAT4/e+/4vx8KTuhCFVdsjw94fPffI4yhqtrIbjVTUU1maKUItcHFFVBFGgwAlYrTJrG4BCf\\nFlAeQtRkPh7JN8nzRjIlP6ge34Ep5U+Ok6qYMB7/+IO/hYdJ9bsbxOMAl4rnwQfXZLI/yrQUcaVk\\n7x7HHlM36LxCoZhUFdc3N8wfXeB9JMugqQz3+/F4jRHZrwu+Lw5CD5X9u4SfiEg14gfXuVrd8fLp\\nI7rRf9dCD+jHIRmmNFhvU0C5ox8GrlYrJlXF6ckJmYZ93wkTNSjuWkuInjrXvN92LOqCsqy4XG3E\\nbk9FrgdFESyDU9Rlznxa45xjN4osBKXRON5uttRFzdN5k64nIyOQhUgxqfBWZGqzpmbdDXgUn57N\\nuNxIQHJMyRp931Nkmt6NTKoKFQLBO0KSvHWjReUFrfXURYEKwrwdrGhPK6NZZpqcwN3trXAwpguJ\\n4tMZb99dcn7xhF3bU9QNtuspQuT97R3zFy9YLOasN1uigmUaPrx3GJPTbbcEJbveEAJKS5pLQNhe\\nmckY944iN3TDwPL0FDuMoPTx3PJECIqIO7r5ZJkWC8NRfJXjKHtYP1pmsxmr6ytUMIyjZ9rMsGPP\\nzV1HUJr5fMF+3FPlBV3fou2ArkpqpWjtKOkrzhMzufV8iNR1Dd6yco5mUrEaOz6/WTFXkdnpGScq\\nkA0d/W5HfTKj1obeeybTCURFXfxxK/fw+t6C+fLZc4Fl6xLrLWOEdr8VokwQBqXd93x7ecVkUmN2\\na85nc6IdE1kg0na9sK7ynKqcMI4D223H2cmSq/fvyRUEfPJi1QzDkATAYIYbfvvPb1G6xGhH24j/\\n4bTKyAuPNxPqssAPA9H2DHsvzNekITNNMtJVGnJDdCLGJTOYqmS12rDatcxmM2yeMew75suKwXmq\\nLMcHGQOcF4aXi7LFcgEint56Bh+wyWA6zzTeBgYnZKGmzPHjiMJzWhj2yzPWQ4+KhyL1UKgOjj5y\\nz4U08MjuMcbD4SpyFnFekbSAYTwYpcNmt2c+mfA6GSeQJsNMa2GzWSuQshfG3Tg6ISBkorm01kon\\nmKanw/Sqo3TMPkaqTB8n1HGwaOVpu57TkzN+/MPPKPKc0si0bH3k337/O9b7HT/84Z9xyD3UWtPu\\n9/z1z/4aYzJ2mxU393f8/Kc/IzPF0SDBo3l0MqPbtnzz+lump2dorXj58ge0XcdmvUab4hjyLISm\\nnPnyNNkdpiKjDxFrYD10o2fZ5Pz6t6+4v1ujUJwsplxd3bLbbTg9v+DZxTn36y2mMvgI1nrcdoMx\\nBZkRM/bgA6v7FTc3d3zy2ScYIv0wUpS5iLy1ONRASCzgcGyyjjXuCL3GP8mSfSiS+vCPeIBxkUk2\\nxu/4uyr9AelIye5Vqwd/WPElVsedJVEaqaosAWGm7jdr6smM2XxGlRusdsyrnE1vESuHQ/qmXI/I\\nmBIpKfCQ95l++fH41TJxHorm+XIp+/vEoA6pSdztdxR5Rttuk0whp+t7jMkggyo9k29vrskyRWky\\nFpOGxXzOq8tL6ukM73p0XhB0TswLiuj59n7LyycXzJwXPaGLXK3WmLxI8i0FKgj8qA1GOeZ1wbod\\n8CpSjI55psiNph+sMOSVAh+ZVCXdMBKzHKJLUi1oypp26DExpnBjTUzN9eicrIZyybXtrUMXOU2e\\nkcWU1BIVi1wxCZH1dktTVuhgcWNPVeQURjNfLtj3O4LWuEFzenLCm7dvefz0Kfv1CpNnzKdT7u9u\\n8TanaSqsC+A9KgVft76lrGu0hr5rRb+uRibzE7754ksunjxmv+9wMaKLgoMcTGcZ435HZrTo7X1A\\nmwIT/DGT8nD/yj0e6HZb6qYmRsWiyQneUWQN2TDS9j3r+zvRVsZAaQr63Y7JZMLt/ZrRezqT8/Ts\\njFfrDb11WCVuTJnJOC0K3t6tUq7xSNHUPNGBfLTc7XuWixlD2zOZz8h9pO0Gmuk08Tz++6/vLZj9\\nYKlmS1SwvHn7reyyylJgtDKXrDWjeTSfCkZuB0xe0A1DcgcROnW0luAcwTvu7u6Jw8C39zeUVcVu\\ncMymFaOXvUNW5hidi8/hpKGuCrQxvL+6ZvH4CbbfClO1LFBObOp0ITvQI0cedXSjEWG8JwRDVCLN\\nqJua3o5MTuZUs4lMCiYXR/4QMWWFTc78KnVQ0UvXNfqADwm68MKOHRNc1ft4PNcKBbmK3NvAfT/S\\nKysmBOl3e0i10AnDOuomkzBZvo/omUyWpb2i/uDrfCrShjEZpl+t73lyeprITA8HlA9izSVFUrr0\\nLBXeEAIxgEmMZNI+VSA70XuSZUJKULL7MlmGc4G8yPEu8Nc/+QXBD6zX10yaGb6aic4yBt5eXlI3\\nBf/ym39JB5DmfHlGefGUtt3z6ps/MJstcM7y1Zd/4KOPPqKopmgtLOir67c8+egZv/r1v/GD2Qlz\\nldH1PXlRcHb+CB/B+cPvhKNEwjvLN69fU5cVVV0ync2Frm8y9qPEFf34k6c4Z+n6ke2u5+zslJPT\\nE4ILWOu4u70hoFkuTtjttpwuT/j8yy94/vyJwGgJUu/7ga//8AceXVzw6pu3sgIoDE+fPcGUpRhw\\na41Ke0SSJvewa9RKCs1D8XyAaQ+pJg+D5MO9kdaTqWbqI/R62J8eXH2y48/mGNdlUsE06kEKEiMM\\nfctu3/Lo8QWTuub92zc8/ugFxMhJnTNYIbjJj9cfXO3h+gSCPVB4lX54WyrdkPFwgel5qCtDN/rj\\nv9cJqr9f35KnJB4fB9FEH57tNDGHmJy2vKPre3Z9jwuB5WxOKAtGZzBk3O52PJ9O0XnBrLK01jE6\\nmE2n5O2arppgvcdUFcY7gTEBN45kRS6m4uNAaUrafs+0zLGZYghCkGm8x3mPTRIt7wOjCxRGM3Qj\\nre+BiEork6bUbJzsX0MQTacMc+LbmsXAMHpKDU0BM6BA0bYtwjDMcMGBs2R5yfXVW54/f8KsOmW9\\nuedkMefq6oqmmbCczbltRbq3Wa844OYxRPIY6KNi24pbUp4rMpPjnCU34hG7urmhzktxOtvuKCcN\\nh5UDIZLlmrzMUcmdZzKdsdtdo5SmmjSpaQeimKccmNj9OFKWM/phRdWcc3d3T65gNptRRdgNPVVV\\nc3l5yXK5JAsBP4z4vuPaOpYnM9aJ3zA4SY0RtEdWGYN31JlBaU1vPWNV0OQ5Mc/onWPaNNjdjiwF\\nng/tnsF7/tTr+83XlaLvOyKaH3z8Qz56/oy8yFksl7IjU4pqMqWZLyibmrquyMuKoqoYByvp5cNI\\nUZXoomAIEp01PZlTTRpMWTBbTNG5QWWamGXoTEzPtSnYW0deFRiT8fL5R8ToKOqaLM8TFCBFRBkj\\nb1RpTJ5j8pyybtB5ASpDm5xIEsgrzdB3lGWNMfIgHCaQ3nn6saMoapwPqTjKjWG9YP5IT0VAsbOB\\n0ctBrbUhICnnCtk3RCArK3ZobIJcP3wJNu/END4Ik1a0SQm3t05cdnxI5JxUAL0kDAZgGEdJjo8p\\nKNc5JlWFtVamde8ZR8cwjEeYyznRVMnPF4ed85NzgedQaJUlUb7o8Lb7vUx9WYaJiiwKoWFe11yc\\nLHh39Zovvn3F1+/fk+X1MaA6xsgvfvpziJAXhmldM3Ydmc548+4N1zdXZHlFjDBrGoos4+rqinEc\\nAEVVT8hn55ydnfK3/+XvOH/8RPSkWS7WiEH0eC7ENPUnTWSWsdtuabKM85MFeWZY36/49vUbbm/u\\nAMW//vYrbm/umDe1hPjW4oAyqyouFjOmVcXLFx+xPDkRuFIrdrs9jx+dUpY5XT9QVRW3V7cUGWy3\\nHe/eXfH06QXTJidGuL29Z7/dPuwKD3vFVLh0knJ8ADQcHrtUiNSxqMq/SZOjPhB0HorqIT7scK3i\\nCauO0VyZOrj3qBROnqzwsuzoDRsV5FXFxaMzNquV7E6zDKMPe0vEw1il76eEfHR4T4fXQRf5ME2k\\nSshDw3j4j8III9z5FKOdqqobR3bdHmOytHd9YKAGJwevDw+WkaIdLHHO8vnr17xtt3Rti/eWoih4\\n9uQJ88mUTClKk7MfLHdtx6vLS64GIDNkVU3lnbhwRSTfNy8o84yMyCQvwVrmZYULsO2c7ERDECmE\\nE/nVrGkknlArVPQ0Rpp2b0dQ0OTmSAyy1uJ8PIZd55kmj47zSc4n85yLPFAjXr259+y3WzSR7WaL\\nQizonLNMpgv2256722smzYS76yuMMbhRdOuz2Vw089aS6RznA8PouN+0xAh51TBfLEUuFxTRObSK\\nXF9f08ymRCUpSXme48YxuRplZHmG0hmlKdAhMAwdKHj06IK27zFaJwTj4T4+yOLwnrFbE5wgkbXR\\n1HVJ33fsdhuilxhHpQu6tmO92XN3v6KuG+oYUXak3e3J84K6KtK9ICuS0TlmRSUcjgSDv9p32Lxg\\nv2uJzmNUxvrmXlZVPoizWPGn58jvnTAv374BAiezCUNuaKYT6qkIWk9OT1MaB+zbjrHvKXPD2HXC\\n+po0dP2Isg6bB0yes9nsmVSlQKaZTg+qxo8jpiqOE5dScHJ2xn67FuPiGHDjnmY6o5pM8SF1mCGS\\nxQBJDGHdgA8OVJZILCWjlV1LDFHgWCWHhO1bsqKkyHO2+5YQLXXd0HYdZdmw3q6IaIJSjIlObX0k\\nKpnCRi/i9EgkI3I+a7jZ97hUBHUhJtPbMR6Wmcdd1CEbEB52NcepIrlsaCXdsyISXdKofTAJxMS+\\ntanAHmQnN6sVs6rm8uYGomIcRFulonSURHkvTnvJAfRODNyHHpQ67hcFLRQ/y8V0gfMjjcmp6jLl\\nfUI/Otb7lvvNDu8dP/+LvxJ7vbSj886z3285XczZtR2ZMjw6v+B+fXfAAclzgyJiR0vb7qmbGS6l\\nOyilWCwW9F5zMjFcbcfj7yzFhh6JUodC45ykv8znc379hz8wm50waypKYyjygnG0fP3Va0KE+63l\\n42dLTGZ4c3XL2clSQgAy+f5lXrCYHvbHkd1+D1mRdpSRz3/3e0yZY/uBsixwzvPt6285OZmhtMbZ\\ngf1WIDhVlEev1ixTmKAZfUIT1KE8hiO8eoRWU9E8dLcqvVFp2z5448e/fzApkKIodndipp5CoXXS\\nDGp93DNJ2gr0bcfNu3ecnp9R5DkXjy+oC5n4Nl2yZMwiKjwYzx/u3Q+L4XemTnX84w/IP/IeSqPZ\\nD+74bg8f5839DSFIQydyjOH4vlQmdozyeVu8DzKljZ4qRcxFFE5rihi4ursjmIxPLp5ydvYYtdmw\\nul8xmc3IicQip923LOoKG2FWGBhGds7RTCSogRAo8oxFlbHtRpGF6IwiBgoiY4z03qMpOJtM2e+3\\nYifpAyqFJEyrKSGx0w9cCAVkmWieCy2hCJPSkOOxzlNWJQWKm/sVKs+ZNA2KwKSpIYHiQ78lyxTz\\n0wush+s3r4UdHCOZKWi3K8rc0I+W2XTOfrsmqIysbKhmy0T2CaxXd0xnU3RRUlQVv/7Vr3j28ilF\\nXZOXxXe9YQ8vnSX4P6KyjDxNp4cOLET5jIpc0pAOtpB5XtC2lrvbe+aTgp21PHr8lHfvLjk7mTOZ\\nF9ys92TdSJ4bOmvJy5q+7yhCxPY9JtdoHWjHERezlICiaLIcFTzejUeSpDaG3GS823X88OVLVu/f\\nYbOcadPQtS1uHJlOJx+2dP/h9b0F82TegILcSIxV8I6irGm7lsLUaMSSqp405FXJvm3Z7beQleIz\\nWJWoqsY6SxgseWnwmaasazJjiMGjAmL2G+WAdVbc8J0P4ukaIuPQUTY1QSm2mzXT5BuotSZoOXBD\\n8OjMyEOnlRQebynK8pj39mCWHSET1xt8gjo9jONI0D15cSIYfGYYvcd6JRAoEmPkk7brsIOJwWPd\\nSIiBMQhO9m5nGRMZ5MMbLCRrqP9wnhyLobj8xAjqCNOEI4GI1FGHdABZ74j+YA6tyEzFLz75Cz5+\\n/pdURcUwDngvkOzV7Tv6oaMdOm7vryWTzhQ0VUNuCpwVqDmkbLyg5bA4qSd0reXR8oR2tNzv9jjv\\nk6zjBZ/9YEEMXpyfDgdmVJRFyThatrs9Js9xzrPebfDOUxYS+WOtxSkJCY7aMJnNuXvzimef/piI\\nmDjsektpCiojOrkPC+VBjHMoMHYcMHXFV1/8Ae88fd/RVCWZEpG3yTLRlqXDatuOfPbxGZO6oO0k\\nIUYne7cQ4tE8IgJF1WCtp+09+33Py4+eE4KnGz1FnuO9Z7PZYp1nOqtp6goUlHmG1wcoVJGHTCwJ\\ntTrugI4VJjzcFUf3HNI0meDyxJ3lsKM8fP2BLHSAYbOUf5lnh9QRfQy5VuqDr1VyaLsQaeZznhc5\\nk8mE9XpNaRTNouR2745rhEMF1wcWbLp/VdrJfnjkHNvBQ2LLB6/KSNPgEypzfAyU4snFM0IIbHZr\\n2mFPDIrH509F8qLAecv1/ZXwKZSi6zucl0klRkForHNiGZkZ9l3Hl5fv2HbCsZhOp1ycnOBGiZLL\\nTC6WhsZwtxNZ0XLSoKNorVsXqfKCYIUdu5jPeXd3z6SuCX5ks91RTCYQYVYWbFYDBh5kNyhGZylN\\nRpdM4oNS4vKVPrfCGMpMoULAB9G2KqWxXYdxjt47xtFJwk6Rs91uOTlZynlcFHzz9Tecn58zn4uU\\nbtMPzIqSzsv9UE/meGfRWc5kMmXTWcqyZL/ZMDUwmVQ4Zzmdn0B0nCymVE2DjZGua5lMpgxtJyud\\n3Ei6S12lDj5K0a/F6m+0jovHj0SWl2DSGAUy1UqTZQUu89RNQ4wWHz0317dUWcR7z+A1RSVpUt3o\\nKauJOKkpS1Ql/XhLNRZUheL29pbm/BEFEj1WJRN5kOhDnWUiiwkS49a2OwpjCLYT5nG69n4Y6eyf\\n9pL93oKZFxlVXkLy1wtBXGZMLsvecRip64qQHqSqmTD6kXoyQ9mdpF0EGN2AKnLquhJ/QiA4S6HF\\nWi4kv1aXQovzvADlGMeeXGeUZUmIiak1esZhoKgaKbrpw8q0QmnJh1QpyFlphU+HTggh0fBFv6a0\\nTKEih1MpBNpS0DGbnYM2tIOTyTTPKTLD6DxjiqPyycxcpB6aVRIYSxSYZ4jxO4UyS/vCA5Ho4fWw\\nQ5IDQx14HCICTzeacxJd9LAQSn6xXoJbf/rJX/HZxz8mRjid5QQlLNDcFMfDdTFboFD0Y896u6Lt\\n97y7eU2eF2QqZ9fvGMaBwuQYbTibzplWFY8fveSffv9rXt/eclhKDcPI4/NnnC0vWG9XhOApyhqF\\nFLO3798wjHsuzp7RDlucF2KUzh5gEzlsFUVZMZ0vWZYVr775kh88OuP+9pbF6QUhyORzt3ecTY1o\\n/w6H8fF3QdqNwPX797SbNU+fPcUOI2/fvIEQWSwWyelGo5J8ghgotObN+w2ni4Zcj4xBBPyHNZu2\\nSgIGouyjCiNByk8fX1BozXQ2oxst664jzw2LxYx9N4h3ppKYl/v7Nc9eviBTQrYJUYhkBpUQFZ8m\\nRvnsk9fBwzV8CN/KO4cUDI466HgP/0juIZOg1zwTN5/CaMlvzdSx+B6mUK0VwVps12IWC4qywgZY\\nzKdMm5rWeqZ1zrZzwtNNRVMB8UAa4tDzHYgdh2IpXT9/VCwlpDtj2wkD9I/fG1F0vB89fcHt6paq\\nLJlP56y2K379m3/kZ3/2c0yKu/rqzZf03UAzqYihY/Ri5m+tpe06Mq1pqopuGLjdbkV/2/Ws25Zp\\nWbGYTJnnOUPXsula8dRtakLwEqwQbDLVkDNq5yNjN2AD3O9EU0luKPKcs7pC+R7rvbBjjRH5gops\\nup5sOk2sUnmOfEhNWnpG+9Fh6pyqyCi8ZXd7R6EU3W5LllZLRXVC7Dsm01nikWTkRcXp+ROGdgNK\\nHI/mkwkqwqzKWa3XNGWFyQzltGG1XlNOF4y7e07qkr7fU+hCDAW84/b9W+rphDhabIT15pbz5Vz4\\nG2WZ8oKTl26yyAPZPzvn0o46oJyXmLUmE19mUpJSYRhdZNf2LOpMiuzQ8/jROWZ6xuWbS+pcUy0f\\n42/XTE4v6G9uefTymaTW3N2z3vZUhSW0e4ayJM6WNFVFjmZnW5Z1ydVqTZGnkGtrmU1qykzTGMN+\\ns0kTc8A5z2674+z87E/Ww+/fYTpPCB6bdDNZZo5J5ypG7NiLn6NSNLMFzaTBjyOEQN91vH/7VmJk\\nmgZMTlSyh9Mx4q2j7zrGYcR5J6SHtM4Zx4GsMORlJY41WU6WS6eY5QVBSV5b1OrAZCBoLZ2cMShj\\niJkWpx4OhVQTMkWUFF3Z5aUxI6T9V5YX7AdLZgyZqSR6xpijQF1o1qJ/dEG6RzFWf+igyiyTrvI7\\nUKE6TmSHQzECSusEkekj+Ud9cLYIavkwFR/YrYfDZbSSFKOiZjk/S41eZLCBKpf3KHZ4aa+UIJIy\\nr7g4fcrzi5dkumDeLHm0fIxGsWymPDk75fHyBI8E3n7z/jIRltLOJYXPmizn6uY97y+/RQG3d5dc\\n3bzj7eUb3l19i7WOQGDoRohBjB5S1JhKloOPTk9ZLBbMFwsUkXazxlnL2O2JyG7Lh0hnHe3gWTQ5\\n3ke8FzeQkKYarUTq03UdWdTcXt3w6Y9/jCpyBucF5fDSYMQgLkMns4p+8DivuN8OzKYlTSH62SzT\\nZHlGnhvK3GB0hlFiqq6iIlrZe2z2PTGkCWIUKLnINWVeMFjHze2K+/s1N5fXaC1oTW5kJZEZgWiP\\nmsn48Ml/eA8c2a1JEpIdZCGZ7JIPrj0Pk6TwOkyGBLlnmjzLKDJ9hGEPdU8rwHu6ds9+t0EFKTS1\\ngaIsWXWW/SgEllmVHQu3VuoYMHywhVMIiajINCQRvFKyl/yjDSZNKUSf/wiApQtTkdXmntv7W6bN\\nlHeXb/nlP/ySf/q3X+G959e//TU+eD7/+vf87NOfojT0XYfzQX7HucHkhjpNKfu+k9iwvqcbLYMd\\n2XUtl5t7vry+4t3dLWVd8+LxE56eX9AUsjraDr2w0on0Poqxtw/03pOXJXmesx8tRVGig6cpE+mR\\nKA14bpJUQ8ne0MmEKnveSKE1zo7E4IU3EQJ1KXBqZXLqPCd4z6SZoLWmbCbSLGsDWqFVQOcFWVlR\\nVwUmN5RlTWEMGihMRtt1kqFrHf3QY/JcOA3dhoyAzk06gzVVM8e7wOX1LacnC4a2ReucpioZ2p5h\\nHMHkDySzZIAfVUTlhsyYI1sbZDgpipzNao3tOnbtyM0u8u31ln7oiUZ8cm0QvfsXb67oPTx5/IjZ\\nfEm729NMpwQf+PjTz4gIT+XZ849ZPHlBZyNPL15y+++/RYeBwVkmTUO0EmLelBUuOHI7UOHpuj3e\\njsIbsSMmEz9qO4wYFPv7+z9ZDr93wqzqWlK/vcM7T3ADZVXRD3uZ+hCT7b7d0Yd7VJYzn88pmwm5\\nySnrinJaY5WEHysVCT7g04QnLOMPoLVMBKw+BOhH6smErt3LQx3FpiuvBGKNBz784aHVwkyTj0nY\\naEISEGam5LTJweRTEfM+YIPFR0VvrRRWoG23NE3D7d210L+DGBEcDKiHMSSmvEC/pENt8IFV1yZN\\nGh/sHGUKPkCxKpFpDgcXCmkktDrCoaTpVXxt5fPwIRDHkUxLsnlIHp4xRr559xWnCyma7RiYVppt\\n79PPDwk3O8yaUpiNyfmffvp3ZFqg5r/84Y/4/NUfeHv7npvVGq01yzlsCIetqQAAIABJREFUti0B\\ni8kM1jrG3qIyze36MgmrK27v3gsEVlW0w4DOC3o7stvvmc1O2O3XidghXVHfD1BECiUOInlmqOqa\\nz378Ey5ffcXZix/iXMBHIV7FCNebgeenNWWujykXB9anAr795hWPLy749vVrPv74B7x5/ZqnT5+B\\ni3gXQUk3XpWG2aRktJ66qiAFAq+3A8tFRe4l2zJTGZiMIoq5uguHyDeFLgt0phkHMZ6wQ88YYH17\\nyb4bOHt0hrP+6IOLOvi3pgk9y8i1R+vDHjPdBBzrhdwjmqR3zo42faRpOsQDGUgfC6x8wimmK1NS\\nLE12hGQPTFmVvjaEKDuuosAWpWSTFnLwbXuX7hVFNwZinjGrDNvOHiHtw471WO6S13PmBz7/8mv+\\n7M//EpWMxlNrQGn0UXbE8Y6UK7d2pEhepk8vnrHerPnX3/0zm+2GzByeNWHff/Xma2L0/PKffikF\\nS8G+b488gcNzViZhfUC8UYcUBXZsOFEMzrLpOk6qmqquWE6nNMzYdi3tMDCgiHZMjS6MzslnpDWT\\nMocozHilc4axozaKzsEq5ee6JG8axpGmEuizznNmecZ936GNwTor8VfWkmWe/a5jHAdWux2TKqUo\\nGeF49NYyn0wY9qKHj2NHFwKT2VzQAmfp+5bm8TO4W1HkJVlRitl936NUQIWRoDWbzT15VYmUK0Zw\\nPRenMxgt3WCZ1xXTxZL1zSU6N2S5SSztxMPw4p+sgEKbhNx5nLVoFSnymp3fE/OSQMH50494//pr\\ninxCoSMXj57zL//+W/7ss0+5Wu3ZdwPWDsznC3y/5fTRY5q6kaD5i3PeX10LryAEJqcX7DYrTidz\\nhutrzPlFQvIiDo/SijmKhVYU1omiICpxSrMWVVSApiwkolH9MTPzg9f3FswxBIqyQitDiLAdR9r1\\nViJXKkVRlhI42kzFWLzrcN4S0bSD5eT0nJA99MsxJjcZ7xHjb3FNOcB46XE4CusPLkEHO6bjxkrn\\nx4eCLEtPrn44eLIsTZAios7SdIROlnOyBBOrMiJBZdigCD4wqXJ2fcekntK5gIpJmuI9vZNEkVSS\\nP9BHRvoQaT/Yk0oc3OEAjMeDTg7Bw5R42CNJYZT37cUrNI2hUmtlMg2peBwZ+N7jnePi/Bl/+enP\\n5VoQtu9CG4F1g/zc74rh5YDKMy2m8DFivacbHMZMaNuegJCC7tcrSiMasr4febQ8I59XXN69k85V\\naZy1oOTa+76XTM8Q6AfLR49LmmXDNhG48iKXzyhq3Ggxdc2+61kNPQpoJhNiUbI4OTuylH2Ix8/t\\n/brn0axk19l0L5AgPejajm634+nFY04mExbTGf3gCDoIQazIyPOM2URYjjrPKXPRtmovO/TbTc9y\\nXjJrMlZ7h45aWNtoSiNT+0H0f4BR991AnlfcXr6jmcx58fGSLMtEY6Zk9gpKMbrU4GSRMssYTYbW\\nshsUD5dUxh76qKN9nUmwLHD0GBZoVaPVg9/rQbJhtEoQ8of7S0nSOACqMUZ8VKi0Rytyw6wydP1I\\n6z68VeQ+7q3ceLM6Z9e7D0g/D/dUTI3itu0l/WIcqOpJgiFlX1rnit3wUIzllVCTYaTMi7QLVXz+\\n9edkOkv2d/YoqzKJLOYPuYvpdCiLHK2l6Q5J8nHQOBulyQoN3uFDWq2MckL2w4D3rUCMduR+t6Mq\\nCpqqZN5MMHnO/XZDb0d0ljP2vezG0vetjWJWSpzV2/stVZljo0NlGX2aLPPUxI/Wcj6b4e0gPs/G\\noEjNjYIsBmLwDNZKmEVRCFdkOsfHSGtHluenhK5nvd+TlSVlVZHZUUKXM8Pdds3LZxfCxjUZRIsp\\nZnTdQN/3zOcn+KGVbNq2JZiI0hmTZsb16y9YLhbc3d1TViX7rmO5PKXclwQj+bh8eJ4oJVaPSjSY\\n0XmR+rgBHzxjOzJZPhJOiNO8efU1wY0UoWMxqdjsWuZNyWbb8uT5C/b7FrQkPRVlhUZMU2LwZFnN\\n6fkjvtm3LE6WXN+tKGcLdr2j/fotRW8xjy4og2fY75gbTeY9s8mE1f09s6ri8t07qiLn2dkjhnEU\\nFnhdJzvRD2/8776+t2AGMjykwFTFyckJQz9QGEPX9xKSGqDKc9bbndCWq5LRWyaLE4aupZzPIMGR\\nWgxzkp2WkV+yToG6CdJEJcebTKZCVZRSYJNrTQgixD+MZfFBfEfw6b9TMTzAkeIrJodEQKA8dMRF\\nOYw9IcGzkhrQuY7Z/JzOpp2iP7AXVTr8pIgFImOEMTHeDo99OBIcHgT1HxZKrfWxMz+8d5l4pSOK\\nTnxGJWNR41xIyewJnvUPk+Pf/PTvePH4ZYJbw5GE0Y2StrDt3UMRj/F4iEqGYKAdHGmgRQFny0f8\\n19P/DR88u/2OEDynyzOCd6w2KxbzJd47dv2a0lT0oyQXZDo7EnsG6yUZZrHg/Pwcax2fffpjfvP7\\n39DuWxRgJhNJqHGeEBxFnoteFs2P//KvGEaHjxGXTCEEko4yFeaa02nB1XYUR6Ion109m3J9dcVq\\ntaJpphSmSKyLKJCRhtmsQj7OIJR3JbKGTGuCUXgf2baWWW04m1ds9y4hCzGRXsC6EeuVJKkEYQEG\\nAhdPnjCMns2ul8P6ZsXT5495++17nr58zvp+xXRxgjKaMs8YnBRCdSTuSCt2INEcsjdNIgtlaYet\\nDn3kcQLVRxlJqhzHqVSILCmHE47QP3Akl5WTGZMiY1pmjEExRPEu/q4tgbx6K4mPs0r2yeFAAjo0\\ngwcUJwpp72F4lu84qQyd9fABCeihedQ0jYTJt0PHrJ7yi5/8gvVuzVevvsB1jqqqsXY8TpCTZiJn\\nkXMUZY5KlpT6YNYxjg8H2oHLoCVzUjI01YOmVSm2uy15Lg35pKrZtC1GK4iKxWTC+ewEHwPvk97T\\nei8EwjFQ1xO23Z4uRrpuEJnY0EmBT1B6ZgzGGK5WK0II1EVOY0wqlpFlnTHNHMbBLgS8zjg7O6fd\\nrKiqMhF5KjY3t7T7Dl0UTKdTps2Eq/eXEDy7/S1Pn1ygTMHm7j3z+RSd5SJH2e4xRhPTfT90e6bT\\nGouw6J2H6J1ELhpx6Qnes9puKJoavRfGqfOBPE8RdWk1ZZItpHeWopyim4abuw1npydkpsLmU4ar\\nayaTCZtt4HbbMpsv+eLrr/jZz35GHzLev7/k448/Zr3ZSEZp26GrEusc08WStu2oyopPfvQjMb4p\\nSqpmyfLxc95/8yV1ZhmurziNniYXz1yc4827G8rCcD+OLE+WDOsV1vZEranqCohpffH/I62kHQcW\\nk5LeiiNMVVV0TthEs5O5+JMGTTGZUEymrO5X7HYts+XIEEV75qOYUWcHc/X4ME1Kx3yIR4hJgyXx\\nSdYH4igQ8G7fikdkmsQkIy85o2iZ5nRUWBfJMkmBIHXSPnii40ikiQliBU1Uit5JAKlKLEYbRDAb\\noiIvavoxZaSlc8ElE4ColcDVB5gVZBJJDjw+BrGWiwf2nzoWRhl2H7qzEB8ecK00mZbgYuC4tzQK\\nBpuSyjPDvJ7zP//if8HonCQ+OUK3MUb2Y2BZG9adyF7KXHZnPkQG63EfQA8fhhg7Z1FayEt1VTG6\\nkb//9f9F2+2oioqf/tnPuLm7wY4WQsIEciNC7a5jOjnhdDnjLW/5yWd/iXeRoe/Z7DYopfjxp59y\\neXVJ0zT0XS9+kkTevn2LtZZP/+IkTZSJqRuixGX5mCBReHXb8aPHE+Zlxm70hCBw+bPnz3l0fsY/\\n/epXfP6H3/PXP/8FbSddbpkbJnXOru3Tg2GOh65JrFif3Jo0YgYRiZwtSva9Y7Qh+SMrrIXRuiPx\\nSylFWRSyl1KKcYx4awl+kAD1xL5UMWIyKS5VYeisOzru2GNh0cdSpZMUxGRixH1kTx/2j1oIPYei\\na47PU4Tkd2uMvD8pDOlnqMOGSf6/KTKKPGN9nBof/v5QMQXRl2dudBGiZ1bl7HqbGpbD95P7+WIx\\n593bKwleT7d6XWT4IFDsdxyMeLgmrQ2rzR0my/hvv/57/tNf/Q3ffPs1gx148ewlZVHw5t239LZH\\nnKNEZ+mcxO254NLvSdyiDs+RiLDEL1WTYd2IMQZTFIzWiuA9yjnlU7D5ZkwMy/Sc9uPA3XbDrGl4\\n9ugRwzAyOMu2bdFasYjw9vYuxfZx3BcfG1ZICR+KMs9wXouov6qZFfKsmyyTNJKxxRcFJi8Zh575\\ncomPgbbdk1nHOIxQGGanSwo0wYs3c/AjzaRCa81mdUs5mVOWBe1+T2dbHl+ccfX+Lao0KB0J1qJK\\nicRqJjPGvuVmvaeocvHu1hm+MKgYGPY7un5gfjrBdi0eQxbBjsJDyUoFueiZ729vWZwsWEwl1GIb\\nGsZ2Q16UTOZzhrbl0cuP2fQjVSkm7Lmpmc/n7Ddrzs6WbLc76qY5Dgpd16agC8X95aWsjE5PuL26\\nxmSKoe+oFjNuX71hPp9A3dAUBZQNo4PlbEJUGa9ev+Hls2e8uVkxa2pKY3h7e8eszKir4k/Ww+8t\\nmMuzJQoYvMXhGLqOusgJmSYzOcP9hunJEjv04DxV9MyfXACRerpgt+rw/UBZVAzDILscee6ERBLD\\n8RDIE7wRvMIr2SmItZilmUxpu1ay+7JctIVK0uxjlMmMEIha4aNkKh53GKgHzSORA0gdElnHBkXU\\nGUEput7LQIIwSZ+cL9nsNkQ067anc46oNDa548QEqWolh5pPOKpRihj10YeVeDgMpbsNQiFEK3Af\\ntO86sXs/hDsOmk35OUJEGceRT559gtF5Osi+C7xHYLQePclZTnJciFgX2H4Aox0Ot8PPID3gX779\\nim8vX/Noec6723eU+YFla3jx5CVV1dDUDT9e/oTPv/ktWmm8k9/3fHbCD198Kgfm2ePj1cxmc+qm\\n4WS+xAdPP76iKComdYVJu+fiIuP502eoyRzrYwrYFvjRu4gN/vj/MUa+vm759PGE3oWj248G8rLk\\nb/72b/n8d78TuUBekxtNVWpW2z1VkVPllVh4pZ2a1pIDkmnDoVgZbfDOs2tl916XmqG3BKUpypzR\\nyWMcU5pHcB5tMooswylPnmeQN2zXG6q6ZHV3z6NHZ6zWG969veT88SPyrBR4Vz1A9AeUUyuSwYCg\\nAQfv4nC0nJPpsjTCfM2USubc8pkeJmJJJjkEQT9MewohBM0qiZxb7cfjtHWQnBwv5o+KJsDoIY6e\\nWSVscn/cuSq8tez7Ae9HikIY7rlRFFnGrv8ubf9gMB+8EK0gslwsAfgff/6f+Md/+xU//bOfMalr\\nvvjmS373xW9RihQ9p1ISkjs+H875D9AcuVZhcCqJ7TMPjM7Ds1IUxXF6D4i8zDuZpENqWKeTiYRV\\nx8imbdl1HcTIvJnwZHl2hFtdDIKWxIffoXzG0uV4L/adWmXUOQx9jw4jo82EFZpLes9dbxkwzA20\\nVmL33K6l0IZxGNgNI/XyhDzTZKbAdi1oKOqKXGWY5EULcHtzS1mW9P2aWVNxtlzixo6AY3oyp+sc\\n6/2O6uQJ47DFe8t0dkZdV1zf3XMyW1D4wKYTI5Q8knbCUJUl/TCiYmBv95ycimnHZNJwf3uPVwVD\\ngKhXNPMloNjfXfPk/BStNLd3t5zPG8kc7jtm54/Ybja8e/ueelJTTWes7+6Yzue0+91R45nlGbc3\\nt+AG/uLPf8yby1uevvgBdZmzjp4CxfXVJRcXT3h7dcPzx4+pyoK7+3t++INP+OKL3/GjH/1IkJ6u\\n59HFE6ztcWPHn3p9L0s2JqJI37fcrdesNjvaXcuw63j/6jVlUeDaHbubW4Zh4OziKeNg2e92FNUE\\n6wLOBbqupyzLY3cr0ybJRUcc79thxDrP4D2DFfp67zzd6FKXqCTX0jrZ41mXnF48Dhh9wIWQtDYS\\nXOwTvCtJhBLlJOxWhQsK52UnqTKDtZGAJtOS8zf2O+qiwo3iU3uYLP2h+iGMx6rIxUIuBPJEbohR\\nfrm5EkZlriAHTDJxz0B2uIeCmx58pZKdmVbo7MOC6RmdTa4toiP8x9//A7er6+Ou5zBdyo5IM6/F\\nxk8D69bTjhEX0u4Vmb6PaRvpFWLg05efsZgueH/3TqKSlObZxQVPn5zy0bOXvLt8x+Pzp2Q659mj\\nl7TdwGa7k73M6RP5TONhbxu5u7/DOsu3b17TDx239zeczOdAoMiNmEiMI1kzIVQNqOy4t5RiKczB\\n0Ud6J2xN6yL7wfJ21fF4LubvH0LwSmf84JMf8m+//x1tu6IsNHfrPc4GrBOiRwjxyPBzbhAda4wo\\nlQgqShoYUvPjnZjNm0yYqmVhjtpe54MY9Y/izJQYXIzDwHq7YTad4O3Iu3fv+fLzr+n2HW++eo1K\\nMJDJOBKXUAfTAimIRolh+tHVJ8lAxKVHdtB1bqgKQ5ln1KWhyjOq/ADFypSTZzrpL+XnzeucWWXY\\nDY794EXXG48Pvbw+HAH/Oy/rI531TCqTCrpi6DqiHQk+Ula1IDcKJrlm3wtZKH7nG0dev3nFf/vH\\nv6fvO/7533/Nr/75V6SNBs47fvkPv+R///v/gz+8+kOaBCEqsXPUSlFWMlFxbCwiIbjjpBgP54S1\\nROcfrCKzD8xASM2ZF01tVZVHFqzJjRTPICHrMQSGcSTEyN1uw+vr97IeIvLJ02eczuZH557jhjZx\\nFeR6BK0o8oJnyxPGvud6sydkBdbD+3VHH0QdMHjFEA3n0xm50gRrccaQz+dMZxN0XrC9veVuvaGZ\\nTammMyHe7fcoU+DdQGE02+2WOs9QmaLQAW8HptM5mIZV1/Hi2TMGF9ntWj76+GMKo9m3HcV8QdPM\\nZFWkFWfLE6Ibid7hhh5AfMSNIQZHsC6hZIqnL36A1RWqmlJNTyjrhvV6JZnKIaDLkgx4/PgZfdfx\\n7uZOJC13d8wWC3RW0LYt8+USnRe0gyUzuYRbBEXVTPjz/+E/8+W7G+6ur4hKM1mcMRZT/u9//g2X\\nt1tev7+j6x3btmW7b6knDdfv37CYLfjmq6+wbcvby2s6G1DeY/8/5sjvnTClW4PlyRnz5RlffP0l\\n7WrF/OyESjegFF0/4nTGZrOhG3vmp+f0w8DJmWRIqoPOK9m9iRhXbOCAJKoXyjVKy3SoZOowpNw1\\n51IIq8gS0BmDtSgnpIcQgQRphhBQmcaj0V4KnHyNTJ8qgs4UHkVQGp1p2n4gpKzAIhMoebXd8vj8\\nCaPzbLwiKiFlmExRGUmaDyEmssYhcUJYuS7tPDPS3ivI+9ZKJVKEFhg57VQOTtla6QQ3H4pamoYR\\nCDcCpTFY79A64/evfsd//vkFikiRaQojHfBgg3hzas3jWQE8LLJjmgSOqY7xw7QMxeff/JbBdeSZ\\n6Kxmk4qubzmdLlBkfPTkBZJsX1NXNY/Pn8p1/pHW7tBhL0+WEOGjZy/ITMbl5Xth7SolD2RRkGWa\\nbrdFPf0o7cREZOxcYHCB0XshzIT4QeeuuNuNzMqcp4uK661Nh74QT+q64fnzZ5RVyf22EzNw5xiV\\npjcDkmyfH983cHRhAXk7MvmJD2sIGYPzFJmirDMKIxKp9Xovsp0YIcgKwPY90TlWmw0/+fPPsGNg\\n145pelMCEVc1Wumj7ONh/5goOYfmUsv9eiSHBSGHZUliUmayDzWZPhbZw/VYf2jCxKJNKwkEqIys\\nIrate9gjHj43DhuSeIQij19wuGU++HrrI+3g024y0DnH0Le8efues7NzYohMSkM7elz80HLhYXx9\\n8fxjvHN88+03bHZihv/L/+f/5L/+7f9KUzeM44APVuwateh4fZCu1MeAHwb58yQPszZQlnK8iQxK\\n4PWoFC76pAEOdH1PYXKZCOE4ecoqJ0hmZfoZwzBSlAVGZShDstMUh60iTZffXL4Xrsd0xtOzc5zz\\nbPd7ukH2nTothjP98Fk6xHIupGZrY0f2QeOiIgyWNiqasqS3nuvVPY8uLqgmDd++ecsw1jQBbu/v\\n+fizT4nOUWioiwmvvvqCejLDo2gWc7Ef1eBtj86UmMNYy+XVPWePzrnb9phK0zSGLHp21tPMGjmT\\n+j3tZkXUKiWPDElTbbDWYnRGCF7eRwjkhaHrI9f3lpCXmLygaqaECEVZoVxP149cbbecn8zoPKzv\\nb8GOvPryK549e06Ikbqpj3UheM+TiwvW63sZToqc87MzNusVdTOhqCrRYOaGH33yCR+9/CHb7ZZu\\nt+P626/o9ntuL9+zWMx48eIF19dX/MWf/7+kvdmPZMl15vkzs7v6HmtG7rWTFCW2KLW6KWkeZsNg\\nXhqYh/lHB5iHwaAbPd3q6UUDqSmJFFVk7ZVbZET47n43W+bh2HWPLJJgA/RCISMzPMLd7zWzc853\\nvvN9f8arr3/Fo6tLlKtpu4bxZPxb4+HvrDC7umU4KNnvt8zfvCDVmmJ2yuZuRePh+m7OvG6xxjA9\\nmZKUQ9bbiu2+pusa8nIUtUr7fhR0VpTs0zQRWn5ncSicV9SdQDud83gUDqkGO2fpfEBpjQMa6zFJ\\nSusF0nQgByoGi6Jz0Np46Hr5u/Ueh2JvPZX11F1fpVpR5wlS8VkvVUwIjrptcZk4nkivS5ECvrNk\\nwCRLyaP3JxCZYsd+jkcqhBC/50NA9f/3N0H1eFe00yLCr+g4+P5u0t/FEQyt4PHFFWWqGOUC1+1q\\ny6ay8VogJBnrGeX3b7WP1V+vjnM/ykFZDNntdtRNQ1PXbPcVOE9ejHCuk8qUY0XXj71ImAvH6vUw\\nQiOHwy8/+yWr1ZLtbivekgick5qE6WiI7TpWN29wzh761Nb56OYQvSbjTKZ1PkKzcLORGeHzsVD7\\nI4GP1ChOZ2PuVhXWSn9ZkgUZ4JdxlR6qj0e3FlhPm4REJyhtYp9dgZIB7dY6qqpDKc/prOT0bEzA\\nYb2lc100Jk/YNC3Pnjyh2bd8881XnI0H3N3eEAK8/+F7fPD9j4U1akysMs0hSEqhFxOD+71vINGa\\nzBjyNCFL4nxlIhVlnmrKzFBm8vcs6rAapRhkhvNxgSKw2HdUjZP7H3pNWnXwsz6SxO5dHDgES7iv\\ntCQG6vvGMcgEcRmNxMJtPB0zzA2tFYQgXmRU35XqF7ZSlOWAtzdv5bMaUcX51Ze/5I8++RE/+fGf\\n8y/++Cc8e/wcEBIi3mPbDqNE//jp5TOm0epKPoa0cw4D9UoUm0yEnSXZFZ9a50SKMk3TQ7KQmYTc\\nJOR5Tp5mok/atLRdE/kCEiy11pyMJ2yqKppNBBbbDV++ec1ivWaQ5zy7vOLB7IQyyyUAW0sXk/t9\\na2kDmFjF1m0r56GzlEWOxjEalDTVjtlkglWaerPBKc1QB7bbHc4YgoYkz1DasKt2VE0lqIVWpElK\\nsJ2se+twTrHbyzzqkycPKZKEYBu8rxlOJljvyDLp/xZZhmtrQlAMywFdUwnzuchxtsE2LU3VECJh\\nyzY1tqpIzp+jTUI5GDGenGCDZ7NZE2xHPhqzs5aL8Qg1ueCX37ym0ymtThmcnVPbjiZ46rZlt98J\\nyuM8jXN0HorRCNu1NE2NCRacpdqsD0kI2rDfbijLAuctOs3ZdYF8fELtFJ99+Q3j0ZDPvvglo8kJ\\nm82KtBgwm51QN/dIYt95/M4KM0tEg7SpatLJmCxJSQclr6ua2gdcUpBlKSF4qsZF5qgcTFVdUQ5G\\nuHYvWbFJQGuaukYpOSS0EaWfummjgXI/XqFEkB/QeNApNjh800UoVfoLRml8AiH0jXWRm6o7EWKv\\nIlFD4JIIhyhxGVFKxk4i+ygaRVuyKDbtPdzu9kyHE9ZVhfZeBr+VNOqd8zS+wyFC612QqofQU/fF\\nX9AAIULQAp/K8EDkyUoSECsBhaiihHuwL8TfF4NslqSMBwPyJCU1Ii3WtT5eg3iYwaFS2taO2SBl\\n2zT3Tr+e/8g7rxNU4PHlEx5dPpZqPXj+7hd/S+c9aZYfDvIDjBui0lGsUu8DbSb2pEHWwmQypqp2\\nMlaCYpLnnA6H3Gw27FfiebdZrUiGM0KSSeLixWfTOelfei8HbV+NqSgo8XrV8Py0ZFIaNrUnT2QE\\nZLVtmI5HUWiDKIjv6SJy0iJkHaUExjWHQfx4n2LAcLFi6ysu5xy+UaAdWWL44PEFd+stq20ts8ko\\nJpMZN/MVZWYYD0r+7h9+xvT0jPPHl4xmU+pOqv5EiSBGou39WyF3KRwrahURCKMQ5R4j7Flhwmpw\\nnYjB396I3qhSqLbj6mwm96B1rPbiZyo53XfX2f2G5fHLXorv/qPXA+3RCg5B0zIqDNdvVyitOZuO\\ncUGg9F4k/VCdRiSgX7SXF1f88vNfigmD8yit+Pb1C+7mt+gk4cc//GM+fP4RiTF89eoriH3K2fiE\\n5w/f4/XNS8qs5H/5i/+V//P/+T8okpzGHz+ni2iPi96SJtG4zsbZQ48OEaEKARM46Cn3UH1iTOw9\\nChs/xGQrMYZROeDbm+vDtemZuHVT07QNd2bNMMs5m8xItGLf1OwqET33BMrE0FnHzXItqFSiQRmq\\nugVlOBlmVDe3LLdbUmvR1nFydgldzc3dHdPLc3rlIBUU8+trTs4vxB9yPCF0nso6yiQVkXhn8Sow\\nPDthMJzy2T/9Ew8eP6WzNcp5vO2og2JyekphMl6+fUUxLBjE1lPd1GRZQbXdY8aiZ50Vom1rW0ty\\n/h6vXr0SQltRMB6PWC6XJEaRz05YzO8YpppF4ykLxQff/4E4Wd28JZAwXy0ZDkdoD9PZlLIo2For\\nFexY5i+zomS5WpIPRqSDIZNUNJ43ywVv53N0EG9M7S3jYSljLtWa8XDCo4+v+Ie//xseP3zIfL2k\\nqSrW2zVPHj1iX/0ePcwsSairOgbAjt1eZKAmkykuGEwiriFZXuC8jpJUUkl1bcWgHKKNASVySUmS\\nEZSQY1rnostEQKfC9Oyso40zS50PtLHHuG9FQd9haD00AfZdYG/dAbJrfKDxgX3b0XoJSxaFRdM4\\naD3RhkvROk/dSb+z6gTqtSEG4OBRKlAFuNtsmI7HEV6VA7tz0u/LuvLPAAAgAElEQVTxSipbj2Q/\\nqRZ2ogoeTTjYJnXekSgtQsg6jqUEed7xuUcmjmy4cBgdiZgak+GIx2cXPDg5obMdX9+84eXtrYgB\\nBPVb7iDUVirnLLIzj94WxLIwxJeRWareMDtJUkzMVuumZZhPv9N/6uXY+oM2vPPH9c0bPv/yM5ar\\npTADteHrF98eAn9b7YVAkKSMhgOyPOfB0+fkZRlVfEJkyIbo3iIwXG92rbVUC5G+xKtFQ5kazscp\\nWarZt54kTTBKRxUcOeySNI2o52E6OA7+Rw+a4CD4wwBz3wPDB9E+PsC3QqRqrPAv0zTldDbgZFww\\nGxWkCVycTTEKKusYTk+YTqecXZxjnVTQPUkmiT3Joz5KvDVeRqDC4TNL0M9TQ54asiQhSwxGCaFs\\nv1mRJ4YUT2kCoa1kH1nPvhWEpx/7gOMMcMwYUOheXe9QZd7/+vBv9yrPPgiCzDgv1ntmkyGPL0+Z\\nz++ou8OFPDBj+zV0+HVBZCz/8if/Hd56dCJ9+jRNqLuG7W7NX/31v+f27obnj97j4cVDSaqt43x2\\nzu3ylte3b/jmzTf8m//yr/no+SecTk6xXliuIgmYSJBNzDufxcdKsW3bw4xnCP6YPqijWYL0q/vC\\nQJCkUTmg6tqDklf/uUwv1qDE33Oz3/Py9i2v57coFE8uHnA+m1FmOcoHUhUY5CmnkxGPxuIyEmIy\\n0+13OJ0wTjO2qzWbuuZ0XMrccdvKOJfzZMWA7XJJpTzD6QSVCPPXK89kNhVxl2DJyoJ96yDJxWXJ\\nSyGRBc9nn/5KED5lUEmGKUouHz9iMp4SuhbXtrGQgSI1eGtRyHy9DoFiesWytqRpSkDhguJusYgx\\npBV+i/KkgzGPn79Htd/huxbvLO9/+CFpnvPo0SNOZhMJzHmBDUKU6rqWar9Hm4S6btBGRPO7ake1\\n3TI+mRGSlIcPHzEYTVDGUJRDiuGYJEkYjKYEAou7W5wDHxyJSTiZndHVe96++pbi93ErsS4wnE4o\\nnaNzlqIY8fbmLUk5IsuyKHTsIpQZDsPJWimq3RbzQLBoay0mkQ+sTYJzMujugmSS3kVyjlZR7kwO\\nxlEpC4EI/+2bFusCOjHSg/QykyizV31vMom9TFnQzvsDrNU5ofuHnhEXQuwnRvhLy0KoLHit2Vd7\\njNHSN7SW/ngWSEYYiPf7OT3T0QVPFjn8Gn3IRr1zUac0ZvdBDmGtDImSg9crYSmioMwyhllBnmVs\\nqz23qyW1bWX+y1o+fPLRrx9q8k7e+bddYxkXhrvdd4dy47tX8PXLz/nixWcMyxE//sE/J88ylusl\\ngcAPPvgj0bE9/M7jpz7O0MnrKjw///Tn7Ksd3gXu5nO+9/HHDIdDnPWoVE6UurXsmpamaaRayguC\\n0ofTt18DPZTv4oxpb4jc21MJZCrVyrrueDQdsKk73rx5S113FCPpFZZ5ig3SCgjeCbFYg4rJQ3Jv\\nFvLeJYzM5170vCczSTVulI6iGhCcou5CZNSmlPmEznbk2ZRnsxlvbxeMJlNpDYT+BaIqTmTAoqW/\\noJB+nIuKJS54kqAPc5R5asiiuHqqA0WWUrVbbLVBaYM1iuubFcvNjmx2waw07CNqcqgnY4AOSqQq\\n3yXi3Fse9y/JOw3PY+15P2VyQeQTp+OSF9dLhrMLlDAJ3rm4h6saA2kIQrL6F3/yE/7L3/xnTCrV\\nvoyGSLD625/9Dc8eP+eHn/yQp1dPeXP7mkeXj/mbf/z/ZJZSKzrb8eXLz7FxXrR/z0Zma+5B3f3S\\n72Hvo4l7keWHz+u8fydhkBlqgXMJYkt3s1oeSG6H/cBRR7/vWysPXedZbtds9jsGecaj0zMKM2W7\\n29K2HSelwdZbRpnG2ZqzszNwFRpFOZligyOfnNDtdyijSbPkcI406xVvF3OuHj+iavaUWRFNJDRl\\nOsRZK4lq3ZDiqVdL/K7CAafnp3z9xZqgAi7N8Cpht62xLlCmabSWE83trq5kMiIiC+WwwCiwNlAp\\nzezklMViTlYOqbY7zs/OuL2bY9uGwSCnspZBnmKCYzoaoZKE62thtNb1moD0RyfTKWjDerMmMQlF\\nJsbbu50YW2MSHpycspjPad2WJM3kymtDOZ7QNRX73SZ6MTsyHLUVp5c/+MMf8cVnn4LveHD1lNNU\\nkWQF9X7z6/sgPn5nhenamma3ZTY7weQ5RTkgKYbsdhW7psETaJynajo6LwaqrbVY72mszAxmWYEy\\nqXgy2i5Wl5KZOxStjz6UzmOD/L1zote62tbYoGici8+L37PEviR0TtNFZqx10lMI2sT+p2xgF2S+\\n0istyh/I18k9ndi+srMhzm8GYWmu93tOxhP5N8JB3N0jm0mDDGUHL3CZ1nGY3h8hWdk1QmYKRzan\\nRwmE5yPtPaoKnYwnPD1/wMlwwq6u+ebtNa8Xc3atiDqLDqJlNj6JG/KYCf+mA25TOyGGvHPHZdv3\\nUnVXF4949vA9uq7lbnlDQNjMJtHs6/07va1+6dyvVA51kYJBUVLVFd57njx+zHA4YrvdCjED6UEp\\nY+i8VHRtXdE0Ndv16lCB+54pG9mywR8PIK2PPo86HvzD3JBow6tVzSA3pErYeiEIGzfJEooyJ89T\\nsjwVEXh6baTf/jgQeqTIxIc4h+s91lkSDU0nowyZFtebqrbcrXbc3K5YrzdsVgseX54wKlPK1JBH\\nIQHTs1/jfVH3kAJRkCJ6pEZ1G6MYZJqTQcrZuOBslHE6ytDtHmMSOjR3yzUvXl+zWK85Oz8XoXEH\\nZaajMhDH6lpJgEeDil+/CwvfQxPuLS3pPBx7rcQ17n0gz0Rw/FdfvOR0NibVfSXbd7n7Z/e9xnsB\\nN4ilmji/+Jhoymxkf9+/ff0N/+Y//WsCgQ+efkSRl/zJD/6M51fP6RM3pRRJasgyIe2gRMGlbUXw\\ngPgcFYUE+uoyMQmDvIgsfmGn904/PTweohSl955hUeC8p26bw7WRWeojiH1/dEvAolj1OSsZTLfF\\nbW8odODx2YmII8xOeTBImWbQNhU6SzFKUeYpGsWrN28oBgMSFSiSBIWYCLi2ZTweob3Ht5bteo3z\\nclZ429I2NeVgxOu3SwyaMs25ma9Jspy3b16R6JbLp4/pvEKTkKQC9S+vr2mqBp1kNG1HUQ5oOhFs\\n0MaQ5QV5OWa52hFQvPjmK87PznDOUQ5LFssV+92Wbr9mnCfMJmOy0KLaiiLPaJqa4WjEcrmgyDOC\\n9yRZjklFys8kqfQsq4rBcCCz71o8RterJePxkLPzc5wVFrxJNIZAMRigk4ysKFktV3RNzelswm67\\n4u76NZfnp5yeX2K0Z1s3cQTr9xAu2KyXpEXJZr1htdng3UY+SJLStg293ZT3Lv4pAcsGMSfd77eY\\nrCSxjl3TRshTglJqBKNXJiEYg7VSCXpkRhMUxqSR4CGs0hA0ljifF2sDHSKD1Pf9QEuaJLQWqSSi\\niIBkQ/4wiNxXJhDHW5DDxAfpg3bxILjbrHkwO+XFfE7PKkmU0PVDEPurBOmB4QXONDEL7WdB9eGQ\\nkE16IJuEEKn+hmFRUGQZaZKw2e+5Xs7ZN2LNJe9XH69v2/FnP/yXh8PmwJ3o+1L3Hv2G3TWOSZkw\\nv19lqmOlmCY5Hz79hA+efnIIvmUx4PvPf8jJ7Oy7xcahr3Z4E30PFnj/+QeMR2Nmkxk//8XPuTg7\\n5+WrV6RpJgSE4MnLEtu2dE1D13YkQWFVzSgGClH5iYQif6xpxYlDXDj6+cJJmZAnmrrzJEax2Fmm\\nowGjwYj5ck/T1OhEMcxyjA4yd+s9WvV91r65dr9e6oNErw8Vg5iPyVXfr/deek3x3maJBDmjNVXn\\nSILmyckF287igxCadCThkBkgYdI5ChNnIq3jdFhwt22k55MahrkIwKexb2nrPW9uNkxPTmWf3i1w\\nWnP54BFZviQAddNyeXmBdVB3lkEC9X2zm/5G9m3M+x+de988fKkOh/7xZ+WH+17cOE+wFHz66adM\\nplO+fvGaq4cPQQWBZr+DThzeR4icbSXmAEVe0OwarANzIKmJEIHzjqqu+Ku/+SseXj6kaWueXD7l\\ne+//AT/44A/5N3/9f7HZbw4ykz2JyjobR0rE49XHatC5qPYTyUBJkhyCXmttZMBLQqwj2S7EUv1s\\nOuN2uYzQ+RHG7ZPLPrBC7L/7mDArRaqN+G82NcMiRXU12/mGQVYS0IxnF0x14G69wTu4OD9nN1/i\\nuo5My+/PiwFhvsAuN9ws1rimJUk1e+/E/jBJGI1mfPvFZ5ycnHEzXzLIG548ecxms2W5XDPOFVdP\\nP+Yff/53PPrwPeq6pa4sj55ckrqaar1hNV+g05RxeirmF7Yhz9LoUZlC5CucXD2izcYMPey3Gyaj\\nMTZAW+9xtmWQKDIDvqlxrcenGdnkAfOXrxmNxyL+4RzohLazmCShqypC1zEaDqnrhrqpGU+mvH75\\nLRfn50Cg2lfstlvGz8bkecHN9Q0n0wntvuJkPKLtLI+fv8frb7+ivblhVBaslgu8bRmNBoSypMxz\\n6rr+DW5Sx8fvDJjKpHz97QuG5+d0XjEZjZhvtjLc2x0VNXT0UHRO8k5LwAfHcr0ROb31AmUSdnUt\\nfU9taL2nA1QQhl2/c4OSoNgfkj7uMakKJeipA7SioyqNOVRprfNob6OXoxK4SRuxDuuFAQh4F4Me\\n0ovsGYLEDRZTcFb7He89eIhR0AUJpD4EEqT31Y+GJHFjGOIoS+yJghwmfaWp4FBp5lnOoCgo8pK6\\nbdjUFavdDoWM9BA3HUq9k6Fqrbg8efDOIRdCHyqPSjH3H5vKcjXL0TpK4R0exwOsP0R6aClNM87P\\nLn/tqbeLN/G9KR6cXclauQfRKqV4cCHv7+MPPwKlqOqawXCAjmbFEOjahqbaM5meMD67lNlZF/uV\\nLsKx/eeKlZHRKqrjSLCclimZEXHwXtj89m7OzfUdP/zoCenpiM1OVGFs1xCMjklNIibmSoLQ8Sy/\\nD6mFdz5634cLXgSc0yShbmUeuGdNpiYhOMd6s6Xa77m6Oj+QSToXZJY4+EMfs3OOxjruNg2rfYPz\\nniJNeb2oIiQpo0RlnjAqMyZFStguqLZLiqJkvVhQ5jkpgdVqxWg0JI3QlHVeKuI4a1zgD3qwhyXO\\nEWH5Lip7hDTjxTlcn+PyCnEPjwqpAq1OKEdjVosFTduy2lZMxwMIRy3afmCq59y9+1C89+x9vn7x\\nFavtShisWh0qM63kumujeTu/xnvPfDWn7Vree/w+Hz79mJ9++tNDwDQxOGqM9I29x3ZdZKXK+9fR\\nzP5+gOuDZdxcUlF7j4vVY56K2MG22h+dhuSpMv4WIg8hrl+jj45EmUkYF7mwPBPNqnbkJsHbmpuu\\nwsWDfJyXnEympEHJDGGR0bRyhmo02+2OtnNcDAaYbMg3X3/JJCmwTlHVe8rxkKbZMzs9A21Ikxzb\\ndSxWW5r9liJVlKMTkixj1zTU+440SzmdDdnfvCJPcr643lAoR1KW0hpIE7rQicNJ01BmGVmWE9BU\\nuz3l8ITtYs7oyVP2+z1OadqqZnN3y2mh2W7WUZrT0JKyuL3lw48+4s2b16JoluWoNNC0HfVmjeta\\nRuMxddNS1zWawPLmmtRo3rx6KQE8BB4+uOKzf/oZ49kZbbXDljm2lZ5pgmcynfDRR59g25rVzWue\\nPn1C3TQs53cMEAZ0F51OftvjdwbM5a7C5znbztO1HV3W4JWiaVtxi/eaJNF0VqjZHlBOZhOD0az3\\nGy4ePKS2YonTeEWGwBb+sKElCBmtaZ0TYkNiaDsPWtMFmW0UzVeBRPsREEKg65xsRK0JWIGg4iyj\\nU6IERJANatRRt7Unt3eHKCaBrJcZC0rFylNYbaeTGderhfw7gVQn1F1LhhjLptG6yvqo9BOhxUTr\\neNZIoEVJ36PMS5SCTd3wcn5D29moaRlT/v4QC+HewS3fl0Af+lP8eHr1P/Mb+lEe2LeOSZGw2Isv\\nnfqNzzyEz3hYKHqpNQW8uXnBYntDZy37fcN0PKPMS/m5nkRCH3cC4/GUf/z05xSDnDJPCT4IQQCF\\nt5YiL3FKs1guGU9PDxCkjdBXENX1d4OlESh2VqakWrFvxatSKyFbvH3zlmdPnrDZWi7Ph5zMRtyt\\nKxHc1uFwACsVWaZai0cqSgQq7iEA90UhYvMSUIc5zu16H2eMJWBWu4qqadhXFePxgAcPHxzmR++P\\na9z/lc7L2FPbyahL00mvRSuB7ZXypM6LPVkInF494u5Fw/rulrppMGlCHhT77ZbNesnFxQPyqOOq\\ntcE7+X1lZmhsX+m9W1C/K8zfQ4i/ua97fJIQkcaFaIu2VmYcT8/OWa+WjAYFX331NR998olU1AgB\\n6n5sfie1i9+YTGb84JMfsttt+PmvfgYcA5l1VnqRSKXeS1P+w2d/z7PH7/Hs4fu8uH7B3frmcM11\\nFGsAYb2K4ARHOcoIwapYVcpluXcw+CNDujdzP51MmK/X716MuB9VfJ0kVl6mN1UIHhPZ2NK6Csxt\\nJwmAUgyygq7rOJ/M2NV7um7DZCjVbpGm7HYVZw+eYp1neXtH52FUliwXc87P4eHlCUFp6tZxcjrD\\ndR3trma9XlMOxwyGE+rOsrp5xeX5jEBgcnrB6+s3TM/OGYwGDLKUzWLNy1uBam2z4vLjDySQGYMy\\nmsSkeCfETZ2lEVnJqZVn/vXXlOOxuLJMZ7z69iXTUcmDqyv2qzneOZI8xQxnhHJKu3nDrqrFhk2p\\nQ/sjTTTbrqPIxGVl9faGtq4wacpkNBJvTW2YzmaMxmOsdSRZwWJxRyBw8/atiL0EWW/7zQLXdQyL\\nnLOLB6xWa1It1mg+eFprGZSlKK/9lsfv7GGqsqCYndCFQAO83ezZ1DWND3gFOjHCcFVaZiI9wmx1\\njn3neLvZsO8srTLsWoFHt41l13n2nQTQve3oQmDXtbQ+UHeWxjqBdV0nEImSqtUp4tfgFLQBOhD7\\nsFideqUIRmBhoZPL4auiDmi/Oz2BDoF2QxBd2d6INziPitBrYgw3qyXnkymZ0YyylDRJ6ZxlWBRs\\nrOd6uZTZpCShSAwqwsVSjUhWXWQFl7MzHp6do7ThbrPixd0Nq92Ggz9eELZgL6IuCkXhMPNGEGjH\\n+mi2LXfp3p7tO0u/+bGuLYPMCMtN9YfmsTLc7Fb8x7/9t/zqm3/EYOiFrL959SVaK+qmIjEZXdvS\\ndRZtFLvd9hB8dtWO65trqmpH00rQePP2Nbv9Tvz5tMwFtm3LbrNGG0PjPWcXl1jnKYbDqNgUCK7v\\nGUoiY5Q6+D4mWnM6zDBGsW9d1IKVLH4xX3B2ckamDYNBwaaSdsGD0wFpIu/TKAmYiTYYbaL7hz5W\\nL9HmLcQE5XgM9htDkWcJLoBWcWTIB9q6oxyUrPZ7Tk5nfPzxhxDU0W2l/+8QOI8VTH/e3v+//5bz\\nns6LEIHc+8DFgyu6IJZyu33FYr1hfndH21r2+x37/Q6IXWqthY2OkJsOic2hZxrefdF+XfzaUrq/\\n1uR3jQojQhmdhJgQAkVZopOUJCvY7DYE79k1jizRDFJzDwPmEIjux+IQIEkyZrMz/uSHf4rWhuFg\\nhEkTGU9TYsBNfI8uJjm387cQAt97/n1cryEb4fO+rSGMWXMPrdHvQqjBH7RgY4v+nXulUNLnVJr1\\nfievEeFYER3RR73piEYFZPzEmITEiJFy1bSs61oEV7zoTq/3NVob2q4jz7OYLEkrZnl3ja83+KZm\\nNpsxHJ8ynZ5AktJ13WGaYViO8CTc3W54c7NEp0PScsau7qg7z3a1oCgynNKMT05Js5JvX7zg0ZMn\\nFKlow1bbJUmWsW/3vP/xh4Sg0drgvJXPZUSFKEnTeK4KoXFcZgwmU8rUMJ5MuL25BaO5fXtNEjza\\ndwyylCQv2bSO169fkZUFu92GIs9ROqFpGjorPWXXCct5fntLCIHJyRmT0Yi8KGi7VuT0mujJnBgG\\nwwEn0wmDwZDJdMrZ6SkmL8mLXNpeQyGrVvsto2FJ5xzT0QCjE9LIZblvhPDdx++sMMNgTN1ZbJAx\\njcbZgztBkiYELUIEOknIdMKuqgTW8wqdKDrgdr2GpGDXbSXTQuGDk8PQi/qGJtD5QAg2QiP+EDRs\\nU8uG0lIVyjyei/OaRkyYFTJ9DShlsEHcM2yEyYySeU+hi+sYBqQSjebuJES7IwQySlBikm1blpuW\\n9x88JNUJtuso0hQX9SJnwyELBa92Ddm24vFsRJFmgsGbhCIvGJYDmq5lud8JGarfSEESAY30PXu3\\nEhd7d/28nIrntsfT257lWXGEkf8bH95D1Tmmg4RV1b0TLJWC+eqO2eSU9x5/jI8CCnjPYnnD48un\\nDAcDyrLg7fwNd8trJqMpv/j857Rdw6Orx/zqy18yX92RGqkkE5PJAVoUMdA7jE4p8xSXJNzdzTk5\\nPed2vuDZ+x+wr+2BFWqjUL+K68MYxI4sMVxMcrTW1J0jieICSVw3X37xJX/0ve8TVCDLDIFA01p0\\nrnlwNqFupYJVSHUJChNVmHo4WuZqPalWeC1zv8o7IR/FYzNLDJvGkRiN1RrnLa1tuX1xS+c85+cn\\nwuYr8oPbSl9ZqnhDe8pR3ys9PNTxi36f9CIU4pDhyYxhtV5TDocMRyO6tkFnOZdXVyyXC+bzOZPZ\\njNnJmSA+QNN58lRjG3evP9C/4L1e7juHwP1FFg7VdpkailSzbVx0u1Hi0hMTvPndnIcPLjk9OaHn\\n4O4axyDXDNCxp6m+KxDFO4hJgOFgxI++/8cMygFpkrCv96y2S/7u05/GUbUE5RVtcPyHv/13/G//\\n8//OQfAh3s9++rk3OUBLkPPu2Ju434p4d+REfj7VJvY8HaejKXerlYySIOeUOlwfIawZkApMyViJ\\nVoo8E2EE16s1mWNCpqNXadN1ZEmBAlIUt+stD09mpMYIc9o13L76miQrGIymPH3/Y6r9Ht9W3L19\\nzezsCnszZzQekW1btIbNZsVkkLNYvGE8GRO0opwMKUZjIWoNCozvwHl2ux3j8ZRfff0p7338DGcd\\nxmSHNWudGLHrRGPSlMSkKO/p/B6dnLC/fsGzp4/pEFH/sF9zejKm3q05HZZSoRZDtCoYKDH5BhkB\\n1D6wr2rGoyFt07DZrKn2O2YnZ0wmU+r9jtHJCVopLi4fUu33bDcrlosFTVPTWbkXRTlgtbgjV55M\\ng21bMUZvOtLRCKUU88VCFMaqlhAcGo+vatKi/K3n5++sMCfTKZV1bNua1nt0ktLFEZDGdmyrhnVd\\ns9ju2VSVqMsohU80jfe0CGkmLwdsbcOua9m2FbW11M6JiaeCFi+VoRbrLK+gw+NUiH8KfVmUHhxd\\n8IemNlqqShs8NgQaLwLjzosuqPU9w002tA0CBzuFvF6EgWwI2HvnRV+pGS1eoMvdltloDCiUcxjv\\nyJRivd9jkIDeGcOL5ZZEac5PzpiNpygFbxd3vJjfcbffsW0aWidSXb1htYsZagjiyRj8MaPVcNj0\\n/aF+NjljkA8Ebv71xtNvDaJKwWbfMchFsPu7udQHTz7kDz/+Z6TaUDc7vBeXmj/94Z/zzcuvJICh\\n+dEnP+aPPvlnJMpQNw2//PJTfv7pz1is54yGQ7IsJy/KOLsGeZZhEkNqUjItB0iapvz4T/45z997\\nn/FkRtu5Q2/PelH26c/OfkC/yAwPZyJY3nQuekUShS4cr795yeXZJW3XYYwmBBsVcBK6TlHVlmGR\\nMCwzkbZTcZzDS89U+qaxUojJWC98LtKGIjdhtMD5ygtzNYtKPZiEfecYjwdisO6PZCE5SDlUmoQQ\\ng2EvNHFPEOLeXQ2xyrbey/xw6+g6y3Kz4/TyAcYYLi8v8N5T1xX7quLq0WOyNKWuKr78/PMDIaux\\nEmhV7M/31/cwARruv/q96vMQEeR5o1ykAddV7N/2b7TfP0rz53/xFxRlQb3fcf3mzeHX7RpPohVl\\nqrkH+MT1eQ/O58jYnYwmpEmKdZYszZiNZ2LsHkLUhe1NuGGxmnN+ckGZD0Qg3Md1FCHz+0mijkIk\\nxpgDSvIudC6Ijb5XZQ7jgbqp9rFP6Qk2Bt/4vMTI2hFpPUlX8lTk8+q2jbravbORJksTRoWMlmVG\\nsW9aautIsyyidg6dF1TVBm0ShpMJGZbF7Vs2b74W8Xbrefr8A5TSPHz4iDQvMaMLms5TlAO80hR5\\nhvMWoxXOerRJeXX9mmfPnuGaltBZtIftdsdsNmC3bQlkktIbg7Od+B8XJbZrD+8/BI9Smu12xaxQ\\nKN+xX69wmyWPLk7BO3Ij4vrGJHgtbFvvPK6zDAYjVJC9WJYFy+WczWbL7PSUy6uHjMYjbCdEo912\\nzfWb1+xWczbLWwKB88srzi6uuLy8JDOBl199ycnZBeRj8sGI/W7H2ekUg6VINFp5xuOJaJjXFXlZ\\n8ODqEeVwxLDIf/PhyX9DhZngCJE8E/FAPNB1FutFTSdE6DCAVHtRWcdE5Zy3ywXPHj1FGYMNcji0\\ncTheG4P1fdWKGKkq6LVGXRAx8SxLcd7FQ02MjfNMGLSHTdD3DeNMpQueDCPmxv0ZAKTaHKTZDj0y\\nxWEQ2XAUF7g/V7rcbbmcnnCzXtKFQBo3+6OzU766fkuRZkwGQwZFwbapmS/m1Ps90+GQnbN4pSUb\\nVQKzoGS8RQUlYgFB3atEIuElQrL9UWWUDMjfre7Y1zuyNP+tqNl3g2GfAASl2DeWSW5Y1Uf5Ojmg\\n5ED86Wc/ZV2teHR6zkfP/whQfPz+9w6/KxB4cHrFoCjY7DdMRiPqrmI0HJMYQ920IkTuPXk0vx3k\\nBd47kjwn0ym3izmz0wvyYsBgNI6EH5G9s50/tHLTGLAGRcKjSYHSIkqQRQcIrWXAcHF3i/We3GRU\\n+z1pCnhNcI48zwVSCoGq8QzLlMxobBeTFRVVaILMyqG8+Kt6gfJkrYIikdEfrWjaaICMJs8zqn1L\\n6xxpmqOV4eWLlzx+8oTNdi9ti3AcizmsvcABbTh4t96DSS6HW3IAACAASURBVD2yHnu4r3NBxD2s\\n52Q8I0sUyrX80y9+IUIOScL5+QU+BIbjMd988w0m6Ve0oDmt9ZSpoeqEtflOghXj5CGevhNAITUw\\nzFNaF9hW4hrRo6vyXEkCtCLO0DoGg5LVasmTJ0/FczaI9dwwNwy0kLXeWaeql2yMv88H9vWWm/kN\\n8/UtygS6rqXzjixJabsmSjV68rTg5esXvLl9zXq7IjiPVQ6Qvrcyx6DYzxWb7wgZfHc0y3NUlvLe\\nczIacbtayGSAfBzpkWodEbTQf5C4p+TvTSsOS4M8p+vEWqwwhlRLv7NqPXXrKbIUhWNft+A9RWpo\\nOstFWbD2YF1H6hO8zumaOwaXF1zPF9T7Pcqf0gXFcDhgNDulun7Fq2+/YlykrL1nNp0wmo4JIVBm\\nKU1l+fqrL/nDH/+IQTEjtA2LuzleG1RW0jQd+6ri4uKURAtxL8sLNps1yhgmpzO0TrB1Q14M+PSL\\nX/H8rGD95pZZBtPCEJodxtVi+K1FuzsbTbl78znnZ5eYNGG724lOsDLgLArNaFzSdR1NXbNZL0mS\\nlM52lKW8L680STnk9PSM/XbD2+vXrFYrrh4/5YPv/wBnLcPxmHY9p6vWVPWArMx4+/a1iKVMxzx+\\neIFyntfffo3yjn3bMMh/j4D56dcvCVkupATn6GwTBQrifx7yrMSGTmCu4AWOxWAIoDRNsFRdzWAw\\nYL5eoWK2Im2fY39O+iwCsXURmhTWpokKK0f9yjRNCV7MV30kBXXWESK0aK1Au5236MTQ2pbEJCRJ\\ndEmJ0JqPogY20s/lLcuiV1rITN5KBbPcbHjv4opRmuJjf4TgGWjNn330MTebDcvtlvnt+nAgKu/Z\\nr1aURcmgLMmVNMuts/F9SKrRuykcAqbs3KNe54GkoXHBoY3m//37/8D/+Kf/07sBU6moU/vucEmI\\n3+v7qtvGczXN2LWtyL45SwieLC/54uVnvLl5zXQyoqor2q4lT399ESmtOJ2dczo7oenaQ1+s6ToI\\ngSQ1tG2gs0IPRyuKJCM4S1M1TMcTyqI4QK9ixCxwowtC29dK5loHecLVtAAUdetJEyH9aCUV+Hw+\\np65awJBmObgujowgUnCtJc2gSBPRD20DRoNJFSoYui6gvEPFoHaomFTfl/JS0SQaEwJZquniyIP3\\nAuPXdU3bWsbjId42XFw+IIRAnmcyY+z7e9n3tvpE08f7fj9A9TGsD6qyVoUQJDPMVWdRGPI05fzi\\nAbatuLm74831ay4uHjAYTfje939AXhRRHUmJzGXnGJcilN6/UuirTMkO7i2noyRjmUlVuWtcFHWP\\n0GXoGxzHn+t78cLUdWRZynwxZzY7idKZgX0r2rNlpqlaf6wqkXGhxXLOq5sXvL25Oc7NqsDD8ZTl\\nxlHmOdZZdKLxXUuqE2ajUx4/esJ//OlfST9fi7KP9z4e1kLu6W0GnXPvVJX3q0sJ3H1wles/Kkqc\\n96x3OzH3jupRPbEI+hE3H0fLOPRZlZLkTHqSVnr6IVB3LoqziCayw4vfvRLls0Gege24vpWxrLau\\nIMtZrtZ4U/Lq7R3jyZi2abm5fs2jqyu28xsW3SvOr644OztFJSlpGqibmsQYdssVs9k5N3cLnn/0\\nDIXHeit92bZlfHqGrizjkwknp0OU6ghWZthVMNRVgxqUUp0nBh885fkjBvlXTKan7DdLMlPI/XFy\\npveXgiTn7atv5X1WNdNhSVGWfPPVV6TRS3M0Hh/O5/V2S54lcaRHYZIMwo6Lyyu++eZblLWs1mve\\n//ATTJrw6aefUtic3eKGx88/5LN/+ArlPPuug4sLnr//IavFnLPJhG+/+JxnT5+T5AWpgkFe4sPv\\nMVbispzdvpLAETNewf/lMH//2fd5/OhD7hZvmY5P+fLFp7ydvxImmxfqcKJgvlkzG024265l/MMF\\nEpNIlYqQNWTAiXiAelR0CMF5ZibF4mk6S+s6SmsYDQZ01pHpmJEm4iZhtCYYcTDpqdypTiN8E9CJ\\nOKEcNncMmiHEAWklCkd12zIsB2ig8/Ke1ts1F+Mpy/WKYVkyKAqatuF6MWfdNqx222MHRslMaZKm\\nBAKZEy/N2jvpxXI0SZYRA6ko7xtLh3Bka/aZfC8mfbu45tDcjM9X4Rgoj20w9Rux921jmQ5SlntL\\nkqZ8+fIzXt58xbAcMJ2OoxqTKCOprHwn8+5hrV9+8Sk+WPH1dA6CFvg1+jt6vxU5Ni3qL8Z7LkYj\\nWoAsi2bekWjhBHIXqb8ImSlFmRuuZjk+EspSEwXSlaj9bJZLPv/FL/jwez9AYZgMcpwSWyajJZlK\\nEkNh0sNAeQ+FBi89vTxTdFYTrEcdZhWPTGtCQMspJoxtJ5VDGu2lPvvyW/atpaoa2lYcbW5vV4xH\\nQ5KiiGM8il5yzUeWZvARko0s2/sPWUcBkBlkr+R5rfNUbUeexqom0aSpoakcWV5yefmAECSpVCqj\\nD719J9Ij7Y3MKBrfrx0Ofcb7sx4hBPJEBN0761lX9tiD7f+8B8Ueeu0CMnHx4Ir9bs3t7R0vXr3E\\ne8/p2Vm8HhI0y9Qwyo1ItSmNCp7r61e8uX0FRlEUGWl02mitpd7tGSqFyTMqlbLebuk6SzEs2bUb\\n/sN//XeEIExsHyNVEkdL+s8kUHkgS9ODpN39YNl/NhckUe9HvM4mU17Pb0XQI+5FHYOvCB+Ywz48\\n7Ml7iE9ijCBiAbbbLXmW048jmQjNqgBt22G9ZzosGRYZ01zTBs/ttez5qmu5vLri1bffkKQjgkro\\nXKAcjrEm4/rmWyYnE/a2Ic+k/RLSIdPhFNtUqFk0mXAiqycSZxq842Q04cX1NWk65PR0RpYrsA3N\\nfidiLnhGkyG7VkQ78IFEaWy1JachDS2jMqdpuzjSI8mKApQ2JIMp+3pLGk0Nqv2e/XbLgwcPKAYl\\n8/mCJLq/LHdbqrpmODyn6zomswnr1ZpyOODLzz9DG81uv+fq0SPW2y2LxZ0YXW9XbBc3fLW54/GT\\np/zsp/8ZuOS8qvjr63/i/Oqct9sd02dP+dubW/77P/9L/u4//RUYQ/b7VJjz9UoaqVrmBrXR4GE0\\nOuGf/eDPOZmeYXTK2eyhNGJPHvJm/pJffPn3bOsNrbU4F3i9uOPjx8/xQWFScUdvouO50eYd+LBf\\ntC4cs9h913A2nhCCuAvsq4qL0UD6n60l4AWmTcRLkcTQdcKccv2CNIbgRabPH8ZS4saPZIB+VCMQ\\nGJQlnbUU8aYbbajalk+ePI2qITWvb1ai1pJmKOcos5QkSdlWlRy6SvQuG+exSSKnyKFXJYe3i8Hy\\n/mvr2JcVaFgWnA9Bep89DA20thWCzb0D8ddGAfrX+k5/aN8GRpkox/z1z/+Wr159wWBQ0FnLqCgZ\\n5kMCMF++YjSasNlvGJXjYwGiYFutD3+J/CnyNCEx0vc9mc4w2tA0NbbrsMFT7/egFarrWC8XDEZT\\n8SWNcoaeY7DME8WDSRkNsMNBc1Ur+XO/r7h+c005HLFezSnSjOHlCSjIUi2mxP0oSmKiBKNEKq1E\\nH9h2Mr6RJlpELroo83ivypUZPJm1TDIt+ryRIGJMwvn5JS9v7sgzS9fVnFxc8uUXL/jRH//hwfD6\\nOKLSIwr+oGDU9237QH28dQqvAoZ75J9YZVatIzUyC935wNu7Ow6SQRCl//o6FQ7waRD7tyIzsp6+\\nU9X299coKHNpkdyvKo+/XyrwQ9DknnFXkNnmLMuZz1tMkmCc58Xrl7x6+4azk1MuTs9FLk7ldNWG\\nxXrBYrvn/PScb2++JU0SlFLkeXYYXcuShB2egVYk1kFdRyGBQNVWOGcl+PsgjNrIMEUdtV37h44m\\nCvdddfr7lBqZ28b5g83XxeyExsoajkXjYXbTaCN9PK1kBCXCvfpwO2IFHgL7ak9iEnEc0oqmlaDs\\nuo4y1XTOSmWmFIMspUigrWuWizuG4xHOeW5v79AmZzgay/V3FoMjTTM2ixt8AoOTE3QihYK3jl2z\\noDKGcjBkOL0kSQqYzyFofOfQmTgHff7ymnJUkqU5RZnh7E4UG4OsUZWlZGrIul6Spjm+aTFpirWd\\nqHZ1VkzPTR8kIxKRJFiV0NkoM6lSEtNRlIV8niTBec9sOmW1WlFVFUYbTk9PSVLDeDphu9lgreXm\\n+g0nJye8/+GHNHXD1199JSgZCBu3KLh89IxRnrC8e8snH/8B1eqOEsUn4wm7+YovO4t67zlbY/jr\\n2zf8yU/+gq9/8Qvq7vdwK/nJv/xXhCALf19vuVveYIzh2aOPSZMMkD4jIeCVkFgenj3lfPaAf/9f\\n/2+W2znOaebrFdnzVOy8rCjx+E6sono4tIc/ZPWGA9xjfbTBqWu01pRZTl3twTmmRc5Ga3ZNgwqe\\nUhus1igfaEJH07THABTZpfczSR2ZtT4eBk6kRQQWQuzFsjxnVpSkxrCr9izWG5q2ZV/tUYgfYHAN\\nZ8MB86blbrWSzRl7QUHJZ9x2LZlJBBaKc3fWifJI74Qhh02IhtkcgNUQ37ePG8tZS+ddD3wfHu/K\\n40kIbduGPC8Ph91yfce2WlOmJZ9/ecPTBxd89epz0ixFKU2aJtjgMTHrXWwX+Fe/5MHp0/toHcE7\\nqqpiPBpgVV8VGjQyy4oWA/DEGGaXD6mrimq1wHvPbDxm27SiPxnl7w4940j2SI3mwbSgcw4XpAfV\\nB8ue6DMaDsgHBU+eP+Hzn/+C7GRCwOO6FuUTsiwlzww6qCiq0V8X3vkqBLGD0wpMCsoZWivNPbEa\\nk+cZI/fbOZFMq2pRKvr65TUqH9C2LX/84x/hgdFohElN1EE99i198MfPG8KBzHO0y+rf0zEN6tuM\\nIYj1XWs9dWvJExGWH07O+OCjlBfffHNInATOvQfOH78hesyxQndHZFb2BAK/pkZRRfj3/to6jpso\\nDhHysFrVIWAqJS2U1WotffjgDiSoN9fX3Nzdcnl6zsOHDzH5kOGg5eHlQ+ouYBtLWzdgRD1p31jy\\nPIUg12ntLJM0RbcVdduSpild2wkCEFVwbCsjab2sXn8NZCz7WBWHCDkcR3zAdU6CL5Blmazh0Yhv\\nb94ePvuh7x+1hFWk2x/ySXW8j6pHLACVJBAiLyOuoxACRimqpj6QhJI0ZVqmKNvi6prz0zParuPV\\ni1ecnMzYbTcYFTg7v+TFi28YDocslkuevf+cvVbiPWoD2oCznexr21FXO4FetxvSrGA6PUEpjzFi\\nFLHbrBlOx4zHJcZokpCyXs/pAqg0o65rymJwMDYISuFtx+rmhvFgQHJPVMZ6T5qInB/FGJ0PWTcd\\n4+mMzWbNbDahaTrGkwmDUszG22jJNhwOD/C51pq6qvDOYW3HcDgUn9v5guXdLbba03nPoMgFUq1b\\nTJLyq09/xvf+4PssFmsGQ/EN/qXzFKdn/DAvWb+65ma1YGlrrvdbHpzO+CAr+G2P3xkwhyORRFMB\\n0nzEdHYleAvhcKAQ+x9a9SxOkVm7PHvMzeYGrcB5zXyz5mQw5vV6TrDdQSFHxV7A/Sy833G9ys2+\\nrYVdFTzeepRJWO4r0raj1SLErqynDSK1Z32gLHLaTg71NNK5QTaxieLrfQWnjei5KoREMshzxmXJ\\nsCjZNw236xV1W8f+imi93qyWpCZWgsGz2e7I8gyl9KGC7M8pIfhouoDoPCKU9T4w+rhpBMZ9t59y\\ncEDwMnCeJzn/w1/+K7Ikk+t2eBEOB5oM3osMYFEM4m1ShOD4q7/7t2QmOSQpZaH45Plzvr15KwlE\\nEC9A1LGS2+6XnM0eAP1iCmx2K8nug6fIMjrrxJsxFeizc57z03OSJOHs5IxPf/lz8tRQ5ilfffMt\\n6WjC+eT83d5RvCdZYriaZnRRbzjVYnHW66/en6WcTCbgPecPLmj2W3716a94/t5T0kQuirXSf+wV\\nkryHoI8U8YM4NzKT623A4KVPacVRRyFBPTGaurF0tq9MAkmW8/DqIXfbCuccq+WK0XjMvmoYRi1M\\n36MHAbwLUacYnA3RH7Z3w+CYLMb72gfKvpepA3Te0VjYtyY6sTgGwwkffvQxzvcyc1ElK8Kj78A4\\nQN15ikSzbWys6IXJmSeaxjpWVU83u/cIx2Dc7/vDG/3OS4Qgc9lFOWCxuOPJoyeYxPDi9Su01jx/\\n9ITZyQkhSPU3Hp9TZIaygJ/86V+y22/565/+Z3SqZd53v6eM2q2JNrTe0TQdShvqppIX9zJqZtI0\\n9i2lrZP0s47hKEAgQIM/XOBDchqZ2iqur0DgYjpjtdtJayqEw7rvg7Gzgmb1/pgcUpxj/5IgaHdm\\nEkJwMhrXB0utyf5/0t6sV7LkytL7zOyMPvsdY47IkUwWq0hWlaRWNyC1AL0L+ouC/oKgp4aEfhC6\\nu9iolorMOTMyxht39Nn9TGamh23nuEcw2SygnAhGxh18OMfM9t5rr71WFFMHrVsF9LMUjxOUxGiU\\nMei6ZnI05uT4lOt3FwFR8Cijsd5jehmzoqQ3GFHXFo2mrKqubSOtkYaq9txeX4cioWQymdJLh8xX\\na1SSk8Q5u7IWYwEdhfOdYBYdsdttydIMlLDHo/6Aqi65mq85Hg8k4Y4yXFXKqFGUEuUjdNYn8WtJ\\ntIcjkdJtKvp5n7puSNMUwn1K05Q0TVmv11RVxXazRRtNXdU8fvSYxjvWiwXbzYY8UoyimE1RcvXy\\nO6ZHE5IkYtnL+Vpp7j++R6+quF5vWNc127JARzG96ZAT4ymMGG28LUpe+NmHq757/MWAuavsAdwn\\nm0qa/G7vg8jePUKyVkDDXz37HdoY/umHfwTtuFrMOB2PeXV3Jaa5SUrd1AJHhBm3dqd5F6AwJQdl\\n4z27piaPY0rkZ60SS5myKOWQ0FIVGi+9gsYJjBrHIqZrQ4XZyuGJXJb8jFLSrB/2+kyGwiLb7Hbc\\nrpZS9WhZGM45rhdzHp6cURvDthacPlIyK7hetEr3h0eHYtjLwqC8bCARht+LOIvAgu8W9qH+pG2s\\nLCI8Hz36lF9/8huyJNsfZgdzck1Tsy5WDPOBjEW0B5kClA+zq2F4P0jE/fjuLb/77HNulguquu7G\\nHhI8qVJkRtNgSOO8+zzWNfz48jsGgz69WPwtyyiiFyeiq6skrzJac3J0iseBdUxHQ25vBDpMkqTr\\n6cnYhTx3YhSno5TSyuxcFGYsjVZd0Iy04vb6muPJhKOjKW9fv+btm7ecHB/z7KOntDLB1lrxElVS\\nJXqMGOqGZKBFMDuA3Hmslh6lrRtUmOUsqxrrLHXjhaFHS9IClKHfE7H5qpfx5tUbPv/VL8nyVAhc\\nAWp1+EDc8YFE56mtiHTUBxXm4aNLgA4DrhMD4KrxlHXDzqhgQK3CugjQLwEk3W+rgyWpqGpH1o8x\\nFWSxJokNZWVZFDU/81YOlnX7hKoTgTr8nj+A/8Hz6OFjHj58RFNXxFHME20YDoYyf+ff3ym7yor5\\ntYG7coc30M9TMWxQUNsG5yxl1dAfT7ChnG1JN+39bAVIWmjUhXkegQL3rY72GoMErVb030TBDgxR\\n0kmThIt3Fx3PwhwgVu0IiXdtW2cPS7eBs7Xfa0l3RpnAXZCAmRgRDHBKEccGb+XMwIqAwc31NZkB\\nnSTgPJvVkspa0sGAi4t3pGnKrq5IRmPSrIetd4CmaUoaa4iigNTVnn6vD3jx/LWOKEqxxY4X7654\\n/vwFg/EEpyNOzh9IX11p0iynWC6wWgQ+288qlbZGxzlXb3/go4dnkghaSPo9Ep3QeM+qdBwj7GZj\\njPha2sCizvvsioKi2DEcDun1evQCP6Xf31fhdWAZj8djlssFVd0wyCIW5YadNnz3/Dv++//hf2S7\\nvGM+u+UmNhT3zih2K9DQ857t3Yw4iRiPzpgUO75brzkaT+iVFRssmYm43az/zOL/ZwTMbcDX21Ut\\nG6HjEXZ5pdYK4wjD48KOM0rxq6e/5dHpUy5u39DP+6R6GxJBR233BALp7RwSXPR70FTjLLqp8daJ\\nUkNVsC5BR6IH6ZQPjf0A6TkbxjUa0NDUTQeJ+aDr2W6qPI4ZBwWI1XbD1WxG1dTd5/UEI2slZUlt\\nLTeLBSfDMS9vrkLwljm8KBao+fChgIHWqDiiCQpGqDAsr4P2bZt8hKq7/dzeOZxt+PjRZzw4e8Sj\\ns8fd97vnV747tKwTNt2bpubXn/32PcKBUopNtSWN4i57blmt725v+ezBI755/UqILk78SA2wqRt8\\nlJAmaWBbembzW8qqEQlD2+CUOD4MYhnX8EoRRQmnJ+d4JEj3sozZekNvNIJdQa/XJ817MvsazvXY\\nKI4HCWUjkolRCI7GiLyYCdyw+c01rnF89cc/8MXf/A2D4Yj+cMTDR0/Ybkt6eYyPPKBpxzfa/l0b\\ndLXZw9ctFC6CGS1pQ3VBzWjFoJdQVjLzVzdWtFkzgbE3m4LZ/A5rHSaR+cskzSiDDFy79qzzMu9r\\n5TnKRuBV8ZA9BDXffwg6IBWj8gJja+cpG0tcy0xqEplQgR+wM9uA8uETeiFPJUZxMkhZFDWLbd22\\nIv/00cGQHwTx9nW6gqp99/vqyinZO2naQynPdDLtnvNDsQClFG+ur3j15gf+9le/IVJibmCdGMEX\\nZRmMnYWMVFoX/C19l3y5YPjs1Z4F3zHxA5rVOpC0s68q/Exb3XnnqV2NiSKOR2Ou5jP5bGEd2ZB4\\nt6iADtfch2sLvltvhPva1p3OOYwRiBGlyJJEft8TEBBHHIlAisojadd4jzMp1A0X8zmPnj0lj4/J\\ntWFxc0ddOVSvT5L1SNOI/mDK/OpKJATDC2+3BZFJ0CaVXqCVM3DYH3JzveTNyxdU9Y75VUESGRJd\\n4xpPb3REamJWq1V3zdr734rJFJVMJRRFhRlN8MYQ5wNe3F4IyatcstlV1E7GUnp5T6TtlPBKyrKg\\n3+vL3Gnj0cpgjGa5XEjAbCxJltJU0vNsqkbabV5R6Zg4ihid3KNY3TIZZ0ymj1hf3bCua7yR9lcv\\nzUiMId2VZMqTJhHjNGU9nzPo9Ylrz81mST7481qyf1G4oKgatlXDtmzYlQ27smZXNhRVQxH+rurw\\nx4p8VxNYn60106g34Ysnv+Z6fsVqu+FoOJbsKjTxgdD83wcLSV72xq0aIR0VtmZbiXyUjwy7uumy\\ntKqqKZ2jsA2NdUFCzXTmu63ahtaafpZxMp7w+PScYb/PYrvh+cVbFpvNnrkbhplb1qpzVqoK77la\\n3HHv+EQOBCum0rWzRFEU3DgOjijvqZoaX9dd5tm6G+RZTi/NiOOYPElDEN3PiuEhiVM+ffI5//Tt\\nP1LV5Z68c/hHSeDsZ31++exXfPbklwJDtpWUkoTkv3z9+zAoHb/3vZfXV0TGcDqZBncUjTKaom6w\\nxqCjpCPBEDaLMRGxNqRJzGg46YK+98ICbGzgVXt49fInqroURrO1RFoxv71is1x0jOtIK06HKbva\\n0jgxSjZKWKkCGcqBcvnmLZcXlySJVLar+Zy7uzuGgyHbzVaE73UrSqD2xBSlUC0J5gDq1pqOZGRM\\nhAr4pfIqwLiGRvgfQvPPE8ajHlkSg29wTmTJkiimrCruZstwADvem6u1vus/Vo2lrBuKWmYqhSW9\\nNxr48NHO9nWzm86JlZGVoFnUNkgKCpt7zwgVZKiNgkYr8kQz7sWksWG1rSmtowyydu9jqu3f+99v\\ngxzQVWr7uHkY6kOVqfY+PW3l6/xevLHribJ/3u12Te0dX7/4hodnJ7QC/NL3tagAWRdNgwuJrQuj\\nbiY8h7UyoylOQYomBNH2jDkk+kSBXOStwwc2bLv3T8Zj0QUuyz2sGT5n6xzUlf4giZbf36uDq9H9\\n2wXnpCRJBQmztpP5E69W+XxV02B0JJ6qx0eYXs5qu8NkPXScsipr6rphenZCbzTEBzOAPE1RdcPF\\nm3diDlCUVLVDCfYD2rBarcUdCk2SZuw2c87Pj0l6A6bTCZ9//IR3L35gtVxQru6YXV8zPj7n6OQB\\naT4QEqJzuMbiTcxqvWFycsp8U1EXWybHJ/jeiHw8ZTZfMJockfYH1I0QmlRIWBtbU1Yy8pflIlHn\\nXHumW27vbtlsNjRNHazCemRZzs3NDaPhgNJajo6P8XVFkvX545dfs1oteXl1w+l4jN2VeOdY7LbY\\nfh9ja+5PJ1xe3/Jd3bCNDNnRhFEvp64qJlmPeru3avvw8RcD5q5qwh/LrrJsK8uuDkO2taOoPWUr\\n2dW0gcPTOPZehoFx+LtP/p7aKh4EdlyreuG970SQga7UbzVYbVhMLauwqGtq27ArC4q6pqgryqqi\\ncZa6aagay842WCviCo1tgui2Ztwf8PD4lLPxFOccF3c3XM1n7OoKFFR1TVXXwSZMfCfrumZXFOzK\\nijIs0rvliqIsOR1Pu+5+01iKsiJJ4m42CyBNUyonIu++/XwhcFsnrD7X9sQOMuEW/fpXf/NvGPYG\\noDRxFO+/Hw75//iH/4dtsaFVrYl0RD/v0crdtdngN6++omzKLmnoenda/Au/f/uGJ6dnImNnRbow\\nShJMFPHLT34bYqWAfafH5xxPjmmqikgbxqNjIhNLdaMkxYjYw5E3N9cUZc3das2bq2vWVUPVCKzZ\\nElCOBgnroqEJPRut2rEgYWy2ijvWiW+hNgaTJFxei4O90ZGQBLQhjSKM2gdDFRR62kTE+dYUWMJJ\\npAWeVcqFKkJhItP1PZPY4BEDgPW2YjbfoLzDNg3ldkcaG6xzfPL5Zzx9+ojXL97Iug8qPjYkklXj\\nQqBsg6WVfqjvhjr+5OH9AemHVl83OJ44R914qtpSVgIZ2wA9tuuvVdUZ5RH9ROaQV0XNqrAUjbzH\\nJPqZ194j/e07+eCN/flzo63YW2n1n6uZP/x10SJec7eY4RXcrVfs6orz6UTGlpwEvbKqZP/XNQ9P\\nTnBWDIGN0kFBC7xSlHUVWKjCemz7jZ1AwSFSc7D3dBiw72UZo7zH9XKxh30PdXiRxFdLRrbvw7cX\\nJyQ3kvjKQhBL0FANi5wXrdyeczYkiJIYisaxxRojGTpy0gAAIABJREFU5hRZTjwckaWJBGulKJoa\\n0+uhkpwoSQQNc7BY7tgWO1brDaBYbzZBoafPZr2WXquRvVUUWx7ePxJZuu2Ks6MxP33/DTrLmY6G\\nzK8vKeotRbFit12gjGYwPmYwPibJe8Rxynq75ezsnEQ7xqcPeHu3lLESrXj67BnLbUEUGabTKb08\\nZ7vdCCnOCPSd532axmKbhjiOGI0GHUs2z3OOT05IkoRer0dZlpycnmKdZzFfkEYxOhuQZSlxnLOY\\nr9mutxxNx6ReJFR3dcFNXfHo0SNG1jK5u+Pm7aUEbufZVRWjQZ+yqemn/wLST1nbLjs87Ie1h7VS\\nisjJQdYeQJowfKtCE9IrlJdq5+n9X7LevJZ5QecF62Sf8XfkHwKtv60OA1RyyCT03mOrEgKk0lhL\\npIR0YvHsAKU0aZwwzPv0s4xdWXC3WrItC1lcQYy5CcG6HTmoQzXYQjcd+BwgWrzn7e0ND45PuF7M\\nus/gvZfGeGQCvVrRj4KtTmsErA4EG0I/pA4b/HDjaRRRnHB2dE5sIv7t3/1PKMLnDPOiX//0FW+v\\nXjJf3/HFR7/mwclDkjjZv18U62LNNz/9gcVmLnNgXlieLYzU+nNuy4Lr+ZyPz+/xzetXKBf0L7WR\\nHmoYKZJnVSw3C1IjDi1Z3iMNruxRkL7bVVKpOe9JMsngRkdnLJcLHn/8KaBpQhCa5BGzbUUd1pIY\\nb0sQa4lHLbwcxRG32w3xbMbTj57x4/OfSJSY2MZRRJbGe3RA7d+vUoSZ0P36bkc8nG6HD+XnLEgj\\npg3ayrFYC4S23VXMZnNeFQX9fp/IGMbDAffOpnhb8uTxOW/eXFHsSjBxN2crwdJSlA1VEGhomsCY\\nZd9L6z7oBw+Hx3Twroy5NNZTK6lYi7ohiTRppElM+Ds2AVZ2rHc11u+bKS0RqKzFXFzaEAev7cMl\\n8chJfxALDh/q4P8PAeX9x3gf5/V4lN8nc2VVksQJzlm+f/E1i+1KUAWjuFutqJuG88mEi9k1TS1B\\nKDKG2jYc5Sn1dUWSpWR5xq4oUAgEZxuPL8TWLY2Tzp2oDWpGt0bOImvnQhBp9+T59IjrxQJrG/lU\\nAbZt+5Dtc7TjIoePPbTr3rtksrfbWWAfZjK9HPpa9pKIq0hQvV1sGKQJZdmw3q0Y9PpMez2ME0jS\\n5Cl1UeO9oCNJJMLyu9WMk+Nj7hYbBsNRmGuEKE5Z3F2hlBK1m0FOvd1gPFjTo2cSLl695PGTBwwi\\nQ7lesV4sSAY9lE3QkaLYLSk2C5IkZzgYkWQ51l+zWdyRjaYkk1MGaslydsu9+w+orePBwwes12u8\\n94xGI3a7HdvtVqDpqibvxWhj6OUpm80mJBBybti6ZrlccnZ2hvdiYTcejymLArzDpBnWzzg5Oubm\\n8oJ+rkgV3M5nxCaiRtbMbFcwMhFZHPNXH33Cm9//ns0gJxtPKIwiVjG/OBny/N27n91/8M8ImJW1\\n+7XQ9jFCH6llKvrQCFeRwFbSJxa6ulKgXNDd1IpRPmW7fcfxaMLNcg6tYE54kfeUN5xsrG4w2H+4\\nU9uxboFVvPfUrsGWMpDcy3JGvQGNtSw2Gy5nt6BU56PYyl15J1WJdTZUSIrmwILow7lGUZjRLIsN\\nn+WPGWY5y922g3u0TkTLVClOB31UkLFJjaZU0CDi9N41SCUt8nGNk4pWjGal0plMj0niGDzkeQ9Q\\nxCrCectXP37Fl8//QJZlaK346sUf+PHNt5xOzxj2hqRxzsX1W97dvRVpuHANW5p2Z0ytpBRzzvF2\\ndstvPvqEe9MpF3czIh1RVBXX80vOju7L+w0XxihDY4N/nPd4Zylrj61qto3lF7/8nQzoA4PBiLOz\\neyilOXcyc9nIPBLDzHC3rqmtrCtjpKprg2V0AJkqFI+fPObx40copfjpx+fcOzsnUhFZEhPHhiR4\\nW+rArG2JCYdU0cOax3lQDoGP0URKdIbbfpzRnqqRDTxfLNBRxG67Jev12RYV3nnulnO89ag4oTea\\ncn7/VCBY7ylri9rVVLXMUNbWUbq9Zq4LBUjL0/QfrvPujRJsreQ9GyCNRAVpmEWM84hRX3rIWkHZ\\nWDalfS8Ad8/sW3UfGZPJU0lKrDgPyGGvDta+UmLQrtrBn/c2YhcP9995v22wf8g98Mp3BeyPL3/g\\nk6efgoLFaoWJBebf7nagFfP1Gu8dp+MJF80Nu7IkSRLyOCGy0gbxCpqqod/rEZuYxXpFVVVilJBl\\nwSVHob2icE33mVxjO2TLGCPJc9MwHQzZFQWbYtchJm2d360dLeuxtRhrZzLbq+xb+PqghyyFgfTF\\nlVKdOLvRhsxobCNVmbiZaAZ5ymZXCIvfOlARR+MeVxevBbb1Cu3FSGA8GjI0MLu74vT8nG++/ZrR\\n8QlZlrJeF2S9nKapRFo0NvT6GZF23NzekWQDtlYzPn3E2xffSuGiFXnWRylDgyINIh0eWTt1U7Db\\nzClVyWQ0Ih6OOe4Nmc/u2G42DAdDVDibQZC2siwpQqJZFAWbzYZ+fygM7STizZs3nJ6ehrPUMhgM\\n8HnG7O6Osiyx1nJ2dsZ6JUmVNmIthhOGu1WG3WZNf5AS1Q16taZJJ0SxYVkVqKMjRlrz44sX/Hef\\nf8Z3t7fcKBgNh4yyIa9XK+J/iR9m3RzebIHkCDCE0QpvFKBBBUk85zGBkOCUUPiVCYHPexrXsNoV\\nnI9PuF7O8d6FbEIWXKvw0DRNCGhhNrLrSx30T5SQM1ptW601w7zHOB+SJQmbouDt7U03v3kIQ7aL\\nt4Ur2/nM9rndz2x8gCg2pHGCQpEaGZV5cn6P7y/eyCB9UVLXMjIzynO0p5sBbZqazl4swKLt68ZR\\nRJ6kVEoJ69d60rzH333x9wfvJPRnXM2/+4d/x7ZckwSrMem1gveOd7cX3C6uu/5IG9CUknvgrMUH\\n8fO62ROv4lj0er9/+4bPHz1ivtmxLUqMbijKHUVZkIYZJe89j+4/5dvv/z+KsubIRHhlqJuK0jnq\\nEFhtOCBOTu4FFxIboEqPVp48jZitS+oAMQkU27KudYBVw9c8rFcbhsMexhiWyyXL5ZosG5BmEf1+\\nFshcItvoHYGMpcMNDPBuOOwImafSQRDde7wXWNMFjVXnGuJEMV9uaOqG7W5LXVmiOOLu9prRZIKO\\nIx4/esK3X34prvCNo6mkmqydKPjUQWhikInINh52tQtyk9KLrJ30PJWCcS/meJDu5f9CcppGmihS\\n4lzhW/hV5iWrZcmiaBhmCb00IjYagqPPn3u0knhV40hjw861Xj0HIbFd/Dr8QvvltgJ979FWjnDQ\\n3JRfD4cgeLQRsh7WMl8t+PK7P9LPhYUdRTIHHOABwLMqtlR1wePTM56/fUMWJ1JFK88oiSmjCG8l\\nmDdNHarAMEIWRYGgYnCNyLTZIADStYJa3oCC0aDPIM95efmu+/gi0dleMwmCsYmkMvW+q1bb9/se\\neyoEzI4W5BVNHUZ5tEb50JpoGtG9DhJwKpxLlW1Idc4gj8iSiKIqIEpJUtvxK9IkIY1ldvbm4h1n\\nzz4mS3JGgwFaOS43G87uPWQbLPXSNCWODJEzVCqnqhXaNaxWcyZHQ8ZHY5Ik5fXLl1QaTkZj2h6R\\nCtdABe3XxWzG6PgeP7x6y0dPH7OZzXn20ccURcF2V+DwImXqxbWoLYqyLKMsS6JIkyYpu922GzP0\\nzhEnCXme8+bNG0bjMZPplDevX5PnOdOjI3568YL7Dx/gvSfvZaRZShLHVEVA5kZjrLX8w2pDNB2y\\n2G24aSZoozieTunnOcpavl5uKKKYmwbOhiOWy/mf3S9/WRrP77Ne6UvIBnOq7c/sN1YToLPGerSW\\nA9AphXKgtMN5jULzbjHj2ckR6nU4qEKG11iLDYO2LUlDXm/fRFdt6cC+t5MnKeNen1HeY1dXzDYr\\nNrdFN9tkjAnSYzbseUXri9fBnwfQrPeeXppRFCVetVml6SoeDcQatHfcreb8zbNPWa5XXK9bsofA\\nNmVTY7yQj6TzJ9moBJEgPq81Wks/I46iUBlnLFdrsiRl0BuKRNTijuPpCVVT8+9//39RVFuSECjl\\nTJLNaIwhQmDLtufSevT54JaulQTXqmk6JZQ2eGul2VUl72YzPnvwmP/3++94cP6YR/ee0QrstQeD\\nOEgkNMEPcpD3uZ1vuX/+mOPjc4GffevQEXw9bVvlKvLYcLeuaCxB6k4O2zZAtEQfFQ6P5XLD1fUd\\n9/0J1tUsZktGkwkEen5d1yJSoA3aBEJKgOvbs8sgNkqqqzb31YD34Oxe+UcOL3DOEMfirjBfLNis\\ntgxGI+49ecJP336HU6J9+ekXvyJJU4pSeufWQ1VbNmXDelexCwSfOlwXrRXt8Ic6iD6mzamQa2dD\\n4tE62Fgvripei0BEbLTMtyYGtMy/Nl4SV2P2bPMPgd4upimx/Rr3IorK7b/oD2DY9ufVPgiEL4Tv\\nqcNCU77WIlLhHlrb8OX3f6CpG377q99hjGFb7nh4fp/X715ydfOO4XgYZq2l7VDVFWiwtmFeVERx\\nxP3TU5T3JE2N9TW5b7hebEnSuBvziONYVJusparEN9HWjXAd6lpmNc37a996YYWfT4+5mt0F3deD\\nSjmsTxdIbc66wPTcazS3sOohsr5XQgrzsQdnqj7o+ZdVHVAgcZRJ44RiV2C9J409Fs1qu2XYy0my\\nBIuQEJXWZFnEME24ff2GdVWTrbc8+vxzYtdwe3vNYNjDNjL0n2UJ02GfWGt+eH1J3u/T1Jb57RWj\\nYca9R5/g8RSrLTbSHJ+edJ+jzQmUl2kDaxtsuFfeiWRjVRYslwuyfp9pf0jd1Hjv2e12ci/SlN1u\\nR6/X6wJoURakaUaWZSyXS0ajEduN9DmHQzF02G7Ee3S9XtPv9cmzjMFgwIsXL9htNpyf38MkKaAo\\nNztWyYZysUQUqEco7/jDm5f8m08+J71bsl2tOD454qGBF0WFURHrsmA8HPLnHn+R9NPOLnYstUDg\\nkX+7QGZoyT1BONuJNVOrYOO6BSJzVKP+lMo2nE2Ou6Anh6MmTqIuCOxzNL+/SWGhxYHu/en9Rzw8\\nPsUreHN3w+X8Tly2jUjuVU0D3ndSeG2b33nfMecIz3n4b9s0pElML81I44QkiTr3dOtEmaX2nqqs\\nuJzdcjqedJCM9+L72LhW0i5caCXB2oS5rZZ40pKcdKh6oigiy1Ksc/zv/8f/xou3Ikq83m34/R//\\nA+tiSRzt3093QIVr1SqIgAyEJ7Fo9uqwqHUw002jiDzPiIJ4ahRFQdPV8ub6irKu+J//23/LR48+\\npTWS7q4XoJTGRAnDoYgPjKdHPH38GUdHZ4El7WTe0Im3pQ0D+5GRYDnb1AGWPShiVJil1UqIPkZU\\nRL775nturq85PR4xn9+xWRcM+gPyJGXS79H2fvE+uB5IoJCkLfSKuornQ8iz7Y+KapVSnl2xpbQy\\ne7nZFhRlwbvLK+7du8fDJ48Y9HO8c9x/8oR798756quvpFKrG/msAXYuGseuaihbUXnru71UVJZt\\n6VgXNYtNzXxdcbsuuV2VzLc116uCm1XB3aZisa1ZlUK2q62jFffwft8Ptw65xs4FcQTYH9M//zhs\\nTdSNjGq0WMb7rNc9vNq1KQ6Cyb6q2idvBz8i99ZENLahsXWXxNzMrnl9+YoqjGIVZSH3KiR6LTLT\\nkvYWmw13yyXT4ZhnR0OK1YyNjApiGysKYs6J2L+S88taS1EUFFVFK5gu/WwX8oL99bl3dMTNbMZ6\\ntxNxkxDsD322ld+PxbUfUNS8XKccti8u31cQ2vNAVHfO6XCdPPJ+0jQB7ynKktoKk76qK6owhyiY\\nnmLQi8kTw2SQMUxiNrd3vLq+Ynx+3tkv5vmQ3XpNmvYpig1KwXQyIVae20UJSZ/lasNk2CPtD7m9\\nu6XZFdJb157p6UlHEjx83y0DWhvN0XTMrig5PTmmqBtMPmC2WlNWjsVyQVmWNE3DybEE3n4ukKfW\\nml5P1LHqWozXp1MZOSqKkuOjU6qyYrlcYsJ9VUrR6/XY7XYkScLV1RW2rvnoo09YrdecnJ5hxme8\\nurxGZQmPPnqGShP6ecZfHd/jrHb84fIt6dGU5WrN2/mcj58940kUsV4sqJqKu/W/YA4zELm6Tado\\nF4+XQs8pwKEB7UA5Re002oF2Hu1End87cMajveLZ6SdYu+TedMvl7E7gjTghCeyvKIo6WNMoTVnV\\nXYU77A2Yjsb0kpSirrjdLNmWQh1umWMej4m0NOuV7gJ2WMFY6DZO1JFDpPJrmqbrYxJgOmOMwESB\\nEu06AW1HZiIuZzO+ePrRwcYLGrFOmIxZFEkvz3m8kt+NgkLPPmDKhpJehcUYQ1FXjAcDLu8u+M/f\\n/IMYJUeGLM26hdsGRu/a5wXCjFgLmzuP9Pe0PGfZWBLj0BiUgTiKQpLTiPSU9+A156cfM+7nLIua\\ni5tbinLH/dOHbSsb5xy//uXv8MhY0Ga94ej4LIxIyIEtMnAuMDsVSaTIYs3duqRuDoFvhdb74XMV\\n0IrF3R3L+ZzecIRvCr777gd6/SGTyRjnYZDExGmMsoYklllY58R+SylAG5Hv4v2DrL3gWu+z5q5L\\n5TxxElPVDuca3l3dMT0+Iktznv/4gv5wSJRlvPvxR7749a8ZDAc8fPZMRM0Dsaux0rssKmFtVyGR\\nbLzbMydpCTzyxv40sKmDvw8SlYPi2KuWiRmEEfyemS4uKJKIHn7u95KsgxZHZT391FDZ/fvolHH+\\nzDt772uqTTwOBllC+6F9/O2v/o75YhZQn4a7+Q0QhEZimadsmoY4SbCuksBZ7+XjAArvuZjdMTk7\\nxvRGpJXDlxVFXaKN6eTljDHkQRyll+2lIVu0yYYRHK0F/Zn2+iiluF0tSOOE1idXRBH292A/jnSg\\negEoLdyHDtnYfytU4PukQu6ZC20HEYLR4XJVdY1zDZHT9LOESEGUiKdrnkRo5VGRJssk2c2MgbLk\\n6uaGfDzGOs1k3GdoDPPrtyRxQqk0TV2RxDGx9ry9mJMNjzC+gHrFsP+ITS2Wcd/9+Ipf/vUvUEZe\\n68M10/5byIM1WZJRupjKVUwmR3z/3bd88vFHmDjCVVLh53nO24u3jEYj4iRmmkzZbcUJaTKZsFgs\\naJqGzWaDUqJGFGURq+WSo6Mj8c8N/rJVWfLTT8958uQJ19dXTEdj0JrNekscGX73u7/l/3z+Ne9e\\nvcKdHuOGPTSay+2aYjIiMoa1MZjRgDg2XF5d88mzRyQv3vDNi5+YPH7yM6tbHn+xwtxvRIFPLVIl\\nSH+nzW7DCImVyrKxMkcn2W6YQZNUFwf00j7fvPmBk+G4I+B4hLmZJAkgTgvaRDgt4sv3jk/4xdOP\\nOJ0csS12vLx+x7vZDWVVhJEDGY3Ya0d6NLqjgRttwmCw7rK0LlOWFdA9hwuwb9v3BMR9BYEOXYBU\\nvRf1ofl2w+V8zr3J8XvXrvEyblPVNV4FKDAM9ePb6+JwgXzT2KarcrVWJGlCnKbcrm7IkkQgiLwH\\nqm3viLB4msTEsQg5GyPBOdHSY1VeRA2U8zjXkMYxWRRjlGZXFNhajKxRAbZ18IsnX/Dbz/8WrzTL\\nXc0oizieHHPvIFj6QOa5vLlgsbzDe8306BTrhMjSeB/Emh02GHfHEWSR5m5dUdYCgXVs4Q56VV2/\\nTisoqpKqrPB1QZRknJ6ekMSGUT/HALPZnLvZnDSJMCG712G8Rmsjc61hFrA9uz+sybtjzUufNE4i\\nlDF4b5ktNyxWO5Sz9Hs5x0cT3r5+zdnZGaOjY57/8BylDQSmsXxmmUEta0dZiTiBC0mDXLx2b3Uu\\nsvw5wYL/2r5s33kbdNtqRr4nbMww8vezj8MzsO2Xee+Izf6o77gCH3IHfuYAPXwuRej1vVelerzS\\nTCdHsneKguVmhVetzZ249dShoiT8dxT2tQ7XGO+5Wy354fqWjz7+lMQ5xoM+R5MpVVNzMpmKU0t4\\nX3ma7bWZWxhUK5QW/1AdRfTznOl4zLvbW1qgvLvAP3Pdu2B5eHnbXubBV1vXkhZiP3im7usO300D\\ngARd7VVnYNCSkcQZB/AOYyLKsibNEpI45asfnsNoTJz1SJOU4WCAMTG2saytYj6bY6KIfhpTN4bV\\nZsfFix+I7Zp8dMS7qyuO+xk3iwWf/fIT2Td/ghoQDNU1BkWktMyu2oZit8N7z9XVJaPRCG8i4jgi\\nSRLiOGazFVLkbrfj6uqK3W5Hf9AjSzOaumY8HksrrNcTe75Q0AxGQ6lkD8b8qromCx6mBpiMxxRl\\nyfm9c8bTY7xSHJ0/RjnP2zfvcN4z325YVCWLnYzZXJQFX13fME8yeifH7DZbTkYjfnvvPtWPP/Ln\\nHn85YB7kvS0k50M/ynuCPdE+q20C1V2CpusOUOnFyGLfVhsuF1fMNyvuT09pLa6c91RVJf9tLcO8\\nx+PjUz46e0AcxVzNb3k7v2G522K7oWE68k57Y9uRDBMZolYF5GDmqt2A3SyV34tpg8CYPrhRGCUE\\nC6k6g5hB6Au5AFFXdcNPFxecTaZB6SjM9hlDYW1HgFGIKoZSAvnGWpFEcbd1Dt+T0ZJR9rOUfpbS\\ny3tEQaEkjqJuw0eh72m0IU4ScFZscwIzNzImuIdI/poqEac2xmAiQ1lX1FUlvRgUWZby7etvma1n\\naDSV9WzLhmkv3i+K9lIqMCai1xuLMpCF2nKwBmQ91NZilNhJXS0KtlUQuQhVVwdbhQpFaZFgXFxd\\nkimDyQYCa+52lFXFeDyhqSyxiRiPJwxHQ4z2ZKlYekWxxkRxkP5Te0FV3j/Y5b677j56pdCxxiip\\nSnt5IlWmbfgv//RHvvzyS7RW/M1vf4PSmt1q3am9SAsCmia4iTSWsq67z9jOTr6/o/b76vDvP0VQ\\n3Qf/Vt3zdNyCsB+tpyNatWL23vmD39wnCQLteYzyXSJRN44sVh1MKF8XglbbS1YhTP9pMN0HSw6/\\nDiFx3QcNpTWvL18RxRG2rnHeYowOKI5AqErLWq3reo/EhLOirmtuNlv+8PYdD+/fZ5rlFLsdxhhu\\nZnfdeaIjgzFRx1doZx5b8ft2X96bTLm4vqYJhJQm9N3az+e8FUZtaG1I31JxmA606FZrHtDem30H\\nQL33t8Q+OetaxMo7R2w0cWSIjQntEksvjcBaKiuGFXUdkmsUu7ogGw1xVkZpyqrk7dUtW61Y64hs\\nekqSRJwcH5EmA77/9jtMU9LLpd+f+pJ+r8fVu7ecnB+RZMnBfQatTHf9tfQAUEHtTTnPbleJkLsP\\nezCKacqSqqoOKsaa6XTawcpN0+Cs59XrV6w3m04nO8syNtsNWZ7LWKH3NLVAtmmad+IzmIhiu2Yc\\nxAvk7IS72xvevHjO2fGUWiUoo9FJHAoUy4MsY1SXjLcr1kZx0dS8LGt2HkwSMxz2+cXTpx9uwP09\\n/rPfOdhWB/lmd8NbmLZ1ibeeEBjbLDv0Nq0PlkrgnMCDV8tL5ps7Lu6uuXd8RBQJOxPvyZOUk+GE\\nT+8/ZtTrM9+seH7zluvljMI2kmWEP2XdiE+dIujBKiJtiKMYE0WdC0o7kxcZmSeMQiUJdBWd0Zo0\\njt/bAFVTUzY1ZVXS+hcqLa+RJSlZmpJmYnhaNhWz1ZJHp2e0s5ptL6Vwjk3dsKkrbFOLCg6iJYon\\nbEj3ng/m+/J3+2HmODKhEo7J4lQ+b3jvzsnG096TaI1XHts02KoiU5pYiyGt9tKDzZKYUb9PmmVU\\nTUNZltRNLaIFB/Onm0oO4EkevVeseO8Zj45Db1qYftb6DpKsAywZKdEXvpjt2NYNZR2UbcLB3mXt\\nHIyCeJFHHI+H9DMZM6id5fjohKS9n1rTyzPyNCZNkkDqEuZgOxDdWhtrzXuznIfXNpwBuMbS1A2b\\n7YayKkWEo9hRVCWbbcHjJ08Y9HrcXt+w3Wx49ulHfP7FL7qqrvGOxosaUFm7ICLg/mTvcLCf3qvL\\n/Ac/qdoD+cOKTnHwJAcqONA6qzRtQPjTj4tqg18XFFUX1BonLjGR3gfNVtlGK9+V6ar7nsxdt++n\\nDZbtI9KGst7x7fMv+eq7P1BVoqLSNDW1rYgig4rEhDhLU0aDYfhMQijR4bAV42fpYcVxTBRFFMWO\\n1a5kVpSc5CnH0yPiKGY6npCH4XNxIdpXhELGksq+vab3j464W6/YVuUeVlZ0vdZOGUgL+uGsFb9X\\n2QTdHZG+qO32xj4s0vXXZb+H6x8usAroUxxFRLEhSxJ6WUaeGtJEY4w8R5ZEDLOMCBH62O52zBYr\\n7jYFOs5wPgi+ABGO11/+gV6UsFnOgzauoVjPMXaHiuHRwyfM5wtGkyk3tzO8Kzg7P0XYjSIIrlpx\\n93ZPoiRYeoEYlff0h0O8tQynY9arJUenJzx89JgXL16gtSaOZX9uNhuBkaOILMtYrZY8efIEYwx1\\n07DZbqnKiqOjY7I0Jc8zhq2u93ZLVRVMp0dY58h6Pb795ltG02PSTBSTri8vwXtu3/xERsnxeMhG\\n604Q/7PTc/56MuaZ98w2BSuluF7MaBRcuwY9yLFGMzyafrhrusdfDJhafdiGOARYCIPAe9i2hWWt\\nDRJ5riUH2QDBwYOjxzw5/ZiLxR2DvM//+m/+Fx4d3+fJyTmnowm1bfjh4jWvrt+xDoPHymhs04Sg\\nYFFGzFazVIKG0Yph3g+Sb/L+kljIL9qEAzSOSdP0vUa89Ce1LDMnA9zKiGZsS/BpvGdXFtR1TRPE\\n1iWjD6a/4bleXl9yPjkiNsJQtc5RVjI7VHuP14ZaiWpH3dQyq2rtvo/RZqTsK2Xp7emuZ9mJO+DB\\ni36otxalYJhkRNrgkArfEIKPVjL35RxiWw2ZkSqqCQ4jkTaYKCKOUjSGeycP31sH811DbBT91FDb\\nksXyBu/lMGnaP9YHlSdPZS1FVROHA+HtbMuuaiXcbNfnDITCMD4RyD54Xj1/wXfffM3Fq5fkccqg\\nl5MnOU1woB8PevQHGVFi0MrjAmTuvECeWss0F2AdAAAgAElEQVRgvwoD8JFSMpT+cw+lgnYoVLuS\\nytZst1veXFxzfX2Dd5Zf//pX7MqSH54/Z70ruby6oSpKTBzviW/WH+jD2q7XLXvl/dDVHaZ+HzZ9\\n1x54/+cOKzU6lOfwK+E1vCSnrWVcW1l2YJ8SNm47cnFYBXaC/FpRW08Wm44R3gVVpbqvGVQr8yDI\\ni2rbBAftDmC9XfLu6jXL9YzVbsk3z7/sene7ckeSClqSpQlt1ZxliQRJDWVZSsKrVDc+1TJTJWmu\\nWGx3bIDfPjzn3mRCWVdUdSXw6IHfZYtkHV6v88kUZx2z5aJjZtN9Lk1T110QlGso8KBwGeRatuxu\\nRRtQ5NN3TO9QKdNedxRtlhZHhsTEksTHEb00JY0iskSTJ5EoNGUJ/UTgVBfGwIqqxikte6q0eCXt\\npziOOJ2O6SvF8Og+14s102GGKjZcPv+Bly9/YnI85eHjJ0RRwvT0HnfrmtXijjxLRaWsq5JDMuDD\\nZ/Ny5oQOjsDJrsG7muFwyM3dgkF/QFWWbDZrgcPznNVqFYhWe73g3W6H857VakWSJFxfX1PXNbez\\nO2a3t2y3664P3e/3OQ+iBUfTKRcXF0zGE7L+gNpazs/Oub69YbNZ8+Kbf2Lai5nPbslHOfRzvFJk\\nqUxS9NBcr9b84+wWHWm0h6vlHBenXGx3lHHMTbX7+XOCf1bADH2ln/leWxmIYkUgeHh/cICGHlbo\\nZwoRREYXhvmIZ2dPBWKkRCt4N7vlp+t33K4WVLYJ9HmZC6urSnwRnUCSUrWozmS2aWqapoagXSr9\\nOE+aJHjvAyTZsu9cR6xRrZJJ+Dx1yB7j0AtVSnUQV900FFXFerOhrkQizzZN1z/alSU3yzlPz873\\ncFWoYFpZv5YEI0ILoa8ZgoZXdO4KSmv573bRIsL2eZJKn03L7ztribVGOYf2nl6kg2ycDtchBM0A\\nIaVxShrFxFqMq5uyZLXddi7jdVORZQl3i2u2xQbrJLCD59s3r7HVnNndS3q9oSQMfi+B2HjJ4Kta\\n5BQjLfXd69st2zIEy9pS1+LOUQfGqDo4dHQ4YKq6Isl6LFcblPMkJmY46HM87JMm4qtqjCaO9iMo\\nQvQR6FyaP1p6x94HSVPfHZTvz8wRkAiNU47lao3zit2upjeaoLTh8uINvX6PdDRlfHQSjJHnkhh5\\nuuSwqhvKWhCQwxDZwvH7Q/M9YO4gKZWTqAP73guW3TvuKtSul+nb/UVg6PpOn7ar/Np31FaM0AVB\\nuQew2Sy5un6Hbba4puyY2CHv6u5PC5fug2moOA+k45RS9PIBHz/5Bd7BerPh84++QGuDUpoHpw9Z\\nb7YSpJsAS3oLyothdPBFbPtXUjH7juEOUNY1KM9sW/D95TXPTk55PJmEnl90oKnbXrW9RvPJaEwW\\nJby7uw2olOkY623g97Rrw3TXKgpEMrzMhrc/c8gqlmDS2vcpvHu/Q621ltnJKKaXpaSRITGGJNjf\\nTXoJkbekkSY2kpCUjch2FlVDXYd2RkD10iRBG02WJaRpymg8YDCekkeOqKhoNmuurq6YnkwhEuRl\\nWxQkvSHff/cNowxG00m3Fto/GhGcaYMmIIEzzNrjoSk2nB5PJTD1xXmknakHgdSTJOkCaFEUJEnS\\nJf/b7ZZ+vw/ek6RpGFURNHG5XIrQvTEcTaestxsmkwlff/Uln376CU3TMJvNyLKMunE4naLSIbuy\\n5MVsjk8TnLVY69AWTFXz9bsbdBJLPzwghsvdjru65vV2x+y/Ehb/IktWh6Ew56TB6kMleYgQeZA4\\npbvWmRzGIajV2mIcaLu3afrXv/x7vnnzVagwFA9OP+Ht3X9ER/u+gPLSV3Ku7TNZojjGOSG7iORY\\n0817OmcxJhYSQfC2VEifEC9kJQ1gTNdTaawVNwqjA0mkhaMUrvO6k6DYZuWuzbi0xllLmiakYQFc\\nr5f81aOnvLy6pLKNmKwaI3J5oY+Ih0SrThoPJSSXxnlRRFLimVeHzxAb3TEhW8aeVsLexVoIUBVe\\nRNONEkUTp6TPaowQCBrboANMXXu5hj5JMLUkG20GWFQFP779ntdXL+llPYG3nCPGYdcRj+49pjYp\\nZd2Sdtr77qhqCYx5LJnI67ttgOqlj63DmkApERjQ+4WkApphrWM0GOIHQ+7mMyyKzETkWdJVpHGk\\nSSKFdT7ozcrqFI0CkdszCMytWpikOzjDYVa3VaDcE+c9WkdEScrzFy94dzPj/N45R0djmsaS9Qd8\\ndHLGDz+8IEkSzs9O9qxUK1ZbZRAh+BAKVUpY5aLoo0ELPtMh722lqfaHVfcPwr1XrXjfQZvTq25d\\ntH3LlqlbW0/iPW1erJSMI7RjJN1coW2Yz2+J44Qo1iyWd+A31I3l7fWM3/z6v8E7Ovur9nd9+/rs\\nYfX3HxKgnPf86tO/5j/903+gKLZkSYrSmvlqTllVWNcGKhE2adEC6xxpmna6znJ/WiRCd4nPaleQ\\nJTHzoqS8uuTjszOSJOHtfBHmj31okUjA1UoJyWc44qfLd12S6kPitB8Pa0lCpktg2kDpEWNpgWfD\\nHWnREiU62F0S5hqRvAtVfDsSlhpNbCIUjkEvx2AZ9VKqqkCrnF6WkMQRdVUK1G9dJ6Eoa0HTeOlj\\nJnFMVRUByXNkgzG3F3dETrNaz1mXBQ+ePeXo+Ijt3RxbVmwrzXI9Y5h6sjxH9bIPVxeH0wJ0CJjc\\nU6VbWNrhbU1ViepVv9+nLEuOj48lmAeFH+9ldK3f73cJz3q9ZjQaUYfphIuLC4FhQXTBgzl4S/Zp\\n6oYsTfnkk49RwSKwCaOD06Mpy+u3bJZ3DPoZP1YlPh8E/ornJDHMd1vuDIhiFXjv2JY7dK/P3WYl\\nIhTvpTbvP/5ihRnpvQeh1u2G+3BbhP+1fZRDElAjjWhjNMPcMMykt7jYNby8ueTh6eecjM/5xcPP\\n+Pj+Z3LR/d7uRyrHpmOlKr/vo7REHRM2m2iiWvIkYZjnRHHUXUwdRkiECKS7jLj1y4yiOPQHFQTV\\nDRCCUBoYdz6IJ7efPzKaXi8ni2NipciTmL4xXM1nPD2/hzZCysnjiFFPBodpg63znW+k1ipU3oos\\nikijNlhKdeituDDEWu4DXsgCnVmth0RpIiUb2yDJhNGaXhLL74XxFk+AZ71iW9dsy5I4aQXdpWeR\\nJgm9NBW7KGWxxZrMV8RNwcXNHT4actRPQAmr0jo5pMvKsq0a0kjjHLy4WUtlWVnK4KZROdep37RG\\nx/LicgBEWnP59h1X19cslisePngog+hG1HF6eSyWWkgV19576bm2LO0G55rQP9zDB62qUhpHWOtY\\nrNasVlvKssJZIYYlaczxdMzpySkPzk8ZDwas1lt+evk6BAjIeyIafXF52c0m14EVWzX+QMIxQJhq\\nD8+Z0HttTbBV2x9qgxm666EdJqVt7PTs4d3DGeK2PdLuvToETWuDyDfsodjwtwnw6s3dOxbLG5pm\\nS1XvWO6WXNzekiQpjx88Q+HZFksurl/S2AprGzbbJS/f/si7m1csV3dU1a4TyG8t2NTBZxj2R/Tz\\nPv/wx//Ev//P/zf/+NXveXv9uhvhkhEYiw/KRFEUkyQJURyHAOa65xJI1HbJtLWW9a7AArV3/PD2\\nLU+Pjng0HhEbgfVU+L1YGwZZzr3JlDdXl7RetO+Tpw7QB1ldgvCEVktbaTYHNn77e7avYPFyvhgT\\nC+tfGdI4Joki8igW55g0IjEQR/LfcaRIYy1krICKmJBct2dFEhuyxJDGAu36QK5sq2fbWLQSM+pK\\nZ/zx+U88/fxzeoMR496I1y9fobWirkrKsmK93sqo3Z9VhJIZ5n0ftu3PupD4yjW+f/8+1jsm4zGz\\n2awrFIQoaYL/rWe9XovKWJAttEEb++XLlxRFQZpl5FnG1eUlaZpSFAVxHDOfzbl4d8F8NqcqCoxW\\n3Du/R2QiIhMxv7zg6YNzsrzH7a5kNxqI9ZtS3BtPuatKviorXD/tUJrW73Oz3VIHQ5AqJGw/9/iL\\nFWYaaWocjVPghfatvToQi95nm97Lz3ilaZwn1zDKY0Z5jPOK+bbG+5okNqRGMcqPsM6zKmp6ccSD\\no4d8//ZryWDCRkjTtGu8t0zKltBhWpUapcjilFQbIX84YcHFxjDMcqpG5gvRhlgbKisWQe0ilMBE\\nqEIdtVMoDWlgqTXa4KynLMuuKtRhERvvRMnIQ+Kl2nlzc81vP/mcy+UM7ywxAqeqOAl6tSIcLabT\\nOsBhIpitEKeXPIpoDvqj3gqLkACR4JXMrjU2JCsyxxUH1wxtDAqHcuJOD7KpJOimVHhyYqxxREqj\\nkgQX5gQTI4wzZR2xc0zSiF1RgrUcH51SWsW6aDgdJry+LbDOUzbiaJPHonnZVZa+nT+V/k7b5bHK\\nE3XYYgvLwma9krm5/oBeL0c56ctY58jjmDQOFZUWRwnlDwJU62asVNdbNioOry8Hmq0bluWWspLP\\nendzG8ZFTtgWO/rekuYZvTzj3bVlvlpy/+FDPvr0U0wcs90VlLsNRjmmR8fCjLUuuI4EWy0vlVXL\\nltQhwVMICqMB46Va00FEvYP/Dvae//ALtFXQwTfUHmZsX9q5tpcp7kEWQwxdz7LtZa43c7yzGGqq\\npsb5mtX8FosVBSdbE8UpLy++C8Pnluv5NVmast5u8N6TpQk3c4v3ijRJiUxEGqckSUZsUqbj/ajV\\nv/rNv+bluxd8++IbbubXqNALlFUvSXUaidRj1dSdCo9RQtxoQj/SWglyWhsirambJiATlgrQcczl\\nesm98YjaOa7W62CM7oijmLPxhIu72z3iFK59J6ISkpm2wsQHMQzAhaitQzbgnCOODc69T4/UgVlv\\nlOkQIWeF2T3qZVRVTRbHKDxZYLenmYxYaCUVdxIZiu2GylqyIF7gnbgiaa3ZFSVCaBNilJzDSua0\\nowRnHZdvfuLZp89onONsOqXYFEQK4qzPMBrz+t0lymjiVObgW3eZ99Zb+5kC1OhajD+gG0pHVI1j\\ntpgzGo9wzjEcDlkul4CMCCZhProJVeNms6GuayEEBeUfZy2nZ2csFwtOT087KHe73VIUBfPFnMl4\\nzPfffsv9X32BRfHu8lLIanVFFGnyNObyzS1m0KfBo70QSWvn+PLyisvdVlo1YXNVTY0qd1LIGI1z\\nFv0vgWSTSKoWrGjFWt737DtAlGTRK8Uoixj1EgxQ1JbFVly2k0iTxvsewbN7v6S2ntXOMs4Tjkcn\\nfPHkr3h+8QO1q0nieB8okaoqiWMSbbqgFUdxl+HYqmZbrhgkmulgSNU4EgM7FVNrzSY08LM4YlMU\\nGNHgC44kEuxqG1i06v9n7D2fJcuu7L7fMdekf76qurqrHTwGA4LAgBopKCpIRlDBCP2vkj7ogxQi\\nQzMiOUMzBgMMbFd1V1d1mWfS53XH6MM+9+YrACNMRtfr51/mveecvffaa69lCJ3DCT2VcSmkhN4q\\nKIaIQyjOjQpyXVC0Sjbc2/WSTx484os3X4mubpSxDe8lWx1lvYtFcgRJKjvii6dR3qOVVA0qXWB1\\njwqviajoeDAd0aQRmKrtGBHQRFksPVIUksmtEnhWxYj2EWKgUBBVRMfUN41IPxSYWENuNF3bUmi4\\n3ta8/+iCEGFdOULUXC0Knr7ZUbWBcW5oOs9XywNdSCMbMQ5bsF+GAXEHibGHy1JvyGh2VY3yHYvF\\njPVyzdlsQZaJakvnOqraMy5HZJlFaQlWJDZq53zyE5Red/SSUNlM5k4BQtSstzXj8Zi67rjbVLRB\\ncXGpyPOc1jnW17fMJmO+++1vsN4eaA8Vpshwh44iL3j/g0dUVcXi/IK6FQeQzgk57HjQxCPhI7UW\\n+uBZWHEQcS5IKwGpUEJKHvrD+/dFUcXxY4VKJ9oRdu2v9ztM9T4QcOy1aQVFXrDd3lJVe3x3IHYj\\nurpGoWjampfXDYvpQdi/3rNvDonY1JJZsTuT6jUm14nD0E9r1x0uOD7lm5wtLod+1Ufvfcyb21fs\\nqm1i1DuCF+hS+n6Rum0YFwXeBxl78mIhh1LvzFOO8nzoZ/ZKMA5oFLxe76jbjicnJ2giL5MbyePL\\nK25XK+pW7PyMMdI3jSQHlVS13SNs0VeNCYKU/ZiCSGoN9bcopq/1yFT0gZ69T4J6fQjMxzm+a9BR\\noVUm3qpBKlbZC0bgXmPAhwRdB3Tyx9Qh4qOiZw73Kjh5lnEyP+H61Ut8d2C1vOH08bexNqPIcl4u\\n30pRkWzm2qZhMRnLefrbSOSwHuPwmhXHClqSFk1MbbHNesXF5RWHg6yD+Xw+6MYaY9hutzjnmE6n\\nTCYT1us1bdtSFIW4/uTSfy2Sgs/V1RXOOSbjycC23Ww2lGUhVmYBqu2W0WhEQPSeTyYl73/0DZ7v\\n1pyMLetqhw+Bk3LEV7c37LpmGDGSlxSok9AFDqzO+PG3/nv+occ/osK0KCWGn10n2YYPMZXox2Uy\\nKSzzUUaZaXaN4+26wgWp3DKrsff6niF6IiLMnhlNtML2WkxHKGWouxprM2JaOP3AckhOHkGLBU6b\\nbHdi01AYw8RayDJudjv2tQMCk9EIY7PUE0xi585R5iKgLnqR4DpHFwK5yaE/iFFYVLIic8xGBZOy\\nZLndDoHIBdlsTaqIR2WJzSxfrW64Ovk683JMXR1QqOSH6NBajIaHeUo0TdeJy4SWfqXRMgZSe59g\\ncLnBZYJyOu9p6pb9ZsPlYozNCm5RxChyXZlW9FNhoWd+oFHJl6+0Fh88RZZRu77fGwVWU4pMG3Id\\nyXTAY7jeHji5eMzJyYXMWsbA9dZxMs54cFIQVpGqdbxYHoaxkj6493svKlABgo5JzCBBYSr1faKM\\nvIzGY1bLFdPJhMJmKKMocssol7naLLNJ2CCBaEEq7aqp0VVMhAND0JrQBYoo/aYYAm0jG3S9Xovg\\ngNLst2tevJDXf/ngATYvqbuO8XjCeFRyd7vk5auv0Fru13ix4MHjx1StT60H8YAdiCX98xrOW2ln\\nxBjJjGaaw7blnUDYf2+IYlTQS98NQ/T9r1Wk+wgDVTH9oj6P7avNfuSrr2CFARokg47w6tVTmnpH\\nMZkyLQrebFZiyKw1zouwwGIyYVcd6PtklW/wwTEyubhHIG2LfgQELWSdfizp1y9/wY9mZxhtqJoD\\nm/2G+WTB3W4JSWVHBOflNXTeyX1K7kNEIRF2ifwzMMaVHWYme5MBMwQvC1qxaRvau1s+ODkBpbHF\\nmJv1in1Tp4sugVF8aO/fDCkDjDYDBOmdJ8vNkPxF+vNMgpYImkjrxOYZmbUC/Ss5ozIt7Q6BS4UH\\nMR2XKCKdCxBlHK8XKIhJuIEUwNvO4QNo05OfBMm5T7BSROaLhSQZ1QobHVcPr1DaSlBpOg7LN5ws\\nZuhiwmZ5i7EZmRXJiH6W8Xht7vUyY9/F7REUqTij9+hyyvpQc3Z2hvcdWVJF6400euH16nBAaY1N\\n0wuj0UgqyxDYJ+JPXhQQIuWoxDvHZDrFOcdms6EoCj5/9own7z+m7TrulisuLi44WZzwH/7j/8uD\\nR4/49a/+ji4L7B89YlqOuJifsKkPfHF3w45ASOe5iv2ekUQ2hij66K5hVv7DWrL/iApTg7IopKem\\nvEd5cAGsVUwKy6zMaF1gU3W8WYlyTC9UjtYoLx0WXL/+0s5P60F7WFUdj+Ylj87f54s3n9E4cTh3\\nzqXswuKiSS7kGhsNZ9O5qOQoza4+sHaOPMvJZ3O6KFXSm6bhyjt0ljG2BW2Qxnlf/XTB4TufmKDg\\nokP5fm5LDn0RBtdUjUh1nc7nbPd7WueIzid4NlC7jnrTspjO0Nbw4uYtH15e8ctnnxG0OEJ0wWGj\\nFdJPykxDEJzfpSwxxEh0DqutiIWnRRrS3FNmDGOtmczG6BBZ1x2jbsf5qKQOOfumI9dykDWdw6GI\\nKXu2qaIsdKRWJPlA4aBEwCpF8I5MC6zXeEXtIRvPyCen+HAkYfkQ+fL2wIeXY67mBf/x15sEA4Y0\\nX/ku+UWEpdMa6N+nz6gV29WG7XrNg8srtLLMp1NxhykyjA7DeAEx0jYCsSkjCUdIEIuLkuTlRUHV\\nNAQfyLwXJCRK//b69ha0ISpRIrm9WWHzklFZ8PzzZ6z3gm5MRhn5eMput+PQduSF4uNPvo7NCprO\\nixhHYGD7QmpPhJ5QcE/lKn3NaEVUBmKXzp44VCX9dSHyDlQIRyKQHJWpz9njOz2Jp68EeLd/2P8S\\nreDm9iXz2QVNvWU+mbIPDcG3eK1QLqYxmUDVVKA1tW8osozb1WrgAVjgkLwIcy1iGk3XCoqhk/5q\\nFCjuUNdCrPKBP/tv/z7NVB6TpSxLxLog5sE9zNkPuPcz0p0TSUrF0S7KdZ18f++8k8wVXPDYaIQl\\nnBnuNmu+8d4TXq7XVMk/16bkNIQg96NvGUTRfz6OcEky0FeG7z6O97zvXwqhR5MbyzizeASut0bG\\nv3QaYdM6DnuSKBKG1spMtVTsFtfUuH7xRCFg1k1HWRbsD9UQKEkgg7YZWmesNmumpycENlyMcrLx\\nTAzOW0dUmvHiEvIRVefYb9ecXU4o8kKCtFLDdR/2bb8oe4g6rVeZJZWq+fT0FHWQM7tt20GVJ8sy\\nlFK8ffuWs/PzofqMMVLXgvQ451ivVlxcXrK6u8PajLvbFR9++GSYk6/reihIsqLEWAm649GIn/7s\\nZ2yvX/No/jGta1hdntPGbvD9nI8mnIznnI5mrHYblocNLnFiIpFJMeNyfkmeiZ/u4f9nrOQfETAV\\nKC1KH6mhn48Mo0wgkvWh4eXyIFnSbzVc5FAUqrs48g7voJJjhtEKr1Sazws8Or3g+5/+iP/0iz9L\\n0nZp7sl58swOrEyUYlsfGFnRWiyLktaJ/uzXFjO2bYfThrtd5KAMuvOUoWFR5jQYWjyHpG6jjaFL\\n5s06iImsCpK5CqVaKurMWrSCjMB7iwX7rmO52xO8MME65/AElpsVi+mc13e3XC1OOL+44Ha1wkeH\\ndwE90nRpHiz6QKFBxUhpDVZbWudQypAbS+WFYae1zBsSI6135GgKJRvI2IyoDN41ZCiylDGpfqHD\\nQNxyUZRE2k76PrnWR5jY5rSuobSW3lqtcU6gPVdzfnY1jA71vbtpaXj2Zsu4yPjGwxl/+8VqYGtK\\nTzsOyyKqezZaKXL2Yy9GKzbrJWU5wlrL+w8XAGS5Tt9jyTKTEgyhbPtWRMZDQLQovcDKbddxd3eL\\nUkbcWRD3FhUVTdexbxx3t9c0TcPJ1RWjxZw317dcnC/48vkrTk/P2G53bMuS7bMvGZ2c8r0f/ABt\\nLc75JNIQ08hUTL1mhsphWP9RnlsfOFX6epNEHVzsTQHUMVhyDIypTZT2i3xdoOUwfGM/yqDS+3r4\\n3HGu9TgCorhb32JNjtvf0ezuyPOCfefFBs2K1rBzjjwXGHx/qLg8PWVvhbRRZBnedayqhvM8Y1nt\\nyUalICPEe+bmUeYh25rMWja7FejIeFJQVTWQZkKHuUrNqCxFNCMdZMGn+cnQWz7FhHRJsiSjHtLf\\n9N4LQdEagpMWhHMel2Wcn19yt7ym1IYHixNeLW8HMQKBCxkq8d5XtyfU6XRWqUyCdpFaKdDvr/5W\\nKHJryK0dWi4EYUHnNhugU4WQeUIIVN4zHxV0UapiOdbkhgfvMZmlqxuyPKetatpORuzquk3FxjFg\\n+hCYjCa0bU1uFLUPzBZzXj7/km89fMzyq+fEouTJR5+w7RS7Q0VZlOy3a4onZ2T57wkF/daNRxa0\\nSotRlJT8EDg3mzW2nGKsGcY87sOo/UyltZYiz7m5vmGxkD2+Xq958OABVS1V6osXL/jWt77B3d0d\\no9GIsiiHsR6b5xitefb0GQ8fXnFze0uWZXz++XMmtmP23gOui14cXhDEu92W292akSlYTGZ8+vAD\\nDm3Far9he9hzaPZ8/nbHbDznZHLKarf+3WuRHn8wYIqXYKDMLJkRv7Vd3bE6dGyrTnpVEZQekIx3\\noaYoIynKyE3VSuOQxd0LGQRpp7GpugTxPeJ8esGmWg9Dxz0sW+QZ0cuB62Jg5ysyY8mtiItPtcKo\\nDt/uOfWB988v+Pl6J/3I6KmrmovFgqVSOKWwUTa5uAz0EF+CMJ1PDW5NSP2ODKmyfWwoY+TjizOu\\nDxX7umY2mbDe7dDWsj/ssXnGr15+yR9/9DVe395J79EaUdQJnlFRMB2V1F1LjkJFhfctZSa9NKsj\\neVBsXAshkNuMWa5RJsfgaVoZWxH4RnN7CCJhpxV58i0dZTZpmcpGjUHRdp7Ky4brnEvsWdhWByFc\\neJmVHBclaE3jPfttg1ImOdNLtTspDKtDy6tVTdXt+fqDKd99POevv1imAJAy8NRA60lbQxTQpIRI\\nmNhN1RBdYGe3zOdTASNjwGiLsTKEfqymxAVmu684VBU+aLq2ExhtXPDqzZJXr17xne98m/1uj75+\\nS5blnF1coLA0MfLk61/j5PQUiDKXGjzl4ozxeMzq7o4sy/joW9+iKApEAlEEF2TWWFCKzvkE3wuc\\nfb/lmHCVIRDGKMFVQWLXyoHai+XFPotXcfjZHjg6HqbHk+v+7J/8S0xclRSTjFTume51eQ8spgt+\\n+Yu/4uOrSyZWswueJgjcSEI8yjwXhrHW4D0qiPRYFwJtXdE0YpH1uqlE7d87mqbFWCNwspa97rzn\\nbHGFQvE3v/wrekhpPB6x2W6H+cp+jnp7cFLBGovvnIxthaP9Vg8rBy8iKFpFIp7WdRhrsUHjG0FM\\n1tstmbW8N5lxvT8Q6hqrYZGPsRdXvLy9wacgFlPw89EPQTSzGcGHewlL0jfWvcDI8U7rdE7KP0OO\\nkvXkZKa0Td6+RWboOgetQ2Uyc3loWrExcw5rc4H4YwAjCZPzQaQajSV27ZDcou5B9YC1OUVRUh02\\njOZT2sMWXYwYz2bQNRBhV7Usishme6BZbrh7+TkfvX/F6dl5sk5/93FfLbdnyUZFImHJ+5GA0gab\\nTzB5xhdffMF8Ph8sFauqoigKNpsNTRT2WxkAACAASURBVNMwGo9xqX9sraVpGiaTCSEEysSInc/n\\nvL2+ZrNe87VPv0bTittJXTd8+snX2O22gii0Avtu1yu+/c2vEZoN11+9Jjx5iLZHJyeV4tChazis\\nGl5tbpiXY04nCx6dXLI+7Lg7bFlVa1aHFWXy/P19jz8YMGejjNyKx+WuctzuO9rOE6IQgkjC4T5o\\nwu94DMaEvCZPRpP+r8VG6X5WHYFDGzifysI6mZxyu715B4LR2tK23ZDVjMtRgmm0LP4Y2beBz79c\\n863HDxiHwOs3r/ijiwueZpauaTnUNdvNktniVJizKBiV7FsnPdF0kW1yFc7Spu6i4G+tdxTWihm2\\n64hdzcNxSZhOeLvfU+Q5ddMQtDhXrPc7rjcrHpxd8KuXz2nbjvFsSqFyRiNpvi/yjIiibWupgnxD\\noSWTVFlJFWXkom4aYsyYWclOLxYz7tY7LmYFh8ahtGF5ELmxUZnRdg4dwRCxmSGGpEEZIihh0Xol\\n8NGuarBZhrCcFVFZ3qw2VHVFzEu+/fH302C8QFMSLDverGuaNL7wky9XfOfxnO8/OeFvPl8Oayim\\nQ/1YQiVCTKqC+kH4+WzCfDrl/OyM3PbjGGLFJIQM+XGDIRIGn0jn5aCKmeLm+pppO2GxOKcDnj77\\njNOTCxRStb98fcOhafnBj38sMmmJwTYpMnY1XFxeSoUwGUtQDoHWJcu6kEQ4+nnHIHq5ARJzGVSv\\nrpOSwNi3fNK8oo8RFcQRJghUIn20e6FxEFUbNrxAfpoEdw6p/3FUpFeT0Wn0yBojw/DWUOYZmsBf\\n/91f4FVgNJ3QNns6m+GjsDI719E0DcoaGhfxBHQQMtzddsuDi3M+f/0KEDMEtPTDD21LoEURk8tG\\ngEZex9XiAX/89X8CRLRV2GgH0o7s56NylTGGUVkOYgUhythVX/GZlDD3SVeM4XguKDXMPPdQYJkX\\nPDo7Y1dVrPc7pkXB1CjCfkM5nvLB1SWvbu9SsJR7Y7VItDnvpAIQOANUmvvUOs0FGgiK+/lL8AFt\\nLQTIC0sM3cCcjSGgMoPrBCVzrYiBNE1LmRyaTBo1ijGSW0ubRilChKqqh3n1PnmK99ZFjDCfn7Dd\\nrQm9xFwXuNuvOF0sqLcHbt7cUnUtf/rRp2St5u6rL1luN3z7k/fQwbG8O3D+4Hw4t3V/fvd/R1Zq\\nqvJ14lQorMnZ1R37dsfp2RlnZ2cD+3W1WnF+fj5UmOvNhnEn62w2E5/f7XbL2dkZRVHw7NkzLi8v\\nyfMCrTWPHjzk5vaGoihYLpfM53ParuHt9Vs++fhjfvbTn9Fsb3nv/Q/YvFjyne98h+dPf4MyRiQi\\ne9/S+1Em3bN1tWNd7ciM5WQy48n5QxFdcZ4ym/6D8fAfNYe5qzzrgxMGqVLkVieBYKE+Z+lmD15w\\nvwPNyhMeqslhv6cF0EN2wK7xTMuM9X5NP1gdY++LJxlFURYQZcxDMkNh07noqYKnzgt+9tVr9s6x\\nGBV09Z5HuuOD+ZTJZEy0hhzH5Tgj15GFCsytZlEUFEphIokc46lcRxtlNMMYi7UZViuIjtNpieta\\nXHNgtVoO1mBH+E1Eon/14gvmkzGTsiQSWG03OO/YVY146IUArgUlm7LpHLu6oXaRTdOIODEwnYxx\\nIbLtPAHNZl+hjEGgU1FV6oJCm4xDK0IJ3nW4kMYdCLQBsiQPGKKCEKjqJmnkirBE1yUSizHMzx5S\\naM352YNBKH1aWna14+2mkuQpCLwYY+Qnz1e4AD/4+Iw0OsnQ/xhWRA8jqgGSvX79BjCczk8wVqXK\\nQ5NncuhnVmN0RmZyvA/UbZOEsC3rzYZfPf0Nt5s1zkY+++Ip62rL48fv8fDhY4iB2+WGt7dLGuB7\\nP/qhKEklFqnVmsaFZEfWayKLqEXrI11IQgD3GKchHR59itj3sX7vQ6VAmNAW50X5KtIzzhX9to6/\\n58djglgHJZn0p3oBAj0kHzqpX8m+zI2myCy3dy9QWvPD7/9zJtbyZJozLoRE5aOMBB26DmUtrfe0\\nvkUrmWkMMbJrK2KEaVlSZhYLlFnJtz/5Y2bljOlozsOz9zidnfPtj7/Hv/jhv+Zf/ejf8Cd/9Kei\\nOqU0h7oZqsUIqWqXANh1ggzUjejM9vBbiNIGiUhC0pskhBDItBmgWKU1o6Ic4PrMGN6/vGRTHXi7\\nvBN+gXccfOSA5m6zYWQMj09PyWwmsH2aj82soFXCXVBD3zyEkJS3BCFRPQOrh0XTMs+t7B/nRfnK\\n0zPdU5976Ekmc3cv8LGgPUH0qzuHNhab3EkEwn2XhHNvaTEeTYgx0NQ1IXoOdY1Os+Wz8ZhtC2o0\\n45sfPsJkOSazzBYnTBbn7PcNt7cblrdL2ZWR3wqWMOCxCceQ803EX0xWoMYnNE2THJMkqejvYZVc\\nTJxzzKZTus5xenpK13bD13u50tlsxnK9RSe3qbZtOT055e7ujgcPHgy97fl8TohwcXHJzfrAT/7L\\nn/GDH/6Am82Gg7XH1k8vIKJ66Uctey0qVJTeeecd15slv3r9nDfrO4yC89k/HDD/YIVZdZJBmwRZ\\nWpDsiySH1ecf7lhVxmMK1K+NIwltAP7jvZ8/LoR97Zgu8gEKBnPcaLEfPpdsJ4QO30purpMKiVKK\\nVim6YsRfvFnxYR45OZ0zDRHd7lksJgSTnMutYjTOUAouspKOSONHdFGxrWrarmVWJhHnCIfWSZYf\\nPNEHqrYV6arMEpVmnpW83Gwpi4I2jZ8EoHWO37x8ztcevc9frlf4ENnXFaezBdPoQWe4IApDHgWZ\\noulk0QVU6tsGtBdFDu88OnomuSHLlECOOqeKMM4LtlXN2MKm85S5laDYivLLpMzYtx2BiFEyJyYz\\nkQGVxBPyzFK7wKFzfOvr3yLPS0EJCEyKjPW+4W7f0rkwaMcOJJ8If/d8yTffm/PDj8756y/u6Nyx\\nIpLWgiyOXvXHaM2oLFju9ixXS6yNnJ2cYJKRbiQSvUXr1DeMDh+haRy73YHxdMbl+w/p2o7FyQl8\\n5ztUh4r1cgXWQpHznR/8gHI0SptEoNCeqGNyTdWGAfZLXXZJBEIgEYuHR0yLWgg4x95lTMnfO9Ap\\nsVdrHD4Oqed0//wTFuK9j+9Vl8fgGBNTtodhjxW6NprMKPLkdFFmkmy4ZoNSgc8++1vm0xkXkxFN\\nU3HwkczKGIaIeytq54bXnAFtI6hD2zmWuw3jYsTruxs676mqipv1HY8v3+d7X/++DPWnTd9XbUcD\\nAcW/+tG/5s//6v9he1hj+qrxHr2/8x1Wm0E9pof0RBM3jVtFqW7uS1b2PIg2OZpMRiOu5idsqj27\\nqgIlsnsK6LQhOoFzv7q749HpOR9fXPA8zfL1U5kxVZX370dfDVutccFTZFZGRtItlqDm6YJBBcgz\\njQqOrpMZTquFRSutlj7wyHkorznivPAKxkWG1ffE2/vgmoLtce49opVhMp2xvLs9BngN1iiKTD42\\noym3P/srHp9+wv5wIDjPeDLl5PScbnvN3WrD2dXFUMgMoyPp/c51g/NPPyOvAaXl/k3HY/7+N8+Y\\nLxbc3NxwenrKer3m9PSU29tbtNZcXV3x7NmzwfsyL0v2+z3n5+fsdjt8EBm88/NzFHLO5VnGqzev\\nB9bteDxmuVxyfn7OerNit98yLy3TySV/+V/+M/sHDzCzcZr9Tv3vd9DOOMCzfdIZSbIUCvZNxaGr\\naY/Swb/z+IMB02iIUYkUk5abbPR99D4Q0Yk1GdAqDgfOsPn7d+IAJKTkrA+aR6OcSOSru7eMciGj\\nlGWOax29aHKf+fW+bL0QQK8NG4LMRFpjycYlL+ua65s1Gk2z2fDhYsL5XOTe9k0H2rJ1Hl/fMY6R\\ny9NTgjbMRwZfFujoaDwoqylQNA7aqGkjBA+ZjVRNSxMNTben9VJlFkVBbjNOplPW+z3rqqL1jo8e\\nPOSL67c0bctyuaIrc3TdUGY5FYGAYZRnjHSJUVDYTA5t51hMSpTvaJSmbhybNtLtaqIy5EbUjPZd\\ny0eLEZtOILK660lSMCnMACWSAnEbPIUFlPjfWWPw0aeD2DCfniaGYGRWWDa1Y111Mqh/r58Ziceg\\nAfz85ZpPrqb86JNz/vbzJYcuDHNe/UGvEiqhlQyEx+CGoW2tFN51YAxWGSJeFIKcWBztq5qbuzW/\\n+s1T8qLgh//sx5TlSDSLiWRFwcn5BcamQzkeHTx64lGIkcyIHVbjj6M1chiJgYAfDpHjau5lEXWQ\\ndDwivzuiMCoShqTg3gaIxw9Cqtz6gHmfS6zuVyzD5+KR1NPvHY5qOkalXqVJKjBDVW7wdeCw2zCf\\nndFWW8rQMSpzjAvcdFLdRWPE2JqjQEjVtuR5MWi53m02fOPxBxitOSRD4NY5Xt58yZvla6y1/OAb\\nP+JscT7s5L6aVCjKYsxsMuNufTN4Wt7XF3XODRJ4IAlYlsYPeuZxz7gcYFx99EnUWkQPHp2dcb1a\\nsdnvk9ylkISwhqptZVRhPMZqxevVHRcnpzy6OOdmtSSi6ZSccW3shpz/WMmnJMs5WS9KifhAutfe\\ny5z2qCiGQKrUUes4coToe+EDg0GRDOObjizLk561oQsJvk6GAsMjHuuOyXRBtT/gfYe1hsm4JFOR\\n7W7PYj4lD7C+uebxo3O5Fk5xc3PNxcU5eW7xRYlrDoSuY787MJ2UxCQl0bcRZJ67T/CUOLUoGbvx\\nzrG/u2Uxn9O1HUWWs16tsZk4kiilxB8zRqbT6ZBEtXWdhFdkz9dVPdh/TadTgU3Xa8q84HZ5l/yR\\nDRcXF9ze3nL9+jWLkSULDXsdmT58xDPXYrN8uFYRMeDIrKWpa2kDDnf1WNH1QTQSKEzJw7N3jSfu\\nP/5gwMyMISa9whgUSkd0UKDBBE3QoHVMxA3w6t2KcXhG9zY6vxUkVVpdCXii6uDJ5SM+f/tCYNjc\\nQjw6dpAOuz5Itq7DGgvpIJICxhGURuUZnbHkQDEa89Y5Xt3s+OBMiYTToSIPAZ/lVG3H6rCDpqFD\\n83q9p7I5LkROpjOC1rQhZS9Jug5yMpsRI+RoLiYWm+V8dXtLriELHeezGYW1NF3DN598zKENjIoJ\\n2/2Ot8vXKCLWWEZFSZ5FYjRM84x5maOVoupy6rrmxAaWXcC7gNEpOTCWsVbsXeDUdDTKcLPZcjYu\\nWKaDNKJoukDduWHGsg2iOxtVpPFAaEQm0EDjPBHDfDqlqveUxZhxbtg2jl3thn7ekQ17b94y9nCO\\n4un1jtYFfvDxGX//YsOmbt9ZEnq4/5E3L7/i8uKC0WhEVIFezC2GQBvEtb1qG+q6oe0crfM8+/wL\\nPv70E04vLqQS92GA/Ps157skmZdOK5+eX0w7apJn7JtE9hgWKASf+o2kGVjVVx/H19krL2kilpD0\\nlBUqJPhUx75cHbZAn1jc39Ty2d/CcxOK0mf8cr2SALrutWHVUFVkSTKtyI69S3xD01ScnT6kdTUf\\nXoxY327otms6bSlsSSCTkae6S5ZrgjIERH0KJKhOxxPuthvmkymtc7SJDeu8p3MtTVvz909/yj//\\nwb+QwXpt+LO/+neUWYEPntY5VutbRqMxznV4L9qg49GIumko0xhQ7zbUW2AJ5JpOlHuJRB/Ypc8X\\nOJlMWUym3G427KoD1prB3za3ls45qWCNcAFCLv361WbF+fyU9y8fcHN3jXHgkEoy3XBCfPeI1cYM\\nc8Bt21EWhcxqIyNowTmCtWm+VItPpeqRgoixoihjlcJaRXBQNy5BhrI+206MJEJU4tPaa0cTxLIM\\nRZaL/+vddkVmLeNxwchaDoctWWYpjGG5XPPm+a/4p9/9hPnpI75cLsmzjNFoyosvXzDNc8ZlLpaD\\nbYeajCQhUTqxYENqi/VrEqKKWJvRHHaYbIw3GUVRUuQ5XdtydnGO1pr9fi+jRYeDJC1KUZblAK07\\n5zHW8NnT3/DNb34DrQ273Y7JeEJdVewPe7pO2nB5XtC2DWZU0jYN9eoto8ZSTnIaa/lKKUajkjaN\\ni/R7LoTAuCgZ25x1tccl+bt30J3h3mqu5g9oUmvg9z3+YMBc7q5ZTC7xwQ2VQUz2GiqZaZieraeE\\nKv5OSd/3X4YNr48wkiLJUCWYQQn0NRufcjEb8Y33bvm7L39Fiegcqni0wOp/fz+nGVIA6N0vCOCR\\npq8PkZglL0elUJMZzw6OB+2tqF/UDm0N2keq0DHPchya6eWEAs2ybrltW6ZFgdIyL1mYHJMZNtWe\\ns4ml6YR0ZLWi0I5HJ6fcrFfo0Zip9njnaRpPe5jwg08/4t/9zd+g0eQ2R2vDeDRiu9tStQ2Z7yiy\\nMVNEV/PBdMxN5zlUB9pO4dN4hLFGoEmlGGeGvfOMck2hS3Y+kpkEexoxij00nRBXUvUYnZN+gVJ0\\n2KSUk9F5x91+hbGZ+N0Vhk3tqVJg6Y3Bpad5b7bynTxJPnixPNA6z3ffP+GL2x1vN02633q47957\\nXPDUhwNNWaI1eKcJidRRNQ37w4G67Viv1qw3OzaHiun8DBekj9K5o31cD12hkkwc/XiHOOqQekbW\\nyNo7tIKMKHVPjSgdDnJ5Bp8RgW+CfG+ICROJ/RxvBDROp0Cb/g3grEoV+DsQLUR+J8U85php/xjV\\nE3v0ULHoVL0YA5kRtaiy7/eqSNPseHD1mNXmLa9f/prR6YRNI8mliwq8J1eaQxqmRzGQOmKQflp/\\nUhqjudusefLwIevDflgDgyKRUqz3K+pWZvGMlhGgt8u3eO8pi5xyVEpPC1HF8t6JNJlS1E0zqNv4\\nGJMYQq8frYb528gxYegP3ovFCZOiTHJ3MuIVnAcjB6ZW4ubjvE9oBTRNyyjPcT6w2q4ZFyPO5ifc\\nrFaUStFZO6AMasDx5M70JvE2adQ2dc24HIES9Eu0fD3WiIiDuKZ4TEyG71YTvEPs8fxwHU1q+nsf\\nBLlILGyR2YtE50WVyxqMNpycnbFe3jEa5ZSZEP0UIgM6zktePfuCyXSWZhenvN52dNWeB1cPCQR0\\ndIRGEDFtYDIZJZhXJ0hTpet8xBOVQryGyXizqnkwz3h7K2Mkb9++xXnHg0cPaWqRUnSuYzwec3d3\\nx3w2YzQes1mvyfOcbb1jPpKRkZubW8bjMaeLE968eUOeizCGsRajxc6w3h/41c9+ysmkoNQtjx6/\\nxy+ub3mjFTt7TNx79Eil/VO7jjLPGYWCum3xSq538AER+pN7m9uMm90N3378vX8gGv4jSD8/ef5f\\nIfp3ZZNSei5lbG8xFNPWZ3iiQ7BUKTNWWujuWmP7kYKByBCPWXeESMm//P7/RKmztDEcIdG+e/x+\\n8LkLwpjsEiGgZ8/1PU2IdD6JcWuD14qYZXzl4Ms2sNOaXec4RMXJ7IQ33rAlY1l73mx3HNqWxkfq\\nKL3FNgTumoq7qsIrKxJ+RIrcEhS0naMgMikKithREoiuYaHhsy8/52615nw6pW4blFFgYN8cyEcF\\nk/GUZeNY7WvquubzN7e4w4agFb++O/B6d+DtdsfBe5ooxt2tSxCX0RyqlkPbcWg6ETwInq5tsSRC\\nkrLMcpkLa5zHKCiNoswsGMv1eksHoDQfPHjC+cmCQyNm3T5BsyFACMml4p3u3P23cYBC324b/vrz\\nJU/OJnx6NXv3+yJkmSQNAcVuvSU4yaC7TiTPotLM53NevHzNy9dvMUXB4uycDz/5kMv3HlInS62m\\nk9fU+UibxODrzlHXnqpxHJqOqnHUnYjBZ1qxS0mE+LYyEH8U0hvTSmOV6JkO69ck3860rm0mgv7i\\nK3oUydeDCHnfe+TdayM58O8ES+i/V2rw4W+aREwxmsyKi4018nGeGemnW2EMawVnJ1dSLaaKb7Xb\\n8+nVBTYEyiKn6hwuClrQJhUtrWTesPNehAEiZDaj6VqZcXUiTaiVJrcWa226LprOdfyf/+n/YFSO\\nMNrwP/7Tf8mPv/un/Js//beEEKmqSsg1mcVkwoxtu45DlUy6qwNVU1N3LbvDnn1d0biOqm2GPd0P\\n1ffV5cOzM3Jr+er2RnqAWuBba61on6aA3jrHaFQm9xCR74tBjNRb51jutxzajgfnl1xMx1iE4Jgl\\n0o+ClBCn+fEEDfeSjtI31MN5Rwh0TrRrxZBbhF7yTMRD8nQf+3nknqeg0oxm07pEMOuFQiSZyTJR\\n+losFuA7QhAoVmDsgFUGnWT9yrNTZoszvHNUejachbFr2d7dcnn1gOvrNzx6/B6d8+RZJqWNsJDo\\nw6RC9RLNKJVUoDpQo1NqCk5OTjk7O5ee5MUFZVmyXq/x3nFycsLhcBj8iJd3d1hrybKMphGv44cP\\nH3KyOOHBgwc47yhSsJwvFkTg9OyM3XrDm1dfkoeatl7y8YdP+PmLl7wuSvb2PrJ1PI0mZUmeZRTJ\\nrUprg0m2gFrrwR83EslsxsXsgg/PP+Jscs4/9PjDFebmLZtqxaRYvEOjlsNO4MlwD99W6ig71MOt\\nonAijWibNGWtkUyt99vsF1r/67tE339wesGr1XW6gZKbeh/fsfdJT2aAbDrnMKqf8YoYa1FEuq7F\\nGCsOCcYQrKFOGVw0hiZEfvL6WqAb3wlhRMsAt4qB+nBAWYMPIpg8LUe0Xcfr1ZpsNCLTmlwrwfaD\\np3OOVRfwPmdTN5gQuG492/CaP/nWtxm/eE7tHVXbYrWW+csgVV4s53SLSyZTT9c1fDI/p+la8qwg\\nEnh784br5RsyG8EYXq42KK2ZFTldA6NMVILaCKM8o24dJh3PRjkmpQgSDxCPEkf7Yjxhezgwn86Y\\nlPJzrT/OCcZ7m/t+VTkkTf0i+a0ocGg7/tvTa/74owu++96CL252wyKvm4YsL3j5+jWnsxk3t9dY\\nA59+/BGzyYgiz9js9iiT8f7HH7M4OSEvR3QhcmjcIMN3hIdJHqZCrumrztBXKEolF56Mu70IVmRG\\ng+0PhtR60ELEInRoY1NFKQP6PTpoVVKtSeSpSMQFjTYB5ROS4iGoePQ9HeDY/hPH3ulxb93vUSan\\njUTo0BxNiQ2KLNOibpTpdHjLAH9fSa83d4zGYxYcuLt5zbrpqB041ZuPK0qbsaur4f6CoDeiQBXB\\ngTKW1W7HYjLhZrVMGswMIgLWCAz6lz/9T5zOzsjznPOTSw6N6NFKv1yqIxMjOjsq6vQQq5iYW2KI\\nyYkGESRJvcqegW614YOLS6qm5qvlnfTDlRZnnyx/N3FGjJqbpmVcloI6OccozzBeGOcxBlb7HdZo\\nFotT9tWBFk0XpFr3QzsIehPdmHqpypjkAamYluMhuBp9ZPnbhKjl1jLKNXXdDGu1F/LwMdCqDhXT\\nGAsylw0CA/dyokU5AmVZrW7pOkfcy992QdFZT6cy8izjYjTjl5+94OLiguXNW148+xWffvwxt8s7\\nnnz4MT/92U8ZlxlNW7NYzMQJinf3sep3foxkxYjDdkfMp6z3B8bzcxrnmI8n7LZb3nv/MePxmN1u\\nx2q94urqCkAUfSYTmrYV2LbrhrGSQ1Xxwfvv8/Szz2jqGmMts/kMGzzbzVaq9KZhtVrSHHboWPPg\\n8RPuOsdtXrCnTfXbuyziXpDGas24KDk0FX1fXRmN6/zAbHe+w3UtXy1f8cNP/jsym//eWAj/iIDp\\nQuDQbJmPTpP3YurDhOOYyKDe0j9Z1T/ptLjVcbA3S1lwZszQlO/Z2sdujTx+/vIpHz14xMvlWzQk\\n3VcFUR9Zc/dIEn1P03tP1JJxdUo2u1fgXKRUBqIf6OieQNcJ4cNoTcgMlfMYK7JWsWsgakaFGLRm\\nNuNQN1RNS9s0nE8nzOYzfIB9fWBkDePCUodIWRSstjuW1QZtDVEZHpyfEb3jbnnLk6srfv7l8wGu\\nbpoaoxSno5JRHlju3hKBPMt5s3xORDMuJ1yePuKPvvF9Qgj87S//ilFe8uH736FuGn759O9YjHMm\\nhaV2csD4BLVWdYfRSirGEBjnBfu6EaBRiYu7LRSPLi6YjkbsDgdevPmS87PHCZk7svNiHxOPb9JB\\n/7vgYg84uqj4yfMl334857sfnPDVnTidoCyPP/qQclTQVBV1VzMezVlvVmS5YbXZ8urVNdlkyoNH\\n7+FjpO4CVSdzUy5R8yMSKGO4Z9id+ppH71EJRItRzqZ2VJ0oH2kVMX1m3Sd5StM2W7zriDEwm1+m\\nVFZQFW1UsiiTTM8p6T9nAZzXOB2IQRF0TAbCqb8+7BV5K9S5d7PRIyTbqzyl6jb9XwzUJVvOraHI\\nNFkK8OYestO1FVMb2NctZJrN7sDk9IRRjNy1EQds93sh02lN07a4ZJuXZRlt16GUxqukwKPgdDpl\\nVBQcmmaA1LXS4sQSI2+Wr/nq9iXRB6bTGc53FFkmA/pdK5WUUpRZPqynpm1QRqy3nPe46GQW0xhI\\nxLJ+n09HIy7mC5a7LdvD/iiAYNTgnZgmdVJPVotBVQjUTZPUijx1K8o9Jrkw+RC5W28otOajhw/5\\n/O0tRM8wkzkgKum+aYFtAUZFQdO2Ai0jraa+Cs1zi0JT5EkDmYgBunS7ZR0ltMxHYasj/XBZJrK+\\nghcCTp5PuLu7xXWttKrSGei7jiaIVuukGPHm7YYYPBdXD2ld5OLiXJSUrKIYlWw3a04yMUw4O5sO\\n9wWkQFDKcNTVhbYNHGJJcJr5ww+ED+A9+8Oe0WjEfD5neXcnCWtKxO7u7nDOUSgRnGjbljz5Y45G\\nI6bTKev1msxm7A57zs7OOdQtRZbxwZMP+K9/8Zd88OQDVnc31Ls73v/et3he1XS2YFdmCZk8VsT3\\nW4Gd98zLEbnSdKlFIAL2imhETUkZmfPuOk8butTa+y0+wb3HHwyYMUae3zxlNjpD65Fk8v0AewwD\\n+eO+WLRK9F2NYOMy35Q2dprhtEZmxgzqWGWqY5B13vHnP/0P/NNPvs7js0u+Wt4QffJfoz8Aj6LN\\nfeYZ043WOgyf6xCYQ/zsAk3qe6qQxM4Jw1hK1/squo4YYDaeszts2e13qYw35DYXqNAH3my3rJVi\\nUowI2qDajgLPvvEs6w6vFOPJ89F2BgAAIABJREFUhJPJBEvENw1RG67vbrg6WfCNqyvW1QHvnECL\\nROrqwG7nidqgglgnOUTerWsOrLc3FPmY9y6f8MPv/Pj42hWcn17yX37y5zy93fDkZELjIyPdcujE\\nXNs7L9WkijRdRyRirDDzGtcxHpUsxmNuNhv2TU1RzI/9LXqG6ZEEkPB03h28gOPh34cGNRw1n73Z\\nc2g8X384Z1111J3D6oyrR494/tkzZtMpk1HJgwfnPH36OberLeV0xtWjRzQ+UHeBuvXUXUfrBE59\\nR3A8xEHCL4ajZm0PQhilKOaG5b4B7s8P33u2SlPtV9zdfslkumB/WDOZnkkwSuxPcVmRn+hcS24s\\nIcqAvU3QT4giHPH7epf0ly6++25/SfvWBorBP1NrmY0elGW0psgEtemp/yb1NrVSHOolcxoWpcJ3\\ngcpF/KFGaUWhRCc1s+Le07QN8/Ep0+mMjx59zGq34u8//xlGx8FGLUKqMqdEpWjadhAXEH5DCqDW\\nYDIZ2cqMleugocwLIQqFo59pnvxm605+V9t12Ez8HLt7bhxaa85mc+bjMW9XKzrvhrZML3zfpCAO\\nx/6v1prQtuj0tzrnwKfuVSdjHFmQi55nhtDu0Drn4dkJL2+Xch5Fh+P4XKQI6887qXiDsTRdi1ca\\nExAVpNRnbbynzDOarsNi0EajowMMrUvnT1KMQokQQgxqkFbUaQ61HE05VAd86v3qlDQVuUUl0uMo\\nt9T7ipcvJBk/O/8a//v/9r/yv/zb/5mvrtdM8zGbzRZ/2FJeTinL7MgNubcOpRVgcU2FLiZQnlG1\\nW3wE7fwwFz8ejzHGJNWe6SA20LYis/jkyROeP3/OZDJJr1OC6WQySZC2jI2cnZ1JAF2uudtWeB94\\n/OQJf/+Tv4ZqzeXVGa13bH3EZlIE3Y9Txz0lULo1hjLPB8KXS0mgkNk6bFGwrfZobcgy0F4zKaa/\\nhfW8+/iDAVMpeL18ybff/7Hc/JBkwUJMOp59r+q464XwQNrcolFZpEw4tzInZkzPJFQpq0/Yf8oU\\nqmZP42qevnnBHz35GtfrpQjmIvNKfcWgkAQsJYASCLUMiPcvQPlev1bRDKQGOeRVUvxv2xYSPBRj\\nJHrPKB9hjUUpxafvf4vpeMaLN8+53V6n4CsuGF4rqhhxTc1dXfOsbQUSOT9nkedY39LWe/ZNh7M5\\nGk+R5/z6i2d895OvU29X+OjBKLIIrcnwNsPFiFYZu6oiRLlZwXV4Hzg0DfVuze7sgvPzDxlPFoBi\\nPl3ww+/+D/yHv/n3vNzWjIzmpBxLpaojjdF0wq+WhZKy89vlikdXj3nv/IJffP5rOteRj0qqesOh\\n2qGz0dDfEFjzHdLi78nJhhqJfl6xf4QYebtt6NyKbz8+YT6CzUGEmmeLOd1hz6s3r1mvbtHZiM57\\nPv3oQ8hy9o2jbj1VJ9KA0rfuUQ8Z2fB9cOoh2gHrl8Qiz8VrtGo9eWbu9eDvP3tF29UoXbLZrKi7\\nlswYluu3jMsJm+UrsmLGdH5G3da8uX7Jw4cfDwbR1kjQlLEbGTfp12lU9ze4utfqUCjuzwDK8dWz\\nifsgaNPfyI1JiWifgEr1qTSD+fDzZz/nUO355uMHWNVwfr7g+aYhw1A7mWcVk3XRXv2T7/0zFtMT\\nYoxcnT1gOprxn3/xF+Lao+V5bOuK0+mMu+1G4NN4NEnoX1fPUO26bpitlPEkn2YShXzTBY9vwyC3\\ntq8qqQR8SmqVwuaWzFguFydEIl/evB1+Xz9mJjyHd907gKE1E9IabJpmGEsJXYcpxB3IGkPTtmir\\nyfOc3X6Ht2Pev7jgermkCqCijLGpvuJMf1unJMEoxchmRKJIBaaqypocrRVV3VDmGS5AmYlqmXfS\\nEujPrph6bdIm6dWgoG07imIko2Or5cCW7iF4pSCGwKgoMF7RUWJdg1OWX//ql1ydnWDKGUHtyMuS\\n29cvGZc5rnOiTiW0aCnYkgm7LUu87zD5hFic8uXtht1uz2yxoG1b+lGRGCNFIYzdr1685O7mjqzI\\nsEl3VyFM7l4zNgQRdJmMxtRtw263A63Ispz9bsdqeUOW57x6+aUgY9slJwVkmWW7qzhoTdvsf2fP\\n9me8xB5DIFKaJNTfNiibHZFQI3aKk6wQ4Y66JmrFtlozysa/83v7xx8MmF9/73tcLt7H6CIRKsS6\\nqRee7rPO45PtS2JZhJkRZaDCGiErJGKETn1NJZyXRAqSfk0g8tnrX2ON5m63oXEtj86v+PzNV3jv\\nOJ2ecb64ZFLMWEwX3G2WON9yu77menONoP0Dw0IO7BQYfHJN0doMIyr9yR+ck54O/cYwzEYzvvPR\\nH/H5q6f87NlP0UpxMjulaSpa1+G6jpho6PPZnKwYMZnNKYyhALrqwF3T4LXlZDrDes9ht+VSOzo0\\nr27e8PjxR/z86S/YdxGvtWS+Keo773FKgs6+bWWDJjiwKEc8++IpfnfD6ePvspg/JMbA6eKUf/LN\\nH/HffvGf0eMRz243nM8meBfE7NVLRouC2HVoFF/78AkPTi7RpuBP/uif8ezVc1abFa+u33B++j6l\\nHQ+VbB8s+x51f8PFbSoO1b98Xt5I8OpFxqUa3DeBn75Y8Y1HC947G7E6dFSHA9V2Q+sVy+sVs0nD\\nN773PYLOqSpH1XZUnXj5da4n6xz7qQHS6EdM/yliUg4QRnZkNrIsD+29VsJ9gDRClB7V5eV7PLhS\\nHHZ3vLl9zctXT3HtljdVw8IY7tZ3VO0KF5IIQLcnyxc4n+zZTMB7RehZnQScjvcCojwfWYKpixnV\\nUQZQ3gzXUSGB0GglJCOryJNYgdW9Ju+Rie59i86nfOvRJc3mFlMU7PcHRlZaBgbQ1jA1JUp3lMWE\\nxexkOOSEaOfouhZblLjO4Z20PbbVgcloxDYpuWS5oBch9RlHRUndNFKNwL2AKeYGGgYCRi9BmFuL\\nCx4TEe3mtEen5YjT6YxD27DabVLiHoaqtuuSlmxvCQaDIpDKMtpO/DR90nQ1iY2rtbBYXduxT0nI\\n+Sji6hq8o263VK3j/OSE1W7LITmExHSW2KOUFTEGcmuIUchqMUrQ6aEY7zyZLei6jhbDqJxgdItP\\nMH3vxBKSKlf0HmvsQHK0xjCaTFmvbhPL1icXI7FrU0A5HmHawM2br5hffkAXIg8u57x+/RWffPIp\\ny/WGyXzOenegcgGTzCS8YKh4B4TIi69egYJPPnzCrm5ZXF6x9Zq8GHNejlFGs1gsZBQlXefe73I8\\nHvPkoymff/YZ5+fn5FnO02dP5T5Op8O9Adi5HZvtlocP3yPGwMsXX5IRCO0Bp4B6x35ZUejAbLHA\\n5QVf7A8wmwycmR4yVkpJD1YdDyeTODKT3LJta6y1LLdbTFnSdS2TyZRcGz4cT3j69DfcWMv//dP/\\ni/+PtPd8kiy5rjx/7k+HSp1ZWVq1lgAIgkOQM1wuZ21tyTEbmy+7/yfHdmfNZs24swSGwEARGt1d\\n3aUrdeh4ysV+cPcXUQX2AGYTZt2VIvLFE+5+/Z577jm39+98bTz8gwHzwbVPUcZQNq1zabBBU3PD\\n6d6fcLCsAT+ppSCNYtJIdFJ6HbQkAi3e74oxjBdjzicveXL+mNlq0gW9z18948NbD/nq1VM+vv8t\\nPrzzcdcUbS3c3L+DRNKomh9//gOenT/xNU6fRfoWhvCgosiRcsL31qwrSBLB0d4hp5Mzkjjhk7e+\\nxT/+/B94efECgeT9+x/zxbPfecsfsEKwPdwjkjFSQiOWlGWFiuCybjHWsrezSx4JLmZTjHApwFcr\\np+l6rs9RIibp7SCXM4x1sl+DJOZkueogLiEgT50DQK1atDKcz2YUScKiUcye/IL793MG/S2uppcM\\nB0M+fPAJP/3tj+gXOWJVMih6VGVFnuYI43odjYzo5Tl7wyHnZ89ZVSv6SYxIMvr9gvff+oQsHVK3\\n2kdIt2uPpPSQVLdibIwF/DiQgaS4rgFZl3FZab3JseDxxYKqzdkd5Ny9c5OnzwSGBTdv3yLv9zBR\\nyqpqWdWKslU0rabRumO2Wn8tXVYZQmAoW/oTkMaS+dLA5bz2C9xGTdZaENJnq5bnz78E2zgrIdNA\\n27ieO63JY4nVsKpKt/lqFTN9xsHRyNcahb9PFhN5UQMbmOOhVroWL/Cdla7kECBFXkNsu9qqlH4+\\nhdJGYP3530V+zSjrBatyzMtXM/YGCeerCimkE9/wmZ5z2HGZeRJHHhYU/lQEv/jin53bhj8H9zmS\\neVVyfXePyXzeIUQAWZ4TecZtVVbuOFIw7A9cRkQwbTYopbEmWF3ZDsaTkST29+Ha7i6RkJyOr1BW\\nezP0UH6xXvDAkQkB6qb2kJv7HKU0sVekibwtiTLGWYLhGMDaGlTbItOEuQKZxWSRoZ9EnM0XGCHZ\\nHW1jlHKG4caSJil169yRhLEdCqCU41bEXvYuTWKMVgjrWlmyNMEArdIdKQjr0IPQ46u1cqSn2Ml+\\nSiEZDLeZTyfEUqK95ZWbdma96Ws1n/32d7z/4cf88Ec/Ym9vyNbOLtPphLKusOWC+XTOaO8QqzIW\\nViNkTGyhsSnjyiEA40XD9evHnE0rev0By9pA5oRZ4jhi2C9I4tj1VvqMeD6fs1qtWC6W1E3N9u4O\\n5+fnbG9ts1qV3Lt3l6qqUK0TYyjLkul06ti1iznlasWzrz4nrefcuv8Wq+WMyclT9kY9Dve3qeOI\\nF02LHRRgFYEo56f5RvnC/TSSITmLUG1NmmZsZRFSZWgR0RiIEURKc9U07B5f4/z8AqMbPj/93dfG\\nwz/YVrKoWpZV6+23NKrVnugQFsEwkW0ndRYHl4QocvR3T/RZk3zWxArp65bzas6jF7/ln7/8Cctq\\n/lq2fT4dM6+W/O13/o6P7n2KY485ZQ0TdD+NIYpS/uy9f81/+Iv/g08efJMH1x9ybec6WZx2vmrr\\ndhQ3abSHd43/ucEw7G/x4e2P+Jtv/S9orTmfnCOFZNgfce/6A2TkBr2Vln7R5+OH3+D6/k2SOOHj\\n+9+kUYpZWZMWBdvb22hjOZ/OWPnWDIOlsdBYw2S15JfPntIf9FFCcjKecDaZ8ujiiqpqHKQF5HFK\\nEsdsZSmjPGfQ7/Pp+3/BpG1ZtYayUTw9+RJrIUkyfv34V9w+vsv9m2+zqhsqIhbKyWRLv5lRxrpg\\n2R9wfnnJtKqoDZzXLbNlxdX0kvFs4mBYH3QiIZCRX5jlOtPcxGe73V9XtVxnlWuWrYP3tUcsrhYt\\nJ5MVVsYc7I0oipzh/gGtTJiVLbOyYVE1rjWkVdStI2u1rXY+o8qZDLehRUSv+wS1WX/uqEiZVe1a\\nlchnvpsCDAaHQFy/cZ+t7WM+e/QZdbmilyT0BBxujQCBVZqdPCeylixLefr0C6aTlw4+jRzZLYpE\\n13bRZeEb9UlXluC1e/VaUbPbkPgMU8h1e8kGJNeJr/tAo9qGulwwzGKKXo5E00tiVsYZdQshqAEt\\npL9nrgViXeawNG1Do1uSNCUWshMJ0EZT1TXLsiRN4vWpCtHNs1Yrsn5BmmcgJFXTOEGDyGnAWuO1\\nWuPI9wnL7p5Ya+llGbcODqnallfjK5QXCgnz9zUXI+2+l1FEhOwE3o0nBobNBPi+bSBLU6LYic4H\\njnLdOClAa+nqr4NEslotmM1n7O7sk2cJiYQkglRYYoFfz5z2aRhTTkPXrYdWe8Z1JDBGUdU1dV07\\nJCLyDkD+XMP1RZEzfxAW+v0BWivKaomTyHPPL/I60gLo94ZgDO+99zZX52f0s5jbN2/z45/8lGs3\\nbyAii2hK9o9vcnJ2jl5N2Tm+R3b0FsveDR5fVSy1c6Lau3GXWW0Z7F/n2dUCHaXMFysOD/bZHm1x\\neXHJ1dWYpm25vLqibRrmszlVVVHVzm1kOBx5JEAjrOXi/JzFfM5iuURrzXQ65fqNm0gpWMxn5HnG\\n1miLeVkxG5/TLCZEkWW4s0ObRszynGm36XZc9VA/jjrExo2fOIpdRp4VrmacpPRjSSIgSaSrpWcZ\\nW1mBNYZ5VfJqsXI92kISEX1tPPwjtGRbT3kWhHQhBLqwG36T0u9gD0ESu8AZJKykr1MG/ccIt3B/\\ndfo5P/jdP5IlKXESd2wtN3jc4newdZeH127xalJ7lMNiO1njELu9aIGUvHPzg24wJXHCf/7J/8Xp\\n+KUb0HptFuvgQujlfcrKFZqTKGVrtMs//fqfwMI33/4TkjjlcOeYSEb83Xf//boG5eGUva1D6i8r\\nvnz5iO988F1++OvvUTcNrXL6kLVSvlFbMygG5EnMaLjN41dfghA8uzjl3vF1zidXTloN54KOFfSy\\nDGEMoqmZLBcQx4yrmj/9aB8pYy6WS4osY1cWWAutallVc2arGR+99QmrasXZ+BUqTUmTmEm5Ypjn\\nFEWPrX6fV1cXbkFMM2qtiJCcX13x0TvfYNjbQkay0810LUJh0Q7QobsPvqy88XJP5s0ieniuRrig\\n2SpNLUE2gtNZxSAfcfftHRarhtmyYdlqmqalUk4EXXk/Sh3amWxAO8Qmwh4+DGcD5QggoyLm5aR0\\nggIhqwxQrv9XWLcBk0qwNdrm5vVjCmm5evaE0cER05fPufXgLW7v7fDs+QuGWzss65rrR8fM5zOy\\n4bGbvFIQaTcXQm8nG8HSu1r79wZ0yU3asKFcQ95uvm3KCQZnktfaT/zf5HnOk5cT0ijlMNaU2hBH\\nPuAmMXkkGdctGtsJIpxPTnl5/oLrhzfAwngx7to+YuHgS20NkVfhWVQV2/0RJ+NLVloR+42B8SbM\\nKIVpnfFzVZVkec5w4GDJGIf4aLVmtFtrSeOEw90drDacjK+8cbQF366mtV4HXR9cpHSBOpaRq4UR\\nO4KH5x8Yr1phPIveyepFTvZTGaxuiaWgl/eYL5cMhgWNNlStZtjPoNRM5jOapmF3e4fZfMaqXJHG\\nzp+39aYLYDZq59pp9BrrA5yz9oqkw7GquHULvfHlI7+Iaa2dS1KWUFcVaZqRpjkXZy9Js6zTSI1j\\n12LkXKAiImnY2t7m/HLBydWMwbDPy5NTtneH1ALuPHyHF198Tk8tyIuCa7du8+LFS4xqEUnCwaGr\\nLY4b95xMnPLsyWNu3H+Lr548xuLGSBLH7O8doLVifHXF0dE1Li+vuLw8Z39vn1hGJFHM0yePOb5x\\nnSRxyUqRF2RZSlM5sfVbt29T5AVzrWil4Onjr5hfnXLjcJ9IaEa7Q8YzzbO6Yt4fMWlqbCQJnPLX\\nN5ihpOGmyfFoh8li5mrWRmFtwm6cclrPAUHZKEwUkUUwGGSUTU3UCGoZIZKIpl4rkr35+oMBs27N\\nRj3KERCSSOL8ZV2tR3kBbq1d0BQeMgrkBGddswEpCRfKIiEpmyU/ffSDrq9SijVxIEyIb7/z52wP\\nr1G3hl4WMV2pbifXLSZ+wRTYznpHCAeV6LbmT975c8p6wfnsnFcXzzifnHk5uj6L1Zxbh3e4mJ5T\\nNzXX9o4ZFTvsDo8IAJ+1rqldK4vQyj0c/4ScELTivbsfsVwtWFQzvvH2t/nJFz9Ct65PaGu4w7Wd\\nY96984EXVHCL261rd/inn3+PYe+QV5eXfPu9D/jp55/T6tb1eFln72MxTJSHkYWkKHL+4/f+nv/t\\nz/8dWMuqXpGnBRZI4hSlNJfTc7b623z01sf8n//4xPWlaY2IY5I0Z2vQ5+TyAqUUrbWYSLrdWRxz\\nvL/H81ePqJThzz/9n319IEizBXUnWDcCbYi7bcIP3Q9cgHLShQJtnKSiMiC1QSqJRaG0YdW0bhOT\\nRgyKiGXdsGo0yvCafu1mkOSNTwvPR6zXIkZFQuWPk25sIrsNnw/iQej6YnpCRMuqXHC6Kjk6OKK1\\nhoN79xkkFj2bcLQzIun3KeKIX331FXfe+pNuXAr8vbKCdqOmHgJhYFmGsR4iaoCQu8DqrtKRGbDe\\nl9Fl+kJIb8AdapfelF23xNJirOLz8YrdrRHVZEZFjMkKpFb0JZRWUjbKN91LDvYOCVj2Lx/9gkS6\\nGxUCp5SR26i0inm5YpgXLnB7OUBrXdkG69R+0iKhnc+xuH68iZxRpFl374MOLMBWv8/2YMh8teLV\\n1QV5mnWMSgve99WJnAf4MhBlnFGBy1qNhCx2mqKRr1PGcUwsXGuJ1U5dJ05SktgRAItejlIORi57\\nPVAtkbVEUY5gQRYLVnWDHU843N93KjbaEV/SJKZpFVEkaFqFtf48tatZR9L1/UkJrTJYaakrp3gV\\nxxGRDWUXN5uMtU40AhiOtrm6dCSnWDoFnjiKGQ2HZKlANTVWKUSrOZ8t+eLRl/TyjKZeomLL4e3b\\nDAcjtFH0BiOuKsn1a9c4u5ySFj2qyslNHhxtOWauvMZsOiGWguMH7zCZTkkzF7TjyFl0JUlCkiZM\\nx2NmkzHFYMCglwOG7e1tkK6lo20VSrlssqkqdF1x4/ZtWmO5enVCv1ewms8Yn70gUQuubeVcLpdk\\nxTaXCqpiyLgt0cYhczKSnosfRCNfZ+YLBEejXWITxDha+klGYjURki0RcalbROQEaE7Kkp2i4Lyc\\nUwtDHblwnKQJX/f6w32YKhRV3X8ygkhYZouXHO3cotUNRTqg1YpaOcZiKLgGyy/ZMWEDu9JllwjD\\n2fTEwSpeA3DTCR3gxsEt3rn5AUorLheag1HGeNH47KKTyn7tpq3p3m6FEhKSOCdNCnYGB7x78wN+\\n9LvvYzAc797gZ1/8kEU15998+tcYZZFRRqVCBmPXzKpu37AJAax39Vq65t69rEcUCf6iN+Lxqy94\\n9Pxz9keHvH37Ay987tZGYy3720f83V/8e5bVgkfPl0zmc+5du8avnnzVkRqqqiIves4dQDv3ht3h\\nIUf7x5RVSZpk5GmPQK3P05yDrWtM5hNA0CsG/M13/le+97P/gkKz0+sTJymfv3yFUm5iFmlCJiKM\\nUizqmrJuUdYQJ5lnQotu4+S9angtKIrfzyTXqafPPj3HygVNg9YRQhhaLcEqtJa0kTumMXA5c4vM\\nTj9DSsHptKRVuiP4uBK1h4stBFuQUMvozsEHo51eynjZvpaBugTVYLyfqxDOCxIko+Eewmq0gUn1\\nJZEQ7PZHrKoVT88u6CWuLeNnj/4r+e4+f/rnf4fSgsasWdlhbGzs6dZjSFikdVsNE8YqXTK5AdvS\\nXc/r9X8/pzoxAxcslVZcTk8oyzloQa8/ZN4olExoZIapK7Ispw03Eqhq19PnXEfg5PwVi3JGkqSk\\nUew9Kr2uqhDYSKDalnm5YpD3uJhPicQafnfOHhFFnlHWVUcOiWXUMWpDW0QWJxxsb6Ot5fnZGWVT\\ndzXBUAO1niHfKf3giDAdq906zdsidZq03boDJHHsfSxjsjRxpg1aexQsI5WCy9mMUa+PFJZFWZNK\\nSR471a44ihBKEQvLoirR56cc7OyxWs2pq6VrUxGGiIjYm1UEUpDAPSfVOI9Ii0PMMK5mLI2DgsMm\\nMPEiK1Vds79/yGwyoakqiiJHIBgOerS6pq6XGJ2QpgmLZcXe7oCXLx4TCUskDNNyxeDwGqp1xMFE\\nxvT3b/KLH/yQ/cPvsFyec/fuHc4ux6hqRbVY0uv3KLKUg/sPWJWVbxmE3b1DFos5i8WCcrVi1br1\\naGd3h7bVvHj6Fe+8/wHauJr+7u6uFybQXJxdcO3oiGfPnvDgrbcZbG3z4sULnn7xOz7+xreoFlMK\\nSoqtLdJIcpTG/GaxopQChFP/CsSedS1f+FH2+iuNE6R0mXw/7zPKc4RW7KUpiWrIZcKyXlGKiH5R\\n0LOCFydnNJHDKhGCIsmYrpZ83esPBszQUAsuCBopAIkQEf/1t/+Rfi9nb3TI9e0P6WcpjXL+iAEu\\neo1VKVyDuCMnWJRp+OWTn/nmaLezwqzh2PvX3+Gt6+/Rqhbtm5CXVcswjzlfNOub9gbhxP27/pnU\\ngrVpsYOuPrr3J8zKCYfbR/SyIYvVFK0FVrosRPvA/RqT0roPEOB7mUU36TuITLidoAF6+YD7x+9Q\\n1RUPbr1N3dZEMgnR8rXFcNAb8fFb3+T06hUJK77x1kf89ukXCAmptlRl5RzthWty/vDBx2Rptia3\\nbGTlAA9uvcNnT3/Twc5bw23++tt/wy8+/zGRgN88fuKcXkREnEgyL5dnpTOFpVG0jeJPP/w3LkiF\\njYldZ3Cb2XeAY+1mVFg/FcJjchacLjBoDEJ7KMsKlDGghIe53fuUtVwuF2zlMcfbBdNVy8W8Wj8X\\nu1aWCp9kumzXTTYhBb3UNSiXShNHr5+fwQXxNtSPjMBGFkREKlP29m9S1jW6vGRlFI8ur7i4vGKY\\nZezt3eQ7//Z/J/Ii/a1Zi6uLjWAZTkcKLzYWyhnuznQKQ66GuLY+c8FRsqm9S5hPhMzSlzg6CFfz\\n4uUjtG4Z5DlFYsgbBYMeZ5WlFhKhFGma0tYuS2qalr/85l8RtkKX0wtAYJRGxG7HHUeuR5LIE1SE\\nYLJccOvgkIv5lLptOsYkQGsM06X3q4xi0jR1fZa+P09KybWdXZI45nR8ycKLXudp2gmLhB5W41tG\\nHGTgM0mzbiMBLymnWieEbpxrUSfgHu4j7u/yNHWtZFGEss5EPYki57NpNdpKFo3FWE0WOVMCa5z9\\n2WK5wBrD8f4BAkM9mxP57DXyzHujNVop8ixDK2es7Zi5nqGOQSn3viSKUKreKF0LRqMtp0aDpshz\\ndne3kWhm43NaA8ZIksTp8462d9BWoVRNniX0en2ePH/K4MBpRmdJgqpqFlXD3miAjVNGOzs8e/aC\\nw+NjTsulY/5aQV70WJYV5WrJbHzFzu4+TVOj2ob5ckGvyJFZigEW0zGD4YjbD97yIvyaLMtYLpcc\\nHBxweXHB1taIJ08ec+vWTeq64enjxyyXSyJd0Rc19eSEw/1tFPB8OmOa96n7QxwhzpMxw+Zxc9IG\\nUqEw63vWHzgTc63J04wYGBYJRjpS4JPZDJsUCKtolMLGCftbW1wuZkSpk9AbT6dkyf+Q0s+GY4Jw\\nO/HWGPa2bjPsjfjy5BfwzjbpAAAgAElEQVQ8OfkS1TbcOfyEIi1QynQu9K/BS9huMUAK/u8f/X1H\\n8An7BYmDfD668ynv3f4ErHC2Tr7382zRcHOnQCwMjXaLZpdghDQ9LD68rgTU7VhxC0yR7zKvGrJ0\\nRBT3qZRwMkna1cqCWsy6NhcYjn5H3wVJL9DgCRk6skTGy//FGe/f/5SL8SUH20fufoZswk/8kJ0J\\nITjau47VDe+Meoz6O/zyy187Z/Y884tIxN2ju6zqJdPlnP2t39c9tECe5Xz04FPvmOB+vj0Y8q+/\\n8Zcsqpb333LSYUJITi9e8vj5F1RN5RzhrSbPCj5++0/Jsz61Ml37kIPGvH6vsZ24+fqT168gayw3\\nhrqxwSYJhLXeIUOgZQD4/fvMpm+k5WKuuFw07A4S7uz3uZjXTFfNa2One8Csvw0Ba6ufMi1VB3tt\\njgtrHYEMa9HC1cRinOiAiQyRkRxfu8f4suCrV1+xtXODTz7+K5R2RJlQknAWX7Y7ZjfyRXDzWftX\\nRsZHT78x6Nh3YoO841EaV3tc20zBG/J4HooNQeF88pKmqWiN4c7eFpmumSCYzJZUUYqQTnC9oaVu\\nG5arJR8+/Ca7oz2MMby4eMFnz36HxZKka+9KIQRCKW/e7K5VG82yqtjqOXi/8SSawFnIsowkikEI\\n0jTtyi6720N2hiOuZjNOxk5fNDiEBPa68vV/WJNhYl83BDpVr/BehHDSeD4oB+Ys4P7OGJR1Mnla\\nmzXBCYE2mlo1nbxf27akcczlvGZ30MNoZy+2KucMioK2bRjPxuyOtrHaBdEgrtD5ZmpNU9eeCSzx\\n0v0doBiCfloUWN+OgzGkeY80zZlcnro+R61RTUvlzReUvyeRMahW0TYalRryPKFfZDx/dQYIpHQm\\n1P08w+qIn/z0B7z99tuMLy8wFuqmYj4dk2cFy1VJrz9kMp1xeHjA+cUZy1VJkY2ptKRZzVlNrzjc\\ne4jICl589Yi79+8TZz3Ozs5phCXJ8s6y7eXLl9y4foNXr14xGA7QTUVjGsanLxBtySAV1MsZg15G\\nFMXMZjNetoZk6GwGHadg7ZksfQZo/fOyYZPpR0gaJ1ijSSOnquRDDHXbMkEzipzOt7QaqaEyTnEq\\nyQpEHLvE0DhyZSvfFGFZv/5gwAwC5gIQBqw0KO0w4H62zSf3/5zv/+o/8eTkMWkUc+foU0SauAWI\\n9SQXNkjguRWsaUvKZuUuVwaHCXcj7h494MO733Au99p4OynrhbEN40XDTi/l5bhcL+J+gXpTmu2N\\nNdR/L7rAGbYv1hq0aZwwgw+Ygcn5+jGFvy7vHiEkkdSeGSxJdEQcu0XOWIhjCSZmZ+uoW1CF2DzO\\nOtOMcIzOSCTMa8vD6zcoigFfPv+SxeKSRjdMFkveuvUWv/rql3zng+9i34ArNu/BpgpSkTqSw6qG\\nOE7XD17A9cObHO0f8+Wzz1mu5qQY35O3Q6MccxjWTFKDgy31Rjb1JmxtCPfN+UNKbxxp8a40nmFj\\nhcVY4Syxwp/7Yxn/vK3POEFxMlZkseRgVLDbz7iYN8zKJlz469tQj2YkkaSfJYwXTkYtjMMAkxof\\nuIP0YiSd5J2OLMoIYmFQkWSwc8T7u9ccy9mAtZKmXrOsA+FIbAS2wOKLpURJSSwNSjoikEW4wrgU\\nOLk96wOm6M4jDkFTCs8y9+cuQ7a60U4iBVW94Pmrx2yPDjGmpKxKLusWZQUqSkiALEtZmIpVVRLH\\nMXevP+T+zQeEdo2qrkjjBO05AJtarwhQRndIN8C8WnG4tc2L8zAWXSDVgC4Nw8EAaf0mNckZ7uwy\\nX614dnbqhCc6+TXvaxtHzsVDa8cW3diswbpuqXz7SkCkEk9KMcaQ5zllVWG88bAxDgaNEmf1ZaUz\\ndGjb1tnaJanjO3i1nO7YwHTljoM1DHsFgzyhRFNXJVfasL+7g8Aync8Jm3RrDNKfcMg6naKZ25il\\niYOG26alqWo33o0hTTOGW9tcXZxhjGExX2BxpKkwl611yYhrbUswxpGSiq0R20WPq8mEquoRxxF7\\neztUsym22KNVLXFWcD6ZcefOHV69eMbuzg51WzHo95gv3PlPpxN0XVMupqxikKZBNZpbD98F6Wrm\\nB8fXmS9L+jKh1++hW8XW1oiyLHn5/JnjR7x4TpolPH30OfL4mHYxIReQ9WK2Dx9ijaJSLQ2CZyIh\\n23ECK+sEzXYJEYRS2zpJ0h2c6O5xTgTWkU0jgROMMRpLjCgyGiMQqTOij7V1dXiWlG3DsOixX6RM\\nVc2LRcnXvf5gwAwPyU2QtVOA9plFHqX81cd/y4uLx3zx4pdE8WfcOfiQULx2FwqOyh57GEnws6/+\\nGWUcxdtagfBU3v3tI/7kre/SaCeyHgQStLVdG8nZvOL+wYA0ESzrIM3nzvNNXdvXvvCQ1yb6vRlo\\nQ0az6fUYZNW6/3lYVxAWNt3Va5PYLbKpdf2HJnJWXBLhPez80/X9iwGSE0HDUbidqLKKz559xbXd\\nHXYGQ9668y6rcsGLs2dM5l/y9OQJkYxpVeN8QAmM39cXa2vdMYvELarL+k1rb3cdq2rJZH7BvJrR\\n7w25d/2huw96UwrPBz+CytMGWWbzfntxcsuavxz4zNK6IGVMgM49e9VagppqeCZr2Nc/TxNyDUHV\\nWp5ereglEQejnL1hxsW8ZF6p7h6sJ51ge5Cyql0GEmT+RGd87jdLfqGTUmCEQEvpRf4FSgqkNjTK\\nBb91pr0W7gjBMvIZ3+tB0/08jiXGSlJ/T7GgXdd6B2UL6FR7gnxkcCeJZXBOgUhEXuhj7Yry/PSx\\nM8xN+sgYRiIiMYpeP+ds1dJoTRa765IyYtTvsygrru1dX2NA1tXADYYsTt3C5LM4KSWqbRHe5SEE\\n0WVZ0vT6DPOCWbnqAqD1z0K1LQe7exxt77AsS15cnFM1zQZqsfa+xH9e+Bq7XjTXEoe2W5NC8Czy\\nvNu4SeFYs0WWsfJwrLvH7pBKOzszC13mbI2m1q5emSUxbdOirGuJ0CjSOGZQ9IikM2/P04yqqWnb\\nhvl8wmi4hVKGxWqB0drVbsuqW+9cHc6xaGXi+kGDWg7awY9xFLO3e8BkfOl6N/28FlgPK68xGCf2\\n4Fyk2sDGF4I4Trl55zbz+S9pmpbV5JJe0mc+nzPKXRa2szXg1fOnDIqcKE6IY6e92+8VPHr0iOFw\\nyGo2ZXdnG9XWVHXDu++9z9n5BXlRkOUJJk5olGJ+dU6/12N8ecb47CVZv8/u9oiL8wui1SVHR0fs\\nDRJGiWEslFN9G4wQacb51RnTwRalspg8xxq9YbYV5k7YmQmnZiFNh5hFxnbqUzvFEK2cVnYWJwxi\\nyUAaXs7n1CLhfFljLChrqTxvw0jnZ2qxjHo9douM4yxhPl3wda8/Qkt2HemtEBiv1oIQTBdnPK/P\\nePf6J9y79g6j3jY/evT/UqQFR1sP0UZ4uM7J0WnTkiQZX558xhevPiMsWy4QWfaHh3znnb/EIjzE\\nZbzjhO+5tGtVl9NpycGgYLpaeE1b34LS6Yq+HjRtN4U3vw8B03YKMSFIGk8msZsHeQP8DT1vUhrf\\n1yjRceRrYpBY6WtMG6xIsd5tdpCD8MFECjCGf/jpf0aZls+eST64c588zTidLLFY8jxmNBhR5AW/\\ne/xrPnz4abdwLVZzR9JIi25F7iWOfr6o1Rqm9gvj2fgF51cvaesKJLy6uuLt2+8xX83pF1vu/lsv\\nf4j1MBwdPOYUtWx3Z8JnWj+4Q2lR2GAFJrr3YKUjughDR9DyB7J+UxOeTWD1WLsOVgCrRvP0ckkv\\ndYHzYJhztaxZVAoIfquw3Us5mVZd/y/WhWfXXmQ7tEEIgTQCLfC1ptBDuYZTw5oVwFdjQ2+l9ao1\\nnswj/SgRQdXK9SNbG8QS3DVIY4mkwZh1gA1ZZeJbdzqRgjjoxzqGbBTaSqSgalacnT8lkSDjhEy0\\npFax189ZLpfc6kc8XRqMh0otlrKqqeuG/Z2D8AQBuH5wg//26x/QiJbIbzqc2IebrQFKDPCj1pqL\\n6YT97R0UlmW5wlpLLCOOdnbZHY6oVcurywtKL01nPfIQAuKyLJHQwbZhjmmvr+oybve9wHbHAEii\\npCPVIJwcnduYeQk8nyFHsWOZRsKpA8VeQMEo41ukPJXfukAlhSRPM7DWZ+/tWp1MCmdM3TqT5fH4\\njNFwFyGgbSvXwiJdDRGj8ar7PjhblHHCJ973BJDs7h0yn09p6mrNxQhbvwCH4NbeSAbzbjfW28Zb\\nuI1n7O3tkg2G3L55A7Oc0xR9fvPzH3Pn+Ii6LGmMpZ9G1CuN1coJoicp5yenFHnOdDymyFLy/haT\\n8xekekWapsxmU3S9opyNkVhUXTLa2ubLX/yIh2+9xcnJCUeH27w8eYmp5rz7/ttIKzBW+TGqiIfb\\n7Bzd4PPTEyob08pgiK0d2GfXZY2NKATWr1selQpIZZFmFFHmwSUnuyqwZEnEeLmitRFozbxpUXHi\\nxDJwblNKSSdsEcfMy5Imy0nihLcO/gfsvWC9e14TPCwCyWI153x6woPDD5Ei4mDrkK1ijydnn3Mw\\nukkkc7+6ucxOCgeX7G8dkiUZbVsTRwm7w33uH73FtZ2bCBGhtWOoGq/kEgg42jofRmUNF4uGYZEw\\nyiNO564doaurhSzzDZgwLO0dQWbj+oCuNtLJuG2G2S7DDGPWCW8L3CKrpCXxu1hjLTaJMNaSRLbz\\nVXS7IbcQ6xAsbYCpXWAIzL+madBxzE+/+B3feutdBlnEbNWSyJSX50/4xjt/xn/6/t+jjeL9B584\\nNZD+Vnf+AL3UEUWWtXbasHGMNpqTiyeY6pLZconRFhvnDHo7fPv4XbaHe1hLBx93GTi+LSRsTGyQ\\nwGMDMguBzY2XUAmwbARNIRxJR4RAuN5AujXBEKTpNp/P61nx+lFYLMtWUV2u6KURe8OMo62c6apl\\nXimGRUKrrK/lyu4x6jehe+Fy4xBUpZVIY/0mZ5OVuh4/3bUJQSyED/7SlxjW9dOwwIJAJKE+qZEK\\n31MKVopu/CUeik0jVwcP7j5JHLwwI59ZSqII6mbJ2cULUmk57KdoBL3III1FWo3WDYqCrSzhqlYs\\n/bwTUnDn+L6TlDNsXJfgW+99mx/++p+I49izSt18SpNkw7XeZT79YoAUwpn0+gDTz3IOd3aYLZc8\\nOz/FAP28eO3exX4zYoWvQRrbEXTAiT8QNq/dBth40ph7FrF096I2BqkUcZL46zE+mOAFSSxpFNPU\\nlSPB+Cw2jpyZogrm6UphcLXTPEnR/hjB/9ONA8eeTeOIOHJWXf0i4+ryjP2DI5YryXI+pV+k1LUi\\nStyaZoP4glemCopEWMve/gF1VbJazv31vw7rg+9t9WuMFBKJcXq02mVdWrUgBKdnZ1y7cYutfs64\\nrHj8+AmZcOtnEcHV+StmxrKzd0BTLrHGkMTOH7KIE7Jsn0gKytUSmhUHtx8wW1a0yymRjen3CwZF\\nSptENG3J4cEeGMV8PkFHtzBFxN277yDynLJu2Lp2SL1covoD8u0D5zGs2m4TLhAI62q8LuzhNYID\\ngU92iFRAJQJpbrvo0zYtWIO0jhymgWlZ0VpBL3PPbLxsscLxElKZeKN0QR6nmAjKpmaiNLPpkoV5\\nM2CvX39UwFwvTf5hW0nTtuxv3+Pa3h2siDAWTiYvubl/i5999d84HT/j9sG7oPVrbabaQi8d8d13\\n/yf6eZ8szpEycplkcD+x1kNlroZlgnqLsR2jUWvD04sFd/YHvJisvNKLXUOp4B0W3O6/owYFWDmk\\nGus1/7XMwRrTjdlN5ucaafNQGBIpXYYYiCrWT3DlvTOTKCKSGmkDM9ITT/zxZEBahMuq37/3CReT\\nE0/F18xK2B0kjOcTFtWK1rZczS75d//6P9Bq1S0wwoas2O0gsYJV42oxJxfPubh8yqA/4vTihKZc\\nkfUGPLzzHrtbB2RZD6VDy8Z6Y7S5iQhjIGSdAQILRfjNzH3zK+hi4/rnNmS7/qgbG5RurIUNNW7i\\ndEEyXC9s3ElL2WpejlfkacT+MGN/WDAqEp6PV4DX67TrsRw+rmOeAl7qFSmczU9HpvF1xQ3Ioftb\\nKVybBcbLBZpwDLuBLghEJDxkuyb/KO30Zt1Gz43NNJJOgzmJiINWbOyVfeJgIu2UhKSQPHn+Gaqc\\ncq2IiJZjJ0OnFY0VLGoNcYa2sFyVznfV1/fatuXk8hUf2U9+73ndOLiF1t/zNV+Xmbl+Y9ORYiIZ\\nc/faPd679z5CCObLS96/9x6/efxbxos5Ty/OKMuKOIlJ46STpMQ4xm1gvsZSIiIwYp3BShmoHmvp\\nty6cC0fiwbp7iXUZA3j7JuiMn5XPNpV28ncuK3Qar1JK8jRhuVyS5xnGB1npz6H1RgeJdMhB63sj\\niyzxpB6L1a6ea6x7tpeXp+xs7SGHI8rlHCdgkGCNoNHaqynZDpGxwM72HgKYT8dh1+ivU3b8BrEh\\nyiZlMMmWThBeG9c/7OdFlhUc7u8RWahJSZKUz18859rBFk0Tcf/+A16cXRGnGRdnp9y4e5/T8wt2\\ntrf51U9+xK37D+nv7TO+vEBGMTJOOX/1DLQmybdojeV0rmmVYXz1nP2jfZpIMLp9h1dNS7azT6k0\\nk+mcZdUSpylaS8T2AXlR8Pz8xCXcLmckRAjhESQh1priNtwn4Uo3cr1yIgUUSUZT1sR+akYItDY0\\nFnaynJG0PLq8oiJCe+2AIkmplfMjLaKYUjh7ulXbsJ3EyFXL173+yIAJQSM2LMitNlRaUYiItnU9\\nRyfjFzy7eoQ2mpeTp9w8eOgo1D72WON3DAK2+vuEOqdWpluI/ZRgHaDXhB+nY+vYiMZYqkYxXtQc\\nbxV8fjp3A9iuM8yQ7ayX+jAY3aoYAkPAAl/LHN6AdLuMSITMwWWhBo0wdIxGa52htraRJ45IVOyk\\ntGJfMA/sYbcYy/Vuygeona1rHOxc9/UPV/97cfoVd4+uU7cN07Lk0fPfcDU95+07HzBdjqnrBW01\\nJUl3uX18C2udDdMvf/s9BoM+aZpjjKJcTdnbGrHoFWil+ezpb9genvHBg28ghCPxGxvOJWSWtguM\\n7trcArGub752o4A3W4rX2UtoQu/y/Y11er0ouhDsKRJrFm4Iul4xIoTNAI8LnwVqA2ezmmWmGeYJ\\nO72UQRYzXTUsavXaMHDn5MaD28B7EpbfaL15bP8HhKuQwolxC+HqkVIYlJRE4R3CTeIA0cYyQsfr\\nemkrLVpqTzryGWbsAmPmA2Yig+F6sPUSxHGEkIZf/PoH3BimJEKSaGfAa6olSxkxyHtcLWYMeg6i\\nz/KcWdUgcO0vFk9IEet7EZ6VEIJhf0RZrzr4FVy2ppTiaPeITx5+g+3RjnMgkoApOb18yYvLc1qt\\nO4m6ODhGeNm8wObV1sFjra8pbs58rTVE7vN8qtHNl/DeUNYISmLGWhKcgby2giRx5KE0SdDaObI0\\njYNipTeSN8aSxDHa21UZY4iSxCn0YMi99J+zxbMUWQbG0np5ulBjXK5K8iRmVddO9Wb/wMkG2iuq\\nusJo58jSbfqsQ1O2RtskScrVxcn6OTg4z681vu7u68L4/lWLQRL53/luhshB571+j36WMr2as5qO\\nMU3F8eE+EksaWcYXp+wOt1BNzY0bNzk/PUG1LVt372JExPH163zxxRfsDHI+fznjYH/C5cun7O+N\\nSJKIydUlu4f7tBpI9mjyAQ0RdRRDq5zGrtfvNYBqGqyFg91dJvNpJ/ln8Z0CfuwZ35fc8UaEQBjb\\n3ZIATTtkxTG18RuknUGfq+mCJIrAtBz1CjIMeRRjlUX0Yvde6cQl0iQlsYZcCMpFTVakVHXNzaNr\\nPFs94+tef7iGycY65WFZY9zFta0mFl76yxiu793m0clvwWouZhc8Of+CB0fv01rVwXRYF/zcAe3r\\nHwSs6gWVKtnpH/kJhq9jbrikhJYPY/nybM4Ht7ZJY0nVNp1uKKwXcrvxzWaAdAHVn5gfkMbSLdQb\\neWmXmawXWRc0Q/bgmroDA1T6nbhBx9LVYyNJHAXndc8YRiKE8oPAS875XbOSXpvXutrKjaMHpLHl\\n24Ntvv+rn3IxuQIh+fGvvo+2hvn0itvHD/n47i20tqwaDUKQZiMm4wu2egVxFLFqGprFnDzJMFJQ\\nFEM+eefbG1n9Zu3Q/9P9539nDRbDJnJhQ3To7rf79k3W8lqdaeO9b3xFCNSbOGw4urBgXRtF+JkQ\\ndPcwwKYAwyLl2XjJbNlSZBGjImGnnzKvWmZlS9Wuw7oUwnkYCh+Cu509rufWL9j45w5+p+8Dq7Eg\\njEUJQYxxLFbWbjiBJLZcnRHHfdK4cMeXBiUid1zv9+psuyKyOPItJaJz9oh80BQYfvPb72NXU2w2\\nIm0dyjJpG3b6OZkQXC5LkiRjZSSTsqWfSvb6OSdl44e8pTHN720Uw7/Xdq/x5YtHGJ/luflhONq9\\nxnc/+lf0i6LTJH588pJ//Pn/x82DI/p5wdV81tU4lVLkWe4RDL85DEHY0sHd2j/LOJJIYn/fcJZg\\nUdRBwCIQdaz1jf6eKOQzTaEVeZ50/ofWGKRwBI/QN62Vs4bDBAstd+w0jjFak8YJrlaq14E5ko5Z\\n67PWIs+p2xaExUpJoyx5klDVFefnrzg6OGY03KKqKheIpQSr3apiYdAb0OsNuDh/Reg5DBvAQEaK\\ngviBlZ6UqLpn5easxqKJ4oRIRuRJSi+JmI7HfPXVU4gzrs5ecXy4y3ixYL/Xo64W7A4LzudXVLML\\njg6OWSrJ6atX7G0PmZw8pZlPyPbvce/6Pqcnzzg82OLewwe8evaC4f4uKklI+33MdEnt70cX8Y2z\\nYBQCRxCzliLvIYRmtVoQWMRhOouwme4OsWbDBunVgEeFdqssShxTv9VgYbGsnf+sbjkY9oi0wkhB\\nZQxaCIJNmu/kQmu3GdrOc5K25aRVWCl4vpjz8PgaX/f6IzLMN4KakAT+o7U+29MO8hgVu+wO9rha\\nXhBHEZPlOUIYT2axfrHrDkRXvApZDJZePmB8eUEeL4lk6loYPFSrfMtHEOwOLSdPLpbc3evxkycu\\nzQ43x53j71/HOmgGOHDjt9ad5xvrfPd+91BF1wMUeqqE9RmnEEhriK3FSOH6vkLA3FQ/CjCdEGur\\nI2GJvI9iYN4aDTLSGCPQWtDLdvjbf/Vv+fXjxzw9e0av2Kbfy5kvZ9y9eQulrYPhcBJ5H7/3pzx7\\n9RWPnvyS4aBHkedkvR5l7Qgfi+qSr55/yc2jW4704O/J7xGnWAdNE3aB3XvW6Zq1Pvv+F4Lla3fz\\nv/O7TYbpm09AWOlZyhtWY2/cT3B1414iOZ2sMBba0jArHWljmMccDDMHI5au1tlq49V2/NQUG0Qf\\na7vNovCLXYBnBS6blsG6zG6Sk+zG8VxLyPbwACti6laRWOHeK8HYiODc4xrlnc9lIPaI0F7iz+vq\\n6hWDJGeVaRZXl2RFgrZOVPqqNox6gpVS5EaRxxq0ZVqBFDVN3RIlCcNRzsXVmKquydMcXn/a3L12\\nn92tfc6unMZsmsTcPrrBt9/7FiBplGJaVShjeHL6FG00Z5Mrru/tczmbdkcy2tC0DUWakaSJg0Tf\\neM5t58DhexPjxKFP1ro1x0Oo4IJtHMUdNGlxASbCZcxF4kQJsiRBGyfKnyYJqjXEsXseSimyPAVr\\n0FqhtSZLEsch8PKLUoBuDVq42m3TtCgBaajrti1pnlAuV+RF7uvUboxorTg/f8Xu7gE7O3tcXp6j\\njHG+osKSZwXD0RYXFyc4rdkQIKJOBSmKIiROFU16ZvKirEnTxKM9gLDsjLZBaLRuyYqItipRjSIq\\nhkxnM/q9hGRrRFGlTMqS7YMDl/nplvc/+Igf/vy33L53n8mqQQnJk6dPuXawRzt+xnQ+Jev1yUcj\\nTmcr+vv7tEazrBsupiuiJEO0TpghyBVasWZ942H37cGQ8/E54CB5t+651TOWMY1uu8w7VKikjzNp\\n7BWdPHzvMkvoZwURrn0ni50EohAupC1Vi5GSIo65u7vFZ1dT2jzz99iVPxIZMSsr0l7GXmXZ6qeU\\n7YzT9uvXpj8iYLrakgxbeb+mWOthOeOcDpQy5EnG/uCIy8UZxmoW1bzL0hwEChvLzvp4ftE11rFh\\nj7bvUrcralUCmcsw1Tq7bDoBbtf68PRixW4/5XiU8WxcdgtXOHfeWAp4IxBgYf2TkFnJ9ffrW9EF\\nWHfqnt3qomcnRGCtcG7p0rUnKKOJlOmE6N2tDEQS97Wzo/EsyNjVTaIQOPHMRCmYV263/8G9+xzs\\n3kBGMYvVlG+/1ydOeiwqTdCq1QiEMdy8dpe6qXhx9hW9fkzbVG5SSsnRaJ+n518yXY65dXSXYSAO\\nvfbfOoA6IpZv5bGGzdvW/bMJS/x3XgGe+5d+3t3yDagu1C1c1ie65v3wvrArtRa2C+dyogJu7F9K\\nW+rWcDGvyWLJqEi4vlNgrGVZK5aVovZykMpCJH3dxAqv7hRk3P0ckA5yfW2QvPEKYyW0ZRmxVq0J\\naEPs9s9EUpBHktSzasOOuquFetZ1Eqc8nZ5xEBt0s2IqB2wngtVyyf7RERfLmr0i5tnCECuLTJy2\\namVBSU0upVP5wfL05DF3ju+SxK4OuKpLijRna7hNkWc8vHGbJPozympFkhWs6pbT8QU/+exHTt1H\\na7Rx9blGKbQx9POcZVWhtbPkStLEsYilIPKtLWF/olrVwdxCCIqscKQ334sZfGvDeAlQaNt6MXUf\\nSIPknbS2a6CX1ge7tsVtbSJHEgF0688tcUpGdds6ko9dz09hXZ24sU1neq+VIk5T6qamEIk3qXYC\\nAtY6MQuBsxY7Ozthd2efna1dppNLtxlIcrZ39rg8P0Xrdj1qrAuMQoQNtERYS+JJSmVVun5REUYh\\nHB1fA1NzfjFhZ2cXgGI4gEaxMhW/++VP+ejTD1jOFmzt7mCt5fTiik8//IBVtWI+n3Hv3n1eXU0Y\\nDUbEWQ+1mkJb8UOuj0YAACAASURBVGpyxbUb15k1ihpBP0kw0mVrWZpRNSuauuoSCOkHe0hUwrjf\\nHm1TVitnVu2DvPXjXUhBmkao5dpI2wjwLBQS6Sy6WqO7TWMaJ44vohT5IGO5stTKkEYJrdei1saV\\nAJIkpUFzvD3kxXQFaYKIXGnMGMMwT0lFQz9KKY3CCMmyqb52zfqDAfO1pUCI7vuQYRrjGJ/KGlrV\\ncPva25wsnoKwlM2SVb2gSAbobvn1E//3FtV12mysJYkLqtWYSDpWnrIuONbaBedQy1TaBZDfvJzy\\nya0dXk1r6lb7DMf3g72ZZXr40J/NG/tq4ZFiG97mc2CH2m6KASAERjhHER8GfR+63WjI9718QqAM\\nCGF8AzNd24PEBcw28uxIE6FiyKzFSIvxgdZ4Zte0dGy43UHC6axmZzCgNQPK2sEz4bqkBYWrgz68\\n/R5KN1zMT73noVPHaEyLUS3Pz54i44R3+tuuj64DA9a15K4ndrP9JoyFMFo6FOH3X1+XVb4ptPDm\\n7wJrUIoAv25uNsJiYzdGGAyLmOdXK8/m3Hy+61epLOWs5mxWkyWSYZ5wMMqII8mq0ZSNpmqd2pNL\\nTDpM1p9vQBnWkHAk3vx5+Mpn6J6V2cG9QhBL11cpwLFgE4GMpdMiFWEh8sf1O4Ysy4hEjNYtUwbc\\nHOxg2wW2v0XdKkpleTydo2VML8vJrOtN1GniYUPXByml5Odf/JSf/u7HfHD/Iz68/yFWN+RJz7XB\\nZEOUMawaA7JAacF/+ed/5Gp2SbDksj4TjP3xllXF7nCLRVlSFC74WWNolKLwTiFZEtG0yll++YCI\\njMjS1GV8nomL9K1n1r42foK9V+SzsUYpijTF8SI0kWfkhyymbdvOS1IKSZqmGK2I4rjr+7SeiR9F\\n7n3GQ8Wuju+k740x9AoHpyutaJQL1qpVPri7bMLJg7qAd3l5ys72Pju7B8ynE/b2D5lcntO2zToR\\n6Rjjtvuc7e1t2nrFZDbFAgf7R0wn4+73W6MhMhJcTeZImSAQFEVGtViSxClPPvsVO/s73Lx9l+fP\\nn3Py6ozrN25gVMvp+SVbe/vML65YqDnbWwcYIRkM+lD1+fzRI/av7VM2DVned04pypWY2lYzHPaY\\nL5be/Np25bYQ9KTfZGdJTp5mnFy8crNPrOdox+FQDaNBj9liRYRTThPSuVZlaYbEkeOKNMNog7Su\\n1coRwoxrAbOGXl4wnZdUTUPsxXCsgYiYZdtwMOxxWTbOJcsjGQWWsm0cGqhByoi6+XqlH/m1v9l4\\nCYGzbfF1tS6AhEXKrpmp9WrOnV6fPMlJoqQTXsdPejoihdtFNGrFy/HTjeDmINHZ8pKqqamVolWG\\nRlkapV0d0zrSQgiWxlrmZcvLScnbx6NOVomQXPj/rF0XlF/LnvDtIP57p22zhhjD34Xvuztg6ej4\\n7vo3MmWzFkFQ2tAq7a/B0LSO0dtoS6MsrXY7pLrVVK2mbBRVrSgbTa2cRmOrNK0PVq2Gy0XDvFTc\\n3u1RKcu8dJuKwC62/nyCX6jB8sHDb/Ddj/+6qwtoa1itVjRK8/Dmu9w9utu1inSEnrAgGpdxNcp4\\nxxDYzMtfS9D/iOzSjavXiVa/93vogk34OpAgup9uPJ8AE/eziLo1VK35F553eF5vjNvWcjFveHy+\\n5MnFkqpRDPKEO3t9bu/12B9m9LKIKAqBzp1jJNaar2JjqK+Xhc1xtK6XhVfo0UwjQZZEpGlEFsVr\\nuTv/WUEDORw+TRJ2hwPuH+9zqyfYoqSuVrRNjY1i5soyHG6TFz1WOMPoUrhsWEQRFtfAH0lnjnx9\\n/4C6GvPk1Wfsbe2gjWVRKZa1olHrVqF/+NH/w8Xk3NcT10o7kZBOSSaOWayc9VWeOtg7SRxDNosT\\nDz06JnxnoOyzkqLI0aqladtuXEjEevJBF+StsV0LlpSu3BHEAVqlaOra9UAinM+l18FtW9V5UEoR\\nIYWlbd11hJ2V6/X0G2fj9F6NVmiliKKYtmlYLkuiQGwx6xqv1oa6bjqRicTXRC/PT4mimHsP3mF8\\neUFV1d04eU060ve1WguLxYLFqqauW9I0pzfo0aqWyXRKkiTEUcJiNqetNb3+AGsFs/mCVVNT9Auu\\n37iFaRqePn/GcDiiPyyYz+bcvHELYxQSyWjvAAs0WmGMJhGW2fiSG8dHHB0ekPd6yP+ftjd9siS5\\nrjt/7h7b2/LlXntVd6MXNNggQQgEQS0UJWoxmUyS6Q+dDzNjNrThzGijZiSSAAmAhIBGr7Uvub81\\nNnfXh+se8TK7qrsazQlYorMyX74X4eFxl3PPPVcb9rZ3uFjMqJuGNBOW8HyxlL7UcB0qGESFkxYt\\nDzvTHc7Oz2QSSiD7JCFzzJOENEDoozQlD2Q3oxXDvEB7Ec4vMsOkMKTakRmP0R5jPEUu80VHmcEY\\nR1WtAMv2aBTIXw2PTy54PKuZN1C6QKBDShsKT+UhMRlt6xkkOdtJymL9DcZ7xahAK4ezFYkZdk9t\\nJF70DkmBSXg0X6IyzfZ4m1E6JiBcqI2NYZTh8+e/5ief/Hd++M4/RhFEj53ldPaMxBSi9bquaVrJ\\nKhvXw7CxNysKGXjg42dzfv+dfQ4nOc8vys0r6MbC4CX7ixlUb9LiN5fzzQgRbL4uILDxXx085wPU\\n1gWNAaawPrpm+TunlLD+gsGwwWg6r9BOyAvSbG2wTtMaQ6I9ifEBqhHGbes9F+uGrUHKslwHwlI8\\nR8HqlZZG+bJakQzGOOe4c/gWnz/9GGcdTdNSZAPu3XiTNM2pm8ttQHhwNiIINvSvEQQi5HPYuL6X\\n7qCvyCzhJdnlRnovur29eH7vsEJLy8YN9F7IPqeLurOzQXly4/72bTCyJXx3jxWK1nrOVw2ztQiE\\njzPDsEjYGWYMcoNzisaKI4nZdlc/3QisIsrgfB+Zbq6ENpGNG6BXJRlm0ywxaluy6LjH4ni8sN+y\\nfMTpqmboHQ9ma76TGsZbY0ZZzkUZh4k5ZquVTOvQufANgFGWszua4L0X1vViRt20nM5n7O/cZd24\\nDgKKd2W5XvD508+YjKc8OX7SMRVBiDfGGOq6ZrVeY7TmYrXkYLrN0/MTYtvOcDBgXZUYbTrnZGJ/\\nciDveIJGbNuitL60R3zYa1edpYuwqhd0J00SIfbYNjT4t13gHIURBEVR2DYMo473jd4BuNDfGOvi\\n3snYrURrsiwlCax4IUIGI+c8g1GBbYOD1YYiy/sG+YszxpMxdbUODqbXkY7X6JxoxNpWxmMVg0Km\\nkszmKA+Tidy75XJBlqfkRcFysSRNEpIsIctyqrJGuZIkz1kt10y3piQm4/x8RpZnOO9ph0Ny5Rhl\\nmto24BwPP/oVO6OUBkWD5ub+AU8fPeTJoxVFPsS2VsZyrdaSDAUhiM3nP5I6p1tT2rairJbh3opR\\nNMZgkoRMG4o8w7UtSnl2RgMu1pUoUSHJmXeOJEu7uZrGaKrGSonEGxHNH6QsypJVYymygqHRWO85\\nGBWcLyou6oaiGNA2QuZySuxXE+QLB6mhLT2nZc10PGTVvjrD/GpIVgnW7L3CbDjLGBnKA92bg/Fw\\nyqqsoPXk6YCyLUlNcclZyqNsaVzDd+78Dtd3bzNfnpHlE5xTTIbXqVrLsmwkM7OeJjJlIyQY+jWj\\ns4yB+68enfOdO9uczEtR9X+JrX4ZRHfJqAcPuOlI/SU4ekOQfuP9escZJmgEYxwNfGwwhkiJFyPr\\nlMc5LfWy+BA6aY9onSMxMhYpTRyJ0+SJYSv3zMuWo1nNwSTj+rTgyfk6zCQO9aEACSkgTXL+9uOf\\nsSzPGKRj0jRjkI85On3GaDDm8ycfc/fG26CSLquWzC209DjfE60ujT2LdeKNEOJLs8xLIcrGPdlc\\n/1jjlrSm22vBeMVgJN6LuAfwXqJUrYNMXnyQI+zuwolJfSTe4Zjpqk24lQiXCnFrVbWsG4teKVKt\\nKDJDkSYUqSFPQ5+ekyEBrd3UVI3P0GWgRQeYmU4JSq6vLC/EoYRAIZ5SNNwqnHlTV6ybmvN8i3/4\\nwbdpZydoDceLJWeVp0knjNKMw90RRS71S+csXimqpuJkdsHxuYx/u3fjHm/efIuPHn5MkQ/6+xTX\\n2XsGubCsr+9d58nRI9IkZ1nOO1FzozTTyRbrSuqWq6pibzJBKY33jkQnlHUto7pU2CM+BOOhz7Gu\\n685xaC0iE1LfNx0C5Zwo8PSsWnGq3jl0UAKyznUi6rYRrd+6aaTNI+4171Ea6lCzjHui27/eb+yF\\n2Jcse1/6bTU+iOL72GLiLOPxGNvUXZYIkKcZu7vbXJwcs14v2d7dY//gGqdHz7v9FzZMQOCE9qKV\\nIU0Cwzrs0f39fV4cH6HwOKWo26aD8zGhLmsMuki5eesWz87OUB7msznOeSaTMXmek2WK2XzGwfYO\\nvqkYDrY4PXrB1mTI8fEzfvvv/S670y2eP3xMOZuR7UzZ3hpxfHrBelWyXMylThyeu83kQyvQJmU8\\nHPP86JmQ2xRYQguQUqRosWkoVJLS2AZc6CLQWkT8PQyLHN9WIuuYpYEAFzNihTcOSCjrFlTCdFAw\\n8B5nDKtWCF9GG+q2xrqW0mrQmmFe4FrHyilsrVlZhUlSUg+j5NVu8TUcptxM6z3e2e6C45c8wY6o\\nBZslObvTa7yYP2ZVLbl/9Am3d96gyEYb6iqev3n4Y7JkwLdvf4/GOgbFBGuhsRWtg7qFurU01nc9\\najJG7jIBBXpn6b3nZNlwOq95+/oWv3h80e3DzW/85Z9+8XjZr+KmiJEgfUylu93Sw4c+GGffGbk+\\n61TI5okkIaUUXgl8qCNZyIuhTqzCGE9iLI01DDLD3ijjomypGpGcenpecm0r5/o05/F5ifdhIowX\\nx62Q/773xnepmlK0Qm1D3Zbcu/kWbVsyGe6hTNLrx3bwcoh4rZfsMtwH2wUUMVrwXTPyly/vl6z7\\nxqGVPGQizcYlZ9bxeLzvZmH6kD1uj3LOlnX37/5To0Hsa4riEVy4r5vZRe8su77HrndWgsOykQG1\\n87IVA6HFkWapIUtEpSdLNEliJCvuMpWenOS6sxHj7T0kw3GniZkmYT/FLDtAv8YoEp1y78ZdbmwN\\nqZYvyAcTMIaD0R71siFtHRfrNVa3rJsK5z2nF+c0YaxXog2NbdFK8fmTz/j0yWfc3L/J3vb+S+/H\\n588+59PHn/LW7bf513//33IyP+a//PV/6notTWr61VUynMGhuLa9w/PTk05ZJ2b22oRaFWFMlxaV\\nq6YVXVMddXMTI3qfvg+URIJQxAtaa0MfZYM30qaShOkT3gthJmlbWkTgw3kXBlEr2tZ2eyQa/ohF\\n+ZDdd4FO2E/eg6XFuRbSlNY5TBJawpRmvZJsygRHniYpe/vXmM/OWa2lpeL89ISdnT32Dq5zevyC\\nTqy9U6IKs351H9CV6wqTNLStkKCsD/VZB8oIYmdbaWcSBq2nxqO9pqob8jD5ZTIeo2kxraZdrnhe\\nV2zv7PHr+49geczB3h63r72PqhueP37O02cvSPKEcZqilOb8Yia92BD6zvtz3ISl9nd2mc3PwxxT\\n2RMJAqenxpAoSJSgCVW5xisdbKNCEUhxRjObL4TZnyfgLRqNSVM0UNqWRiu5dutIci1TavIxKvAw\\njFbQiu/Cg7cO6ywL50mzlNPFkpaWQV7gnOfz2Zzaf4MMswco+kMrOsZYJDWIMZHs6Hfe/AH/+Rcn\\nLBYLLopzrk9rYNQ/LEpzbXqXrdEOdaBwOyc9XiAGsQ3Qq8jk9Q7SbWaXxExI3jdmmr96cs6P3j1g\\nf5JzNJdawZfVyjaPl73usqC5/8LP4vLq7m+Di1QEbcQueNyIWnvDGeXXFOC19FvpkNVZrdHOklgY\\npIpJkfN8tqJq5B4YJQ/ro7MVN7YH3NwueHK6xkaCiiy41GK8I0lyPJ4nJw/4+P6vcTh+8P4fCI7v\\nLouJA4Hs42md7Qg/l9dInLMND0pnW16eSH752tMbrs2Mvq8Nqku1ZYj9XJItGq0ZFwmfvlgQa9Hy\\n4m61BY6HDcWQ/oPj50UHaIJua2Y0MpNSXih7nW7/O+elzu5F31ZrmbwRHW6eKIosIU8TEh2k75J+\\nhmp0MmKcUxQFg9Rgi5dAISETBWlNOF3MqY6PuTEpOF1XzKqG41Ka72065nx+Ib2F3gvM52WgsFGa\\n4WAgs2hRTAcTvv/tH/RG+8px5/AeN/dvkiXCPt3d2iNNUpq2YTQY0FhL2dSBvRxbINbsjMecnJ+j\\nAnQqZ+4xwfSIcIIIr7uAyKRJ2kFDtpU+SB36MI2W6GhzilIk9+ClH9PETRPuaJZlrMt1V2vUSuNc\\nGyYqCeJwFT/qqIDhnCI0Hg8VarhKATa0HmnTyWAKOWjI/v4h89kZVblmUOSd2PfF+SnTnV0Orl3n\\n9EjGcSVJgkfuU/Q/OrSqWCcjvuTS5DxcCLjFzXu8tSivqVYVp80ZVVOxs3+NxWope8xo9ne3efTJ\\nh+zcvoeaGj75/D533/g26//xEdcObjJILAfXb/PrTz7n4vgZWWE4vHmdi1VDfXLOzWvXWJWrHoJG\\nhezfy1OlYDLawjnLejkXp65EBtF5qdcmKpDFnLT0aC0jIxsbpAzRAZKV666aitYZUiOphlGyVlkw\\n+lVdhSxcMzaGdbmmcQi8i+nG5bXOdvfStjVt6O/VHlarNWhFYRLWXzKt5LVIP91WiVE9McLnUvQc\\nCRk6KJ+Y1OCDBmKvRQq1tUxHh+ASnI2MSzHobUi1rZV2lahZ2nrfRY0Rio1ZZZfv+QBXOfjFowve\\nvzXFqKsGnh5O9P3vNr+/erzsd5f+PmY+4ctvrNGmGo73dCShTcKJrEtsqwmiDNaG8WaWxopT2Bln\\nPLtYczyvWVcN67JhVbes64aytjw4XnE6r7mxXUAwjNHJRWdoQ3AyGeyQ5RmpSVgs51eUe+T8o2Zs\\nG+BYGzQw3YZJ3YQZvYowp//azrLbY9DVvOKgZQjBxkbgFLNfUVuSFqfJIGW2Dj2VIbvG98Cs71LR\\nvsWp+7xwAkrFKFig3dgKFOvN0VlGyTgxbL6DryMzL+4L+WxZW1lDqZ1UradsPevas24cq8ayqCzz\\n0jIrLavGMl+3zMuWeWlZ1JZlLWzVVWDvfvjgUy5Ky41bNynXayxQTKb4NOOstmxv7fKH3/ujjs0a\\nDZLSGh2zOe8x2vC9936X1GT9vbyy3xOTsKpWnM3POqdzuHONLM0wxpBp0zNXjfSUzsKMyPFgGLK7\\nEKyEYdjeuw4ximSX2LYBIYBW4lSFoyqSd5vnFnszleqZs1XbhkTHU1cVSinGoxF1LS0wUbpSqR4u\\n7/eyDvdtAxnqvvfhbkbyow/nKVKd0j8t55elGbt7B+Isq1ICOw9ZmoTrV8zOz6jWJXsH16WuZwxZ\\nmoljjxcYbANxmk/Yby5wCHoJUQkynbOsq4rlqmS1LgHPaDBEKc1oOMAD+4fXSJKUqhRd3eVyyXg0\\nIsnHnJzPOXr6iIPDa9y8fZflYoVvW0aDlLpc4ZylafqacESxcARNWsNoPOH87LQL7OI6KVTXV6oI\\nIvStRSmDt67rMU6VJgmBoTExIfJB9c11tjJLUhIlSATOkRrNRdPgdCK6v8AgNxhch4R0GI8C71qa\\nRgZ3L8uS+XLJebXG5N9ggLRkjf2/47cuMhk26i9KQW1r/tuH/4naliI/FB4kj+3YmrFXLja9x39X\\nTQmYHoLdcI7e9o4tPAvBwMdoKxpFec3xvOLFRclv3d7hZw/OLv3t1ePqzy+1jnyN1/WRX79SPZoT\\noUX5Pi6qV/4ybAsydxTRmPUeskxxuJXz7HzNunFoBY2KUzBsgOikZ/P+iaO2BTd3Bjw5W8tGMQYf\\nBWu1x3qNNilFPuL9936bxOS0Xgz9ptPsHWwkIcVrjW5gwy8q1fVYXdooX/PYzPpUaITrHF64x9Hv\\nuY3ajwJ2h6LsEz86mreNvCC8Nqx9eITjd4orMygNgcmpJLv0wWF2AhRCLojjmWJNN/akKQJUa/ps\\nVabX6I4hqoC6KWmsDO32Xm/UKun/P46d3zi++873sX7N6vwz0kGOLS3HFwvWjeUPfucP2d/eE8Pk\\nRVNVG40xCYMso6rrTo/VOst8uWB3so8CVtWKpm26nlzCdfzXv/7PeOV59/Z7vH3nHc5mZ9SN9EtK\\ndiyRf6I0tRVnfLFcsDUcUrVNx6gF0MrQBOjZex8IRCHfsxI4p0kqji0IN9g4ZDkc/TQSgf06oxic\\nbqz7+aCyowkzXUPg0O2F+J6qH4iwuYH7xjPJRg+u32O8fcCjj368cZ+gqiqMNowGQ3Z3D7g4P8E7\\nKy0qtq/7p2lKXdcYrUU/FsfuteucPH+GC32nMQNz4bzpUIggxylRTXCecgZWBXF3DyqUz0Dg2qIo\\n2N2ZsljMGKQpi9Wak3lJmhjOTk65ffce56dn1K1nvZizN91nfO0689PnXJxdMB7ktHXN2ZmTFhPv\\nuucp2l2A7ek+s7NTvG1ARf6vMKLjjGSxFZIg+TBoIc9SqGvA0+JBG1onYhLOScuPCkIOEOwTMsrr\\nbDGHJGGU59DIunol5L1xkrBwYZRcCKQdoghVNjLCbe0qsjRFJybIUKa86vjKDFMr1UV33fSbKwmE\\nCpZEoVlVM2brM5Isk+hPK8bFRNoQvPuiE6KPzI3OQkbkuj6/jjHaOdYg4db9ddzvX2RpfvjsgnGR\\ncGN78IXP/bLjy7LN1/q7DcO2kf92Tl6yyvCoebqxYnYjo5bWFE+WKK5NBzw+XXG+aqhb27V21NZJ\\nO0rtQs+gpWwaHp4seXIqEK2G0AbS14Gt9eT5iA/e/iH6krPst/7VzLhf4d7Z91fYHx0J7Ipxf53j\\ni32YvVPuzseJIION6x3+N8wNtXWX5O7gFafhN3eL3K/Y16k7R9lrlm7e14ikGN0LqXd+2/fPRu8s\\nRYwiVVKTTIOKT/x7rZUotKQZJsy67Nx88Kiqi7QuH9PJDveffMqz2Yqj0vFsUTO3nuuH99ib7nbn\\n/u7db2OdJc9yBkVBGnRUR0VBGiTYuvmTwN988jOMSS4FdjLaSxzbR49/zf/+Z/8rp/PTDub0wTnh\\nfNdD6fBcLJfkodk+yr3F9wPfMWE3YcbNexRVbzb1bCMzFiIJqP99J33nRc1HKdUNFUjTlCgYAXGO\\nqN64fyE0uyTZGW2MI0lStqaHvPu9f8ydt77HtTvfuXRbvHUMigG7e4ecnR3R1pXUVl1fK/VemLZZ\\nmnYau4vZjNX8gv1r1zBJ0qMql4K93v6pcD50JqTntUfGbQy5jE5I85RiUKCNYZhlNAGpOLx2neHO\\nNTyaFy+ekxvPweF1TDHi9PkjTLPAZAWHu3ucnFygXYNR4RkPgUh8RhWera0pbduwWi9l2eJD62L2\\n74N6Fkj7iYxis9YKUqGFCJQoYcTsTyYMtGaYpmQGjBcoXOT05N/OSTue0oKY5GmCw7OuG1oP85VM\\nKcmMtGtlJmWYF0yynGGSkPjwLCsZR6eMEWLXK46vdJgmGAdtQt2mi3Q2ILTuf+L4Ei1Jd9XUnC1O\\n0UgNxRKNSl8sdpHyjfTNREZmZML22SidI+m3hidWEDso1Pcb01rP3z48593rE4rkNdHn120i3Py8\\nl2SiVyFfiEZajIrDdxmyD9Bh/Fup0TqKVHN9OuDx6ZJF2YYsTxxr3Up/p8C28lW1lrK2lE3L4/M1\\nD44X3NgdkBn62ZY+9LF2fw/Oqb6PNF5O9E4IbIXq+x+jkenq15f2gjQt/8aHpCp0+FPIkHpE4eVI\\nwe4442xVf+n6d+8PdFVMFaFfOmUlFWssfhNd6a8yTtPQWkYsweVgI15CqrUIqRsT9FEtT158IqLr\\nTckn9/+WX3321/z0ox8HkYII+0aY8bLJvFpL93ju3niXs9bwtISl12TFhPff/C062Fkp7hzeRSlN\\nlqQUWUae5xR5HpyLvOfp7LhDD9q2pW5iL5oPvz/BJEn/3HphxsZnVCkltUfEGW6iLxfrJZPBoM/C\\n494Kjg6k9hjnViqthX27cf+is4zvGddD4OH+ZzGj9NZ1rwFo2jpizd0zG0sOPQ4kEL8KXTVKxd5C\\n2D24xQ/+8b/nd/7gX6F1Aji+9cE/IMgaoLxnNJ6wv3/I2ckLmlqgYBEJEclDraWfPUtS8iyntaJw\\n5L1nOZuzmM/YO+ydpt+wtwRVnAjngu5tRv+NYCY6DiBPyLKEosjIMoOrKuan57w4W4BOUCYRuFgr\\nUgVJPqQYbXF2ds7IeM5OT7h2/TquWpFmCYPRiHs3DnHlAtfUopYTlZzSnGEx4vz0uLPZm6UzEXAR\\n9bJoxKW3WNNYS1U3jIZDFJ5MSw0TjwR3QcYuNZpMJ2iP1Km9oHExmWqdZ1lWFEXB+Xwho+mCctIo\\nT0MQ6hlkIonYOotJU4o0Q+tExBaso6pf3Yf51RmmZgPyiwNrYw+h6uyaDNdVHE6vsTPap2lqEm0Y\\n5kItF1JPNHwbxts2gMI6S91WeB9HHW04SnfV+MVNL0bt6mzDTSN5vqp5fLbiu3d3vupSryZLr314\\nvtxIX/pdfD2bTn/DOIdrHmSGa9OCRycrlpUN01qCGELIGMVZivOTLxFBqFtxns9mJZ++WHBtq2Cc\\n655cFQyFjfeCy86yy9t8rOmpPnDayKw6xxENPbDpWH7jYzPICM47ntfLYr8szImcreovrP3V9Y9H\\nV7bUvfOTzJGOcLG5IZTqHaW8JgLFqkM9+jsbMlUd2JNaBMUfv/icIhui8Xz08G95+Pw+s+WM3333\\n90h1zL4Ca/YL2faVawprs799yB98/4852L/HG7fe40ff/Udkad69rm4q0iThh9/5fdpQf1qt11Ir\\nDzXD6HhWlQwp//63f4/d6S59rgyJMbRt061jlmWSsUU74EO2vbFsqZF+yPlqxXS81QuKR21Q3/cv\\nxvfVWgvEq6TGZZQQu2KNczNLVUraOuLzEEXWYwYmI7pCPdQKFBrcvXx5wFmBBjeeyi5WiyutPHfe\\n/C55PkBr6Gz3vwAAIABJREFU07XGONeyc+0NkiRne3uXvT2BYdu27qZpaG2CfF9CmmTkeQYKWUtC\\nzT9EWOvFksVsxv7hddIsC4zs3qELXN12WXjXgxySCIH6pfaeFzmTrRGNaymGBVuTMcYr8uGE8XjC\\ncnbGyYun2KbG1RXetYy3pjw/OiUZblHXNdokFImUyCajgq3BEOUsb964zkA5CuM6jsj2zi5nZ8eC\\nVAipQK7OR6a9D6UKdaVG6yE4Ka0TGWWnpc6ZKHGU8WbHW5IEOcRUh3sdZPBq21JbSRhK62mDfyji\\nSDcF47wgwVI1wrzeGU0wHoZpxiTN0M6h1RdtSDxew2GGlNVIo3KamhA1xzpM1Pd0eCdST+/cfB8X\\n58Q52xFKorpMXEAZrVPgrPTMJWYUMqGeBNMTY3oT1rnNjWzj6n83j4+ezdFK8a3DyZdfrKKvwV39\\nlVJf+Pqy41Ww7qW6LfHRjT8T8YJRnnJ9OuidZcg4+zUhBCCBLOEjOaeXrqsbR9t6zhY1H79YsDPM\\n2RkmoW7AJbZxH5AEpxSCEVkS2ZQSsRqSECwlwUkJIeYbu8iXrpmca1938FdeF792hhnnq/prxTt9\\nItszvFW0lH3A3r2u65UMBCAQI9w4QTJkSECsNQXoKQSXRmnatuTmwR0Odw9ZrC8YJp6tQcF79z5g\\nkA+6ztAOgr3iNDedhJxlhDBlH7x959u8dfsdjOmzwKcnT/mrD39CY1tu7N8kMyI+3dgmOCDJ5rRS\\nnC3OmS1mgGKQDUTgWvUfuD3eIYqiR+WeSO4BOvg6arvmaU+cMEbqleOiEOSntWF9xXBGBwl02rEd\\nA3ZjP8gIL9u1TOF6RznIC4oslUHe3onyD3FGZAhMvQt/G3eArKvm8v71G98oNMPRlL2D28TKtHfR\\nsWpGkwMOD2+S5wWr2Tm2bUTIQYXWmKCFqtA4a6nrirKsWK7XNEG0HBXaz5SiWq+Yzy7Y278mNeYN\\ni9e2tpsL2zt3IdKkxpCnGUVWMBgOGE0GJKm0IBmjybS0XOSDEVVVMtnep8hStsYTJoOcpmlYLRfs\\nbG+hlWe+WpFiGaSwNd1hkBh8XXH64jnKaLYGBeM8Y3uYsbe9w/JiRrVehXUL6YDznbBJTCpkpqjv\\nWLN12wT2qmNdlQyLAnCkxgRWsIhUJEr3JMCwJ5JALLPAaDCgbVoq51iXFU2A3qXNCyl/IB0JidEo\\nD4O8YJhq6b3MFAbRITbK8KrjNWqYkkYnRjNIDcPcUKSWQSpwUxz02vqGXz38C35+/79z/+jTrraA\\ncp2DcDa0irT9eK6mrWhc3aHwUaKqy8D8poHsodmIpLjXTGh+9uCUO3tDdoavZkC96niVc/w6jvNL\\nM096xzkZJFybFjw8WbGug5sIa9Bn5nSydzIjtFc/ik7TBQfaOseyavn4xZxhZjiYZCGijgGH6v9L\\nf56xxzVep5BVCDU4FTRPZZBxVwd6qdX5eocOf+tDoGTD2sSfXX1jrWBrkHK2fA04lnDRqoexOgLQ\\nJnpBv9/k+mUNRO5MfuYCLN40tmPlxvfrGOMhM9chw3TO8fNf/4Sj48eUiwuOzi+YL1/wk1/+GX/2\\n0z/lP/zln4B3G2vpX7rH/Ms2/AYE6r1nvprz//7sv1LVlUT3KP749/5ZmFUoTtVaS1VWOOuYjrY4\\nX5yG7Hrj3nefCcNiKDDWhtHahExFQSW28vQZY9u2nM5mjDdgWetEnCMOfPdEYllfm4xDoOXyFNJw\\nII5UZk0GhxigwTYY4whti71pqeuqY96qcG4uGOlYgbtkCsP5oCRou3br7UvMcHmN5uFHP6Y6e0Bb\\nrTk+es5ytQqSdaYbO6a12igtyUhEhydNE7x1tE1oddjQQRSnecbu/iFpXgB0s1J7FCKUBwJCkiUZ\\nSWpIc/lKEoFwh4NCBB2altVywfz8BKoF1eKMne0p84sTlss5q/kFw+GAPM9Yzi6oQ6KoTI6t1xil\\nKLKU6XjMxcUFw9EIjef6wSG7WxOePHkg97GvmXUwSB+Q9EZCoHO59rYVHd6qqhhOtnFNg8IFXV4o\\nGwlC8jQFb7s+XG0E3WqDkH+eSG/tsqxxSjSLUwPDPCc3iiIVxbqqrvFKJpko3zIaJhQJDFKBehP1\\narf4epBsYPrliUH5ktny1xS5NGbrsKkMKdenb2Ncydn6mCQRhf1FecFifS4PgQ9QYqxRekdZlWiV\\ni1ZqaCrts57oHP2l7Rof4k0Vn68i6pSN45dPLvjg7javW858FVv2Va97nePLIMPpMOVgq+D+yZKy\\nsZ1Tc/R1zkgQ2mxJkcw0Gpye2dpaH0ahWarG8+nREq00N7cHoDa0b5F70VG2I9Xfd8FiSLei7qkm\\nNzowyrT05BKdT29sfpNjw29ddnwbCMPmMR2kLKqmMyhf+f4b77EZkMXP3qwnowJkq6LYu9wHZ0VT\\nt25bEdfYLBmE15kI4YYs1nkYDcbcvfEWWZZzMV/y2/fucXH8hFujEQq4eXhXID/6dq3NoyeDgEx8\\n6H6D9z1p58e/+nMePPsMrWGQD8nSVO5dkvJH3/8nXWbjvJdhy84xHW9z7/qbX/ycjX//k9/7Z1SV\\nyN+VYRLJJinHKE0epoR47wR+RNZ3XVfgZcpKRDlAbIKsU1/5FhIRVHXdOVEd+xsDU9aGGYxZaGKv\\nGxnt1/UzR0euVOfEIgnIe98hCn0P4Yaj3wj+8mzAG2//ThcAyJeinB/h16fUbcPxyXNhYFqBvNdl\\nRdt6Vus1y3UperLO0bZy7nmWYZQhzzPStNejjWiH857Vcsn56THTnT1hucfnfWNz9k+dEqUcL2Pk\\nOqjci2PeGo05en7EcnbOYDDEec8gFcGA6WRMXa7Y3R5TLS6oqyVV07C9vY01BXVdYm3LYFBglKKq\\nKoFxceTDEYPJDqvZKbcO9mUKjO+zypjVeDHkgdVvuntqnQ+iFJFh7jk7P2c0GGCAVblmbVtOl0tO\\nV2tK62i8BNFpkrKuGp5fLMhDPX6nGIjDrBuKvCA3kEXBfWNItEZ7UGHwdtKxeAM5SmtGhWGavdp4\\nvYbrkBsjs/oUdbPi4uwp89V9MhMeFu+prWOydYOd7Dq/s3/I3nSKUoo8zyjbhezFaOw3MiGTFjRO\\nGJ+NtaKdaoMhu9IkH82xsPfUpQfsdY7nFyWni4oP7uy+1uvh9bLIr3u8LAPaGWXsj3M+P1pQtW7j\\n4disfYYnuduP0XFu1Ar85ZacCBnGAdyPTpas65Y7OzL8t3O0ITqUPkLfsZVjcGN9zDQVaWJIEhOy\\nyy9P8V/33nSvj3DblWz10nzNKO8D7IxzTufVKwOmzUzx0vuFaKTrg+3eO7w9ffO6vFTResnYK9tS\\nNyKMX7cRIouEqA1CUAffQ56KYMT2ZIfHL+5TDEfMLs5IXM1ifk69Lrl77Y0vkqnUS+qZV0b9yP4Q\\npEApmAy2+OThJzRtw9HZc3752S/Cc+MpsgFYR21bFusV77/5Af/6H/xb3r33HsNi9IX123Se69UK\\nr6BuG4FSg8OMKFRkPKZJQhocWXRQAOeLBdvDoays98jQZMl+xWHIoQFci9+AoaNjiwOVU5P0fVeh\\n7QCk1NA0jQhHhAw1smhj24uOzNywza7ukbgF8VKnbJv60nonGranO1ycnzGfz8jznIPDA4ajQWhx\\nEY1eibigdS1KCzSdZSlZmpEkQsZJTCL7JDxfgryJpvZqteLk6Dm7u3ukeR7udQjiO7KZRSupkVoX\\nVasU1aqkKmXM1XK5Yn52zK17b4r0nLMUwzFZlnJ+fo4xCc+eH2G0Y3F+QpZo8uGQvb1tjo6OKYoC\\npYVktFov2doas15VFFu7lOWS2rVs700ZDdJ+JqYn+I2QCZswu9S5IIbSdtfZra2HtmmYTncoUhEU\\nOJ8t8N6xbqowXMKjdcKqKnl4eobKM8bDAQOjaKpKWhLx7I4H7BQZxorCT2tbGR5hpXXIaGmBLNsW\\nYxRlW6O0Zns4YPQlGdVXOsxorMUYgFGe3WHOfl6RJEJsAEmLy2pNMb3Ls3MrqgvFkPWy5HRx1sFg\\n0irSGymtRPJINufllpI+g9rYKME4uQ7E/XrHLx9dMMwMd/e+aBxeeu2vafC/iVPdHWfsjnPuHy9o\\nbN9645FoSkFohg9fIAHDBgOmI5aHc7ax3rkB20Zd2BeziuNZza3tAaPMSEuJC3qxzgXtXk/thFBk\\nrUR/Ud2mI77onpLfQS0b5/GbHpeIytGPde8XnaVilCc45yk3xZJfeh+unMtGEhkDjT4o2UxxI6HH\\ndzNfm9bSNEJlD3OQuyO2ppggeBCdZgcrovj44Yd8+43fFmZhnjOzis/mJbdufIvx8HKNvaupfuGy\\nXvbYCqRunePnn/yM2tbkWQ5KMZ1s4/G0jeW//c3/B1ok5/a29njn7nvCJlTm0mdsBooe6ZF+cvJU\\noD9jSNOUrfG4I+E0wSFFHdmmbanqpoNYwTNfrxjkGSr0BXjXr3Z0aHG9nQPtLbE30m4IDkhUIQLd\\nNkxIyXNxQqkxDPJiwxF66qrXqDVGRogJwLu5Ly7vEXm1Y2v7ELPRl5caSLSjcoZ3vv8v2dneJUtS\\n2qbuatxFJizkIk9FOCFMTUkSQ5JKb+mqXGM9AnEnCc5LsCHQcY+DNFXF2ckRO7t75HlxGSMPr9FB\\nXCNJUpy3nJ5csF63OK9IkpRyOWd7f598MKSsW3RWsFzMaZuGtlmzXs24e+8eJy+es729Q7VeMhgW\\nWNtysarIiwEmG/JitmQ0mWKrmnwoym3L1ZzhZIx3nuFgSNM0RKzDhX2klQr3DlkDrcMEGMm4x4Oh\\niEo4R17knM6XWJMzGBQM04TMJLRtS1k3VK2jbCtmZUkDTIYjhomirhseXsxJi4I8S5lkCTiLBeHN\\nILZTK2HM5tqxVSSMjGakDVvakGuDsS1re+XB/oon79LhXID1nNB2h4M9fvLJff7qo19S1mcSvQUn\\n2FjwKuH2ze9ia8fAJLx5eJNvXXtXIguvuub3ePKJ8lhXdxJhPUS2CcX5DQhKbezoLx8PFY9LRBLg\\np/fP+Na1CdvD7Gs5xf8/jt2RFM4/P5rT2P48Ns8rijJE4kusYUJwpKFu4H1PBvKe4CTpIFq7YYwu\\n1tKvuTNK2R2l1K0V9ZngDKrGUtXCOmuc7SDy3ohLHatzNp3TUVfd02sdVzP5Pmjwl2pqEfaMa3e6\\nrC7fv1fcy5dnn9CvZF+X69bSB8H51lK3LWVrKduWshGN4ygjKOdPR47LQo03ihR0gYbSvHPvfc5X\\nFyiV8NmLEyyaNMt4++678Z36N+wqVa6Du79wDZtriEBek+Gkc1R72/sU6YDnx894+OJzLuZn/PA7\\nv0dd1SzXC2zbbLQqqEuZ/eYT9x9+/H/zi09+TlWtydKM0WAolH8kc1BeCH3WWbbHI7zv65HxrZ23\\nzNcrpqNxgBgvt5TE+9SdC0htMkCpkSnrnMO2YnDj36sQBdVtQ9O2tG274az7cpBzDm0MWVFsBHaX\\n1zVm4845rt9+h0gBH2RCdFtWDY8//gs+/fn/xXx2ynK1Yl1W1AHqrpuasixpglata23ouVQkykhg\\nYy11I2xaay3aJNjQm4iL5yRUsLqqOD15wXQnOE08IkMne6pt5RlVypPohLxIQUOaJiSJIp+M2d7e\\nBus4O3nOdHubpq5obctwOMGYjBdHz9nenrK/M+Vgd8p6XTNfLLl392Yg6kCWZmxt7XC2qLEq4+HD\\nB1SN7fxDWdYMimJDIlRsRpZKkFVkaSdNFBMgPMwWogiVpinLxZK6qnl+fMqysTRAE2qdddvIrFIr\\n4x6N92TeMlusOV3X6FTs+e54jKtrQAQ0ou6uAsZFTqE1O4Mh2jsyk4BzVBYW1tJojXPfIMPs62BC\\ncoCUP/rBv0eZMYaaSI12PvQCNi1ZPmGSbFM1NU214un5fWJvmd/4srZmvp6BD8X/CC3S99xJNrm5\\npa+aUDleh3wTj1Xd8j8en/Pbd3fIXpF+X2bD+m6x/i7B2YNJznSYcv9oEWDol5BV/OWa4KU5ndBB\\ncZGoAqoz9hLI9MO2rQ3/DRnounZ8+mJBahS3diSiLBuR2ltXTdfT2bS2g2hF39d2PaA2ZKZ9vbM3\\nsl8nEHkVnLp5RHgTxCEVWcJs1V76/dXjpU54M1u9NLh3sz4cZ496qla+6sZSt2G02aXPICgEKTJj\\nBLIO0npRx1ID62rFi1NRc3nrre9ClpLkBfdu3OHRs1+LrubG+fZu41Ja8aoV7BzANCj0eOe5c3iH\\nhy/u4/H85S//gvFwyv72If/ih/+Kf/OP/h1pkvHh57/acI6KVbXipx/+mD/76X8EpSjrNVVdMh6N\\nyNKMLE2xtpHJJF5UdHqIlU6fNjpJ5+OMR8/5csHWcHQJDYnZZRuk+lrb4oKqeNv2mWt3L8PfRSWf\\nKLqwLtcbzuby+/sgqBAdb5am4f5c7u/0G6t989Y77B3eQSsY5zImb1U7tE7Zv/Nd3v7hv+OdH/wb\\nhtvXWK/X1HVL27R4nSLwsWIwGDO+/i1p9chyGtuQJSnj4ZA8zSUbzTJs0xLreOESu2fae8k0T49f\\nsL29RzEYhba+KNQQmOxJIiL2iaEoUkwiqEGuEx589jkPHnzGIC/wbSNtLdbRNBXey3BmleecHD9H\\npymDYYFXkBUD8tGEzx484HB/h+VyTbF3wNlihjI5datYry117SjrGhfWWCN62Dip7UZ7IOL6obbv\\nwTtHU9focI/rphYhFe9ZrFZUTS37oXXUjWXVNBydz8mzjIPpFs9OzpiVFWhFlqYYpRgkBoXDuqZr\\nmUMrJoOCsVFoJ3s1MRqcZdm2LJqG1KTMlmvW32S8V2NbPBrfekDgl1G+w1Zxg9npYwbbB6E2Cagg\\nQq0d1w/e5eknTxmxYFqfwfitjYc+Zg8ZaRLVfUSNo29ni44xbOGYeUYo4ort+DLj/DJD+vyiZDpI\\n+d69Xf7y05MvWQGBhKJYt2ziGBv/5u7z2lbBME+4f7x8JWGlj7avfE/oyfJBsk1eEAxUmGLgBeZy\\nVmN1gBSt69h4cXSUdY4Pn83ZH2e8sT/i4+cz5nW70VeIEEbCBIGOgRicSW1DX2iIGnvH/fWP17uH\\nEsDsjnPOl9UXs8/XfE/fWcZQv1J9oOGdPOwiqC2ft7mFYt1WIUFUFGhPEkOWGvLEkBkRX0/D33vv\\nGA1GGK058ZCkOZPxBGMU8/MXPJ/PeXz8gj/4rmi/9qlrMCxsZuAxVFKXdmAM7X7w/u/zX376H1mv\\nl9RVxdOTp/zWm9/ln/7gn3M+O8Now3Q8JZIt3r3zLnhwOM5mZ/zVhz/G2po0Tfjf/tP/gvWi0+m9\\nl1FhIUOKhs8kGuUEivZIvchuQKxx73rvKeuaqmmY5ENm60XXd9k6cXpRjF0uXKGNpg1zLeO9lDpp\\nkFpzPszPFCm4rrd0Q3LPGENVSUN7dNyttQwGA8r1Oqi0+ssOWUlmMxoMyIyibFzQdJYjSYW9asZT\\n3v17/5KLo4ciMN82TA9vc/7kI44//TnXv/Mjtg7uYW+/x/z4EblznD/6FVlW4EObS92ENQo323sd\\niDOAkbYRbTTeOZazM/YOrtHWJeV6jnOQpHL9zstYs/VqzWg0IMsNw6JgfnTM6WzOe++8zeL8jCdP\\nnzAZDkm0x1qo6jUHh4d4pG80Hw5wGsbTbS4uztnZ3mc4KtCJIRmOefTwPpgEZQyr9bpTXtJh4sjm\\nIWxyT+Il8QqF+e4+y3g1T1M3lGWF91A1CxFjd1bqlt7hMdRlhQ1lkXXdcLi/y+PTc67vbFPWFeBx\\nSuOaBq8NNrhLYwypSdjJc9q6QhkJJlsrAxNKZzE6p6wbaqtf6i+6+/7K34SjiTqZRuMJTs079g/f\\nY7E8IrE2TBMBvMIqT922DIptPnjj93n46Cecr5Zs74rzE+dDZ1xlz7d4H/XugtG9UseKFabeXPBK\\n+O11Du89Hz6d8cNv7fPtm1N+9eSi+93lBQuGUcuZWdfbsqvv97rH9WlBkSXcP17wCl95KRt6udPc\\nrObRxxChPuecjIyKerCt9TTaXb42LzWUunF8vKwY5wlvHIzJ0pL7R0HeSkfHIX1n0UF7jwgltC1t\\nbEJ/Seb/d3nENTEapoOMT17MfuP3ijUss1F7jcQnpTQuOkkrr44s2dhzqrQiAbQhTCUxDLKEItVk\\nicwxzFLDp49+wfH5KVvjLeq25jtvfMCta3f56MEv8E3NxawkSROuTbc5Wcw5n5+yN92/VHlQCgR3\\n0ZfO//Li0CGqxhj2tw/4bDnHpIY/+OAfsK5X/PrBh3z3rQ9QSnOxOMcrz/Zop7trs8UFf/3hX2Jd\\ng040ZVXisSSJQYXWMqmPS+9vEgh/dqMVLA5IUGE94zMas8g0SbhYLpiOJpyv5t3Ir7iHdXC+3lus\\nIkC+vhv6Li/ztK0nyqup6PBQJOmGGpESOLeqqg76BBn/5b1HGy2DiWmwttlYynC/fYXBsqql7KQC\\nar9JZCKA5VsHd+Tn4Vq2rr3J6YNfkeZD+cx8xM6t9/DesTr6HFuthESFJAvoOPUj3kwRvM/yjCzL\\nyBLDeDTg7PyEXFWMhgXV/ESgcDTjrQnOOupqzc7uNnW1pkgzFqdnPHn+jNv3blOWFTrJqMuSc9sy\\nDAOecR6TGQbjIa5q0L6lGI1wYRScTg3apKSjKaY+I/M1ZeuwrWTNLW23Hj0ipzp7JSWhRnp+dRio\\nbsRQxX1sraUua9Aq2BOZExw5Bq0XiLVpJVBLTMKTs3OK4YiD8ZjlGs5WoihVNRWrpsYC2hgGScoo\\nTfBtg0UEDSzC0ajC7GGHY7G2+E679+XHVzpM6ecjJLZ9pmcTQ1YcUrW2M/oKQjuDpmpaJqPr3Lv9\\n+5yvPqd1Ihicas26mTFIt2S0V1hU5xtQScT1+s37EgvcAye/uXmOm/5nD8740dsHXCwrnl6U4TM3\\nb764zDCgHbdZ8/oNjhvbA7JE8+BLnOXVY/N8Ln2PMP0cqmPahbglQCBx8LML0KC8n9swYk2A2hvr\\nWVQlx8uK969v8f6tCb98PKNqYv1MXIxWMmLLR2g3jmejAwYunfPf1dG/n2drkLGoGlr7DT4jbJ84\\nysl5jwr2SqleASc6LLfhNCOpp1O/Cj3KRaIp0oQ8FedyPjvi2fET1k1N06w5W8xIVcK33/qAp0fP\\nqBs4OLjN06PHDLwjTRJmywv2tw86tmy8drXhLLv8chNuvsKcvb57g199/kt++uFf8y9+9K8w2vD9\\nb/+AYV7wVx/+Fav1ipuHN5kMJjjv+MUnf8Ojo4egRMKsbRriBAwZzeVRiergTec8VoHWceyWpnUb\\nzLTgXbqnOWYVQNnUHKQJo6JgXVUhowtCBVaGpVsbWkQS8NZ37EtZfyXTLYz0MsbWkY6RqTTLcolz\\njqqqe5EFLeIrYshFIUgEtxXGGZpKRgFmecH1/T1a65mXNUkaW2UixnTVaV7eox7QacGd7/0xR/d/\\nyc33ttEmwzYr6vkpzWqOdRaTJlRlBWGvGW0oBgOMVgyyFG9bhsMCrGVxfkrTFuRtw9Gjh9S25fZb\\n72B9S1NXbI1GLC8uWK6WzBRMp1ss5zOquuT2W28wSoYcP31M27YURcpoa0q5WjAcDFB6QN20ZE1N\\nmqfsDQ/QngCvwsXslOt330R5xyBV3L52jYvFgvsvzvDI1BChAEQPqLo2Ho+M9JIhBRbnBMkx3nTP\\nF4Q6tg+BTeiPbRHE0XppNQxDSVBhr3nv0B5KaxkmmnQ8luyzbWSWbtShRWRXK+soXYtDESVCWyus\\n28SIRF+WJNRfIo33Wg7TQ1AI8KF+IHUxpSTSxkvkjYobKU6dbykGu+wmRRjWKrWej578mHdu/T0S\\nNUChcV6jdU4YT09HwOgiyrgh6ZzVN3OXciilqFvHz+6f8P0391lWx8zKfuJB/HTrfciKibHwpeN1\\nnIMCbuwMSLTmwcnymyTHGx/cfyMGJwQ0DlxQ1VIxw2w9CglQTCvXEGubTeu7WmfdeH7y+Sn3DoZ8\\n740dfv3kguN5FSBLfwkNj/W+OLD26zrLzYz56xw7o4xn56+eWfc6n9mxLQFQeKewOjbuRJq/2zDC\\nKsjdaYo0iHmE+YdposlTTZ4kpAlczJ8xGY6xrulqZotyjVKK49kxWZLxD3/3jwJEuca4NXVToZ3l\\nk8cfcfvabUb56FJGE89dzvbycZWwY7RhazRBa81iveRP//xP2Bnv8Iff+yN+8enf8Mnjj6jrhsdH\\njzibnVHWJY9fPCQvChKlRaycXkkqrlndiAHcVN9x3nUjt6RVw19yJiYIoUeBgTSMFZuvV2yPJ1Sh\\naR0vcKD1ogMr1+6wzgSFHxe6aYIMX6ibxihQa7GmddOIZGTILNUGTAtQ1zJdBSUDrHXQrbVBzHxn\\nus10NOH49JiyKrkzO8GkOZPJbldUVl59Ye++bM+nwy1uvf8jvLMcff63nD7+kGFm0MF572xP+PCT\\nTxkWI/Z2d7Fty3AwoBgWDHJNvVywXq0xSUI6HDIaD6mqEqUMZVUye/6U0e4BW4MxF0cvKJuabFCQ\\nJALRkiRsHxxSLubMlqd4rRhtbWEVOK3Y2d3j+PiIyXSLyrZYB8MsB9vSNlUXoqX5gHw0Zn12BECe\\npQyKEdPhipP5HLO1jQ5ckBiMRMlTsRcSSLmre8OYLspOkoS2dTjirOOgAhWcsFZw69a7PH12H4Vm\\nsVjggdFwxLJu2Z5u0ZyfYD0yak45jDIMtGZRrWmVwjrhw9ReRkgKkiACLN5bjPLkqaauXm2/vtJh\\nWhtUKLQDp1ChemcwHbwqGUgciRQxYKlrttaRJoPetiv4rbt/n7IuIYkSWO6yHmmsLfEK+PPqv7+h\\n9zlfNfzq6QXfe2OPP//4iPpS5hKu5SUu+tX1sasZKtzaHaIUPDz9zZ3lyzJNCEQYDy6KRSNBjFcC\\nIUeqkMPTONFpFIO4oU0bs4Hwtp8+X3B0UbI7zjnUmufna6KIB9FoeoHWv6CC8jWv6es4zWEmslWr\\n2n7186QkAAAgAElEQVTFK1/vcHh0rAVv9vYqj/Z9th57ULNUS59hIqQeY6ROmSWaNFFoPB8/+CVl\\nU8mOsRYH4W8Mtw7uoJRmsTpna7RNnhR8/uQZ1w/2ydOMdVnxX//6P/DHP/inKPIudoQv1uI3/602\\nzhNEJOFw55DnJ88pq5In6yf8n3/xJ5zNz4SxWBQ45/js8adEBrpE+aFXLnymDlCmMWZDlk2OOJIL\\ngmGLBrE7IclOlQvjtYKTM9owWy25d3ids/mcBmQEVhAxIDBalVIyAizAeyaKG3gbWiaSLsApK1Hz\\naTpxAoc2ikFegFLS7uAlo9RIW4wQhySyLPIxO+Mxbdvy6PlTYeZ6z5//9/+DRBtu3nqb937rR+gw\\nYeSqs/yyPay0wSQprlrS+Ix1WWLblta2oaXLYYwmNTnLxYKsyCnrllXVYIohKR5XVTx/dkSeabJi\\nSNNa0kxjvAWfo3WKrxaUyzWD7Sk+z6mrhtFoi9VyyfZ4i3W9Jh3kVDPHrZt3ODk6ZjCdkgxyXF3j\\nndQSi0FOXa5xWqPyjMF4l/nsBJMlDIdjzo+es7ezw+nZCTd2t6iV9I9WtUxh8R3YIQ9QVCiTPSUO\\n0Id9JYplYkEi/yoS8Iosp3aWxlowCYfX7nHrzrdZLmf8/Od/hvOO+WrOdDLheLFkXAyxVYW2LSSG\\nLBU5QIuidVLmax3IqEUVCEKJiO84jUsdGkfyamW813CYEWJxCqU9bej/c4EIIGO5VJiCrTBqRmqm\\nOK+DeHLQnNWi3G+dR1GQJxmtj+xKoUQbo1CtZEVh6boF3vjPN08tX3I8PVszzhO+/+Yef/7JMS4m\\nu0Af0395RPmynyvg9t4Q7+HRyerv5NQvPaCqd2IdKhc+xCGRmQ33y/mgiRocZsci7DJDeZ8YqJyv\\nWy5WDfuTAXf3Rzw9X4uj8v196GSrIxz1NaOBr5th7o5zThevhkxe9v5feq8CxKwAfaVwHkdWeSV9\\np5kRdas80SSJlj0flI6MNqE5uyExivWiwiSmi57ruuHujTd47977AKRJDl4US370/T/kZ7/8c7RS\\nLFdLXF5w/+ln3LnxvsDur7wGuVOKQBJRsK5K6rZitV5wsbggTVPaVjLds9mZwJNJigtO0FqLta2M\\n8wLapu3gaB1mTRLYmD6wC5US2TWF7zI4ABUcqAEhBGkVGs11N8YrttlYZ1lVJdPRiIvlkjQReLVu\\nGmKDcdcWEj7bGY93llSmCgORAJQSd2KSJNy+9Q6fP/iQNO2HVkuvoxBAiBCuloHPgyxnazTm7OKU\\n47MTYXhGUQQv++TJo19jtOHbH/x9OjGoLpDpg5pXHds33mJ29IC0GDFIUp5/8jNm8zlFkWMSw3K1\\noshyqqZhtVqTZobpdJd6Pef07Izd/T2KrRGJNniv0HnGztYW3nqePPyMw1t3SYoBF2fHKO85PToh\\nHw5FSCEx6CKjaAYs1iuK4YhyXVLVNUUxoG0d2iR431Ku15gkJR0Madua0dY+1XqOdy2182RJznhr\\nm/nZCVvTHVCOyjqK0ZiPPn1Aok1gHqsOpo1ljQ4xdB5vnZS4Nkc+KsVwWDBbLCmKgjTLUG0DVlM3\\nNQ8++xuu3fwWN26+w3R6SFOXfP7gl5ycP6WdTNCTkbS/qQRjWyo8a2dx4T7XriUzWuB+YxiEntCq\\naanD/NBBatDu1R7zKx2m91KXjIiPx4HSoQhvQ0YpUZ5rK+r5L8gPf5vE7ISeJ0VdXbBYP2F/531o\\nPa2ztD5K23kRjG5rMVohMqWLHQkG+iqhpP/XlxnF1zmi0f7o2ZxhnvDdOzv87P7Zb/Se8Vwim/TO\\nrgjKPz1b/536+UvQonxw+AWXnaZHCDtyu3CduHw/uqpb2wi/x7mAgVDx/KJkVmpuTAesqpbnFyXW\\n9x/3TZ3l1Yz8VUdqFMMs4fHZ6mu9/1fvDx9iwh6SDcFx9/cyJ1MgRdGyDNNb1MbkFhRVXbEqW1Ge\\n8WJ0nbV45cOAdHnTPLAsAe4/vY/eUMZZ16VowPowjR7/krWS85b76EQaDfh//vJPWVdrppNtymot\\njfp5Lq1AoW0jshrjAGalxJk1TYM2WhiZfoOjrkIYpcTBJ2kqTe/OYcI5xwZ1kGfYeqk7CpStuxYQ\\nFVJmrTTL9ZrD7R1O5zOiupExfRaqPP3fGYFoO4qPF6erdUBLAkry9lsfMBnvcP/BhzjrQTuU6Web\\nosQZDgYDbN1wY29fns+j53jvxfla19U6sdKnmmcJ89NHHD/+kOnhPXSSopUBhKDSPfe8PJ5X2vDG\\n7/5zrK1Ynjxl58a3qMsVq7NnNE3NaiXrb61Fac3u9pTV+QnVekU2GjIaDnFNw9PnLzi4fsh0kOFa\\nz2effkIyyFiXM8a7ByTDIS+eP2UwHGJMwtnxCePJVJCSNMOu1l0NcDQeY20r97NtgmydZzmfM93d\\nJsvHtG3NcjFHp9JHul6vZaZqljNy/5O99/yRLUnT+34RcXzayvLXm+7p6ZmeHtMzu8OllqRILrHi\\nBwkS9DdKEAhBgCAIBLUSRJC72B3venp62tzb15XLSp/HR4Q+xDlZedvd29M9u0tA8aGysirNsa99\\n3ucx5FXhSPWxHB+MODmfkHQ6fNwQCWjYulygVTfE/7pBUVpwimXWOOaihqWpFyeoMncI4eUl7717\\ngVQhBwe3iKKEb73xT/nVb/4TWTHjvdOlCx6l4s1re6TpEhBORDov2Ot1MXlGpTxMk+RlZUlpW6J8\\nR8q+PWr08fVSTD+tykgr5ux+N9S2IVFuuEwDP+L9h89YL54S+gLfdwamlwy5uHxMUTxGKbEZ5HaG\\nyjYN/gBrJda2iLs2b9ouzDYR9ccURb5KgMmvH0+JfcmrR90/+DOc8YBbux0qbXj2FTvLz1ru6DSo\\nZgw0nJ2uItDKg7WPW+e0MW7tSEU7w4ltqPKwpIXmw/MV2sKdgy7d8Hm9wi97Dl4m09zphMzT8oXR\\n/LZz2R5raNf2tjrHcPX6tn9y9YIr3b5tUen29S7LciVdIQTD3g5vfeMHDHo9BBD6AQJBHEV0fH/z\\noetsSVYsuZifkRYrZqslSeDTSRKkkKTlmmcXT7cco3vcxJNcdfJ//M7f8r/9x/+Vf/d//y+kRUat\\nayaL8aZn1PbpWkNQVVXjPBsB4Gb0JYrCTdm0vY5kM9cnpCBJEsIgcIP2wv0dIYgCN5jeKpW0aFil\\n1Obv4FCfqtkW1zd0/fVhp4svPZQSBL5HqPyNyHXLsbphUrWGuqo3YyiB52N0Ta0N3//uv+SV+2/y\\n81/+Z6qq3PTdBYAUKM9zItCdDvu9PrePr+MrwcX0EiuFG5vwfITn+oxRGJLECfduXOdPX3uFg1AQ\\npU94+vP/g/d/9lcsL59RFWu0qTbn5lMgDlvLojyf/v4tbr35z/jaD/8b9u99yyFEy5K8QfOu0zVF\\nqcnSlNoaRru75GnOR8+eMTzYJ5Qe6WzOo2dPCYdDRoeHiMBjvZoRxTGD4YhOr0utryoxeV6h/ICq\\ndoocZeHuo7q2jfSVO+91rUFKwrALGKhyZpNpcy0pJpMp8+WS7v4hyvcQ0nHXlnlOp5tw4/phQygh\\n6HQ6JEnczIO2zLfN/WndfKY2jmzBWoMnJHlesLN/w4HMmkpA2Mz+SuWuq3Q93yRyxsI3vv6nWBFS\\nljVZnlOUOc+WKdLz6fg+yli6vk+gXXUiCgM8KUnzglI45aVIeY4kw9qGve7T14szzK1fdIutphn7\\nkM4AS2OahqrPa2/8Ky6nv2NnN6WuK6KwizUlAsl6eU7v6D51o1Zi2pIXNNyyBmMbSdZm7OTKVtjn\\nN8p+Viz3gv3Z+pxPM9LGwM8eTvjhK/tkhebJS2Yz26t1lnltXhqc8llG/fMcyaf2NDeZZpMhNr25\\nDSr/ucMmtg7rtuNwr9vmV20fT2YZcag4GkT0E5/TWUb9knDfz9qnl3GWQsAwCXhwsfy8V/FZ18Sn\\nIRqfO944HOpGY7AB/tBK2DXjgaIp0Tl1GbkJUkTTeqhNxW/e/yVZntONE4qq2jiZ6WrVXNMCbTXv\\nvP1jtITzyzG9Xo9ZmlHVNZ5wCMOsSCmrHM8LaJURW4MsrEQqwU9+9yM+fPYBEukUMIy+EqNWjc5s\\nWSI9NycqG5kn0Wh16obYXEhJpeuNvZfKCRALCxpnzDb92IZermwyIikFSvnPqXJs+ojCRe6mkWey\\nm7Ek62SkspRhr09WjNG1RTVi5TTBuWzIs8E6YgcLptb4QeBaPMqSZRl3bn+D4XCfH//s/0WbhgCh\\nrp0QNeA1fdBOnHC0u0+k4PTyHE95RIFPUemNTeuGCYM4Is8zOmHI3f0hTz98H4oCnS25ORxwOpny\\n5Fd/RXc4ZJWWjK6/zt6NryGlh64LsOAFV1WELUwWVgpMVSFlwI2v/4BiuWBy+gG6qtHGsioKjo6O\\nqGuNigNsXbIuC67dvkHiJ8zOT7FYgl5Cp9tDKtUQZyjWl+d0OwPqqiRAMp9NQFnq0pB0u82xF01F\\n0J2vqqyIuwm6dhJ5u3uHKCUpZ0uU8sjTFF1pkl7CfL4gimO0tQjPR8oKpGRn0GOe5kRhwO5oQKUd\\nUtYIpyDiqgC1C+KFK8tqd+NRa00YBCSdDqtVzu3Xf8B7P/33gCXPc2qjHRmIEKR5Tre30/Q9m1q4\\n9Pn+d/6C2fyCk/MHZNmS0/kC+l1iYejGMTJPmWQZMgioRIUVgloJOsqnMjWeElglAcNOb/iZFuaF\\nDjPxJWl11dDT1oLZLneB1IZKSvKyph/vkabHnJ7/nhuHr2N1xsOnPyFWllpn5OWc0E8aqjbhep1W\\nXxGAt2VBXC/zE+Zvy2q43fvD1ucZ6bI2/OzhJd+/u0tRay6WxfOb8DlNfiUFt3Y7rnS5yF9qW75s\\ndvZJ4w+b/MOCFVfYXrs5flvOsCmV2K0/iC1n2b6lPRtpUfPgfMVuL+TuQZfLZcFkVbwwfPmiAcH2\\n+waJT1rWz9EHfrLU+odlu20vsylwskH6uH8+lzRoLNYF4RjZTHJad6zLIuXh09+hPEsQ+Ji6RuBK\\nbdZa7t57ZXO8O3GP3s4+5xfP2BkMyYsCgcX3FCoIsNbw9oe/4dcf/AJrLf/dP/8f8ZQPbQdaWBbr\\nBe88+K37i2zKndIJNHtKobUhiSKKhlIs8gOKqnJzfUHgADI4OjmJwPf8zfmomxJu5AcNnZvrAwKN\\nKLYgDgPS3Ok7qoZfVinlxICbQfZuFFFUJUo4MgbVoJOqxjtlRcGo18P3JLU2DRmcc+Z1I8mljUFg\\nGmBQM0fZIGKrquL68Svcv/8trIXvvvnnLFdT/vNf/5+O6cUapHXhxv5ol9FgyDpNWZuSXidB6ZxR\\nklCWmeuN+wF7/T6RqLi/e0A+nbA6O0NXFvBYzDJSr6bIMmy24uD4gC6acvw7Pnr2NsvaBVV33/zn\\ndBuHKTY/ANuw4AQB7T16761/QfY3KxazMyrjRnUwNfPFjFt7d1guVyAVvh+ALjHCss4KhvsHYC29\\npMvi7ITKWIypoSzp7x1RrpZIXaJXK/A8FtMJUnquHLoVLPuBR11XmNoSR12E9CmzNRLJbDwm7iQO\\nHFgZV4UQktrUhFFMul5Sa03UGVDMlvSGXYfQFZBlBf1+F2uhKAsq2xDS29Zuu4qC8hRJEpOXBUHS\\nxffDjU5rXhQoLyAOfZRUrNKMIOhsBO/b294A/f4+/f4e2lQ8ePhrTsePqI2m37UkgeT2/hHFckKt\\nLaaZpw6koOvH5GWOLyXS81HelQD7x9cLS7KvXRu0jSpnPJuN2y7fORJhN8+Xl5rdwU3SxQRfFKRZ\\nikqXBKGPkpLT2fsYkxMGHp4EIZpovVXK2E5pt+wWm0tPbAy33fx4+dX2W160VnnNrx5NeePmDv3o\\nahj604xxG1UrKbi912GVVy/tLD9rm152Oz9rWywtIfvV8O+mwW5bwojtMuRVz8paF/21ifzH2Xva\\nz79Y5Hw0XtOJPO4edDcI1q96WetEolvNS/jiYKHP+tytJ1uJ9HYbgM3v1rreS6U1pXbqL61uqLAw\\nXYyZL8bUtSb0fCpdE6iATtxnf+ca1/ZvbG2/CxSjMKAqc6xxRsZpxNZ4yqcocwewEZJ1nm6u/nY9\\nePoBoimNtqLWYRhsyrBxHBEGAcN+n2ujXfpRiPJ8LI6+zja6qIEfEISByyqb0q0QTtm+1m4GLgwC\\nfKUIPAduysuKNC+oqsqBM4SjtjPWkISutDuIY2ytN0LCUtgNN7UjM9JYNPPVgqQRlzba9VZbaS/H\\nY9qgKJUjL8faDVjp7u1v8I3X32JDvWktv//9ryirfHMtJ1HMjcNrBF7A5WxK5Am6gWDgw53dXXZt\\nST9bciuEb+x16Huag05CMZtT55rL8ZiqyCmylPH4gsePHzptVBXwi1+/y/hszMnJM4JQQZWze+M1\\neqMjWuL4bSMmmpr6hqVJuOz8zX/23zM8ukehDYPhDr7RjIZDRF4yH19S5wXr5YrZOqUWkt3DYx49\\neAg085LKMSY5IfmKKlvieQHHx3eYXozxUXQ7EdITGFNfAbUE5HlOkdf4YUKU9Fgvp2721lqWZU7c\\n79Hv9zk9PSPudrFY178VgkrDuiiZrlYYAe+995C8dAFot5uwWq1dP1Kp1nwDzrYFQUyn43iHszxH\\nE3Dv2/+C+eUpEttIorm2khCKRboijoeEUcdxZptWValpNTUKWODx6is/4Pvf/Uvu334DoQKezRf8\\n9OkzHpUGFcYkQUQkJR4QSYWxhlJrwrjPLPvsKtYLM0whBNdH8aYP12YtrWEx1jE5SG2oZU1eC4LY\\nZ2fvm7z/+7/D73Y4K2qwK448SdE1/O37/54fvvpvCT2PqnZghRZ55pzglXN+vsrWOtGWP/WPuybr\\nkt8+nfHdO7v86IMLsupzOAal4M5+l3laurnFv+f18YztuRJtcxy3Bava1tin7dFVFfzFR7isDY/G\\na7qRx/FOQl5pzubZC0kFvojDiwPnENbFZ6sI/KHr07LzlvW4vR5NE1y0fL9CgGiElLVqAj3P0usO\\nicIep7NTPOXzzXtvcuPoFkp47vOs+9QHT3/HyfgJuq4JAs9pP1Ylw7DfqMVofN9jOBiQ5TmBF7Lb\\nGzVSSO12G3770Tu0KF/JNgLa9ac9XHsgQLLOC3S6pB8lzLQDEnlegDWGKAwJGvYb3/cJpSQtCsqq\\npjR1Q0bihrqVkARSsBK6QVlGRIEjvTbGIJpZysj3yKtqQ+7Q8u8KLJXRoK+O+7rIuTbaY7pYODH5\\nylWclBSbkm87B26tI163xhBFXe7efn0DjGpDHqU8hBCEvs/B7h6e5zNdzonDkMNeDKbmIHKjEzoF\\nVVVUlUeRFbDIqaoCHceMxxeEvmBRVMikS+EFdK7tkEiBCXzq+YLD/X3KumJ9OScfz/E8j+n5I67f\\n//ZVFa79uW3Lnks7HWjpm9//C3of/IKbYc6vf/JT9g9HVGj29w84n82IOx2sBV/5VEXOvVfus1qt\\nCKOQuDfAmim+7+P7AbPFArRGeiGH12+DLrl8ekYw6BLEMUVeIZAo4e6tMIrww5jpZMxw1KeuS5Z1\\nRtzvU1WaPC3xPZ9A+QjpeHWXywVxJ0Jpy3K5QgiP0c4O48kEU1ck3T6dTsxqvaIsK2e+hRtB8ZM+\\nvdExvdER/mJMd+eI4eEdBJaTD39GVWmEFNRViTaGvC4JOrvce/UtEJ4bE2luWNH4hQab6pLYusZT\\nIdePXuPG8Wtk+Zpn4/c5nTzhfDkmDHxu9LtERjIvapA+oR8yTzMK/dn2+4UO873TBW/e2mG8LNx8\\n4tVW0iQrWOs8fKvskFeSXm8fint0qo+YaMP949uQrumP9pitVjy++CVHu9/B24ySPN9/2lTFPsXu\\nbobOP+FQv/p1OsuIPMlb9/b4u/cvqD5l/C/wJLf2ukzXxRcaefj7WFfD7lszhp/zuqs/PP+/j5c/\\nP55xr/KadbFktxty76DHdFVwuSpems3o89aoEzJZXV3EG/CAhe3T8WXR0tAGbS2aUzTQ9yazBIx0\\nn+8phzQ01hkPYwVR1OMbX/seb0qFUp6b8bOOAL8FgoGgKCpu7N7h/afvAhpbWwa9Pus8wxrod7sY\\nYynKkn5nwJ9968/d4H5bNrbw8OIRFpepbe4DcCW6TodBp8dO6HO2WGJxDiwLIpZ57rI54XpenSTC\\nR7g+p4ChB16xxBceC2HB87BN/9JaS15V5LreIDklOIYUqUh8z/VMhaR0YHryusITrpVTV8bJcYUO\\nZdtmpWEQkhUF/TjmcpYjpBvxaYkQbKNwZLR2pAHGYI3m1ftvopR/1cYRgroqqOqUm8fX6EQJRV2S\\n5jlR6LOTxAyFQeoCvcyQxnI2nhGFMReXY6SA5XzO0fEB56dP8QIfG8XcPDggLysq6UBNl7MVReCT\\nFTXT9cQ5aiHQ2qB1RVmcMTn9iN3j22w7xcZsXp3Hq1MGAj784Ofs6EtW4xVhpNg72kVKxXy6IE4S\\nF+aKNoizm+w5Wy0ZDIZ4QUitNXm6RkrBYDBiuZiTdHqE8S6T2czJyvmKqnBIbg0EQUAUd5lPxvi+\\nm231rOTBh485ODpCWkmR5YRhiLWWIAhQ8or319YVUiqXPFnDaDAkI+LVH/wb6jLl/OHb3Hvlu6zn\\nY87e/wVWKG5984cEnSHWCrqHdwCB1hXLyTPKoqC7f5vO4IAwGTp8QBghlY81UOpmmG0rcWuR7QjX\\nTtJCNChrF+D6Qcy9G9/heP8Vnp1/yOn8Ge9eXKKkQtc1nU7MmzcPePjwfaIk/kz78EKHucprTmcZ\\nXzvu8/YTx7e6DSBxpT+LaBRHdMMcU9Wa7u51Hv3iR6Seojo74zRb8Weex53dfVZpiqREyXCLtKAB\\nqbS4leY7Plkd+/Je8mUG5lvj+3C8JvQV37+7y48/HLNNZu8rya3dhMmqeK5k+A+1rhwkm77z9t8/\\n/U2f/Tkv9f7Na2C8LJilJfu9iPuHPS6X7rj8IWfLDRdDJ/I4mTnwlTvzV/1zgfiE8/6i67lrobm+\\nbButNYa71gYJ1MaNkyAMnhENjZfBWkVtDL5y/Y+23C222gpaCDws37j3LTdydO0Wv33wDof7ivcf\\nv0ccxVRVxXK9ZtTpgjG8dvt13CgXm+j0g5MPeXz6uCGgECjPIRy9Btm50+0SYSiWczwh8cOQoiwZ\\ndDv01ZAsz/CUU1Q5iHxml5cUNdRKsihKrDEEnuE48YmV4HyZMhUO1NMLfQZBh1AJ8jwlChOWaYoQ\\nhsNuiK6gG/rMsoKV8ZghqJoSoBKSSljqqnYOphlrMVozWy04Go6YzGeu5Gpcltnr7LBOl2jtZNxM\\nVTfBNXz44LfcuvEq1loWizHvffBrrh0cc+PwmMV8zDJLKYs1BoVHje9bFtMl68WSKi/odLss1ys8\\nzyeOAlbLOQd3b5Fpze6tm/T6PWxZcj6do70QYZzjHwwHhIHPSkrmaUpZtohjZ7+ssZx+9Ft2j29v\\nzv0nWi5bv0sJJ6ePSecnfO3WMePlkiRJqPOCXm/EePaYo8Oj7dvZjQMZHE+uNTz+/XvE/R5BJyaI\\nIwLlsc5yVBBS5isqo7lx71WqMiOvKgZJxNl4Shh16Q5GLOcTrDXEnRghDRfTObt7e06NS1hUk8Eo\\n5RFGAVWRoQIPUzshaM8PmEzmruKhNTt3X0MoHxkNOPzan4KAZOc6d75/TNtWK1uecgutlFkwuM71\\n/lHD7tTYedtUMusGv263miabSpojINngNbZGxFxZXCC1xlcJd669wZ1r33AlbF1isVTVjJPpBWH/\\nECE+2y2+0GECfHi+4k9f2WO/H3KxKJpdaxy6tQ4wIuxmXMEhXqHSazKtiZKIt2dTru/v8vTkFPZH\\nTBdTdvpTQv9aI1Xjvsu2jTO2nj93pQmkdcw7Dkz3xY3ktoF9WdDJ70+XfPOG5K27e/z4gSM2CDzJ\\nrd0OF4uceVa98HO2v++LgF7+kNVeTC/z+V/lWA64SsPJLCP0JAeDiJ1uyMUiZ/ESx+jj27XTDVmk\\n5XN8xVfrqy0vtHOC7rp2t5vZyjCxoJQDqwjRao+yGbcyRlJbV0p0MHrX195srTFY6Uozp5NnvP/4\\nHXwV8OTxCUkUk+Y5GOMEjqVE+R5/8+v/xD9548/oRB1OL0/58e9+xDpbbnphXkPRF4Uh2hqKqmKZ\\npqgoIq0qTBASGM0ojinLmh2/ourGrLOcm33HPzq3giCJHMGBFVgpUGHI2sCz2YLdboCfaW4c7eNX\\nBapKiYOEXIKuc7I8J0VSlBWJL5lP5xwOu/R1ysCHBQFFGJKVNZra9UWb0QPb9G3nyyWjbp8kilms\\nHKl+r7PD3ZuvcXr2iGfnH3Hn+ivcOL7H2+/8mOV6ziqd8R/+6n/m+rW7WFvx6p1XGO3eJKsMvVGJ\\nUj5lXeB7PkZXZOs548snKH/FxZOHBLLD0hes65rE8/B6u5yta75+45ghFSrLuJxc0u0PiKKYIi8Y\\n9rrYskR5itnskjiKSboJldZ0kgRPKsaXl8TdIe1s6YtWVVW89+5PuHc4wlQlwjp1j8VkRp7XhFF0\\n1f6jqehVmqTT5WI8pt/v093bp6pr8qImiGLSokR6ijAKqTPFYr1i1OsTdTsE6xVVumJ30EMkfdbL\\nCZ6yWFOTZ0uy3KGrA8/HNlmj9CRBGOB5irp0kltBlDCbzkAofOVKu37gM10L9m6/Tlnr5xjE3Kib\\naAJMu2EO27TXGoCdFRZqfcUsZXkOh9d28t1rr95XW7txjjTvbccX2znpWrCp9kjhxoySQCDlgF7n\\nhgOxfo7NfKkeJsDvTxZ8/dqA2bqi0i3VmlvSXvV6Wqq1Whvm6QSd7LLf28OoKcvxBdfjLicXY+ZG\\n82T8AXeP9h1y7rl1ZQQ/YRLtZpz6uW38pNF/rlnwufv2sv9/+8mcN28OeevOLr95NOXGXofz+cs7\\ny0/7/Y+9/j6/6+OrqA2PL1OSQHEwiNnthoyXOcv85XqRAhh2Aj4arzd/a8tSomkNfAqO+sut5uOQ\\nwKAAACAASURBVJQbHGNNG90KY9DCsVxdzTXbzc3qKL7sBrqtGrIT3SDKPdF28ARpseSX7/2UJHSl\\nyKPdYy5nY/rdAcPuDp6nuH1wiPK73D2+Ryfu8v/89D8wmU8wxpGTK6WcIRUOrdqLYjpKsq5qlnlO\\nXlUYzycvCgLtYS2kFparil5g6AeSx5dThpFPx3Mlro7vE0gPhaEbeKR5yt7OkKerFQe9Dn6dM1tn\\njDyo0xSTp9RR7BCWVvAsN0RK0Qk8VpdLdjwYdWIiU9Dp9Xm0KDHap1Qeq3SNxRLIACHBWo/zyzFJ\\nlGCMY/D5s7f+NUJIBt0Rr73yHfrdAWfnj6jqnLBB+l7b26cTR9y49SekFSyzupktVlSNfm9Zg5QB\\nSf+Au8MjBIY7r/9X+Mp32UVd8Fd/87/jCckP79xEnz/DJjHGQuDHyLKgyAus1qzSBX4QscoKksBH\\nSENtK5T0Od7dIw4VfQ/OLz5kMbnPYHTozvsmQL660IQQ1GXOkw9/wnEs6OuKs0djJpMJw70+YRSD\\n8hg2aFtrLZ7vU1WVK5sqj07kY3WBDEL6vQ7VOuP8/JJekoC1VGWNHwUk/QHL1YzBcJ/OcEQdhKxW\\nKYEwLIs1UirqNKUsC8JOB6U8vEBRlSXCSOLYp9vtcnp6QhCFRHFIpWtQEqtpQI+Ssja89qf/lko7\\nWlTdAgptc+82QLPWYW7AdlugO4etaGfAr27LtqJ0la1dvf75e/h5r7GZAZUt2Kol1BD4UiJQzNJy\\nE9yILzOHKYVLn+dZzXhZ8Npxn7efXklhtTtvDVglHGJWO+HhYecWR1+7TRh47NYZf/eTf8dpqFHD\\nIf56BSLC9zzyqqat725S7fZ4bB+c53qWzzvNL7K+TFb3q8czfnBvj//6jWP++ndnzBpn+UWc7x8r\\nq/zHutJS8/BiRTfy2O9F7PVgvCpYviDQ6Mc+RdUKl7tlRaNVKba0U7fWJ0BPL7me69MK0UxYurje\\n2qZgZIwDmDRNpxbJvEEbN4IEjr5XNAoxtuljOk3Ry/mY9x//jr3BEReXT/k3/+S/xfN8qrok8AOK\\nsiAOY5JAsNO/3iCbDctsSRRHlEWJ1powCol8pxKhpCRREqocX1uoK4RS1EVFZTWq0+UkXTuB4aZ/\\ntswLBt0Ok8USLT36SpAbQ0iN70mKqmBd1JRoOkFIaSxPLxeMYlfetRLOVjlVYVnWljj00XXO2hiK\\nAgZRyCD2mGnYFYJ8OmE3CPATxUmuKTwf3/PAWPLSjaUUWnPc63K5mFBVDokphKDbHWKtpSxLPvzo\\ntwjhMewnvHrrFjrLKYM95pl24ujmqr/XtLQAR4WthEBJl/X7SqGtO3ZhGPOXf/4/8P77PyI7eYKP\\nJV2uGY12mOYXKCUJow55XeMFId1Oh8J6rNKCYr1EdjrsHx9hdU6xqEk8wX4oGP/ur1Fv/Es6/SFg\\nubhwQK+Dg2vk2RpVL/BW5+zkEx6fXzIbWnxbM53P2Lt5RG9nyGK2oAWYeoED3CgMVVGSZylxt0ug\\nFPPZ1Alie42kB7IRhxZUxoG56kqzmE0Y7Izojo6o6qeYbEUcBKRpShL41LomHV/S39mlXK/wg4Bi\\nucJ6gvXUzbdGvueo9fIaJT0C3+PhR48Jw4hw7z4i6lLWNWVtNxlmE1sCV3ScFtvQLTpnekW6ciXm\\nsFE8a4hBZOv8xItqS1dOs329FFd6vo48R9LtKiZpTaXN1f8/B3jxYoepxIYp5oPzFd+/O+JwEHE2\\nz69qyaIl83aWpcQiyhpjLZVWVAY6QUJncJN3VifUVcHOYEASBWhdIoQbR9iOMNzD8zvdPr0S2eH5\\n933igH31Kw4Ui7TE9+DuYZ9ffDT5o3zPV7W+CiDMV7VWec0qX9GNfPZ6Ifu9kPGy+MxS7U6TkbZL\\nNHfJ5xViv6p9dXw/Bmkb+jPREmm4kE60r7IO3m61Q8qajWylQRhX3qRpHWijKcqCt77+QwLfJyve\\noKWl85Qb9wiCoHGSCmsNSkjWZeZktIzjSg3DkND3CYMABfTCgCLLSRu1+p1ul8liybqqGO0MqK0m\\nimN8pajrCo3AIJmmrlzZj2LKumzGZDRSWPKyZlVrKsBWFUfdHmsDuZAURYknFTMV4vsRVufM0swx\\nuiiJbt43XmYcdCMmtWG/00UVBUGxItGSVEl8T1GVGmHBazhpp4sFw+4AnaiNc984QCH54Vv/mtBX\\nTKZnzMcPELLH4OCYoqrd9revbR1m07oxUmzJ84nmJLlrRSHw/YDjvWtIs8KWBbbMmU7GSCXYOzjk\\nvUdP2Rl20Qjef/gIPwjwhSEZjpjnGXv9LvOzEzIsR4c3mV5OuHa4x0/+r/+JG2/8CeezMdlqwjdu\\nHJHP3+fxk2fs7Oxi6pLVKkcJSzYfk0lLf2+XbqfLcr7e9NSRkMQJp6endHtdV473fWbzNeGgtynb\\nrrIcqRR5UTQjT5b1ZI3vBXhKgJbovmA2naDChGy9pEhT4jik2x0yuThjPp8xHAwIPY+6qlBKUZcF\\nXhTTTxKKSrOYr/CDhKqsqEtNZSzD/XsMb3+TotQUTZVRm/Z8tHfVFercNvzWWNtoUtrNPdYyk7U9\\nuSuMy5VIhxVN60Rc4QWuoiS7+b3NKNtSbPsZg1iyLmqWeY0Sju+8xdN81nqhw1TNxYZ1xuDdkwVv\\n3BgwXZeUVYNaA0etBg37j5PNMk1P05ESwOuv/DmL1Rm/efIj1umao2HgaLPq9mDaqwNk20r1FtCj\\nDVU2wo+fdJ7bJo/Nu/+w9fEeZxIoro8SHk/WvHe64K17u3z71g6/fDT9g7/jj73+sTjLdgkhWOUV\\nq7yiE3rOcfYjpisHFmqDu8h37CXrQm/e54Im8ccoxD63rlDYdgMicHibNgpudPpoWhBWUtvmDpdu\\nW11g7F7oRgQdKvVodH2j6xcF8dbNLqh11eg9OiWgrMz48OQ9zqenxEFAXhaOirK2DuGoDb4UVFWJ\\nJwVaKbq+R1HkxN2EbLZAV7Wjt/N9QmFRwvVhpZUUzX4O64JdD1ZKsTAtF7LEAHEYskzXnC/mGCBf\\nrVEGqrJi0O+hdU1VZFhf4SunTBEFPoHvk3S7nK8WKD9Az1eEumTQ7RJhqVPNsqrRtds+2/SbJssF\\n96/f4pW73wPExrAGUuAHAaUxLApDLTtUxAz37jny7GZ+c6NDT2s3mkqBdVhKax1hirStbF9jvxB0\\npebdp0+5f/MmRZGzXqVcu3bM46dPGIz67B4espjN2b1+RJUXDId9xucTlFJUqwXTyZjBYIfzxx+h\\na818NuHN20c8/fBnSKU46A3w0iXLvKAXB6SLGboumc7nXL9+iPAkQnp0u93NDGoU+oBFosjynP29\\nAy7OTul0EkxZUC4WTHVNfzCgyFKUUgzDhAePHjEa7ZGmGQLHy1tVmuFowOX5BGtK/CCgPzpEhRHV\\nasFyOcMPfW5cO0Yp2fQqDZ1O0vDxKi6mC7woxBpBluZUVU1Z1+zf+w4H979HVlYUlaaoNVXjMNsS\\nq20rMc1jW5Vp/aK7t1zZ1rBdsv0Yrd7H+pm099onEqzWc1xxHcsm3YwCN+50schBOF5ZKR3I8PMq\\ngC/hMBuP3kTOq6LmdJbz9Wt9fvVoRrs7BpCmUTGT8oqbtOnoWlthrcdoeJNXbcE7T37M2fQRo+5t\\nhA0aQ7VN1eZ27Dm2n60Mc5u+YGNIP4kQetHuvXC1n9mNfK7vJDy+XG3mMX/68JK37uzy7duOrP3/\\nXy9e2+doXdSsi5rYV4y6IXu9iFlaMl0VjLoBs9ShjrcVVhrbt/WBfGXFhE+CwK4MLaJxlE0QqLWl\\nrg21NI6fUzSYVYFTsJdsJOGUpRlPaZ43MjKi2X5jDVIqfvXez1FS8c17b2ADj3c+epuPTh4SNAw7\\nWEei7/uutFpWFdbzkJ5HiaOOW1qDUh4BAmM0pTXUGnbDyLHvWItvDetag5Ks6pqs1IRYhoGkFwZ0\\nfI+HWYq1gnWagpSs0hTle1icgr0nDIv1iizPscqRVtd1jZKSUadD4CssBhpC7wvhc7MbUegKpQR9\\noSmEItUaP/BZrddun8qS2XSKf1+hrSXyJJ5QZJVhkjoDbixIGbFz/K2GkahxlvZzynS2RT5/Ei8h\\nEJgqIzEZpiiZTWd0Owk9Y7icXNLvDfCl5PLJCdPJJVG3y/HN6wgr2dkb4U1mZKslcRTR70Ro41iY\\nQqCoHbOSqWtYL1HDaxSTMZ2e41n1vC4H1/cQXjPfaBwBvDbajdcIiVAK33dVuKrMsb4i7HaptXbl\\nXl1RzBdYXVNmKV6nx/WDXSazOaOdIU9PJoT9AYOdfcoiJ09XBKFPka0psogw7hN1EvLFDGkFy8sx\\nNSXdTo/15ZhaOfapebbEIMjSgslkTq/Xd2xLVnHz9rfIq5qiNhS1c5h17fR2W4KMlpjmiiylcZyN\\no9yuDNg2wNmg/TZp0+Z5w/pJy6ncQvbE1nlFXPE8t0o5Ugr2woDzeU6hTeMs2/9J10b5jPXikmwj\\ncyMaMIMFHl6u+V5vxPWdiKfTrL3uNgmgAz+INiwHq91BEQIouLbzCvP0nOn6lOnqgk54/coINolk\\nq1e9CSfaFzzX8L2KHj9/fTmr2o085ywn6+fIC4yBn354yffujfjunRG/fDj5g6n6/iHXy6KF/1gr\\nqzTPZhm+EuwkIa8c9dnpBvy6ydxd6cVdUtvH1378mviq19Z1ZlunZ0CLZsxEgJT6anTEd7epByAk\\nrpjrgj5XFBGba9sTV5OxUkqyIuXOtfv8x5/9FWezZ9wYHXAxPyeJY8f9iqviKM8DKfCV57JNnPqI\\n1rX7W0OYPk9T/CDg/t4el8sVQlg6nmBSalJj8AMfqzWB76OtB1LxdL3Epo7Or9/t8doo4p3Hp67X\\n53tNUOCMeKkrNHYj6dXe/EoqwPL1YQcsfFDlzLVASY+l9CgF+LqmnySY9ZrzPCPNc5rKNcJaSp2j\\nSAnDPlmpWRaaymhq7QywMW2zxtmVj4NDthGVsim1XZXjwBOOwlLJVmHFotInUJb0ej0CJcjT3I3C\\neAG6zBE2II5iwr19Kq25ePQYv9fD1hWB7yE9xe0795iOz8nyjEEnIeol9AKfQdxIVvke64sz/MCn\\n1+/ieRHz2ZQwicnykjCJXXXC1CjP53Bvn3S5oCgyYpmwWCzwAo/A84mCkGdPnuCHAYNOgtSGdLF0\\nssVFzXx2ye7OHuvpAlFnDEa3WK2mWANJL0ZKqArDej5DWkuyu4uH4Pfv/JbR3gihPOZFwbW79xmf\\nPKWqHYHFcpkSJ12EkFR1Rakt+197ixpBWWrKqm6yS0ulteMHb25aY8yG1c05R1eRaM9dW7bdyA02\\nTsU9NZvGpWifb6Waz1mvpjrk4qMreTEhNFLAfi9knVcssgop3biXlE73WZlWI/XT1wsdZqAEdUM9\\nZGVTizaCd08WvHlzwCwtSYuWhE1sSqWmcZC2mUuyddtRAE+WDJIjzhfPyKqS2G/vuKt0+qrqatmM\\nZoqmdPOx9Puz15ezokIIerHPYT/i0eWa/FNYCwzwsw8nfPfuiO/cGfGL/0Kd5suuFzvXLx6ctJ9X\\nacv5IndGEDgYxBwAs7RsRkvEJ0/pxysyX3J9HPzzHGGelThAgqEywpVgK9Nkic4hGk9sAkeUdJmm\\nkQ48K5tjZ51aibE108WEO8f3iMMIISxJHBN6AUHgEQcRRVW5ERMpMVWFsRZfCOotPUslnRSSbeYa\\nh1HIYrngeDikWlwQq5jjTsDj8ZRO0sVaZ8i80Kcsa5SAfqDoyB6dMCBW0LWGsso46sU8SwuMsCRR\\nROx5LFeOP7QFYLS7C46XVgCzvIbFGL/Q4CfEQYCSAmM8Cik4GPRZXF4QKo+yLrEG7hxfJwlCOsnA\\ntXUKTVEbR6BnnbPU2mwMrrOX8iqubrZHyrZP9TxIRAqBJ5yjdOjImnI1pV6fcigrfvLTn3Dn1g2k\\nEJw8fcLXXvs6Hz34kGs3rlEbxcnpGYfDhDD0Wa1zbJriSYXxLYN+nxrJk6fPuHHjmqNqKwpMGJGm\\nM3pJBMbgKUWlNTYvWZVrqrKiBLrD4SYz8oIQX0ouTp6C59Eb9LBWEMQBfhRRr1MkEAPj0zPU9WO6\\nSUw8HFBOaqJYse/vumzVt/zgrbc4PZ9w+fgjBqMhZaUgDIiSBF06esJstSAIY+JejzSviWMPgcIP\\nIiwCjWKxXBEEIYvFHN8PKIua3Vd+QPfwHllRkteavDZUtWOrapmxHG94o4LUtOfcPWI2wL0ND9Qm\\nw2wD4uaoONWDj93iW+VXms9oMjsBSCQI20hGOr/RiTyUVJu5bq8hYJDGOUwn+/UlMszAUwjttBmw\\njTqJtGSV5tFlyuvXhvzkwQSLQy+2NF0Y0WSLFiMEFRbXrHSi0v34mO/c+QsEPmWtXQK9lV5LwUbw\\n2GA3DtTZG0dgrBuXutGie64s++Wt5yD2ORhEPBqvKerPdoMG+PmDCd+5M+K7d0f8/MF/WU7zi2SX\\nL3aWX/jLr37Fne9hEvLowh3zOFTsdHz2e31WecVsXbEu6+fL71/AWX6RbHo78m2LPVY0wJ6PSYC1\\nVQ4nJgBWWZyYlwTp0JsIUFg+fPouzy4fIZXb60U2Y7qYAhapPEeGLiRpluF5HlEQkJclSLEhHRdi\\nS0aryZ6sUPieIjIaXWl6SULfOumik9mMvZ0B0hqU55OX2oGKJHQ8hdQlh0qQLSZorbm0hjDpcHZ6\\nhj8YIjxF7PssJhOs5+FJSW2c8Ltt5qbbXlGla07mSwQ+4+klN+7sUlclgyhBLtcknQ4f/v49zoWP\\nFwXcinfxPY+iyDhZzfmTm99BeF0CYckr5yCNbjKU50AkzgjKJj11GaN06GSEK2kiNs5SNM5SSRC2\\ngvHb1OdPqYuSd5cLrh3ss14sSHo9kiRhenlG4EsWiwVSSEaDEIMg9AMGvQ7vP3zIK6/coT/okZcF\\nxWLB4fEhvU4HYyXrLOfRg4d0ej26nqIoDP1+jyzNqCrDxcUlWkr2ex18II5iHj97yuHREVrXdIZ9\\nojBC1pbTJx+hAh9rDd04Is9W9PdHCE8SS0k6mVBb8P2ANHOAH2sMvZ0jijyjShcEShEKgS4LyrKE\\nssQKSacbMxmfUaNI+nsU6yVGC3YP9plcnCLDgPViTUuWby2URUG0d4P+0R3ysqSoXCm2qp/nWdZ2\\n69FecZDb9nfYcpJmc79t39jbM+sfb7tdBbdtCqVpgV664QFzY4sC34OdJOR0llJpuymCmmZ2WjXB\\nl+FT6Nya9UKHGTa1c4GjJGolvRSW01nOMAl59bDHe2fLzaCpaUqoG9QSYE27swZhS7RVeNIhA7W+\\n0u2TUje0WBIhzCZaF2w18YVjFhIvU439A9cwCdjvh3w0Xj831vBZywC/eDjhzdsj3rq3y88ejNH2\\n0w3zH5u04GXXV0+k8HLvbb/jitWtja2hE3uU2qm4CwF5qTmpapTM6Uc+h4MIKQWzdcE8q17q3HzR\\ntTku1gV7kivu4qtH15upDa6/WTflViMdO0rbw7PWDXR6rs9nrGV354AHZ+/hWx9tDCcXT6m1RmuN\\nF/goICsKsrKgIyVlk1ma5miFYYRCYKymE8VILMKAsZqBH5LojCiOuN4JmY4v+WBVEsYxVlcMQx9P\\nSSokerWitIbi8hIV+NRSIoxlPBnTHR3y0XxNuH9AqWuENSzXSzr9HmXpVELacqawV2cx9APWWQFx\\nxF7o8b3vfo/dbMHJbMr56SMKofDnS+r+HqOkyzrPWaRLFpcpVmuiIKSqCso6phM6lZR23zfAkM11\\nRGM3WgIHia8EthFFxuqmJ+V0dqUUVMUKo1Oi8hKbLTCVRgnDwf4eRZ7T7/ewFpIkYbFe0e12Ge3s\\n8Oz0jL1hglARq8Wai+mCmzdusV6sUEoQhTHdXgfbzB5qa1ktZo6EwjpR6KSTOIHopjx4eHgAymNy\\nfkkZr/CPrrO+mDLHx1eS6WzGaNjDUx5JGLBYrYijCF2XzOYTBoMBZV4SBQF1rfE8RRR6aE/Q6fQJ\\nezssLs4oVgXJcJ9FbkmNZTqfMhoOmC1W1BZEELFe53jKQ1rFYOcAqTSB73E+X7CqDWWlWa5cn1lr\\njZ8M2H/1+5TGzVvndU1Zm8ZZ6k3pvNXWbbUrHQi0qUBu+GA/ydzT3mtiqxRrm8yxOfvN4wZe18Te\\nruZguLIzLWvXUTdmui5Yl6406+5tNo7cWEcJ+HG95e31QocZtQ6zbUlqjWk2RAh473TOd+/sMlkX\\nXDY8qlcBgqW2Lu1ty6ilblgesASeREqFVJKgsZ6maRIbc5VlNsdro8CubZNZNsetCTCag/blxyh2\\nOgG73ZCHF2sq/fIGWVvLzx9e8sbNIT+4v8/PHk7+KAb9q1x/rP7l5jw02d+GQWfrqzaMUZsLVLDb\\nDZmsy0Y78eq1Wlsmq4rLVUnoSQZJwO29DrW2zNOSRV69kPC93a4vtJqIbXPzcQVQ0E09qW73o24l\\n6tobUGFVI8gsFAKFEhCHPW7t32c8PWOdTx17Su1KrHVdY6VykljKo2rUQlxv0o1ieFJQN9Jduq7o\\nRAGLwmn8jbOCcrlkONzhNx9+RGkEh/sjPASjbg81u3AD61FEnmUEQhJGMdLzmM9mGKvxu32KqsCE\\nXUdK0JR6tYG8qvCFwCpFbcGaq+vb2Ham0sPDMsSSfvgec+B0sqR36w67nR6FrlkuFjw9P0Hruimz\\nmoY43OOXv/tb/un3/5K81ASeIit0g6xsrqMGxCEEKE/iK0moJJ4UCJMzvfg13dFdnjz+LUnUQ/kd\\n1vMn7Pc7hAL6kc/l40cs12vSLOPu7ZukeUVWFEipEEJSlgVKSXxfOSKBXoy2grqsHUpUeez0YvJC\\nsVzkLM2Ka7duoHyPMs8xuuLmzZs8e/SA4aDPcrFk2OtT5yl+4COkQ5nKKmdvp0OUdDk/PeHWzesI\\nBMvZGJ+KqlhjPB8hYDga0h8MqdKUfqfHYpmTrxeEGILAR6oAJQLGswm7h3c5Oz0hCkPOzscU2Rhl\\ncvA8wiDAeAH71/fJ05SqcKw+DnRVYG1NECasFlMWRQV41JUhCELH4yt8bnz7XyH8LkWDiq1qp+JT\\nN6XYq6yyzSzBtD3o1iluj5U8f7tt/GFbmt0E1Vv2fvP69v5r3tyCu1rAqBSWUTfEWJoxtbaf7Vom\\nVrt2i7Hu/v1STD9R4G1SZpdCS6x28k9SulLJuycLvn6tz08fPO8gWr/lnJ9wxNXGFWZF7RxipCSB\\n0mi7wpd9Z1CLys1NWYkR5mr4tVlSuMHW50ZKvqJ0c7cbMuwEPByvXsoAX+3r1Wt/83jGa8d9/uT+\\nLj95cEle/uN2mvCHZ5afFaC0zvL5ZvynfefVe6PG+KV53VQU3P8Nz39+URvOFznnC+iEHr3I414/\\noqwNy8w1879IoPOiZYTLhgVNOUnSlCGvwAu1sWANZYP0to0hML7CokC4nl8tJIGv+Nqtb/KNe98m\\ny5a8++g3vP/0A5R2Mk3CuH03xkXULfG6kpJeSwxtSrpRiC4Fs8WKUkASxQzjkOuHd5lOJ6ySmIHv\\nERrNji6pzhYMen1IBGenp6xWC4yFJIqogWAwYFXkJMMRnpDspimXQUjaKEbUmxEQp42o5dU96EJY\\nd86TKOT+wT49YfjJyZj+zdscHN1mulpxOR2zyjLq2vVRMQ601O0nBEqBhensEiEEeV0T+94mo2+X\\nbEq/nicJlCTwFL6eoeaPWV084fzsknJ2QQDsRIaqmKKkJq7W1GnGBDhfrvj6K69Q5Rm/+vXbjHZ3\\n6XW7ZIUj+S9rzc7OEKNrlKe4nC5RfkhVlvT7A7L1kp+9fUG31yVQEoFBV5rZYkIQRaBr0jyn0xtQ\\nZDlYJ1Ct/IhSG3RROik2FHVVUNcWYTRB4HF5OUcJQbfXQ3mek29TkqqqKbJlA3Kq6SaCg9F1prMp\\nw26PfJ1xeXHG0bWbLC+fwXqC0RGjWLCqKnrDfcazFVW2oLc7Ik1TPKEcqwM43ckkxPfh8YP36Qz2\\nCKM+ZZ5T1zXCi+nsXWPvle+hpUe+cZa6cZamySCds9SmkRi0zk/ULTp26xHa1Kd1omwet63Dxs7b\\nredb3nLbcW5e09gb35P0Y5/H4xXaNvexlEgMaEd/Ka1rJTbQhc9cL9HDFGijGnSTYjNH0zRyhYR5\\nVnE2z3j9Wp9fPpp97BNsM4AqGpSjc5oCx5cohOHZ5NeM+orQv00n7LubE/c9ygrsBlFoN0dUCAnW\\nXGUoW+nIH5pl7vVC+rHPRxcrZwC/wPq4w/n96ZJKG/7k3j4/e3jJaosO7h+6FPv3ttoSgNh+Khrn\\n05D2b8qzLrt0NIMNCf9myHwrntyKPsGNpqzyitNZRtI4zzv7HbSxLLOKVVGTFvUXPuabnjhb0W3r\\nwi2b+UxjXUnS2AZqYHFMIbVDsJqN91QgaoTwkbVFeAbqiihM+PZrP8BayQdPfk/oeURxgBBQ1xXS\\nD/CU10TZ1vU1fR+pFCNfcllYSqsRvlPjeHXY4eTklMLzef1wl9OTMwaBx2R8SRBE/O7kA6Ik4uHJ\\nCTeu32C3EzFbrljWNZ6uOd7Z4dF0yjrPiXb3WWcpkR8wKzIC30cKQVFWDTnD1SiAVE7wNw4ihp0e\\nnvBYW4sYDLlYzimqqplZrdHaabMKwPN8hr0unlRUdYVC8o1Xv4dtwCGVMQSeoC7ba8KhXpUUBJ5w\\nmWX+hCh9ynR8znD/GmVV4scRuiwoypo8XdOLY7QRRJ0OlBU7ccTF6TNqrbl28xZFuuJyNuPatWNO\\nT08Y7e4zWcyQQpCVJYPBkE4Qsl4tGY/PCXzFaPcIbS3D0QjqmrffeZfRaMgwCDBVzbxYoGtHzBL5\\nPlmacXY5YXdvn/F0yv3bt8jzkkqDTlM6SYe6diN6RWXw6pQwTvB8HyEUi+X0/+PuTZ8kOdL0vp+7\\nx513Zh1d1TeAxgAYAHMtNMPVXhwaSUlGyiiZ6aP+Pn3QZ0mkSDNyTWu7s9rZuWfQg6vPdRE19QAA\\nIABJREFU6ror78y43F0fPCIzqxrdjWs4a3KbxnRXZUZGRoT76+/zPu/zMFBdCu1EMJRSFKag3WlT\\nZEvCJKC7+ybFcsJ8McWPI9K8oNVss8hyPn50QH/QI0qalKWh02kxnznyS7vVxPMU1hQcPD1gNl1Q\\nGkWj1aEsCwoDd977c4L2NnmhSfNyFSxzbSm1rWRRK49Ku4Zda4jaVD35tso465BoNoKly3vWsOzq\\n5xv/vrTECFtBVeLSe+rZKgXsdmKOxqlz26qXI60dKdUV/5FCu1gjJUK+eO1/pYG0EhAoVxvwKyd3\\nT4pKZqoqpAMPTucIAXe2ks1lZ7XbNlSFYL2BGRuHSsfBDudPD7HlM4TIiAOPQCk85XbVSqxlyKqr\\ntFqMBYIa0fs6gWinHdGKfB6dzVfB0m7sgr7KeHA657PjKX9yd0AvCb7ycf5Q49I1/YrjZddHiI1g\\nWTEWXbAUFVRSuc8L95y14oDJolhZ8qxxmecrF1c/3+KC59E45eOjKYejJUIIrnVi3txrc72f0E18\\nvJfoRL74S8JG2vhczltLQ1bCKitB9lK79pO8NOSFIa9cfMqqj1NX7hdYwd5gDz/w8AIfvzJKDoJw\\ndXzlKaSnsMplnnlZcDCckJclWgo85bHXDJmPLpBxzJ0kJJ3MSIzh8PFTPCmxRlMUOcIImp02Oztb\\nPD05YWItH80Lfr4seZIbklaTndt3KIzBM4bhfIaRkOuS680msXX1QjBEoc9Ot8d+b8Cd3X26zSbz\\nxYKPjp7xs48+5MnwnKzIKcqCUheUZYk2JdY6yl6v3cZXHiYv0EXJdDrlrde+A8LN7bQwxIETcpBS\\noKpaZVDBsMOT+zA7IJvPabd7DI+PmEzmTGczpIDlck670wWliKRjrs6XC3r9PnlZkhaa6XhEGAdE\\njZjcOGYuHnS3t1BJg97ODmHS4GIyRgYemYC9O7cYThcY6ZG0Ohgp8VtduoNtCiswyqPZboIXkFtQ\\nQchsmeKHEVbCtd1rfPb4gDRbAE7qUPlOW7hIl3TaHcIgcjVYI5hNFzTDBFNaRFmQThdAQFFKLi6m\\naOuRE7CcDskXS3w/4ux8RBBEXFwMGc3mNAddrl2/ThhHYC3LZY6QPjdv3QSdMxmdM5pOndGAkIzG\\nI06OD2kNbvDOX/zPhK1t8tKQaU1e1jCsYy5v1iZXwgTGVh0WdsWQXXEAqF7Lep11pTjjapuG1R+q\\nMgeX/lR1TSNcicTYlcyeqUlFxnCtEzNNC6Zp4ZI+ywredz6vpiqvWApTGYe8BFl8tXCB8lGU+Mar\\nLHdkRQ0WWG1XwV0i+PBgzPfv9BnNC4aLy1ZXrhbpLqawFm0lRWlIM023eYM8nXB4+IjGALrxW+RK\\nup2o1Fjr/Pg01SJsK43rDYYU9nLq8WWyzN1ORBJ4PD6fV47dX/4YLxpPhwsybfjunT73n404HKWv\\nftPXGH+snsqr10pcuhdQp4YC9xxcST7pNgMWeVmRusTG/aynVgXdvKQgvzmWuWaZO7abJwWNyKMR\\neux0YkptWOSaRVayyMtXQu91vHZntLGHtc/LaK3Dt1wFTmksuTaIsmZp6lUDtTBuw3Bt6zpJ1GC6\\nHCNsw21GpVtglCdpRCG+VORlQeQHLPOMQggS30eVGZ0oQmYp95+dsb/VYzpNmU3n5HnBUpcMhyNa\\nSULUiPFCSTqBk/NzzqTP0Twn9RR7rQF+7xpROaRjMjIpGEpBEAaEnk9aFjwZDel0OjTjeCXWMM9S\\nJss5Np1Tag1C0u106CYR0+MTJtMZUrmbLYXEl054wZNO8shXCisgVAFxO+R8eEyr2XPuEpU8YOQp\\niooIpoQk8AXp7Izl/ITjxZR+p43NCmYzR8Jpdzr4wpWOYt+JsUrrmMHNRsxkPGIw2Ob0+JjSl2zv\\n7TNZLEinM5JOE5THMs1IGk18T5DnBSKMyKxksLPvMrdujzCMKPKSoizZ3t7CbyTouWU6zfAaPmEc\\nMz89ZYwh8H18X+ILSaEL/CBEeqHLMpcFJycHXLu2D0GDEsmy1CRBxCzNaHf6HJ+c0oubID2idsj5\\n+QVJ6NPr9xFBwvHThzSbDYQuuZjN6Xdb+L5HVhbELSfrl2YpizxnOprSaBSEQUCaFkgpmS2XtDp9\\nxuORM0wvSgjaRDffc3XytCQvNXnhAmVpqlafDZh1pRG7SdKya4LP+s8GJFtH0fWKQb0yrOQILJey\\nSLEqZF7ZTNtqlbGWXiNACjgdLSsNgOoQm5PWgq6eK7eGvTyJ+ALCBeBZgfFcoNRGUhqJMhZtKjq7\\nBKEdi/b+4ZRv7bc/l/BiLRhhkcZdCC0seaFZyJxB/3VOjjMuzg5Iru3jeQ18XTclO21NY3QVG2tY\\ntsoxVzXOy60lXyTg7XVjQk/y6Gz2uWbH30TwOZ2k/ONnZ3z/7oAk8Pn0ZPq1j/l5448pg3c1WNYi\\nA+5BrH9adc1tZJ31b3qNkKPhgnX+Wc8hsQqgL4JlXjVKYxkvCsaLAlgS+ZLYV7Rin2udGGMti7xc\\nBdms0Jc+5zLBYHMrcHWTtn4m66qLrXxijTCUWlCUYtX+oKSpGJwumPz4B/+Kn/7u7zm6cKxZgDAI\\naAQhjSBAWk3Lj9E6R0YRtizYinwWuY8SkqdHJ7xx5yb+ZMJksURKn/OLI6atDq1+i2lZsFwsYDIj\\n7Hb59fmIeRARNpv84NbbfPu193ly+inHz47RrT266hn+YIusKqfEUYw0Ti4gyzNm8wkY19wfhyFU\\njd/jLENbw8PPHrC9f53xckGlSYbF0ooa2NLB5GHos9docriYMiws09mcP3mv7RAJZVEISm1pRYpZ\\nVotmCzAls+En7DUTtM6JcXCvjAKE9ImDAJst2Go1ybIlnXaXi5Njp7lqNI1GzGwywgiL12hydHSC\\nJyWT+YKbd25SFiX9QZ/I9xkOL0jTckUqybOcvMjxPI8sy5jNFHlu6SUReZqh/IBGq8FsvmTQa9FO\\n58RJjOf5LNOMRZ5TGo2fxARRwrw0ZEXG7du3eHZ0TKvV4mI0xpYFBidRp/yI0JeUpaZIl2R5jvID\\nbNjAqJDzoyfEzYYT5g8ifFUwm6ZMZynLPCdLNTvXdjk6OUPjs1jMiJoRaWk5P7+g22lhGTIazZgv\\nFkgv4vq7f05jsI9FMZznxKGHpwSLTLv6ZK3is4nErbJB1/hX/WijdikuB8vVoruxFbVQl2NWv72y\\nttn6vRvlnpVOu7DEgaLXjHh0NmMtq7oO1ALpNlEbs1hUJZ+vxZL1lYNyPGnJbI6vAqfsLi1SuD/G\\ngpAWYQTDec7RaMm3b3T4+cPn5eKshdI6pr0QlgLNsrAIEbC7+z5CFGjrDEs9JZ14L2CVri52tX5e\\nqey+iPPzsqC534vxpFhf1D/gmKQlP/n4lO/fHRCHit89GVVs368elP8p6sQCK33OOrOsiSGiyh5r\\nn8D6azdDH2MsuXbvdaIXUPf+CrsOlZe+8Vf8+mlhSAvDcOFE3wNPkgQecaDoJgGB52yKloWu+suM\\nI6msPk+s/itYQ9u1kkz9vdbuCGKF6mrjbI+UNCjj3DNq4pAnffrtASfDZ24hMgZfSTwJ0pRsNQIC\\nJZhMNUs/RKZT8sxlaVmRkWvNznSCzDPS5ZLpYuEMnBsNlsaikZiogQ0TTKnJvACpJK2kxbuvv48n\\nBfu9PWbTC27fuostrjEaPiJPU6QUzCYXeJ5iJ4k4KZdsdxsIY5lmOUoKJouUuYatJOJkOmXr+j4A\\nb/TbPJotCaOEeVnQbyREwlKkKcIUPHj6hHFR0mi36QU+//C7v+Zbt75LuzWoIG63AGpb92KWpIsT\\n9lsJIlsi45DpZESeLglaLfZ7W1gEaWpYzOc0mglozWQ64VanjdUlk+mMZZYRhDHNQZ/FaMxskdLb\\nHhAqxfnxCe1ej8NnRyBk5X8bEIQheZ4itVM90lpTameQPRyN8ZQiCAMH9xUabSTNTodimbLMF1xM\\n51zbvUZZLIm7fSYnZ5ycn9PvDzgcDonbLSd7ZzWLaUbDV/R62xw8fUYShWSFwWinwdsaDGi1Wiwn\\nF3RbTXJtyYuS+cQZRm/vbmGtoJhNacUNRpMpeW7RWvPad/6M/XvvAYLRs485ePwh1krKvCTqXmP/\\n3vdJ+nuU2mnCFtqynOU0I0Xhu17hGu1ZP/NV6BG19Ahrzgl13XIToXGrxap0t/qtqdYQx6Nf/XyV\\nVUK9775KBnIohmC/G/NsuCAvL6NZ4srLzfptTiDnFQjWKwPm8fEnnF8ccOfOnxCGETY3jtZeFd5N\\nnWXamsQBj84XtGKfe9dafHz0fDZlcVCVqM+2MFhbUBjHeJNVSi+VJUBWtHpRQViVi73d+KJVn1V9\\nAV+VWQpgv5egpODx+eyVzKhvaqSl4f/99JTv3unz/bt9fv7w4kuTizbH5wk2/FEJRZsoyUYWKajr\\nlWJV16yzMCFcG894ka3bh+q6dLUxWj3kgkrs/yqE8/VGXhryMqcS/0AIiDxFFCjiwKPXCAg9hcW6\\nWqQ2lbC0qWoiVdYoa4FnZ+2lRB0018bUrl4iXN1HSbdLlzXPVNJstJEVyUdKJ0AvqCy8rMZqS5IX\\ndNoxpFPKVoPj+YJGEDDoD8iyJbKiJx6lKeXeTUrhSCJCOMKMJ1wtt9NsO5JREKDLGUnSwZMt3r7z\\nPkZIcq04PDymE7vMZicKKU3Ocpaj8gKhJC0FRb4kiGPiZoxQPoUxID2G4xH73Q5TXbIrDTqb0osS\\nmmXK09MzUqmYW5haS6fTQQpBO4rIsyX/8LP/yF/+t/8OKX08KVlmS/J0yv3PfkOZztlKAqLYRxY5\\n0/GYuNUk8H3m8wVFI3cqS5XOrrWCoszp9PrM50uklIxGY6Sn2Bn0SXPN0ydP6e3v44cRmTGIIGQ0\\nXuD7EVoblOf6KNM0Zbl0z2pRuuc7TVPAUuQZwlrSLHMKTEqR5SXtZouiNOjlAs/zGU7GCCFZDsec\\nnI/xwyZprilKQZxEKOXT3RrQbjYZn59xMJ0w2B4wHV4QKEHcbCGUTzP0mZ4dYqVy9dpFynyxoN3v\\n0vRaiErHN44Tt4YWDgYejWbshiFCKQSC/o23SHr7nD38Na3mNv3rr4MQFKV2LT/W2dNpaxnOM9pR\\ngLEwXuZOgapGDkUt77Huka99a+t55agAGzVJ6qKL+1sdXivLhfUkvbqs1QgW62PUf9nrxozmObO0\\nXK/rVlSvX5eFsLaSUZSrgC0uHez58cqAmS5nrg9LemgrUcqglESWrhF4M4hJUTd4Wz48GPGDuwO2\\n2yEn47S6YBtglnVFYgGUAih1HXXxVKVhi0UIi6egNNWCVCsIVczY2m+w9lNbA3ob13YzoADX+46Y\\n9OR87i7hf8UYUxr46WcXfPtGh392b5ufPRyyyL+YoXI9NoPjCtb8IzNvV24iiBWDVCBdU30VIKUU\\nq9fVMK2vJHGgOBo7bVaLq4k46B2EldWOtZ6BYv1kX9kUfVNWZtY6fdtloRktCnfOFkJPEAYeoa9I\\nQkXkBQS+IvAU2IrAUBEc6h21sRUIstoc24p84FiFpRV4pjKlFnA2PCL0Xe3FmJJ5mlIoj9JT2Bxu\\nD3ootWC8mBBJuBZ6/C5Nsc0mZpnSFlAKiQh82rdep729TVaUtJOYvVaLSZpyPJ0wWyxYZCnnszHL\\nvODbb4AqjOt1VhESUJHP3hs/5B9+/V+IlMBqSIQhUZLZMkVrw1RCo9lGYImVpMiWpJ5PdznGjwJK\\nKbkbNpgo11LjK8XT8xGPS4sMnR1YIABrGE1mjCQoodBS8JNf/iekDGglHUwx5+bODuV8RCMK2G9E\\nlMs5k+US64U0o5hG6DwfC1vS8RNm8wleGILVzLKCMIyxxZLFYkma59zYv8Ozw2O8Rkxja4tms4UU\\nMJstcUpKDlJXSmEtVbBM8X0fY5zQhDYlUjj9XKzL3orSLffaGk5PTlnOE5QnCOMWqhw5QXqtuTg5\\nR0jHDckWOdZazs/PSZcJylM0GwlRu0c6m2KjkLDVwgYRe3dfZ3Z2zPj0lCzLUL6z4coKjQojvCBk\\nMpmz3WoiioLZcI7n+ZSlwZYpO7fuYa0BK8gXI1TYJGi02H/nT7E4YQKjN3gDGPd8WoGQHrOspB37\\neBJGi3KF+BnjtJW12XzeoUZEN4PkmhFQ92XWE4XnguPL5rRDstbthdttZ6p+OsvWn4trG3FLjljN\\n8fV6UW3TX4BSbo5XBkzhe9x7/QcEfoguSox2sGyuBMpItLRIKzDS4cyyOsnSwIcHY759s8s8da4U\\nV0cteSeMC5pCGKQWaJPh+0lV/3Hfrt65Kykwuq5nrfLy1Tp6lfyzecGFgJv9Btpanl0svskk5UuP\\n3z4dc2vQ4IdvbPGbpyNnM/OS8U8Nfq3Hug5Z/Xf1zFebmQ24svacU5WgvwH6zZBpWlZG5W6s6p/V\\nvx0SI7GiACuvbIo2IZ5v9hptbkKEcLXQMtOkhWa2XCvM1EE/9hWRrwg8V+vxpNv41So0SlaZtajs\\nhBSVOo0kqBjhoQf9ZpNmnLDT7RH6gSO5KI9+HBH7IHtbbEchVkhmiyV7gx3ubm+jsjkyL5iMx7Sa\\nbUZBiNEG3wNTZIyeHJNqzU6c0G7FTHzFLPQ5Ho94cPgJ79797qVVzgKtRpfvvv3nfPz0N4womJiC\\nvpCcLlL2goBYeXhZjjElEyytwCdbLJhqw24rYJEuKGzAbLFAhAFdGxAWGd/aGdBQkoWxnGQlnpCM\\nq9tZ2pIgDIijNq/dfIt2o0eRzZhPnnJz0KPUJcvcmSP7nk/SSBCeJFCKditGKMUyS7FegJEKXWok\\ngslkQuh7LNOUOGnx6YNHBFLQaiY0mm2iMGA+n7NMCzxPoZQkSWIWizlp6mqWqvIvBYHneVhrnD+o\\n53R9ddVOBDj9bGNZpkuU8ojiCF1YtE0p8wwpPSzWBeTSwbrWWiazKb7vs1wuaSYNGkmbdFmytXuD\\npN3h8cMHRHFAGQZESUy5WLBMM7JSEzQaFMbS6LScnKI2RFHEZDpDeT6TyZStzhaDG6+7pXN+yvD4\\nEYO731kxVetAJqr5rJBIZUHKitgjSUtDpxES+h7n82yVmRlrKa3beK1KK+bKimyv/rUKnhsv+jJz\\nuX5tK/LpxD4PTmeXORVCbECvtWpt7YxVl482A/aLP/uVAZPsHMt1l+lJKPI5QRQ5w1dh1lBb9aE1\\nXCqEYJKWPDiZ8+6NDj99cFE1ja6+pguIOJk7KRxUVQqLr0LKOihagRASKQ3SCpR1MHDdm2mNW0yF\\nNRsX43JWLarX3hw0KLThWe2w8kcej8/nzNKC92/1eBJ9MTLQF9ZB/a8I0W6GzBpmraHZ2oPOZZeS\\nwHNZpTauIb+dBDw+n7vAWp13/f4alnXfx1C1Gq/3pldv9BcYX+W6uGy5hpA3VGaqDZynpHMfEQKD\\nqPrQLLXulRD68nuEQCpB4EkCTxL6CmudUk1WlkzSGV3dYr50mVAUhNgQDobnjJXiVtNjNl2SLRb8\\n5OkJRRjTjxViMmS5WNLwA84Oj2hub9HpNjk4HbF74/ss/Tknhx9hFmNuJD5eoWkrxd61PabLU37+\\nyU+4s3uPbrOHkBKsIPBCrm3tsTvYJc1STi6eUcxHHHtzQj8glJBnC4o8J4kTtIHPpimvXRswyVLG\\npSEJLGaZYfyQ6XTKXqtJOhvh+SGT5YIoaRMoiYdjvvvKY7dzjffe/pG7psbw6Se/h2zInRu3GF0c\\nY8qMwFPEUYLCZXaaqqZYLogbCbGV5FlGEsWMZjMGW30mozHGU6A1QbNNp9sliRTj2YLZYrm629Y4\\nh5bZfEGRlyuhe6M1fhC4BdZYjFIoqSnK0rlcCFYtDg5lsNhKLi7LM/f8VXq4QroAWUP1riVQYIqS\\nsnDiHYtFSpzEDHb2mWeaw/v3mc7mbG0NaDRaLOZTkq0tyskEURiEknheWAVin8ViSpblRFGT8WSC\\njFps3XzT+RBjMWGH2YMPUXGLzu5rUM8/4Z53r7IYM1qTXTygnF8wTzVJb4sLEdCJA5LFDNG4jrHO\\nr1UbQV5NL2ntSplVUKMt4lKJ4kUJzsvm76X5KQShJ7nWiZ7rdNh8T80lcLNyzVqpg70LnHw9SPaz\\nZ0+4yAre/9ZfIYQkjiq3byUplEBqVrCsFA6Ok1KsemKOxkuakce7N7v8ohI12AyasFZGMTUcp8HD\\nVH5wwnkHClcGzq1AS1lZJtk1AegFuq3gMpYb/QZ5aTgcPR8sv25wedX7X9bqcTHP+cnHp3zv7oBW\\n7POrJxcrO5zNcfX9/xRqlpc+W7ggtgp2ovLyW/29Ci5SIUWBER7dKCArdCWyv4ZXgBWbdrMob80r\\nnuYXjM+DsL/U9wQQ67JDLTIupQv+kSeJQ8/1D/sevlrDPuuJuYnL1kQgR2PXFcIiBRwPTygpKSs4\\nNvB9Cl2yyJb044QwECy1JVGKj0/Ose0WyzTjeL7khu8jmgqZZeApzHjCZ8cn9N/7Md3+Dn0huX79\\ndYzW/Pu/+d/R1rDb78FkTOApSgw//+j/od/Z53tv/IgVm9G6OxKHDXYG19HdXbrbtxmNn3E0Pqbv\\nh9w/PuP1MGF5cERr0CcQHkfnI85Kg/D6RFHCvU6TX35yijKa7mDA8dk5hZAssjHG88jSFBUEaA23\\nr7+J1gVPDj/l409/y62tLgOpiYqUbQ9k5FxXSlvVf0vHoPc8b+1nGIaurjlZoLyAyXDGZ0+eUAYh\\nVvq8cWMPIxRZtsRaS54VKOUhhHXavuVyJdmX5277o42BIieOE7QpkAi8wEenzvD5qoRiPUx1nsKy\\nkhO0pVnBhmAxRiA2enyDVhedzti+fpcbb/+Iw8efkk9SsnLC0fEp+/vXCMKE8WhKWRqEkCRBjLWS\\n6XRCEEbkuROKmM6mBM0trt95G6lkdS6WfLnElgXZ4X0O50uu3XkLIWUF1FmkheXklPToY3xShC4Q\\n8zmxP+PZ0wOGIiBu9xm8c4OiVJSV8Lo0xrX9bawLVKUNwdrCa1XX/ALz90VDCldmOxovn3OUuloG\\nvPLL6sqvtkkumL9kiXhlwOzvbHF0fsrh6QOubd8jCBpk2QJT5igRuszPOPp3veBJjOsrrTDiT46n\\nfOdWl7eutfjoCgnIIcjVKYuNf9dQXiVcUJDiCx+tQQmDFsJp2q6w6Q2pvA18Wgq4tdVgWTW1/yHG\\ny2pnL/t5/b60NPz9J6e8d7PHj17f5hePXl3XfNXC/4cMpM8dewXLVkX/6r4J3MO8ZovWghUeYOk2\\nfU7GWaXd6O6llM41y1ZYjjXW9VBZu/Eg21Wt4YvWLb+uqLzLLN13qn0UlRL4ShAFHq3YJw59fGko\\nijG+33Q7/frRtuuFoS7FSuHIPJ5010tbQ2m1Y4Jqi5ACbQ1FURL5AWezKdLEXGRLbrQbnAuPVLuF\\nelwUTOczet0OjTjCNJuMVIfb73+Xfv8aeWlRwlQZsc/+3m1yPUNnOfMgwsMQasOtMOTZ7Jij4QE7\\n3Wtc2t5aS+hFCB+SsEm/s4O1hqOTJ8RTzc9HY243GuwoRSfP2Go22c5zPl2kGF/xs8MzpoWm1+3w\\n8HxE4UUor8mff/Av8KSrC44mQ7Jiyoef/pxWFHAxumB/q0Mv8pAmQiqL32iTzsboPMcCQRxjlLum\\nQqoV2oR15sCFzWm3+zz67DOmhaGQEIYKL0w4Pb+g3YwIw8DVAbPcPbvW6ec2mg3yLFvBlUIItNEg\\nqJw73KbC1Tldc37NAq8zqM1sZj3vuXRdrdwoM1XPyLU7b3Hr9bcwMqAQHtu3vsXW7bdJp0OOH/yG\\nyeyM+XjoxNmTyJGSsgybZhhrGI6HSBVRGo/Xf/CXNLf2AXuJKZrOxxS6JMaSH/+O3z78Ff3rr9Pa\\nug7GMj9/QjY65NruDstUEIQ+8xlMpgvCpIWnDZPhCdvFnO1Wm7QweMpQaodAClH3X1bYLFxGhq5M\\n3atz+YvM7eu9ZCWJeXW8LFmx60VkdSpi4/p83nhlwJyPxrTaDebZtIrCIFVIGHiYQiOVcgGzqkkZ\\nIwCJFC5o1hnCb5+O+P7dgaP7jhz0UZNB1p1tVXZRPXBS1IpCEikjssI4M9iq4XvzMqxvR/XVK+bi\\nrUGDeVZy/FywtKt3fhPB5UXHeNmxNxt3tYVfPh5yZ8vVNe8fjjn8JwIdPzc2UJTNVpKq1W6VyUns\\n6u/1ZdBVBGmGCmsFWekspqqjuc2OESsFHCNqJEGs3HCqD351hX710q9+f9dkJveRK3UrWSnOeIok\\n9Am9EiUyjs8eEJol01yz3d0FIZnlOc24Q6uxReDFDnYzOZ70V0QoJSzD2TnL5Zxm0kQqQRgElVOK\\nW5RDP6DbSPCl4CcPnmH9gGyZUxY555MJ+90BozTjk7NzWp19Pvjuj9HGkmalY+9LWdViNd+99yP+\\n/jf/nqLI6TYSJmlKgeFer4N9dsyv7v8dP/7hv6uIfJevs6nSA6MdM3136yb7u7f58LNfcT55CqHi\\n2XjIcpmx8BR3mm32Ol3+r08/486NG4znM3Srx73tNxgMdkiiJkXhGJeNRoPi4gjPZKSLlCRyWZL0\\nJe0kYTmbuHYTNF4YsFguWUzHeIFPUUnYjcYjdKHxQo/CSuJGi8APOB1NGJWGQScmjJvoZI/zTz4j\\nCLeRQJbmjtCjDVIpgiCg3WzwdHiBEMr5JVqXCWZpRrPZxOqSNMtddolDVawxKCmdYg41mcTWT/j6\\nsRV1QKWCBM1qLex0e8S+wHiNlc2hC7yGuNnl9nt/irWWswe/5vjRh0wWGcn1dxlsXSOIYvRiggwS\\ngqSNNeUKa1wFblxwVkGIsu5e7mzv0i2XTIYHjIaPEQLiRoOklaBMyenhAe1ulziMnTuK8LCixJOS\\nw4f32Xv7R+x0Qg7Otesxrs5bSidyU28iRIW01BvHzWm9Ob5IsNxtRwCcvIQDshk0PzeAVvdg1f71\\nks97ZcC0SYTRmkW6cAVhUV3qDZjNKAct1fTi2lpF1RmjdYSJ3zwZ8Z3bPeZZyaQO3rKnAAAgAElE\\nQVTWVhXVzrD+w+V/e9I9cNrW2Uv9OlFlobYihdT5pVtoPSm4uZUwWRacTbNXfc0/+LgKob5oEX94\\nNmc4z3j/dp9+I+DDp+Mv7a35h4JrBZePtyk8UCeJ7r7LSmp0U9LwchNyKw4YL9xEVqJuyaiY08Ig\\nNBRCVEL7cGmDs0EVqM/jmyD8XG3PcUIL7pPW7N4qwClQkhXh5+T8l8wXU7abLZZFwXYc0SzH+AI8\\nXdBVIGbnnE/nmKSFsZplLsiNYXdwg0AoHh5+SLfTJvI82kmD4WSMpzzysnTzzRgePXnG09GYuNXh\\nL3/wr4mjBst0ge+FfPjpPzI+O2N79w5vv/mnLIuSstZtFgIhLfPFKa3GLmmRsr/9Fp8d/ZbJcAh+\\ngAYuJjMGscdyNOOvf/l/86ff/ucEfujqboJVADVVIK94XWitefPOt/mbXx9QLDMaYUy726UsC4Kk\\nxX/48D7zouDh+TmDKGYnabC9e4MkbrusrDJbCP2Ig5MDbrWajNOU3BhCz0NIj8lwiFSQtCzaBuh0\\ngS0NnvJgkeEnMfl8zsXpKfu3buD7IUYXxH7Co88eMisKUCGT+Zzvvf2nRHFAoTVBEDEZjyv3l5Iw\\nDDFaEwYepyenrvYeBpVJd+X5q5xSUakFUeCIWblwxt7aGvKirJxPKhi3enbtleW4tp6qURmJYGv3\\nOp3dmzT23qwY42Jjoa+PIZHCsv3ad9h+/TsVxFoHJPDa2+4DjPOH3PzYNYonsWVJaQ1aa6bjMVEj\\nZv/6NsPhOUJIdrYHPPz4I8Z+QJI0wEBRlmR57jazuPKcsJZF4czNd9oRBxcLpAEhHOHKCFGhgGsG\\n/KUTqs+p/tEXmM/dJKAZ+zz4AtyPzePVPAmurGf18/yS6t6rA+bpeEjkNfjh2z+oMsya9VixBC1o\\nKZHSoszascxBspWEXXUbl4Xh46Mp71zv8IvHQ7LSXMpA6mBZV2FdjqJdMd26ACk/R1bNvRrqJV15\\ngpv9BqNFzvnMBcvnF9U/HGT5dcd4WfJ3H53w/q0+P7q3zS8enbP4IzuevCz4bmZgtWFvfX+uvsta\\nS6Aksac4m2QoAaHnHDyEgEJrRAFg0NpphYgrnwNiZSj+TY7LakUb36+CVEXFcq21jZWQTutYgi9a\\n+DKlEYfIMqPpCeR8TjqfIouci8MnJHEDu1gQtppE7Taq0NggZnZyn14j5mYIb0Q9VBgTe5Lubp/x\\nMqMgqoQdDM2dbeaPnvDPP/i3gHQlCpVQWsu9u3/CW/d+RF46cexCGwwWJQS+UphyysHTD/nWm7sE\\nXsT+zh0CP+JXn/yUhpLky5RTT3DT8/AFfMeH33/y1xi/SRS2aJDR7d2lkfTW14x1pqAQ7Pau8/un\\n9/nBVh9fCpqRx1m65Pv71/jNdM7br33Ak6NPmSxhnk5JojYAo/E53fY20lM0wx7d29/HO/oF9x8/\\nIfR9ZLkAK4mkR7yc0uoPOHw4Aq0pbcF2p8OyWNCOE6SxSARFlpLETdCG+WSICbr863/zv6KkQnkB\\ny/Epd2/dIM0zfE9Q5AVR7JifcZQggdGoIAgCwiDEWIMuNZ4Q+L7vHDyUy558T6FkRJYXlBKUUs48\\nQkoWy8Wqz3ATjQRQqjICD3xCP6K3tc1sOuHk4BFBZx8vTKhF559/PjeShNVPnn9+N/aa662mBTAs\\nxmeVKXlOqSWlMSzmS4xxZLWL0wsG1/YZj4bo0lLkJaXRjm9S1i0l4IcxSsA0NfhSsNuNeHo+R2+s\\nBWtrxivn9rI08wWjEXpst50F41dpZbeXNuHV+vYCmHhzvDJgfvDaG8St12nGTdKiXElTSeF8Lo2V\\n+NJiqqAprRO/lRb3HyNWFGMDnM9y4mDBeze7/PLRkFqPsw7EbvfhdlBW1Au1rOAw9wBKUa6ynfVG\\nwRWJPKW41W9wPs+5mL04s/ymspIvM75Mxlca+NnDC+5uN/jhGztfE6Ktp+kGlvqSc7xKyX7R66AO\\nkHUGtgn4PL97q+Gpdhys6g2uNcMQiSWF9fBFiPKdVrGo+0s2voGACqZ99bX8Opn25W9QwRqskY+1\\nQIFEILlz8x3OTn7G7z/5hBs7O8wmM6yAnX6fZbYklI4RK+IAFYSESpJbOJ2NaSnJpw9P6PS6xLZE\\nP37MtWs3GB0+ptPuOL9Mo+n3B9w/OmA5n+OrkGWek9cC2MbZ4JllsXKFqDcbwnP1tXB4n/nZEXwL\\nCu2W2V57hx//yb/h4bP7/Hb4c6Io4nC+wFc+y/EFcRBCU/Dg4oiz0Tl/8b0tgqiFr4Iq03TR0sme\\nQeTHtNoNcmuwowlFFBImMUfjKXcl2Czlz773LxnPxsRhssoqup0dd2+NoZE0ePr0Y9Icdgd9Ds+H\\nZAY6nk8r8pksFrRaPXq7u4xPT0g8Hy0tJydDfBTbO9tk85Q0y3l6cEpeZGipeO3bH6CkD+AyyUYX\\nK3ymwzPCMKDdapJEEY1GQjaf8PjwmCCISRoJvi/ReYmWgFQoqdDatUO5zE4ghCEMfFQp0Mo5shjj\\n2jq0cSIAtVWP0QZjjWs3CUMarTatVofR8JRssSTu72NMUcG5xmEdV55jWz2QX0Q00l75Rw3w+mHC\\nXGuMlFVbjCPNKKXwpCLPCvKiJMucQbW25eq7ONUl9z2z+QU95fxap4uU0BPsdWOeDBdIgQuc9urZ\\nVOjTC5ivL1qfQ09yvZfw5OLL+RW/9PpUmfyG8cnnjlcGzOLijN3+TTRmRaNXFXxiLXjWYJWsiBq1\\ntiDgWu2QwmKVq0vV1+lwtKQReLx3s8evn44qUggbt706YwsCZ+gqHWn8c3dQwoKwgkBJbgxiziYZ\\no8XzBeCrF+jLjFeJmn+h4vJXGA9O5wznBe/d6rLdCvnd0xGv8qR+/jy+WLBcvZ/6XqxfX8vdbR5C\\nrl57GaytWc/unq5rkPWpNCPFs2GKFbZiiQoyEWGFaxDP0jlWNqrgtFbKqQlewlbw6Cvgk6/CWr56\\nIUR9NVanb1ffV8hKDk9ajs8fMpsveO/OLQJAZEv0bMbFfEIy6COVI/XIMMTznX3XTpxgsKRGUDba\\nnC9y7m210dIjaDaYSoGejMjyEgQcnZ6zzHJajTZPjh7Q6+w5MexSU9Q+hJfsk1zNVRkDKO6fTzCz\\nmXMMwbFzhQCpNTeuvUkj6fLz3/8NS13ghyFFpulRUoyG3Gk1SYI9zkYPGY0eUAqPdtyj296jlXQB\\nt7fptbYYnX/KZLEgQZJNZlDmhI2Y4WzJwcOfoSTcvP0upmaLWnvpyt64/R661BTFkt/+5r/QDjza\\nSUQ2GaPLgLP5Askzdgc7eEHExcUZxE3KIODg9Izr13YZnp7ihQGYgmavx8W8oLd7i7ph31ab8+be\\nOzRDxbPDIwSSMAyYTUaMx1OSRpPQD0niiCJfopRAKY/SaGcuHSjKQiM8hdGGsnQbKU95rp3CWoSH\\nM63Qbvviylq4+rx1DNzeYAcsDM+OAYvnKZbDQx6ePyVqdNl755/R7u2sFnU2Yoyt0I9qalXzdiOl\\n3Pg/K3DWc6xDlooSrJCUpRMnKCujcmMNpdDkRQ5CrJ1GrK1q2O4IutoULIbnFNNzBCFKCkaLgiRQ\\n7HVinl4sKM1aW5Zqk22xlfrl8+vji9ZMJQU3Bw2OxsuVucLzowpAL9pEXLk86x+/4Bcb45UB07Ra\\n/P6jX/LWzYygfw+vUr2oH3GHSjuJLyOdv5hTp3cLrpXV5JUWbwNjf3A2450bHb613+KTo1kN5FIv\\nwbZana117gfj2VPiaA9jdeU9uHHjhbMg2+/HnE6WLlhezfz/gNnkq479dXsnR4ucv/3ohHeud/ng\\n9W1+8ejiJQ/L5x75S7wW2AiOq9pddWs29oYb6b0FsUle2Kgtr7R+Xc9uJwlY5JpMO7KAxpIWkGsQ\\ntnSMWNlwi2mFPtQKUqtd52a6+RXGKxnGV+Ct1d9XAXyjv1S4SudkPma5WOB7lpPFkkagwFN0+x2W\\nrumUxA/QxuB7CgnMZyOSTJMAC2mYRhGPJiloQxGX5EmLXjMiG8+IfI/RfE7YabGcF+Rl7iT6SnM5\\nyzSXGcRQGfla6Gy/wdOHD0BKysJU2qy2UtCytBrb/MX3/kfGszNsWTCaXfDJ099yvdVif2sLeXqG\\nIudgsiQvCg6zA0r5azwv5P03PmC7u0/gN9np3uHpw19iA0G3keClGU/HI07zkqTT5tPDj0jzlNde\\n+x5aa6SU5EXmaqWALjIQisNnn+Arj/PREE8pJvMlOYI7OwNEUWCKgkApjLXcf/wY6TfoD64xGxcU\\nheRWI2B8ek6aB7zx3b8iabSdP6MpENItfXF7i7wc05qOK1s5S1HkbO9su0ze8zBljkA6SL5qoSuK\\ngiDwUTiZQ+pNmHCG2BiD5zlDbCMVRuqVok1tf9VI2rQ6PRazCXm6WDFtZdV3bqyiTKd8+pP/g8Zg\\nn9vv/zl+1HCfZesFvpqrdiOQ1gSXepJUU3XN/nTtHrrImF0crYKss8WqekRFTbQDKulRaxU157W2\\n9NLaIKQkanZcJKi+vxCS4aIgCRX7vZjH5wuHPFZzeH0qDi0Sdo1U1M/u1XVVCLg5cKW2lzNiX5Fx\\nr+DXz3/Ny9bzVwbMppQkgzaHD3/DTeHhde44Tz67zjKtNWgl8IzESIfEehiEsauc0LLeDQnciz5+\\nNuXdWz3ubDd5fDZf31gcpV4JZy8mBTTjPYoS56m5cWPBBcvr/YTD8bJypLhKC/n646U1vG+IXPOy\\n42gDv34yoh173OgnjOY5Z9PsC37DDXDxC57rJqR/5RBANRk3epZsLVlY/3yD2Vo7mVsB7cjjbJZi\\njKWwpqqV1McRiI3tspCCQHiUOFumVa3icxQLvmmIvZ529bUQsBboqNAxh7jUsnaSIIk5TUtKrZkY\\nH6sNkdaUxpGD5ssF1lik9p2Cj+9R5BlSKu7GMb+fLZlqQSMKsX7AhYGzszGNOGGvGVMIn3I5p5H0\\n6XX2KLQh15q8NGu3+xrhqSBjId1cMNayNbjDux/8D9Tm6/WiZ0vXQ11U6FG7uYtSgkH/Bu1Wn/nk\\ngH/85BFboeJm3OYkT5lpyV/88N8yX85YZHM+efprBt0bGKvp7bxOISVHB78lCRvk2rJdFITdFjaK\\nifKUMG5xfvGYXmcXrV2rS3XbUV7M0dFnbG3fpte/Tm9yzNnhp3S3d9gOPQwGWzhvz35/wPjijF7S\\n470/+5+QXoitdHNno0OarTFv3H6LwPex1jIbnRA2e9TCF54VBN07pI/us5wNaSUBQeDje5LcE7is\\nqAqWQqA85Rj6FYdD46yhpHRBTlv3sEsp3TMsnI+w0euWN6kUcaON8jxGF2cYo/GEwuB6KY21Vb2v\\nYp5bmJ0/5Vf/6X9j6/ZbmCKnf+MeQkqEUIRJgzBuuaBnDQ/+4T9Sas29H/731fvr+VLxbHWBLgtO\\nH/6OdHhU6cFWLVzVHHLZfz0DHBKgMShPYYxjERs00jjFo/23f4hM+pRZjkjzqvUKRvOCZqS40U94\\ncjYnN7qa3DwPxW7886pGNrj2kbzUz5E4v/y8t195s/3KgPm7w0OuJQkdX/Hwd3/L3XdKvN5bWFti\\nK2aPtaCkdcxBW3cdObqPW+U2cPbVDXQT+fcHY9692SEvQ47G1YUQbnFSnoc2pyBaSAJKna/MSOtr\\nFHqK/X7C4cWCaVZUuyz7OSv+/z/GZFkyz2bs92JubzcrRf5vmBC0eelsvafcyC43drDa1lmWg1fc\\nr6o+3LouUEGbkS+xwCxd18LNqjZYmUtXGa1SkshzbNRlrigrKM3F1Vp/cj2+3KRZp6ebdPPV119l\\nztXvNyJnfX5SCKSSKOVgxE6zzyefPKDVbZAay+xiyPV+mzCMaElJVpboICRUikAKlAEvSpBCUZQl\\nkzyj5Xv0ui0enY9oN5zT+s7WDv3IZ3hxznCWs7f3Nn9y/VukhaHQ5cqXUGtDadfXRVgLUlIrZVGZ\\nE4RqypODD9nduYctrdNErXbbQrsgUGjXMuMpwVbvJjuDG+xdu2D09FdM85LddpOm7BD4IVKFNBoD\\nkrhLVhQYa/GkoN3ao3Wvz4NPf8rT0wvuDTrcjEImOudovCBoj9FlxLNnDygLzXe/85erNcJi2L12\\nF4DJ+Jhma4vzk0cEEoSSHBydogSU1hKFMY1WH336AOXHZEW5UhQL2/s0ezdAuJrh8PQB5yfPuPvO\\nnznmapVBSc/n7Tff5md/+585fFQQNBPKwtUnjZB4vlzBdaIyIBemehaqnk0pXT3emLUClVSiCp6V\\nCpSBIIxotrtky4zJxVlloC6rrLR63ozbZtYejp4nQAiKsmB48BFlnnPx7FMHj2pNs3+NvTfexwsT\\nwriJVB7z06c8+e3fcfPt/wYh/XVdzlrKPOPw439kcfrYGYtXQb/WBbHVGm6NWdWo62e+LuJbwPPq\\nXmvJfHhIO+lVm4cN3WgBw3lBK7Rc7yc8PpthKvNn93HruHA16duck9e6MVIIDi4WX2Kef/PjlQGz\\njEJOK9+xC89j+snPePtbAUHrNbQtMNZlm54UFSQrqy9tEEIh0AiDq1etC0GrC1Ray/2jCe/sdyhK\\ny3DuCt1CCqRNKfOUYXpBu3WvKjBXOLq1RL7kWjfhcLhklukXXux/6uPLklO0sTw5X9BNAu5sNTmf\\nZVzMvmi2+fxnX/rcKzD+5x3z8vmuxcZdnXENx7pj2Op/lm4j4mLums21pcpCq/fW5IXa9aA6L99O\\nKFWHdduQO+Zqg/q1kvt6R/2ig2xm5qzOU4hatajqx1SCk+EBd7Z6KJ3SjwN6nRa9KMCrdv2Rp1zP\\nMhApj+PDE4psSXfQI1SKuNOmZwQGQ9mKicKQQbvDcDjkPNXs3Xyb793dJ4w6ZEVJoTVlBcNqa12w\\nNGY1x9wUNFjrNiLTyVPKRc5sdES6nGO27mJxMn7lCsa1lYyhoZQSzzjWpCcFYTigff0D8mxM/uin\\nLJWPNgXGeOSFQcjYqawIKDR4yiMIQ7793o/ZPv2Mh49+SxRYVF7QVoLx8UOMH9CIIqKkw2R6QRBE\\nCOnhBw6aLfIlrc6u8w69p7g4e8TMKmxDYoKQs2JJMJlyMlvQe+0D0tJ5mepKlk4phbaW2FOMn/2S\\ni+PHeM3rlEajjSPpCCTSGAIJg+1tnn72Kdebr+N7PnmW4/sKrSujCU+5wCYU2urq5yBwsnl11rme\\nG+7B0aXG8xXNdpsgjJmOL8jSFOmYkdSqFlJRzR+xCpxSKGd7Uz2DpdZ4UbyeCcayGJ1y8NEvePNH\\n/x3KC2jv3mF0/JCLJx+ii4K73/urFWybLkY8+vl/xuZzx9ClkhetNrDCk46kaQ1GSIQxq0XACrDG\\nFcPqTbLnKbQxzA4/Jtm+g1ThZdce3HcZLXKaxufWoOG8h/XlyqXYqLRcun7AoBkQ+6qyYfycWfoN\\nIXxfZLy6hikgxzIE7uzucP/4mEc//2v+5T/bw1OBu7CVd2W99AjhJPNKYRGolXO8g1HFKlg6XXVL\\nmrt2kzf32pijqVOLkM6ex/OvI4OSvCwpa2KDhdiX7Hac59ks06wvf/UQblzaP0bw/DJB8Kve8NEi\\nZ5YV7HVj2nGTw9GCtHh5tvl5UMfm+a5/8DnndukJZxWxamWmtTlPlS1uHNNTgtj3OBqmrJb1CgVw\\nk7nq0XIPEFpAqgVG9sjL8nJGad1nmM+dPl92fD5cfSmx3Pi9E5F3/cF+JbwuBRxfHFBEMf1mk47Q\\ndJMQbAXbApEfUWYpxsJnz56SSku/36O0hjzNCPMcLQSB73MniRBJgJgJDuYp/+Iv/heskOSFIStK\\ncu2Cwop5vFGAqOtDAidT6er9hkZjwPDgH6HMXe9dkWNFgLZUNU63YgkrkNZlnsZatBZoz2n/+mFC\\nI2kxOXvMO9tNHv3yP5Dc/IAo7pNrB/HW979UAu2BlD6txoAw6nFWaraaHe7cvQFezPnFIRqPUpf4\\nQUIYRRRFQZ5n+H6I50cVkalACI/dvbdYLqfs3vo2yIA8S5mNj7m5/z4L0WSZFc5Gq9rAedatP3r+\\niHz8jEVqeP3b77qNhnWbA4HFs5Ycn+2tAePxkCgOV8Gv1pBFyI1srC4hOPUnKi9g5ylQZfSs55oX\\nhrS7PbJ0yXh4itG6ClAKIR2hZl0ktyuxilXLnbZEQUhRFijlGLplJdbuRwGIJcX8gk9/8n/S2b1F\\nZ/c2O3ff5fzxfSbnz7AYKEuEH3Dy2a/R6RQl1cZzY6vkpppPUmB1BUErRZ2yW5xGrqyURmT1PYUS\\nlNmCsw//hu4bP8SXEZ4wVY1/hdUwnOcUWnFz0ODx+YLsigboZsCsRyf26SQBD09nX6l95JserxYu\\nwGKlJLXw2bMDbu/s8mi2pBE2KHWBloJSWryKM+kERaosQRu3i1u5c7tjGuv+LTaKv9O05NPjKfeu\\ntXh4OqOwOVl2gfL3KLQkLQoXMK0lDiRbrZCn50uWRR0sHQVpU7/xld/tS2Z2X/a4f4hjXj3XUrts\\ns5P43Bo0Gc6zV9Y2PzdYviDRuvraynTNnUuVOdYw+Ca86ViIojqooZcETJY5uqLIV3dr9Z4NxMj9\\nRlskhlzlrgXCbpQ8ak/ML3DbPp8RezUUVq+tf3Ll9avskjpoCpSSVdBUFMWC2PfZ7nSQixmnWqN1\\n6ej4pebNdpuT0wuGsymZkHjNBBUGWCuYFzmdRoNIa4aTETJpERoB3oJff/wp3/nev8IiKErnwVn+\\nf+S92bMkx5Xm9/MlttzuWvuCneAikATJJtUaTVv3jNmMSWYySSY96H+UXuZR1m0jqdXqEWeaTXZz\\nAwEChdrrLpn35h6LL3pwj8i8hUIVlgJASW5WVbfyRmZGeIT7Oec73/mOcxtDKYLRds7TxLnbNLCP\\n682DdwKdD9BJQeMtb/R3OC9XqDztuADtsUgf+QngcQExMhaviGtL8OpbP2P88d/z+NEjdodHXMn3\\nqE2IeFtOlrIBCZECyvmYd9/5C07Hd0iXj1k//A2PJmcw2OP26z9hd+8axlnKqiRNc1xTMj97TD48\\nREgdjE4+REnFMMk4enyH4aU3ME6iBtdwicKsDWVt4/yEmxaeNUFZKpbyMjffeRuLwsRO4EGIIZzz\\nurYs5gv6gyFaKUajHSaTU9I8o67Xod2Vi3KF1oT1JUNJCd4jZDSOOhIgHUipGIx2UDphMTsHb0NO\\nUQqEUN3aklJineuer5ZAF1jYUY9ZStAa5T1SqYjIeJSUDPt9Vus1wlXMHr3P7MmHpMMDQGDKJccf\\n/TPTo49Ji10mDz8gT9MN7Oo9CBmD3OD4hnKRMHdBH7xtt0gkvMEFZMaHSLOan3D8m3/PwQ/+a6Rq\\nJTGDU9JqMU9XDda6YDRPV1Rb2q8+Xn87L8N8U2v5PEH1r3O8OMIMKw4hJNVgwOPZjMs7+yzrGXnS\\nR3mLJN5EghaotD4YSuGRVtEIgcVGODUIa3sRVUK2gLb52vDwbMWbV0fcOZlSN3tUtaUylsYE+KlI\\nFXv9lIeTZYCAPBEruKjX+E2Or/smQngQl6Xh6m7Ba5cHPDlfs4pM2pdJiGkLsPGhs0RL6HnaaMan\\nP+SIkOz0Mu6eLkJOtCP2bBvqTaQqIqTfOPDeYJyObYXcRhjjS17HM1l4z/h5g6yJbrNo4VidSBIl\\n+ODeP9HPc6azGdYYaimZL2uk0mR4/uaPH5P3++wNRqSJRiVB+WplDNd39/HGsFyt8C448/dPT6gW\\nNYfX3+bSpZtB0Nq5uGkItFIIDBJF2ZyjZR6gwG3idHtPImTunKORPWo358HsnNGO/CRgsDUBm0c4\\nGFBcSLFY63BKIQZXuPxmwfXb7zBbVTQmnGMrm6ek6CI9WRwyX82ZzWecPn7Ea/tDenlKsneD+XJG\\nf3QpGBiVYo1ByZRicBDLHQwQNm1nHLZZUxpHWoVI2zhPbRqKVFMZF0lPYVdxYWcn611ld3QdB2GD\\nbr2ftnGwFzQOHp2eszssAkyrFGa1YuYcvV5B0wT5Oy10h5ZJIWNfXhCS2OLLo5QkK3J6gxFNVbKc\\nTSKY4lFKIGWYRyFkLNcIJKGOPSpi44KtpaSUQrqNalCqdGSuh+chT7OgcSvDft3MjjrW7/Eff4lp\\nDEtxAgS4Fdc6vgGrcYgLC0BEw+ndxdeUkt26uaCe4xzOwejKW6A0AruVx7xYdjZbN1gvuHVYcP9k\\ndVEwPdqEIpVc3S24d7p4bq3lZy4Re0njxe294mKrhQubo5LMFjOOpw94/dK38VJSpFnIpYhQ29Ww\\n2RB9B8BJsC7CsFsbUIxGIAgTTNeWyWLNm1eG/P7RkqY2NI3DOE+RavaKlHuTRTfJns2+2yaQhdiK\\nRp4zvgnD9lnGs7ynz3KuxnkeTFYMcs31vR6rOmjoPu2dPWsIwedul+W9j5JX2y+ycTzjS8MiYd04\\nagv4TYy6AXBbtRJoBa5B4K3Dxk3Fed/V+m5/9ouv69nz9lkciE+8N0bNAaoL/RfP5w+oyjnj+ZRR\\nMSDPMwY6CednDYnOeee7hzhjGBRpMADOhOjUO+q6DPJlSYL0jsl8xumqor8z5Ptv/RddrjK2KUQJ\\nsNWEex//jluv/xQpE7zZ1kP65O1o/1za2eWX937Dtf19rHcoNusw5C/DO9vIQMooQStEVDQKEY/3\\nYFYTMCrkU2N0WdvWYIbPas+7yFLWdc21m+9wcHCT07s/Jyv62MkdDt76FzgXNIa9c3hv0VpioxC1\\n9zL8znvA4UTB6PANqrqmNj4o09RQJBrvXGiSLNpSqFay06Cd3PRkjXCrly0u5aicZL5YsLd/iE4y\\nVqsV6aDHYlmipCLNsmDQjEfowNPQiaKumzhXKjy7UtIb7ACC1fwMawxah+jMWttBvEp1mEXYD324\\nViF853SI6EiGMo2A9GGJka5EqiSwx20QTFhXgdGLCHtuohVCpOHzo/HqEAV8R9Jrmxu0hn8bDvbe\\nd0xrAbFeU8SIc8Oqtc6jh4ccvPZ9FmXZwjEbYxlzni1cM183OOe4edDjwab7yIwAACAASURBVHjF\\neqvZRJ6EqocH4yVl457t3G6jWV+j0ZQvOiDQ/sMJNsbQOMtRueC9+79lWS9i9wNBqhTelwgMmVYk\\nSnU5HhVLRDoJvAt/QmJYRxWfREnunt7neHbOG5f74AP7r59K9vsJ98+WVMZ2EN0Gjt3aTP8EsO4v\\nO77MA7AoDR8dzzHW88blIXv99LN84RcK255peHxbJxtKSvb7KefLulN7Cn1QZTwi5MAdseEsIZ8W\\n+uqFVkHGtcaSeI5f/Q3ufOIWjm03YRENiJAo4bn/5HccnY3p93oYH3Q7XVVh64ZEaVIpWC+mHB8/\\nYTWfkjcVuWlwpqGylrVpMEhq76hkAkXOkVfcuv2djtCzTY8Q3nP33h+Yzo4wjcHLDCcSWn3MVti6\\n3WzZms9l47g02uHwxp+R5qEMoYuYVSAvJUqQaEKfTq1IEk2SKFKtyLREKwfNnOnpEYMrb1LWJtaB\\nxjIV6zE2KAnVxrJuDIvKULmE+arGq4KzpcEkBzyc1dz5+Hc40XZqESA0jQkRj4mwrovEpKpaUlcl\\nVd1QGxfqUK2jMYbzVU2WSBoXIl0Tn59AatqU3bTPUYeKRHctyUeMdnY4Pj5mvpgxXywxBrRMsc5T\\nNwZjGoQSNFWNqULkG1JN4TNkWrC7fwlTVyGqxKG1iE5gK4NH53DBZg8UApIkNK0OUWiAQZUKryVJ\\n0hkqpRVStU6NRKoA2yZSh9xkdOyEACUDUSm0PQtSjkSYtV1JbVgDwRi3tZJCBCZ4Cwtvw/ddwCNj\\nKk5AsXsN5ywtm11GRyysIRGNZvtceuZlw9F0xc2DgiJVQFDxuXkQiJwtQvY857YjSH1N44URZp4F\\n8kJVh7xY1VR4D7Nqxq/v/wPfuf4Og3wP0Yb/5/c4HF0n0QWgwgboPKFm0wWVmriYhdxMROgtqEmk\\nxOshp4sU7xu+dW3EhycLBmnC3dNFZyw7CI/WS/ykXsQ3Dc1+3vEycXnng4L/+arm2m7BTi/lyfkn\\n+8U9/d1farTGrPX+gF4aujwsqoZ28YS9avN3awlbHrUXoiMMtZQW13rA0ei+7HMXW4t5M/v+wv8u\\n/Cw8zlu8U6HDhbMUeY80SZhXFVYIjHdUZcXOcMhgfx+UZ42nXK9RiabIC3p5TrleUXvQWYowKYop\\n+OBetJemYp7LAq+99edUVYlD02zn7diGkOPPPsydsTAY3ULIHvlgj8YCxnb1cttzoKQk0VGYQSkU\\nAoGh78ZMTu4idt4mv/oDdNpjUTbRWFqsjSVfcbZkJB2FAncfjJbXvPa9f0OWKIaXX6M/2Kdcr0Dq\\nmFnZZL7jx0Cba5MFxkJtmkgyCsbQec90VXFtr4df+m7Tdy50u5FOtFkb2iSrkKLFvRAIVD7g8OAS\\ndz78AJ1eI9M+wsNBGMGa4AytmjXWOnSiqJYVUknSJCPN+1TrksXsHO8MKkah3tPBmG3TZu9bg9RG\\ncoq6ruM9C0X/ASmLtZ7WdQQcIVxXIymEjL8L6JDWCmNteGpFK/MW66DbfAJteNHOcCswEl4X0XFu\\njW7o3bnZl9qIUyqNi9KAXliEVCzPj9kXYoP6tfhy+2y2CNHW/r2oLPZszY39HsezksNhzpPzNYvK\\nfK5U0teV03yxNF5Vo5MEpTWuaVA6IdfhbU9mT7D1jLcu38Sn+2g54Nr+LRKd05igL6tV8BC3Nzkr\\nXCD8hGc3ej5Q2zNq25Amlymbho+OS96+OuIHt3b52/eOqa27MIF+CxKIYGy42X7jMf2/bXxeVaAX\\nvac2jrunS3Z6CbcOeiwrw/G0xLxEylkHiWyCsg4G3OklnC2rLjfh2UiDIYg6wxcaswUoPxrf7ne+\\ne/dLH11t5SZzyQXjLlrnbBMBB+RFc+XSG5TV7yhSha1qZmVFkRcUaYp3niujHby3mKam8p4iS9nb\\n3UfgGSY66DED/dEOrioxzYprB3uUy1MyPQzRuHM8vP97rtz8Hi7mCo1XAa512+IPFyH8tg+p99A4\\nR2Uh6+1RG9/lo6Tf3LRWvSmRUC8eIs2cer1gp9/DlVN+dfc+5bLm23/1YwY7BavaUDcWE3OJISIO\\nUxc239iQQYZifOMExjkyraiNQ6sdlqUJRlq0HVWi0cDhvIFyjpKSymgaq6isxVg2Qg1u8/SUtWWQ\\naeZtJ6TWMGw5ETJGeHILMQAQMmG+XHDz5nU++MNHvPO9N+iPhtRViWkMwviOFKOUIrSekwz7Oyil\\nmU5Oo0yiQKh2H3oK7WrVr2Sbp2/ZtEGnFYLRc85GQ6s6w6rUJh8opYpN5kMeNDSPDu6mFJFQJzzC\\nbYIIrSQ2kp0ubI3tfd9KdbR7ctvoGuiYsTgiwzbeJyljdyJPOX1EXS7wviUVbaF/3Y4guNAW3sOq\\nspzMKn74yj6/fzxlXj5f1vRZ42ULl3zaeKHBrJ3F1KG/W5IkcQMJk5YoTWMs6fgYdmvOKFB6xPX9\\nV3Heob3DeYnWYfKaKGYgffD+iLqFjT1DyhGp2MMjaJrQaaGfaybLmmVl+faNHX55Z9x5L08bxM3+\\n7z93Lu7LjJeJn3+ez/m8D8h01TBfNxwMMl6/MmSyqBgvqpcGX3flKuF/QFDB6eWax+fr7naIlmgR\\nj9u6bRdGWFYtbhRu6FdlLOXFF7b/2XjcF+JOj3GBoXzj8utMzu5wtpwyzAqqqgpRS1My6mdIGhCe\\nXpEGiNMZyvWKnV4Rmv0Cq7Nzzk/PKHoFFsGeEpyfHrObX0foHC8El669FcQJnI0wtdvkNuOQ3fxv\\njILqjKYP9YkxPdIe3xbib6KIkCf93R9+wW6RU/iG8Yljb3+XhUyxe3uAiJCro3GuiyAb57v1Gd3X\\n0MvR2wC7O4mL86aVREuDliqkdPBQj9GrR7jsEHvyG57MS0wt2X/zZ4gkp7Y2RrO+M5Y2GjF8EPU4\\nHKadwRRCoGhrAjfGUkjZ5WXb3xGjqWvXbvLPv/otXn4LYww60cHQtWLkTYM1nt5ghE4zqvUSrSJW\\nIhVKK5DgrLlgFIUPka93wch5sXnGWpGXlokacpaiIwR574OEoNJdNyBjLAKF9zZyNnzXrOBprK3d\\nV0Kk62LP4o35am98x3jHd+35LsjNCU+SJUjkZr1HwpJAoFSCEDoKy4OzoRHHZs/ePGeeNqqFREsu\\njTLeezRl1EtYlA2LcpPT/Kzj64BmX1xW4hxOytAiSGsyncRiVU9fJVS+5m9+/x5v7u9x7fZtPigf\\nkkg4GN4OurJK4FFxnhyJlDS2ximNdYCXaL2LdaGTebv4RnlCmigejJdUjeOtawN+8Mo+v7wzvqh8\\n31H73Evb/D/r+Doh36cN8xeBIJyHk3nFdN1waZjz5pUhx9OS6TN0GdvP/lzX2Dqw0anf7aUhuQ/b\\nQVvX8s372NVmKzTtbqvftqFfzTwLRPh+sakp25yO6E4o/ooO0IqbkvVBd3S9WjBIM6pqTZH3KZKE\\n3UxgmgYnBXVjSZXg/KymaWockqPZmss7A14ZjehfTnny6DHzlWF2do7uX+Jf/cW/ZLLaEGqcV7Hg\\n3mN9jOTiSamOWAE+dvbZdFMJBlCJENUEI0qMWNqoP0xCB+tKwc9+9t/ivef4yUdMH71HNVnQ7xdc\\nGfSYnN5H967EDilsSDtAG5TE5R42c0mUXQtr1DqHtTKII2iPdsGJWq9LMn0ZoQa4wx+zewBpb4+y\\nMZSNjQQoOmPZYUsxIVkbFwVNFI31AW5WkdGsQgmQUuHnjfBEuM9tHnB6NuXgcI/lvKIoHEWvhxCS\\n2OSEPOvTK4aU6zWL88A6lb2cpMjAW4T0nfFqnS06w6AwLjJU/UZkIujpBgPZGk2lNVgb0BY8WV5Q\\nVxUBqpW46Dg569FaAwJjwueY0mKcJU10KA/ZWscB7t1I8G2b1xBZsh2Uo3X4jPZ171tSVRvxhg5R\\nZVmx/9ZPQefYZr1hdtvgKABdVLO9dWshuLXfYzyvGC9rzlcNNw8KnpyXzMvma4scP+t4cVmJteRp\\nSqI0xhi8cQx7fby1nJcLhBSM3niFe/M51d17zBPJ/zX+O370+k+4cfAWToWemWgJgjCBdo0SIywh\\n2W8dQaIr1mru9lMSJbg/WYXaSzy/fzjjO9dHfP+VfX758WSL7dXeB/EnN7kve2wbzS/jTdXG8fBs\\nRZEqruwU7A8yjmcly+qiV/dF5lKwiTB2eyn3xstPHNPlm6PX2cU4/pPe8ZcZn9XBaIkXF0dbhB//\\njl52e47t8xcYjY5ekuKMoakrJtWatQ5dLcpVjXGGNE1J0ozd4R69LKEvBMM05Te//mf2Dw+4vT/i\\n94/OUEWfb33rXRxgXCitsX4jrB5yuWGjaiHAoFDZ4scgvejkyWSrySxlZyxSFUh57SW7GBnaLl8l\\ncD4YkCvX3uLytTdRKE6OP+T9D/6Jb33/XZq45lr90ZYM0m7B3Xx7DzYyUkXACVQ0cg6LM0F/2ntP\\nmh9iVaz90ykOz7KsMNYHco+NcLjf3uTbbHhwAOalYbdIOFsZko7IJEkSiZYKJekUmtrOS0pKVueP\\nEVHyJs8yFsslvSSnrkqEgLw3pNcfUa5W1M2a87MT8jSPz0A0dDFal1vPkiUU+muVYJqGRAlEqijr\\nuusXLL2MSkGbZ9/GchoXIYQk0SA9VVlT103nqGR5EXLpbW9KAWmSgIkdWbr9Qsbn117I317Io7DZ\\nW1oSGS2ELVs2r4y51HCsVgotJcvhVYZX32RZVRuinvNdUiXUZtM5zYJwH27u9zlfVpwvawRQNpZ7\\np0tuH/YRU5iu6k+s129yvNhgek9jTVCnUEEgeLFaIJWKfdGCnmGjJR+ulwgvOdi5xnKx5AkfceXg\\nTbwy4KNsHo6MEbUJS6vr3Re9j/1BSqokD8+Xm4jRhwn/zYNzvndzlx++GiPNFhZ/apP9uozm18nO\\n+iq+a11bPj5ZMMw1V3ZyrPMcz8oXdkLZpnR/2hgWCVUThMFbo9MulO5d0bp6nr6DYWx7vp/43QuY\\nc5/ltfbDffc7T8A45eZL2w1n60S8j+IbHharMc26YuXDBlSZ0GS4UjWmCWUEaZ6TJBlXdncZSst+\\nqqjXKxpv+O4P3+XkyUM+PpogVI6SKf3hXtcJwkUVnvZypQi7rAz/uTBX7Zm20KNWG+ixrZHWSiK9\\n4/z0A+4/uEtRDHj77T9ncvaIon+I1AnWmqhDGz5YCHDesHf5Nd49eDUwVGvT5QAv3L8ud7U5L0dA\\nEhwBhhMuFPp34qVYPAqHQdmAZgVHOETx7f7gP+W+h/MIzmTZWC6PcgrjEFKQKUWiVccCbudERzF1\\nrTSLyUOO3v85eSLpFQX7+7vM5yuyfB9Hw3DvEkJ4quUM31SsVmt0kgQGqVZ4HNaFPKQQIGIkLQhO\\niNQagaBerUiLDOklKkbe4VkL662FYjuGaux6IoVmtVh20KjOEoSxNKYKOd/WuMU1ppUGITBRoShE\\ns7FutG3U97xUUodYbFSL2o5B7XkqKfHOB0g9URwc3sI6E5ybyFJ2EQnxMcRu3+8JJYQ3D3pM5jVn\\nywbfBjsirKG7p0tuH4QWf+fLPx2j+UKDqaTCNE1QR5KSPM0wLhQFWAKcYJwN8lFZgkew09/lnTd/\\nwv2jO6zrc7J0B69sKB2IaiGtN+XZbAb7/YxES57MxghR4IXdIN5xbf36/jnfu7nDu6/u86uPx9Su\\nvRHPbw3zpzy+KdWKdsxLw7xcsNNLuLHXo2wsJ7OS6lNE3Z83t20D1r1+yjg28I6AX1wUn+1z4NmG\\n8nnv+7zz15lCH4hoAe5qCw02NVedgxD/tEpVxjrSZMSPf/LfU63PqMsVB3tXcN7yD7/6G8pqyXAw\\n5NLePr1EkSlIbKi1PMwLsjTl9OEDHi1Kar3POz/4C5BJJBUFybo2F6ikuBARbDYyus2yhSZVLKL3\\n9RylcyYnH9Lr7yGSlHK1RDfnnB/fw9ZLThdnFHnG2dkZ77z7r7FW4GVgt7YIQItTCxtgxPB3MMBd\\nqVhcf8/iD4Q1Hn4RA+TutVYuMyjdSLwMJSXBifGdQtjGWPqt50JA17w8/FdLRdk4dnopVeNJk1Av\\nm6YJSnjq5RlJMQAH5XLK+O6vSXyJNxW1DZ04nLX0en12L11lfnbCYjqmNg2D/gBrLM5ZsiJEwC0Z\\nxrkG70MJCDZE0o0xSK2D3rAI5+GNxeCD0EGSUFZ1d21a65g/jGLvsVWWi1reHsiTJJS2yIZ1WeK8\\nQ0oFwgTiT5x/SSh9st18RSMc84+hVrO7ud06EDKGqcIjY4kKPtR9BiMbhOIlApVq0lSTFhnr1RPK\\n4gBrg3hDq3bko6HsuLY+RJY39vuM5xVny5gO2ramBBTs45MFrxz2kUIwWVzsUPJNjRcazH6vF4qQ\\njWG+WlIJ0EmKdC11OSSyXezA7fEkSQoSbl59jdo0eO9IlMK70NnbCY99alUdDDKkEBzPSpqmQYrs\\nAv7vY5SJF/z6/jnfvb7Du68e8os7Y+rtj3rJsN7/n8Z01TBbNez1U24f9llVltP5pxvOp0c771kS\\nanDbxL2MnnEbYbYQzdYbwxAb2PlZTsTnjSqfNzZRb+TeeugkyegQ2A7yu3CdPjTOrY0lVQnGSXr9\\ny+yMFFqFDeFH7/wVv/jV37A/HHFz1Gcxn3I+LTlbl9zY3+O9j+5gGsN51eCzjJ/95X+DVAmNjeUq\\nXnZkFNUZpXBiUoQcl3cWW8+hCTWI68aRDw6RdkF1/pjx4w/xKmW5WjB2lksHl8DWHI0n9Ad98jSh\\n3y94/PBDvvXdv0Ago8MQWKr+KevnnaOxDYi001lV0qOkw8qo3OWfjRa0k+63Z1/4yIgVsXSlfUrY\\nYrq372gFFsJoIcI2FSMIkU+iJLWxHA4ywMZnEWYPf8/4wfuYesXO5VeYjR+QSBj2cvr9AalSNFVN\\nXVVcv/kKTWM4Pj5C+pLGWXDQNAapJHmWY4zFRT3hdVXH5yLUILZIhSDkJYUHayosMZ+XKJQQEUHw\\nISJ0niTRWBymMaRJgvW2E6wQIjhpdV3TGwyoqioyax0KhZIi5Nb9pk5SdolHNl1spIySpLF0K0Z1\\nnUQeGyJYYAPHrisiwO9ShnultAz1uXnKzs6Io/c/hPQW5DvRWAaD6broMny/VnB9LxjL81UT523z\\nVGzvNI113D1dcPtwgIDOAf8mx2eAZIPnJ6SkX/RomgZT10EKT8oLx1nvUULQmIon54+4PLqKEorj\\n+SMqs+Lqzhs44yKU0HUX5GAYaMjH03WAb8QIY11H3/9EjsR7fvvwnLev7fCT1w/4Tx+NabbKJFr4\\n51NDlM85vggT9tOixi+q4vN1DQ9MljXnq5rdaDjXdehB92k1nE+P/X7GWYRRuiJq2igT2m1xYwCj\\nr9s6mVuG8Qsp8rzoWB/1MKMxlMiQafEhs7OdS9q2Ga3D5gmbV2M9pbHdZ4ZTDbDacLhPlmRoIbh3\\n/yFOZIwuv87+wXW8ddy49kMa0/Ba1g8QVZJh3PbGFuDTrjwEF2A831CtVzz66NfsDXq4ZgXGUNYN\\naa8gsYdUszFNVbE/HDLoZSxXCVXTUGhYNpbXb9ygqtZc3hlx/+SUqqw5OLhGG1t71yav6EIDB4EF\\nisD5QCLS8RyNbUtYWrnLi0azJYgEpmorMSg6Rq6OBfkhWhaR9blpoeAhQp3tDbkIQbef14qoJLER\\nxLAIZJh7v/prlpPH2KbGezj+6NdIKcl3d0iTFOGD0dq9ekCRZzRNSVVNWVeea4cDpmcTvExwxuCV\\nRGUZmXeUdYV3DmsMMpaAIILWLEKidMhN4j2mbhBKoqSmMg3oBPChJEMI0AqZaKypkFri8CRpimlq\\n0BJrBMJaTGNZL5dorRGyIYrWbmY6PMI4G+Y9BIwBhpdCx5xoEMNr8fRoPgF/ganbwvktpCqi4IJS\\nCpkodJKQ5Rnn03PGa8mN0R7LynRwbOuItvt2oiTXd3OOZ2XsW7xZkxfLAzejsZ67J4uQ0xQwXtSf\\nuid81lK7LzNeaDAPh7usqpLS1BQ6NL31xgY4wFmSPNscLMDgeP/xe+RZn0xlfHT8AWfLI5aLNdm3\\nBvSKQ7xvgASE53CQYpzneFZt5SxisbMLKWPfbUZtoXv4ut8/nPH2tSF/9voh/+nOacyLPsfD/QJj\\nWzfxc0cxzzn+69ZA/LzDeZgsas6WNbu9lFsHParGcTJ/fo5TScEwT/jweA7ETU60P2yFklvjeffr\\nZcDrW1lIYFveKpyc2zryaemrDdoZju1g2Zh7N1ZQS7ch2XhQLjQavn3rO5wefcilq9/j1u23N/Jp\\n3WeG73A+MMSh7e4QhN1TLRG2ojx/yOzsEdgG4RqSLOX1vR7jyZi9vV2WSwNFxiBXyHJMIi3JIA3m\\nz9YUqSRXmvlyQV1bdncEg2zAyXjCYrYgTQR/fO8/cPPVd0izQTSU0Vy1Umm+NVyBbSukRElPojxG\\nq9hVJHRIEU/dX9EZtZC7UjEHpqQg0aELiJJys+97gFCS1uW/aOdrYypbBGC7ZEQpSaIU1sHeIOUP\\n//i/MT36OJxTjICV0uR5TrVaYwdDDq9cZb1aUVcrvFlT9HKMMfQvvYmzYxKhqE2FTRSiFUlPNFrr\\nYCwJ1yRiXk+pBOGhriuUCkiAwSGkCgiBlCilsdYglESooNzjvCXVCU1TY5wlkynIIA2JFFghETHK\\nFEaxs7PHYj4Nka9s7Wa4F7SRJrEFtlAR6WlFEVrx9ygXIgLM3+VJVYBdpQzNs70Lsn9ChevUWlP0\\ncj66c486v8atH/9X1NGJtLHZu/WbjkKpllwd5ZzOS2ZPsfIv1tZ/chjnuXu6DPCslBxP15845uk9\\n4qvaX18cYRobupXjqIwhT1KMMFgfFEtMbUC0dZEBzMF77hx9wDAbsts/4MHpXd55/T9n1L9MbRxK\\n9Gi8Zb+fUBnD6bwMbDNPZyw30eXG6942lp7ADvz94xnfujrkz14/4Bd3xpTNy5Us2E7Cf973fZnf\\nb49vMsfpPZwta86XNTu9lOt7PYx1jBfVM2uldnsp87IJNX8bXDN8VjROLWIAL44gX04uOnre3nfR\\nTtvdvvseWsQjkH46WLb9hO3/xA28zTW2z2pLzvHRrb55401GgyHD4WGnvXnxHsZri3k4FXNiwago\\nqvkDHt/9NdVqyeVLh+z0BygJ3tQ0dcnVS/scHx+T5Rl1VWE1sQNHjOe9C2kQ60nTlOFwwGFWIASc\\nnp5S1Q15mrA2lsnxx1jr+PZ/9i9DH81Y09cZdkuXRglwXzRQkWUqlUS7uPm6ixufpC1ticzUKMGX\\nKkWqJYkOkKKIUX9g0G6Uctha/53/JS5GrkiJFmHetAxw4ur0PuvT+yHH1+qzShjujDjYP2QwHDEv\\nLWfjM9IkRO/9wS44w3q5RO1JJvMVPZlAXQUykoQsTZFK0pjQMDvsjx6sRajoLvjQojBRiqpcY60h\\nyUJP1EyFNmFOBFJQKHtpHRyLszYoDLlQJmJNIFcKrVEKvAqpLyECXFrWBiVUzC+CcyGf2UaQuoPA\\n480UEUkQIkyI2BgYGZ2hto+l1jIKMQQDqxIdYekgtzlewbd//OesKkNVhzrZUH+5QQYzLbg8ynl8\\nvmZWbhS/urzpp0SY26/baDRvH/S5spNzNC0vrvCnHOtvLMKsnUEgKLKCYa4w3tHIhjxJkcMRZVmx\\nrkrqpgm0bEJuYbqcsqxX/OCVH3Ft5yZCpjTWxk3FsTdIKCvDZNEyoNoykZC7CF0H/MUNM2563Yg/\\nv/9kTm0tP33jkH/8aBwKl1/ifL3Myf9TjiqfNzyh/+Z0VTMsEi4Nc67shGT8+arubsteP+XB2VZX\\ndNFudBFK85t62a+amCUicaRdm8RFtV2g3XYBbCGpcKzvopf2rbCJlET3u84TwHtH7IEVP0Pw6MEf\\nmI/v4m58l0tX3/jE9XbOGLFHsJA0zZLTo7ssteLRww9xpuHW9avkSiDtmvVsRdnU9Po9Tk4XZEVO\\nXTVUjWVxNEZKwWg4QCPo9VJUkuGUwzuHcg5hSmarkiZKvfWzjCTx1MYgt1QQZBuMuBhVSx9THe01\\ni06YXUuBkRInXXQ83FabsdAPVCkRjJmCRCnSRJFrSZYoUgnC1yBDdCR1vlVn2U1xt5PKmNAVeHC2\\nE2CQwlMvTzFe4L1h/OA3HB5c5ujxQ1CCLEtIsx63X3kVTMOTJw9JD16jlBnrxSlp4miamnoxJ89T\\nmnKOcwlzM8fUlsR5cqmoV2u0lLhqhY35UysEKklR0uFbuTrnKKsS4UKTCbxH+VYiMECirZiAEApn\\nTGDFRrKNMSY8WzLU1tZ1gyJK53lPuVqRpTnlug7GToX3SClj/5HWiGwMU9sVpf1dS+oSEV1RKvTo\\nbCFynehoU32HkCSJIitSTscTDm9/h8ZCbYKxDDXDrgtyeqniYJDx8Gx1sWzNd198cU9/xhreNpr3\\nxktu7fe4ulvw5Hz9iWO/6vFCgymj9JEzltJWpCqJXpGir1MOdnukOuHDo8ccz89CTig6Mk8mD3n7\\n+nfROqMxtqMY7/RTVpVhsmw2LKotr5022oz/tsd4NpT1bYDPe8+d4yVN4/jJ6wf86u4ZZ39i9Ttf\\ndvypGFpPaM8zWzcUcTFcGuacLatYrOypmm2Qs4XT2ogyfs4XMJbPgmefPS8bEyi37ZoPGIjv8JAt\\nI/lU9mSbgrKRJNsQTESHNbPJM24f4x0nTz7m2k5BefQe1WBE2t/f+g6BNQ1Jmne1lfX6nHt//I/k\\niWS4s8/1/R2apqZQgiJTeKcRFGT00FoxGo6CLq1USGXpFUWQP4sR9ORkjHWW3nCEkoI00axWNVVZ\\nAaFZdd0YUhki/8Mrr2JMhVRpZ/iViIL57Vx5MESt1paMJAIxyUqJaoviI7MzzI3ciAfoYCjzVJMm\\nktzN6TfHnD65S2Uk4+mMt3/639H4pttU6+UY4T357g2cNazG93HNGrueYuoVznpEbB9YLc5CJKQS\\nBHDl2i329vfI8oL+YEi5XvHRB3/gxvWrHB7s0+8v+c3HM974wZ9zjRAjjgAAIABJREFU9uE/kleG\\n/nBEfXrC8fiEwyu3ePDBY/JUkZaOaTWnLGuuXL2KqRqsNeyMRpydnbF7+Uro2iEkdVUxXS4Z7Oyw\\nOxxx8vh+aFqd+U7YQ6sU5234Y4MCkRChlMTGJtONi8YNOhH1pjZID8Za8l4OeHSiwjyI7UbWYiMa\\nIFT37G2vI8VmLbZC7giPVFHMQUd41hObXYNKFBKYNgX7r71Jac1GqMC7Tt2nl4VWjI8mq2ekcUQn\\nHRiDXghZz9BlpoVxnlrezsP9yYqb+z2u7xU8OvskPPtVjheXlQiJsaHY1QhB09QIJambmnFZkgkY\\nZTm7vQFlY1jV6xBB9vbQKqExEV7w4cbsFJpVZThfm+61ltATqOyi81AuwDGCWLf16eP+ZEVtHT98\\ndZ/f3D/jZP7lWFVfNxT6TUGvX/R717XlwWRFoiQHg5S3r+9xPC3ppYpVbemCFP/JGrovkpv87MaS\\nwLoUMbcUV53bcrm27CgXjGUXQW6gP+K/QkS6foQhtRKxs0Ts6iBbhR3J0eMPaJo1k7ml7w2Lj/8j\\nOweXWJclZWM4rwxV47l0+Ra7u4c05Yzl+WNu7g+xxpIriU01hXRkiSRxDmtCo2gpQEtJWa4Z9gck\\nieZ0fI4XMJlMWS7XeOdYLuboJEGfL9jd22V3d4AxLjBxI4RsjGFd1QwPb3Pp2qs4G8VA2JBu2vsI\\nEic90l1kYmrhsUqQtK3bfCT1tBF5ZJOmSpImkl6qybRAzf8I9ZTjxYzpfIl1oKTn4W//Gu8M1jRo\\nFcgxzhjSwT2q1RRNQL26dlHWBjKLhyLvMZ/Nqcs1+4eH5L2C0c7rTCcnnJ8ec3R8jJCSxjhsU3H/\\nwSk3r15nPjnm6vf/kvv/8L9yYifIZsXkfMyNb/+E7/7l/4gzFad3fs1sfEaaJEyncwQSrVKWVUOS\\n5Tx59IRef0ivyGisRegEEDRYRrt7nJwcI9MUnaRgmgDBtqIH1iFa7dj22RYiiuCHZthKpRjT4EVs\\nxWZDvJ9laZwPh1KtIHtMN0gJPij1OB/ELpyLZX0C8BuNWqWiDq4IuUqlBVqr0BAjphPSXkaiNE+O\\nzxjd+jOsUDSmiRKJrhOyGGQJo57mwST0L30KbA0rtV1bxBKjraM+jW3tfeg4EyLNPjf2ejw6W73U\\nNNzzxgsNZi9JqEQoJnY6nHBpGqzweCVwUjGpalKluLx3ics7NymSgqZpuLp7o4suEZ6dImFZGWZr\\nE42k65L57UYW4NioQdhutLAVmmy8j2dN6tG0pDETvn97jw+ezHj4NXggX7Wh+6oN6Kd9/me9rsY6\\nzpY1x9M1p4uaq7sFQgjmy4bpuonOz8U79ax8w+cxoJ92Thtlk9gBR6u4SQQjYKwLheWEZy46tk+9\\nd2uzj0ZByY0mqVbBEGsZ8m9tHi5R4TUt4f33/gEpPJUUyEQjypKzP/yWyweHCAE7ScF4NUeXx5z8\\n8X3y/pDLgyHCNYhcMxwNWC/qUP8sPMvFDOssXmoGgyFSqQAPrudMz0qc1Bwfn7EuG7z3VOsqEC+s\\nR+uE1bpBa8tqPacxltFol+VyTlPXDHYu8+0f/atNVOg3hfAhJo+C+W5T0I4IourKg1Me7SUi7iZh\\nDUfGrGidC0mqJXmqSKTHj3+HN0uOJ2Oq2oTjVcihmXpNMLwC24SclwCaxTFayLjBbpxthEAJxdHJ\\ncWC77u9zsH8Jj+d8fMLe3gHlcsXJ+BShJFop6rqiXpfsDPe4fOWAux/9lo8fvcfNd/6S2lSMP/on\\n1OwBSmUBIs36XHn7ZwyvvsHDX/8t9WxOlucM+0N0rpmfHrG7v8diXgY1M1wQ4e/1qJsGrRXFaAAi\\nCAoIpYOQi1dRkSdAs2xFk20xrCAYR+cDpFkUOavVKsC5hGfSmWB4rY2lXCFQDc+xDMLuMuDYKBE+\\nOjz7GyKlECHKLXo5dV2R5zngYneSUH+Jhw//+DHr/AY3BgcsVutYdxnXmA8NF7JU8/BsRWMdwl8s\\nzop2+iLOzpaxFM82ltvDe7g/XnJjv8eN/R4PJ1+P0XyxlmzTUKgk5CmARV1hrEHLhNrUNM6y0+vx\\naHzKujnmfD7jB6/+iLeufZvSVIHhJWBUpCxLw7I0XeG38+Bd2wKIKE7sPxF5bsOv3Xm1r20HB3HD\\nmyxrfnFnzI9ePSDTio9OFl9ocj6roXpZBu1PTXDh81zX3iDjbNUwWVScLWv6qWZ/kPH6MGO6rjlb\\n1R0s82WN5QvOuosKlRAMM0GRJszWgbR2sdXy5hz85u3xX99Brar7Q1CLkbE/ZKrIk9A3MtMhgkq0\\n5oM//D2JBrwgSTOMs/i0x97NXabnE6ra0MiSmzduUSjHbnZAIqBaTVHCI1A0es56OsF4h9YpWRHq\\noa1zKCFplkvmiwVJUXA6mWG9RMmUQS9lXZbUogo5Kmcpy6DMczqekKYp/UHBeDzG+7gxe4sQktXi\\nBKUT0mxwYYODkIeU0gfhbhlgbUW4RrQCLMZG0fCQWwlvjeIGqRKkiSLTCdXkQ1hMEM5RVjXEeaZF\\nmWxwaKyz6Ji/8z5GvJExCmHZNyY433VjuHzlGr1eD9M0TM9PwYWWWrYxeBFUfZQKW15jDDpLGY6G\\nnBwdkfcyRmnOnb//d6yN4bt/9T9x+4cFtLBmhA6L0SFv/Zf/A7PxA6Yf/iPT6TnLlULpHqZxGNuw\\nWKwY9geYxlKWDav1gr3dHayPBVZeoqUOEZ8K/Sq9sUgZ0TxCLtERcs/OW7TSOG9RIugXyyiQ0FQV\\nSZZSu5ptlETGFmGtRu32wx0i1FbAYEvKLyIyWkuMESRJEiPRwMytqobHD0+Yrixv/Yvv0jRV17PV\\nRQ7KwSCQgR6frTE2zhutIRRtXoSWk9Tex6A+JDqd6adrsZ+1P3jg4WTF9f1eaEQ9WT0vHfpSxotz\\nmCh6UjBIEsZVyboqcQLK9QIdxX1X1ZxBXqCzlNlyyt/+9n+n+Y7ltUtv0biaUZ4EY1kbNjqcbJix\\nLcEHutfbCdmMdifzn9zcnjHmpeHnH53w41cPKDLFbx9MP//sfAPjTyVX+XmGFDAqEj48CqUk3nuW\\ntWF1ZtFSMCw012LUeb6smK5qGru5u5/bWD5njrahViHAOElVLvE+7+QX4elHJyzZDRQbMp2qLRWJ\\nsKtSIsCKWpGnijxRFGnQZU11EPc+O/oDvj7n9tWrOOfIo+6mB0aDEaKqyIea3Z1dBlnK8eMHZHkW\\n2JNaUpUlXlm8FIhEk0mNlipyeD1KaCanx0itsUrRVBXFYIBWKcZYVqt1qJ3zsf9ijBTbOmqhJMvF\\nEqU0O7u7KKVJEnjv//xfEMWQ0f4Nrr72PbAWLzwbYcMwoaH1d4CdpfQYIRC2FXx3KOeD8ysj9C1C\\nbXboTiLQWjCZPMKuZ0GcwLhgxGLxtIzwnLUBlXJOIKNCvvWBSdw+Llon9HpDkjRlPp1TrpfMzicM\\nhsPQKiw+J9PzMb3BiLoO5MTVKkDWCMFyuWQ47CGkY71aM9zpwWLN3V/9e17/yb9BRAN7gWuIZ7R/\\ng9HhTR7/7ueMH75PlmoSvUuvGHDlypD5fMr+/h7z+Ry8ZLlqaBpPXiTgg0KaTnQoI4rweLjm8B0t\\n6UkIGSNvERi0BAJRy8y2riErctZmHY0soUa+jRjj/XeO2ERAROO/qbnscphKoVONc54sS1E6GKr5\\nfImzjuWixBiLThRK55i4YbcG73CUUhvH0XkZEIP4de2a3IZcX9bojOZewa39Pg8my63OVS9/vNBg\\n3s4SMqk4LVekUtF4hzcOEoGTAucFRoMQwejpRFNVFX//+/+D5WrOT9/6CbN1xboteg/rArvFgt0m\\n/GxIPu1t2Ah1d/PwnAnZ3nzL2vHzP57w7qsH/Pi1ff7p7oTPKFrz/7nxVdZ97vZSFrGU5OnvbKxn\\nsqiZLGryRDEqNK9dGlIZy3RVMy8N9vPay099PRJyfNgonA9izrXMQyf4+Oan6wSBjvEJLZwrI5y4\\nEepOVVA3yVNNkSqKmItr1uecnjymXBzT04pRlpFqyag/ZKefk0nNbHaOqVcUeCbzOYOdEeP7d8iz\\nlDxLMFWDsQ0oidIapTVZmoMNrfVs3VBWJavVCpNqpA+KW0pphPes1hW2MSRSYaUNjd/jxbT33jQG\\n0zS05RhVVZJlGb0iI1UVy/MnPDi6x2z8kMHuJQ5vfguV5Hhgdno/zK1UpP0ddFKghcZISyMswkYp\\ntigS3zrA7f3SSoaidyDvHzCePAzXFfkR2M0zKtjsFdYHA+1bBq/WFL0+/f4QIQSz8yn3Pn4vXmpg\\nd5arNUWex2ewQQgoBkNUV5Qf1J2s8SRSUVU1SSYxxtIfjEjzjPWq4qO/+5+58aN/Sz7Yv/BMd3uR\\ng6vf/gnDa69x/vCPzM7uc3hwgFbhuEePH7M7GtDUFU4Lil5B0euzmJ6hswwtFc40yNgIuvEOnSYh\\nLRubgss2B6kUWmSsyxVRkzA6FK5D4ZyLEWKSYBsT1ZBaw7gdrQWz1eahW0lDqTf1siLem6pagw+p\\njOVyHe6/cyxO75Idvt4CxhwOchZlzXhedSGNjxGlkB7VCk6IrSgyQoTb4ZHAbxlXgf+M5vXR2Zpr\\nuwW3DvrcH391RvOFBnN2dsbZ5Bz2d7DWkWhJJdsb1T7YPhTGRq3ZLEmDMr82rJs6CgpsYJSW2t/B\\nrW1ecmMe44jBfPuSbw3o83Nu28M4+MVHY753a5efvnGJf7wzjknor39806Ser8po7g0yHk1WF157\\nFpyyrg3rOjSw7ueavX7GlZ0i5rUbFmXzhR/0NircvjrvQ+9F4UyHXjzrfQi6PoPAFuM11g2KYCy1\\nVmSJokglRaoRds1H7//faGHx3nNpd5dBltBUa4ZFjq2X1BhmZ5NQJ6g0H374EW+9+wNcU5HnGSoW\\nwyeJwqcx1+Rl2EysxdY1jXMsVyEfpPKMXpGjVNLJUTZ1TShNEJi6wRgbmw93Fwneo7VCo7rCc50k\\nJErjrGU4GHREkcXpQ6bH9zj68J/IhyMSpamrJXt7u+A898enpL0R1kE2usLVN3+MFC52VRGx+0l7\\n32PtqwzKQAi48to7rM4fY5YTvAi5um6Jb+W7WyKLF4q86FEUfdIspypXTCen1HUVvssFo+AJeqxZ\\nmiGixqsi6MOuFgvy3pB1uSZNUxbzBXu7uxhjEI1ACE2Wpcznc7IkY/9gn+n0jMe/+TtuvvuvSbLB\\nJ/cXH64z7+9y5c13MfXbzD/+BX0ngCDC//DxKQf7e5xNzjnY36Nar9jZ2WN89AQ37NPqr0spcLVF\\nigThfSey1DaT9p5wH4wjzXRnSryDqqzQaYI3jjRJY/1mvPWxjZdsu+9061F2z4z3QXtYJrrr5CJT\\niY/fvVqsQsvFwYgkT5hOpxz98Z+5sXMDLRWHwyykYlYmsHOjyRO0+41HKhAuEIpcS1IhGs54NZu0\\n5mbP3+Q1n5euCkc9Pl9zdSfn9uGAe6eLr8RovtBg5lnBt96+gvaOUinem55hmhIlAqxgowKEkhLT\\nNDTGkGnF7YNr3Dt5xJXdt0iTfnfh3dLwsVVS7BzehvbdwukmYnts//azb/yOINr+xuUhP3vrEr/6\\neMx0/fkblH7Z8U3nKL8KYznINdb5DYLw1Pi03MOiNCwri8AzLBJGRcK13ecbz+ed/4aMsg3eb0nN\\nbb33ws8+QkYtdCS3ZNZiL8lEhkL7TCtyrSnSFNFMuf/hz3GmYjAccDAcUGiJwqC0xNQ1pqroKw3W\\nMz49wivNwY1r7A36NNPzkJ8jUPa380xeWIT31GVFWVUhB6oUSZaF7hie0BvRh7y/NRZjbMh3xbyV\\ncS1TdTMXSigg6IAqqWIHilBfZ01DXVUoJSmkxDqNUhJpSpTIuHb5MkWecXJyQq8oKNdzyqphdjbm\\nyivfJU0LrCeiDK14+9Y6FZu+nFIIhrtXOVtOcNbiWwMv2gjOo5Sk1+tTFD10krBerZhMTqnLkjxL\\nsdayXpcksWtIkiTkWQGAThPwDq101FzVlOsVg9EOUoTuIl4I6qbB2wB/y6i2kyYJ88UCIRWjwRA/\\nm/Pol3/NwRs/oX944ylMMSISSiOVRqc5691XePjodyym55HIpFiWJVr3qGqLNWtm8xV7oxHGWZyI\\nz55WKK8DQceGdSEhkL4Iz0ftDWlehDlyQWdWqgC/50XOar6M+rHbghybSI4YsbZOScjnBuOZpilC\\ngtKhTVmiNKtyzXqxJk0SslEPIQRnZ2dMz6eMbv+QPM/RCo6mVbeWxdb6E7QSlLLDZaUPOuINBIEM\\n3x4vN7ahfWTaSPipdfvsPTQc+2RacnmU88rhgHvj5SdQry87Xmgwk3XJsCgom4rD4ZA9oExzUgS7\\nox4n6yXT1ZLaVCilyLKcS8M9Hk2OOV8ueHL+iNcufQvzCSisDcT9xjj6zSvBRwuvd4F5p1W5oSRs\\nT8eLDNKHx3NWdcOPXjvkdw/PP6EW8XWMbyJH+WW/83mRadCN/WLlOy1sN101TFcNUoS2YDu9YDxX\\ntWG+NiyqJhAIPmUEebStXBv+wqoV8aX22G2MIrL3t3RMI6tTxq4fkQSRak2ehpyl8iV33vtbkiRh\\nd7SDFjCfL1h5G0U8BXmacri7T72Ycj47Y3d/H5+kXLp0CdEY5rMZzll2dvdZzpcIJSmKHk1TUzUW\\nmVWs6gYnJTpLsC7kmCAwIp1zIeKwDdZ6bGM7iM0520HM7Spr156UIf8oaOsjWxUcQZFlVKYK5R1K\\noBPNoNdnd2eI95ZyPQfvgqaqcyHH6z0Pfv8fePWdf0mWFjRNFfODMrIrdXRaXAcFg+f8+G6wp1Hq\\nLcsykiQl7wVmqU4SynLNdHpGVa6DYRchyjfGYIxD64QkTUNPUOex1lAURYjWjAswvGhFxD1VtWYw\\nHDGbTcnTjKqqUVqxWq9CgX4UFxdSsFgtQfRJ0wStPdM7v2A5ecDB6z9E63zTfNnHHUsIhPPsXn+T\\nfPeQ7OQ+UqWYas35k49QdoUXQ1bzBYeHeyR5HzM+phEg8hylVVAMikIMzobGzLaqkWkSQgQXU1lx\\nLbSOg/ehf2aWZ2GelIxkn7i7Wo+THuFlx/oO5y5ibWdAB1UiI0nLs1os8I7gQPX6FL0ex0dPODzc\\n5+6DE968/SZZqjiaranis7eBVkWUeIxrTAZpPiFD3rUyDm9CPe8GuvedcZcIXKvn7D8LMHvxt8ez\\nkkvDjFcO+9w7XWJeotF8ocF8dPQEh2N/NGJ+esobec6VnT0EntlqytI4lkrR4Ml1yrXdA+6Pj1hU\\noZzjbDHm9cstocJ3eaaYbApGMH7XxgxuuXFiC6b13Rt5epI+63h8XlLWE37wyh7DPOGPkajyTY4v\\nCpV+Hoj3y8LB2+fY/pxqSZZIZuPmBe/+bJ9v/UXj2c8Thrnm8iinsY5lZZiX5oIIfPc8waa2jE+7\\nTt89PV1EGS1mJwreGksRIsxERxZsEkg+eaoozx9xMApQZaLAmFD+0ThIdU6Rpxz2ClZnE3KtGe4c\\nMl9M6ReOk0dHrFcLhJb0ipzx7Dx+jmI6OyNNc5brimRoMQJ6vSIQROIG2NQ1UshY4G2xNha5E3RM\\n2+vWWkPkBCCIsKVCtGrqEaYNG64nSRLSNKHoFQgpSZOENNWYqmS5mAGOddlgmra0IUQ6ZVlST5/w\\n/t//O9Kihwo1DlhrSdIcmRRkw332rr+FzvoIKf4f6t6zSY7szPf7HZeZ5drCDsYPySV5yV2SK66L\\nuy+0saGQQqGQ3ug76iNIEVerWK2u1mgd/ZAccgYYAA2gG+3KpTlGL56TWdUYzGAMZsjNiIZpV1kn\\nM8/j/oazB7/Gry7o2pbxZMp4PGW6M8Mow7peMb88p+t6BxABL21fZ4CicPTEfKM0xbgkhojSCZ9F\\n1lOKuF7k3FjaumZn74DlaiEtaWtx1tK0LUdHjzg8PCDFyGKxYjQec3Z2TjWqmIxHTCeW+eKYJz/9\\nWw6/9kPK2bUrc/FNJaQoJ/tU0wN6kYubb3+Xk3u/5Pzhr9BKsVo1rNYdFCWua0khgDO52yCcU4VY\\nlS3rFpcTpd6wQhkZgTnnwCMJSgRTOIKXGXVSoEI/aohIxZIGYQ1JrqQVa50jEtFW5qWhC8yXK6qi\\nJEaF94HLyzk+Ku4fHfPqH/yA8WyP48uatrvKlwfheAqFRaG0iK4X1lAqT1evuHRjCZRRnKvUVkse\\nten69HAi1f/6rWf7RZ2647nQqt64PuXuyeITE+7PcrwwYL7xzpsUxrBer3HG8PjsDOs9O7MJJsHp\\naoktC6pyzI3dAx6dn9B2bRYXjqzb1SbG5WxeNqg+u980jTZE1q2ma8qKEFs15RY6+VP0tz96nK1a\\n/uHXx3zvzUNmI8dP7v3uwECfd774VbZ2P+689icF58v2c6Yun3zEBPN1x3zdAWvGpWVaOV7ZH2G0\\nYtV4Fo2nbgIhboLlwKVko8rTP3Fq64HrP60zbUEphc0C4CKmLWR7Z/PDnsE+qT6nu7wnLVHrMc5Q\\nFgUqSRAZlwX1+RmhrpmMJzy4f5/1asV0NsbHSNvOcWXJaDrFx8B4VDEdzVhcnqNdxdl8ia0qRpOK\\nppEKoreB6neNEBPBR9zIDu4vKcVB8GMQ2lYb0fSkt2zWlBqeGUkQRAVGa4OzIrYdfMPK11iVNWlV\\nHkCiGFUjgu9Ytw0K4bomAsqvSEp0eHVKxG5FbJeszh5y+eg93OQaReFYnz9md2+fsqrwXcdqteDk\\nyRFd18kMsqyy2MlG3q1PbgRY6IgxCs1ECc8wegG5xCDuHYUtiDGybmqm4wkpBWnHxsRkPMU3DcZK\\ndT0djbBG0zYtq3XNdLbDul6zt7sjLeMI67qlqkq6ruPpu3/H9PU/ZOfWO/J1NhtSTGT7rJT5h4B2\\n3Hjnu0wObnL0y39msTrHnRU4Z5iNR6wWFxRFAbFDKaGY+CDyoylGUgi4QvZTaww+t1aNMYS2lU6c\\nQWgn1mzQthL/NmCqvoNiReIupSRrYMiVXaLznlVd44qCLkgVr1TmGVuDGV/jxlvf4XTZim5sypSS\\n/qnrdZHzs2aNoiosU91hT9/llAPcaEozdHVyTLjSOdwUVsOYbmsL+rR739OFUKveyDPN7iUEzRf7\\nYU4nhKYl5hnKwWjEo3rN0ZPHFK7E60RhNLd3D/nw7AnrrsEVBaEVvzYfMypP8ieUijm7l7G26hc5\\nv95mb97qX39kbpDTkL7w3FrATxs8ax/5p/eO+U4GA/373aes2v84ENrPWim+bD1crWB3XPDbJy+n\\nQn/RdVu3gXUbOL6Uh3BaOmaV4/bOmERi1XpWrWfdRRF+pxe363//VqDc+nsQEM+JnMkB05neKsrk\\nStpgUkt7/j6z0uAYYU12H0mJ/d1dxtZiEhSjKYv5gifnp5ycnDA72KVOiYnVzKa7hARN6AS4g+bx\\nyRNW65pyPGbnYE/mWMPsKhFDJAQhtfdJQOEc3vvBpJf+cRioBKLoAnHr2guVojfy1j2fMUWsFo/L\\nEAImCxT0fopGGcpxhQKCj/i2zVWoGDHEmCgLQeVqrTNNTE7MWMtstoOxFm0VwTfEsuDy4oKzpyck\\nIia/X6OMtBtDHETke4WZ/j3obGTct/yU2jYB69vzQrJvmoaubamtZVwK4rhdLdnb3efi9ERmjEp8\\nKa3W2MJSlSVlVXF5Occay3JdQ6ZZtDFSFAUhBi7e/1fqixNmt78mhtTKbI2UNtdDzjERQ2K8e523\\n/+S/4+HP/4HLs4dMJxPK0jHe3Zf2el3j80w5RTG+MFqhYyB2UmGa3H4l32s6QUoBYx1dCJBnuiKt\\nJ+4xWmtSlnJUCqwzgpRNaeDJqiSv5ztPCInReAQJXGEZ24Ljk6dcrBL7b32LOjl8EFT8tsH3s3u4\\nqAcZZizp7v0762IXdXiL1PXKP5tuodra5AcxwGFP+PwdxdOc0L95fcrdkyXtCyqjFxUuLwyYhXGk\\nkZHhsg88Pjtj7gxpXLE33mFnodmb7nE/B8sYIo1vsj5izPOhHDB7MjhXvfEkSVZXARh9ad4nb0rl\\nm3ArOLL52c9TcUXgxx+e8+b1CX/yznV+8uEZTxcvR4P207ZAf9dAoM977I4L8b57Sa0OeP5afeRz\\nCYJPXMaOxdqj1JrCaqYjx9644HYhM7MmB9i6i7QhDoGm70jo3HaVmV62nlKiwym+iqJOI4FTZPBs\\nbBlXht3RLsv1iqoaAYlCOxanp5wsl4SQuPvhPVTq0Nqwf/MaB9cO8SSM1uL207V0OZsOgCorKmOZ\\njKXNGaLw7WIQ6bSUMmXASDAyWXw7+HDFNDj1n0sBsHntNsGmF9btVYz69VUoou8Ag7ZCS4ghoJQI\\ngRuT8G0LKWCtycbRBh/EMLmN4lFYOIc1jqIqMMZhXCGbdQisVysuL07ouo4Qg7QZrUFpK36kSZS/\\nYoyEGDa0njx/7Gefcg/0z5ZoryaVSEE2Qq0NXespiwqtDWVRohGAj7MW37UYaymKkqap8TFQFeXg\\n7Ssz7ITViqdPTzDGoo2hbT3Wisl0YUsO71zj5MlDPjz6LXtvfpdrr31bqjau3q99t3JAcWvDza99\\nn/f+6/uUZcHl5ZLZzhRnFL6pKcuK9XolPNvgcdagkqJrOmxhZf6c2+hRWwpjuLiYM5ro7Msp76O0\\nDh8DPonQg4qyNv3c1VgLMRBJIkABNE1D03XYoqTMXpzjouTyfMHxeU0YXcNMD2j8tnZsj2pOw17f\\nCw8MwvyqJN34T+hqj+jl/u5BnrLmw9Mu1bnqL7MEhE/aHj/N/nm2bHOlOfnEoLnd7fu444UB88Pj\\nY66PR7jS0dUt1yYT9KjEGINbNlwb7fJkcYmPQUTalRBuhWYSmVY7JISEDFk5JcOo9SA3JvJbSYmg\\ns2TFkpltQD6bHqy0fNi0Qj7lwn3c8cHxksW647uv7XPvZPmJykDPU6n5pOPTtFr/I4oV7E+Kj7gF\\nfBnHc9cvz4z6nLaLictVx3ztUQpKq6mcYVxZDncsVkHjI40eIO+jAAAgAElEQVQP1F0aWklD0MwP\\ntlaipWmU8NmcNVhjJFgaDd0alTrmpxeYBMePHnOwt8e7d++zXJ7iipIE3Djc4ebNW9TrJSsfWKzW\\nFFUJCursnygIc9lk1uua/b09QvC5nSYVQps5k0OVlaunXq8TGBwtQLJ9eQ6EOiJtQSN/azFVVrmy\\n3G7RQV+pMdBM+t8fY5B2MAI6qkaW2EkQN9oxGU/pzRjLosQaQ9esaZqWy/mJzF+zz2L0ER8C3nvK\\n0uTg1FNpICU9vF9pw0r1H0LMQTMT7fskG1BZGDyZTYBVSlCz5xcXjHOlJIlIJALr1ZLJzg7dacdy\\ntUYZw8g62cST2FMdHOxwfrkAZM7rvafSFSrIuV2cXlIUJfu7muXpEeq1b8moCTUgszdVkbynmEDH\\nhCtG2MkBdT0nuIJ0saB0B5ycXTCbJlaLOTuzKclafNviXIFvW4zNYK2shlYvV0wm4yEhsWUJIaBz\\nW1ZoI3roeSoje2kkUtiC5JMEqK6j80L/S8oyHpWQIoWzGF1x99FdFk3i7T/8Pj57Xvqeb5vfZl/Q\\nKI1wLnMCColOF6jRIa0PtD5kn2NFpkhcqR1TLpwELd1Xn2x1UD7rDiLH+UpQ929cE55m3V0Nmp82\\ndrwwYL57ecll23J7OqZd1YwnE7422aVV0NgxPzv6kHNfI62bXig44ZTl1Rtv8Ydv/GAIcn0G0n+Y\\nXGmqnAENCisK/HARNm4SSXGlFZvb9LyMRurJouUff3PM9944ZHdSfKq55icFwz6A/74Gwy9ybpPS\\nkhKsPsFI+ss6ctGUwQwp2zr1NBCdqRJybqs2oGlljuLEUmp/IsAdqxQ+xCwanfBZmhHNgJJ12xWm\\n0nifeHzvPvPLS+rViqIsqYPncH+Hnalld38fW46Zz5f89u5dAonDW9cx2oKWjTwBISXRV0ZhrWM6\\nmYiodgbh6HxTi51SEkCHUr3A8lb3Qtak5zxqnYgho0LlO+gTi4QYKaA2ogxW6WwGLfJpzsqMLxKz\\nWlAcDISNsShrcaZAaYPS8r1N0xC8Z71esbw4E/BQZQmxo20bmrZjOpmI9Z+S91dVFc46Cutk7hoz\\nojWRHY1yqzhGUmR4/vtNRA0liHzofN21sXRNg3WWtm2pqoLJZELKfFWltAiV68B4NuHy8pSmbVku\\nV9y58wrRd4McXFUWlIWjbT3Be6rRKHfH5D5JWoJ/6CLriwc8uftzbrz5bSANpI4ojhEwkPbJIh2K\\nr/3p/8iHP/oblk/vk9KYxXJNayecd5FkCh7PVxzERGjWxCqgtaFZrtBJGgXBtyhtWC3mTEZjLuZz\\nRqMxq+WKcjpGKS3yeP1S9Wunc/s1BozWtOuargvUdU1As394iFYeFRXT2QEf3L3H0dFjvvNX/yvK\\nVnStlw7IloQpZJ1avbl22x3EGMXuzefrEPOMt0fCbq7vs1FRLvDVhu3V47MUSZdrecZeO5xw/+nq\\nChXu0xZcLwyYC2f4sGswteFwd0bnPU+OHjHZP+Te8gxvFCr0mb5wwbQyzMZ7YrSqzbAGSoOKW5Wl\\nFviziYmgdW7hqtxqkQ1FRITTEHTp5xogffjn7Pmft9pct5F//PUx37qzy59/4wY/vnv6iXzNZ/UO\\nn/f138fjiwoZ7E+Kz00l+SzHs+fWP1iq/4/qkywBnswKTUTR9m4abEJGHSKNjywaj1FQGMO4FLWe\\nSSXAnsLqK6bQfYVTWDF1pppSR8PN117njVdewaWOejGnXcyp28T5xQVl2aKLktnhAePJ+ApP2TmH\\n71pCEtm9mBTBR7BIph+liurbqzGmDbgl9a3X/kPoI0oJ0CUNG43MrELWcCa3ontDYKUUZSnglT6w\\n9vdBTKCspapKrLZYK5Wq1oYQPF3XErynW69om2Y4P9GWFe3Rpm4IydO1HSnCuKpo2pZJWVG6cjPz\\nSkLsl9cwkAXxrVVbxgt5s3ymCha/x5QrbvC+YzQa0XYthXMorYkxUNiStmmJXUfddSJYsFogguIO\\n60Rtp6NjPl8wG48BCEFgLJpcsWrRCLZGS6s6I5adK6jXS6bTGU9/86/41SU7N14HBePd62hth3HR\\nRshBgqYGbn3jT3j4c8/86UOauqGoKubnc1zhKIsKX+6IEtblmtdfuc36/JR6uaQoHcSI0xCVAaXR\\nJFYX5yjnSK1HOQta0+OipbUtSZhKEd91RBTLtgMUxWjCeDIm+Q5nCiY7ezx6+CHvvvsut//gz6im\\n+xkf0BtE93LBG6qQ7dvmSpKznu7jYxTqU05Or+7NfdDMHSOViL0gDlv0weduU599tjmvPel8LYpA\\np8srtmOfZi98YcAMJC5j4P3liovo2Z9Mee3wGierM2oChVIsUsIamZlI4As8uTjiwdMH7I72ee3w\\nrX5ykgE+veSYiASL3mQiKiX2QUhZH+itgkQRojewHTBZCrZ5Os8GsM871/zZgwvu7I/4wVvX+M3j\\nOfeeLq8s6nPJ+OmjgsG/78enPcft9+SMYpydCD7Lz72cYwtdnX+vUtIRtEYzied0xR4qGOF+5faY\\n6HL2ma/CGaGLaKMJW9WoUuKoIS1dRWntQCUprOZiecHX33mHO9f26BZzHh09IoSOR4+PSUphyoI7\\nO7ucnZ9RzXbp2hbjHM45lsul6HBqNYB0QMx2VVQDSGajwpIyXWRjei2Bpp9CSEC11hJ8D+xRAkQC\\nrDEoNsINWmucKzDOQoKd0QRnHdoYXOEw1gyYA1QgdB3r1ZoQujxH3dzzMbsOK6Xk9XMXqOu8aJAa\\nSzEq6Iw4jdRNQ207fCMo2P5ZEQpo72oigu7Seu1nlbJPQAYpKT0AlQxGZp3G4IPYfVljM3pUk8dk\\nxBQJSgBKSimKoiTFSLOumcx2GY3OCUF4pQnwPtAnHZPJhMVqTdeJLOGoqiTBtxajE846jNHMZlOM\\nUTSnH/Lo+AMmkzHroqLxcONbf4Etp0PrUv6SpMwUI974wV/Trpc8ePcfqRcXVHu38M2axXopogvO\\nMZ7ucVEH7GgHf3FC8J7ZRMRgTIz4GHBKczpfcHjtkBgixsg6phgGYJRSGg00bUci4VFYJ76hhStI\\nvqMqR2IjtpwzPz9jtV5z7bVvZDeSeMVFqu8aquy/Kc9m1hWWx5UOuQ4hpY30ad4bhspyAPt8dF/N\\nW/xH5pj9T3yenWVRex6crQbt2c/SKXthwIyAKwoZdB9eY+xKjk6PGc3G7DPifH1MTIku+LygccgA\\nQ/Ism+WgODK0ZI1GZ+Jz/xGjxqjs1K0Ao0ghLxY5A2ezUaahqf38POOLAmkenK25WHX80Rv77E0c\\nP71/3qs5fWwA+H0F7zxbSX7WQLl97E9KzlftJw7iP+5nX8YxTC7zW1AISEepxKq4htOayipGVqG0\\npWtXtNEgmBBJ0pzVVM4y1Sti1xDKa/TdjB596UOP1ItENKvTe3TzIw5297j//nukIBuxHs249fYe\\nVTXGWMN8vmDnxowYAmXWBW2blrKayeafAlZBMlLJKDbGA73aiULneZ7hClowd1/6TTsGIblbV+V2\\nsqjlFIU4Rmgtc8Ie0JKCtOF8CATvaZuGpBLBC5rdKAmevqs/shFtJ4M667kpJXZSZVlQFCVt2wyz\\nx2a9RhupxHamM1DgtRGcQwgCFslXdODbqc2ARSmGlp+8DwaSvYrgs6yb1QbtKkhQOptnkEHk/7QV\\nPqi1dL4DJZ9TCmLXQoqUxZi1qem6jtZ7YhcYVQVSOKdsBdbS+Y4iFGhrhrWtmzW7e1NSgvG4ZDIZ\\n0zQ1dd2xf1ARTp/y4Y//ljf/+L8XukfcrGFCeMcpJlw54c0f/DUqB2oUJO+5OLnP0c//ge78nOls\\nB2M01++8yaMP3qNQhunOlKQL1hfnaBIWWK9qJuMx7XpNUZby/stC+LP5emXROmnPK82oFL3jGKRs\\nfHryIUcnp+wdHDIqR4SuRZVyvjHFnsk7dHhkX9+M21T//nrBgL47kkXmJXnYgM96QN6wx/fjLDbj\\ntivFyOfcO7aPZeO5f7rk1YMJD85WLJtPp/z2woAJcqMe7OxhsNw9ecRBVXB0/yFP5nOK/UMKa6nb\\nNs+SMuItyuyl821eBDI6VmaUPejHak3UEE0g5Q0jj3cYRjERopL5ZkT1+KErWdswrdmaHX7RTXvR\\neP7+vWO+c2ePv/j6dX5894zL+urC/kdFuX7Wo6/m9sYF7x+/mEryZayLyjPsnjLSa74arQV1mRJa\\nRcbU3A7HPHl4hL7zFwLFzxmy6B0bJjZizYhU7mAGE10GofV+dlkYQ1o/wawfUzjF6uQBp0+PwRp8\\nUvgAzhQYd4G1jsJZojJif9d2YjBtLSplxKeWWZpRSjagIDez7olrObCPJztCH4BceUI/z6kqR13X\\naKVFUN17tNK0XSeI0HqN76SKEL1ZUf6xxuJDyKMQqQTKcUXEZ0eWRNuusVk+bTsxjRmglLauRSLL\\nq4VIs1qJLmu+JiL4rcWRQxtBcCpomnaYSPVqNAMnlExkRzZYAQTJfNUYm/NoAzrhkkJbB4Ar7VCV\\nam1IWiTpVJLWYAgeY3oeacIaR9PW1KsF09kOi6Xcz03mbHY+yP5kLW3XCIUneDrf4r0S5G3QolHr\\n15RlyWg0pigtd++eorCsV9Jipen45f/9v3H7W3/O3u13sqj6ZsuPeXNUvbZqylW+1uzeeJ3dwzvc\\n//nfc3H0Gw4Or7FeNbz+tT/g6IP3OX10zOPzC9547VX2xlPW61rk5ryn9YHYeiJCewopiEF1lvEj\\nC7OnFHFVhTUVF0+OWKwuicYymu1wOV9Q7Ahv0udgt6lMNtWdQUBz1kgxRBLxeBBE7GbWzuYfA6Dz\\n6u/bdF+26s/0/OrzsxzP63at2sCHT5e8ejjm6HzNIu/tXwglC4lpOaYLgXeP77I7GvH4Ys6TEHH7\\nezShobAl0/GEdb0mxJj5YWI8erE+x2iTXcBBZS82qxMxq3KJl1reYJNwNX1fJQ+yiNmiZnth2bRl\\n5UxffuCKUagnd/ZH/PHb1/jgeM77x8uPfF9/IX7fgD5fJGg9G/R2R45V6z9CAP7q5rgbibf+Kewz\\nVBvWJCY0AbSbcOR24JXXMcZxXZ0xDxUtDq0Uo0KTHv0I/dqf5FbfZjywLYeXujXnD3/J+uIIqxRG\\nRaaTEasARluMMYzLAqMVs+lYABVACB7vV+Lz6pMYCkfybNFLoM+txbqumU5mrFe1zG5y6zH4wOmT\\nhwCDDVNRFNjSsli0mCTtZt9JdqkUpO0KZmveqRCEa887NFZI7z5m9OdI1HBUApUTD4C2bUWHtW5R\\nGXnZ0zyMlWovND47lGzMj60VI+2iKNCFJXWeViVSkkCUC/cr90c/B1OJjILtHTl6kFLE2QJnZfRT\\n1zWuyCR9RIovQZ43il4skdye1Hl/keqzacX9o2trqvGMqqxYrpYUpqDznuiDdMuahqoYkaIAfET4\\nQHR7dQawiDtTi7OOYALWGKrxmLZdM5vtMh6JoMTlw1+zd1NGUx/phtHP3NMwr+6pTxjDq9/5S872\\nbtKd32exmvPwyWNxzqkmvPrWIUYnOjRtSOyNDToFVIy07ZraSzXf+Mje4Z7wYbXsogEYT3YpihF+\\nuYAUeHJyxmi6w2KxZhUcX/+z/xlsSfBe+LX9c5ivl0HO01mZ9bM4JnQ15cE7rNqaEBUhU7fkqm6t\\ngFLZrFxd6SAO5tJDhrYRaO8bkC/rWHcSNF87nPD4ouZi9cm0whcGzDsHN3DGcO/pY2IK6LZlZBzV\\nCLoUCEaz7jpSU28G3MNDG6nblWwSGZ1l8pzSJI014ncXe2cFpfA6yAxKBXTPbwVIaljIQQU/bdaV\\ntFH474/tIPZFjwdna86WLX/0+j6H04off3g28Hk+rt35VQXPlxmwnv1d2z+/Pyl5cvnp9Xe/6Hvf\\nXr9hQ83AAJlsyddijKx1iQ5BHsgMmLHG4khcPnofDr4pHEOjUL7G3f6eVFrkqtJkG69caV4+/Bnn\\nj95jdzbjcG9XfCpj5IN7D3GuojCO6WRMioGikI02xVztbfkMpgwg0v3wNc9zVE6lrTaslivJyAfN\\nywRBuKSjsqTPzXVW3LJGnEa6th2Ac33yH2OuQzMgrg/iSkmlZq0lITZ8TjtQSRSAMk0gRuHXSZEj\\nllcpge8CRhusdfiuE+RvlApCKib5sNaitNraSA1B5zltm8UQoiKpCH01laTNK65FWhC1ekiNcoKt\\nZWbojACK8n1q8usBwsVUIiCulQCVFFIJhpAGhCZJ9FabOrBczBnPdliulkL1SfLeU0yUZQkpUWcR\\nlqqqaNbiO+ny64YUMFbTec9kNh5a5TJf1iyWC65f2+e99z7g+P0fc/NrPyAEP8zytu52eupJX4Hn\\nHARN4uC1rxNfeZuTu78grX/FxeJCkpJ1iyKxu7fLwa1XePLwAdORk9nkeIILgeneHlMFyujhNbU2\\nTCZ7kCLLxSkOw5PTp0x2d6nXHXUq+OZf/i8kZWh8IA592Pzz8lAOXUJnNOHsA8zlh7SNZ3L7D/BR\\nuo0hgYkGE/r3JK3bpNJw/eXZ6Fu4W/spbMdN6Sx+ju38k/aiuovcPVnyxrUJkLhYfbzU54vdSoxj\\n3ayZlBUL1py3a87Cksl0wkg56hgorWPVig9aijFbCyUm1ZTbu6/Q+prSjjA6C/5mTUOTFNbmgXT/\\npoJCEVDKoFSUTDEpSIqYelNbhkz0eUXll9UmXbWBf3jvhK/f3uHPvy5WYfP6+b3vr1q67mW93rNJ\\nRv//USFzpOf1+r+6inp4uvo+AyCtTRUixiiIOlOSggSONqGu/9HQvlUolJugrMjHOadZnT6E0KKI\\nqG5Fs3wKvmUyHlGvVoNQulKK2c4eo6qSWVrmF7dZN1kpRYrQhTCAIPrLEmIi0OUkQOfWqKhfpRg2\\nGXS2xmp8R1WVkBCEphOwjDVWRNqbNSqff4zi7tF1HqWlgxN1FPpA5uIlEq4oMEbapDFFQtuQAO81\\nOs8EY/9c5TOS17CY7ISSckDpGiGDey/BQWlNWRTEEHLbVCpt0ynQMb9H2SCjktfSOQmW+1cRiHnc\\nYrIWqVzyXm6tFzWIKVCUTpDEIWSOa48O7lu80oqsqoLlck1Ika3dGd9KNV+vFhzcvD0I2xulMwVF\\nVI8a7ymcwygRsmgQAYqmaXDOUo4rfNeiTaJtGqwRQ3EVU3aUUVhXcP3GISf3fsH+nW9gyzEbYFN/\\nb29mm30zMuUsKOYAo43lxtvf5fqb3yb6jnp5wb2f/B3d8hJrpQ0/PTikKrRU9XVD6Ry+8XS+YTKd\\n0DYtk509yvEOwXccP3rAbGdGUYrQQ0Rxfj7n+jf/DG0sjY9CudoaC5CTSp3XwlpF4TSuADVyrFZn\\nvP/P/wev/dF/m4FXiRA10eR7NfNhVR8Nt9q7/WUf3v/WU9//+8qyvaSj9Zug2fumPu94YcA8Ojvm\\nIGmulSXF2DK3a2KIdN7TpIjShkW9zrOOzdtQyHziJ/d/xMniCf/5m3+F1oUoqeTFSJlMLQRWPQTN\\noBQqIwR75GACecBQpLAR0e4zjs2j8PIXEzbVTgJ+dXTJ0dmKm7sjJmXH48v6I1nPV92W/bJfT1xJ\\nXo4K0qc51ObJ2VSXajNL7d9u/64jYPoqiyTuHirhVYQgKFr7jKpPYeD8/rs8ef/fKJ0o7YyqEfVy\\ngbOWUTnFVokqOUBoEyZTFryXgCFV1kZ+rkerKjIdarhTN/ZdIWTOoY+E6EFrqTil6CKphMtoVucs\\nMQWapgalqcYVILNCY5Qo22SzYOcMrZfAroJsVEabzKXUA2BjXa9FwSdXcUZrSHJeMl7K7cvtjpFS\\npBBom5YUsqIQWfTBOfGf1JB8IEVNUiI4UNeN0Asy4V+qWHIlLi26RA8cya3UoeW3mVH3cncgr5di\\n3LSIEdqEIZE6j7NWeIq5feu9F9eUPBZ6Fu25Wi6ZTndYrxaDoXVZlHgvPqrGOQpjAbEBq+t6sNUy\\n1pKSyAP64BmPKrq2GxKY0WiMb1vG4wp7fsmDX/y/vP3D/wF6cXnkej/Lm0hJbYIqeWvNj4Q2Bq00\\nk/0bfOPP/ifmT+9z/MHP8MsF01u3KMeO+fkZ04ObPD0+YjQa8+TklG7dsn/zDm2bOD+9J9c9Rgpl\\nOHpwxPHZnIPDa2As+7ffpAsxz1yvAi7N8CwarBFUuU2edn5CqNcoLPHyIU9/888cvPk9ohWDbK81\\nWocN9z5tRiq9qs+GZr+9ofbP/wb8xlZC/2mLhReh9lsf+eB4wevXph/7Oz4+lObDEzkOLXUMTLRo\\nZ+p+18nAAa0F0GCtHWyWRMarI8XEg9MH/Pjev6E02LxZCdG8HxYLQVxU7fv/920yK3qKw9xHXd00\\nVZ4SK9mm+qX4PKjQFx3bF2Zee377ZI7WireuT6ncxy/l9jzpd3V80us/72s9eAPAasWksi/s77+s\\n43lXS29d3aEBqDbXtoex99+h+69reThFjEDE1G3mXPrVOZdHv2A8ElNm5xyh61ApMZtMs4iGpizE\\nuSNGT9us6VqRgIx5Q4lXuGVq6+/+I125B1TaoAlV1kaNmacmQS1vDjGSCHljdpRVKeCVJGbQztmB\\nUmOMyUHIoG3PcdZDpdRXTVolnJF5bMwAoK7zNJ2Y/xKlS7QNnlN5g9JKo5HXKqqSqiopqyKLR8i8\\nUBIC4Wy2radtu6GFGnq+aBK3EWssRhsRPEEN+0If5KVlrnIqvZ0sKWIQkIPO/G1rLeRK2uYqWhtN\\nk3Vv9Xa5j4yLkpK1Xi8F/BNTzNZYucKNibIoRPGpsJhCU1RZmMHL7HZ5eUnXSjej67rcBmdolfcV\\n2Wg0oqoccfmU49/+WAJG/376Pe3Kvb99rmmoNlP/FaWYHx/RNSv2b7/Fm9/7K+z0kLOzM1Z1R1Ti\\neepcidGGspywf3CDZr2inZ/hl3MMkULB+fkl798/oijHXFwsuPH1/wZTjHNl2GvFyoarUIPwuzNa\\njNV1Qp+/R2wXjMcFKSZGo5LLo1+xPLmLUTFX3moA1fUTCrGhY3j/zz7//edFCWpTlX/WcVtK6bn/\\nfvboQuLuyccrvb0wYGpj0GXBWb3i6Owsez1nSnG/WRkzCBFY5yiLAuMcxogJrUqJdx/8lJPLxzS+\\nwWYitxk2MiWtIqOywkoGDhiNMZIB66350nYW31eZwObm+zQr+AnHxwWXZwNvTPDgdMXjizWvHky4\\nsVOhnvPiz5sHfpnHx90QH/f6Lzqv/UnBxar9UhzMnz2GJsxQQapBHrHPcIfNZVjXrc8rseXSRm+C\\npFEUVmV+paXKAfP03o9RKeR7Ue6vXgpusZjTNg1aK7zv8G1H9H6LeJ8ykE1jrdzrKrvJ94Lo8pHb\\nkTH1bnb0AbS/g1WmaOTQOsyx+vscej3mRIyB84sFdec5v5yzWtdgNU3XEkLaiJNrsfjq0aV5exFo\\nf7wKCBpUkrQIs6M228KgCmRtbudqtJHAJiBWoV6kuAFkpZTwIeXfZTDGobWsj3MijOBc3hvUloAJ\\nGqNEjtCgsxJYrjKNYORF/Scr92hpn8YQZMZqDaPpmEgSb0iVaL2nGle4wgofN27iZlSy3j54SIlR\\n5qY6I9ZfMSfiMYjgfNd6ykrEWGQWyzCjThG6VpxzUhIqj7FC3vddSwiB3d0Zk0nJ+d2fsDh5KOuY\\nRwOyf6qt+/iZ52IIFmmY+ZXTHbS1JGUoJzu88q0/pekCF/Ml1o04vzxD24LT+Yobd15jeX6MX5xR\\n6MSksOBbrNa8/+A+r735GvPLC/TsJtff/LbYxuXxQI6Vkgj0yldaOMuF1Yy7U4rVMaZesF7MWa1X\\nkKAqS0aTXVxR5T0/3zfZOWdIGq4kRM/fixJsvo9PHyifd7xov/skfewXtmTHRSkcy1GJAUyuMutW\\nlF5Sn82jqGyZ4fEKHwOL9QqTpGUSfODvf/W3EDV/+e2/Ynd0APTIJwEJyO+TIyaR54o6DlqzSimM\\n0uJI3yeMSuV5Zt8S/mJB6bMGGxAi7PvHC27tjnjr+pSjs/UV2aUX/fzLOJ4978/KvfzYYArsTQru\\nnnwUGfx5zvGF5zH88cz/c0nZAyK2f8/2ZtPrwNrBmstKsHSG0loKDbG+4OmT3xJWpzJvQuGDVEM9\\nf5KU8CGwWtUYI0ovqXeAzxmuc8J3FAuuTcUDZNQo9EIdbLWe+tZ+zLqx2/D6bQFvpRioGjFGVISu\\n9ShlaOqOFETFZTkX0FBVlOKt2Fc4StO2LeSglBkrfcNYPtefJz2KVzAIOldGwoPcBNCQv7/vJMUY\\nB06kJAjyt7GGonCEGLI6TJCuU4SQwVE2J9S9bN0QJVIiDS0DmdsmEcFFaUXXiFm91hqT9Va7pqUo\\nx5SFzXScKGIo0dM0kHwckpR+ZqpSAq3QEZbLObOdXdbzS5wVMYQUIqYwWTawyGtk6EKLzuetMcL7\\nTDLH7Rp5betcrjrb4X6cTCdY6yhdw9HP/pazh68RYsdk7yaT3UO0cZTTPdH9TVxpO+aFyX/K3+V4\\n2meLpJSoJruUs2ucP73PZFSxDoFXbt3k8dFD6stTxrMdzi8uWB2fMJmOMdrw2wePuHXnFpeLmsmd\\n/8Rr3/4hMaYsqr55TTUkL32CJbP1sDwh1Y8Z7exyeXnOxWJFCOLusjcbU5/eZTrZk26h0ZvOgRI2\\naP9cyD6+4WFuugGbOWb/UGxz8L/q44UBU+XSnmwXU2b9R6MMXfD4GBjZQh5KJW2V4ANd2w5vXmuN\\nsorlekGIgb9792/44dt/zo2dV0hKk3TcBM6cIRutsEkk84yOWad2a44F9MPilP8YFICGJOyzK/98\\n1sDW/94QEw/OVsxGjlcPx1ysOo4v69/VdX1pAXpn5Gi6SOuvqr18FdXyUEFudw22Wlh9oNRKoxVY\\npXJrX9CZZTZ/LpzYc6l2weP3/5lmfopS4qtojcFkCoQ1hqZp8N5nFKTI2RW9pF3YwLZTghi7IZj1\\n8zQJiJtsOeNlMdbkCjQQfUaW9muoEN6y9Noy8CcLGyLUIyMAACAASURBVGSjgr6tWtc1zhUM0nKm\\ngJQoXTlwK/veXYyC4AxB5nC+aXDOZXEQ1Uv2ANIlQitcNp5WW8EUpQb0b1WN8N4L2Cf4LLlHphzk\\nrpA2YCRJ0FqzWq8YlWWm1wiS1TmHznxPa8xQMfatR9CkKLJ3plC03jOqRrRtPewpV9Zaa3wXWM3n\\nKKPYme3Qdl1eXNnHrHH5PfVi9hqd5B7ydU25d0hwjhC7PKcUD0iQBACd6Nour7MAf3SS9q0gomOu\\n7qVlnIIEBFs5kUA0UFYlZSGz2qOjX9P5iL94TFMVXM6X7N5+m5tf+z6umpBibunyCc/bdlDTmhtv\\n/xEfLs/xybB//RW60GFU5PxyyfVr11BoYoLjszluFGlC4PhkyejWt/j6t35A58WBaBvFO3R2EG68\\n1QqdAvXxb2D5mM6vuEyBhU+4cszByDKqSroQWD+9h5pcp9h7VTqJunerUsO9g5aWa1QKlakrm9wp\\nbZ6R/FYVm2/4tJXmy9qvXtiSjTHmOU4hg/AgvK++jaW1JqRAYQxFUoS2Y9XWtGyg6dZaXJYIc8Zx\\nsTzj/3n3b/jw9ION2s+w+eWZ6NZGKA/Ept8Nmxnmpt20aTs9ty/Kl7fJb//e+brjt08WOKt5++aM\\ncWE+4Sdf/jl8Ulvj8xz705LTrBv7RVvLn+bnnn0Nub79pgEbG2NyRakwhjxXUblVZKiy6XP/UTnH\\n8tG7dPNTAf0Yi1ESIArnBuF2rTSj0YiyFIsjDfiuy6Lom9laX3n193dZlkMQ6O9faw3GbqTnFAzB\\nMKN78lAqDZu+yYLrPlesKQqlw3eBrhX/y671BJ8oipKuayXYynfLqMRobOEwhaMcjdBGkQiUpQRa\\ndKZeOLvBHuhN63dQm1FC94gxMJlOqcaVzHC7LkuuyfmnlHKLTbwWY/S4LIDeD928z1zNIeFVmyJC\\nCZhG5YpRG7ETVCproBpFURbEKG1xneX/JMiDMmCMInpP2WvWJmkHV2Up6HoiSif56PcY1Yv1J4wx\\ntM2acjSmbTvIQhM9nzymiPeB9apGRZWTenFoFqm7OMxVQ4iD8Te54g4pUS/WdHVH2wZu3b6JdZay\\nLLh54yZ3XrlNaaE++ZAP/+2/sDx9lJMlBLux1RbdPEZ9VOm7Boqd3T2+/ad/TXQjfvveLzk7v2Td\\nehrvMUXJ46dPuWg6gnGsU8InSyr3ef2b388OJ1wRKFCq94wFZ5GZpVFwcZ908h7dxTGL+TmJxGQ8\\nZlQV7O/PMBbatsZqzfzBzyC0Q0Gl9BbGgD4IqeFe2Pof9N2Z3MnsdaE+y57yMo8XV5jIAxFDhCQg\\ngV5b0mhNj5+K5P9rhScxKSux8onCjZP5T0AbTakcrW/40Qf/xMhVHO7cJiUBHSQlah86V5Tyd24B\\n9aW72pTy/RwmN+oQBQkxpo7PZB9fFegmxMSD0xXTynLnYMyi9jy+WH/pM8CXffNUTpCUiy3qzFd5\\ng8p13brewznkB1nJTEV4ekrI3NmVpHR9O1YCaHfxkNXTB9k8V9SmnDEDiCalhO98NgzQmeyeBhcR\\ngJRElzMhGzQIErQH1PQJIjGhnXgPorKebe0FLTrMouKW60bK7Wa5R6212f5rUwEGL6bMPkac1RgF\\nTdtkPdNs55RXaDYZ0XWeyWSK7xp88Bgro5RiVGGNpW7WWKspnBsy+qZppZptfQYeyVXY2Z2RVGJ5\\nucRqWVOZgwq/sZ/JaiXIYFtK+1KMoIPQXHrQ0dCyHnZkmTdnH0eVEj7KfFgbTVGVOXhq6tUyczst\\nPopgQfKSmGN6pK0k8yl6Ugh0TTuYWzfrVq6filvTMOF5AqwWcw5u3Jbzy5W6sRZI+DaLsaien+rR\\nygz3gIjpb+ngsmmdphipVzVGGYLvqKqKGBJ1XWNdKdfCQGk0UYnDzr1//7949bv/mZ0br20VBLni\\n3BSeQ7WlFVRWgyqoteHgze+xnM+5OH/K7Tuv0DVLOX8i0509TucLRrakbTtu3XwVSUvVIF2Xcpuu\\nD2xiySguP7q7xIZTFusF0cDB/p4kisbQU3rafmSnFKFZk0KL0aMc8HPQDP2rXi16ho5s7hz2lCt5\\nqx/tFH5WANAXOV5YYdreIiZJtdn5Dp9bsT5nJFopOi/qEhNtORjPsCFzwIDWd6A1VVlmkrjFWcey\\nWfCju/+CD3XO+LY1CRkCpurTXdVXGc8Ae7ZWu38QNpvty9vgPyvadVF7fvN4TkyJd27O2Bm5j/2d\\nv4/HwfSrcSXpj49Wl5soeQVJ2Lf/tcaqHigmKGtpwZrBfcQZhSVyevfHhDwXL4si34cyf3LWoZUZ\\npPPats2aoGu6LuSKQagIwYcsN5eGDdTlxEK0YjcI03622WWx6544L2bJavuNb0YH+Q0brWmDp/Ud\\nre9YrlesmjURQdL6GEBBSDIvI4HSBucKlNJ0XYdSIos3nYwpy4LRaIRRkdGoYG82Ymc2ZToZAZG2\\nqalGFSZveIUV7qExBm0MTbPGGHEwSSmSsqNEP5cCIEmlaK1ltV5Lcg2b+bLZdFvkc2TgT8IZg9vq\\nMFlrcvkh/06DqTRAwmUwYeqpO6mnKQhwKSIqSEVRSYBtW7bbm70mr8plTozyfrq2oawmpCjIzrZp\\n8a24r/QI0WEfYMNLTSmbWW+Bc8S7NBJ96LvkwnWMKXt1jimslS6B1uii5Nat25QafLvk/k//K+v5\\n+YCo9m1Dyi171bczlaIwirHT+BhZt4mYFK4c88b3/5py9wbHT44pR7s0MXD71VepJhWuKGiajnKy\\nw8Gdd6Slnoaw1JMghAaotWj2Jk+aP6S4+C3nD9/HObh5/YBRVVBkxHYMnrquc3ciC1kQSNEPVnyZ\\n7DBUzKhew1lt7QGwjYbNS/qR2eVXzUB4YcDcH48pcpuohxPr7LWmlLgjlE4kq1qkbdG1LV5JKzcJ\\n5I+6a1nXNaUrKIoC55w41qvE4/OHOGPBcCUD2WyQyKKyvYlufU3+y2YovgmuX8bxWYJwTPD4oub+\\n0xWH05LXDyc48yWd2Es8jFZMK8f5V8i9hGfWdquk1L26j+ol7HrEncYZsesqnc3gHpk9OatwFpJv\\naNYXGXxhcEoI7t57qkLoJCmFLANXk1Ki68Lg0BC2AuSQ+OaNUuf2Vb/59g9uDwLq54AxRhHAjlGU\\nbNImI77ysOeWutJKWsVWBMWds1jjctCyGG2xrsji4xpXyOd3DvYJoaW0hq5tZZMyUDpZD2LE10su\\nnp5SLxacPH5E03ZgNFqLVJ9SIu7e0zdSktmtqATJ+3GFcKp7AJMEHtnRQpAWdpc7Sj3trL++A7pe\\nSZArnNB2qlEps9McwMTAW+aOIfNeJWBtqlWX3Ta0gi4bFHvvcVoQwl0IrJsGpfrWs96MePJ7FrSq\\n+H62zZLJbJblPKUo6IFQcq3kMmltJEhrhSssqF76c1PtpHztJeHKbh1AGzyrZc1sOmNnZ4cUIlpp\\nqvGUyWzGpCpp1gsKFbn7r/+FZnmRA27i6Jf/zPriBEgYYOKk0Fi2gdanq0EmRV759p8zuvEOTx4/\\nImAYz3Zp6o7VqqVuW269/YdYV25anWnT2TFK2tYug3y4vM+sfcT80QeMqoJJVeHbhhhagm8JvsOH\\nMLR0pc0uXOHYrocWeO+brPRmjtlfkyuPPfmEntkOvsjxWel1zx4vDJj1cgE+iKxSn+3nm7VvIQXv\\n0REqW9ApWHtR/m/ahn6LqVyBLQpI4PLKFMbS+DU/ufcvrNuVLGjP0RmqxM2/5cXT0IYYbgy1+b5E\\nnr88Z3m/6n739rHuAh8cL1g2nrduzLg2Kzc3ymc4r68qk9qfFMzX3VdCJXne0WtO9uowA2ct/92b\\nBjutMho2e1oaEWIvjBJB9q7l8a//Aas1o0ICUMzjhN6J4vLykqZpB8BPr4W8/bENHtMZ2ZoG9Zs0\\nVI59J0aTfSpDpkGEmGkIihi2fP62rqc8sHFoLfY80MIWgFAQyrJEI4mqyu1b6yxRJ6bTCaPC0a6b\\ngbJQViVV6SBFdPSUhcUZx95shm8biqLicDpmai069vJuMns1TuaKyceBZiHUMUvq510EIpFk1OaZ\\nzAnEdDIdOgKCyh2mKkMSrIzYjrXek3KgDN6jtEYZQ9PWNF2L0nao7p4Nvlprog+oBM26xjjH5fxS\\n0K3OsDObUpailzv8HJnvOcxKhXbStS2+85LMk9H61mzN8nSe9QmK1GhZo0jfmt66rikNgLAYE2TU\\ncgjSHTBacXCwT9e2LBdznLOslgtMNeLO9etMpwXt8pxf/9P/zsmDX+PKkp1rrzDdv0lpDZXTND6x\\nbnx+3TxPzSdhjMPYile++UOK3Vuslmu6pLlctRhjufWNH3L7G99DkNoqz9IZgHQmjzq0VigiVVoz\\nP3nCYjkXRSVrROQ+Jyc9D7+fxwsVEMaTitXlk2G+P/gh5+twZfsbbo7nFDxq64Mvthd+3p994Qxz\\nZzrhad2BbymMxWlDm0RgHSWbkkays2VTo3ygKAsSiYICEASpUZqD8YSj8zM679mf7TBfLTMaK3B0\\ncY83Dr4uKNvtQNJ3YzfdOQFtAJ7N+m0jutIzn3tZxxcNuAl4umi4WLfc3B3x9o0Zjy/WV2aEn/jz\\nX3Kw7H+/Vor9ScG9l0Al+bTHFQrM1sd2m30DCNMb4QsrAdJZQ2FF7aawisI65kc/5/L+L1AKyqqS\\nakwrjBOgTtu2ciOxTQPZVIrPQwWnmIg6bL4nJgYlcaQKsMYKiZ3ehUNBgKASKXh0IldrW1VmjAQ2\\ns/lNCsgQJLSR+ejQedGa0WTMYrFgOh0xGpfUy0suL8+5Xt2UlrFHlFmsgi7SNS11O0dpxf7uIWen\\nx3RaULEmyPl3bcNoPKHtWogIxUKnLByiSWGzKae+DZqfcd+Kf6YrCgpnWNRLjHFb65kl7LI0YFE5\\nfOgybUeoNs4YyKo/xiiccXSt6HtukMhqUy1u3TtFUXBxdsGoKrKYAfi2zdKG/X684TqaDCUZgDo+\\noc2a0WTKar1EKTVQe2wyQyu9BwMpMecQA+u6HtZkc0OknHSKOlKMiRA8JMRUOXiUNcSQKIoCpRKh\\nWzOdjJnNplwcP8U3K/yTX/LrD3/BdPcQ5g+pQ+Tow/fZ2dknNktsNWV8/Q32b72FcZYUGYReYtdx\\n+OZ3Ofrl/8fpb39LlzQ3vv4Drr/5nQxUkhmhUr0jVN8ilefOWIs/fcAIT6sT09GIhMx5rZE5rrJS\\nQBkTsYAOkTr4nGAafD1H0bMdGNrM0phQAsbKVJ8rncPNqJu05X3cP5OfdU/cpnZ93Nc/6XhhwKxX\\nK9F31Uo841KL6v3plMIqg9WGqbWcRVjTDG7vKbeeWt/hO8/i4pKyKolGs16tJZvW4iTw03v/wu7o\\nkJ3RgdyIeWOQxd260dmAC4cbP27pL3K1dO+/78UL+2WE2OcfPggoaFwYbu2NOJgkHl2sBzH3jz3D\\nr6hCno4crY80LzifL+1Q8mAwXPNNdamVRhsyMb9XhxIKSV9hmuh58KP/E5pLqmpMiD4bkWfKRr4X\\nupz09ebBPQ1EWq+i+dqveMz3slH6yqylDwT9/LMHhIRhI1KEPB+KIaKMQnwJe5RpIsDQvenblPld\\nkxC1mSAGjYhkndytWivW9Uo4p86xXF7SNTUHN65TjApIGk2kXtdZeN5QlAWNj7jCcf/ePYJOdDFk\\n88lIYS1aaSHza5Hfa9uW3d0d1s06U1IkWJDdg3JxJZWDMbRdx6gcUdfrXA32WrAMa9avVVmUNG2N\\nsZkakj0+nbVoa4T3WDc54OkBFNWvef/7+plkjIlxVQ4Aq+DDleffOZurvTQoALV1k+djcp/VqxWT\\n6Q7OFrS+xYcgoB5Z9Qx0AltI29o4SyKgrYbI4MyUMnIl5ffdI5/J2rtKy5z7YH+f+XzBfLmiKhyu\\nrDg7Pwdref3VO9TLJc4EymnFuEz89Of/jm86IoFZ6didTlA68vhX/8j8wS8oCsf55Zxq5xav/sEP\\ncLakGE9564//mtXFCdVkD/KYLfZm4FuVnoRAeQ51CiyPfsXTuz/ltTu3cdVIqmClUNqilEErWK1r\\nlqs1ZTWi62Rur40lKVgtV9jZbEMD2xJokMB8FfhD7lL0fcJhV1Y5QbsSNj/P9vL5giV8ioC56iLK\\nFcTQyAJmlKC1AsuPPuC0oalXoITPFmOk7brBTd1myLgqBF6NhtZnmL412JRo2pa7T97j+2/+KR1q\\nK1hqtIrPbJo5A+mDYF+i54d3s8ifZWm/+t7jqg28/2TB/qTgzWtTzlctJ/P6d9YG7W+Yg0nB6eJ3\\nCfZ59usbMFhPQbJKDeIE8mHy35blg3ehXcqcTYEryiGghSDVfIiCwkz5871M3KBilYNT6gNYkjPr\\nq8Ltc95wAoWT2XXSylYKsexCsmhjpRvTc417kMVWzkxfVW5amXI4aymqkrbrRPi7KgUYA4O+rdIK\\n7ZzMO61lvVihFIzGEzrf5MABaGhCy3h3SiRRjceQAr7z6KRgMYfQUpUVKXqaGGiaJm/0m6Q0bqCM\\nsgZ9N8gotFXQynuydlNxC/p4s4ZN06CNGDvH0A2JCmQheiV7id4KlsDAC415vmiMGgKo7hG3W0mN\\nQmELAd3J/mFofUe36gRg1XppywZJjFarJcVoRLfImq8qI2e3kiu5FxhcUqQxkDIoKnfH8hXVW3gO\\nYAArNW0ndI3CoFKAZEEbdg8PGU3GTIuKuNPgfWCxmLN+usDPTxnvXePp2TkPjx5yMR5z/fo1isIx\\nHTnKynHyeIEpTrj8zd/z/r277F+7SYqaw9e/QTndH8Qy1Nbe2fsMSzJm6C4f8fT+L9ipFKORZbW6\\nYHdnh50bN1g3NQFFChFrRc84YahrLzP/JH6ore/AiFyiHlR+hFsv7dteTRipbp+NkIM/KsM5P8t+\\n+CqPF84wlynS+G5ApiYE2VYoTaVEkLhtO7EcSpKtF8ZxMN1lVmaPwNxGq5xjVJa4rPBhjYEQ8U2L\\nRrFsFlJdDLMOPVSySm+QVNB/fasC6b+iYBMuN5vvF+UQfllHAk6XLb95MsdoxTs3Z+yNi5fzuz8H\\ngqzM88CPc2F53rFpcX30o//6J/3slf9vpzv9nENt/t3Latlee9gYCmNwTgJne/GI+cN3KQoBywQf\\nWK/XAuDwYiTcuxH0FVcv6divWUxJxAViGiy3Yg6kKHG96GebSqncXpRA0HVd1oSV3+NcQWEL4WJq\\nczVYpl5bdus+TlfXZIDgG4jRo1JkMhmJwADgCoc2CqXSEPxBhOhHzrKYL2TOFiSYGGOwLv9sVVCN\\nspyjNtiihMLiJlNWF5fEzKMsCkfngwTlDSpEzi0l4bM6Q1GWdN5nHmsh4vBmw1m1VmdUbN8hMBjF\\nABpcnp/LPVgKDYSYCG2gLMocFCVB6ELY4pCaTWWUaUI9OFGpLJhvzBYKtFcqAoIk4qSM9s2/J6TI\\ncjlnNJ1IYFTyc0oL+lZplV1TsnOS0bm9mvCRXKlKxUy+m+P/T96bPUl2JOd+v1jOkkvt3Y0GGg3M\\nYIZDDkkjda9kui8ymUky04NMf67erplMD6JMxktxGXJIznCwNAYN9FJ7bmeJRQ8ecc7J6g0YYoaX\\nZABlXZWVlZknTkS4++eff57qNeXnnJ2Xe2aLkuViznxWE/EoYLFYcnx0wmx5RNf3XDz/irLUfPDo\\nER88eEi/XXHv7JiuF9Whp0+/5na95eX5FUpZ1psNh0eH9N2WHz5+zEwHHhzXPP3b/5vzL3+R5my6\\nVzMjVhxA37XcPv17SuUBz6yyzGc1ITh6Jw5hbpvWtD2tC4R03dZaDg6XzOoiCfxr2s0F/W6VeCqM\\nuc4BImew3uO5/eq5ddeR/F2Pd9dhGiMyST6AUVhtKJUhOM/O92hriBqqQhO0ojIlKsLNak0Xfcq7\\npJZfPkgPNOSmlNbSup6gJYez7Va40GGUCDK7IYRPWpsqkPsZJERoMpJLHwODS/JKrPJf7/Ah8s31\\njrrQ3D+sOV2WvLhtvnV+8/sap8uKy/W7mbHfdtG+zmhOCTTkHBjJgxwKsydGI5nRqbG0A7lHp/IR\\njQ4966d/hy7EWEYfUYWisGV6X/B9UnohOWRR1nSIYYRFUUniUXRXx48exwgzPTKyYYXwYW2JUVFq\\n9CblByRImDCShGI2PoMb+Pp5jTEQQj6Ahd0r4utFakgNvo/DpijLEud7jNL0TUOz26IC0nS6D/i+\\nF7GFohyQIAl4pA7PVCVGQ0ScX2nWXOE9qXvF9JDVgzHrug4dYzLmMp/GimKPVWZAplAdRheY1GYs\\nqog2htKWXN/esFwuic5RllKC4VJD5zw/JpWcaMWEgCN56dyPMyJnlgi/pwgl9ZSyVqfOJSo1iUgi\\nCFEUmaIHHz2u65ktDmh2a4F3vbAmsgxeViVqdk2K3uX1fIokxYny4szs7QVZSTobzUgS0leoHmbL\\nJcYWhOC4On/Jer2inM9YHpywurri7PiQECLfXF8wq2u6TmDjUoum8K8+/Uxk/pqGuq4x2tC2cHx8\\nwNOnBmVLSJFdPkU1yZFLn6+9+RJciy4K6tmC5XzGZnPLuu3QxpACcUJwqVVdQVlIpK9QlKVhtd4J\\nTB4Dfd/RN2uKw4UY0RRt5sAnp2FG43nHWCZZyjFRMe6X75Pb8c/OYYaYPSupySyz4SMSNJgErax2\\nDQ7JORRKPEsf/BDC+hAojJFLDRGlzYR9KAdG2zVsmzWL+jg19mXIKxk1eiUwYuxDUfsAMeSkboJL\\nfoe5ye9j7DrPl+cblnXBe0c1Z8vI85uG5o427XS8qZD3uw6jFQezgk+fr976vH+uh3f37/MmHe5h\\nNpBKNCd1gmEz2pANZmn1wJAtNNx++ff4ZpVgW3HkMroxFReQQy9M3l0EtmOCFvMmFkJLFluX0gql\\n7HCw5NeU54nSUMyGUS50gGlTSmYwoAExmjrBy68QEcaXGJyKrCQj30diIeoyPkRMKQ2dbVHgnUfH\\nQAyRs8MllTVYM6PvW3zn2G13LA+WbDdrfARdiPHKxlByfIakro41cpgHFCrqJJTQS7pFi4qRSMbJ\\nh+27jlTTnyIwpEMLSlR7bIVP6zkzUJWPPH/+HBbzIWKX7iZhNFQqs6OzTyx5TJtKYCBBtUbET/Ah\\nOSYKpc14u2PuriRiFZAK9pW8sEURnaLZbTg8PqVrNgP8qjSpm4rUo6IjOkqkm8+zkA72qW7w9BRK\\nWd0hqnOuJwRBOY5OH0h5z26dPyz1bIY1Ct82fPPsKY8ff0RpNKvtiupgyaZppadlWXFzfcPp2Smz\\nukYRubmVVnUoTVlVbJmzWB6xvviaan5IUc+miwytFLvbF4TdJcuDJZYet12xC5LT1kkoI/YurXsx\\n9mVZCpKhIl3Xst12IlqfGMLBeXzfUSolaQnlR9QIGNjwk9xl/hoyb28wjt+n0XzX67zTYLrgJf+C\\n2vOQ0aIr67wTDyIIZipF1amom8Q+TDekdz21FeYsVrPpGhSKwlh8lEhq1dxyMDvN7uAgpSQ98bxA\\nswHpmTlkCGDsKA1DJHDnWr7rxO5FQr/h+E1fY9M6Pnux5nhe8PhszqZ1vLxt6f23J+J81/c8npes\\nm1485O/h9d468imSoXV5gxGOGe4vKbocmbGFTsxYoykLS9hd89Wv/hztd0LOcBEMQxeNgDAg9UQK\\njeTVQypaV9lMJ9gsDtt1ZA+nQvyQQ5b0mLxGGP4mt7G6awRDjmh8EC/Ze0gEltfO7ZBekPxXrk2W\\nX0mHjOgDxgjD1KSax+zklkVBXZSYCN45rl6+oPeearkUQ4amKA2mKIWQEkkqOREdpIelMxGsEHky\\nJGqthRhQlZSYtE2H9w5pfO1RzmGcpapqhobTvcMWYlwNiia2oJLakjK0uy2Nd3z04D5t06Axg35u\\njpxHRi7J5YiDscz3QVScDERPl64ppoglKyuFEClLi3MeZRTRi8NDMsAxRpETDA6loKpntM0Oa4tk\\noEVPO/hAZSuC96mzSRwQB2MkCs59qwdFsrTYx5x4RFvLweGxiPn7jqvLC+azGb1zKKOoMNxcXdLu\\ndhyd3adxsv5mxkJwHB8sUFa0hGfvPWCxXAh8rxTb3Y7jk2O+/PJLfvazFR98+GN2z37O0y+/AFty\\n9OBjDs4+oJ4t0cawvvyasrvm+Khmd7Wi2a6lRreqQMHBcpFEKRRFVnRKsKpzXcplCmzeukDXSwQa\\nifRux3IoHZymWwagBa2UgNJpK34bBsrrAoY3nfPfxgZU9s2ZyncazKPZgnUj7LjgPd6oRFcG33US\\n2mtN1CO81MeAKSwqiS1nLyEQ2fQthTb024ags0TWeHqum1tiDBOMWw2RZlaKyGH54MgzjSTHQ3Y6\\nplP0bSbtdxnmv+2519ue213P6bLih/eXrJqel6tmrwXN92XIThYlX11uv9Xn+ueO4dUGA6D2fptQ\\nmkldl9x7m5ynTPYxynP+qz+n21wxq6vhQDPW4vp+IOSIURRkIyKRp3P9ZC2I0EaYNHrOB7SQWkw6\\nVGX1+eAxNsmhpb8Yoth0TTpFF6IfGwfySYwBlYyTNNIdr32aU5K3VoMaSs556oRlhSB9Ma0x6fm5\\nXMNgbUFpK7SHm4sXXF68wEWolktsWaHKgsJL9Lbb7gTOQaUuJXogWfjeS6Wl8xhbAF5kz0LAFJre\\n9QlalhrDwlp87+lMT1UXdE1LVZdYI9cqij0G3Tu8EihUx8D5xZVEMDrDpUkcIEWp3js0OkWCCXlK\\n9dzWWoGpC0MIck+8kwb0UWUHiAEaRyt8BGVHR8Rom9SRRCmoLAtikF6Z9WyBdyLdaa3FFIaYoH3X\\nS7nLiDrESZ5YWocNCkDZOCR/S1vL4uCQoijZ7bbs1tdUVcXN7ZqyLInBobXFVhWtD1BWaBVxfUOp\\nFY8+/JBPnzyhjOB14PTsVNqaeXFgtk1PXVWsVrfEAKawnFQ9bdMyq0t8VGxefM7m5RPpLlPVLOcz\\njh6cYVVke30B1lAvFlKvqjPiEynLGpRUTmRxfm7CqQAAIABJREFUjxhJcLSid9C7QJcQkBAVzeoa\\n3o8TNbfkpKrBTU7ygiTuwLi39k7jyB1gdn8Mkf1vcIYfzAoeHtVv/P07DeYffvAJv/jmC1bdTrDq\\nINTzEMXz1Okz5T3fB5deWHqmeTIMhcACSmOVoQ/iPdoiFQxH0X7cdTuM1hLZ5gWmMt49Jodz26QB\\n745jGD8VLcjHcfiOsOwI7X6/8ONvMkKE81XL5brlLBnOm23H5brDfU+U2mVtcT68Fvr9NtcwZSRO\\nH9v7mSkBLruV4++m3+yZT5UhWpLuqEkMWYPfXrJbXWKNSvlGWWtt2wpRzahE9pE6xhADubcq2IEI\\nRBCDo6NEL2SmZhTptvypsji6LSyouFemMswBYSglkHIH0VyWhrwSclitUUFJdHgHNRjaUcaJ4ZzO\\naZo/H6RFlk+ts5yTKNCkchjft1w8f86zFy9FwGC5oJjNpAwiaTY3fU8wVmDrXtqNCSs9CcB7cQTK\\nsqTZtVQzIfYYpWmalr7t8TGAT05FQp+CD3RNJ7kxZbAq4poOXRgCkW63lRixrCkKy9XFOQeHc3zf\\nD+eF99J/NABFruUMAZK2bEyZQIn6AzGaRPaJo+DEZO4CpAg84roeY6AsSnwUSF5pUHGU+uv7Hucc\\nB0cneNeyWW+wtqQoLTF6vBeDkQX4UaB0pKzLQTQhRC+pKT0ad6U0s/kBxlp22zWb1XVSb1JsNhvm\\n8zmzes7LFy+YLTU6ON57+D7nF9c8e3nORx99iO86jk7OaH/xTyyWNVFFqsLSNju6tiMEuR9FYQnR\\nU5YF89kcraCalVR1gTUFZSXGwRjDYlFDDNjY45qGclZDUaBtgVYK5z2994Cm60UbPIE1srZ9JOnN\\nD4o/IfULRSk2txcE12BUMeYv74hZTFMQuSbzbrCT24C9bfwmxvJ4XnD/sH5r/fk7WbK/ePaEP378\\ne5wtj0Say1qBbBBGX/bgUenCtMYT8UTmRcWymqXO6nqog1tvNigUZdLylO4HIZEbPFfbi+RBjkng\\noclqYs5OobxpTKkGKC/ytnP+uxiB72N8HxFriPBy1Q45xk/eO+DBYS0apv/McbqouHyNDN53mqeY\\nFHomUdY+YzahA3eM5bgIc7Ji/+8yO3ZsOJ6ajivF9uIrgnNDRKqUxvVJu5VA57rhvfqUrxLEQ4TV\\nbSEHmPRXFN3SLGguJB47+Rw6OWbSFFpqF1MEo9JXimIiUl/ZuY5d20jN5/6kCaxl7GQu4nBgQDpM\\nslzYdA5VZn/q4bkZSsxwcwiSk7NlweHxIQcnx9jZDIzBRREj6GLAlCXG2OGQikn4PIQ4sH3FAREm\\nu2s6UdTpulQqY4X9a4sU0cnGjD4QvCBHzWaHKQqqokC1LVaXHCwPWcwWbG+uWN9ecnw4J/ae9XqD\\nVmbgNkjUm1qoBSnzIcTk8IggRYx+uA/GCk8iO9aQf5UndRKzKE1ZlJCEWHJO1KfSoEww6l3LfLnE\\nBzeoAZVVgbagrRCTlFZ7aIYPjrbrhtKbGCVini8OOT69Twg915cvaXfb9BlFhs8WBUdHR/RNQxUj\\nNy/OsUoxPzjCmQW//PRzur6nqCq67ZqyqohRU1eWstTM64LFvMIYTV2LuDoU2KIk4Om9QyvFvbMT\\nzk6POF6UzEvF6aIiNhtqFek2K3bbNbouKaqKqBQuRsSN0gNDVq5L1okLkT4Iwzhk7WUy5qdSl50N\\n6+sXKDPyBBRZ8jQjSsMRMN6ryf5I4eWrEOK3HG/KgZ4tK+4fznhyvn1r/fk7Debl5oZ/ePoZv3f/\\nMY+OTjHoBJMKkzUzXrUSdRVJMCv64Nl1HZ1zQt9PnnQbHKouQEVpTJ2iuBDl5394+rdcbi+EIJCw\\nbA3DwSmRRiIQ5InLM6/yhKpXJvR1c/y7pid/XzBviPDituWz5yu0gh8lw2l/Q8NZWk1ViGTWdLxr\\nfsZDfJSwQ42keU3ca8f1poWekQLR+U+owTTxr+TxLNxsdGacOg7OPhQtzDgyJoFB6FzOKzUYLJ8g\\nIqU0tihTFOyT3ukYXUQvXjlJXMOaLK02RpN793PILEzc5HRYhrTWu75P4tsinh4QIo+C1HoqnxHq\\njXMfIYmJt8SkTiOHdBz+znlP1/dc3VzTKzB1BUUBWlPYkqIo6RtHcFHq5YJEbjHEobZRLifi43hp\\nznm6TpyTwhSpFtKS83+QUi9ejPfAUA2Rru0pq4rri2s+/+Uvubm85PLFOc9eXLJar2naHlMVEkFm\\naDZmwtVIPkTBbFYPsJ7zHcZaQhQhAR9FnD7fhzjMGnvrIkN/u7al7fvB4clD53POWFzXgLYYW6OM\\nHuB5kYPTKaIKwz3rOkfwiuAhBo3WlsXymMOjezjnuLx4zm67QuuILQROz2zsEAOb9QpHpDo4YH29\\nYn2z4tnTp3z96c+5f3KINYZFPWd1e8OH790D7/C7HX3XDq3Rcg7Te0dRaA4PDjg8PMBqcTYWiznL\\nuibsthjnUH0DTUO3XtNsNsQQKbQ0Eleph+goOpDJcYpMevMhJhUjcKm3KcTB4ZMcu2G3OpdUBDmH\\nmR3r8TYNZ8V4QAxnwjS0/D7Ob6UU9w4qjhclX5yv6Sf38XXjnQZTK83Nbs2vXvyax2fv8/DwlMoU\\nlEqxKAp8Jx6nClLs69puL4rI3RXavksNRxSuF63Z3A5GIDIRdC9NzUl9mspPRh8zG82R6ZjjlVHG\\naUIZSePtUea/9uFC5NlNw2cvViglEed7h/V3Fnc/XZRcbaTh97cx6kPUj0CgoxrTJE8z+DtiNOVL\\njKgajuA4/M2dLMUQIQwSWkycpvwexlAf3WN+70N8gka925e401rjnQiIhwQX1nWdXhtc36GMqMrY\\nUpiixlpsYSB4QpQ6wZBaON39nNOhlRp+nSPW3LGhKArQ0PRCdjFJncf5XAaVT+xxjd9du5mV6HK0\\nGl+tsw0hiDxdBFNVUNUU8wWmKACVWk1tBYKMCtdKFw4Vki5qelMxmuIUSDlqxIWAjwiESUQbS1FY\\n6rqk9z6tn0RoSo5w3wsc3nYt27bFFZpYl6iy4Hq9xlSW2ekJhydnXJ1fEZMM3RDdKolaPCJ6orWw\\n77tOoNvgA9umAS36tzpxLIb2ZGo8i7KUndIxdUARFqfkGBUagykStJq5GsmIbTdbjo6OkpqQpIvq\\nWiRAtVaDYc/RpOSaLcuDE46O7+Od4+bqgs16jXceoriSJuWfp23PbFFIvtdojt67x/LgAGLkYF7w\\nk9/7EQa4vLrEo3BmxrYD13Zcn1+ggse7PkGxktcUElNEa4mOpRF6JIaeajZnVhZsV7eE4Oi6ZlzL\\nIQpEHiQ/KSL4bmhpJ+L0El3GmBdogmeTE5elBFVyMvqukTMhq/2M1vDO5p8u/Nw7VQ9sl/2n/2aH\\nvFKK945qDuqCJ+ebgRfythPwnQbTe49Vms12w8+ffsrD43s8Or0vbCbvwRp6J8woU5ZoK6IEuW7K\\nKC1RKZm6LfmfoiiGg0/Cek/nOj689yEHsyNZdMOEyqEspQVxYFnlAzRffPru1Yl57aO/2zGFKb/v\\n4byUnvzq2S0hRn5wf8kHJzPKt7C98tBKEt1X6+/WJDqrLWlSxK/1AJcO3dSTED6DIdUZNBgMoGIs\\n7lZkqGa8k3F8MneXcl4jj/7gPxHVCE/GwAivJYZeRh3KohwIQE3bUlQzlst5EsQWdmTf9wMlviwL\\n2raR2r80psZYPoecliEEgTr7LkVJCltYSlMIUckUQ02xMTZptObPKyxa0oGOHnPo03sx/U4pJfCW\\nzWLkemSSOk/oPdFFYbv2XvKOu4bgchcNn1qcJUZ66quplJBi8rsNYg7pGpumFfmzvse5XnKaRmoA\\nfYruci9PiVIlMvAhMj865uj0FDubcfbwAfPFHBdhfnjAcnHI+dU1TdOKAIDSUgNOdkZkPnZNC8ZQ\\nlCW7psGYgqPDJUTJV9/ebhAGcJn+hsFo5sNYGyUqNAktK4pRAEGlg0mlVlzbzZauaSirmtxEPEY/\\n5EnH9RiTc1RyeHzG4fEZfd9yef6C3XaLS2IYMSp8AOeypOIouFCWqWY4RGb1jIPZHFuWrFcbtNHU\\n1hD6hugdNgZOjg6ZLY9o+4AKcHt+Tt+1ojM8qwYG+HI5HzRxtdZYU1KYitIUrK+uJPccUzvFGInO\\n0Ts31BeLwWT43gdBH/oE4ceQ4HgYnNckdkREDQ5I126Efcx+E2kysqTymsv3QlbR9DiKqLvB5nc+\\nWxXw/nFNXRienK/fWBlwd7zzRC1SDqdPPfg+/+YJR7MlH57cp21bmZAU0ZRaimFLpYaQPATJDzjv\\npGOJtmgUtS1QUfJKfVpIB/UxP330H7CmHBhQ42IfDymtxihHAaPiZ2bPKu7O32BO7/zidw3L/jaH\\nD5EXtw2fPl/R9oEf3Fvy6HTOrDRv/JvjecmmcbjwboLTFIKN6efc9UPYq2IojdKJ1cqANojdiySL\\nIEYj3zm1/95q771yRBmHV8ibNoqVQRtLtTwRgkhSVLnbYWSUTTODvNzR8TFVrbm5vZY13nqCG9s0\\nFVa6cmhtUEjhepZm2/uwaWRi2ZjLCsk796nAX1HbkrospaYyKQlFstxXpO9cgvJGaEhsV454ZNFn\\nElyZXkvt1Y9KeUPwufG7lIh0zmOriqjHbilTstYeyzdOiDMD3JpKM0KGv2G3a3F9TAd8LYbRR1BJ\\n8ATplaszX6Ht8Z2nd4H5gdQObm7XeCK32408v5d+u70bI1UfpHSj9x4fxZnZtS1lXaNQ3Nzesl5v\\nuLq6ZT6X/p99245Rpt4nUGXHQGtNPatEFCMpJo1zAs4FjLbE6MiSiaAJQdpWlWVBURZAwBYVRyf3\\nOTg6oW12XJ4/Z7fdCLpBHNdukPuiU2TbdY6ulXpFhdx7751o2i4W3Lx8zqwIHC2PWK137HY9Xe/Y\\nucjV5SW765f85Ec/QkXF7eU1NxfXBO+whUUrj/cdXddjtKIorLSC0+Jg/vrJ57gYJpBqyoWn+doX\\n895f8NP9FSco0SDMMRi1mM4KlSLWmPgp4z4fjOZrtlY+IxiyohmU+s1SXFrB47MFWimenK9fkSJ9\\n2yn47gjTeaqikAkJkW3b8Iunn2JMyY8efTIy2nLiPDXMBfEEqqIS79xIU1sdxcA55weIRSfo4/G9\\njzk9OMUHqZ/KDNyMdxstVHxttHwPe30zszrEPjT7/eQN/zWMvPhClK4ov3p+y7Z1fHAy5wf3lxy8\\npoH1ybLi6jvkLvPMyrEhBrO0RvpQGoM1KfIcYLDxD4dNlVu05dzn8Mqvuyb5d39vZJUcQR+2t+d0\\n60vpo5h+n3MsuV0XpLZbSnF2dsq9e2fsdmt22wajbTIOSRhg8llCiAKduoC11VicPmHHMtn0Kh3O\\nuWdj3h/CKBSExSeCSb6SQC6V0IKqWENU6SvPz/Be6QxLCju+dxTGJrJEHLp5iPMwleADoy2udyJs\\noKTzRjauMUb6VGuZOxHdnfHhX0kqpluZ2IzK4FyC4JKDIlGJ3Iu+62nalqqq8QR8FEm1aKRry2qz\\n4d6jh9x/8JC6riai7RohFkvkliFulEpCCZ7OtSij6b3j4PCAxWIBOiZyiX5laeV7WFVCjgGp2dWJ\\nZJW1aAVWVSJraDRds6OezUVowMeBMV3VNaf3HjKbz1mvbrg8f06ze315Vr6V8hXwLkguOQRi0EmH\\nFYwtiUZKn3brGwoDhwcV9+6f8fCjH3Gz83zx5Au8a/jpH/4hTllmR6c0nWNZWdrdjpvrG5SyVGVF\\nCI6yqqhnNbUtMX3glz//uTQgL0sconJkU647REnHEaQawShSaUnSB4rTuoPRsYsxOxVqSIP4CFFF\\nFou5yB6K/uMAyU5Rpbz/BjZ4fmQv4syI1HcPdoxWfHxvSedDKqF73Wu8+XXfWVay2m0hRqpSOgBU\\nhaHte/7+m8/5g4c/4Mfvf8wvvvkCqzWt64WZpw0ZDbxdr3DBJSq4wkWfDlSh9rsYk0al5oPTR5Oa\\ntCHGTM+XQ9goUmNTkc4beyYq2bz5ML173f9+7OYwQoSrTcfVpmNZW86WFe8d1lxuWq43HbPS4n1g\\n27pvFV3C6GRm42y15rCEk6LhRT+n6eQQ8yEmuHxyRxI8Jos9MN4gNWy5nJfeez+yoY4pb6dy2EXw\\nPS8++xuJ/jAoa6VZbxChgqIoEmRomc/mhODZrleEKFBtVlIcIo8YsFpKGBQRbaT7SIZkBfIUJ885\\nyX8x6ITKugykrijD7GXFl/H6Mxkpwh7ZJkd3gwG0dui4QUJujJHIfCDopOhUa0mTZKWcLFKts3EP\\nUk+ZI24x6Fn1xqbrSwIJGd4ePqzMd4zJKcnJpBgxVuN9Ry6hGa56gPLEOVZKEfuOGJQ40M6JgUqk\\nnb6XNeHDaGi9TxFfQMozJlG39yPEGEKgqmoOl0v6riEGn8g/QuCJjNGOMSa1iJOoPHhPVdWEsBnP\\nnzgqhiklTaL7fks9vz84+UU1o6gP6LqW1e0lruulo0kuSSKgkJKTITUwGSHkfKgarqftIkVhB3h3\\nVlYsZku0jRS2orIFu9tLZtozrysODpYU9Zwvnj3l9vw5Z6cPWG03aN2B1pSliPRrJbn4ebXArdd8\\n8cXnFAUUVQ2pHKgHCmvSHtcEbXDBMa9K+q5LTpLszxACYW9eZRdnh1WWsUqpeXEuCmvpQkQNlM6M\\nEt7Z64NxHIl1woQIaSlKiid/+21HaTWPzxZcbzou1q28b17H+b3fcQ6+02Bqo2mcsPt0jIReXrAo\\nSv7u60/54Ow9fvLoE3719Re0TjoSdLKbOahqWiP90AJJwDoGrJLcgOQ+BZ44rI95cPhQciB7Bm/M\\n40j7II0OgdxoWivJ/eRaOTGc+ZjOL5UNaz7wJjfiNwzr/7WNdeNYN466MJwuS3788JBZYXh69S17\\nXsbMVh6jeLkn8std31NojTce6QU/NtmNalStHHRY765LFYeSlOmvR7KXPJors/I6d82a7c0L2eCA\\n73thcJukPBVEJKB3js51UtpkJA8lsmgM0nsx5mL5dAykdaKNbKxc6pBbeIkMnRs+rYAwce9gHDpv\\npHUWGA+KTGwb82x68MpD8CmCkvpSUwghx6YQXoyafM4hL2Ut290uwamkkog+zyJ974e9FAFjLZrc\\n7UM6B7m+w/hU0J9v/WBE9jU8c05xWq+cowwyimfSalGiQBScT3WbsvdJ+WbJr+XWWQGtbbo3hizW\\noKb7XEuZj7Wa2aymLEu0inTNRrSBp2zHuO+aFUUh+VstrQl3/Y7gU5PsRMIKSeVH0kpeOBdW433P\\n6f2HxBBYr1d07U0qY5LPNJ0TYiQqT0S6twSXZX+m61/EFfLa10ozn83AO14+f86j997nZrvlaDGD\\nMnJxdcXNzS3KKJaHRzgXuHr5gt3F17x37xRbVtSLA/7pH/+WHxwfyfrAc3h4jAngtluefPEEozVt\\nH6iMrBsXA7PZjLZrMWWFMnJml+VCJBy1oEfbtpca+XQBWul0ZjMYz6EkSClIIjS5ftr1m+RclYOo\\nxN3jIKq8BTOmO91P8gQVJ0bzW4xZYfjwbM7LVcvNph8c84n9fTsWm8a3Iv24hIfXpdTkdFFyG0Yb\\nPn35lJc3l/z+B59QV7MBXjqcLShjZtmFVFgsXy44fEwHahBI6KN7P8SabL/VKGelRiasEH8YPLxp\\nXzVx2KdXPHBoX/Fc/j2Ppvd8fbXj1+cbSqs5XVQDXPuu9aJemUBpDt4Ew44jAjGVEOmxa0T6Ej3g\\nKZU89ZUcvMzXvR+y70heqEqOUP4YMVItTpgfPsQ7P3Szd95JbZhztH1Plxic6/UG5zxN17HdNinf\\nJlFLyHuT3LdRDSSHkNaoS63BnHfC3vRuchKrIT5+U778lZFOmLyelYKhBZIxoNUQ3YpBNFgtQvM2\\npTGsNQOs6HOxOKmwP2Q5uOSwKGFW+4ioc5EMuAJbFWMpzuQG7BnCdD9k6keHM8QAOgPl2WEQwpMP\\no1Ra17tErlCJuSxzG3xEp85HOuUIlVJjPm2STwXRRD08mLNY1CzmNYtZhVUB3zboENnttgkWTrDx\\nxAkzxkjtJaJ8M9aQS+cSY+yonJTyxCiFKWoOj++LYTaWq4sXdM1W4PU0JyGGwcjKG4ujMkDjef6G\\nWqsoeafJGlJa0XYdrXMcHB4SteL0wftsNzvKsmaxPCQEx+OPfohSll/84h9R9Nw/PWA+q3Fdw9zC\\n4eExlxdXIs5QVhRKxO1vL865ur0Ba1ksD9FFAVbmZOc9ZT1PurAaFSNd19G2Pa3zdA422x1G28GR\\nGcoChzKuxFYPUfrAZlRES13zrtkQgx/39GhmJ9ti3+lkWHcZTxy2/7cay9ry+GzB11c7rlOt+WAs\\n9yCsd1vMd0aYEakxanY7lks7yN5FpehSLdiTi2/YupY/efx7/MPTz7jardhsNtTJkzcKohfx9c67\\n/FGFqu49RlsenjyU6C+5DdKDUAEjESGt3TsiBrm0QRPUqFIziSmH93vdqTwlPfx7GoczoVK/vG1Y\\nzgpOFyUPj2putj3X247+rvReFF815x/zERSikEmi1lgLVdyidIlHD/cFIs4rqVVDyeF0d7r38hnD\\ng8O9z3BtwtjkzipFVBpdlJIz1CPT0Xv5TFrrRLGXLdj1PTFIdBGIlLbYc7b2jdwYCkQlayR702Jk\\n4wBhjs+fxpvjdUx+DfvHATrlHYeAaEKmyBBnWRYyn+m9jVEElQ16kMPJj5G3R+oqfVI/kujNJaQg\\nl20ECmsJCDO42WzQpR0dlRwkjAHm3uXkKFy6fWR1Lj3mj3uJGp3zCVrVCeJNwYPPJUA65VJDckqS\\n9KCR0oh8tIJca9f1aC3m3toZ/W4LwRGcIypFiEIqUum18zzmvGUIwn62WrNtW6pqhtYR51J06AXB\\nKssSY2fM5nOCD9zcXOH6juXhKaYo8b107ojBT07g6T0fnYp8T40ZRdrzvcr3OM97CJ5ZXUmLrZtr\\njhcHbIxht+s4v1lzcvoAp2surq65d3LCpmkxVjrXlGXJbFbzg09+j7/66z/n3gcPOTw8Au9p2oYn\\nXz7h/v17LBe19CQ2ls57keLrO5wSiN6ABDjIvtrsGprOo3SBNgV96o+cc7FRS8mS1MGqlONU5D60\\nZVmgiDgnuc+s+ZEwErnwCSdg2Cd3jvC7W2rPrr7mLD9dlJwdVHx5saHp/eCvTLbiq8byLYbznRFm\\n00q3823Xcr1eMasqlkXFYVmyqCpUKiG5WF3z86ef8vsffMzj04e03rFxPZ3zkquIEd/7VNScmHDO\\n0ffSS/Or8y/ZtGuyqPIr1k2pVEycuxVkRtt45fnAHWbi1VP520Td/yIjTryz3/bQCo4WJZeblgis\\ndj1Pzjc8Od+gteKH9w94fDrnoLZT53dvPgcvMEUJLgScjzRqjkdyJoVWVBZqGqy+Uy+bwsqcw1CT\\n677rIAnCoIbPPiXt5cNwmj+bbpxMasEHrDGpcbRBmZx/iUMfRWPGrhfT14GxdGYkVsVXnqOSN41S\\nk2vIX0NPhuFL7I08LhGVH94DJLfmvRddXC8ybVIkL3CmJ9D1Ig4iIteJaBNDMlJCKolxFBOQtx3L\\nRHwMUj7gA/ViTgxCOslnSI6KXhstJ7Zz1sidqt3ESNrjcSg3CJGhb2QIY0u0VxjNiQUrBfjJuVAq\\nnQxyvHZdTwxijNdNS4cmGjsQVgBsaqQtak6i2hNjSPCixbueGAPGaBbVbGAm17MlJ2fvcXRyj4jn\\n8vIFze4W17dorWh3G+r5YlzQvGF+8l6ZGMu76yLD2TbV7RJTHaZS+M5zeXFBUVU8fPxDNtstF8++\\nZlYXdKsrHp4dUZUlfddibYHShgfvvYfSmma3kbxkI43D6/mSF8+ecnB0AIAjpbe0sJhJf++8B23Y\\ntR2Ni7R9oHMS2ed1sG1beh8xxqZ9kR1qALlPPoZBlznHi03fEZRBaztAoTlHvmcT42DFsrs6zvPk\\n621DAQ+PZxwvSj5/uZ5Ifqr991L7VkEQzTefwe/uh0nKMQTP9VYgrVIZlpWlqmYcFjXBlJRFQes9\\nf/3kl/zRox8xryo+ff7V0MG8C27wuVyCzrwXSSvvHWVRMa8W+IBAf0MeJEcXMqY5LenWHYZDd9/d\\neGVW9mL4aR7zdT//Lsfdz/HbeO3p6x7OSrat2xNwB+hc4PlNw4vbhsNZwcmi4uHxjNudCMB3vR+m\\nUl5c8nBySCcIjJCafUuO2RpDNAfork8OTkDHRBxJketolPOWG5lze8iCVoMI+9AWKHp616JsuuEZ\\nns9bbeK1utzoGWk3t5wvqOta6ASJIesnUrrT+3J3bUzXy+vW0mv/PkWpUl8sc2CUEmUUICJlBvmA\\nz3Ct955ioguqgL5zuASD+aTUI4Xd06hGDQbSh/HCXCqpMMlpECZqZDaf0zatRIu9z68kqMBwiOVr\\nFMMuxKk4wNj5vZUmdfeIg9XNDsywFBW44IbXGrR2EeOy3e0oC2lCPCXyeQImCGS7Xm0pyoJd6zk6\\nmBG7DlsUWCUSGWVZ0nUdkUhVVPSuRxtNaSyXV1fYesZ8NuPls2dQ1Byd3qPvW3a7NW2zo+8lCuud\\nJx+2vWuZm2MplZmU/0zvu1JqOG6STUCFCDoOOdm99QGpJ6gleEePx6nAo48/om87trfXuG6LMoqy\\nsLx8+jVKay6uV/z+j35M3zsurq44WGxBGdbrFdpIjta1LVsHvm8oCukX2ncdKJXKSyLbpsF7z3w+\\nHxjNSlva3qGclJ1U1Yyua3EuSp2wC6gYKWxJpIeQFafGCw8IclCGSJ9EQpQ2RC/SlWJo70CwTOdt\\nIoqQ11Cez/Gd9vaZVvDh6YJI5IuXY9nI3aA0G97913i7DXinwZy+QDmr8dqwdo7VtqVoWvmAgFUK\\nW5TEwvC3X/+KP3z/E376/g/4qy9/KRJ4aSJzgn+EJSK7fsfnLz7lo/s/orKz4f1Uens1noVjRKKy\\nv57/u3OhMTH90iH1usn9r2V830byXeOg7CRkAAAgAElEQVR0WfLsevfG38cIN9uem21PYRTH84oP\\nT+aEGLnZdax2/YiekIyZkp+yX6gAqw2Vv8FHTafm6Tqzq5iNTX5kPHTyfGg9gLBDHZ3O9Z46NXmO\\ngXZ7MzhXw1f+HHluFQnCl8eWyyV1WaLSga+1HnKUd43j2xypKeHlbc/J0ex002ZDmw9PpUBbjfMh\\nKdqEIbq2hQh+D2IGWuO6FmXsEHcpmY7xkCFKx5EkLDL9nDl/GIcDC3zvWF/fUC8POThccn1zI8ZM\\nI824J0ZgyvrViWGbCU5aZ6m8dG1DoZugPpmdHJLSzkDkikjdqgLlPTblC/OY3lu0wMry3pH5fMbt\\nes3RcklsXBI8tzRNk4TQC0L0FKXFKEXXd5iy4vDwBFvNOX7wPl3bcH35Qu5JJqk4L/M4pIYk1dC2\\na2azBbfueo/hPBrL7CCNJ5MLkcLHoTQmpAYU0mtVD5rafdcym8+Zzw+wSrFdr3n64gVFYTg4vceX\\nXz9n3TTM53MeP/6Q69trjDYcHi6xRc3zF88Ju1tODxfMy4JFNeerJ0/FkSgsplzQux7XNXhtMGWR\\ncsyemTK02w3rXcvyoKLrG6TLT0Zgurx66fpOcplBtIcLK2vUpdKYQdMr+UxN12LsHBK5LYZMBGV0\\nvhPnc8/RH5y1pAwW70SJk1EYxeOzBZvW8fymufPbibefbcOdB7LNeNN4p8G0JjWW9Z7tbocPntpW\\nQ8fs1nli6mASux30MK9n/PWXv+ST+4/4jz/4A/7y079n7XayoZLXTIy43OU+Yd6k3nmDkRxbaZKT\\n7/nixqgEuaHZmKqRSTfUjOXJesMk/1sed6OfeRIx2HZvbkg9Hb2PnK9aLlYti0pzNC+5/6Cmc4F1\\n42g6N5GB0wMxiwTR+XiAixHfZ7j5db7kBDVIPw8RZHpMSorUYDAzmWh3+4xud4NOAv9KJ5JXMlIh\\nRgz7ENiAR8QgqJpRBOdTfkxy7FmQPa+Y3BT4zoceEJi78zzS4V+5UPmHVyOSTHQRyTw/GEKjdYoK\\npcVeYW2CzlJpyyDhFofryw2v5eDOWRudck5ZXEEE0oMOWCPR1/xwmdiegv4YO0oEDvVzg4sT0+E2\\ndnfJU5SJOsakfFZKWEUmNaJBri/XXQ9zHKEYGhLnEpi7Bn+UV0MhrcWKMuXQQCFnjLVCUMl3QiGl\\nFroqqJdyDTeXL3Chp+/9MGcxG/v0maw1dF0/fI6u2XJ09gC7FWWrrhvne8rOjQOylc8fNeTDjRE1\\nns1mm1j/hrIq8SFwdHDI9uqK86tLjNY0zvPho0dUtuDm5paqKggBurZht2t4//1HdE3D+YvndNsV\\nh4s5L29uIURm1YyXL15w/96Sg8WS86tbjpcH9O0OaxVBy62NRNa7HXVVUbjAbtcK4mcg9F4Ysi5J\\nJPYOlEbHyK7dgYKZqlBKU5UFXaqzz3vA+8iu61ge3xcFpZjTB3mfpvlidCaHMhbFsK5Hw8kwx1Jw\\nAnWh+fB0zvmq5eo1jST2tq+aPDA4+6/Z43fGOw3mvJ7R9K1AQCmLsOtbyqKgQA4pH4SS3Xc9Smtu\\ntxtCCPzN5hc8Pn3Af/fJH/E3T37J5fpmCL+NsiyqGVVZU5qKHz/8Ccv6UAgkg1MRh0arQw0eMHUD\\nBk9DDcfgGE1OJnV4Ttxbv/8uxvSgOV1Wr19Mb30Bmb9tJ+3Xnt82HNUFR4uSRydzfIx413LbaQoV\\nkuybx4VAG5AuBm7CwFSTZRlzxjoZsrswLLl5uBjKQuWOJQoVAy+//DlKGyEXDRsrRSt5w6pRxSbn\\nCCU6UjRty3bX0PZOVFtSiYX3Hte5BF3ulzaQorfRs3uNx6smMOIkKssjxJjaF43Enul7CftQnJvM\\n9hTxdiWCH0YR++QQhVHLNIsRZHrC4JvHyQcZPqJJ72npupbCGg6PT3Bdy+16g/MeH5MyS4Ifczwq\\nO3I8YJTOnv/oFOXIKyQlGWIcpO6II1sBVCqxEfUcpVO+MeT5nczpHgwuD2eW8Ww2IwQhDUktY84b\\nQlHOMLZOpSM9u/UtMQocvN1tsbbYu0chRUC5aXafxRyUqJ/1sYPgmM8XrFa3wzXb1EXG+8QI9oHM\\nCY3pmkGaU5Q6OXkqUpZmWOMk7e3WS8eTTbPjow8/xnrHp599xsc/+oTdZk3sW2JZ8cNPPuHXXz7F\\n7W44WC4oFEStuH/vmMPlIevVCqMi19dr6qrm9PiYzWrFfFYnVaWAMkKMcy5w3azxXsg+UpqF5EXb\\njnldE7pWlJcScueTYP5qt6MoSmyS+Au99OUUhMTQ9x317GDMXef9GeN0JZCRyHE/D4fFGLUyIlOR\\nyNG84MFhzdPLLZv2TmegN43xEJr8/HbE6N0RpjYsyxl6LpqxbdeKtFjX0XUddVFhtMiNCUV/AgFF\\nzfOba46X9/jf/uP/wtdXFzw5/5q6mHGyPOVocczJ/EygFTXpgTmcM5NDZ7hINRYXq9FA7l34ayZm\\n7KGmXveqv/X85beB7n7bwxrFvLJ8ffVmFZK3jYh4zDpGblvHtvNcbTrOliX36sjxck5or2i259yG\\nBd6X9F7Te48jQy/TA5DBick/whQpUAOpwGiFTeUquazi4qufs7r4Cp1gScmjejIIpnJHnXRwC2QU\\nmc1nGC3F5NLdwTBP6jFKxQGaHVVzwp7BDdNa4elKunt7B3BjcOFkDidrTac8ok55OonKDKTD3jkH\\nMdI0HUVZyP7yUaLDmBuwJ4P5Bj3M163tkJxc+b2Up1RVhfKB7Vba72ljURiCEhGKIU0ZRxnAfMhE\\ncpSbnxLTPQkDcWds9zQ6sCEGTGHkgFbyOUjPtdakw1TQitxhZNA9TdcWo6gwWaO53WxTRAlVPaOs\\n5lhjcH1L327ptj1WKfrdlqKu6LtucJCMFcWnsijY7CT6ylvW+5BILtLjMsRIs10zWx5xu7ohKpmv\\n6APLwwVtuyMExXq724NoxVFSKC2lNk3XUxYlCsW8rtluNxTW0LYtO+coDpecHR9xeDDn6WefYetK\\n1iBS6tJ0PVeXVywPDmhjy/0HD/nyq6+IIfLw3j0ODo/4p8++4OHZIU0fePLrp/z0pz8ldg1X657Z\\n0SGz+YzeBZqmoayFlFOWEgV2vZCiqkLKforCsFrdUs9nSdEKtBJIeb1r2IaWGCJ1XVFYgXqjGZGY\\nerYkMKpQDZAs2eGbbqRsNMdTXtbReH4rIg+OapaV5cn5hvY1/XyJkjKaBkp3z2LhQ6hRmewN450G\\ns65rOtdhlCEUMLcW27X4RO9uXcesmnO6OONi/ZK2a7Gm4GB2wMnihPeOH/LHH/4J31x+xdlyycf3\\n/nv+4vOfs27WOOeZF0tKW6Uk7r4pk0hZDrExcTsyqlRafKO7oYYNPP37wSuN+x7374roM61Z+5c0\\nmqeLiptN94p24rtGzq9NA/MYI0EJUeh83XO1VRRmzdFsxmI+50FVsd71XG86Ou8mjZZfoWcNh9I0\\nB6lVFnbPZRRaIkwtJRXt5oKXT/6OiB6k3bSedH7IcGf64LmvZZbHq6uSGDzPX16grUFq8IyUJSTi\\nSUCK2JW86NCRIysYyRswrNk33tvJBU+h2IFRSiSGnJ8LQqqAPUMtRd+5mF7mI/N4pvDrHmyZJ1eN\\n923wy3Uya3E0nlVZETqJCspqBloiDlTExTj6Oncuc48ABeP8hTC50emP9BQOl/sk+bwOa23al+RT\\ncnCq8mEncHLqRpIic2NENq9rGhbzA2w1E+Pnerp2w6bdgVJUZYWN0tg6BC9dk7wnJgaoy0IqHakM\\nJg6fJ0SR4pTnyXx3vaOOQoZxiachTo2wXm/WtyjEmZtC2SJWLsISbe+FeOMcQWnKCO12w+LklN5o\\nivkBiohTitm9e5wsFhRlBUFakp2dHOGi5vzFC04P51Af8OLimg8fnhGJtEFxuWp5cfM1J6f3+Pjx\\nR+xub9htNnQo5lElZnTgYD5HG3GcFvM5z16+pLDSkUYh+sXrzZp6XjOrSwyIWE20uBAoraFxHk9k\\ntd1Q2BJjpUWeMLIVVbUQkliMAwy7vzQlnTBUDOx5WKMSmOwfxaNj4VZ8/vINAupxYhJeOXjGb4aT\\nKcZX1vd0vNNgNm0jEKkeL6YoCkyCk5x3tH3Lo9MP+eOP/pSvr79i22z4+vIpF7fnuOD5gw/+iAcn\\nH/Dps3/iaH7Kf/PRj/mrJ78AO2NWzvEhy0ZNXI0UeqjBUqq9w2CEoPNOSt/HV6PHIW+893dvjEe/\\n9zElA/xLDaVEaP3zl+u3Pu9dn3OM/oUZ60Ik9pJH612g6V1SzdlRFYZZafnobEHbB9atY930tC6g\\nhnuW7vPEWGajolRWCEGaRptcvK94/tUv6Lp2j8iTyS4+OIFxU8G0Qos8m+9BadquY7vd0radHLrp\\nYLfWpggwX2McN6oaBQyISWicHEklQxHja/faAD3x5iA0v6kn6/CKUUBLzksiz1yAGoeGxxl+VSoH\\n73I4xwRr6aEhIYPxzAeRRIl6YLj2rufyxQucBmV8agOl9zfQuBKG9ZKdiOn1hxBTUYEaykqmV6xS\\nVGxtkea+GKD4PGfa6NRJJd8LNTgM8r/CFCWz2YJyVlMslriuoWk29H07MKIzMkCUHp27ZgdFSdv1\\n2KIQIXwYIiYfpK7QxwzthwEJiCETK+Ta+65huTxgvboSixKjIHBNm5APRfSvnjQhys02Rt4HD9td\\ny6yoMKwogYVShGbH85cvOTg+pZrVGGO5fHGOj/K3bd/x/Nk3PHzvAfdOTnFKhOCrqsTakths+ZM/\\n/D3+8s/Puff+Y/x2hVKaTeeoqooYPb3rUabAWBFNUASM8hADVWFQMdAHT9sFfFCUpaUuS3QMeNcL\\naSFqnNEUSkHwuNbTup6iKAbH0NiSol5KeVFI8PwkyoxxDIz2jNvkzMnrsC4Mj07nXG9azldjp6U3\\nBkCTNTjUYU43pFKv3Z93x7sjzLKSImckSS/K9uIBKK0oEO/wZ1/+FT999Mc4J+2Nfnj/E94/+ZDH\\n9z/EKM1fffkX/D+//DP+r59X/M9//D/wJ49/xF89+UdWu0fMq+X4hvHOLJFh14nlV/mgEvdzX/9h\\nMjdxErFmIxnvxDjpMPhtR5v/0nDs4axg1zt6/+Zu4m8bOSKfPCKlGDn/5qVcgjD+wa73Q760Lg3L\\nuuCDkzlKwbZ1bFshEtyNNrMohYjtizZxaRSFFRUh5R3r6+eihgMoPRbLhyC1vlk82oeAMdD1Lkng\\nybUUZYmLYii1MdR1jdWGLtXm+ST/FaPk0VWqWYtRDeLq2TMVIztC/pBYwxPnbfpv3pgud1CZ/j5G\\nISHFsL8etRLRhBAG6HXq3IQYh6L4icUfRRUED9+zeyEGIdYVErH2XUd1uMSmGk609Kn1GeZS4/6J\\njAzfbHxzKUvIjlCen0n0OcBiySkYXIkM+U6uK+f8hrINBEWo6xlVXVNWVZpnR9ts2TRrilxLmJyM\\nqMb6R6LUDra9o0qdVXKdrsplHjHngyNh6NWaC/GlQXIWgaiqCqMVZVFS2oLcSsu7frhGQQj0niM2\\nzH/wBAJFMsZN16DrGbPFkt3qehBFODs+QRH45stfs/j9n9D3Dbvdjvsff8zTX3/JrulwUWGrOTer\\nW5Rz7LYNi2XH6b0P+adffUZB5L1HH/P185f8f3/+Z7z//gkHpyfEqCiKUoy/bAbWLrLb9ARlsEEi\\n7z7vBxSkmmfXd3R9Tx8ZHlPeJ2dJyp1c22KUpa5NyiOXdH6skMgKW/HO2sw/DI/lI1vBYV3w4Kjm\\nm+sdq13PW0f6uxzly2rbP4tzxmQqr/qm8U6D2XlH17WUVSVvFcfiZOcyfVsOzZ/9+q+x2qAx3Gyu\\n+fXFl3x99REhen7x9d9jtYUY+T//7s/4bz/5U/70ox+zaW8JsUYpM0aBSg2qUaMmvhr3Wfop/0vy\\nTAddWeBtxm8and+N0v+tjtNFxYvbuzTr7z5SzLFXsiPyaqNxmXpqMd3DbecHZm5hNMvKcjgreHhU\\n0/nArvc0nceFiNZSS2W1wmpFYRWF1ZTGUFhDc/sS1zegkoiz0mkv5Ga+eihZ0EYnBy+AsqKIg9Qi\\n5txakXKVJumTesYDOxKHDg1yxqeODVpg3Jhx0bzJ0r7c2/vT71/jxmZDoWIWrJcnTksVQoivIiV5\\nKCXCID7r9+YuQDpJWOpUCikvIBBoP8KbIbDeNngnbc/m85mAXyoLuEemZj3mPYegDAyR+Jvp/sOH\\nzkYyva7kJXMULJ89lwNJS0yNLUrqshJI1Vo619G2LdvtCqMl97eYl+iyxEQpzzCFoXWOoiiwSVw8\\n5hpPbQjOUZTShQZthps21ormKBKyupUECRJxa22T+EJgs74BU9L3G2Z1heu7obQi36kwmYK9+YlS\\nblJZw3J5wEFVYWczrl60tJ3jaDGj6x3WaO65I8JuR/SOhw/uE4Pjo48/5vD6hufPn3P64D02qyvu\\nnR1T1yWzxQHRO7r1JT/5wz/i2bNn/Oy//L+4dsVi8Yijs3t0mw2rzRZTVTgPB/MZV6sVjZfr7FyH\\ntZoYNVVZ0jYtvXNsmjb1Qy6YGUPvAxEv0HLM9ZgAmqAiffBUswOULvB9L+3EhggzO52MAc5kIU1t\\n5v3Dmnll3pyvfNO6e9vjMduUtzw3jW8BybZUpSSle9dLPkXJJBhrhmgzIALC3nv64OjosNpitOV2\\nc43G5M9GjIG//Pxv2PUt/+lHf8qyrrncSn2PmlzEUC6SfBuUGkSKs+GUxbx/iuSLf51B1G86dP4N\\nj1kpZR9vYo/t1TyxH7ncfd7ITswsyZF88VrPLI5/K28CvQ9cbzuutz1aRWal4XBW8uCoZlYYOh8F\\n6k01a5W1lNZSWkO/u+L5p38haj1II98Mioo90EKVT+uyaVoR244QfZCefDFijYhGayWlKN571s7T\\n9R2FHRspD+VM6bD33lHogrqq6buWsLeKJM832MTpfAy7Ps1T3HtweEqEkaGYhkCdAaNy1JaaMqMo\\njEFryW9meCvnBvM9jemzCDQp/1pbJPGQAAiJxWjDbJYaDaMpbFbt8TDotgAxUhZFOg+SkQl37jNq\\n757LRkwi90xh6jj5u3RNZcmsnlHVMzGQXYfrW1brG5zrQcsZpLXkJcuiYL26Ff1iBN7dNQ26ECc8\\nOxuF0mirOJjNuVpdUZujoZco6XloJapII8Y9+ANKgdIG75yURhBpug7X9xwc32ez2+B8oOu9sP0H\\n0dj0InnvZNg53U0fIp0T9aKL9QqrpMj/+PCQlxfnzOqaophT1zXr1Yqjg0N2bcPTr5/x0UcfEyMs\\nl3N26w1HyxrVL5nN5py/fMEDU3J6fMz1puXF01+yrBWPH33M0eEBzXZNHwOtD9A6jC242TSSD/Yd\\nXQ8YDV6jtaF3wnxXIa1RZZjPKjQB1XZ0wQkRLRvMlBowSuFd4ODknjRIiKPubCDDseP6GdGacVit\\n+OBkgfOBJy/XuO8Q5UTeEDWOG3UP+XnbeKfBXC4WFEYKgDNxIudxYqKKG50hh3zwQoiO5eyYq/U5\\nz66/SR9QCDxKKVzs+dmTv+WLl0/43//D/8qHp+9zvm7pfILAQBRRhtKS8fzJeprjgcZoRNWd0H4y\\nOxnY+c1AyX+942RRcrVuX/u77wpDx8mOz0nyjAJIB4H8u+nIh3d+kfywfNP0gd63XG468bRnBQe1\\n5XBesqyFem+MoVm95Kt/+i8061u57zqHc1kzVchCkRGGk8c1ZanJijLGGJpmh9LS/kshkV3re1Ci\\nahUU2KEv4oTAktaac/0r6+xtUaXUTrrxd3fc2YyYxAECjLg4rnWpV4xjfjNCWRQoJTWI+Vp1kvkL\\ngh0O9yxD1loLii7qPrm3Z45k0yVqS1kUeO/ok8B8cklkF2lhEwo5Q7/qFMF+Jwo1uf95DgfoWlMU\\npUSCtkydRBxt23Fzey3wJgzsRU02OmLgQbFrtxAikZBqAB0xlUPolJcsdOrEojSLgxN+9dlnvLdY\\nCkM2xHSdeihlEaGGdE1DVAzKByl70aKlrcqKznlq7yjLGV3fsd61AvmnexDieOLkSCrf/ew7uRi5\\n3e6YV8L2jUXPar3j5YsL7n/wPoWPKFtwfO8Y3+zY3K6pqpJmu8XoyPHZQ56/vGBZV3Rdi9nBfHFI\\nBM5fvgRjefz++7y4eCa5UF2w2Ww4PD2h3ewIxnLvYMmnXz2lmC8oi5IQHbuuJ5agvCAxXYhYpemd\\nH9qQBaSXbOcCvQ9CamKMGG2EgOL03mN8CPjA0LA6w997DmT6Pu+TRWl576jmct1xuW7fSFr8tmdZ\\nzlbG8YFvPd5pMF3f4/ueuqxBJekqpXB9R9/LYh5knnTuNC8faFYsWLe3eBIjbjgpokQHOnK7W/F/\\n/OV/5n/6o/+RP3j0Q642LZsm5ONj+By5OHWMLrPyC+ig8HsdvJnAKDBGRWMNWR7Z+/hdsGX/JYbV\\niv+fujf9kRzLsvx+byNpZm6+xZZLVdbW3VLNdE9PCwIGEAToT9cHAYIACerumdbMYLr2yi1W32wh\\n+TZ9uO+R9EiPjMjah5nu4W5ubm4k37vLueeee9K5e8o+y2xwmVV+6LGsB0BRx5jgurIY72X7NYvg\\nntXU9+6xwKEJ6EfJeEaf6EPitEnE289Jww0XZ6eEzQrvPYMfCENZh7kESlrWRsqgtJynD34KsKy1\\nKKMYQqBxjsbJvMDDbkfMaYJfdYEja0BWMzQQaTZfgsZvXLsHNp8xurRXpKl8MT19isLnXkyRvC6v\\nqSewSK61Ugu4C0KIEgCmPGm5hiA1PK2Ki5sy02K8i0SbsQZbTUAR6A0hFJ3VOIkATKIEWdx6SkV/\\nFhbtWrkMwZ4DpjruahnhK2OxzuFsg3UNSmnGsWccR477Oxk+X07X6MUFXWZ51H0u9zZrBSnitAPk\\n+igtEziGccB2nTyPjLOGV6++FnF3BJoPNejLC0OqFEvAr97imDPOSNZLyboyit3ulu3JBfvDnoQM\\nGnDOMXoZeTj5hOVrAhMfKEPIkQMD+xhYr1ZstEIZhW5XxHFkvepwRgK7rDUpZm5vbhi9Z8w7/Dhw\\ncXnO9mTNF199xenFI0EljEHnwNcvn9N1LXGMWBJOZYabW/bDwA+efczh7obvf/IxL169IubEMErG\\nOHpPzpkjRTweyCljUmIMcsWu9z3HmPBF8L+2BOYswhTnj7/Pan3KYRAh/iXRZ3KuS8ClfP3opGXb\\nWb682nMY0++c7Nyr99dVpeafVaUyFkH/Q8f7tWSLBNWu31Ol4+sWaZpmilBhdjxaaTKadbfi0fYR\\n+cXPue1vMaX4PQ22LVqXN/0N//t/+T9o3JofPH5KYyJvdp46gFSTicVoVR3ROnqnapZKVlpdYXWK\\nBSZm0RZxL6y4n/r/Ls7jL/FYLo7zTcPtYW4leajF5V2PfVvxOy8WlvipEgjlkl1Ml3BBaVni5MyU\\n/dkdVCNf6qRaSWBlHLQX/OJn/0gIga5tWbUrutWK0+0ZxhjGcWAcBoIPhOClBSMhRmuUwebGWFCa\\n3f5IyoqmaVEK7u7uZtJMTgL7FwdiTBmTtUAyZqEBPQl2V1EGhejnkmXgsdJ1VNU8KHg61yrmnufW\\njtr2IhenXLICU9W2F6U1FG3TVIx8ykmyggWLE7g3wcUUMXOMmt6zShIkpKnth0myTQYw19eqQgiS\\nsdcxXdM0CSrCU2u/CmMt1lbn6LBF0GL0A8M4cLu7vZepT/D3tF5ELP1+FDKTcmKWbEY3jpwVOglD\\nNcTEerXCB4/3gaYRe9C6VhjRGr7/4x+StUZN+qfzMOQ8vwPJqPO8R0IU9EzY1RntjAQXo2edE0o7\\n0EmcbciEUlZ4K/7/xlGD+zqUvPdB5hCfP8K2LWrIeB/oj1eEBFe7A08eP0Jrw/7NG54+eczzr77i\\n0Pcc/SCEJWvQ1glbdxxpneXi7Iyb3S27w4GYJdD45NlTVBzRKhHGA48uzuhHzxgSWhn6cZDsG7Hd\\nQ/A0VhOQgCmEyCFEYhaUI1KEHyRqQtmGp89+Qsyim1yzS4Fu6/XOZV5uRUQUn5x1xKz41ct9Eb6Q\\nVfiuymUNLWvAA99iy6cllafP80PvvlEfJL6ulEYZhVUGn6JAMW81c1fHqbUWTcCY+PrqS3707Cf8\\n8NlP+OrqC17cfD1BO1UGK0TP09On/PR7/47ObXlxO3C+dnx0JiSVuv9VFmMaVZ5787Qi1NqMKiSB\\nhUxehW7u2e9yLWtB/m0Rg78EZ/l2TfF9z73Xe7d4/wqBY3+9aCX5LmzdD3Ka5Ugli5F50nlucSg0\\noSqjNhnECZZbtpRUp8n9wAjF6eWnrDbn3Fy/YBgHQgjs9ncTuaVtO5xzNOsVJ80ZzlhimYYzDp7+\\neAQJnRgLpT6mzKEf8EkyD2MNqszMUpWBqzTovGhRqeO0ioD5Al3RWaDXlOKkfgJ51kst12TpLMsf\\nkfmaSZxgzdFUUdHSSpyPsHfn6++9ZGMZiWVTlvFK9fcn1iwapSVvRavJASulpH2iQId16kQobM98\\nb5iRmu7ZXJuUv6SNxlhxiNZatLWY5fX3I4fjHu9FdjDlCtnl+69/H5aYJPGWxzR/sTiXOtvSxyhD\\nj0MqMKs04mvjJKDqezrruL65wZd6ba0F11VaM52yhAsyWOzDQnCjCsSrDHH0RJCB0ocdq82Go+8l\\nw8ppmorzbc6y/jgrcRomQwoRqx3by8c03YrjzTV3d3c8vrjk5Vdfcnm2xenMcRx4c3NLt1lzdThw\\n+vgJ2/NHXN/t0ShG3xOGnsaAbS2QaFoRFdhYw91ux3F/4E5LXTsrUdPSWuOM4jh4lKlKWpI8DaOn\\ndWtReCtoBKqgKLmq9KgS82XOzj6maU84ljFvMdUh6bWGmaegJCOci2dnLVd7z8vbceJMTMmaur92\\npq/vZajfrIM+fBR7tPjdb/vN9zrMlPIEr2itZR5CFJX6Jby0VO2vbLLDeOSff/2PXK4f8aOPfsJP\\nv/9v2Q97fvbVv3J7vOHRyWOenUFHw0MAACAASURBVH/MX3/8N2zaE4F8lOL64Fk5zbPzjqv9yM0x\\nSPaY82RYtZZJD0bPQ4kVC5KQmlmF9bF7F/c9Ed+f6/hO0OgDmeHSwW1XjsEnhpC+8bPl8W19oh/a\\nP1rZjXUKyXIi+tThdG/Cunrr35phMAU8oidbXiFHTi8+5vb6ZdEdLpBuFn3V1B859vvy6uKMWtfQ\\nNi1d07HarIv4gZ5KCTF4jocjCcNmc0JO85AAYyxVIFoZWWMxxkkerh66qMTkJGveh0BMhVyVqxZp\\n8S3VwaAk8i5N/BPByJgSbKhJx7QK6tZpI7pmuaaKsyupKSY115K1vE4q+7BtHBL3q2nItFIUofY0\\nywWW34GiaFSyyHr7tbZSG9aijmStRRsrY8SiZHOHYSDs7hiDL/HBfN+rOPsMwd1Heea2EsGKZtoT\\nU/ifS8Bc+zhDjOSsJNuJiVUrNbxhKCxgEne7g5C8kLY4jJUsBwlfshIxAqX0ZBfSZPTfcuqLe58p\\n4hLlZ/vjkfXmFLQhxbDIUueVvmxpWAbrIpMofyKSxWmW1qJ9f8RdnHOxPeFws2NUmjwMqMZx0/c8\\n+/4nNK7hyePHaKPZbM949fqKYfQcjm/45KMnfPHVF6y2W5Iy9NHTacAajHMMKXO6OcFHGRR9N0aO\\nY2SISK+mgs45rnd7UDAGT8yi+BVT5hgEwRFCzwy31rX5+NmPiEqyz1Czy1IZqFegZpePNsJb+Opq\\nz36I842/dy3fcdTg8Ru/9b5EofiGYk9+r7aSrMqIpiSRoVaKpPVERgh1TFd9U8VYZrIsbm14tXvJ\\n/vMdP/34b/npp/+WHz35K3754l/5649/yuB7fvPqV3zx5rdsmi0/evoTPr78lIPP9H7gfNPQOsPL\\n24GkamSeC3lBNo8pzjMVLFqi7Tl7oSzGKVXPPJB1zk7jLyHLfJ+T+pD3eLlpeLUbPsjhvS+TvKfm\\n8s7nzku1QllLE6PeuvBKL0PCkl0WyG/J6Kyrf3NyKc6gMk1VbfdYQJ1qhjCHcRAtTrVHKUVIUk+q\\nTtRay2a75fL8Amc0IfiSHUZIkLKowcQYC3Q41wMpazGmJLBrTrMzYimyMRNGKvlmKhikPK3JSsKh\\nOLnS+YzKqvSSmhK5U1pj5t+tkXmKpYUkywDklBOm9CUqJW1g8r5LMJBBKVMcmGSkVotqjlx7yRyN\\nKQOeQ5Q+6+gZxoHdfkcMYcoY5VxTsRXymnoxlabepFpLre0E4lAzLDLa2uetSlmmttxU0pMukGjN\\nVDRmIlaFGGmbhjF4GbKtJEg59D3J2FKDS1NmL8pKIjdHdVQIpyyyRKrmTKZmyDX5qSvwcNyxXq25\\n290uMta6J+47ybd3cFW+USpLe1xxPGNMaGPpVmuyUvzN06e8/PILXrx6TbM9ZbVas2k69tc3qJS4\\nvbmVySbbM372s1/wydNHAo83jQxb7wdoHN5aomu53G7B90QyXWPJPjJqaNqWrnXEKKzybdcwJjgk\\n0ZJVRjN4jy99+tXBzyzXxGb9mFV3Rh8EPp8FC2T9Sd9rwmr4+GxNSInfvNrjYy42fL5S+YFrRt3/\\nOS+e+c3H54W1cKbq3pMnBOz3yjBdEeXVxqDLQq+wWYVn6maW1DpP/V8aabY+X1/w2ZMfklLi//7F\\n/8WqWXNzeIOzLS+uvuLXr3+F1oqX8QUvb5/zk4/+hs+e/Jh1u+Xl7cDpyvLpZcfzm5HdGKUBPVHG\\nPGm0yhgTJepRiqwL07Y4ywdPf8Js/7KSzQ912O9zgp0zWKPZ9R8oRPye40Pe07Rk67WfcsvZecwZ\\nhxACtK5ZF9QQe8ouVWV0Ajmy2Wxpuo5xOLLcHjHFwpisD9W6YpVRS9SZj9ZZUk7c7G9IKXOyXhP9\\ngDFK+vNypmsbtHYoZehWTsYaFfJNiqlM1yjCDWRiCML+K/JodWRTvWam7J3as+yMFTFvgBLo5RhL\\nQKrLtPsKC86ElJxS6f+L03Wtkz6m6zGVHQ1GW3HmxmC0pukMbdOW9ykOUltpS6mvLzVQaWtIcSAe\\nD4TSPgLgYyDGXCDTvIjo53UbYyYVubmUmZChmkNOIgfl/GtH9XxH5wWlWJRUikB7LpB/LioUGhl/\\nFkrAkZWWto4sddsqlZi1lnaQGKf2H5kZWu4tM9yqlbQfpcV7SZN0+lJwYfk97I8HHl8+5W5/x8wo\\nXxr8+3RGdW8ll4w713mxMhnEaakZt9bhw45ejwzRc352yvPDyONna3xIaNdwfbfjwlo+/vgjXr1+\\nzZOnj7i7veL87ARNYtO1HI89vh/ZXG5obUPyAyGGKQh1RtM5Gcg9eC+OFmgbSxxkLfgQUMhe8jER\\nssySnQXVZe0+evwZGEP0vhCCUtkjuZDb4KS1XKwbXtz1XO/HGQ5fhBc5w1ur48FjsjRv2cd7/Awo\\nwc8cjEMxTe95/Q+oYcoF1EpLf5aaYSSjBfYa/Pjwm0wytPVsfcbr2xe82r3G+xFtpT/pandF73vZ\\nqMpgrGU33vEvv/knbo43/Ie//l/RSnF9iLQu8vS0pes9L27TBMdao4hJkbJ8HxMoxImi0sSIWm7J\\nvIwy+GZE8efOMr9LnfFdx8Wm+e5TST7g+F3e2/JaV8WD5eKckIkKNy6/RtCM/e4Nv/3lP3F2/hHD\\nsGccDtL24Afmezirz9QMJRWDa5VCW5lEcrJeE7ynH0e2mw3Je6zt2B/2sp6HgWG8kyyj1C2lnlom\\npxQHqIzBKAPG4ZymUXrSvZ0GLBdJO61Fv3boR+52dxiXcU1DjoW8k0pGqUBpTbdac3JyRmXR1n0n\\nxCRh8YpRRxrLhRZcaq1lcktKwi7PdaajZE/7w1G+K+PCcnHIIK8rziyVoEWCCKtFUrDKxS0d3JRB\\nI/15UUF1NdVlzFus3HhVgqdc/5mVkuq6SEr0WytUWRVzGufkPZYssQbGPkZiCFLvJdM40QuWWp6f\\ngpiYQWYyqokFLK0R85pNb73l+sX0WHHaFQasJ5tSYhgH2m7N8bi/315TnjSd6+IPzD+rzxTY0qtI\\nCkKCuTkcePPiJe5kw9mjR9y9uWY/jESlyQTOT7f86vPfcvH0Mefnlzx/8RWf/ehHXD79iH6/I/ie\\n4XiEFGhMC96Tk4iFhII61LKKtQaM4nAc8UkIZdY4Vg0chpHd4cBmsyFn0RmOkzOcA8WM4uzsiRB9\\nas0yz2IFisyTbYvVms+v9hz9vGaKV6tkE5jzmwdt89s2+95z7qWd80NLU3avrLUcEPHW8QEDpOfb\\nGQouH1PEWMeq6ejHgUqdqZnlMi2PKfLi9gUhjhhtcM6Vxa84elHyrzMMc4GQUsz8/Ov/xuXmCX/z\\nyf8oM9fGzGHs2baWy03D1W4kZOkBNSoTdUYr+Uhz0IhaXOmJ4iN/7N41rJj7tGT/QqDZ3+UwWrFd\\nOX7+/O4P/to1av4ufrMSvKZa5jtffF5rMh9Qnq6VYuz3vHn9pWR7Rk1ZrDYyZUTE3cWAChlBk8jT\\nTEVtDWEsmrcxEEIgpMjheODi5ITBC3ljiIEWS8igtCqCCRbva5aViUFEuyVKXnCzp7QhTeejteHk\\nZMW6aen3V9zcHQpzUTMewvSaqgqRG4MupIiQIlnJxHppG5HySChavNYa+mFAaxnmO/micm0m16Og\\nMYa2aeiHHh8TbdM8CD3JxCA11Um9D6WNRFCmHFJxEjXzlZsUEYMYM4VYVC4A1VXXSzK3dd2zS4sF\\nlUrgYIyeGMMSiJjpPddxWwpIKRASGGXIWktzvVLE0WP0rOoEpTZbMvG68UMRZcjT+8h1MU5X6F4G\\nCMJWfsAQA+z7PWen5xymmvryNcrX5fdr7HHf1LzVEodc1yFGzj/9HpenW1xO3Ly+Yhh6eh/JIXFy\\nfs7FYc9+GHl8ccHj+Fjq66tO5CONo++PrFYruraV8VsJxhCwXcdxDGSliYj4w83uQMAQQyKrgG4a\\nRh8YfBDR+EZIZzEJU3ZiWWc5g7Zb07QnDDHNRJ/iLJ3VPDtZses9n1+J4AO5Qvnlauc5hOAdTuxt\\n/sa7bHbFu/K3pJGCXHw73vj+GiaZMXihUBtDSFF62bSmH3qpjxR23bLGlbPAXxVys8pOxBClFzqU\\nyzebi9q/Bp01v375c37y7K/QymAUxKx4c/Q0WvHopOF6DzfJC5xWMkytZSGaksnEMpqIPEu41ShP\\nbJVcSl0Yakto6b9Xp3m+brg7+km9/7uwbj/suC8O8b7XnePnsgkU9zRQ6muKASw1zFyBOsnqjsMB\\n13YyOi5mjFlkOIWdrZWlQu0xCvwZc5KyQpK+uJSStKasOsZdoA+BXT/QDz2ubUlk9uOAsTJKK+VI\\nTNA4WzKoMlE+yeYSNiRU6D/NPgqQWntKiX448ubqWiT14sz01kZPz1VKWN85Zdpuw36/n8aJ+RRQ\\nSNN4Kn/TGUNIMm/Qp4gu48xMYSyS9ZSxJ6Xovec4jqxWa5m8kkUOEKpxLplr2Zc+SKiTStZR2e21\\nbSQtU0tKT+G0p0vGv/iuhkv3dpR6e3+Vu66YbYeRWZGN0vRhlGukptbRaTRgrG4vM5HObC7vJ4Gz\\n0mKTspfabRaexVxLXazlPK/xCt9O77ycyCKfmd66QuTkcs40Tcvoh0X2XPfCNwOGt7dQfT8+J6wS\\nhO/RdsNxv6PR8PKr5+yj55OPPmLXH9nv91w8esRn3/sev/7qOT4pzrdbXl+9wTlHt15jjOXu+jVa\\nQxxHhtGDMTRtK0LuSuGzTBUJPpFLL3LTOI7DSMiJ/TDQrjqikjmyzjrGUorIqY7Ck6Tl7OwjlDKk\\nLD+v7SRna0frNC9uem574QYI6We+SrOrXK6nh4/32je1WH/vyCqX1/3brNl7pn/N6znmNEV1Rkkd\\nRmuNntRQFlR1RK9RpisUMo3WGGvlsfraizdbm8qL3cRozW1/zb9+/V+KwlB5jlIMIXHTRzad5fFJ\\nQ2NU2egirq0L5FPraPfYlw+c4xw9/umOqeb7RzguTxqu9g8r+/wxjlq7XnrR++e23AgLiE4tfq4W\\nT8kIlFWfqDXH/a3Q15F1EGMNBijzD0svYYWEtC4zCh1C6ChSjhmZBp+EGBOTQIi26USMwGhW6zWN\\nc4VdqiaR93EcGMMoffxGF0Oa5D9Vg7DZtCakZKEVXN/uCMqQtWRB0rxvCoxXooisiEWDNGaZmAFM\\nYiEJxYgQUVIxpjGLo6rQYkYzpozPEFWdHagIhURhrGMYvZCVlDjCBKVYrKfZoSGEoodamKWqsCOp\\nfXazQ8xKhMqzhqzrY+qtD8nYqT2JKjPVsqkkpto2II4sIwhS6wS2s1ZjSw0ShJEZc8THIG05pcoY\\nVSaoTCAz5Ignk62RID8ERjKeNImNa6NlcLguNqTUfZXR8qHeQlQWsF4916ktpRj6/XHPZr25/7z6\\nu2991MCQqTRRrln9c0bjUySMnnHoubm9YXt+ijYW7Sw+J1Tj2I0Dyhm0ga/vbjhmcF0j76px7PY7\\nUEb6kGPEGrHHx+OB2/0BX4ZCxyyDAQQskKAuxMRxDIwpTxq9KUtbSy56/6HAuwkJLC8ffSrrM4qj\\nNFrx7LTDaPj8zUFYsHkWiLhnMfLy62XrCe88qv9Rk+1/+Dnv+z5/iy94f1tJFsKEMrk0lQtJIXlP\\nSEJSyJqiMBImso928tIppcngVJhWwTRuJzFPNZ8bp1P5iPznz/8jj86e8HjzTCKYEpnHBDfHQGPg\\n2dmK13cjbw5JmLOlf0vpLCpA+T5Ltl6OPH2SY6km9HbU8iG4+R/7+JBMcdtZxpDo/VyQ+cNllh9w\\n1GC7XnC18I3LL0p2Vp5df1DgxBmSAkgx8MMf/z3kxIsXvxSnqGUTGSViA5JlGiYh7/LHlKQgGG2L\\nOIHUu2POdF2H99Io7hpDp1uUSuQo5k8rU4ggkZgzprGoWEgOlQ6imJv31WJYgFK4QkQ6DgNeU4S7\\npT5kSuZJyR4ShSwE0ntas7wcSCWVioUoVHs/hYkuxq1pGihRPKqUR7LslxADzlq8H/El85bhzGmq\\nhWpVWsi0IsUsjjHnUkYqtadFyWJa9XV/qPm5c+Y0BzX3f6u6lrcXzr2iiBhv0iQmsR8GlLVYregn\\nmcEMeo78Z5FzQRqE5a8ZvSeZSEhRxhMWwX6nTFEBkncgwyOE3KSyBABJQU6QqkZ8pbMy25BKRKqn\\nvu8PbE+2hUkdp6WfFkIMerEvpxr8MrhUQrIMIRKIXFmLcy0pRL6+vWOzPeHqbk9cdTRdw+1+z8Zu\\nefTogqsvX+C3pxxDpCVx9IFeK7RzxLblavTsjwPtelXkFC1kRdc44nEUoQ+l6ENgOEaiBu+DCCto\\nPSc+WRFyLKznIthQgteTk4uiFpVYN6JN/OKm5+boC2GshBf3koc5W7+3QApytHz822zv72WXv+V3\\n35thSoF9nhlitMYVTVlnDFZraeAtPWryRvMUKb1trKsuKJQtUr5XpUYxyewBSmlCCvzzL/+fQjLS\\nGKUm5qRSiuOYuD54tivL022LKz1z0p7A4qPAPNR6Ws0+89Jcf3Ce+VDf43c5fh8n9m2L4fKk5c0f\\ngezzvmPe5ot8/R48+fYzl4Dd4jnTL85rJyWwtuOjj39EtzqR7C2KREjdqNoadBELt8UA1LFfJOmz\\nG3zRGFWJ9WZN2zhW646+PxCJ+DBQEifatpH1aSiGKzOMgYMP+CSU+Cl7YkoQ718LrUGLoa/tDRWy\\n9CHhi8pJKA3fCXFwqeStgUQkT8IJ2hjQkIjEHCQLUyKiIMSKRFSBQMQnT1SRmCPZQECcoGucCDQg\\nIgsK6SXVpbcyJIHichbnE+u5Lc4swZQ11nOfz3zOsxb0lsVjqXxeHooJpC+oELlmx5CUQNLJCPQ8\\nxDC/+lvZHvf+muwVHyPaGulEtbUOmkuCW86soFqmGObGWmrXaM1c6tg5+Ob+zfW6lPedcmJ/3LNa\\nbebAr0KPi3d6b+28tX7qpYkFwbjtj9yGwKANBx8ZjEOtV2xcy/7Qc/CeV/ueAc12u6Vdr0hKMcTE\\nYRxpVxs2p1uazRq7XnP29BF2tca0K0JU9DGzGwJDSvjgWa3XUDLOXFEMFCFE+l5EZXKWuZmVyJOR\\nMsZ6c4Z1HQpRG7NW89VVz36oE3XqcxfnWx9/l3l7B0J47z58iKN8z1Pyt9jm9zpMq8SJtbbB6jkT\\ntFrUH0LOZVbgDM1O7RwFim0aEVWuDDyyaGfKlPcGY8w8aBQwxSErJAp7ffeKnz3/z2gokGthUWqB\\neVLOvNmPpASfXKzYrsyUDU85jFo6w7y4+DPcsojtPsih/b6w6ruEAt5ZuH6Pk26tprGa3ftmxP2B\\njncZDcm07j8OGSXpI9+wDDXIqp/VMrMXyDEDF5ef8Ld/97/x45/8PZ999m/Ybh8jYheiUyzBu8hv\\nVRUYrRRnp6dsNyecn51hjEUbw+3dTup5XctqtZrKCz7FSRN1GAcaK7X3UB431tC2zZQBLsUjgFJL\\nFVivsYbDcCTWtafKe1UCqebiIKZ8S5Ua4fQYJJWLnFyQ6SoV0tR6IuHVoCHkKJBkFkWf2vZRM5Ws\\nlcCrVCdYGL9al5qhnvZILHtksYEWH0yQqtQA72egk7OqLlKlCWKd3acioVHKoZBRafNyUuQstdyY\\nEmMssxhDcTelP1fOffH3a9ZdHysBSHWsM+wq93rTNSgVsYWD0bUNRmW61pKjhyIwj6JIJDKViyTA\\n11MdtSIqs7wb3O33dO267It5feeakb69oZYljXL9q9OvqFvMiaAMwVq8yjy+uCTtDzSSvnJzOLBP\\nMgw9+AHTtdwNnn2IbDanHG6PHPuRRCYEaQXpQ2DIiSElDsEzpMiQMnd9T1QSbIaQJvlHGWuWyTkW\\n+D4TUz132awn2wtaqzldWfZ94NXtOE22Wp77Ima5H/B8w3E+RFF7+HifTZ7Qrt/h9z+ohrl1K07a\\nltY4yJkhjIzBS69WitOcuZpRxilDhMZYaZhGstXGOtqmRZt5Xl9d5CHKa9UahSkQm1Ka3775DSnH\\nCfawSraZmSI/uOsDr+88F5uWj846rFnUJIoRnLRnmTPPyXsu0yL+xFDmW8f7nOZDx8VJy9V+/OCF\\n9fse71uY32wmXqRhy2MRCMw2Ok//1s85Z5xt+OEP/x2nZ4/EKags9Uqty8QLS9e1JQgznJ1s+ORs\\njU4ySf50s+LR6RalNScnW3RKDH6kbRo27UrWRnEcq9UKp03RlZXaozZaxlqV+qIyUhtTM+wBzApA\\nffBSX8s1mMyFoCLOZqxfa5GpqzV/qPtIaouUWZ2jDxN5IhSBAJSMJ4sLwQCBnUXAo45aiqU8oqzU\\npbKSbDwX0XlFnljEUpOdM7WkivNWku3Uj1pznNAEvUSVjLx/tGStaFL5qOdVr0MqQda9j6IGM4bA\\nPnqSlQwYJfVLX6DymPMkoC4w7vxRs3yNorWOVkuf68o6muLwYgpsVg2d0SKRqOfzqH2zUxtbIVax\\nWNtaK5y1sw0pAVHIkT70dF0na6LA9BWhW8KRU4a63D9T0CKMaGmTguvhQG6M6GvHiG4dT8/P2TYC\\nu++GkdxYIjImbj8Gjkmu193dLXf9yJgNEYGbQ0qMORFJhHItfcrsh0CIktWHIrsYcpIAqGaVSWrc\\nUoeXj7Z1/PjTH2CM5s1u5DAURKA6xsz0/dKGVJv83hSQD8wkHziWNmVxoT/kTwIf4DCNEm3BNAZa\\nbWiNm5rEKySTloZRCTW+cQ5nHZmyKVOaGphzzjjnBNI1BpVVmcHHRP6Z6N+F+Tr6gX/57T9zd7wu\\nRq1AvuXnWsm/Pie+vjkSYuL7j9acrd29QvCUpbFIdpSAsbo+9gA0+za8/Idgnv4hiT9awenK/VF6\\nL3/fQy7xMl3J9f/7R2ZCBWp9pxrtlDK74y1KW2IKPH/5OVc3L0s2lrDWYJ1hUwg7ztki36ZIPrDW\\nitP1im3TsNKGrrG0xnB9e82q64osXiBEGYpulCKMA4f+yL7vyTqDzozRi0EJQYw9CWMlIKvQWtaS\\nffgUZ7m38rMxhkKiUUVIXU11yLqPKEgLaibXoAS6NcYiGVgu2bcYwqREJMQn0S+NUepmuuiq+iRs\\n9hhlAkXtUR3jLGwhfa+qQNmm7G81BTIPrVRVpLa0kelDE3qjigOWE4JcHL9S4jyVpurrVmZxZlnX\\nhoTGR4gJfEqMo5dAIEYZnVVTsOJhQyzi6CWgqPZIegUTQ/CEGGmM5XS1guDROdM6x0nTYcmYnAg+\\n0FiDVZquOFmylHgarWm1MKa1Ujhnp45Ta5b6wPLW9sc9J+sTAOIUYJR7ntMElU+Z8pSVQ1YahaKx\\njs44LjcbVmohuJ8ydzGgu5bNqiP1PSFEjsOIj2CNzAXtU+QwBn7x8iX7tiE4S9Ia2ziGGBiSQPqh\\nBGEhqeI0ZT2FzEKhp2TwyPczYUdy6Efnlzx79AzMituDn4K6VMhA03rOSwb1HDTke3ZhgTxNzvb3\\ntLv5/pf3QvoPMMXvdZg+Bu6OBw7jgFWgY0JXSKG850owqF83RYi5jvRqbVOcp/zcKEWOiehDQXZk\\nEelSz6xOOGdRUQkhcLO/4V8+/0eudq9x1hVpPibjKpDSnHHe9oHn10e2neN7lx1dMWrAIsus/+ZS\\ny6zQ2fTpGzfmIQWJP8bxXRfE+aZh18+tJO868mLR/6kOzQPncu+hQnmoCZpa9Kgh7znmjLUrlLYo\\nZfn4o5/gmpYYa5tTYWdbzWbtWK06/vrZEz69fETXOi5PT3BRHOGqsTw52TD2BzbbLY3VrFzDuhUV\\nnJgzr497jlGgKt1II3xllmqjJkgWxIA3jcNZjTWG9aqjtbYoFelp0o7ougu8FkpbR8gixRdSFJZp\\nzsWpFcUZY8gafI5So1XCjsVoqcsVKLSyYbWxjCEQsgSbMYuEWSoSftYZjFFTcDr1QReIsWb2FSLN\\nmkVdtZxxzbycpXEWZzQoEbDXhWk6ZZ0KlEr324Dy/MXEflwsjKTmD/GHRWw9VwZnRuZwirdNWZXg\\nZdEqVrKYkCKpKIKFnMEaGmvQKgnzVsGmFaZ9GAf8OMo8yiQOcO0sJovNq+fuQ5j6e3OS8pIMJZfa\\ncOOMtMYZRUgBYwxd2xb0LTFGX3qTmQKRpOZrUGFko6TO3FrL2jnWRrPSmo2xuIS0GMWIMpYxJrpV\\nh9XgUyACu35g1Uod8fH5Ocdjzz5kcC3bzYaTpmEYekLKRRQdQpRrLR+pyDAugaE5662BkdYymeqz\\njz/FGM3nX3+Fa7eTg61QLm9/Lvd+blea18ACV1qaifsm5Ls6zXwfAq6KjMWFf9DxXoeJ1ujGYRqH\\nIXO2alm1nbSVlD9TYVmp20jvpTMGZ+wEJWgosIWam1iBwY+EFCfiz6TgAaQo2pWSkVqccry4+YoY\\n/aQrWYXXja5SavPjMWW+vuk5DJHvXa55fNLOmeIiAlZLqvviorzrIn4bbfm7HA+9zu/62heblqvd\\n+Ad1hH+oGu1sH+tKve8MeeDreRJ7nhxmLnqfMWceXX7CX//4H9huL+jHoThcTessxMS6azne3RCP\\nB1oimsz5uuOsazje3XLoe4w1Mvg2R3o/8tXr10W1R0TGMWbqGQ5J2I25pH7yj2RiCVE/kZaQyPHY\\nM/hRBhkjJQFTEJGwgAlrrbRen1g0OVXZC7VkkXIuvaRyHcwC/jWFsJNKFjBBuiUTSVkJ2almWhOT\\nVk3G2JbanlGiOBRKq0yuQagVGqpSUsPVWlAgVyQx98eejDhKbXVFkKcgdrvuWDvL2s2h01SRyov1\\nUT7ISvpwy1MkW9SSRSdIsWQouUK8SgQeVMbrRFSJZBFR+pIdNsawshanDSvnCOMorGltGIeBGAPa\\nGNbrFVaLrGRrFarIHxoFrXGSKWmBd3MSh9yUjoDaY66Vxmkz2aFjv+d0c4pV1S5qnJbgylgz75OC\\nQqCUOHMtz22MZuUcnTGsrWFlFCfW4HLGalhbi/cDyjraVvqHbw57Xh6PBG3orOPJakUejjingMR2\\ntWbc7TjsDyJZh5og2ipKotB/DQAAIABJREFUr97qgVySdSZnaQxPLx/z9NETXl694svXLwtyYu85\\nyyrIXklPaTIMhevytrWt+2wxwebtas632aYH7egUwM22/e31+JaL/sbx3rYSa0wRN86olFEpYKM0\\ng4cKXZVoeBk5O2VwSoMygts7icLSOGC1IeSEc5ZWN5Azg/dSfyi6nOQitmwMKRYSR8H+dbmxta2r\\ninTX7FIVeNYkMRp3feBuCDxaN/zw8YbnNz37YR5Uq8qVEqObIReaPapAMeobN+gP4Zju4ffvcJIP\\nNde+fZx0Mn3h6N81KW4+/th12QeJTG//bHqAKcRWy+cuIsGUxBEoBQIeJkAmfXz6yd8Qoufnv/zH\\nMusSQULIXKzWvLm54ROjGUJkP4y44DkMAZxDpcxuGEhWY9Ac4ojuXJlmMhb9ZE2KaQqocplMoWog\\noQyiUyvOOiSEvqJFMchnUMqI6EES2TayKKmYkjUJOmvIORKiBHBVdCMUdZqchaErzfo1I9Slpi+s\\nX8kgZRhCVsI41coCUv5orYOS6WlEI7rVpgxplr87juI0miz1TaW1MFJzwlhDKOLuMUVSihyDjHoK\\nCWyGE9fiwyCG12hyEl3nR6enjLd33PgBW0Z5Lrnpb62gt8AHNRGGZKmIg0o5iTrMtKaUqKWjhBnI\\nrNqqi0zh2jk6a3HAGGXihk8JbcUM+hAwyuKDx1onknrTTNOMJTNGUVyqvbe6aBRXyFYQL0GzEhGl\\noR+PnGy2OOeIMeC0IYSIMZaE2Dkf4jSD1WlNoy1NTpAiKhvO1it2z1+wv7vGrjpOt2ekuzs8RgIX\\nPxIQeFllaSs7+CzXCMgxYkKkM6YQeQLX+z25kDFTgXjrx9S1kIRMtqTcKCArJUS603Nu9zt+89Xn\\n+Cww+Wp9Mq3pmq1O9crEdCfnF2NK+6bASdXHfnd79a0OtT6nfJqW0Xv+5Hsd5nnb8vpwIObM9TCg\\nckI1LVppTlzHwQ/EJP1LGSHukDKHeGTdrmiMFeNA5jgMUCOnQsaJMbAf+mkItZxMWepl40svqOLx\\n9hnPzj6a5/xpUFmhc6Hd6zzVMnUW6EyXCCdmeHk30DnDR+cdh9Hy4naQhVrwQM2EEpT4SqYGPMho\\n+ws6LjctVzsRKvhDOsTv+loPwdX63mPVAagpy8y5wH5LJ0nRpsyJmA06CdNSKU3MSgI3QKvMZnOO\\nLoOJBz+AWuEax8s3b2SUkVKs0FycnfP1q1dcXFyw7wfejEeSFbKOLe0UmmK4SpQvG1dWY52baLQp\\nRDXJymxhT8YYJapGNFhHH1Fa2q7GUWT1SifMwllIvF21TVOSnlCj9VRPkt+TayGGTLOqotiL7KQK\\nx/sgKjaRRMwBoxWdMWg7CyRMUbSSqqFRAiu3jZRDtA+grWQYOoKy+CROMwPGWPphQGnLqltx148k\\nFCvryMHjEShP0CbDmXP8pt8zkIuYhCEVDDLnhYss/8ydiqpcz3LdUuE3GEV/lL8/ry5JQTrniMg4\\nQKsNVitWtuGi7WjIEDxDlrmMIUUoA7bHIDBpKrNLE9IBZLLCKl2gyQKxksvcUVXWgOhZK6Vpih00\\nqox2U5JdRT9wut6y399JBm5lD5gsCUcoAUpnXNHPzSStIYj4+vX1FVlDt16zWZ8w7PfYYZAJM2W6\\nSe89KJljKUpMBXkDctvw0eNHeGDMmRfXbxgby0nXiEBFSJDVzC6OIggxj04E18gwdmcNjy8ekXPm\\n69fPOfYjIIlMyIlVt0WX4d8TSnTfMDD9VzPWt23JtE+gEs8Wbva9xztJk9O6WrzWdzBz74Vk97s7\\njBLYcrSGURt0GaNze9hNswUV4jQb68QU5MTgB5m+UCa0O2vZrta0TiL56AMp5knKK3hPisLAmjRp\\nU0ZhUMry+PQJ55uLqQZplJ6ySqMri01N7FgRZCkZZ6lzHsfI52+OxAQ/fLzhctOWyHBWBJpSzuny\\nzm0BNRT5Q0Kyywz2oRv9bXXHxmpap7n9E7WSvOt46Hq8vcDr5pG2o/uPyxelTSDWOp9oUIbysZTX\\nqrP1zrfPeHz5Gcd+RCnNGAIxK8YM7apllwK7Q8/VzRUnmw0qJ45+pHVOJONIYjhzxnuZcFHFD8RQ\\n1LmQpszIFOYpxRBWtmy3anEOlNaMIZGzGPeYohBiROoGrQxaGWH4QlGJqfVRcZR+FFkEpQ0+JryP\\nZXC07KP9cRBSUyWjoHGuxWiLMQ2gJ83OlBUhw+hjqcsZjFaMceTgRzGsBdFJMUKKdM7QaIUxiifr\\nE7oks0OddVIzU5LZna5XGBLBiyj7PoysreV4OE5KX4227Pc7xhgJSs5XNtM8oizP0rsz6QURLUAJ\\nzOuc1FdjiBz6sdSyl2tO0TYOYxVtcZRn3YrLtmNrLQQvYv0xEGMqpBvmtVWud1UKijnhY4ERUyxc\\niyT2RSlSiqUWLBbfKI0i4YwhpSA6xMbilKHRFj8MnLRrOmtRKdNZhysOttWWtnx/3q04tY7ztuWs\\nbXh8ssH5wMvnL9hst3KvtcIZaBX43R2tUlyuNty9eoXTisvVlhNruVg1OKf5/skpv/rNFzAGwu6O\\nM2vBe7bdio11rJyDHHFOAjBrZ6g4pDSTklLi4uycT59+zO6w58uXzxm9nOvMjs6cbi+J9U5Odmu+\\nryBj6xILpnBdATlPBmGhdVTsxB8mbVEPfqOmz99m2d+bYXqEDp7rCCVjGUZfmoEFBktFy9NqYeRF\\nKILNgePxwLo0sK5dy/7YM0YvZIhYxiQhtYREIpfpCanQx0NMnG3O+NvP/j3n20d8/eZzTrpTGtsB\\nYphS+TBGYFibZJRPNpCLUojk3ZqsEzohg6kPnsfbhtOV4+ubnuNYxbCLu8wycz6peTbe0gn8qZV+\\n4JsQ7cWmkZE4f9J38e3HvMyl5qcfqD0off83UhYnqFUiJNBRqAC6xHSyvcBiBJpVkmlqY/g3/8N/\\noGvX/OK3/4nH5xdo4Nl2w9e7A14lHj99wss3r9BqYJMimcQujkKoQWA5MvOUE1WcYnlv9T5XJiOV\\n5JYzhyFwvhL5Ma0t0SiiNaA0+2EgZlBlSoM2huBFcEDmUBaFH0SMI5QgMVDYhCW7zHp2arWfWZyx\\nlECMdQA45xj9KIY812HRCWssobSB6BhRpV/TNi0ZaX+oDfkUo+eKrKUi43PAOZlm0TrH3dCjrOWm\\n30nZpTjul7d7boBuvaFpHCbCunEcjkeC0Yw+gypqSyxmmmZxYHkJzZdyizFCRlpZzfVwICmLwhTb\\nWgyrkuvnNIx+KM7I4LSmU4rovZCIiuH2UWDWQGLdrCAJ6zaVxZoS6EokSgm0xiCBuCHgg9SRE6CM\\nmgLumGWKUmOsSDumUi7KMhQix8Dp6oRjvxM2smtKBdZxOxw5JhFoWBtFayQZGO72xGHg7uqa/skl\\nXdfx+vVrTk/WdF0Do2fc3TIqS9c41trQKMVaa3zMWOC2PxKN4dmTRxxu3vDy5SvsqmO9WjHkzMo1\\n5DYIVBspc0Vjsf0ekmK93nCxPWPXH/jtyy+lVKHK+adcao0anQ1dt5nuT0VU8iKlq/268zCGeyDt\\nPSOilujt72tvF9DrN3+0fN13u8z31zC1ZufHUiMsA0ODn6bXx4WzTCkRgy+/Z8hKcwwjVltRx48J\\nA5yfbBmGgWyF+j+OnuPQT1lelQjLScgOzy4+5vn1F/x/n/8nPrr4iF+//Bmb7oyzzQVOtzTGknLA\\n6IzVmWQKtKdrXKMFD1OFUagzBtEj/eq6Z9MaPrlYcRgCL257fKoRESWCl4uo+OO6pXcxcB+S6gMx\\nKGfrhl/8EaaS/D7HNzPLAkKqTMHR70GDKcnmSaoIPycglPpUCGTMvOkAMCiVBARSCWsUP/7h3zGG\\ngdubL4QU04/c7He0xvLfvvyKoBWrxnEYhyJz1xRiWCzknxLZplzIF7mo2UiWUck/ExpQvweu9j2b\\n1mHL/MKYxJFmrbGF/CCDimMh0piptlMNy3EcRcbN2pLlCrU/U/oMUyytWpazrQizS91StFBjjCJS\\nboSIVMlyopWbCSHRNQ0+BlnTWuFjxCuDzkVEvsCfgGRSQOscnZb2r6w0++FIJDEOA12zQmKDzHEU\\nSHNzsuGi0Ty/u8O6hkzk1fU1yRgCloyiyYUAqFOpGZf8ohCrRKpNrrUfAwrDzd2RmMr8VHVf5AAl\\nmendMKKVjFQ7b1tsFsbxmMK0h0zpmx1ixDpBw4YY0NqUXlkx5j4XsQQl/I2MqEvFmCVzBIYcCxqR\\nobBJg0o0xpHrxBwFTknYl+LAqttg0sBQpsw0xknXQbD045EYEma9xo9HxpzpNivcesUPnJ44HK7r\\n0KVWuVpZQUdi5GKzYdM43tzdcH52ShhHYu9RneXm1R189ilmZzHOcrLa8OrlCy6ePQEUW9twDBG3\\n6Ri8BzIxwma15nRzCgpevnnFocyiFUi/7PSCyKkS/IS0UPOpTy7X9h4GWx3Y0hHeMx56croLwG+2\\nM9/RgcrbVA/6w8lhqm9Xe3uvw9z1PZRU3Zceta5tmSSntDDlVMoiG6EogtNlkvsg43N0yjJGxhiS\\nlxrD2jUlkhEyg7EaShSacyyLNPLLr35WtKENn7/6Fb9MP6fRDqcdF9un/N0P/mcas5ogXLlZJXqP\\nVV2IsqjFgSqdMaWmtB8iu37H5UnDD5+c8GY/8GY3shwXm/IcBS0h1D9mlrl0jg9BnmfrRpqL39NK\\n8qHH79Lj9K7ltWQap3vfZ3KaF21SWQx2yTBVqavVJn+MKfVOQzZpXuxKGJdaCYlBa816dc6vfvtf\\n2cbIeREzSEZx5SNPLy4IfsS1Fl0yGlSdiFFrUsICleUiDfWiWTunP3WwsFJSu5TA2nDX+zIEWoIy\\nY6wY7ywqMbE4S1ATMc4Ygw+BIXpxfkqRQ8Yaka+zWjPmhI+BphGlrbPNGgjEHKHuGSp0KCOuFEJg\\n8TGgjLBtrZEsNCVNP444Z9A6czUc2VhLYxStEaEQq4umaoZhGGisI6fIfuzJgNOGQxhRnTjQzXrF\\nulkxDD2v73bsjObuOLLdWF5eXbE9PWU49hhn2LRlBmi59iHKWDKjNTEbms4QYmAck5RjlKHvA1lZ\\nWQpTbbksAzXvSWs0aAkSdjGgU2YjTduTwIGgHtKkr3NmiAHrnNSEnWUIHlPGuWUlPeUxSXCWtQTh\\nogyUabNk4SlDNpo86cRmnJb7osu9scZgcmTlHIfBAgM6g/eeVdPSWsOZs+TxyNVxj7aWtmtxChpr\\ncG2LNpYUE42zBQYVcQxjDeu24fDqNeP1Dfvra9rG8uj0jOura4IxnG/XZBQ+ZValVaZNmbQ/stqe\\nMKjMk+2G68Ne2qCMYnVyQrda8/r6irujjCrT2og27sLmZcU0qaQqP819lTPECgtfWE1y/VSfk6vV\\nqJ5W3fvZu+ztffD2m8cyvkrfeLB8o+bhCe863lvDjDpPQ1HHklkO4yBC6znhx4GcIpUY0ViHLXBI\\n8n6aAHAsLK6UZXOcrdaomDn2RyiwgCr2UIg8AoN2TStUbNtIzdIYmqYhaxjzyG9f/pxfPP+vWKMK\\nVVuX54s0WWM0zmislWHTVmuR14NCAVcTy/H13cjnr/d0VvPjpxtOOztFJaJPuxTbW1zqRR3yT3lc\\nbJqJ7PPnPO7h/jWahDm8VEzO4q3cc/q2wp0xSd0y5owPmTFEBi/ScGNIjEHEz2OR4wplo8ac+cH3\\n/oqLy484BM8X+53U0XNifbIRoYLGFG1WGRhcBTDIMqA3ZYQZS0IZI/1+COtVFIWQHnwyVelEyEEi\\n/I5xGNeixLLjrJX+w7LBJyJIESYI05SN0ryuRRayaxuij7y+veYYRjBSDz3fnuBs5rDfy3vJCqMs\\nJCG4dI2TpDwm6WdWTPX7ECM+eG6PPWPORAw3+4E+KnYpcogjY040xmCywo+ifrTSmkZB8p7sNF0n\\nSFEdtnAYEgbFF8+f84uvv+Jqf8edH9HOcn3ck9uG3TASkEHaow+MMWGtxfuI1lamccSMaxUxDgx9\\nXwRJLNZWQfJCZFlkNLL4JLDSWh5PRUdx9J69SvQFDo85T5NYhhSJWYIsH0OplwdSisLizCLYYkpf\\nZS7BUihTY0T5JuJLjdVqjcmZRhs6YyCLFmtIZdC9Kj3mgO/3ONvgisqQLsIrjbNsVytONxs65zgc\\ne4x1ZCTAadpOxCZSRDuHTzCMowRNxqLIPLo8pWsNT05Pef3F1xz3R55cnPPRas2pUaQwslmtuH75\\nkv3dLdvtBsj0hwMfXZyTjj3H3Q6jHY/OnhFT5jfPv5SRjqqyoyWNqNc0LTLGDKWWqQsiNNctp3r1\\nNwxGuY2qGolFJLT42QTVvyc5eedPK/z/wN+fuCTfmlvK8f5pJYg6iC7svRilZ9IZR8iBrASuiSFg\\nrWXdrqBEpiGV1hHTkHKUSMlackzsd3vB61drrNIcBtkkKlWSji4nNfdV+uCJC0gmI+zAX339r3x8\\n/hlnJ5e4Iq8uxWSFYEZJKvwZ5vl7szEnipFECZX/65uezmmenK44P8k8v+7pxyQbM8+Mzj9HDbMe\\nm1agu8P4/laSDz2+q9PXUBCx+3WI5RXmga+X8WCFwIBSJxQFFJL0/uVIidzrr+aKwpU9UNdLRhnL\\ndvuYVzdf0jQtSWUarfExsutH2kaTiMLQThJgxZyIOtMoh7JyBqu2I6PYjwM5i0JVpkyhJ6ONxftQ\\n5O9S0VGeCQ0hZ5xRMoyZyhRWU/O/0ZoUg9DwcxbpSCViHrfjnv3Qs9lssa0r5BLNEAZCbNj1I4GI\\n0Q5tpI6pLBg0IQq3QKT2xPkrrfG51OdSwpNQEXo/oLKS+ZxaoYzU7HyKmJzo2hZrNcNuoN/tOD09\\nZRiOoBJJyTBjP45YrfjZF1+gu47zR0/QCvpxLC5OpOa0MsQoQXMi05mGfd/TOot1qmjkahGUT4pu\\n1cqQbKukphgy2lT0o6AUk11VhSSIwOkZXKNpWkOKgSPiwNoCk/pyjl3TcPA9jZX6IUaUolKMIhKB\\ntLglCgMfYav6GKZSlFKGpBIxirqSrCdhWdceVkqLCEm4u+G45/z8KbrfY4vucKM0++OAtY43hx23\\n3vPx06c0JbuMxHKOQtqqMoimEVFzrTXX17ecnZ5iDBiX+PH3vs8//b//xE///d9iUNw+f8Fwcs6J\\nsTy9uODN3R3WGbrNBuMaXFakMbI5ucC3Da/ubklpZN1KohLKGg4pTL2Uuf5b4Neq67zpNrIbFlBA\\nLrjqQxZGOAMs4ukaZFb5+xlRuGdcJtsjQzXuC/rP62P+5q2f1c8T3KsefuLieK/DBBm/JSm3qKpY\\nY0khYpDJJTFLL1NrHQ2GvlC0K4OQcqO99wx9X1it0nB93B8kcgFa6xhTjZAl8pyIACnjrCuQjCpQ\\nX6ZtGmIM/OuX/5F/+Mn/QudWkD0qWxRRYOIisaBQqDLZuxSKoBgXkhL1rgQqZ3qf+M2rAycry/cu\\n1uyHyMu7Iz7MN/3P4yrluNj8aWdePnTU2kI1YNP1eMe6Kx2Ui2gRlFpQxnMWmBM1wWcSoSA1zQrw\\nesn2tUpone417l9sL/i1tpQ5YYKKOMumcxz6HY2SDOq06wg5s/MS6J02LSEmhhjotMCCVkHIajKa\\nNTAdoui5WpNJSQmzG2F7B6SkEFIkIYxKZRQpBxEkKLvaGk2MidYJ1Ho3HEXbtbE0tpU+5wJdGqUx\\n2pCVgbZBB0WIoLK0czhnSrZiGH2VjBPYOIaI10EIdgh0dvQj2+ZEyB4lGPA5YTFEDSZpnt9coa41\\nP768JHqPDpHj7Y6vUwJtChs5EHzAnqx4ev6Ei1XH66trVk3L5XrN1e5OjLxFIPQsymG6MTS2QalE\\nTAGjYUwesDStI6eRqMFZTQge64qDQk0DGlQx0ilnbIWlS7UxkzkeR9rW0mcvI7ySSOL1ozgmRRkr\\nqITMpyncCYX0git5bWfdlCj0YRQHHosge6lZZ/LkkF2pH2srtfrWGAihZPly8/uxxzUdKQxY44j9\\ngCGz391JPdUZzrqOMBwJ/WEKSGsdXwunDE1DShGfIq5zJJ05DCNd09I2lv/p7/+Br15eES/g0fk5\\nXWvp2jW3xnC62bBuOvLo0Vmzjz2BTIgDaUw8XTXcHBOjyhwHXxxWQVQWLYCVAVtJao8vP2G7fYqP\\nsxD/7JDqRi8vVTPTpSGtf+eecc2T8ldVV7r3/OJkJyyiJjLvSQAeZqV8+++8X3xdyWIS+TFDThC8\\nFNFPVxvxuFnIOXVBHv1ASonWOnQZFqhLOhyRWubRiyqNaxq2m1POT07ZuFZaU5zDFPq7UcjopiLW\\nrsqbVqgy006gk5e3X/GzL/+Fu+MVjTM0Vgks6wytFdUOeVzmslmrJRrTc0uKKbMBpzYUMndHz69e\\n7fAx8sMnG56etaVVpV6fPz0U64xi3Vhu/sytJLX+UFWbgIfXW347siwRqSo5WZ6j1EpfTzmXVhIm\\nBm0IkRCl3cTHOLWdxNJykjK0qzXn2yfzOK2ywXf9SMIxYDC24c3djtu+p3MNa9fwqNvw2LU0PhL6\\nXmqpYZ5QknLiOISiUiWQZyzFWV37E530YVrjhJ1qrbQtxDTpKVf2be9leoOzjuvd3YLJLWsq5TSh\\nHlprVk1DirJnZHKFIiSFUkb6NEmMcQQynWtk7yk11ed8Jf/kCFoyzNvjnpiEi5BSYm0bXry54de3\\nN7xInnHleHl9w/Pra/7Pn/2CN16GCPc5MqokmQ+JdbeGFFnnzHnX0Bpp71l7EVE4sY6TxtJYy7po\\ns6YQsFqzaVra/5+7N3uS5LyuPH/f5ktEZGZl1oqdICGJlEhxps0ktdmMtc3DzDzM39wPM/PSZjKZ\\nWtaU2KRIEMRae26xuPu3zsP9PCKzUECBmzTWDkOhkFtk+HKXc889pxrLWyMFUYx5D+fPAt9az/PC\\nw3qV1nXmreRe0qZgjNwH1jmgsLAdvZHXHv1UJfJEUYlC7e5Fdm8XQoXsRZbP1gI9piryrsBah7VG\\nmLClOitpvbfgoggpyGRoMBBTlfQTs2ulFdO4o+tXtauWe6xxoqQGBecaxmmq9xqUnGR7ICcpMOuu\\ne8pRCDaAaxq0tpyc3CH6CZMyOo2snOLx559Dht1uB0VVWzf5WacP3uL+e3/Gp1885Vc//xfiNHG6\\n6FkZS4uGer/nLAiOuNscrsN+ppwTbb/gBx/8NdaY/bWbu8NbI5saJ4o+fODV+HC7MVS3vn/OAa8e\\nB0/ycgvGfd0x/96vefFvPd7YYc6JycfArPnnjGXVdYy7HVGJ3qVVmhQCY/CiEKEUrWnAFlSWoNU3\\nDSFqcHIKYgpoBIoa/UiIoe7GiRKJ1oqs5abZv8H93KiqUlR1CYPm48e/4JPHv+bvfvS/cf/4LQgR\\nnTRaCUyiVcajD7BhmaFAuXh6/tl1bUDpgqrEoJfbicvdxN2jlu8/POJiM/FyW9cG9pDkDaumP+Fx\\numy53Pnbldm/07H/FeZz9w3EofnhutVl3mBuHIhBM7tOyDZ7bdw6a1Sprp5oRdAFk2ROlGqSefH8\\nC87Xz+i7dq9vbIwkST9NdJ1lGz1TUliriNPIPbXk/OqCB6tjeufwFE7apQQlY7gax8qitVWaTSj1\\nJSPrCTFKQYdCI0Ew1T1kVFV+YdZjzaQiCEtIkfPNtXQrRqO0IeZIQ7XDUqJHKmLyDQtnCGEiIFJ7\\nY0xYLbuRRityqqSMPZQIu1q85pKxyopQfFGEFNBNwxAntmOgb1smf8ngZU3Fto5nl2t+ud5QUkEf\\nr4QApQ2xulfoDF3XoZTCp8TL6yuWMWEaRZgyOwyta6UY2Fxg+wXZGM6OlhxV9aPN1RVDnmiahjEn\\nTrqeixRQSaQCwdB2Roy+tUGpIjNUY2qHWlVZlcwBDQkfxEg+xEDWorOakq+IlXQ9Y8z7wjgjRt1K\\naRolxhI5ixBFCEH2b/ezuwxVtMAqCF4Uw4qCxjhS7T5npRpfqjpSyZWQVW8cMrZpSdOAtgaCCD20\\nUbS7H5fCg+USisYZR4pBfof6e6CEXT5LI5YcscZizYp//PXPuHf8jLcfPeTOsuWLxyP37pzx2ae/\\n5ahbYKzj+PQB3WJFGHb4CL/95GMWDeRhwKXMFCe5Z6vkqTMaSiYXTS5KplxJ9pZlfKbZDuvKJr+Z\\nBl8dGHKjw6yfUzdmkzXpzbjTa8deSt2OsTOCqw6ff22QejUx/h69zhsTZkqJhDDytBJqs9MWP06y\\nl1iFirfTQEqJvmv3wutOG1pj2Yw7fAw4LK2xpAKTl2AyjaPMADT73TKheyshRdTZi6pyU3NQzqm6\\nn0CFrmpANYlff/7P2A8MZ8eP8CGIWAkHvPtmkN9j7TeonDZXFLAoYWqWAzvy6dXEuQ7cO2r5/oMj\\nXqynf9M9SKXgzqLhk+e/3yrJd5Ha++6/zNd/9qt/v/laNytS+cD+sh2QFVTdyausO61QeV5FAZUl\\nUOskqECc0YEi88QP3vsJnz37hJjDHj4LKbHebbHWsBtmWryi0Za2UQwp0FTrrDFGGutYb9fkGJlU\\noKQMZRYDMOQsQhtaa7kHq2bpMI1ghF3qnNuLb0SyFJSIsbAymhzrPUVBWZnjyb6mMDhnMoVWMg5R\\nRdRi1psdxrZibYWweyUJHNxAnBWtGLKS5fYQyMrg63rY3NH7OvPUwMI4rNNMuzUqJdgmpmmExpGU\\nuGrM3xsraccaU591RYmZ9s4JpyXz8tkTSrditWpQCsZYWN29T5MifdvyrjVcPHvGZUmwaFk2R/jg\\nabuOM+PYlURsGiBTrOiE5ZJonCHFakVWuxNtFCCz4lKZzqF4slcsmo6Y5JoqhbiRINCw1qZ2KkpI\\nYDntJ2Yxpz2crxFyVkauV67JMtabNitpIHSBUnV4UxHRFI8Uc6XMhg/SfATvGXcblssVm+2aVLWD\\n+8WS7TByR1uuxpF113LiWqyWZqQ+MkIaK2W/NywdmSHFQPRXbLYbWtOirKWxDT/8wQ949vQJx31H\\n8J77H/wF508+Z30QOVV4AAAgAElEQVRxwfPnT1itFjy403N2/y7Nskerwqrr2Fxd4YyR9ab6TCqt\\nUVmkGfXs1VrEl1Vrg1buIIc3/8Zl3tOcg0bh1RyqbiS9In/Uj9VooQ4RQmLJ62LR6xPlq2jwqz9r\\nTsy3ftQ3HG9MmNpIdWy0JLveNozTiE9JApIXfN5UdR2nZUZUKqw2RSEMHbULqXqlNKJzshOWjcZY\\nW78+7+ebIQRxBKhzhYJUMiklUhTikXViH2Z0IVXNR+ccF7un/P0v/x/uH7/F+w8/YrU4pXcOozOE\\nwwUppe73Uf2BalVT0UKpQOfPzDM1IKTCVxcDrdPcO+o4WzU8W09c7/x8+v9kZKCT3rHzkZB+/5//\\n+6yP/DGPG2d5vz5SSt1nqwEmI4LXJQtTmyxNZkKJ5mplRsq/Ga0TWhk65zhanvL8+vFeNNznQMqR\\nhekIQUQzVv0Ca8StwmcPpuXTl+e4ZQt+out7rnMg1GdL5lYi1i6FsYwIUi6EmGi1wedM3zakILM9\\nrTUxJ7qmhTqvHKZJINIkYw1T9/90HegoLclYiG2yGjX7VW6GAKbZV/BaSzCTojFXoXQoRWj/OUSc\\ntjRdw+gnXC87oWNdYwkx0ratsBo1vLxaV0KM5Xyzo1t0tSgVFCiXsif9zWYJYwpYCvcWR+zGgSfZ\\n0y87Qmu5u+g5My2PL895cHwHv1mzsi0vXzzHdi33WkfX9ZAzlz5y/+iU6WrNqe2YDOxSRlmxUjNO\\nUVTcu4KUkrBO1fcqMTDmUIlXwqb3UUwhipLxjM8Jq4Qghco0GHyKDDkQyTjlBD6taJHWh3tRF2G7\\nzjEg5Yy1DoNAtkYJC1aQKYlVhjprp4qpKyXwM4UcRow6wdmGHD3TMHJyfIdl23BxvWaiEE5OMNaR\\nQyDFgDbma8/SnDQPClqZH//wQ3FksY4QJvqu48HDt9hsB8I4sDl/wvnLFzSLE756uaW/XnN6doez\\nszs0fS+oSM7E7UB3csyUJ3SqmrxV+hGtRdJGQQweYxx/9dHfslycEFK+laQqQXWPxDE//xVOL3PL\\nPH9OfT0hzjDsPPr5nQiXN5LpTBa99enDgPTVfvhrx5sTZt3HaqoXZhgntkkWhHOUfcqYKxOwLkMb\\naypjTD7uqog1iOt5ybLA5iptWyFar7nOD1NNsnNXmJgFjcUUt3HiyTnvA0kFLkvWfdtyd7nkarvl\\nq/Pf8vz6Mcf9Hd5/+EPOjh7SWguqqgmVwxyuZF29DOuMRFeR9ywXt3JIxP+tQsRTyHxxvqNvNPeO\\nO+6tGp5fT6zH8Cdj0J4uW55dj3/wz/l3S5a3oNl5L66ynmtltFdWyqUisZmSZa6EFu1VnbKMA1R1\\nqVH1QSZxcnSPVCIv1k+EsGY0xWhZYLeaxnV7tuEuVh/A4Ekm42KgU5qL7ZXoyBqDNg4VRUgaXaXP\\noM6PDvf9XPnL3EY6FOscIYu4AinvxwhFFYYw7ccBs0SeMYZqtwtGFudPVkv8tGOMCWcaWWtA5CM1\\nGoPBVFwqpERVQUZbWTPR2mB7EaGP0dO5Bqct62mHj5G+aRmmkU0ascaw262xncNpy2bc4ppWBA+A\\ntu5ggySSZdtRinRh0UdU44SwQsb4RAlr7mvDqiRM2xDGgYdnd7DGsr2+JviAV5qHJ2fYceJ62rHo\\ne3IKrKyTVTQFyWSCB6Mc2ihSyqQssUWlwpgiuSi61kKKldks8old16CozFJTCDmitCPkxCaHvaPJ\\nDC0XpbC109RKYWamJ/I+Z93eXGRXlBqXlFZYZbA3OiSQFRWrD6QnWwXg8zTgjGMYd3RtR5xG+q7n\\n06+e4o96rLX0xrHbXFf0gFro30CJbpBbqmY/p3dPyVG0hnHH7IIgAqpcs70aGK4ucK5B+S0ffu99\\nPvnNrzihoHMmjV7uYw1Wyx585xpinkhRVIuoKzJZQWNEi/docY/3Hn1EKqoKchwUk+eEWdHXmr/m\\n+POaGDmjszWRKsWt9ZXvDKV+DaKt52l/cQ4w8Hz+lPoDE6ap1PBF0xJHzzZOAoFV5Q9uXrhK3Cg5\\n45qGvuLeSoGu+rExJigao2VJ2CpDShFjRcjYh4AIB89QadmTe2JIIrSuZB+JUrDWyEqBAlUKBnjU\\nd6yvrmmsIRO52D7n6jfnfP+tv+S9hz+kVJir2FIx+YItEvxKRlRcsqgBFSW6h1pJgp2LlVKTJqUw\\n+sxnL7YsWsu9o4b7xy3Pryeuhz+umfOikZtzO8VbH/9dOsY/hTj7txUG3wYBy7eVWtnNHaewYWfR\\n81kNB9iXlwmpCnUu6CjkhcPDaXl47/soDM8uv6q7c+zJHoumo6ssydGL5FvMlbChjCRmi8xHjSWn\\nIg49WrP1cR8QpNqV/c0kigFiMVWkIypa7iOllECAiAOItUbslEqurFgn6IhS5JRExN2IG08qkhCX\\nTcOwWVOUyLlFJXB1KEGg6Koqk5J0VxTRbY5ZXIWUKvSto+wyWjVYIzJqCtgFj0KxHXaglOinIlD2\\n5bSWjtf7veeo0kpmmlWl5sOze4ybDQbDRQh8cHyCGiZca7nerrFNi8ni9FFyZtE25Jy5vnjB9W5H\\nch2P7t/FTDtiksX+tmlpkuH5sCFpiEmY7NYK3Kwq2WhW/RHWMmhlSQlCkCKhaeu1Q1WdYC0FD7LG\\nE2uyTNU+LMRq5VUlPkvtLIvSVVicqntdMNbuDSOMFqHxXLvflDOmSg0apbHKQBXSn4ttgHFY0y5O\\nMNZidUOadhil2SnF4uSY46YjbNfEENDO7nswYYoKtK/RlJyrSEapz0yhX52ibMt6c816fcm9s7s8\\neviIzfWay8tzcsjEsOPo+A5Hi67+DAjBE3PBNI47JydErWkKbIeBed1PrE3lPrFGCFvDuGGctrTt\\nEYcJ5KGzPPy97OOmmj9TY/d8fmYoV1W06aZ4u+KwgnJLAW2OKbfi0/w9whi/kS7nCel+Fqq5qR71\\nzccbE2ZrLa1xpMmLZFK9eeZl/sYcbpxZPizEyDYmnBGWq1Ki7tJYYZ4trMyLVMqyO5XlAdT5QAgx\\nCDtQiFoyoE8kbHWcTxXD1wUa15CqJFNrHHfahqOuw08jy6ZlF0Vo4ZPH/52jxRknRw/JVpOyxmWR\\nY8t59g+Ual/MfKl7ofX01ou6vyjl5kVTbKfIdoosGsO9o7YmzvGPJox+umq52H5zEv6jzie/wzG/\\n3psS56sJ/eYscw4Ch3mHfE/mAIHP5sdz5SqzzEKYv//GWy5A37S8/9aPWC5P+PUXP+Ny9xx0oHWO\\nlLOsFmhN17SsNxuy1izbDlWTWOMs1ishwkwTbdex3WzxfqJfLiHJTA9EUSeRsLahBLFCCqom/SKs\\nVGHzRnonC+ZCzAm4pmPVdUxhYvCTdCo5A2mvE3u6OCIFj/eBYht8kXctll71fGiBWRXQmEYSdqa6\\nnkQWbUMpkWWrQDvp6EmESZR7NuPI0XLFUb9kip6dH2RP02qssfuAV2rCUHUBVwHTMHC/bXl+cYVp\\nG1SOuDixu75kWF9z9t472LYjTxNFKa4H4SwkrTl98EDmuMOWMhceSglsGAJYTas1U67OJ4DTipzE\\npH5Isj6Gqs+vKthiySUIlJo0IWSiUzjj8D7WlTj5/HZKNAuHj140eikkBDlQReajuRSCyoJgKVv3\\nsG8ydRU5x3rvCTxrtUGj9smyFBFtmHFGozQpBZlLp4BzC+I0kFLmfLMh9Y7jfsmiwG63FaIWyN5s\\nHVulUvZatZLwha3e9kvafkUMnsvnX6G04v333iPsdoTNJSV6nFGEmHny7CUoxYOHD9htNyhlgUyO\\nIyFFuuNjGYtEj7UaH+cZptoz0FPOdeUm8PTlV7z/9l8IfKpuxE1uQLI3k2U1GVc15rKPEzUMFOon\\nKxKlbsfguXi4mSVvh79y47XZx5PD3VsOCfvmt31LCH1jwmy0pcTEbpqI1H00pWrSlOA1Z2dVlUyK\\nkf2zECJjilgt8Ov5xtO7QOecUOWdISNd6nY3MHox9W2tEwZWvSGVFgkxRdXaROAQXcAay7LtoBR2\\nYeKoaUjjxDCMHC86GCeuY0ArTUgTnzz+F36yuIM1DmeEkm/LzLildrYiiC0Y+80VkgM0K4Shw5mt\\nEzgyIibw6Ysty9Zy76jl3lHLi/X0ByVOaxTL1vL4Yvd7/4zf5/hjzjtvabHO1TDz0vHN+UGBMgt0\\ny//mGiBub3Pm+tWl7nQpCXlF1lDOjt/mb3/0gJ/95r/w5PIzjDZsy0B2LX1xPL+6QFvHom1JpTCM\\nE4uuJUyJ7eBxypB9JMWBp+eXlL4n1OCYcj50FVWTVCldDdGjqLRoRcmzDFzDNkak2BNd2Psnd9hs\\ntoyj36vyWFlarOYDIqC+Hid0Y5lS3CsT+SQz/il4+rbF54BSmnHa4Kyr3VaS4BUirVMEHyiIupAP\\nhVAKgczx8ojjfilJvGh0kvWJnMV9I1Zxeio05pysi1ml+Xy9xt25w1v9gk1OHKF5vt2yWC5oFg1Z\\nC1lvs9niFh26bURNSCnC5PEx0LUtMXowGmcdPiWmojjpF0w+MBAxlcHqw0RSCgMstSVRGHOq6zqG\\n3RgoGFQuTBGMaWW2WxQ+JrQXy7PRR0JW4KUrL7nI7M3O4wGqUxL7+79QiNVqUMyn1a1nQ8ZIB7RE\\n1yIb5LwJNGsoVTFHFZn/Nc2S7AcSmt+8fMHq3Yec9kvibot1du/mVOprm4rKzchKLgXX9iwWK1KM\\nbNcXRD/iGs1qeULygc3lC5wxUiSFCZ0TR8uWnZ946+yMFCPBD7SuJRRZ7Skpoa1A860R0YcSBYpV\\nSpqVUHV4rRE2bZnhzhooD/Zgav/7sj+nlUjFnBTZV9CHp5t9B1r2yNO+vL593IR8ORTkcyeramc8\\nx5hSbrwYHK6c+tpP3h9vVvqJie00kGs7LkLruv62h6rAGoPTVqBSoLOGgcKErIUMPqKtrQPhhABr\\nkshSzjSmYXHUMIUozFYlD2SmMAwDWUnFLOQ4BUosaJbdglXbYUvidLHk0WqBv74gpsKwG0gx0LRC\\nlLDGcLF9xtOXv+Xdh39BsgWXBZLJWv5NRaGzTIHMjfIkz93UHJz3gf82NHDzmDvOZWu5u2q4f9zx\\ncj1xtfvdWbWni4arneePJBv7nY5XWa+vS5q/65z2VtLkMM+cj3pPfw1a2c8GM1WknfrkZkgC/c+q\\nI8Jkla9tG8OPvvc3jL/ccTG+kB005fHF0/cdTjtijCKMbsSaa/JC7HmyG0ghSmd1dCQkt3mWUu+N\\nkjIpJ4YsASYWUfxRJYsaVgqUXEgq7W3oyKK3nEJg50e007UAy0Joq56KmULWiq5rudM61s9ekFoR\\n4I4p74kRxhjG6ClFoF1DYYqegjA4QxI1Gh9ErL2UgjKGZd+Thh3DNOK0ZvSBVdfLMnxlqKeURNbN\\nGjTy/K+UwxrNWdsyRs+YCo1VnGwnrp8PdI3BNZZipPsbU0K1DbZaqikUOSaygr5fkGNgytC2DeMU\\nMNZw96iHLEm91YaZgCeC+zBlIW8FH5mConBwmrHWClEIi9LiCbMZpZuevVNTKgJx5yqOUgzaZKh7\\njbLRIoRApUCEhjKmxqE9ElJmJacaB5WqZJ/9ktReJc0ZAzkzTRO26QhRElekUFyDRZOsoW06bIrk\\nLIztGCIpyOpSqmxerRSkgul6VkvRSd6tL8gp1HvCMrMlQxgZx4nUOlzXk6Kc48WyMCSYFBzfvUue\\ndgzDbi/YoFH0bUMeRhZag1WsCZSoyQmKlXtJVz6A1jJN16RDRzcnzNp4aMXeAao2mLXZOqwNzqlF\\n3fi7/Kc2KeUVyQF1+I/a/62OeObOtl4NdWj094mxHHLm/nf5puONCXPrR4pS9E17eDN1zcNZgR5K\\nEdq80zIHSjFVNX5N2/ZoRF4PJYvCo/cYrenbDmvEYLd1Qnt3xuBMSyyFafJM00TX9Thr2Q4D0Qec\\nc3RNIyc5RtZhjS6F1aJlGDLnl2up6Iy4pEiSVxy5li8uL/ji+cfcvfMWjT0iGY1Nmag1RhdMKdVO\\nCamOSzWwVbUDKkJEmTm1hz8qs4551nnAx7dTZDMG+sZwdyVQ7fnGc7GdvlMCVMCdZcOnL7av//y/\\nAQz7x3yNr3WataSfz+Lh9eoK0QyZzDBYLpXmL0SxqERllFTnE/WcFiUQa+dafvrn/4lfff5feXb9\\nuRRICiYfiLoqCOla0zpHDrkSfDSqOSj8hKq8o7R0Eiknsq4dRBL5sqJKLQCNuPvUWVNRisY25JSw\\nWgzON+OItU7YnzFQgKYV4wJdWahKaVrjePr0KcFockrSMdUuLcXMdjeSkI7FGC0JobqwxJhIxor0\\nn7GEqgyjS8KHCR8Tuar/nJ6c0DYNYSO2YEqih3TRSomCTF3Kf3u55NgZbLSEVEg+McXMdpq4068o\\nWrSbEwqjHE5LFynzwSCL/0mus9KGQuDFNJJK4a52hGnAaEtIiYVx+xUarXVd0UmMKRIyULVLlVIs\\nu1bWK5SqxtuFy50nRrCukbFSgVLq+lKdbxUgRRGpQAniFJPA56a6Hxkr/pBKie2fGILXaFsyWmnG\\nFHDK0mhhYM87haWIYIvKmZCr/47WFKPwYWS5POHFF5+RFSyaBpsyIUOKiRwiOUVyEXQv54LrljT9\\ngug9u+uX+0Zl5gVoY9mu11WQRXbLpyDdYNMv0SimkHhxcUnz1kNO75wS15rdMKK0RVvR4zalUIYJ\\nwkS77NkpkTtVxtXnVd6bc5bNcAUzVrTvLA9J6OY8U3N4rufub+7G92DpoQ6ZU+WNzvN2p3lIlvNX\\nS4crXesMzR463fn75iLr22UODsebST/OsnItOQkMJBZAswJEqdCqpXeWUgKTF1k7Zxu5uZABtdVG\\nKOG2oVhHqNT75CU4rac1trEsnSPERBynWok5ll1HSdWcVcsMNUy+yoUJe2zRNizbjnH0PBllp0tb\\nI0pBRujjnz19StIKn655efmY9x4e47IiWU3MArOZrOo8paoT6UMnOXeZZUYd6sMy/38p8w3xikRT\\n/buYV+9orObeUctHj4653HrON9O3Oo4c944piPD4v8fxp0jIr02aMJ+5+Ys4tJuHYX2uNlBJi2MI\\nYkxfH4B8KGKKpZQEBbq246cf/a/8/c//My93T3DOkowwVp11WC1L5bNhrmscuhRCyHVeWEDLlbW1\\n450dU0ou4tKgECirButcJmIprKxI0A1h3EuwJSU7yk4rbNb4amysdJGutIiWaSmZ3TBxnTLZsZcM\\nVEpJAZkSrmmZ9XaV1gxexApMNtKVqUqTSQkfZaWlcQ3OWXKM+OqGsmhbdsOOUGZRcSMuHo04A73V\\nHTH5AGFkEQOuFFb9Ar/d8t+//AJ3vOL43ilDSpy2HSUFUspMOWCNrLTEnDDWMiVRjpmvf64L/ovG\\n8dwPnPUrpuAZS0LlQo/dGzfoAg2a0cMUC6tlVwvrBqMLS2vR08SUZGdUaYO2YirdOIhJEWPBqMrY\\nr2Qma2W26KeCUk4A/iQ2VwAhSCB2TaGzAJmmmk5bLRKhQ4hgMgsFFivG2TnTOYtF0Ln9HqXR+JAw\\nGpx1vBxHPvre9zlVlvHqCm01JWtC9Ghr0cbSr47RpiH4gc3Vi0N1eONZLQVi8iyPjkSSbxpYrI64\\nurrEOCMm4zGKhu12yzZGTNuhQyTlZ1inaNuGtmnEZINMVoreWNqm4Woc2ZREKMJS9tVK7vL6hfiA\\nKmkq9h1l/VdWdQo6V+JefdRnBLe+ARRlr4hZP3hrVqnn5+9GP7mPK3OUuNGV6n2iVK98R02qN157\\n/gnfdLwxYR51LS2Gy2EgkzHOyo4ZkkwWbUujC9M0iG6nbuisvOnRByiFkDPHywWuOgeEkKtyBNQd\\nYYFtp1B3wxpcY1k5wzQFpnESu52CwFYKGuvo2o6uaShAZzTTZsfnL16wOruLcUZYbwp617Beb3B9\\ni86QS+L5+ec8PHsfozuMEV3QNMOzpRrEVmf2XK/oHNRVraoUVMLJzAauuPhcGX3Dmfcx89XFgDUj\\nd5eiHLQeAuebiek1SfF01fJi/Yevkvyux781gWiuCL+mPznnTVWZqQjcmm6m15tLWpJWKUW8NPc2\\nOKXwo+/9Db998nMeX30iFH8tMl45JtBi0eSsMFfnHeI8w6z1J8ccxS6rVANzJRBUDEHIaCi00YzT\\nhDaGkEK1LgNnHFOM3O2WXG/WaOdwRbGyPVs8w7iT14+RXKBpZNY66nklRZ6naZpY9AtKZbDGIGSg\\n3ThJs6zEOFkrQyojwQdyKTTOseh7Fm2HRjNNnlBkj3k7TZicSSVhjK0wr8I4R28sF8OG1TDxwdtv\\ns7SGMo68ePyYx199RWwd905PcK6hs7Z225oxTCQNYxIRAFncFz7C2eKYGMfq2KIIWskOa8pscmCh\\nNEfWsS6ZizjRGlsLdc314LmcJNCHmMg54SfPomu42GzBinevteIbGUshacswRqDU7jXjrDiiQCZ4\\nj1KarrUoVdm5UZ7pPUlNWXJW9W5IIq5CIanMVI2qfQwMJbONGpVEIlShmHLEROibhilFnBYxvNY5\\nrq4vOT49ZaksTz/9LcdHSywNWSWabkG3PBbRfz+wWz876HS/5lD1D9c4wjjiU2azvma5XOFcK+bi\\ngM+Z1aJjvVkz5oSOkRATymbarqNrl2zGa16+eMG9d9/lzvExcQpMwzV0DeuQCKnyPyhsh80NmLXO\\nLytTfD/LVEoU1ITqfiDkqJsFdH0fs870vmA+dKDftrYnMaE+rXus9UaTs+9Hb85Ub3zht6TMNybM\\nldFcrjcUrWltS4iB1opOo0aR/cioMm3T0XQduoBKkethIsSMcw33j4/YbddoK8k2J+j7Thhf2qGy\\nYjMM5CLD+BhTlZOSN9c1jtZZ0JrNdocxhrZp6JyBEjlbLclT4Mth4OjkDs5ojvteHBzItMZwPfRs\\n/ETXGqAhhA0ff/7f+PPv/Q1WG5yRva6YFSkr0aQEStJolauZsMTkWWGIGSqU9XLmq6urA0cGbnZR\\nNw+ZQxWeXo+82EzcWTS8f2/JFDLn24nNKOSS3omm7vz//6Mf8wOyh8vmD84ATC2uQKGLrDHFXG9k\\nNT9Us0Zt/a5iofqrtnbJX33v7zh+dodffvVP9Ru9CLjHTG9bUhIJstkYPaSZ7KX2BgS5roKQy77r\\nzUinaZ3sNapKgphyEOKEFhWUomGzXYsGbYExRRpjOXIiWr4dpThqm4arzYb1uMM2rvpDKvwU0VgZ\\nfYwiKZmTvP6qXeJcx+XmXMyrdWGa8t4WDwpTDIwp0NqWru+ZYsRVrdVeSydHzuRUZGYcIhdxCxmu\\nlOKtzQYmz6JfcHF9xff+7ENhv+aMU5oQI9oJo1RgNkNWwmL1tRi11pKJQihCYayh0ZpJJXxR+Glk\\nUxRd3XHNBoYcMOgqNpBxXUtRiilmEay3mhgi2yh7465K4SUK1lhCFPGInGR1rW0sRokm6ywzp0rV\\n6k2i+1tQOGfrzaQr9FkYJxnKKDUTBqVrz9V4WqFICpTTDETGKdI4x0nX1wJQ7XkZSmuuri95+53v\\n8y//8I9srtc8eHgf4zr6xRGUwjhs8JOsdqSUMNp+I4g4P0NhGokpop3j7v2HOGPYbddY6wh+wted\\nUq0N0QdMyZAzumSapidmwz/96is++ddf8L/cvQvLnnEaaduGpBW6zLL1MiLomh4fPFo7tJLOzqDq\\nDn25nTTVgfGqNeQ8Q7A3dGRvrPPJub7ZUL8+qalDUN4nY/n7q6DrzMKf0/CcSMs3nlf4DglzGgcy\\nVX0nBtkvmhNGKehGiyl09CSfGX0gxwLGsOp7emsxObJadOzGIKIDvaMkj3MW7z2jTzLjLIW+bYgx\\nEeKsKKKFaVjEO9Maw6LvaIwoTSz7nmNreXp1jbZGoARnibtrSh2iP9usMarh7uqYu13Hs+srnk87\\nrstLSglo5cTlPRWsLiQtD8sswK4VwngsB/hwdku4VY/sZxbwrWf9lSPlwsvNxPlm4qh33DvqeHii\\nuNhMLFrz7+5K8m921AJp/+crqs2zQpP8pwqZF0mcCUTtoKhKCAIlYq8UREt4vj4KxQePfsQYd3z6\\n4peyGxk9VhuxBFMKX1nTwU8yj9O6qkmJhmcqiUY3OOHdC+M0JpqmIVUYzimznwXGKphNEc1TnxLE\\niKmmBetpK8/REHm4PGLZdWij2Ew7lDP77jLVXUEKONXx6PQeR8tjFs2CxvWcHT9AgosQTQa/4We/\\n+QeeXn0lVmFaE5SiaSzZJMbRE2LkeHWE0oqUCjFkWdZXhqNVjzKKGBNZZRZtz2+j505O3Lu64mgh\\nLFQNbL2nOEtnLBfn57JLqqA1LSnLAj9KiypOLgw+4IPsuIaS8SlhlZGuchwYjKZoK3PPFIi5kjbI\\nlSAUKcLMY/CBKcouq7JmL0pitEgg5ooA+RhwxgKFEDy5ihBIQJWEmIFURFBj0XbEFLC2elGKWxgZ\\n4TnMnY+pqyQo+R2d0ViqcYUW04eSZLfbIKLtRiviJJqtnTaYlGQ3eHXC6dvfJ2zX+O0VwU/iPZln\\nDd3biMzrDqUU2ljZga2G2NvNNYuuRytNv1xi1xsWXYc2hhdPn3DiGk7O7mKteBlfXDyBeMVH/+Gv\\noG05n7zswy46FqWQDeQhsBkndjmQrKZx8q5jiRij0Emhq2rjAZ6d3YbKYbylDr+35M1y470cZrM3\\nA+se0bvxng+KbLcZzhoqq7nyIm4EaVV/jzkpf9vxxoQ5+EgpYoSbSyGmiDMWZ4wIAxSPD0nmOCgU\\nDW1naVtHThEfJzSiQKK1OLuL8YAlpkgIicmL7JNrBIpy1rJcLPB+IiQB3rqmYT7zi8bh/YR1jg7Y\\nbjdsppGmaSFHcipoZTjfDaIHaxyUwvr6imePvyQajTKWKeyYpi2L/oxYCrZqk2qtUVlo4FnNdOY6\\npJ6Dbk2mlG/qIgXmmf+u5iv/muPm918Pgesh0DvZ5fzorWN+8eUlzmhC+uPMMP+t9zW/CT553e8x\\nM2f39V6ZT15B02EAACAASURBVFudThQ167AD7NcvyKDIEvTqbAKN6MAyg7TyjfKsFL7/6CdsxjXP\\n119VBxrRL1b1Hsg5UnR1mVBWNF5jFhagkdl5ShnvZQ5YapAOUZSeQgp1jiP3rYiDG0Lw+BxEsD0q\\nrtMg+rLAwzv3eXl1wfIIdruJmFLVMC2cdKe8/85H3FncZdEdY22zJ0fBHHxuEy36dsV/+uv/i599\\n8vf84sufEaMs9RNgmkKVctNM1Rw+xshi2QvrUWmOV0tsKoxOxAGOup6r7ZZ37j7AP/6SO8uecfJg\\nDcZqxhi58gPZKazR6KKYVKpM4aqCU/e1r6aBrDW2iFZpLjBm4SYoI13JEDKdNTjdiA1YjORiZFfW\\nKK62AeUaIWJpvYfQtTEicmI0KUoxXqqcXSpFinBUtYpSWG3FkBnpdELMLPoFKQeoxVkUlg/OGXKM\\nEhcKWCez6Jyr8k9lW2ddaKzDp0AqBlcUIUa81iyNwwQ5xzpkypS4ur7kL3/yEzbrF/hxy/XLF0y7\\na9qur/6a4qOqrZ0foG+MKVprMZxOiavNmq7vZB3I2lrsF1bLJT7Do5N7/Pq3nzAtF7x1/wGdgs8+\\n+4JtGPmLn/wlL4cdZyenbC5e0ix6tjFw7UeuQ2IXqlNRLjw6fYe26dkMa15cPuVocU8KCSXFUqpx\\nVWQtFSodVslmEtDchMxFwf7d3UiuN0PJvOs5R485VtyMPQp5fWeEYZ1SdULa/6ADEWiGe7/peGPC\\ndE2DLuIPqBBR6VIKRkPnLBT2tjkAyqm6KB0QvUdHDB6wOO1obPWTKwU/eaaQsK6ha6Tqm4oIqw+D\\n7BsarQg+0ltH3zT0bcd22KK14aTv6Yzhehpo+46sDI11LBvH6CeaDsIkXbFCAoNbLqGKYuccuVq/\\nYLW8K+QgrTAarBb3i6L0fkhdFMjYVYK5zuxXHb7WZdZuaN9tfgMk/m04/BASQ0j861fXxFT44N4S\\nHzMXO89657/2oHzXJDi/3retifyxk+mblIDg9TfpXILtZxp1JlHmxel69vMMqhQoOWPQVeJQuoEZ\\nokXFWt9HNHXl5P2/Yfz4/2UzXVG5tvsHUyst6xkyPK+ehiIHKbPIiZwhlETMsXYeWeDaukJgrayI\\nWCVd2tbvZD5OwWEYkxfYT8vKxuPzF4JmVJPiQsGnyFF7wt/95f9O63qxDMvgY5Vnu3HO5mpa6yr2\\nbWSV5ac/+I/EkvnyxW8Z/FaW0OUb0GhCDKKApSCmQAqZxjWcr68wGc5WR/gcCd7z3t179CkyLXou\\nB0+InvbOSXUFyexUYtl1sptRTROMUSJIn0RgZIieXAujyMHSzZcshDstij0pF7rGYsjkIHAnBaYg\\nghD7rkLNnUMlhFWXjRCEfazr8xyLzMBRdT0OMW+YqpgBSrpCU9dvUi7154hoilKFlKLMNwFnrexw\\nVvZs60QW1CpqoyA8CKOqx6tWdE7Y0spotHHYdsHd+z2/+vhXvP29Dzg9PYYQ2F5fYZ0Ubs515JII\\nIdE3LamyYm8+NwfVG/knTF7EXBbLqoimqvatOJ/EGGgbhzWOl8/PeffkhJPjYz791S9IJN567x1O\\nlgvCsONqc8XiaEX2nvXlFXm5wJZCazQsLGGT0FaQgNZ1PDv/jL5ZYdxSBGZ02SfOXDvNuahT9foY\\npZD+Xh22EcqNOLmHbPXhni+F1wZYdfjP3IfPkHCpprQ3k+8t6PYPSZijT2gtu17OCB5dUiLGTNKy\\npD15gTWt1hijabXC6hYs7KaRnAqrvsMADk1Uic00MXiBsI4WMs8cvOxfmkYWklWdd4SUiLlw1Boa\\nMtlZWZalEMNEiomubSgo+pqgTS6kcRQVjCwOAcooEW5GWvKUCk9ffsk7D3+A0ZKcrdZEXbBak0vC\\nVCk06S4rc1aJN54Ep8MaCermTXtD2EAB85Lsq9f1m7pOxCT6sxdbpph5fj1y1DtOly0Pjzuudp7L\\nXbjVdX6XZPddk+ofN2m+/r1/02vOcFP9zAycMEORczcyV6ZQ5QyyXKdCrtqqCEwrSwAobsA04nxI\\n17T8+P2/4x9+/X8z+UHgVi3mAD4GfJTZU9M4xmnaB2SVRIQgxkicYl2+lmIw5VQX/mXAKlVtoJQA\\nSiQgU0gUq/A50bUtISWS9yQSrji0cvzkw7/jZHFGQdPYDkXLdorCsq3jgdmuaq7clJLueN4XtEVT\\ntEYZ+OmHf4szlo8f/ys+DRXSq0krZ6IXyFY0ALQIiSSDMmD9yE/f+YC43RJjIGx3fH59xboUjozh\\nHS1FzPng6RdLHBCKlCqNsVil0NXDMSAyc9TglGqHmZDrF0Nm4TqGaRIjeVVNnRESV0GYq0Y7wrzL\\nXZEobgS8OEPyZZZxE2uyXIPz/Kz6GMHofTKWlTRTZToLKUvcyyhRmIpQEKMHSpHGwMruyeQzwWR6\\np+mNZWHktWJM5KoZHDMs+hWrboXfrSFHVAhMfoexhX5xjL94Qdc5jLWM00hG1phc3WX1UVSLXu0y\\nZzH22TC87XuZI9duLScpeqCgreXunROeP/4SS0TFyPMnX3F6drpHbkpMLLuWtQ8821zT9gta69hd\\nb3i36zm/eMlL11JS4nzzXFjHSvNn7/4Yn3KV0hO0ThuFyVVjvGS5N2vBJ8SgigLV9yGm4TeE5ed5\\nYznEhEPh/Jqos0df5D6M9ffZWwZyAwrex+pvj3tvXivRshMm7EFZSy1KYAtrCk4Z3GKJQuF9gJKF\\nql6Nc4vW3Fkeocjil5kCuyCdpbKORddTcpJhMWIfNgYv3ayVbtbW6qSxhhgCOSUWTYszmt04EpTM\\nAow2RBSX1xuuh4GIRjuBfouWOVKJpT5wQlff7s7ZDRsW/QnJSEVrc6lzKplj5DqvLHperq0XStci\\n+sYFnJOmfFrNdKA35oxXu7BV72SJPh4gxfUYWY8RZzSny4YP768YQ/rGrvN3Pf5UDity1AT2LSoa\\nt7rNuWtgPm01U3L7VB5c2Wa6zyzczgHb2XsN1a+o3zyPP1p3wn/88/+DXz35Z55efkIBdn6klIzK\\nisa2eD8xjAOq3v8hBJxpUcpyvDqhbXqu1ucsuhXHp8dY7Xh+9YTNdE1jG5xruNpc0nWdrFtpJeIA\\n9X3PcOCqO+WHb/81Hz58l8td1YytBJxdFMPglA/GAbeSJXPCLLX4k/s2GykarDXcP37Ez7/4b8zK\\nmiBIS4yVdKHnAKxq8hEizUXc8PHTJ7TXay4U5HFkNIq2X3B0fMLqzh12z58SiqJXmutppGsNnXa0\\nxhJjJOSZdVxdXVLC51yDqbDmbdZ0vaq6uKperyoqkkTrNlWiVSqZBOL+UfLezWPu+nI9z3qG76v9\\nlzVGAnESZqsylsY5ge+17JXL6pL8DvN5TjFRisCottqDhSrskHKpageCaoQEy8bQ5IIqMkctzrFa\\nHLNQFpJn2JyTpwnjOh5/9Zl0PClitWPtI7kUxu0W23RViEDet9aiTFR0oeoU3oIShRRkhHSWCzlF\\n0f5Oqe7xioFA37e8PD/nYn3Nvfv3uXvvDs42rNqGcX3FNE1MSQrNRS5sJ0+yDWXZYazm6ZQoRpTg\\nOjRX6yuGsKNvV6wWJ6SsGEOsiVKSZTYZXWQPWVckoVoaC+xc5lJ2VgGan315l3X6cgOXPSzyqflE\\n3PrMHFsQFKBGlNdGoRvjjG863iyN1zTCDFPzTleSBWkFbePQSTFEz+A9GU1JkHRi6Rpc7yglsx0n\\n1sOGKSQoBm1kIH206CtLTRiJBcU4BUKUSiDng1bpMHn8NBJj5OhohauyY6L/OlvyyIxVN5ZVc4xP\\nslA+hcP+aMhxL3aNghgnvnrxMT9473+SJV+tsTqLoEENTGZOmBWaLXOFmtP+IlW04FYzOXdAaX89\\nvhmCfXUOerZsON9MX/s8QEiZZ9fjvus8WzY8Oum43gUud/61qynf5fjTzTXnZPfmTnP/u8ChKoa9\\nmspNmsMMoc1JsswPQj7E/PlBREt3MDOa50RcakfRuI6/eu9vOV094JPH/8xuWlcbKdiNI9OwQ2tH\\n1y64s7rL2eohp6v7tLYTqF9bfPIoxJFea83334rspjWQWQ/XfBx/zhg2TMGzWi4FBkyFdnHMu4/+\\nnFV/wr3jRyjliFkxBC9ITpYkkesi/kwAkqXxA3St9x2m3MfzDnFmFl8orPq7Qm7KqcYhuR6pOnMk\\naV2FXFIUj87e5WR1h6cXj3m2uYQSRRjBFlzT8OjkmLe6nqv1Nc92A3ZxjDNQTEOjFK4Kj8cUZS4J\\nOG2rPJy8ptby+jGDT5FV1xNS4GzVsZsm2W2sV63UeXBBktX8PM7MEWfM3oeUen7mIlbXGacPUfxH\\nUeimoTEWU0BbJTqySoHVdc4lr5xr+DW2wSqBDHNlU2dU3eU0NEbjVKG1smqmreO4O2bRdFxt14Rp\\nx/k4smxbUTOzjhx8bTQSJRXRbjWOMAVijPQLS04ipO9jZhw9KMs4eRa2P4yGlMxRjTH7EUKprO6U\\nEqVKJVpriTHjfeJqu+XDP/sIk+HJxXM++OB7+N0W4xxhvYFCFbwv3LFiuzgZS+Msk9aM9pgwBrKf\\naqzfsGhWNTHbKgajyDNxrki3WZR8TD4HKgtFpUAdbdyIA/UulvtVRg6z5eM+TnxT6FI3YkW9i179\\n2le/9Q+CZJeNIkQr0IpSpBxlCVcrVsbRWc3TMNG4jpg82hi2YWI7DjRK47Rm5yNTAdcuWC5aci6s\\nuhZVCj54UoUshilQlKaxDSD7brqI8EBRArQtFytyjjy9uGRhDZ2zLPueIQSB0HIRRxQtFPFYl2pj\\njPsT3BiLbRoKhRgzT57+hnsnb3N68g45B3IWkYVcTJ1pySOTawBXWu2Ng8kyE8nlgInPupP7S/o7\\ndm6d0zRWs37DKkmBPUnIGc3JwvHe3SUpF652nqsh3IIf/l2PW8P5bz9uQ7Lzx24WIzfg2yoocOjD\\nJTkrpUSpR5jvKCAevoJ94syCKsRciFZz//h9Tpf3+erlJ3z58mOmONC6hkcPPuCts/dYtMc426OU\\nsGZztR6LqSAWxaombJm1LNsztFIc9/d5dPoBw7jm+dVjvrr8hKO256O3fsKqP8FUL8YpZnL2ON2w\\nGyWwp3QjUeYq/1ff7qHOLvuZkNGKYgqlGDIZV+bawdC4nh+99z/z80//kVxFHWLJHPcrrHE8PH2b\\no/4I7wOnR2ecru6ybJf8+L2f8ttnP8dtn/BfvvysijNkBu/55eU11lpMt+T+8ZKVU5RYTblTkMre\\nqP0MTYwVKv8hR2IUe66opGO6DhNL54g+ME2BqGQlphSZvRVmWE1V1rJ4885uR9TEqurseYb0YjrM\\n/aw20n3W5zPOWor1MKIEVxmz7JPuDP2WilhUBb3anQg8q42hcS3HixVGKXzwxKsNMUWsdThrxOQ7\\nSVwa1xtSLqzalu3lFcftCpQihsAUAqdaVKl002MK7HYD/aITb9YyNxUCb+pKtMolk6J4rqaUDkVE\\nyVIsKMPzqyuOzs5YLZeE3cBwvWHc7ohebN5c1+JTJhbhrlhrQBuB/LFMNnPOSFCWRoGfRqYwYIzm\\n06/+laPulJOjB7hSyDpjdZEdVqP3hWouGlNkzitzy4MC0zzLrLjTIXrsA8H8/zUWvFqM32o5bzwp\\n8zm79bWHWap+TUyajzcmTD+M2Kahd0YegFxYGFkGVjmRyTw6PiEWOB92hJwpxRFVYucjOSSapuW4\\nbVk0DUYVJh9whb0tTQiSKPvFQqjidaZh6qIyWix9dN2DskZjG0dIYtyrslR6KZeaNDNxZt5ayzSO\\n9G1XYTCpIEOMolyUxXHixeUXHK3OcKYhF1mOLiVTqm1JKaLrmfed5iFAzddM1hi+nh/nhPpdDqUU\\np0uRzvtdjpAyL9YTL9YTi8Zwsmj4wXHHbopcDYHNEL5jb/enPF7f+b6u8351jnqo8mci0Px5tU8c\\n8+fnpDrPYXJR9cGsxY/Yx4sHao6krIlJ45OmtQqnO969/yPun7zPFHccdaeVbBPFMHoSo+Ky/3mv\\nPqwHaEeruYuSRNa6E95/cMrDsw+x2qGUEdJJlrWJVI2aO2cYvBgmpyTdVa430W2N3/kV5Q89v9+s\\nkLGavhEYpAv7waMf40zLr7/6Oc46lu2K06N73D95xHF3TN8tDwxFNf9czYPjt/n0yWf0donrei6H\\nczZ+RDtD3za0tuG4dZhpFEjb2r0nLpWQE0veO3mIgL3MLYVBPIuiwyZHVMlMOaONkwRZNIlMmju/\\n/YhEznWMhwQiNddsOSRB2GpTdWDlTkkpoY2Y188uIPMtl4vwGLQqsmtb6nxNQVEitVlvgD2HYdn2\\nYkyuNGMYeLG+wPuBZduxwtAaw8JZ/DCQplBX8BKT97imoVG6Kpx5NtsdRydnhOdP2O12tG3LZr3h\\n+OSEyXvaxhG8p6SMtpbGNfhprN182pMwBb4VR5FcUmUSC+N3eXLEg/sPiWNApcyw2TJu1vjgWa2W\\nuLbFKZHQs21DCYHONZSi2IQJKPRKMSkhtSkUL66e88GDH6C14hef/RPff+cvuXvyjqyqFMTooiiy\\nkWcy5yRSpDXmK1VHLLMaUE1gMza0R/JugFU3wdnDw7C/dW98xat/3wegAxSrbwK5Xz/evFYSCgsr\\nJIqVdSisVDCVEacBlQcUiqYIPbtU5XqjDNY6jvoOXQqN0qQUyVrjtNAwTIJl16O07FJpI/Mnow22\\n+mmGlIQOHiK+ZNqm4aRf0BpN8BPX24FApmkcu1KVVpTo10rwcRwvl6x3u6prm/De7x+MZd+x3j7l\\nk89/xg8++A90zu1Pq8B2Ivadi0Bj88mdNRAF9jmU+6pGmnm+tEfZ1SHYf9NhtOKod3z8dP2mS/ON\\nx84ndn7g6dXAUec4XTS8dadnXbvR3RT/f5A8D8d3n51KshMXE7XvNG9+u0Ixjy8FAJh3hisjaE6w\\nhVoYyTWNKWGTIUZdnRcURjW0riMkGONYWZhzl3eAdGeI9yCxxa17RBirlbmKln0wZYgpy+9QGaIx\\n5X0nuWwSUxC1n73t3Jyc5yxxeLnbM8wiZtuZsh/f7iGpAtlq3rv/Q966+6HQ7a0TsfAahHyUTmx2\\nIdIaosr07Qkf/fj/5IMw8uunv+Dis5dMPuIcjCHwYHVMChNT8hSj984eocKcs0xZAaacxKBZzw4f\\niPi4VoypMAZhMStjiFSGY6mkQ1VJQqXsv/cw5pp9KNW+EzdG/3/tndmPJMmR3n/m7nHkUUdXz9HT\\nQxKkiCWkpbCQBElv+tf1phfpRYAErADucrkzO8OZPurMzLj80IO5R0ZVV3cPOeRSK5QB09NdFRkZ\\n4ZeZfWb2GTYfijGEmclLrMk1mAX6T3ONYDlsC7wegnqaobSay9SZq2bFum5VYfmR28MtwzRiDBij\\nKJSzjkYqHPDm7hY/ec5awUfPpm05Xa2ZDnuccVzfXPL9t99ydX3Jy5c/zXzAHltVVJWj6zqsCMM4\\n4ayl6ztOTk7UMIylw4ib4dmUDUc17nNcUy05np+fY2LCh5HD7oaT7Zqx2xNFEGOYxommaXAmzo2k\\nidAPPV1/wLdNTs7U87qpK757+zVvPvsFv3r5a148+yk+aG/QaERrWVPCJg1zhTy2hkxJWrRhqZfM\\nG3kmgMmOS4HaZ0RvcUKUvId5X3xIirNT9mpRnB9wMT+qME3laKylEmFtHH4amUZluy8vkkiMk+fQ\\njwRJjLEQJVvNkAqe1mppSgqBunIE73MRcaXpxFHp8pqqYpgCtbMa2wg5FTkGqkYZf56vV2ydkk97\\nSVhXEwW6vicljT1MIdCFyHq94uX5OTfXl9wEz7pp2fUdq6Yhor3vUkpcXl5yeXuHdRUX5y/ZrC9y\\nx4HM65kSMRpsjrfookrZDGKuzVwGo8sJmsqsHPHA98r5uubuTwCllhZlN93ETTfhjHC6qvj0pKF+\\ntua2n7g9jBzG8PGb/QXkQ+HUYsSY+dpsRyZmZYmQY+DZw1eKJkjaTb54/XrwJazNDYJDUZaF0ivM\\nqEH53rlzSlo+0X1k4VgPmZVO9jKFiFlsyIRyLRdPspSJ+KDcwSnDayU0kO9+z8iQecD0XSSVLFBD\\nsmF+5hLvj9HgXcQZR0QYfZzNjJLtXQwOYxRCjdM1t9ff8emLv6ZenfLF+U/47Xd/T0SRnLNVy8ro\\nM1TW5DILMiXgMXnLh5AzVZXUQHLAqrZOO7oYsCkiyWKtDrw1wuRD5sfPRkWux6UYoxy3VmmmnKS0\\n3NLvLUiGy16XmCNZv8lxSbIhVWpfC7xOrsFFDJt2xapu2bZrDkPHvtvz+vZSod6svQsKBYl+8gQ/\\n4oPnbjjw2bNn9CFQOUfX9WAdzjn2h47rm2u+/OInXL+dSAZWp2cIEes0yej6+o7tdqtJPflcDDFS\\nJyWK9xl+hVyLKXlcjJ2Vgta1rzEB/NThp16bWFe1VkMYy9gNjNOk8csEvh9wK41HHw4HxuAZnGHn\\nI1HUB7Ri6cKB//a//iu/+PJXnG8vuDj5BCFQiVVP0hiigWBzcqXRxMpkypzJfJba3CKs7Lt7kOyj\\nInlHvXvh8jx5l/Unr6O8Tz+kaD+e9GMdThIVhjAppZStaqbgEYGrQ8em1i4AXfKMPuZuC47KWfZd\\nz36/pyZpj0DADVrMOqXIsN8DQgyavRazeg9xorThc5WycVgilsjYHbiaRlZNzaptebZquD10dAlW\\ndcVhHLQ7SNNSC7x++4p+mpR71lli0zD6oDV2UTH9uqoQ5/jq+7/l1c03nJ+84JOzn7BePdcSlJx4\\nYXI8Rsom4whdmXzsHKGCpZYsZKfHnz4mzzY1X7/dfWxafpAsYU0fE5f7kcu9NpA9XdV8frbCWZnj\\noN3/E8pTR2fpRd0Pws/RjaKmVHnmdVMU2wyJpgzlpETKVHUxKn1hIdeX3C7MhpgTQ3If1gwzFU7L\\n2YtZPFsqX/pwmy28TDh6mksIv3w+5bhk4TEGhdincKT4y3b1O0p6OWqgRoCRcm2gtL0q7CYxQTAJ\\nFyPeKjxnFu8JaIweMJJwSROE7l79H6R9qco7JD45/Zz/8K/+E//zq/8BJGov+DhRVZZp1IbJ+v5p\\nhmWDwi3YykGKOBHGEEhiSGIp6ZJh8rjK0XcTTd3QDxPitPl3idUdT0CZw1mlX2SidMIoUHA6jlHx\\nTLMmKcmA2jZLD21rzHGejPLfVsZxsl6zbtZMYaIfB77rbrWsKB73WczzWQ7lKHBgoM4Ka91s2fuR\\nPsHK1mwbR0omd7uBzWaDMYGL5xeM3Z52vcHGQWuCh4HKWYZxoFk1DIOiZJISfd8hSebvTUlLY0CN\\nBVs5nLPEoI6MM47d1RV1rVD4EAZWqwYxBlc5xslzst3Sdzs26y3dfp/HQ8uU2qqlshXfXF/hTs8w\\nJmBToq5qDl3HV69+w7eXFictddXwq5/+e9bt+dx3OKREtNodKpas2WQy3WUeT6NJkyk+bAEoM6pz\\nfy88NFzfPWVnmHahGNWQyEatfNhY/6jCnPzIQKI1LRNeeWQFjDhlqTeWw5Rr2pqGGsmdCSB6z6Zd\\nEQiQmy9rVlnBpi1Vq+TJOOg9WIwGrk1STzSWNOiAMwZnNVV65z37yVMdOlaVY7Nas60t317fMsRI\\nW9cYgf0w4v2oyrOuCD4QQmDynk2jjCZ3Y6dUVc7hrMNYw/X+aw7TNS8v/jWr9rkWgHs5QjYZximW\\naDnUlhM1K1BYTPH7Z2PbKiF0P/1pGH3eJ1NQKr63u2FOFnqRleeu99z1E/v+LwXbvvutM9kCC29y\\noTjN4pr5IEyAud+YWmKikOgrrJe7RghEo4TUJsW5w4fMm2dh4iyncLFPY1YG9+Ch/EfZoCYUmHb+\\n7QwbzmUiSb3ikBJTPMJPeu29QeHIfpIt66I4RM24lISQIs5I7vWavUtrcFHUmxaTmbcWFnbIB0cu\\nsZAUuOwbfvrpZ/iYMmKW+Pz8JZtXJwTfIVXFebPi8uY1tnLYEPO7KOxZW4fk7kUOm1EaqKzDimWc\\nPN0UiAGmYBiTRqwkeIIYTCr9P9OcITnDcYmcU3BUWmWNlCJ+KUZENg7IKEExtIyoQtM6Wi2HW9cN\\nJ6sNbV3TDR13Xc/N4U6z+jMZimbcF2MtFzeIQYx+lzFGWYBC4tP1lrHvEWfYNC3iI6112FHLPayr\\nGPoB6xwnZxfcXr2maT7hsOtYNzXDeMv56QnDOCpXrPcLTm6tT0UEs8gULuUxTdPgx5GZ3EGgaWuI\\narAMU8SYUpObSQhSxFW1xoxRvljvPbWz1I3WBI+DZ+0cyVjGzms7spSo64pV3XB5dc1OhHHsON08\\nI0WFs9XTjERjCAZMSljUZko5QVD1hJaekORYpTDvu8wvO+8l7u2DpRS0h2JILc6SjFDP+/1HZckG\\nwOfi/U2t5OtkeMkZnfjRB8Y4UhnDpt3k5suGq/2etqkVX0+JQ38goPELSDixiBgtTzE2w1NJ23Kh\\ngeSUDFjBSU0lhjh5+rFjsqpEex+47gbOfeDTkxPOT0+5zTWZKSWl5EqRwWs5ya4b5jKUw6Gjrio+\\nf/acMWpAHDFc7m7AGa7uXtNPnl//7L9oAoAJc4xoWU54jGUeGUdKQoqwPOhk3saPycWm4Wr/hyX7\\n3OdclEf//iFZJgtVVjhpK55vG758tmY3eO66iV0//eCkpT+3vEOqkGEceJgMVBw/HW8FjdSqyUgP\\nSWImpBBMVI8qRjMrNTFZJS+g9Ye6Eo6zSrr/s+WfxR++5yuL/jGXucyHOPdipXCsu6R81/L78/OV\\nTZ8K+b9JmCg5pCA54SJhg3qXLveALdmihedTeT8FmwwxRpw1vPzp35AkaUMFq0onJk0kMcbw8vSU\\n2/1b7e+YlI2GKRBFjZYuTAo3SyIFz5Th8hQiJgbWdcUUPAcvtKuWfpg4O9mwPxwgmUxCUJShZNi0\\nzOrji/Ne+kYOl5hyqOY5LDHuhBrr29WWzaplVdWM00g3DLy5vmLIjZldpqVT5WTmUoijh589VArc\\nr1nIIKWR5QAAFQZJREFUxgm9nwgp8ov1BTZGknhub25Yt2vqqsZPXU5EqrGmoj907G4uaTcbhv6g\\n3mxSntvb3Z66rpRrO+nbpqQlJZJRgyLr1Zo4TkqrZy2jD4TocXXN2B+orVUoX4SqqhXKdg4kKXGC\\n9zSrlY5/jASE2/2er3d73OmG2lkOXafdf6ydW+U1rp5JFlbVStcnkcnvcW5DyMlpocCyGX5Pc4No\\nAatJnRKTsq8tDePl/putp/s/f7BRFqiPzPu8JHsVpWo+cHZ+VGG2dcMXJxvOBMI4ZsghYqJaoCtj\\nsABBB/a7yzdUItTGsl1tZuVymAYlPZagvfliIhBZuwqfIj5Mmj1nHbWBxlQMY884ai+4IXhiUEjG\\n5EyviCC1o6orRuDV1TVjTHQp8fz0VC1Dq3Vxu75n3w80TUNKaghURptYWyIXbUsXE28OO6y13PUd\\nU4zUXhtfy3Iyjn79fBDemzhkPujmEzoVz+jxyaidoakMd930sSmZ5WM0d3+oTOEI21ojbFvH6ari\\ni/MV3RjYDRO73v/F+nI+JgtjE8PRYIHi1eu8RHQdKlSXcqeEDGGJKl295lh6MPfrQ12a9w/x0lBZ\\nPlxkuWZm42m+Jt27tMCumsWYadUe0QWPJUmJFIscRDRJTxCiaDzOZDjWxqBJMCnhReO1ysYiOVFF\\nMFEPjWhTJqxWr8Aa5YctLEIpGyjPT0558/YNsU5glJqwn0bldC3Wv2hC25yEFLMnnIQohjFBbQyH\\nDMlV1mKZ8ONI1a7wUzGMFut+aY1mz6PkERhjMNnKi6inldDAyOxtpERbN6zqllXTUllHP/bsu47r\\nu2u811yCJJBKXDt/1lqjGalLA20xP3PtsCTWriWGgV4iJ42W0+2vr3i9u+X5+QVx8iQJnGy2fIfh\\nn779lk3b4irH7eUbzp9/xjiN7O72bM8u2O93tO1K4WbROQkhzMrSe5/bE2qJS/B+XiPBa0ZwSNo4\\n3NYNoDzH3TARxpHkHCebRgnyD3uu7nZcfPKcqnLa8Dwkvvr6KzpXs96sWdcO8ZabuwhW16AzFsnk\\nEVVd8fvLf2SzPiGkkX7ccVJttLepSVirRoCJkpm5ZKYeVPRGFZo1IMnMiN5iQ9zbeMUAerhdj5cc\\noWtdD0c0qXih75OPK0wn1H7K/JVxjhd0wTMNyigxRa3ViUDV1JrQQ6Lzg3JoBsXTLcKU29+AUtPd\\nhS6nPBuaytIIDIOniwctHAZs8EfWDqPFxQYz09vZTDk1GYutDF9sTxj7gcu3b+lDYMTiXM12vSF4\\nzzDpAgoxsh+0iPi67/LgC8M0ao1lijhTUxnH5P383AUymDFzjt5BsWTnn1Mil8Uy4tFD8Nmm5mo/\\nLgiB/7KitZwTNwclEN80jm1b8bPnDQC7wbPrNeP2n9v7fB//rLL+3Cc3KPBsRmqPSRDHm8HsmS42\\nVVre/xinXtg+M/y7ZA95N2XgPgIgkAm/j78vU164ZZKke4fCY2UkcF85H42nYgAwF4OXZ0sBksmt\\nyKwqvRiVGSiIFpHbKEhWiiUeh+XIFS2a3WgXzzJMCg0SB8RUM11hbV3eq8fEpWEMSO7jMQ9DSnST\\n8kWfr7QF2CEEhiAYp8rgaGOogiqK9x0jJOX66MS8lxJA5tatxLKqG9q6oXU1PgZ6P3K1u6Efh1np\\nmnz3gPbOLPhvTMd5FElKoELmiZ0fI2dzZjh6jBPkevBbPDYkbvs9vYCVxCdNg/ET/dhxdnrK66tL\\nzHrF+uwEf3XHYXdH27ScPTuj7wesq2nbln7qqWkIMeIqbf8W82Iv5SNlXRjRCTV5zUYfcJXDIYRx\\nwlU1dUSbSVuLa1sYR2xdcfH8gikmGtdwefOGylaqDP2EQzhtG/zrS42jrhoQYfITN5NnvVkzTSN/\\n983f8vb2Nf/2l/+Zzy5eZkIOgzURm4yyUs3ksZC059cchjFkIySP8LHumnnHH5fAkSdZHihT5vux\\nUJIcQzAU5rDH5aMKc2XVgwz54VN+IWOEYFU/344jU7aM8xNlWEI4TBNjiFzUW3ZdR3Ki5OZB4deQ\\n66ysaJp3N06zxRQT2oVAdFAUYdPP+KTDV1VGy1uMwVWWxjnSpJ3q7WoN48TG1VqaYkSpuKyhrSpE\\nDE6EdW0xpuHVzS37ocNWVgPNMXK6eTZvxFggveK7SIHilgfa0uLNG2f+9WN2j0JpZ6ua3776w0pJ\\njhj8j/cuPyQpwa73c0/O2hm2jeNio9BtPwV2vWc/ePopvBcm/rHysF5zCYEXOdZqMmtIPVTjfODN\\nFmZaeo9pPoyPyvG+JTC/y735fPiMRXE9+NyDez38173rjczUbinf7FGb5LjUyhdRjg8tAjfKOWtQ\\nBWeY40B4iEbjVraUayQUxkUPLEciZNjTEQliMutVmpV+UzWctivGrgOEOjk2rWXwI8RIYx0uGXo/\\nMsWIFZvjy0rVVuZPE24MjSSuDjvENoSgiNMwRSTnLhRIVg2Po/GSt+MMjaeUVEkirOqKtmpY1Q3O\\nWIZx5DD0vLm9opBkaMG8zDcKCUSOWabHOdTSJkUEBGtV+ZSEG52DY+aCYOgmj0RBJJIqw2XoMRtt\\nHCHW4mtHHSNXN1d88eWXiGv47vvfE198xhd/9Vf461umJLR1zauraz777FNVaslhUAdCS0f0jDRW\\nk9uKt+lymUlZ91ZE4/k+gnOUOC9o/NJa5e+uc07HMPSIdXRDT71qNb57ccF6HKmN4bSqOUTo315S\\nv/gU6yyDV5YigyrPKQS+v/49v+x3bFdnxBybdLlnaTKGaPM5n0I29NSLD5IyW1VczH+adULRC+T3\\nn3HXWVcukEFKQhsz6qP6bLGFfoyH+cXJKWG/Y8oJBjHfLCbNHrvdHwhGkLzhBF3EMSbu/KAMQVXF\\nzWGvacWlWDlpJltV16zqhnVbsz/0dKI9NTWuYufMJS+i1FEmIWIzMS8Mg2662hpNTQ+Bw9TRupqf\\nffIZox+xptIeFdETknA3DNoeKBWo2HDoO2pn+PzZc76/vSSGwORHLk4+h2LRplxAnvRwLbGDUoO5\\nTE5ZqIyj1fmeMT5f1+wGLVz/lyCjj1x6hW6L97lpHC+frXDWsM+e537wjOFP807vU7zvEBwsNw4c\\noVfyNM02S5mPNFvd9+6XMy4ffNn9Z3rkGedLlt7NB55/fmaOhz35OFiiDR8CHubvKG+USqasQsIx\\n17iVlnWIWvOSwOQm0c5mVl5Rq8JIUj5e5RxUxq18x9kGjEkbbE8Tn61aujCwahv80OFQJWwwTFHh\\nfIwe3NYYPRTLOZYzlX30eGuonCMghHhMhoLMy1yUWjk05Xh8pmxYN1XNqipeZMU4TXTjwNu7a/pJ\\nOXsLo0tR2CmfnCZ7Ns7YnHBoFoevZJskE6uI0bITC40x2uYwadiozEt5drECBiyWIY04sbS6zAgI\\n9WrDeUxI1J7AJ5sTvvnmGz794gtiiEy+p21aarcHEUJSdA+YPUstXTJHL9yHOexQGqJPRYGmRPSe\\nISu1lLRvppu0L2w/jtTbExgHZXUKkcrC7eUNTVNTpYjgsTbx5u6Of+j3rF98jq0rhmlkiiE/F5ys\\nt0QiwSdudm95cfETnDFUVg2vlCko752e8eixi0jOIJd58c2Q/oIz+qjsltBLPp8z7Mp817SIWzIb\\n1iKPoURH+XjSz35HDOH4hSkn0YRA7xOubQkhEijUS/mDJrfIEU2w0X572qC3shVt5ZScXVDqqMOe\\npm64WK2IZsMUPDEGunHCD9r0xeZmr8RIVdUYY/Cj0oeNwVMZTVKYEkjw/O6r39HUdY5daNd1I1ov\\ndOgSKUbqpiYE7QQQDcRpmNPX1/UJTdU+ILvmqCzT8ahicVjp/8s1+d+zd3m8tsizTc231woJP8Z6\\n8+eW90GcP+yz971Pa9RjXzeOZ9sGZwz70XMY9L8/luf2Q2PyqNKE2eIu5QaSCnRzNF5k9jqPUNts\\n+N3zlB/53oc/yOjLo3O4vBfy7mcXWyeTy8yw47tftlhDcjweynPL3KX++B7axYIZc5rh2gxb+QiV\\nqPLEysLwyGQRSQ/mJAmceq9vurfsu1u+/PwFaTjQSCKMPSIFOtMmzXfjQBI3J+JNIWi2fellSi55\\nIRGN1qBGNDs+RBBnZ2P1OH7zxqKqK1ZVTesamqpSmHUcuD3s+H4ctOZSCoGEHCczT75Iqd0kd9uQ\\nbJAfIfREwqAlRwXyk2x8aJWMzyhGVmIpUUiljRUqB7VYfDdQNQ5nDSdVgx0jTRK+ff0d52fn7HZ3\\nVBZ++uVL3t7dcru/w/qBxlaMQRj9hHWOFAK2UiIC57T+klwzO3+/ZNh98bxCrs/Ma8agynQKHkRY\\n1Q11CoxDYHQOYx2mqrk57DCrFtPUWOcY315xtm7Aj9wOHqlqnm+27LsD0ToS4JNnvVrxyek5xMjb\\nmyu++v7veXbyCRenL6iNqLI8tqhAmY8NXiKepcOekLjY4+ZoJJVG80KaM751Px2dmvL+hTVIs5kX\\nNHgFSSjz+h75qMIUUE5OymI6ftnZyYZVbsj6er+jzzyOghL2xqBclePkEWuJIlSuoaks/dAruYE4\\npUxqWow1tE3FxlhGat7ud1TWEXOhscmd060YrBXGccwMPDD5xN14YAraALeuaqqmJcXItmnxwWPF\\n4MOkqdYGJhLj0FNXFaumIfnAq8NtrluqlF7MtTPZ9ZLhpQzwfcX5UNKDv747EZtG3/8vVQP5Y5Tz\\nY4o2xDR3VQFwVtjUjnXjuNg2WCP0o9ImdmOgG/80MdDHEp+W2cr6qHlLpOIxLOo980eXfuG9TLwH\\nz/hwJo+OqxS9ubjT/esX/uS9a8o/TDa24ntti/vrKnFfoRf/S8+ZXISflYbGJTWBQpWFyQQCx6zO\\nRCDECWvaHOMUrYnOp0tK2lPxd6//DucsN3d3ODzbxuEEumEiGmH0yhOdTAVB8D5irXo8PiVVMHkj\\nmdxYfvCjkoWXTN+soMohZ8SyqmtqpzBr7SrG6Bmnidt+R387zqNQykdMcWXj/ZEP+jK5kUI2aYsC\\nz58tqtmKnSdRu5METWgkMU3lvXLYBlF+2aQUfkiiHwLJgQ+eKoCIpbYVKxc53Nywi4GX21O6Q0/T\\nNvhhJA4DV1dXnJ+d8qxdcXfTc3JyhjWWfhhw4jS5yVrCNGErl8sutIRPjMm0flp5QA5d2SVzRoj0\\nfa9JQ05rNa0RXIhMXYetKwJoJmvlCE3FbvT0RlhLRb8b+brrabcn9MNAINHUNc46xmnCR8/V4Y7W\\nVIQQGXzP//7tf+fl85/z85f/hqZqSPjjHhHwAhJ0f4agcyBo2VeKOuYl3r8s57IiWGepTcz0kAmf\\nCuKi1JROAj6aTLMnueZY91sBwz7kNnxUYcai/xcLyonQrGpSSoShwwAvT7YcfOCm056CkhJDpqHS\\n7vTCs82WT9dbfn9zqaz1zgKJtmr42ekzVgb85On7Du89TNrKKogheGVjSR6ayhFymnrJ8PI+MKWI\\nqxsS2p6pNo7GOq52t2zbNYjSkPUhaJKSqNKcppHDMHC12/Pm0EFTM40Dn138HOdqhnHZHSIpcf5j\\n8Ot7Dv5ZVz7y+4tNzeX+2JXkn9u7/HPHP31IM9sQqAe6qi3r2vHJSUNbrRl9pCsKdAp/dBbuYwr8\\nXpkJZE8heylz9k46Wj8LSGcRjn7Qd++edl2IPPh7eZ7jpWr5P6JGF/eKMXs7DzUp79vRJRV/gUAV\\nFS7M2arF0y4KUIzMdHBKo7coIjeKvBhBm3HPRofK6AemaeL1zRWcnvOT8y0ez+QjV91I3bYYVxG9\\nejkhRSRnrIfsjZX8hKKkpikyTlBXRksopkRdN9RVjTWOVTaqh3Gkn0Yu97eMYZoTbpQggZkak6TK\\nLWZqvocdMAQdg/Kv2bso98neWTFItGuT5MziXIsteoiHkCgdllJxY9D1FqO69T5CbGqqumIaB/Zh\\nxATh7dBxfv6MKkZiZbm7vSYFbUUVBq3LvJxGmlXN1p0xTZ5hHJRtLS+Uqq7mXpiRnCWMkExe3hkx\\nIKMfYdISE0LQLFqrOSRKoHEMR3jvOQSPqxva3EXn1e0dZy8+Y50M3eWlvrcRfa/e0zYN27plnAbu\\nDh1ETQCNApvVmpA8X736Dd2055df/g11tZmhYyncy2IwMeIlIrk6QmuoNVQgMREoNIY62NYKjU1s\\nXA/i2E8tknNhrIC1HuvfkuQZRuo8Pz0iEWc2SEjZG/8xkGxM95SCEQ0om5xUQMbDbfBsxDCIpRef\\nMejcLss6VnXNy5MTpv0OQuB0vVEydO+12Wy3ox8OHHrPbd8TK8fJ6SkrV3M5HAgGvJ+oXMUURkKI\\nOKfdIZrK4Y3Qp9zo2gmnzYpu6Dn4kcpZrYUqgXpXEaLPdFjaCWDXD/QxYZqalFPr1+0WIxUhDjMb\\ny5wNWw605SlSYJ3Fwapjd1QAS4VYWUNbW/7p6vDO7/4lyB+jbENM9yBcAdrKsqot27bik9MWZ4R+\\nCvrfH6FE04NFv4weHw9OZfvR8/GoUOWezixGkTyKIiwZgB7CpIsvf/cHaflEj1zz6LAWHzbBO4+i\\nSjhhkHSEqrRQJtcEplLQndRzNYJEEFuUocz0e9rUQzVvypCtOQZ/EaCp1/z6Z/+O3W9uGLxn33ue\\nry19DEQsYhxxmhj6gXa1Opb15Ibn92rdRFluiJbNak1bt1hrqXPJ2WEYmILn1c1brYcsMeoH8Wor\\nQiDey3JcGkxHJPY43wXCnONZFHKD41wpxJ4VPJoR66TAu4FxGrDWYsQdydplgTIkqCqLtbBxNS/q\\nDf94OHA3DkxBWJ+dsqor3r59hQNu9wdefP6CT16MjKJtubwkVusNbdry/Tf/QNs22MqpoRA1/lgG\\nosQ2y1jfK7XKSjEXRRJTwlWV0pYm7aUpRjCVnckSe+85PXtGK4ZqteI6RM5tzavff8fOj/RJ2JpT\\nYtBKgsY6Nk3FykYkRmKEu6CecwqJyjqaVcWrt19BEv76F/9RiWamhM0E+c5EpqAJSgZhUnqPwkSK\\nZ4H2LeYwiSNFjxy+R9znuGp75JNG8OYZoOQ7GtsNeD8irl2shveLfOiQFnkIRD3JkzzJkzzJk/z/\\nL2mGoI7yQYX5JE/yJE/yJE/yJCofqtF8kid5kid5kid5kixPCvNJnuRJnuRJnuQHyJPCfJIneZIn\\neZIn+QHypDCf5Eme5Eme5El+gDwpzCd5kid5kid5kh8g/xewXL7akE2h1AAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10bd225f8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig = plt.figure(figsize=(8, 8))\\n\",\n    \"m = Basemap(projection='lcc', resolution=None,\\n\",\n    \"            lon_0=0, lat_0=50, lat_1=45, lat_2=55,\\n\",\n    \"            width=1.6E7, height=1.2E7)\\n\",\n    \"draw_map(m)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Other projections\\n\",\n    \"\\n\",\n    \"If you're going to do much with map-based visualizations, I encourage you to read up on other available projections, along with their properties, advantages, and disadvantages.\\n\",\n    \"Most likely, they are available in the [Basemap package](http://matplotlib.org/basemap/users/mapsetup.html).\\n\",\n    \"If you dig deep enough into this topic, you'll find an incredible subculture of geo-viz geeks who will be ready to argue fervently in support of their favorite projection for any given application! \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Drawing a Map Background\\n\",\n    \"\\n\",\n    \"Earlier we saw the ``bluemarble()`` and ``shadedrelief()`` methods for projecting global images on the map, as well as the ``drawparallels()`` and ``drawmeridians()`` methods for drawing lines of constant latitude and longitude.\\n\",\n    \"The Basemap package contains a range of useful functions for drawing borders of physical features like continents, oceans, lakes, and rivers, as well as political boundaries such as countries and US states and counties.\\n\",\n    \"The following are some of the available drawing functions that you may wish to explore using IPython's help features:\\n\",\n    \"\\n\",\n    \"- **Physical boundaries and bodies of water**\\n\",\n    \"  - ``drawcoastlines()``: Draw continental coast lines\\n\",\n    \"  - ``drawlsmask()``: Draw a mask between the land and sea, for use with projecting images on one or the other\\n\",\n    \"  - ``drawmapboundary()``: Draw the map boundary, including the fill color for oceans.\\n\",\n    \"  - ``drawrivers()``: Draw rivers on the map\\n\",\n    \"  - ``fillcontinents()``: Fill the continents with a given color; optionally fill lakes with another color\\n\",\n    \"\\n\",\n    \"- **Political boundaries**\\n\",\n    \"  - ``drawcountries()``: Draw country boundaries\\n\",\n    \"  - ``drawstates()``: Draw US state boundaries\\n\",\n    \"  - ``drawcounties()``: Draw US county boundaries\\n\",\n    \"\\n\",\n    \"- **Map features**\\n\",\n    \"  - ``drawgreatcircle()``: Draw a great circle between two points\\n\",\n    \"  - ``drawparallels()``: Draw lines of constant latitude\\n\",\n    \"  - ``drawmeridians()``: Draw lines of constant longitude\\n\",\n    \"  - ``drawmapscale()``: Draw a linear scale on the map\\n\",\n    \"\\n\",\n    \"- **Whole-globe images**\\n\",\n    \"  - ``bluemarble()``: Project NASA's blue marble image onto the map\\n\",\n    \"  - ``shadedrelief()``: Project a shaded relief image onto the map\\n\",\n    \"  - ``etopo()``: Draw an etopo relief image onto the map\\n\",\n    \"  - ``warpimage()``: Project a user-provided image onto the map\\n\",\n    \"\\n\",\n    \"For the boundary-based features, you must set the desired resolution when creating a Basemap image.\\n\",\n    \"The ``resolution`` argument of the ``Basemap`` class sets the level of detail in boundaries, either ``'c'`` (crude), ``'l'`` (low), ``'i'`` (intermediate), ``'h'`` (high), ``'f'`` (full), or ``None`` if no boundaries will be used.\\n\",\n    \"This choice is important: setting high-resolution boundaries on a global map, for example, can be *very* slow.\\n\",\n    \"\\n\",\n    \"Here's an example of drawing land/sea boundaries, and the effect of the resolution parameter.\\n\",\n    \"We'll create both a low- and high-resolution map of Scotland's beautiful Isle of Skye.\\n\",\n    \"It's located at 57.3°N, 6.2°W, and a map of 90,000 × 120,000 kilometers shows it well:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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kXImMmCSzfuJEveP9GYYnXl1j3Yd+6Py/hZjJgwC11dje5OKwiK0KSC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\\nQgghhBBCa0lYFUIIIYQQWut/QdEIzy4gwwIAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10f2d6320>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig, ax = plt.subplots(1, 2, figsize=(12, 8))\\n\",\n    \"\\n\",\n    \"for i, res in enumerate(['l', 'h']):\\n\",\n    \"    m = Basemap(projection='gnom', lat_0=57.3, lon_0=-6.2,\\n\",\n    \"                width=90000, height=120000, resolution=res, ax=ax[i])\\n\",\n    \"    m.fillcontinents(color=\\\"#FFDDCC\\\", lake_color='#DDEEFF')\\n\",\n    \"    m.drawmapboundary(fill_color=\\\"#DDEEFF\\\")\\n\",\n    \"    m.drawcoastlines()\\n\",\n    \"    ax[i].set_title(\\\"resolution='{0}'\\\".format(res));\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Notice that the low-resolution coastlines are not suitable for this level of zoom, while high-resolution works just fine.\\n\",\n    \"The low level would work just fine for a global view, however, and would be *much* faster than loading the high-resolution border data for the entire globe!\\n\",\n    \"It might require some experimentation to find the correct resolution parameter for a given view: the best route is to start with a fast, low-resolution plot and increase the resolution as needed.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Plotting Data on Maps\\n\",\n    \"\\n\",\n    \"Perhaps the most useful piece of the Basemap toolkit is the ability to over-plot a variety of data onto a map background.\\n\",\n    \"For simple plotting and text, any ``plt`` function works on the map; you can use the ``Basemap`` instance to project latitude and longitude coordinates to ``(x, y)`` coordinates for plotting with ``plt``, as we saw earlier in the Seattle example.\\n\",\n    \"\\n\",\n    \"In addition to this, there are many map-specific functions available as methods of the ``Basemap`` instance.\\n\",\n    \"These work very similarly to their standard Matplotlib counterparts, but have an additional Boolean argument ``latlon``, which if set to ``True`` allows you to pass raw latitudes and longitudes to the method, rather than projected ``(x, y)`` coordinates.\\n\",\n    \"\\n\",\n    \"Some of these map-specific methods are:\\n\",\n    \"\\n\",\n    \"- ``contour()``/``contourf()`` : Draw contour lines or filled contours\\n\",\n    \"- ``imshow()``: Draw an image\\n\",\n    \"- ``pcolor()``/``pcolormesh()`` : Draw a pseudocolor plot for irregular/regular meshes\\n\",\n    \"- ``plot()``: Draw lines and/or markers.\\n\",\n    \"- ``scatter()``: Draw points with markers.\\n\",\n    \"- ``quiver()``: Draw vectors.\\n\",\n    \"- ``barbs()``: Draw wind barbs.\\n\",\n    \"- ``drawgreatcircle()``: Draw a great circle.\\n\",\n    \"\\n\",\n    \"We'll see some examples of a few of these as we continue.\\n\",\n    \"For more information on these functions, including several example plots, see the [online Basemap documentation](http://matplotlib.org/basemap/).\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Example: California Cities\\n\",\n    \"\\n\",\n    \"Recall that in [Customizing Plot Legends](04.06-Customizing-Legends.ipynb), we demonstrated the use of size and color in a scatter plot to convey information about the location, size, and population of California cities.\\n\",\n    \"Here, we'll create this plot again, but using Basemap to put the data in context.\\n\",\n    \"\\n\",\n    \"We start with loading the data, as we did before:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import pandas as pd\\n\",\n    \"cities = pd.read_csv('data/california_cities.csv')\\n\",\n    \"\\n\",\n    \"# Extract the data we're interested in\\n\",\n    \"lat = cities['latd'].values\\n\",\n    \"lon = cities['longd'].values\\n\",\n    \"population = cities['population_total'].values\\n\",\n    \"area = cities['area_total_km2'].values\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Next, we set up the map projection, scatter the data, and then create a colorbar and legend:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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8fdJ58w6Pd5uLtld39PuztwvV6yXtYom3Hsem4fHtkdjmR5yfvXz6mr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\\nSUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x11d095400>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# 1. Draw the map background\\n\",\n    \"fig = plt.figure(figsize=(8, 8))\\n\",\n    \"m = Basemap(projection='lcc', resolution='h', \\n\",\n    \"            lat_0=37.5, lon_0=-119,\\n\",\n    \"            width=1E6, height=1.2E6)\\n\",\n    \"m.shadedrelief()\\n\",\n    \"m.drawcoastlines(color='gray')\\n\",\n    \"m.drawcountries(color='gray')\\n\",\n    \"m.drawstates(color='gray')\\n\",\n    \"\\n\",\n    \"# 2. scatter city data, with color reflecting population\\n\",\n    \"# and size reflecting area\\n\",\n    \"m.scatter(lon, lat, latlon=True,\\n\",\n    \"          c=np.log10(population), s=area,\\n\",\n    \"          cmap='Reds', alpha=0.5)\\n\",\n    \"\\n\",\n    \"# 3. create colorbar and legend\\n\",\n    \"plt.colorbar(label=r'$\\\\log_{10}({\\\\rm population})$')\\n\",\n    \"plt.clim(3, 7)\\n\",\n    \"\\n\",\n    \"# make legend with dummy points\\n\",\n    \"for a in [100, 300, 500]:\\n\",\n    \"    plt.scatter([], [], c='k', alpha=0.5, s=a,\\n\",\n    \"                label=str(a) + ' km$^2$')\\n\",\n    \"plt.legend(scatterpoints=1, frameon=False,\\n\",\n    \"           labelspacing=1, loc='lower left');\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This shows us roughly where larger populations of people have settled in California: they are clustered near the coast in the Los Angeles and San Francisco areas, stretched along the highways in the flat central valley, and avoiding almost completely the mountainous regions along the borders of the state.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Example: Surface Temperature Data\\n\",\n    \"\\n\",\n    \"As an example of visualizing some more continuous geographic data, let's consider the \\\"polar vortex\\\" that hit the eastern half of the United States in January of 2014.\\n\",\n    \"A great source for any sort of climatic data is [NASA's Goddard Institute for Space Studies](http://data.giss.nasa.gov/).\\n\",\n    \"Here we'll use the GIS 250 temperature data, which we can download using shell commands (these commands may have to be modified on Windows machines).\\n\",\n    \"The data used here was downloaded on 6/12/2016, and the file size is approximately 9MB:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# !curl -O http://data.giss.nasa.gov/pub/gistemp/gistemp250.nc.gz\\n\",\n    \"# !gunzip gistemp250.nc.gz\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The data comes in NetCDF format, which can be read in Python by the ``netCDF4`` library.\\n\",\n    \"You can install this library as shown here\\n\",\n    \"\\n\",\n    \"```\\n\",\n    \"$ conda install netcdf4\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"We read the data as follows:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from netCDF4 import Dataset\\n\",\n    \"data = Dataset('gistemp250.nc')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The file contains many global temperature readings on a variety of dates; we need to select the index of the date we're interested in—in this case, January 15, 2014:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from netCDF4 import date2index\\n\",\n    \"from datetime import datetime\\n\",\n    \"timeindex = date2index(datetime(2014, 1, 15),\\n\",\n    \"                       data.variables['time'])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now we can load the latitude and longitude data, as well as the temperature anomaly for this index:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"lat = data.variables['lat'][:]\\n\",\n    \"lon = data.variables['lon'][:]\\n\",\n    \"lon, lat = np.meshgrid(lon, lat)\\n\",\n    \"temp_anomaly = data.variables['tempanomaly'][timeindex]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Finally, we'll use the ``pcolormesh()`` method to draw a color mesh of the data.\\n\",\n    \"We'll look at North America, and use a shaded relief map in the background.\\n\",\n    \"Note that for this data we specifically chose a divergent colormap, which has a neutral color at zero and two contrasting colors at negative and positive values.\\n\",\n    \"We'll also lightly draw the coastlines over the colors for reference:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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PvJDMbGx7n08suwaoljS7PZ9IRI6HTaFAoFEvXtuOHTmzCQfM8TFlab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\\nxnSdBSDGGGOM6ToLQIwxxhjTdf8C8Py1bjxDsywAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1138b64e0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig = plt.figure(figsize=(10, 8))\\n\",\n    \"m = Basemap(projection='lcc', resolution='c',\\n\",\n    \"            width=8E6, height=8E6, \\n\",\n    \"            lat_0=45, lon_0=-100,)\\n\",\n    \"m.shadedrelief(scale=0.5)\\n\",\n    \"m.pcolormesh(lon, lat, temp_anomaly,\\n\",\n    \"             latlon=True, cmap='RdBu_r')\\n\",\n    \"plt.clim(-8, 8)\\n\",\n    \"m.drawcoastlines(color='lightgray')\\n\",\n    \"\\n\",\n    \"plt.title('January 2014 Temperature Anomaly')\\n\",\n    \"plt.colorbar(label='temperature anomaly (°C)');\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The data paints a picture of the localized, extreme temperature anomalies that happened during that month.\\n\",\n    \"The eastern half of the United States was much colder than normal, while the western half and Alaska were much warmer.\\n\",\n    \"Regions with no recorded temperature show the map background.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<!--NAVIGATION-->\\n\",\n    \"< [Three-Dimensional Plotting in Matplotlib](04.12-Three-Dimensional-Plotting.ipynb) | [Contents](Index.ipynb) | [Visualization with Seaborn](04.14-Visualization-With-Seaborn.ipynb) >\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.4.3\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "matplotlib/04.14-Visualization-With-Seaborn.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<!--BOOK_INFORMATION-->\\n\",\n    \"<img align=\\\"left\\\" style=\\\"padding-right:10px;\\\" src=\\\"figures/PDSH-cover-small.png\\\">\\n\",\n    \"*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\\n\",\n    \"\\n\",\n    \"*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*\\n\",\n    \"\\n\",\n    \"*No changes were made to the contents of this notebook from the original.*\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<!--NAVIGATION-->\\n\",\n    \"< [Geographic Data with Basemap](04.13-Geographic-Data-With-Basemap.ipynb) | [Contents](Index.ipynb) | [Further Resources](04.15-Further-Resources.ipynb) >\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Visualization with Seaborn\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Matplotlib has proven to be an incredibly useful and popular visualization tool, but even avid users will admit it often leaves much to be desired.\\n\",\n    \"There are several valid complaints about Matplotlib that often come up:\\n\",\n    \"\\n\",\n    \"- Prior to version 2.0, Matplotlib's defaults are not exactly the best choices. It was based off of MATLAB circa 1999, and this often shows.\\n\",\n    \"- Matplotlib's API is relatively low level. Doing sophisticated statistical visualization is possible, but often requires a *lot* of boilerplate code.\\n\",\n    \"- Matplotlib predated Pandas by more than a decade, and thus is not designed for use with Pandas ``DataFrame``s. In order to visualize data from a Pandas ``DataFrame``, you must extract each ``Series`` and often concatenate them together into the right format. It would be nicer to have a plotting library that can intelligently use the ``DataFrame`` labels in a plot.\\n\",\n    \"\\n\",\n    \"An answer to these problems is [Seaborn](http://seaborn.pydata.org/). Seaborn provides an API on top of Matplotlib that offers sane choices for plot style and color defaults, defines simple high-level functions for common statistical plot types, and integrates with the functionality provided by Pandas ``DataFrame``s.\\n\",\n    \"\\n\",\n    \"To be fair, the Matplotlib team is addressing this: it has recently added the ``plt.style`` tools discussed in [Customizing Matplotlib: Configurations and Style Sheets](04.11-Settings-and-Stylesheets.ipynb), and is starting to handle Pandas data more seamlessly.\\n\",\n    \"The 2.0 release of the library will include a new default stylesheet that will improve on the current status quo.\\n\",\n    \"But for all the reasons just discussed, Seaborn remains an extremely useful addon.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Seaborn Versus Matplotlib\\n\",\n    \"\\n\",\n    \"Here is an example of a simple random-walk plot in Matplotlib, using its classic plot formatting and colors.\\n\",\n    \"We start with the typical imports:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"plt.style.use('classic')\\n\",\n    \"%matplotlib inline\\n\",\n    \"import numpy as np\\n\",\n    \"import pandas as pd\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now we create some random walk data:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Create some data\\n\",\n    \"rng = np.random.RandomState(0)\\n\",\n    \"x = np.linspace(0, 10, 500)\\n\",\n    \"y = np.cumsum(rng.randn(500, 6), 0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"And do a simple plot:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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fkVIIoiq+rq0CuV5NrtPW5ns8nEt/X1XT6+2Onk/sOHMXu97LPZenzd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\\nSKoIUuBLJBJJFeH/OyKinWS1qMsAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1023b2cc0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Plot the data with Matplotlib defaults\\n\",\n    \"plt.plot(x, y)\\n\",\n    \"plt.legend('ABCDEF', ncol=2, loc='upper left');\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Although the result contains all the information we'd like it to convey, it does so in a way that is not all that aesthetically pleasing, and even looks a bit old-fashioned in the context of 21st-century data visualization.\\n\",\n    \"\\n\",\n    \"Now let's take a look at how it works with Seaborn.\\n\",\n    \"As we will see, Seaborn has many of its own high-level plotting routines, but it can also overwrite Matplotlib's default parameters and in turn get even simple Matplotlib scripts to produce vastly superior output.\\n\",\n    \"We can set the style by calling Seaborn's ``set()`` method.\\n\",\n    \"By convention, Seaborn is imported as ``sns``:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import seaborn as sns\\n\",\n    \"sns.set()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now let's rerun the same two lines as before:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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+OKHfkW8uJlTkzm/HKqlmGFHQzE8AyF6evysfryGjpb3Xnj1qyvwWzV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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x11a0d73c8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# same plotting code as above!\\n\",\n    \"plt.plot(x, y)\\n\",\n    \"plt.legend('ABCDEF', ncol=2, loc='upper left');\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Ah, much better!\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Exploring Seaborn Plots\\n\",\n    \"\\n\",\n    \"The main idea of Seaborn is that it provides high-level commands to create a variety of plot types useful for statistical data exploration, and even some statistical model fitting.\\n\",\n    \"\\n\",\n    \"Let's take a look at a few of the datasets and plot types available in Seaborn. Note that all of the following *could* be done using raw Matplotlib commands (this is, in fact, what Seaborn does under the hood) but the Seaborn API is much more convenient.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Histograms, KDE, and densities\\n\",\n    \"\\n\",\n    \"Often in statistical data visualization, all you want is to plot histograms and joint distributions of variables.\\n\",\n    \"We have seen that this is relatively straightforward in Matplotlib:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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ypSjvX5iuR0jWZlu8D5jGNUAAAYiAAHAMBABDgAAAYiwAEAMBABDgCA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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x11a191080>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"data = np.random.multivariate_normal([0, 0], [[5, 2], [2, 2]], size=2000)\\n\",\n    \"data = pd.DataFrame(data, columns=['x', 'y'])\\n\",\n    \"\\n\",\n    \"for col in 'xy':\\n\",\n    \"    plt.hist(data[col], normed=True, alpha=0.5)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Rather than a histogram, we can get a smooth estimate of the distribution using a kernel density estimation, which Seaborn does with ``sns.kdeplot``:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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BHgbpMry5XMXa21GgBwebAxu4UQUVIY4ERz0OntggABbnP+B3iVpRIA\\ncK6X/eBE+YABTjRLsiKj09sDu7YIKlGV7XLmzK4rgkltwvWxFu5QRpQHGOBEs9TvH0A4FoYjT7YQ\\nTUQQBFRbKhGUQ2gd68h2OUSUAAOcaJY6vfH+b1uej0C/2cRr9HrPpSxXQkSJMMCJZmlyAJvZmeVK\\nUqfSXA4RAi4OXsl2KUSUAAOcaJYmnsBLzfm5hOpUtJIGpaYS9AQ8GA97s10OEd0BA5xoFhRFQed4\\nNyxqM3QqbbbLSalqywIAwJXBq1muhIjuhAFONAtj4XGMR7wFM4DtZhP94Oc9F7NcCRHdCQOcaBYm\\nthAtKsAAd+js0EoatHo5Ep0olzHAiWah09sDAHDm6Q5kdyIIAkoMboyER+EN+7JdDhFNgwFONAud\\nN57AS62lWa4kPUqM8ZXlmkfbslwJEU2HAU40C53ebugkLcwaU7ZLSYtSoxsAcHXwepYrIaLpMMCJ\\nZigQDaI/MAiHphiCIGS7nLQoMUw8gbdmtxAimhYDnGiGum70f+f7HuB3olVpYdfZ0B3wcF10ohzF\\nACeaock9wPWFNwL9ZiUGNyJyBD0+T7ZLIaIpJBXgR44cwbZt27B161a8/PLLt33+wQcf4KmnnsJT\\nTz2F733ve2hoaEj6XKJ8M7ECW4mlcFZgm8pEP3jTcEuWKyGiqSQMcFmW8cILL+DVV1/Fhx9+iH37\\n9qGpqemWYyorK/Hmm2/id7/7Hf7yL/8Szz//fNLnEuWbzvEuqAQJjgKcQnazkhsB3jjIn1miXJQw\\nwOvr61FVVYXy8nKo1Wps374dhw4duuWYtWvXwmw2T/63x+NJ+lyifBKVo+jxeVCstUMUCrsHyq6z\\nQSOq0e7tzHYpRDSFhL+BPB4PSku/nuvqdrvR19c37fFvv/02Nm/ePKtziXJdj68PUSWG4gJcge2b\\nREGE2+DCUHgY/og/2+UQ0TeoUnmx48eP491338Wvf/3rOV/L6TSnoKLsKoQ2AIXRjlS14eL4IACg\\nosgNm82QkmvORKbvWV1cjg5vF0bFIVQ53Sm7Lv9N5Y5CaEchtGE2Ega42+1Gd3f35Ncejwcul+u2\\n4xoaGvD888/jl7/8JaxW64zOnUp//3hSx+Uqp9Oc920ACqMdqWzD5Z54f7BFsmFkJLNPpTabIeP3\\nNAvxn+VzrQ0olSpSck3+m8odhdCOQmgDMLs/QhK+Qq+rq0N7ezu6uroQDoexb98+bNmy5ZZjuru7\\n8cMf/hA//elPsWDBghmdS5RPOsa7IUCA25zcH6L5zqEvBgC0jXJjE6Jck/AJXJIkPPfcc9i9ezcU\\nRcHOnTtRW1uLPXv2QBAEPPPMM3jppZcwOjqKH//4x1AUBSqVCu+888605xLlI1mR0enthl1bBJWY\\n0t6nnGXTWqAWVegNcOwKUa5J6rfQ5s2bJwemTdi1a9fkf//kJz/BT37yk6TPJcpH/f4BhGNhOAyF\\nP4BtgiAIKNbZ0efvR0SOQj1P/nAhygeFPQ+GKIU6bizgUlTAS6hOxaEvhgwFvT4+hRPlEgY4UZIm\\nllCdL/3fEyb6wTvHurJcCRHdjAFOlKSOiT3ALambTpUPnDcCvGWYe4MT5RIGOFESFEVBh7cLVrUF\\nWkmb7XIyyqGPLxnb5evJciVEdDMGOFESRkKj8EX8cGoLe/3zqWgkDawaCzzBfiiKku1yiOgGBjhR\\nEtpvvD6fbwPYJjj0dgRiQYyGx7JdChHdwAAnSkLnjQB3GouzXEl2TA5kG+9OcCQRZQoDnCgJE1PI\\nyiylCY4sTBMD2VqH27NcCRFNYIATJaFjvAtGlQFGjTHbpWTFxBN4+xi3FiXKFQxwogTGw16MhEbh\\n1M7P1+cAYNGYoRHV8AT7s10KEd3AACdKYKLf1z5PB7AB8SVV7boiDIVGEJNj2S6HiMAAJ0qowxsf\\nwDbxGnm+suuKIENGf2Ag26UQERjgRAlNrMBWZp2fA9gmFE8s6DLem+VKiAhggBMl1DneDZ2ohUVr\\nznYpWWXXxbsQ2kc5kI0oFzDAie4gEA2iLzAAh7YYgiBku5ysKtbFn8C7x7mkKlEuYIAT3UGXNx5W\\nds382QN8Oia1ERpRjb4Q+8CJcgEDnOgOJvq/HXoGOEeiE+UWVbYLIMplk1uIWkuyXMmtIhEZrV1B\\n+PwxaLUidBoRWo0Is0mC2Zi+H2u7rgi9/j70BwZRYpxf+6IT5RoGONEddIx3QS2oYM+BJ3CvP4rz\\nDV5ca/WjtSuIaHTqncGqynRYt8KExdUGSFJq++2LdfH/Hbq9PQxwoixjgBNNIxKLoNffB7fWAVHI\\nXm9TNKbgxLlRHDs7hmgsHtpWs4BSpwSLWUI4En8iD0cUDI/KaOsOoq07CKNexOplJty92gK9TkpJ\\nLfYbAd4+0on17jUpuSYRzQ4DnGga3b5eyIoMuzZ7T9/t3UEc+HwQgyNRGPUili+QUFmmg82qmfac\\n0fEYrjUH0NIRxrGzY6hv9OLbDzlQu0A/53om3kRMDO4jouxhgBNNY6L/u1ib+SVUgyEZnxwfRn2D\\nFwCwqErCfRvtiMYSDx6zmiVsXGPC2lUKGq4HcLEhiLf392H1UiMevdcOnXb2bxPMahPUohp9wcFZ\\nX4OIUoMBTjSNiS1E3ZbMDmAbGongnQN9GBqJosgiYH2dFm6nATq9Gl5v8qO/VZKAVUsNqCjR4MvT\\nXtQ3+tDSGcSTjzqwoEw3q9omRqIPBAYQk2OQxNS8mieimeM0MqJpdI13Q4QIl9GZsXs2twfwH7/t\\nwdBIFEtqJHzrISvcTsOcrmmzqrDtYStWLdXB649hzz4PLl71zvp6dp0NMUXGQIBP4UTZxAAnmoKs\\nyOjy9sCutUGVgadMRVFw4vwo3v5DH6JRBXevUWPjGiskKTU/oqIoYPVyAx69zwxJBD78dBBHT49A\\nUaYeyX4nEyuydXm5JjpRNjHAiabQ5+9HWI6gOAMrsMViCvZ9NohPj49ArxXw8L06LKpJz7rrbqca\\n33rICqNewNFTo9j32SBisZmF+M0j0YkoexjgRFPomNwDPL0BHgzF8F+/9+DiVR+KbQIee9A451fm\\niVjN8VfzdpuIi1d92Lu/D5GInPT5N88FJ6LsYYATTWFiD3CXOX393yNjEbz+Xi/au0OocIt49D4r\\nTMbpp4elkl4n4rEHrCh3q9HWFcQ7B/oQiSYX4maNCSpRhf4Q+8CJsokBTjSFzhtP4KVpGoHe1RvC\\nf/y2F0MjUSytkfDAJgvUmsz+OKpUAh7YZEJ5iRptXSG8e6B/2tXdbiYIAuxaGwZDw5CV5J/ciSi1\\nGOBE36AoCjrHu2FVW6CVUv9EfPGqF7/+sBfBkIwNq1TYsMYKUczOj6IkCnjgbhPK3Gq0dAbx7kd9\\nk6u93YldV4SYEsNgYDgDVRLRVBjgRN8wHBqBL+qHQ2tP6XUVRcFnJ4bx4aeDkEQBD27UYukiS0rv\\nMRuSKODBu00odanQ3BHEbz/qSziwbWIgW4+PI9GJsoUBTvQNXw9gS90KbKGwjN8c6Mfxc2MwGwU8\\nep8eFeXGlF1/riRJwOZNZpQ4VWhqD+LA0cE7TjGz6+L/23SMdGWqRCL6BgY40Td03lhC1WksTsn1\\nRsaj+M/3e3G9LQC3Q8SWB0ywF81uJbR0mgjxIquE+gYfTl4Yn/bYiSfwrht/7BBR5jHAib5hYgnV\\nUkvpnK/V2RvE67/tQf9QBIuqRDx8rwUGvXrO100XlUrAQ/eYodcJ+OTYMK61+qc8zqq1QBJEeIID\\nGa6QiCYwwIm+oXO8G0bJAJNmbq+4L13z4q0PPAgEZaxbocLd62wpW1ktnQx6EQ/dY4YkAe8f6odn\\nIHzbMaIgwnZjJPpsVnMjornL/d8mRBnkjfgwHBqZ0wA2RVFw5OQIPvhkEJIEPLhRi+VLsj9YbSbs\\nNhXu22BCNAq8vd8Dry96+zE6GyJKBCOh0SxUSEQMcKKbTMz/LprlADZZVvD7w4P48swozEYBj9xr\\nyKnBajNRWabB2pV6eP0yfneo/7YnbfvkimwciU6UDQxwopt03uj/dhhmPoBtIrwvNPpgtwl47H4T\\niu25N1htJpYv0qG8RI32nvBtg9omllTtHOVIdKJsYIAT3eTrFdjcMzpPluMbkkysaf7QPWboDbk7\\nWC1ZgiBg0zojtBoBn50YRt/g1/3hRTcCvIMj0YmyggFOdJMObzc0ohpFuuRfocuygg8/HcClaz44\\nbAIeuscCvU6VxiozS6cVcc96I2QZeP/g18ut2rRWCBDQx5HoRFnBACe6IRwLw+Prg0NrhyAISZ2j\\nKAr2Hx7E5et+OIri4a3TpX//8EwrL9FgUbUWgyNRHD4ZXz5VJUqwaa0YCN550RciSg8GONEN3b5e\\nKFBmtAf4qYvjuDD52twCbQGG94T1qwwwG0WcrB9Ha1cAQHwgW0gOYyw8/aIvRJQeSQX4kSNHsG3b\\nNmzduhUvv/zybZ83Nzdj165dqKurw69+9atbPnv00Ufx1FNP4emnn8bOnTtTUzVRGkwuoapNLsDb\\nu4P45Ngw9FoB9200Qqst3PAG4ou83LfRBEEAfv/ZAKIxBcX6GyPRuSY6UcYl7KiTZRkvvPACXnvt\\nNbhcLuzcuRNbtmxBbW3t5DE2mw0/+tGPcPDgwdvOFwQBb7zxBqxWa2orJ0qxiSVU3UkMYBvzRvHe\\nx/0QANyzTguzKTP7eGdbcZEKSxZq0dgUwqkLYyiuis+Xbx/pxHL7kixXRzS/JHwCr6+vR1VVFcrL\\ny6FWq7F9+3YcOnTolmPsdjtWrVoFler2vwcURYEsc89gyn0d3m6IggiX0XHH46JRBe9+1A9/UMaa\\n5SqUlhgyVGFuqFuqh0Yt4IvTIzAg/od5+2hnlqsimn8SBrjH40Fp6ddrQrvdbvT19SV9A0EQsHv3\\nbuzYsQN79+6dXZVEaRaTY+j29qBYUwRJvPOr8I+ODqK3P4zqchHLFpszVGHu0GhErF6uRyQKnK+X\\nIQoiPMH+bJdFNO+kfa7LW2+9BZfLhaGhIfzgBz/AwoULsXHjxoTnOZ35/4uxENoAFEY7ErWhc7QH\\nETmKUrMTNtv0T9QXr46hvtEHp13CI/c7oNZktt/bZMqNhWHWrtLielsYFxsDKH3AhsHQEIodRohC\\ncuNi58O/qXxRCO0ohDbMRsIAd7vd6O7+eqEGj8cDl8uV9A0mjrXb7Xj88cdx4cKFpAK8vz+/R7U6\\nnea8bwNQGO1Ipg3ne68CAEwwY2Rk6h24AsEYfvuHHkgisGG1FqFwBKFwJOX1Tsdk0sHrDWbsfoms\\nXaHDZ8e88A3pETYO4WpHB4r1ideQny//pvJBIbSjENoAzO6PkIR/LtfV1aG9vR1dXV0Ih8PYt28f\\ntmzZMu3xN88HDQQC8Pl8AAC/34+jR49i8eLFMy6SKN06Jgawmaf/4/Tgl8PwBWSsWKSC3ZYbT8LZ\\nVObWoNSthm8o/saii2uiE2VUwidwSZLw3HPPYffu3VAUBTt37kRtbS327NkDQRDwzDPPYGBgADt2\\n7IDP54Moinj99dexb98+DA0N4dlnn4UgCIjFYnjyySfxwAMPZKJdRDPSPt4JAQJKzVOPQL/e5sel\\na/E1zlcsNWW4uty1fpUBfzgT/9+jbbgDq50rslwR0fyRVB/45s2bsXnz5lu+t2vXrsn/djgcOHz4\\n8G3nGY1GvP/++3MskSi9ZEVG53g37Bob1NLt65cHQzIOfD4EUQQ21unzYk/vTLGaJVTa7fAAuNDT\\niic5k4woY/ibiOa9/sAggrEQiqfZA/zT48MY98WwfKEKjmK+Ov+muho7lJiIbl8/ZC6pSpQxDHCa\\n9zrG4nOY7VMsodrWFcT5Bi+KLAJWLeer86mYTWqoY2bIGi/OXed0MqJMYYDTvNfunRjA5rzl+7GY\\nggNHByEIwIY6LV+d34HDUARBlPHeifpsl0I0b/A3Es17HWPxAC+zlNzy/RPnxzA0EkXtAgku5/xa\\nbW2mHIaJNdH7cLVjJMvVEM0PDHCa1xRFQYe3C0UaKzTS1+uZj4xF8OWZUei1AlYvN2axwvxgVsWX\\nVBX1XvzuaFOWqyGaHxjgNK8NBIYQiAbh0Nw6gO3gl8OIxhTULVVBp0v7goV5z6q2AQD0Vh8ut42i\\no8+b5YqICh8DnOa1jhv93zdvIXqt1Y/rbQG4ikXU1nDgWjJ0oh4aUQvJFF8Ri0/hROnHAKd5rf3G\\nCHS3KT4QritDAAAgAElEQVSALRyR8fEX8Tnf61fqIAhCNsvLG4IgwKqyISR44SiScObaIPpGAtku\\ni6igMcBpXptYQrXMEt9x74vToxjzxrCkWoLdzjnfM2FVx99iLF2igqIAh061Z7kiosLGAKd5S1Zk\\ntI93wqq2QKvSorc/hK/qx2AyCKhbNj93N5qLiQDXWcag10o4eqEXkaic5aqIChcDnOatfv8A/NEA\\n3DoHZFnB/iNDUBRg/Sot1Br+aMzURID3BXtQV1OMQCiGU419Wa6KqHDxtxTNW61jHQAAh7YYX9WP\\nwTMQRk25hIoyzvmeDbPKAhEiBqP9WLPIAQA4eLIty1URFS4GOM1bLWPxPlqz6MTRU/E532tWMbxn\\nSxREmNVWjMZGYDWpUV1iRkuvD139nFJGlA4McJq3WkfbIAkSTp+REI0pWLNcBYP+9t3IKHlWVRFk\\nxDASGcLaG0/hh05zMBtROjDAaV4Kx8Lo8vXCqBShozuCcreImirO+Z6riQVd+oK9qC23wqhX4dil\\nPoTCsSxXRlR4GOA0L7WPd0FWZIx4TFCrgfV1Bs75TgHbjYFsPb5OSKKA1QsdCEVknLjiyXJlRIWH\\nAU7zUtNwKwAgOm7FhpVqmE2aO59ASbHcCPCBcDyw19QWQwAHsxGlAwOc5qWjTVcAAOVmOxZWc853\\nqmhEDQySEcOxIQCAxajBwnILOgcCaOsdz3J1RIWFAU7zzldXPBiI9AJRDe5e6cp2OQXHqi5CSAnC\\nF42PPp8YzHaQK7MRpRQDnOYVz7Afrx08B1EbhF2yQ6ORsl1SwZnoB/cEewAANSUWmPRqnGrsRzjC\\nwWxEqcIAp3kjHInh5+9dQlgzCABwaO0JzqDZsKnj/7t2++IL5YiigJXVdoQiMs5e689maUQFhQFO\\n84KsKPjlh5fR5hmHs8wHACjWObNcVWEqUhcDAHqDXZPfW7UwHuqfne3ISk1EhYgBTvPCu4ebcaqx\\nHxUOHdS2EUiQ4DSWZLusgqSVdNBLBgzFBqAoCgCg2KJDWbEBVzvHMTjKbUaJUoEBTgXvwPE2/P54\\nG+xmNb51bwkGI/0okoohCez/TpcidTFCShDe6Njk91bWFENRgE9OcjAbUSowwKmgXWoZwku/OQ+D\\nVsJ37q/CCOK7YxVJxVmurLBN9IP3Brsnv7e8ygZJFHDgROvkkzkRzR4DnApWV78XL713AaIAbN9U\\nDmeRGV2B+IIiTj1fn6dTkSb+B1KX7+unbZ1GhcUVVniGgmjp4ZxworligFNBGvWF8bO36xEIxfDU\\n/ZWoKY8HSmegLd7/beD873SaeALvC/Xc8v1VNfH/P3AwG9HcMcCp4IQjMfzv39RjcCyIe5YVYePK\\nSgCAP+rDUGQAdskBkf3faaURNTBKZgxFB255XV5dYobZoMaphn5EopwTTjQXDHAqKLKi4P/7/RU0\\ndY9heaUJD66tmvysKxh/ncv+78wo0tgRQQSjkeHJ74migHVLXAhGZJy9OpDF6ojyHwOcCsr7n7fg\\nqyt9qHDosHVTzS07jLH/O7O+ng/efcv31y2Nd1/wNTrR3DDAqWAcv9SLD75sRZFJjSfvr4ZGrbrl\\n885AOySo4GD/d0ZM9IN3eW+dNua2G1BiN6Cxcwyj3lA2SiMqCAxwKgh9w378x4FGaNUinrp3AcwG\\n/S2fj0dGMRwZRLHkgCjwn30mxANcQF+457bPVtXYoSjAlxdv/4yIksPfZJT3YrKMVz64jFA4hofX\\nOOF2WG47psV/HQDgUPHpO1NUogpWlQ1DsQHElFsHrC2rKoIoCvj8fPc0ZxNRIgxwynsfftmGpu4x\\nLKswYc3isimPafFdAwBUmKum/JzSw65xQIaM/lDvLd83aFWoLbOgdziIdg/nhBPNBgOc8lpT1yg+\\n+KIVFoMKWzZOHc6hWBCdgTZYxSIYNeYMVzi/FWvie4HfvKDLhFU18T7yz893ZrQmokLBAKe8FQhF\\n8fIHl6AoCh7fUAqjXjPlcU1j1yFDhlNyZ7hCsmviO751+ttu+2xhqQU6jYTjl/sQk+VMl0aU9xjg\\nlLf2HLqG/pEgNiy2orbCMe1xV0caAABlxspMlUY3GCQjtKIO/RHPbZ9JkojlVUXwBWO41DKUheqI\\n8hsDnPJSc/cYPq/vgcumxea10/dry4qMa6NXoRcMsOunD3lKD0EQYNc4EFD8GL9pZ7IJE6/RD3NO\\nONGMMcAp7yiKgv/6JD4o7cE6F1Sq6ZdF7Ql2IhgLwKUqvWVRF8oc+41+8G7/7SFdYjfAbtGivnkE\\n/mAk06UR5TUGOOWdM1cHcK1zFIvKDHd8dQ4ATb5GAIBLy9XXsqVYHe8H7/C23vaZIAhYVWNHTFbw\\n1ZXbX7MT0fQY4JRXojEZb392HaIA3L/qzqEsKzKuea9AK2pRZq7IUIX0TTaNHQJEeMJdU36+oir+\\nGv3Iuak/J6KpMcApr3x6tgt9wwGsrrHAXWy947EdgRb4Yz5U6hZw97EskgQJNnURhmNDiMq3vya3\\nGDWocpvQ6vHBM+zPQoVE+SmpAD9y5Ai2bduGrVu34uWXX77t8+bmZuzatQt1dXX41a9+NaNziZLl\\nC0bwu6Mt0KlF3LuqPOHxDeMXAQBV5oXpLo0SsGscUKDAE5p66dRVC+Mbnxw+yznhRMlKGOCyLOOF\\nF17Aq6++ig8//BD79u1DU1PTLcfYbDb86Ec/wp//+Z/P+FyiZO37sg2+YBQbF9tgNurueGxYDqHZ\\ndxUm0YxSy9Srs1HmFN+YD97ubZny8yUVNmjVIr640Ms54URJShjg9fX1qKqqQnl5OdRqNbZv345D\\nhw7dcozdbseqVaugUqlmfC5RMgZHgzh4ugNWowp3rUzcn93kbURUiaJUVcHR5znAoYmvQd/hb53y\\nc7VKxPIqO8YDUVxs5pxwomQkDHCPx4PS0tLJr91uN/r6+pK6+FzOJbrZgZPtiMYU3L3UDvUdpo1N\\naPDGX58vMNekuzRKglbSwayyYiDad9vGJhNW18Zfo39y+vZlV4nodhzERjnPG4jgyPluWAwq1C0q\\nTXj8eGQUnYE2FEsOWLS2DFRIyXBonIghil7/1DuQuYv0cNn0uNQ6glFfOMPVEeUfVaID3G43uru/\\n/oHzeDxwuZLbknEu5zqd+b/pRCG0Ach+Ow5+1IhwRMbDa91wFJsSHn+260sAQI15IUymeF/5xP/N\\nd/ncjnK5Ai3+62geacKD5VMva3v3yhJ8+EULzjcPYsejSzJc4cxk++ciVQqhHYXQhtlIGOB1dXVo\\nb29HV1cXnE4n9u3bhxdffHHa4xVFmfW5N+vvz+8tBp1Oc963Ach+O0KRGH53pAl6jYhllcUYGbnz\\nNKOYEsOZ/lNQCxqU6Krg9QZhMung9QYzVHH65Hs7TEr8bUjz6HXUGe+e8pgatwmSKGDf0WY8uKok\\nZ8cvZPvnIlUKoR2F0AZgdn+EJAxwSZLw3HPPYffu3VAUBTt37kRtbS327NkDQRDwzDPPYGBgADt2\\n7IDP54Moinj99dexb98+GI3GKc8lStbR+h54AxFsWloEnVad8PgW3zX4Yz5UqxdBLSY+njJHLxlg\\nlEzoCXZDVmSIwu09eHqtCosrrGhoH0FT9xgWld95rj/RfJYwwAFg8+bN2Lx58y3f27Vr1+R/OxwO\\nHD58OOlziZIRk2Uc+KodKknA+qWJ+74B4MLYGQBAjWlROkujWXJo3WjzN2Eg3Dft8rZ1C4vR0D6C\\nT890MMCJ7oCD2ChnnbzSh4HRIFZWmWE2ahMePxweRGegDQ7JBZvenoEKaaYmp5P5pp4PDgBVbjMs\\nBjVONw4gGI5mqjSivMMAp5ykKAr2n2iHIAAblrqTOufi2FkAQKVm+u1FKbsmAny6BV0AQBQFrFpY\\njHBUxleXucEJ0XQY4JSTLrUMoaPPi6UVJjhsiUeeR+QwroxfgFbQYYGNS6fmKoPKCKPKBE+0B7Iy\\n/YprqxcWQxCAA1+13TIwloi+xgCnnPTRqfje0esX3Xm70AmN3ssIyUFUqqq5cUmOKzGUIqKEMRCe\\n/unaYtRgcYUNPUNBXO8azWB1RPmDAU45xzPkx8XmIVQ4dKgoKUp4vKIoqB89DQECaiwcvJbrSg3x\\ntelbvXfeF2H94vgfbwdOtKW9JqJ8xACnnHPoTHxHqpXVyY1A7gl2YjDch1JVOUxaSzpLoxQoSTLA\\nK10mOKw6nLs+iOHxUCZKI8orDHDKKcFwFF9c6IFJr8Kq2uSmjtWPnQYALNCx7zsf6FV6WFQ29Ec9\\niMrTjzIXBAHrlzghK8CnZzoyWCFRfmCAU045drEXgVAMq6rMkMTEq3B5o+No8jbCIlpRYk68Rzjl\\nBpe2BDJi6Aneef/vFdVF0KpFfHauG9EYtxkluhkDnHKGoig4dKYLkihg7eKpF/n4pstj5yFDRqW6\\nOmeX3aTbObXxqYGt49fveJxGJaFuYTG8gShONXAnQ6KbMcApZ1xpG0b3gA9Lyo2wJLFph6zIuDR+\\nHiqoUG1bnIEKKVUcGhcECGgPNCc8dt1iJ4D4lDIi+hoDnHLGodPx16mra5ObOtbub4Y3OoYyVSU0\\nkiadpVGKqUQ17BoHhmKDCMYCdzy2yKzFwjIL2jw+tPXm/6YVRKnCAKecMDASwLnrAygt0mJBSXJ7\\neF8cOwcAWGDk4LV8NLEWeoe/NeGx6288he8/Pv0KbkTzDQOccsKnZ7ugKMDKGktSfdne6Dha/ddh\\nE4vgNCa31CrlFueNAG8ev5rw2JpSM4otOpxsHMDAyJ2f2InmCwY4ZV04EsOR890waCXUJTl17PLY\\neShQUK7muuf5yq4uhlrQoCPYmnC5VEEQcM8KNxQF2HesNSP1EeU6Bjhl3YkrHviCUaysMkOtSrwM\\nqqIouDxef2PwGldey1eCIMKtK0VA8WMw3J/w+OVVRbAaNTh6oRejXi7sQsQAp6xSFAWHTndCEIC1\\ni5N7Fd4VbMd4dBSlqgoOXstzbm18VbYWb+LX6KIoYNMKN2Kygv1cXpWIAU7Z1dQ1hnaPF4vKjCiy\\nGJI6p2H8IgCgXL8gnaVRBri18S6TpiQCHABW1dhh1Knw2dlueAORdJZGlPMY4JRVE+ue1y20J3V8\\nRI7gurcBBsGIEhNXXst3WkmHInUxBqJ9CMWCCY9XSSLuWuZCOCrj45PtGaiQKHcxwClrRrwhnGro\\ng9OqQW15cVLntPiuIaKEUaoq58prBaJEVwYFCtr9yU0RW7vIAZ1GwsFTHQiEpl9LnajQMcApaw6f\\n60ZMVrCqOrmpYwDQ4I2/Pl9g4tzvQuHWxt+kXBu7ktTxGrWEDUudCIRlfHb2zmupExUyBjhlRTQm\\n47OzXdCqRaxelNzUMX/Uh3Z/M2ySHTZ9cq/cKffZ1EXQijp0htoSTiebsGGJE2qViP3H2xEM8ymc\\n5icGOGXFmav9GPWFsWKBGVqNKqlzrnovQ4GCUhX7vguJIAhwa8sQUoLwhLqTOkenUWHjUhe8wSgO\\nnGBfOM1PDHDKiol1z9feWCIzGdd98VesCyx8fV5oyvQVAIDG0UtJn3P3chf0WhX2n2jHmC+crtKI\\nchYDnDKupWcM1zpHUVNigLPInNQ53ug4eoJdKJacMKiNaa6QMs2lLYUkqNDsv5r0a3StWsL9q0oQ\\njsp47/PEu5oRFRoGOGXcga/irzzX1Cbfj93kbQAAuFXJ9ZdTfpEECSXaMnjlcQyFB5I+b01tMWwm\\nDY6c74ZnyJ/GColyDwOcMmpwNIhTDf1w2bRYXJnctqEAcN3XCACotNSkqzTKslJd/DX61bHkX6NL\\nkojNa8ogK8DeT6+lqzSinMQAp4w6eLoDsqJg9cLkp475ol50Bzv4+rzAlejKIULE9RtvW5K1tNKG\\n0mIDzl4bRHP3WJqqI8o9DHDKmEAoiiPnu2HSq7A6yV3HAKDpxtO3S1WSrtIoB6hFNZxaN0bkYYxG\\nhpM+TxAEPLQ2vqb6noONSfehE+U7BjhlzJHz3QiEYqirtkCVxK5jEyaeyCrN1WmqjHJFma4SAHBt\\nfGZP4QtcZtSWWXC9exynGxPvbEZUCBjglBExWcbBUx1QqwSsW5L8k7Q/6kN3sAN2yQGjJrkR65S/\\n4v3gAhrHLs743EfWlUMSBbz5cSMXd6F5gQFOGXG6sR+DYyGsWGCGyaBN+rxW/3UoUOCS+Pp8PtBK\\nOri0bgzFBjASGZrRuXaLDncvd2PUF+G0MpoXGOCUdoqi4MBX7RAArF/qmtG5zb74yOIyU2UaKqNc\\nVKmvBgA0jM78KfyeFW5YjRp8fKoTnX3eFFdGlFsY4JR2VztG0NIzjkXlRjhtyb8Gj8gRdARaYBYt\\nsOqK0lgh5ZJSXSVESGgYvzDjAWlqlYjHN1ZAUYDX9l+BzAFtVMAY4JR2v/uiFQCwfgbLpgJAR6AV\\nUSUKJ1+fzytqUY1SXTnG5TH0hz0zPn9hmRVLKm1o7hnHFxd60lAhUW5ggFNaXe0YwZW2YVS79agq\\nndlTdMuN1+elem5eMt9U6KsAAFdG6md1/pb15VCrRPzXoWvwBiKpLI0oZzDAKa0++LIVAHDXspk9\\nfcuKjBb/NWgFHZxGdxoqo1zm1pVBLahx1XcZsiLP+HyzQYMH6krhD8Xw5seNaaiQKPsY4JQ2TV2j\\nuNQyhCqXHjVlxTM61xPqRiDmh1tVAkHgP9P5RhIklOkXIKgE0BWY3XahG5Y4UWo34MTlPs4Np4LE\\n34yUNhN93zN9+ga+Hn3u1LD/e76q0se3ja0fPj2r80VRwLfvrYIkCnht/xWM+bnlKBUWBjilRUvP\\nGC40D6LSqcPC8pk9fQPx/m8JEsrMC9JQHeUDu8YBk8qC1uB1BGOBWV2j2KLD5jVl8AWjeH3/FS6z\\nSgWFAU5p8cHE0/fS5HccmzAaGcZwZBBOyQ2VqEpxZZQvBEFAtaEWMmQ0zGJltgkbljhR4TTizLVB\\nnLgy81HtRLmKAU4p19Y7jnPXB1Dh0KG2YuYBPjH63KGe2aIvVHgW6GsgQET96OlZPz2LooA/2lQF\\nlSTgjQONGB4PpbhKouxggFPK/eZIEwBg49LipLcMvVmL/zoAoNxcldK6KP9oJR1KdeUYjQ2jLzT7\\nOd1FZi0eWVeOQCiGVz+8xAVeqCAkFeBHjhzBtm3bsHXrVrz88stTHvOTn/wE3/rWt/Cd73wHly9f\\nnvz+o48+iqeeegpPP/00du7cmZqqKWddahnCxeb4yPPFlTMfvBaKBdEd6IBNtHPvbwIAVBtqAcx+\\nMNuEtYscqCk143LbCA6cmN3IdqJckjDAZVnGCy+8gFdffRUffvgh9u3bh6ampluOOXz4MNrb2/HR\\nRx/h7/7u7/C3f/u3k58JgoA33ngD7733Ht55552UN4Byhywr2PvpdQgA7l9VMqun7zZ/M2TIcKo4\\n95viXNoS6CUDrvkbEIoFZ30dQRDw7XuqYNSp8JvDTWjqGk1hlUSZlzDA6+vrUVVVhfLycqjVamzf\\nvh2HDh265ZhDhw7h6aefBgCsWbMG4+PjGBgYABDfyEKWZ74QA+WfY5d60dHnxfIFJlS4bbO6Rov/\\nxuYlxopUlkZ5TBBELDQsQQxRXBo7P6drGXVqPHFfNWQFeOm9C/AFuUob5a+EAe7xeFBaWjr5tdvt\\nRl9f3y3H9PX1oaSk5JZjPJ74aE9BELB7927s2LEDe/fuTVXdlGNCkRjePdIMlSTg/tVls7qGrMho\\n8zdDLxhQpJv54DcqXNXGWkiQcG7k5KxWZrtZlduM+1aVYHg8jF/t49Qyyl9pH8T21ltv4be//S1e\\neeUVvPnmmzh16lS6b0lZ8PHJDgyPh7Cu1ooi8+z6rnuCnQjJQbik2b1+p8KlEbWoNFTDJ4+jzd+U\\n+IQE7ltZgkqnEWeuDeDTs10pqJAo8xJOsnW73eju7p782uPxwOW6dXqPy+VCb2/v5Ne9vb1wu92T\\nnwGA3W7H448/jgsXLmDjxo0JC3M6k992MlcVQhuAxO0YGQ9h/4l2mPQqbL1vMQx6zazuc2K8GQBQ\\nXVQFk0k3q2tMJ9XXy5b53I5V6jq0tjXh/OhXWFe+Zs41/Mm25fjXt89hz6FrWLe8BEsWzGyznfny\\n850PCqENs5EwwOvq6tDe3o6uri44nU7s27cPL7744i3HbNmyBW+++Sa+/e1v49y5c7BYLHA4HAgE\\nApBlGUajEX6/H0ePHsWzzz6bVGH9/eOza1GOcDrNed8GILl2vPFRIwKhKB5a7UA4FEU4FJ3xfRRF\\nwZWhS1BDDZvKBa939oOVvslk0qX0etky39uhhgEOjRsdgXY09bWhWDPzWQ7ftP2eKrzzWRP+7pfH\\n8Le7N8FqTO6Pz/n0853rCqENwOz+CEkY4JIk4bnnnsPu3buhKAp27tyJ2tpa7NmzB4Ig4JlnnsFD\\nDz2Ew4cP4/HHH4der8c//MM/AAAGBgbw7LPPQhAExGIxPPnkk3jggQdm3jLKWc3dY/jsTBeKzRps\\nWDb7bT/7wx6MR8dQrloAUZBSWCEVklrjEgyEPTg1+CW2ln5nzterKbXgwTWlOHK+By+9W4//+Sfr\\noZK4PAblh6TWqdy8eTM2b958y/d27dp1y9fPP//8bedVVlbi/fffn0N5lMuiMRn/8YcGKAAeWVsy\\np198zb74lo9OTWmCI2k+K9VVwKSy4Jr/Cu6LPAyz2jrna25a7oZnKIDGjhHs/eQ6/uTxJSmolCj9\\n+KcmzdrHpzrQ0efFyiozFlbMfMOSmzV5r0KEiApzZYqqo0IkCAKWmFZAgYJTQ8dSds1tmxag2KLF\\nwdOdOHaxN/FJRDmAAU6z0j8SwPuft8Cok7B57dzmbI+EhzAUGYBLVQK1NLsBcDR/VOqroBcNuOKt\\nRyDmT8k1tWoJf7x5IbRqEb/afwUtPWMpuS5ROjHAacYURcEbHzUiHJVx/0oHzIa5jYxu8l0FADhV\\n3PubEhMFCYtNyxFDDOeGv0rZde1mHZ64txqxmIJ/efs8hsbyf8AgFTYGOM3YV1f6cLF5CNVuPdYs\\nnnufdbOvEQIElFu4eQklp8pQC42oxfmx03NaXvWbasuteGR9Ocb8Efzz3nMIzGJGBVGmMMBpRsZ8\\nYbx18CpUkoBH1pXPecGV0cgwekPdKJZc0Kv0KaqSCp1KVGGxcTkiShhnho+n9NobljixbrEDXQN+\\n/Pz9i4hxKWjKUQxwSpqsKHh13xWM+SO4d7kdzqK5L55w1Rvfua5UPbvlV2n+WmhcAq2ow7nRkynr\\nCwfig9q2rK9ATakZF5qHsOfQtZRdmyiVGOCUtI9PduBC8yCq3Xrcs2ruo8UVRUHj+CWIELHAujAF\\nFdJ8ohJVWGpaiSiiODX0ZUqvLYoCnrq/Bg6rDodOd+Gjkx0pvT5RKjDAKSmtvWN457MmmHQqbNtU\\nlZK1ygfCHgxHBuGWyqCRtCmokuabauMi6EUD6sfOwBtN7WpcWrWEHQ/VwqhTYc+ha/jiQk9Kr080\\nVwxwSigQiuLn719CTFbw+Ho3LMbU9FU3jt94fa7l1qE0O5IgYZl5FWTEcGLwSMqvbzVq8N1HFsWn\\nl/3+Cs5c7U/5PYhmiwFOCf3nR1fRNxzAhsVWLK5yJT4hCbIi46r3MtSCBhVWjj6n2VtgWAiTZMFl\\n7wUMhlMfsE6bHv/Hw4sgiQL+/b2LuNI6lPJ7EM0GA5zu6PdftuDYpV6U2rV4aF11yq7bHeyALzaO\\nUlU5JK59TnMgCiLqrOsAKDjS93Fa7lHmMOKPN8fHafyv39TjavtwWu5DNBMMcJrW+esD+MW79TDq\\nJPzRpsqUbvJwcewsAKBctyBl16T5y60tg1NTgs5QW0r2C59KdYkFT95XjXBExnO/+BLXu0bTch+i\\nZDHAaUqtvWP4+fuXoJJEPHFvBRy21O2364t60eRthEW0wW3i9DGaO0EQUGddDwA43PcxZCU9c7eX\\nVNqw/d4qBEJR/ONbZ/k6nbKKAU63GRgN4F/erkc4EsMfb16AqhJ7Sq9/cewsZMioVFenZDQ7EQBY\\n1TZUG2oxGhvG+dFTabvPimo7/nTrMsRkBf/89nmcuz6QtnsR3QkDnG7hD0bws7frMeoL48G6YqxZ\\nOvs9vqcSU2K4OHYWaqhRY1uc0msTrTCvgVrQ4Pjg4ZRPK7vlPjXF2HGjT/x//6YeX13xpO1eRNNh\\ngNMkbyCCF/eeR/eAD+tqrbhnVer7p5u8jfDHfKhQVUEtqVN+fZrftJIOKy1rEUUUR/rTM6BtQnWp\\nBd99ZBFUkohfvH8JH3zRAllR0npPopsxwAkAMOoN4ae/PoPm7jEsrzTh0Q3VablP/dhpAECNhU/f\\nlB7VhloUqYvR5G9Eu78lrfeqcJrwvS2LYdKr8NvPW/DSby9yAxTKGAY4YXA0iP/nzTPo7PdhTY0F\\nT9y/CFIKR5xP6Aq0oyfYCbdUCquuKOXXJwLiA9rW2u4CAHzS93tE5HBa7+e2G/D9bctQ6TTizNV+\\n/OT1U/AMpW5tdqLpMMDnud4hP/7hzdPwDAdw1xIbvrVpYVoGlimKgmNDhwEAi4zLUn59opvZ1HYs\\nMi7HeGwMxwYPp/1+Bp0azzy6GBuWONEz6MffvXYSxy71QuErdUojBvg8dqF5EP/366cwNBbC/Svs\\neGRDTdpGhbcHWm48fZfBZZr7HuJEiayw1MEomXF+7BR6gp1pv58oCtiyoQLb76lCJCbjlQ8u419/\\ncwHD46G035vmJwb4PCTLCt77vBk/23sewXAMj6134f416VvOVFEUHL/x9L3EtDJt9yG6mSSosN62\\nCQDwce8HiMqZ6ZteWWPH7m8vR6XTgHPXB/CjXx7H5/XdfBqnlGOAzzNj/jD+ee85/O6LVliNanz3\\n4SqsT/FUsW9q8V9DX6gXpaoKOIzOtN6L6GYOrQu1xiUYjY3gi8FPMnZfm0mLXVuW4Ft3VSIWk/Gr\\n3zfg//31WbT0jGWsBip8qmwXQJlz/voAXj/QiOHxEBaWGLB1UxXMBl1a7xmVI/h84BAECFhiXJHW\\ne0KLQA8AABaHSURBVBFNZYV5LTzBXtSPnUaVYSGqjYsycl9BELB2kQMLSy34+FQ7rnaM4IX/OIVN\\nK9zYsXkhHLbU7OpH8xefwOeBUW8I//7eRfzLO/UY9YVw33I7djy8JO3hDQAnh7/EWHQE1epFsBsd\\nab8f0TepRBXutt8PESI+9nwAX9Sb0ftbjBrseGgRdj26CC6bFicue/DXrxzHnkPXMOpL7wh5Kmx8\\nAi9gsqLgaH0P9n5yHf5QFGXFWmxZV45SpzUj9x8M9+PMyHEYBCNWFK3OyD2JpmJVF2GlZS0ujJ3B\\nR57f4TtluyAKmX1+WeA24/vbluNy2zCOnOvCRyc78OnZLjyyrhx/tGkBrCZtRuuh/McAL1CXWofw\\nzqdNaPOMQ6sW8fAaB+5aXpGxtccVRcGn/X+ADBkrDGugUfGXE2VXrXEp+kO96Ay24djQYdxf/EjG\\naxAEASur7VhaaUN90yCOX+65Jci3bVoAG4OcksQALzCtvWN457MmXG6N71e8rNKEB1eXochizGgd\\nZ0e/Qk+wEyVSOSqt1Rm9N9FUBEHAhqL78Fn/H3Bm5Dhc2hIsNi3PSi0qScT6JU6sri2+EeS9+Ohk\\nBz4504nNa8rw7XuqYLekv4uL8hsDvEC09o5h37E2nG7sBwBUu/W4f1UJyl22jNfiCfbg2OBn0Ak6\\nrCnamPH7E01HI2pwj/0hHB44gI89H8KmLoJTW5K1em4O8ovN8SD/5EwXDp/rxv11pXji3ioOdqNp\\nMcDzmKIouNoxgg+PteFSS3xf4lK7FveucGFRZXYGjIXlEA70vQ8ZMlYbN8CgyeyTP1EiFrUVG2z3\\n4cTwEbzf/V/4/9u79+CoyruB499z9n7LZbPJ5gYBAuEiCSDWC32tCCggICCoM307OtDWdt4Zo5YO\\nHUHbzoC2omM78/7hyLTqW19feb1RX+uMWqMkAnKHgGIaETAkIZvL5rKb3eztPO8fKwEUyAXN2cXn\\nMxOy2Zyz+T3s2ed3znnO+T13Ft9Dpknf0r5Gg8r0CbmUl3o4eqKDjz9toaa2me1HTnNjRQGLbxhD\\nTqY8IpfOJxN4GkpoGgfr23l3bwNfNCXvKx2dZ2NmWQ7jiz26zbF9Zty7O9bJOFMZRRnfXXEYSboc\\nhbZiKrSZHO7ez9am/+HO4ntxGJ16h4VBVSgv9XDV2Bw+a+hkx5Fmqg81s/3waW6cVsjiG+Spdeks\\nmcDTSDgS56PaZt7f30h7dx8ApQV2rpmYR0mB/pODHOzeQ33wKNlqDlNzpusdjiRdUqljItFEhLrg\\nJ/y9+WXuKPp3bAa73mEBybKsV41xM3l0Np992cn2I81sO9jE9sPN3DyjmEU3lJDhMOsdpqQzmcDT\\nQGtXmKp9jWw/0kw4ksBkUKgYm8HVE/PIy3bpHR4AX4a+YGfHh9gUOz/ImoVBlZuWlPomucqJiijH\\ne+t5vfFFlhX9GKcxNT5T8FUiH+tmckk2n570s/1wM//cd4qa2iZu+cEoFlw7GrvVpHeYkk5kL5ui\\nzoxvv7f3FIc+b0cATpuRGya7mVGWj9OeOrea+KPtvON7EwWVq53X47CmTgcoSZeiKAoVGTNRUTnW\\nW8frjS+yvOjHZJhG/uLPS1FVhfJxOUwuyab2i3Y+/qSFf+z8kg/2N7HgutHMu6YYq1l259838h1P\\nMfGExp7PfLy39xQNvmTFqIJsCxXjsrhqnBej0aBzhOcLxLp5s3kLUS3CNOs15Ln0u6JXkoZDURSm\\nZszAqBipC37CK43/xe2Fd5FnSb1Z84wGlZlleVSM87C/vpU9R328UXOcf+47xaIbxnDzjEJMKdZH\\nSN8dmcBTRDAcY9vBJqoONNIdjKIoUFbsYHqph5KCbN0uTLuUcCLE309vIZgIMMk8lXHuMr1DkqRh\\nURSFyRkVmFULh3v283rTfzPfu5RxjtTcpk1Gleun5DNjfC576nzs/1crW6o+5909Ddx2fQk3VhRg\\nNslEfqWTCVxnPn+I9/adYsfh00TjGhaTytWlmVw9yYt7hIuvDEUo3subp/+XrpifcaYyJntkqVQp\\n/ZU6J2I3Otjr38HbLa9zg3s2M7OuT8kdaACL2cCNFYVcMzGPXZ+2cPBYOy/9s55/7DzJ/GtHM3tG\\noTy1fgVTRIpOUtvWFtA7hMuSm+u6aBvOjG+/u+cUtceS49uZDiPlYzOZUVaAzZI6F6VkZdnp6gqd\\n91xPrJs3T79MV6yT0caxXJ2buh0cgNNpJRjs0zuMyybbMXI6o352dVTTJ8KMd0xibt4izOrZq74v\\n9LlIBaG+GHs+83HwWDuxuMBpMzHn6iJmzyi6YInWS/VT6eJKaAMk2zFUMoF/Ry60UUVjCXYd9fH+\\nvkYa25Lj24VuKxWlWUwdl4+qpl4S/HpH1Rbx8dbpV+lNBCg1lVHumZnSyRvSI2EMhmzHyOpLhNnt\\n344/1obb5GFh/nLc5mSBpFRN4GeEI3H21fk48Hk7kZiGqir8YFIe82YWM64wo/8zeyUkvyuhDSAT\\neEo5d6Nq7QxRXdtMzaFmevviqApMKHIyrTQnZce3zzjTUQkhONJzgO0dVSREgknmciZ7yvUOb1DS\\nJWEMRLZj5GkiwZHugxwP1WNUjNycu4BJrvKUT+BnROMJPj3hZ/+/fPgDMQBG5TmZNTWf66Z4mTDW\\nc0X1telMJvAU4sq08e6O42w/fJq6hi4AbBYDU0tcTJ+QT3ZGetQ3zsqy09DWzEcdVRzvrcesWKiw\\nz0yrCUrSKWFcimyHfprCDRzo3EWcOGXOKdxeupTIyE4rflmEEDT4guyta+FkSxBNgKLAjLI8ZozP\\nYdp4D05b6gzdDYVM4CkoHd+QaCy5t3ugvo2Dx9oJ9cWBZJnTicUurhqXn1ZXhobivXwa3see1t1o\\nJMhRc7k66zqc1gy9QxuSdEwYFyLboa9gPMBe/3a64p04jU5mexYy1jFe77CGLBSJ89lJP5+caMfX\\nGQFAVRTKRmUyfbyH6RM85GWnRkW6wZAJPAWlyxvS3hWmrqGL2i/aOXK8g2hMAyDLaaKs0MnUUg+e\\nLP1rLA9WVItwKvwldYEjnOw9hoaGXXEwwTqZsVkTUvp0/8Wka8L4OtkO/WlCoz54lH8FPkFDY4y9\\nlH/LmUu2OUfv0IYljsKeI00ca+qi5atkDpCbZWXKGDdTxriZNDoLlz11y7bKBD6AmpoaHn/8cYQQ\\nrFixgvvuu+8by2zcuJGamhpsNht//OMfmTx58qDXvZBUfEOisQRN7b00+ALUn+qm/lQnHT1nN3q3\\ny8S4fAfji7OYOrGQnu6wjtFenBCCcCJEbyJIMB6gO+anM+anNXKatogPQXKTyFSzGOcspdheijGN\\nS6Omc8I4l2xH6oiZQnzctIOOeBsKKlNcFUzPuhZ3miXyc8fyg+EYxxq7+LzRT3NHH5GvDkYUoDDX\\nwYTiLCYUZTK+OBNPpjVldua/zwl8wF5Z0zQ2bNjACy+8QF5eHitXrmTu3LmUlpb2L1NdXU1DQwPv\\nvfcetbW1/O53v+OVV14Z1LqpRhOC7mCUtq4wrZ1hWrvCtHaGONUapMUf4tzdHZvFwIQiB/nZVsYW\\nZuN1O/s3alXHjTucCNEZ7aA71kl3vItgPEBvPEBvopdwopdwItSfpM+louI2eMhS3RQ5RpFjz7si\\nOltJ+rZlW9zcmDuP5r5GPuk+wKeBQ3waOESJvZSJzqsY6xiPWU2dcseD4bSZmD4hl+kTctE0QYs/\\nxPHmThp8AVr8IZraetl2sAmADIeZEq+LknwXY/JdjPY6cWdYde33vo8GTOCHDx+mpKSEoqIiABYt\\nWkRVVdV5Sbiqqoply5YBMG3aNAKBAO3t7TQ2Ng647kjQNEEoEicYjhEMxwiEogRCMXp6o/SEonQF\\no3QFInQGInQFIyS0byY3s1GlKMdKToYZt8tCsTeLfLdD173QM4naH22nPdqKP9pOR7SNPu3CR/5G\\nTFhUC9kGN2bFikWxYFYsOE0uMq1uMsyZGJT0GaOXJD0pikKRbRSF1iKa+5qo7/mEL0Nf8GXoCwyK\\ngSLraAqsxeRbi/CY87AZ7Clz1DoQVVUo9Dgo9CSLSSU0kTyQ8fVwqjVAa3eEI8c7OHK8o38di8lA\\nvttOocdOfo6D3EwrOZlWcjKsZDktKXmbbLobMIH7fD4KCs7WBPZ6vRw5cuS8ZVpbW8nPP1sDOz8/\\nH5/PN6h1B0sIwfHmHjoDEaLxBNGYRjSuEYkliEQT9EXjRKIJwtEEob4Y4UiCUCRGqC9OqC9+gePN\\n8ylKcrIQb5YFh81Aht1Eht2EO8OOJ9tJht00oh++7lgngXgPUS1CJNFHOBEilOglEA8QiHfRHeu6\\nYKJ2qE7yjYXYFAd2gwOXJQOXJQOrasOopudVppKUyhRFpcg2iiLbKHpi3ZwKnaQ5fIqG8Akawif6\\nl7OqVjJN2TiNGTiNLmwGO1bVhsVgxayYMalmjIoRg2LEbnBgN6ZOJUaDqlCQ46Agx8G1U5J9eqgv\\nhq8zTFNbD21dYbqCURrbgnzp++bpbIOq4LKbyLCbcdlNuOxmHFYTVosBq9mAzWLEYjJgMqoYDckv\\nk0FBVRUURUFVFBSFZD8ukmdKIZkXmjv78Hf2ktDE2a+Edt7P2pnnRPKxEMl1haA/Nyhf/ZP8e2BQ\\nVVT1q8cGFYOqJL8MCkZVxWBQMKgqRoNCToYVr3vkL/z7TgY2v4vr4lr8IR57cf+glzcZFawmAzaz\\nSrbThtWsYjGpWEwGrGYVu9WMy27GZbfisJlxWI0ps4fYFfPzYsOzF/29iopNdeA1FOBQnTgMLrJt\\nbrKsbgxK+o5VS1K6yzBlclXmNK7KnEYk0UdHtI32kI9APECvFqA14sMXOT3g6ygo3FvyH7iMqXvH\\nh91qYmyBibEFZ2PUNEF3b5SOnjD+7hA9vRGC4TiBcIxQRKPFH6KhVdMx6u+GosB/PnDjiE/tOmBv\\n7/V6aW5u7v/Z5/ORl5d33jJ5eXm0tLT0/9zS0oLX6yUWiw247sV8fUA/N9fF/z11O3X1x5Lj0IqK\\nwWBAVVUMavJ7aonhyhvemzkKL08UPsoXx4+hKQJVVVENKiaTaeTbmQC3LT3uWb+oK6ENINuRSgbV\\nBhsTyIaLTPIjhCAejxOPJxCahtAEQtMABZPRyLjCkbggbvj91MWZgcHdeSOEQNM0ElqCRCKBpiW+\\n+oWWPBAUov+AUAiBoij93/t99VhRkkfrguQRe/J5pf95VVWTjzm7/Lnfzz3w7P+biP4YRX8sZ96n\\n5BKapmE2mykZ5R76f9VlGjCBl5eX09DQQFNTE7m5ubz99ts8/fTT5y0zd+5cXnrpJW677TYOHTpE\\nRkYGHo+H7OzsAdcdCkVRmDxxwrDXTzdjSwr1DkGSJElKUQMmcIPBwKOPPsrq1asRQrBy5UpKS0vZ\\nsmULiqJw9913c9NNN1FdXc0tt9yCzWbjD3/4wyXXlSRJkiTp8qRsIRdJkiRJki4u1QaOJUmSJEka\\nBJnAJUmSJCkNyQQuSZIkSWkoJRN4XV0dd999N8uWLWPlypXDLv6SCl588UUWLlzIkiVLeOqpp/QO\\n57I899xzTJo0ia6uLr1DGbJNmzaxcOFCli5dyv33308wmD5zQdbU1LBgwQLmz5/P5s2b9Q5nWFpa\\nWrjnnntYtGgRS5Ys4W9/+5veIQ2bpmksX76cX/7yl3qHMmyBQIDKykoWLlzIokWLqK2t1TukYXnh\\nhRdYvHgxS5YsYc2aNUSjUb1DGpR169Yxa9YslixZ0v9cd3c3q1evZv78+fz0pz8lEBhEfXeRglav\\nXi0++ugjIYQQ27ZtEz/5yU90jmh4du3aJVatWiVisZgQQoiOjg6dIxq+06dPi9WrV4ubb75ZdHZ2\\n6h3OkO3YsUMkEgkhhBBPPvmkeOqpp3SOaHASiYSYN2+eaGxsFNFoVNx+++3i2LFjeoc1ZK2treLo\\n0aNCCCGCwaC49dZb07IdQgjx/PPPizVr1ohf/OIXeocybL/5zW/Ea6+9JoQQIhaLiUAgoHNEQ9fS\\n0iLmzJkjIpGIEEKIBx54QGzdulXnqAZn79694ujRo2Lx4sX9z23atEls3rxZCCHEs88+K5588skB\\nXyclj8AVRenf+wgEAni9Xp0jGp6XX36Zn//85xiNybv13O6Rv9H/2/L444+zdu1avcMYtlmzZvUX\\nwZk+ffp5hYdS2blzEZhMpv75BNJNbm5u/wyFDoeD0tJSWltbdY5q6FpaWqiurubOO+/UO5RhCwaD\\n7Nu3jxUrVgBgNBpxOtNnyuNzaZpGOBwmHo/T19c36EJhervmmmvIyDi/yl5VVRXLly8HYPny5bz/\\n/vsDvk5K1t18+OGH+dnPfsYTTzyBEIItW7boHdKwnDx5kn379vGnP/0Ji8XC2rVrKS8v1zusIauq\\nqqKgoICJEyfqHcq34rXXXmPRokV6hzEo3+Z8AqmisbGRuro6Kioq9A5lyM7syA7q9GaKamxsJDs7\\nm4cffpi6ujqmTp3K+vXrsVqteoc2JF6vl1WrVjF79mxsNhs//OEPmTVrlt5hDZvf78fj8QDJHV6/\\n3z/gOrol8FWrVtHe3v6N5x966CF27tzJ+vXrmTdvHu+88w7r1q3j+eef1yHKgV2sHQ8++CCJRILu\\n7m5eeeUVDh8+zIMPPpiyR0+Xasezzz7Lc8891/+cSNHSAZfapubMmQPAM888g8lkOm/sSRo5vb29\\nVFZWsm7dOhyO1JmsYzC2bduGx+Nh8uTJ7N69W+9whi0ej3P06FF++9vfUl5ezmOPPcbmzZuprKzU\\nO7Qh6enpoaqqig8//BCXy0VlZSVvvfXWFfPZHszkWbol8Esl5LVr1/LII48AsGDBAtavXz9SYQ3Z\\npdqxZcsWbr31VgAqKipQVZXOzk6ys7NHKrxBu1g76uvraWpqYunSpQgh8Pl8rFixgldffZWcnJGo\\n1Tx4A+3kvfHGG1RXV6fVBVSDmYsgXcTjcSorK1m6dCnz5s3TO5whO3DgAB988AHV1dVEIhF6e3tZ\\nu3YtmzZt0ju0IcnPzyc/P7//bOD8+fP5y1/+onNUQ7dz505GjRpFVlYWALfccgsHDx5M2wSek5ND\\ne3s7Ho+Htra2QQ25puQYuNfrZc+ePQB8/PHHjBkzRt+AhmnevHns2rULgBMnThCPx1MyeV9KWVkZ\\nO3bsoKqqig8++ACv18vWrVtTLnkPpKamhr/+9a8888wzmM1mvcMZtHPnIohGo7z99tvMnTtX77CG\\nZd26dYwfP557771X71CG5Ve/+hXbtm2jqqqKp59+muuuuy7tkjeAx+OhoKCAEyeSU53u2rUrLUtc\\nFxYWUltbSyQSQQiRdu34+pnMOXPm8MYbbwCwdevWQX3OU3IMfMOGDWzcuBFN07BYLGzYsEHvkIbl\\njjvuYN26dSxZsgSTycQTTzyhd0iX7cxsQOlm48aNxGIxVq9eDcC0adP4/e9/r29Qg3ClzCewf/9+\\n3nrrLcrKyli2bBmKovDQQw/xox/9SO/QvpceeeQRfv3rXxOPxxk1alT//BXppKKigvnz57Ns2TKM\\nRiNTpkzhrrvu0jusQVmzZg27d++mq6uL2bNnc//993PffffxwAMP8Prrr1NUVMSf//znAV9H1kKX\\nJEmSpDSUkqfQJUmSJEm6NJnAJUmSJCkNyQQuSZIkSWlIJnBJkiRJSkMygUuSJElSGpIJXJIkSZLS\\nkEzgkiRJkpSGZAKXJEmSpDT0/yrJsHWX4UslAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x11a195a90>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"for col in 'xy':\\n\",\n    \"    sns.kdeplot(data[col], shade=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Histograms and KDE can be combined using ``distplot``:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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8Nv+jB0o9zhlMzlduHxeea83xCrx5lwMJwIYylVtZPyhNgo5Bm4ECs0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\\nTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x11acced30>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"sns.distplot(data['x'])\\n\",\n    \"sns.distplot(data['y']);\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"If we pass the full two-dimensional dataset to ``kdeplot``, we will get a two-dimensional visualization of the data:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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iAqqk1LX5JwDUQQC00igvjmI0kS+/fv4csvPyM/PxeNRsP99z/MHXfc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\\nYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x11aebdbe0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"sns.kdeplot(data);\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We can see the joint distribution and the marginal distributions together using ``sns.jointplot``.\\n\",\n    \"For this plot, we'll set the style to a white background:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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YhIJHZIXakqkIiIyHNxFpGIiBSBgURERIrAQCIiIkVgIBERkSIwkIiI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\\nSBH+H3jVAOxlSfUyAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x11ad139b0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"with sns.axes_style('white'):\\n\",\n    \"    sns.jointplot(\\\"x\\\", \\\"y\\\", data, kind='kde');\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"There are other parameters that can be passed to ``jointplot``—for example, we can use a hexagonally based histogram instead:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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tpw3PHxIzjz9J044/TT8cwzz+DgwYO45JJL8M1vfhPXX389MpkMfvnL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\\nKwAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x11543ac18>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"with sns.axes_style('white'):\\n\",\n    \"    sns.jointplot(\\\"x\\\", \\\"y\\\", data, kind='hex')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Pair plots\\n\",\n    \"\\n\",\n    \"When you generalize joint plots to datasets of larger dimensions, you end up with *pair plots*. This is very useful for exploring correlations between multidimensional data, when you'd like to plot all pairs of values against each other.\\n\",\n    \"\\n\",\n    \"We'll demo this with the well-known Iris dataset, which lists measurements of petals and sepals of three iris species:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>sepal_length</th>\\n\",\n       \"      <th>sepal_width</th>\\n\",\n       \"      <th>petal_length</th>\\n\",\n       \"      <th>petal_width</th>\\n\",\n       \"      <th>species</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>5.1</td>\\n\",\n       \"      <td>3.5</td>\\n\",\n       \"      <td>1.4</td>\\n\",\n       \"      <td>0.2</td>\\n\",\n       \"      <td>setosa</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>4.9</td>\\n\",\n       \"      <td>3.0</td>\\n\",\n       \"      <td>1.4</td>\\n\",\n       \"      <td>0.2</td>\\n\",\n       \"      <td>setosa</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>4.7</td>\\n\",\n       \"      <td>3.2</td>\\n\",\n       \"      <td>1.3</td>\\n\",\n       \"      <td>0.2</td>\\n\",\n       \"      <td>setosa</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>4.6</td>\\n\",\n       \"      <td>3.1</td>\\n\",\n       \"      <td>1.5</td>\\n\",\n       \"      <td>0.2</td>\\n\",\n       \"      <td>setosa</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>5.0</td>\\n\",\n       \"      <td>3.6</td>\\n\",\n       \"      <td>1.4</td>\\n\",\n       \"      <td>0.2</td>\\n\",\n       \"      <td>setosa</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   sepal_length  sepal_width  petal_length  petal_width species\\n\",\n       \"0           5.1          3.5           1.4          0.2  setosa\\n\",\n       \"1           4.9          3.0           1.4          0.2  setosa\\n\",\n       \"2           4.7          3.2           1.3          0.2  setosa\\n\",\n       \"3           4.6          3.1           1.5          0.2  setosa\\n\",\n       \"4           5.0          3.6           1.4          0.2  setosa\"\n      ]\n     },\n     \"execution_count\": 12,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"iris = sns.load_dataset(\\\"iris\\\")\\n\",\n    \"iris.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Visualizing the multidimensional relationships among the samples is as easy as calling ``sns.pairplot``:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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G07HA5YLJaIyh3vRCeTyRDTJKlUPj+V6y7V+XKQclJerNcgEWV6y/O4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\\nQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x11ae9d160>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"sns.pairplot(iris, hue='species', size=2.5);\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Faceted histograms\\n\",\n    \"\\n\",\n    \"Sometimes the best way to view data is via histograms of subsets. Seaborn's ``FacetGrid`` makes this extremely simple.\\n\",\n    \"We'll take a look at some data that shows the amount that restaurant staff receive in tips based on various indicator data:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>total_bill</th>\\n\",\n       \"      <th>tip</th>\\n\",\n       \"      <th>sex</th>\\n\",\n       \"      <th>smoker</th>\\n\",\n       \"      <th>day</th>\\n\",\n       \"      <th>time</th>\\n\",\n       \"      <th>size</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>16.99</td>\\n\",\n       \"      <td>1.01</td>\\n\",\n       \"      <td>Female</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>Sun</td>\\n\",\n       \"      <td>Dinner</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>10.34</td>\\n\",\n       \"      <td>1.66</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>Sun</td>\\n\",\n       \"      <td>Dinner</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>21.01</td>\\n\",\n       \"      <td>3.50</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>Sun</td>\\n\",\n       \"      <td>Dinner</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>23.68</td>\\n\",\n       \"      <td>3.31</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>Sun</td>\\n\",\n       \"      <td>Dinner</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>24.59</td>\\n\",\n       \"      <td>3.61</td>\\n\",\n       \"      <td>Female</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>Sun</td>\\n\",\n       \"      <td>Dinner</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   total_bill   tip     sex smoker  day    time  size\\n\",\n       \"0       16.99  1.01  Female     No  Sun  Dinner     2\\n\",\n       \"1       10.34  1.66    Male     No  Sun  Dinner     3\\n\",\n       \"2       21.01  3.50    Male     No  Sun  Dinner     3\\n\",\n       \"3       23.68  3.31    Male     No  Sun  Dinner     2\\n\",\n       \"4       24.59  3.61  Female     No  Sun  Dinner     4\"\n      ]\n     },\n     \"execution_count\": 14,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"tips = sns.load_dataset('tips')\\n\",\n    \"tips.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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Xd/+tOfcPDgQRiNRjQ0NOD999/Hpk2bRMf1aoFFeXk5zp8/j/Hjx6O6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\\n4jGsiIhI8f4/8A46ouL3SiQAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x11c3a3fd0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"tips['tip_pct'] = 100 * tips['tip'] / tips['total_bill']\\n\",\n    \"\\n\",\n    \"grid = sns.FacetGrid(tips, row=\\\"sex\\\", col=\\\"time\\\", margin_titles=True)\\n\",\n    \"grid.map(plt.hist, \\\"tip_pct\\\", bins=np.linspace(0, 40, 15));\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Factor plots\\n\",\n    \"\\n\",\n    \"Factor plots can be useful for this kind of visualization as well. This allows you to view the distribution of a parameter within bins defined by any other parameter:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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bNOJxoMHDgQ+/fvx4ULF3DixAk0Nzdj7NixDjWQV2rbt2/HsWPHWj1X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\\nYImIBGHAEhEJwoAlIhLk/wApsnpaZFKY5AAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x11c6f0518>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"with sns.axes_style(style='ticks'):\\n\",\n    \"    g = sns.factorplot(\\\"day\\\", \\\"total_bill\\\", \\\"sex\\\", data=tips, kind=\\\"box\\\")\\n\",\n    \"    g.set_axis_labels(\\\"Day\\\", \\\"Total Bill\\\");\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Joint distributions\\n\",\n    \"\\n\",\n    \"Similar to the pairplot we saw earlier, we can use ``sns.jointplot`` to show the joint distribution between different datasets, along with the associated marginal distributions:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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nnT1CRxDJTdQ4cO4cILL8S2bduwfPnyoKdDC0hXVxduuukmbNmyJeip\\nHNNuuukmfPvb34aqjr223bx5M15++WV87nOfC2hmx57Sevfggw/i5JNPDno684I7IiKq2WQ7CqJK\\nsRDRorZs2TLuhubQVVddhauuuiroaVCdY9NTIiIKFAsREREFioWIiGgSQgiYlgPX84KeyoLHe0RE\\nRKMIIWC7HjKGDc8TkCQgEQlBCym+PUpPY7EQERGNcF0PhmmjYB3dBQkBpA0bquIgEdGgKBILks9Y\\niIho0RNCoGA5yOadKd/HcQWGsiaiYRURTYUssxj5hYWIiBYtIQQc10N65BiuEkbBQd50eFznIxYi\\nIlp0ih3VBfKmg7xVfZR36bgupDiIRzUoMo/rasGn5oho0fEEMJgxZ1WERrNdgaGMifpvlFbfAitE\\nP/7xj8udg0uBU0REtPgEUohef/113H///fjlL3+JBx98EI899hgOHjwYxFSIiChggRSiN998E6ee\\neio0TYOiKNi4cSMefvjhIKZCREQBC6QQrV27Fs8++yxSqRTy+TyeeOIJHD58OIipEBFRwAJ5am71\\n6tX4xCc+gRtuuAGxWAwnn3wyFEUJYipERBSwwB7fvvrqq3H11VcDKMYEd3Z2BjUVIqK6E4/Hg57C\\nvAnsqbnBwUEAQHd3N/7whz+UY3uJiMi/iPhjQWA7oltuuQWpVAqqquILX/jCoqr+RER0VGCF6Kc/\\n/WlQn5qIiOoIOysQEVGgWIiIaNGRAEQ0f57UDSmL517OXGHTUyJadGRZQiwSgq4pyBg23Ao7b48m\\nSUAyGkJIZQfuWrEQEdGiJEkSQqqCpoQM03aRMeyKP5aZRP5iISKiRU2SJIQ1FSFFhmE6KEzTkVtV\\nJCQY++A7FiIiIgCKIiMeCSGsKROC8iQJDMKbQyxEREQjSsd1zQm5HB0eDSuIaCEew80hFiIionEk\\nSUJED0FTFcg8hptzLERERFNQFP6Ey3zgd5mIiALFQkRERIFiISIiokCxEBERUaBYiIiIKFAsRERE\\nFCgWIiIiChQLERERBYqFiIiIAsVCREREgWIhIiKiQLEQERFRoFiIiIgoUCxEREQUKBYiIiIKFAsR\\nEREFioWIiIgCFVhC649+9CPcf//9kCQJ69atw1133QVN04KaDhERBSSQHVFvby/uu+8+PPDAA9iy\\nZQtc18VvfvObIKZCREQBC2xH5Hke8vk8ZFlGoVBAe3t7UFMhIqIABVKIOjo6cMMNN+D8889HJBLB\\n2WefjbPOOiuIqRAR1SUhRNBTmDeBHM2l02ls27YNjz76KJ588kkYhoEtW7YEMRUiorqUzWaDnsK8\\nCaQQbd++HStWrEBjYyMURcFFF12EF154IYipEBFRwAIpREuXLsXOnTthmiaEEHj66aexevXqIKZC\\nREQBC+Qe0YYNG3DxxRfjyiuvhKqqWL9+Pa699togpkJERAEL7Km5m2++GTfffHNQn56IiOoEOysQ\\nEVGgAtsREdHCZtkujIINWZYRi6hQZF730uRYiIjIV67rIVdwYNpu6Q2wXRcRTUVEVyFJUrATpLrD\\nQkREvhBCIG86yFsOPG/sn3kekCs4sGwX0XAIWkgJZpJUl1iIiKhmpWM4252+G4DtCqRyFvSQwuM6\\nKmMhIqJZcz0PufyoY7gKmbYL2/UQ1VSEdYXHdYscCxERzYpRcJC37AnHcJXyPIFswYZpO4hHQlBV\\nHtctVtwXE9GsFCa5FzQbtisAbogWNRYiIiIKFAsREREFioWIiIgCxUJERESBYiEiIqJAsRAREdWh\\neDwe9BTmDQsREVEdWkw/5MtCREREgWIhIiKiQLEQERFRoFiIiGhWFNnHexii/u6HCCHguC6EmL6j\\nONWOTU+JaFaSMQ2G6aBgOvBmuVarioRYOARVra9rYtfzkC84yFsuQqqEeESDqtTXHBcSFiIimhVJ\\nKhaRcEgZm8haAVkCInr9JbYKIWDaLjKGXX6b7QgMZUzEwirCmgrZz50gAWAhIqIaKYqMZEyDORKO\\n58wQjqeHFMTCKpQ62mEUj+EEsoYFZ4rtXa7gwDAdJKMaQqpcVwX0WMdCRES+0EMKNFWGUXCKERHj\\n1nNVkRANh6DXWUz46GO4mQgBpHIWtJHjunoqpscyFiIi8o0kSYhFQghrCnIFG6btQZaAsKYiGq6/\\nYzhr5Biu2ltcliMwyOM637AQEZHvisd1OkzbhSpLdbdz8DyBdM4shvLVIFdwkDcdNCXCLEY1YCEi\\nojlTb8dwJQKouQiVzPaJQToqkEK0d+9e3HbbbZAkCUIIHDx4EJ/61KfwkY98JIjpEBFRgAIpRMcf\\nfzwefPBBAIDneTj33HNx0UUXBTEVIiIKWOAHt9u3b8fKlSuxZMmSoKdCREQBCPwe0W9+8xtceuml\\nQU+DiKiuZDIZpFIpAEAymayrJw79FuiOyLZtPPLII3jve98b5DSIiOrOH3d2YeuO/fjvx3YjnU4H\\nPZ05FeiO6IknnsApp5yC5ubmIKdBRFR3ItE4YvFk0NOYF4HuiB566CG8733vC3IKREQUsMAKUT6f\\nx/bt2/m0HBHRIhfY0VwkEsHTTz8d1KcnIqI6Efjj20TjuZ4Hy6k8UmC+lHqT1WNQWr3Oq5751ZJn\\nAT/MNm8Cf3ybqEQIgYLlwjAdeJ4oxgVEVChy8NdLoyMOQqqMeESFqgTfvsZxXWTzDmzHg6rIiIbV\\num2rU08UWUJzQkfBcpDNO7MeJ6IriOghFqMasRBRXbAdF7m8Pab/l2m7sF0XES24ADXX9SaEvtmO\\nh+GMhbCmIhYJZl5CCOTyxbiF0nfMcT2kcxb0kIxYOFR3jUbrjSRJiOghaOrRTuGVUhUJiYgGRZEW\\n9M/3zBcWIgqUEALZvA3Tcidtxe95xQ7Hlu0iGg5Bm6erfSHEtDHYAkDecmA5LqK6irA+f/+UCmax\\n4/NUAW6m7cF2TIR1FdE6S0CtR4oiIxHVEHY9ZHI2vGmOOCUAiWjxdcjvq39YiCgQpWO4vOnAraB9\\nse0KpHIWwiEF0Tk+rrNsF7kKkkYBwPUEMnkbpu0iFglBncNdiON6yOVtWM7MV+6eAIyRAh6bxwJ+\\nrJIkCZqqoCkpw5ziuK54DFcfR8ULDQsRzTvbcZErFO9rVKtgu7Bcr7gL0fy9Kp3sGK5SluPBzprF\\n4zqfA+CEEMiNpJ5W+zyCM1LA6zGeux7Jo4/r8jZMx+Mx3DxgIaJ5ZRQcGIXqEzFH87zicZ7neYhF\\nNF/mZdkuMnkLXvW1sUwIIG86sF0XjTHdl0VLCIHhrFnR7mw6pfttTXEdMq/oZ6QoMhIxDRG3+BAI\\nC9DcYiGieeV5Xk1FaDQ/FwfH82oqQmMI/+YmBGouQiXFr48LaqUkSUJI5ZHmfOClERERBYqFiIiI\\nAsVCREREgeI9IiKiOmQYGYSzURhGLuipzDkWIiKiOnTqCQ048cRVAIoJrQsZCxERUR1KJBJoaGgI\\nehrzgveIiIgoUCxEREQUKBYiIiIKFAsREREFioWI5pUWUqAq/rSZKVgubMfzJZlUVxVoqj//HDxX\\nwLScmuclhIDtuL415dFUmSmuVJf41BzNKy2kIKTK02b9zESSij3YXK/YENSP9vyKIqMhrqNgOjAq\\njKaYal4egLRhQ1MdxCLarKIhipEPFizn6DxK41dLkSVE9GK4IFE94iuT5p0kSYiFQwiHqkvGlFAM\\npBu/GOdNFwXT9SWwLKyr0DVlQvrpbOZlOQJWxkQsrCKsqZDlmefleQIFy0GuMDEPpzR+6fNVMq+w\\npiAWCbF7NNU1FiIKjKLISMZ0mLYLY4YgupkWX4HiLkRVnJqzYyRJQjwaQliTkZ0hN6mSopArFBNV\\nE1ENIXXySIHiMZyHjGHNuEsUFXzekCojHlahsns0HQNYiChweqh4f8YYCX8bvRCXjqMqPZFyXIEh\\nn47rVFVBQ0yeNEm2vAuqcCxPAKmcBU2VEY+ExgTUuSPJq2YVQYGlzzu+IJWO4fwODSSaSyxEVBck\\nSUIsEoKuKTAKxThsIWZ3TwQYOa6zXCQitR3XSdLRhT07EgleTWEcz3I8DGZMxMMqNE2BZbnITnIM\\nV6lyQZKKBT3OYzg6BvGpOaor6shxnR/peUIUj+u82TwRMY4kSUhENSgV3OepRLbgYDhj1lSERouF\\nVSSiGosQHZMCK0SZTAa33nor3vve9+LSSy/Fzp07g5oK1aF6XU/rdaGv13kRVSKwo7k77rgD5513\\nHv71X/8VjuOgUCgENRUiIgpQIDuibDaLZ599FldffTUAQFVVxOPxIKZCREQBC6QQHTp0CE1NTbj9\\n9ttx1VVX4fOf/zx3REREi1QghchxHOzevRt//dd/jc2bNyMcDuN73/teEFMhIqpLmUwGqVQKqVRq\\nwbdmCqQQdXZ2orOzE29729sAABdffDF2794dxFSIiOrSzjdT2LpjP/77sd1Ip9NBT2dOBVKIWltb\\nsWTJEuzduxcA8PTTT2P16tVBTIWIqC5FYwnE4klEo7GgpzLnAntq7nOf+xz+/u//Ho7jYMWKFbjr\\nrruCmgoREQUosEJ00kkn4Ze//GVQn56IiOoEOysQEVGgWIiorgghkDcri1+ohB6Sfe3SEA1XFudQ\\nCb8ehJIlaVaZR0T1gk1PqW7YjotcwYY9EgZXae7OZGRJQiIamjJ2YbY0VUFTQoZpOcjmZ9cnbnzn\\n7lq+znhEha6pkNnih45hLEQUOCFEsbO15Y5ZkEd3lq5m9zDXi7MsSYjoIWhqdcF+wORFp5J8ofH0\\nkIxYeGycBNGxioWIAiOEmDTrZ+L7FX+dabHWVRmxyPwtzooiIxHVEHY8ZAwb3jTVcqZcpUqLrixh\\n2oA9omMRCxEFwnHcGdNPx5tq5xDk4ixJErSQgqbk5Md1U8WIT2W6ohsPj+z0fLpHRVQvWIhoXgkh\\nkMvbKIw7hqv440d+Le0cYmEV4TpYnEcf12UNC5Yrarr3M7roTpbqSrSQ8JVN8yqXt5GfZREaTQgg\\nqquIhkOBF6HRFEVGJFy8vqv5a0Sx4CZjGosQLWh8ddMxq15vkfDeDVF1KjqaGxgYwHPPPQdFUbBx\\n40Y0NDTM9byIiGiRmHFH9Ktf/QqXX345fv3rX+OBBx7A+973Pjz++OPzMTciIloEZtwR3XPPPXjg\\ngQfQ0dEBAOjq6sJNN92E8847b84nR0REC9+MhSgej6Otra38/8uWLUMoFJrTSRERLXZDgwMQkJHP\\n55BKNVb0Mclk8pi8RzljIVq3bh0+8YlP4Oqrr4aiKPjtb3+L9vZ2PPjggwCAK6+8cs4nSUS02Hie\\nA8+zoesadrwyBEkanvb9DSOHy89ff0zew5+xEAkh0N7ejieffBIAEIlEEIlE8MwzzwBgISIimgst\\nrR1oaesMehrzYsZCxMA6IiKaS1MWor/927/Fd7/7XWzatGnMmaMQArIsY+vWrfMyQSIiWtimLERf\\n+tKXAADr16/HZz/7WQghIEkShBC4/fbb522CRES0sE1ZiP75n/8Zr7zyCo4cOYI9e/aU3+66LpYs\\nWTIvk5sLYqSr5LH4ZMlCMF2H6tmMVbpAqnksz4MkST6N5d/XCBztO1fzOCPfL1muv4YqrudBqcN5\\neZ5Xl9+vhWbKQvSVr3wFw8PDuOOOO/C5z33u6AeoKlpaWuZlcn5zXQ+5gg3XE0hENCiKPwsPzayU\\nOdTdbyAaVhDRa/sRAEkC8qYLAIjo6qwXMSEECqaL7v4somEVrY0RhFRl1mMZpoM3D6UgPIH2lmhN\\nyakSij31Ulmz5terabs42JPGcM7CiSuaEI+G6uK177oeUlkTR4YLWNYWQzxSH/MSQsCyXWTyNmI6\\nu57PtSkLUTweRzwexz333DOf85kTnhATWvQPZU1EdKWmRYxmVvoH3TtgIFsofv+zhgMj76AhrkNV\\nq/vel3N9RjYdedNFwXKRiISghZSqFjHbcTGYKmAgbQIATNvCcNbC0pYYEjGtqoXHdlx09+ew73Cm\\n/LbhnIWlrVE0xPWqF9fRnbsdV2AoaxYLuFZdk1fPE+gfzuPVg8Plndrzr/VheVsMyzsS0EOzK7q1\\nEkLAKDjo6svCcYvzOtibRSysorMlWvXfpZ/zcl2BTN4qzytbcGCY7pwk/lLRgo6BEELAdkdCyyY5\\nLsmbLgojL7CgXvgLmet6GEoXcGS4MOHPPAEMZUyENRnxqFbR9760QxhPCCBt2FAVp6KdgycEMjkL\\n3f25CeMEaQCXAAAgAElEQVQJAXT156Cn8ljaGkdYn/51IYTAYMbEK/uG4LgTs5W6+w0cGcpjRUcC\\nEX3mf27TBegZBRd5s7KiW9qBvn5gCJlJIs0P9eXQPWDgxJWNaG2IzOvVvmW7ODJkIJ2zJ/xZruDg\\nza402hvDaEqG57XruOt5KJjFojOeJwRSOYvJuHNkwRYi1/VgmDYK1vTBawLVLWI0s1LmUFdfbtrk\\nVQAoWB4KVgGJWAhhbfKXYzlcbobPO9POoZQI29OfRX6G14Vpe9h7OI2WhI7mxvCkx3VGwcFbXcPl\\nHdV089rbnUZjXEN78+THdeN3elOppOhatotDvRkc7MtNO5bnCezZN4RENIO1K5rm/FjM9TykshZ6\\nBowZ3/fIcHGnuqwthtgcz2v0MdxM33/T9mDa5pzH0S82C64QFRebiUmZMyktYjyuq41luegdyiFj\\nVPf9z+Rs5Ax7zHHdTPHaU5ls5+C4LgZSBQykpi8a4w1kTAxmTSxtHTmukyQ4rofD/Tm81Z2uaqzh\\nbPHob1lrDMn40V3gVDu96RwtuioiI/cvPE+gP5XHqweGq3pgImM4eP7VPqxoj2F5ewKaz8d1Qgjk\\nTQddR3KwJ9k1TsX1BA70ZpGIqGhvjkHX/J/X+GO4SmXzDoyCi0QshJDC47paLahCZDnulMdwlSrd\\nc2iMaVBnedN6MfI8gaF0Ab1D+dmPUTqu0xUkolrVi/Noo3cOkgT09BuY7ctCCKCrL4dIKo94VMPr\\nh1JVRZyP19WfQ99wHsctSUJV5ZoC9IyCg7zpQJEk7OtJT3rcVamDR3Lo7jewYU0rkjGthlkdZVoO\\n+lMFpLLWrMfI5B1kulJY0hJFY6L6+22T8TyBgukgZ1Z3wTRmDCGQyloIa8XjOj5dN3sL6jtXMB1f\\nHp0VArCrvEJa7IQQ6BuefREarTDJGf1sOa5A31Bh1kVotLzlYd/hTE1FqMRyvKp2B9Mp3deqpQiV\\nuJ5Axph90Rgvm7drKkKjpQ3Lt52HAGoqQqMVj/+5I6pFYDuiTZs2IR6PQ5ZlqKqK+++/P6ipEBFR\\ngAIrRJIk4b777jsmO8USEZF/AjuaE0LA8/w5miAiomNXoDuij33sY5BlGR/84Adx7bXXBjUVIqK6\\nUwrGA4BwRIc0w30ow5j+cf16Flgh+tnPfob29nYMDg7ihhtuwAknnICNGzcGNR0iorpSCsbLGwbO\\nPe3Eim5jJJPJeZiZ/wIrRO3t7QCA5uZmXHTRRdi1axcLERHRiFIwXi6bRkNDw4K+nx7IPaJ8Po9c\\nrriNNAwDTz31FNauXRvEVIiIKGCB7Ij6+/tx8803Q5IkuK6Lyy67DOecc04QUyEiooAFUohWrFiB\\nX/3qV0F8aiIiqjMLqrMCVacUlLbQ+fk1sqVYdRbBy4t8sKAKUTyqIRkN1dxsIxZWA8tpmS+m5WI4\\na2I4a8Kya2+pI8sSVi9rQEO8th5lsiyhuUH3pWGKZbvo6stiIFVAwXJqKkiyLGFFRxynrm3FuhUN\\nNRUkSQJWdMQR0VVfvk4JQEdzBKs6E1BqjHM4fkkS7U2RmudUanQqyUBzg17zvBriGpa0xHy7qJAl\\noDmhQ6syD2s8VZbQGNd5gVKjBdX0VJYk6JoKVZWRLzjIW9UtsJoqIRbRakrVrHellFrTPvrDxMWc\\nFQWxsDrrnBVJkhAKKVjaGkNjQkd3lZ2WAaAhFoI2EgVRWm5KHbirIYTAQCqPoYxV7gtnOR4czUNY\\nU6puZtvaGEbLqGycJa1xNCbC2H84XXWT15YGHa0NkfJYpRjw2S6vRz9WQiwSwtqVjcXms4PVzas5\\noeOE5Q2I6mrN/dxsx0Wu4JS/94oso7khDMt2q+47F1IkLG2P+zKv0SRJgqJISMY02I6HjGFV3Y8w\\nEQ1BZ46ZLxZUISpRZBmxSAi6piJrWHBmeIVJEpCMags6fbGUiFmwnEn/wZm2C9t1EdHU4pX6LL8P\\nkiQhFg7hhOUNSGdNHK4geyYSVhALT545UypClS7WmZyFgXQBRmFiQ8u85cK0XUTCKnRVmbHoRsMK\\nOltiky42EV3FiauasKQlij0HhmDOkG+kazKWt8WhT5K5NJuiO1VEhixJaGmIIBHTcLg/h9wMcSiq\\nIuGkVc1oSuo1Z+uUwvhMy53070oLKWhtDCNXsJEvzHyR2NkSRUNcm9NIFkmSoIUUNCXCKFgOcpO8\\nbsaLaAoiYUbF+GlBFiJg5ApdldCY0GHaxXiIycTCKsILPI/etF0YBXvGzBXPKyZkWraLaDhUUy6N\\nIktoSoYRi4SmTOOUZQmNicoWmpl2Drbjom8oj1TOmnYx9wSQyzuwVA8RTZk06VSWJCxtiyERnT6Q\\nTZIkNCTCeOdJHegdNPDGodSE+UkSsKwtjmQshJk6NJeL7jQFqRwSOEPB0lQFqzqTyOUtHDySm7Qr\\n/XGdcSxti08a+leNUuBg3nRmDEKUJAnxiIaI7mE4Y006r2QshPamqO+5SNORZan8ms/lLVjOxHkp\\nsoRENASV+UO+W7CFqESSJIQ1FaFxx3WqIiERXQzHcA7MKu8B2a44elwXqe3KTwspWNYWR1PCQVdf\\ntlwMk7HQpLuD6Uy2cygewxVG7nVVfhRoOx5sx0NE9xAOHT2ua20Io7khXNXrQlFkLG2LoympY29X\\nGn2pYjR6c1JHW2Ok6uNOISYvurM5poxFNKxbEcJgOo8jQ8V5NcZDWLO8CdFw7cddjuMiO+oYrlKK\\nLKOlIQzTcsoXKaoiYWlbHDEf5jVbqiIjGdNhOx7SxtGLGh7Dza0FX4hKjh7XKRCeQGiBv6iKgWl2\\nTTk8peO6eCQEPTT7l4okFe9frF7WgMGMCSFETd/70uJQMG30DuYrOk6ZSt50YdkuGuM61qxorGmx\\nieghnHx8MzrTBRimU3WhHW2yv7bZ3qeXZQmtjVEkYzrCIQVNybAvJwDZvI2C6dQU7KdrKlpDClRF\\nRkNMm/U9Sj+VjuuaE2GYtgOtgmNcqs2iKURA6bhuYT8NV2I5ri9hcJ6HGZstVkpRZEQ0peqHSKaS\\ny1d2pj8T1yve8wnXUDhKJElCIqahHnMVNR+LEFB8KtGPL1OSJCRjobpb7GVZQkQPBT2NRaG+/uaJ\\niGjRYSEiIqJAsRAREVGgWIiIiChQi+phBSKiY0UpoTWfzyGVapz1OMlksu6fEGYhIiKqQ6WEVl3X\\nsOOVIUjScNVjGEYOl5+/vu5D9ViIiIjqUCmhdTHgPSIiIgoUCxEREQWKhYiIiALFQlQD1/PgOF7d\\npZx6QmD2CTfjxvIEDvZm4Di1t+XxhMBwxoRbZU7RVMK6Aj3kz0s4l7d9CQgUQsDzhE9NkYoBbori\\nU4sluZbko7GKXbP9C6mrs39CNM/4sMIsCCFgjURLCABhTUFUn32onJ/zsl0PGcMut9evZenpGzLw\\nm+37sb8ng5WdcVz3V+uwvD0+q0dBh7Mmntl1GG8dziAaVvCuUzrR0jC7JNBSF+pYRMOqTgV9wwWk\\nsuaseuupigwhPAxlLTyzuxcnrmxEa0NkVv3YPE8gbznlLKSaAu8kIKypiIWL/0TzpoO8OXmW1Ixj\\nAdA1BfHI9LEWlSi/9vN2RbEVM9FCMmLh0ILugk8zYyGqghACriuQyVtjsn0KlouC5QbaKt51PRim\\njcK4gLZydAIqXxQLpoMdu3vw2PPd5bcd6Mniqz95HhduXI4L37kCiWhlkeCW7WLP/kH86aWe8uc3\\nCi4efa4Lxy1J4JQTWhDRK38ZShi76KmqgiWtMSRiIQykCjMGwZUocjF3aHSKrOcJ7Nk3hEQ0g7Ur\\nmipeuIUQsB0XacMeM7fZpsxqarFT/OjFORouvraqjfUIKcXO5340+3XcYpLp+FyrqWIrpqPKUrHR\\nbBV/97Rw8VVQIdfzRq5Kp14EMoYNQ3bmNTyrGErmIDvDAlxJJLXnCbzVlcIvH31zysVu27OH8OTO\\nbvzv95yIt53QClWd/ErWEwKH+7LY9uyhKTtk7zucwf6eDDae1I7lHYmRo6PJTZVIWhKPaIiFQxhM\\nFzCUmT6bKKRKcByBqXpHZwwHz7/ahxXtMSxvT0wb0Oa4HrJ5C/YkQWolle4cZlqcFUVGMqbBsl3k\\nZgg6lGUJUV1FWKv9wsjzxIzppZUW3dE7vXr/IUuaPyxEM5jsKGI6ricwnLXmPE5YCAHH9ZAedQw3\\n48eM/DrZYjGQyuN3f9qPN7vSM45j2R5+uGUPTliWxAcuXItlrbExi0o6Z+GZl3vwxqFUBV8H8D97\\njuCV/UM4Y30HmpLhMX9eaSJp8esqxmQnYxr6hvNIZ60xR1mqIgNCTFs0Rjt4JIfufgMnrmpES3Ls\\ncV0li/N4UwbeYWRxjlS2OGshBSFVhmE6KExyXBcOKYjWGGhYnO/kO73pP6b462Rf52Q7PSKAhWhK\\nUx3DVSpvucjPwXFd6Wa4YToozDLXZ/SiUrAcPLvnCB559lDV47zVlcZX7n0O737XClxw+nJoIQWv\\nHRjCH186XPW9jIxhY9uzh7B6WRInH9+MsKbO+t5DSFWwtDWOZNTCQKqAvOlAUaSqU0SB4oXF7r1D\\naIhlsWZ5I2KR0IT0zmqMvxgoLs4qVKW6ozNJkhALhxAOKcgVbJi2h5Ai1RzxDoy89j0x405v2jFw\\n9GtUSrszHsPRFPjKmIJRcGCYtYeuZQwbIiJ8C9gq2C6yhu3LWL2DOfz4oVdmXdBKHn7mIJ7ceRin\\nrm5Busa5vdmVxt7DGVx+zvFTHvtVKh7VEIuE8FbXMEy7tseyUjkbz73ahxNXNfqyyy0+bKEiWuPr\\nQhmJtnYcF4pPx8F505/AQSEAXZWQiOk8hqNpBbpH9jwPV111FW666aYgpzEp4dOjqQB8SUot83Gs\\n4vGSP2mppuX6sngBI48GS/58oZIkQZb9S+X1fIxeVX08tlVVP3fdvgwDAJDl+blXSse2QAvRvffe\\ni9WrVwc5BSIiClhghainpwePP/44PvCBDwQ1BSIiqgOBFaI777wTn/nMZ7htJyJa5AJ5WOGxxx5D\\na2srTj75ZDzzzDNBTIGIqK6VgvFmKxzRkTcMH2c0dwIpRM8//zweeeQRPP744zBNE7lcDp/5zGfw\\n1a9+NYjpEBHVnVIw3mzkDQPnnnYiGhqOQzKZ9Hlm/gukEH3605/Gpz/9aQDAjh078MMf/pBFiIho\\nlFqC8XLZNBoaGuo+mbWEP+JMRESBCvwHWs844wycccYZQU+DiIgCwh0REREFioVoCiFFhl9PltuO\\nC8enMDi/QuUAYGC4UM67qVVTQoOu+dPBwPNcvHZg2JfAQSNvo3/Y8GUsRZEQjfjTqgkoBsL5QQiB\\ngVTel/BCAFBVybe5+RXq5zfLdquK06C5FfjRXL3SNRUhVRkTdDYbkgTYjsBw1qyp/b3jusjlHVgj\\njTtrCV3LGjaeebkHe7tTSMY0NCZ0dPfnZtXaRZElnLauFcmoBgEgGdXQN2xgNvVSCAEhgFzexRMv\\ndGP/4Qzeub4drY3RqsfyPIFXDwxhX3cauYKDxoSOpoQ2655/KzvjWN4WR0hV4Loesnm7/HdRLT+D\\nFDM5C/2pPPKmi8G0ieakjuZkuKafz9NUBU0Jperu4qPJsoRktP46bbueh1z+aKaTPhLMF3So5WLH\\nQjSNUqaLripVd+EeH18gRLGZpOW4IzkxlX3rhRDIFRwULGdC6Fq1xcgTAi++1oc9eweRzo16LNQV\\nWN4WR8Fy0Tecr3i8tcsbsLw9Dk8c7afnCoH2pigsx8NAqlDxWMITMEdSb0v2Hc6gd9DAupWNeOf6\\nToQqbILa05/DqweG0Dd89PMPZ0xk8xaaE2E0xjWoFQbFNcRDxa7b4aMheaVcoGq7cMuyhEQ0NLLb\\nrm2nYNsuegeNckowANiOh97BPLKGjbbGyKx3b5IkQZJQ7uSdy1uwqujCHWRA5FSEEMU8McuBN+r6\\nwbQ92I6JsK4iqjMjKSgsRDOQJAmqKqExrlecSzRdgXBdgYxhw7TdGSOSCyPx0M4UXVOrSQA92JvB\\n86/0obs/N/nnGml+urIjjoHhAnLTdB5vTmhYf3wLFEWetKGr4wrIkoSlLVGkcta0V9We68ETmDLq\\nO2+62Pn6ALr7ctiwthXrVjZNOZZRsPHy3gF0HclNetHgOAJHhvLI5W00JXUkotqUC48iSzhpVSOa\\nk5PHhkuSBC2koDkRrmjnEI+oI9EWtUd19w/nMZyxxqTLjpYrOMj3ZpCM62hvitS0K1FHuntXUnTD\\nmoyoXn+7C8t2YRRs2FNcSHqi2G3fGvk3WWuMBlWPhahCkiRB11SoqoyC6cCYJKl1phTR0Szbg+MU\\nj+ui447rHNdDrmBPmzI62nQJoLmCjR1/7sGbXemK8njypouGhIaGhI7DA2OP62RZwmlrW9EY1yo6\\nerNdgVgkhHg0hL6h/JhCI4SAJwSMQmW5Sn3DBTz67CHs7Upj4/oOtDQcDdATQuC1A8PY251GNj/z\\nDwDmCsXC0ZRw0JTQEB53XLeiPY7l7fGKFiRZlqbdOeiajJhPi3PGsNA/nJ82JbjEE8VdoJG30ZwM\\noyk5+yiGmYqunzs9P40/hpuJ4wqkchb0kIJY2J+jU6oMC1GVFFlGNByCHlLLx3XVpIiO5gnAKB/X\\nhaCF5JGFeWLqZiVGJ4AKIbDzjX7sfmsQqaxV1TjFMDSBFe1xGAUH/akCVi9rwMqO4jFcNfd/hCj+\\n19kcQ8F2MJg2ITwPpu2NOYarhCeAt7rT6Bk0cOLKRmxc34GBVB6v7h/GkaHKjxRLhjImsnkbTQkd\\njQkdzQkda1cUw++qXVCP7hyKiaYSJCRiPh3DOS56B/PI5Kyq7wtajoeeQQOZvIW2pkhN+Ueji27G\\nKL72/drp+al4DOcib9ljjuEqZdoubMdFRFcR4XHdvGAhmoXRx3WD6ULNeUOOK5A2LCiSBLfGp7tK\\nH/3os4fw6oHhmsbKmy4kScLGk9oQi2g1fZ2260GRZYQUCb0pc1YPM5QYBQcvvNaPwwM5yJI05ZFL\\nRfNyPBwZyqMpoePUtW2THsNVqrhzUNGcUAAJkH1YwFzPw77D6VknpZbk8g7yZgZrlzfWfKWvKjIa\\n4zo8IXwJCfRbNm/XnLPlieLOWZaliu/n0uzxO1wDSZJqe3xtHM/HRDI/H02Nhv17ZFmSpJqK0Giu\\nB7g+ffMjulpTERrNr3GA4m7SqbEIlcxmdzAVSZKg1OlOwfMxibI+v8KFp/4uZ4iIaFFhISIiokCx\\nEBERUaB4j4iIqA7VEoyXz+eQSjX6PKPJJZPJmp8sZCEiIqpDtQTj6bqGHa8MQZJqe3J2JoaRw+Xn\\nr68594iFiIioDtUSjHes4T0iIiIKFAsREREFioWIiIgCxUK0QPn4w+XwfPyRfD9/6t23lhaArz9C\\n70cI37HAz69zsXzPaHIsRDWK6iEoPrR00UMyomEVao1jCSGw91A/Xvzz65Ax+0C/MreAPzyxA5lM\\nuuahLMvCS3vehOcUak4AbUmGsaojgaWtMag1poDGwiqG0gX0DRs1F0rX9TCYLmAoU6h5LM8TSOdM\\nSDJQa0s3WQKaEnptg4wQQiBrWOgdMGDX2EpKCAHLcUeaqPpzwRPRVYR8SIZVFYkNT+cJn5qrUURX\\nEdYUZPM2TKv6zmeqMhK+N9JYMaKrkwbhVSKdzWPzH3bi14+/PPKWAzh1/fHo7GiDXWVNUmWB/fv3\\n4cWXdgMAdr38Bs79Xxuw8e3roSjV9p4TeG1vN/7w1J9HrnwP4oSVnVhz/Ao4XnX/0CO6glVLkjh5\\nVVO5eWdbYxgHj2QxmDarGktVpWJQXkKHqsjYvXcIDbFsMQivyg7cQgjk8ja6+nJwRwrQQKqApa2x\\nqjs4l0LcuvtzY6JAFFkqj12NWFgtdt72oWegZbnoHcohYxRfUENZE50tUTTE9Kp77Lmuh5xpw7SK\\nX6Npm4iFi928a+nXp4UUhFR51h24ZQkMyptnLEQ+kCQJiaiGsOYil586gGs0WcKkWUSSJCEeCSGs\\nKchVGEftOC7+Z9d+fPs/noA17gp15+69ePWNQ3jn209EJBzFTMOpCpBODeGp7Ttgj6teTzz9Ev70\\n3G5cfel5WLl86YxFVwIwMJTCQ4+9gFRmbFrrWwd6sPdgLzZuWIuWpiZYM3zPJAlY3hbHScc1oSE+\\n9sq+MRFGQ1xHV18WPQNGRfHWU0WHp3I2nnu1Dys7RqLBK8gksmwXRwZzSBvOuLd72Hc4g6aEhtbG\\nCEIVpMLajov+4TyGMhOjO1yvGDkiV1iQQqqM5mQYzTVkEZU/t+thOGuid3Bs3IYQwOF+o6qiK4RA\\nwXKQzU/8e8oVimGQiaiGkDr7CA1JkhANq9A1uapMIkaHB4OFyEchVUFDXEbBcmGYzpRHM9rIi326\\n5ExVkdEQ16dPaRUC+w8P4nu/+CNe29835VgFy8aTz/wZyztbcfK6VXDFFAuiZ+LZF/6Mru6eKcey\\nbQc/f3AbjlvZifdu+l+IxxOTvp9lWdj+/Ov482sHpxxLCIH/2fkaGuJRnL5hHWRVm3QX2JTUsXZ5\\nI1Z0xKdcmCRJwvL2BNoaIzjQm0XvoDHpYh0Nq2ieIZ0VAA70ZtHVl8NJq5rQnAxPeoXuuh5SWRM9\\ng9NnIQ1lLAxnLSxpiSI5xc6hdAx3eMCYdicsUCxIilz8/WRX+7IEJGNaMZ21wkj0KT/fSFR9V18W\\n7jQXC5UUXSEEbLeYQzXdsaUngFTOgqbKiEdqKwqKXIx1nymlVVWkkZwxprMGgYXIZ5IkIaKrxZC7\\nvIPCqCsxVZYQGTl6qFRYV6FrCnL5keO6kbdncgVsefQlbN66q+KxDvX0o6unH6euX42OjtZyxk1I\\nBg4c3I/nX/xzxWPtO9CDe370IC44+zScvuFkyErxa5Ig8Pq+w3j4yV0Vx1qksgYe3f4i1hy3FCes\\nWlY+rgvrClZ1JnDycc0Vx13rmoq1KxrR2lA8rhvKFI/rVEVCczKMxrhW8eLsegIv7x1EYzyENcub\\nyrtXMZIs29WXnTSSfDJCAN39BvrH7RyEECiYLrr7szArTOQtzq346/jjumhYRVtjBLGID8dwtosj\\nQwbSucp/ur9UdJe2xJCIaeWi67oeDLOyNN7y53c8DGZMxMPFo2t/jusc5C2nXMCnOpmg+cVCNEcU\\nWUYipkG3i7sjVZERm+WLXZIkxKMhhHUZfUMGnt99CN/6yeMoWNU/jCAAvLj7TUTeOojT37YWrmNh\\n+5+ehWlVl+Ja8ugfX8T2/3kZ77/0PESiUfzu8Z0YShuzGuuNfd1468BhbNywDmuP68Qpx7egMRme\\n+QMn0ZQs3vvpGilGjZNEgldqOGvj2VeO4LjOONqaohjMmEhXmXpbMnrn0JQII5UxMZCp7t7WaKXj\\nOi1U3EG3NIRrXlAdx0PaMNEzUH3qLVAsul39OeipPJa2xQGgogj3qWQLDgzTQSKm1ZR4WzyuK+56\\nise3gsdwdYKFaI5pIQWaT9t9VVHw4p6D+NoPH6l5rHzBwh+f2Yl8dqjmsUzLxs82b0NjawfMWpMx\\nPYEdL76Kv75kA7RQbVf1kiRheUcCyYRWviFei309WeQK/gQODmUsZAy74h3VdASAtsYIknF/noob\\nzBTQP1yY+R1nYNoeegcNX3ZnngCMgo3G+OwuTEZTlOJxHdWPQAqRZVn40Ic+BNu24bouLr74Ytx8\\n881BTIWIiAIWSCHSNA333nsvIpEIXNfF9ddfj3PPPRcbNmwIYjpERBSgwA5HI5EIgOLuyHF8+MFL\\nIiI6JgVWiDzPw5VXXomzzz4bZ599NndDRESLVGAPK8iyjAcffBDZbBaf/OQn8cYbb2DNmjVBTYeI\\nqK7UktA6lXBEh+RjY0XDyPkyTuBPzcXjcbzrXe/Ck08+yUJERDSiloTWyeQNA+eedmLNaarjJZPJ\\nmscIpBANDg4iFAohkUigUChg+/btuPHGG4OYChFRXfI7oTWXTaOhocH3QuSHQApRX18f/vEf/xGe\\n58HzPFxyySU477zzgpgKEREFLJBCdOKJJ2Lz5s1BfGoiIqoz7G1BRESBYiGaY+mchV1v9mNvd6ri\\nJqBT6T6SwfaXetDZ3urL3BqTcXR0dqLWeFJdC+Fvrns3PnzFWWhMRmoaS1Vk3PzX56KlMVpTk8uS\\n9qYwTjmuBQ3x2tvMtDWG0ZTQoYdqn1djQsOK9gSaErW3mklEVUTC/h1uNCfCaG+q7e8RKPa/a2+K\\nIh7xZ26OK5DOWXB9TAym+hD4U3MLlet6eKs7jSNDBhxXYDBtYihjYmVHAq2N1f0jtx0XP/vdn/GH\\nZ/ZiMFVsRLl0yRIYRhbDqUzVc2tMhOEJgeFssZ9Yx9JlcEwDAwODVY910V++HeeduQGaXuxz9qn/\\ncxFe3HMAW7btrLrw/tWZ63Dte05HU0MMQLErsmk5VXV/LolHVHQ0R8uBgxtWt2EwXcAr+4eqDpeL\\n6AqWtcXLPQOT8TAcp5jPU+21RUiVsaztaPftsB5DQ1yfEIJXCUWWsKwtVnWI30xUVUZrYwTJqIbe\\nwRwyk+QGTUeSMCEsT1MV5Ap2VR3Gy+ON/CoEYNoubNdDdCSQkh2zFwYWIp8JIdAzaKDrSHZCQFvG\\nsLFn3yBaGiI4YVmyojiI7TsP4r/+sBuvHRhbJFI5C4qsY9mSGPoGBmBZMy/WYV1FLKxhIDW2O3Y6\\na0KCgqXLlmNocAD5/Mxdl49b0Y5rLzsPrS1NY94uywpOP+V4rDuuEw8/uQs7X+2acawlbUnc8qFz\\nsWZV+4SFRddUtIaKIYF5c+aGo1MtzrIsobUxgnfFNBzsy+Jgb3bGsSQJWNEeRzw6cddSWqzzpo2s\\nMfNCLQHobJ2YZFrqCH3C0gakciZ6+o2KUn7bm8JoSoTntHO0pilY3pGYkDw7nca4hrbGyIRAQUWR\\ni+GRzkgeUYUVXJIwodh7nigmItsOYuFQRYGDVN9YiHyUNSzsPZyeNrLaE0DfcB7pnIklLTGs7ExM\\nemqESSAAAB4DSURBVFXXO5DFD3/1Ina83DXllbLrCQznbDQ0NkOGi94j/VN+3uaGKIy8NaEIlQgA\\nQxkT4XgjGhob0HO4d+StY4VCKv73+zfh5LUrAWnqRTAei+D97zkDZ5w2gJ8/tAOZ7MRuzqoi4xMf\\nOAvnvGM1tNDUL8ViDIaGiO5hOGtNGarW3hhGU3L6xTkUUnD8kiTaGyN4/dDwlLutlgYdrQ2RGRf6\\niB6CHlKRMUxY9uTzahhZnKfrwi7LEpoSYcTDIRwZziM1RcxEPKyioyUKLTQ/u4HS937NchVD6QKO\\nTNGVe/xOb6qxtJCCpqQMc4qE1vL7ovjqm65e2Y5AKmtB1xTEfd4V0vxiIfKB6wns7U7hyGAetlvZ\\n0YNpe9jXk8FgpoBVnUk0j+TuOK6HXzz8Mh7+05voH64sD8YY2XktXboEuWwGqfTRq/1irLaEwSkK\\n0HgF00HBBDqXLoNlGhgcdVy36exTccHZp0HXK48bWN7Zgts++m48//J+PPTYzvLCcv471+C6S96B\\nlqZ4xWMpioyWhvCE47rYyOKsV7g4lxbXU9e0YSCVxysHhsvFLazJWNYWLx/pVUKWJTRMclwXUiQs\\nbY8jWkF8dkkopGBpawxNI1lKpUTRuTqGq5SiyGhpjCAR0ybEsS9piSIZ16FUeE9PliRE9FDxuC5v\\nwxyXX18qQpUQAAqWC9vxEOFx3TGLhahGPQM5HJrkGK5S6ZyNl98aQGtjBEOpHH65bQ9e2Tcwq7FS\\nWQuqEsayJTEMDg0hoqsYSlV21DNxLBOSVDyu01UHV19yDtpbm2c1L0VR8M4NJ+CkEzqx/YXXcclf\\nnox1x3XMesEoHdcZeRstjZFZXw3LsoS2piga4joO9mYASaopp6Z8XFewEY+G0BDXocjVH52Vj+uW\\nNyCVteC6Hppn2OnNB0mSoGsqVnYmkM3byOQstM6w05uOohTDI8OOh3SuuAMUqLwIjeaWj+tcxMNq\\nzRHpNL9YiGq0vydTVfzxZDwBHBnK4xe/24X9h1M1jeW4AsM5B/Gojr7B6h9kGE2I4nHdJz74l7Mu\\nQqMl4lF87Oqz0BCrPcBNkiS0NUcRC9f+NJwWUrCkNTbri4nxErFi+mqtFFlGczIMIURdXeVLkoRE\\nVPPlOKx0XKcoki8hgbbjocpnUagO8PHtGvm5PPjZjFCexZX4lGNNcy+o+rH8+xp9XZrrZ52foJ6K\\n0Gi+zqtOv0aaHyxEREQUKBYiIiIKFAsREREFig8rEBHVIb+D8fL5HFKpRt/Gm0oymaz6/iELERFR\\nHfI7GE/XNex4ZQiSNOzbmOMZRg6Xn7++6swjFiIiojrkdzBePeM9IiIiChQLERERBYqFiIiIArXo\\nCpEQYsruzdXyPFFxk9OZCRi52tr7lMgSkMumfRkLADwfg8hsp7Z2SKMVTKfmsMESP/8heJ5geFuV\\nFt1CRGMsmr9/IQRsx8NwxsRgpgDLciBmuYgJITCQyuMXW1/HY891IZ21auoQM9zfg69/6f/DvV/+\\nCHJdOxCpoX1aVLVx+OXf4ZGf/T+YR3YhFp79zDpbErj2Padjw4nL0BALQVNn/3JRFAmqIqFnII+e\\ngRzcGgq4JwQOHcni/kffxNYdB5DKTh27MRMhRDGSYKTPXK2dZvIFB6/sH8Lzr/Yhk7Nm/RpbbJIx\\nDVFdRS2hvLKMkXyiRbOsLRiL4qk51/OQLzjIj2pOmjJshBQH8agGtYquxgXLwQuvHsHW/zlUbvf/\\nwmt9aEpqOPm4lqr+ETi2iUd//yC+8v/+EY5TfEzz8Qf+BU3tq3DOFbcA0aWodL3WQxJyva/gd7/9\\nPlyn2Ml45x83Q3/hEWzcdD0Q6YTtVDZYNBzC6etX4H2bTkMyVmze+f+3d+/hVdV3vsff67bvO/eE\\nQIhcgkCE4gW5CM7UIjNwGJFbqT3jhSlVsFXi7ciUPJ0+c06tc87T5zg+duhBfNrTsUPro1ZQ2tM6\\nj1QuVgWFKnNaQEoBgSQk5Lqz73vtNX9sEsl9Z2fjTrK/r398THZ++a21yfqu9Vtrfz+aqqJpKuGo\\nSSgcS3peComu1Ff+7o602rJi9+Vu18kffVrbwxw+dpEmX2IbT11o4881bSz8wlimTczHlmTXZcuy\\nME0LXzDSpdlmx3s6mCgCSMR31DcGaLncRTpmxjjySQPjilxcU5qDPcUO1dlCURTcTgO7LZHkOti0\\nWruh4XbqKXU7F5k3qguRZVmEoybtgWivB5WoadHsC+N26DhsepfkzO7icYvTtW3s2ncKfy+BXs1t\\nEd49WsuU8TmUlXgHmhknj33M0995nNOnPuk5Vv1Zdr/w35gx7w6mz19FMN53tLgCGGYTh9/6GQ0X\\nTvb4fjjQyu9+uY3ya29iyg1/jT/Wf8zB9MljWLJwBtMm93xsVFNVXHYVm6YSipoDJqYauko8Hu+1\\nAFoWnK/3Y7cFGZ9E/k8kanLi02ZOnO35GQjLgneO1vLRJw3cPqecsUXufj9QF49bBCNRAqG+52+R\\nXDGyLIvW9jA1l3rPe6q5FKCuMcjUa/IoynMmndmTrXRNJddtJxSJEQzHBuzIbWiJyIxUoyjE8DAq\\nC1FfZ7t98YdiBMIxclw2DF3tcRBragux59A5jp1tHnCsP51v43Stj1lTish123ocyFqb6vnJ9mfZ\\n+fKLA471h4O/5NiHb/LFVVXkXzObULfPtrn0GGf/8Db///1fDjjWuZNHOP+nj7j+1pXkj5+FP9S1\\nOJQUeLh19hQWzZ8+YOduXddwayqGphKKmES6FRpNU1AVkroCC0finLrQ1mciqmVZ1F7yc/CPFzEH\\neC/bQzFeP3Caa8tzmXvdGHK6xU1YlkUkauILRvtN/ux8/eX/9lWQQuEY5y76OsPr+hK3LI6fbcZT\\n72NqeT4el6SJDsRh07EbGv5QjFAk1uP9UlVw2vR+E2HFyDHqCpEZjxMMxwY8W+/OsqDVH8GmK7id\\nieW6cMTko5P1/PvBc0kduDrnYFr8/kQDhbl2pk0owNBVzFiEfW/9kv/5j08SiSR/TyNuRnn71f9N\\n4bgKFvzNQ1jOUmy6gr/hE37z/17AjCY/lmXF+ejAa7i8+7jptruwHMVoqs5NM8Zzx5euJ8/rSnqs\\njpA0XU8s1wVDMeIWGLpCNGYx2EcSGlvDNLWFKSv2dIbTtbVHOPJJPZf6iKfuy8lzrZw638rCWeOY\\nNiEPQ9eImXF8geROTLrrXpBiZpyG5iDNvsHdm2oPJpbrxhe7KR/jlbP4ASiKgsdp4Oi2XGc3NNwO\\nPeNBgSJ9RlUhCkVitCd5ttuXSMwi4gvT2BrkN++epS2QeouNxtYw7x6tRQ+c5idbv8vJE39Ifaya\\nU+x+4XFm3rISf1sjdeeOpzxWwNfIO7t/yI23LOXRx59kxrVlKY/VsVynqwrtwSjRWOo7P7Fc147T\\nphIImUldgfYlbsGBj2s4+qcG/suCiWkJXbNI3KOqafCnlCLa4XyDn5rGALOnFeNKQ7DfaNe5XBeO\\noaqKFPBRaFQVokjUHFIRutLJc61DKkJXOvj+e0MqQle68MnveizRpSrcdn5IRehKuqYB6ZlYMBLn\\nTF16Hj9v9UfxB6MD3oNKVps/MqQi1CEet4jE4iR/DSoc9lF1uBJXyMg7W1dXx+bNm2lsbERVVdau\\nXct9992XiakIIYTIsIwUIk3T2LJlC5WVlfj9flavXs3ChQupqKjIxHSEEEJkUEbu9hUXF1NZWQmA\\n2+2moqKC+vr6TExFCCFEhmV80fX8+fMcP36cWbNmZXoqQggxbKQ7GK87h9OOMqSeMD0FAv6Ufi6j\\nhcjv91NVVUV1dTVutzuTUxFCiGEl3cF4VwoGAvzlDdMGHWCXjJycnEH/TMYKUSwWo6qqihUrVrB4\\n8eJMTUMIIYalqxmM529vIzc396oUolRk7BNh1dXVTJkyhXXr1mVqCkIIIYaBjBSiw4cPs3v3bt5/\\n/31WrlzJqlWr2L9/fyamIoQQIsMysjQ3e/Zsjh07lolfLYQQYpgZVc2a0tn8cCh5Od2FQqk9SdKb\\nvPxiDKP/DtrJcru9mPH0BdWl6wEcRUnkyqRLOiOBhpLJ1J206hQiIeOPb6dTR4PEtkA05RTWSNTk\\nTG0bFjBjUgGf1vnwBVN7ciUaCXP03Tc4efwPTP3CLVyqO0tTQ01KY2mazjcf/+/cungF4VCAXS//\\nhF/vfiWlsRRF4f4Hq/ibFWtpD8Rw2i0MXUu5kKsKaLpCYY6TUMSk7XImTyrG5DuZMDYHQ1f40/lW\\n9v2+JuX3cmyRkxuuLcFh1wedL9Sdpip4XTYKcx20+MIcO9NMLMWTFbtNpXJCQWdzVyGy3agqRIqi\\nYOgaBV71cgPUnrlBfbEsi5pL7dQ0BAiEPvu5ieO8hCImp863Mpjj4eljB/n4/Tc5e+Z059cc3hKu\\nLSnj9CcfE4smf7BetHQl99z/BN78xBM0To+d/7r+Mb54+zJ++M9P8enZU0mPNXf+Qr5ZtZkx464B\\nIBKLE4nFcdrj2G0ahja4hpKapiTiGS7vG7uhUZznxBcIE4okf6C2GyqVE/LJ8dg7C+L0CQWML/bw\\nwbGLHO8lh6gvhq5yy8xSivOdnWMlmy/UG6/LwG58VqgLchzMm1FCTUOA07XJ98RTFJhSlktJgWtQ\\nYYxCjHajqhB1UBQFp93Apmv4w1HCAxwQW9vDfFrno6mtZ1t/Mw6GrjFjciGNrSFqLvW/zNZyqYbf\\n/+51jh09QjTWtRCGwhFC4QgTpt5INOjj0z//sd+xSkrLeOIfnmHy9BvpbSFn3IRpPPXMjzn0uz1s\\ne+6fOlNee+P2ePj2P/4vbpg9D5SexSYYNolETZz2REjgQFdHmqp05j71xuuy43bEafKF+10aU4CK\\nslxKC129tvX3uGzcdtN4KicW8NsPz9M6wNXWzIoCppTloveS1NoZ56Akt1zntGk47b3HDeiaRvkY\\nD0V5Dk6db6VpgEiIkjwnE8fl4JTGnUL0MKr/KjRNxeu04bDF8fmjxLsdfaIxkzM1PuqbAwPGBJhx\\nizyvnTyvnU8vttEe6FpkYtEo//Hebv7jyDs0N/cfX9Dc4gNg2hduoaHmNE2NdV2+r6oaGx/5B764\\nZDWa0Xc6a+LFBnP/YikzbpjDqz97gT2/eaPHS752/ze5c/VXcbj6T44144nMnEg0jtOu9xoSqCqJ\\nQm8mcXmoqirFeU6CERNfLwWkONfBpLIcnPb+7wcpikJpoZu1t0/h5LkWDnxU2+O9LClwctPUYjyu\\ngZe7BooDV1WFHJeBrvXc/u7zcjkMZlQU0twW5vjZph7/juyGyvQJ+eRecaUnhOhqVBciSBwsbLpG\\nfo5K+PJynWVZ1Db6qWnw9xr7PZBJY3MIhk1OXWjFsuDsiQ/4+L03OX06+SUygIamVpx5Y5kypozT\\nJz7GNGP8xaJlrHvw78ktHDeosdzeQtZt/HsW/dWdbP3n71Jz/iyzZ8/loce2MHb8xEGNlViui+C0\\nazgMrfPq4rNluOQXuCwLHIaGI89JWyBMOBLHpqtMn5hP3iAPzoaucd2kQspLvBz6Yx2fnGtF11Tm\\nzxzDmALXoA/0vS3XeZzJXRFeSVUUCnMdzL1uDDUNfs7UJU40KspyGdvHlZ4Q4jOjvhB1UC8v1+ma\\nyjsf19DYOrh0zSuZcbAZGjMnF7LtB9/n4yPvE42m9kBDMBQmGIJJ02/mq/es5/q5t5H681QK5ZOv\\n45+e/VdaLp3j2qmVKGrqIWKJ5bo4uW4lkTI7xHC5HJcdT5HOuCLPkO6ReN02Ft1czvSJ+Z33BVPV\\nsUW6ppDjsg2paBi6xjWlXorznKiqIvk5QiQp6/5SFBTa/Onp32TGLeounE65CF2pqaWNaytvIB0P\\n9SqajcrrZpKOJ9DNuIWqKYN6UKM/uW57Wm7UK4pCnsdBIDz4K9reGJqalisXRVFwOSV1VYjBkDUD\\nIYQQGSWFSAghREZJIRJCCJFRWXePSAghRoJgoB1/e/IfmB6MVAPsrhYpREIIMQxpZju66Rj0z1Vc\\nU0R52dgBX5dKgN3VIoVICCGGIZu3FHvu4IPxFDU6bALvkiX3iIQQQmSUFCIhhBAZJYVICCFERkkh\\nEkIIkVEjqhBZQ4zatCyLiGmS47ahpqERcp7Xxq0L5pOT039X62RcVzkdl01NSwKo12WQ57FjN4Y+\\nls1Q0TUFXRv6DtM0ZWjpdN3YdDUxZhqkcVppY1kW0VginmOo//aFGM5G1FNzgXAU04yn1BMsZsZp\\nag1xqTXEmAIXHqdOY2uY9hTSVx12jXFFbsaXeLj+O99i+fKl/MvW7bx/8INBHzCKi4pYunQJf/e1\\n9dhsNkLhGE1tIVraB59yaugq5WM8zJxciM3QCIVj/LmmjUutwUHHZasK5HhsFOc5MXQN04wTDMcI\\nRcyUore9LoOSfBd2W+oNSrszDI18XSUQihGKxFLqh6drCm6Hgc1I37zSwYzHCYZiBCOJKHe7ruJ2\\nGtLJW4xKI6oQhSOJoLXuiZn9sSwLXyBCTUOgS4aN22nD5TBobgvR7AsTjg7cIVRTofhywJnd9tmu\\nm33jDfxo+7/wbz97iZ+99ApnzpwbcCzD0Fm4cAH337+BCRMmdH7dYdcZW+TG4zJobgvjDyXX1LMk\\n38mMyQUU5n6WX+Sw61w3qYCGlgDnLrbjCyRXdN0OncJcR5dsH01T8bhs2AyTYDiRWZQMh02jMNdB\\nrsee1OsHS1EU3E4Du03DH4omPS9VSewfl31wkQ9Xm2VZhKNmj/cqHIsT9oXxOHXsNh11GM1ZiKEa\\nUYWogy8Qxa/G+g0v6/iDrmv0EwiZvY6jKAoFuU48LlviKsQX7vOsOtdt45oxXgryev+Amaqq3HfP\\n37L8jmU888xz/Obf9+Br7/3Ty9OnTeWrX72L2xf/dZ/zynHb8TgT82puCxPto5W2x6kzpTyPirLc\\nPg+oxXkuCnOdnK1to64p0OfB2mao5HntFOY4+hzLZmgYukooHCMUNon1scM0TSHXbackPxGJcLXp\\nmkqu204oEiMYivU5L0jEmbsdvSevZoplWcRMi/ZApN+5twdjBEImXreBMUBwnxAjxYgsRADxuEVL\\ne6TXOGfTjNPYFuJSSyipsWyGRmmhG4/ToMkXpv2Ks1GHTaW00E15qTeps9D8vDy++z++wx13LOOH\\n/+cFDn5wuPN7hQX5LFmyhPVfvx+7feArBFVVKMpzJubVbblO1xXKSxLLcFdenfU5lqIwaVwupQVu\\n/lzbSmNLqEt0dq7bRnG+M6lsH0VRcF5ezgpeLkhdw+UMSvKdGcnjcdh07IaG//Jy3ZXLiLqWSFS1\\nD8dluHCMYLj3E6bu4pZFa3sEu03FbZflOjHyjdhC1CEYMQlGTLyuxBliIBzjQoOfeAo3DDwuG+7L\\nB/3W9jA5HjsTx+bgTOGAOm/uzcy5+Sb+77/+G6+8spNrJlzD+q8/QEVFxaDH6liuczsNmn1h3A6D\\nGZMLKMobIEa8F06HzoxJhdQ3Bzh30YdlQUGOA6974Ijt7rov1ylAYa6TPO/VWYZLlqIoeJwGDpuG\\nPxglFo/jMHRcjuG1DBe3LKKXl+FSeRQhHIkTjlxerjP0z+XKU4irYcQXog6+QBR/MEogyXsqfVEU\\nhcJcJxNSLEBXUlWVr3/tPlauWoUZT+6eVn/zyvXYKR/jpazYPeQDakm+i1y3jVZ/ZMhj2QwNm6FS\\n4HUMq7NzXVPJ9dixLGtYFaAOvkAk6Xta/WkPxlAVJakrYyGGo4wdNaqrq1mwYAHLly9P25jpfMJV\\nS+PZpdPR9z2XwdJUJW1jqWkcS1GUYXtGPhyLULrJw91iJMtYIVq9ejU/+tGPMvXrhRBCDBMZK0Q3\\n33zzsGpDLoQQIjOGz4K+EEKIrDQi7m6aZuKx1vr6i/2+zh+KEU7yEdiBBN0GRhra7QCJR3P7+CzT\\nYIXabZihwT8t15uYGafNP/gODr1RFAi22bPifky6+IIRoml4WAGgzTn8HksXvSstLUXXBz70RgJN\\nhNsH/57aiotSmVZGjYhC1NDQAMBDG9dneCZCCDE0e/bsYfz48QO+7q9unZXU60aDjBaiZPuyzZw5\\nkx07dlBcXIymyVmfEGLkKi3tP3W1tLSUPXv2DPi60USxMtTW94knnuDgwYO0tLRQVFTEpk2bWLNm\\nTSamIoQQIoMyVoiEEEIIkKfmhBBCZJgUIiGEEBklhUgIIURGjYjHt4e76upq9u7dS2FhIbt37wag\\ntbWVxx57jAsXLjB+/HieffZZvN6hR4oPR3V1dWzevJnGxkZUVWXt2rXcd999WbMPIpEId999N9Fo\\nFNM0WbJkCQ8//HDWbH+HeDzOmjVrGDNmDNu2bcuq7V+0aBEejwdVVdF1nVdffTWrtn+o5IooDXrr\\nm7d9+3ZuueUW3nzzTebNm8fzzz+fodldfZqmsWXLFn71q1/x0ksvsWPHDk6dOpU1+8Bms/Hiiy+y\\na9cudu3axf79+zl69GjWbH+HF198sUvMSTZtv6Io/PSnP2XXrl28+uqrQHZt/1BJIUqD3vrm7dmz\\nh1WrVgGwatUq3nrrrUxM7XNRXFxMZWUlAG63m4qKCi5evJhV+8DpTHS7iEQixGKJKJJs2v66ujr2\\n7dvH2rVrO7+WTdtvWRbxeNcuGdm0/UMlhegqaWpqoqgo0WqjuLiYpqamDM/o83H+/HmOHz/O9ddf\\nT2NjY9bsg3g8zsqVK1m4cCELFy5k1qxZWbX9Tz/9NJs3b+7S4imbtl9RFNavX8+aNWt45ZVXgOza\\n/qGSe0Sfk2zoweb3+6mqqqK6uhq3u2d432jeB6qqsmvXLtrb23nooYc4efJk1mz/3r17KSoqorKy\\nkoMHD/b5utG6/QA///nPKSkpoampifXr1zNp0qSsef/TQQrRVVJYWMilS5coKiqioaGBgoKCTE/p\\nqorFYlRVVbFixQoWL14MZN8+APB4PMydO5cDBw5kzfYfOXKE3/72t+zbt49wOIzf7+fJJ5+kqKgo\\nK7YfoKSkBICCggIWL17M0aNHs+b9TwdZmkuT7g0qFi1axGuvvQbAzp07uf322zMxrc9NdXU1U6ZM\\nYd26dZ1fy5Z90NTUhM/nAyAUCvHuu+9SUVGRNdv/+OOPs3fvXvbs2cMzzzzDvHnz+P73v8+XvvSl\\nrNj+YDCI3+8HIBAI8M477zB16tSsef/TQVr8pEFvffMWL17MI488Qm1tLWVlZTz77LOjNgjw8OHD\\n3HPPPUydOhVFScSPP/bYY8yaNYtHH3101O+DEydO8K1vfYt4PE48HmfZsmV84xvfoKWlJSu2/0qH\\nDh3ixz/+Mdu2bcua7T937hwPP/wwiqJgmibLly9nw4YNWbP96SCFSAghREbJ0pwQQoiMkkIkhBAi\\no6QQCSGEyCgpREIIITJKCpEQQoiMkkIkhBAio6QQiRGro51Of7Zs2UJtbW2/r7n33nv54IMP+vz+\\nhQsXWLRoUa/f27hxIw0NDezcuZMtW7YAiQ/y1tTUDDB7IUQHafEjRqyWlhaOHz/e72sOHjzYo+tF\\nKvrqEyat/YUYOrkiEiPW9773Perr69m0aROvvfYay5cv584772TLli0EAgG2b99OfX09GzZsoLW1\\nlV//+tfcddddrFy5kqVLl/Lhhx8m/bvC4TCPPvooK1asoKqqqrOlj1z9CDF0UojEiPXtb3+bkpIS\\nqqqq2LZtGzt27OCNN97A6XSydetWNmzYQElJCS+88AI5OTm8/PLLPP/88+zatYsHHnigR5hhfxob\\nG1m3bh2vv/465eXlbN26FZCOykKkgxQiMaJZlsWhQ4dYtGhRZx+vr3zlK7z33ntdXqMoCj/4wQ84\\ncOAAzz33HDt37iQQCCT9eyZPnsyNN94IwJ133smhQ4c6xxZCDI0UIjHiWZbVoyCYptnl/wOBAF/+\\n8pe5cOECc+bM4d577x1UEdE0rcvv03W5vSpEukghEiOWruvE43HmzJnD22+/TVtbGwAvv/wy8+fP\\n73yNaZqcOXMGTdN48MEHmT9/Pvv37+8R7dyfU6dOdT4Y8Ytf/IIFCxakf4OEyFJSiMSIVVhYyNix\\nY3n66afZsGEDd999N8uWLcPn8/HII48AcNttt/HAAw/g9XqZPn06S5YsYfXq1bjd7s6HDJK5zzNh\\nwgS2bt3K8uXLaW5uZuPGjX3+rNw3EmJwJAZCCCFERslCtxAkws02bdrU5Wqm4yGHp556ihkzZmRw\\ndkKMbnJFJIQQIqPkHpEQQoiMkkIkhBAio6QQCSGEyCgpREIIITJKCpEQQoiMkkIkhBAio/4TSoYp\\nGWRbiq0AAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x11d25ef98>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"with sns.axes_style('white'):\\n\",\n    \"    sns.jointplot(\\\"total_bill\\\", \\\"tip\\\", data=tips, kind='hex')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The joint plot can even do some automatic kernel density estimation and regression:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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QKhOC2eELVu/yi26e34pawzURkNOkxGHSajHnPnfyY9WU4daGA1G0Bn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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x11c3b0550>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"sns.jointplot(\\\"total_bill\\\", \\\"tip\\\", data=tips, kind='reg');\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Bar plots\\n\",\n    \"\\n\",\n    \"Time series can be plotted using ``sns.factorplot``. In the following example, we'll use the Planets data that we first saw in [Aggregation and Grouping](03.08-Aggregation-and-Grouping.ipynb):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>method</th>\\n\",\n       \"      <th>number</th>\\n\",\n       \"      <th>orbital_period</th>\\n\",\n       \"      <th>mass</th>\\n\",\n       \"      <th>distance</th>\\n\",\n       \"      <th>year</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>Radial Velocity</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>269.300</td>\\n\",\n       \"      <td>7.10</td>\\n\",\n       \"      <td>77.40</td>\\n\",\n       \"      <td>2006</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>Radial Velocity</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>874.774</td>\\n\",\n       \"      <td>2.21</td>\\n\",\n       \"      <td>56.95</td>\\n\",\n       \"      <td>2008</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>Radial Velocity</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>763.000</td>\\n\",\n       \"      <td>2.60</td>\\n\",\n       \"      <td>19.84</td>\\n\",\n       \"      <td>2011</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>Radial Velocity</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>326.030</td>\\n\",\n       \"      <td>19.40</td>\\n\",\n       \"      <td>110.62</td>\\n\",\n       \"      <td>2007</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>Radial Velocity</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>516.220</td>\\n\",\n       \"      <td>10.50</td>\\n\",\n       \"      <td>119.47</td>\\n\",\n       \"      <td>2009</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"            method  number  orbital_period   mass  distance  year\\n\",\n       \"0  Radial Velocity       1         269.300   7.10     77.40  2006\\n\",\n       \"1  Radial Velocity       1         874.774   2.21     56.95  2008\\n\",\n       \"2  Radial Velocity       1         763.000   2.60     19.84  2011\\n\",\n       \"3  Radial Velocity       1         326.030  19.40    110.62  2007\\n\",\n       \"4  Radial Velocity       1         516.220  10.50    119.47  2009\"\n      ]\n     },\n     \"execution_count\": 19,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"planets = sns.load_dataset('planets')\\n\",\n    \"planets.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAkEAAAEWCAYAAABhZ0N/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\nV0ZGhrZu3SpJ2rZtmzIzM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     \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x11ade0dd8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"with sns.axes_style('white'):\\n\",\n    \"    g = sns.factorplot(\\\"year\\\", data=planets, aspect=2,\\n\",\n    \"                       kind=\\\"count\\\", color='steelblue')\\n\",\n    \"    g.set_xticklabels(step=5)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We can learn more by looking at the *method* of discovery of each of these planets:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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Nr776qiSp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\\nERERERERERGpcf4XgBnpZwZT4DcAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x11bd8d668>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"with sns.axes_style('white'):\\n\",\n    \"    g = sns.factorplot(\\\"year\\\", data=planets, aspect=4.0, kind='count',\\n\",\n    \"                       hue='method', order=range(2001, 2015))\\n\",\n    \"    g.set_ylabels('Number of Planets Discovered')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"For more information on plotting with Seaborn, see the [Seaborn documentation](http://seaborn.pydata.org/), a [tutorial](http://seaborn.pydata.org/\\n\",\n    \"tutorial.htm), and the [Seaborn gallery](http://seaborn.pydata.org/examples/index.html).\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Example: Exploring Marathon Finishing Times\\n\",\n    \"\\n\",\n    \"Here we'll look at using Seaborn to help visualize and understand finishing results from a marathon.\\n\",\n    \"I've scraped the data from sources on the Web, aggregated it and removed any identifying information, and put it on GitHub where it can be downloaded\\n\",\n    \"(if you are interested in using Python for web scraping, I would recommend [*Web Scraping with Python*](http://shop.oreilly.com/product/0636920034391.do) by Ryan Mitchell).\\n\",\n    \"We will start by downloading the data from\\n\",\n    \"the Web, and loading it into Pandas:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# !curl -O https://raw.githubusercontent.com/jakevdp/marathon-data/master/marathon-data.csv\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>age</th>\\n\",\n       \"      <th>gender</th>\\n\",\n       \"      <th>split</th>\\n\",\n       \"      <th>final</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>33</td>\\n\",\n       \"      <td>M</td>\\n\",\n       \"      <td>01:05:38</td>\\n\",\n       \"      <td>02:08:51</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>32</td>\\n\",\n       \"      <td>M</td>\\n\",\n       \"      <td>01:06:26</td>\\n\",\n       \"      <td>02:09:28</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>31</td>\\n\",\n       \"      <td>M</td>\\n\",\n       \"      <td>01:06:49</td>\\n\",\n       \"      <td>02:10:42</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>38</td>\\n\",\n       \"      <td>M</td>\\n\",\n       \"      <td>01:06:16</td>\\n\",\n       \"      <td>02:13:45</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>31</td>\\n\",\n       \"      <td>M</td>\\n\",\n       \"      <td>01:06:32</td>\\n\",\n       \"      <td>02:13:59</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   age gender     split     final\\n\",\n       \"0   33      M  01:05:38  02:08:51\\n\",\n       \"1   32      M  01:06:26  02:09:28\\n\",\n       \"2   31      M  01:06:49  02:10:42\\n\",\n       \"3   38      M  01:06:16  02:13:45\\n\",\n       \"4   31      M  01:06:32  02:13:59\"\n      ]\n     },\n     \"execution_count\": 23,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"data = pd.read_csv('marathon-data.csv')\\n\",\n    \"data.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"By default, Pandas loaded the time columns as Python strings (type ``object``); we can see this by looking at the ``dtypes`` attribute of the DataFrame:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"age        int64\\n\",\n       \"gender    object\\n\",\n       \"split     object\\n\",\n       \"final     object\\n\",\n       \"dtype: object\"\n      ]\n     },\n     \"execution_count\": 24,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"data.dtypes\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's fix this by providing a converter for the times:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>age</th>\\n\",\n       \"      <th>gender</th>\\n\",\n       \"      <th>split</th>\\n\",\n       \"      <th>final</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>33</td>\\n\",\n       \"      <td>M</td>\\n\",\n       \"      <td>01:05:38</td>\\n\",\n       \"      <td>02:08:51</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>32</td>\\n\",\n       \"      <td>M</td>\\n\",\n       \"      <td>01:06:26</td>\\n\",\n       \"      <td>02:09:28</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>31</td>\\n\",\n       \"      <td>M</td>\\n\",\n       \"      <td>01:06:49</td>\\n\",\n       \"      <td>02:10:42</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>38</td>\\n\",\n       \"      <td>M</td>\\n\",\n       \"      <td>01:06:16</td>\\n\",\n       \"      <td>02:13:45</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>31</td>\\n\",\n       \"      <td>M</td>\\n\",\n       \"      <td>01:06:32</td>\\n\",\n       \"      <td>02:13:59</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   age gender    split    final\\n\",\n       \"0   33      M 01:05:38 02:08:51\\n\",\n       \"1   32      M 01:06:26 02:09:28\\n\",\n       \"2   31      M 01:06:49 02:10:42\\n\",\n       \"3   38      M 01:06:16 02:13:45\\n\",\n       \"4   31      M 01:06:32 02:13:59\"\n      ]\n     },\n     \"execution_count\": 25,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"def convert_time(s):\\n\",\n    \"    h, m, s = map(int, s.split(':'))\\n\",\n    \"    return pd.datetools.timedelta(hours=h, minutes=m, seconds=s)\\n\",\n    \"\\n\",\n    \"data = pd.read_csv('marathon-data.csv',\\n\",\n    \"                   converters={'split':convert_time, 'final':convert_time})\\n\",\n    \"data.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"age                 int64\\n\",\n       \"gender             object\\n\",\n       \"split     timedelta64[ns]\\n\",\n       \"final     timedelta64[ns]\\n\",\n       \"dtype: object\"\n      ]\n     },\n     \"execution_count\": 26,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"data.dtypes\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"That looks much better. For the purpose of our Seaborn plotting utilities, let's next add columns that give the times in seconds:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>age</th>\\n\",\n       \"      <th>gender</th>\\n\",\n       \"      <th>split</th>\\n\",\n       \"      <th>final</th>\\n\",\n       \"      <th>split_sec</th>\\n\",\n       \"      <th>final_sec</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>33</td>\\n\",\n       \"      <td>M</td>\\n\",\n       \"      <td>01:05:38</td>\\n\",\n       \"      <td>02:08:51</td>\\n\",\n       \"      <td>3938.0</td>\\n\",\n       \"      <td>7731.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>32</td>\\n\",\n       \"      <td>M</td>\\n\",\n       \"      <td>01:06:26</td>\\n\",\n       \"      <td>02:09:28</td>\\n\",\n       \"      <td>3986.0</td>\\n\",\n       \"      <td>7768.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>31</td>\\n\",\n       \"      <td>M</td>\\n\",\n       \"      <td>01:06:49</td>\\n\",\n       \"      <td>02:10:42</td>\\n\",\n       \"      <td>4009.0</td>\\n\",\n       \"      <td>7842.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>38</td>\\n\",\n       \"      <td>M</td>\\n\",\n       \"      <td>01:06:16</td>\\n\",\n       \"      <td>02:13:45</td>\\n\",\n       \"      <td>3976.0</td>\\n\",\n       \"      <td>8025.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>31</td>\\n\",\n       \"      <td>M</td>\\n\",\n       \"      <td>01:06:32</td>\\n\",\n       \"      <td>02:13:59</td>\\n\",\n       \"      <td>3992.0</td>\\n\",\n       \"      <td>8039.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   age gender    split    final  split_sec  final_sec\\n\",\n       \"0   33      M 01:05:38 02:08:51     3938.0     7731.0\\n\",\n       \"1   32      M 01:06:26 02:09:28     3986.0     7768.0\\n\",\n       \"2   31      M 01:06:49 02:10:42     4009.0     7842.0\\n\",\n       \"3   38      M 01:06:16 02:13:45     3976.0     8025.0\\n\",\n       \"4   31      M 01:06:32 02:13:59     3992.0     8039.0\"\n      ]\n     },\n     \"execution_count\": 27,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"data['split_sec'] = data['split'].astype(int) / 1E9\\n\",\n    \"data['final_sec'] = data['final'].astype(int) / 1E9\\n\",\n    \"data.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"To get an idea of what the data looks like, we can plot a ``jointplot`` over the data:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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LgQw4YNw6RJk9DW1pZ13HvvvRcvvvgiOOcYN24cpk6dCsuy8Oqrr2Le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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x11b646ac8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"with sns.axes_style('white'):\\n\",\n    \"    g = sns.jointplot(\\\"split_sec\\\", \\\"final_sec\\\", data, kind='hex')\\n\",\n    \"    g.ax_joint.plot(np.linspace(4000, 16000),\\n\",\n    \"                    np.linspace(8000, 32000), ':k')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The dotted line shows where someone's time would lie if they ran the marathon at a perfectly steady pace. The fact that the distribution lies above this indicates (as you might expect) that most people slow down over the course of the marathon.\\n\",\n    \"If you have run competitively, you'll know that those who do the opposite—run faster during the second half of the race—are said to have \\\"negative-split\\\" the race.\\n\",\n    \"\\n\",\n    \"Let's create another column in the data, the split fraction, which measures the degree to which each runner negative-splits or positive-splits the race:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>age</th>\\n\",\n       \"      <th>gender</th>\\n\",\n       \"      <th>split</th>\\n\",\n       \"      <th>final</th>\\n\",\n       \"      <th>split_sec</th>\\n\",\n       \"      <th>final_sec</th>\\n\",\n       \"      <th>split_frac</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>33</td>\\n\",\n       \"      <td>M</td>\\n\",\n       \"      <td>01:05:38</td>\\n\",\n       \"      <td>02:08:51</td>\\n\",\n       \"      <td>3938.0</td>\\n\",\n       \"      <td>7731.0</td>\\n\",\n       \"      <td>-0.018756</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>32</td>\\n\",\n       \"      <td>M</td>\\n\",\n       \"      <td>01:06:26</td>\\n\",\n       \"      <td>02:09:28</td>\\n\",\n       \"      <td>3986.0</td>\\n\",\n       \"      <td>7768.0</td>\\n\",\n       \"      <td>-0.026262</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>31</td>\\n\",\n       \"      <td>M</td>\\n\",\n       \"      <td>01:06:49</td>\\n\",\n       \"      <td>02:10:42</td>\\n\",\n       \"      <td>4009.0</td>\\n\",\n       \"      <td>7842.0</td>\\n\",\n       \"      <td>-0.022443</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>38</td>\\n\",\n       \"      <td>M</td>\\n\",\n       \"      <td>01:06:16</td>\\n\",\n       \"      <td>02:13:45</td>\\n\",\n       \"      <td>3976.0</td>\\n\",\n       \"      <td>8025.0</td>\\n\",\n       \"      <td>0.009097</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>31</td>\\n\",\n       \"      <td>M</td>\\n\",\n       \"      <td>01:06:32</td>\\n\",\n       \"      <td>02:13:59</td>\\n\",\n       \"      <td>3992.0</td>\\n\",\n       \"      <td>8039.0</td>\\n\",\n       \"      <td>0.006842</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   age gender    split    final  split_sec  final_sec  split_frac\\n\",\n       \"0   33      M 01:05:38 02:08:51     3938.0     7731.0   -0.018756\\n\",\n       \"1   32      M 01:06:26 02:09:28     3986.0     7768.0   -0.026262\\n\",\n       \"2   31      M 01:06:49 02:10:42     4009.0     7842.0   -0.022443\\n\",\n       \"3   38      M 01:06:16 02:13:45     3976.0     8025.0    0.009097\\n\",\n       \"4   31      M 01:06:32 02:13:59     3992.0     8039.0    0.006842\"\n      ]\n     },\n     \"execution_count\": 29,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"data['split_frac'] = 1 - 2 * data['split_sec'] / data['final_sec']\\n\",\n    \"data.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Where this split difference is less than zero, the person negative-split the race by that fraction.\\n\",\n    \"Let's do a distribution plot of this split fraction:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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vVwOKzVq1drw4YNcrvdeuSRR9TY2Kg333xTXq9XL7300ljWCQAAohjS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\\nAAxBqAMAYAhCHQAAQ/w/HaCoKnHAZlAAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x11bb754a8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"sns.distplot(data['split_frac'], kde=False);\\n\",\n    \"plt.axvline(0, color=\\\"k\\\", linestyle=\\\"--\\\");\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"251\"\n      ]\n     },\n     \"execution_count\": 31,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"sum(data.split_frac < 0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Out of nearly 40,000 participants, there were only 250 people who negative-split their marathon.\\n\",\n    \"\\n\",\n    \"Let's see whether there is any correlation between this split fraction and other variables. We'll do this using a ``pairgrid``, which draws plots of all these correlations:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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4kM034GL7hvo6ytRrS2mZr0KMTVhmjdlx5CT58nEScN29JaI+T1ncOV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\\nRCKRSCSS3/h/L/d2XtJO7wgAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x11aeb5908>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"g = sns.PairGrid(data, vars=['age', 'split_sec', 'final_sec', 'split_frac'],\\n\",\n    \"                 hue='gender', palette='RdBu_r')\\n\",\n    \"g.map(plt.scatter, alpha=0.8)\\n\",\n    \"g.add_legend();\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"It looks like the split fraction does not correlate particularly with age, but does correlate with the final time: faster runners tend to have closer to even splits on their marathon time.\\n\",\n    \"(We see here that Seaborn is no panacea for Matplotlib's ills when it comes to plot styles: in particular, the x-axis labels overlap. Because the output is a simple Matplotlib plot, however, the methods in [Customizing Ticks](04.10-Customizing-Ticks.ipynb) can be used to adjust such things if desired.)\\n\",\n    \"\\n\",\n    \"The difference between men and women here is interesting. Let's look at the histogram of split fractions for these two groups:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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O69995q1EZEY5QxMsib+pi3Ltk0WeOMmMhhJe8RT5kyBVOmTMG8efMA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\\n/wdn9tHNYmsjqgAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x11bdb3a58>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"sns.kdeplot(data.split_frac[data.gender=='M'], label='men', shade=True)\\n\",\n    \"sns.kdeplot(data.split_frac[data.gender=='W'], label='women', shade=True)\\n\",\n    \"plt.xlabel('split_frac');\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The interesting thing here is that there are many more men than women who are running close to an even split!\\n\",\n    \"This almost looks like some kind of bimodal distribution among the men and women. Let's see if we can suss-out what's going on by looking at the distributions as a function of age.\\n\",\n    \"\\n\",\n    \"A nice way to compare distributions is to use a *violin plot*\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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GDr1udobKxndko6NxfMvurxQUFB3DF3MelxCZSWnuKll/5TXfpT1JjC\\nPikpiczMTAoLCykrK+OOO+6gqkoLLshH6+zs4I03XuO5535NQ0MdiRk5LLztTlx5RePa3R4eE8vs\\n1evJW3oDvvMT/5599leUlZVqBrHIRQzD4PTpk/zH757G623lmqxcNsxdNKbfx1C7nbsWX0uBI4W6\\nuhp+97vfUFtbPfFFy7gaUzd+REQE+/fvp7CwkB07djB37ly6urTYglyqs7ODw4ff4/jxo/j9PiLj\\nE8mev4SE9KwJG1O32Wyk5BeTkJZF3YnDeCpK2bbtJZzOFJYtu54ZM/IvWYJXZLrxett4663t1NZW\\nYw8K5tbiuZRkzvhErxFqt3PngiW8dfY0+6rLeeGF/6C4eA433HATUVHRE1S5jKcxhf3DDz/M888/\\nz3e/+12ef/55br31Vr71rW9NdG0yBRiGQW1tNUePHhzdjjYsOoaseSU4svOwTVLQhkVGkb/0BtKL\\n51F7/CCemgpeeeUFYmPjmD9/EXPmzCc8PGJSahExm2EYNDTUcfjwe1RUnAUgP9nJuqK5JERGfarX\\nDLLZWF0wi1kpabz2wTFOnz5J2ZnTFBXPZtGiJSQnO8fzW5BxZjPG0N/5+OOP88ADD0xGPeOmpaXb\\n7BIsrb+/j9LSDzh+/MjoIjfRSQ5SC+eQnJX7qcflD778OwBKNnzpM9XX2+Gl6cxJWqrK8fuGsdvt\\nFBXNZs6c+aSkpGn2vlhSb28vFRVlnDhxBI/HDUBabDzXz5hJoTPlqj/3T+x+E4D7Vtzyse/jNwyO\\n1Newv6YCb18vAFlZOcyePY8ZM/IJCwsbh+9GPimH48qTlMfUsn/rrbe4//779QE5zfl8PqqrKzl1\\n6gSVlWfx+/3YgoJw5OSTWjiHmAA6s4+KTyR/6QqyF1yDp+IMTWWnOHnyGCdPHiMhIYnZs+dSXDxH\\nM/hlyuvq6qS8/Azl5WWja0/YgCJnKsuyc8mITxz3z+4gm43FmTksysjmbIubAzWVVNdWU1tbTXBQ\\nMFnZOeTnF5KbO5PIyMhxfW/5dMYU9vHx8axbt47Zs2dfcsb26KOPTlhhEhgMw8DjcVNa+gGlpR/Q\\nd/4sPjIuAWdeIY6cfEIjAveXOSQsnPRZ80krmktHcwOeyjO01dWwZ8/b7N27i+zsGRQXzyEvbyYh\\nH3GtsUigGRgYoKGhjvr6Gmprq2lp8Yw+lxmfSJErlWJnKnGT8Htps9kocKZQ4Eyhpaeb0+5GSj1N\\nVFVVUFVVgc1mIzU1nczMbDIzs0lNTcduH1PsyDi76r96TU0N2dnZbNy4cbLqkQDR0dE+GvAXlqa1\\nh4aRWjAbZ14hUQlJU6qnxxYUREJaJglpmQwPDNBSU4Gnsozq6kqqqyux20PIz59JYeFssrNn6Fpi\\nCRhDQ4M0NjZQV1dDXV0NbnfT6NUmwUFB5CY5KHKmUuhMIfpTLFQ1XhzRMTiiC1mRV4i3r5czniZK\\n3U00NNbT2FjPgQN7CQ62k5Y2Ev4ZGdmkpKTqd22SXDXs77//fl566SXefPNNfvnLX05IAbt37+Yn\\nP/kJhmHwhS98gXvvvfeS51999VV+9atfARAVFcXf/u3fUlhYOCG1THdebxuVlWc5e/YMzc2NAAQF\\nB5OUlYsjJ5+EtMxxuUbebPawMFILZpFaMIu+zg5aqstprS6ntPQUpaWnCA+PYObMIvLyZpKZma2W\\niEwawzDwettobm6kqamR5qYGWttaRsM9yGYjPTae7MRkZiQmkxGfQEhw4P18JkZGcW1OPtfm5HNu\\naIia9jZqvK1Ut7eOnrQABAcF43S5SElJJzU1jZSUNGJj46ZUQ2KquOoEvS984QuEhoZy5swZ5syZ\\nc9nzzz777Gd6c7/fz9q1a3n66adxOp1s2rSJxx57jLy8vNFjjh49Sl5eHjExMezevZt//ud/5ve/\\n//3HvrYm6H08v99PU1MDFRVnqaw8++HmMjYb8SnpOHLySczMwT6J3dvjNUHvkzIMg562lpHgr6lg\\n6NzIapEhISFkZ+eSm5tPbm4+EQE8ZCFTi2EYdHd30dLiwe1uoqmpAXdzEwODA6PH2IOCSI2NJyM+\\ngZxEB1nxiYRO0MnnJ5mg91n0DQ6cD/826ju9uLu78F8UQ5ERkaSkjoS/y5WCw+Ei8lNeQTDdfOoJ\\nes888wynT5/mb/7mbybkUrvjx4+TnZ1Neno6AOvXr2fnzp2XhP2CBQsuue12u8e9jumkv7+Pmpoq\\nqqsrqaqu4Nz5JZCDgu0kZuaQmJ5NQnoWodPsMjWbzUZMspOYZCczFi2jq9WNt74Gb33N+clPZ0bH\\nH2fMyCMnJw+Hw6kWiIyJ3+/H622jpcVNS4sbj8dDS0vzZavRJUZGUZDkID0ugYz4BJzRsQRbbJ2I\\nyNAwil1po8v0DvmGaerqpKGznYbOduo72qmsHGmAXBAVFY3T6cLhcI3+GRcXr9+/T+CqYR8dHc2S\\nJUvYunUriYmJH3nMN77xDZ588slP9eZut5vU1NTR+y6XixMnTlzx+D/84Q+sWLHiU73XdGUYBm73\\nyISZ6urKS8b7QiMiceUXk5iRTXxKGkEB2B1oBltQEHHOVOKcqcxYtIy+rg689TW019fQeH78ce/e\\nXURGRpGTk0tOTh7Z2Tm6jl8wDIPe3h7a2lpoa2ulra2V1lYPLS0t+HzDlxybEBFJjjOVlNg4UmLi\\nSI+LJzJ0+l2yFhJsJyshiayEpNHHus7109DZTnN3F+6uTpq7O0cn/V0QGhqKw+EiOdlBUlIySUkO\\nEhOTNfv/Csb06X6loAcmraW9f/9+XnzxRX73u9+N6fiEhEjs9qk/vvxp+Hw+ysvLOXHiBKWlpfT1\\n9QEXWq8uEtIyiU/LnHKT7MwSGRtP5Kx4MmbNZ2jgHB1N9bQ31tHRWMepUyc4deoENpuNrKws5syZ\\nw9y5c4mNjTW7bJlAhmHQ1dWFx+PB7R5pqV/4+p+t9SCbDUd0DCkxI6GeEhuHMzqW8JAQk6oPfLHh\\nEcSGR1yySU/v4ADu7k6auzpHTgK6O2lsqLtsq+uoqCicTiculwun0zl6Oypqeg8FfOam3GcJC5fL\\nRWNj4+h9t9uN03n5tdqlpaV8//vf59e//jVxcXFjeu329r5PXddU5Pf7qauroazsNOXlZ0Y/cEIj\\nInHmFY4EfEo69mnYchhPIWHhOHLyceTkj4zze1vpaKyjvbGOmtpaampq2LZtG+npmRQWFpOfXzTt\\nP2SmMr/fT2dnB15vG15v6/k/22j3tl0ytg4jn4WJkVHkOFPPz0wf+UqKjLZcV7wZokLDyE1ykpv0\\nYUYM+YZp7e2hpaf7w6/ebqqqqi7bvyUiIpLExKSLvpJJTEwiJibWMo2ez7yozkSZO3cutbW1NDQ0\\n4HA42LZtG4899tglxzQ2NnLffffx05/+lKysLJMqDVx9fb0cOXKQEyeP0n++BR8aETmykl12LjHJ\\nLsv8IAcam81GTJKDmCQHmXMXMdjfR1ttFa01FTScb3G89dabZGfPoKRkGRkZE7dHgHw2Pp8Pr7eN\\ntraW0UD3elvpaG/H5/ddcmyQzUZCZBQz4hM/DPWoGJKiFOqTLSTYTmpsPKmx8Zc8Pjg8TFtfD57z\\nJwCt508CGj6iJ8BuD/kfJwFJJCU5iI9PsNS+GqaGfXBwMA8//DD33HMPhmGwadMm8vLy2Lp1Kzab\\njc2bN/PLX/6Szs5O/u7v/g7DMLDb7Tz//PNmlh0QOjs7OHToACdPHsfnG8YeFkbKzFkkZ+cS60iZ\\ntDXp5UMjJ1mzSS2czUBfL221lbTWVIxey+9ypXLNNdeRlzdToW+i3t5eWlvdtLS00NLiprXVg9fb\\ndtn2yKHBdlzRIyGeHBVDcnQ0yVHRJEREKdQDXKj9o08Chnw+vH09tPb20NrTTWtfD609PbS1evB4\\nmi851m63k5TkwOFwkpzsPP+nY8rOzRnT2vhXs2HDBl5++eXxqmfcWPXSu76+Xvbu3c0HHxzDMAzC\\noqJJL56PM6+QYAtcD37w5d9hGAZLNv6F2aWMm+5WD/WnjuKtqwYgKSmZFStWk5OTa25h08C5c/00\\nNNTT0FA3EuwtHvr6Lx3iCwkOxhkde/7rQqjHEBMWPq1Oyp7Y/SaGYfB/Vq4xu5RJ5zcMOvv7Rk4C\\nervxdHfh7umipaf7kssCAWJiYklOduJ0ukhPzyQtLT1gVt+8Wjf+mMJ+7969XH/99Zc8tn37dtas\\nWcPTTz/NV7/61c9c5HizWtj7fD6OHTvEvn17GBwcICI2now5C0nOzrNMV1Nvh5djr7+AYRiEx8RR\\ntOIWouKvPDl0qunrbKfh1DE8VWfBMMjNzWfFitUkJFjnezRbX18v9fV1NDTUUl9fS2tryyXPx4VH\\n4oqJxRUzEu6umFgSIqMImkah/lE83V38av8u/IZBYmQUd85fgjNGk0x9fj+tvT14erpwd3fh6e7E\\n3dNFz8CH8zWCgoJwuVJIT88iIyOTtLRM0zYC+tRh//rrrzM4OMgTTzzBfffdN/r40NAQW7Zs4c03\\n3xzfSseRlcK+vr6WnTv/iNfbhj00jKx5i0mZOctyXfWHXvlPznV3jt6PiI1j0ec3m1jRxOhtb6Py\\n4Lt0eZoICgqipGQZS5deh92u2dmflGEYtLa2nJ+YWobX2zr6nD0oiIy4RLISk8hOSCIlJk4z4K/g\\nX/bsHN29DiApMpr/tXyViRUFtt7BARo7O6hpb6O2vY3Gro7RS5ptNhsuZwr5M4soKCgiLi7+Y15t\\n/HzqCXo9PT0cOXKE3t5eDhw4MPp4cHDwlNvydirq7+/jnXfe4oMPjgOQMrOYrHlLCAk3b/3riTLY\\n33dJ0AP0d3Uy2N8X0BvtfBpRCUnMufk22uqqqD60j/fee5czZ06xevU6srNnmF3elHAh4MvKTn+4\\nd0NQMLlJDrLPX7OdFhePPWh6Xn77SfQMnLsk6AHa+nroGThn6lr7gSwqNIyZDhczHS5gZEJgfaeX\\nGm8bNe1tNHiaaXY3sWfPW7hcqRQUFFFQUExs7NiuJpsIY+rG37dvH9dee+1k1DNupnrLvrGxnlde\\neYH+/j6iEpLIu+aGgNpCdryd6+nm0H/9x2WPL779/yPcwtvQ+oaGqD1xiKbSExiGwYIFi1m58mbL\\nDM2Mt66uTnbt2kl5+RlgJOBnOpzMcqWTn+ycsKVkrayjv49/emfHZY//1Q03E2+xE+3J0jc4yBlP\\nE6fcjVR5WzEMA5vNxrx5C7nuuhUTNsnvU7fsH374YX70ox/xy1/+kn/913+97PnPuja+fLTKynK2\\nbXsJn89HzsKlpBXNtVyXvYwIDglhxqJlOHLyObvvbY4ePURvby/r1n1eG/BcxDAMDh16j337djM8\\nPExGfALXZOUyM9mlgJeAExkaysKMbBZmZNM3OECpp5n91RUcO3aYsrJSbrrpFgoLZ01qTVf9Ldm8\\neWS89K/+6q8mpRgBj8fNK688jy0omKKVa0lM19oC00F0YjJzb/kzTu96g7NnSwkNDWXNmvVmlxUw\\n2tu9vPPOnwiz2/nc7AXMS8ucVjPlZeqKDA1jUUY289My2V9TwVvlpfz3f7/CzJlFk9qDd9V36u/v\\n5/3338dms33kl4y/xsZ6DMNgRsl1Cvppxh4ayuxVt2IPC6e+vtbscgJKfHwCMTGx+A0DR7R1VjyT\\n6SPIZiMpKhrDMMjMzJ70obqrtuyfeOKJKz5ns9nUjT8Bent7ACw9Ti1XFhRsJzQikt6eqT3nZLyN\\nXLWwlLfeepN/O7CbQmcKN+YV6fIwmRJqvK28VV5KXcfIZNKSkqWTXsNVw/6555675H5HRwfBwcHE\\nxCiIJkpSUjIA7Y11xKekm1yNTLZzPd30dbbjcqaYXUrAWbCghMTEZN7du4szzY2c8TSzID2LVTOL\\nidKeD59ZSEgIsbGxdHV1MTQ0ZHY5luDt62F76QecbR3ZMC43N5/rrluB4/ws/sk0ppktpaWlPPjg\\ng7jdbgzDIDc3V2vVT5CZM4vYtWsnnopSsucvIShYlw5NJ+6KUjg/K18ul5WVQ2ZmNlVVFezZ8zZH\\nG2opdTdxY34RizNzpv3iOJ9WSEgIn//85ykpKeHgwYO8+uqrZpc0pQ35fOypOsu+6nJ8fj8ZGVks\\nX34jqanmNeDGNGjwve99jwceeIADBw7w3nvv8bWvfY2//uu/nujapqXg4GBmzixkeHCQnraWj/8L\\nYikdTfXMbjprAAAgAElEQVQEBQUxc2ah2aUELJvNRm5uPnfddQ833ngz/iAbfyw9wSsnj1y2tKmM\\nTWxsLCUlJQCUlJRoi+bPYHB4mN8d2seeyjIiIqNYv34DmzZ9ydSghzGGvWEY3HTTTaP3b7nlltE9\\n0mX8ZWbmANDpaTK3EBOEhISQlJREyDRc6cw3PESPt5WUlLSAWWs7kAUFBbFw4RK++tVvkpqSxomm\\nel49eYRhn+/j/7Jcoquri4MHDwJw8OBBurq6TK5oauofGmTrkQPUdniZObOIr3zlXgoKigNiQumY\\nuvFLSkr4l3/5FzZv3kxwcDCvv/46eXl5o3vRp6WlTWiR082F/c9902zcbLp3JfqGh8EwiIqKNruU\\nKSUqKoqNd2zmxRe2crypnqauTm6fu4hUE1crm2qGhoZ49dVX2bVrl8bsP6XyFjevnTpG98A5Zs4s\\n4nOfuz2gFscaU9jv3LkTm83GCy+8MHqGYhgGd911FzabjZ07d05okdPX9OqS/J9dibt27TK5Ipkq\\nwsLC2XTnl3jnnbc4duwwTx3YzYL0LBZlZJMSExcQLatANzQ0RFtbm9llTCmGYVDtbeVQfQ2n3Y0E\\nBQVx/fUrKSlZFlBBD2MM+8cff5xDhw5x11138c1vfpMPPviAv/u7v2PdunUTXd+0dGHYcbp9QF3o\\nSrzQsp9uXYkX/rc/467T01ZISCirVq0lL28mO3b8kcP1NRyur8EVE8vC9GzmpKYToeERGQdd5/o5\\n1ljH0YZaOs5vmex0ulizZr0pM+3HYkxh/8gjj/Cd73yH7du3Ex4ezssvv8y3vvUthf0EuZDx060b\\nf7p3JfqGp9f3O1Gys3O5++5vUlNTycmTx6msPMsfS0+w/cxJshOSKHCmUOBI0brvMmaGYdDS001Z\\nSzNlLW4aOtsBsNtDmD17HnPmzCc1NT2gG2hjCnu/38+SJUv49re/zZo1a0hNTcWnSTATxul0ER4e\\nQUtNBdkLlxI8jdb+ns5die7zm7vk5OSaXMnUFxQUxIwZ+cyYkU9vby+nT5+krOw0Ve4mqrytvFF6\\nEmd0LAXOFIqcKerql8v4/X5qO7yUeZo509I82oK32WxkZGRRVDSbgoJi0/au/6TGlCIRERE89dRT\\nHDhwgO9///s888wzo5PIZPzZ7SHMm7eQ9957l7Pv/omZ162aVoE/HbXVVdNYeoLw8HCKimabXY6l\\nREVFUVKylJKSpfT0dFNZWU5l5Vlqa6vZU1nGnsoy4sIjKHKlUuRMJSM+UdfrT1PDfh/Vba2c9jRR\\n5mmmb2gQgNDQUAoKisnLm0lOTu6E7Vo3kcaUIP/4j//IH/7wB5544gni4uLweDz8/Oc/n+japrWS\\nkmU0NNTRUFfNwI5XKVqxhrBInWBZjWEYNJ4+TvWRA9jtIaxb9/lpednhZImOjmHevIXMm7eQwcFB\\namqqKC8/Q2XlWQ7UVHKgppKo0DCKnCnMTskgKyFRLX6L8/n9VLR5+KCpgbOtbgaGhwGIjIxiXvFs\\n8vMLyMjIJniKL3A2pv3sp6Kpvp89gM/nY8eO/+bUqRME2e2kF88jrXgedgtOMpqO+9l3NNVTffQ9\\ner2tREVFc/vtd+JyaZlcM/h8Purqqjl7toyKijL6z3fZxoSHM8eVzuzUdMt29U/H/ez9hkFtexsn\\nmxoo9TTSf35+UGxsHPn5heTnF5KamhZwM+o/zqfez17MFRwczJo160lLy+Ddd3dTd+IwzWWnyJiz\\nCFd+kbr2p6juNg+1R9+no7kBgKKiWdxwwyqiLXpSMxUEBweTk5NHTk4eq1evpa6uhjNnTlF+9gz7\\nairYV1NBQkQk+Q4X+ckushOSCJniLb0L7FcItCs9PlWdGxqisq2F8lY3Fa0eegYHAIiKimbhnAUU\\nFc3C5Uq15AkdqGU/ZQwODnLkyPu8f3A/Q4OD2MPCSckvJrVwNqEWOPu2esve8Pvx1tfQWHqCrpZm\\nALKzZ7B8+Y04telNwBoeHqa6upIzZ05RXVXB4PkxXHtQMDmJSeQnu8hNcpAYGTWlQ+Jf9uzE29c7\\nej8pMpr/tXyViRV9dn7DwNPdRUWbh/JWD3Ud3tHLWiMiIsnLm0lR0WzS0zOnXAv+Sq7WslfYTzF9\\nfX0cOfI+x48f5ty5c9iCgkjOziOtaC7Riclml/epWTXsh4cG8VScofHMSQbOb1ubk5PL4sVLycrK\\nMbc4+UR8Ph+NjfVUVVVQXV1BW1vr6HNRoWFkJSSSlZBEVnwSzpjYKTXJz9Pdxa/278JvGCRFRrNp\\nfsmU2z7Y5/fT2NVBbXsbte1t1HV4R8ffAVJS0sjJyWXGjDzLtuAV9hY0NDTE6dMnOXz4fdrbRy5V\\ni3WkkFo0h6SMHGxT7EzVamHf391J05kP8FSewTc0RHCwnVmz5rBw4ZLRbYxlauvq6qS6upK6uhoa\\nGuro7e0ZfS7MbiczfiT8ZyQmkxIbH/Dh/8TuNzEMg/+zco3ZpYzJsN9HfUc71d5WatvbaOhsZ9jv\\nH30+Li6ejIwsMjOzyc7OJTJy6veAfhyN2VtQSMjI5Xlz5y6gurqSI0fep6amiq6WZsIio0ktnE3K\\nzFkEa2b3pOp0N9Fw+hjtDbXAyHjggmuuY+7cBURYYLhFPhQbGzc6s98wDDo7O0auoDn/Vd460n0M\\nI+GfnZDMjMRkchKTcUTHBGTLMhBrusDv99PU1UmVt5Vqbwt1Hd5Lwj052UFGRhZpaZmkp2cSHa09\\nJi6msJ/ibDYbM2bkMWNGHm1trRw9epBTp05SfeQA9aeOkTFrPikFszWZb4J1eZqpPX6QTvfI5lAp\\nKWksWrSE/PzCKX/Jjnw8m81GfHwC8fEJzJ49D4Cenh4aGmqpra2hrq76/OprI/M1okLDmOlwMTc1\\ng+yEpIAOWTP5/H7KW92caGqgss1zSbd8crKDzMwcMjOzSU/PmJLXvk8mdeNb0Llz/Rw9eohDhw4w\\nODhISHgEmXMWklIwO2A/VAb7+3j/xX+/7PEld9wV0BMQezu8VB/eT0dTPTAyHr9s2XLT966WwNPV\\n1UldXQ11dTXU1laPdvvHhkcwJyWduakZpo6TP7H7TQDuW3GLaTXAyNoT9Z3tnGis55S7YfSyuLi4\\neLKyZpCZmU1mZhaRWnfkMhqzn6bOnevn8OH3OHz4IENDg8SnZlJw3Y2EBOgZ8KFX/pNz3Z2j9yNi\\n41j0+c0mVnRlhmHgrjhD1cG9+H0+MjOzufbaFaSnZ5hdmkwBhmFQX19LaekHlJWdZnBwZJb/zGQX\\nq2YWmxL6gRD2Nd5WdpSdorGrAxhZ2KaoaBbFxXNwOFwB21gJFAr7aa6vr5c33niN6upKQiMiKVqx\\nhphkp9llXaa3w8ux11/AMAwiYuMovOEWouITzS7rMn6fj/L9u2ipLicsLIw1a24jP7/A7LJkihoe\\nHqKyspyjRw/R0FCHDZiXlslN+cXEhIdPWh1mhn1LTzc7y05xttUNQH5+IfPmLSAzM8cyl8VNBk3Q\\nm+YiI6PYsOHPOXhwP3v37uLMO2+y8LY/D7jJe1HxiYRGRmEYRsC26AEaTh2jpbqclJQ01q/fQGxs\\nnNklyRRmt4dQUFDMzJlFVFVVsGfP2xxrrKPU08yawtnMT8u0bIvW5/fzbnU5uyvO4DcMMtIzuWHF\\nKlJS0swuzXIU9tOEzWZjyZJrGRoa5MCBd6k9fpAZi681u6yPFMgfbP1dHdSdPExUVDR33LGZsLDJ\\na3mJtdlsNnJz88nJyeXEiaPseectXv3gKB80N7BhziKipsjuamPV2tvDi8cP4u7uIioqmlWr1pKX\\nNzOgf/+nMvWPTDPXXHM9UVHReKrKzC5lSmqtrcLw+7n++pUKepkQQUFBzJ+/iL/8ytfJycmlsq2F\\nfzuwG/dF81mmuopWD08deAd3dxezZ8/jL//y6+TnFyjoJ5DCfpqx2+0kJiYxPDCA3+czu5wpZ7B/\\nZElRLXErEy0mJpYNG/6ca6+9gc5z/fzmvT00dnaYXdZndtrdyH8c3s+w4Wfdus+zZs16widxbsJ0\\npbCfhi60SH3n1/mWsfOdnzUdZrEuVQlMNpuNZcuW87nP3c6Qz8cLxw9y7vylaFORt6+XV04exR4S\\nwp13/gXFxXPMLmnaUNhPQ77zLXqbFnv5xC78m/n96hWRyVNYOItrrrmOjv4+3i4vNbucT+31U8cY\\n9A2zevU6rUUxyRT205D9/Gp6atl/chf+zYKDNbdVJteyZcuJjYnlSEMNvQMDZpfziTV2dlDlbSUz\\nM0ctehMo7Kchh2PkGvvu8+t2y9gYfj89ba2Eh0do73mZdMHBwSxavJRhv5/366rMLucTe7e6HIAl\\nS5aZXMn0pLCfhi5srVr5/l76u7vMLWaKMAyDivf3MNDbTXb2DM0aFlPMmTOP8PBwDtZVT6nWvbu7\\ni1J3I06HS1s7m0RhPw2lpqZz0023MHSunw92vkZbXRUWXUhxXJzr6aZ8/y7c5aU4nS5Wr15ndkky\\nTYWEhLJkybX0Dw3y28P7psRkvfa+Xn53eB8GsOzaG3SibBINPE5TCxaUMDAwwLvv7qZ095tExMaR\\nXjwfx4yZBGniHgC97W3UnzpGW00FhmGQmJjMxo2bNRNfTLV48VI6Ojo4ceIITx14h3XFc8lNcphd\\n1mUMw+BEUz07yk7ROzjAypWrycubaXZZ05bCfhpbuvR68vMLOXToAKdPn6T8wG5qjx8kKSuXhPQs\\n4pwpBE2ziWj9XZ20N9bira8Z3a42OdlBSckyCgqKtV2tmM5ms7Fq1RpCQuwcOXKQ3x7aR5EzlZsL\\nZpEQIDvBNXV18EbpSeo6vNjtdlauvJlFi5aYXda0Nr0+yeUySUnJrFmznmuvvYEjR97nxImjNJ05\\nSdOZkwQF24lPTSchLZOEtCzCoqLNLnfc+X0+Oj1NtDfU0t5Yy7mL5jCkp2eyZMkycnLy1PUoASUo\\nKIiVK2+muHgOf/rTdkqbGjjjaaI4JY3rcvJJjY2f9JoMw6DK28K7VeVUeVsByM8vYOXKm7V/RAAw\\nPex3797NT37yEwzD4Atf+AL33nvvZcf8+Mc/Zvfu3URERPD3f//3FBcXm1CptcXExLJixWquv/5G\\nGhrqqKqqoKqqAm99Dd76GgAi4xKIc6UR60wl1pkS0PvMX4nf76enrYUuTxNdniY6PU34h4eBkfHQ\\n/PwCZswYWZ9cM+4l0DmdKWze/GXKyk7z/nv7ONXcyKnmRmYkJnNNVi75DhdBE3yiOuzz8YG7kfdq\\nKmk+v6RvZmY211xznSbjBRBTw97v9/OjH/2Ip59+GqfTyaZNm1i9ejV5eXmjx+zatYva2lq2b9/O\\nsWPH+MEPfsDvf/97E6u2tuDgYLKycsjKymHlytV0dLRTVVVBdXUFdXW1NHV+QFPZBwBExMYT60wh\\nzplGrCuVsADpQryY3zdMd2vLaLB3t7jx+4ZHn09ISGTGjDxmzMgnPT1T3fQy5dhsNgoLZ1FQUExt\\nbRXvv3+AqrpqqrytxEdEUpKZw4L0LCJCQsf1fTvP9XOorpoj9TX0DQ1is9koKChm8eKlpKSkjut7\\nyWdnatgfP36c7Oxs0tNHVlJav349O3fuvCTsd+7cyYYNGwCYP38+3d3dtLa2kpycbErN0018fAIL\\nF5awcGEJw8PDuN3NNDTUUl9fS2NjA+7yUtznV/QKj44lLiWd+NR04lxphJiwUYzh99PT3kpHUwOd\\nzQ10tbgxLlrtLikpmYyMLNLTs0hPzyQ62npDEzI92Ww2srNzyc7OpaXFzdGjhyktPcmOslO8XV7K\\nvLRMrsnKxfEZe6zqO7zsr6mg1N2EAYSHh1Myfxnz5i0kLm7yhw9kbEwNe7fbTWrqh2eALpeLEydO\\nXHKMx+MhJSXlkmPcbrfC3gR2u5309AzS0zO45prr8Pv9eDzN1NfXjZ4AuMtP4y4/DUB0koP4lHTi\\nUjKIdbgmbJZ/f3cXnc31IwHvbmR48MPrjx0OJxkZ2WRkZJKenknEFBx6EPmkHA4Xt9xyKzfccCMf\\nfHCCo0cPcri+hsP1NeQnO1mWnUdOYvKY56L4DYNSdxP7aypo6Gw//x5OFi5cQmFhMXZ7yER+OzIO\\nTB+zl6krKCiIlJQ0UlLSKClZit/vp7m5kdraamprq2lqaqCnrYX6D44SFGwnKTMHZ14hca60zzzh\\nbaCvl5bKMjxVZfR3fbj1Z0xMLNkFRaNDEQp3mc7CwyNYvPgaFi4soaLiLIcPv0d5Yz3lrR7ykhys\\nKZpDctTVW/p17W38sfTk6Hh8bm4+ixZdQ0ZGliauTiGmhr3L5aKxsXH0vtvtxul0XnKM0+mkubl5\\n9H5zczMul+tjXzshIRK7XeOvk83limP+/JEJlAMDA1RVVVFeXk5paSkt1eW0VJcTFhWNM7cAZ24B\\n4dGxY35tv8+Ht74ad0UZHc31YBjY7XZmzZpFfn4++fn5JCUl6QNI5CO4XCVcd10JdXV1vPnmm5SX\\nl/Pku29zTVYuq2ZePum5f2iQP54+wcnmBgAWLFjAqlWr1Ks6RZka9nPnzqW2tpaGhgYcDgfbtm3j\\nscceu+SY1atX89vf/pbPfe5zHD16lNjY2DH9sLW3901U2fIJJCWlk5SUzjXXrKChoZ5Tp45TVnaa\\nuhOHqTtxGMeMmcxYfO1Vx/cNw6C1poKqQ/sYOtcPQEpKGrNnz6OgoHh0L2zDgNbWnkn5vkSmqvDw\\neG67bROVlWfZtWsn+2sqaO3tptCZSvD5E+XegQH+/dA+PD1duFwp3HjjLaSlZWAY0NLSbfJ3IFfi\\ncFy5l8bUsA8ODubhhx/mnnvuwTAMNm3aRF5eHlu3bsVms7F582ZWrlzJrl27uOWWW4iIiODRRx81\\ns2T5lGw2GxkZmWRkZHLjjbdw9mwpR468T0vVWTqa6slbspykrBmX/b3B/j4q3nsHb30NdrudxYuX\\nMnv2PJKS1LoQ+bRsNht5eQVkZc3gtddepLy6ktwkgy8tWsbA8DDPHtxLa28P8+YtZNWqteotswCb\\nYdFF0XX2Gfj8fj+HDh1g37538Pl8ZMxZREtVGQAlG75Ef1cHx7e/wvDAOdLTM1mzZj3x8QkmVy1i\\nLcPDw7zyyvPU1FSxce5iWnu7eaeyTEE/BV2tZa+wF9N5vW28/PLv6ezswB4WTrDdzoLPbeL4Gy/R\\n39XJDTesYvHia/ShIzJBOjraeeaZLUSFhHJueIiQsHDuvvubhIaO77X5MrGuFvba9U5Ml5iYxO23\\n30lISCjDA+dGlt089C79XZ0sWnQNJSVLFfQiEyg+PoHc3Hy6B84x5PMxZ858Bb3FKOwlICQlJbNi\\nxSoAhgcHaakuJzExiRtuuMnkykSmh4yM7ItuZ5lYiUwEhb0EjOLi2QD4h4cw/H7mzVtEUJB+REUm\\nQ2Ji0uhtTYC1Hn2SSsAICQnFbv/wApGcnFwTqxGZXmJjP1zzIsqCO1xOdwp7CSgXt+S1zrbI5AkP\\njxi9rTky1qOwl4By8YeMuvBFJk9oaJjZJcgE0qepBBS1KETMoZNra9P/rgQYhb2IyHhT2EtAUcNe\\nxBx+v9/sEmQCKexFRASfz2d2CTKBFPYSUNLSMswuQWRaUsve2hT2ElBmzZpndgki05Qlt0mR8xT2\\nElA0Zi9iDptNcWBl+t+VgGLNPRhFAl9wcLDZJcgEUtiLiIjC3uIU9iIiogWtLE5hLwFFnzciIuNP\\nYS8BRWP2IiLjT2EvIiJicQp7ERERi1PYi4iIWJzCXgKKJuiJiIw/hb2IiIjFKewlwKhpLyIy3hT2\\nIiIiFqewFxERsTiFvQQYraojIjLeFPYSULSCnojI+FPYi4iIWJzCXkRExOIU9iIiIhansBcREbE4\\nhb2IiIjFKewloGhtfBGR8aewFxERsTiFvQQUm5r2IiLjzm52ASIXy8qaQWZmNgsWLDa7FBERy1DY\\nS0Cx2+1s2vQls8sQEbEUdeOLiIhYnMJeRETE4hT2IiIiFmfamH1nZycPPPAADQ0NZGRk8Itf/IKY\\nmJhLjmlububBBx+kra2NoKAg7rzzTv7yL//SpIpFRESmJtNa9lu2bOHaa6/ljTfeYOnSpTz55JOX\\nHRMcHMxDDz3Etm3b2Lp1K7/97W+pqKgwoVoREZGpy7Sw37lzJxs3bgRg48aN7Nix47JjHA4HxcXF\\nAERFRZGXl4fH45nUOkVERKY608Le6/WSnJwMjIS61+u96vH19fWUlpYyb968yShPRETEMiZ0zP7u\\nu++mtbX1ssfvv//+yx672sppvb293HfffXzve98jKipqXGsUERGxugkN+9/85jdXfC4pKYnW1laS\\nk5NpaWkhMTHxI48bHh7mvvvu4/bbb+fmm28e83snJERitwd/4ppFRKY7hyPm4w+SKcW02firVq3i\\nxRdf5N577+Wll15i9erVH3nc9773PfLz8/nKV77yiV6/vb1vPMoUEZl2Wlq6zS5BPoWrnaSZNmb/\\n9a9/nXfffZe1a9eyf/9+7r33XgA8Hg/f+MY3ADh06BCvvvoq+/fvZ8OGDWzcuJHdu3ebVbKIiMiU\\nZDMMwzC7iImgM1MRkU/m8ccfBeCBBx4yuRL5NAKyZS8iIiKTQ2EvIiJicQp7ERERi1PYi4iIWJzC\\nXkRExOIU9iIiIhansBcREbE4hb2IiIjFKexFREQsTmEvIiJicaZthCMiIoHluutWEB2tHe+sSGvj\\ni4iIWIDWxhcREZnGFPYiIiIWp7AXERGxOIW9iIiIxSnsRURELE5hLyIiYnEKexEREYtT2IuIiFic\\nwl5ERMTiFPYiIiIWp7AXERGxOIW9iIiIxSnsRURELE5hLyIiYnEKexEREYtT2IuIiFicwl5ERMTi\\nFPYiIiIWp7AXERGxOIW9iIiIxSnsRURELE5hLyIiYnEKexEREYtT2IuIiFicwl5ERMTiFPYiIiIW\\np7AXERGxOIW9iIiIxSnsRURELE5hLyIiYnGmhX1nZyf33HMPa9eu5Wtf+xrd3d1XPNbv97Nx40a+\\n+c1vTmKFIiIi1mBa2G/ZsoVrr72WN954g6VLl/Lkk09e8dhnn32WvLy8SaxORETEOkwL+507d7Jx\\n40YANm7cyI4dOz7yuObmZnbt2sWdd945meWJiIhYhmlh7/V6SU5OBsDhcOD1ej/yuJ/85Cc8+OCD\\n2Gy2ySxPRETEMuwT+eJ33303ra2tlz1+//33X/bYR4X522+/TXJyMsXFxRw4cGBCahQREbG6CQ37\\n3/zmN1d8LikpidbWVpKTk2lpaSExMfGyYw4fPsyf/vQndu3axcDAAL29vTz44IP89Kc//dj3djhi\\nPlPtIiIiVmEzDMMw441/9rOfERcXx7333suWLVvo6uri//7f/3vF49977z2eeuop/t//+3+TWKWI\\niMjUZ9qY/de//nXeffdd1q5dy/79+7n33nsB8Hg8fOMb3zCrLBEREcsxrWUvIiIik0Mr6ImIiFic\\nwl5ERMTiFPYiIiIWp7CXgFBUVMSDDz44et/n87Fs2TLthyAygR599FGeffbZ0ftf+9rXePjhh0fv\\n/8M//ANPP/20CZXJeFPYS0CIiIjg7NmzDA4OArB3715SU1NNrkrE2hYtWsSRI0cAMAyD9vZ2zp49\\nO/r8kSNHWLRokVnlyThS2EvAWLFiBW+//TYA27ZtY/369eYWJGJxCxcuHA37s2fPUlBQQFRUFN3d\\n3QwODlJZWcmsWbNMrlLGg8JeAoLNZmP9+vW89tprDA4OcubMGebPn292WSKW5nQ6sdvtNDc3c+TI\\nERYuXMj8+fM5cuQIJ0+epKCgALt9QhdalUmi/0UJGAUFBTQ0NPDaa6+xcuVKtASEyMRbuHAhhw8f\\n5siRI9x99900Nzdz+PBhYmJi1IVvIWrZS0BZtWoVP/3pT7ntttvMLkVkWrgQ9mVlZRQUFLBgwQKO\\nHj3K0aNHWbhwodnlyThR2EtAuNCK37RpE9/61reYOXOmyRWJTA+LFi3i7bffJj4+HpvNRlxcHF1d\\nXaPd+mINCnsJCBe2OHa5XNx1110mVyMyfRQUFNDR0cGCBQtGHyssLCQ2Npb4+HgTK5PxpLXxRURE\\nLE4texEREYtT2IuIiFicwl5ERMTiFPYiIiIWp7AXERGxOIW9iIiIxSnsRWRCvffee3z5y182uwyR\\naU1hLyIT7sKiSSJiDm2EIyKX+PnPf8727dtJSEjA4XCwatUqbDYbzz77LIZhMHv2bL7//e8TGhrK\\n8uXLWbduHYcOHcJut/OLX/yC9PR09uzZw9///d8TFhbGjBkzRl+7traWv/3bv6Wjo4OIiAgefvhh\\nioqKeOihh2hvb6euro7vfOc73Hjjjeb9A4hYkFr2IjLqrbfe4siRI7z++uts2bKF06dP09/fzx/+\\n8Ae2bt3KSy+9RGJiIk899RQAra2tXHfddbz00kuUlJTw7//+7wwODvLXf/3X/NM//RMvvPAC4eHh\\no6//3e9+lwcffJAXX3yRH/7wh9x///2jzyUkJLBt2zYFvcgEUMteREbt3buXW2+9leDgYGJjY7n5\\n5psxDIOamho2b96MYRgMDw8ze/bs0b+zfPlyAGbOnMnBgwcpKyvD5XKNtug3bNjAE088QV9fHydO\\nnOChhx4a3fjo3LlzdHZ2AjB//vxJ/m5Fpg+FvYiMCg4Oxu/3j943DAOfz8ett97K3/zN3wDQ39+P\\nz+cDRsbiQ0NDR28bhoHNZrvkNez2kY8Zv99PeHg4L7300uhzbrebuLg4gEt6AERkfKkbX0RGXXfd\\ndWzfvp2hoSF6enp4++236erqYseOHXi9XgzD4Ac/+AFPP/008OHWxBcrLCzE6/Vy5swZAF577TUA\\noqOjyc7O5pVXXgFGehG0w6HI5FDLXkRGrVy5kiNHjnDHHXcQFxeH0+kkPz+f//2//zdf+cpXMAyD\\n4uJi7r33XuCjZ9nb7XZ+/vOf853vfAe73X5Jl//PfvYzfvCDH/DrX/+a0NBQfvGLX0za9yYynWmL\\nW+HgkHQAAAB2SURBVBEZdfToUaqrq9mwYQPDw8Ns3ryZRx99lIKCArNLE5HPQGEvIqM6Ozv59re/\\nTUtLC4ZhcMcdd/DVr37V7LJE5DNS2IuIiFicJuiJiIhYnMJeRETE4hT2IiIiFqewFxERsTiFvYiI\\niMUp7EVERP7/DXMAAPmmNk33GAG5AAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x11d2aeeb8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"sns.violinplot(\\\"gender\\\", \\\"split_frac\\\", data=data,\\n\",\n    \"               palette=[\\\"lightblue\\\", \\\"lightpink\\\"]);\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This is yet another way to compare the distributions between men and women.\\n\",\n    \"\\n\",\n    \"Let's look a little deeper, and compare these violin plots as a function of age. We'll start by creating a new column in the array that specifies the decade of age that each person is in:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 35,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>age</th>\\n\",\n       \"      <th>gender</th>\\n\",\n       \"      <th>split</th>\\n\",\n       \"      <th>final</th>\\n\",\n       \"      <th>split_sec</th>\\n\",\n       \"      <th>final_sec</th>\\n\",\n       \"      <th>split_frac</th>\\n\",\n       \"      <th>age_dec</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>33</td>\\n\",\n       \"      <td>M</td>\\n\",\n       \"      <td>01:05:38</td>\\n\",\n       \"      <td>02:08:51</td>\\n\",\n       \"      <td>3938.0</td>\\n\",\n       \"      <td>7731.0</td>\\n\",\n       \"      <td>-0.018756</td>\\n\",\n       \"      <td>30</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>32</td>\\n\",\n       \"      <td>M</td>\\n\",\n       \"      <td>01:06:26</td>\\n\",\n       \"      <td>02:09:28</td>\\n\",\n       \"      <td>3986.0</td>\\n\",\n       \"      <td>7768.0</td>\\n\",\n       \"      <td>-0.026262</td>\\n\",\n       \"      <td>30</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>31</td>\\n\",\n       \"      <td>M</td>\\n\",\n       \"      <td>01:06:49</td>\\n\",\n       \"      <td>02:10:42</td>\\n\",\n       \"      <td>4009.0</td>\\n\",\n       \"      <td>7842.0</td>\\n\",\n       \"      <td>-0.022443</td>\\n\",\n       \"      <td>30</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>38</td>\\n\",\n       \"      <td>M</td>\\n\",\n       \"      <td>01:06:16</td>\\n\",\n       \"      <td>02:13:45</td>\\n\",\n       \"      <td>3976.0</td>\\n\",\n       \"      <td>8025.0</td>\\n\",\n       \"      <td>0.009097</td>\\n\",\n       \"      <td>30</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>31</td>\\n\",\n       \"      <td>M</td>\\n\",\n       \"      <td>01:06:32</td>\\n\",\n       \"      <td>02:13:59</td>\\n\",\n       \"      <td>3992.0</td>\\n\",\n       \"      <td>8039.0</td>\\n\",\n       \"      <td>0.006842</td>\\n\",\n       \"      <td>30</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   age gender    split    final  split_sec  final_sec  split_frac  age_dec\\n\",\n       \"0   33      M 01:05:38 02:08:51     3938.0     7731.0   -0.018756       30\\n\",\n       \"1   32      M 01:06:26 02:09:28     3986.0     7768.0   -0.026262       30\\n\",\n       \"2   31      M 01:06:49 02:10:42     4009.0     7842.0   -0.022443       30\\n\",\n       \"3   38      M 01:06:16 02:13:45     3976.0     8025.0    0.009097       30\\n\",\n       \"4   31      M 01:06:32 02:13:59     3992.0     8039.0    0.006842       30\"\n      ]\n     },\n     \"execution_count\": 35,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"data['age_dec'] = data.age.map(lambda age: 10 * (age // 10))\\n\",\n    \"data.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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W5siQUFew6NjAgv41n4PXthX1AfFkIMjCaTsGt4WHv2MpnMzS3h3uTP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\\nhBBCCOMo2BNCCCGMo2BPCCGEMO7/A2wATbzbo2s4AAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x11bd81c18>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"men = (data.gender == 'M')\\n\",\n    \"women = (data.gender == 'W')\\n\",\n    \"\\n\",\n    \"with sns.axes_style(style=None):\\n\",\n    \"    sns.violinplot(\\\"age_dec\\\", \\\"split_frac\\\", hue=\\\"gender\\\", data=data,\\n\",\n    \"                   split=True, inner=\\\"quartile\\\",\\n\",\n    \"                   palette=[\\\"lightblue\\\", \\\"lightpink\\\"]);\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Looking at this, we can see where the distributions of men and women differ: the split distributions of men in their 20s to 50s show a pronounced over-density toward lower splits when compared to women of the same age (or of any age, for that matter).\\n\",\n    \"\\n\",\n    \"Also surprisingly, the 80-year-old women seem to outperform *everyone* in terms of their split time. This is probably due to the fact that we're estimating the distribution from small numbers, as there are only a handful of runners in that range:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 38,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"7\"\n      ]\n     },\n     \"execution_count\": 38,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"(data.age > 80).sum()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Back to the men with negative splits: who are these runners? Does this split fraction correlate with finishing quickly? We can plot this very easily. We'll use ``regplot``, which will automatically fit a linear regression to the data:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 37,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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csWbrjhBtrb28fc71vf+hYbNmyY6SnFLCrNcMpGF9MnG4RUBsuy6OiL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\\nAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x11e0dfba8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"g = sns.lmplot('final_sec', 'split_frac', col='gender', data=data,\\n\",\n    \"               markers=\\\".\\\", scatter_kws=dict(color='c'))\\n\",\n    \"g.map(plt.axhline, y=0.1, color=\\\"k\\\", ls=\\\":\\\");\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Apparently the people with fast splits are the elite runners who are finishing within ~15,000 seconds, or about 4 hours. People slower than that are much less likely to have a fast second split.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<!--NAVIGATION-->\\n\",\n    \"< [Geographic Data with Basemap](04.13-Geographic-Data-With-Basemap.ipynb) | [Contents](Index.ipynb) | [Further Resources](04.15-Further-Resources.ipynb) >\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"anaconda-cloud\": {},\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.4.3\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "matplotlib/04.15-Further-Resources.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<!--BOOK_INFORMATION-->\\n\",\n    \"<img align=\\\"left\\\" style=\\\"padding-right:10px;\\\" src=\\\"figures/PDSH-cover-small.png\\\">\\n\",\n    \"*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\\n\",\n    \"\\n\",\n    \"*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*\\n\",\n    \"\\n\",\n    \"*No changes were made to the contents of this notebook from the original.*\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<!--NAVIGATION-->\\n\",\n    \"< [Visualization with Seaborn](04.14-Visualization-With-Seaborn.ipynb) | [Contents](Index.ipynb) | [Machine Learning](05.00-Machine-Learning.ipynb) >\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Further Resources\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Matplotlib Resources\\n\",\n    \"\\n\",\n    \"A single chapter in a book can never hope to cover all the available features and plot types available in Matplotlib.\\n\",\n    \"As with other packages we've seen, liberal use of IPython's tab-completion and help functions (see [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb)) can be very helpful when exploring Matplotlib's API.\\n\",\n    \"In addition, Matplotlib’s [online documentation](http://matplotlib.org/) can be a helpful reference.\\n\",\n    \"See in particular the [Matplotlib gallery](http://matplotlib.org/gallery.html) linked on that page: it shows thumbnails of hundreds of different plot types, each one linked to a page with the Python code snippet used to generate it.\\n\",\n    \"In this way, you can visually inspect and learn about a wide range of different plotting styles and visualization techniques.\\n\",\n    \"\\n\",\n    \"For a book-length treatment of Matplotlib, I would recommend [*Interactive Applications Using Matplotlib*](https://www.packtpub.com/application-development/interactive-applications-using-matplotlib), written by Matplotlib core developer Ben Root.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Other Python Graphics Libraries\\n\",\n    \"\\n\",\n    \"Although Matplotlib is the most prominent Python visualization library, there are other more modern tools that are worth exploring as well.\\n\",\n    \"I'll mention a few of them briefly here:\\n\",\n    \"\\n\",\n    \"- [Bokeh](http://bokeh.pydata.org) is a JavaScript visualization library with a Python frontend that creates highly interactive visualizations capable of handling very large and/or streaming datasets. The Python front-end outputs a JSON data structure that can be interpreted by the Bokeh JS engine.\\n\",\n    \"- [Plotly](http://plot.ly) is the eponymous open source product of the Plotly company, and is similar in spirit to Bokeh. Because Plotly is the main product of a startup, it is receiving a high level of development effort. Use of the library is entirely free.\\n\",\n    \"- [Vispy](http://vispy.org/) is an actively developed project focused on dynamic visualizations of very large datasets. Because it is built to target OpenGL and make use of efficient graphics processors in your computer, it is able to render some quite large and stunning visualizations.\\n\",\n    \"- [Vega](https://vega.github.io/) and [Vega-Lite](https://vega.github.io/vega-lite) are declarative graphics representations, and are the product of years of research into the fundamental language of data visualization. The reference rendering implementation is JavaScript, but the API is language agnostic. There is a Python API under development in the [Altair](https://altair-viz.github.io/) package. Though as of summer 2016 it's not yet fully mature, I'm quite excited for the possibilities of this project to provide a common reference point for visualization in Python and other languages.\\n\",\n    \"\\n\",\n    \"The visualization space in the Python community is very dynamic, and I fully expect this list to be out of date as soon as it is published.\\n\",\n    \"Keep an eye out for what's coming in the future!\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<!--NAVIGATION-->\\n\",\n    \"< [Visualization with Seaborn](04.14-Visualization-With-Seaborn.ipynb) | [Contents](Index.ipynb) | [Machine Learning](05.00-Machine-Learning.ipynb) >\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.4.3\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "matplotlib/__init__.py",
    "content": ""
  },
  {
    "path": "matplotlib/matplotlib-applied.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This notebook was prepared by [Donne Martin](http://donnemartin.com). Source and license info is on [GitHub](https://github.com/donnemartin/data-science-ipython-notebooks).\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# matplotlib-applied\\n\",\n    \"\\n\",\n    \"* Applying Matplotlib Visualizations to Kaggle: Titanic\\n\",\n    \"* Bar Plots, Histograms, subplot2grid\\n\",\n    \"* Normalized Plots\\n\",\n    \"* Scatter Plots, subplots\\n\",\n    \"* Kernel Density Estimation Plots\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Applying Matplotlib Visualizations to Kaggle: Titanic\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Prepare the titanic data to plot:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%matplotlib inline\\n\",\n    \"import pandas as pd\\n\",\n    \"import numpy as np\\n\",\n    \"import pylab as plt\\n\",\n    \"import seaborn\\n\",\n    \"\\n\",\n    \"# Set the global default size of matplotlib figures\\n\",\n    \"plt.rc('figure', figsize=(10, 5))\\n\",\n    \"\\n\",\n    \"# Set seaborn aesthetic parameters to defaults\\n\",\n    \"seaborn.set()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"df_train = pd.read_csv('../data/titanic/train.csv')\\n\",\n    \"\\n\",\n    \"def clean_data(df):\\n\",\n    \"    \\n\",\n    \"    # Get the unique values of Sex\\n\",\n    \"    sexes = np.sort(df['Sex'].unique())\\n\",\n    \"    \\n\",\n    \"    # Generate a mapping of Sex from a string to a number representation    \\n\",\n    \"    genders_mapping = dict(zip(sexes, range(0, len(sexes) + 1)))\\n\",\n    \"\\n\",\n    \"    # Transform Sex from a string to a number representation\\n\",\n    \"    df['Sex_Val'] = df['Sex'].map(genders_mapping).astype(int)\\n\",\n    \"    \\n\",\n    \"    # Get the unique values of Embarked\\n\",\n    \"    embarked_locs = np.sort(df['Embarked'].unique())\\n\",\n    \"\\n\",\n    \"    # Generate a mapping of Embarked from a string to a number representation        \\n\",\n    \"    embarked_locs_mapping = dict(zip(embarked_locs, \\n\",\n    \"                                     range(0, len(embarked_locs) + 1)))\\n\",\n    \"    \\n\",\n    \"    # Transform Embarked from a string to dummy variables\\n\",\n    \"    df = pd.concat([df, pd.get_dummies(df['Embarked'], prefix='Embarked_Val')], axis=1)\\n\",\n    \"    \\n\",\n    \"    # Fill in missing values of Embarked\\n\",\n    \"    # Since the vast majority of passengers embarked in 'S': 3, \\n\",\n    \"    # we assign the missing values in Embarked to 'S':\\n\",\n    \"    if len(df[df['Embarked'].isnull()] > 0):\\n\",\n    \"        df.replace({'Embarked_Val' : \\n\",\n    \"                       { embarked_locs_mapping[np.nan] : embarked_locs_mapping['S'] \\n\",\n    \"                       }\\n\",\n    \"                   }, \\n\",\n    \"                   inplace=True)\\n\",\n    \"    \\n\",\n    \"    # Fill in missing values of Fare with the average Fare\\n\",\n    \"    if len(df[df['Fare'].isnull()] > 0):\\n\",\n    \"        avg_fare = df['Fare'].mean()\\n\",\n    \"        df.replace({ None: avg_fare }, inplace=True)\\n\",\n    \"    \\n\",\n    \"    # To keep Age in tact, make a copy of it called AgeFill \\n\",\n    \"    # that we will use to fill in the missing ages:\\n\",\n    \"    df['AgeFill'] = df['Age']\\n\",\n    \"\\n\",\n    \"    # Determine the Age typical for each passenger class by Sex_Val.  \\n\",\n    \"    # We'll use the median instead of the mean because the Age \\n\",\n    \"    # histogram seems to be right skewed.\\n\",\n    \"    df['AgeFill'] = df['AgeFill'] \\\\\\n\",\n    \"                        .groupby([df['Sex_Val'], df['Pclass']]) \\\\\\n\",\n    \"                        .apply(lambda x: x.fillna(x.median()))\\n\",\n    \"            \\n\",\n    \"    # Define a new feature FamilySize that is the sum of \\n\",\n    \"    # Parch (number of parents or children on board) and \\n\",\n    \"    # SibSp (number of siblings or spouses):\\n\",\n    \"    df['FamilySize'] = df['SibSp'] + df['Parch']\\n\",\n    \"    \\n\",\n    \"    return df\\n\",\n    \"\\n\",\n    \"df_train = clean_data(df_train)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Bar Plots, Histograms, subplot2grid\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.text.Text at 0x11357ac50>\"\n      ]\n     },\n     \"execution_count\": 3,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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VBgwYIFCAkJwV133YXo6GgkJCTgjjvuwKOPPoqioiIATc8eBIA+ffpg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\\nBMIUAACACf8PA3yXZdwarecAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x113145b10>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Size of matplotlib figures that contain subplots\\n\",\n    \"figsize_with_subplots = (10, 10)\\n\",\n    \"\\n\",\n    \"# Set up a grid of plots\\n\",\n    \"fig = plt.figure(figsize=figsize_with_subplots) \\n\",\n    \"fig_dims = (3, 2)\\n\",\n    \"\\n\",\n    \"# Plot death and survival counts\\n\",\n    \"plt.subplot2grid(fig_dims, (0, 0))\\n\",\n    \"df_train['Survived'].value_counts().plot(kind='bar', \\n\",\n    \"                                         title='Death and Survival Counts',\\n\",\n    \"                                         color='r',\\n\",\n    \"                                         align='center')\\n\",\n    \"\\n\",\n    \"# Plot Pclass counts\\n\",\n    \"plt.subplot2grid(fig_dims, (0, 1))\\n\",\n    \"df_train['Pclass'].value_counts().plot(kind='bar', \\n\",\n    \"                                       title='Passenger Class Counts')\\n\",\n    \"\\n\",\n    \"# Plot Sex counts\\n\",\n    \"plt.subplot2grid(fig_dims, (1, 0))\\n\",\n    \"df_train['Sex'].value_counts().plot(kind='bar', \\n\",\n    \"                                    title='Gender Counts')\\n\",\n    \"plt.xticks(rotation=0)\\n\",\n    \"\\n\",\n    \"# Plot Embarked counts\\n\",\n    \"plt.subplot2grid(fig_dims, (1, 1))\\n\",\n    \"df_train['Embarked'].value_counts().plot(kind='bar', \\n\",\n    \"                                         title='Ports of Embarkation Counts')\\n\",\n    \"\\n\",\n    \"# Plot the Age histogram\\n\",\n    \"plt.subplot2grid(fig_dims, (2, 0))\\n\",\n    \"df_train['Age'].hist()\\n\",\n    \"plt.title('Age Histogram')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.text.Text at 0x1138e6f10>\"\n      ]\n     },\n     \"execution_count\": 4,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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hmoli0vP6/p58x5Urfc0kO/+9315zX9mRDQAIA6ibRFK/qKpg22vJ07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\\nAQAw0P8DObo8PxWIIQIAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1132fcd90>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Get the unique values of Embarked and its maximum\\n\",\n    \"family_sizes = np.sort(df_train['FamilySize'].unique())\\n\",\n    \"family_size_max = max(family_sizes)\\n\",\n    \"\\n\",\n    \"df1 = df_train[df_train['Survived'] == 0]['FamilySize']\\n\",\n    \"df2 = df_train[df_train['Survived'] == 1]['FamilySize']\\n\",\n    \"plt.hist([df1, df2], \\n\",\n    \"         bins=family_size_max + 1, \\n\",\n    \"         range=(0, family_size_max), \\n\",\n    \"         stacked=True)\\n\",\n    \"plt.legend(('Died', 'Survived'), loc='best')\\n\",\n    \"plt.title('Survivors by Family Size')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Normalized Plots\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.text.Text at 0x113ccbc50>\"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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tO3ql27Dq71IiOj9K9/fSlJWrkyRVFRPfXeewvUqtW9mjlznl566f9q\\n6tQkZWVl6eOPP9Jbb72nqVNnyGKxXPPom8PmAIBK7Xxmrqz+vmrYo5kkKffoWe1/f4f8Avz/s9Jl\\nT66+8cb68vW9GI/du/fUypUpOnXqlNq3f+CyJ2Va9NBDXfX88wMVGdlDdrtdjRvfrP37f1Z6+hZ9\\n881qSVJ29lkdOXJIjRo1drV5551hRR6VXVaENwCgUjv3u12nthxR4yfuksXqI//A6rJW85VvdT85\\nss/L/4bqOp+RK9W9uL6Pz38OSrdqda/mzp2pjIwMjRjxcqF2a9SwKTT0Ns2cOU3dukVJkho1aqzb\\nbmumhx7qqoyME1q9+l9q0CBEv/66X+fPn1OVKv7atesntWnzX9e0T4Q3AKBSq3V7kM5l2LV3/hb5\\nVLFKhqH6D98ii4+PDqfsUZVaVSXf/xzK/uMh7Y4dH9SWLWmqX/+mIm1HRfXUyJFDNGbMOElSv35P\\nKTExQZ9/vlx2u11PPz1ItWvXVr9+T+m55waoZs2aslqvPXotxrWO3ctJRXtMmyT98ss+vbppCo/b\\ncyP76GmNa/uSmjRp6u1SKjT6U+nRp0qHPlV6FbVPlfRIUE5YAwDAZAhvAABMhvAGAMBkCG8AAEyG\\n8AYAwGS4VAwAcF1w5hfIfqL4K5fsJ87q4MEDZW6zYcNGqlKlyrWWVmaENwDgumA/ka3Mf4eqeq16\\nRZZZJb35r2OSjpW6vdwzJzTjpagrXl7mdDo1bVqSfvnlZ/n5+WnUqFd0000NrqL6wghvAMB1o3qt\\nerLVKXqzFU/59tt1cjgcmjdvgX76aadmz35diYnTrrldfvMGAMBDfvhhh+677+KtUO+4o7l27971\\np7RLeAMA4CG5uXbVqFHDNe3j4yOn03nN7RLeAAB4SPXqNZSbm+uaNgyj0INPrhbhDQCAh9x1V5i+\\n/36jJGnnzh/VpMktf0q7nLAGALhu5J45Ua5t3X9/R6WlbdZzzz0lSRo9etyfsm3CGwBwXahRL0Bq\\nv0fSniLL7CfOanCLAQoJaVSmNhs2vPL6FotFI0eOLlObpUF4AwCuCz6+1is+HjUkpFGFeyRoSfjN\\nGwAAkyG8AQAwGcIbAACTIbwBADAZTlgDAFwXeKoYAAAmYz+RLXtGtmoEBRRZVqNeTS088rF0pAzt\\nZWTrn1ET3J6h/tNPOzVv3izNmjW/rCWXiPAGAFw3agQFXPFysT/b4sXvadWqlapWrfqf2i6/eQMA\\n4CENGjTUpElTZBjGn9ou4Q0AgIc88EAnWa3WP71dwhsAAJMhvAEAMBlOWAMAXDfsGcVfKubptiwW\\ny5+2XYnwBgBcJ2rUK3qJ2CWeeqqYJN14Y33Nm7egTO26Q3gDAK4LPFUMAAB4DeENAIDJEN4AAJgM\\n4Q0AgMkQ3gAAmAzhDQCAyRDeAACYDOENAIDJEN4AAJgM4Q0AgMkQ3gAAmIzHwtvpdCo+Pl6xsbGK\\ni4vTwYMHCy1fvXq1evXqpejoaH300UeeKgMAgErHYw8mWbNmjRwOh5KTk7Vjxw4lJSVp7ty5ruWJ\\niYlasWKFqlWrpm7duikyMlIBASU/8QUAAFzksfDetm2bOnToIEkKCwvTzp07Cy338/PT2bNn5ePj\\nI8Mw/vRnnQIAUFl5LLxzcnJks9lc01arVU6nUz4+F4/U//3vf1evXr1UrVo1denSpdC6AACgZB77\\nzdtms8lut7umLw/uo0ePavHixUpNTVVqaqpOnTqlf/3rX54qBQCASsVjI++WLVtq7dq1ioiI0Pbt\\n2xUaGupadv78efn4+KhKlSry8fFRYGCgsrOzr9henTrV5etr9VS5VyUri6MFpRUYaFNQEOc0XAn9\\nqWzoU+7Rp8rGTH3KY+H90EMPaePGjYqNjZV08QS1lJQU5ebmKiYmRj179lRsbKz8/f3VqFEj9ezZ\\n84rtZWXleqrUq5aZmePtEkwjMzNHGRlX/oJ2vaM/lQ19yj36VNlUxD5V0pcJj4W3xWLRq6++Wmhe\\n48aNXa/79++v/v37e2rzAABUWtykBQAAkyG8AQAwGcIbAACTIbwBADAZwhsAAJMhvAEAMBnCGwAA\\nkyG8AQAwGcIbAACT8dgd1gAA3uVwOGSvYLf7rKjsGdlyOBzeLqPUCG8AqMROb2ms8wGB3i6jwsvL\\nzpQivF1F6RHeAFBJ+fn5qW6DZrLVucnbpVR4OVlH5Ofn5+0ySo3fvAEAMBnCGwAAkyG8AQAwGcIb\\nAACTIbwBADAZwhsAAJMhvAEAMBnCGwAAkyG8AQAwGcIbAACTIbwBADAZwhsAAJMhvAEAMBnCGwAA\\nkyG8AQAwGcIbAACTIbwBADAZwhsAAJMhvAEAMBnCGwAAkyG8AQAwGcIbAACTIbwBADAZwhsAAJMh\\nvAEAMBnCGwAAkyG8AQAwGcIbAACTIbwBADAZwhsAAJMhvAEAMBnCGwAAkyG8AQAwGcIbAACTIbwB\\nADAZwhsAAJMhvAEAMBnCGwAAkyG8AQAwGcIbAACTIbwBADAZwhsAAJPx9VTDTqdT48eP1969e+Xn\\n56dJkyYpJCTEtfyHH37Q5MmTZRiGgoODNXnyZFWpUsVT5QAAUGl4bOS9Zs0aORwOJScna+TIkUpK\\nSnItMwxD8fHxSkpK0ocffqi2bdvq8OHDnioFAIBKpVThbbfbtXv3bjmdTuXm5paq4W3btqlDhw6S\\npLCwMO3cudO17Ndff1Xt2rW1cOFCxcXF6ezZs7r55puvonwAAK4/bsN706ZN6tGjh55//nllZGSo\\nY8eO+vbbb902nJOTI5vN5pq2Wq1yOp2SpKysLKWnp6tv375auHChNm3apO+///4adgMAgOuH29+8\\np02bpsWLF+uZZ55RcHCwPvjgA7344ouuUXVJbDab7Ha7a9rpdMrH5+J3hdq1ayskJMQ12u7QoYN2\\n7typNm3alNhenTrV5etrLdVOlZesLJv7lSBJCgy0KSgowNtlVGj0p7KhT7lHnyobM/Upt+HtdDpV\\nr14913TTpk1lsVjcNtyyZUutXbtWERER2r59u0JDQ13LGjZsqNzcXB08eFAhISHaunWroqOjr9he\\nVlbpDteXp8zMHG+XYBqZmTnKyMj2dhkVGv2pbOhT7tGnyqYi9qmSvky4De+//OUvSk1NlSSdPXtW\\nixcvVv369d1u8KGHHtLGjRsVGxsrSUpMTFRKSopyc3MVExOjSZMmacSIETIMQy1bttQDDzxQlv0B\\nAOC65Ta8J0yYoEmTJunYsWPq3Lmz2rRpowkTJrht2GKx6NVXXy00r3Hjxq7Xbdq00dKlS6+iZAAA\\nrm9uw3vPnj16/fXXC81btWqVunTp4rGiAABAyUoM7y+//FIXLlzQzJkzNXToUNd8h8Oh+fPnE94A\\nAHhJieGdk5Oj9PR05ebmavPmza75VqtVL774YrkUBwAAiioxvHv37q3evXtr06ZNatu2bXnWBAAA\\nrsDtb95+fn569tlnlZeXJ6fTKafTqWPHjrnOQAcAAOXL7R3WxowZo86dO6ugoEB9+/ZVo0aN1K9f\\nv/KoDQAAFMNteFetWlXR0dFq3bq1atasqYkTJ+rrr78uj9oAAEAxShXep0+fVuPGjbVjxw5ZLBZl\\nZmaWR20AAKAYbsO7f//+GjZsmDp16qTly5erW7duuuOOO8qjNgAAUAy3J6xFRETo4Ycflo+Pj5Yt\\nW6YDBw4oJCSkPGoDAADFKHHkferUKU2dOlXvvPOO61Ge1apVU3p6OjdoAQDAi0oceY8cOVI2m03b\\nt2+Xw+HQ/fffr5dfflm5ubkaPXp0edYIAAAuU2J4Hzp0SKtXr5bdbldsbKwWL16sJ598Uv3791eV\\nKlXKs0YAAHCZEsPbZrPJYrHIZrPp9OnTmjVrllq0aFGetQEAgGK4PdtckurWrUtwAwBQQZQ48s7N\\nzVVaWpoMw1BeXp7rtcVikSS1bt263IoEAAD/UWJ4BwcHa+bMmUVeX7Jo0SLPVgYAAIpVYngTzkD5\\ncjgcsmdke7sMU7BnZMvhcHi7DMBr3N6kBUD5Ob2lsc4HBHq7jAovLztTivB2FYD3EN5ABeHn56e6\\nDZrJVucmb5dS4eVkHZGfn5+3ywC8plRnmwMAgIqjxJG3u7uoJSYm/unFAAAA90oM79atW8tiscgw\\njCLLLl0uBgAAyl+J4f3oo4+6XmdlZSkvL0+GYcjpdOrw4cPlUhwAACjK7Qlr06ZN04cffqj8/HzV\\nrl1bx48fV5s2bdS2bdvyqA8AAPyB2xPWvvzyS61bt04RERFatGiR3n33XTVo0KA8agMAAMVwG95B\\nQUEKCAjQrbfeql27dqlNmzb6+eefy6M2AABQDLeHzW02m1asWKHbb79dH3zwgerVq6dTp06VR20A\\nAKAYbkfer732mjIzM9WmTRs1aNBA48aN07Bhw8qjNgAAUAy3I++VK1cqKipKkjRq1CiPFwQAAK7M\\n7cj7+PHjiomJ0dNPP63PPvtMeXl55VEXAAAogdvwfvnll/XNN9/o2Wef1Y4dO/S3v/1NI0eOLI/a\\nAABAMUp9b/P8/Hw5HA5ZLBZVqVLFkzUBAIArcPubd0JCgtasWaNmzZopKipKY8eOlb+/f3nUBgAA\\niuE2vP/6179q+fLlCgzkGcMAAFQEJYZ3cnKyYmNjdebMGX344YdFlg8ePNijhQEAgOKV+jfv4p4u\\nBgAAyl+JI+/Y2FhJF++wFhkZqRtuuKHcigIAACXjOm8AAEyG67wBADAZrvMGAMBkuM4bAACTcRve\\ngYGBXOcNAEAF4vaw+RdffEFwAwBQgbgdeTdt2lSzZ89WWFiYqlat6prfunVrjxYGAACK5za8T58+\\nrc2bN2vz5s2F5i9atMhjRQEAgJK5DW9CGgCAisVteMfFxRWZZ7FY9P7773ukIAAAcGVuw/vyB5Dk\\n5+frm2++Uc2aNT1aFAAAKJnb8L7vvvsKTbdr107R0dEaNmyYx4oCAAAlcxveR48edb02DEP79u3T\\nmTNnPFoUAAAomdvw7tu3r+u1xWJRnTp1NHbsWI8WBQAASuY2vFNTU8ujDgAAUEpXvMNaamqqDh06\\nJElavXq1Bg0apBkzZig/P99tw06nU/Hx8YqNjVVcXJwOHjxY7HqvvPKKpk2bdhWlAwBwfSoxvP/7\\nv/9bs2fP1rlz57R7926NHDlSnTt3lt1u1+TJk902vGbNGjkcDiUnJ2vkyJFKSkoqsk5ycrL27dsn\\ni8VybXsBAMB1pMTD5itWrNCSJUtUvXp1TZ06VQ8++KAee+wxGYahiIgItw1v27ZNHTp0kCSFhYVp\\n586dRZb/8MMP6t27t/bv33+NuwEAwPWjxJG3j4+PqlevLknavHmz2rdvL+niSWulGSnn5OTIZrO5\\npq1Wq5xOpyTpxIkTmjNnjuLj42UYxjXtAAAA15sSR95Wq1VnzpxRXl6edu3a5Qrvo0ePytfX7Xlu\\nstlsstvtrmmn0ykfn4vfFb7++mtlZWVp4MCBOnnypM6dO6cmTZqoR48eJbZXp051+fpaS71j5SEr\\ny+Z+JUiSAgNtCgoK8HYZFRr9qWzoU+7Rp8rGTH2qxBR+5pln1LNnTzkcDkVHR6tevXpauXKlpk+f\\nrn/84x9uG27ZsqXWrl2riIgIbd++XaGhoa5lcXFxrtuuLl++XPv3779icEtSVlZuafep3GRm5ni7\\nBNPIzMxRRka2t8uo0OhPZUOfco8+VTYVsU+V9GWixPDu2rWrWrRooaysLN12222SpGrVqmnixIlF\\n7rpWnIceekgbN25UbGysJCkxMVEpKSnKzc1VTExMoXU5YQ0AgNK74vHv4OBgBQcHu6bDw8NL3bDF\\nYtGrr75aaF7jxo2LrNezZ89StwkAANxc5w0AACoewhsAAJMhvAEAMBnCGwAAk3F/wTZK5HA4ZK9g\\nlxVURPaMbDkcDm+XAQCVBuF9jU5vaazzAYHeLqNCy8vOlNzfURcAUEqE9zXw8/NT3QbNZKtzk7dL\\nqdByso7Iz8/P22UAQKXBb94AAJgM4Q0AgMkQ3gAAmAzhDQCAyRDeAACYDOENAIDJEN4AAJgM4Q0A\\ngMkQ3gAAmAzhDQCAyRDeAACYDOENAIDJEN4AAJgM4Q0AgMkQ3gAAmAzhDQCAyRDeAACYDOENAIDJ\\nEN4AAJgM4Q0AgMkQ3gAAmAzhDQCAyRDeAACYDOENAIDJEN4AAJgM4Q0AgMkQ3gAAmAzhDQCAyRDe\\nAACYDOFU/jBoAAAMq0lEQVQNAIDJEN4AAJgM4Q0AgMkQ3gAAmAzhDQCAyRDeAACYDOENAIDJEN4A\\nAJgM4Q0AgMkQ3gAAmAzhDQCAyRDeAACYDOENAIDJEN4AAJgM4Q0AgMkQ3gAAmAzhDQCAyfh6qmGn\\n06nx48dr79698vPz06RJkxQSEuJanpKSovfff19Wq1W33nqrxo8fL4vF4qlyAACoNDw28l6zZo0c\\nDoeSk5M1cuRIJSUluZadO3dOM2bM0KJFi/TRRx8pJydHa9eu9VQpAABUKh4L723btqlDhw6SpLCw\\nMO3cudO1zN/fX0uWLJG/v78kKT8/X1WrVvVUKQAAVCoeC++cnBzZbDbXtNVqldPplCRZLBYFBgZK\\nkhYtWqS8vDz913/9l6dKAQCgUvHYb942m012u9017XQ65ePjU2h6ypQpOnDggGbNmuWpMgAAqHQ8\\nFt4tW7bU2rVrFRERoe3btys0NLTQ8vj4ePn7+2vOnDmlOlGtTp3q8vW1eqrcq5KVZXO/EiRJgYE2\\nBQUFeLuMCo3+VDb0KffoU2Vjpj7lsfB+6KGHtHHjRsXGxkqSEhMTlZKSotzcXDVv3lyffvqpWrVq\\npSeffFKS1K9fP3Xu3LnE9rKycj1V6lXLzMzxdgmmkZmZo4yMbG+XUaHRn8qGPuUefapsKmKfKunL\\nhMfC22Kx6NVXXy00r3Hjxq7Xu3bt8tSmAQCo1LhJCwAAJkN4AwBgMoQ3AAAmQ3gDAGAyhDcAACZD\\neAMAYDKENwAAJkN4AwBgMoQ3AAAmQ3gDAGAyhDcAACZDeAMAYDKENwAAJkN4AwBgMoQ3AAAmQ3gD\\nAGAyhDcAACZDeAMAYDKENwAAJkN4AwBgMoQ3AAAmQ3gDAGAyhDcAACZDeAMAYDKENwAAJkN4AwBg\\nMoQ3AAAmQ3gDAGAyhDcAACZDeAMAYDKENwAAJkN4AwBgMoQ3AAAmQ3gDAGAyhDcAACZDeAMAYDKE\\nNwAAJkN4AwBgMoQ3AAAmQ3gDAGAyhDcAACZDeAMAYDKENwAAJkN4AwBgMoQ3AAAmQ3gDAGAyhDcA\\nACZDeAMAYDKENwAAJkN4AwBgMoQ3AAAmQ3gDAGAyhDcAACZDeAMAYDIeC2+n06n4+HjFxsYqLi5O\\nBw8eLLQ8NTVV0dHRio2N1dKlSz1VBgAAlY7HwnvNmjVyOBxKTk7WyJEjlZSU5FrmcDiUlJSkhQsX\\natGiRVqyZIlOnTrlqVIAAKhUPBbe27ZtU4cOHSRJYWFh2rlzp2vZL7/8opCQEAUEBMjPz0/33HOP\\n0tLSPFUKAACViq+nGs7JyZHNZnNNW61WOZ1O+fj4KCcnRwEBAa5lNWrUUHZ2tqdK8ajcMye8XUKF\\nx2dUenxWpcPnVHp8VqVjts/JY+Fts9lkt9td05eCW5ICAgIKLbPb7apVq9YV2wsKCrjicm8ICmqp\\nzZ+29HYZqCToT/iz0acqL48dNm/ZsqU2bNggSdq+fbtCQ0Ndy26++WYdOHBAZ86c0YULF5SWlqa7\\n777bU6UAAFCpWAzDMDzRsGEYGj9+vPbs2SNJSkxM1E8//aTc3FzFxMRo7dq1mjNnjpxOp6Kjo9Wn\\nTx9PlAEAQKXjsfAGAACewU1aAAAwGcIbAACTIbwBADAZwhuAW+fPn/d2Cagkzp07pwsXLni7DNPj\\nhDUTi4uLk8Ph0B//hBaLRcnJyV6qCmaWmpqqhIQEWa1WDR8+XN26dZN0sa8tWrTIy9XBjPbt26fX\\nX39dtWrVUmRkpF555RVZLBaNGTNGnTp18nZ5puWxm7TA80aOHKmxY8dq9uzZslqt3i4HlcCbb76p\\nFStWyOl0aujQoTp//rweffRRb5cFExs3bpyGDRumI0eOaMiQIfr6669VtWpVDRgwgPC+BoS3iYWF\\nhSkqKkp79uxRly5dvF0OKoEqVaq47nY4d+5c9evXT/Xr1/dyVTAzwzB07733SpI2b96sG264QZLk\\n60v8XAsOmwNweemllxQYGKghQ4aoRo0aOnbsmJ566illZ2fr3//+t7fLgwmNHj1aPj4+mjBhgusI\\n4fz587Vr1y698cYbXq7OvDhhDYDLa6+9ptDQUFksFknSjTfeqEWLFqlr165ergxmNXHiRHXs2LHQ\\nT3vBwcGFHhONsmPkDQCAyTDyBgDAZAhvAABMhvAGAMBkOFcf8KLDhw+ra9euuuWWW2SxWORwOFSv\\nXj0lJiYqODjY2+X9KdatW6f58+crNzdXTqdTnTt31pAhQ2SxWBQXF6cXXnjBdSkRgNJh5A14Wb16\\n9bRixQotX75cKSkpat68uRISErxd1p9iw4YNSkhIUFJSkj777DN98skn2r17t2bOnOla59KZ7QBK\\nj5E3UMHcc889Sk1NlSStXLlS7777rs6dO6dz585p0qRJatWqlRYuXKgVK1bIx8dHd955pyZMmKDd\\nu3dr3Lhxys/Pl7+/vxITE9WoUSNt2LBBs2bNUn5+vho0aKCEhATVrl1bnTp10t/+9jf9+9//Vl5e\\nniZPnqw77rhDe/fu1ahRo+R0OnXPPffo22+/1apVq3Ty5EmNGzdOx44dk4+Pj0aMGKG2bdtq1qxZ\\n2r59u37//Xf17dtXjz/+uGtf5s2bpxdeeEGNGjWSJPn7+2v8+PHav39/oX0uKCjQuHHj9PPPP+vk\\nyZNq3LixZs+eLYfDoRdffFEnT56UJA0ePFidOnUqdv+B6wkjb6ACcTgcWrlypVq2bCnDMLRkyRLN\\nnz9fn332mQYOHKh33nlHBQUFeuutt7Rs2TItW7ZMVqtVx48f13vvvae///3v+vTTT9W3b1/t2LFD\\nmZmZmj59uhYsWKDly5erXbt2mjp1qmt7derU0dKlSxUbG6v58+dLkkaNGqVhw4ZpxYoVatiwoQoK\\nCiRJkyZNUq9evbRs2TLNnTtX8fHxstvtrrq//PLLQsEtSbt27dJdd91VaF5wcLDatm3rmjYMQ+np\\n6fL391dycrJWr16tc+fOaf369VqzZo0aNGigZcuWacqUKdq6dWuR/ffx8dHx48c98vcAKipG3oCX\\nnThxQj169JAkXbhwQWFhYRo5cqQsFotmz56t1NRU/frrr0pLS5PVapXValWLFi3Uq1cvPfjgg+rT\\np4+Cg4MVHh6uCRMm6Ntvv1XHjh3VtWtXrV+/XseOHVNcXJykiyPc2rVru7bdoUMHSdItt9yiVatW\\n6cyZMzpy5Ijuv/9+SVJ0dLTef/99SdJ3332nX3/91XXIu6CgQIcOHZLFYlFYWFix++bj41PkwTl/\\nZLFY1KpVK9WqVUuLFy/W/v37deDAAeXm5qpFixaaPn26jh8/rvDwcD333HNF9v+JJ56oNOcHAKVF\\neANeduk37z+y2+3q1auXevbsqXvvvVe33XabPvjgA0kX7zu+Y8cOrV+/XgMGDNDUqVP18MMP6+67\\n79a6dev03nvvaf369QoPD1fLli315ptvSrr4aM9Lo2Xp4mFs6WKAGoZR5AE3lwevYRh6//33VbNm\\nTUnS8ePHFRQUpDVr1rja+aPmzZvrxx9/VJMmTVzzfv31V82bN0+TJ092tfvNN99o1qxZ6tevn3r1\\n6qXTp09Lkho1aqSVK1fq22+/1dq1a7VgwQKtXLmy2P1v3bp12T54wMQ4bA5UUL/99pusVqsGDRqk\\n++67T+vXr5fT6VRWVpYeeeQRNW3aVEOGDFG7du20Z88ejRgxQj/++KN69+6tIUOG6H//938VFham\\n7du367fffpN0MfSnTJlS4jZtNptCQkK0YcMGSdIXX3zhOqGsTZs2Wrx4saSLj3mMiopSXl7eFUfW\\nAwYM0Jw5c3TgwAFJF7+QJCYmFnnYyaZNmxQREaGePXuqbt26SktLU35+vj766CPNmjVLXbt2VXx8\\nvDIzM5WVlaWIiIhC+793796r/pwBM2LkDXhZSWdbN2vWTM2aNVNERIQCAwP18MMP6/vvv1edOnUU\\nExOj6OhoVa1aVTfddJMeffRRtWrVSmPHjtXcuXNltVo1evRo3XDDDXrttdc0bNgwFRQU6MYbbyw2\\nvC0Wi6uOpKQkjRkzRm+88YZCQ0NVtWpVSdLYsWMVHx+vqKgoGYahqVOnqkaNGlc8W7xDhw4aPny4\\nhg8froKCAuXn5ysiIkKDBw8utO2YmBiNGDFCq1atUlBQkB588EEdOXJETz/9tEaMGKHu3bvLz89P\\nL7zwgurUqaPevXsX2v+ePXtey58AMB3ubQ6gkDlz5igmJkZBQUFatWqVUlJSCl3aBcD7GHkDKKR+\\n/fp66qmn5Ovrq1q1amnSpEneLgnAHzDyBgDAZDhhDQAAkyG8AQAwGcIbAACTIbwBADAZwhsAAJMh\\nvAEAMJn/BxSJuuk2dCC9AAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x113b53b90>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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PLly2vXrl0aNmzYTX8nLS2jhKorutTUdFWs7i//WiX7sg0zSk1NV0oK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     \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x113c19f10>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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gAAmAzhDQCAyRDeAACYTJl7JeiSJe/q22//rdzcXFmtVg0bNkohIQ1u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\\nrkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x113cc3d90>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"pclass_xt = pd.crosstab(df_train['Pclass'], df_train['Survived'])\\n\",\n    \"\\n\",\n    \"# Normalize the cross tab to sum to 1:\\n\",\n    \"pclass_xt_pct = pclass_xt.div(pclass_xt.sum(1).astype(float), axis=0)\\n\",\n    \"\\n\",\n    \"pclass_xt_pct.plot(kind='bar', \\n\",\n    \"                   stacked=True, \\n\",\n    \"                   title='Survival Rate by Passenger Classes')\\n\",\n    \"plt.xlabel('Passenger Class')\\n\",\n    \"plt.ylabel('Survival Rate')\\n\",\n    \"\\n\",\n    \"# Plot survival rate by Sex\\n\",\n    \"females_df = df_train[df_train['Sex'] == 'female']\\n\",\n    \"females_xt = pd.crosstab(females_df['Pclass'], df_train['Survived'])\\n\",\n    \"females_xt_pct = females_xt.div(females_xt.sum(1).astype(float), axis=0)\\n\",\n    \"females_xt_pct.plot(kind='bar', \\n\",\n    \"                    stacked=True, \\n\",\n    \"                    title='Female Survival Rate by Passenger Class')\\n\",\n    \"plt.xlabel('Passenger Class')\\n\",\n    \"plt.ylabel('Survival Rate')\\n\",\n    \"\\n\",\n    \"# Plot survival rate by Pclass\\n\",\n    \"males_df = df_train[df_train['Sex'] == 'male']\\n\",\n    \"males_xt = pd.crosstab(males_df['Pclass'], df_train['Survived'])\\n\",\n    \"males_xt_pct = males_xt.div(males_xt.sum(1).astype(float), axis=0)\\n\",\n    \"males_xt_pct.plot(kind='bar', \\n\",\n    \"                  stacked=True, \\n\",\n    \"                  title='Male Survival Rate by Passenger Class')\\n\",\n    \"plt.xlabel('Passenger Class')\\n\",\n    \"plt.ylabel('Survival Rate')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Scatter Plots, subplots\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.text.Text at 0x113f4d710>\"\n      ]\n     },\n     \"execution_count\": 6,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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hGvvPIkMAdob3nMtcRERETE2ahBcxEvvrgRmEbBJc44S0xERKR458+f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\\nAADDUNAAAAAMQ0EDAAAwzP8H7tN4VQuAfaYAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x113b53250>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Set up a grid of plots\\n\",\n    \"fig, axes = plt.subplots(2, 1, figsize=figsize_with_subplots)\\n\",\n    \"\\n\",\n    \"# Histogram of AgeFill segmented by Survived\\n\",\n    \"df1 = df_train[df_train['Survived'] == 0]['Age']\\n\",\n    \"df2 = df_train[df_train['Survived'] == 1]['Age']\\n\",\n    \"max_age = max(df_train['AgeFill'])\\n\",\n    \"\\n\",\n    \"axes[1].hist([df1, df2], \\n\",\n    \"             bins=max_age / 10, \\n\",\n    \"             range=(1, max_age), \\n\",\n    \"             stacked=True)\\n\",\n    \"axes[1].legend(('Died', 'Survived'), loc='best')\\n\",\n    \"axes[1].set_title('Survivors by Age Groups Histogram')\\n\",\n    \"axes[1].set_xlabel('Age')\\n\",\n    \"axes[1].set_ylabel('Count')\\n\",\n    \"\\n\",\n    \"# Scatter plot Survived and AgeFill\\n\",\n    \"axes[0].scatter(df_train['Survived'], df_train['AgeFill'])\\n\",\n    \"axes[0].set_title('Survivors by Age Plot')\\n\",\n    \"axes[0].set_xlabel('Survived')\\n\",\n    \"axes[0].set_ylabel('Age')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Kernel Density Estimation Plots\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.legend.Legend at 0x113175ed0>\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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KX+m2qhWdAfNmwYGzZs8O4vnjdvHitXrqSsrIybb76ZadOmMXHiRFwu\\nF2PGjCEuLo64uDjS09MZM2aMN3mK9OKFEEIIdbSIffot9dsctOxvq9Cy29eS2wbSvuZO2td8+dPT\\nlzS8QgghxAVCgr4QQghxgZCgL4QQQlwgJOgLIYQQFwgJ+kKcY2W7d1G89ReUM851F0IILUjQF+Ic\\nKly3lqPPL+DE4lc49eHSc10dIVqsnTsz+PvfJ9d5T3b2STZs+KHG65WVlSxa9E/+9rd7uP/+STz6\\n6AOcOpUNwJgx7uO1mwsJ+kKcI67KSnI/+xR9UBDG6Gis69ZSeezoua6WEC3Ohx++x8KFs+sNzr/8\\nks6OHdtrvP7yyy8QH9+KV199k0WL3uCGG0YzY8YTAM0ul4xmyXmEEHUr/nkLrrJSokbegLlDR04s\\nfoWijRuIHXvLua6aEJr4JG0/6btPqVrmZSlx3Dykc533tGvXnjlznmPWrBne1z7/fAWrVn2NXq8n\\nJaU7U6Y8zAcfvEtlZSW9evXm6qvdB8bZ7XZ+/HE9jz76D+97Bw4cTJ8+l1R7xoED+1m06J84nS6s\\n1kKmTp1Gz54XMXfuTI4dO0plZSVjx45j+PDreP31V9m27RccDieDBw9h/Pg7VfxE6iZBX4hzpHjT\\nJgDCBw7CEBaGPjCQkl9+JmbMzc2u9yDE+WzQoCGcOHG82mvffvsVjzzyBCkp3fjii09RFIUJE/7C\\n4cNZ3oAPYLVaiY6OrlFmWFiY978VReHgwYPcf/+DJCV15r//XcXXX39FUlJntm//lTfeeBeALVvc\\nf+fXrFnNK6+8QXR0NN9885UGLa6dBH0hzgFXZSXl+/Zgbp+AKToGgJCL+lC8ZRO2Y0cxt2t/jmso\\nhPpuHtK53l55U3niiadZtuwDjh8/Rs+eF6Eoivd/Z4qIiKC4uKTG+7/7bhVDhgwF3EP8MTGxvPvu\\n25jNZsrKSgkJCSU4OJgpUx5hwYI5lJaWMnz4CABmzJjFv/71Mvn5eVx55VXaN/YMMqcvxDlQvm8v\\nisNBcPce3teCu3VzX8vcf66qJcQF46uvvmDq1CdYtOgN9u7dQ0bGb+j1elx/2EVjNBq54oor+fTT\\nZd7X0tLW8OmnyzAa3f1mRVF46aXnmThxMk8++QxJSZ1RFIW8vFz27NnF3LnPsXDhiyxe/DJ2u521\\na9cwc+ZcXn75Nb79diXZ2SebrN3S0xfiHCjbsxuA4G7dva8FdnL3gCr274dBqeekXkK0ZGdOm3Xq\\n1Im//e2vBAeHEBsbR48evQgJCeH9998hObkb11wzzHvv3//+EK+88iL33ns3oCMsLIw5cxZ6SgVg\\n+PARPPXU48TFxZOS0p28vFyio2PIz8/j3nvvRq83cNttEzCZTISFhTNp0l2YzWYuv/xK4uNbNd1n\\nIAfunN9a8qER0LLbV1fbjr6wkLJdv9Pp5VcxBIcAoLhcZD54P4ZQC4lzFzRlVRulJf/uQNrX3LXk\\n9smBO0I0I4qiUHHoIKb4eG/AB9Dp9QQmJmE/lY2zrPQc1lAI0VJJ0BeiidlPncJVXk5gx8Qa18zt\\n2gFgO3asqaslhLgASNAXoolVHskCILBDxxrXAtq4g74k6RFCaEGCvhBNzHbcvV84oG27GtfMbT1B\\nX3r6Qgj1SdAXoonZqpKEBLRqXeNaQJs2oNNhk56+EEIDEvSFaGKVJ06gM5sxRkXVuKYPCMAUHYOt\\nCfftCiEuHLJPX4gmpLhc2E+eIKBtu1pT7Zpi4yjbtRNXZSV6s7mJayhEy+JwOJg3byYnT57EZrNx\\n550T6d9/YIPe+9hjD/Hww4/R6g+jcv/5z+f897+r0Ol0OBwOJk26j4sv7sucOc8wdOhwrriinxZN\\nUYUEfSGakD03F8XhIKB1zaF9D1NcLOwCe84pSccrhJ++++5bIiIieeqpWRQVFfGXv9zW4KDvVv3L\\n+Zo1q/n55y289NK/MBgMnDhxnL/97R6WLPmwWZyZIUFfiCbknc9v3abWe0yxce57T0nQFy3L5/tX\\n8uupHaqWeXFcL/7c+fpar6emDmXw4GsAUBSXN3Xu/fdPomvXZA4cyKS0tJRZsxbQqlUr3n77dTZu\\n/JHo6BhOncquUd6XX/6bv//9YQwGAwCtW7fh3Xc/9h7Ao9PpKCsrZd68WZSWlpCbm8Of/zyWUaPG\\n1DjZ78EHp7JuXRoffvg+RqORmJhYZs6cq+mXBwn6QjQhX4K+PUfdI0iFuBAFBQUBUFZWyvTpj3PP\\nPfcC7uDcvXtPpkx5hDfeWMyaNau47LIr2br1Z95+eymVlZXccUfNY65zc3No27Zttdf+eOLe0aNH\\nGDp0OIMGpZKbm8P9909m1KgxNU72czqdrFnzHePH38GgQUNYteprSktLCQ0N1ezzkKAvRBPyLNAL\\naFV7ru2AuKqgf0qCvmhZ/tz5+jp75VrJzj7Jk08+xp//PJahQ4d7X+/aNRmAuLh48vPzOHz4EMnJ\\nKQCYzWZSUroD1TPVt2rVmpMnT5KU1Mn72ubNP9G5cxfvz5GRUXzyycesX59GcHAoTqcDOPvJfn//\\n+0MsXfouK1Yso2PHRAYOHKzRp+Amq/eFaEKO3FwATDGxtd5jinVfk56+EP7Lz8/j4Yfv5777pnDd\\ndTf84Wr1YfSOHZP4/feduFwu7HY7+/btqXHPyJE38t57b+F0OgE4fDiLBQtmo9cbvPcsW/YhPXv2\\n4qmnZpGaeo33uN6znez35Zf/5u67J7Fo0RsoisK6dWtV/wzOJD19IZqQPTcHQ3g4+oCAWu/RBwZh\\nsIRJ0BdCBe+/v4SSkhKWLHmTJUveRKfT8dxzL9W4T6fT0aVLV/r3H8g999xJZGQk4eERNe675po/\\nkZeXy333/RWTyYTT6eTpp2cTGRnpLefqqwfwz38+x/r135OYmERwcDB2u73GyX7du/ektLSUxx57\\nkODgEIKDg7n6al8WGfpOTtk7z7Xkk6KgZbfvj21TnE723XsPgYlJJDwxvc73Hp43m4qDB+iy+A10\\nxvPzu3lL/t2BtK+5a8ntk1P2hGgGHAX54HLVObTvYYqJAZcLh7WwCWomhLhQSNAXoonYc3IAMMXG\\n1HuvMdKdrc+RX6BpnYQQFxYJ+kI0EXtuVdCPiav3Xk+KXntBnqZ1EkJcWCToC9FE7N6V+/X39E3e\\nnn6+pnUSQlxYJOgL0UROD+/XP6fvHd4vkOF9IYR6JOgL0UTseblgMHgDel08w/vS0xdCqOn83Ask\\nRAvkyM/DGBGBTl//d22DxYLOaMReIEFfCH84nU4WLJjNkSOH0el0TJ36RLVsemdzxx238P77y6u9\\nVllZyZtv/otdu3ai0+kICgri0Uf/QVxcPGPG3MDHH3+OyWTSsimqkJ6+EE1AcTpxFBZiiopu0P06\\nnQ5jZKR7m58QotE2bvwBvV7Pv/71Nvfccy9vvrm4UeW8/PILxMe34tVX32TRoje44YbRzJjxBECz\\nOF3PQ3r6QjQBh9UKioKxKmtXQxgjoyjftxfF4ThvE/QI4YucFcso/jld1TItl15G7NhxtV4fMGAw\\nV101AICTJ09gsbgPx7n//klERUVTXFzEvHkv8OyzT2G1FtK2bTtcLle1Mux2Oz/+uJ5HH/2H97WB\\nAwfTp88l1e47cGA/ixb9E6fThdVayNSp0+jZ8yLmzp3JsWNHqaysZOzYcQwffh2vv/4q27b9gsPh\\nZPDgIYwff6daH0md5F8SIZqAp8fua9BHUXAUFjQooY8Q4uwMBgNz5jzD+vVrmT17IeDunQ8bNpwB\\nAwazbNkHJCYmcc8993L48CEeffTBau+3Wq1ER9ccpfvj6XoHDx7k/vsfJCmpM//97yq+/vorkpI6\\ns337r7zxxrsAbNmyCYA1a1bzyitvEB0dzTfffKVRy2uSoC9EEzgd9Bs2vA9n7NXPz5egL1qE2LHj\\n6uyVa+nJJ5/h3nv/zqRJd/HBBysASEjoCLgPzenX72rvaxER1b+cR0REUFxcUqPM775bxZAhQwH3\\nl4iYmFjeffdtzGYzZWWlhISEEhwczJQpj7BgwRxKS0sZPnwEADNmzOJf/3qZ/Pw8rrzyKq2aXYPM\\n6QvRBDyZ9Xzr6bvvdRZKKl4hGmvVqq9ZunQJ4D4uV6/Xe+fgPf/fsWMSO3ZsB+DYsaNY/5D+2mg0\\ncsUVV/Lpp8u8r6WlreHTT5dhrJp6UxSFl156nokTJ/Pkk8+QlNQZRVHIy8tlz55dzJ37HAsXvsji\\nxS9jt9tZu3YNM2fO5eWXX+Pbb1eSXXXsttakpy9EEzjd069/u56HMSzc/d4iqyZ1EuJCkJp6DXPm\\nzOT++yfhcDiYMuURzGZztXtGjbqJefOe5d57J9K6dRvvvP+Z/v73h3jllRe59967AR1hYWHMmbOw\\n6qr7y8Pw4SN46qnHiYuLJyWlO3l5uURHx5Cfn8e9996NXm/gttsmYDKZCAsLZ9KkuzCbzVx++ZXE\\nx7fS+JOoqqmcsnd+a8knRUHLbt+ZbTv+2quU/JxO0vMvYoxoWG+/fN8+jiyYQ+S11xE75mYtq9oo\\nLfl3B9K+5q4lt09O2RPiPOcoKACDAUNV770hDOHue53S0xdCqESCvhBNwFGQjzG8YYl5PIxVQd9h\\nlaAvhFCHZnP6LpeLZ555hr1792IymZgzZw4JCQne62lpaSxevBij0chNN93E2LFjARg9ejShoaEA\\ntG/fnrlz52pVRSGahOJy4SgsJDAxyaf36c1m9IGBEvSFEKrRLOivWbMGu93OsmXL2L59O/Pnz2fx\\nYncmJLvdzvz58/nss88IDAzk1ltv5ZprriEkJASApUuXalUtIZqcw2oFl8unRXwehvAInBL0hRAq\\n0Wx4f+vWrQwY4M6C1Lt3bzIyMrzXMjMzSUhIwGKxYDKZ6Nu3L1u2bGH37t2Ul5czceJE7rzzTrZv\\n365V9YRoMp6V+6Yo34O+MTwcZ0kxitOpdrWEEBcgzXr6JSUl3mF6cGdEcrlc6PV6SkpKsFhOrz4M\\nCQmhuLiYpKQkJk6cyNixYzl06BD33HMPq1evRu/DPKgQ55vGZOPzMIaHg6LgLC5q8Kp/IYSojWZB\\nPzQ0lNLSUu/PnoAPYLFYql0rLS0lPDycjh070qFDBwA6duxIREQEOTk5xMfH1/ksf7YvNAfSvuYr\\nNtaC3V4GQFSHNsT42NbiVrEUAxadndDz8HNqyb87kPY1dy29fY2hWdC/5JJLWLt2LSNGjGDbtm0k\\nJyd7ryUlJZGVlYXVaiUoKIj09HQmTpzI559/zp49e3j66afJzs6mpKSE2Nj604+21L2Y0LL3mkLL\\nbp+nbYXHTgFQpjP73FZ7QDAAOVnHKQ+PU72O/mjJvzuQ9jV3Lbl9/nyZ0SzoDxs2jA0bNjBunDvP\\n8rx581i5ciVlZWXcfPPNTJs2jYkTJ+JyuRgzZgxxcXGMGTOGJ554gvHjx3vfI0P7orlzFhUBYAir\\nmeWrPp59/c5CWcwnhPCfZkFfp9Mxc+bMaq8lJiZ6/zs1NZXU1NTqlTEaee6557SqkhDnhCe5ji+J\\neTyMERGApOIVQqhDutFCaMxRVITOZEIfGOjze73592XbnhBCBRL0hdCYs6gIQ1iY90QvXxgiqob3\\nrXLSnhDCfxL0hdCQ4tlu14ihfQBDSCjo9dLTF0KoQoK+EBpylZWhOBzew3N8pdPrMYSFSVY+IYQq\\nJOgLoSFPD93YiJX7HsawcFnIJ4RQhQR9ITR0euV+44O+ISwMxWbDVVmpVrWEEBcoCfpCaOj0Hv3G\\nDe8DGC1h1coSQojGkqAvhIYcVYHan+F9Q5g7+5ajuGVmFxNCNB0J+kJoyJ/EPB6G0KqefrH09IUQ\\n/pGgL4SG1OzpS9AXQvhLgr4QGlJlIZ9nTl+G94UQfpKgL4SGHEVF6IxG9EHBjS7DaKnq6ctCPiGE\\nnyToC6EhZ5EVQ1h4o1LwenhGCRwyvC+E8JMEfSE0oiiKN+++Pwyhnjl9Gd4XQvhHgr4QGnGWulPw\\n+rOID0BvNqMzB0rQF0L4TYK+EBqxFbpPxvNnu56H0WKR1ftCCL9J0BdCI/aq43D97emDe9ues7gY\\nRVH8LksIceGSoC+ERuyF/ifm8TBYwlAcDlzl5X6XJYS4cEnQF0Ij9kIVe/oWSdAjhPCfBH0hNGLz\\n9PTD1enpAziLZDGfEKLxJOgLoRE1e/rek/ZKpKcvhGg8CfpCaMRWoN7qfc/wvkN6+kIIP0jQF0Ij\\n9kKrOwW2sJjZAAAgAElEQVRvcONT8Hp4EvzInL4Qwh8S9IXQiN1aiMES5lcKXo/TC/mkpy+EaDwJ\\n+kJoQFEU7IVWv1Pwehilpy+EUIEEfSE04KqowGWzqbKID07n33fISXtCCD9I0BdCA84i9RLzAN61\\nATK8L4TwhwR9ITTgrOqRqzW8D+69+jK8L4TwhwR9ITTgqOrpG1VIzONhsFhwlpSguFyqlSmEuLBI\\n0BdCA06rZ3hfvZ6+0RIGLheusjLVyhRCXFgk6AuhAc+CO6NKc/rgPmnvzLKFEMJXEvSF0IA2c/py\\n6I4Qwj8S9IXQgHdOX82evkX26gsh/CNBXwgNOIuK0BkMqqTg9fAeuiPD+0KIRpKgL4QGnEVFmMLD\\n0enV+yvmPXRH9uoLIRpJgr4QGnAUWTFFqDe0D2cculMiQV8I0TgS9IVQmauiAsVmIyAyQtVyPal4\\nZXhfCNFYEvSFUJlnS50pXO2gHwo6naTiFUI0mgR9IVTmScyj9vC+zmBAHxIiQV8I0WgS9IVQmWe7\\nnilC3Z4+uFfwS9AXQjSWBH0hVOaZcw/QIOgbLBacpZJ/XwjROBL0hVDZ6Z6+usP7ULVtT1FwlpSo\\nXrYQouWToC+Eyjw9fS2G9yUrnxDCH5oFfZfLxYwZMxg3bhwTJkzg8OHD1a6npaUxZswYxo0bx4oV\\nK6pdy8vLY9CgQRw8eFCr6gmhmdPD+xr19EHm9YUQjaJZ0F+zZg12u51ly5YxdepU5s+f771mt9uZ\\nP38+S5YsYenSpSxfvpy8vDzvtRkzZhAUFKRV1YTQlKPICno9xqoArSZPmbJXXwjRGJoF/a1btzJg\\nwAAAevfuTUZGhvdaZmYmCQkJWCwWTCYTffv2JT09HYCFCxdy6623Ehsbq1XVhNCUs6gIgyVM1RS8\\nHp7hfYdk5RNCNIJmQb+kpITQ0FDvzwaDAVfViuOSkhIsZ/SCQkJCKC4u5vPPPycqKor+/fsDoCiK\\nVtUTQjOOIitGFY/UPZNBevpCCD8YtSo4NDSU0tJS788ulwt9Vc/HYrFUu1ZaWkpYWBhLly5Fp9Ox\\nceNGdu/ezbRp01i8eDExMTF1Pis2Vv1h1POJtK/5cFZUoFRWEhQTBajftrLyVhwFAhwV58Xndj7U\\nQUvSvuatpbevMTQL+pdccglr165lxIgRbNu2jeTkZO+1pKQksrKysFqtBAUFkZ6ezsSJExk+fLj3\\nngkTJvDss8/WG/ABcnJa7lBnbKxF2teM2HJOAeAKDAHU/7PpcBgAKDmVd84/t5b2u/sjaV/z1pLb\\n58+XGc2C/rBhw9iwYQPjxo0DYN68eaxcuZKysjJuvvlmpk2bxsSJE3G5XIwZM4a4uDitqiJEk/EM\\nuxu0Gt4Pkfz7QojG0yzo63Q6Zs6cWe21xMRE73+npqaSmppa6/uXLl2qVdWE0IyzKjGPVkFfp9dj\\nCA3FIfv0hRCNIMl5hFCR54Q9Y5j6e/Q9DBaL9PSFEI0iQV8IFXmH98O1DPphuEpLURwOzZ4hhGiZ\\nJOgLoSJP3n2ttuzBGdv2SiX/vhDCNxL0hVCR0+qZ09e2pw/gLJIhfiGEbyToC6EiR1ER6HQYzkhM\\npTZvKl7JyieE8JEEfSFU5E7Ba9EkBa+HNxWvZOUTQvhIgr4QKnIWWTUd2gc5aU8I0XgS9IVQictm\\nw1VRoekiPjidA8Ape/WFED6SoC+ESrROzONhCJWevhCicSToC6ESb2IeDffow+ntgBL0hRC+kqAv\\nhEpO593XNujrg4NBr5dUvEIIn0nQF0IlTZGYB07n35eevhDCVxL0hVBJUyTm8TBYwmQhnxDCZxL0\\nhVDJ6cN2tO3pg3vbnqusTPLvCyF8IkFfCJU01ep9wJvx77esXyioKNT8eUKIlsF4risgREvh9Kbg\\ntWj6nF15e9lbuo9uwOfblpGXFcBVbS5jTJcbCTAEaPpsIUTzJj19IVTiKCrCEBqKzmDQ7BnpJ39l\\n8W/vUGRyD+tfE3kprUPi2XB8C4u2vYXdadfs2UKI5k+CvhAq0ToF76Giw3yw6xPMBjNXdhkMQJ+Q\\nJB67bAoXx11EpvUQH+7+FEVRNKuDEKJ5k6AvhApcdhuu8nKMGgX9SqeNtzM+xKm4mNhzPK3iOgLu\\nKQWT3sid3W4hMSyB9Oxf2ZaToUkdhBDNnwR9IVTgOdteq0V8qw+lkV9RwNCEQXSL6lojK5/JYGJC\\n91sw6o18svcLKhwVmtRDCNG8SdAXQgUOq3aJefLKC1hzeB2R5ghGJA4FTp+0d2ZWvvjgWP6UMJgi\\nWzHfH92oej2EEM2fBH0hVHB6u576w/urs9JwKk5u7HQt5qrV+bUdujMkYSAhxmDWHF5HuaNc9boI\\nIZo3CfpCqOB03n11e/oFFYVsOvEzcUEx9I3r7X1dHxwMBkONoB9kDOSahIGUO8rZcHyLqnURQjR/\\nEvSFUIE37364ukF//bGfcCpOhnVIxaA/vRWwrvz7/dteiUlvYv3RjbgUl6r1EUI0bxL0hVCBFifs\\n2V0ONh7fQogxmEvj+9S4Xlv+/RBTMJe3uoS8igIycnepVh8hRPMnQV8IFWiRd//XU79RYi+lX5vL\\nCDCYalw3Wiy4ystx2Wsm5BnYth8Am07+olp9hBDNnwR9IVTgLLKqnoL3h2Ob0KFjQNsrz3rdYPFs\\n26vZ229naUPb0NZk5O6ixF6qWp2EEM2bBH0hVOAsKsIQEorOqM5xFrnleRywHqJrZCdigqLPeo8h\\nPNz77LO5vNUlOBUnW7O3q1InIUTzJ0FfCBU4iopUXbmffvJXwB24a+PJ/ufJEfBHl8VfjA6dDPEL\\nIbwk6AvhJ5fdjqusVLWgrygKW7K3YtIb6R3bs9b7jJ6efi1BP9wcRkpUF7KKjpBXnq9K3YQQzZsE\\nfSH85JlTVyvv/uHio5wqy+WimB4EGQNrvc8zvO/ZLng2faq+NGzP3alK3YQQzVu9Qf/NN98kJyen\\nKeoiRLOkdmKeX0/tAKDvWbbpncnT03dYC2u956LYHujQse2UHMIjhGhA0K+srOT222/nnnvu4dtv\\nv8V+lu1BQlzIvIl5VAj6iqKwPTeDAL2JblFd67zXUM/wPkBYgIWk8A4csB6i2Fbid/2EEM1bvUH/\\n/vvvZ9WqVUyePJnNmzfzP//zPzz77LPs2iVJP4QAdRPznCw7xamyXLpHJ591b/6ZDCGhoNfXupDP\\no3dsTxQUfsuRIX4hLnQNmtOvqKjg6NGjHDlyBL1eT3h4OHPmzOH555/Xun5CnPc8Qd8z3O6P7VWB\\nua4FfB46vR5DWJj3sJ/aXBTTA4CMvN1+108I0bzVu6n4kUceYdOmTQwcOJB7772XSy+9FACbzUb/\\n/v2ZOnWq5pUU4nzm8J6w5//w/vacDPQ6PT2jUxp0vzEsHNvJEyiKgk6nO+s9scHRxAXFsKdgHw6X\\nA6NenVwCQojmp96//f369ePZZ58lJCTE+5rNZiMgIICVK1dqWjkhmgO1hvcLKgo5XHyUlMguBJuC\\nG/QeY3g4lYezUCor0AUG1Xpf9+hkvj+6gQPWLLpGdvKrnkKI5qve4f0VK1ZUC/hOp5ObbroJgLi4\\nOO1qJkQz4ZlTN1r8S8HrGX7vFdu9we/xbturZ16/e3QyAL/n7Wlk7YQQLUGtPf0JEyaQnp4OQErK\\n6aFGg8HANddco33NhGgmnFYrhlCL3yl4d1UF5IYO7UP1rHwB8a1qva9LRBJGvZHf8/cwiuv8qqcQ\\novmq9V+ppUuXAjB79mymT5/eZBUSorlxFFkxRkb5VYbT5WRPwX5ig6JrzbV/Nqfz79fd0w8wBNAl\\nIold+XsprLQSYVbvCGAhRPNRa9Bfu3Ytqamp9OjRgy+++KLG9VGjRmlaMSGaA3cK3jKMHTr6Vc4B\\naxYVzkquiO7r0/uMDRzeB/cQ/678vezK20u/Npc1qp5CiOat1qC/Y8cOUlNT2bx581lXBUvQF0K9\\nRXy/57uH9rtHJfv0Ps9z60rQ4+FJ9rOnYH+Dgn5ZhZ2DJ4oBSGoTRpBZVv0L0dzV+rd4ypQpAMyf\\nP9/7WnFxMSdOnKBr17ozhQlxofAu4vNzj/6uvD0YdQa6+Liy3peefqvgOCwBoewt2F/nFj+H08V/\\nfjzIf9OPYHO4ADAZ9Qy/vD03Xp2I0SBHdgjRXDVo9f4TTzxBXl4eI0eOZMqUKbz44ov1FuxyuZgx\\nYwbjxo1jwoQJHD58uNr1tLQ0xowZw7hx41ixYgXg3hnwxBNPcOutt3Lbbbexb9++RjZLiKbhmUs3\\n+BH0rZXFHCk5TqeIRMyGAJ/eawyPABoW9HU6HV0jOmG1FXOq7OznadgdTl78ZDtf/5RFaLCJ66/q\\nwMh+HQgNMrFyYxYvrdhOpd3pUx2FEOePeoP+Rx99xOOPP87XX3/NNddcw8qVK/nhhx/qLXjNmjXY\\n7XaWLVvG1KlTq40Y2O125s+fz5IlS1i6dCnLly8nLy+PtWvXotfr+fjjj3nwwQcb9OVCiHPJ29P3\\nY3h/d/5e4PS2Ol/oAwPRmc31LuTz8OzR31uYWeOaS1H41xc72ZVVQJ/OMcz+6xX8eWAnbhrUiTn3\\nXEGfzjHsPFTAm1/9jktRfK6rEOLca9A4XUREBOvWrWPQoEEYjUYqKyvrfc/WrVsZMGAAAL179yYj\\n4/QpX5mZmSQkJGCxWDCZTPTt25f09HSGDh3Ks88+C8CxY8cIVyGtqRBaUqOnv6sq6Nd3wE5tjGHh\\nDerpw+mgv6egZtD/bssRtu3PpVuHSO4d1ZPAgNOzf4EBRu4b3ZOUhAi27s0h7ZejjaqrEOLcqjfo\\nd+7cmcmTJ3PkyBGuuuoqHnjgAXr16lVvwSUlJYSGhnp/NhgMuFwu7zXLGYlMQkJCKC4u9t43bdo0\\nZs+ezfXXX+9zg4RoSv7O6SuKwt6CTCymUNqE1L7Pvi6G8HCcxUUoVX+/6hIbFEOEOZx9BZkoZ/TW\\nj+aU8Pn6TMJCApj8Pz0wGWv+02A06Jl8Yw9Cg0ys+D6To6eKG1VfIcS5U+9y3Llz57Jt2za6dOlC\\nQEAAo0ePpn///vUWHBoaSmlpqfdnl8uFXu/+h8RisVS7VlpaWq1XP3/+fKZOncrNN9/MN998Q2Bg\\nYJ3Pio31LxPa+U7ad/7KrXD/OY5PbIMpvGY76mvb8aKTWG1F9Gvfl7i4xuXuz4uNpmL/PiLMEBBR\\n/2d5UasU1mdtpiKgmISItiiKwj8//Q2HU+GBWy6mU4fa8wTExlq4f2wf5r+fzttf7uTpv17ZqDo3\\nF835z2ZDSPsuPPUG/bKyMvbs2cPmzZu9r2VkZHD//ffX+b5LLrmEtWvXMmLECLZt20Zy8un5yqSk\\nJLKysrBarQQFBZGens7EiRP54osvyM7OZvLkyQQGBqLT6bxfFOqSk9NyexyxsRZp33msLCcP9HoK\\nKkBnq96OhrRt07HfAOgQnNDoz8ER6E6TnZ15hMCE+v++JAQlAJvZdOA3gtqH8eveHH7bn8tFnaJJ\\njAuptx5dWofSrUMkP+/KZu2WQ/RMbHgyoeakuf/ZrI+0r/ny58tMvf9CPPDAA2zZsqXaUGBDDBs2\\njICAAMaNG8f8+fN54oknWLlyJZ988gkmk4lp06YxceJExo0bx5gxY4iLi+Paa69l165d3H777fz1\\nr3/lySefJCDAt9XMQjQlZ5EVQ1gYugZ8OT2bvVVz610jGn8IjikyEgBHYUGD7vfM6+8ryMSlKHy2\\n/gB6nY6bUzs36P06nY5bhnRGp4PPvj/g878NQohzp96efl5eHu+++67PBet0OmbOnFnttcTERO9/\\np6amkpqaWu16YGAg//znP31+lhDngqIo7pz3rVo3+v37Cg4QHmAhLji20fUweoJ+QWGD7o8OiiIq\\nMJL91oNs3ZPD8dxSrurZijYxIfW/uUpCvIWrLmrDhu3H2Xkov8X29oVoaertnnTr1o3du3c3RV2E\\naFaUygoUm63Ri/hOlGZTbC+hS2SnWhPlNIQxwhP08xv8nk7hiZTay/jy5x3ogJH9Ovj83LFDugDw\\n9cYsn98rhDg36u3p7927l9GjRxMdHe0datfpdPzf//2f5pUT4nzmWbnf2O16nr3yyZENG1avjeew\\nn4YO7wN0juhIevZWTlQeoW9yX1pHN7yX79GpXQQ9k6LIOJDP/qNWOreTLbZCnO/qDfqLFi0C3IFe\\n5u6EOM3fxDz7PPP5Pqbe/aPTw/sND/qdItxTbXpLIdc1opfvMfLKDmQcyOe/Px+RoC9EM1Dv8H67\\ndu3YunUrn3zyCZGRkfz888+0a9euKeomxHnNm5inEUHfpbjYV3CASHME0YH+HcurN5vRBwf7FPSN\\nNguK3YQ5opCOrRq3VRCga/sI2sWGsnVvDoUl9SftEkKcW/UG/eeee45169bx3Xff4XA4+Oyzz5g3\\nb15T1E2I85qj6oS9xszpHy85SamjjK5+zud7GCMifRreX7f9BK6SSJzGMgoqGrYA8Gx0Oh1DLmmL\\n06WwfvvxRpcjhGga9Qb9H3/8keeeew6z2Ux4eDhLlixh/fr1TVE3Ic5rTj/m9D3z+f4O7XsYIyNx\\nlZXhakCKbLvDxQ+/HcdY7l5xv7/woF/PvrJHPIEBBtZtO46zAVkBhRDnTr1B32AwVPvZZrPVeE2I\\nC5E/c/r7Cg4A6gZ9aNi8/i97TlFcZqdPG3fCrEzrIb+eHRhg5OqerSkoruS3/Xl+lSWE0Fa9Qf/a\\na6/loYcewmq18u677zJ+/HhGjhzZFHUT4rzW2MN2FEUh03qQqMBIogIjVamLd9teA4b4121zD8Nf\\n16cXJr2JTD97+gADertzFfy444TfZQkhtFPv6v1BgwYRFxfHkSNH+OWXX5gyZUqNpDpCXIgcViu6\\ngAD09ZwN8UfZZTmU2svoHuX7Ubq1Od3Tr3uvfk5hOXuOFJKSEEGbKAuJYQnsLcyk1F5GiCm40c9P\\niLeQEBfKb5l5FJXZCAuWTJpCnI9q7enn5eUxfvx4JkyYwIcffojBYGDTpk189NFHFFUtYBLiQuYs\\nsmIMC/d5IV6m1d2z7hTRUbW6NHR4/6edJwHo17NVVR3cW/cO+DnED3B1r9Y4XQqbd2b7XZYQQhu1\\nBv1nn32Wvn37smHDBlasWMGKFSvYsGEDKSkpzJ07tynrKMR5R3G5cBQVYQjzfbtbZuEhwJ0VTy0N\\nGd5XFIWNGScJMOm5NDkOgM5VQd9TJ39c0SMeg17HBhniF+K8VWvQ37NnDw8//DAmk8n7WkBAAA89\\n9BA7d+5sksoJcb5ylZWB09molfuZ1kMEGYNoFRKnWn1MVVn57HX09Pcfs3KqoJy+XWMJMrtn9jqG\\nJaDX6f1ewQ8QFhzARZ2iOXyqhMPZLfN0MyGau1qDfm1n2Ov1elm9Ly54Dqt7b7uvK/etlcXklufR\\nKbwDel3jTuY7G31oKDqjsc7h/Y0Z7qH9q3qePiAo0GimXWgbDhcfxea0+12P/r3cZW/YcdLvsoQQ\\n6lPvXx0hLiCOwqqgH+nb6nvP3LmaQ/vgTpJjjIrGkXf2LXM2u5Mtu04RaTHTrUP1OneOSMSpOMkq\\nOux3PXp1isYSbOKnnSdxOGXPvhDnm1pX7+/fv58hQ4ac9dqpU6c0q5AQzYFn7twYEeHT+zyL+JJU\\nXMTnYYqOoWzXTlw2G/qA6qvnt+3PpbzSweCL26DXV1942CkikbQjP5BpPUQXf88BMOi5ols8a345\\nSsaBfPp0ifGrPCGEumoN+qtWrWrKegjRrHh7+r4G/cJDGHUGOljUP7/CGO3OsOfIyyWgdZtq1842\\ntO/RKbwj4H9mPo+rerVizS9H2ZhxQoK+EOeZWoO+HKojRO28QT+84cP7FY5KjpYcp2NYe0wGU/1v\\n8JGpKujb8/KqBX1rSSUZB/Lp2MpC25iaR+haAkKJC47hoDULl+Lye61Bh3gLbWJC2LY/l9IKOyGB\\n6rdVCNE4MqcvRCM4G9HTP1R0GJfiUn0+38MU7e5V2/8wr7/592xcisJVVXvzz6ZzeCIVzkqOlfi/\\n3U6n03FVz1Y4nArpu2UqUIjziQR9IRrBYS0AgwF9aGiD3+PJca9mUp4znTm8f6aNO09i0Ou4vHt8\\nre9NUnG/PsCV3ePRcXpaQQhxfpCgL0QjOAoLMUZE+JSN70BVQE0M76BJnUwxnp7+6aB/LKeEw9kl\\n9EyMqjM1bueq0QfPQkN/RYUF0q1jJPuPWjlVUKZKmUII/0nQF8JHisuFw2r1ZsFrCKfLyYGiLFqF\\nxBNqqjmvrgZjRCTo9dWG9zf+Ie1ubWKCoggLsJBZeBBFUVSpj2c6QXr7Qpw/JOgL4SNnSQk4nT7N\\n5x8rOYHNafOulNeCzmDAGBHp3avvUhQ27cwmyGygT+e6V9HrdDo6hXfEaismr6LuQ3sa6pKusZhN\\nBjZmnFTti4QQwj8S9IXwkXePfnjDg753Pl/DoA/uFfyOwgIUh4M9WQUUFFdyaXIcAab6s2h6Dt9R\\na+teYICRvsmx5For2HfUqkqZQgj/SNAXwkeN2aOv9SI+D2NMDCgK9oJ879B+Xav2z+Spm1qL+c58\\ntgzxC3F+kKAvhI9Ob9dr2Jy+oigcKDxIeICF6MAoLavm3atfnp3Dz3tyiA4z06V9w76ctA1pTaDB\\n7P2CooaUhEgiLWbSd5/CZneqVq4QonEk6AvhI89hO4YG9vTzKvKx2opJikj0abV/Y5ii3HP3mbsO\\nUmlzcmWPVugb+EyD3kBieAeyy05RbCtRpT56vY5+PVpRXulg2/7c+t8ghNCUBH0hfORr3n3PcLnW\\n8/kApthYAE7sOwJAvx4NG9r38CQOOqBib7+fDPELcd6QoC+Ej3yd0/fsfdd6Ph/AFBcHgP3UKTq0\\ncqfD9YWnjmot5gNoGxNCx1YWMg7kU1RqU61cIYTvJOgL4SOH1YouIAB9UHCD7s8sPITZEEDbkJqH\\n3ajNGBmFojcQYS/i6gYu4DtTx7D2GHQGVef1wb2gz6UobP49W9VyhRC+kaAvhI8chQUYwxuWja/E\\nXsrJslMkhnXAoK9/25zfdDqsAWFE2ou50sehfYAAQwAJlrYcKT5GpVO9Xvnl3eMx6HUyxC/EOSZB\\nXwgfKC4XTqu1wUP7B61ZQNMM7QPsO2olRx9CkMtGkLOyUWUkRXTEpbg4ZD2sWr3CggPolRRNVnYx\\nR3PUWSQohPCdBH0hfOAsKgJFwdDAxDyeRXxJTbCID2D99uMUmCwA2E417oQ7tfPwe3j27P8kvX0h\\nzhkJ+kL4wFHgTlHryyI+vU5Px7AELasFQFmFg593n8IR7t6rb89p3Py55wuKmkl6AHp3jiHYbOSn\\nnSdxulyqli2EaBgJ+kL4wJ7vDvqmqPqT7NgcNrKKjtIutA2BRrPWVWPzrmxsDhcdunUEwJ7duKAf\\nGhBCq+A4DhRl4XSpl1DHZNRzZY94CktsbN+fV/8bhBCqk6AvhA8cBVV79BsQ9DMLsnAqziaZz1cU\\nhXXbjqHX6eh1aQoAtlONXynfKSIRm9PG0ZLjalURgNSL2wKwdutRVcsVQjSMBH0hfOAocPdQjZH1\\nB/3dOZnA6YQ3Wtp/zMrh7BL6dIkhOqE1GAzYGzmnD6cTCam9da9tbChd24Wz81AB2fllqpYthKif\\nBH0hfOCoGt43RkXXe+/uXHfQb4pFfGt+dvech13aDp3BgCkmxq+g37nqxD215/UBBl/i7u1/v+2Y\\n6mULIeomQV8IH9jz88FgwBgeXud9LsXF3txMYoOiCTdbNK1TflEFv+zJoV1sKF2rDtcxxcbjLCnG\\nWVraqDKjAiOJMIeTWXgQRVHUrC59u8ZhCTbx428n5BAeIZqYBH0hfOAoyHcn5tHX/VfnRGk2pfby\\nJunlr/31GC5FcffyqxIGBbR2Z/+znTzRqDJ1Oh2dwjtSbC8huyxHtbqCe0HfgIvaUFrhIH1340cj\\nhBC+k6AvRAMpLheOwsIGLeLzHFjjGSbXSqXdybptxwkNMnFF93jv6+bWbQCwHW/8EHrXyE4A7C3I\\n9K+SZzG4Txt0Ove0hNojCUKI2knQF6KBHIWF4HJhioys996mSsqzfttxSsrtDL64LQGm02l+A9p4\\ngn7jV997g36h+kE/JiKIvl1jycouZvfhQtXLF0KcnQR9IRrIm5inIdv1rIewBIQQHxyrWX3sDhff\\nbs7CbDIw7NJ21a55gn7licYH/digGCLM4ewryMSlqJ9MZ/gV7oRFqzarl+5XCFE3CfpCNJB35X5k\\n3Sv3CyoKya8oIDm2c4MO5WmsDRknKCyxMfjiNliCA6pdMwSHYIiI8Kunr9PpSI7sTIm9lBOl6p+O\\n16lNOF3ahbPjQJ7k4xeiiWgW9F0uFzNmzGDcuHFMmDCBw4erf5tPS0tjzJgxjBs3jhUrVgBgt9t5\\n9NFHGT9+PGPHjiUtLU2r6gnhM3t+1R79enr6nr3tKTGdNKuL0+Xim5+yMBr0DL/87Cl+za3b4sjP\\nw1VR3ujndNFwXh/g2qq6f7fliCblCyGq0yzor1mzBrvdzrJly5g6dSrz58/3XrPb7cyfP58lS5aw\\ndOlSli9fTl5eHl999RVRUVF8+OGHvPXWW8yaNUur6gnhM8/wfn0peD3z+VoG/R9/O0GutYIBvVsT\\nEXr2FL/eef0TjVvBD9A1Qtug37tLDPGRQfy08yQFxY07FVAI0XCaBf2tW7cyYMAAAHr37k1GRob3\\nWmZmJgkJCVgsFkwmE3379iU9PZ1rr72WKVOmAO6RAoOhCc4fF6KBTg/v19fTP4hJbyQpUptDdips\\nDr744SABJj3X9+tY633eeX0/hvijgyKJCYxiX6E28/p6nY7hVyTgdCms3iJz+0JoTbOgX1JSQmho\\nqPdng8GAq+pkrZKSEiyW0wlLQkJCKC4uJjg4mJCQEEpKSnjggQd46KGHtKqeED6z5+ejMxoxWGpP\\ntlPuKOd4yUk6hiVgNBg1qcfqLUewltoYflkCkZbaD/IJ8Gzb82MxH0DXyM6UOyo4WqxuHn6P/r1a\\nE3L/MD0AACAASURBVBVmZu2vx7CWSG9fCC1pFvRDQ0MpPSMbmMvlQl+V0MRisVS7VlpaSnhVhrMT\\nJ05w5513MmrUKEaOHKlV9YTwmaMgH2NkZJ2JeQ5YD6OgeHPXq81aUsmqzYcJCzZx7RV1jySY27jT\\n3fqzVx8guWpef0/Bfr/KqY3R4B6xcO9GkN6+EFrSpisCXHLJJaxdu5YRI0awbds2kpOTvdeSkpLI\\nysrCarUSFBREeno6EydOJDc3l7vvvpunn36aK6+8ssHPio3VNs3puSbtO/dcdjt7i4oI6dG9zvqe\\nPOkOsJd06A6o37YP1uyj0u5k4o09SGhXT76AWAuHIyOxHzvqVz2uDO3Nkt8/5lDpoRrlqNW+UUO6\\n8s3mw3z/6zFuv647kWGBqpTrr+bwZ9Mf0r4Lj2ZBf9iwYWzYsIFx48YBMG/ePFauXElZWRk333wz\\n06ZNY+LEibhcLsaMGUNcXByzZ8+muLiYV199lVdffRWAt956C7O57rPIc3KKtWrGORcba5H2nQfs\\nOTmgKCihYXXWd8fxvejQEYV7f76abfv9UD5pPx+hQ7yFiztFNajsgHbtKd3xGycPHK9zWqJuelqH\\nxLPz1D6On8zHZDAB6v/uRlyRwNLVe/jgm98Zd00X1cptrObyZ7OxpH3Nlz9fZjQL+jqdjpkzZ1Z7\\nLTHxdErS1NRUUlNTq12fPn0606dP16pKQjSavaD+RXwOl4NDRYdpE9qKIGOQqs+32Z28v3oPOh3c\\nNSIFQz25/z3MCR0o3fEbFYezCOnRs9HP7xbVlbQjP5BpPURKlDYBuX+v1nz90yHW/nqMP13Wnqjz\\npLcvREsiyXmEaABHbi4AppjaM+wdKT6O3eWgU7j6+fa/3HCIUwXlDLu0PR1aNfxbvrm9e96/8oh/\\nc+Xdo93Tc7/n7fGrnLqYjHr+p38idoeLL344qNlzhLiQSdAXogHsue6T5kwxMbXek2l1B6pOER1V\\nffaewwV8uymLmPBARg3w7QuFOaEDAJWH/Qv6ncMTMelN/J6vXdAHuLpna9rGhrAh4wRHT0mWPiHU\\nJkFfiAZoSNA/UJWUR82V+6UVdt5c+Ts6nY5JN/YgMMC3GTlTTAz6oCAqD2f5VQ+TwUSXyCROlGZT\\nUKHdATl6vY4xgzqhKPDpOm0SAglxIZOgL0QD2HNzQafDGHX2vPuKopBpPURUYCSRgRGqPFNRFN79\\ndjf5RZXc2L8jnduG+1yGTq/H3D4BW/ZJXJX+7YHvHuUe4t+Vv9evcupzUadokttH8FtmHruzCjR9\\nlhAXGgn6QjSAPTcXY0QkepPprNdPleVQYi8lKbyDas/8z48H+WVPDl3bR9SZea8+5vYJoChUHvUv\\nv333qK4A/K5x0NfpdIxN7QzAJ2v341IUTZ8nxIVEgr4Q9VAcDhwF+fXM5x8CUG0R36adJ/lywyFi\\nIwK5b3RP9PrGn9bnndfPOuRXneKCY4kKjGR3/j6cLqdfZdUnqU0Yl3eL49DJYn7KOKnps4S4kEjQ\\nF6Ie9oJ8UBSMdQV9z3y+Cov49h+18s43uwkyG3lgTG/C/nBsrq+CkpIAKD94wK9ydDod3aO6Uu4o\\n54DVvzUCDTFmcCdM/7+9+46PqsobP/650yczKZPeQ0JIo1epoi6isnaKqIu7igV9WHUtq/52ddl9\\ndHXro6usWHYt6IqKoLg2RHqvARJCAkkI6b1NJlPv/f0RDC2UJJPKeb9eiJm599zz5d7Md+45556j\\nUbF8Qy7NDneXH08QLgUi6QvCBVzM43pH6vLw0RiJMIV16lhVdc28uuIAsqzw0M1DiAw2dao8AG1Y\\nOCofH+y5nR8YNzS4ZabBA1WZnS7rQoL9jVx3WSz1Vidfbev6LxmCcCkQSV8QLsBVeWLkflDbd/q1\\n9jqq7TUMDIhHJXX8V8pmd/PK8gM02lzcOT2JwfHnX83vYkkqFYb4BFwV5XgaOzdDWbIlEZ1ax4Gq\\nQyjd0Nd+3fg4Av30rN51nPJaW5cfTxD6O5H0BeECXNU/3um3nfSP1LU0mycFJHT4GB5ZZsmqDIqr\\nmpg2JporR0Z1uKy2GBJaFs1pzu/c3b5WrSUtMJmq5mqKGkq9UbXz0mvVzLkyEbdH4eMfumbBH0G4\\nlIikLwgX0PqMfkjbzftHalsSaaKl40l/2ZqjZOTVMGxgEHOv8v40t8aBLUnfntf5Jv5hJ5r4dxcf\\n6HRZF2NsSihJMQGkH60iI7+6W44pCP2VSPqCcAGuqipQqdAEtL2q3ZG6PIwaA9HmyA6V/8OeIn7Y\\nW0R0iIkHbhzcqZH652IY0PKFxJ7bucF8AEOCU1FJKnYX7+90WRdDkiTumDYISYKP1hzB7ZG75biC\\n0B+JpC8IF+CqrEAbGISkVp/1Xp2jnsrmagb6D+hQf/7BvGr+syYHP5OOh2cNw6jvmjWw1GYz2vBw\\n7Pm5KHLnkqZJ68NA/wEcqTlGvaPBSzU8v9gwX6YOj6S02sbaPUXdckxB6I9E0heE8/A0N+NpaEAb\\n1vao/CO1LXfOgywD2112aXUTS77IQK1S8cuZQwn29+7KfGcyJg5Ctts7vfgOwLCQwUD3jOL/0S2X\\nJ2AyaPh8cz61jZ2bXVAQLlUi6QvCebgqygHQhp4j6Z8YxDeonYP4bHYX//jsIM0OD/fMSGFgZPun\\n2G0vn+RUAJqzD3e6rBEhLcv07invniZ+AF8fHTOnDsTu9PDJOjGoTxA6QiR9QTgPV3lL0ted606/\\nLheDWt+u/nxZVliyKpPyGhvXXRbL+MHhXqnrhRiTW+bOt3kh6QcaLCQHD+RoXT51jvpOl3exLh8e\\nSXyELzsOlZN1rKbbjisI/YVI+oJwHs7z3OnXOxqosFWREDAAters/v5zWb4hl4y8GoYmBDFzavu7\\nBTpKGxiENiSU5pzsTvfrA0yKHYOCwt6K7hnFDy2r8P1sejIS8MH3OWJQnyC0k0j6gnAe57vTP9qB\\npv30o1V8u+M4YYE+PHBjWpeM1D8fY0oKcnMzjuOd79cfHzMKCalbm/gB4iP8uGJkFKXVNlbv6twi\\nQoJwqRFJXxDOw1lRDipVm7Px5bQz6dc2Ovj3V1lo1CoeunkIPoa2V+zrSj7JKQDYsrM6XVaAwY9k\\nSyLHGo5T1dy9z8/fOjUBXx8tq7bkU11v79ZjC0JfJpK+IJyHq7wcbXAIkubsR+mO1uahU+uI9Y2+\\nYDmyrPDWl5lYm13cdlUiMaHmrqjuBRlPDOazZR3ySnmjw0YA3TugD8Bk0DL7ikScLpmPfjjSrccW\\nhL5MJH1BOAePrQmPtbHN/vxGp5UyWwUD/S+uP/+bHQUcPl7HyEHBXDXKu1PstofWYkEXFU1z9mFk\\np7PT5Y0IGYJGUrOzfF+3zMV/qolDw0mM9mdvTiUHcsVMfYJwMUTSF4RzOF9/fk5tyyNjF9O0X1LV\\nxBeb8/E36bh7RiqS1L39+GcyDR2G4nJ55dE9H62RYSGDKWsq51hD58cJtIdKkpg3PRmVJPGf73Nw\\nuT3denxB6ItE0heEc2gdud9G0j9c09KknBJ4/nnyZVnhnW+ycHsU7romGbOx+/vxz2QaMhSApoPe\\naZKfGDEOgK0lO71SXnvEhJr5yehoKuqaxfK7gnARRNIXhHNovdM/o3lfURSyao7gozES43v+pvo1\\ne4rILW5gXGooI5PaXrCnuxkTB6EyGGg6eMArTfLJgYkEGizsrtiP3d39g+punhJPgFnHV9sKKK5q\\n6vbjC0JfIpK+IJyDo6QEAF3E6RPvVDZXU+uoI8mSeN759msa7KzclIfZqOWOaUldWtf2kDQafNIG\\n46qsbP1i0xkqScX4iDE4Pc5ufWb/R0a9hnnTk/HICu99cxi5m8cWCEJfIpK+IJyDs6QYlcGAJjDw\\ntNeza39s2k887/7vfXUIh9PDrVMT8DPpuqyeHWEaNhwA6769XilvQsQYJCQ2FW/v9gF9ACOTQhid\\nHMLR4no27Cvu9uMLQl8hkr4gtEFxu3GWl6GLjDxr4N3hmpZBfMmWc/fn5xbXs3Z3IbFhZi4f1rEl\\nd7uSecQoUKlo3LPLK+UFGiwMC07jeGMR+Q0907d+59VJGPUaPl2fS02DeHZfENoikr4gtMFZXg4e\\nD7rI0/vsZUUmp/YoQQYLIcagNveVFYUPv88B4I5pSd0+697FUJvN+KSm4TiWj6uq0itlXhkzGYB1\\nhZu9Ul57BZj13HZVInanhw+/z+mRFgdB6O1E0heENjhLWpqI9Wck/cLGYmzuZpItg8756N22jDKO\\nlTVy+cgokmICuryuHWUePQaAxj27vVJeYkACUeYI0iszqLHXeqXM9poyLILkmAD2HaliT7Z3vswI\\nQn8ikr4gtMFxIumfead/8lG9tvvzXW6Zzzflo1Gr+MVPB3dtJTvJd+RoUKmw7vZOE78kSVwZMwVZ\\nkVlftMUrZXakDj+/LgWNWsUH3+dgbXb1SD0EobcSSV8Q2uA8V9I/MSlPkqXtpL9xfwnVDXauGhVF\\niMXYtZXsJLWvLz6padjz83CWlXqlzDGhw/HX+bKpeDtWZ888Phce6MPNU+JpaHLywersHqmDIPRW\\nIukLQhucxcWojEY0Fkvra3a3g7y6fGLMkfjqzp473+H08OXWY+h1amZMiOvO6naY36SWfvj6zZu8\\nUp5WreXquCtxepysK/ROmR1x7bhYEqP82ZlVwY5DnX8sURD6C5H0BeEMssuFs6IcXWTUaf322bVH\\ncSseBgeltLnfmj2FNDQ5mT4mBj+f3vWI3rmYR45C5WOiYetmFLfbK2VOihyHr87M+qIt2Fw2r5TZ\\nXiqVxPzrU9Fr1XywOpvaRkeP1EMQehuR9AXhDK6yMpBl9FGnN+1nVrfMVT84+Oykb7O7+Wb7cUwG\\nDdeMi+2WenqDSqvDb/x4PA0NNGUc9EqZOrWOabFTsXsc/NCDd/thFh9uuyqRJrubd77OEqP5BQGR\\n9AXhLPbjxwDQx5xsolcUhczqw5g0PgzwOzupr9tXhM3h5trLYvExnL0Mb2/mN/lyAOrW/eC1MqdE\\nTcBf58sPxzdS56j3WrntNXVEJEMSAsnIr2GdmLRHEETSF4QzOQpaJpfRx51M+iVNZdQ56kkNSjpr\\n6l2ny8P3uwox6tVcOTK6W+vqDYbYOIxJydgyM3AUFXqlTL1ax/UJ1+CSXazK/dYrZXaEJEncfV0q\\nZqOWZT8c5Xh5Y4/VRRB6A5H0BeEM9uMFoFKhj45pfS2jKgugzf78TQdKabC5uGpUdJ+7y/+R5Zrr\\nAKhd7b0EPT5iDFHmCHaW7eV4Y5HXym0vi6+e+T9Nxe2Ref2LTOxO74xdEIS+SCR9QTiFIss4Co+j\\ni4hEpTs5GC+z+jASEmlByadt7/bIfLujAK1GxdVjYs4srs8wDR2GLjyChh3bcdVUe6VMlaTi1sTr\\nUVBYlr0SWZG9Um5HDE8M5ppxMZTX2Fj6Xbbo3xcuWSLpC8IpXOVlKA4HhlOa9ptcNvLqCxjgF4tZ\\nazpt+x2HyqlucHD58Mhet6hOe0gqFZbrZoDHQ/UXn3ut3JTAQYwJG0FBQyEbirZ6rdyOmDl1IPER\\nfmzLLGfLwbIerYsg9BSR9AXhFPaCYwDoY08m/czqwygoDDlj1L6sKHy9vQC1SuLaPjRi/1z8JkxC\\nFxVNw9bNXuvbB5g16EZMGh9W5X1LdXON18ptL41axYKbBmPUa/hgdTaFFdYeq4sg9BSR9AXhFM25\\nuQAY4hNaX0uvzABgRMiQ07Y9cLSa0mob49PCCPI3dF8lu4ikUhEyaw4oCpWfLPNaE7ivzszMQTfg\\n9Dh579AyPLLHK+V2REiAkfk/TcXplnltxQExTa9wyRFJXxBOYc89iqTRtN7pOzxODlVnE+YTSrgp\\n7LRtv9/dcjfcl57LvxCfIUPxGTwE26FMGrZ6b7W8ceGjGBk6jNz6Y3xzbI3Xyu2IUUkhXD9xAJV1\\ndt5clYksi/594dIhkr4gnCDb7TgKj2OIT0Cl1QKQVZ2NS3addZdfVGklq6CWlNgAokPPnpK3r5Ik\\nibC7foHKYKBy2X+8NqhPkiTuSJ5JkMHCt8fWklWd45VyO+rmyfEMGxhERn4NKzfl9WhdBKE7dXnS\\nl2WZ5557jrlz5zJv3jyOHz9+2vtr165l1qxZzJ07l08//fS09/bv38+8efO6uoqCAIA9Pw8UBcPA\\nk4vpnKtpf83ulkfQ+vKI/XPRBgUTMud25OZmSpf8E9nl9Eq5Ploj9wy5E7VKzdsZH1Da1HNz4qtU\\nEvffkEaoxchX2wrYfbiix+oiCN2py5P+mjVrcLlcLFu2jCeeeIKXXnqp9T2Xy8VLL73EO++8w9Kl\\nS/n444+prm65s3jrrbf47W9/i8sl+tyE7tF8tGXZXOOJpO+W3RysyiLQYCHG9+SUvNZmF9syywj2\\nNzA8MbhH6trV/KZcju/4Cdjzcil/7x2v9e8P8IvlZymzsXvsvL7/HRqcPTdZjo9By8Jbh6LXqvnX\\nV1kUV4qBfUL/1+VJf+/evUyZMgWA4cOHk5GR0fpebm4usbGx+Pr6otVqGT16NLt2taztHRcXx2uv\\nvSaepxW6TXNOyzKshsSWpJ9Vk4PdY2dEyJDTFt7ZkF6Myy0zbXQ0KpXUZll9nSRJhP38bgwJCTRu\\n30bFf5aiyN55zn5s+EhmxF9Ntb2Gf+x7k0ZnzyXb6BAz9/w0FYfLw6srDtJkFzcZQv/W5UnfarVi\\nNp/s81Sr1cgnPjysViu+vr6t75lMJhobW775T58+HbVa3dXVEwQAZKeT5iM56GNi0Pj6AbCzbC8A\\nY8NGtm7n9sis3VuMXqdm8rDIHqlrd1FpdUT98lfoomOoX7eW8vff8dpKfDMGTOOK6EmUNpXzavpb\\nPZr4x6aEMmN8HBW1zby56pAY2Cf0a12e9M1mM01NTa0/y7KMStVyWF9f39Pea2pqwt/fv6urJAhn\\naT6Sg+J245M2uOVnt52DVYcI8wk9rWl/b04ltY0OJg+J6LNT7raH2teXmCeeQh8bR8PmTRT9/S94\\nGjvfJC9JErMG3ciUqAkUW0v56+7XKLdVeqHGHXPr5QkMSQjkYF61GNgn9Gtd/qk1atQo1q1bx3XX\\nXUd6ejrJySenMU1ISKCgoID6+nqMRiO7du1i/vz57T5GSIjvhTfqw0R8Xc+a3zKaPGLCWCwhvqzP\\nz8Alu7ly4HhCQ/1at9uwLB2A2dOTCQm58Kj93hBbp4X4EvKXP3Lk5Vep3radohf/QMpTT7a83sn4\\nFobMIyzTwvLMr/n73n/yxKT7SQtN8lLF2+c3d1/GY69s5KttBQxODCHEC/H1diK+S0+XJ/2rr76a\\nLVu2MHfuXABefPFF/vvf/2Kz2ZgzZw5PP/008+fPR5ZlZs2aRWho6Gn7n9qXei6Vlf135ayQEF8R\\nXzeo2r0PSaPBGRpDZWUjPxxpmTI21ZzWWr/80gayjtUwbGAQOpQL1ru3xOYtgXffD6ERVK/6nANP\\n/T8GLrgP1YjLOl3ulWFXoPf48FH2Cn6/7mVuSLiGq+OuOGs1w+7w0E2DeX7pHl5etpfoUDNmbf99\\nqrm/XZ9n6s/xdebLjKT0g5Fy/fXEQv++cKF3xOesqODY//s1PkOGEf3oY9Ta63h264vE+8fx+OiH\\nWrd768tMtmWW89htwxkSH3TBcntDbF2h6eABSt96A9nWhN/kywm982eotJ1fd+BoXT7vZP6HOkc9\\nKZZB3Jk6i0CDxQs1bp892RUsXplBWKAPv5k3GrNR2+116A799fr8UX+OrzNJv/9+jRWEi2TdsxsA\\n3zFjANhSsgMFhQkRY1u3qbM62JlVQUSQD4MHBPZIPXsL09BhxD27CFNCPA2bN1L44gu4qjrfH58Y\\nEM8zYx9lSFAKh2uP8PyOv7GxaFu3r843OjmUGyYOoLzGxhtfZODx0lMLgtAbiKQvXPKse3eDSoV5\\nxCg8soetJTsxagyMDhveus36fcV4ZIVpY2Iuqsupv9OGhDD0pRfwmzwFx/ECCv53EbasQ50u16wz\\nsWDY3cxLnYNKUvNxzkpe2fcG5U3dO3nOTVPiGZsWRuaxWj5bLwb2Cf2HSPrCJc1ZWYE9Pw+f5BTU\\nZjMHqw5R72xkXPho9OqWJmuXW2b9vmJ89BomDg7v4Rr3Hmq9nvBfzCfsrrtRHA6KX/k71n17Ol2u\\nJEmMjxjDs5c9zvCQIRyty+ePO/+Pr/JW45K988jghagkicfvGE14oA/f7jzO9kNiKV6hfxBJX7ik\\n1W9YD4DfxMkArC/aAsDkyJMD1HZmldNgc3H5iEj0OjF3xJn8L59K1COPgVpNyeuLadix3Tvl6v24\\nf+hd3D/0Lsw6M18fW8OLO/+PI7W5Xin/QkxGLb+cORSDTs27Xx+moKx/9g8LlxaR9IVLluJ207Bl\\nEyqTCfOYMRxrOM6RujxSA5OINLfc0SuKwve7C5EkuGpU1AVKvHT5pKYR/diTqAwGyv71Jtb96V4r\\ne3jIEH572eNMjZ5Eha2Kl/e9wQdZn9LksnntGOcSEWTivhvSTizFK2bsE/o+kfSFS1bjzh14Ghvx\\nnzgZlVbH9wXrAbg69orWbY4U1XO83MqopBCC/Y09U9E+wjgwkaiHf4Wk0VC6ZDG2E9Mae6VsjYE5\\nSTfxxJj/IcocwbbSXfxh+1/YWba3y6fqHjkohBsmDqC6wc6/v8oSU4MLfZpI+sIlSXG7qf7yc1Cr\\nCZh2NWVN5eyvzCTWN5oky8DW7b7fVQj0z9X0uoIxcRCRDy5EkWVKXn0ZR0mJV8sf4BfLU2Me5pbE\\nn+L0OHnv0DJeS3+b6uYarx7nTDdNjiclNoB9R6paV1gUhL5IJH2h13KWllC+9F3yn/k1RxbcS+5j\\nD1P8j/+jfvNGZIejU2XXb9qAq7KSgKlXoA0K5ovcb1FQuG7AT1pH51fWNbP3SCVx4b4MihbTQ18s\\n09BhhP9iPnJzMyWvvYLnlKm2vUGtUjMtdiq/vexx0oKSOVx7hBd3vcL+yowL79xBKpXE/TcOxs9H\\nyyfrjpJX0tBlxxKEriSSvtDrKIpCzXffcGzRs9RvWI/H1oQuKhpJq6XpwH7K3/03eU88StUXK/HY\\n2p9QXFWVVC7/FJXRSOCMGzhal8+BqkwS/AcwNDitdbsf9hShKDBdPKbXbn4TJmK5dgauinJK33wd\\nxePx+jGCjIE8NOwe7kyZjVt28+bB91meswp3F43wDzDrue/GwciywpIvMkT/vtAn9f8VQ4Q+RVEU\\nqpZ/TO1336L28yP0jnmYR41GOrFIk6uqkvotm6lfv5aaL7+g7ofvsUy/Fsu0q1EZLtznLjudlL71\\nBorDTujd9yL5+fLZnvcAuCVxRmtyb3a42XSgBH+zjrGpoecrUjiH4Ftn4SwppunAfqpWfkbIrDle\\nP4YkSUyMHMsAvxj+lfEB64o2U9BYyP1Df46v7sJrI7TX4AGB3DBpAKu2HOPfX2Wx8Nah4guh0KeI\\nO32hV6lbs5ra775FGx5O3HN/wHfM2NaED6ANDiH4pluIf+mvBM+cDZJE9ecryPv141St/Ax3w7mb\\nXT1WKyWvvow99yi+l43Hb+IkfijcyPHGYsaGjSLBf0DrtpsPltLs8HDVqGg0avFr0hGSSkX4vQ+g\\nDQuj9tuvsR7w3oj+M0Waw/n12IcZHTqcvPoC/rL7NUqbyrvkWDdOEv37Qt+lXrRo0aKerkRn2WzO\\nnq5ClzGZ9JdMfM15eZS+tQS1ry/yg3extiGdlUe/4ovcb/g6/3s2l+wgp/Yo9c4GgszBWFKG4n/F\\nVaj0ehz5+dgyD1K7ZjX2gmPIdjsAiseNq7yM+i2bKH37DZwlxZhGjCT83gcoba7g3UMfYdL68ODw\\nu9GpW+ZYl2WFt788hNMtc/8Naei1HXs2/1I6d+ei0moxDkqiYctmmg4ewHfceNTGrnkKQqNSMyJk\\nKApwoCqTXeX7iPWNJth44XUS2nKu+CRJYnB8INsyykg/WsXQhCAsvvpO1r77ieuz7zKZOn69iaTf\\ny/XnCxdOxqe43RS//Dc8jY1sn57ActsOChoKcckugo1BBOj9cXqcHG8s4nDNEdYVbuZYQyGBpmCi\\nh48n4MqfoAkIwFVTgz0nm6b96dRvXE/dmtXUb9pA8+EskFQE3zqT0Ll3YFMcvLrvTayuJu5Ou50Y\\n35PP4KcfqWLdvmImD43gsrSwTsfWX11sfBr/ANQmM9Y9u3Acy8dvwsTTWm+8SZIkkiwDCTEGkV5x\\nkN3l+4gwhxNuan8XzfniM+g0xIT6sjWjjEMFNUwaEoFW07dahMT12Xd1JumLPn2hV6j57hucpSUc\\nHOTDdt9q0gKTuSp2CoMCEtCoTl6m9Y4GDlRlsr10D5nVh8msPsxA/wFMj7uSwVf+hICrpuEoKaH5\\naA6OwkJkezNqkwlD7ABMI0ag9jFhc9lYnP42VfYarh3wE4aFDD6tLt+1PqYX3a3/Bv2Z/xVXYsvO\\nwrp7F9VfrCT41llderxx4aMI0Pvx+oF3+VfGB8xLncO48FFePcbg+EBmTIjjq20FvPtNFg/ePET0\\n7wu9nkj6Qo9z1NVQ8eXnOAwSB8aE8uCw2xgSnNrmtv56P6ZETWBK1ATy6o/x3bG1ZFQf5vUD7xBl\\njmB63JWMDB9KQGRkm/sXNpbw74wPqGiuYmLEWH4af/Vp7x8triensI4hCYFEhXh/INilSpIkwu66\\nG0fBMWq++QpjSiqmtMEX3rETkiyJPDziPhbv/zfvHVqG3e3g8ugJXj3GzVPiySmsY3d2JevTS7hy\\npJi1UejdRPN+L9efm6gAjEYt3736e/xL6sgYH828nz5BrN/F3WFbDAGMDR/J8ODBNLubyanNZV/l\\nQbaW7KLGXousyEhINLubyasv4Ov8H/g053Oa3Daujr2CWUk3opJOb5L9YHUOZTU27r4updMz8PX3\\nc9fe+FRaLcaBidRv2Ywt8yB+Eyah0ndtX7jFEMDgoBTSKzPYW3EAH42ReP/Yi9r3osYsSBKDco7Z\\n7wAAHjdJREFUBwSyNaOM9CNVDE8Mwt/cN/r3xfXZd4k+/X56YqF/X7iKovDx9veJ/e9e7GY9kx59\\nHrPBt93l+Ol9GRk6jLFho/AoMsXWUo7U5bG7PJ0NRVtYX7SF3eXplDSVEm4K4+dpc5kSPeGsptii\\nSisf/XCExCh/bp4S3+mm2v587qBj8WksFiSdjqa9e3AUF+E7bnyXN4n76XwZGpxGesVB9lUexFdr\\nJs7vwjMsXmx8Rr2GyGAT2zLLyDpex6Qh4X2if19cn32X6NMX+qSv87/H+e1GNDKEzpyL0WDqVHkh\\nPkHMTb6F2YNuJLv2KAUNRVQ1V6OgEGSwkGRJZGDAgLPu7lvrs70AgBkT4kTfbBeyXH0NtkOZ2DIO\\nUvv9dwRec12XHzPMJ4SHRz7Ay3uX8HHOStSSiklRl114x4s0PDGYa8bF8N3OQpauzua+69PENST0\\nSiLpCz1if2Uma7NXMz/PjjooiKAJl3utbLVKTVpQMmlByRe9T0VdMzsPVRAdYmL4wI494iVcHEml\\nIvye+yj4/bNUrViOT3IKhgHxXX7ccFMoD4+8n1f2vcFH2StQqdRMiBjjtfJnTh3IkaJ6tmeWkxpn\\nYcqwtseVCEJP6v1tUEK/U9ZUwfuHljHiqBONRyFw2nQkdc+uU//V1mPIisKM8eIuvzto/P0Jn38/\\neDyUvvE6sr25W44baQ7nlyPuw0dj5MOsT9lZttdrZWvUKhbcOBijXsOHq3MorrR6rWxB8BaR9IVu\\n5fS4eDtjKS6nnbG5btQ+PvhN9t5dfkeU19jYcrCMiCAfxqV2/Ll8oX1Mg4e0zM9fWUH5B+9323Gj\\nfSNZOPJeDBoD7x/6mN1l+7xWdnCAkXtmpOJ0y7z+RSYOl/fXHBCEzhBJX+hWn+d+TWlTOTc0xKCy\\n2gibPq3LZmi76DptzkdWFG6ZkoBKJe7yu1PwzbdiiE+gcfs2GrZu6bbjxvpG88sR92LQ6Hn30DL2\\nlO/3Wtmjk0P4yehoSqqa+OC7bBRF8VrZgtBZIukL3Saz+jAbirYQ7hNK4sEKUKmIvH5Gj9apqMLK\\nzkPlxIaZGZUc0qN1uRRJGg3h9y9AZTRS/uH7OMvKuu3YcX4xLBxxL3q1nncPfcTeigNeK3vOlYkM\\nCPdlS0aZmJ9f6FVE0he6RaPTytKsT1BLauZpx+EqKsJ3zFj0IT2baJdvyEUBbr08AZXoy+8RupBQ\\nQuf9HMXhoPTN15Fd3bdk7QC/WBaOmI9OpeWdzP+QXpnhlXK1GhULbx2Kn0nHx2uPknmsxivlCkJn\\niaQvdDlFUfjw8Kc0Oq3cOPBaNJt2AxAw7ZoerdeB3CoO5FaTEhvA0AQxYr8n+Y0bj9/kKTiOF1D1\\n2afdeux4/zgeGj4fjUrDvzI+YH9lplfKDfQzsPCWoahUsOTzDCpqbV4pVxA6QyR9octtLtnOwaos\\nkiyJTFYnYMs4gHFQEsaEhB6rk9sj89GaI6gkiTuuThIj9nuB0Nt/hi48gro1q7Gme29w3cUYGDCA\\n/zkl8R+sOuSVchOj/Zk3PZkmu5tXlh/A2tx9rRiC0BaR9IUuVW6rZMWR/+KjMXJX6hzqf1gDQMDV\\nPXuX//3uQsprm7lyVBTRYo79XkGl1xPxwINIWi1lb7+Bo6SkW4+fGBDPQ8PuRi2peOvgUrYe3+2V\\ncqcMj+SacTGUVtv4x/IDOMWIfqEHiaQvdBmP7OG9Q8twyi7mJt+Kr1NFw9YtaENCMY8Y2WP1qqhr\\nZtXmY5iNWm6e0vWTwggXTx8TS9gv5iPb7ZS89gqepqZuPf4gy0AeGn4PWpWGV7b9m41F27xS7uwr\\nE7ksLYyjxfW8sSoTjyx7pVxBaC+R9IUu823BWgoaChkbNpLRYcOpW7sGxe0m4OrpXbae+oXIisK7\\nX2fhcHm4fdogTAZtj9RDODe/y8a3PL9fUU7pm6+jeLr3zniQZSCPjlqAn97Mxzkr+Sb/h04/dqeS\\nJO6ZkUpqnIV9R6p45+vDyLJ4lE/ofiLpC13iWMNxvj32AxZ9AHOSbkZ2OKhb9wMqkwn/SVN6rF5r\\n9xRx+HgdIwcFMz5NTMTTWwXfOgvTsOHYMjOo/PTjbj9+jG8Uf/jJEwQaLPw3/zuWZa/AI3fuy8eP\\nI/rjI/zYmlHGO19nicQvdDuR9AWvc3icvJe5DFmRuSttDj5aI/VbNiE3NRFw5U+6fDnVc8kvbeCT\\ndUcxG7XMuyZZDN7rxSSVivB7H0AXEUndmtXUrv622+sQ4RvK46MfIsocweaSHbya/hZWZ+e6G4x6\\nDY/fNoL4CD+2ZJTx76+zRFO/0K1E0he8bnnOKiqaq7gqZgpJlkQUj4e61d8habUEXDWtR+pkbXbx\\n+ucZeDwK99+QRkAfWfP8Uqb28SHq0cdQ+wdQ+ckyGnZ4p3+9PQL0/jw26iFGhAzhSF0ef979D4oa\\nOzfA0MdwMvFvzShj8YoMHE4xuE/oHiLpC161o3QPW0t3Em2O5MaEawGw7t2Dq6oSv4mT0fj5dXud\\nXG4Pr312gKp6O9dPHMAQ8Ux+n6ENCib60cdRGY2U/fttmjK9M3lOexg0euYP+RkzBkyj2l7LX/a8\\nxvqiLZ3q5/cxaHhi7ggGxweSfrSKP3+0j4am/rn2u9C7iKQveE1pUznLsldgUBuYP+RnaNVaFFmm\\n+ssvQJKwTO/+x/Q8ssybqw6RU1TP2JRQbhKj9fscfUwMkf/zMJIkUbL4H9iyvPMMfXuoJBU/TZjO\\ngmG/QK/W8WnOF7xx8F3qHY0dLtOo1/DIrGFMGhJOfmkD//veLvJLG7xYa0E4m0j6glfY3Q7ezvgA\\np+ziZ6mzCfUJBqBx106cJcX4TZiELiy8W+vkdHlYvCKDPTmVpMQGcO/1aWKq3T7KJyWViIcWgixT\\n/OrLPZL4AYYGp/H/xv2KJEsiB6uy+N8df2VLyQ5kpWP98hq1int+msotU+KpaXDw4gd7WLevWCzS\\nI3QZkfSFTpMVmXcy/0NZUzlXRE9iZOhQABSPh+pVn4NaTdANN3VrnWobHfz143TSj1aRNsDCw7OG\\nodWIy70vMw8bQcRDv2xJ/P/4P5oyDvZIPQL0/vxyxL3MSboZRZH5z+HPeHnvEo41HO9QeZIkccOk\\neH5123AMOg1Lv8tm8coM6kVzv9AF1IsWLVrU05XoLJut//5ymEz6Xh/f8iOr2Fm+lxTLIO5Kuw2V\\n1JJc69evpXH7Vvwvn4rfhElt7tsV8aUfqeLlT/dTVm1jXGooD948FL1W7dVjXIy+cO46oyfi04WF\\no4+Nw7prBw07tqMJDMQQG9clxzpffJIkMcAvhssiRlNjryWrJoetJTspbSonyhyBWWtq9/FCLT6M\\nSw2joLyRjPwathwsJdDPQFSwqUueNBHXZ99lMnV8ILJI+r1cb79wfzi+kW+O/UCEKaxltTK1DgB3\\nYwMl/3wVSaMh8sFfojIY2tzfm/EVlDXy7jeHWbXlGB6Pwu0/GcTsKxNRq3vmDr+3n7vO6qn4dGHh\\nGJOSse7dg3XXDhRFwZjk/UcwLyY+g8bA6LDhJAUkUNpUweHaI2ws2kaxtZQgo4UAvX+7julj0DBx\\naDgmo5aMvGp2ZlWQfbyO2DAz/l5+4kRcn32XSPr99MRC775wNxRtZfmRVfjrfHl45P346U+OzK/4\\n6APsubkEz5qDKW3wOcvobHwNTU62Hyrjk7VH+WxDHuW1zaTGWfjlzKGMGBTSo8/i9+Zz5w09GZ82\\nKBjziJFYD+6nKX0fjmP5mIYMRaXTee0Y7YkvyBjIxMhxRJojqGquIrs2l60lOzlc07KoU4hPMBrV\\nxbU2SZLEwEh/xqWGUlVvJ/NYDRv2l1BV30xUiAmz0TuzSIrrs+/qTNKXlH4wYqSysuMjaHu7kBDf\\nXhnf2sJNfHbkS3x1Zh4duYBwU2jre9b0fZS89gq66Bjifvs7JI3mnOVcTHyyrFDf5KS6wU51vZ2a\\nBjul1TbyShsorWrixws4bYCFa8bFMiQ+sFdMvNNbz5239Ib43I0NlL39JrbMDDQWC6F33uW1dR06\\nGp+iKOTU5vL98fVk1eQAYFDrGR02gjFhI0gMiG/tArsYGXnVLFt7lJKqJiQJLksLY8b4uE4vFNUb\\nzl9X6s/xhYT4dnhfkfR7ud524cqKzIqj/2Vd4Wb8dL78csR9RJpPjsp319VRsOhZZHszsc8uQh8V\\nfd7yfozPZndTXGWloraZqno7VXUtf1c32KltdOBpY7pSvU5NfLgvIxKDGZUUQnCA0evxdkZvO3fe\\n1lviU2SZmq//2/JoqMeDefQYgm+ZiS48olPleiO+quYatpfuYlvpbuoc9QD4as0MDx3CyJChDApI\\nQH0RLQCyrLA7u4L/bj1GUWXLrICJ0f5MHR7J2JRQdB0Ys9Jbzl9X6c/xiaTfT08s9K4Lt97RyPuH\\nlnG49gjhpjAeGnYPQUZL6/uyw0HRX/+EPT+PkNvvxPKTq9ssxyPLFJRZySqoobjaxtHCOqrq7Wdt\\nJwEBvnoC/fQE+RkI9DOc+FtPaICRiCATKlXP39GfS286d12ht8XnKC6m/P13sOceBUnCd+xl+F8+\\ntaW/vwMLPHkzPlmRyanNZV/FAdIrM7C6WhK3UWMg2TKIwUHJpAUlX3AMgKwo7D9axbq9xWTm16DQ\\n8uV3+MAgRiWFMDQhCKP+3C1rp+pt58/b+nN8Iun30xMLvePCVRSFPRX7WZ6zikaXlaHBqdyVehs+\\nWp/WbWSnk5J/voYt4wB+EyYRds+9rU3ssqJQWG7l8PFasgpqySmsw37KtKO+PlpiQ83EhPoSGmgk\\nxN9IcEBLgtf00CA8b+gN564r9cb4FFnGmr6P6lWf4ywqBEBjCcQnNRVD4iB04RFog4NRGYyt/f+y\\n04niciI7nMj2ZmS7Hdlux6yTqK+obfnZYUeRZVRaLZJGi6TXo/H3R+PvjzrAgsbf/6K/WHhkD7n1\\n+eyrOEhm9WGq7bWt70WawhkclEKSZSAJ/gMwaM7dd1tZ18ymAyXsPFRBRV0zAGqVxIBwX5JjLaTE\\nBjAgwu+cYwB64/nzpv4cn0j6/fTEQs9fuHn1BXyZ+y05dbloVBpuHjiDK6InndZn7m5ooHTJYppz\\nsvEZMpSI/3mY0joH2cfryCqoJft4LU12d+v2YRYjKXEWUuMsjB8ehcfh6hV98N7W0+euq/Xm+BRZ\\npjknm4btW7Hu3Yts69xCORciabVoQ8PQhYejCwtHGxaGLjQMTVAwmoCAc34hUBSFClslmTXZHKrO\\n5khdHm655XdFJamI9Y1mUEACgywJJPgPwKg5+ykYRVEoqmxiT3YFmfk15Jc2Ip/ysR7opyc21JeY\\nUDMRQT6EBfoQZjESFxPYa8+fN/Tm67OzemXSl2WZRYsWkZOTg1ar5YUXXiA2Nrb1/bVr1/LPf/4T\\njUbDzJkzmT179gX3OZf+emKhZy5ch8fJ/soMtpbs5EhdHgBDglKYnXQTwcaT89YrikLjzh1UfvIR\\nnvp6mhIGs3XQNLJLrFibXa3bBfnpW5N8SqyFQL+TH1z9/Rezv8YGfSc+RZZxlhRjz8vDWVmBu7oa\\n2WFHdjiQJAlJp0PS6lDpdKgMhtY/fsEB2NwS0omfJZUKxe1CcbmQ7Xbc9fUtf2prcVWU4ywvR3G0\\n0U2l0aAJDEIbFITKZEbtY0Rl9GlZbfLUL7uShEfxUO2oo6q5hmp7DTXOBmQU3BpwaNWY/QMJCowk\\nJCKeqPBBRPtFoVGd3pzf7HCTW1xPdmEdBeWNFJZb25zox9dHR2iAgVCLkSD/lpa1ID8DQf4tXWk9\\nMbeFN/WV67MjOpP0L67zpwPWrFmDy+Vi2bJl7N+/n5deeol//vOfALhcLl566SU+++wzDAYDt99+\\nO1dddRV79uw55z5C13HJbsqaKsitzyen5iiHa4/g8LR8SKQGJnFN3FUMsiQgKwq1jQ4qSqqo37Mb\\nzb5tmGvLcEsqNgaNYqc0GHJrCfTTMyEhrKWJMc5CiL+hX97JC32DpFKhj45BHx3Trv3amzQURcFT\\nX4+zvAxnWRmuygpcVVW4q6twVVViyyq/qHLUQNiJP2erB/KBLdhUsM9HjcPXgGzxQxMUjCkkHP/w\\nWKIi4kibFIfqxJMz9VYHhZVWymuaKa+xUXFioOyxskZyS9qe799s1J7+ZcBPT5C/gQCzHn+TDj+T\\nrkMDCIWe1WVJf+/evUyZMgWA4cOHk5FxcnWs3NxcYmNj8fVt+bYyevRodu3aRXp6+jn3uRhuj0yz\\no6Vp7NTmC0VWUGxNoCgnX1cUUBTcsgeH7GjdoaVZ7OTeiqK0zIOttJTaWkLL7oCC3Ppay3Y/btPa\\nhnJiXm6l9UXllGOd3FA5bZuWOxRfPwMNDXakE1u0VPvk9q1HO1Gu0lJwax3dihu37MKpuHHLTlyy\\nk2Z3MzZ3EzZ3Ew2uBhpcda0RSwqEqswEkYS/IwwpF3av3cFu62r09VUE2WsJc9QQgIKMRLY5jsNJ\\nkwkaEM090QGkxAb0ulH0gtAdJElCExCAJiAAn+SUs96XnU7kZhuyzYanuRnF6Tz5u64oJz8wTvx/\\n62uKguxwINuacFutNNRV0FRVhrumGn29Ff/SJihtAkqBgziBSqBMAptJi8PfiNvfhGI2ofMxMsDH\\nRKK/L/4DA2l2KjhkFc0uFU0uaHJKNNo8NNo81Nsaaah2UlMBRxRQkECSWj6nlJZPJL1Wjcmow+yj\\nw2zQYdCpMeg06LUq9Do1Rr0GnVaFVqVCrVGhliTUahUatYRa1fL/alXLoN0fWz2kE/+RkJBO/Lv+\\n+PaP213MPYSt2Ye6WttFnz+1pD7vGIqLIqlQ+fhceLtOuNiBmufSZUnfarViNp98jlStViPLMiqV\\nCqvV2prwAUwmE42Njefd52L8/t1dFFee3Xd3ZdVuLqvrmQU6TnXmdXox35EdQPev/F4DtD2PuCyp\\naA6JRhmYQuDkyVybGMMNYk57Qbggla6lCwH/gE6VE3zGzx6nk5qyAqpL8mgoL8JeWY5UW4+mzoqh\\n0U5wcQMUt30373Pij6XNd7ueArgvuFXHWDuwT70XjrsmeAy7A9K8UFLbwgJ9ePs3bT8ZdTG6LOmb\\nzWaamk4m4FOTt6+v72nvNTU14efnd959zufH/o0lT087xxbdu9iL0D6d6Z/q7fpzbCDi6y3Co4Jg\\n9KieroYAtL3KSO/RZbdoo0aNYuPGjQCkp6eTnJzc+l5CQgIFBQXU19fjdDrZtWsXI0eOPO8+giAI\\ngiB0TpeN3lcUhUWLFpGdnQ3Aiy++SGZmJjabjTlz5rBu3ToWL16MLMvMmjWLO+64o8194uPju6J6\\ngiAIgnDJ6RfP6QuCIAiCcGFiBJYgCIIgXCJE0hcEQRCES4RI+oIgCIJwiejTST83N5cxY8bgdLbM\\nHpeens6cOXO4/fbbee2113q4dh3X2NjIggULmDdvHnPnziU9PR3oP/HJssxzzz3H3LlzmTdvHseP\\ntz0nQF/icrl48sknufPOO5k9ezZr166loKCA22+/nTvvvJNFixbRH4bPVFdXM3XqVPLz8/tdfG+8\\n8QZz585l5syZrFy5sl/FJ8syzzzzTGs8eXl5/SK+/fv3M2/ePIBzxvPJJ58wc+ZMbrvtNtavX9+D\\ntW2/U+PLysrizjvvZN68ecyfP5/q6mqgA/EpfVRjY6Ny3333KRMnTlQcDoeiKIpy0003KcePH1cU\\nRVHuu+8+5dChQz1ZxQ77xz/+obz33nuKoihKXl6ecssttyiKoig33nhjv4jvu+++U55++mlFURQl\\nPT1defDBB3u4Rp332WefKX/84x8VRVGUuro6ZerUqcqCBQuUnTt3KoqiKM8995zy/fff92QVO83p\\ndCoPPfSQcs011yi5ubnKAw880G/i2759u/LAAw8oiqIoTU1NyiuvvNKvzt+GDRuURx55RFEURdmy\\nZYuycOHCPh/fm2++qVx//fXKbbfdpiiK0ub1WFFRoVx//fWK0+lUGhsbleuvv741X/R2Z8b3s5/9\\nTMnKylIURVGWLVumvPjii0plZWW74+uTd/qKovDcc8/x2GOPode3zFdntVpxOp3ExLTMrz158mS2\\nbt3ak9XssF/84hfcdtttALjdbvR6PVarFZfL1S/iO98UzX3Vtddey8MPPwy03FVpNBoOHTrE2LFj\\nAbj88sv77Pn60Z///Gduv/12QkJCAPpVfFu2bCE5OZmHHnqIBQsWcNVVV5GZmdlv4jMYDDQ2NrYs\\nktXYiFar7fPxxcXF8dprr7Xe0bd1PR48eJBRo0ah1Woxm83ExcW1PhLe250Z39///ndSUlqmd/4x\\nLxw4cKDd8XXZjHze8umnn/L++++f9lpkZCQzZsxo/QeAs6f9NZlMFBYWdls9O6qt+F588UWGDBlC\\nZWUlv/71r/nNb37TZ+NrS2enW+6NfE7Mt221WnnkkUd49NFH+dOf/nTa+42NfXfFrxUrVhAYGMjk\\nyZN54403Tq5JcUJfj6+mpobS0lLeeOMNCgsLWbBgQb+Kb9SoUTidTq699lrq6upYsmQJu3btan2/\\nL8Y3ffp0ioqKWn8+9XydOrX7mVO+W60dmaC3+50Z349ftvfu3cuHH37Ihx9+yKZNm9odX69P+rNn\\nz2b27NmnvTZ9+nSWL1/O8uXLqaqqYv78+bz++uunTeFrtVrx8/Pr7uq2W1vxAWRnZ/P444/z1FNP\\nMWbMGKxWa5+Mry0dnW65tystLWXhwoXceeedXH/99fzlL39pfe/Hqab7qhUrViBJElu3buXw4cM8\\n/fTT1NbWtr7f1+OzWCwMHDgQjUZDfHw8er2eioqK1vf7enxvv/02o0aN4le/+hVlZWXcdddduN0n\\nZ73v6/EBp32G/Pj5eOZnTV+P8+uvv2bJkiW8+eabWCyWDsXXJz9pV69ezdKlS1m6dCnBwcH861//\\nwmw2o9VqKSwsRFEUtmzZwpgxY3q6qh1y9OhRHnnkEf72t7+1NoP3p/j643TLVVVV3HPPPTz55JPc\\neuutAKSmprJz504ANm7c2GfPF8AHH3zQ+juXkpLCn/70JyZPntxv4hs9ejSbNm0CoLy8HLvdzvjx\\n4/tNfM3NzZhMJgD8/Pxwu92kpaX1m/ig7d+3YcOGsXv3bpxOJ42NjeTm5jJo0KAermnHfPHFF3z4\\n4YcsXbqU6OhogA7F1+vv9C/k1HXaf//73/PEE0/g8XiYPHkyw4YN68Gaddzf//53XC4Xzz//PNDy\\nS7p48eJ+E9/VV1/Nli1bmDt3LtDSndHXLVmyhMbGRhYvXszixYsB+M1vfsMLL7yAy+Vi4MCBXHvt\\ntT1cS++RJImnn36aZ599tl/Ed8UVV7Br1y5mzZqFLMv87ne/Iyoqqt/EN3/+fJ555hnuuOMO3G43\\njz/+OIMHD+4X8f2YA9q6HiVJ4q677uKOO+5AlmUee+wxdDpdD9e4fSRJQpZl/vjHPxIZGcnChQsB\\nuOyyy1i4cGG74xPT8AqCIAjCJaJPNu8LgiAIgtB+IukLgiAIwiVCJH1BEARBuESIpC8IgiAIlwiR\\n9AVBEAThEiGSviAIgiBcIkTSFwThvHJyckhJSWH16tU9XRVBEDpJJH1BEM5rxYoVXHPNNSxbtqyn\\nqyIIQif1+Rn5BEHoOm63my+//JIPP/yQuXPnUlhYSExMDDt27OD5559Ho9EwfPhwcnNzWbp0KQUF\\nBfz+97+nrq4Og8HAs88+S2pqak+HIQjCCeJOXxCEc1q/fj1RUVEMGDCAadOmsWzZMtxuN0899RR/\\n+9vfWLlyJVqttnUq1Keeeoonn3ySFStW8Ic//IFf/epXPRyBIAinEklfEIRzWrFiBTNmzADguuuu\\nY+XKlRw6dIjAwECSkpIAmDlzJoqiYLPZyMjI4JlnnuHmm2/miSeeoLm5mfr6+p4MQRCEU4jmfUEQ\\n2lRdXc3GjRvJzMzk/fffB6ChoYGNGzfS1pIdsiyj1+v5/PPPW18rLS3F39+/2+osCML5iTt9QRDa\\ntGrVKiZOnMiGDRtYu3Yta9euZcGCBWzevJmGhgZycnIA+PLLL1GpVJjNZuLi4li1ahUAW7duZd68\\neT0ZgiAIZxCr7AmC0KYbbriBxx9/nCuuuKL1terqaqZNm8bbb7/N888/jyRJxMfH09jYyJtvvkle\\nXh6/+93vqK+vR6fTsWjRIoYMGdJzQQiCcBqR9AVBaBdFUfjrX//KwoULMRqNvPPOO1RUVPDUU0/1\\ndNUEQbgA0acvCEK7SJKEv78/s2bNQqvVEh0dzQsvvNDT1RIE4SKIO31BEARBuESIgXyCIAiCcIkQ\\nSV8QBEEQLhEi6QuCIAjCJUIkfUEQBEG4RIikLwiCIAiXCJH0BUEQBOES8f8BMjBn+8647pEAAAAA\\nSUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x113175d50>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Get the unique values of Pclass:\\n\",\n    \"passenger_classes = np.sort(df_train['Pclass'].unique())\\n\",\n    \"\\n\",\n    \"for pclass in passenger_classes:\\n\",\n    \"    df_train.AgeFill[df_train.Pclass == pclass].plot(kind='kde')\\n\",\n    \"plt.title('Age Density Plot by Passenger Class')\\n\",\n    \"plt.xlabel('Age')\\n\",\n    \"plt.legend(('1st Class', '2nd Class', '3rd Class'), loc='best')\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 2\",\n   \"language\": \"python\",\n   \"name\": \"python2\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 2\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython2\",\n   \"version\": \"2.7.10\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "matplotlib/tests/__init__.py",
    "content": ""
  },
  {
    "path": "numpy/__init__.py",
    "content": ""
  },
  {
    "path": "numpy/tests/__init__.py",
    "content": ""
  },
  {
    "path": "pandas/__init__.py",
    "content": ""
  },
  {
    "path": "pandas/tests/__init__.py",
    "content": ""
  },
  {
    "path": "python-data/__init__.py",
    "content": ""
  },
  {
    "path": "python-data/tests/__init__.py",
    "content": ""
  },
  {
    "path": "scikit-learn/__init__.py",
    "content": ""
  },
  {
    "path": "scikit-learn/tests/__init__.py",
    "content": ""
  },
  {
    "path": "scipy/__init__.py",
    "content": ""
  },
  {
    "path": "scipy/tests/__init__.py",
    "content": ""
  },
  {
    "path": "spark/__init__.py",
    "content": ""
  }
]